Identification of abnormal motor cortex activation patterns in children with cerebral palsy by functional near-infrared spectroscopy.
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.
Khan
Bilal
B
University of Texas at Arlington and University of Texas Southwestern Medical Center at Dallas, Joint Graduate Program in Biomedical Engineering, Arlington, Texas 76019, USA.
Tian
Fenghua
F
Behbehani
Khosrow
K
Romero
Mario I
MI
Delgado
Mauricio R
MR
Clegg
Nancy J
NJ
Smith
Linsley
L
Reid
Dahlia
D
Liu
Hanli
H
Alexandrakis
George
G
D000465:Algorithms; D000704:Analysis of Variance; D016022:Case-Control Studies; D002547:Cerebral Palsy / Q000503:physiopathology*; D002648:Child; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D009044:Motor Cortex / Q000503:physiopathology*; D009473:Neuronal Plasticity / Q000502:physiology; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D018709:Statistics, Nonparametric