Although autism spectrum disorder (ASD) is a serious lifelong condition, its underlying neural mechanism remains unclear. Recently, neuroimaging-based classifiers for ASD and typically developed (TD) individuals were developed to identify the abnormality of functional connections (FCs). Due to over-fitting and interferential effects of varying measurement conditions and demographic distributions, no classifiers have been strictly validated for independent cohorts. Here we overcome these difficulties by developing a novel machine-learning algorithm that identifies a small number of FCs that separates ASD versus TD. The classifier achieves high accuracy for a Japanese discovery cohort and demonstrates a remarkable degree of generalization for two independent validation cohorts in the USA and Japan. The developed ASD classifier does not distinguish individuals with major depressive disorder and attention-deficit hyperactivity disorder from their controls but moderately distinguishes patients with schizophrenia from their controls. The results leave open the viable possibility of exploring neuroimaging-based dimensions quantifying the multiple-disorder spectrum.

When collecting large amounts of neuroimaging data associated with psychiatric disorders, images must be acquired from multiple sites because of the limited capacity of a single site. However, site differences represent a barrier when acquiring multisite neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multisite, multidisorder dataset to demonstrate that site differences are composed of biological sampling bias and engineering measurement bias. The effects on resting-state functional MRI connectivity based on pairwise correlations because of both bias types were greater than or equal to psychiatric disorder differences. Furthermore, our findings indicated that each site can sample only from a subpopulation of participants. This result suggests that it is essential to collect large amounts of neuroimaging data from as many sites as possible to appropriately estimate the distribution of the grand population. Finally, we developed a novel harmonization method that removed only the measurement bias by using a traveling-subject dataset and achieved the reduction of the measurement bias by 29% and improvement of the signal-to-noise ratios by 40%. Our results provide fundamental knowledge regarding site effects, which is important for future research using multisite, multidisorder resting-state functional MRI data.

Identifying both the commonalities and differences in brain structures among psychiatric disorders is important for understanding the pathophysiology. Recently, the ENIGMA-Schizophrenia DTI Working Group performed a large-scale meta-analysis and reported widespread white matter microstructural alterations in schizophrenia; however, no similar crossdisorder study has been carried out to date. Here, we conducted mega-analyses comparing white matter microstructural differences between healthy comparison subjects (HCS; N = 1506) and patients with schizophrenia (N = 696), bipolar disorder (N = 211), autism spectrum disorder (N = 126), or major depressive disorder (N = 398; total N = 2937 from 12 sites). In comparison with HCS, we found that schizophrenia, bipolar disorder, and autism spectrum disorder share similar white matter microstructural differences in the body of the corpus callosum; schizophrenia and bipolar disorder featured comparable changes in the limbic system, such as the fornix and cingulum. By comparison, alterations in tracts connecting neocortical areas, such as the uncinate fasciculus, were observed only in schizophrenia. No significant difference was found in major depressive disorder. In a direct comparison between schizophrenia and bipolar disorder, there were no significant differences. Significant differences between schizophrenia/bipolar disorder and major depressive disorder were found in the limbic system, which were similar to the differences in schizophrenia and bipolar disorder relative to HCS. While schizophrenia and bipolar disorder may have similar pathological characteristics, the biological characteristics of major depressive disorder may be close to those of HCS. Our findings provide insights into nosology and encourage further investigations of shared and unique pathophysiology of psychiatric disorders.

Recent functional magnetic resonance imaging (fMRI) studies on autism spectrum condition (ASC) have identified dysfunctions in specific brain networks involved in social and non-social cognition that persist into adulthood. Although increasing numbers of fMRI studies have revealed atypical functional connectivity in the adult ASC brain, such functional alterations at the network level have not yet been fully characterized within the recently developed graph-theoretical framework. Here, we applied a graph-theoretical analysis to resting-state fMRI data acquired from 46 adults with ASC and 46 age- and gender-matched controls, to investigate the topological properties and organization of autistic brain network. Analyses of global metrics revealed that, relative to the controls, participants with ASC exhibited significant decreases in clustering coefficient and characteristic path length, indicating a shift towards randomized organization. Furthermore, analyses of local metrics revealed a significantly altered organization of the hub nodes in ASC, as shown by analyses of hub disruption indices using multiple local metrics and by a loss of “hubness” in several nodes (e.g., the bilateral superior temporal sulcus, right dorsolateral prefrontal cortex, and precuneus) that are critical for social and non-social cognitive functions. In particular, local metrics of the anterior cingulate cortex consistently showed significant negative correlations with the Autism-Spectrum Quotient score. Our results demonstrate altered patterns of global and local topological properties that may underlie impaired social and non-social cognition in ASC.

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Psychiatric research has been hampered by an explanatory gap between psychiatric symptoms and their neural underpinnings, which has resulted in poor treatment outcomes. This situation has prompted us to shift from symptom-based diagnosis to data-driven diagnosis, aiming to redefine psychiatric disorders as disorders of neural circuitry. Promising candidates for data-driven diagnosis include resting-state functional connectivity MRI (rs-fcMRI)-based biomarkers. Although biomarkers have been developed with the aim of diagnosing patients and predicting the efficacy of therapy, the focus has shifted to the identification of biomarkers that represent therapeutic targets, which would allow for more personalized treatment approaches. This type of biomarker (i.e., “theranostic biomarker”) is expected to elucidate the disease mechanism of psychiatric conditions and to offer an individualized neural circuit-based therapeutic target based on the neural cause of a condition. To this end, researchers have developed rs-fcMRI-based biomarkers and investigated a causal relationship between potential biomarkers and disease-specific behavior using functional MRI (fMRI)-based neurofeedback on functional connectivity. In this review, we introduce a recent approach for creating a theranostic biomarker, which consists mainly of 2 parts: (1) developing an rs-fcMRI-based biomarker that can predict diagnosis and/or symptoms with high accuracy, and (2) the introduction of a proof-of-concept study investigating the relationship between normalizing the biomarker and symptom changes using fMRI-based neurofeedback. In parallel with the introduction of recent studies, we review rs-fcMRI-based biomarker and fMRI-based neurofeedback, focusing on the technological improvements and limitations associated with clinical use.

Fragile X-associated tremor/ataxia syndrome is a neurodegenerative disorder that primarily affects older male premutation carriers of the fragile X mental retardation gene. Although its core symptoms are mainly characterized by motor problems such as intention tremor and gait ataxia, cognitive decline and psychiatric problems are also commonly observed. Past radiological and histological approaches have focused on prominent neurodegenerative changes in specific brain structures including the cerebellum and limbic areas. However, quantitative investigations of the regional structural abnormalities have not been performed over the whole brain. In this study, we adopted the voxel-based morphometry method together with regions of interest analysis for the cerebellum to examine the pattern of regional grey matter change in the male premutation carriers with and without fragile X-associated tremor/ataxia syndrome. In a comparison with healthy controls, we found striking grey matter loss of the patients with fragile X-associated tremor/ataxia syndrome in multiple regions over the cortical and subcortical structures. In the cerebellum, the anterior lobe and the superior posterior lobe were profoundly reduced in both vermis and hemispheres. In the cerebral cortex, clusters of highly significant grey matter reduction were found in the extended areas in the medial surface of the brain, including the dorsomedial prefrontal cortex, anterior cingulate cortex and precuneus. The other prominent grey matter loss was found in the lateral prefrontal cortex, orbitofrontal cortex, amygdala and insula. Although the voxel-wise comparison between the asymptomatic premutation group and healthy controls did not reach significant difference, a regions of interest analysis revealed significant grey matter reduction in anterior subregions of the cerebellar vermis and hemisphere in the asymptomatic premutation group. Correlation analyses using behavioural scales of the premutation groups showed significant associations between grey matter loss in the left amygdala and increased levels of obsessive-compulsiveness and depression, and between decreased grey matter in the left inferior frontal cortex and anterior cingulate cortex and poor working memory performance. Furthermore, regression analyses revealed a significant negative effect of CGG repeat size on grey matter density in the dorsomedial frontal regions. A significant negative correlation with the clinical scale for the severity of fragile X-associated tremor/ataxia syndrome was found in a part of the vermis. These observations reveal the anatomical patterns of the neurodegenerative process that underlie the motor, cognitive and psychiatric problems of fragile X-associated tremor/ataxia syndrome, together with incipient structural abnormalities that may occur before the clinical onset of this disease.

Older male premutation carriers of the FMR1 gene are associated with the risk of developing a late-onset neurodegenerative disorder, fragile X-associated tremor/ataxia syndrome (FXTAS). Although previous postmortem and in vivo MRI studies have indicated white matter pathology, the regional selectivity of abnormalities, as well as their relationship with molecular variables of the FMR1 gene, has not been investigated. In this study, we used diffusion tensor imaging (DTI) to study male premutation carriers with and without FXTAS and healthy gender-matched controls. We performed a tract of interest analysis for fractional anisotropy (FA), axial and radial diffusivities of major white matter tracts in the cerebellar-brainstem and limbic systems. Compared with healthy controls, patients with FXTAS showed significant reductions of FA in multiple white matter tracts, including the middle cerebellar peduncle (MCP), superior cerebellar peduncle, cerebral peduncle, and the fornix and stria terminalis. Significant reduction of FA in these tracts were confirmed by a voxel-wise analysis using Tract-Based Spatial Statistics. Analysis of axial and radial diffusivities showed significant elevation of these measures in MCP even among premutation carriers without FXTAS. Furthermore, regression analyses demonstrated clear inverted U-shaped relationship between CGG repeat size and axial and radial diffusivities in MCP. These results provide new evidence from DTI for white matter abnormalities in the cerebellarbrainstem and limbic systems among individuals with the fragile X premutation, and suggest the involvement of molecular mechanisms related to the FMR1 gene in their white matter pathology. Fragile X-associated tremor/ataxia syndrome (FXTAS) is a late-onset neurodegenerative disorder that is caused by premutation expansions (55 -200 CGG repeats) in the 5' untranslated region of the fragile X mental retardation gene (FMR1) 1 . Males have higher risk of developing FXTAS than females, affecting more than one third of male premutation carriers older than 50 years of age 2 . Although intention tremor and ataxia constitute the core clinical features of FXTAS, the main symptoms also include cognitive decline, autonomic dysfunction, neuropathy and psychiatric features including anxiety, depression, and apathy 3 . KeywordsThe pathogenetic mechanism of FXTAS is not yet fully understood. However, an RNA "toxic" gain-of-function model has been supported by several lines of evidence including the observation of abnormally elevated FMR1 mRNA levels for premutation alleles 4 . Previous postmortem histological studies examined the pathological processes in the FXTAS brain and revealed eosinophilic intranuclear inclusions in neurons and astrocytes throughout the cerebrum and brainstem with particularly pronounced concentration in the hippocampus 5 . Prominent neuropathological features were also found in the cerebellum, including spongiform changes in the deep cerebellar white matter 5,6 .There have been only a few in vivo MRI morphomet...