text:
uke and Fujii, Masahiko and Sugimura, Kazuro and Ostergaard, L and Weisskoff, R M and Chesler, D A and Gyldensted, C and Rosen, B R and Staring, Marius and Xiao, Changyan and Shamonin, Denis P and Stoel, Berend C and Lee, Jaesung and Reeves, Anthony P and Feuerstein, Marco and Kitasaka, Takayuki and Mori, Kensaku and Murphy, Keelin and van Ginneken, Bram and Schilham, Arnold M R and de Hoop, Bartjan J and Gietema, H A and Prokop, Mathias and Kirby, Miranda and Pike, Damien and Coxson, Harvey O and McCormack, David G and Parraga, Grace and Altes, Talissa A and Johnson, Mac and III, John P Mugler and Miller, G Wilson and Flors, Lucia and Mata, Jaime F and Salinas, Carlos and Tustison, Nick J Nicholas Nick Nicholas J and Lee, Po-Shun and Song, Tao and Froh, Karl Yen Deborah and Botfield, Martyn and Zhu, S C and Yuille, A and Caselles, V and Kimmel, R and Sapiro, G and Zosso, Dominique and Bresson, Xavier and Thiran, Jean-Philippe and Yushkevich, Paul A and Piven, Joseph and Hazlett, Heather Cody and Smith, Rachel Gimpel and Ho, Sean and Gee, James. C and Gerig, Guido and Boyes, Richard G and Gunter, Jeff L and Frost, Chris and Janke, Andrew L and Yeatman, Thomas and Hill, Derek L G and Bernstein, Matt A and Thompson, Paul F M and Weiner, Michael W and Schuff, Norbert and Alexander, Gene E and Killiany, Ronald J and DeCarli, Charles and Jack, Clifford R and Fox, Nick C and {ADNI Study} and Ashburner, John and Friston, Karl J and Awate, Suyash P and Whitaker, Ross T and Buades, Antoni and Coll, Bartomeu and Morel, Jean-Michel and Cline, H E and Lorensen, W E and Kikinis, Ron and Jolesz, F A and Dempster, A and Laird, N and Rubin, D and Geman, S and Geman, D and Gohagan, J K and Spitznagel, E L and Murphy, W A and Vannier, M W and Dixon, W T and Gersell, D J and Rossnick, S L and Totty, W G and Destouet, J M and Rickman, D L and Gubern-M{\'{e}}rida, Albert and Kallenberg, Michiel and Mann, Ritse M and Mart$\backslash$'$\backslash$i, Robert and Karssemeijer, Nico and Held, K and {Rota Kops}, E and Krause, B J and {Wells 3rd}, W M and Kikinis, Ron and M{\"{u}}ller-G{\"{a}}rtner, H W and Shenton, M E and Gerig, Guido and Martin, J and Anderson, M and Metcalf, D and Guttmann, C R and McCarley, R W and Lorensen, W E and Cline, H E and Ribes, S and Didierlaurent, D and Decoster, N and Gonneau, E and Risser, L and Feillel, V and Caselles, O and Shih, W J and Tustison, Nick J Nicholas Nick Nicholas J and Gee, James. C and Zhang, H and Yushkevich, Paul A and Gee, James. C and Awate, Suyash P and Cai, Jing and Altes, Talissa A and Miller, G Wilson and de Lange, Eduard E and Mugler, John P and Gee, James. C and Siqueira, Marcelo and Gee, James. C and Avants, Brian B A and Gee, James. C and Siqueira, Marcelo and Gee, James. C and Awate, Suyash P and Gee, James. C and A, Talissa and Altes and Song, Gang and de Lange, Eduard E and III, John P Mugler and Gee, James. C and {Van Leemput}, Koen and Maes, F and Vandermeulen, D and Suetens, P and Vannier, M W and Butterfield, R L and Jordan, D and Murphy, W A and Levitt, R G and Gado, M and Wells, William M and Grimson, W L and Kikinis, Ron and Jolesz, F A and Zhang, Y and Brady, M and Smith, Stephen M and Wang, Hongzhi and Suh, Jung W and Das, Sandhitsu R and Pluta, John B and Craige, Caryne and Yushkevich, Paul A and Hankinson, J L and Odencrantz, J R and Fedan, K B and Altes, Talissa A and Mata, Jaime F and de Lange, Eduard E and Brookeman, James R and {Mugler 3rd}, John P and Miller, Michael R I and Hankinson, J L and Brusasco, V and Burgos, F and Casaburi, R and Coates, A and Crapo, R and Enright, P and van der Grinten, C P M and Gustafsson, P and Jensen, R and Johnson, D C and MacIntyre, N and McKay, R and Navajas, D and Pedersen, O F and Pellegrino, R and Viegi, G and Wanger, J and {ATS/ERS Task Force} and Hankinson, J L and Brusasco, V and Burgos, F and Casaburi, R and Coates, A and Enright, P and van der Grinten, C P M and Gustafsson, P and Jensen, R and Johnson, D C and MacIntyre, N and McKay, R and Navajas, D and Pedersen, O F and Pellegrino, R and Viegi, G and Wanger, J and {ATS/ERS Task Force} and Qing, Kun and {Mugler 3rd}, John P and Altes, Talissa A and Jiang, Yun and Mata, Jaime F and Miller, G Wilson and Ruset, Iulian C and Hersman, F William and Ruppert, Kai and Dregely, Isabel M and Mata, Jaime F and Miller, G Wilson and Ketel, Steve Stephen and Ketel, Jeffrey and Hersman, F William and Ruppert, Kai and {Mugler 3rd}, John P and Ruset, Iulian C and Altes, Talissa A and Mata, Jaime F and Miller, G Wilson and Ketel, Jeffrey and Ketel, Steve Stephen and Distelbrink, Jan and Hersman, F William and Ruppert, Kai and Gee, James. C and Reivich, M and Bajcsy, Ruzena and Kovacic, Stanislav and Broit, C and Messay, Temesguen and Hardie, Russell C and Rogers, Steven K and van Ginneken, Bram and {Armato 3rd}, Samuel G and de Hoop, Bartjan J and {van Amelsvoort-van de Vorst}, Saskia and Duindam, Thomas and Niemeijer, Meindert and Murphy, Keelin and Schilham, Arnold M R and Retico, Alessandra and Fantacci, Maria Evelina and Camarlinghi, Niccol{\`{o}} and Bagagli, Francesco and Gori, Ilaria and Hara, Takeshi and Fujita, Hiroshi and Gargano, Gianfranco and Bellotti, Roberto and Tangaro, Sabina and Bola{\~{n}}os, Lourdes and {De Carlo}, Francesco and Cerello, Piergiorgio and {Cristian Cheran}, Sorin and {Lopez Torres}, Ernesto and Prokop, Mathias and Asman, A and Akhondi-Asl, A and Wang, Hongzhi and Tustison, Nick J Nicholas Nick Nicholas J and Avants, Brian B A and Warfield, Simon K and Landman, B and McErlean, Aoife and Panicek, David M and Zabor, Emily C and Moskowitz, Chaya S and Bitar, Richard and Motzer, Robert J and Hricak, Hedvig and Ginsberg, Michelle S and Zhao, Binsheng and Tan, Yongqiang and Bell, Daniel J and Marley, Sarah E and Guo, Pingzhen and Mann, Helen and Scott, Marietta L J and Schwartz, Lawrence H and Ghiorghiu, Dana C and Wang, Yi-Xiang and Deng, Min and Song, Gang and Tustison, Nick J Nicholas Nick Nicholas J and Gee, James. C and Lo, Pechin and van Ginneken, Bram and Reinhardt, Joseph M and Yavarna, Tarunashree and de Jong, Pim A and Irving, Benjamin and Fetita, Catalin and Ortner, Margarete and Pinho, R{\^{o}}mulo and Sijbers, Jan and Feuerstein, Marco and Fabija{\'{n}}ska, Anna and Bauer, Christian and Beichel, Reinhard and Mendoza, Carlos S and Wiemker, Rafael and Lee, Jaesung and Reeves, Anthony P and Born, Silvia and Weinheimer, Oliver and van Rikxoort, Eva M and Tschirren, Juerg and Mori, Kensaku and Odry, Benjamin and Naidich, David P and Hartmann, Ieneke and Hoffman, Eric A and Prokop, Mathias and Pedersen, Jesper H and de Bruijne, Marleen and Gevenois, Pierre Alain and {De Vuyst}, P and Sy, M and Scillia, P and Chaminade, L and {De Maertelaer}, Viviane and Zanen, Jacqueline and Yernault, J C and Stolk, Jan and Putter, Hein and Bakker, Els M and Shaker, Saher B and Parr, David G and Piitulainen, Eeva and Russi, Erich W and Grebski, Elzbieta and Dirksen, Asger and Stockley, Robert A and Reiber, Johan H C and Stoel, Berend C and Warfield, Simon K and Zou, Kelly H and Wells, William M and Manj{\'{o}}n, Jos{\'{e}} V and Coup{\'{e}}, Pierrick and Mart$\backslash$'$\backslash$i-Bonmat$\backslash$'$\backslash$i, Luis and Collins, D Louis and Robles, Montserrat and Hoffman, Eric A and Lynch, David A and Barr, R Graham and van Beek, Edwin J R and Parraga, Grace and {IWPFI Investigators} and Tustison, Nick J Nicholas Nick Nicholas J and Gee, James. C and Yang, Yang and Salerno, Michael and Song, Gang and {Gee James C} and Avants, Brian B A and van Rikxoort, Eva M and van Ginneken, Bram and Yunwen, Ye and Kishida, K and Lo, Pechin and van Ginneken, Bram and Reinhardt, Joseph M and de Bruijne, Marleen and Sluimer, Ingrid and Schilham, Arnold M R and Prokop, Mathias and van Ginneken, Bram and Rosas, Ivan O and Yao, Jianhua and Avila, Nilo A and Chow, Catherine K and Gahl, William A and Gochuico, Bernadette R and DeBoer, Emily M and Swiercz, Waldemar and Heltshe, Sonya L and Anthony, Margaret M and Szefler, Paul and Klein, Rebecca and Strain, John and Brody, Alan S and Sagel, Scott D and Doel, Tom and Gavaghan, David J and Grau, Vicente and Qi, Shouliang and van Triest, Han J W and Yue, Yong and Xu, Mingjie and Kang, Yan and Korfiatis, Panayiotis D and Kalogeropoulou, Cristina and Karahaliou, Anna N and Kazantzi, Alexandra D and Costaridou, Lena I and Agam, Gady and {Armato 3rd}, Samuel G and Wu, Changhua and Nakamura, Masanori and Wada, Shigeo and Miki, Takahito and Shimada, Yasuhiro and Suda, Yuji and Tamura, Gen and Zheng, Bin and Leader, Joseph K and McMurray, Jessica M and Park, Sang Cheol and Fuhrman, Carl R and Gur, David and Sciurba, Frank C and van Rikxoort, Eva M and de Hoop, Bartjan J and Viergever, Max A and Prokop, Mathias and van Ginneken, Bram and Wang, Jiahui and Li, Feng and Li, Qiang and Prasad, Mithun N and Brown, Matthew S and Ahmad, Shama and Abtin, Fereidoun and Allen, Jared and da Costa, Irene and Kim, Heeyoung Hyun J and McNitt-Gray, Michael F and Goldin, Jonathan G and {De Nunzio}, Giorgio and Tommasi, Eleonora and Agrusti, Antonella and Cataldo, Rosella and {De Mitri}, Ivan and Favetta, Marco and Maglio, Silvio and Massafra, Andrea and Quarta, Maurizio and Torsello, Massimo and Zecca, Ilaria and Bellotti, Roberto and Tangaro, Sabina and Calvini, Piero and Camarlinghi, Niccol{\`{o}} and Falaschi, Fabio and Cerello, Piergiorgio and Oliva, Piernicola and Avants, Brian B A and Klein, Arno and Tustison, Nick J Nicholas Nick Nicholas J and Woo, J and Gee, James. C and Zaporozhan, Julia and Ley, Sebastian and Gast, Klaus Kurt and Schmiedeskamp, J{\"{o}}rg and Biedermann, Alexander and Eberle, Balthasar and Kauczor, Hans-Ulrich U and Eberhardt, Ralf and Weinheimer, Oliver and Iliyushenko, Svitlana and Herth, Felix and Kauczor, Hans-Ulrich U and Yuan, Ren and Mayo, John R and Hogg, James C and Par{\'{e}}, Peter D and McWilliams, Annette M and Lam, Stephen and Coxson, Harvey O and Yoo, Terry S and Metaxas, Dimitris N and Xu, Ye and van Beek, Edwin J R and Hwanjo, Yu and Guo, Junfeng and McLennan, Geoffrey and Hoffman, Eric A and Sonka, Milan and McLennan, Geoffrey and Guo, Junfeng and Hoffman, Eric A and Woodhouse, Neil and Wild, Jim M and Paley, Martyn N J and Fichele, Stanislao and Said, Zead and Swift, Andrew J and van Beek, Edwin J R and Wood, S A and Zerhouni, E A and Hoford, J D and Hoffman, Eric A and Mitzner, W and Vikgren, J and Bake, B and Ekberg-Jansson, A and Larsson, S and Tyl{\'{e}}n, U and Uppaluri, R and Mitsa, T and Sonka, Milan and Hoffman, Eric A and McLennan, Geoffrey and Sonka, Milan and Hartley, P G and Hunninghake, G W and McLennan, Geoffrey and Temizoz, Osman and Etlik, Omer and Sakarya, Mehmet Emin and Uzun, Kursat and Arslan, Halil and Harman, Mustafa and Demir, Mustafa Kemal and Stoel, Berend C and Stolk, Jan and Stavngaard, T and Shaker, Saher B and Bach, K S and Stoel, Berend C and Dirksen, Asger and Sled, J G and Zijdenbos, A P and Evans, Andrea C and Samee, Saba and Altes, Talissa A and Powers, Patrick L and de Lange, Eduard E and Knight-Scott, Jack and Rakes, Gary and {Mugler 3rd}, John P and Ciambotti, Jonathan M and Alford, Bennet A and Brookeman, James R and Platts-Mills, Thomas A E and Reinhardt, Joseph M and D'Souza, N D and Hoffman, Eric A and Christensen, Gary E and Hoffman, Eric A and Ding, Kai and Cao, Kunlin and Vestbo, J{\o}rgen and Hurd, Suzanne S and Agust$\backslash$'$\backslash$i, Alvar G and Jones, Paul W and Vogelmeier, Claus and Anzueto, Antonio and Barnes, Peter J and Fabbri, Leonardo M and Martinez, Fernando J and Nishimura, Masaharu and Stockley, Robert A and Sin, Don D and Rodriguez-Roisin, Roberto and {Perez 4th}, Andrew and Coxson, Harvey O and Hogg, James C and Gibson, Kevin and Thompson, Paul F M and Rogers, Robert M and Peng, Hanchuan and Long, Fuhui and Ding, Chris and Parraga, Grace and Ouriadov, Alexei and Evans, Andrea C and McKay, Shayna and Lam, Wilfred W and Fenster, Aaron and Etemad-Rezai, Roya and McCormack, David G and Santyr, Giles and Park, W and Hoffman, Eric A and Sonka, Milan and Orlandi, Ilaria and Moroni, Chiara and Camiciottoli, Gianna and Bartolucci, Maurizio and Pistolesi, Massimo and Villari, Natale and Mascalchi, Mario and Nakano, Y and Sakai, H and Muro, S and Hirai, T and Oku, Y and Nishimura, K and Mishima, M and Sakai, H and Hirai, T and Chin, K and Tsukino, M and Nishimura, K and Itoh, H and Par{\'{e}}, Peter D and Hogg, James C and Mishima, M and Coxson, Harvey O and Bosan, S and Rogers, Robert M and Sciurba, Frank C and Keenan, R J and Walley, K R and Par{\'{e}}, Peter D and Hogg, James C and M{\"{u}}ller, N L and Staples, C A and Miller, R R and Abboud, R T and M{\"{u}}ller, N L and Vedal, S and Morrison, N J and Staples, C A and Mentore, Kimiknu and Froh, Deborah K and de Lange, Eduard E and Brookeman, James R and Paget-Brown, Alix O and Altes, Talissa A and McNitt-Gray, Michael F and Goldin, Jonathan G and Johnson, T D and Tashkin, D P and Aberle, D R and McMahon, Colm J and Dodd, Jonathan D and Hill, Catherine and Woodhouse, Neil and Wild, Jim M and Fichele, Stanislao and Gallagher, Charles G and Skehan, Stephen J and van Beek, Edwin J R and Masterson, James B and Madani, Afarine and {De Maertelaer}, Viviane and Zanen, Jacqueline and Gevenois, Pierre Alain and Koumellis, Panos and van Beek, Edwin J R and Woodhouse, Neil and Fichele, Stanislao and Swift, Andrew J and Paley, Martyn N J and Hill, Catherine and Taylor, Chris J and Wild, Jim M and Kiryu, Shigeru and Sundaram, Tessa A and Kubo, Shigeto and Ohtomo, Kuni and Asakura, Toshio and Gee, James. C and Hatabu, Hiroto and Takahashi, Masaya and Lee, Linda and Heckman, George and McKelvie, Robert and Jong, Philip and D'Elia, Teresa and Hillier, Loretta M and Hu, S and Hoffman, Eric A and Reinhardt, Joseph M and Simon, Brett A and McLennan, Geoffrey and Sundaram, Tessa A and Gee, James. C and Gould, G A and MacNee, W and McLean, A and Warren, P M and Redpath, A and Best, J J and Lamb, D and Flenley, D C and Gee, James. C and Sundaram, Tessa A and Hasegawa, Ichiro and Uematsu, Hidemasa and Hatabu, Hiroto and Ederle, J R and Heussel, C P and Hast, J and Fischer, B and van Beek, Edwin J R and Ley, Sebastian and Thelen, M and Kauczor, Hans-Ulrich U and Dougherty, Lawrence and Torigian, Drew A and Affusso, John D and Asmuth, Jane C and Gefter, Warren B and de Lange, Eduard E and {Mugler 3rd}, John P and Brookeman, James R and Knight-Scott, Jack and Truwit, J D and Teates, C D and Daniel, T M and Bogorad, P L and Cates, G D and Altes, Talissa A and Patrie, James T and Parmar, Jaywant and Brookeman, James R and {Mugler 3rd}, John P and Platts-Mills, Thomas A E and Gaare, John D and Knake, Jeffrey J and {Mugler 3rd}, John P and Platts-Mills, Thomas A E and Coxson, Harvey O and Rogers, Robert M and Whittall, K P and D'yachkova, Y and Par{\'{e}}, Peter D and Sciurba, Frank C and Hogg, James C and Mayo, John R and Behzad, H and Moore, B J and Verburgt, L M and Staples, C A and Par{\'{e}}, Peter D and Hogg, James C and Cook, Tessa Sundaram and Tustison, Nick J Nicholas Nick Nicholas J and Biederer, J{\"{u}}rgen and Tetzlaff, Ralf and Gee, James. C and Chen, C C and Daponte, J S and Fox, M D and Aykac, Deniz and Hoffman, Eric A and McLennan, Geoffrey and Reinhardt, Joseph M and Altes, Talissa A and Powers, Patrick L and Knight-Scott, Jack and Rakes, Gary and Platts-Mills, Thomas A E and de Lange, Eduard E and Alford, Bennet A and {Mugler 3rd}, John P and Brookeman, James R and Ashburner, John and Cox, Robert W and Jenkinson, Mark and Beckmann, Christian F and Behrens, Timothy E J and Woolrich, Mark W and Smith, Stephen M and Fischl, Bruce and Menze, Bjoern and Reyes, Mauricio and {Van Leemput}, Koen and Wang, Hongzhi and Suh, Jung W and Das, Sandhitsu R and Pluta, John B and Craige, Caryne and Yushkevich, Paul A and Durst, Christopher R and Raghavan, Prashant and Shaffrey, Mark E and Schiff, David and Lopes, M Beatriz and Sheehan, Jason P and Tustison, Nick J Nicholas Nick Nicholas J and Patrie, James T and Xin, Wenjun and Elias, William Jeff and Liu, Kenneth C and Helm, Greg A and Cupino, Alan and Wintermark, Max and Qing, Kun and Altes, Talissa A and Tustison, Nick J Nicholas Nick Nicholas J and Feng, Xue and Chen, Xiao and Mata, Jaime F and Miller, G Wilson and de Lange, Eduard E and Tobias, William A and {Cates Jr}, Gordon D and Brookeman, James R and {Mugler 3rd}, John P and Yoder, Jonathon H and Peloquin, John M and Song, Gang and Tustison, Nick J Nicholas Nick Nicholas J and Moon, Sung M and Wright, Alexander C and Vresilovic, Edward J and Gee, James. C and Elliott, Dawn M and Said, Nicholas and Elias, William Jeff and Raghavan, Prashant and Cupino, Alan and Tustison, Nick J Nicholas Nick Nicholas J and Frysinger, Robert and Patrie, James T and Xin, Wenjun and Wintermark, Max and Tustison, Nick J Nicholas Nick Nicholas J and Elias, William Jeff and Patrie, James T and Xin, Wenjun and Demartini, Nicholas and Eames, Matt and Sumer, Suna and Lau, Benison and Cupino, Alan and Snell, John and Hananel, Arik and Kassell, Neal and Aubry, Jean-Francois and Teague, W Gerald and Tustison, Nick J Nicholas Nick Nicholas J and Altes, Talissa A and Wintermark, Max and Huss, Diane S and Shah, Binit B and Tustison, Nick J Nicholas Nick Nicholas J and Druzgal, T Jason and Kassell, Neal and Elias, William Jeff and Shrinidhi, K L and Wintermark, Max and Durst, Christopher R and Kandel, Benjamin M and Gee, James. C and Grossman, Murray C and Avants, Brian B A and Siqueira, Marcelo and Latecki, Longin J and Tustison, Nick J Nicholas Nick Nicholas J and Gallier, J and Gee, James. C},
doi = {10.1109/TPAMI.2012.143},
editor = {Camara, Oscar and Mansi, Tommaso and Pop, Mihaela and Rhode, Kawal and Sermesant, Maxime and Young, Alistair},
institution = {Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA 19104, USA. tustison@picsl.upenn.edu},
isbn = {978-3-319-14677-5},
issn = {0270-5257},
journal = {IEEE Trans Med Imaging},
keywords = {,18F-FDG PET/CT,3-D texture,3D dissolved-phase imaging,ANTs,COPD,CSSR,CST = corticospinal tract,CT,Classification,Collaboration,Collaborative software,Computer languages,Computer science,DBS = deep brain stimulation,DTI = diffusion tensor imaging,Death and Euthanasia,Fissure,GEE = generalized estimating equation,GPi = globus pallidus internus,Heterogeneity,Image registration,Insight-Toolkit,Lobe,Lung,MPRAGE = magnetization-prepared rapid acquisition gradient echo,Mechanisms,Motion estimation,Myocardial perfusion,NEX = number of excitations,Neuroimaging,Non-small cell lung cancer,Open source software,PD = Parkinson disease,Prognostic factor,Pulmonary,STN = subthalamic nucleus,Segmentation,Software engineering,Solid texture,Stereotactic body radiation therapy,TBI,Textural analysis,Texture primitive,Vim = ventral intermediate nucleus,Volumetric texture,advanced normalization tools,asthma,brain imaging,clinical development,clinical trials,compressed sensing,corrective learning,deep brain stimulation,deep learning,diagnostic radiology,diffusion tensor tractography,dual energy CT,functional neurosurgery,gas uptake,human factors,hyperpolarized gas,hyperpolarized xenon-129,joint label fusion,learning theory,legitimate peripheral participation,lobe segmentation,lung segmentation,machine learning,magnetic resonance imaging,medical imaging,motivation,multi-atlas label fusion,new drug,open source implementation,open source software development,pathophysiology,pharmaceutical industry,phenotypes,public domain software,pulmonary MRI,pulmonary image registration,pulmonary imaging,pulmonary ventilation and perfusion,quantitative CT,random forest decision tree,reviews,software engineering},
month = {aug},
number = {1},
pages = {409--422},
pmid = {22732662},
publisher = {Springer International Publishing},
series = {Lecture Notes in Computer Science},
title = {{No Title}},
url = {http://fsmsh.com/2845 http://dx.doi.org/10.1007/978-3-319-14678-2{\_}1 http://dx.doi.org/10.1016/S0734-189X(89)80014-3 http://www.goldcopd.org/Guidelines/ guidelines-resources.html https://masi.vuse.vanderbilt.edu/workshop2013/index.php/Main{\_}Page http://hdl.handle.net/1926/1323 http://hdl.handle.net/10380/3141 http://hdl.handle.net/1926/1524 http://hdl.handle.net/1926/1497 http://hdl.handle.net/1926/1503 http://hdl.handle.net/1926/470 http://hdl.handle.net/1926/500 http://hdl.handle.net/1926/475 http://hdl.handle.net/1926/171 http://dx.doi.org/10.1002/jmri.22137 http://hdl.handle.net/10380/3053 http://hdl.handle.net/1926/1372 http://hdl.handle.net/1926/1452 http://hdl.handle.net/1926/1374 http://hdl.handle.net/1926/140 http://picsl.upenn.edu/software/ants/ http://www.itksnap.org https://www.nlm.nih.gov/research/visible/visible{\_}human.html http://www.ukbiobank.ac.uk},
volume = {31},
year = {2014}
}
@article{Kay2008,
abstract = {Functional magnetic resonance imaging (fMRI) suffers from many problems that make signal estimation difficult. These include variation in the hemodynamic response across voxels and low signal-to-noise ratio (SNR). We evaluate several analysis techniques that address these problems for event-related fMRI. (1) Many fMRI analyses assume a canonical hemodynamic response function, but this assumption may lead to inaccurate data models. By adopting the finite impulse response model, we show that voxel-specific hemodynamic response functions can be estimated directly from the data. (2) There is a large amount of low-frequency noise fluctuation (LFF) in blood oxygenation level dependent (BOLD) time-series data. To compensate for this problem, we use polynomials as regressors for LFF. We show that this technique substantially improves SNR and is more accurate than high-pass filtering of the data. (3) Model overfitting is a problem for the finite impulse response model because of the low SNR of the BOLD response. To reduce overfitting, we estimate a hemodynamic response timecourse for each voxel and incorporate the constraint of time-event separability, the constraint that hemodynamic responses across event types are identical up to a scale factor. We show that this technique substantially improves the accuracy of hemodynamic response estimates and can be computed efficiently. For the analysis techniques we present, we evaluate improvement in modeling accuracy via 10-fold cross-validation.},
author = {Kay, Kendrick N and David, Stephen V and Prenger, Ryan J and Hansen, Kathleen A and Gallant, Jack L},
doi = {10.1002/hbm.20379},
institution = {Department of Psychology, University of California, Berkeley, CA 94720, USA.},
journal = {Hum Brain Mapp},
keywords = { Neurological; Models, Theoretical; Photic Stimulation; Time, blood supply/physiology; Cerebrovascular Circulation, physiology; Humans; Magnetic Resonance Imaging; Models,Artifacts; Brain Mapping; Brain},
month = {feb},
number = {2},
pages = {142--156},
pmid = {17394212},
title = {{Modeling low-frequency fluctuation and hemodynamic response timecourse in event-related fMRI.}},
url = {http://dx.doi.org/10.1002/hbm.20379},
volume = {29},
year = {2008}
}
@article{Kandel2015a,
abstract = {{\textcopyright} 2015-IOS Press and the authors. All rights reserved. Background: Psychometric tests predict conversion of mild cognitive impairment (MCI) to probable Alzheimer's disease (AD). Because the definition of clinical AD relies on those same psychometric tests, the ability of these tests to identify underlying AD pathology remains unclear. Objective: To determine the degree to which psychometric testing predicts molecular evidence of AD amyloid pathology, as indicated by cerebrospinal fluid (CSF) amyloid-$\beta$ (A$\beta$) 1-42 , in patients with MCI, as compared to neuroimaging biomarkers. Methods: We identified 408 MCI subjects with CSF A$\beta$ levels, psychometric test data, FDG-PET scans, and acceptable volumetric MR scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We used psychometric tests and imaging biomarkers in univariate and multivariate models to predict A$\beta$ status. Results: The 30-min delayed recall score of the Rey Auditory Verbal Learning Test was the best predictor of A$\beta$ status among the psychometric tests, achieving an AUC of 0.67±0.02 and odds ratio of 2.5±0.4. FDG-PET was the best imaging-based biomarker (AUC 0.67±0.03, OR 3.2±1.2), followed by hippocampal volume (AUC 0.64±0.02, OR 2.4±0.3). A multivariate analysis based on the psychometric tests improved on the univariate predictors, achieving anAUCof 0.68±0.03 (OR 3.38±1.2). Adding imaging biomarkers to the multivariate analysis did not improve the AUC. Conclusion: Psychometric tests perform as well as imaging biomarkers to predict presence of molecular markers ofADpathology in MCI patients and should be considered in the determination of the likelihood that MCI is due to AD.},
author = {Kandel, B.M. and Avants, B.B. and Gee, J.C. and Arnold, S.E. and Wolk, D.A.},
doi = {10.3233/JAD-142943},
issn = {18758908},
journal = {Journal of Alzheimer's Disease},
keywords = {Alzheimer's disease,magnetic resonance imaging,mild cognitive impairment,positron emission tomography},
number = {4},
title = {{Neuropsychological Testing Predicts Cerebrospinal Fluid Amyloid-$\beta$ in Mild Cognitive Impairment}},
volume = {46},
year = {2015}
}
@article{Adler2014,
abstract = {Recently, there has been a growing effort to analyze the morphometry of hippocampal subfields using both in vivo and postmortem magnetic resonance imaging (MRI). However, given that boundaries between subregions of the hippocampal formation (HF) are conventionally defined on the basis of microscopic features that often lack discernible signature in MRI, subfield delineation in MRI literature has largely relied on heuristic geometric rules, the validity of which with respect to the underlying anatomy is largely unknown. The development and evaluation of such rules are challenged by the limited availability of data linking MRI appearance to microscopic hippocampal anatomy, particularly in three dimensions (3D). The present paper, for the first time, demonstrates the feasibility of labeling hippocampal subfields in a high resolution volumetric MRI dataset based directly on microscopic features extracted from histology. It uses a combination of computational techniques and manual post-processing to map subfield boundaries from a stack of histology images (obtained with 200. $\mu$m spacing and 5. $\mu$m slice thickness; stained using the Kluver-Barrera method) onto a postmortem 9.4. Tesla MRI scan of the intact, whole hippocampal formation acquired with 160. $\mu$m isotropic resolution. The histology reconstruction procedure consists of sequential application of a graph-theoretic slice stacking algorithm that mitigates the effects of distorted slices, followed by iterative affine and diffeomorphic co-registration to postmortem MRI scans of approximately 1. cm-thick tissue sub-blocks acquired with 200. $\mu$m isotropic resolution. These 1. cm blocks are subsequently co-registered to the MRI of the whole HF. Reconstruction accuracy is evaluated as the average displacement error between boundaries manually delineated in both the histology and MRI following the sequential stages of reconstruction. The methods presented and evaluated in this single-subject study can potentially be applied to multiple hippocampal tissue samples in order to construct a histologically informed MRI atlas of the hippocampal formation. {\textcopyright} 2013 Elsevier Inc.},
author = {Adler, D.H. and Pluta, J. and Kadivar, S. and Craige, C. and Gee, J.C. and Avants, B.B. and Yushkevich, P.A.},
doi = {10.1016/j.neuroimage.2013.08.067},
issn = {10538119},
journal = {NeuroImage},
keywords = {Hippocampus,Histology,MRI,Postmortem,Reconstruction,Subfields},
title = {{Histology-derived volumetric annotation of the human hippocampal subfields in postmortem MRI}},
volume = {84},
year = {2014}
}
@article{Hagspiel:2000aa,
abstract = {Hyperpolarized gas MR virtual colonography was performed in plastic phantoms and in the dog colon. (3)He was laser polarized in a prototype commercial system. 2D and 3D gradient echo sequences were used to image the noble gas-filled structures. The hyperpolarized (3)He within the plastic tube and colon lumen produced high signal, providing excellent contrast from the surrounding structures. The virtual colonoscopic analysis of the canine dataset allowed visualization of the colonic features and the colonic wall from inside the colon. (3)He colonoscopy is a novel technique to visualize the colon with MRI with the application of an inert gaseous endoluminal contrast agent.},
author = {Hagspiel, K D and Altes, T A and {Mugler 3rd}, J P and {Spellman Jr}, M J and Mata, J F and Tustison, N J and Brookeman, J R},
journal = {Magn Reson Med},
month = {nov},
number = {5},
pages = {813--816},
pmid = {11064418},
title = {{{\{}MR{\}} virtual colonography using hyperpolarized {\{}(3)He{\}} as an endoluminal contrast agent: demonstration of feasibility}},
volume = {44},
year = {2000}
}
@article{Calhoun:2009aa,
abstract = {Independent component analysis (ICA) has become an increasingly utilized approach for analyzing brain imaging data. In contrast to the widely used general linear model (GLM) that requires the user to parameterize the data (e.g. the brain's response to stimuli), ICA, by relying upon a general assumption of independence, allows the user to be agnostic regarding the exact form of the response. In addition, ICA is intrinsically a multivariate approach, and hence each component provides a grouping of brain activity into regions that share the same response pattern thus providing a natural measure of functional connectivity. There are a wide variety of ICA approaches that have been proposed, in this paper we focus upon two distinct methods. The first part of this paper reviews the use of ICA for making group inferences from fMRI data. We provide an overview of current approaches for utilizing ICA to make group inferences with a focus upon the group ICA approach implemented in the GIFT software. In the next part of this paper, we provide an overview of the use of ICA to combine or fuse multimodal data. ICA has proven particularly useful for data fusion of multiple tasks or data modalities such as single nucleotide polymorphism (SNP) data or event-related potentials. As demonstrated by a number of examples in this paper, ICA is a powerful and versatile data-driven approach for studying the brain.},
author = {Calhoun, Vince D and Liu, Jingyu and Adali, T{\"{u}}lay},
doi = {10.1016/j.neuroimage.2008.10.057},
journal = {Neuroimage},
month = {mar},
number = {1 Suppl},
pages = {S163--72},
pmid = {19059344},
title = {{A review of group {\{}ICA{\}} for f{\{}MRI{\}} data and {\{}ICA{\}} for joint inference of imaging, genetic, and {\{}ERP{\}} data}},
volume = {45},
year = {2009}
}
@article{Kandel2014a,
abstract = {Although much attention has recently been focused on single-subject functional networks, using methods such as resting-state functional MRI, methods for constructing single-subject structural networks are in their infancy. Single-subject cortical networks aim to describe the self-similarity across the cortical structure, possibly signifying convergent developmental pathways. Previous methods for constructing single-subject cortical networks have used patch-based correlations and distance metrics based on curvature and thickness. We present here a method for constructing similarity-based cortical structural networks that utilizes a rotation-invariant representation of structure. The resulting graph metrics are closely linked to age and indicate an increasing degree of closeness throughout development in nearly all brain regions, perhaps corresponding to a more regular structure as the brain matures. The derived graph metrics demonstrate a four-fold increase in power for detecting age as compared to cortical thickness. This proof of concept study indicates that the proposed metric may be useful in identifying biologically relevant cortical patterns.},
author = {Kandel, B.M. and Wang, D.J. and Gee, J.C. and Avants, B.B.},
journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
number = {Pt 3},
title = {{Single-subject structural networks with closed-form rotation invariant matching mprove power in developmental studies of the cortex.}},
volume = {17},
year = {2014}
}
@article{Avants2009,
author = {Avants, B. and Khan, A. and McCluskey, L. and Elman, L. and Grossman, M.},
doi = {10.1001/archneurol.2008.542},
issn = {00039942},
journal = {Archives of Neurology},
number = {1},
title = {{Longitudinal cortical atrophy in amyotrophic lateral sclerosis with frontotemporal dementia}},
volume = {66},
year = {2009}
}
@article{Avants2010b,
abstract = {We present a new algorithm for reliable, unbiased, multivariate longitudinal analysis of cortical and white matter atrophy rates with penalized statistical methods. The pipeline uses a step-wise approach to transform and personalize template information first to a single-subject template (SST) and then to the individual's time series data. The first stream of information flows from group template to the SST; the second flows from the SST to the individual time-points and provides unbiased, prior-based segmentation and measurement of cortical thickness. MRI-bias correction, consistent longitudinal segmentation, cortical parcellation and cortical thickness estimation are all based on strong use of the subject-specific priors built from initial diffeomorphic mapping between the SST and optimal group template. We evaluate our approach with both test-retest data and with application to a driving biological problem. We use test-retest data to show that this approach produces (a) zero change when the retest data contains the same image content as the test data and (b) produces normally distributed, low variance estimates of thickness change centered at zero when test-retest data is collected near in time to test data. We also show that our approach--when combined with sparse canonical correlation analysis--reveals plausible, significant, annualized decline in cortical thickness and white matter volume when contrasting frontotemporal dementia and normal aging.},
author = {Avants, B. and Cook, P.A. and McMillan, C. and Grossman, M. and Tustison, N.J. and Zheng, Y. and Gee, J.C.},
journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
number = {Pt 1},
title = {{Sparse unbiased analysis of anatomical variance in longitudinal imaging.}},
volume = {13},
year = {2010}
}
@article{McKeown:1998aa,
abstract = {Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski ([1995]: Neural Comput 7:1129-1159). We decomposed eight fMRI data sets from 4 normal subjects performing Stroop color-naming, the Brown and Peterson work/number task, and control tasks into spatially independent components. Each component consisted of voxel values at fixed three-dimensional locations (a component "map"), and a unique associated time course of activation. Given data from 144 time points collected during a 6-min trial, ICA extracted an equal number of spatially independent components. In all eight trials, ICA derived one and only one component with a time course closely matching the time course of 40-sec alternations between experimental and control tasks. The regions of maximum activity in these consistently task-related components generally overlapped active regions detected by standard correlational analysis, but included frontal regions not detected by correlation. Time courses of other ICA components were transiently task-related, quasiperiodic, or slowly varying. By utilizing higher-order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and a related fourth-order decomposition technique (Comon [1994]: Signal Processing 36:11-20) were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. For each subject, the time courses and active regions of the task-related ICA components were consistent across trials and were robust to the addition of simulated noise. Simulated movement artifact and simulated task-related activations added to actual fMRI data were clearly separated by the algorithm. ICA can be used to distinguish between nontask-related signal components, movements, and other artifacts, as well as consistently or transiently task-related fMRI activations, based on only weak assumptions about their spatial distributions and without a priori assumptions about their time courses. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations, especially for uncovering unpredictable transient patterns of brain activity associated with performance of psychomotor tasks.},
author = {McKeown, M J and Makeig, S and Brown, G G and Jung, T P and Kindermann, S S and Bell, A J and Sejnowski, T J},
journal = {Hum Brain Mapp},
number = {3},
pages = {160--188},
pmid = {9673671},
title = {{Analysis of fMRI data by blind separation into independent spatial components}},
volume = {6},
year = {1998}
}
@inproceedings{Kim2012,
abstract = {We are interested in investigating white matter connectivity using a novel computational framework that does not use diffusion tensor imaging (DTI) but only uses T1-weighted magnetic resonance imaging. The proposed method relies on correlating Jacobian determinants across different voxels based on the tensor-based morphometry (TBM) framework. In this paper, we show agreement between the TBM-based white matter connectivity and the DTI-based white matter atlas. As an application, altered white matter connectivity in a clinical population is determined. {\textcopyright} 2012 IEEE.},
author = {Kim, S.-G. and Lee, H. and Chung, M.K. and Hanson, J.L. and Avants, B.B. and Gee, J.C. and Davidson, R.J. and Pollak, S.D.},
booktitle = {Proceedings - International Symposium on Biomedical Imaging},
doi = {10.1109/ISBI.2012.6235479},
isbn = {9781457718588},
issn = {19457928},
keywords = {brain network,structural connectivity,tensor-based morphometry,white matter atlas},
title = {{Agreement between the white matter connectivity based on the tensor-based morphometry and the volumetric white matter parcellations based on diffusion tensor imaging}},
year = {2012}
}
@article{Dhillon2014,
abstract = {We present a new framework for prior-constrained sparse decomposition of matrices derived from the neuroimaging data and apply this method to functional network analysis of a clinically relevant population. Matrix decomposition methods are powerful dimensionality reduction tools that have found widespread use in neuroimaging. However, the unconstrained nature of these totally data-driven techniques makes it difficult to interpret the results in a domain where network-specific hypotheses may exist.We propose a novel approach, Prior Based Eigenanatomy (p-Eigen), which seeks to identify a data-driven matrix decomposition but at the same time constrains the individual components by spatial anatomical priors (probabilistic ROIs). We formulate our novel solution in terms of prior-constrained ℓ 1 penalized (sparse) principal component analysis. p-Eigen starts with a common functional parcellation for all the subjects and refines it with subject-specific information. This enables modeling of the inter-subject variability in the functional parcel boundaries and allows us to construct subject-specific networks with reduced sensitivity to ROI placement.We show that while still maintaining correspondence across subjects, p-Eigen extracts biologically-relevant and patient-specific functional parcels that facilitate hypothesis-driven network analysis. We construct default mode network (DMN) connectivity graphs using p-Eigen refined ROIs and use them in a classification paradigm. Our results show that the functional connectivity graphs derived from p-Eigen significantly aid classification of mild cognitive impairment (MCI) as well as the prediction of scores in a Delayed Recall memory task when compared to graph metrics derived from 1) standard registration-based seed ROI definitions, 2) totally data-driven ROIs, 3) a model based on standard demographics plus hippocampal volume as covariates, and 4) Ward Clustering based data-driven ROIs. In summary, p-Eigen incarnates a new class of prior-constrained dimensionality reduction tools that may improve our understanding of the relationship between MCI and functional connectivity. {\textcopyright} 2014.},
author = {Dhillon, P.S. and Wolk, D.A. and Das, S.R. and Ungar, L.H. and Gee, J.C. and Avants, B.B.},
doi = {10.1016/j.neuroimage.2014.05.026},
issn = {10959572},
journal = {NeuroImage},
keywords = {Data-driven parcellations,Default mode network,Delayed recall,FMRI,MCI,PCA,ROI},
title = {{Subject-specific functional parcellation via Prior Based Eigenanatomy}},
volume = {99},
year = {2014}
}
@article{Zhang2007,
abstract = {Spatial normalization of diffusion tensor images plays a key role in voxel-based analysis of white matter (WM) group differences. Currently, it has been achieved using low-dimensional registration methods in the large majority of clinical studies. This paper aims to motivate the use of high-dimensional normalization approaches by generating evidence of their impact on the findings of such studies. Using an ongoing amyotrophic lateral sclerosis (ALS) study, we evaluated three normalization methods representing the current range of available approaches: low-dimensional normalization using the fractional anisotropy (FA), high-dimensional normalization using the FA, and high-dimensional normalization using full tensor information. Each method was assessed in terms of its ability to detect significant differences between ALS patients and controls. Our findings suggest that inadequate normalization with low-dimensional approaches can result in insufficient removal of shape differences which in turn can confound FA differences in a complex manner, and that utilizing high-dimensional normalization can both significantly minimize the confounding effect of shape differences to FA differences and provide a more complete description of WM differences in terms of both size and tissue architecture differences. We also found that high-dimensional approaches, by leveraging full tensor features instead of tensor-derived indices, can further improve the alignment of WM tracts. {\textcopyright} 2007 IEEE.},
author = {Zhang, H. and Avants, B.B. and Yushkevich, P.A. and Woo, J.H. and Wang, S. and McCluskey, L.F. and Elman, L.B. and Melhem, E.R. and Gee, J.C.},
doi = {10.1109/TMI.2007.906784},
issn = {02780062},
journal = {IEEE Transactions on Medical Imaging},
keywords = {Diffusion tensor images,Spatial normalization},
number = {11},
title = {{High-dimensional spatial normalization of diffusion tensor images improves the detection of white matter differences: An example study using amyotrophic lateral sclerosis}},
volume = {26},
year = {2007}
}
@article{Maga2017,
abstract = {{\textcopyright} 2017 Anatomical Society Laboratory mice are staples for evo/devo and genetics studies. Inbred strains provide a uniform genetic background to manipulate and understand gene–environment interactions, while their crosses have been instrumental in studies of genetic architecture, integration and modularity, and mapping of complex biological traits. Recently, there have been multiple large-scale studies of laboratory mice to further our understanding of the developmental basis, evolution, and genetic control of shape variation in the craniofacial skeleton (i.e. skull and mandible). These experiments typically use micro-computed tomography (micro-CT) to capture the craniofacial phenotype in 3D and rely  on manually annotated anatomical landmarks to conduct statistical shape analysis. Although the common choice for imaging modality and phenotyping provides the potential for collaborative research for even larger studies with more statistical power, the investigator (or lab-specific) nature of the data collection hampers these efforts. Investigators are rightly concerned that subtle differences in how anatomical landmarks were recorded will create systematic bias between studies that will eventually influence scientific findings. Even if researchers are willing to repeat landmark annotation on a combined dataset, different lab practices and software choices may create obstacles for standardization beyond the underlying imaging data. Here, we propose a freely available analysis system that could assist in the standardization of micro-CT studies in the mouse. Our proposal uses best practices developed in biomedical imaging and takes advantage of existing open-source software and imaging formats. Our first contribution is the creation of a synthetic template for the adult mouse craniofacial skeleton from 25 inbred strains and five F1 crosses that are widely used in biological research. The template contains a fully segmented cranium, left and right hemi-mandibles, endocranial space, and the first few cervical vertebrae. We have been using this template in our lab to segment and isolate cranial structures in an automated fashion from a mixed population of mice, including craniofacial mutants, aged 4–12.5 weeks. As a secondary contribution, we demonstrate an application of nearly automated shape analysis, using symmetric diffeomorphic image registration. This approach, which we call diGPA, closely approximates the popular generalized Procrustes analysis (GPA) but negates the collection of anatomical landmarks. We achieve our goals by using the open-source advanced normalization tools (ANT) image quantification library, as well as its associated R library (ANTsR) for statistical image analysis. Finally, we make a plea to investigators to commit to using open imaging standards and software in their labs to the extent possible to increase the potential for data exchange and improve the reproducibility of findings. Future work will incorporate more anatomical detail (such as individual cranial bones, turbinals, dentition, middle ear ossicles) and more diversity into the template.},
author = {Maga, A.M. and Tustison, N.J. and Avants, B.B.},
doi = {10.1111/joa.12645},
issn = {14697580},
journal = {Journal of Anatomy},
keywords = {geometrics morphometrics,image processing,image-based shape analysis,landmarking,microCT,segmentation},
number = {3},
title = {{A population level atlas of Mus musculus craniofacial skeleton and automated image-based shape analysis}},
volume = {231},
year = {2017}
}
@article{Avants2008,
abstract = {Rationale and Objectives: Diffusion tensor (DT) and T1 structural magnetic resonance images provide unique and co mplementary tools for quantifying the living brain. We leverage both modalities in a diffeomorphic normalization method that unifies analysis of clinical datasets in a consistent and inherently multivariate (MV) statistical framework. We use this technique to study MV effects of traumatic brain injury (TBI). Materials and Methods: We contrast T1 and DT image-based measurements in the thalamus and hippocampus of 12 TBI survivors and nine matched controls normalized to a combined DT and T1 template space. The normalization method uses maps that are topology-preserving and unbiased. Normalization is based on the full tensor of information at each voxel and, simultaneously, the similarity between high-resolution features derived from T1 data. The technique is termed symmetric normalization for MV neuroanatomy (SyNMN). Voxel-wise MV statistics on the local volume and mean diffusion are assessed with Hotelling's T 2 test with correction for multiple comparisons. Results: TBI significantly (false discovery rate P  {\textless}  .05) reduces volume and increases mean diffusion at coincident locations in the mediodorsal thalamus and anterior hippocampus. Conclusions: SyNMN reveals evidence that TBI compromises the limbic system. This TBI morphometry study and an additional performance evaluation contrasting SyNMN with other methods suggest that the DT component may aid normalization quality. {\textcopyright} 2008 AUR.},
author = {Avants, B. and Duda, J.T. and Kim, J. and Zhang, H. and Pluta, J. and Gee, J.C. and Whyte, J.},
doi = {10.1016/j.acra.2008.07.007},
issn = {10766332},
journal = {Academic Radiology},
keywords = {Diffeomorphism,diffusion,morphometry,traumatic brain injury,unbiased},
number = {11},
title = {{Multivariate Analysis of Structural and Diffusion Imaging in Traumatic Brain Injury}},
volume = {15},
year = {2008}
}
@article{Avants2015,
abstract = {Magnetic resonance imaging (MRI) captures the dynamics of brain development with multiple modalities that quantify both structure and function. These measurements may yield valuable insights into the neural patterns that mark healthy maturation or that identify early risk for psychiatric disorder. The Pediatric Template of Brain Perfusion (PTBP) is a free and public neuroimaging resource that will help accelerate the understanding of childhood brain development as seen through the lens of multiple modality neuroimaging and in relation to cognitive and environmental factors. The PTBP uses cross-sectional and longitudinal MRI to quantify cortex, white matter, resting state functional connectivity and brain perfusion, as measured by Arterial Spin Labeling (ASL), in 120 children 7-18 years of age. We describe the PTBP and show, as a demonstration of validity, that global summary measurements capture the trajectories that demarcate critical turning points in brain maturation. This novel resource will allow a more detailed understanding of the network-level, structural and functional landmarks that are obtained during normal adolescent brain development.},
author = {Avants, B.B. and Duda, J.T. and Kilroy, E. and Krasileva, K. and Jann, K. and Kandel, B.T. and Tustison, N.J. and Yan, L. and Jog, M. and Smith, R. and Wang, Y. and Dapretto, M. and Wang, D.J.},
doi = {10.1038/sdata.2015.3},
issn = {20524463},
journal = {Scientific data},
title = {{The pediatric template of brain perfusion}},
volume = {2},
year = {2015}
}
@inproceedings{Sundaram2004,
abstract = {We present methods for extracting an average representation of respiratory dynamics from free-breathing lung MR images. Due to individual variations in respiration and the difficulties of real-time pulmonary imaging, time of image acquisition bears little physiologic meaning. Thus, we reparameterize each individual's expiratory image sequence with respect to normalized lung capacity (area, as a substitute for volume), where 1 represents maximal capacity and 0 minimal capacity, as measured by semi-automated image segmentation. This process, combined with intra-subject pairwise non-rigid registration, is used to interpolate intermediate images in the normalized capacity interval [0,1], Images from separate individuals with the same normalized capacity are taken to be at corresponding points during expiration. We then construct an average representation of pulmonary dynamics from capacity-matched image sequences. This methodology is illustrated usi ng two coronal 2-D datasets from healthy individuals. {\textcopyright} Springer-Verlag Berlin Heidelberg 2004.},
author = {Sundaram, T.A. and Avants, B.B. and Gee, J.C.},
booktitle = {Lecture Notes in Computer Science},
issn = {03029743},
number = {1 PART 2},
title = {{A dynamic model of average lung deformation using capacity-based reparameterization and shape averaging of lung MR images}},
volume = {3217},
year = {2004}
}
@article{Schmaal2017,
abstract = {The neuro-anatomical substrates of major depressive disorder (MDD) are still not well understood, despite many neuroimaging studies over the past few decades. Here we present the largest ever worldwide study by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Major Depressive Disorder Working Group on cortical structural alterations in MDD. Structural T1-weighted brain magnetic resonance imaging (MRI) scans from 2148 MDD patients and 7957 healthy controls were analysed with harmonized protocols at 20 sites around the world. To detect consistent effects of MDD and its modulators on cortical thickness and surface area estimates derived from MRI, statistical effects from sites were meta-analysed separately for adults and adolescents. Adults with MDD had thinner cortical gray matter than controls in the orbitofrontal cortex (OFC), anterior and posterior cingulate, insula and temporal lobes (Cohen's d effect sizes: -0.10 to -0.14). These effects were most pronounced in first episode and adult-onset patients ({\textgreater}21 years). Compared to matched controls, adolescents with MDD had lower total surface area (but no differences in cortical thickness) and regional reductions in frontal regions (medial OFC and superior frontal gyrus) and primary and higher-order visual, somatosensory and motor areas (d: -0.26 to -0.57). The strongest effects were found in recurrent adolescent patients. This highly powered global effort to identify consistent brain abnormalities showed widespread cortical alterations in MDD patients as compared to controls and suggests that MDD may impact brain structure in a highly dynamic way, with different patterns of alterations at different stages of life.Molecular Psychiatry advance online publication, 3 May 2016; doi:10.1038/mp.2016.60.},
author = {Schmaal, L. and Hibar, D. P. and S{\"{a}}mann, P. G. and Hall, G. B. and Baune, B. T. and Jahanshad, N. and Cheung, J. W. and {Van Erp}, T. G.M. and Bos, D. and Ikram, M. A. and Vernooij, M. W. and Niessen, W. J. and Tiemeier, H. and Hofman, A. and Wittfeld, K. and Grabe, H. J. and Janowitz, D. and B{\"{u}}low, R. and Selonke, M. and V{\"{o}}lzke, H. and Grotegerd, D. and Dannlowski, U. and Arolt, V. and Opel, N. and Heindel, W. and Kugel, H. and Hoehn, D. and Czisch, M. and Couvy-Duchesne, B. and Renter{\'{i}}a, M. E. and Strike, L. T. and Wright, M. J. and Mills, N. T. and {De Zubicaray}, G. I. and McMahon, K. L. and Medland, S. E. and Martin, N. G. and Gillespie, N. A. and Goya-Maldonado, R. and Gruber, O. and Kr{\"{a}}mer, B. and Hatton, S. N. and Lagopoulos, J. and Hickie, I. B. and Frodl, T. and Carballedo, A. and Frey, E. M. and {Van Velzen}, L. S. and Penninx, B. W.J.H. and {Van Tol}, M. J. and {Van der Wee}, N. J. and Davey, C. G. and Harrison, B. J. and Mwangi, B. and Cao, B. and Soares, J. C. and Veer, I. M. and Walter, H. and Schoepf, D. and Zurowski, B. and Konrad, C. and Schramm, E. and Normann, C. and Schnell, K. and Sacchet, M. D. and Gotlib, I. H. and MacQueen, G. M. and Godlewska, B. R. and Nickson, T. and McIntosh, A. M. and Papmeyer, M. and Whalley, H. C. and Hall, J. and Sussmann, J. E. and Li, M. and Walter, M. and Aftanas, L. and Brack, I. and Bokhan, N. A. and Thompson, P. M. and Veltman, D. J.},
doi = {10.1038/mp.2016.60},
isbn = {1476-5578 (Electronic) 1359-4184 (Linking)},
issn = {14765578},
journal = {Molecular Psychiatry},
pmid = {27137745},
title = {{Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group}},
year = {2017}
}
@article{Tustison2009,
abstract = {Previous contributions to both the research [1] and open source software [2]  communities detailed a generalization of a fast scalar field fitting technique for cubic B-splines based on the work originally proposed by Lee et al. [3]. One advantage of our proposed generalized B-spline fitting approach is its immediate application to a class of nonrigid registration techniques frequently employed in medical image analysis. Specifically, these registration techniques fall under the rubric of free-form deformation (FFD) approaches in which the object to be registered is embedded within a B-spline object. The deformation of the B-spline object describes the transformation of the image registration solution. Representative of this class of techniques, and often cited within the relevant community, is the formulation of Rueckert et al. [4]  who employed cubic splines with normalized mutual information to study breast deformation. Similar techniques from various groups provided incremental novelty in the form of disparate explicit regularization terms, as well as the employment of various image metrics and tailored optimization methods. For several algorithms, the underlying gradient-based optimization retained the essential characteristics of Rueckert's original contribution. The contribution which we provide in this paper is two-fold: 1) the observation that the generic FFD framework is intrinsically susceptible to problematic energy topographies and 2) that the standard gradient used in FFD image registration can be modified to a well-understood preconditioned form which substantially improves performance. This is demonstrated with theoretical discussion and comparative evaluation experimentation. {\textcopyright} 2009 IEEE.},
author = {Tustison, N.J. and Avants, B.B. and Gee, J.C.},
doi = {10.1109/TIP.2008.2010072},
issn = {10577149},
journal = {IEEE Transactions on Image Processing},
keywords = {B-splines,Directly manipulated free-form deformation,Free-form deformation (FFD),Image registration,Preconditioner},
number = {3},
title = {{Directly manipulated free-form deformation image registration}},
volume = {18},
year = {2009}
}
@article{Wu2015,
abstract = {BACKGROUND AND PURPOSE: Traditional methods of dating a pregnancy based on history or sonographic assessment have a large variation in the third trimester. We aimed to assess the ability of various quantitative measures of brain cortical folding on MR imaging in determining fetal gestational age in the third trimester. MATERIALS AND METHODS: We evaluated 8 different quantitative cortical folding measures to predict gestational age i n 33 healthy fetuses by using T2-weighted fetal MR imaging. We compared the accuracy of the prediction of gestational age by these cortical folding measures with the accuracy of prediction by brain volume measurement and by a previously reported semiquantitative visual scale of brain maturity. Regression models were constructed, and measurement biases and variances were determined via a cross-validation procedure. RESULTS: The cortical folding measures are accurate in the estimation and prediction of gestational age (mean of the absolute error, 0.43 ± 0.45 weeks) and perform better than (P = .024) brain volume (mean of the absolute error, 0.72 ± 0.61 weeks) or sonography measures (SDs approximately 1.5 weeks, as reported in literature). Prediction accuracy is comparable with that of the semiquantitative visual assessment score (mean, 0.57 ± 0.41 weeks). CONCLUSIONS: Quantitative cortical folding measures such as global average curvedness can be an accurate and reliable estimator of gestational age and brain maturity for healthy fetuses in the third trimester and have the potential to be an indicator of brain-growth delays for at-risk fetuses and preterm neonates.},
author = {Wu, J. and Awate, S.P. and Licht, D.J. and Clouchoux, C. and {Du Plessis}, A.J. and Avants, B.B. and Vossough, A. and Gee, J.C. and Limperopoulos, C.},
doi = {10.3174/ajnr.A4357},
issn = {1936959X},
journal = {American Journal of Neuroradiology},
number = {7},
title = {{Assessment of MRI-based automated fetal cerebral cortical folding measures in prediction of gestational age in the third trimester}},
volume = {36},
year = {2015}
}
@article{Avants2011a,
abstract = {We introduce Atropos, an ITK-based mul-tivariate n-class open source segmentation algorithm distributed with ANTs (http://www.picsl.upenn.edu/ANTs). The Bayesian formulation of the segmentation problem is solved using the Expectation Maximization (EM) algorithm with the modeling of the class intensities based on either parametric or non-parametric finite mixtures. Atropos is capable of incorporating spatial prior probability maps (sparse), prio r label maps and/or Markov Random Field (MRF) modeling. Atropos has also been efficiently implemented to handle large quantities of possible labelings (in the experimental section, we use up to 69 classes) with a minimal memory footprint. This work describes the technical and implementation aspects of Atropos and evaluates its performance on two different ground-truth datasets. First, we use the BrainWeb dataset from Montreal Neurological Institute to evaluate three-tissue segmentation performance via (1) K-means segmentation without use of template data; (2) MRF segmentation with initialization by prior probability maps derived from a group template; (3) Prior-based segmentation with use of spatial prior probability maps derived from a group template. We also evaluate Atropos performance by using spatial priors to drive a 69-class EM segmentation problem derived from the Hammers atlas from University College London. These evaluation studies, combined with illustrative examples that exercise Atropos options, demonstrate both performance and wide applicability of this new platform-independent open source segmentation tool. {\textcopyright} Springer Science+Business Media, LLC 2011.},
author = {Avants, B.B. and Tustison, N.J. and Wu, J. and Cook, P.A. and Gee, J.C.},
doi = {10.1007/s12021-011-9109-y},
issn = {15392791},
journal = {Neuroinformatics},
keywords = {BrainWeb,Cortical parcellation,Evaluation,ITK,Image segmentation,Multivariate,Open source},
number = {4},
title = {{An open source multivariate framework for N-tissue segmentation with evaluation on public data}},
volume = {9},
year = {2011}
}
@article{Kay2013,
abstract = {In task-based functional magnetic resonance imaging (fMRI), researchers seek to measure fMRI signals related to a given task or condition. In many circumstances, measuring this signal of interest is limited by noise. In this study, we present GLMdenoise, a technique that improves signal-to-noise ratio (SNR) by entering noise regressors into a general linear model (GLM) analysis of fMRI data. The noise regressors are derived by conducting an initial model fit to determine voxels unrelated to the experimental paradigm, performing principal components analysis (PCA) on the time-series of these voxels, and using cross-validation to select the optimal number of principal components to use as noise regressors. Due to the use of data resampling, GLMdenoise requires and is best suited for datasets involving multiple runs (where conditions repeat across runs). We show that GLMdenoise consistently improves cross-validation accuracy of GLM estimates on a variety of event-related experimental datasets and is accompanied by substantial gains in SNR. To promote practical application of methods, we provide MATLAB code implementing GLMdenoise. Furthermore, to help compare GLMdenoise to other denoising methods, we present the Denoise Benchmark (DNB), a public database and architecture for evaluating denoising methods. The DNB consists of the datasets described in this paper, a code framework that enables automatic evaluation of a denoising method, and implementations of several denoising methods, including GLMdenoise, the use of motion parameters as noise regressors, ICA-based denoising, and RETROICOR/RVHRCOR. Using the DNB, we find that GLMdenoise performs best out of all of the denoising methods we tested.},
author = {Kay, Kendrick N and Rokem, Ariel and Winawer, Jonathan and Dougherty, Robert F and Wandell, Brian A},
doi = {10.3389/fnins.2013.00247},
institution = {Department of Psychology, Stanford University Stanford, CA, USA.},
journal = {Front Neurosci},
pages = {247},
pmid = {24381539},
title = {{GLMdenoise: a fast, automated technique for denoising task-based fMRI data.}},
url = {http://dx.doi.org/10.3389/fnins.2013.00247},
volume = {7},
year = {2013}
}
@inproceedings{Contrella2012,
abstract = {Although 3He MRI permits compelling visualization of the pulmonary air spaces, quantitation of absolute ventilation is difficult due to confounds such as field inhomogeneity and relative intensity differences between image acquisition - the latter complicating longitudinal investigations of ventilation variation with respiratory alterations. To address these potential difficulties, we present a 4-D segmentation and normalization approach for intra-subject quantitative analysis of lung hyperpolarized 3He MRI. After normalization, which combines bias correction and relative intensity scaling between longitudinal data, partitioning of the lung volume time series is performed by iterating between modeling of the combined intensity histogram as a Gaussian mixture model and modulating the spatial heterogeneity tissue class assignments through Markov random field modeling. Evaluation of the algorithm was retrospectively applied to a cohort of 10 asthmatics between 19-25 years old in which spirometry and 3He MR ventilation images were acquired both before and after respiratory exacerbation by a bronchoconstricting agent (methacholine). Acquisition was repeated under the same conditions from 7 to 467 days (mean ± standard deviation: 185 ± 37.2) later. Several techniques were evaluated for matching intensities between the pre and post-methacholine images with the 95 th percentile value histogram matching demonstrating superior correlations with spirometry measures. Subsequent analysis evaluated segmentation parameters for assessing ventilation change in this cohort. Current findings also support previous research that areas of poor ventilation in response to bronchoconstriction are relatively consistent over time. {\textcopyright} 2012 SPIE.},
author = {Contrella, B. and Tustison, N.J. and Altes, T.A. and Avants, B.B. and {Mugler III}, J.P. and {De Lange}, E.E.},
booktitle = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},
doi = {10.1117/12.911032},
isbn = {9780819489661},
issn = {16057422},
keywords = {Bias correction,Hyperpolarized 3He,Lung,Segmentation},
title = {{4-D segmentation and normalization of 3He MR images for intra-subject assessment of ventilated lung volumes}},
volume = {8317},
year = {2012}
}
@article{Hotelling:1935aa,
author = {Hotelling, H},
journal = {J. Educ. Psychol.},
title = {{Canonical Correlation Analysis (CCA)}},
year = {1935}
}
@book{Avants2003c,
abstract = {This work explores a priori constraints based on human landmarking for defining the parameters of  {\&}  variational curve registration algorithm. The result is a method for designing variational energies that adjust the optimization process over the solution domain such that features which are salient in the human decision-making process are used. The application here is locating correspondence of corpora callosa that agree with expert user data. General principles that guide our particular application are first stated. These principles involve the definition of a generic variational problem and an associated optimization algorithm, given here for the case of matching curves. A small set of specific similarity criterion for curves is then defined. The ability of each feature to find correspondences that relate to human decisions is individually tested. Following the results of this study, a Maximum a Posteriori (MAP) automatic landmarking and correspondence method is developed. The probabilities associated with the MAP estimates are also used to define variational weights that vary over the solution's domain. These methods are both evaluated with respect to known correspondences and inter-user variability. {\textcopyright} Springer-Verlag Berlin Heidelberg 2003.},
author = {Avants, B. and Gee, J.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
issn = {03029743},
title = {{Formulation and evaluation of variational curve matching with prior constraints}},
volume = {2717},
year = {2003}
}
@inproceedings{Das2007,
abstract = {We present a measure of gray matter (GM) thickness based on local surface models in the image domain. Thickness is measured by integrating GM probability maps along the white matter (WM) surface normal direction. The method is simple to implement and allows statistical tests to be performed in the gray matter volume. A novel topology preserving segmentation method is introduced that is able to accurately recover GM in deep sulci. We apply this methodology to a longitudinal study of gray matter atrophy in a patient cohort diagnosed with frontotemporal dementia (FTD) spectrum disorders. Following image-based normalization of GM thickness maps, results show significant reduction in cortical thickness in several Brodmann areas spanning temporal, parietal and frontal lobes across subjects. {\textcopyright}2007 IEEE.},
author = {Das, S.R. and Avants, B.B. and Grossman, M. and Gee, J.C.},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
doi = {10.1109/ICCV.2007.4409136},
title = {{Measuring cortical thickness using an image domain local surface model and topology preserving segmentation}},
year = {2007}
}
@inproceedings{Wu2014,
abstract = {{\textcopyright} 2014 IEEE. In the absence of a neonatal template with cortical subregion labels, it can be extremely difficult to obtain cortical parcellation of new neonatal brain images automatically. This paper addresses this problem by utilizing adult templates with rich cortical annotation and a neonatal template with simple tissue labels. Theoretical feasibility is assured because of the preservation of brain putative cytoarchitectonic boundaries from birth to adulthood. We use large deformation registration to propagate neuroanatomical labels from adult to neonatal brain and perform multi-atlas labeling based on accurate prior-based tissue segmentation. We evaluate the repeatability of the labeling by cross-validation with training and testing data. Preliminary results show interesting relationship between the volumes of labels and independent measures of neonatal development and maternal characteristics.},
author = {Wu, J. and Ashtari, M. and Betancourt, L.M. and Brodsky, N.L. and Giannetta, J.M. and Gee, J.C. and Hurt, H. and Avants, B.B.},
booktitle = {2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014},
isbn = {9781467319591},
title = {{Cortical parcellation for neonatal brains}},
year = {2014}
}
@article{Hotelling:1936aa,
author = {Hotelling, H},
journal = {Biometrika},
pages = {321--377},
title = {{Relations between two sets of variants}},
year = {1936}
}
@article{Ash2010,
abstract = {The nature and frequency of speech production errors in neurodegenerative disease have not previously been precisely quantified. In the present study, 16 patients with a progressive form of non-fluent aphasia (PNFA) were asked to tell a story from a wordless children's picture book. Errors in production were classified as either phonemic, involving language-based deformations that nevertheless result in possible sequences of English speech segments; or phonetic, involving a motor planning deficit and resulting in non-English speech segments. The distribution of cortical atrophy as revealed by structural MRI scans was examined quantitatively in a subset of PNFA patients (N = 7). The few errors made by healthy seniors were only phonemic in type. PNFA patients made more than four times as many errors as controls. This included both phonemic and phonetic errors, with a preponderance of errors (82{\%}) classified as phonemic. The majority of phonemic errors were substitutions that shared most distinctive features with the target phoneme. The systematic nature of these substitutions is not consistent with a motor planning deficit. Cortical atrophy was found in prefrontal regions bilaterally and peri-Sylvian regions of the left hemisphere. We conclude that the speech errors produced by PNFA patients are mainly errors at the phonemic level of language processing and are not caused by a motor planning impairment. {\textcopyright} 2009 Elsevier Inc. All rights reserved.},
author = {Ash, S. and McMillan, C. and Gunawardena, D. and Avants, B. and Morgan, B. and Khan, A. and Moore, P. and Gee, J. and Grossman, M.},
doi = {10.1016/j.bandl.2009.12.001},
issn = {0093934X},
journal = {Brain and Language},
keywords = {Phonology,Progressive non-fluent aphasia,Speech errors},
number = {1},
title = {{Speech errors in progressive non-fluent aphasia}},
volume = {113},
year = {2010}
}
@article{Agarwal2012,
abstract = {Many statistical {\$}M{\$}-estimators are based on convex optimization problems formed by the combination of a data-dependent loss function with a norm-based regularizer. We analyze the convergence rates of projected gradient and composite gradient methods for solving such problems, working within a high-dimensional framework that allows the data dimension {\$}\backslashpdim{\$} to grow with (and possibly exceed) the sample size {\$}\backslashnumobs{\$}. This high-dimensional structure precludes the usual global assumptions---namely, strong convexity and smoothness conditions---that underlie much of classical optimization analysis. We define appropriately restricted versions of these conditions, and show that they are satisfied with high probability for various statistical models. Under these conditions, our theory guarantees that projected gradient descent has a globally geometric rate of convergence up to the $\backslash$emph{\{}statistical precision{\}} of the model, meaning the typical distance between the true unknown parameter {\$}\backslashtheta{\^{}}*{\$} and an optimal solution {\$}\backslashhat{\{}\backslashtheta{\}}{\$}. This result is substantially sharper than previous convergence results, which yielded sublinear convergence, or linear convergence only up to the noise level. Our analysis applies to a wide range of {\$}M{\$}-estimators and statistical models, including sparse linear regression using Lasso ({\$}\backslashell{\_}1{\$}-regularized regression); group Lasso for block sparsity; log-linear models with regularization; low-rank matrix recovery using nuclear norm regularization; and matrix decomposition. Overall, our analysis reveals interesting connections between statistical precision and computational efficiency in high-dimensional estimation.},
archivePrefix = {arXiv},
arxivId = {1104.4824},
author = {Agarwal, Alekh and Negahban, Sahand and Wainwright, Martin J.},
doi = {10.1214/12-AOS1032},
eprint = {1104.4824},
isbn = {9781617823800},
issn = {00905364},
journal = {Annals of Statistics},
keywords = {Convex optimization,High-dimensional inference,Regularized Mestimation},
title = {{Fast global convergence of gradient methods for high-dimensional statistical recovery}},
year = {2012}
}
@article{Avants2008a,
abstract = {One of the most challenging problems in modern neuroimaging is detailed characterization of neurodegeneration. Quantifying spatial and longitudinal atrophy patterns is an important component of this process. These spatiotemporal signals will aid in discriminating between related diseases, such as frontotemporal dementia (FTD) and Alzheimer's disease (AD), which manifest themselves in the same at-risk population. Here, we develop a novel symmetric image normalization method (SyN) for maximizing the cross-correlation within the space of diffeomorphic maps and provide the Euler-Lagrange equations necessary for this optimization. We then turn to a careful evaluation of our method. Our evaluation uses gold standard, human cortical segmentation to contrast SyN's performance with a related elastic method and with the standard ITK implementation of Thirion's Demons algorithm. The new method compares favorably with both approaches, in particular when the distance between the template brain and the target brain is large. We then report the correlation of volumes gained by algorithmic cortical labelings of FTD and control subjects with those gained by the manual rater. This comparison shows that, of the three methods tested, SyN's volume measurements are the most strongly correlated with volume measurements gained by expert labeling. This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals.},
author = {Avants, B.B. and Epstein, C.L. and Grossman, M. and Gee, J.C.},
doi = {10.1016/j.media.2007.06.004},
issn = {13618415},
journal = {Medical Image Analysis},
keywords = {Cross-correlation,Deformable image registration,Dementia,Diffeomorphic,Human cortex,Morphometry},
number = {1},
title = {{Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain}},
volume = {12},
year = {2008}
}
@article{Kandel2016,
abstract = {Introduction: Cognitive tests and nonamyloid imaging biomarkers do not consistently identify preclinical AD. The objective of this study was to evaluate whether white matter hyperintensity (WMH) volume, a cerebrovascular disease marker, is more associated with preclinical AD than conventional AD biomarkers and cognitive tests. Methods: Elderly controls enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 158) underwent florbetapir-PET scans, psychometric testing, neuroimaging with MRI and PET, and APOE genetic testing. Elderly controls the Parkinson's progression markers initiative (PPMI, n = 58) had WMH volume, cerebrospinal fluid (CSF) A$\beta$1-42, and APOE status measured. Results: In the ADNI cohort, only WMH volume and APOE $\epsilon$4 status were associated with cerebral A$\beta$ (standardized $\beta$ = 0.44 and 1.25, P = .03 and .002). The association between WMH volume and APOE $\epsilon$4 status with cerebral A$\beta$ (standardized $\beta$ = 1.12 and 0.26, P = .048 and .045) was confirmed in the PPMI cohort. Discussion: WMH volume is more highly associated with preclinical AD than other AD biomarkers.},
author = {Kandel, Benjamin M. and Avants, Brian B. and Gee, James C. and McMillan, Corey T. and Erus, Guray and Doshi, Jimit and Davatzikos, Christos and Wolk, David A.},
doi = {10.1016/j.dadm.2016.03.001},
issn = {23528729},
journal = {Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring},
keywords = {Aging,Alzheimer's disease,Amyloid,Leukoaraiosis,MRI,PET,Preclinical Alzheimer's disease,White matter},
pages = {18--27},
pmid = {27489875},
title = {{White matter hyperintensities are more highly associated with preclinical Alzheimer's disease than imaging and cognitive markers of neurodegeneration}},
volume = {4},
year = {2016}
}
@article{Isgum2015,
abstract = {{\textcopyright} 2014 Elsevier B.V. A number of algorithms for brain segmentation in preterm born infants have been published, but a reliable comparison of their performance is lacking. The NeoBrainS12 study (http://neobrains12.isi.uu.nl), providing three different image sets of preterm born infants, was set up to provide such a comparison. These sets are (i) axial scans acquired at 40. weeks corrected age, (ii) coronal scans acquired at 30. weeks corrected age and (iii) coronal scans acquired at 40. weeks corrected age. Each of these three sets consists of three T1- and T2-weighted MR images of the brain acquired with a 3T MRI scanner. The task was to segment cortical grey matter, non-myelinated and myelinated white matter, brainstem, basal ganglia and thalami, cerebellum, and cerebrospinal fluid in the ventricles and in the extracerebral space separately. Any team could upload the results and all segmentations were evaluated in the same way. This paper presents the results of eight participating teams. The results demonstrate that the participating methods were able to segment all tissue classes well, except myelinated white matter.},
author = {I{\v{s}}gum, I. and Benders, M.J.N.L. and Avants, B. and Cardoso, M.J. and Counsell, S.J. and Gomez, E.F. and Gui, L. and Huppi, P.S. and Kersbergen, K.J. and Makropoulos, A. and Melbourne, A. and Moeskops, P. and Mol, C.P. and Kuklisova-Murgasova, M. and Rueckert, D. and Schnabel, J.A. and Srhoj-Egekher, V. and Wu, J. and Wang, S. and de Vries, L.S. and Viergever, M.A.},
doi = {10.1016/j.media.2014.11.001},
issn = {13618423},
journal = {Medical Image Analysis},
keywords = {Brain segmentation,MRI,Neonatal brain,Segmentation comparison,Segmentation evaluation},
number = {1},
title = {{Evaluation of automatic neonatal brain segmentation algorithms: The NeoBrainS12 challenge}},
volume = {20},
year = {2015}
}
@article{Pecli2018,
abstract = {For the last years, a considerable amount of attention has been devoted to the research about the link prediction (LP) problem in complex networks. This problem tries to predict the likelihood of an association between two not interconnected nodes in a network to appear in the future. One of the most important approaches to the LP problem is based on supervised machine learning (ML) techniques for classification. Although many works have presented promising results with this approach, choosing the set of features (variables) to train the classifiers is still a major challenge. In this article, we report on the effects of three different automatic variable selection strategies (Forward, Backward and Evolutionary) applied to the feature-based supervised learning approach in LP applications. The results of the experiments show that the use of these strategies does lead to better classification models than classifiers built with the complete set of variables. Such experiments were performed over three datasets (Microsoft Academic Network, Amazon and Flickr) that contained more than twenty different features each, including topological and domain-specific ones. We also describe the specification and implementation of the process used to support the experiments. It combines the use of the feature selection strategies, six different classification algorithms (SVM, K-NN, na{\"{i}}ve Bayes, CART, random forest and multilayer perceptron) and three evaluation metrics (Precision, F-Measure and Area Under the Curve). Moreover, this process includes a novel ML voting committee inspired approach that suggests sets of features to represent data in LP applications. It mines the log of the experiments in order to identify sets of features frequently selected to produce classification models with high performance. The experiments showed interesting correlations between frequently selected features and datasets.},
author = {Pecli, Antonio and Cavalcanti, Maria Claudia and Goldschmidt, Ronaldo},
doi = {10.1007/s10115-017-1121-6},
issn = {0219-3116},
journal = {Knowledge and Information Systems},
month = {jul},
number = {1},
pages = {85},
publisher = {Springer},
title = {{Automatic feature selection for supervised learning in link prediction applications: a comparative study}},
url = {http://dx.doi.org/10.1007/s10115-017-1121-6},
volume = {56},
year = {2018}
}
@article{Kandel2013a,
abstract = {We present a new framework for predicting cognitive or other continuous-variable data from medical images. Current methods of probing the connection between medical images and other clinical data typically use voxel-based mass univariate approaches. These approaches do not take into account the multivariate, network-based interactions between the various areas of the brain and do not give readily interpretable metrics that describe how strongly cognitive function is related to neuroanatomical structure. On the other hand, high-dimensional machine learning techniques do not typically provide a direct method for discovering which parts of the brain are used for making predictions. We present a framework, based on recent work in sparse linear regression, that addresses both drawbacks of mass univariate approaches, while preserving the direct spatial interpretability that they provide. In addition, we present a novel optimization algorithm that adapts the conjugate gradient method for sparse regression on medical imaging data. This algorithm produces coefficients that are more interpretable than existing sparse regression techniques.},
author = {Kandel, B.M. and Wolk, D.A. and Gee, J.C. and Avants, B.},
issn = {10112499},
journal = {Information processing in medical imaging : proceedings of the ... conference},
title = {{Predicting cognitive data from medical images using sparse linear regression.}},
volume = {23},
year = {2013}
}
@article{French1947,
author = {French, N R and Steinberg, J C},
doi = {http://dx.doi.org/10.1121/1.1916407},
journal = {The Journal of the Acoustical Society of America},
number = {1},
pages = {90--119},
title = {{Factors Governing the Intelligibility of Speech Sounds}},
url = {http://scitation.aip.org/content/asa/journal/jasa/19/1/10.1121/1.1916407},
volume = {19},
year = {1947}
}
@article{Avants2011,
abstract = {The United States National Institutes of Health (NIH) commit significant support to open-source data and software resources in order to foment reproducibility in the biomedical imaging sciences. Here, we report and evaluate a recent product of this commitment: Advanced Neuroimaging Tools (ANTs), which is approaching its 2.0 release. The ANTs open source software library consists of a suite of state-of-the-art image registration, segmentation and template building tools for quantitative morphometric analysis. In this work, we use ANTs to quantify, for the first time, the impact of similarity metrics on the affine and deformable components of a template-based normalization study. We detail the ANTs implementation of three similarity metrics: squared intensity difference, a new and faster cross-correlation, and voxel-wise mutual information. We then use two-fold cross-validation to compare their performance on openly available, manually labeled, T1-weighted MRI brain image data of 40 subjects (UCLA's LPBA40 dataset). We report evaluation results on cortical and whole brain labels for both the affine and deformable components of the registration. Results indicate that the best ANTs methods are competitive with existing brain extraction results (Jaccard = 0.958) and cortical labeling approaches. Mutual information affine mapping combined with cross-correlation diffeomorphic mapping gave the best cortical labeling results (Jaccard = 0.669. ±. 0.022). Furthermore, our two-fold cross-validation allows us to quantify the similarity of templates derived from different subgroups. Our open code, data and evaluation scripts set performance benchmark parameters for this state-of-the-art toolkit. This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling. {\textcopyright} 2010 Elsevier Inc.},
author = {Avants, B.B. and Tustison, N.J. and Song, G. and Cook, P.A. and Klein, A. and Gee, J.C.},
doi = {10.1016/j.neuroimage.2010.09.025},
issn = {10538119},
journal = {NeuroImage},
number = {3},
title = {{A reproducible evaluation of ANTs similarity metric performance in brain image registration}},
volume = {54},
year = {2011}
}
@article{McMillan2014,
abstract = {Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) that are sensitive for tau or TDP-43 pathology in frontotemporal lobar degeneration (FTLD). Neuroimaging analyses have revealed distinct distributions of disease in FTLD patients with genetic mutations. However, genetic influences on neuroanatomic structure in sporadic FTLD have not been assessed. In this report, we use novel multivariate tools, Eigenanatomy, and sparse canonical correlation analysis to identify associations between SNPs and neuroanatomic structure in sporadic FTLD. Magnetic resonance imaging analyses revealed that rs8070723 (MAPT) was associated with gray matter variance in the temporal cortex. Diffusion tensor imaging analyses revealed that rs1768208 (MOBP), rs646776 (near SORT1), and rs5848 (PGRN) were associated with white matter variance in the midbrain and superior longitudinal fasciculus. In an independent autopsy series, we observed that rs8070723 and rs1768208 conferred significant risk of tau pathology relative to TDP-43, and rs646776 conferred increased risk of TDP-43 pathology relative to tau. Identified brain regions and SNPs may help provide an invivo screen for underlying pathology in FTLD and contribute to our understanding of sporadic FTLD. {\textcopyright} 2014 Elsevier Inc.},
author = {McMillan, C.T. and Toledo, J.B. and Avants, B.B. and Cook, P.A. and Wood, E.M. and Suh, E. and Irwin, D.J. and Powers, J. and Olm, C. and Elman, L. and McCluskey, L. and Schellenberg, G.D. and Lee, V.M.-Y. and Trojanowski, J.Q. and {Van Deerlin}, V.M. and Grossman, M.},
doi = {10.1016/j.neurobiolaging.2013.11.029},
issn = {01974580},
journal = {Neurobiology of Aging},
keywords = {Biomarkers,Frontotemporal lobar degeneration,Genetics,Neuroimaging},
number = {6},
title = {{Genetic and neuroanatomic associations in sporadic frontotemporal lobar degeneration}},
volume = {35},
year = {2014}
}
@article{Song2006a,
abstract = {Brain MRI segmentation remains a challenging problem in spite of numerous existing techniques. To overcome the inherent difficulties associated with this segmentation problem, we present a new method of information integration in a graph based framework. In addition to image intensity, tissue priors and local boundary information are integrated into the edge weight metrics in the graph. Furthermore, inhomogeneity correction is incorporated by adaptively adjusting the edge weights according to the intermediate inhomogeneity estimation. In the validation experiments of simulated brain MRIs, the proposed method outperformed a segmentation method based on iterated conditional modes (ICM), which is a commonly used optimization method in medical image segmentation. In the experiments of real neonatal brain MRIs, the results of the proposed method have good overlap with the manual segmentations by human experts.},
author = {Song, Z. and Tustison, N. and Avants, B. and Gee, J.C.},
journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
number = {Pt 2},
title = {{Integrated graph cuts for brain MRI segmentation.}},
volume = {9},
year = {2006}
}
@inproceedings{Das2008,
abstract = {Asymmetry analysis of brain activation in functional magnetic resonance imaging (fMRI) experiments is commonly used to determine hemispheric specialization for cognitive function. Asymmetry is typically expressed in terms of differences in the number of suprathreshold voxels activated, normalized to total activation, within specific regions known to subserve the function of interest or over the entire hemisphere. We introduce methodologies for carrying out asymmetry analysis both for region of interest (ROI) and whole brain based studies that take into account information about spatial correspondence of voxels on two sides of the brain. We apply this methodology to make determination of activation asymmetry during a memory encoding task in patients with refractory temporal lobe epilepsy (TLE). Memory lateralization is an important step in the presurgical evaluation of such patients for temporal lobectomy. Using structure specific analysis, our asymmetry scores in hippocampus are found to have a strong correspondence with hemispheric dominance given by Intracortical Amobarbital Testing (IAT), which is the widely accepted gold standard for determining laterality. Whole brain analysis, on the other hand, is used to locate regions in the brain with asymmetric activation in an exploratory analysis. These methods of quantifying asymmetry using spatial correspondence have the potential for generating more robust asymmetry measurements. {\textcopyright}2008 IEEE.},
author = {Das, S.R. and Mechanic-Hamilton, D. and Korczykowski, M. and Avants, B.B. and Detre, J.A. and Gee, J.C. and Yushkevich, P.A.},
booktitle = {2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI},
doi = {10.1109/ISBI.2008.4541071},
isbn = {9781424420032},
keywords = {Asymmetry,Epilepsy,IAT,Memory,Wada,fMRI},
title = {{Spatial correspondence based asymmetry analysis in fMRI}},
year = {2008}
}
@article{Song2013a,
abstract = {In this paper we propose a novel way to construct a diffeomorphic polyaffine model. Each affine transform is defined on a local region and the resulting diffeomorphism encapsulates all the local transforms by a smooth and invertible displacement field. Compared with traditional weighting schemes used in combining local transforms, our new scheme guarantees that the resulting transform precisely preserves the value of each local affine transform. By introducing the trajectory of local regions instead of using regions themselves, the new approach encodes precisely each local affine transform using a diffeomorphism with one or more stationary velocity fields. Experiments show that our new polyaffine model is both accurate and efficient.},
author = {Song, G. and Liu, Y. and Wu, B. and Avants, B. and Gee, J.C.},
issn = {10112499},
journal = {Information processing in medical imaging : proceedings of the ... conference},
title = {{Using region trajectories to construct an accurate and efficient polyaffine transform model.}},
volume = {23},
year = {2013}
}
@article{Grossman2008,
abstract = {Background: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative condition affecting the motor system, but recent work also shows more widespread cognitive impairment. This study examined performance on measures requiring knowledge of actions, and related performance to MRI cortical atrophy in ALS. Methods: A total of 34 patients with ALS performed measures requiring word-description matching and associativity judgments with actions and objects. Voxel-based morphometry was used to relate these measures to cortical atrophy using high resolution structural MRI. Results: Patients with ALS were significantly more impaired on measures requiring knowledge of actions than measures requiring knowledge of objects. Difficulty on measures requiring action knowledge correlated with cortical atrophy in motor cortex, implicating degraded knowledge of action features represented in motor cortex of patients with ALS. Performance on measures requiring object knowledge did not correlate with motor cortex atrophy. Several areas correlated with difficulty for both actions and objects, implicating these brain areas in components of semantic memory that are not dedicated to a specific category of knowledge. Conclusion: Patients with amyotrophic lateral sclerosis are impaired on measures involving action knowledge, and this appears to be due to at least two sources of impairment: degradation of knowledge about action features represented in motor cortex and impairment on multicategory cognitive components contributing more generally to semantic memory. {\textcopyright} 2008 by AAN Enterprises, Inc.},
author = {Grossman, M. and Anderson, C. and Khan, A. and Avants, B. and Elman, L. and McCluskey, L.},
doi = {10.1212/01.wnl.0000319701.50168.8c},
issn = {00283878},
journal = {Neurology},
number = {18},
title = {{Impaired action knowledge in amyotrophic lateral sclerosis}},
volume = {71},
year = {2008}
}
@inproceedings{Avants2004b,
abstract = {Average anatomical shape is important to the spatial normalization process used in both functional and structural studies, as it provides a least-biased reference frame. Linear averaging of transformation fields is often used to construct average shapes from a given population. This type of averaging will not necessarily preserve topology of the anatomy and may not result in an average shape that lies within the permissible shape space, as defined by the continuum mechanical or variational model. These problems may be addressed by using a formulation where one finds the average configuration of the domain in time, as opposed to the average displacement. This is achieved by solving a transport ordinary differential equation of the form g -. v̄ (ḡ), which imposes the diffeomorphism constraint directly on the average transformation, g. An energy formulation for this problem will be given along with an algorithm for its minimization. The new diffeomorphic shape averaging algorithms are compared with the traditional linear averaging method. {\textcopyright}2004 IEEE.},
author = {Avants, B. and Gee, J.C.},
booktitle = {2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano},
isbn = {0780383885},
title = {{Shape averaging with diffeomorphic flows for atlas creation}},
volume = {1},
year = {2004}
}
@article{Guo:2008aa,
abstract = {Independent component analysis (ICA) is becoming increasingly popular for analyzing functional magnetic resonance imaging (fMRI) data. While ICA has been successfully applied to single-subject analysis, the extension of ICA to group inferences is not straightforward and remains an active topic of research. Current group ICA models, such as the GIFT [Calhoun, V.D., Adali, T., Pearlson, G.D., Pekar, J.J., 2001. A method for making group inferences from functional MRI data using independent component analysis. Hum. Brain Mapp. 14, 140-151.] and tensor PICA [Beckmann, C.F., Smith, S.M., 2005. Tensorial extensions of independent component analysis for multisubject FMRI analysis. Neuroimage 25, 294-311.], make different assumptions about the underlying structure of the group spatio-temporal processes and are thus estimated using algorithms tailored for the assumed structure, potentially leading to diverging results. To our knowledge, there are currently no methods for assessing the validity of different model structures in real fMRI data and selecting the most appropriate one among various choices. In this paper, we propose a unified framework for estimating and comparing group ICA models with varying spatio-temporal structures. We consider a class of group ICA models that can accommodate different group structures and include existing models, such as the GIFT and tensor PICA, as special cases. We propose a maximum likelihood (ML) approach with a modified Expectation-Maximization (EM) algorithm for the estimation of the proposed class of models. Likelihood ratio tests (LRT) are presented to compare between different group ICA models. The LRT can be used to perform model comparison and selection, to assess the goodness-of-fit of a model in a particular data set, and to test group differences in the fMRI signal time courses between subject subgroups. Simulation studies are conducted to evaluate the performance of the proposed method under varying structures of group spatio-temporal processes. We illustrate our group ICA method using data from an fMRI study that investigates changes in neural processing associated with the regular practice of Zen meditation.},
author = {Guo, Ying and Pagnoni, Giuseppe},
doi = {10.1016/j.neuroimage.2008.05.008},
journal = {Neuroimage},
month = {sep},
number = {3},
pages = {1078--1093},
pmid = {18650105},
title = {{A unified framework for group independent component analysis for multi-subject fMRI data}},
volume = {42},
year = {2008}
}
@book{Avants2018,
abstract = {{\textcopyright} 2018, Springer Science+Business Media, LLC. Human brain mapping is increasingly faced with the need to efficiently interrogate small sample size, but high-dimensional (“short and wide”) data-sets. Such data may derive from rare or difficult to identify populations wherein we seek to detect subtle network changes that precede disease. Few prior hypotheses may exist in these cases and yet, due to small sample size, exploratory analysis is power challenged. We overview how to use sparse canonical correlation analysis to produce biologically principled low-dimensional representations before proceeding to hypothesis testing. This strategy conserves power by taking advantage of the underlying neurobiological covariation across modalities to compress large data-sets. We provide an example that maps voxel-wise cortical thickness measurements to resting state network correlations in order to identify structure-function sub-networks with little further supervision. The resulting network-like, sparse basis functions allow one to predict traditional univariate outcomes from multiple neuroimaging modalities even when sample sizes are relatively small. Importantly, these data-driven functions are anatomically and edge-wise specific, allowing a nearly traditional neuroscientific interpretation.},
author = {Avants, B.B.},
booktitle = {Neuromethods},
doi = {10.1007/978-1-4939-7647-8_6},
issn = {19406045},
keywords = {Brain,DiReCT,Dimensionality reduction,Eigenanatomy,Hubs,Network,Neuroimaging,Resting state,SCCAN,Sparse canonical correlation analysis,Thickness},
title = {{Relating high-dimensional structural networks to resting functional connectivity with sparse canonical correlation analysis for neuroimaging}},
volume = {136},
year = {2018}
}
@article{Libon2012,
abstract = {Objective: Amyotrophic Lateral Sclerosis (ALS) is associated with impaired executive control. The aim of the current research was to test the hypothesis that concept formation deficits associated with an extramotor neurocognitive network involving executive and semantic resources can be found in some ALS patients. Method: Forty-one patients with clinically definite ALS were assessed with Delis Kaplan Executive Function System Sorting Test (D-KEFS), a measure of concept formation requiring patients to manipulate verbal and visual semantic information and neuropsychological tests measuring naming, semantic memory, and executive control. Using D-KEFS scale scores,  a k-mean cluster analysis specifying a 3-group solution was able to classify ALS patients into groups presenting with mildly impaired, average, and above average sorting test performance. High-resolution T1 structural MRI was used to examine cortical thickness in a subset of 16 ALS patients. Results: Stepwise regression analyses related free and recognition sorting test performance to measures of action naming, single word semantic knowledge, and mental search/working memory. MRI studies found widespread cortical thinning involving bilateral frontal, temporal, and parietal regions. Regression analyses related recognition sorting performance to reduced MRI cortical thickness involving the left prefrontal and left parietal cortex. Conclusions: An extramotor cognitive network is associated with impaired concept formation in ALS. {\textcopyright} 2012 American Psychological Association.},
author = {Libon, D.J. and McMillan, C. and Avants, B. and Boller, A. and Morgan, B. and Burkholder, L. and Chandrasekaran, K. and Elman, L. and McCluskey, L. and Grossman, M.},
doi = {10.1037/a0028668},
issn = {08944105},
journal = {Neuropsychology},
keywords = {Amyotrophic Lateral Sclerosis (ALS),Delis-Kaplan Executive Function System (D-KEFS) so,Executive control,Neuropsychology,Prefrontal cortex},
number = {4},
title = {{Deficits in concept formation in Amyotrophic Lateral Sclerosis}},
volume = {26},
year = {2012}
}
@article{Tustison2011a,
abstract = {We propose a new approach to front propagation algorithms based on a topological variant of well-composedness which contrasts with previous methods based on simple point detection. This provides for a theoretical justification, based on the digital Jordan separation theorem, for digitally "gluing" evolved well-composed objects separated by well-composed curves or surfaces. Additionally, our framework can be extended to more relaxed topologically constrained algorithms based on multisimple points. For both methods this framework has the additional benefit of obviating the requirement for both a user-specified connectivity and a topologically-consistent marching cubes/squares algorithm in meshing the resulting segmentation. {\textcopyright} 2010 IEEE.},
author = {Tustison, N.J. and Avants, B.B. and Siqueira, M. and Gee, J.C.},
doi = {10.1109/TIP.2010.2095021},
issn = {10577149},
journal = {IEEE Transactions on Image Processing},
keywords = {Digital Jordan separation theorem,front propagation,marching cubes,simple point,topological well-composedness},
number = {6},
title = {{Topological well-composedness and glamorous glue: A digital gluing algorithm for topologically constrained front propagation}},
volume = {20},
year = {2011}
}
@inproceedings{Yushkevich2008,
abstract = {This paper presents preliminary work on the construction of a computational anatomical atlas of the human hippocampus. The atlas is derived from high-resolution 9.4 Tesla MRI of postmortem samples. The main subfields of the hippocampus (cornu Ammonis fields CA1, CA2/3 and CA4; dentate gyrus; and the vestigial hippocampal sulcus) are labeled in the images manually using a combination of distinguishable image features and geometrical features. A synthetic average image is derived from the MRI of the samples using shape and intensity averaging in the diffeomorphic non-linear registration framework, and a consensus labeling of the template is generated. The agreement of the consensus labeling with manual labeling of each sample is measured, and the effect of aiding registration with landmarks and manually generated mask images is evaluated. {\textcopyright}2008 IEEE.},
author = {Yushkevich, P.A. and Avants, B.B. and Pluta, J. and Minkoff, D. and Pickup, S. and Liu, W. and Detre, J.A. and Grossman, M. and Gee, J.C.},
booktitle = {2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI},
doi = {10.1109/ISBI.2008.4540957},
isbn = {9781424420032},
keywords = {Brain,Hippocampus,Image registration,Magnetic resonance imaging},
title = {{Building an atlas of hippocampal subfields using postmortem MRI}},
year = {2008}
}
@book{Avants2004,
abstract = {Structural image registration is often achieved through diffeomorphic transformations. The formalism associated with the diffeomorphic framework allows one to define curved distances which are often more appropriate for morphological comparisons of anatomy. However, the correspondence problem as well as the metric distances across the database depend upon the chosen reference anatomy, requiring average transformations to be estimated. The goal of this paper is to develop an algorithm which, given a database of images, estimates an average shape based on the geodesic distances of curved, time-dependent transformations. Specifically, this paper will develop direct, efficient, symmetric methods for generating average anatomical shapes from diffeomorphic registration algorithms. The need for these types of averages is illustrated with synthetic examples and the novel algorithm is compared to the usual approach of averaging linear transformations. Furthermore, the same algorithm will be used for shape interpolation that is independent of the multi-scale framework used. {\textcopyright} Springer-Verlag 2004.},
author = {Avants, B. and Gee, J.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
issn = {03029743},
title = {{Symmetric Geodesic Shape Averaging and Shape Interpolation}},
volume = {3117},
year = {2004}
}
@article{Behzadi2007,
abstract = {A component based method (CompCor) for the reduction of noise in both blood oxygenation level-dependent (BOLD) and perfusion-based functional magnetic resonance imaging (fMRI) data is presented. In the proposed method, significant principal components are derived from noise regions-of-interest (ROI) in which the time series data are unlikely to be modulated by neural activity. These components are then included as nuisance parameters within general linear models for BOLD and perfusion-based fMRI time series data. Two approaches for the determination of the noise ROI are considered. The first method uses high-resolution anatomical data to define a region of interest composed primarily of white matter and cerebrospinal fluid, while the second method defines a region based upon the temporal standard deviation of the time series data. With the application of CompCor, the temporal standard deviation of resting-state perfusion and BOLD data in gray matter regions was significantly reduced as compared to either no correction or the application of a previously described retrospective image based correction scheme (RETROICOR). For both functional perfusion and BOLD data, the application of CompCor significantly increased the number of activated voxels as compared to no correction. In addition, for functional BOLD data, there were significantly more activated voxels detected with CompCor as compared to RETROICOR. In comparison to RETROICOR, CompCor has the advantage of not requiring external monitoring of physiological fluctuations.},
author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T},
doi = {10.1016/j.neuroimage.2007.04.042},
institution = {UCSD Center for Functional Magnetic Resonance Imaging and Department of Radiology, 9500 Gilman Drive, MC 0677, La Jolla, CA 92093-0677, USA.},
journal = {Neuroimage},
keywords = { Computer-Assisted, blood supply/physiology; Energy Metabolism, blood; Photic Stimulation; Principal Component Analysis; Regional Blood Flow, methods; Image Processing, methods; Linear Models; Magnetic Resonance Imaging, methods; Oxygen, physiology, physiology; Fourier Analysis; Humans; Image Enhancement, physiology; Software; Visual Perception,Adult; Algorithms; Artifacts; Cerebral Cortex},
month = {aug},
number = {1},
pages = {90--101},
pmid = {17560126},
title = {{A component based noise correction method (CompCor) for BOLD and perfusion based fMRI.}},
url = {http://dx.doi.org/10.1016/j.neuroimage.2007.04.042},
volume = {37},
year = {2007}
}
@article{Yushkevich2010,
abstract = {We present and evaluate a new method for automatically labeling the subfields of the hippocampal formation in focal 0.4×0.5×2.0mm 3 resolution T2-weighted magnetic resonance images that can be acquired in the routine clinical setting with under 5min scan time. The method combines multi-atlas segmentation, similarity-weighted voting, and a novel learning-based bias correction technique to achieve excellent agreement with manual segmentation. Initial partitioning of MRI slices into hippocampal 'head', 'body' and 'tail' slices is the only input required from the user, necessitated by the nature of the underlying segmentation protocol. Dice overlap between manual and automatic segmentation is above 0.87 for the larger subfields, CA1 and dentate gyrus, and is competitive with the best results for whole-hippocampus segmentation in the literature. Intraclass correlation of volume measurements in CA1 and dentate gyrus is above 0.89. Overlap in smaller hippocampal subfields is lower in magnitude (0.54 for CA2, 0.62 for CA3, 0.77 for subiculum and 0.79 for entorhinal cortex) but comparable to overlap between manual segmentations by trained human raters. These results support the feasibility of subfield-specific hippocampal morphometry in clinical studies of memory and neurodegenerative disease. {\textcopyright} 2010 Elsevier Inc.},
author = {Yushkevich, P.A. and Wang, H. and Pluta, J. and Das, S.R. and Craige, C. and Avants, B.B. and Weiner, M.W. and Mueller, S.},
doi = {10.1016/j.neuroimage.2010.06.040},
issn = {10538119},
journal = {NeuroImage},
number = {4},
title = {{Nearly automatic segmentation of hippocampal subfields in in vivo focal T2-weighted MRI}},
volume = {53},
year = {2010}
}
@inproceedings{Dhillon2012,
abstract = {We propose a new multivariate method, partial sparse canonical correlation analysis (PSCCA), for computing the statistical comparisons needed by population studies in medical imaging. PSCCA is a multivariate generalization of linear regression that allows one to statistically parameterize imaging studies in terms of multiple views of the population (e.g., the full collection of measurements taken from an image set along with batteries of cognitive or genetic data) while controlling for nuisance variables. This paper develops the theory of PSCCA, provides an algorithm and illustrates PSCCA performance on both simulated and real datasets. We show, as a first application and evaluation of this new methodology, that PSCCA can improve detection power over mass univariate approaches while retaining the interpretability and biological plausibility of the estimated effects. We also discuss the strengths, limitations and future potential of this methodology. {\textcopyright} 2012 IEEE.},
author = {Dhillon, P.S. and Avants, B. and Ungar, L. and Gee, J.C.},
booktitle = {Proceedings - International Symposium on Biomedical Imaging},
doi = {10.1109/ISBI.2012.6235759},
isbn = {9781457718588},
issn = {19457928},
keywords = {Medical Imaging,Multivariate modeling,Spectral Methods},
title = {{Partial sparse canonical correlation analysis (PSCCA) for population studies in medical imaging}},
year = {2012}
}
@article{Olm2016,
abstract = {{\^{A}}{\textcopyright} 2016, Springer-Verlag Berlin Heidelberg. The objective of the study was to evaluate the prognostic value of regional cerebral blood flow (CBF) measured by arterial spin labeled (ASL) perfusion MRI in patients with semantic variant primary progressive aphasia (svPPA). We acquired pseudo-continuous ASL (pCASL) MRI and whole-brain T1-weighted structural MRI in svPPA patients (N{\^{A}} ={\^{A}} 13) with cerebrospinal fluid biomarkers consistent with frontotemporal lobar degeneration pathology. Follow-up T1-weighted MRI was available in a subset of patients (N{\^{A}} ={\^{A}} 8). We performed whole-brain comparisons of partial volume-corrected CBF and cortical thickness between svPPA and controls, and compared baseline and follow-up cortical thickness in regions of significant hypoperfusion and hyperperfusion. Patients with svPPA showed partial volume-corrected hypoperfusion relative to controls in left temporal lobe and insula. svPPA patients also had typical cortical thinning in anterior temporal, insula, and inferior frontal regions at baseline. Volume-corrected hypoperfusion was seen in areas of significant cortical thinning such as the left temporal lobe and insula. Additional regions of hypoperfusion corresponded to areas without cortical thinning. We also observed regions of hyperperfusion, some associated with cortical thinning and others without cortical thinning, including right superior temporal, inferior parietal, and orbitofrontal cortices. Regions of hypoperfusion and hyperperfusion near cortical thinning at baseline had significant longitudinal thinning between baseline and follow-up scans, but perfusion changes in distant areas did not show progressive thinning. Our findings suggest ASL MRI may be sensitive to functional changes not readily apparent in structural MRI, and specific changes in perfusion may be prognostic markers of disease progression in a manner consistent with cell-to-cell spreading pathology.},
author = {Olm, C.A. and Kandel, B.M. and Avants, B.B. and Detre, J.A. and Gee, J.C. and Grossman, M. and McMillan, C.T.},
doi = {10.1007/s00415-016-8221-1},
issn = {14321459},
journal = {Journal of Neurology},
keywords = {Arterial spin labeling,Frontotemporal degeneration,Functional neuroimaging,MRI,Semantic variant primary progressive aphasia},
number = {10},
title = {{Arterial spin labeling perfusion predicts longitudinal decline in semantic variant primary progressive aphasia}},
volume = {263},
year = {2016}
}
@article{Xie2017,
abstract = {{\textcopyright} 2016 Elsevier Inc. Rational The human perirhinal cortex (PRC) plays critical roles in episodic and semantic memory and visual perception. The PRC consists of Brodmann areas 35 and 36 (BA35, BA36). In Alzheimer's disease (AD), BA35 is the first cortical site affected by neurofibrillary tangle pathology, which is closely linked to neural injury in AD. Large anatomical variability, manifested in the form of different cortical folding and branching patterns, makes it difficult to segment the PRC in MRI scans. Pathology studies have found that in {\~{}}97{\%} of specimens, the PRC falls into one of three discrete anatomical variants. However, current methods for PRC segmentation and morphometry in MRI are based on single-template approaches, which may not be able to accurately model these discrete variants Methods A multi-template analysis pipeline that explicitly accounts for anatomical variability is used to automatically label the PRC and measure its thickness in T2-weighted MRI scans. The pipeline uses multi-atlas segmentation to automatically label medial temporal lobe cortices including entorhinal cortex, PRC and the parahippocampal cortex. Pairwise registration between label maps and clustering based on residual dissimilarity after registration are used to construct separate templates for the anatomical variants of the PRC. An optimal path of deformations linking these templates is used to establish correspondences between all the subjects. Experimental evaluation focuses on the ability of single-template and multi-template analyses to detect differences in the thickness of medial temporal lobe cortices between patients with amnestic mild cognitive impairment (aMCI, n=41) and age-matched controls (n=44). Results The proposed technique is able to generate templates that recover the three dominant discrete variants of PRC and establish more meaningful correspondences between subjects than a single-template approach. The largest reduction in thickness associated with aMCI, in absolute terms, was found in left BA35 using both regional and summary thickness measures. Further, statistical maps of regional thickness difference between aMCI and controls revealed different patterns for the three anatomical variants.},
author = {Xie, L. and Pluta, J.B. and Das, S.R. and Wisse, L.E.M. and Avants, B.B. and Yushkevich, P.A. and Mancuso, L. and Kliot, D. and Wolk, D.A. and Das, S.R. and Mancuso, L. and Kliot, D. and Wolk, D.A. and Pluta, J.B. and Das, S.R. and Wisse, L.E.M. and Avants, B.B. and Yushkevich, P.A. and Ding, S.-L. and Wang, H. and Manj{\'{o}}n, J.V. and Xie, L. and Ding, S.-L.},
doi = {10.1016/j.neuroimage.2016.09.070},
issn = {10959572},
journal = {NeuroImage},
keywords = {Alzheimer's disease,Anatomical variability,Cortical thickness analysis,Magnetic resonance imaging,Manifold learning,Perirhinal cortex},
title = {{Multi-template analysis of human perirhinal cortex in brain MRI: Explicitly accounting for anatomical variability}},
volume = {144},
year = {2017}
}
@article{Klein2010,
abstract = {Establishing correspondences across brains for the purposes of comparison and group analysis is almost universally done by registering images to one another either directly or via a template. However, there are many registration algorithms to choose from. A recent evaluation of fully automated nonlinear deformation methods applied to brain image registration was restricted to volume-based methods. The present study is the first that directly compares some of the most accurate of these volume registration methods with surface registration methods, as well as the first study to compare registrations of whole-head and brain-only (de-skulled) images. We used permutation tests to compare the overlap or Hausdorff distance performance for more than 16,000 registrations between 80 manually labeled brain images. We compared every combination of volume-based and surface-based labels, registration, and evaluation. Our primary findings are the following: 1. de-skulling aids volume registration methods; 2. custom-made optimal average templates improve registration over direct pairwise registration; and 3. resampling volume labels on surfaces or converting surface labels to volumes introduces distortions that preclude a fair comparison between the highest ranking volume and surface registration methods using present resampling methods. From the results of this study, we recommend constructing a custom template from a limited sample drawn from the same or a similar representative population, using the same algorithm used for registering brains to the template. {\textcopyright} 2010.},
author = {Klein, A. and Ghosh, S.S. and Avants, B. and Yeo, B.T.T. and Fischl, B. and Ardekani, B. and Gee, J.C. and Mann, J.J. and Parsey, R.V.},
doi = {10.1016/j.neuroimage.2010.01.091},
issn = {10538119},
journal = {NeuroImage},
number = {1},
title = {{Evaluation of volume-based and surface-based brain image registration methods}},
volume = {51},
year = {2010}
}
@article{Muschelli2018,
abstract = {Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience.},
author = {Muschelli, John and Gherman, Adrian and Fortin, Jean-Philippe and Avants, Brian and Whitcher, Brandon and Clayden, Jonathan D and Caffo, Brian S and Crainiceanu, Ciprian M},
doi = {10.1093/biostatistics/kxx068},
issn = {1468-4357},
journal = {Biostatistics (Oxford, England)},
keywords = {Bioinformatics; Image analysis; Statistical modelling},
month = {jan},
pmid = {29325029},
title = {{Neuroconductor: an R platform for medical imaging analysis.}},
year = {2018}
}
@article{Avants2015a,
abstract = {{\textcopyright} 2015 Avants et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. What are the long-term effects of childhood experience on brain development? Research with animals shows that the quality of environmental stimulation and parental nurturance both play important roles in shaping lifelong brain structure and function. Human research has so far been limited to the effects of abnormal experience and pathological development. Using a unique longitudinal dataset of in-home measures of childhood experience at ages 4 and 8 and MRI acquired in late adolescence, we were able to relate normal variation in childhood experience to later life cortical thickness. Environmental stimulation at age 4 predicted cortical thickness in a set of automatically derived regions in temporal and prefrontal cortex. In contrast, age 8 experience was not predictive. Parental nurturance was not predictive at either age. This work reveals an association between childhood experience and later brain structure that is specific relative to aspects of experience, regions of brain, and timing.},
author = {Avants, B.B. and Hackman, D.A. and Betancourt, L.M. and Lawson, G.M. and Hurt, H. and Farah, M.J.},
doi = {10.1371/journal.pone.0138217},
issn = {19326203},
journal = {PLoS ONE},
number = {10},
title = {{Relation of childhood home environment to cortical thickness in late adolescence: Specificity of experience and timing}},
volume = {10},
year = {2015}
}
@inproceedings{Sun2008a,
abstract = {The cm-rep (continuous medial representation) is a powerful shape representation method that models a 3D object by describing its medial axis (skeleton) and boundary as continuous parametric manifolds. It provides parametrization of the entire interior of the object, which can be used for combined statistical analysis of shape and appearance. This paper extends the cm-rep to more complex shapes with multi-figures, i.e., shapes whose skeletons have branches. Along the branching curves, the equality constraints enforced by the medial geometry are implemented as soft penalties in the deformable model. The remaining small violations are corrected by local adjustments. As a proof of concept, the branching continuous medial representation is applied to a 2-chamber heart model data set consisting of 428 cardiac shapes from 90 subjects. The results show that our model can capture the heart shape accurately. {\textcopyright}2008 IEEE.},
author = {Sun, H. and Avants, B.B. and Frangi, A.F. and Ordas, S. and Gee, J.C. and Yushkevich, P.A.},
booktitle = {2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI},
doi = {10.1109/ISBI.2008.4541289},
isbn = {9781424420032},
keywords = {2-chamber heart model,Branching Medial Model,Cardiac Thickness Analysis,Medial Axis},
title = {{Branching medial models for cardiac shape representation}},
year = {2008}
}
@article{Pustina2018,
abstract = {Lesion to symptom mapping (LSM) is a crucial tool for understanding the causality of brain-behavior relationships. The analyses are typically performed by applying statistical methods on individual brain voxels (VLSM), a method called the mass-univariate approach. Several authors have shown that VLSM suffers from limitations that may decrease the accuracy and reliability of the findings, and have proposed the use of multivariate methods to overcome these limitations. In this study, we propose a multivariate optimization technique known as sparse canonical correlation analysis for neuroimaging (SCCAN) for lesion to symptom mapping. To validate the method and compare it with mass-univariate results, we used data from 131 patients with chronic stroke lesions in the territory of the middle cerebral artery, and created synthetic behavioral scores based on the lesion load of 93 brain regions (putative functional units). LSM analyses were performed with univariate VLSM or SCCAN, and the accuracy of the two methods was compared in terms of both overlap and displacement from the simulated functional areas. Overall, SCCAN produced more accurate results - higher dice overlap and smaller average displacement - compared to VLSM. This advantage persisted at different sample sizes (N = 20-131) and different multiple comparison corrections (false discovery rate, FDR; Bonferroni; permutation-based family wise error rate, FWER). These findings were replicated with a fully automated SCCAN routine that relied on cross-validated predictive accuracy to find the optimal sparseness value. Simulations of one, two, and three brain regions showed a systematic advantage of SCCAN over VLSM; under no circumstance could VLSM exceed the accuracy obtained with SCCAN. When considering functional units composed of multiple brain areas VLSM identified fewer areas than SCCAN. The investigation of real scores of aphasia severity (aphasia quotient and picture naming) showed that SCCAN could accurately identify known language-critical areas, while VLSM either produced diffuse maps (FDR correction) or few scattered voxels (FWER correction). Overall, this study shows that a multivariate method, such as, SCCAN, outperforms VLSM in a number of scenarios, including functional dependency on single or multiple areas, different sample sizes, different multi-area combinations, and different thresholding mechanisms (FWER, Bonferroni, FDR). These results support previous claims that multivariate methods are in general more accurate than mass-univariate approaches, and should be preferred over traditional VLSM approaches. All the methods described in this study are available in the newly developed LESYMAP package for R.},
author = {Pustina, Dorian and Avants, Brian and Faseyitan, Olufunsho K and Medaglia, John D and Coslett, H Branch},
doi = {10.1016/j.neuropsychologia.2017.08.027},
issn = {1873-3514},
journal = {Neuropsychologia},
keywords = {Aphasia; Brunner; Motor; Nonparametric; SCCAN; Sparse; Stroke},
month = {jul},
pages = {154--166},
pmid = {28882479},
title = {{Improved accuracy of lesion to symptom mapping with multivariate sparse canonical correlations.}},
volume = {115},
year = {2018}
}
@article{Avants1999,
abstract = {We describe an undergraduate experiment for measuring the electrical conductivity of the earth with a four-electrode Wenner array, at scales approaching tens of meters. When analyzed in the context of a simple two-layer model of the earth, such measurements yield information about what is underground. In our case, this is the depth of the water table and the electrical conductivity of both the upper dry layer and the lower water-saturated layer. We also performed conductivity measurements in a water tank, to test the theory in a known situation. The experiments are discussed in the context of several boundary value problems in electricity and magnetism. {\textcopyright} 1999 American Association of Physics Teachers.},
author = {Avants, B. and Soodak, D. and Ruppeiner, G.

license_type: permissive

branch_name: refs/heads/master

extension: bib