Segmentation and Volume Estimation of the Habenula Using Deep Learning in Patients With Depression.

Background: The habenula is involved in the pathophysiology of depression. However, its small structure limits the accuracy of segmentation methods, and the findings regarding its volume have been inconsistent. This study aimed to create a highly accurate habenula segmentation model using deep learning, test its generalizability to clinical magnetic resonance imaging, and examine differences between healthy participants and patients with depression. Methods: This multicenter study included 382 participants (patients with depression: N = 234, women 47.0%; healthy participants: N = 148, women 37.8%). A 3-dimensional residual U-Net was used to create a habenula segmentation model on 3T magnetic resonance images. The reproducibility and generalizability of the predictive model were tested on various validation cohorts. Thereafter, differences between the habenula volume of healthy participants and that of patients with depression were examined. Results: A Dice coefficient of 86.6% was achieved in the derivation cohort. The test-retest dataset showed a mean absolute percentage error of 6.66, indicating sufficiently high reproducibility. A Dice coefficient of >80% was achieved for datasets with different imaging conditions, such as magnetic field strengths, spatial resolutions, and imaging sequences, by adjusting the threshold. A significant negative correlation with age was observed in the general population, and this correlation was more pronounced in patients with depression (p < 10-7, r = -0.59). Habenula volume decreased with depression severity in women even when the effects of age and scanner were excluded (p = .019, η2 = 0.099). Conclusions: Habenula volume could be a pathophysiologically relevant factor and diagnostic and therapeutic marker for depression, particularly in women.

Accurate segmentation of the habenula, a brain region implicated in depression, is challenging. In this study, we developed an automated human habenula segmentation model using deep learning techniques. The model was confirmed to be reproducible and generalizable at various spatial resolutions. Application of this model to a multicenter dataset confirmed that habenula volume decreased with age in healthy volunteers, an association that was more pronounced in individuals with depression. In addition, habenula volume decreased with the severity of depression in women. This novel model for habenula segmentation enables further study of the role of the habenula in depression.

Generalizable neuromarker for autism spectrum disorder across imaging sites and developmental stages: A multi-site study.

Autism spectrum disorder (ASD) is a lifelong condition, and its underlying biological mechanisms remain elusive. The complexity of various factors, including inter-site and development-related differences, makes it challenging to develop generalizable neuroimaging-based biomarkers for ASD. This study used a large-scale, multi-site dataset of 730 Japanese adults to develop a generalizable neuromarker for ASD across independent sites and different developmental stages. Our adult ASD neuromarker achieved successful generalization for the US and Belgium adults and Japanese adults. The neuromarker demonstrated significant generalization for children and adolescents. We identified 141 functional connections (FCs) important for discriminating individuals with ASD from TDCs. Finally, we mapped schizophrenia (SCZ) and major depressive disorder (MDD) onto the biological axis defined by the neuromarker and explored the biological continuity of ASD with SCZ and MDD. We observed that SCZ, but not MDD, was located proximate to ASD on the biological dimension defined by the ASD neuromarker. The successful generalization in multifarious datasets and the observed relations of ASD with SCZ on the biological dimensions provide new insights for a deeper understanding of ASD.

Generalizable neuromarker for autism spectrum disorder across imaging sites and developmental stages: A multi-site study.

Autism spectrum disorder (ASD) is a lifelong condition, and its underlying biological mechanisms remain elusive. The complexity of various factors, including inter-site and development-related differences, makes it challenging to develop generalizable neuroimaging-based biomarkers for ASD. This study used a large-scale, multi-site dataset of 730 Japanese adults to develop a generalizable neuromarker for ASD across independent sites (U.S., Belgium, and Japan) and different developmental stages (children and adolescents). Our adult ASD neuromarker achieved successful generalization for the US and Belgium adults (area under the curve [AUC] = 0.70) and Japanese adults (AUC = 0.81). The neuromarker demonstrated significant generalization for children (AUC = 0.66) and adolescents (AUC = 0.71; all P<0.05, family-wise-error corrected). We identified 141 functional connections (FCs) important for discriminating individuals with ASD from TDCs. These FCs largely centered on social brain regions such as the amygdala, hippocampus, dorsomedial and ventromedial prefrontal cortices, and temporal cortices. Finally, we mapped schizophrenia (SCZ) and major depressive disorder (MDD) onto the biological axis defined by the neuromarker and explored the biological continuity of ASD with SCZ and MDD. We observed that SCZ, but not MDD, was located proximate to ASD on the biological dimension defined by the ASD neuromarker. The successful generalization in multifarious datasets and the observed relations of ASD with SCZ on the biological dimensions provide new insights for a deeper understanding of ASD.

Distinctive alterations in the mesocorticolimbic circuits in various psychiatric disorders.

AIM: Increasing evidence suggests that psychiatric disorders are linked to alterations in the mesocorticolimbic dopamine-related circuits. However, the common and disease-specific alterations remain to be examined in schizophrenia (SCZ), major depressive disorder (MDD), and autism spectrum disorder (ASD). Thus, this study aimed to examine common and disease-specific features related to mesocorticolimbic circuits. METHODS: This study included 555 participants from four institutes with five scanners: 140 individuals with SCZ (45.0% female), 127 individuals with MDD (44.9%), 119 individuals with ASD (15.1%), and 169 healthy controls (HC) (34.9%). All participants underwent resting-state functional magnetic resonance imaging. A parametric empirical Bayes approach was adopted to compare estimated effective connectivity among groups. Intrinsic effective connectivity focusing on the mesocorticolimbic dopamine-related circuits including the ventral tegmental area (VTA), shell and core parts of the nucleus accumbens (NAc), and medial prefrontal cortex (mPFC) were examined using a dynamic causal modeling analysis across these psychiatric disorders. RESULTS: The excitatory shell-to-core connectivity was greater in all patients than in the HC group. The inhibitory shell-to-VTA and shell-to-mPFC connectivities were greater in the ASD group than in the HC, MDD, and SCZ groups. Furthermore, the VTA-to-core and VTA-to-shell connectivities were excitatory in the ASD group, while those connections were inhibitory in the HC, MDD, and SCZ groups. CONCLUSION: Impaired signaling in the mesocorticolimbic dopamine-related circuits could be an underlying neuropathogenesis of various psychiatric disorders. These findings will improve the understanding of unique neural alternations of each disorder and will facilitate identification of effective therapeutic targets.

Aberrant Large-Scale Network Interactions Across Psychiatric Disorders Revealed by Large-Sample Multi-Site Resting-State Functional Magnetic Resonance Imaging Datasets.

BACKGROUND AND HYPOTHESIS: Dynamics of the distributed sets of functionally synchronized brain regions, known as large-scale networks, are essential for the emotional state and cognitive processes. However, few studies were performed to elucidate the aberrant dynamics across the large-scale networks across multiple psychiatric disorders. In this paper, we aimed to investigate dynamic aspects of the aberrancy of the causal connections among the large-scale networks of the multiple psychiatric disorders. STUDY DESIGN: We applied dynamic causal modeling (DCM) to the large-sample multi-site dataset with 739 participants from 4 imaging sites including 4 different groups, healthy controls, schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD), to compare the causal relationships among the large-scale networks, including visual network, somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network, and default mode network. STUDY RESULTS: DCM showed that the decreased self-inhibitory connection of LIN was the common aberrant connection pattern across psychiatry disorders. Furthermore, increased causal connections from LIN to multiple networks, aberrant self-inhibitory connections of DAN and SMN, and increased self-inhibitory connection of SAN were disorder-specific patterns for SCZ, MDD, and BD, respectively. CONCLUSIONS: DCM revealed that LIN was the core abnormal network common to psychiatric disorders. Furthermore, DCM showed disorder-specific abnormal patterns of causal connections across the 7 networks. Our findings suggested that aberrant dynamics among the large-scale networks could be a key biomarker for these transdiagnostic psychiatric disorders.

Altered white matter microstructure and neurocognitive function of HIV-infected patients with low nadir CD4.

Altered white matter microstructure has been reported repeatedly using diffusion tensor imaging (DTI) in HIV-associated neurocognitive disorders. However, the associations between neurocognitive deficits and impaired white matter remains obscure due to frequent physical and psychiatric comorbidities in the patients. Severe immune suppression, reflected by low nadir CD4 T-cell counts, is reported to be associated with the neurocognitive deficits in the patients. In the present study, we examined white matter integrity using DTI and tract-based spatial statistics (TBSS), and neurocognitive functions using a battery of tests, in 15 HIV-infected patients with low nadir CD4, 16 HIV-infected patients with high nadir CD4, and 33 age- and sex-matched healthy controls. As DTI measures, we analyzed fractional anisotropy (FA) and mean diffusivity (MD). In addition, we investigated the correlation between white matter impairments and neurocognitive deficits. Among the three participant groups, the patients with low nadir CD4 showed significantly lower performance in processing speed and motor skills, and had significantly increased MD in widespread regions of white matter in both hemispheres. In the patients with low nadir CD4, there was a significant negative correlation between motor skills and MD in the right motor tracts, as well as in the corpus callosum. In summary, this study may provide white matter correlates of neurocognitive deficits in HIV-infected patients with past severe immune suppression as legacy effects.

Development of a classifier for gambling disorder based on functional connections between brain regions.

AIM: Recently, a machine-learning (ML) technique has been used to create generalizable classifiers for psychiatric disorders based on information of functional connections (FCs) between brain regions at resting state. These classifiers predict diagnostic labels by a weighted linear sum (WLS) of the correlation values of a small number of selected FCs. We aimed to develop a generalizable classifier for gambling disorder (GD) from the information of FCs using the ML technique and examine relationships between WLS and clinical data. METHODS: As a training dataset for ML, data from 71 GD patients and 90 healthy controls (HCs) were obtained from two magnetic resonance imaging sites. We used an ML algorithm consisting of a cascade of an L1-regularized sparse canonical correlation analysis and a sparse logistic regression to create the classifier. The generalizability of the classifier was verified using an external dataset. This external dataset consisted of six GD patients and 14 HCs, and was collected at a different site from the sites of the training dataset. Correlations between WLS and South Oaks Gambling Screen (SOGS) and duration of illness were examined. RESULTS: The classifier distinguished between the GD patients and HCs with high accuracy in leave-one-out cross-validation (area under curve (AUC = 0.89)). This performance was confirmed in the external dataset (AUC = 0.81). There was no correlation between WLS, and SOGS and duration of illness in the GD patients. CONCLUSION: We developed a generalizable classifier for GD based on information of functional connections between brain regions at resting state.

Depressive symptoms reduce when dorsolateral prefrontal cortex-precuneus connectivity normalizes after functional connectivity neurofeedback.

Depressive disorders contribute heavily to global disease burden; This is possibly because patients are often treated homogeneously, despite having heterogeneous symptoms with differing underlying neural mechanisms. A novel treatment that can directly influence the neural circuit relevant to an individual patient's subset of symptoms might more precisely and thus effectively aid in the alleviation of their specific symptoms. We tested this hypothesis in a proof-of-concept study using fMRI functional connectivity neurofeedback. We targeted connectivity between the left dorsolateral prefrontal cortex/middle frontal gyrus and the left precuneus/posterior cingulate cortex, because this connection has been well-established as relating to a specific subset of depressive symptoms. Specifically, this connectivity has been shown in a data-driven manner to be less anticorrelated in patients with melancholic depression than in healthy controls. Furthermore, a posterior cingulate dominant state-which results in a loss of this anticorrelation-is expected to specifically relate to an increase in rumination symptoms such as brooding. In line with predictions, we found that, with neurofeedback training, the more a participant normalized this connectivity (restored the anticorrelation), the more related (depressive and brooding symptoms), but not unrelated (trait anxiety), symptoms were reduced. Because these results look promising, this paradigm next needs to be examined with a greater sample size and with better controls. Nonetheless, here we provide preliminary evidence for a correlation between the normalization of a neural network and a reduction in related symptoms. Showing their reproducibility, these results were found in two experiments that took place several years apart by different experimenters. Indicative of its potential clinical utility, effects of this treatment remained one-two months later.Clinical trial registration: Both experiments reported here were registered clinical trials (UMIN000015249, jRCTs052180169).

A multi-site, multi-disorder resting-state magnetic resonance image database.

Machine learning classifiers for psychiatric disorders using resting-state functional magnetic resonance imaging (rs-fMRI) have recently attracted attention as a method for directly examining relationships between neural circuits and psychiatric disorders. To develop accurate and generalizable classifiers, we compiled a large-scale, multi-site, multi-disorder neuroimaging database. The database comprises resting-state fMRI and structural images of the brain from 993 patients and 1,421 healthy individuals, as well as demographic information such as age, sex, and clinical rating scales. To harmonize the multi-site data, nine healthy participants ("traveling subjects") visited the sites from which the above datasets were obtained and underwent neuroimaging with 12 scanners. All participants consented to having their data shared and analyzed at multiple medical and research institutions participating in the project, and 706 patients and 1,122 healthy individuals consented to having their data disclosed. Finally, we have published four datasets: 1) the SRPBS Multi-disorder Connectivity Dataset 2), the SRPBS Multi-disorder MRI Dataset (restricted), 3) the SRPBS Multi-disorder MRI Dataset (unrestricted), and 4) the SRPBS Traveling Subject MRI Dataset.

Cognition and interpersonal coordination of patients with schizophrenia who have sports habits.

Team sports activities are effective for improving the negative symptoms and cognitive functions in patients with schizophrenia. However, the interpersonal coordination during the sports and visual cognition of patients with schizophrenia who have team sports habits are unknown. The main objectives of this study were to test two hypotheses: first, patients with schizophrenia perform the skill requiring ball passing and receiving worse than healthy controls; and second, the patients will be impaired in these functionings in accordance with the previous studies regarding schizophrenia in general. Twelve patients with schizophrenia and 15 healthy controls, who had habits in football, participated in this study. The participants performed three conventional cognitive tests and a 3-vs-1 ball possession task to evaluate their interpersonal coordination. The results showed that in the 3-vs-1 possession task, the displacement in the pass angle for the patients was significantly smaller than that for the control. The recall in the complex figure test, the performance in the trail making test, and that in the five-choice reaction task for the patients were worse than those for the control. Moreover, we found the significant partial correlations in the patients between the extradimensional shift error and the pass angle as well as between the time in the trail making test and the displacement in the pass angle, whereas there was no significant correlation in the control group. This study clarified the impaired interpersonal coordination during team sports and the visual cognition of patients with schizophrenia who have team sports habits.

Publisher Correction: Primary functional brain connections associated with melancholic major depressive disorder and modulation by antidepressants.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Early and late effects of electroconvulsive therapy associated with different temporal lobe structures.

Recent studies examining electroconvulsive therapy (ECT) have reported that early sessions can induce rapid antidepressant and antipsychotic effects, and the early termination of ECT was reported to increase the risk of relapse. We hypothesized that different neural mechanisms associated with the therapeutic effects of ECT may be involved in the different responses observed during the early and late periods of ECT treatment. We investigated whether these antidepressant and antipsychotic effects were associated with temporally and spatially different regional gray matter volume (GMV) changes during ECT. Fourteen patients with major depressive disorder, with or without psychotic features, underwent 3-Tesla structural magnetic resonance imaging scans before (time point [Tp] 1), after the fifth or sixth ECT session (Tp2), and after ECT completion (Tp3). We investigated the regions in which GMV changed between Tp1 and Tp2, Tp2 and Tp3, and Tp1 and Tp3 using voxel-based morphometry. In addition, we investigated the association between regional GMV changes and improvement in depressive or psychotic symptoms. GMV increase in the left superior and inferior temporal gyrus during Tp1-Tp2 was associated with improvement in psychotic symptoms (P < 0.025). GMV increase in the left hippocampus was associated with improvement of depressive symptoms in Tp2-Tp3 (P < 0.05). Our findings suggest that different temporal lobe structures are associated with early antipsychotic and late antidepressant effects of ECT.

Neurocognitive impairment and gray matter volume reduction in HIV-infected patients.

Although neuropsychological studies of human immunodeficiency virus (HIV)-infected patients have demonstrated heterogeneity in neurocognitive impairment and neuroimaging studies have reported diverse brain regions affected by HIV, it remains unclear whether individual differences in neurocognitive impairment are underpinned by their neural bases. Here, we investigated spatial distribution patterns of correlation between neurocognitive function and regional gray matter (GM) volume across patients with HIV. Thirty-one combination antiretroviral therapy-treated HIV-infected Japanese male patients and 33 age- and sex-matched healthy controls were included in the analysis after strict exclusion criteria, especially for substance use. Fifteen neurocognitive tests were used, and volumetric magnetic resonance imaging was performed. We used voxel-based morphometry to compare GM volume between groups and identify regional GM volumes that correlated with neurocognitive tests across patients. Using the Frascati criteria, 10 patients were diagnosed with asymptomatic neurocognitive impairment, while the others were not diagnosed with HIV-associated neurocognitive disorders. Patients showed a significantly lower performance in five neurocognitive tests as well as significantly reduced GM volume relative to controls, with volume-reduced regions spread diffusely across the whole brain. Different aspects of neurocognitive impairment (i.e., figural copy, finger tapping, and Pegboard) were associated with different GM regions. Our findings suggest a biological background constituting heterogeneity of neurocognitive impairment in HIV infection and support the clinical importance of considering individual differences for tailor-made medicine for people living with HIV.

Overlapping but Asymmetrical Relationships Between Schizophrenia and Autism Revealed by Brain Connectivity.

Although the relationship between schizophrenia spectrum disorder (SSD) and autism spectrum disorder (ASD) has long been debated, it has not yet been fully elucidated. The authors quantified and visualized the relationship between ASD and SSD using dual classifiers that discriminate patients from healthy controls (HCs) based on resting-state functional connectivity magnetic resonance imaging. To develop a reliable SSD classifier, sophisticated machine-learning algorithms that automatically selected SSD-specific functional connections were applied to Japanese datasets from Kyoto University Hospital (N = 170) including patients with chronic-stage SSD. The generalizability of the SSD classifier was tested by 2 independent validation cohorts, and 1 cohort including first-episode schizophrenia. The specificity of the SSD classifier was tested by 2 Japanese cohorts of ASD and major depressive disorder. The weighted linear summation of the classifier's functional connections constituted the biological dimensions representing neural classification certainty for the disorders. Our previously developed ASD classifier was used as ASD dimension. Distributions of individuals with SSD, ASD, and HCs s were examined on the SSD and ASD biological dimensions. We found that the SSD and ASD populations exhibited overlapping but asymmetrical patterns in the 2 biological dimensions. That is, the SSD population showed increased classification certainty for the ASD dimension but not vice versa. Furthermore, the 2 dimensions were correlated within the ASD population but not the SSD population. In conclusion, using the 2 biological dimensions based on resting-state functional connectivity enabled us to discover the quantified relationships between SSD and ASD.

Primary functional brain connections associated with melancholic major depressive disorder and modulation by antidepressants.

The limited efficacy of available antidepressant therapies may be due to how they affect the underlying brain network. The purpose of this study was to develop a melancholic MDD biomarker to identify critically important functional connections (FCs), and explore their association to treatments. Resting state fMRI data of 130 individuals (65 melancholic major depressive disorder (MDD) patients, 65 healthy controls) were included to build a melancholic MDD classifier, and 10 FCs were selected by our sparse machine learning algorithm. This biomarker generalized to a drug-free independent cohort of melancholic MDD, and did not generalize to other MDD subtypes or other psychiatric disorders. Moreover, we found that antidepressants had a heterogeneous effect on the identified FCs of 25 melancholic MDDs. In particular, it did impact the FC between left dorsolateral prefrontal cortex (DLPFC)/inferior frontal gyrus (IFG) and posterior cingulate cortex (PCC)/precuneus, ranked as the second 'most important' FC based on the biomarker weights, whilst other eight FCs were normalized. Given that left DLPFC has been proposed as an explicit target of depression treatments, this suggest that the limited efficacy of antidepressants might be compensated by combining therapies with targeted treatment as an optimized approach in the future.

Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias.

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.

A prediction model of working memory across health and psychiatric disease using whole-brain functional connectivity.

Working memory deficits are present in many neuropsychiatric diseases with diagnosis-related severity. However, it is unknown whether this common behavioral abnormality is a continuum explained by a neural mechanism shared across diseases or a set of discrete dysfunctions. Here, we performed predictive modeling to examine working memory ability (WMA) as a function of normative whole-brain connectivity across psychiatric diseases. We built a quantitative model for letter three-back task performance in healthy participants, using resting state functional magnetic resonance imaging (rs-fMRI). This normative model was applied to independent participants (N = 965) including four psychiatric diagnoses. Individual's predicted WMA significantly correlated with a measured WMA in both healthy population and schizophrenia. Our predicted effect size estimates on WMA impairment were comparable to previous meta-analysis results. These results suggest a general association between brain connectivity and working memory ability applicable commonly to health and psychiatric diseases.

[FMRI Neurofeedback and its Application to Psychiatric Disorders].

Functional magnetic resonance imaging (fMRI) neurofeedback can train subjects to control their brain activity with real-time processing and high spatial resolution, as many advances in MRI data acquisition methods, computer hardware, and processing algorithms have improved the sensitivity and speed of fMRI neurofeedback. FMRI neurofeedback has been applied to psychiatric disorders, including schizophrenia, major depressive disorder, attention-deficit hyperactivity disorder, and obsessive-compulsive disorder. Some studies reported that fMRI neurofeedback improved the symptoms of patients with psychiatric disorders, although it remains unclear if the regulation of the targeted brain regions or the functional connectivities themselves improved the symptoms. It is necessary that researchers pay enough attention to their study's design, because many non-specific factors such as introduction, mental strategies, self-efficacy, attention, motivation, learning ability, and reward influence the results in fMRI neurofeedback studies. If the long-term effect of fMRI neurofeedback on the symptoms of psychiatric disorders are recognized, fMRI neurofeedback will be useful in treating heterogeneous patients with psychiatric disorders without side effects.

Effect of phase-encoding direction on group analysis of resting-state functional magnetic resonance imaging.

AIM: Echo-planar imaging is a common technique used in functional magnetic resonance imaging (fMRI); however, it suffers from image distortion and signal loss because of large susceptibility effects that are related to the phase-encoding direction of the scan. Despite this relation, the majority of neuroimaging studies has not considered the influence of phase-encoding direction. Here, we aimed to clarify how phase-encoding direction can affect the outcome of an fMRI connectivity study of schizophrenia (SCZ). METHODS: Resting-state fMRI using anterior to posterior (A-P) and posterior to anterior (P-A) directions was used to examine 25 patients with SCZ and 37 matched healthy controls (HC). We conducted a functional connectivity (FC) analysis using independent component analysis and performed three group comparisons: (i) A-P versus P-A (all participants); (ii) SCZ versus HC for the A-P and P-A datasets; and (iii) the interaction between phase-encoding direction and participant group. RESULTS: The estimated FC differed between the two phase-encoding directions in areas that were more extensive than those where signal loss has been reported. Although FC in the SCZ group was lower than that in the HC group for both directions, the A-P and P-A conditions did not exhibit the same specific pattern of differences. Further, we observed an interaction between participant group and the phase-encoding direction in the left temporoparietal junction and left fusiform gyrus. CONCLUSION: Phase-encoding direction can influence the results of FC studies. Thus, appropriate selection and documentation of phase-encoding direction will be important in future resting-state fMRI studies.

Additive Effect of Cigarette Smoking on Gray Matter Abnormalities in Schizophrenia.

It is widely known that there is a high prevalence of cigarette smoking in schizophrenia. One of the explanations is the self-medication hypothesis. Based on this hypothesis, it has been suggested that nicotine has procognitive effect or even neuroprotective effect in schizophrenia. However, cigarettes contain numerous neurotoxic substances, making the net effect of cigarette smoking on brain function and structure complex. Indeed, recent studies have called into question the self-medication hypothesis. We aimed to test whether there is an interaction between diagnosis and smoking status in gray matter volume, ie, whether smoking has specific effects on gray matter or whether main effects of these 2 variables additively affect common brain regions. Magnetic resonance imaging (MRI) images were obtained from 4 groups: (1) normal controls with no smoking history, (2) normal controls currently smoking and/or with a past history of smoking, (3) schizophrenia patients with no smoking history, and (4) schizophrenia patients currently smoking and/or with a past history of smoking. We used voxel-based morphometry to compare gray matter volumes among the 4 groups. We did not find any interaction between diagnosis and smoking, but we did find negative additive effects of schizophrenia diagnosis and smoking status in the left prefrontal cortex. The decrease in left prefrontal volume was associated with greater numbers of cigarette pack years and severe positive and negative symptoms. The current findings do not support the neuroprotective effect of smoking on gross brain structure in schizophrenia, emphasizing the necessity of longitudinal studies to test causal relationships among these variables.