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.

Keywords: Bayesian hierarchical modeling; dynamic functional network connectivity; ischemic stroke; stroke recovery; stroke severity.

FIGURE 1

Spatial maps of 49 intrinsic connectivity networks of all ischemic stroke subjects (n = 41). Networks were assigned to seven functional domains: Subcortical (SC, 3 networks, light blue), auditory (AUD, 3 networks, blue), cortical sensorimotor (SMN, 8 networks, dark blue), visual (VIS, 10 networks, yellow), cognitive control (CC, 14 networks, orange), default mode network (DMN, 9 networks, brown), cerebellar domain (CB, 3 networks, moccasin)

FIGURE 2

Three discrete connectivity states representing re‐occurring dynamic connectivity across time and subject space. These states demonstrated varying connectivity configurations between seven functional domains (c.f., Figure 1). Darker red color implies stronger positive, darker blue stronger negative connectivity. Stated percentages correspond to state‐specific fraction times across all subjects. The ordering of states corresponds to the one introduced by the k‐means algorithm

FIGURE 3

Fraction and dwell times for each of the three dynamic connectivity states and stroke severity defined subgroups of mildly, moderately and severely affected patients (asterisks mark statistically significant differences between patient subgroups based on one‐way ANOVAs, p < .05). (a) Fraction times. Severely affected patients (NIHSS >9, upper row) presented with a markedly different dynamic pattern than moderately (NIHSS 3–9, middle row) and mildly (NIHSS <3, bottom row) affected patients: In contrast to the other two patient groups that preferred State 2, a particularly weakly connected state, severely affected patients spent significantly more time in the densely connected State 1. (b) Dwell times. In parallel to the fraction time findings, severely affected patients spent significantly more time in State 1 at any one time in comparison to the less affected patient groups

FIGURE 4

Significant dynamic connectivity differences between mildly, moderately and severely affected patient groups in State 1 (one‐way ANOVAs: p < .05, post hoc t tests: p < .05, FDR‐corrected). The functionally segregated state 1 comprised the most significantly altered connectivity pairs. Severely affected patients comprised numerous dynamic connectivity pairs with enhanced connectivity compared with both mildly and moderately affected patients. These changes primarily involved subcortical and cortical motor networks, as well as multiple connections to the default mode network

FIGURE 5

Recovery in the first 3 months after stroke is linked to specific acute dynamic connectivity pairs. (a) Recovery‐correlated connectivity pairs are highlighted within dynamic connectivity State 1 (upper row) and State 3 (bottom row). These connectivity pairs were located in subcortical (SC), auditory (AUD), cognitive control (CC) and default mode network (DMN) domains. (b) Brain renderings of involved networks. In State 1, the connectivity between the bilateral intraparietal lobule and left angular gyrus was significantly correlated with recovery after correction for multiple comparison. In State 3, the connectivity between bilateral putamen and anterior insula was significantly correlated with recovery after correction for multiple comparisons, while the connectivity between bilateral putamen and superior temporal gyrus was significantly correlated with recovery before correction for multiple comparisons. These latter findings may motivate a re‐examination in future studies. (c) Correlation plots. Recovery, measured as realized change in NIHSS and adjusted for NIHSS_scan, is plotted on the x‐axis, dFNC strength on the y‐axis (p‐values are FDR‐corrected). The size of the dots corresponds to an individual's stroke severity at time of scanning

FIGURE 6

Bayesian hierarchical modeling of the stroke severity at time of scanning: Posterior parameter distributions. (a) Acute baseline model. The model based on the NIHSS score at admission, thus on average 3 days earlier, could predict the NIHSS at time of scanning with an explained variance of 32.3% (obtained via posterior predictive checks). The intercept for patients with a higher white matter hyperintensity load indicated a higher predicted NIHSS score at time of scanning (light blue) compared to the group of patients with a lower white matter hyperintensity load (dark blue). The parameter posterior mean of 0.51 for the NIHSS scores at admission denoted a decrease in NIHSS stroke severity until the time of scanning (right plot). (b) Acute dynamic model. A higher dwell time in any of the three states predicted a higher NIHSS score at the time of scanning, the explained variance was 32.8%. This effect was particularly strong for dwell times in State 1 (dark green). The effect of the white matter hyperintensity load on stroke severity did not differ between the groups of moderate and high white matter hyperintensity loads. (c) Acute extended model relying on the NIHSS at admission as well as the derived dwell times. A higher NIHSS score at admission, as well as higher dwell times, mainly in State 1, continued to be predictive of a higher NIHSS score at the time of scanning. Explained variance of the joint model was 62.1%