The relevance of rich club regions for functional outcome post‐stroke is enhanced in women

This study aimed to investigate the influence of stroke lesions in predefined highly interconnected (rich‐club) brain regions on functional outcome post‐stroke, determine their spatial specificity and explore the effects of biological sex on their relevance. We analyzed MRI data recorded at index stroke and ~3‐months modified Rankin Scale (mRS) data from patients with acute ischemic stroke enrolled in the multisite MRI‐GENIE study. Spatially normalized structural stroke lesions were parcellated into 108 atlas‐defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS > 2) was modeled in a Bayesian logistic regression framework. Effects of individual brain regions were captured as two compound effects for (i) six bilateral rich club and (ii) all further non‐rich club regions. In spatial specificity analyses, we randomized the split into “rich club” and “non‐rich club” regions and compared the effect of the actual rich club regions to the distribution of effects from 1000 combinations of six random regions. In sex‐specific analyses, we introduced an additional hierarchical level in our model structure to compare male and female‐specific rich club effects. A total of 822 patients (age: 64.7[15.0], 39% women) were analyzed. Rich club regions had substantial relevance in explaining unfavorable functional outcome (mean of posterior distribution: 0.08, area under the curve: 0.8). In particular, the rich club‐combination had a higher relevance than 98.4% of random constellations. Rich club regions were substantially more important in explaining long‐term outcome in women than in men. All in all, lesions in rich club regions were associated with increased odds of unfavorable outcome. These effects were spatially specific and more pronounced in women.

[1]  J. Wassélius,et al.  Association of Stroke Lesion Pattern and White Matter Hyperintensity Burden With Stroke Severity and Outcome , 2022, Neurology.

[2]  S. Kautz,et al.  Association of Modified Rankin Scale With Recovery Phenotypes in Patients With Upper Extremity Weakness After Stroke , 2022, Neurology.

[3]  A. Karch,et al.  Development and Validation of Prediction Models for Severe Complications After Acute Ischemic Stroke: A Study Based on the Stroke Registry of Northwestern Germany , 2022, Journal of the American Heart Association.

[4]  M. Etherton,et al.  Association of Infarct Topography and Outcome After Endovascular Thrombectomy in Patients With Acute Ischemic Stroke , 2022, Neurology.

[5]  OUP accepted manuscript , 2022, Brain Communications.

[6]  S. Ourselin,et al.  Reclassifying stroke lesion anatomy , 2021, Cortex.

[7]  M. Hausmann,et al.  Sex/gender differences in the brain are not trivial—A commentary on Eliot et al. (2021) , 2021, Neuroscience & Biobehavioral Reviews.

[8]  S. Cramer,et al.  Domain-Specific Outcomes for Stroke Clinical Trials , 2021, Neurology.

[9]  Adrian V. Dalca,et al.  Outcome after acute ischemic stroke is linked to sex-specific lesion patterns , 2021, Nature Communications.

[10]  D. Tranel,et al.  Cognitive impairment after focal brain lesions is better predicted by damage to structural than functional network hubs , 2021, Proceedings of the National Academy of Sciences.

[11]  F. Hummel,et al.  Disconnectomics of the Rich Club Impacts Motor Recovery After Stroke , 2021, Stroke.

[12]  David J. Lin,et al.  Cognitive Demands Influence Upper Extremity Motor Performance During Recovery From Acute Stroke , 2021, Neurology.

[13]  J. Wellmann,et al.  Female Stroke: Sex Differences in Acute Treatment and Early Outcomes of Acute Ischemic Stroke. , 2021, Stroke.

[14]  Joanna K. Bright,et al.  Sex is a defining feature of neuroimaging phenotypes in major brain disorders , 2020, Human brain mapping.

[15]  A. Ahmed,et al.  Dump the “dimorphism”: Comprehensive synthesis of human brain studies reveals few male-female differences beyond size , 2020, Neuroscience & Biobehavioral Reviews.

[16]  Ben D. Fulcher,et al.  Genetic influences on hub connectivity of the human connectome , 2020, Nature Communications.

[17]  Hong Li,et al.  Sex Differences in Anatomical Rich-Club and Structural-Functional Coupling in the Human Brain Network. , 2020, Cerebral cortex.

[18]  V. Feigin,et al.  Burden of Neurological Disorders Across the US From 1990-2017 , 2020, JAMA neurology.

[19]  V. Calhoun,et al.  Dynamic connectivity predicts acute motor impairment and recovery post-stroke , 2020, medRxiv.

[20]  Nick A. Weaver,et al.  Generative lesion pattern decomposition of cognitive impairment after stroke , 2020, bioRxiv.

[21]  L. Concha,et al.  Network-based atrophy modeling in the common epilepsies: A worldwide ENIGMA study , 2020, Science Advances.

[22]  Adrian V. Dalca,et al.  Brain Volume: An Important Determinant of Functional Outcome After Acute Ischemic Stroke. , 2020, Mayo Clinic proceedings.

[23]  M. Greco,et al.  Female , 2020, Definitions.

[24]  Jie Xiang,et al.  Hemisphere and Gender Differences in the Rich-Club Organization of Structural Networks. , 2019, Cerebral cortex.

[25]  C. Hilgetag,et al.  Revisiting 'brain modes' in a new computational era: approaches for the characterization of brain-behavioural associations. , 2019, Brain : a journal of neurology.

[26]  Nick A. Weaver,et al.  Extent to Which Network Hubs Are Affected by Ischemic Stroke Predicts Cognitive Recovery. , 2019, Stroke.

[27]  J. Wassélius,et al.  Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data. , 2019, Stroke.

[28]  Ben Glocker,et al.  Ensemble of Convolutional Neural Networks Improves Automated Segmentation of Acute Ischemic Lesions Using Multiparametric Diffusion-Weighted MRI , 2019, American Journal of Neuroradiology.

[29]  David C. Funder,et al.  Evaluating Effect Size in Psychological Research: Sense and Nonsense , 2019, Advances in Methods and Practices in Psychological Science.

[30]  Daniel Rueckert,et al.  Brain connectivity measures improve modeling of functional outcome after acute ischemic stroke , 2019, bioRxiv.

[31]  Pankaj Sharma,et al.  White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts – The MRI-GENIE study , 2019, NeuroImage: Clinical.

[32]  Sofia Ira Ktena,et al.  Rich-Club Organization: An Important Determinant of Functional Outcome After Acute Ischemic Stroke , 2019, bioRxiv.

[33]  Michael D Fox,et al.  Mapping Symptoms to Brain Networks with the Human Connectome. , 2018, The New England journal of medicine.

[34]  James E. Tcheng,et al.  2017 Cardiovascular and Stroke Endpoint Definitions for Clinical Trials , 2018, Circulation.

[35]  L. Cohen,et al.  Biomarkers of stroke recovery: Consensus-based core recommendations from the Stroke Recovery and Rehabilitation Roundtable , 2017, International journal of stroke : official journal of the International Stroke Society.

[36]  R. Levy,et al.  Advanced lesion symptom mapping analyses and implementation as BCBtoolkit , 2017, bioRxiv.

[37]  Mark E Bastin,et al.  Sex Differences in the Adult Human Brain: Evidence from 5216 UK Biobank Participants , 2017, bioRxiv.

[38]  John Salvatier,et al.  Probabilistic programming in Python using PyMC3 , 2016, PeerJ Comput. Sci..

[39]  J. Lee,et al.  Loci associated with ischaemic stroke and its subtypes (SiGN): a genome-wide association study , 2016, The Lancet Neurology.

[40]  C. Rorden,et al.  Preservation of structural brain network hubs is associated with less severe post-stroke aphasia. , 2015, Restorative neurology and neuroscience.

[41]  Á. Pascual-Leone,et al.  Network localization of neurological symptoms from focal brain lesions. , 2015, Brain : a journal of neurology.

[42]  M. Breakspear,et al.  The connectomics of brain disorders , 2015, Nature Reviews Neuroscience.

[43]  Andrea A Kühn,et al.  Lead-DBS: A toolbox for deep brain stimulation electrode localizations and visualizations , 2015, NeuroImage.

[44]  Hester F. Lingsma,et al.  A randomized trial of intraarterial treatment for acute ischemic stroke. , 2015, The New England journal of medicine.

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

[46]  E. Bullmore,et al.  The hubs of the human connectome are generally implicated in the anatomy of brain disorders , 2014, Brain : a journal of neurology.

[47]  Michael Eickenberg,et al.  Machine learning for neuroimaging with scikit-learn , 2014, Front. Neuroinform..

[48]  S. Baron-Cohen,et al.  Neuroscience and Biobehavioral Reviews a Meta-analysis of Sex Differences in Human Brain Structure , 2022 .

[49]  Andrew Gelman,et al.  The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo , 2011, J. Mach. Learn. Res..

[50]  Alex R. Smith,et al.  Sex differences in the structural connectome of the human brain , 2013, Proceedings of the National Academy of Sciences.

[51]  C. Sudlow,et al.  Stroke Genetics Network (SiGN) Study: Design and Rationale for a Genome-Wide Association Study of Ischemic Stroke Subtypes , 2013, Stroke.

[52]  Joaquín Goñi,et al.  Abnormal rich club organization and functional brain dynamics in schizophrenia. , 2013, JAMA psychiatry.

[53]  N. Volkow,et al.  Energetic cost of brain functional connectivity , 2013, Proceedings of the National Academy of Sciences.

[54]  Cedric E. Ginestet,et al.  Cognitive relevance of the community structure of the human brain functional coactivation network , 2013, Proceedings of the National Academy of Sciences.

[55]  Yong He,et al.  Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain , 2013, Proceedings of the National Academy of Sciences.

[56]  L. McCullough,et al.  The Effects of Estrogen in Ischemic Stroke , 2012, Translational Stroke Research.

[57]  O. Sporns,et al.  Rich Club Organization of Macaque Cerebral Cortex and Its Role in Network Communication , 2012, PloS one.

[58]  O. Sporns,et al.  High-cost, high-capacity backbone for global brain communication , 2012, Proceedings of the National Academy of Sciences.

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

[60]  Joanne Odenkirchen,et al.  National Institute of Neurological Disorders and Stroke Common Data Element Project – approach and methods , 2012, Clinical trials.

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

[62]  G. Fink,et al.  Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches , 2011, Brain : a journal of neurology.

[63]  Keith A. Johnson,et al.  Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease , 2009, The Journal of Neuroscience.

[64]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

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

[66]  Koroshetz Wj,et al.  Tissue plasminogen activator for acute ischemic stroke. , 1996, The New England journal of medicine.

[67]  M M Mesulam,et al.  Large‐scale neurocognitive networks and distributed processing for attention, language, and memory , 1990, Annals of neurology.

[68]  R. Bloch,et al.  Interobserver agreement for the assessment of handicap in stroke patients. , 1988, Stroke.

[69]  Adrian V. Dalca,et al.  Design and rationale for examining neuroimaging genetics in ischemic stroke The MRI-GENIE study , 2022 .