Content » Vol 45, Issue 6

Original report

Combined information from resting-state functional connectivity and passive movements with functional magnetic resonance imaging differentiates fast late-onset motor recovery from progressive recovery in hemiplegic stroke patients: A pilot study

Tae-Du Jung, Ji-Young Kim, Jee-Hye Seo, Seong-Uk Jin, Hui Joong Lee, So-Hyun Lee, Yang-Soo Lee, Yongmin Chang
Department of Physical Medicine and Rehabilitation, Kyungpook National University Hospital/Kyungpook National University School of Medicine, 200 Dongduk-Ro Jung-Gu Daegu, South Korea
DOI: 10.2340/16501977-1165


Objective: To investigate the value of combining information from resting-state functional connectivity and passive movements, measured with functional magnetic resonance imaging (fMRI), in acute stroke patients with severe motor impairment.
Subjects: Eight patients with severe left upper limb motor impairment underwent a passive movement task with fMRI and resting-state fMRI, 3 weeks following stroke onset. According to the patterns of motor recovery, patients were divided into groups with, respectively, good or poor motor recovery. Patients with good recovery were further divided into two subgroups: progressive and fast late-onset motor recovery.
Method: Activation and deactivation maps from a passive movement task with fMRI were obtained. Interhemispheric
connectivity analysis was conducted using resting-state fMRI.
Results: Interhemispheric connectivity score in patients with progressive motor recovery was much greater than the scores in patients with fast late-onset and poor motor recovery. For passive movement, patients with progressive recovery exhibited activation in the ipsilesional sensorimotor area and no deactivation in the contralesional sensorimotor area. Patients with fast late-onset motor recovery showed strong deactivation in both sensorimotor areas. Patients with poor recovery showed no activation or deactivation in either of the sensorimotor areas.
Conclusion: Interhemispheric connectivity alone is not enough to predict delayed motor recovery.

Lay Abstract


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