Bridging the Gap between the Human and Macaque Connectome: A Quantitative Comparison of Global Interspecies Structure-Function Relationships and Network Topology

Resting state functional connectivity MRI (rs-fcMRI) may provide a powerful and noninvasive “bridge” for comparing brain function between patients and experimental animal models; however, the relationship between human and macaque rs-fcMRI remains poorly understood. Here, using a novel surface deformation process for species comparisons in the same anatomical space (Van Essen, 2004, 2005), we found high correspondence, but also unique hub topology, between human and macaque functional connectomes. The global functional connectivity match between species was moderate to strong (r = 0.41) and increased when considering the top 15% strongest connections (r = 0.54). Analysis of the match between functional connectivity and the underlying anatomical connectivity, derived from a previous retrograde tracer study done in macaques (Markov et al., 2012), showed impressive structure–function correspondence in both the macaque and human. When examining the strongest structural connections, we found a 70–80% match between structural and functional connectivity matrices in both species. Finally, we compare species on two widely used metrics for studying hub topology: degree and betweenness centrality. The data showed topological agreement across the species, with nodes of the posterior cingulate showing high degree and betweenness centrality. In contrast, nodes in medial frontal and parietal cortices were identified as having high degree and betweenness in the human as opposed to the macaque. Our results provide: (1) a thorough examination and validation for a surface-based interspecies deformation process, (2) a strong theoretical foundation for making interspecies comparisons of rs-fcMRI, and (3) a unique look at topological distinctions between the species.

[1]  John O. Willis,et al.  Wechsler Abbreviated Scale of Intelligence , 2014 .

[2]  Samuel D. Carpenter,et al.  Structural and Functional Rich Club Organization of the Brain in Children and Adults , 2014, PloS one.

[3]  D. Fair,et al.  Dietary Omega-3 Fatty Acids Modulate Large-Scale Systems Organization in the Rhesus Macaque Brain , 2014, The Journal of Neuroscience.

[4]  Timothy O. Laumann,et al.  Methods to detect, characterize, and remove motion artifact in resting state fMRI , 2014, NeuroImage.

[5]  Nikola T. Markov,et al.  A Weighted and Directed Interareal Connectivity Matrix for Macaque Cerebral Cortex , 2012, Cerebral cortex.

[6]  Beatriz Luna,et al.  The nuisance of nuisance regression: Spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity , 2013, NeuroImage.

[7]  Jonathan D. Power,et al.  Evidence for Hubs in Human Functional Brain Networks , 2013, Neuron.

[8]  R. Cameron Craddock,et al.  A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics , 2013, NeuroImage.

[9]  Adam G. Thomas,et al.  The Organization of Dorsal Frontal Cortex in Humans and Macaques , 2013, The Journal of Neuroscience.

[10]  Kathleen M. Gates,et al.  Inferring functional connectivity in MRI using Bayesian network structure learning with a modified PC algorithm , 2013, NeuroImage.

[11]  Hang Joon Jo,et al.  The perils of global signal regression for group comparisons: a case study of Autism Spectrum Disorders , 2013, Front. Hum. Neurosci..

[12]  David M. Groppe,et al.  Neurophysiological Investigation of Spontaneous Correlated and Anticorrelated Fluctuations of the BOLD Signal , 2013, The Journal of Neuroscience.

[13]  M. Corbetta,et al.  Evolutionarily Novel Functional Networks in the Human Brain? , 2013, The Journal of Neuroscience.

[14]  Simon B. Eickhoff,et al.  An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data , 2013, NeuroImage.

[15]  Swathi P. Iyer,et al.  Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRI data , 2012, Front. Syst. Neurosci..

[16]  Ravi S. Menon,et al.  Information Processing Architecture of Functionally Defined Clusters in the Macaque Cortex , 2012, The Journal of Neuroscience.

[17]  Torsten Rohlfing,et al.  The INIA19 Template and NeuroMaps Atlas for Primate Brain Image Parcellation and Spatial Normalization , 2012, Front. Neuroinform..

[18]  O. Sporns,et al.  Functional connectivity between anatomically unconnected areas is shaped by collective network-level effects in the macaque cortex. , 2012, Cerebral cortex.

[19]  P. Hagmann,et al.  MR connectomics: a conceptual framework for studying the developing brain , 2012, Front. Syst. Neurosci..

[20]  Abraham Z. Snyder,et al.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.

[21]  O. Sporns,et al.  Rich-Club Organization of the Human Connectome , 2011, The Journal of Neuroscience.

[22]  Joseph S. Gati,et al.  Resting-state networks in the macaque at 7T , 2011, NeuroImage.

[23]  Timothy Edward John Behrens,et al.  Diffusion-Weighted Imaging Tractography-Based Parcellation of the Human Parietal Cortex and Comparison with Human and Macaque Resting-State Functional Connectivity , 2011, The Journal of Neuroscience.

[24]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[25]  M. Schölvinck,et al.  Neural basis of global resting-state fMRI activity , 2010, Proceedings of the National Academy of Sciences.

[26]  Itamar Kahn,et al.  Functional connectivity of the macaque posterior parahippocampal cortex. , 2010, Journal of neurophysiology.

[27]  Gary J. Robertson,et al.  Wide‐Range Achievement Test , 2010 .

[28]  Justin L. Vincent,et al.  Precuneus shares intrinsic functional architecture in humans and monkeys , 2009, Proceedings of the National Academy of Sciences.

[29]  R. Kahn,et al.  Functionally linked resting‐state networks reflect the underlying structural connectivity architecture of the human brain , 2009, Human brain mapping.

[30]  Rupert Lanzenberger,et al.  Correlations and anticorrelations in resting-state functional connectivity MRI: A quantitative comparison of preprocessing strategies , 2009, NeuroImage.

[31]  Doris Y. Tsao,et al.  Functional Connectivity of the Macaque Brain across Stimulus and Arousal States , 2009, The Journal of Neuroscience.

[32]  M. Fox,et al.  The global signal and observed anticorrelated resting state brain networks. , 2009, Journal of neurophysiology.

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

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

[35]  Kevin Murphy,et al.  The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? , 2009, NeuroImage.

[36]  M. Greicius,et al.  Resting-state functional connectivity reflects structural connectivity in the default mode network. , 2009, Cerebral cortex.

[37]  Cornelis J. Stam,et al.  Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain , 2008, NeuroImage.

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

[39]  D. V. Essen,et al.  Surface-Based and Probabilistic Atlases of Primate Cerebral Cortex , 2007, Neuron.

[40]  Daniel S. Margulies,et al.  Mapping the functional connectivity of anterior cingulate cortex , 2007, NeuroImage.

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

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

[43]  Justin L. Vincent,et al.  Intrinsic functional architecture in the anaesthetized monkey brain , 2007, Nature.

[44]  E. Bullmore,et al.  A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs , 2006, The Journal of Neuroscience.

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

[46]  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.

[47]  Guy A. Orban,et al.  Visual Activation in Prefrontal Cortex is Stronger in Monkeys than in Humans , 2004, Journal of Cognitive Neuroscience.

[48]  G. Orban,et al.  Comparative mapping of higher visual areas in monkeys and humans , 2004, Trends in Cognitive Sciences.

[49]  D. V. van Essen,et al.  Surface-based approaches to spatial localization and registration in primate cerebral cortex. , 2004, NeuroImage.

[50]  Jeffrey M. Zacks,et al.  Neural correlates of incongruous visual information An event-related fMRI study , 2003, NeuroImage.

[51]  M. Corbetta,et al.  Functional Organization of Human Intraparietal and Frontal Cortex for Attending, Looking, and Pointing , 2003, The Journal of Neuroscience.

[52]  D. C. Essen Surface-based Comparisons of Macaque and Human Cortical Organization In : From Monkey Brain to Human Brain , 2003 .

[53]  Thomas E. Nichols,et al.  Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate , 2002, NeuroImage.

[54]  A. Field Meta-analysis of correlation coefficients: a Monte Carlo comparison of fixed- and random-effects methods. , 2001, Psychological methods.

[55]  D. V. van Essen,et al.  Corticocortical connections of visual, sensorimotor, and multimodal processing areas in the parietal lobe of the macaque monkey , 2000, The Journal of comparative neurology.

[56]  A. Toga,et al.  The Rhesus Monkey Brain in Stereotaxic Coordinates , 1999 .

[57]  M. First,et al.  Structured clinical interview for DSM-IV axis I disorders : SCID-I: clinical version : administration booklet , 1996 .

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

[59]  Jack L. Lancaster,et al.  A modality‐independent approach to spatial normalization of tomographic images of the human brain , 1995 .

[60]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[61]  J. Benjamin,et al.  Further evidence for family-genetic risk factors in attention deficit hyperactivity disorder. Patterns of comorbidity in probands and relatives psychiatrically and pediatrically referred samples. , 1992, Archives of general psychiatry.

[62]  P. Goldman-Rakic,et al.  Preface: Cerebral Cortex Has Come of Age , 1991 .

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

[64]  J. Endicott,et al.  A diagnostic interview: the schedule for affective disorders and schizophrenia. , 1978, Archives of general psychiatry.

[65]  L. Freeman Centrality in social networks conceptual clarification , 1978 .

[66]  D. C. Essen 1 Surface-Based Comparisons of Macaque and Human Cortical Organization , 2022 .