Neuropsychiatric disorders are diagnosed based on behavioral criteria, which makes the diagnosis challenging. Objective biomarkers such as neuroimaging are needed, and when coupled with machine learning, can assist the diagnostic decision and increase its reliability. Sixty-four schizophrenia, 36 autism spectrum disorder (ASD), and 106 typically developing individuals were analyzed. FreeSurfer was used to obtain the data from the participant's brain scans. Six classifiers were utilized to classify the subjects. Subsequently, 26 ultra-high risk for psychosis (UHR) and 17 first-episode psychosis (FEP) subjects were run through the trained classifiers. Lastly, the classifiers' output of the patient groups was correlated with their clinical severity. All six classifiers performed relatively well to distinguish the subject groups, especially support vector machine (SVM) and Logistic regression (LR). Cortical thickness and subcortical volume feature groups were most useful for the classification. LR and SVM were highly consistent with clinical indices of ASD. When UHR and FEP groups were run with the trained classifiers, majority of the cases were classified as schizophrenia, none as ASD. Overall, SVM and LR were the best performing classifiers. Cortical thickness and subcortical volume were most useful for the classification, compared to surface area. LR, SVM, and DT's output were clinically informative. The trained classifiers were able to help predict the diagnostic category of both UHR and FEP Individuals.

Multisite magnetic resonance imaging (MRI) is increasingly used in clinical research and development. Measurement biases—caused by site differences in scanner/image‐acquisition protocols—negatively influence the reliability and reproducibility of image‐analysis methods. Harmonization can reduce bias and improve the reproducibility of multisite datasets. Herein, a traveling‐subject (TS) dataset including 56 T1‐weighted MRI scans of 20 healthy participants in three different MRI procedures—20, 19, and 17 subjects in Procedures 1, 2, and 3, respectively—was considered to compare the reproducibility of TS‐GLM, ComBat, and TS‐ComBat harmonization methods. The minimum participant count required for harmonization was determined, and the Cohen's d between different MRI procedures was evaluated as a measurement‐bias indicator. The measurement‐bias reduction realized with different methods was evaluated by comparing test–retest scans for 20 healthy participants. Moreover, the minimum subject count for harmonization was determined by comparing test–retest datasets. The results revealed that TS‐GLM and TS‐ComBat reduced measurement bias by up to 85 and 81.3%, respectively. Meanwhile, ComBat showed a reduction of only 59.0%. At least 6 TSs were required to harmonize data obtained from different MRI scanners, complying with the imaging protocol predetermined for multisite investigations and operated with similar scan parameters. The results indicate that TS‐based harmonization outperforms ComBat for measurement‐bias reduction and is optimal for MRI data in well‐prepared multisite investigations. One drawback is the small sample size used, potentially limiting the applicability of ComBat. Investigation on the number of subjects needed for a large‐scale study is an interesting future problem.

Although many studies have demonstrated structural brain abnormalities associated with auditory verbal hallucinations (AVH) in schizophrenia, the results remain inconsistent because of the small sample sizes and the reliability of clinical interviews. We compared brain morphometries in 204 participants, including 58 schizophrenia patients with a history of AVH (AVH + ), 29 without a history of AVH (AVH−), and 117 healthy controls (HCs) based on a detailed inspection of medical records. We further divided the AVH+ group into 37 patients with and 21 patients without hallucinations at the time of the MRI scans (AVH++ and AVH+−, respectively) via clinical interviews to explore the morphological differences according to the persistence of AVH. The AVH + group had a smaller surface area in the left caudal middle frontal gyrus (F = 7.28, FDR-corrected p = 0.0008) and precentral gyrus (F = 7.68, FDR-corrected p = 0.0006) compared to the AVH− group. The AVH+ patients had a smaller surface area in the left insula (F = 7.06, FDR-corrected p = 0.001) and a smaller subcortical volume in the bilateral hippocampus (right: F = 13.34, FDR-corrected p = 0.00003; left: F = 6.80, FDR-corrected p = 0.001) compared to the HC group. Of these significantly altered areas, the AVH++ group showed significantly smaller bilateral hippocampal volumes compared to the AVH+− group, and a smaller surface area in the left precentral gyrus and caudal middle frontal gyrus compared to the AVH- group. Our findings highlighted the distinct pattern of structural alteration between the history and presence of AVH in schizophrenia, and the importance of integrating multiple criteria to elucidate the neuroanatomical mechanisms.

Background and Hypothesis Machine learning approaches using structural magnetic resonance imaging (MRI) can be informative for disease classification; however, their applicability to earlier clinical stages of psychosis and other disease spectra is unknown. We evaluated whether a model differentiating patients with chronic schizophrenia (ChSZ) from healthy controls (HCs) could be applied to earlier clinical stages such as first-episode psychosis (FEP), ultra-high risk for psychosis (UHR), and autism spectrum disorders (ASDs). Study Design Total 359 T1-weighted MRI scans, including 154 individuals with schizophrenia spectrum (UHR, n = 37; FEP, n = 24; and ChSZ, n = 93), 64 with ASD, and 141 HCs, were obtained using three acquisition protocols. Of these, data regarding ChSZ (n = 75) and HC (n = 101) from two protocols were used to build a classifier (training dataset). The remainder was used to evaluate the classifier (test, independent confirmatory, and independent group datasets). Scanner and protocol effects were diminished using ComBat. Study Results The accuracy of the classifier for the test and independent confirmatory datasets were 75% and 76%, respectively. The bilateral pallidum and inferior frontal gyrus pars triangularis strongly contributed to classifying ChSZ. Schizophrenia spectrum individuals were more likely to be classified as ChSZ compared to ASD (classification rate to ChSZ: UHR, 41%; FEP, 54%; ChSZ, 70%; ASD, 19%; HC, 21%). Conclusion We built a classifier from multiple protocol structural brain images applicable to independent samples from different clinical stages and spectra. The predictive information of the classifier could be useful for applying neuroimaging techniques to clinical differential diagnosis and predicting disease onset earlier.

Many studies have tested the relationship between demographic, clinical, and psychobiological measurements and clinical outcomes in ultra-high risk for psychosis (UHR) and first-episode psychosis (FEP). However, no study has investigated the relationship between multi-modal measurements and long-term outcomes for >2 years. Thirty-eight individuals with UHR and 29 patients with FEP were measured using one or more modalities (cognitive battery, electrophysiological response, structural magnetic resonance imaging, and functional near-infrared spectroscopy). We explored the characteristics associated with 13- and 28-month clinical outcomes. In UHR, the cortical surface area in the left orbital part of the inferior frontal gyrus was negatively associated with 13-month disorganized symptoms. In FEP, the cortical surface area in the left insula was positively associated with 28-month global social function. The left inferior frontal gyrus and insula are well-known structural brain characteristics in schizophrenia, and future studies on the pathological mechanism of structural alteration would provide a clearer understanding of the disease.