Abstract
ObjectiveAlthough distinctive structural abnormalities occur in patients with schizophrenia, detecting schizophrenia with magnetic resonance imaging (MRI) remains challenging. This study aimed to detect schizophrenia in structural MRI data sets using a trained deep learning algorithm.MethodFive public MRI data sets (BrainGluSchi, COBRE, MCICShare, NMorphCH, and NUSDAST) from schizophrenia patients and normal subjects, for a total of 873 structural MRI data sets, were used to train a deep convolutional neural network.ResultsThe deep learning algorithm trained with structural MR images detected schizophrenia in randomly selected images with reliable performance (area under the receiver operating characteristic curve [AUC] of 0.96). The algorithm could also identify MR images from schizophrenia patients in a previously unencountered data set with an AUC of 0.71 to 0.90. The deep learning algorithm’s classification performance degraded to an AUC of 0.71 when a new data set with younger patients and a shorter duration of illness than the training data sets was presented. The brain region contributing the most to the performance of the algorithm was the right temporal area, followed by the right parietal area. Semitrained clinical specialists hardly discriminated schizophrenia patients from healthy controls (AUC: 0.61) in the set of 100 randomly selected brain images.ConclusionsThe deep learning algorithm showed good performance in detecting schizophrenia and identified relevant structural features from structural brain MRI data; it had an acceptable classification performance in a separate group of patients at an earlier stage of the disease. Deep learning can be used to delineate the structural characteristics of schizophrenia and to provide supplementary diagnostic information in clinical settings.
Highlights
Structural brain alterations in schizophrenia have been thoroughly investigated with the development of neuroimaging methods [1,2,3]
Our results imply that a deep learning algorithm trained with large structural magnetic resonance imaging (MRI) data sets could discriminate patients with schizophrenia from healthy participants
We used qualitative methods rather than precise cortical parcellation to divide brain regions [49], these results suggest that a deep learning algorithm could be used to identify certain brain features of schizophrenia, complementing the findings of previous studies
Summary
Structural brain alterations in schizophrenia have been thoroughly investigated with the development of neuroimaging methods [1,2,3]. There remain some controversies regarding the use of antipsychotics and the duration of illness, a number of studies have found overall gray matter loss [2], decreased volume of the bilateral medial temporal areas [3] and a left superior temporal region deficit [1] in brains with schizophrenia. As these structural abnormalities are thought to be linked to the positive symptoms of schizophrenia [4, 5], it has been suggested that the neuropathology and etiology of schizophrenia might be related to alterations in brain structure [6]. Another reason is that certain cortical features found in schizophrenia are shared with other neurodegenerative diseases; the patient’s clinical history of psychiatric problems is needed to discriminate these mental illnesses [10]
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