Resting-State Functional Connectivity in the Human Connectome Project: Current Status and Relevance to Understanding Psychopathology.

A key tenet of modern psychiatry is that psychiatric disorders arise from abnormalities in brain circuits that support human behavior. Our ability to examine hypotheses around circuit-level abnormalities in psychiatric disorders has been made possible by advances in human neuroimaging technologies. These advances have provided the basis for recent efforts to develop a more complex understanding of the function of brain circuits in health and of their relationship to behavior-providing, in turn, a foundation for our understanding of how disruptions in such circuits contribute to the development of psychiatric disorders. This review focuses on the use of resting-state functional connectivity MRI to assess brain circuits, on the advances generated by the Human Connectome Project, and on how these advances potentially contribute to understanding neural circuit dysfunction in psychopathology. The review gives particular attention to the methods developed by the Human Connectome Project that may be especially relevant to studies of psychopathology; it outlines some of the key findings about what constitutes a brain region; and it highlights new information about the nature and stability of brain circuits. Some of the Human Connectome Project's new findings particularly relevant to psychopathology-about neural circuits and their relationships to behavior-are also presented. The review ends by discussing the extension of Human Connectome Project methods across the lifespan and into manifest illness. Potential treatment implications are also considered.

[1]  Chongwon Pae,et al.  Connectivity-based change point detection for large-size functional networks , 2016, NeuroImage.

[2]  M. Raichle The restless brain: how intrinsic activity organizes brain function , 2015, Philosophical Transactions of the Royal Society B: Biological Sciences.

[3]  Steen Moeller,et al.  ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging , 2014, NeuroImage.

[4]  M. Raichle The brain's default mode network. , 2015, Annual review of neuroscience.

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

[6]  Andreas Meyer-Lindenberg,et al.  Functionally altered neurocircuits in a rat model of treatment-resistant depression show prominent role of the habenula , 2014, European Neuropsychopharmacology.

[7]  M. Szyf,et al.  Impact of Early Environment on Children's Mental Health: Lessons From DNA Methylation Studies With Monozygotic Twins , 2015, Twin Research and Human Genetics.

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

[9]  Neil D. Woodward,et al.  Review of thalamocortical resting-state fMRI studies in schizophrenia , 2017, Schizophrenia Research.

[10]  Dardo Tomasi,et al.  Structural and functional connectivity of the precuneus and thalamus to the default mode network , 2017, Human brain mapping.

[11]  Elisabeth B. Binder,et al.  Gene × Environment Interactions: From Molecular Mechanisms to Behavior , 2017, Annual review of psychology.

[12]  Benjamin A. Ely,et al.  Resting‐state functional connectivity of the human habenula in healthy individuals: Associations with subclinical depression , 2016, Human brain mapping.

[13]  J. Talairach,et al.  Co-Planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System: An Approach to Cerebral Imaging , 1988 .

[14]  Justin L. Vincent,et al.  Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. , 2008, Journal of neurophysiology.

[15]  Timothy O. Laumann,et al.  Parcellating an Individual Subject's Cortical and Subcortical Brain Structures Using Snowball Sampling of Resting-State Correlations , 2013, Cerebral cortex.

[16]  Xiangchuan Chen,et al.  Spatiotemporal Modeling of Brain Dynamics Using Resting-State Functional Magnetic Resonance Imaging with Gaussian Hidden Markov Model , 2016, Brain Connect..

[17]  G. Glover,et al.  Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control , 2007, The Journal of Neuroscience.

[18]  D. Perkel,et al.  Simultaneously Recorded Trains of Action Potentials: Analysis and Functional Interpretation , 1969, Science.

[19]  Roland N. Boubela,et al.  Big Data Approaches for the Analysis of Large-Scale fMRI Data Using Apache Spark and GPU Processing: A Demonstration on Resting-State fMRI Data from the Human Connectome Project , 2016, Front. Neurosci..

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

[21]  Jason S. Nomi,et al.  Correspondence between evoked and intrinsic functional brain network configurations , 2017, Human brain mapping.

[22]  P. Matthews,et al.  Multimodal population brain imaging in the UK Biobank prospective epidemiological study , 2016, Nature Neuroscience.

[23]  Baoci Shan,et al.  Inefficient DMN Suppression in Schizophrenia Patients with Impaired Cognitive Function but not Patients with Preserved Cognitive Function , 2016, Scientific Reports.

[24]  Detre A. Godinez,et al.  Familial risk and ADHD-specific neural activity revealed by case-control, discordant twin pair design , 2015, Psychiatry Research: Neuroimaging.

[25]  M. Chun,et al.  Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity , 2015, Nature Neuroscience.

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

[27]  Justin P. Haldar,et al.  Temporal Non-Local Means Filtering Reveals Real-Time Whole-Brain Cortical Interactions in Resting fMRI , 2016, PloS one.

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

[29]  Deanna M. Barch,et al.  When less is more: TPJ and default network deactivation during encoding predicts working memory performance , 2010, NeuroImage.

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

[31]  Meng-Chuan Lai,et al.  Annual Research Review: The role of the environment in the developmental psychopathology of autism spectrum condition. , 2016, Journal of child psychology and psychiatry, and allied disciplines.

[32]  Dustin Scheinost,et al.  Can brain state be manipulated to emphasize individual differences in functional connectivity? , 2017, NeuroImage.

[33]  Julia M. Sheffield,et al.  Cingulo-opercular network efficiency mediates the association between psychotic-like experiences and cognitive ability in the general population. , 2016, Biological psychiatry. Cognitive neuroscience and neuroimaging.

[34]  G. P. Moore,et al.  Neuronal spike trains and stochastic point processes. II. Simultaneous spike trains. , 1967, Biophysical journal.

[35]  Mark W. Woolrich,et al.  Large-scale Probabilistic Functional Modes from resting state fMRI , 2015, NeuroImage.

[36]  Peter A. Bandettini,et al.  Principles of BOLD Functional MRI , 2011 .

[37]  R. Buckner,et al.  Parcellating Cortical Functional Networks in Individuals , 2015, Nature Neuroscience.

[38]  Jonathan D. Power,et al.  The Development of Human Functional Brain Networks , 2010, Neuron.

[39]  Vince D. Calhoun,et al.  Chronnectomic patterns and neural flexibility underlie executive function , 2017, NeuroImage.

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

[41]  R Cameron Craddock,et al.  A whole brain fMRI atlas generated via spatially constrained spectral clustering , 2012, Human brain mapping.

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

[43]  N. Martin,et al.  Progression in substance use initiation: A multilevel discordant monozygotic twin design. , 2015, Journal of abnormal psychology.

[44]  Steen Moeller,et al.  The Human Connectome Project's neuroimaging approach , 2016, Nature Neuroscience.

[45]  Xenophon Papademetris,et al.  Groupwise whole-brain parcellation from resting-state fMRI data for network node identification , 2013, NeuroImage.

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

[47]  Jeffrey M. Zacks,et al.  Coherent spontaneous activity accounts for trial-to-trial variability in human evoked brain responses , 2006, Nature Neuroscience.

[48]  Arthur F. Kramer,et al.  Behavioural Brain Research Age-related Differences in Cortical Recruitment and Suppression: Implications for Cognitive Performance , 2022 .

[49]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[50]  M. Fox,et al.  Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging , 2007, Nature Reviews Neuroscience.

[51]  J. Jovicich,et al.  Fast computation of voxel-level brain connectivity maps from resting-state functional MRI using l₁-norm as approximation of Pearson's temporal correlation: proof-of-concept and example vector hardware implementation. , 2014, Medical engineering & physics.

[52]  M. Potenza,et al.  A Targeted Review of the Neurobiology and Genetics of Behavioural Addictions: An Emerging Area of Research , 2013, Canadian journal of psychiatry. Revue canadienne de psychiatrie.

[53]  Christoforos Anagnostopoulos,et al.  Real‐time estimation of dynamic functional connectivity networks , 2017, Human brain mapping.

[54]  J. Smoller Disorders and borders: Psychiatric genetics and nosology , 2013, American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics.

[55]  Evan M. Gordon,et al.  Long-term neural and physiological phenotyping of a single human , 2015, Nature Communications.

[56]  Ludovica Griffanti,et al.  Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers , 2014, NeuroImage.

[57]  Jinglei Lv,et al.  Sparse representation of HCP grayordinate data reveals novel functional architecture of cerebral cortex , 2015, Human brain mapping.

[58]  Aapo Hyvärinen,et al.  Group-PCA for very large fMRI datasets , 2014, NeuroImage.

[59]  S. Petersen,et al.  Development of distinct control networks through segregation and integration , 2007, Proceedings of the National Academy of Sciences.

[60]  S. A. Burt Research review: the shared environment as a key source of variability in child and adolescent psychopathology. , 2014, Journal of child psychology and psychiatry, and allied disciplines.

[61]  Evan M. Gordon,et al.  Functional System and Areal Organization of a Highly Sampled Individual Human Brain , 2015, Neuron.

[62]  Steen Moeller,et al.  Advances in diffusion MRI acquisition and processing in the Human Connectome Project , 2013, NeuroImage.

[63]  Mark Jenkinson,et al.  MSM: A new flexible framework for Multimodal Surface Matching , 2014, NeuroImage.

[64]  Cheryl L. Grady,et al.  Inter-individual differences in the experience of negative emotion predict variations in functional brain architecture , 2015, NeuroImage.

[65]  Gustavo Deco,et al.  Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? , 2016, NeuroImage.

[66]  Steen Moeller,et al.  Evaluation of 2D multiband EPI imaging for high-resolution, whole-brain, task-based fMRI studies at 3T: Sensitivity and slice leakage artifacts , 2016, NeuroImage.

[67]  Bernard Ng,et al.  Coupled Stable Overlapping Replicator Dynamics for Multimodal Brain Subnetwork Identification , 2015, IPMI.

[68]  Matthew W State,et al.  The Genetics of Child Psychiatric Disorders: Focus on Autism and Tourette Syndrome , 2010, Neuron.

[69]  Jordan W Smoller,et al.  The Genetics of Stress-Related Disorders: PTSD, Depression, and Anxiety Disorders , 2016, Neuropsychopharmacology.

[70]  Peter T Fox,et al.  Shared Genetic Factors Influence Head Motion During MRI and Body Mass Index , 2016, Cerebral cortex.

[71]  George L. Gerstein,et al.  Identification of functionally related neural assemblies , 1978, Brain Research.

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

[73]  Michael W. Cole,et al.  The role of default network deactivation in cognition and disease , 2012, Trends in Cognitive Sciences.

[74]  Maurizio Corbetta,et al.  Dorsal and Ventral Attention Systems Underlie Social and Symbolic Cueing , 2014, Journal of Cognitive Neuroscience.

[75]  Steen Moeller,et al.  Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project , 2013, NeuroImage.

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

[77]  Stephen Dager,et al.  Subregional differences in intrinsic amygdala hyperconnectivity and hypoconnectivity in autism spectrum disorder , 2016, Autism research : official journal of the International Society for Autism Research.

[78]  Angus W. MacDonald,et al.  Fronto-parietal and cingulo-opercular network integrity and cognition in health and schizophrenia , 2015, Neuropsychologia.

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

[80]  Wei Zhang,et al.  Temporal Dynamics Assessment of Spatial Overlap Pattern of Functional Brain Networks Reveals Novel Functional Architecture of Cerebral Cortex , 2016, IEEE Transactions on Biomedical Engineering.

[81]  M. Sperling,et al.  Functional connectivity abnormalities vary by amygdala subdivision and are associated with psychiatric symptoms in unilateral temporal epilepsy , 2013, Brain and Cognition.

[82]  Steen Moeller,et al.  The Human Connectome Project: A data acquisition perspective , 2012, NeuroImage.

[83]  D. Barch,et al.  Shared Predisposition in the Association Between Cannabis Use and Subcortical Brain Structure. , 2015, JAMA psychiatry.

[84]  Timothy O. Laumann,et al.  An approach for parcellating human cortical areas using resting-state correlations , 2014, NeuroImage.

[85]  V. Menon,et al.  Saliency, switching, attention and control: a network model of insula function , 2010, Brain Structure and Function.

[86]  Mark Jenkinson,et al.  The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.

[87]  P. Liddle,et al.  Neural Primacy of the Salience Processing System in Schizophrenia , 2013, Neuron.

[88]  Yu Zhao,et al.  Supervised Dictionary Learning for Inferring Concurrent Brain Networks , 2015, IEEE Transactions on Medical Imaging.

[89]  Steen Moeller,et al.  Multiband multislice GE‐EPI at 7 tesla, with 16‐fold acceleration using partial parallel imaging with application to high spatial and temporal whole‐brain fMRI , 2010, Magnetic resonance in medicine.

[90]  Jonathan D. Cohen,et al.  Dissociating working memory from task difficulty in human prefrontal cortex , 1997, Neuropsychologia.

[91]  Steen Moeller,et al.  Tradeoffs in pushing the spatial resolution of fMRI for the 7T Human Connectome Project , 2017, NeuroImage.

[92]  M. Corbetta,et al.  Functional evolution of new and expanded attention networks in humans , 2015, Proceedings of the National Academy of Sciences.

[93]  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.

[94]  M. Osler,et al.  Commentary: Strengths and limitations of the discordant twin-pair design in social epidemiology. Where do we go from here? , 2009, International journal of epidemiology.

[95]  Timothy O. Laumann,et al.  Evaluation of Denoising Strategies to Address Motion-Correlated Artifacts in Resting-State Functional Magnetic Resonance Imaging Data from the Human Connectome Project , 2016, Brain Connect..

[96]  Kaustubh Supekar,et al.  Distinct Global Brain Dynamics and Spatiotemporal Organization of the Salience Network , 2016, PLoS biology.

[97]  Jason B. Mattingley,et al.  Functional brain networks related to individual differences in human intelligence at rest , 2016, Scientific Reports.

[98]  Evan M. Gordon,et al.  Evidence for Two Independent Factors that Modify Brain Networks to Meet Task Goals. , 2016, Cell reports.

[99]  Timothy O. Laumann,et al.  Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. , 2016, Cerebral cortex.

[100]  Dardo Tomasi,et al.  High-Resolution Functional Connectivity Density: Hub Locations, Sensitivity, Specificity, Reproducibility, and Reliability. , 2016, Cerebral cortex.

[101]  Jieping Ye,et al.  Holistic Atlases of Functional Networks and Interactions Reveal Reciprocal Organizational Architecture of Cortical Function , 2015, IEEE Transactions on Biomedical Engineering.

[102]  S. Faraone,et al.  Genetics of aggressive behavior: An overview , 2016, American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics.

[103]  Thomas E. Nichols,et al.  A positive-negative mode of population covariation links brain connectivity, demographics and behavior , 2015, Nature Neuroscience.