Altered topology of large-scale structural brain networks in chronic stroke

Abstract Beyond disruption of neuronal pathways, focal stroke lesions induce structural disintegration of distant, yet connected brain regions via retrograde neuronal degeneration. Stroke lesions alter functional brain connectivity and topology in large-scale brain networks. These changes are associated with the degree of clinical impairment and recovery. In contrast, changes of large scale, structural brain networks after stroke are less well reported. We therefore aimed to analyse the impact of focal lesions on the structural connectome after stroke based on data from diffusion-weighted imaging and probabilistic fibre tracking. In total, 17 patients (mean age 64.5 ± 8.4 years) with upper limb motor deficits in the chronic stage after stroke and 21 healthy participants (mean age 64.9 ± 10.3 years) were included. Clinical deficits were evaluated by grip strength and the upper extremity Fugl-Meyer assessment. We calculated global and local graph theoretical measures to characterize topological changes in the structural connectome. Results from our analysis demonstrated significant alterations of network topology in both ipsi- and contralesional, primarily unaffected, hemispheres after stroke. Global efficiency was significantly lower in stroke connectomes as an indicator of overall reduced capacity for information transfer between distant brain areas. Furthermore, topology of structural connectomes was shifted toward a higher degree of segregation as indicated by significantly higher values of global clustering and modularity. On a level of local network parameters, these effects were most pronounced in a subnetwork of cortico-subcortical brain regions involved in motor control. Structural changes were not significantly associated with clinical measures. We propose that the observed network changes in our patients are best explained by the disruption of inter- and intrahemispheric, long white matter fibre tracts connecting distant brain regions. Our results add novel insights on topological changes of structural large-scale brain networks in the ipsi- and contralesional hemisphere after stroke.

[1]  S. Shapiro,et al.  An Approximate Analysis of Variance Test for Normality , 1972 .

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

[3]  Edward T. Bullmore,et al.  Network-based statistic: Identifying differences in brain networks , 2010, NeuroImage.

[4]  Mark W. Woolrich,et al.  Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? , 2007, NeuroImage.

[5]  E. Schlemm,et al.  Altered topology of structural brain networks in patients with Gilles de la Tourette syndrome , 2017, Scientific Reports.

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

[7]  Andrew Zalesky,et al.  Fashion Safety Hot Sale Running Shoes Sport Men Soccer wqp8Zq - inversiontablepro.com , 2018 .

[8]  T. Dawson,et al.  Cortical interneurons become activated by deafferentation and instruct the apoptosis of pyramidal neurons. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Mark E. Bastin,et al.  The effect of network thresholding and weighting on structural brain networks in the UK Biobank , 2019, NeuroImage.

[10]  Danielle S. Bassett,et al.  Classification of weighted networks through mesoscale homological features , 2015, J. Complex Networks.

[11]  Danielle Smith Bassett,et al.  Small-World Brain Networks , 2006, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[12]  Dorothee Auer,et al.  Reproducibility of Graph-Theoretic Brain Network Metrics: A Systematic Review , 2014, Brain Connect..

[13]  Edward T. Bullmore,et al.  Small-World Brain Networks Revisited , 2016, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[14]  Nikos Makris,et al.  Microstructural status of ipsilesional and contralesional corticospinal tract correlates with motor skill in chronic stroke patients , 2009, Human brain mapping.

[15]  W. Otte,et al.  Modified structural network backbone in the contralesional hemisphere chronically after stroke in rat brain , 2017, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[16]  A. Alavi,et al.  Contralateral cerebellar hypometabolism following cerebral insult: A positron emission tomographic study , 1984, Annals of neurology.

[17]  Olaf Sporns,et al.  Network attributes for segregation and integration in the human brain , 2013, Current Opinion in Neurobiology.

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

[19]  Bastian Cheng,et al.  Cortical atrophy and transcallosal diaschisis following isolated subcortical stroke , 2020, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[20]  Maurizio Corbetta,et al.  Why use a connectivity-based approach to study stroke and recovery of function? , 2012, NeuroImage.

[21]  Derek K. Jones,et al.  Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data , 2015, NeuroImage.

[22]  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.

[23]  E. Bullmore,et al.  Human brain networks in health and disease , 2009, Current opinion in neurology.

[24]  Bastian Cheng,et al.  Modeling of Large-Scale Functional Brain Networks Based on Structural Connectivity from DTI: Comparison with EEG Derived Phase Coupling Networks and Evaluation of Alternative Methods along the Modeling Path , 2016, bioRxiv.

[25]  R. Righart,et al.  Acute infarcts cause focal thinning in remote cortex via degeneration of connecting fiber tracts , 2015, Neurology.

[26]  Michael Breakspear,et al.  Graph analysis of the human connectome: Promise, progress, and pitfalls , 2013, NeuroImage.

[27]  Alexander Münchau,et al.  Altered intrahemispheric structural connectivity in Gilles de la Tourette syndrome☆☆☆ , 2013, NeuroImage: Clinical.

[28]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[29]  G. Tononi,et al.  Diaschisis: past, present, future. , 2014, Brain : a journal of neurology.

[30]  Chun-Hung Yeh,et al.  Is removal of weak connections necessary for graph-theoretical analysis of dense weighted structural connectomes from diffusion MRI? , 2019, NeuroImage.

[31]  J. Rothwell,et al.  Intracortical circuits modulate transcallosal inhibition in humans , 2007, The Journal of physiology.

[32]  Andreas Daffertshofer,et al.  Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory , 2010, PloS one.

[33]  Zoltán Toroczkai,et al.  The role of long-range connections on the specificity of the macaque interareal cortical network , 2013, Proceedings of the National Academy of Sciences.

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

[35]  B Mazoyer,et al.  Effects of capsular or thalamic stroke on metabolism in the cortex and cerebellum: a positron tomography study. , 1990, Stroke.

[36]  Desmond J. Higham,et al.  Network analysis detects changes in the contralesional hemisphere following stroke , 2011, NeuroImage.