Effective connectivity extracts clinically relevant prognostic information from resting state activity in stroke. Issue 4 (23rd October 2021)

• Record Type: Journal Article
• Authors: Adhikari, Mohit H; Griffis, Joseph; Siegel, Joshua S; Thiebaut de Schotten, Michel; Deco, Gustavo; Instabato, Andrea; Gilson, Matthieu; Corbetta, Maurizio
• Is Part Of: Brain communications. Volume 3:Issue 4(2021)
• Publication Date: 2021-10-23
• Subjects: functional connectivity -- effective connectivity -- classification -- whole-brain modelling; 616
• DOI: 10.1093/braincomms/fcab233
• Languages: English
• ISSNs: 2632-1297

Abstract

Abstract: Recent resting-state functional MRI studies in stroke patients have identified two robust biomarkers of acute brain dysfunction: a reduction of inter-hemispheric functional connectivity between homotopic regions of the same network, and an abnormal increase of ipsi-lesional functional connectivity between task-negative and task-positive resting-state networks. Whole-brain computational modelling studies, at the individual subject level, using undirected effective connectivity derived from empirically measured functional connectivity, have shown a reduction of measures of integration and segregation in stroke as compared to healthy brains. Here we employ a novel method, first, to infer whole-brain directional effective connectivity from zero-lagged and lagged covariance matrices, then, to compare it to empirically measured functional connectivity for predicting stroke versus healthy status, and patient performance (zero, one, multiple deficits) across neuropsychological tests. We also investigated the accuracy of functional connectivity versus model effective connectivity in predicting the long-term outcome from acute measures. Both functional and effective connectivity predicted healthy from stroke individuals significantly better than the chance-level; however, accuracy for the effective connectivity was significantly higher than for functional connectivity at 1- to 2-week, 3-month and 1-year post-stroke. Predictive functional connections mainly included those reported in previous studies (within-network inter-hemispheric and between task-positive and -negative networks intra-hemispherically). Predictive effective connections included additional between-network links. Effective connectivity was a better predictor than functional connectivity of the number of behavioural domains in which patients suffered deficits, both at 2-week and 1-year post-onset of stroke. Interestingly, patient deficits at 1-year time-point were better predicted by effective connectivity values at 2 weeks rather than at 1-year time-point. Our results thus demonstrate that the second-order statistics of functional MRI resting-state activity at an early stage of stroke, derived from a whole-brain effective connectivity, estimated in a model fitted to reproduce the propagation of neuronal activity, has pertinent information for clinical prognosis. Abstract : Focal stroke alters resting-state functional connectivity that measures inter-regional statistical associations but ignores directed, causal interactions captured by effective connectivity. Adhikari et al. find that a model-driven effective connectivity outperforms functional connectivity as a predictive biomarker for distinguishing patients from controls and, far more importantly, for prognostic prediction of patient behavioural deficits. Graphical Abstract: