Network curvature as a hallmark of brain structural connectivity

Studies show that while brain networks are remarkably robust to a variety of adverse events, such as injuries and lesions due to accidents or disease, they may be fragile when the disturbance takes place in specific locations. This seems to be the case for diseases in which accumulated changes in network topology dramatically affect certain sensitive areas. To this end, previous attempts have been made to quantify robustness and fragility of brain functionality in two broadly defined ways: (i) utilizing model-based techniques to predict lesion effects, and (ii) studying empirical effects from brain lesions due to injury or disease. Both directions aim at assessing functional connectivity changes resulting from structural network variations. In the present work, we follow a more geometric viewpoint that is based on a notion of curvature of networks, the so-called Ollivier-Ricci curvature. A similar approach has been used in recent studies to quantify financial market robustness as well as to differentiate biological networks corresponding to cancer cells from normal cells. The same notion of curvature, defined at the node level for brain networks obtained from MRI data, may help identify and characterize the effects of diseases on specific brain regions. In the present paper, we apply the Ollivier-Ricci curvature to brain structural networks to: i) Demonstrate its unique ability to identify robust (or fragile) brain regions in healthy subjects. We compare our results to previously published work which identified a unique set of regions (called structural core) of the human cerebral cortex. This novel characterization of brain networks, complementary to measures such as degree, strength, clustering or efficiency, may be particularly useful to detect and monitor candidate areas for targeting by surgery (e.g. deep brain stimulation) or pharmaco-therapeutic agents; ii) Illustrate the power our curvature-derived measures to track changes in brain connectivity with healthy development/aging and; iii) Detect changes in brain structural connectivity in people with Autism Spectrum Disorders (ASD) which are in agreement with previous morphometric MRI studies.

[1]  John V. Carlis,et al.  Where the brain grows old: Decline in anterior cingulate and medial prefrontal function with normal aging , 2007, NeuroImage.

[2]  L. Mottron,et al.  Local bias in autistic subjects as evidenced by graphic tasks: perceptual hierarchization or working memory deficit? , 1999, Journal of child psychology and psychiatry, and allied disciplines.

[3]  Y. Ollivier Ricci curvature of metric spaces , 2007 .

[4]  Daniel P. Kennedy,et al.  Enhancing studies of the connectome in autism using the autism brain imaging data exchange II , 2017, Scientific Data.

[5]  Karl-Theodor Sturm,et al.  Transport inequalities, gradient estimates, entropy and Ricci curvature , 2005 .

[6]  A. Vespignani,et al.  The architecture of complex weighted networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Anders M. Dale,et al.  Thinning of the Cerebral Cortex in Aging David H. Salat , 2004 .

[8]  Rachid Deriche,et al.  Mathematical methods for diffusion MRI processing , 2009, NeuroImage.

[9]  K. Walhovd,et al.  Structural Brain Changes in Aging: Courses, Causes and Cognitive Consequences , 2010, Reviews in the neurosciences.

[10]  Cheryl L. Dahle,et al.  Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. , 2005, Cerebral cortex.

[11]  Piet Van Mieghem,et al.  A Mapping Between Structural and Functional Brain Networks , 2016, Brain Connect..

[12]  Brian Barton,et al.  Visual cortex in aging and Alzheimer's disease: changes in visual field maps and population receptive fields , 2012, Front. Psychol..

[13]  C. Rainville,et al.  Do high functioning persons with autism present superior spatial abilities? , 2004, Neuropsychologia.

[14]  Edward T. Bullmore,et al.  Whole-brain anatomical networks: Does the choice of nodes matter? , 2010, NeuroImage.

[15]  T. Ohnishi,et al.  Changes in brain morphology in Alzheimer disease and normal aging: is Alzheimer disease an exaggerated aging process? , 2001, AJNR. American journal of neuroradiology.

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

[17]  C. Villani,et al.  Ricci curvature for metric-measure spaces via optimal transport , 2004, math/0412127.

[18]  Anita E. Bandrowski,et al.  The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis , 2012, Front. Neuroinform..

[19]  S.N. Sotiropoulos,et al.  High resolution whole brain diffusion imaging at 7T for the Human Connectome Project , 2015, NeuroImage.

[20]  C. Villani Topics in Optimal Transportation , 2003 .

[21]  Mark D'Esposito,et al.  Focal Brain Lesions to Critical Locations Cause Widespread Disruption of the Modular Organization of the Brain , 2012, Journal of Cognitive Neuroscience.

[22]  Andrea Caria,et al.  Anterior insular cortex regulation in autism spectrum disorders , 2015, Front. Behav. Neurosci..

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

[24]  F. Otto THE GEOMETRY OF DISSIPATIVE EVOLUTION EQUATIONS: THE POROUS MEDIUM EQUATION , 2001 .

[25]  I. Holopainen Riemannian Geometry , 1927, Nature.

[26]  Tzu-Chao Chuang,et al.  Deriving and validating biomarkers associated with autism spectrum disorders from a large-scale resting-state database , 2019, Scientific Reports.

[27]  Y. Ollivier A visual introduction to Riemannian curvatures and some discrete generalizations , 2012 .

[28]  Gereon R Fink,et al.  Structural brain abnormalities in adolescents with autism spectrum disorder and patients with attention deficit/hyperactivity disorder. , 2007, Journal of child psychology and psychiatry, and allied disciplines.

[29]  Richard S. Frackowiak,et al.  Normal variation in fronto-occipital circuitry and cerebellar structure with an autism-associated polymorphism of CNTNAP2 , 2010, NeuroImage.

[30]  Lloyd Demetrius,et al.  Boltzmann, Darwin and Directionality theory , 2013 .

[31]  Julien Cohen-Adad,et al.  Pushing the limits of in vivo diffusion MRI for the Human Connectome Project , 2013, NeuroImage.

[32]  Olaf Sporns,et al.  The Human Connectome: A Structural Description of the Human Brain , 2005, PLoS Comput. Biol..

[33]  Nathalie Boddaert,et al.  MRI Findings in 77 Children with Non-Syndromic Autistic Disorder , 2009, PloS one.

[34]  D. Schacter,et al.  The Brain's Default Network , 2008, Annals of the New York Academy of Sciences.

[35]  Karl-Theodor Sturm,et al.  On the geometry of metric measure spaces , 2006 .

[36]  S. Rachev,et al.  Mass transportation problems , 1998 .

[37]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[38]  Ed Reznik,et al.  Graph Curvature for Differentiating Cancer Networks , 2015, Scientific Reports.

[39]  P. Basser,et al.  In vivo fiber tractography using DT‐MRI data , 2000, Magnetic resonance in medicine.

[40]  Jesse A. Brown,et al.  Altered functional and structural brain network organization in autism☆ , 2012, NeuroImage: Clinical.

[41]  Karl-Theodor Sturm,et al.  On the geometry of metric measure spaces. II , 2006 .

[42]  D. Head,et al.  Selective aging of the human cerebral cortex observed in vivo: differential vulnerability of the prefrontal gray matter. , 1997, Cerebral cortex.

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

[44]  J. Mendelson,et al.  Age-related changes in the visual cortex , 2002, Vision Research.

[45]  Anders M. Dale,et al.  Estimation of Thalamocortical and Intracortical Network Models from Joint Thalamic Single-Electrode and Cortical Laminar-Electrode Recordings in the Rat Barrel System , 2009, PLoS Comput. Biol..

[46]  J. Jost,et al.  Ollivier-Ricci curvature and the spectrum of the normalized graph Laplace operator , 2011, 1105.3803.

[47]  A. Dale,et al.  Thinning of the cerebral cortex in aging. , 2004, Cerebral cortex.

[48]  Krishna Somandepalli,et al.  The Neural Correlates of Emotional Lability in Children with Autism Spectrum Disorder , 2017, Brain Connect..

[49]  Tryphon T. Georgiou,et al.  A new approach to robust transportation over networks , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[50]  V S Caviness,et al.  Brain asymmetries in autism and developmental language disorder: a nested whole-brain analysis. , 2004, Brain : a journal of neurology.

[51]  M P Young,et al.  On imputing function to structure from the behavioural effects of brain lesions. , 2000, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[52]  Timothy D. Verstynen,et al.  Deterministic Diffusion Fiber Tracking Improved by Quantitative Anisotropy , 2013, PloS one.

[53]  C. Keown,et al.  Repetitive behaviors in autism are linked to imbalance of corticostriatal connectivity: a functional connectivity MRI study , 2017, Social cognitive and affective neuroscience.

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

[55]  Henry Rusinek,et al.  Age-related changes in brain: II. Positron emission tomography of frontal and temporal lobe glucose metabolism in normal subjects , 2005, Psychiatric Quarterly.

[56]  Mikko Sams,et al.  Abnormal wiring of the connectome in adults with high-functioning autism spectrum disorder , 2015, Molecular Autism.

[57]  S. Holm A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[58]  David C. Van Essen,et al.  The future of the human connectome , 2012, NeuroImage.

[59]  Li Hai Tan,et al.  Localizing Age-Related Changes in Brain Structure Using Voxel-Based Morphometry , 2017, Neural plasticity.

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

[61]  Hugh Garavan,et al.  Disrupted Functional Connectivity in Dorsal and Ventral Attention Networks During Attention Orienting in Autism Spectrum Disorders , 2015, Autism research : official journal of the International Society for Autism Research.

[62]  Bruce R. Rosen,et al.  MGH–USC Human Connectome Project datasets with ultra-high b-value diffusion MRI , 2016, NeuroImage.

[63]  O. Sporns,et al.  Identification and Classification of Hubs in Brain Networks , 2007, PloS one.

[64]  Matthew J. Hoptman,et al.  Age-related changes in brain: I. Magnetic resonance imaging measures of temporal lobe volumes in normal subjects , 2005, Psychiatric Quarterly.

[65]  R. Kerwin,et al.  Left Temporal Lobe Damage in Asperger's Syndrome , 1990, British Journal of Psychiatry.

[66]  A. Tannenbaum,et al.  Ricci curvature: An economic indicator for market fragility and systemic risk , 2016, Science Advances.

[67]  P. Mundy,et al.  A review of joint attention and social‐cognitive brain systems in typical development and autism spectrum disorder , 2018, The European journal of neuroscience.

[68]  Francis Comets,et al.  Large Deviations and Applications , 2011, International Encyclopedia of Statistical Science.

[69]  D. Badcock,et al.  Abnormal global processing along the dorsal visual pathway in autism: a possible mechanism for weak visuospatial coherence? , 2005, Neuropsychologia.

[70]  Karl J. Friston Functional and effective connectivity in neuroimaging: A synthesis , 1994 .

[71]  D. Long Networks of the Brain , 2011 .

[72]  J. Constantino,et al.  Validation of a Brief Quantitative Measure of Autistic Traits: Comparison of the Social Responsiveness Scale with the Autism Diagnostic Interview-Revised , 2003, Journal of autism and developmental disorders.

[73]  Anqi Qiu,et al.  Adaptation of Brain Functional and Structural Networks in Aging , 2015, PloS one.

[74]  Y. Ollivier Ricci curvature of Markov chains on metric spaces , 2007, math/0701886.

[75]  M E Newman,et al.  Scientific collaboration networks. I. Network construction and fundamental results. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[76]  O. Sporns Structure and function of complex brain networks , 2013, Dialogues in clinical neuroscience.

[77]  Simon B Eickhoff,et al.  Brain structure anomalies in autism spectrum disorder—a meta‐analysis of VBM studies using anatomic likelihood estimation , 2012, Human brain mapping.

[78]  C. Sander,et al.  Graph Curvature and the Robustness of Cancer Networks , 2015, 1502.04512.

[79]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[80]  Wim Fias,et al.  Brain networks under attack: robustness properties and the impact of lesions. , 2016, Brain : a journal of neurology.

[81]  Brian Barton,et al.  Changes in Visual Cortex in Healthy Aging and Dementia , 2016 .

[82]  Olaf Sporns,et al.  THE HUMAN CONNECTOME: A COMPLEX NETWORK , 2011, Schizophrenia Research.

[83]  Jean-Claude Dreher,et al.  Differences in Cortical Structure and Functional MRI Connectivity in High Functioning Autism , 2018, Front. Neurol..

[84]  Jonathan D. Power,et al.  Network measures predict neuropsychological outcome after brain injury , 2014, Proceedings of the National Academy of Sciences.

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

[86]  John Suckling,et al.  Frontal networks in adults with autism spectrum disorder , 2016, Brain : a journal of neurology.

[87]  O. Lund,et al.  NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence , 2007, PloS one.

[88]  Olaf Sporns,et al.  Modeling the Impact of Lesions in the Human Brain , 2009, PLoS Comput. Biol..

[89]  Bin Hu,et al.  A review of structural and functional brain networks: small world and atlas , 2015, Brain Informatics.

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

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

[92]  Julien Cohen-Adad,et al.  The Human Connectome Project and beyond: Initial applications of 300mT/m gradients , 2013, NeuroImage.