Back

Electrophysiological Signature of Stroke Recovery: Investigating EEG Biomarkers for Prognostic Insights

Khalili-Ardali, M.; Sharma, V.; Mandahar, T. S.; Pascoa dos Santos, F.; Tiesinga, P.; Ramsey, N.

2026-06-04 neuroscience
10.64898/2026.06.01.728505 bioRxiv
Show abstract

Stroke is a leading cause of long-term disability, often resulting in persistent motor impairments reflecting disruptions in large-scale brain networks. While electroencephalography (EEG) has long been used to monitor neurophysiological changes following stroke, an integrated framework capturing spatiotemporal dynamics would help understand changes over time. In this study, we analysed resting-state EEG from stroke patients at one week (Session 1) and three months (Session 2) post-stroke to investigate electrophysiological biomarkers of motor recovery, indexed by changes in the Fugl-Meyer scale ({Delta}FM ). We quantified spectral properties, focusing on the relative alpha band power, microstate metrics such as mean duration, complexity, and transition probabilities, and measures of metastability and synchrony derived from the Kuramoto Order Parameter. Among all the EEG measures, the longitudinal change in relative alpha power emerged as the strongest single correlate of motor improvement, accounting for the largest proportion of variance among the examined EEG measures. Although metastability and synchrony alone did not reach statistical significance, they showed moderate positive correlations with {Delta}FM, particularly in the alpha and theta ranges, and once combined with alpha power, added 26% in explaining the variance in {Delta}FM . Microstate parameters did not explain additional variance in recovery once alpha power and network-level dynamics were considered. A hierarchical model combining alpha power, metastability/synchrony, and microstates explained over 78% of the variance in {Delta}FM, indicating that stroke recovery involves restoring balanced alpha oscillations and flexible large-scale brain coordination. Future research with larger samples and more frequent longitudinal assessments is required to confirm the prognostic utility of integrated EEG biomarkers for guiding personalised stroke rehabilitation strategies.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Clinical Neurophysiology
50 papers in training set
Top 0.1%
23.1%
2
Scientific Reports
3102 papers in training set
Top 9%
8.6%
3
NeuroImage: Clinical
132 papers in training set
Top 0.7%
6.5%
4
Brain Communications
147 papers in training set
Top 0.3%
5.0%
5
Frontiers in Neurology
91 papers in training set
Top 1%
4.1%
6
Neurorehabilitation and Neural Repair
17 papers in training set
Top 0.2%
3.7%
50% of probability mass above
7
Neurobiology of Disease
134 papers in training set
Top 2%
2.8%
8
NeuroImage
813 papers in training set
Top 3%
2.7%
9
Journal of Neural Engineering
197 papers in training set
Top 0.9%
2.5%
10
Experimental Neurology
57 papers in training set
Top 0.4%
2.4%
11
PLOS ONE
4510 papers in training set
Top 47%
2.1%
12
Brain
154 papers in training set
Top 2%
1.9%
13
Journal of NeuroEngineering and Rehabilitation
28 papers in training set
Top 0.5%
1.9%
14
Imaging Neuroscience
242 papers in training set
Top 2%
1.9%
15
eneuro
389 papers in training set
Top 5%
1.8%
16
Human Brain Mapping
295 papers in training set
Top 3%
1.5%
17
Frontiers in Aging Neuroscience
67 papers in training set
Top 2%
1.4%
18
Frontiers in Human Neuroscience
67 papers in training set
Top 1%
1.4%
19
Progress in Neurobiology
41 papers in training set
Top 1.0%
1.4%
20
Brain Research
35 papers in training set
Top 1%
1.3%
21
Frontiers in Neuroscience
223 papers in training set
Top 6%
0.9%
22
Journal of Neurology
26 papers in training set
Top 1%
0.9%
23
Communications Biology
886 papers in training set
Top 18%
0.9%
24
PLOS Computational Biology
1633 papers in training set
Top 22%
0.9%
25
Brain Stimulation
112 papers in training set
Top 1%
0.8%
26
iScience
1063 papers in training set
Top 28%
0.8%
27
Heliyon
146 papers in training set
Top 5%
0.8%
28
The Journal of Neuroscience
928 papers in training set
Top 8%
0.8%
29
Journal of Alzheimer's Disease
43 papers in training set
Top 1%
0.7%
30
Neuroscience
88 papers in training set
Top 3%
0.7%