Open Access
1 May 2010 Identification of abnormal motor cortex activation patterns in children with cerebral palsy by functional near-infrared spectroscopy
Bilal Khan, Fenghua Tian, Khosrow Behbehani, Mario Romero-Ortega, Mauricio R. Delgado, Nancy Clegg, Linsley Smith, Dahlia Reid, Hanli Liu, George Alexandrakis
Author Affiliations +
Abstract
We demonstrate the utility of functional near-infrared spectroscopy (fNIRS) as a tool for physicians to study cortical plasticity in children with cerebral palsy (CP). Motor cortex activation patterns were studied in five healthy children and five children with CP (8.4±2.3 years old in both groups) performing a finger-tapping protocol. Spatial (distance from center and area difference) and temporal (duration and time-to-peak) image metrics are proposed as potential biomarkers for differentiating abnormal cortical activation in children with CP from healthy pediatric controls. In addition, a similarity image-analysis concept is presented that unveils areas that have similar activation patterns as that of the maximum activation area, but are not discernible by visual inspection of standard activation images. Metrics derived from the images presenting areas of similarity are shown to be sensitive identifiers of abnormal activation patterns in children with CP. Importantly, the proposed similarity concept and related metrics may be applicable to other studies for the identification of cortical activation patterns by fNIRS.
©(2010) Society of Photo-Optical Instrumentation Engineers (SPIE)
Bilal Khan, Fenghua Tian, Khosrow Behbehani, Mario Romero-Ortega, Mauricio R. Delgado, Nancy Clegg, Linsley Smith, Dahlia Reid, Hanli Liu, and George Alexandrakis "Identification of abnormal motor cortex activation patterns in children with cerebral palsy by functional near-infrared spectroscopy," Journal of Biomedical Optics 15(3), 036008 (1 May 2010). https://doi.org/10.1117/1.3432746
Published: 1 May 2010
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Cited by 30 scholarly publications.
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KEYWORDS
Control systems

Digital filtering

Electronic filtering

Image processing

Hemodynamics

Sensors

Brain

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