Title of article :
Combining clinical assessment scores and in vivo MR spectroscopy neurometabolites in very low birth weight adolescents
Author/Authors :
Tone F. Bathen، نويسنده , , Tone F. and Christensen Lّhaugen، نويسنده , , Gro C. and Brubakk، نويسنده , , Ann-Mari and Gribbestad، نويسنده , , Ingrid S. and Axelson، نويسنده , , David E. and Skranes، نويسنده , , Jon، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2009
Abstract :
SummaryObjective
ow birth weight (VLBW) survivors are at increased risk of neurological impairments that may persist into adolescence and adulthood. The aims of this study were to identify the most important clinical assessments that characterize differences between VLBW and control adolescents, and to look at the relationship between clinical assessments and the metabolites in in vivo MR spectra.
s
15 years of age, 54 VLBW survivors and 64 term controls were examined clinically. Several neuropsychological and motor assessments were performed. The magnetic resonance (MR) brain spectra were acquired from volumes localized in the left frontal lobe and contained mainly white matter.
s
ilistic neural networks and support vector machines demonstrated that clinical assessments rendered a possibility of the classification of VLBW versus control adolescents. The most important clinical assessments in this classification were visual–motor integration, motor coordination, stroop test, full scale IQ, and grooved pegboard.
h the use of outer product analysis-partial least squares discriminant analysis on a subset of adolescents (n = 36), the clinical assessments found to most strongly correlate with the spectral data were the global assessment scale, Wisconsin card sorting test, full scale IQ, grooved pegboard test, and motor coordination test. Clinical assessments that relate to spectral data may be especially dependent on an intact microstructure in frontal white matter.
Keywords :
In Vivo MRS , Probabilistic Neural Networks , Support Vector Machines , Outer Product Analysis , Very low birth weight , Clinical assessments
Journal title :
Artificial Intelligence In Medicine
Journal title :
Artificial Intelligence In Medicine