利用腦結構和症狀特徵預測前驅期病患發展為思覺失調症之研究

思覺失調症是一個神經發育疾病。在醫界，及早偵測、及早治療會擁有較好的康復機會是一個普遍認知。因此，會給予所有的高風險族群預防性臨床用藥，儘管很多並不會發病。這顯示出使用臨床資源沒有效率，也引發倫理問題。預測高風險之人是否會發病成為一個重要的議題。主要的方法來自醫學影像標記。TBAA 計算出白質擴散指標、CAT12 計算出灰質體積、皮質厚度、腦迴化指標、碎形維度、腦溝深度。使用「正性與負性症狀量表」，替35位前驅期病患的症狀嚴重程度評分。其中31位作為統計分析與機器學習的訓練集，4位較晚發病者作為獨立測試集。所有的醫學標記都在受試者處於前驅期時收集的。首先，排除抗精神疾病藥物的因素後，發病者對於「N3.會談關係不佳、N5.抽象思考困難、 G12.判斷力及病識感障礙」的症狀嚴重程度較高(通過多重比較檢定 p<0.05)。第二、在腦結構上，發病者與未發病者的軸向擴散強度、徑向擴散強度、平均擴散強度在「杏仁核胼胝體、楔前葉胼胝體、右邊終紋神經束」達到顯著差異(通過多重比較檢定 p<0.05)。第三、未發病者的灰質體積在「左半腦額上回、右半腦扣帶回」大於發病者和正常人；但未發病者的「右半腦下側顳葉皮質」只有大於發病者(p<0.05)。最後、發病者在兩側的「距狀裂皮質周圍」有較淺的腦溝。這些顯著的神經影像標記、症狀標記分別被用在支持向量機模型中(6種模型)。31 位前驅受測者使用在訓練集，4位前驅受測者使用在獨立測試集。白質影像標記的成果高於其他標記，不管是在ROC曲線下面積(0.96)或是測試集(75%正確率)。

關鍵字

思覺失調症；醫學影像標記；正性與負性症狀量表；白質神經束；灰質；支持向量機；徑向擴散強度

並列摘要

Background: Schizophrenia is a neurodevelopment disease, and the notion that early detection and treatment provide the best opportunity for recovery is well established across medical fields. At present, potentially preventative clinical interventions are offered to all patients even though most of them do not subsequently become psychotic. This represents an inefficient use of clinical resources and raises ethical concerns. Therefore, whether a patient at risk will or will not develop schizophrenia becomes an important issue. Methods: Tract-Based Automatic Analysis (TBAA) was used to calculate the white matter diffusion indices, Computational Anatomy Toolbox (CAT12) was used to calculate gray matter volume, cortical thickness, gyrification index, fractal dimension and Sulcal Depth, and Positive and Negative Syndrome Scaling (PANSS) was used to scale the symptoms severity in 35 prodromal patients. Among all of the subjects, 31 prodromal patients were used as statistics and training set in machine learning and 4 slow-converters were used as independent test set. Results: All the features were collected from high-risk subjects in prodromal state. After controlling the antipsychotic factor, converters had significantly more severe symptoms in N3, N5, and G12 after multiple comparison correction. In brain structures, axial/radial/mean diffusivity of the corpus callosum (CC) connecting the amygdala and the precuneus, and the right stria terminalis” was significantly different between converters and non-converters. Non-converters showed significantly larger gray matter volume than converters in the left superior frontal gyrus, right cingulate gyrus, and right inferior temporal gyrus. Converters showed significantly smaller bilateral pericalcarine sulcal depth. The resulting neuroimaging and symptomatic markers were then used as features in the training set using 6 models of support vector machine. The model of best performance was validated in the test set. Among the features, white matter features outperformed the others presenting with AUC of 0.96 in the training set and accuracy of 75% in the test set.

並列關鍵字

Schizophrenia ； Neuroimaging markers ； Positive and Negative Syndrome Scaling (PANSS) ； White matter tracts ； Gray Matter ； Support vector machine (SVM) ； Radial diffusivity

參考文獻

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利用腦結構和症狀特徵預測前驅期病患發展為思覺失調症之研究

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