From connectome to cognition: The search for mechanism in human functional brain networks

&NA; Recent developments in functional connectivity research have expanded the scope of human neuroimaging, from identifying changes in regional activation amplitudes to detailed mapping of large‐scale brain networks. However, linking network processes to a clear role in cognition demands advances in the theoretical frameworks, algorithms, and experimental approaches applied. This would help evolve the field from a descriptive to an explanatory state, by targeting network interactions that can mechanistically account for cognitive effects. In the present review, we provide an explicit framework to aid this search for “network mechanisms”, which anchors recent methodological advances in functional connectivity estimation to a renewed emphasis on careful experimental design. We emphasize how this framework can address specific questions in network neuroscience. These span ambiguity over the cognitive relevance of resting‐state networks, how to characterize task‐evoked and spontaneous network dynamics, how to identify directed or “effective” connections, and how to apply multivariate pattern analysis at the network level. In parallel, we apply the framework to highlight the mechanistic interaction of network components that remain “stable” across task domains and more “flexible” components associated with on‐task reconfiguration. By emphasizing the need to structure the use of diverse analytic approaches with sound experimentation, our framework promotes an explanatory mapping between the workings of the cognitive mind and the large‐scale network mechanisms of the human brain. HighlightsMechanisms are needed to link descriptive network maps to cognitive function.Network mechanisms involve explanatory interactions amongst brain regions.Task manipulations and advanced analytic methods can reveal network mechanisms.Each mechanism is characterized by its spatial, temporal and directional components.Cognition emerges from mechanisms at macro network as well as micro cellular levels.

[1]  Erik P. Hoel,et al.  Quantifying causal emergence shows that macro can beat micro , 2013, Proceedings of the National Academy of Sciences.

[2]  Robert W. Zmud,et al.  An Empirical Validation of , 1987 .

[3]  Walter Schneider,et al.  Identifying the brain's most globally connected regions , 2010, NeuroImage.

[4]  M. Ding,et al.  Decomposing Neural Synchrony: Toward an Explanation for Near-Zero Phase-Lag in Cortical Oscillatory Networks , 2008, PloS one.

[5]  Rajan S. Patel,et al.  A Bayesian approach to determining connectivity of the human brain , 2006, Human brain mapping.

[6]  N. Sigala,et al.  Dynamic Coding for Cognitive Control in Prefrontal Cortex , 2013, Neuron.

[7]  J. Fodor,et al.  Connectionism and cognitive architecture: A critical analysis , 1988, Cognition.

[8]  Hillary D. Schwarb,et al.  Short‐time windows of correlation between large‐scale functional brain networks predict vigilance intraindividually and interindividually , 2013, Human brain mapping.

[9]  Michael W. Cole,et al.  The Frontoparietal Control System , 2014, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[10]  Theodore P. Zanto,et al.  Causal role of the prefrontal cortex in top-down modulation of visual processing and working memory , 2011, Nature Neuroscience.

[11]  Klaus Scheffler,et al.  Differences Between MEG and High-Density EEG Source Localizations Using a Distributed Source Model in Comparison to fMRI , 2014, Brain Topography.

[12]  Maxym Myroshnychenko,et al.  High-Degree Neurons Feed Cortical Computations , 2016, PLoS Comput. Biol..

[13]  T. Sejnowski,et al.  The Book of Hebb , 1999, Neuron.

[14]  M. Corbetta,et al.  Learning sculpts the spontaneous activity of the resting human brain , 2009, Proceedings of the National Academy of Sciences.

[15]  Jeff H. Duyn,et al.  The future of ultra-high field MRI and fMRI for study of the human brain , 2012, NeuroImage.

[16]  Timothy Edward John Behrens,et al.  Task-free MRI predicts individual differences in brain activity during task performance , 2016, Science.

[17]  Danielle S. Bassett,et al.  Cognitive Network Neuroscience , 2015, Journal of Cognitive Neuroscience.

[18]  Rainer Goebel,et al.  Information-based functional brain mapping. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[19]  L. Yao,et al.  Top-Down Regulation of Default Mode Activity in Spatial Visual Attention , 2013, The Journal of Neuroscience.

[20]  Cyriel M A Pennartz,et al.  Population-Level Neural Codes Are Robust to Single-Neuron Variability from a Multidimensional Coding Perspective. , 2016, Cell reports.

[21]  Stephen M Smith,et al.  Fast transient networks in spontaneous human brain activity , 2014, eLife.

[22]  W. Drongelen,et al.  Localization of brain electrical activity via linearly constrained minimum variance spatial filtering , 1997, IEEE Transactions on Biomedical Engineering.

[23]  Nikola T. Markov,et al.  Weight Consistency Specifies Regularities of Macaque Cortical Networks , 2010, Cerebral cortex.

[24]  Michael W. Cole,et al.  Rapid Transfer of Abstract Rules to Novel Contexts in Human Lateral Prefrontal Cortex , 2011, Front. Hum. Neurosci..

[25]  Charles Lance,et al.  State and Trait Components of Turnover Intentions , 2017 .

[26]  S. Petersen,et al.  Concepts and principles in the analysis of brain networks , 2011, Annals of the New York Academy of Sciences.

[27]  Justin L. Vincent,et al.  Distinct brain networks for adaptive and stable task control in humans , 2007, Proceedings of the National Academy of Sciences.

[28]  O. Sporns.  Contributions and challenges for network models in cognitive neuroscience , 2014, Nature Neuroscience.

[29]  Krzysztof J. Gorgolewski,et al.  The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance , 2015, Neuron.

[30]  Jonathan D. Power,et al.  Intrinsic and Task-Evoked Network Architectures of the Human Brain , 2014, Neuron.

[31]  Jonathan D. Cohen,et al.  An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance. , 2005, Annual review of neuroscience.

[32]  M. Rushworth,et al.  Functionally Specific Reorganization in Human Premotor Cortex , 2007, Neuron.

[33]  Maurizio Corbetta,et al.  The human brain is intrinsically organized into dynamic, anticorrelated functional networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[34]  Russell A. Poldrack,et al.  Large-scale automated synthesis of human functional neuroimaging data , 2011, Nature Methods.

[35]  L. Yao,et al.  Causal Interactions in Attention Networks Predict Behavioral Performance , 2012, The Journal of Neuroscience.

[36]  Timothy O. Laumann,et al.  Data Quality Influences Observed Links Between Functional Connectivity and Behavior , 2017, Cerebral cortex.

[37]  M. Brass,et al.  Unconscious determinants of free decisions in the human brain , 2008, Nature Neuroscience.

[38]  A. Ishai,et al.  Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.

[39]  J. S. Guntupalli,et al.  Decoding neural representational spaces using multivariate pattern analysis. , 2014, Annual review of neuroscience.

[40]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.

[41]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[42]  Tao Zhang,et al.  Directionality index of neural information flow as a measure of synaptic plasticity in chronic unpredictable stress rats , 2011, Neuroscience Letters.

[43]  Ethan R. Buch,et al.  Noninvasive brain stimulation: from physiology to network dynamics and back , 2013, Nature Neuroscience.

[44]  Sterling C. Johnson,et al.  A generalized form of context-dependent psychophysiological interactions (gPPI): A comparison to standard approaches , 2012, NeuroImage.

[45]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

[46]  A. Aertsen,et al.  Spiking activity propagation in neuronal networks: reconciling different perspectives on neural coding , 2010, Nature Reviews Neuroscience.

[47]  Karl J. Friston,et al.  Dynamic causal modelling , 2003, NeuroImage.

[48]  M. Bedau.  Weak Emergence * , 1997 .

[49]  Eswar Damaraju,et al.  Tracking whole-brain connectivity dynamics in the resting state. , 2014, Cerebral cortex.

[50]  J. S. Guntupalli,et al.  A Model of Representational Spaces in Human Cortex , 2016, Cerebral cortex.

[51]  D. J. Felleman,et al.  Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.

[52]  Torkel Klingberg,et al.  The role of fronto-parietal and fronto-striatal networks in the development of working memory: a longitudinal study. , 2015, Cerebral cortex.

[53]  Kathleen M. Gates,et al.  Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples , 2012, NeuroImage.

[54]  Michael W. Cole,et al.  Prefrontal Dynamics Underlying Rapid Instructed Task Learning Reverse with Practice , 2010, The Journal of Neuroscience.

[55]  K. Müller,et al.  Robustly estimating the flow direction of information in complex physical systems. , 2007, Physical review letters.

[56]  G Tononi,et al.  Measures of degeneracy and redundancy in biological networks. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[57]  R. Douglas,et al.  Neuronal circuits of the neocortex. , 2004, Annual review of neuroscience.

[58]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[59]  Michael W. Cole,et al.  Higher Intelligence Is Associated with Less Task-Related Brain Network Reconfiguration , 2016, The Journal of Neuroscience.

[60]  W. Bechtel,et al.  Explanation: a mechanist alternative. , 2005, Studies in history and philosophy of biological and biomedical sciences.

[61]  Patricia S. Goldman.  TOPOGRAPHY OF COGNITION: Parallel Distributed Networks in Primate Association Cortex , 1988 .

[62]  Laura D. Lewis,et al.  Fast fMRI can detect oscillatory neural activity in humans , 2016, Proceedings of the National Academy of Sciences.

[63]  Thomas R. Knösche,et al.  White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI , 2013, NeuroImage.

[64]  Michael W. Cole,et al.  Activity flow over resting-state networks shapes cognitive task activations , 2016, Nature Neuroscience.

[65]  Iroise Dumontheil,et al.  Influence of the COMT Genotype on Working Memory and Brain Activity Changes During Development , 2011, Biological Psychiatry.

[66]  Timothy O. Laumann,et al.  Functional Network Organization of the Human Brain , 2011, Neuron.

[67]  F. H. Lopes da Silva.  Neural mechanisms underlying brain waves: from neural membranes to networks. , 1991, Electroencephalography and clinical neurophysiology.

[68]  Linda Geerligs,et al.  State and Trait Components of Functional Connectivity: Individual Differences Vary with Mental State , 2015, The Journal of Neuroscience.

[69]  W. Schneider,et al.  Neuroimaging studies of practice-related change: fMRI and meta-analytic evidence of a domain-general control network for learning. , 2005, Brain research. Cognitive brain research.

[70]  Csaba Petre,et al.  Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network , 2016, ArXiv.

[71]  Michael W. Cole,et al.  Rapid instructed task learning: A new window into the human brain’s unique capacity for flexible cognitive control , 2013, Cognitive, affective & behavioral neuroscience.

[72]  D H Hubel,et al.  Brain mechanisms of vision. , 1979, Scientific American.

[73]  E. Miller,et al.  An integrative theory of prefrontal cortex function. , 2001, Annual review of neuroscience.

[74]  Jonathan D. Power,et al.  Multi-task connectivity reveals flexible hubs for adaptive task control , 2013, Nature Neuroscience.

[75]  R. Passingham.  The hippocampus as a cognitive map J. O'Keefe & L. Nadel, Oxford University Press, Oxford (1978). 570 pp., £25.00 , 1979, Neuroscience.

[76]  Jon Williamson,et al.  What is a mechanism? Thinking about mechanisms across the sciences , 2012 .

[77]  N. Balderston,et al.  Prefrontal cortical regulation of fear learning , 2014, Trends in Neurosciences.

[78]  G. Shulman,et al.  Evidence for separate perceptual reactivation and search processes during remembering. , 2006, Cerebral cortex.

[79]  R. Desimone,et al.  High-Frequency, Long-Range Coupling Between Prefrontal and Visual Cortex During Attention , 2009, Science.

[80]  Joerg F. Hipp,et al.  BOLD fMRI Correlation Reflects Frequency-Specific Neuronal Correlation , 2015, Current Biology.

[81]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[82]  S. Dehaene,et al.  Characterizing the dynamics of mental representations: the temporal generalization method , 2014, Trends in Cognitive Sciences.

[83]  Richard F. Betzel,et al.  Resting-brain functional connectivity predicted by analytic measures of network communication , 2013, Proceedings of the National Academy of Sciences.

[84]  Xiaoping Hu,et al.  Effect of hemodynamic variability on Granger causality analysis of fMRI , 2010, NeuroImage.

[85]  W. Singer,et al.  Modulation of Neuronal Interactions Through Neuronal Synchronization , 2007, Science.

[86]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

[87]  Ravi D. Mill,et al.  Pupil dilation during recognition memory: Isolating unexpected recognition from judgment uncertainty , 2016, Cognition.

[88]  M. Spira,et al.  Multi-electrode array technologies for neuroscience and cardiology. , 2013, Nature nanotechnology.

[89]  Markus Siegel,et al.  Cortical information flow during flexible sensorimotor decisions , 2015, Science.

[90]  Kosuke Imai,et al.  Experimental designs for identifying causal mechanisms , 2013 .

[91]  Sepideh Sadaghiani,et al.  Ongoing dynamics in large-scale functional connectivity predict perception , 2015, Proceedings of the National Academy of Sciences.

[92]  Stephen M. Smith,et al.  Temporally-independent functional modes of spontaneous brain activity , 2012, Proceedings of the National Academy of Sciences.

[93]  L. Nadel,et al.  The Hippocampus as a Cognitive Map , 1978 .

[94]  James L. McClelland,et al.  What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated , 2016, Trends in Cognitive Sciences.

[95]  Laurence Dricot,et al.  Functional connectivity and structural analyses in the bilingual brain: implications for arithmetic. , 2015 .

[96]  P. Fries.  A mechanism for cognitive dynamics: neuronal communication through neuronal coherence , 2005, Trends in Cognitive Sciences.

[97]  M. Corbetta,et al.  Large-scale cortical correlation structure of spontaneous oscillatory activity , 2012, Nature Neuroscience.

[98]  Mark W. Woolrich,et al.  Resting-state fMRI in the Human Connectome Project , 2013, NeuroImage.

[99]  Scott T. Grafton,et al.  Dynamic reconfiguration of human brain networks during learning , 2010, Proceedings of the National Academy of Sciences.

[100]  Remco J. Renken,et al.  The effect of intra- and inter-subject variability of hemodynamic responses on group level Granger causality analyses , 2011, NeuroImage.

[101]  Mark W. Woolrich,et al.  Biophysical network models and the human connectome , 2013, NeuroImage.

[102]  V. Haughton,et al.  Mapping functionally related regions of brain with functional connectivity MR imaging. , 2000, AJNR. American journal of neuroradiology.

[103]  Alvaro Pascual-Leone,et al.  Measuring and manipulating brain connectivity with resting state functional connectivity magnetic resonance imaging (fcMRI) and transcranial magnetic stimulation (TMS) , 2012, NeuroImage.

[104]  M. Nitsche,et al.  The Importance of Timing in Segregated Theta Phase-Coupling for Cognitive Performance , 2012, Current Biology.

[105]  Mark W. Woolrich,et al.  Network modelling methods for FMRI , 2011, NeuroImage.

[106]  J. Martinerie,et al.  The brainweb: Phase synchronization and large-scale integration , 2001, Nature Reviews Neuroscience.

[107]  Gustavo Deco,et al.  Resting brains never rest: computational insights into potential cognitive architectures , 2013, Trends in Neurosciences.

[108]  John R. Anderson,et al.  Tracking cognitive processing stages with MEG: A spatio-temporal model of associative recognition in the brain , 2016, NeuroImage.

[109]  Robert Klee,et al.  Micro-Determinism and Concepts of Emergence , 1984, Philosophy of Science.

[110]  Karl J. Friston,et al.  A systematic framework for functional connectivity measures , 2014, Front. Neurosci..

[111]  M. Gazzaniga,et al.  Understanding complexity in the human brain , 2011, Trends in Cognitive Sciences.

[112]  Walter Schneider,et al.  The cognitive control network: Integrated cortical regions with dissociable functions , 2007, NeuroImage.

[113]  Yaroslav O. Halchenko,et al.  Pattern classification precedes region-average hemodynamic response in early visual cortex , 2013, NeuroImage.

[114]  Richard F. Betzel,et al.  Cooperative and Competitive Spreading Dynamics on the Human Connectome , 2015, Neuron.

[115]  Ernest Sosa,et al.  Supervenience and Mind: Selected Philosophical Essays , 1993 .

[116]  Stephen M. Smith,et al.  Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging , 2010, PloS one.

[117]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[118]  Joseph Ramsey,et al.  Bayesian networks for fMRI: A primer , 2014, NeuroImage.

[119]  J. Duyn,et al.  Time-varying functional network information extracted from brief instances of spontaneous brain activity , 2013, Proceedings of the National Academy of Sciences.

[120]  F. D. Silva.  Neural mechanisms underlying brain waves: from neural membranes to networks. , 1991 .

[121]  Takuya Ito,et al.  Large-scale multivariate activation states of the human brain , 2016 .

[122]  Judea Pearl,et al.  Direct and Indirect Effects , 2001, UAI.

[123]  William A Catterall,et al.  The Hodgkin-Huxley Heritage: From Channels to Circuits , 2012, The Journal of Neuroscience.

[124]  Ravi S. Menon,et al.  Resting‐state networks show dynamic functional connectivity in awake humans and anesthetized macaques , 2013, Human brain mapping.

[125]  J. Pillai.  Functional Connectivity. , 2017, Neuroimaging clinics of North America.

[126]  C. Kelly,et al.  The extrinsic and intrinsic functional architectures of the human brain are not equivalent. , 2013, Cerebral cortex.

[127]  Mark D'Esposito,et al.  Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses , 2004, NeuroImage.

[128]  Linda Geerligs,et al.  Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation , 2016, NeuroImage.

[129]  Dimitri Van De Ville,et al.  Transient brain activity disentangles fMRI resting-state dynamics in terms of spatially and temporally overlapping networks , 2015, Nature Communications.

[130]  S. Petersen,et al.  Brain Networks and Cognitive Architectures , 2015, Neuron.

[131]  Keith Heberlein,et al.  Imaging human connectomes at the macroscale , 2013, Nature Methods.

[132]  B T Thomas Yeo,et al.  Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

[133]  Darren Price,et al.  Investigating the electrophysiological basis of resting state networks using magnetoencephalography , 2011, Proceedings of the National Academy of Sciences.

[134]  M. Raichle.  Two views of brain function , 2010, Trends in Cognitive Sciences.

[135]  Danielle S. Bassett,et al.  Activity flow over resting-state networks shapes cognitive task activations , 2016 .

[136]  Vince D. Calhoun,et al.  Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering , 2011, NeuroImage.

[137]  R. Buckner,et al.  Transcranial magnetic stimulation modulates the brain's intrinsic activity in a frequency-dependent manner , 2011, Proceedings of the National Academy of Sciences.

[138]  M. Corbetta,et al.  Control of goal-directed and stimulus-driven attention in the brain , 2002, Nature Reviews Neuroscience.

[139]  O. Sporns,et al.  Key role of coupling, delay, and noise in resting brain fluctuations , 2009, Proceedings of the National Academy of Sciences.

[140]  M. Hallett,et al.  Identifying true brain interaction from EEG data using the imaginary part of coherency , 2004, Clinical Neurophysiology.

[141]  M. Corbetta,et al.  Temporal dynamics of spontaneous MEG activity in brain networks , 2010, Proceedings of the National Academy of Sciences.

[142]  Angela R. Laird,et al.  Networks of task co-activations , 2013, NeuroImage.

[143]  Clark Glymour,et al.  Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study , 2011, NeuroImage.

[144]  Riitta Salmelin,et al.  Tracking neural coding of perceptual and semantic features of concrete nouns , 2012, NeuroImage.

[145]  Terrence J Sejnowski,et al.  Communication in Neuronal Networks , 2003, Science.

[146]  David Badre,et al.  Ventral fronto-temporal pathway supporting cognitive control of episodic memory retrieval. , 2015, Cerebral cortex.

[147]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[148]  Sharon L. Thompson-Schill,et al.  Informational connectivity: identifying synchronized discriminability of multi-voxel patterns across the brain , 2013, Front. Hum. Neurosci..

[149]  J. Haynes.  A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives , 2015, Neuron.

[150]  A. Engel,et al.  Spectral fingerprints of large-scale neuronal interactions , 2012, Nature Reviews Neuroscience.

[151]  Adrienne L. Fairhall,et al.  Intrinsic Neuronal Properties Switch the Mode of Information Transmission in Networks , 2014, PLoS Comput. Biol..

[152]  Karl J. Friston,et al.  Dynamic causal modelling of induced responses , 2008, NeuroImage.

[153]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1990 .

[154]  Stephen J. Gotts,et al.  High-frequency, Long-range Coupling between Prefrontal and Visual Cortex during Sustained Attention , 2022 .

[155]  N. Kanwisher.  Functional specificity in the human brain: A window into the functional architecture of the mind , 2010, Proceedings of the National Academy of Sciences.

[156]  Danielle S Bassett,et al.  Learning-induced autonomy of sensorimotor systems , 2014, Nature Neuroscience.

[157]  M. Raichle,et al.  Lag threads organize the brain’s intrinsic activity , 2015, Proceedings of the National Academy of Sciences.

[158]  O. Sporns,et al.  Network hubs in the human brain , 2013, Trends in Cognitive Sciences.

[159]  J. Schoffelen,et al.  Source connectivity analysis with MEG and EEG , 2009, Human brain mapping.

[160]  Leonardo L. Gollo,et al.  Time-resolved resting-state brain networks , 2014, Proceedings of the National Academy of Sciences.

[161]  Anil K. Seth.  Measuring emergence via nonlinear Granger causality , 2008, ALIFE.

[162]  Joseph E LeDoux.  Emotion Circuits in the Brain , 2000 .

[163]  Ravi D. Mill,et al.  Differentiating the Functional Contributions of Resting Connectivity Networks to Memory Decision-making: fMRI Support for Multistage Control Processes , 2015, Journal of Cognitive Neuroscience.

[164]  Olaf Sporns,et al.  Network structure of cerebral cortex shapes functional connectivity on multiple time scales , 2007, Proceedings of the National Academy of Sciences.

[165]  A. Aertsen,et al.  Dynamics of neuronal interactions in monkey cortex in relation to behavioural events , 1995, Nature.

[166]  T. Braver,et al.  Vive les differences! Individual variation in neural mechanisms of executive control , 2010, Current Opinion in Neurobiology.

[167]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[168]  Nikolaus Kriegeskorte,et al.  Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .

[169]  Walter Schneider,et al.  Empirical validation of directed functional connectivity , 2017, NeuroImage.

[170]  C. Stevens,et al.  Heterogeneity of Release Probability, Facilitation, and Depletion at Central Synapses , 1997, Neuron.

[171]  Rainer Goebel,et al.  Mapping directed influence over the brain using Granger causality and fMRI , 2005, NeuroImage.

[172]  Evan M. Gordon,et al.  On the Stability of BOLD fMRI Correlations , 2016, Cerebral cortex.

[173]  Jesper Andersson,et al.  A multi-modal parcellation of human cerebral cortex , 2016, Nature.

[174]  C. Craver.  Explaining the Brain: Mechanisms and the Mosaic Unity of Neuroscience , 2007 .