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@article{calhoun2017prediction,
  title={Prediction of Individual Differences from Neuroimaging Data.},
  author={Calhoun, Vince D and Lawrie, Stephan M and Mourao-Miranda, Janaina and Stephan, Klaas E},
  journal={NeuroImage},
  volume={145},
  number={Pt B},
  pages={135--136},
  year={2017}
}

@article{Pallares201624,
	author={Pallares, Vicente and Insabato, Andrea and Sanjuan, Ana and Kuehn, Simone and Mantini, Dante and Deco, Gustavo and Gilson, Matthieu},
	title={Subject- and behavior-specific signatures extracted from fMRI data using whole-brain effective connectivity},
	year={2017},
	doi={10.1101/201624},
	publisher={Cold Spring Harbor Laboratory},
	abstract={The idea of fMRI-based personalized medicine is emerging to characterize brain disorders at the patient level and to develop tailored therapeutic protocols. A main limitation in this direction, as well as with fundamental studies of brain function, is the variability observed in fMRI data. In practice, such measurements exhibit a mixture of session-to-session variability with subject- and condition-related information. Disentangling these contributions is crucial for real-life applications where only a few sessions per subject are available. The present study aims to define a new reliable standard for the extraction of signatures from fMRI data. These signatures correspond to subnetworks of directed interactions between brain regions (typically 100 covering the whole brain) supporting the subject and condition identification for single fMRI sessions. The key is to step from functional connectivity to effective connectivity, which is estimated using a whole-brain dynamic model. Our method shows that as few as 4 sessions per subject are sufficient to perfectly identify more than 40 other sessions of 6 subjects. We also demonstrate the good generalization capability for 30 subjects. Using another dataset with resting state and movie viewing, we show that the two extracted signatures correspond to distinct subnetworks, suggesting some sort of orthogonality. Our results set solid foundations to follow longitudinally a subject{\textquoteright}s condition from fMRI data.},
	URL={https://www.biorxiv.org/content/early/2017/10/11/201624},
	eprint={https://www.biorxiv.org/content/early/2017/10/11/201624.full.pdf},
	journal={bioRxiv}
}

@article{miranda2014connectotyping,
  title={Connectotyping: model based fingerprinting of the functional connectome},
  author={Miranda-Dominguez, Oscar and Mills, Brian D and Carpenter, Samuel D and Grant, Kathleen A and Kroenke, Christopher D and Nigg, Joel T and Fair, Damien A},
  journal={PLoS One},
  volume={9},
  number={11},
  pages={e111048},
  year={2014},
  publisher={Public Library of Science}
}

@article{finn2017can,
  title={Can brain state be manipulated to emphasize individual differences in functional connectivity?},
  author={Finn, Emily S and Scheinost, Dustin and Finn, Daniel M and Shen, Xilin and Papademetris, Xenophon and Constable, R Todd},
  journal={NeuroImage},
  year={2017},
  publisher={Elsevier}
}

@article{hoptman2012decreased,
  title={Decreased interhemispheric coordination in schizophrenia: a resting state fMRI study},
  author={Hoptman, Matthew J and Zuo, Xi-Nian and D'Angelo, Debra and Mauro, Cristina J and Butler, Pamela D and Milham, Michael P and Javitt, Daniel C},
  journal={Schizophrenia research},
  volume={141},
  number={1},
  pages={1--7},
  year={2012},
  publisher={Elsevier}
}

@article{rissman2012distributed,
  title={Distributed representations in memory: insights from functional brain imaging},
  author={Rissman, Jesse and Wagner, Anthony D},
  journal={Annual review of psychology},
  volume={63},
  pages={101--128},
  year={2012},
  publisher={Annual Reviews}
}

@article{deco2014great,
  title={Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders},
  author={Deco, Gustavo and Kringelbach, Morten L},
  journal={Neuron},
  volume={84},
  number={5},
  pages={892--905},
  year={2014},
  publisher={Elsevier}
}

@article{woo2017building,
  title={Building better biomarkers: brain models in translational neuroimaging},
  author={Woo, Choong-Wan and Chang, Luke J and Lindquist, Martin A and Wager, Tor D},
  journal={Nature neuroscience},
  volume={20},
  number={3},
  pages={365--377},
  year={2017},
  publisher={Nature Research}
}

@article{kaufmann2017delayed,
  title={Delayed stabilization and individualization in connectome development are related to psychiatric disorders},
  author={Kaufmann, Tobias and Aln{\ae}s, Dag and Doan, Nhat Trung and Brandt, Christine Lycke and Andreassen, Ole A and Westlye, Lars T},
  journal={Nature neuroscience},
  volume={20},
  number={4},
  pages={513--515},
  year={2017},
  publisher={Nature Research}
}

@article{finn2015functional,
  title={Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity},
  author={Finn, Emily S and Shen, Xilin and Scheinost, Dustin and Rosenberg, Monica D and Huang, Jessica and Chun, Marvin M and Papademetris, Xenophon and Constable, R Todd},
  journal={Nature neuroscience},
  volume={18},
  number={11},
  pages={1664--1671},
  year={2015},
  publisher={Nature Research}
}

@article{mueller2015reliability,
  title={Reliability correction for functional connectivity: Theory and implementation},
  author={Mueller, Sophia and Wang, Danhong and Fox, Michael D and Pan, Ruiqi and Lu, Jie and Li, Kuncheng and Sun, Wei and Buckner, Randy L and Liu, Hesheng},
  journal={Human brain mapping},
  volume={36},
  number={11},
  pages={4664--4680},
  year={2015},
  publisher={Wiley Online Library}
}

@article{pannunzi2017resting,
  title={Resting-state fMRI correlations: from link-wise unreliability to whole brain stability},
  author={Pannunzi, Mario and Hindriks, Rikkert and Bettinardi, Ruggero G and Wenger, Elisabeth and Lisofsky, Nina and Martensson, Johan and Butler, Oisin and Filevich, Elisa and Becker, Maxi and Lochstet, Martyna and others},
  journal={Neuroimage},
  volume={157},
  pages={250--262},
  year={2017},
  publisher={Elsevier}
}

@article{rahim2017joint,
  title={Joint prediction of multiple scores captures better individual traits from brain images},
  author={Rahim, Mehdi and Thirion, Bertrand and Bzdok, Danilo and Buvat, Irene and Varoquaux, Ga{\"e}l},
  journal={NeuroImage},
  volume={158},
  pages={145--154},
  year={2017},
  publisher={Elsevier}
}

@article{kurth2015recognition,
  title={Recognition of personally familiar faces and functional connectivity in Alzheimer's disease},
  author={Kurth, Sophie and Moyse, Evelyne and Bahri, Mohamed A and Salmon, Eric and Bastin, Christine},
  journal={Cortex},
  volume={67},
  pages={59--73},
  year={2015},
  publisher={Elsevier}
}

@article{gordon2017precision,
  title={Precision functional mapping of individual human brains},
  author={Gordon, Evan M and Laumann, Timothy O and Gilmore, Adrian W and Newbold, Dillan J and Greene, Deanna J and Berg, Jeffrey J and Ortega, Mario and Hoyt-Drazen, Catherine and Gratton, Caterina and Sun, Haoxin and others},
  journal={Neuron},
  volume={95},
  number={4},
  pages={791--807},
  year={2017},
  publisher={Elsevier}
}

@article{amunts2014interoperable,
  title={Interoperable atlases of the human brain},
  author={Amunts, Katrin and Hawrylycz, Michael J and Van Essen, David C and Van Horn, John D and Harel, Noam and Poline, J-B and De Martino, Federico and Bjaalie, Jan G and Dehaene-Lambertz, Ghislaine and Dehaene, Stanislas and others},
  journal={Neuroimage},
  volume={99},
  pages={525--532},
  year={2014},
  publisher={Elsevier}
}

@article{Filevich_BMC_2017,
	Abstract = {BACKGROUND: Most studies of brain structure and function, and their relationships to cognitive ability, have relied on inter-individual variability in magnetic resonance (MR) images. Intra-individual variability is often ignored or implicitly assumed to be equivalent to the former. Testing this assumption empirically by collecting enough data on single individuals is cumbersome and costly. We collected a dataset of multiple MR sequences and behavioural covariates to quantify and characterize intra-individual variability in MR images for multiple individuals.
METHODS AND DESIGN: Eight participants volunteered to undergo brain scanning 40-50 times over the course of 6 months. Six participants completed the full set of sessions. T1-weighted, T2*-weighted during rest, T2-weighted high-resolution hippocampus, diffusion-tensor imaging (DTI), and proton magnetic resonance spectroscopy sequences were collected, along with a rich set of stable and time-varying physical, behavioural and physiological variables. Participants did not change their lifestyle or participated in any training programs during the period of data collection.
CONCLUSION: This imaging dataset provides a large number of MRI scans in different modalities for six participants. It enables the analysis of the time course and correlates of intra-individual variability in structural, chemical, and functional aspects of the human brain.},
	Author = {Filevich, Elisa and Lisofsky, Nina and Becker, Maxi and Butler, Oisin and Lochstet, Martyna and Martensson, Johan and Wenger, Elisabeth and Lindenberger, Ulman and K{\"u}hn, Simone},
	Date-Added = {2017-10-11 12:09:30 +0000},
	Date-Modified = {2017-10-11 12:09:54 +0000},
	Doi = {10.1186/s12868-017-0383-y},
	Journal = {BMC Neurosci},
	Pages = {65},
	Title = {Day2day: investigating daily variability of magnetic resonance imaging measures over half a year},
	Volume = {18},
	Year = {2017},
	Bdsk-Url-1 = {http://dx.doi.org/10.1186/s12868-017-0383-y}}

@article{Rousseeuw1987,
	Author = {Rousseeuw, PJ},
	Date-Added = {2017-10-11 10:00:00 +0000},
	Date-Modified = {2017-10-11 10:00:38 +0000},
	Doi = {https://doi.org/10.1016/0377-0427(87)90125-7},
	Journal = {Journal of Computational and Applied Mathematics},
	Pages = {53-65},
	Title = {Silhouettes: A graphical aid to the interpretation and validation of cluster analysis},
	Volume = {20},
	Year = {1987},
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/0377042787901257},
	Bdsk-Url-2 = {https://doi.org/10.1016/0377-0427(87)90125-7}}

@article{yahata+kawato+computational_biomarkers+17,
author = {Yahata, Noriaki and Kasai, Kiyoto and Kawato, Mitsuo},
title = {Computational neuroscience approach to biomarkers and treatments for mental disorders},
journal = {Psychiatry and Clinical Neurosciences},
volume = {71},
number = {4},
publisher = {John Wiley \& Sons Australia, Ltd},
issn = {1440-1819},
url = {http://dx.doi.org/10.1111/pcn.12502},
doi = {10.1111/pcn.12502},
pages = {215--237},
keywords = {biomarkers, computational psychiatry, machine learning, neuroimaging, resting-state functional connectivity},
year = {2017}}

@article{Gilson_NeIm_2017,
	Author = {Matthieu Gilson and Gustavo Deco and Karl Friston and Patric Hagmann and Dante Mantini and Viviana Betti and Gian Luca Romani and Maurizio Corbetta},
	Date-Added = {2017-10-10 20:42:44 +0000},
	Date-Modified = {2017-10-10 20:42:58 +0000},
	Doi = {https://doi.org/10.1016/j.neuroimage.2017.09.061},
	Journal = {NeuroImage},
	Title = {Effective connectivity inferred from fMRI transition dynamics during movie viewing points to a balanced reconfiguration of cortical interactions},
	Year = {2017},
	Bdsk-Url-1 = {https://doi.org/10.1016/j.neuroimage.2017.09.061}}

@article{Sheehan_JCP_1998,
	Abstract = {The Mini-International Neuropsychiatric Interview (M.I.N.I.) is a short structured diagnostic interview, developed jointly by psychiatrists and clinicians in the United States and Europe, for DSM-IV and ICD-10 psychiatric disorders. With an administration time of approximately 15 minutes, it was designed to meet the need for a short but accurate structured psychiatric interview for multicenter clinical trials and epidemiology studies and to be used as a first step in outcome tracking in nonresearch clinical settings. The authors describe the development of the M.I.N.I. and its family of interviews: the M.I.N.I.-Screen, the M.I.N.I.-Plus, and the M.I.N.I.-Kid. They report on validation of the M.I.N.I. in relation to the Structured Clinical Interview for DSM-III-R, Patient Version, the Composite International Diagnostic Interview, and expert professional opinion, and they comment on potential applications for this interview.},
	Author = {Sheehan, D V and Lecrubier, Y and Sheehan, K H and Amorim, P and Janavs, J and Weiller, E and Hergueta, T and Baker, R and Dunbar, G C},
	Date-Added = {2017-10-10 20:07:49 +0000},
	Date-Modified = {2017-10-10 20:08:28 +0000},
	Journal = {J Clin Psychiatry},
	Pages = {22-33},
	Title = {The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10},
	Volume = {59},
	Year = {1998}}

@article{Yan_Neuroinf_2016,
	Abstract = {Brain imaging efforts are being increasingly devoted to decode the functioning of the human brain. Among neuroimaging techniques, resting-state fMRI (R-fMRI) is currently expanding exponentially. Beyond the general neuroimaging analysis packages (e.g., SPM, AFNI and FSL), REST and DPARSF were developed to meet the increasing need of user-friendly toolboxes for R-fMRI data processing. To address recently identified methodological challenges of R-fMRI, we introduce the newly developed toolbox, DPABI, which was evolved from REST and DPARSF. DPABI incorporates recent research advances on head motion control and measurement standardization, thus allowing users to evaluate results using stringent control strategies. DPABI also emphasizes test-retest reliability and quality control of data processing. Furthermore, DPABI provides a user-friendly pipeline analysis toolkit for rat/monkey R-fMRI data analysis to reflect the rapid advances in animal imaging. In addition, DPABI includes preprocessing modules for task-based fMRI, voxel-based morphometry analysis, statistical analysis and results viewing. DPABI is designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies. We anticipate this open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies.},
	Author = {Yan, Chao-Gan and Wang, Xin-Di and Zuo, Xi-Nian and Zang, Yu-Feng},
	Date-Added = {2017-10-10 20:01:15 +0000},
	Date-Modified = {2017-10-10 20:01:41 +0000},
	Doi = {10.1007/s12021-016-9299-4},
	Journal = {Neuroinformatics},
	Pages = {339--351},
	Title = {DPABI: Data Processing {\&} Analysis for (Resting-State) Brain Imaging},
	Volume = {14},
	Year = {2016},
	Bdsk-Url-1 = {https://doi.org/10.1007/s12021-016-9299-4},
	Bdsk-Url-2 = {http://dx.doi.org/10.1007/s12021-016-9299-4}}

@article{Zuo_SciData_2014,
	Abstract = {Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals' resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included.},
	Author = {Zuo, Xi-Nian and Anderson, Jeffrey S and Bellec, Pierre and Birn, Rasmus M and Biswal, Bharat B and Blautzik, Janusch and Breitner, John C S and Buckner, Randy L and Calhoun, Vince D and Castellanos, F Xavier and Chen, Antao and Chen, Bing and Chen, Jiangtao and Chen, Xu and Colcombe, Stanley J and Courtney, William and Craddock, R Cameron and Di Martino, Adriana and Dong, Hao-Ming and Fu, Xiaolan and Gong, Qiyong and Gorgolewski, Krzysztof J and Han, Ying and He, Ye and He, Yong and Ho, Erica and Holmes, Avram and Hou, Xiao-Hui and Huckins, Jeremy and Jiang, Tianzi and Jiang, Yi and Kelley, William and Kelly, Clare and King, Margaret and LaConte, Stephen M and Lainhart, Janet E and Lei, Xu and Li, Hui-Jie and Li, Kaiming and Li, Kuncheng and Lin, Qixiang and Liu, Dongqiang and Liu, Jia and Liu, Xun and Liu, Yijun and Lu, Guangming and Lu, Jie and Luna, Beatriz and Luo, Jing and Lurie, Daniel and Mao, Ying and Margulies, Daniel S and Mayer, Andrew R and Meindl, Thomas and Meyerand, Mary E and Nan, Weizhi and Nielsen, Jared A and O'Connor, David and Paulsen, David and Prabhakaran, Vivek and Qi, Zhigang and Qiu, Jiang and Shao, Chunhong and Shehzad, Zarrar and Tang, Weijun and Villringer, Arno and Wang, Huiling and Wang, Kai and Wei, Dongtao and Wei, Gao-Xia and Weng, Xu-Chu and Wu, Xuehai and Xu, Ting and Yang, Ning and Yang, Zhi and Zang, Yu-Feng and Zhang, Lei and Zhang, Qinglin and Zhang, Zhe and Zhang, Zhiqiang and Zhao, Ke and Zhen, Zonglei and Zhou, Yuan and Zhu, Xing-Ting and Milham, Michael P},
	Date-Added = {2017-10-10 15:45:24 +0000},
	Date-Modified = {2017-10-10 15:45:44 +0000},
	Doi = {10.1038/sdata.2014.49},
	Journal = {Sci Data},
	Pages = {140049},
	Title = {An open science resource for establishing reliability and reproducibility in functional connectomics},
	Volume = {1},
	Year = {2014},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/sdata.2014.49}}

@article{Guyon_ML_2002,
	Abstract = {DNA micro-arrays now permit scientists to screen thousands of genes simultaneously and determine whether those genes are active, hyperactive or silent in normal or cancerous tissue. Because these new micro-array devices generate bewildering amounts of raw data, new analytical methods must be developed to sort out whether cancer tissues have distinctive signatures of gene expression over normal tissues or other types of cancer tissues.},
	Author = {Guyon, Isabelle and Weston, Jason and Barnhill, Stephen and Vapnik, Vladimir},
	Date-Added = {2017-10-10 15:09:36 +0000},
	Date-Modified = {2017-10-10 15:10:02 +0000},
	Day = {01},
	Doi = {10.1023/A:1012487302797},
	Journal = {Machine Learning},
	Number = {1},
	Pages = {389--422},
	Title = {Gene Selection for Cancer Classification using Support Vector Machines},
	Volume = {46},
	Year = {2002},
	Bdsk-Url-1 = {https://doi.org/10.1023/A:1012487302797},
	Bdsk-Url-2 = {http://dx.doi.org/10.1023/A:1012487302797}}

@article{Tzourio2002,
	Author = {Tzourio-Mazoyer, N and Landeau, B and Papathanassiou, D and Crivello, F and Etard, O and Delcroix, N and Mazoyer, B and Joliot, M},
	Journal = {Neuroimage},
	Pages = {273-289},
	Title = {Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the {MNI} {MRI} single-subject brain},
	Volume = {15},
	Year = {2002}}

@article{Vanderwal_NeIm_2017,
	Abstract = {Naturalistic viewing paradigms such as movies have been shown to reduce participant head motion and improve arousal during fMRI scanning relative to task-free rest, and have been used to study both functional connectivity and stimulus-evoked BOLD-signal changes. These task-based hemodynamic changes are synchronized across subjects and involve large areas of the cortex, and it is unclear whether individual differences in functional connectivity are enhanced or diminished under such naturalistic conditions. This work first aims to characterize variability in BOLD-signal based functional connectivity (FC) across 2 distinct movie conditions and eyes-open rest (n=31 healthy adults, 2 scan sessions each). We found that movies have higher within- and between-subject correlations in cluster-wise FC relative to rest. The anatomical distribution of inter-individual variability was similar across conditions, with higher variability occurring at the lateral prefrontal lobes and temporoparietal junctions. Second, we used an unsupervised test-retest matching algorithm that identifies individual subjects from within a group based on FC patterns, quantifying the accuracy of the algorithm across the three conditions. The movies and resting state all enabled identification of individual subjects based on FC matrices, with accuracies between 61% and 100%. Overall, pairings involving movies outperformed rest, and the social, faster-paced movie attained 100% accuracy. When the parcellation resolution, scan duration, and number of edges used were increased, accuracies improved across conditions, and the pattern of movies>rest was preserved. These results suggest that using dynamic stimuli such as movies enhances the detection of FC patterns that are unique at the individual level.},
	Author = {Vanderwal, Tamara and Eilbott, Jeffrey and Finn, Emily S and Craddock, R Cameron and Turnbull, Adam and Castellanos, F Xavier},
	Date-Added = {2017-08-20 20:18:14 +0000},
	Date-Modified = {2017-08-20 20:18:51 +0000},
	Doi = {10.1016/j.neuroimage.2017.06.027},
	Journal = {Neuroimage},
	Pages = {521-530},
	Title = {Individual differences in functional connectivity during naturalistic viewing conditions},
	Volume = {157},
	Year = {2017},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuroimage.2017.06.027}}

@article{Papageorgiou_PNAS_2013,
	Abstract = {Brain-computer interfaces (BCIs) can convert mental states into signals to drive real-world devices, but it is not known if a given covert task is the same when performed with and without BCI-based control. Using a BCI likely involves additional cognitive processes, such as multitasking, attention, and conflict monitoring. In addition, it is challenging to measure the quality of covert task performance. We used whole-brain classifier-based real-time functional MRI to address these issues, because the method provides both classifier-based maps to examine the neural requirements of BCI and classification accuracy to quantify the quality of task performance. Subjects performed a covert counting task at fast and slow rates to control a visual interface. Compared with the same task when viewing but not controlling the interface, we observed that being in control of a BCI improved task classification of fast and slow counting states. Additional BCI control increased subjects' whole-brain signal-to-noise ratio compared with the absence of control. The neural pattern for control consisted of a positive network comprised of dorsal parietal and frontal regions and the anterior insula of the right hemisphere as well as an expansive negative network of regions. These findings suggest that real-time functional MRI can serve as a platform for exploring information processing and frontoparietal and insula network-based regulation of whole-brain task signal-to-noise ratio.},
	Author = {Papageorgiou, T Dorina and Lisinski, Jonathan M and McHenry, Monica A and White, Jason P and LaConte, Stephen M},
	Date-Added = {2017-08-20 20:12:11 +0000},
	Date-Modified = {2017-08-20 20:12:43 +0000},
	Doi = {10.1073/pnas.1210738110},
	Journal = {Proc Natl Acad Sci U S A},
	Pages = {13630-13635},
	Title = {Brain-computer interfaces increase whole-brain signal to noise},
	Volume = {110},
	Year = {2013},
	Bdsk-Url-1 = {http://dx.doi.org/10.1073/pnas.1210738110}}

@article{Xie_NeIm_2017,
	Abstract = {Functional connectivity (FC) has been widely used to study the functional organization of temporally correlated and spatially distributed brain regions. Recent studies of FC dynamics, quantified by windowed correlations, provide new insights to analyze dynamic, context-dependent reconfiguration of brain networks. A set of reoccurring whole-brain connectivity patterns at rest, referred to as FC states, have been identified, hypothetically reflecting underlying cognitive processes or mental states. We posit that the mean FC information for a given subject represents a significant contribution to the group-level FC dynamics. We show that the subject-specific FC profile, termed as FC individuality, can be removed to increase sensitivity to cognitively relevant FC states. To assess the impact of the FC individuality and task-specific FC modulation on the group-level FC dynamics analysis, we generate and analyze group studies of four subjects engaging in four cognitive conditions (rest, simple math, two-back memory, and visual attention task). We also propose a model to quantitatively evaluate the effect of two factors, namely, subject-specific and task-specific modulation on FC dynamics. We show that FC individuality is a predominant factor in group-level FC variability, and the embedded cognitively relevant FC states are clearly visible after removing the individual's connectivity profile. Our results challenge the current understanding of FC states and emphasize the importance of individual heterogeneity in connectivity dynamics analysis.},
	Author = {Xie, Hua and Calhoun, Vince D and Gonzalez-Castillo, Javier and Damaraju, Eswar and Miller, Robyn and Bandettini, Peter A and Mitra, Sunanda},
	Date-Added = {2017-08-20 19:29:37 +0000},
	Date-Modified = {2017-08-20 19:30:03 +0000},
	Doi = {10.1016/j.neuroimage.2017.05.050},
	Journal = {Neuroimage},
	Title = {Whole-brain connectivity dynamics reflect both task-specific and individual-specific modulation: A multitask study},
	Year = {2017},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuroimage.2017.05.050}}

@article{Li_FP_2012,
	Abstract = {The default mode network (DMN) has recently attracted widespread interest. Previous studies have found that task-related processing can induce deactivation and changes in the functional connectivity of this network. However, it remains unclear how tasks modulate the underlying effective connectivity within the DMN. Using recent advances in dynamic causal modeling (DCM), we investigated the modulatory effect of (gender judgment) task performance on directed connectivity within the DMN. Sixteen healthy subjects were scanned twice: at rest and while performing a gender judgment task. Group independent component analysis was used to identify independent spatial components. Four subject-specific regions of interest (ROIs) were defined according to the ensuing default mode component: the posterior cingulate cortex, the left lateral parietal cortex, the right lateral parietal cortex, and the medial prefrontal cortex. Effective connectivity among these regions was then characterized with stochastic DCM, revealing enhanced (extrinsic) between region connectivity within the DMN during task sessions - and a universal decrease in (intrinsic) self-inhibition - relative to resting sessions. These results suggest a distributed but systematic modulatory effect of cognitive and attentional set on the effective connectivity subtending the DMN: an effect that increases its sensitivity to inputs and may optimize distributed processing during task performance.},
	Author = {Li, Baojuan and Wang, Xiang and Yao, Shuqiao and Hu, Dewen and Friston, Karl},
	Date-Added = {2017-08-20 19:26:48 +0000},
	Date-Modified = {2017-08-20 19:27:19 +0000},
	Doi = {10.3389/fpsyg.2012.00206},
	Journal = {Front Psychol},
	Pages = {206},
	Title = {Task-Dependent Modulation of Effective Connectivity within the Default Mode Network},
	Volume = {3},
	Year = {2012},
	Bdsk-Url-1 = {http://dx.doi.org/10.3389/fpsyg.2012.00206}}

@article{Frassle_NeIm_2017,
	Abstract = {The development of large-scale network models that infer the effective (directed) connectivity among neuronal populations from neuroimaging data represents a key challenge for computational neuroscience. Dynamic causal models (DCMs) of neuroimaging and electrophysiological data are frequently used for inferring effective connectivity but are presently restricted to small graphs (typically up to 10 regions) in order to keep model inversion computationally feasible. Here, we present a novel variant of DCM for functional magnetic resonance imaging (fMRI) data that is suited to assess effective connectivity in large (whole-brain) networks. The approach rests on translating a linear DCM into the frequency domain and reformulating it as a special case of Bayesian linear regression. This paper derives regression DCM (rDCM) in detail and presents a variational Bayesian inversion method that enables extremely fast inference and accelerates model inversion by several orders of magnitude compared to classical DCM. Using both simulated and empirical data, we demonstrate the face validity of rDCM under different settings of signal-to-noise ratio (SNR) and repetition time (TR) of fMRI data. In particular, we assess the potential utility of rDCM as a tool for whole-brain connectomics by challenging it to infer effective connection strengths in a simulated whole-brain network comprising 66 regions and 300 free parameters. Our results indicate that rDCM represents a computationally highly efficient approach with promising potential for inferring whole-brain connectivity from individual fMRI data.},
	Author = {Fr{\"a}ssle, S and Lomakina, E I and Razi, A and Friston, K J and Buhmann, J M and Stephan, K E},
	Date-Added = {2017-08-20 19:07:01 +0000},
	Date-Modified = {2017-08-22 12:51:59 +0000},
	Doi = {10.1016/j.neuroimage.2017.02.090},
	Journal = {Neuroimage},
	Pages = {406-421},
	Title = {Regression {DCM} for {fMRI}},
	Volume = {155},
	Year = {2017},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuroimage.2017.02.090}}

@article{Friston_NeIm_2014,
	Abstract = {This technical note introduces a dynamic causal model (DCM) for resting state fMRI time series based upon observed functional connectivity--as measured by the cross spectra among different brain regions. This DCM is based upon a deterministic model that generates predicted crossed spectra from a biophysically plausible model of coupled neuronal fluctuations in a distributed neuronal network or graph. Effectively, the resulting scheme finds the best effective connectivity among hidden neuronal states that explains the observed functional connectivity among haemodynamic responses. This is because the cross spectra contain all the information about (second order) statistical dependencies among regional dynamics. In this note, we focus on describing the model, its relationship to existing measures of directed and undirected functional connectivity and establishing its face validity using simulations. In subsequent papers, we will evaluate its construct validity in relation to stochastic DCM and its predictive validity in Parkinson's and Huntington's disease.},
	Author = {Friston, K J and Kahan, J and Biswal, B and Razi, A},
	Date-Added = {2017-08-20 17:55:13 +0000},
	Date-Modified = {2017-08-22 12:51:36 +0000},
	Doi = {10.1016/j.neuroimage.2013.12.009},
	Journal = {Neuroimage},
	Pages = {396-407},
	Title = {A {DCM} for resting state {fMRI}},
	Volume = {94},
	Year = {2014},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuroimage.2013.12.009}}

@article{Boynton_JNS_1996,
	Abstract = {The linear transform model of functional magnetic resonance imaging (fMRI) hypothesizes that fMRI responses are proportional to local average neural activity averaged over a period of time. This work reports results from three empirical tests that support this hypothesis. First, fMRI responses in human primary visual cortex (V1) depend separably on stimulus timing and stimulus contrast. Second, responses to long-duration stimuli can be predicted from responses to shorter duration stimuli. Third, the noise in the fMRI data is independent of stimulus contrast and temporal period. Although these tests can not prove the correctness of the linear transform model, they might have been used to reject the model. Because the linear transform model is consistent with our data, we proceeded to estimate the temporal fMRI impulse-response function and the underlying (presumably neural) contrast-response function of human V1.},
	Author = {Boynton, G M and Engel, S A and Glover, G H and Heeger, D J},
	Date-Added = {2017-08-20 17:53:31 +0000},
	Date-Modified = {2017-08-22 12:53:34 +0000},
	Journal = {J Neurosci},
	Pages = {4207-4221},
	Title = {Linear systems analysis of functional magnetic resonance imaging in human {V1}},
	Volume = {16},
	Year = {1996}}

@article{Power_NeIm_2012,
	Abstract = {Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements.},
	Author = {Power, Jonathan D and Barnes, Kelly A and Snyder, Abraham Z and Schlaggar, Bradley L and Petersen, Steven E},
	Date-Added = {2017-05-19 13:21:36 +0000},
	Date-Modified = {2017-08-22 12:49:17 +0000},
	Journal = {Neuroimage},
	Pages = {2142-2154},
	Title = {Spurious but systematic correlations in functional connectivity {MRI} networks arise from subject motion},
	Volume = {59},
	Year = {2012},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuroimage.2011.10.018}}

@article{Talavage_HR_2014,
	Abstract = {For much of the past 30 years, investigations of auditory perception and language have been enhanced or even driven by the use of functional neuroimaging techniques that specialize in localization of central responses. Beginning with investigations using positron emission tomography (PET) and gradually shifting primarily to usage of functional magnetic resonance imaging (fMRI), auditory neuroimaging has greatly advanced our understanding of the organization and response properties of brain regions critical to the perception of and communication with the acoustic world in which we live. As the complexity of the questions being addressed has increased, the techniques, experiments and analyses applied have also become more nuanced and specialized. A brief review of the history of these investigations sets the stage for an overview and analysis of how these neuroimaging modalities are becoming ever more effective tools for understanding the auditory brain. We conclude with a brief discussion of open methodological issues as well as potential clinical applications for auditory neuroimaging. This article is part of a Special Issue entitled Human Auditory Neuroimaging.},
	Author = {Talavage, Thomas M and Gonzalez-Castillo, Javier and Scott, Sophie K},
	Date-Added = {2017-05-15 08:58:00 +0000},
	Date-Modified = {2017-08-20 20:53:25 +0000},
	Journal = {Hear Res},
	Pages = {4-15},
	Title = {Auditory neuroimaging with {fMRI} and {PET}},
	Volume = {307},
	Year = {2014},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.heares.2013.09.009}}

@article{Arsigny_MRM_2006,
	Abstract = {Diffusion tensor imaging (DT-MRI or DTI) is an emerging imaging modality whose importance has been growing considerably. However, the processing of this type of data (i.e., symmetric positive-definite matrices), called "tensors" here, has proved difficult in recent years. Usual Euclidean operations on matrices suffer from many defects on tensors, which have led to the use of many ad hoc methods. Recently, affine-invariant Riemannian metrics have been proposed as a rigorous and general framework in which these defects are corrected. These metrics have excellent theoretical properties and provide powerful processing tools, but also lead in practice to complex and slow algorithms. To remedy this limitation, a new family of Riemannian metrics called Log-Euclidean is proposed in this article. They also have excellent theoretical properties and yield similar results in practice, but with much simpler and faster computations. This new approach is based on a novel vector space structure for tensors. In this framework, Riemannian computations can be converted into Euclidean ones once tensors have been transformed into their matrix logarithms. Theoretical aspects are presented and the Euclidean, affine-invariant, and Log-Euclidean frameworks are compared experimentally. The comparison is carried out on interpolation and regularization tasks on synthetic and clinical 3D DTI data.},
	Author = {Arsigny, Vincent and Fillard, Pierre and Pennec, Xavier and Ayache, Nicholas},
	Date-Added = {2017-05-14 14:41:00 +0000},
	Date-Modified = {2017-05-14 14:41:24 +0000},
	Journal = {Magn Reson Med},
	Pages = {411-421},
	Title = {Log-Euclidean metrics for fast and simple calculus on diffusion tensors},
	Volume = {56},
	Year = {2006},
	Bdsk-Url-1 = {http://dx.doi.org/10.1002/mrm.20965}}

@article{DiCarlo_Nrn_2012,
	Abstract = {Mounting evidence suggests that 'core object recognition,' the ability to rapidly recognize objects despite substantial appearance variation, is solved in the brain via a cascade of reflexive, largely feedforward computations that culminate in a powerful neuronal representation in the inferior temporal cortex. However, the algorithm that produces this solution remains poorly understood. Here we review evidence ranging from individual neurons and neuronal populations to behavior and computational models. We propose that understanding this algorithm will require using neuronal and psychophysical data to sift through many computational models, each based on building blocks of small, canonical subnetworks with a common functional goal.},
	Author = {DiCarlo, James J and Zoccolan, Davide and Rust, Nicole C},
	Date-Added = {2017-05-13 08:35:17 +0000},
	Date-Modified = {2017-05-13 08:35:39 +0000},
	Journal = {Neuron},
	Pages = {415-434},
	Title = {How does the brain solve visual object recognition?},
	Volume = {73},
	Year = {2012},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuron.2012.01.010}}

@article{Farbood_FrontNeurosci_2015,
	Abstract = {Music, like speech, is a complex auditory signal that contains structures at multiple timescales, and as such is a potentially powerful entry point into the question of how the brain integrates complex streams of information. Using an experimental design modeled after previous studies that used scrambled versions of a spoken story (Lerner et al., 2011) and a silent movie (Hasson et al., 2008), we investigate whether listeners perceive hierarchical structure in music beyond short (~6 s) time windows and whether there is cortical overlap between music and language processing at multiple timescales. Experienced pianists were presented with an extended musical excerpt scrambled at multiple timescales-by measure, phrase, and section-while measuring brain activity with functional magnetic resonance imaging (fMRI). The reliability of evoked activity, as quantified by inter-subject correlation of the fMRI responses, was measured. We found that response reliability depended systematically on musical structure coherence, revealing a topographically organized hierarchy of processing timescales. Early auditory areas (at the bottom of the hierarchy) responded reliably in all conditions. For brain areas at the top of the hierarchy, the original (unscrambled) excerpt evoked more reliable responses than any of the scrambled excerpts, indicating that these brain areas process long-timescale musical structures, on the order of minutes. The topography of processing timescales was analogous with that reported previously for speech, but the timescale gradients for music and speech overlapped with one another only partially, suggesting that temporally analogous structures-words/measures, sentences/musical phrases, paragraph/sections-are processed separately.},
	Author = {Farbood, Morwaread M and Heeger, David J and Marcus, Gary and Hasson, Uri and Lerner, Yulia},
	Date-Added = {2017-05-13 08:30:37 +0000},
	Date-Modified = {2017-05-13 08:33:01 +0000},
	Journal = {Front Neurosci},
	Pages = {157},
	Title = {The neural processing of hierarchical structure in music and speech at different timescales},
	Volume = {9},
	Year = {2015},
	Bdsk-Url-1 = {http://dx.doi.org/10.3389/fnins.2015.00157}}

@article{Emerson_FrontHum_2015,
	Abstract = {Better understanding of the developing brain's functional mechanisms is critical for improving diagnosis and treatment of different developmental disorders. Particularly, characterizing how the developing brain dynamically reorganizes during different cognitive states may offer novel insight into the neuronal mechanisms of cognitive deficits. Imaging the brain during naturalistic conditions, like movie watching, provides a highly practical way to study young children's developing functional brain systems. In this study we compared the network-level functional organization of 6-year-old children while they were at rest with their functional connectivity as they watched short video clips. We employed both a data-driven independent component analysis (ICA) approach and a hypothesis-driven seed-based analysis to identify changes in network-level functional interactions during the shift from resting to video watching. Our ICA results showed that naturally watching a movie elicits significant changes in the functional connectivity between the visual system and the dorsal attention network when compared to rest (t (32) = 5.02, p = 0.0001). More interestingly, children showed an immature, but qualitatively adult-like, pattern of reorganization among three of the brain's higher-order networks (frontal control, default-mode and dorsal attention). For both ICA and seed-based approaches, we observed a decrease in the frontal network's correlation with the dorsal attention network (ICA: t (32) = -2.46, p = 0.02; Seed-based: t (32) = -1.62, p =0.12) and an increase in its connectivity with the default mode network (ICA: t (32) = 2.84, p = 0.008; Seed-based: t (32) = 2.28, p =0.03), which is highly consistent with the pattern observed in adults. These results offer improved understanding of the developing brain's dynamic network-level interaction patterns during the transition between different brain states and call for further studies to examine potential alterations to such dynamic patterns in different developmental disorders.},
	Author = {Emerson, Robert W and Short, Sarah J and Lin, Weili and Gilmore, John H and Gao, Wei},
	Date-Added = {2017-05-13 08:30:25 +0000},
	Date-Modified = {2017-05-13 08:31:28 +0000},
	Journal = {Front Hum Neurosci},
	Pages = {631},
	Title = {Network-Level Connectivity Dynamics of Movie Watching in 6-Year-Old Children},
	Volume = {9},
	Year = {2015},
	Bdsk-Url-1 = {http://dx.doi.org/10.3389/fnhum.2015.00631}}

@article{Gao_MIC_2010,
	Abstract = {The question of how large-scale systems interact with each other is intriguing given the increasingly established network structures of whole brain organization. Commonly used regional interaction approaches, however, cannot address this question. In this paper, we proposed a multivariate network-level framework to directly quantify the interaction pattern between large-scale functional systems. The proposed framework was tested on three different brain states, including resting, finger tapping and movie watching using functional connectivity MRI. The interaction patterns among five predefined networks including dorsal attention (DA), default (DF), frontal-parietal control (FPC), motor-sensory (MS) and visual (V) were delineated during each state. Results show dramatic and expected network-level correlation changes across different states underscoring the importance of network-level interactions for successful transition between different states. In addition, our analysis provides preliminary evidence of the potential regulating role of FPC on the two opposing systems-DA and DF on the network level.},
	Author = {Gao, Wei and Zhu, Hongtu and Giovanello, Kelly and Lin, Weili},
	Date-Added = {2017-05-13 08:30:03 +0000},
	Date-Modified = {2017-05-13 08:32:05 +0000},
	Journal = {Med Image Comput Comput Assist Interv},
	Pages = {298-305},
	Title = {Multivariate network-level approach to detect interactions between large-scale functional systems},
	Volume = {13},
	Year = {2010}}

@article{Mennes_CeCo_2013,
	Abstract = {The brain's intrinsic functional architecture, revealed in correlated spontaneous activity, appears to constitute a faithful representation of its repertoire of evoked, extrinsic functional interactions. Here, using broad task contrasts to probe evoked patterns of coactivation, we demonstrate tight coupling between the brain's intrinsic and extrinsic functional architectures for default and task-positive regions, but not for subcortical and limbic regions or for primary sensory and motor cortices. While strong correspondence likely reflects persistent or recurrent patterns of evoked coactivation, weak correspondence may exist for regions whose patterns of evoked functional interactions are more adaptive and context dependent. These findings were independent of task. For tight task contrasts (e.g., incongruent vs. congruent trials), evoked patterns of coactivation were unrelated to the intrinsic functional architecture, suggesting that high-level task demands are accommodated by context-specific modulations of functional interactions. We conclude that intrinsic approaches provide only a partial understanding of the brain's functional architecture. Appreciating the full repertoire of dynamic neural responses will continue to require task-based functional magnetic resonance imaging approaches.},
	Author = {Mennes, Maarten and Kelly, Clare and Colcombe, Stan and Castellanos, F Xavier and Milham, Michael P},
	Date-Added = {2017-05-13 08:18:55 +0000},
	Date-Modified = {2017-05-13 08:19:42 +0000},
	Journal = {Cereb Cortex},
	Pages = {223-229},
	Title = {The extrinsic and intrinsic functional architectures of the human brain are not equivalent},
	Volume = {23},
	Year = {2013},
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/cercor/bhs010}}

@article{Cole_Nrn_2014,
	Abstract = {Many functional network properties of the human brain have been identified during rest and task states, yet it remains unclear how the two relate. We identified a whole-brain network architecture present across dozens of task states that was highly similar to the resting-state network architecture. The most frequent functional connectivity strengths across tasks closely matched the strengths observed at rest, suggesting this is an "intrinsic," standard architecture of functional brain organization. Furthermore, a set of small but consistent changes common across tasks suggests the existence of a task-general network architecture distinguishing task states from rest. These results indicate the brain's functional network architecture during task performance is shaped primarily by an intrinsic network architecture that is also present during rest, and secondarily by evoked task-general and task-specific network changes. This establishes a strong relationship between resting-state functional connectivity and task-evoked functional connectivity-areas of neuroscientific inquiry typically considered separately.},
	Author = {Cole, Michael W and Bassett, Danielle S and Power, Jonathan D and Braver, Todd S and Petersen, Steven E},
	Date-Added = {2017-05-13 08:17:48 +0000},
	Date-Modified = {2017-05-13 08:19:17 +0000},
	Journal = {Neuron},
	Pages = {238-251},
	Title = {Intrinsic and task-evoked network architectures of the human brain},
	Volume = {83},
	Year = {2014},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuron.2014.05.014}}

@article{Dinstein_TCS_2015,
	Abstract = {Although we may not realize it, our brain function varies markedly from moment to moment such that our brain responses exhibit substantial variability across trials even in response to a simple repeating stimulus. Should we care about such within-subject variability? Are there developmental, cognitive, and clinical consequences to having a brain that is more or less variable/noisy? Although neural variability seems to be beneficial for learning, excessive levels of neural variability are apparent in individuals with different clinical disorders. We propose that measuring distinct types of neural variability in autism and other disorders is likely to reveal crucial insights regarding their neuropathology. We further discuss the importance of studying neural variability more generally across development and aging in humans.},
	Author = {Dinstein, Ilan and Heeger, David J and Behrmann, Marlene},
	Date-Added = {2017-05-13 07:19:05 +0000},
	Date-Modified = {2017-05-13 07:19:32 +0000},
	Journal = {Trends Cogn Sci},
	Pages = {322-328},
	Title = {Neural variability: friend or foe?},
	Volume = {19},
	Year = {2015},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.tics.2015.04.005}}

@article{Faisal_NatRev_2008,
	Abstract = {Noise--random disturbances of signals--poses a fundamental problem for information processing and affects all aspects of nervous-system function. However, the nature, amount and impact of noise in the nervous system have only recently been addressed in a quantitative manner. Experimental and computational methods have shown that multiple noise sources contribute to cellular and behavioural trial-to-trial variability. We review the sources of noise in the nervous system, from the molecular to the behavioural level, and show how noise contributes to trial-to-trial variability. We highlight how noise affects neuronal networks and the principles the nervous system applies to counter detrimental effects of noise, and briefly discuss noise's potential benefits.},
	Author = {Faisal, A Aldo and Selen, Luc P J and Wolpert, Daniel M},
	Date-Added = {2017-05-13 07:18:05 +0000},
	Date-Modified = {2017-05-13 07:22:14 +0000},
	Journal = {Nat Rev Neurosci},
	Pages = {292-303},
	Title = {Noise in the nervous system},
	Volume = {9},
	Year = {2008},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/nrn2258}}

@article{Stein_NatRev_2005,
	Abstract = {Sensory, motor and cortical neurons fire impulses or spikes at a regular, but slowly declining, rate in response to a constant current stimulus. Yet, the intervals between spikes often vary randomly during behaviour. Is this variation an unavoidable effect of generating spikes by sensory or synaptic processes ('neural noise') or is it an important part of the 'signal' that is transmitted to other neurons? Here, we mainly discuss this question in relation to sensory and motor processes, as the signals are best identified in such systems, although we also touch on central processes.},
	Author = {Stein, Richard B and Gossen, E Roderich and Jones, Kelvin E},
	Date-Added = {2017-05-13 07:17:38 +0000},
	Date-Modified = {2017-05-13 07:21:17 +0000},
	Journal = {Nat Rev Neurosci},
	Pages = {389-397},
	Title = {Neuronal variability: noise or part of the signal?},
	Volume = {6},
	Year = {2005},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/nrn1668}}

@article{Wink_HBM_2008,
	Abstract = {Fractal processes, like trees or coastlines, are defined by self-similarity or power law scaling controlled by a single exponent, simply related to the fractal dimension or Hurst exponent (H) of the process. Multifractal processes, like turbulence, have more complex behaviours defined by a spectrum of possible local scaling behaviours or singularity exponents (h). Here, we report two experiments that explore the relationships between instrumental and cognitive variables and the monofractal and multifractal parameters of functional magnetic resonance imaging (fMRI) data acquired in a no-task or resting state. First, we show that the Hurst exponent is greater in grey matter than in white matter regions, and it is maximal in grey matter when data were acquired with an echo time known to optimise BOLD contrast. Second, we show that latency of response in a fame decision/facial encoding task was negatively correlated with the Hurst exponent of resting state data acquired 30 min after task performance. This association was localised to a right inferior frontal cortical region activated by the fame decision task and indicated that people with shorter response latency had more persistent dynamics (higher values of H). Multifractal analysis revealed that faster responding participants had wider singularity spectra of resting fMRI time series in inferior frontal cortex. Endogenous brain oscillations measured by fMRI have monofractal and multifractal properties that can be related to instrumental and cognitive factors in a way, which indicates that these low frequency dynamics are relevant to neurocognitive function.},
	Author = {Wink, Alle-Meije and Bullmore, Ed and Barnes, Anna and Bernard, Frederic and Suckling, John},
	Date-Added = {2017-05-13 06:53:55 +0000},
	Date-Modified = {2017-08-22 12:47:11 +0000},
	Journal = {Hum Brain Mapp},
	Pages = {791-801},
	Title = {Monofractal and multifractal dynamics of low frequency endogenous brain oscillations in functional {MRI}},
	Volume = {29},
	Year = {2008},
	Bdsk-Url-1 = {http://dx.doi.org/10.1002/hbm.20593}}

@article{PonceAlvarez_PCB_2015,
	Abstract = {How a stimulus or a task alters the spontaneous dynamics of the brain remains a fundamental open question in neuroscience. One of the most robust hallmarks of task/stimulus-driven brain dynamics is the decrease of variability with respect to the spontaneous level, an effect seen across multiple experimental conditions and in brain signals observed at different spatiotemporal scales. Recently, it was observed that the trial-to-trial variability and temporal variance of functional magnetic resonance imaging (fMRI) signals decrease in the task-driven activity. Here we examined the dynamics of a large-scale model of the human cortex to provide a mechanistic understanding of these observations. The model allows computing the statistics of synaptic activity in the spontaneous condition and in putative tasks determined by external inputs to a given subset of brain regions. We demonstrated that external inputs decrease the variance, increase the covariances, and decrease the autocovariance of synaptic activity as a consequence of single node and large-scale network dynamics. Altogether, these changes in network statistics imply a reduction of entropy, meaning that the spontaneous synaptic activity outlines a larger multidimensional activity space than does the task-driven activity. We tested this model's prediction on fMRI signals from healthy humans acquired during rest and task conditions and found a significant decrease of entropy in the stimulus-driven activity. Altogether, our study proposes a mechanism for increasing the information capacity of brain networks by enlarging the volume of possible activity configurations at rest and reliably settling into a confined stimulus-driven state to allow better transmission of stimulus-related information.},
	Author = {Ponce-Alvarez, Adri{\'a}n and He, Biyu J and Hagmann, Patric and Deco, Gustavo},
	Date-Added = {2017-05-10 15:57:17 +0000},
	Date-Modified = {2017-05-10 15:57:39 +0000},
	Doi = {10.1371/journal.pcbi.1004445},
	Journal = {PLoS Comput Biol},
	Journal-Full = {PLoS computational biology},
	Mesh = {Adolescent; Adult; Cerebral Cortex; Computational Biology; Entropy; Female; Humans; Magnetic Resonance Imaging; Male; Models, Neurological; Rest; Task Performance and Analysis; Young Adult},
	Month = {Aug},
	Number = {8},
	Pages = {e1004445},
	Pmc = {PMC4552873},
	Pmid = {26317432},
	Pst = {epublish},
	Title = {Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling},
	Volume = {11},
	Year = {2015},
	Bdsk-Url-1 = {http://dx.doi.org/10.1371/journal.pcbi.1004445}}

@article{Ciuciu_FrontPhys_2012,
	Abstract = {Scaling temporal dynamics in functional MRI (fMRI) signals have been evidenced for a decade as intrinsic characteristics of ongoing brain activity (Zarahn et al., 1997). Recently, scaling properties were shown to fluctuate across brain networks and to be modulated between rest and task (He, 2011): notably, Hurst exponent, quantifying long memory, decreases under task in activating and deactivating brain regions. In most cases, such results were obtained: First, from univariate (voxelwise or regionwise) analysis, hence focusing on specific cognitive systems such as Resting-State Networks (RSNs) and raising the issue of the specificity of this scale-free dynamics modulation in RSNs. Second, using analysis tools designed to measure a single scaling exponent related to the second order statistics of the data, thus relying on models that either implicitly or explicitly assume Gaussianity and (asymptotic) self-similarity, while fMRI signals may significantly depart from those either of those two assumptions (Ciuciu et al., 2008; Wink et al., 2008). To address these issues, the present contribution elaborates on the analysis of the scaling properties of fMRI temporal dynamics by proposing two significant variations. First, scaling properties are technically investigated using the recently introduced Wavelet Leader-based Multifractal formalism (WLMF; Wendt et al., 2007). This measures a collection of scaling exponents, thus enables a richer and more versatile description of scale invariance (beyond correlation and Gaussianity), referred to as multifractality. Also, it benefits from improved estimation performance compared to tools previously used in the literature. Second, scaling properties are investigated in both RSN and non-RSN structures (e.g., artifacts), at a broader spatial scale than the voxel one, using a multivariate approach, namely the Multi-Subject Dictionary Learning (MSDL) algorithm (Varoquaux et al., 2011) that produces a set of spatial components that appear more sparse than their Independent Component Analysis (ICA) counterpart. These tools are combined and applied to a fMRI dataset comprising 12 subjects with resting-state and activation runs (Sadaghiani et al., 2009). Results stemming from those analysis confirm the already reported task-related decrease of long memory in functional networks, but also show that it occurs in artifacts, thus making this feature not specific to functional networks. Further, results indicate that most fMRI signals appear multifractal at rest except in non-cortical regions. Task-related modulation of multifractality appears only significant in functional networks and thus can be considered as the key property disentangling functional networks from artifacts. These finding are discussed in the light of the recent literature reporting scaling dynamics of EEG microstate sequences at rest and addressing non-stationarity issues in temporally independent fMRI modes.},
	Author = {Ciuciu, P and Varoquaux, G and Abry, P and Sadaghiani, S and Kleinschmidt, A},
	Date-Added = {2017-05-10 15:38:51 +0000},
	Date-Modified = {2017-08-22 12:46:30 +0000},
	Journal = {Front Physiol},
	Pages = {186},
	Title = {Scale-Free and Multifractal Time Dynamics of {fMRI} Signals during Rest and Task},
	Volume = {3},
	Year = {2012},
	Bdsk-Url-1 = {http://dx.doi.org/10.3389/fphys.2012.00186}}

@article{He_JNS_2011,
	Abstract = {It has been shown recently that a significant portion of brain electrical field potentials consists of scale-free dynamics. These scale-free brain dynamics contain complex spatiotemporal structures and are modulated by task performance. Here we show that the fMRI signal recorded from the human brain is also scale free; its power-law exponent differentiates between brain networks and correlates with fMRI signal variance and brain glucose metabolism. Importantly, in parallel to brain electrical field potentials, the variance and power-law exponent of the fMRI signal decrease during task activation, suggesting that the signal contains more long-range memory during rest and conversely is more efficient at online information processing during task. Remarkably, similar changes also occurred in task-deactivated brain regions, revealing the presence of an optimal dynamic range in the fMRI signal. The scale-free properties of the fMRI signal and brain electrical field potentials bespeak their respective stationarity and nonstationarity. This suggests that neurovascular coupling mechanism is likely to contain a transformation from nonstationarity to stationarity. In summary, our results demonstrate the functional relevance of scale-free properties of the fMRI signal and impose constraints on future models of neurovascular coupling.},
	Author = {He, Biyu J},
	Date-Added = {2017-05-05 14:22:49 +0000},
	Date-Modified = {2017-05-05 14:23:35 +0000},
	Journal = {J Neurosci},
	Pages = {13786-13795},
	Title = {Scale-free properties of the functional magnetic resonance imaging signal during rest and task},
	Volume = {31},
	Year = {2011},
	Bdsk-Url-1 = {http://dx.doi.org/10.1523/JNEUROSCI.2111-11.2011}}

@article{Ciuciu_NeIm_2014,
	Abstract = {Studies employing functional connectivity-type analyses have established that spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals are organized within large-scale brain networks. Meanwhile, fMRI signals have been shown to exhibit 1/f-type power spectra - a hallmark of scale-free dynamics. We studied the interplay between functional connectivity and scale-free dynamics in fMRI signals, utilizing the fractal connectivity framework - a multivariate extension of the univariate fractional Gaussian noise model, which relies on a wavelet formulation for robust parameter estimation. We applied this framework to fMRI data acquired from healthy young adults at rest and while performing a visual detection task. First, we found that scale-invariance existed beyond univariate dynamics, being present also in bivariate cross-temporal dynamics. Second, we observed that frequencies within the scale-free range do not contribute evenly to inter-regional connectivity, with a systematically stronger contribution of the lowest frequencies, both at rest and during task. Third, in addition to a decrease of the Hurst exponent and inter-regional correlations, task performance modified cross-temporal dynamics, inducing a larger contribution of the highest frequencies within the scale-free range to global correlation. Lastly, we found that across individuals, a weaker task modulation of the frequency contribution to inter-regional connectivity was associated with better task performance manifesting as shorter and less variable reaction times. These findings bring together two related fields that have hitherto been studied separately - resting-state networks and scale-free dynamics, and show that scale-free dynamics of human brain activity manifest in cross-regional interactions as well.},
	Author = {Ciuciu, Philippe and Abry, Patrice and He, Biyu J},
	Date-Added = {2017-05-05 14:19:48 +0000},
	Date-Modified = {2017-08-22 12:46:20 +0000},
	Journal = {Neuroimage},
	Pages = {248-263},
	Title = {Interplay between functional connectivity and scale-free dynamics in intrinsic {fMRI} networks},
	Volume = {95},
	Year = {2014},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuroimage.2014.03.047}}

@article{Newman_PNAS_2006,
	Abstract = {Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure is one of the outstanding issues in the study of networked systems. One highly effective approach is the optimization of the quality function known as "modularity" over the possible divisions of a network. Here I show that the modularity can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which I call the modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times. I illustrate the method with applications to several published network data sets.},
	Author = {Newman, M E J},
	Date-Added = {2017-05-05 14:13:40 +0000},
	Date-Modified = {2017-05-05 14:14:04 +0000},
	Journal = {Proc Natl Acad Sci U S A},
	Pages = {8577-8582},
	Title = {Modularity and community structure in networks},
	Volume = {103},
	Year = {2006},
	Bdsk-Url-1 = {http://dx.doi.org/10.1073/pnas.0601602103}}

@article{Blondel_JSM_2008,
	Author = {Blondel, VD and Guillaume, J-L and Lambiotte, R and Lefebvre, E},
	Date-Added = {2017-05-02 16:53:13 +0000},
	Date-Modified = {2017-05-02 16:56:22 +0000},
	Journal = {J Stat Mech},
	Pages = {P10008},
	Title = {Fast Unfolding of Communities in Large Networks},
	Volume = {10},
	Year = {2008}}

@article{Stephan_CON_2004,
	Author = {Stephan, KE and Harrison, LM and Penny, WD and Friston, KJ},
	Date-Added = {2017-05-02 16:43:21 +0000},
	Date-Modified = {2017-08-22 12:47:59 +0000},
	Journal = {Curr Opin Neurol},
	Pages = {629-635},
	Title = {Biophysical models of {fMRI} responses},
	Volume = {14},
	Year = {2004}}

@article{McIntosh_HBM_1994,
	Author = {McIntosh, AR and Gonzalez-Lima, F},
	Date-Added = {2017-05-02 16:39:16 +0000},
	Date-Modified = {2017-05-02 16:50:19 +0000},
	Journal = {Human Brain Mapping},
	Pages = {2-22},
	Title = {Structural equation modeling and its application to network analysis in functional brain imaging},
	Volume = {2},
	Year = {1994}}

@article{vanEssen_Sci_1992,
	Abstract = {The primate visual system contains dozens of distinct areas in the cerebral cortex and several major subcortical structures. These subdivisions are extensively interconnected in a distributed hierarchical network that contains several intertwined processing streams. A number of strategies are used for efficient information processing within this hierarchy. These include linear and nonlinear filtering, passage through information bottlenecks, and coordinated use of multiple types of information. In addition, dynamic regulation of information flow within and between visual areas may provide the computational flexibility needed for the visual system to perform a broad spectrum of tasks accurately and at high resolution.},
	Author = {Van Essen, D C and Anderson, C H and Felleman, D J},
	Date-Added = {2017-05-02 15:52:36 +0000},
	Date-Modified = {2017-05-02 16:03:08 +0000},
	Journal = {Science},
	Pages = {419-423},
	Title = {Information processing in the primate visual system: an integrated systems perspective},
	Volume = {255},
	Year = {1992}}

@article{Sui_NeIm_2009,
	Abstract = {Extraction of relevant features from multitask functional MRI (fMRI) data in order to identify potential biomarkers for disease, is an attractive goal. In this paper, we introduce a novel feature-based framework, which is sensitive and accurate in detecting group differences (e.g. controls vs. patients) by proposing three key ideas. First, we integrate two goal-directed techniques: coefficient-constrained independent component analysis (CC-ICA) and principal component analysis with reference (PCA-R), both of which improve sensitivity to group differences. Secondly, an automated artifact-removal method is developed for selecting components of interest derived from CC-ICA, with an average accuracy of 91%. Finally, we propose a strategy for optimal feature/component selection, aiming to identify optimal group-discriminative brain networks as well as the tasks within which these circuits are engaged. The group-discriminating performance is evaluated on 15 fMRI feature combinations (5 single features and 10 joint features) collected from 28 healthy control subjects and 25 schizophrenia patients. Results show that a feature from a sensorimotor task and a joint feature from a Sternberg working memory (probe) task and an auditory oddball (target) task are the top two feature combinations distinguishing groups. We identified three optimal features that best separate patients from controls, including brain networks consisting of temporal lobe, default mode and occipital lobe circuits, which when grouped together provide improved capability in classifying group membership. The proposed framework provides a general approach for selecting optimal brain networks which may serve as potential biomarkers of several brain diseases and thus has wide applicability in the neuroimaging research community.},
	Author = {Sui, Jing and Adali, T{\"u}lay and Pearlson, Godfrey D and Calhoun, Vince D},
	Date-Added = {2017-05-02 15:52:16 +0000},
	Date-Modified = {2017-08-22 12:47:41 +0000},
	Journal = {Neuroimage},
	Pages = {73-86},
	Title = {An {ICA}-based method for the identification of optimal {fMRI} features and components using combined group-discriminative techniques},
	Volume = {46},
	Year = {2009},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuroimage.2009.01.026}}

@article{Spadone_PNAS_2015,
	Abstract = {Fundamental problems in neuroscience today are understanding how patterns of ongoing spontaneous activity are modified by task performance and whether/how these intrinsic patterns influence task-evoked activation and behavior. We examined these questions by comparing instantaneous functional connectivity (IFC) and directed functional connectivity (DFC) changes in two networks that are strongly correlated and segregated at rest: the visual (VIS) network and the dorsal attention network (DAN). We measured how IFC and DFC during a visuospatial attention task, which requires dynamic selective rerouting of visual information across hemispheres, changed with respect to rest. During the attention task, the two networks remained relatively segregated, and their general pattern of within-network correlation was maintained. However, attention induced a decrease of correlation in the VIS network and an increase of the DAN→VIS IFC and DFC, especially in a top-down direction. In contrast, within the DAN, IFC was not modified by attention, whereas DFC was enhanced. Importantly, IFC modulations were behaviorally relevant. We conclude that a stable backbone of within-network functional connectivity topography remains in place when transitioning between resting wakefulness and attention selection. However, relative decrease of correlation of ongoing "idling" activity in visual cortex and synchronization between frontoparietal and visual cortex were behaviorally relevant, indicating that modulations of resting activity patterns are important for task performance. Higher order resting connectivity in the DAN was relatively unaffected during attention, potentially indicating a role for simultaneous ongoing activity as a "prior" for attention selection.},
	Author = {Spadone, Sara and Della Penna, Stefania and Sestieri, Carlo and Betti, Viviana and Tosoni, Annalisa and Perrucci, Mauro Gianni and Romani, Gian Luca and Corbetta, Maurizio},
	Date-Added = {2017-05-02 15:44:00 +0000},
	Date-Modified = {2017-05-02 16:34:09 +0000},
	Journal = {Proc Natl Acad Sci U S A},
	Pages = {8112-8117},
	Title = {Dynamic reorganization of human resting-state networks during visuospatial attention},
	Volume = {112},
	Year = {2015},
	Bdsk-Url-1 = {http://dx.doi.org/10.1073/pnas.1415439112}}

@article{Mantini_NatM_2012,
	Abstract = {Evolution-driven functional changes in the primate brain are typically assessed by aligning monkey and human activation maps using cortical surface expansion models. These models use putative homologous areas as registration landmarks, assuming they are functionally correspondent. For cases in which functional changes have occurred in an area, this assumption prohibits to reveal whether other areas may have assumed lost functions. Here we describe a method to examine functional correspondences across species. Without making spatial assumptions, we assessed similarities in sensory-driven functional magnetic resonance imaging responses between monkey (Macaca mulatta) and human brain areas by temporal correlation. Using natural vision data, we revealed regions for which functional processing has shifted to topologically divergent locations during evolution. We conclude that substantial evolution-driven functional reorganizations have occurred, not always consistent with cortical expansion processes. This framework for evaluating changes in functional architecture is crucial to building more accurate evolutionary models.},
	Author = {Mantini, Dante and Hasson, Uri and Betti, Viviana and Perrucci, Mauro G and Romani, Gian Luca and Corbetta, Maurizio and Orban, Guy A and Vanduffel, Wim},
	Date-Added = {2017-05-02 15:40:43 +0000},
	Date-Modified = {2017-05-02 16:06:39 +0000},
	Journal = {Nat Methods},
	Pages = {277-282},
	Title = {Interspecies activity correlations reveal functional correspondence between monkey and human brain areas},
	Volume = {9},
	Year = {2012},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/nmeth.1868}}

@article{Hutchison_NeIm_2013,
	Abstract = {The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations.},
	Author = {Hutchison, R Matthew and Womelsdorf, Thilo and Allen, Elena A and Bandettini, Peter A and Calhoun, Vince D and Corbetta, Maurizio and Della Penna, Stefania and Duyn, Jeff H and Glover, Gary H and Gonzalez-Castillo, Javier and Handwerker, Daniel A and Keilholz, Shella and Kiviniemi, Vesa and Leopold, David A and de Pasquale, Francesco and Sporns, Olaf and Walter, Martin and Chang, Catie},
	Date-Added = {2017-05-02 15:40:06 +0000},
	Date-Modified = {2017-05-02 16:04:56 +0000},
	Journal = {Neuroimage},
	Pages = {360-378},
	Title = {Dynamic functional connectivity: promise, issues, and interpretations},
	Volume = {80},
	Year = {2013},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuroimage.2013.05.079}}

@article{Honey_PNAS_2007,
	Abstract = {Neuronal dynamics unfolding within the cerebral cortex exhibit complex spatial and temporal patterns even in the absence of external input. Here we use a computational approach in an attempt to relate these features of spontaneous cortical dynamics to the underlying anatomical connectivity. Simulating nonlinear neuronal dynamics on a network that captures the large-scale interregional connections of macaque neocortex, and applying information theoretic measures to identify functional networks, we find structure-function relations at multiple temporal scales. Functional networks recovered from long windows of neural activity (minutes) largely overlap with the underlying structural network. As a result, hubs in these long-run functional networks correspond to structural hubs. In contrast, significant fluctuations in functional topology are observed across the sequence of networks recovered from consecutive shorter (seconds) time windows. The functional centrality of individual nodes varies across time as interregional couplings shift. Furthermore, the transient couplings between brain regions are coordinated in a manner that reveals the existence of two anticorrelated clusters. These clusters are linked by prefrontal and parietal regions that are hub nodes in the underlying structural network. At an even faster time scale (hundreds of milliseconds) we detect individual episodes of interregional phase-locking and find that slow variations in the statistics of these transient episodes, contingent on the underlying anatomical structure, produce the transfer entropy functional connectivity and simulated blood oxygenation level-dependent correlation patterns observed on slower time scales.},
	Author = {Honey, Christopher J and K{\"o}tter, Rolf and Breakspear, Michael and Sporns, Olaf},
	Date-Added = {2017-05-02 15:38:44 +0000},
	Date-Modified = {2017-05-02 16:15:37 +0000},
	Journal = {Proc Natl Acad Sci U S A},
	Pages = {10240-10245},
	Title = {Network structure of cerebral cortex shapes functional connectivity on multiple time scales},
	Volume = {104},
	Year = {2007}}

@article{Hlinka_NeIm_2011,
	Abstract = {Functional connectivity (FC) analysis is a prominent approach to analyzing fMRI data, especially acquired under the resting state condition. The commonly used linear correlation FC measure bears an implicit assumption of Gaussianity of the dependence structure. If only the marginals, but not all the bivariate distributions are Gaussian, linear correlation consistently underestimates the strength of the dependence. To assess the suitability of linear correlation and the general potential of nonlinear FC measures, we present a framework for testing and estimating the deviation from Gaussianity by means of comparing mutual information in the data and its Gaussianized counterpart. We apply this method to 24 sessions of human resting state fMRI. For each session, matrix of connectivities between 90 anatomical parcel time series is computed using mutual information and compared to results from its multivariate Gaussian surrogate that conserves the correlations but cancels any nonlinearity. While the group-level tests confirmed non-Gaussianity in the FC, the quantitative assessment revealed that the portion of mutual information neglected by linear correlation is relatively minor-on average only about 5% of the mutual information already captured by the linear correlation. The marginality of the non-Gaussianity was confirmed in comparisons using clustering of the parcels-the disagreement between clustering obtained from mutual information and linear correlation was attributable to random error. We conclude that for this type of data, practical relevance of nonlinear methods trying to improve over linear correlation might be limited by the fact that the data are indeed almost Gaussian.},
	Author = {Hlinka, Jaroslav and Palus, Milan and Vejmelka, Martin and Mantini, Dante and Corbetta, Maurizio},
	Date-Added = {2017-05-02 15:38:09 +0000},
	Date-Modified = {2017-08-22 12:50:54 +0000},
	Journal = {Neuroimage},
	Pages = {2218-2225},
	Title = {Functional connectivity in resting-state {fMRI}: is linear correlation sufficient?},
	Volume = {54},
	Year = {2011},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuroimage.2010.08.042}}

@article{Hipp_Nrn_2011,
	Abstract = {Normal brain function requires the dynamic interaction of functionally specialized but widely distributed cortical regions. Long-range synchronization of oscillatory signals has been suggested to mediate these interactions within large-scale cortical networks, but direct evidence is sparse. Here we show that oscillatory synchronization is organized in such large-scale networks. We implemented an analysis approach that allows for imaging synchronized cortical networks and applied this technique to EEG recordings in humans. We identified two networks: beta-band synchronization (~20 Hz) in a fronto-parieto-occipital network and gamma-band synchronization (~80 Hz) in a centro-temporal network. Strong perceptual correlates support their functional relevance: the strength of synchronization within these networks predicted the subjects' perception of an ambiguous audiovisual stimulus as well as the integration of auditory and visual information. Our results provide evidence that oscillatory neuronal synchronization mediates neuronal communication within frequency-specific, large-scale cortical networks.},
	Author = {Hipp, Joerg F and Engel, Andreas K and Siegel, Markus},
	Date-Added = {2017-05-02 15:37:53 +0000},
	Date-Modified = {2017-05-02 16:01:13 +0000},
	Journal = {Neuron},
	Pages = {387-396},
	Title = {Oscillatory synchronization in large-scale cortical networks predicts perception},
	Volume = {69},
	Year = {2011},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuron.2010.12.027}}

@article{Hasson_Sci_2004,
	Abstract = {To what extent do all brains work alike during natural conditions? We explored this question by letting five subjects freely view half an hour of a popular movie while undergoing functional brain imaging. Applying an unbiased analysis in which spatiotemporal activity patterns in one brain were used to "model" activity in another brain, we found a striking level of voxel-by-voxel synchronization between individuals, not only in primary and secondary visual and auditory areas but also in association cortices. The results reveal a surprising tendency of individual brains to "tick collectively" during natural vision. The intersubject synchronization consisted of a widespread cortical activation pattern correlated with emotionally arousing scenes and regionally selective components. The characteristics of these activations were revealed with the use of an open-ended "reverse-correlation" approach, which inverts the conventional analysis by letting the brain signals themselves "pick up" the optimal stimuli for each specialized cortical area.},
	Author = {Hasson, Uri and Nir, Yuval and Levy, Ifat and Fuhrmann, Galit and Malach, Rafael},
	Date-Added = {2017-05-02 15:37:24 +0000},
	Date-Modified = {2017-05-02 16:13:30 +0000},
	Journal = {Science},
	Pages = {1634-1640},
	Title = {Intersubject synchronization of cortical activity during natural vision},
	Volume = {303},
	Year = {2004},
	Bdsk-Url-1 = {http://dx.doi.org/10.1126/science.1089506}}

@article{Hagmann_PB_2008,
	Abstract = {Structurally segregated and functionally specialized regions of the human cerebral cortex are interconnected by a dense network of cortico-cortical axonal pathways. By using diffusion spectrum imaging, we noninvasively mapped these pathways within and across cortical hemispheres in individual human participants. An analysis of the resulting large-scale structural brain networks reveals a structural core within posterior medial and parietal cerebral cortex, as well as several distinct temporal and frontal modules. Brain regions within the structural core share high degree, strength, and betweenness centrality, and they constitute connector hubs that link all major structural modules. The structural core contains brain regions that form the posterior components of the human default network. Looking both within and outside of core regions, we observed a substantial correspondence between structural connectivity and resting-state functional connectivity measured in the same participants. The spatial and topological centrality of the core within cortex suggests an important role in functional integration.},
	Author = {Hagmann, Patric and Cammoun, Leila and Gigandet, Xavier and Meuli, Reto and Honey, Christopher J and Wedeen, Van J and Sporns, Olaf},
	Date-Added = {2017-05-02 15:36:57 +0000},
	Date-Modified = {2017-05-02 16:20:23 +0000},
	Journal = {PLoS Biol},
	Pages = {e159},
	Title = {Mapping the structural core of human cerebral cortex},
	Volume = {6},
	Year = {2008},
	Bdsk-Url-1 = {http://dx.doi.org/10.1371/journal.pbio.0060159}}

@article{Greicius_PNAS_2003,
	Abstract = {Functional imaging studies have shown that certain brain regions, including posterior cingulate cortex (PCC) and ventral anterior cingulate cortex (vACC), consistently show greater activity during resting states than during cognitive tasks. This finding led to the hypothesis that these regions constitute a network supporting a default mode of brain function. In this study, we investigate three questions pertaining to this hypothesis: Does such a resting-state network exist in the human brain? Is it modulated during simple sensory processing? How is it modulated during cognitive processing? To address these questions, we defined PCC and vACC regions that showed decreased activity during a cognitive (working memory) task, then examined their functional connectivity during rest. PCC was strongly coupled with vACC and several other brain regions implicated in the default mode network. Next, we examined the functional connectivity of PCC and vACC during a visual processing task and show that the resultant connectivity maps are virtually identical to those obtained during rest. Last, we defined three lateral prefrontal regions showing increased activity during the cognitive task and examined their resting-state connectivity. We report significant inverse correlations among all three lateral prefrontal regions and PCC, suggesting a mechanism for attenuation of default mode network activity during cognitive processing. This study constitutes, to our knowledge, the first resting-state connectivity analysis of the default mode and provides the most compelling evidence to date for the existence of a cohesive default mode network. Our findings also provide insight into how this network is modulated by task demands and what functions it might subserve.},
	Author = {Greicius, Michael D and Krasnow, Ben and Reiss, Allan L and Menon, Vinod},
	Date-Added = {2017-05-02 15:36:15 +0000},
	Date-Modified = {2017-05-02 16:00:34 +0000},
	Journal = {Proc Natl Acad Sci U S A},
	Pages = {253-258},
	Title = {Functional connectivity in the resting brain: a network analysis of the default mode hypothesis},
	Volume = {100},
	Year = {2003},
	Bdsk-Url-1 = {http://dx.doi.org/10.1073/pnas.0135058100}}

@article{Friston_NeIm_2010,
	Abstract = {This article reviews the substantial impact computational neuroscience has had on neuroimaging over the past years. It builds on the distinction between models of the brain as a computational machine and computational models of neuronal dynamics per se; i.e., models of brain function and biophysics. Both sorts of model borrow heavily from computational neuroscience, and both have enriched the analysis of neuroimaging data and the type of questions we address. To illustrate the role of functional models in imaging neuroscience, we focus on optimal control and decision (game) theory; the models used here provide a mechanistic account of neuronal computations and the latent (mental) states represent by the brain. In terms of biophysical modelling, we focus on dynamic causal modelling, with a special emphasis on recent advances in neural-mass models for hemodynamic and electrophysiological time series. Each example emphasises the role of generative models, which embed our hypotheses or questions, and the importance of model comparison (i.e., hypothesis testing). We will refer to this theme, when trying to contextualise recent trends in relation to each other.},
	Author = {Friston, Karl J and Dolan, Raymond J},
	Date-Added = {2017-05-02 15:32:59 +0000},
	Date-Modified = {2017-05-02 16:16:16 +0000},
	Journal = {Neuroimage},
	Pages = {752-765},
	Title = {Computational and dynamic models in neuroimaging},
	Volume = {52},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuroimage.2009.12.068}}

@article{Friston_NeIm_2003,
	Abstract = {In this paper we present an approach to the identification of nonlinear input-state-output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters that mediate intrinsic coupling among the states, and (3) [bilinear] parameters that allow the inputs to modulate that coupling. Identification proceeds in a Bayesian framework given known, deterministic inputs and the observed responses of the system. We developed this approach for the analysis of effective connectivity using experimentally designed inputs and fMRI responses. In this context, the coupling parameters correspond to effective connectivity and the bilinear parameters reflect the changes in connectivity induced by inputs. The ensuing framework allows one to characterise fMRI experiments, conceptually, as an experimental manipulation of integration among brain regions (by contextual or trial-free inputs, like time or attentional set) that is revealed using evoked responses (to perturbations or trial-bound inputs, like stimuli). As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling (cf., psychophysiologic interactions). However, unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.},
	Author = {Friston, K J and Harrison, L and Penny, W},
	Date-Added = {2017-05-02 15:32:25 +0000},
	Date-Modified = {2017-05-02 16:17:13 +0000},
	Journal = {Neuroimage},
	Pages = {1273-1302},
	Title = {Dynamic causal modelling},
	Volume = {19},
	Year = {2003}}

@article{Friston_ARN_2002,
	Abstract = {Unsupervised models of how the brain identifies and categorizes the causes of its sensory input can be divided into two classes: those that minimize the mutual information (i.e., redundancy) among evoked responses and those that minimize the prediction error. Although these models have the same goal, the way that goal is attained, and the functional architectures required, are fundamentally different. This review describes the differences, in the functional anatomy of sensory cortical hierarchies, implied by the two models. We then consider how neuroimaging can be used to disambiguate between them. The key distinction reduces to whether backward connections are employed by the brain to generate a prediction of sensory inputs. To ascertain whether backward influences are evident empirically requires a characterization of functional integration among brain systems. This review summarizes the approaches to measuring functional integration in terms of effective connectivity and proceeds to address the question posed by the theoretical considerations. In short, it will be shown that the conjoint manipulation of bottom-up and top-down inputs to an area can be used to test for interactions between them, in elaborating cortical responses. The conclusion, from these sorts of neuroimaging studies, points to the prevalence of top-down influences and the plausibility of generative models of sensory brain function.},
	Author = {Friston, Karl},
	Date-Added = {2017-05-02 15:31:23 +0000},
	Date-Modified = {2017-05-02 16:12:43 +0000},
	Journal = {Annu Rev Neurosci},
	Pages = {221-250},
	Title = {Beyond phrenology: what can neuroimaging tell us about distributed circuitry?},
	Volume = {25},
	Year = {2002},
	Bdsk-Url-1 = {http://dx.doi.org/10.1146/annurev.neuro.25.112701.142846}}

@article{Fries_TCS_2005,
	Abstract = {At any one moment, many neuronal groups in our brain are active. Microelectrode recordings have characterized the activation of single neurons and fMRI has unveiled brain-wide activation patterns. Now it is time to understand how the many active neuronal groups interact with each other and how their communication is flexibly modulated to bring about our cognitive dynamics. I hypothesize that neuronal communication is mechanistically subserved by neuronal coherence. Activated neuronal groups oscillate and thereby undergo rhythmic excitability fluctuations that produce temporal windows for communication. Only coherently oscillating neuronal groups can interact effectively, because their communication windows for input and for output are open at the same times. Thus, a flexible pattern of coherence defines a flexible communication structure, which subserves our cognitive flexibility.},
	Author = {Fries, Pascal},
	Date-Added = {2017-05-02 15:31:04 +0000},
	Date-Modified = {2017-05-02 16:35:46 +0000},
	Journal = {Trends Cogn Sci},
	Pages = {474-480},
	Title = {A mechanism for cognitive dynamics: neuronal communication through neuronal coherence},
	Volume = {9},
	Year = {2005},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.tics.2005.08.011}}

@article{Engel_NatRev_2001,
	Abstract = {Classical theories of sensory processing view the brain as a passive, stimulus-driven device. By contrast, more recent approaches emphasize the constructive nature of perception, viewing it as an active and highly selective process. Indeed, there is ample evidence that the processing of stimuli is controlled by top-down influences that strongly shape the intrinsic dynamics of thalamocortical networks and constantly create predictions about forthcoming sensory events. We discuss recent experiments indicating that such predictions might be embodied in the temporal structure of both stimulus-evoked and ongoing activity, and that synchronous oscillations are particularly important in this process. Coherence among subthreshold membrane potential fluctuations could be exploited to express selective functional relationships during states of expectancy or attention, and these dynamic patterns could allow the grouping and selection of distributed neuronal responses for further processing.},
	Author = {Engel, A K and Fries, P and Singer, W},
	Date-Added = {2017-05-02 15:29:19 +0000},
	Date-Modified = {2017-05-02 16:13:00 +0000},
	Journal = {Nat Rev Neurosci},
	Pages = {704-716},
	Title = {Dynamic predictions: oscillations and synchrony in top-down processing},
	Volume = {2},
	Year = {2001},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/35094565}}

@article{Destexhe_NatRev_2003,
	Author = {Destexhe, Alain and Rudolph, Michael and Par{\'e}, Denis},
	Date-Added = {2017-05-02 15:29:03 +0000},
	Date-Modified = {2017-05-02 16:35:25 +0000},
	Journal = {Nat Rev Neurosci},
	Pages = {739-751},
	Title = {The high-conductance state of neocortical neurons in vivo},
	Volume = {4},
	Year = {2003},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/nrn1198}}

@article{Dehghani_SR_2016,
	Abstract = {Balance of excitation and inhibition is a fundamental feature of in vivo network activity and is important for its computations. However, its presence in the neocortex of higher mammals is not well established. We investigated the dynamics of excitation and inhibition using dense multielectrode recordings in humans and monkeys. We found that in all states of the wake-sleep cycle, excitatory and inhibitory ensembles are well balanced, and co-fluctuate with slight instantaneous deviations from perfect balance, mostly in slow-wave sleep. Remarkably, these correlated fluctuations are seen for many different temporal scales. The similarity of these computational features with a network model of self-generated balanced states suggests that such balanced activity is essentially generated by recurrent activity in the local network and is not due to external inputs. Finally, we find that this balance breaks down during seizures, where the temporal correlation of excitatory and inhibitory populations is disrupted. These results show that balanced activity is a feature of normal brain activity, and break down of the balance could be an important factor to define pathological states.},
	Author = {Dehghani, Nima and Peyrache, Adrien and Telenczuk, Bartosz and Le Van Quyen, Michel and Halgren, Eric and Cash, Sydney S and Hatsopoulos, Nicholas G and Destexhe, Alain},
	Date-Added = {2017-05-02 15:28:43 +0000},
	Date-Modified = {2017-05-02 16:28:21 +0000},
	Journal = {Sci Rep},
	Pages = {23176},
	Title = {Dynamic Balance of Excitation and Inhibition in Human and Monkey Neocortex},
	Volume = {6},
	Year = {2016},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/srep23176}}

@article{Deco_Nrsc_2011,
	Abstract = {The authors review evidence that spontaneous, that is, not stimulus or task driven, activity in the brain at the level of large-scale neural systems is not noise, but orderly and organized in a series of functional networks that maintain, at all times, a high level of coherence. These networks of spontaneous activity correlation or resting state networks (RSN) are closely related to the underlying anatomical connectivity, but their topography is also gated by the history of prior task activation. Network coherence does not depend on covert cognitive activity, but its strength and integrity relates to behavioral performance. Some RSN are functionally organized as dynamically competing systems both at rest and during tasks. Computational studies show that one of such dynamics, the anticorrelation between networks, depends on noise-driven transitions between different multistable cluster synchronization states. These multistable states emerge because of transmission delays between regions that are modeled as coupled oscillators systems. Large-scale systems dynamics are useful for keeping different functional subnetworks in a state of heightened competition, which can be stabilized and fired by even small modulations of either sensory or internal signals.},
	Author = {Deco, Gustavo and Corbetta, Maurizio},
	Date-Added = {2017-05-02 15:27:53 +0000},
	Date-Modified = {2017-05-02 16:02:01 +0000},
	Journal = {Neuroscientist},
	Pages = {107-123},
	Title = {The dynamical balance of the brain at rest},
	Volume = {17},
	Year = {2011}}

@article{dePasquale_Nrn_2012,
	Abstract = {We used magneto-encephalography to study the temporal dynamics of band-limited power correlation at rest within and across six brain networks previously defined by prior functional magnetic resonance imaging (fMRI) studies. Epochs of transiently high within-network band limited power (BLP) correlation were identified and correlation of BLP time-series across networks was assessed in these epochs. These analyses demonstrate that functional networks are not equivalent with respect to cross-network interactions. The default-mode network and the posterior cingulate cortex, in particular, exhibit the highest degree of transient BLP correlation with other networks especially in the 14-25 Hz (β band) frequency range. Our results indicate that the previously demonstrated neuroanatomical centrality of the PCC and DMN has a physiological counterpart in the temporal dynamics of network interaction at behaviorally relevant timescales. This interaction involved subsets of nodes from other networks during periods in which their internal correlation was low.},
	Author = {de Pasquale, Francesco and Della Penna, Stefania and Snyder, Abraham Z and Marzetti, Laura and Pizzella, Vittorio and Romani, Gian Luca and Corbetta, Maurizio},
	Date-Added = {2017-05-02 15:27:18 +0000},
	Date-Modified = {2017-05-02 15:59:02 +0000},
	Journal = {Neuron},
	Pages = {753-764},
	Title = {A cortical core for dynamic integration of functional networks in the resting human brain},
	Volume = {74},
	Year = {2012},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuron.2012.03.031}}

@article{dePasquale_CC_2016,
	Abstract = {Spontaneous brain activity is spatially and temporally organized in the absence of any stimulation or task in networks of cortical and subcortical regions that appear largely segregated when imaged at slow temporal resolution with functional magnetic resonance imaging (fMRI). When imaged at high temporal resolution with magneto-encephalography (MEG), these resting-state networks (RSNs) show correlated fluctuations of band-limited power in the beta frequency band (14-25 Hz) that alternate between epochs of strong and weak internal coupling. This study presents 2 novel findings on the fundamental issue of how different brain regions or networks interact in the resting state. First, we demonstrate the existence of multiple dynamic hubs that allow for across-network coupling. Second, dynamic network coupling and related variations in hub centrality correspond to increased global efficiency. These findings suggest that the dynamic organization of across-network interactions represents a property of the brain aimed at optimizing the efficiency of communication between distinct functional domains (memory, sensory-attention, motor). They also support the hypothesis of a dynamic core network model in which a set of network hubs alternating over time ensure efficient global communication in the whole brain.},
	Author = {de Pasquale, F and Della Penna, S and Sporns, O and Romani, G L and Corbetta, M},
	Date-Added = {2017-05-02 15:26:54 +0000},
	Date-Modified = {2017-05-02 15:58:38 +0000},
	Journal = {Cereb Cortex},
	Pages = {4015-4033},
	Title = {A Dynamic Core Network and Global Efficiency in the Resting Human Brain},
	Volume = {26},
	Year = {2016},
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/cercor/bhv185}}

@article{Cordes_AJNR_2000,
	Abstract = {BACKGROUND AND PURPOSE: In subjects who are performing no prescribed cognitive task, functional connectivity mapped with MR imaging (fcMRI) shows regions with synchronous fluctuations of cerebral blood flow. When specific tasks are performed, functional MR imaging (fMRI) can map locations in which regional cerebral blood flow increases synchronously with the performance of the task. We tested the hypothesis that fcMRI maps, based on the synchrony of low-frequency blood flow fluctuations, identify brain regions that show activation on fMRI maps of sensorimotor, visual, language, and auditory tasks.
METHODS: In four volunteers, task-activation fMRI and functional connectivity (resting-state) fcMRI data were acquired. A small region of interest (in an area that showed maximal task activation) was chosen, and the correlation coefficient of the corresponding resting-state signal with the signal of all other voxels in the resting data set was calculated. The correlation coefficient was decomposed into frequency components and its distribution determined for each fcMRI map. The fcMRI maps were compared with the fMRI maps.
RESULTS: For each task, fcMRI maps based on one to four seed voxel(s) produced clusters of voxels in regions of eloquent cortex. For each fMRI map a closely corresponding fcMRI map was obtained. The frequencies that predominated in the cross-correlation coefficients for the functionally related regions were below 0.1 Hz.
CONCLUSION: Functionally related brain regions can be identified by means of their synchronous slow fluctuations in signal intensity. Such blood flow synchrony can be detected in sensorimotor areas, expressive and receptive language regions, and the visual cortex by fcMRI. Regions identified by the slow synchronous fluctuations are similar to those activated by motor, language, or visual tasks.},
	Author = {Cordes, D and Haughton, V M and Arfanakis, K and Wendt, G J and Turski, P A and Moritz, C H and Quigley, M A and Meyerand, M E},
	Date-Added = {2017-05-02 15:26:29 +0000},
	Date-Modified = {2017-08-22 12:53:01 +0000},
	Journal = {Am J Neuroradiol},
	Pages = {1636-1644},
	Title = {Mapping functionally related regions of brain with functional connectivity {MR} imaging},
	Volume = {21},
	Year = {2000}}

@article{Cabral_NeIm_2011,
	Abstract = {Spatio-temporally organized low-frequency fluctuations (<0.1 Hz), observed in BOLD fMRI signal during rest, suggest the existence of underlying network dynamics that emerge spontaneously from intrinsic brain processes. Furthermore, significant correlations between distinct anatomical regions-or functional connectivity (FC)-have led to the identification of several widely distributed resting-state networks (RSNs). This slow dynamics seems to be highly structured by anatomical connectivity but the mechanism behind it and its relationship with neural activity, particularly in the gamma frequency range, remains largely unknown. Indeed, direct measurements of neuronal activity have revealed similar large-scale correlations, particularly in slow power fluctuations of local field potential gamma frequency range oscillations. To address these questions, we investigated neural dynamics in a large-scale model of the human brain's neural activity. A key ingredient of the model was a structural brain network defined by empirically derived long-range brain connectivity together with the corresponding conduction delays. A neural population, assumed to spontaneously oscillate in the gamma frequency range, was placed at each network node. When these oscillatory units are integrated in the network, they behave as weakly coupled oscillators. The time-delayed interaction between nodes is described by the Kuramoto model of phase oscillators, a biologically-based model of coupled oscillatory systems. For a realistic setting of axonal conduction speed, we show that time-delayed network interaction leads to the emergence of slow neural activity fluctuations, whose patterns correlate significantly with the empirically measured FC. The best agreement of the simulated FC with the empirically measured FC is found for a set of parameters where subsets of nodes tend to synchronize although the network is not globally synchronized. Inside such clusters, the simulated BOLD signal between nodes is found to be correlated, instantiating the empirically observed RSNs. Between clusters, patterns of positive and negative correlations are observed, as described in experimental studies. These results are found to be robust with respect to a biologically plausible range of model parameters. In conclusion, our model suggests how resting-state neural activity can originate from the interplay between the local neural dynamics and the large-scale structure of the brain.},
	Author = {Cabral, Joana and Hugues, Etienne and Sporns, Olaf and Deco, Gustavo},
	Date-Added = {2017-05-02 15:25:11 +0000},
	Date-Modified = {2017-05-02 15:55:10 +0000},
	Journal = {Neuroimage},
	Pages = {130-139},
	Title = {Role of local network oscillations in resting-state functional connectivity},
	Volume = {57},
	Year = {2011},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuroimage.2011.04.010}}

@article{Brookes_PNAS_2011,
	Abstract = {In recent years the study of resting state brain networks (RSNs) has become an important area of neuroimaging. The majority of studies have used functional magnetic resonance imaging (fMRI) to measure temporal correlation between blood-oxygenation-level-dependent (BOLD) signals from different brain areas. However, BOLD is an indirect measure related to hemodynamics, and the electrophysiological basis of connectivity between spatially separate network nodes cannot be comprehensively assessed using this technique. In this paper we describe a means to characterize resting state brain networks independently using magnetoencephalography (MEG), a neuroimaging modality that bypasses the hemodynamic response and measures the magnetic fields associated with electrophysiological brain activity. The MEG data are analyzed using a unique combination of beamformer spatial filtering and independent component analysis (ICA) and require no prior assumptions about the spatial locations or patterns of the networks. This method results in RSNs with significant similarity in their spatial structure compared with RSNs derived independently using fMRI. This outcome confirms the neural basis of hemodynamic networks and demonstrates the potential of MEG as a tool for understanding the mechanisms that underlie RSNs and the nature of connectivity that binds network nodes.},
	Author = {Brookes, Matthew J and Woolrich, Mark and Luckhoo, Henry and Price, Darren and Hale, Joanne R and Stephenson, Mary C and Barnes, Gareth R and Smith, Stephen M and Morris, Peter G},
	Date-Added = {2017-05-02 15:24:55 +0000},
	Date-Modified = {2017-05-02 15:57:30 +0000},
	Journal = {Proc Natl Acad Sci U S A},
	Number = {40},
	Pages = {16783-16788},
	Title = {Investigating the electrophysiological basis of resting state networks using magnetoencephalography},
	Volume = {108},
	Year = {2011},
	Bdsk-Url-1 = {http://dx.doi.org/10.1073/pnas.1112685108}}

@article{Betti_Nrn_2013,
	Abstract = {Spontaneous fMRI fluctuations are organized in large-scale spatiotemporal structures, or resting-state networks (RSN). However, it is unknown how task performance affects RSN dynamics. We use MEG to measure slow (∼0.1 Hz) coherent fluctuations of band-limited power (BLP), a robust correlate of RSN, during rest and movie observation and compare them to fMRI-RSN. BLP correlation, especially in α band, dropped in multiple RSN during movie although overall topography was maintained. Variability of power correlation increased in visual occipital cortex, and transient decrements corresponded to scenes perceived as "event boundaries." Additionally, stronger task-dependent interactions developed between vision and language networks in θ and β bands, and default and language networks in γ band. The topography of fMRI connectivity and relative changes induced by the movie were well matched to MEG. We conclude that resting-state and task network interactions are clearly different in the frequency domain despite maintenance of underlying network topography.},
	Author = {Betti, Viviana and Della Penna, Stefania and de Pasquale, Francesco and Mantini, Dante and Marzetti, Laura and Romani, Gian Luca and Corbetta, Maurizio},
	Date-Added = {2017-05-02 15:23:37 +0000},
	Date-Modified = {2017-05-02 16:01:34 +0000},
	Journal = {Neuron},
	Pages = {782-797},
	Title = {Natural scenes viewing alters the dynamics of functional connectivity in the human brain},
	Volume = {79},
	Year = {2013},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuron.2013.06.022}}

@article{Aertsen_JNP_1989,
	Abstract = {1. We reexamine the possibilities for analyzing and interpreting the time course of correlation in spike trains simultaneously and separably recorded from two neurons. 2. We develop procedures to quantify and properly normalize the classical joint peristimulus time scatter diagram. These allow separation of the "raw" correlation into components caused by direct stimulus modulations of the single-neuron firing rates and those caused by various types of interaction between the two neurons. 3. A newly developed significance test ("surprise") is applied to evaluate such inferences. 4. Application of the new procedures to simulated spike trains allowed the recovery of the known circuitry. In particular, it proved possible to recover fast stimulus-locked modulations of "effective connectivity," even if they were masked by strong direct stimulus modulations of individual firing rates. These procedures thus present a clearly superior alternative to the commonly used "shift predictor." 5. Adopting a model-based approach, we generalize the classical measures for quantifying a direct interneuronal connection ("efficacy" and "contribution") to include possible stimulus-locked time variations. 6. Application of the new procedures to real spike trains from several different preparations showed that fast stimulus-locked modulations of "effective connectivity" also occur for real neurons.},
	Author = {Aertsen, A M and Gerstein, G L and Habib, M K and Palm, G},
	Date-Added = {2017-05-02 15:23:10 +0000},
	Date-Modified = {2017-05-02 15:53:03 +0000},
	Journal = {J Neurophysiol},
	Pages = {900-917},
	Title = {Dynamics of neuronal firing correlation: modulation of "effective connectivity"},
	Volume = {61},
	Year = {1989}}

@article{Battaglia_PCB_2012,
	Abstract = {Anatomic connections between brain areas affect information flow between neuronal circuits and the synchronization of neuronal activity. However, such structural connectivity does not coincide with effective connectivity (or, more precisely, causal connectivity), related to the elusive question "Which areas cause the present activity of which others?". Effective connectivity is directed and depends flexibly on contexts and tasks. Here we show that dynamic effective connectivity can emerge from transitions in the collective organization of coherent neural activity. Integrating simulation and semi-analytic approaches, we study mesoscale network motifs of interacting cortical areas, modeled as large random networks of spiking neurons or as simple rate units. Through a causal analysis of time-series of model neural activity, we show that different dynamical states generated by a same structural connectivity motif correspond to distinct effective connectivity motifs. Such effective motifs can display a dominant directionality, due to spontaneous symmetry breaking and effective entrainment between local brain rhythms, although all connections in the considered structural motifs are reciprocal. We show then that transitions between effective connectivity configurations (like, for instance, reversal in the direction of inter-areal interactions) can be triggered reliably by brief perturbation inputs, properly timed with respect to an ongoing local oscillation, without the need for plastic synaptic changes. Finally, we analyze how the information encoded in spiking patterns of a local neuronal population is propagated across a fixed structural connectivity motif, demonstrating that changes in the active effective connectivity regulate both the efficiency and the directionality of information transfer. Previous studies stressed the role played by coherent oscillations in establishing efficient communication between distant areas. Going beyond these early proposals, we advance here that dynamic interactions between brain rhythms provide as well the basis for the self-organized control of this "communication-through-coherence", making thus possible a fast "on-demand" reconfiguration of global information routing modalities.},
	Author = {Battaglia, Demian and Witt, Annette and Wolf, Fred and Geisel, Theo},
	Date-Added = {2017-05-02 15:22:15 +0000},
	Date-Modified = {2017-05-02 16:26:24 +0000},
	Journal = {PLoS Comput Biol},
	Pages = {e1002438},
	Title = {Dynamic effective connectivity of inter-areal brain circuits},
	Volume = {8},
	Year = {2012},
	Bdsk-Url-1 = {http://dx.doi.org/10.1371/journal.pcbi.1002438}}

@article{Cabeza_JCN_2000,
	Abstract = {Positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have been extensively used to explore the functional neuroanatomy of cognitive functions. Here we review 275 PET and fMRI studies of attention (sustained, selective, Stroop, orientation, divided), perception (object, face, space/motion, smell), imagery (object, space/motion), language (written/spoken word recognition, spoken/no spoken response), working memory (verbal/numeric, object, spatial, problem solving), semantic memory retrieval (categorization, generation), episodic memory encoding (verbal, object, spatial), episodic memory retrieval (verbal, nonverbal, success, effort, mode, context), priming (perceptual, conceptual), and procedural memory (conditioning, motor, and nonmotor skill learning). To identify consistent activation patterns associated with these cognitive operations, data from 412 contrasts were summarized at the level of cortical Brodmann's areas, insula, thalamus, medial-temporal lobe (including hippocampus), basal ganglia, and cerebellum. For perception and imagery, activation patterns included primary and secondary regions in the dorsal and ventral pathways. For attention and working memory, activations were usually found in prefrontal and parietal regions. For language and semantic memory retrieval, typical regions included left prefrontal and temporal regions. For episodic memory encoding, consistently activated regions included left prefrontal and medial temporal regions. For episodic memory retrieval, activation patterns included prefrontal, medial temporal, and posterior midline regions. For priming, deactivations in prefrontal (conceptual) or extrastriate (perceptual) regions were consistently seen. For procedural memory, activations were found in motor as well as in non-motor brain areas. Analysis of regional activations across cognitive domains suggested that several brain regions, including the cerebellum, are engaged by a variety of cognitive challenges. These observations are discussed in relation to functional specialization as well as functional integration.},
	Author = {Cabeza, R and Nyberg, L},
	Date-Added = {2017-05-02 15:21:40 +0000},
	Date-Modified = {2017-08-21 01:55:43 +0000},
	Journal = {J Cogn Neurosci},
	Pages = {1-47},
	Title = {Imaging cognition {II}: An empirical review of 275 {PET} and {fMRI} studies},
	Volume = {12},
	Year = {2000}}

@article{Ashburner_NeIm_2007,
	Abstract = {This paper describes DARTEL, which is an algorithm for diffeomorphic image registration. It is implemented for both 2D and 3D image registration and has been formulated to include an option for estimating inverse consistent deformations. Nonlinear registration is considered as a local optimisation problem, which is solved using a Levenberg-Marquardt strategy. The necessary matrix solutions are obtained in reasonable time using a multigrid method. A constant Eulerian velocity framework is used, which allows a rapid scaling and squaring method to be used in the computations. DARTEL has been applied to intersubject registration of 471 whole brain images, and the resulting deformations were evaluated in terms of how well they encode the shape information necessary to separate male and female subjects and to predict the ages of the subjects.},
	Author = {Ashburner, John},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Neuroimage},
	Pages = {95-113},
	Title = {A fast diffeomorphic image registration algorithm},
	Volume = {38},
	Year = {2007},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuroimage.2007.07.007}}

@article{Bakker_FNI_2012,
	Author = {Bakker, R and Wachtler, T and Diesmann, M},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:53:59 +0000},
	Journal = {Front Neuroinform},
	Pages = {30},
	Title = {{CoCoMac} 2.0 and the future of tract-tracing databases},
	Volume = {6},
	Year = {2012}}

@article{Bratsch_JMRI_2006,
	Author = {Bartsch, AJ and Homola, G and Biller, A and Solymosi, L and Bendszus, M},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:46:43 +0000},
	Journal = {J Magn Reson Imaging},
	Pages = {921-932},
	Title = {Diagnostic functional {MRI}: illustrated clinical applications and decision-making},
	Volume = {23},
	Year = {2006}}

@book{Bernstein_Matrix_2009,
	Author = {Bernstein, D},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Isbn = {9780691140391},
	Publisher = {PUP},
	Title = {Matrix mathematics: Theory, facts, and formulas (second edition)},
	Year = {2009}}

@article{Biswal_MRM_1995,
	Abstract = {An MRI time course of 512 echo-planar images (EPI) in resting human brain obtained every 250 ms reveals fluctuations in signal intensity in each pixel that have a physiologic origin. Regions of the sensorimotor cortex that were activated secondary to hand movement were identified using functional MRI methodology (FMRI). Time courses of low frequency (< 0.1 Hz) fluctuations in resting brain were observed to have a high degree of temporal correlation (P < 10(-3)) within these regions and also with time courses in several other regions that can be associated with motor function. It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.},
	Author = {Biswal, B and Yetkin, FZ and Haughton, VM and Hyde, JS},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-08-22 12:53:44 +0000},
	Journal = {Magn Reson Med},
	Pages = {537-541},
	Title = {Functional connectivity in the motor cortex of resting human brain using echo-planar {MRI}},
	Volume = {34},
	Year = {1995}}

@article{Chang_PB_2015,
	Abstract = {Neuroimaging has identified many correlates of emotion but has not yet yielded brain representations predictive of the intensity of emotional experiences in individuals. We used machine learning to identify a sensitive and specific signature of emotional responses to aversive images. This signature predicted the intensity of negative emotion in individual participants in cross validation (n =121) and test (n = 61) samples (high-low emotion = 93.5% accuracy). It was unresponsive to physical pain (emotion-pain = 92% discriminative accuracy), demonstrating that it is not a representation of generalized arousal or salience. The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience. Furthermore, it was not reducible to activity in traditional "emotion-related" regions (e.g., amygdala, insula) or resting-state networks (e.g., "salience," "default mode"). Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes.},
	Author = {Chang, LJ and Gianaros, PJ and Manuck, SB and Krishnan, A and Wager, TD},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {PLoS Biol},
	Pages = {e1002180},
	Title = {A Sensitive and Specific Neural Signature for Picture-Induced Negative Affect},
	Volume = {13},
	Year = {2015}}

@article{Ching_PRE_2013,
	Author = {Ching, ESC and Lai, P-Y and Leung, CY},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Phys Rev E},
	Pages = {042817},
	Title = {Extracting connectivity from dynamics of networks with uniform bidirectional coupling},
	Volume = {88},
	Year = {2013}}

@article{Cloutman_FNA_2012,
	Abstract = {The parcellation of the cortex via its anatomical properties has been an important research endeavor for over a century. To date, however, a universally accepted parcellation scheme for the human brain still remains elusive. In the current review, we explore the use of in vivo diffusion imaging and white matter tractography as a non-invasive method for the structural and functional parcellation of the human cerebral cortex, discussing the strengths and limitations of the current approaches. Cortical parcellation via white matter connectivity is based on the premise that, as connectional anatomy determines functional organization, it should be possible to segregate functionally-distinct cortical regions by identifying similarities and differences in connectivity profiles. Recent studies have provided initial evidence in support of the efficacy of this connectional parcellation methodology. Such investigations have identified distinct cortical subregions which correlate strongly with functional regions identified via fMRI and meta-analyses. Furthermore, a strong parallel between the cortical regions defined via tractographic and more traditional cytoarchitectonic parcellation methods has been observed. However, the degree of correspondence and relative functional importance of cytoarchitectonic- versus connectivity-derived parcellations still remains unclear. Diffusion tractography remains one of the only methods capable of visualizing the structural networks of the brain in vivo. As such, it is of vital importance to continue to improve the accuracy of the methodology and to extend its potential applications in the study of cognition in neurological health and disease.},
	Author = {Cloutman, Lauren L and Lambon Ralph, Matthew A},
	Date-Added = {2017-04-26 10:46:45 +0000},
	Date-Modified = {2017-04-26 10:46:45 +0000},
	Journal = {Front Neuroanat},
	Pages = {34},
	Title = {Connectivity-based structural and functional parcellation of the human cortex using diffusion imaging and tractography},
	Volume = {6},
	Year = {2012},
	Bdsk-Url-1 = {http://dx.doi.org/10.3389/fnana.2012.00034}}

@article{Cox_BSAF_2014,
	Abstract = {Manual volumetric measurement of the brain's frontal lobe and its subregions from magnetic resonance images (MRIs) is an established method for