Acute ischaemic stroke alters the brain's preference for distinct dynamic connectivity states

被引:77
作者
Bonkhoff, Anna K. [1 ,2 ,3 ,4 ]
Espinoza, Flor A. [5 ]
Gazula, Harshvardhan [6 ,7 ]
Vergara, Victor M. [6 ,7 ]
Hensel, Lukas [1 ,2 ]
Michely, Jochen [1 ,2 ]
Paul, Theresa [1 ,2 ]
Rehme, Anne K. [1 ,2 ]
Volz, Lukas J. [1 ,2 ]
Fink, Gereon R. [1 ,2 ,3 ]
Calhoun, Vince D. [5 ,6 ,7 ]
Grefkes, Christian [1 ,2 ,3 ]
机构
[1] Univ Cologne, Univ Hosp Cologne, Dept Neurol, Cologne, Germany
[2] Univ Cologne, Med Fac, Cologne, Germany
[3] Inst Neurosci & Med INM 3, Cognit Neurosci, Res Ctr Juelich, Julich, Germany
[4] UCL, Inst Neurol, Queen Sq, London, England
[5] Mind Res Network, Albuquerque, NM USA
[6] Georgia State Univ, Triinst Ctr Translat Res Neuroimaging & Data Sci, Atlanta, GA 30303 USA
[7] Emory Univ, Georgia Inst Technol, Atlanta, GA 30322 USA
关键词
hand motor deficits; dynamic functional network connectivity; sliding window analysis; functional segregation; functional integration; FUNCTIONAL CONNECTIVITY; MOTOR RECOVERY; NETWORK; FMRI; REORGANIZATION; ICA; SEGREGATION; IMPAIRMENT; DISRUPTION; MODULARITY;
D O I
10.1093/brain/awaa101
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Acute ischaemic stroke disturbs healthy brain organization, prompting subsequent plasticity and reorganization to compensate for the loss of specialized neural tissue and function. Static resting state functional MRI studies have already furthered our understanding of cerebral reorganization by estimating stroke-induced changes in network connectivity aggregated over the duration of several minutes. In this study, we used dynamic resting state functional MRI analyses to increase temporal resolution to seconds and explore transient configurations of motor network connectivity in acute stroke. To this end, we collected resting state functional MRI data of 31 patients with acute ischaemic stroke and 17 age-matched healthy control subjects. Stroke patients presented with moderate to severe hand motor deficits. By estimating dynamic functional connectivity within a sliding window framework, we identified three distinct connectivity configurations of motor-related networks. Motor networks were organized into three regional domains, i.e. a cortical, subcortical and cerebellar domain. The dynamic connectivity patterns of stroke patients diverged from those of healthy controls depending on the severity of the initial motor impairment. Moderately affected patients (n = 18) spent significantly more time in a weakly connected configuration that was characterized by low levels of connectivity, both locally as well as between distant regions. In contrast, severely affected patients (n = 13) showed a significant preference for transitions into a spatially segregated connectivity configuration. This configuration featured particularly high levels of local connectivity within the three regional domains as well as anti-correlated connectivity between distant networks across domains. A third connectivity configuration represented an intermediate connectivity pattern compared to the preceding two, and predominantly encompassed decreased interhemispheric connectivity between cortical motor networks independent of individual deficit severity. Alterations within this third configuration thus closely resembled previously reported ones originating from static resting state functional MRI studies post-stroke. In summary, acute ischaemic stroke not only prompted changes in connectivity between distinct networks, but it also caused characteristic changes in temporal properties of large-scale network interactions depending on the severity of the individual deficit. These findings offer new vistas on the dynamic neural mechanisms underlying acute neurological symptoms, cortical reorganization and treatment effects in stroke patients.
引用
收藏
页码:1525 / 1540
页数:16
相关论文
共 72 条
[1]  
Aggarwal CC, 2001, LECT NOTES COMPUT SC, V1973, P420
[2]   Tracking Whole-Brain Connectivity Dynamics in the Resting State [J].
Allen, Elena A. ;
Damaraju, Eswar ;
Plis, Sergey M. ;
Erhardt, Erik B. ;
Eichele, Tom ;
Calhoun, Vince D. .
CEREBRAL CORTEX, 2014, 24 (03) :663-676
[3]   A baseline for the multivariate comparison of resting-state networks [J].
Allen, Elena A. ;
Erhardt, Erik B. ;
Damaraju, Eswar ;
Gruner, William ;
Segall, Judith M. ;
Silva, Rogers F. ;
Havlicek, Martin ;
Rachakonda, Srinivas ;
Fries, Jill ;
Kalyanam, Ravi ;
Michael, Andrew M. ;
Caprihan, Arvind ;
Turner, Jessica A. ;
Eichele, Tom ;
Adelsheim, Steven ;
Bryan, Angela D. ;
Bustillo, Juan ;
Clark, Vincent P. ;
Ewing, Sarah W. Feldstein ;
Filbey, Francesca ;
Ford, Corey C. ;
Hutchison, Kent ;
Jung, Rex E. ;
Kiehl, Kent A. ;
Kodituwakku, Piyadasa ;
Komesu, Yuko M. ;
Mayer, Andrew R. ;
Pearlson, Godfrey D. ;
Phillips, John P. ;
Sadek, Joseph R. ;
Stevens, Michael ;
Teuscher, Ursina ;
Thoma, Robert J. ;
Calhoun, Vince D. .
FRONTIERS IN SYSTEMS NEUROSCIENCE, 2011, 5
[4]  
[Anonymous], 2011, Group ICA of fMRI Toolbox (GIFT)
[5]   Functional brain network modularity predicts response to cognitive training after brain injury [J].
Arnemann, Katelyn L. ;
Chen, Anthony J. -W. ;
Novakovic-Agopian, Tatjana ;
Gratton, Caterina ;
Nomura, Emi M. ;
D'Esposito, Mark .
NEUROLOGY, 2015, 84 (15) :1568-1574
[6]   Unified segmentation [J].
Ashburner, J ;
Friston, KJ .
NEUROIMAGE, 2005, 26 (03) :839-851
[7]   Brain Network Modularity Predicts Exercise-Related Executive Function Gains in Older Adults [J].
Baniqued, Pauline L. ;
Gallen, Courtney L. ;
Voss, Michelle W. ;
Burzynska, Agnieszka Z. ;
Wong, Chelsea N. ;
Cooke, Gillian E. ;
Duffy, Kristin ;
Fanning, Jason ;
Ehlers, Diane K. ;
Salerno, Elizabeth A. ;
Aguinaga, Susan ;
McAuley, Edward ;
Kramer, Arthur F. ;
D'Esposito, Mark .
FRONTIERS IN AGING NEUROSCIENCE, 2018, 9
[8]   CONTROLLING THE FALSE DISCOVERY RATE - A PRACTICAL AND POWERFUL APPROACH TO MULTIPLE TESTING [J].
BENJAMINI, Y ;
HOCHBERG, Y .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 1995, 57 (01) :289-300
[9]   A method for making group inferences from functional MRI data using independent component analysis [J].
Calhoun, VD ;
Adali, T ;
Pearlson, GD ;
Pekar, JJ .
HUMAN BRAIN MAPPING, 2001, 14 (03) :140-151
[10]   The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery [J].
Calhoun, Vince D. ;
Miller, Robyn ;
Pearlson, Godfrey ;
Adali, Tulay .
NEURON, 2014, 84 (02) :262-274