The Functional Connectivity Landscape of the Human Brain

Functional brain networks emerge and dissipate over a primarily static anatomical foundation. The dynamic basis of these networks is inter-regional communication involving local and distal regions. It is assumed that inter-regional distances play a pivotal role in modulating network dynamics. Using three different neuroimaging modalities, 6 datasets were evaluated to determine whether experimental manipulations asymmetrically affect functional relationships based on the distance between brain regions in human participants. Contrary to previous assumptions, here we show that short- and long-range connections are equally likely to strengthen or weaken in response to task demands. Additionally, connections between homotopic areas are the most stable and less likely to change compared to any other type of connection. Our results point to a functional connectivity landscape characterized by fluid transitions between local specialization and global integration. This ability to mediate functional properties irrespective of spatial distance may engender a diverse repertoire of cognitive processes when faced with a dynamic environment.

[1]  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.

[2]  Lars Kai Hansen,et al.  Webmining: learning from the world wide web , 2002 .

[3]  Olaf Sporns,et al.  Weight-conserving characterization of complex functional brain networks , 2011, NeuroImage.

[4]  C. Grady,et al.  The Importance of Being Variable , 2011, The Journal of Neuroscience.

[5]  D. Margulies,et al.  Regional Variation in Interhemispheric Coordination of Intrinsic Hemodynamic Fluctuations , 2008, The Journal of Neuroscience.

[6]  Olaf Sporns,et al.  Connectivity and complexity: the relationship between neuroanatomy and brain dynamics , 2000, Neural Networks.

[7]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[8]  Fabrice Labeau,et al.  Discrete Time Signal Processing , 2004 .

[9]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[10]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[11]  Alan C. Evans,et al.  Growing Together and Growing Apart: Regional and Sex Differences in the Lifespan Developmental Trajectories of Functional Homotopy , 2010, The Journal of Neuroscience.

[12]  M Davies,et al.  The Structured Clinical Interview for DSM-III-R (SCID). II. Multisite test-retest reliability. , 1992 .

[13]  T. Paus,et al.  Functional coactivation map of the human brain. , 2008, Cerebral cortex.

[14]  Stephen C. Strother,et al.  PHYCAA: Data-driven measurement and removal of physiological noise in BOLD fMRI , 2012, NeuroImage.

[15]  Anthony Randal McIntosh,et al.  Partial least squares analysis of neuroimaging data: applications and advances , 2004, NeuroImage.

[16]  David Poeppel,et al.  Reconstructing spatio-temporal activities of neural sources using an MEG vector beamformer technique , 2001, IEEE Transactions on Biomedical Engineering.

[17]  M. N. Rajah,et al.  Interactions of prefrontal cortex in relation to awareness in sensory learning. , 1999, Science.

[18]  O. Sporns,et al.  The economy of brain network organization , 2012, Nature Reviews Neuroscience.

[19]  Manfred G Kitzbichler,et al.  Cognitive Effort Drives Workspace Configuration of Human Brain Functional Networks , 2011, The Journal of Neuroscience.

[20]  V. Mountcastle.  The columnar organization of the neocortex. , 1997, Brain : a journal of neurology.

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

[22]  P. Reuter-Lorenz,et al.  Neuromarkers of fatigue and cognitive complaints following chemotherapy for breast cancer: a prospective fMRI investigation , 2014, Breast Cancer Research and Treatment.

[23]  Nick C. Fox,et al.  An automated algorithm for the computation of brain volume change from sequential MRIs using an iterative principal component analysis and its evaluation for the assessment of whole-brain atrophy rates in patients with probable Alzheimer's disease , 2004, NeuroImage.

[24]  Yingli Lu,et al.  Regional homogeneity approach to fMRI data analysis , 2004, NeuroImage.

[25]  O Sporns,et al.  Predicting human resting-state functional connectivity from structural connectivity , 2009, Proceedings of the National Academy of Sciences.

[26]  Karl J. Friston,et al.  Robust Smoothness Estimation in Statistical Parametric Maps Using Standardized Residuals from the General Linear Model , 1999, NeuroImage.

[27]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[28]  A. McIntosh,et al.  Multivariate statistical analyses for neuroimaging data. , 2013, Annual review of psychology.

[29]  Anthony Randal McIntosh,et al.  Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review , 2011, NeuroImage.

[30]  Natasa Kovacevic,et al.  Groupwise independent component decomposition of EEG data and partial least square analysis , 2007, NeuroImage.

[31]  J. Jonides,et al.  Depression, rumination and the default network. , 2011, Social cognitive and affective neuroscience.

[32]  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.

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

[34]  Liang Wang,et al.  Default mode network as revealed with multiple methods for resting-state functional MRI analysis , 2008, Journal of Neuroscience Methods.

[35]  Stephen M. Smith,et al.  Probabilistic independent component analysis for functional magnetic resonance imaging , 2004, IEEE Transactions on Medical Imaging.

[36]  G Tononi,et al.  Theoretical neuroanatomy: relating anatomical and functional connectivity in graphs and cortical connection matrices. , 2000, Cerebral cortex.

[37]  Caterina Breitenstein,et al.  Development and validation of a language learning model for behavioral and functional-imaging studies , 2002, Journal of Neuroscience Methods.

[38]  John A. E. Anderson,et al.  A multivariate analysis of age-related differences in default mode and task-positive networks across multiple cognitive domains. , 2010, Cerebral cortex.

[39]  Natasa Kovacevic,et al.  ICA-based artifact correction improves spatial localization of adaptive spatial filters in MEG , 2013, NeuroImage.

[40]  Douglas C Noll,et al.  Pretreatment worry and neurocognitive responses in women with breast cancer. , 2014, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[41]  A. McIntosh,et al.  The interplay of cue modality and response latency in brain areas supporting crossmodal motor preparation: an event-related fMRI study , 2011, Experimental Brain Research.

[42]  E. Bullmore,et al.  Neurophysiological architecture of functional magnetic resonance images of human brain. , 2005, Cerebral cortex.

[43]  Edward T. Bullmore,et al.  Efficiency and Cost of Economical Brain Functional Networks , 2007, PLoS Comput. Biol..

[44]  E. Tulving,et al.  Age‐related differences in effective neural connectivity during encoding and recall , 1997, Neuroreport.

[45]  G. Edelman,et al.  A measure for brain complexity: relating functional segregation and integration in the nervous system. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[46]  Douglas O. Cheyne,et al.  Reconstruction of correlated brain activity with adaptive spatial filters in MEG , 2010, NeuroImage.

[47]  Leslie G. Ungerleider,et al.  Network analysis of cortical visual pathways mapped with PET , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[48]  Ethan Kross,et al.  Does resting-state connectivity reflect depressive rumination? A tale of two analyses , 2014, NeuroImage.

[49]  C. Genovese,et al.  Statistical Issues in fMRI for Brain Imaging , 2001 .

[50]  D. Sheehan,et al.  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. , 1998, The Journal of clinical psychiatry.

[51]  Egill Rostrup,et al.  Motion or activity: their role in intra- and inter-subject variation in fMRI , 2005, NeuroImage.

[52]  Karl J. Friston,et al.  Lateralized Cognitive Processes and Lateralized Task Control in the Human Brain , 2003, Science.

[53]  Lily Riggs,et al.  A complementary analytic approach to examining medial temporal lobe sources using magnetoencephalography , 2009, NeuroImage.

[54]  M. Greicius,et al.  Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI , 2004, Proc. Natl. Acad. Sci. USA.