Poststroke acute dysexecutive syndrome, a disorder resulting from minor stroke due to disruption of network dynamics

Elisabeth B Marsh, Christian Brodbeck, Rafael H Llinas, Dania Mallick, Joshua P Kulasingham, Jonathan Z Simon, Rodolfo R Llinás, Elisabeth B Marsh, Christian Brodbeck, Rafael H Llinas, Dania Mallick, Joshua P Kulasingham, Jonathan Z Simon, Rodolfo R Llinás

Abstract

Stroke patients with small central nervous system infarcts often demonstrate an acute dysexecutive syndrome characterized by difficulty with attention, concentration, and processing speed, independent of lesion size or location. We use magnetoencephalography (MEG) to show that disruption of network dynamics may be responsible. Nine patients with recent minor strokes and eight age-similar controls underwent cognitive screening using the Montreal cognitive assessment (MoCA) and MEG to evaluate differences in cerebral activation patterns. During MEG, subjects participated in a visual picture-word matching task. Task complexity was increased as testing progressed. Cluster-based permutation tests determined differences in activation patterns within the visual cortex, fusiform gyrus, and lateral temporal lobe. At visit 1, MoCA scores were significantly lower for patients than controls (median [interquartile range] = 26.0 [4] versus 29.5 [3], P = 0.005), and patient reaction times were increased. The amplitude of activation was significantly lower after infarct and demonstrated a pattern of temporal dispersion independent of stroke location. Differences were prominent in the fusiform gyrus and lateral temporal lobe. The pattern suggests that distributed network dysfunction may be responsible. Additionally, controls were able to modulate their cerebral activity based on task difficulty. In contrast, stroke patients exhibited the same low-amplitude response to all stimuli. Group differences remained, to a lesser degree, 6 mo later; while MoCA scores and reaction times improved for patients. This study suggests that function is a globally distributed property beyond area-specific functionality and illustrates the need for longer-term follow-up studies to determine whether abnormal activation patterns ultimately resolve or another mechanism underlies continued recovery.

Keywords: magnetoencephalography; recovery; stroke.

Conflict of interest statement

The authors declare no competing interest.

Copyright © 2020 the Author(s). Published by PNAS.

Figures

Fig. 1.
Fig. 1.
Overlay plots of cerebral activation time courses corresponding to each of the predefined areas of interest highlighted in green (created by combining aparc labels as described in our Methods section): the occipital lobe (visual response), fusiform gyrus (M170 word form response) and lateral temporal lobe (M400 semantic processing response) for stroke patients and controls at visit 1 (n = 9 patients and 8 controls). Responses shown correspond to the average response to all words included in the analysis. Each trace corresponds to one virtual current dipole in the source model. dSPM, related to a standardized Z score, represents the current estimate normalized by the variance of the noise estimate (34). Note the decreased amplitude and lack of clear peaks for stroke patients compared to controls.
Fig. 2.
Fig. 2.
Clinical MRI scans of the nine stroke patients (diffusion weighted imaging sequences) obtained during their hospital admission showing small acute infarcts without involvement of areas typically associated with cognitive dysfunction.
Fig. 3.
Fig. 3.
MEG rms analysis demonstrating a significant difference in activation patterns between 50 and 600 ms for both words and images at visit 1 for stroke patients versus controls. This difference was no longer significant for words at visit 2. Activation patterns appear more visually similar between groups at visit 2, especially when compared to the visit 1 activation patterns of only the participants (n = 6 patients and 6 controls) who had repeat imaging performed (Inlays). Error bars represent the within-subject SEM (48).
Fig. 4.
Fig. 4.
(A) At visit 1 there are task effects by the group (n = 9 patients and 8 controls) present within the fusiform gyrus and lateral temporal lobe. Bars indicate the temporal extent of significant clusters found in the spatiotemporal cluster-based analysis. Time courses are shown within ROIs defined from significant clusters, outlined in red. Times series demonstrate that for controls, the shape of the waveform varies as a function of task while remaining relatively flat and typically around zero for stroke patients. dSPM, related to a Z score, represents the current estimate normalized by the variance of the noise estimate but is averaged across sources in the ROI, which accounts for the lower amplitude compared to Fig. 1. Error bars indicate the within-subject SEM. (B) Waveforms remain consistent for both stroke patients and controls across visits. Only subjects with complete data from visits 1 and 2 (n = 6 patients and 6 controls) are displayed for comparison.

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