ABSTRACT
Schizophrenia and obsessive-compulsive disorder are common disorders in clinical psychiatry， accompanied by varying degrees of painful experience and social functioning impairment， and a high prevalence rate of co-morbidities. In this paper， a review of comparative study of structural and functional magnetic resonance imaging of the two diseases is presented， so as to explore the similar and specific biological markers of them and to provide a forceful imaging basis for subsequent studies.
ABSTRACT
A systematic characterization of the similarities and differences among different methods for detecting structural brain abnormalities in schizophrenia, such as voxel-based morphometry (VBM), tensor-based morphometry (TBM), and projection-based thickness (PBT), is important for understanding the brain pathology in schizophrenia and for developing effective biomarkers for a diagnosis of schizophrenia. However, such studies are still lacking. Here, we performed VBM, TBM, and PBT analyses on T1-weighted brain MR images acquired from 116 patients with schizophrenia and 116 healthy controls. We found that, although all methods detected wide-spread structural changes, different methods captured different information - only 10.35% of the grey matter changes in cortex were detected by all three methods, and VBM only detected 11.36% of the white matter changes detected by TBM. Further, pattern classification between patients and controls revealed that combining different measures improved the classification accuracy (81.9%), indicating that fusion of different structural measures serves as a better neuroimaging marker for the objective diagnosis of schizophrenia.
ABSTRACT
A systematic characterization of the similarities and differences among different methods for detecting structural brain abnormalities in schizophrenia, such as voxel-based morphometry (VBM), tensor-based morphometry (TBM), and projection-based thickness (PBT), is important for understanding the brain pathology in schizophrenia and for developing effective biomarkers for a diagnosis of schizophrenia. However, such studies are still lacking. Here, we performed VBM, TBM, and PBT analyses on T1-weighted brain MR images acquired from 116 patients with schizophrenia and 116 healthy controls. We found that, although all methods detected wide-spread structural changes, different methods captured different information - only 10.35% of the grey matter changes in cortex were detected by all three methods, and VBM only detected 11.36% of the white matter changes detected by TBM. Further, pattern classification between patients and controls revealed that combining different measures improved the classification accuracy (81.9%), indicating that fusion of different structural measures serves as a better neuroimaging marker for the objective diagnosis of schizophrenia.
ABSTRACT
Objective To investigate the changes of cortical thickness and surface area in patients with bipolar depression(BD),and to explore the relationship between abnormal changes in gray matter and clinical symptoms.Methods 28 BD patients and 28 healthy controls underwent T1-weighted MRI.The Freesurfer software was used to process the T1 images,which used a set of automated sequences to analyze cortical thickness and surface area on 66 regions (33 regions of each hemisphere),and the correlation with clinical features was also calculated.Results Compared with controls,BD patients showed thinner cortical thickness in left medial orbitofrontal cortex((2.40±0.12) mm vs (2.55 ±0.18) mm,P=1.2× 10-3) and left rostral anterior cingulate((2.66±0.21) mm vs (2.88±0.27) mm,P=3.1 × 10-4),and smaller area of left cuneus((1 443.13± 131.00) mm2vs (6 634.70±600.16) mm2,P=2.7× 10-4) and right superior frontal gyrus ((6 634.70±600.16) mm2vs (7 300.50±653.39) mm2,P=1.3× 10-3).In addition,the negatively correlation was found between the cortical area of left cuneus and effective illness duration (r=-0.471,P=0.018),and the cortical thickness in left rostral anterior cingulate and total score of HAMD-17(17-item Hamilton Rating Scale for Depression) (r=-0.508,P=0.009).Conclusion There are abnormal altertion of cortical thickness and cortical areas of emotional circuit in bipolar depression,but the brain areas are not completely overlapping.Correlation analysis suggests that cortical thickness and area is related to different clinical features.
ABSTRACT
Post-traumatic stress disorder ( PTSD) is an anxiety disorder that can develop following a traumatic event.Neuro-imaging techniques offer a noninvasive means to elucidate the brain circuit underlying PTSD, and may help to find effective biomarkers for diagnosis and treatment evaluation of this disorder.In this article, we review recent brain structural MRI studies in PTSD.Problems of the current research and possible directions for future research are also presented.
ABSTRACT
This paper presents an automatic approach which classifies structural Magnetic Resonance images into pathological or healthy controls. A classification model was trained to find the boundaries that allow to separate the study groups. The method uses the deformation values from a set of regions, automatically identified as relevant, in a process that selects the statistically significant regions of a t-test under the restriction that this significance must be spatially coherent within a neighborhood of 5 voxels. The proposed method was assessed to distinguish healthy controls from schizophrenia patients. Classification results showed accuracy between 74% and 89%, depending on the stage of the disease and number of training samples.
Este artículo presenta un método automático para la clasificación de individuos en grupos patológicos o controles sanos haciendo uso de imágenes de resonancia magnética. El método propuesto usa los valores de deformación del sujeto analizado a un cerebro plantilla, para entrenar un modelo de clasificación capaz de identificar las fronteras que separan los grupos de estudio en un espacio de características dado. Con el fin de reducir la dimensionalidad del problema, un conjunto de regiones relevantes es automáticamente extraído en un proceso que selecciona las regiones estadísticamente significativas en una prueba t-student, con la restricción de mantener coherencia en dicha significancia en una vecindad de 5 voxeles. El método propuesto fue evaluado en la clasificación de pacientes con esquizofrenia y sujetos sanos. Los resultados mostraron un desempeño entre el 74 y el 89%, el cual depende principalmente del número de muestras empleadas para el entrenamiento del modelo.