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Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
Published on: July 14, 2023
Alterations in spontaneous brain oscillations during stroke recovery
Kristina Laaksonen1, Liisa Helle, Lauri Parkkonen
1Brain Research Unit, O.V. Lounasmaa Laboratory and MEG Core, Aalto Neuroimaging, Aalto University, Aalto, Espoo, Finland. laaks@neuro.hut.fi
Plos One
|April 18, 2013
Summary
Stroke recovery involves changes in brain oscillations. Temporo-parietal 10-Hz oscillations increase in the affected hemisphere, while abnormal low-frequency magnetic activity (ALFMA) may indicate poorer outcomes.
Area of Science:
- Neuroscience
- Biophysics
- Medical Imaging
Background:
- Spontaneous brain oscillations' role in pathology and recovery is unclear.
- Temporal dynamics and functional significance of altered brain oscillations post-stroke require investigation.
Purpose of the Study:
- To investigate the temporal evolution of spontaneous brain oscillations after stroke.
- To correlate changes in brain oscillations with clinical recovery and lesion characteristics.
Main Methods:
- Whole-head magnetoencephalography (MEG) recordings in 16 stroke patients at 1-7 days (T0), 1 month (T1), and 3 months (T2) post-stroke.
- Clinical examinations were performed concurrently with MEG recordings.
- Analysis focused on temporo-parietal oscillations (≈10 Hz) and abnormal low-frequency magnetic activity (ALFMA, ≈1 Hz) in the affected hemisphere.
Main Results:
- Significant clinical improvement observed from T0 to T1/T2.
- Increased temporo-parietal ≈10-Hz oscillations in the affected hemisphere during recovery (T1, T2).
- Abnormal low-frequency magnetic activity (ALFMA) detected in the perilesional cortex in 7/16 patients at T0, persisting in 4/7 at T2.
- Larger lesion size and worse clinical outcome associated with ALFMA, particularly persisting ALFMA.
Conclusions:
- Temporo-parietal ≈10-Hz oscillations are enhanced in the affected hemisphere during stroke recovery.
- Stroke induces ALFMA, which may serve as a biomarker for predicting poorer clinical outcomes.

