Abnormal dynamic functional connectivity is linked to recovery after acute ischemic stroke

Abstract

The aim of the current study was to explore the whole‐brain dynamic functional connectivity patterns in acute ischemic stroke (AIS) patients and their relation to short and long‐term stroke severity. We investigated resting‐state functional MRI‐based dynamic functional connectivity of 41 AIS patients two to five days after symptom onset. Re‐occurring dynamic connectivity configurations were obtained using a sliding window approach and k‐means clustering. We evaluated differences in dynamic patterns between three NIHSS‐stroke severity defined groups (mildly, moderately, and severely affected patients). Furthermore, we built Bayesian hierarchical models to evaluate the predictive capacity of dynamic connectivity and examine the interrelation with clinical measures, such as white matter hyperintensity lesions. Finally, we established correlation analyses between dynamic connectivity and AIS severity as well as 90‐day neurological recovery (ΔNIHSS). We identified three distinct dynamic connectivity configurations acutely post‐stroke. More severely affected patients spent significantly more time in a configuration that was characterized by particularly strong connectivity and isolated processing of functional brain domains (three‐level ANOVA: p < .05, post hoc t tests: p < .05, FDR‐corrected). Configuration‐specific time estimates possessed predictive capacity of stroke severity in addition to the one of clinical measures. Recovery, as indexed by the realized change of the NIHSS over time, was significantly linked to the dynamic connectivity between bilateral intraparietal lobule and left angular gyrus (Pearson's r = −.68, p = .003, FDR‐corrected). Our findings demonstrate transiently increased isolated information processing in multiple functional domains in case of severe AIS. Dynamic connectivity involving default mode network components significantly correlated with recovery in the first 3 months poststroke.

Dynamic Functional Connectivity Better Predicts Disability Than Structural and Static Functional Connectivity in People With Multiple Sclerosis

“…The BOLD time series that were extracted from 86 regions of FreeSurfer atlas (same atlas used for static FC and SC analysis) were used as an input to the GIFT toolbox. As suggested by Allen et al (2014) and previous studies (Bonkhoff et al, 2020(Bonkhoff et al, , 2021, dFC between two regional time courses was computed using a sliding window approach with a window size of 22 TR (50.6 s) in steps of 1 TR (2.3 s). A rectangular window of 22 time points convolved with a Gaussian of 3 TR (6.9 s) was used for tapering along the edges, resulting in 153 tapered time windows per subject.…”

“…Dynamic FC (dFC) approaches allow assessment of the varying topology of FC over time by using sliding windows to assess dynamic FCs (Allen et al, 2014 ). There is increased interest in using dFC to investigate pathological mechanisms in psychiatric disorders, and stroke (Damaraju et al, 2014 ; Rashid et al, 2016 ; Sambataro et al, 2017 ; Mennigen et al, 2018 ; Bonkhoff et al, 2020 , 2021 ). In MS, recent studies have used dFC to (1) compare clinically isolated syndrome (CIS) patients to HC (Rocca et al, 2019 ), (2) analyze relationships with information processing speed in relapsing-remitting (RR) pwMS (van Geest et al, 2018 ), and (3) classify cognitively impaired vs. preserved pwMS (d'Ambrosio et al, 2019 ; Eijlers et al, 2019 ).…”

Low-Frequency Repetitive Transcranial Magnetic Stimulation Restores Dynamic Functional Connectivity in Subcortical Stroke

“…As dFC analysis can extract more time-varying characteristics of information exchange between brain regions on a time scale and because these characteristics are significantly related to many physiological parameters ( 12 ), pathological features ( 13 ), and even intervention effects ( 14 ), dFC analysis seems to be particularly suitable for evaluating the complex and changeable characteristics of brain networks after stroke and exploring the neural mechanism of functional rehabilitation. Previous studies have shown that the proportion between integrated and segregated states was not balanced in patients who suffered from strokes ( 4 ) and that the temporal dynamics of FC were closely related to clinical severity ( 15 , 16 ). However, these studies focus mainly on patients with acute or chronic stroke; there are few reports on the dynamic relationship between brain networks in patients with a convalescence period of 1–3 months after stroke.…”

Bayesian stroke modeling details sex biases in the white matter substrates of aphasia

“…All analyses were adjusted for variation that could be explained by age, age 2 , sex, education in years, pre-morbid cognitive performance, and total lesion volume. We also included sex as a covariate in the model to explicitly account for a-priori differences between female and male stroke outcomes (Bonkhoff, Schirmer, Bretzner, Hong, et al, 2021). For example, worse language outcomes in females could be linked to an underlying cause independent of the lesion distribution, such as a decreased likelihood of receiving acute interventions.…”