目的
神經發育假說（Neurodevelopmental model）認為思覺失調症的發生是源自於腦部發育的異常。細微體質特徵 (Minor physical anomalies, MPAs) 與軟性神經學症狀 (Neurological soft signs, NSSs) 被認為是胚胎發育異常的神經發展標記。先前研究已經報導思覺失調症患者相對於健康對照組有較多的神經發展標記異常，但對於其家屬的神經發展標記仍無一致性的結果。另外，思覺失調症的好發年齡與疾病的徵狀十分相關，因此我們想要探討早發和成人思覺失調症的預測效度是否有差異，並且利用神經發展標記估計早發和成人思覺失調症之家族聚集性程度。
實驗設計
我們結合質性與量性項目改編細微體質特徵量表，測量個案的各部位特徵，並使 用神經功能評估量表 (Neurological Evaluation Scale, NES) 作為神經軟性功能的測量 工具。 研究共收集了 187 位思覺失調症病人及其 147 位家屬，與 241 位健康對照組，並測量其神經發展標記。首先，利用兩種資料探勘方法（類神經網路及決策樹）以及一種常見的統計方法（羅吉斯回歸）作為預測模組來估計神經發展標記在早發（發病年齡< 20）和成人（發病年齡≥ 20）思覺失調症的預測效度，並利用十折交叉驗證來確保模型之準確性及正確度。最後使用再現風險比分析神經發展標記在早發及成人思覺失調症的家族聚集程度。
結果
研究結果發現，在三個預測模組當中，類神經網路模式有較高的預測效度，以下預測效度結果僅顯示類神經網路之準確度(accuracy)。首先，在細微體質特徵（MPAs）中，在質性測量部分，早發和成人思覺失調症之準確度分別為78%及73%；在結合質性與量性測量的MPAs中，早發和成人思覺失調症之準確度分別為91%及81%。再現危險比的結果發現，質性測量的MPAs（MPAs切點 ≥ 10）早發思覺失調症家族為 4.16 (95%CI: 2.05-8.43)；在成人思覺失調症家族為 2.56 (95%CI: 1.06-6.19)；在結合質性與量性測量的MPAs（MPAs切點 ≥ 19）中，再現風險比在早發思覺失調症家族為 9.27 (95%CI: 3.88-22.16) ；在成人思覺失調症家族為2.47 (95%CI: 2.03-11.52)。此外，在軟性神經學症狀（NSSs）中，早發和成人思覺失調症之準確度分別為85%及78%。而再現危險比的結果發現，在感覺統合及運動協調之軟性神經學症狀的子類別中，早發思覺失調症家族之再現危險比皆大於成人思覺失調症家族。如：感覺統合（切點 ≥ 1）在早發思覺失調症家族為 2.63 (95%CI: 1.36-5.09) ，在成人思覺失調症家族為2.15 (95%CI: 1.19-3.89)；運動協調（NSSs切點 ≥ 1）在早發思覺失調症家族為 24.54 (95%CI: 7.81-77.05) ，在成人思覺失調症家族為19.91 (95%CI: 6.49-61.10)。而早發及成人思覺失調症家族在其他軟性神經學症狀子類別中並沒有顯著的差異。
結論
據我們所知，這是第一篇探討神經發展標記在早發及成人思覺失調症及其家屬之預測效度及家族聚集性之研究。我們的研究發現神經發展標記可作為思覺失調症的一個易感受性指標，並且不論是在細微體質特徵或軟性神經學症狀，早發思覺失調症之預測效度皆比成人思覺失調症好。因此，我們認為神經發展標記可以更加準確的區分早發思覺失調症及健康對照組。此外，在顯著的細微體質特徵（MPAs）及軟性神經學症狀（NSSs）中發現早發思覺失調症家族相對于成人思覺失調症家族具有較高的家族聚集性。因此，我們的研究支持早發思覺失調症可能具有較大的家族性及基因危險因子的神經發育假說。

Background
The neurodevelopmental hypothesis proposes that schizophrenia is originated from abnormal brain development. Minor physical anomalies (MPAs) and neurological soft signs (NSSs) are suggested as biomarkers associated with disruptions of fetal development. Schizophrenia patients have been reported to have more deficits in neurodevelopmental markers than healthy controls, but family studies have produced different results. Moreover, the onset age of schizophrenia is related to the severity of the subsequent symptoms, and thus it may be possible to estimate the predictive abilities of neurodevelopmental markers from early-onset and adult-onset schizophrenia. We aimed to examine the familial aggregation of neurodevelopmental markers in early-onset and adult-onset schizophrenia families.
Methods
We developed a modified physical measurement scale composed of both qualitative and quantitative items, and used the Neurological Evaluation Scale (NES) to assess NSSs. Participants included 182 schizophrenia patients, 147 unaffected first-degree relatives of schizophrenia patients, and 241 healthy controls. First, we estimated the predictive abilities of neurodevelopmental markers between early-onset schizophrenia (EOS) (onset age < 20) and adult-onset schizophrenia (AOS) (onset age ≥ 20) using two data mining algorithms (artificial neural networks and decision trees), and a commonly used statistical method (logistic regression), we also used a 10-fold cross-validation method to measure the unbiased estimate of the prediction models. Second, we assessed the magnitude of familial aggregation for neurodevelopmental markers for EOS and AOS families using the relative recurrence-risk ratio.
Results
The results of artificial neural networks were significantly more accurate than the other two methods. Therefore, we only showed the accuracy of artificial neural networks. For the measurement of qualitative MPAs, the accuracies for EOS and AOS were 78% and 73%, respectively. For qualitatively and quantitative measurements of MPAs (combined MPAs), the accuracies for EOS and AOS were 91% and 81%, respectively. The recurrence risk ratio for the total score of qualitative MPAs (Cut-Off Score MPAs ≥ 10) in EOS families was 4.16 (95%CI: 2.05-8.43), and in AOS families was 2.56 (95%CI: 1.06-6.19). The combined MPAs (Cut-Off Score MPAs ≥ 19) had a higher recurrence risk ratio than qualitative MPAs. The recurrence risk ratio was 9.27 (95%CI: 3.88-22.16) in EOS families, and 2.47 (95%CI: 0.61-9.97) in AOS families. For the measurement of NSSs, the accuracies of EOS and AOS were 85% and 78%, respectively. For sensory integration and motor coordination subscale, the recurrence risk of EOS families was greater than that of AOS families in all NSS cut-off scores. For example, for the cut-off point of ≥ 1 in the Sensory Integration subscale, the results showed that risk ratios were 2.63 (95%CI: 1.36-5.09) in EOS families, and 2.15 (95%CI: 1.19-3.89) in AOS families. For the cut-off point of ≥ 1 in the motor coordination subscale, the results showed that risk ratios were 24.54 (95%CI: 7.81-77.05) in EOS families, and 19.91 (95%CI: 6.49-61.10) in AOS families. However, no significant differences were found between EOS and AOS families for the sequencing of complex motor acts subscale and the others subscale.
Conclusion
To our knowledge, this study was the first to examine the association between familial aggregation and the age of onset among schizophrenia patients and their relatives in terms of neurodevelopmental markers. These findings provide support for the potential of neurodevelopmental markers as a vulnerability indicator to schizophrenia. Both MPAs and NSSs had higher predictive abilities for EOS than for AOS. Therefore, neurodevelopmental markers may have more accuracy in distinguishing EOS patients from healthy controls. Furthermore, evidence suggests that EOS families might have higher familial aggregation than AOS families in terms of MPAs and NSS. Hence, the findings of this study support the neurodevelopmental hypothesis that EOS might have greater familial and genetic risks.

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