title: Automatic statistical shape analysis of cerebral asymmetry in 3D T1-weighted magnetic resonance images at vertex-level: application to neuroleptic-naïve schizophrenia.
abstract: The study of the structural asymmetries in the human brain can assist the early diagnosis and progression of various neuropsychiatric disorders, and give insights into the biological bases of several cognitive deficits. The high inter-subject variability in cortical morphology complicates the detection of abnormal asymmetries especially if only small samples are available. This work introduces a novel automatic method for the local (vertex-level) statistical shape analysis of gross cerebral hemispheric surface asymmetries which is robust to the individual cortical variations. After segmentation of the cerebral hemispheric volumes from three-dimensional (3D) T1-weighted magnetic resonance images (MRI) and their spatial normalization to a common space, the right hemispheric masks were reflected to match with the left ones. Cerebral hemispheric surfaces were extracted using a deformable model-based algorithm which extracted the salient morphological features while establishing the point correspondence between the surfaces. The interhemispheric asymmetry, quantified by customized measures of asymmetry, was evaluated in a few thousands of corresponding surface vertices and tested for statistical significance. The developed method was tested on scans obtained from a small sample of healthy volunteers and first-episode neuroleptic-naïve schizophrenics. A significant main effect of the disease on the local interhemispheric asymmetry was observed, both in females and males, at the frontal and temporal lobes, the latter being often linked to the cognitive, auditory, and memory deficits in schizophrenia. The findings of this study, although need further testing in larger samples, partially replicate previous studies supporting the hypothesis of schizophrenia as a neurodevelopmental disorder.
authors: Department of Signal Processing, Tampere University of Technology, PO Box 553, FIN-33101 Tampere, Finland. antonietta.pepe@tut.fi
authors: Antonietta
authors: A
authors: Pepe
authors: Lu
authors: L
authors: Zhao
authors: Juha
authors: J
authors: Koikkalainen
authors: Jarmo
authors: J
authors: Hietala
authors: Ulla
authors: U
authors: Ruotsalainen
authors: Jussi
authors: J
authors: Tohka
mesh_terms: D000328:Adult; D000465:Algorithms; D014150:Antipsychotic Agents / Q000627:therapeutic use; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012559:Schizophrenia / Q000188:drug therapy / Q000473:pathology*; D012680:Sensitivity and Specificity; D055815:Young Adult
chemical_list: D014150:Antipsychotic Agents