Manifold alteration between major depressive disorder and healthy control subjects using dynamic mode decomposition in resting-state fMRI data.

Background: The World Health Organization has reported that approximately 300 million individuals suffer from the mood disorder known as MDD. Non-invasive measurement techniques have been utilized to reveal the mechanism of MDD, with rsfMRI being the predominant method. The previous functional connectivity and energy landscape studies have shown the difference in the coactivation patterns between MDD and HCs. However, these studies did not consider oscillatory temporal dynamics. Methods: In this study, the dynamic mode decomposition, a method to compute a set of coherent spatial patterns associated with the oscillation frequency and temporal decay rate, was employed to investigate the alteration of the occurrence of dynamic modes between MDD and HCs. Specifically, The BOLD signals of each subject were transformed into dynamic modes representing coherent spatial patterns and discrete-time eigenvalues to capture temporal variations using dynamic mode decomposition. All the dynamic modes were disentangled into a two-dimensional manifold using t-SNE. Density estimation and density ratio estimation were applied to the two-dimensional manifolds after the two-dimensional manifold was split based on HCs and MDD. Results: The dynamic modes that uniquely emerged in the MDD were not observed. Instead, we have found some dynamic modes that have shown increased or reduced occurrence in MDD compared with HCs. The reduced dynamic modes were associated with the visual and saliency networks while the increased dynamic modes were associated with the default mode and sensory-motor networks. Conclusion: To the best of our knowledge, this study showed initial evidence of the alteration of occurrence of the dynamic modes between MDD and HCs. To deepen understanding of how the alteration of the dynamic modes emerges from the structure, it is vital to investigate the relationship between the dynamic modes, cortical thickness, and surface areas.

Resting-state functional magnetic resonance imaging indices are related to electrophysiological dysfunction in degenerative cervical myelopathy.

The age-related degenerative pathologies of the cervical spinal column that comprise degenerative cervical myelopathy (DCM) cause myelopathy due spinal cord compression. Functional neurological assessment of DCM can potentially reveal the severity and pathological mechanism of DCM. However, functional assessment by conventional MRI remains difficult. This study used resting-state functional MRI (rs-fMRI) to investigate the relationship between functional connectivity (FC) strength and neurophysiological indices and examined the feasibility of functional assessment by FC for DCM. Preoperatively, 34 patients with DCM underwent rs-fMRI scans. Preoperative central motor conduction time (CMCT) reflecting motor functional disability and intraoperative somatosensory evoked potentials (SEP) reflecting sensory functional disability were recorded as electrophysiological indices of severity of the cervical spinal cord impairment. We performed seed-to-voxel FC analysis and correlation analyses between FC strength and the two electrophysiological indices. We found that FC strength between the primary motor cortex and the precuneus correlated significantly positively with CMCT, and that between the lateral part of the sensorimotor cortex and the lateral occipital cortex also showed a significantly positive correlation with SEP amplitudes. These results suggest that we can evaluate neurological and electrophysiological severity in patients with DCM by analyzing FC strengths between certain brain regions.

Cerebral cortical structural alteration patterns across four major psychiatric disorders in 5549 individuals.

According to the operational diagnostic criteria, psychiatric disorders such as schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and autism spectrum disorder (ASD) are classified based on symptoms. While its cluster of symptoms defines each of these psychiatric disorders, there is also an overlap in symptoms between the disorders. We hypothesized that there are also similarities and differences in cortical structural neuroimaging features among these psychiatric disorders. T1-weighted magnetic resonance imaging scans were performed for 5,549 subjects recruited from 14 sites. Effect sizes were determined using a linear regression model within each protocol, and these effect sizes were meta-analyzed. The similarity of the differences in cortical thickness and surface area of each disorder group was calculated using cosine similarity, which was calculated from the effect sizes of each cortical regions. The thinnest cortex was found in SZ, followed by BD and MDD. The cosine similarity values between disorders were 0.943 for SZ and BD, 0.959 for SZ and MDD, and 0.943 for BD and MDD, which indicated that a common pattern of cortical thickness alterations was found among SZ, BD, and MDD. Additionally, a generally smaller cortical surface area was found in SZ and MDD than in BD, and the effect was larger in SZ. The cosine similarity values between disorders were 0.945 for SZ and MDD, 0.867 for SZ and ASD, and 0.811 for MDD and ASD, which indicated a common pattern of cortical surface area alterations among SZ, MDD, and ASD. Patterns of alterations in cortical thickness and surface area were revealed in the four major psychiatric disorders. To our knowledge, this is the first report of a cross-disorder analysis conducted on four major psychiatric disorders. Cross-disorder brain imaging research can help to advance our understanding of the pathogenesis of psychiatric disorders and common symptoms.

Subcortical volumetric alterations in four major psychiatric disorders: a mega-analysis study of 5604 subjects and a volumetric data-driven approach for classification.

Differential diagnosis is sometimes difficult in practical psychiatric settings, in terms of using the current diagnostic system based on presenting symptoms and signs. The creation of a novel diagnostic system using objective biomarkers is expected to take place. Neuroimaging studies and others reported that subcortical brain structures are the hubs for various psycho-behavioral functions, while there are so far no neuroimaging data-driven clinical criteria overcoming limitations of the current diagnostic system, which would reflect cognitive/social functioning. Prior to the main analysis, we conducted a large-scale multisite study of subcortical volumetric and lateralization alterations in schizophrenia, bipolar disorder, major depressive disorder, and autism spectrum disorder using T1-weighted images of 5604 subjects (3078 controls and 2526 patients). We demonstrated larger lateral ventricles volume in schizophrenia, bipolar disorder, and major depressive disorder, smaller hippocampus volume in schizophrenia and bipolar disorder, and schizophrenia-specific smaller amygdala, thalamus, and accumbens volumes and larger caudate, putamen, and pallidum volumes. In addition, we observed a leftward alteration of lateralization for pallidum volume specifically in schizophrenia. Moreover, as our main objective, we clustered the 5,604 subjects based on subcortical volumes, and explored whether data-driven clustering results can explain cognitive/social functioning in the subcohorts. We showed a four-biotype classification, namely extremely (Brain Biotype [BB] 1) and moderately smaller limbic regions (BB2), larger basal ganglia (BB3), and normal volumes (BB4), being associated with cognitive/social functioning. Specifically, BB1 and BB2-3 were associated with severe and mild cognitive/social impairment, respectively, while BB4 was characterized by normal cognitive/social functioning. Our results may lead to the future creation of novel biological data-driven psychiatric diagnostic criteria, which may be expected to be useful for prediction or therapeutic selection.

Prefrontal cortex activities during verbal fluency and emotional words tasks in major depressive, adjustment, and bipolar disorders with depressive states.

BACKGROUND: It can be difficult to differentiate psychiatric disorders from depressive states, with little knowledge on how to differentiate them. This study aimed to evaluate changes in brain activity during cognitive and emotional tasks in patients with depressive state to help with differential diagnoses. METHODS: Sixty-two patients with depressive states [17 with adjustment disorder (AD), 27 with major depressive disorder (MDD), and 18 with bipolar disorder (BD)] and 34 healthy controls (HC) were recruited. We used a verbal fluency task (VFT) and emotional word tasks with happy and threat words. Functional near-infrared spectroscopy measured the relative change in oxygenated hemoglobin in the frontotemporal areas. RESULTS: During the VFT, patients with AD or MDD showed significantly reduced activation in the bilateral frontotemporal region (all p < 0.01), whereas patients with BD demonstrated significantly reduced activation in the right frontotemporal areas compared to HC (p < 0.01). During the emotional words task with happy words, patients with MDD showed significantly increased activity in the frontopolar area compared to HC (p = 0.023). Binary logistic regression analysis showed that MDD or BD was significantly associated with brain activity during the happy word task. In distinguishing MDD or BD from HC, the happy words task performed equally well, with an area under the curve of 0.70. LIMITATIONS: All study patients were taking psychotropic drugs. CONCLUSIONS: Brain activation in response to a combination of cognitive or emotional stimuli could assist in distinguishing patients with depressive states from healthy controls.

Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts.

Large-scale neuroimaging data acquired and shared by multiple institutions are essential to advance neuroscientific understanding of pathophysiological mechanisms in psychiatric disorders, such as major depressive disorder (MDD). About 75% of studies that have applied machine learning technique to neuroimaging have been based on diagnoses by clinicians. However, an increasing number of studies have highlighted the difficulty in finding a clear association between existing clinical diagnostic categories and neurobiological abnormalities. Here, using resting-state functional magnetic resonance imaging, we determined and validated resting-state functional connectivity related to depression symptoms that were thought to be directly related to neurobiological abnormalities. We then compared the resting-state functional connectivity related to depression symptoms with that related to depression diagnosis that we recently identified. In particular, for the discovery dataset with 477 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a brain network prediction model of depression symptoms (Beck Depression Inventory-II [BDI] score). The prediction model significantly predicted BDI score for an independent validation dataset with 439 participants from 4 different imaging sites. Finally, we found 3 common functional connections between those related to depression symptoms and those related to MDD diagnosis. These findings contribute to a deeper understanding of the neural circuitry of depressive symptoms in MDD, a hetero-symptomatic population, revealing the neural basis of MDD.

Distinct epigenetic signatures between adult-onset and late-onset depression.

The heterogeneity of major depressive disorder (MDD) is attributed to the fact that diagnostic criteria (e.g., DSM-5) are only based on clinical symptoms. The discovery of blood biomarkers has the potential to change the diagnosis of MDD. The purpose of this study was to identify blood biomarkers of DNA methylation by strategically subtyping patients with MDD by onset age. We analyzed genome-wide DNA methylation of patients with adult-onset depression (AOD; age ≥ 50 years, age at depression onset < 50 years; N = 10) and late-onset depression (LOD; age ≥ 50 years, age at depression onset ≥ 50 years; N = 25) in comparison to that of 30 healthy subjects. The methylation profile of the AOD group was not only different from that of the LOD group but also more homogenous. Six identified methylation CpG sites were validated by pyrosequencing and amplicon bisulfite sequencing as potential markers for AOD in a second set of independent patients with AOD and healthy control subjects (N = 11). The combination of three specific methylation markers achieved the highest accuracy (sensitivity, 64%; specificity, 91%; accuracy, 77%). Taken together, our findings suggest that DNA methylation markers are more suitable for AOD than for LOD patients.

Generalizable brain network markers of major depressive disorder across multiple imaging sites.

Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable marker of major depressive disorder (MDD) that would distinguish patients from healthy controls based on resting-state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD classifier. The classifier achieved an approximately 70% generalization accuracy for an independent validation dataset with 521 participants from 5 different imaging sites. The successful generalization to a perfectly independent dataset acquired from multiple imaging sites is novel and ensures scientific reproducibility and clinical applicability.

Characterization of the signature of peripheral innate immunity in women with later-life major depressive disorder.

The prevalence of depression in later life is higher in women than in men. However, the sex difference in the pathophysiology of depression in elderly patients is not fully understood. Here, we performed gene expression profiling in leukocytes of middle-aged and elderly patients with major depressive disorder, termed later-life depression (LLD) in this context, and we characterized the sex-dependent pathophysiology of LLD. A microarray dataset obtained from leukocytes of patients (aged ≥50 years) with LLD (32 males and 39 females) and age-matched healthy individuals (20 males and 24 females) was used. Differentially expressed probes were determined by comparing the expression levels between patients and healthy individuals, and then functional annotation analyses (Ingenuity Pathway Analysis, Reactome pathway analysis, and cell-type enrichment analysis) were performed. A total of 1656 probes were differentially expressed in LLD females, but only 3 genes were differentially expressed in LLD males. The differentially expressed genes in LLD females were relevant to leukocyte extravasation signaling, Tec kinase signaling and the innate immune response. The upregulated genes were relevant to myeloid lineage cells such as CD14+ monocytes. In contrast, the downregulated genes were relevant to CD4+ and CD8+ T cells. Remarkable innate immune signatures are present in the leukocytes of LLD females but not males. Because inflammation is involved in the pathophysiology of depression, the altered inflammatory activity may be involved in the pathophysiology of LLD in women. In contrast, abnormal inflammation may be an uncommon feature in LLD males.

White matter microstructural alterations across four major psychiatric disorders: mega-analysis study in 2937 individuals.

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 cross-disorder 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.

Altered expression of long noncoding RNAs in patients with major depressive disorder.

Although major depressive disorder (MDD) is a leading cause of disability worldwide, its pathophysiology is poorly understood. Increasing evidence suggests that aberrant regulation of transcription plays a key role in the pathophysiology of MDD. Recently, long noncoding RNAs (lncRNAs) have been recognized for their important functions in chromatin structure, gene expression, and the subsequent manifestation of various biological processes in the central nervous system. However, it is unclear whether the aberrant expression and function of lncRNAs are associated with the pathophysiology of MDD. In this study, we sought to evaluate the expression of lncRNAs in peripheral blood leukocytes as potential biomarkers for MDD. We measured the expression levels of 83 lncRNAs in the peripheral blood leukocytes of 29 MDD patients and 29 age- and gender-matched healthy controls using quantitative reverse transcription PCR (RT-qPCR) analysis. We found that MDD patients exhibited distinct expression signatures. Specifically, the expression level of one lncRNA (RMRP) was lower while the levels of four (Y5, MER11C, PCAT1, and PCAT29) were higher in MDD patients compared to healthy controls. The expression level of RMRP was correlated with depression severity as measured by the Hamilton Depression Rating Scale (HAM-D). Moreover, RMRP expression was lower in a mouse model of depression, corroborating the observation from MDD patients. Taken together, our data suggest that lower RMRP levels may serve as a potential biomarker for MDD.

Distinctive Neuroanatomical Substrates for Depression in Bipolar Disorder versus Major Depressive Disorder.

No neuroanatomical substrates for distinguishing between depression of bipolar disorder (dBD) and major depressive disorder (dMDD) are currently known. The aim of the current multicenter study was to identify neuroanatomical patterns distinct to depressed patients with the two disorders. Further analysis was conducted on an independent sample to enable generalization of results. We directly compared MR images of these subjects using voxel-based morphometry (VBM) and a support vector machine (SVM) algorithm using 1531 participants. The VBM analysis showed significantly reduced gray matter volumes in the bilateral dorsolateral prefrontal (DLPFC) and anterior cingulate cortices (ACC) in patients with dBD compared with those with dMDD. Patients with the two disorders shared small gray matter volumes for the right ACC and left inferior frontal gyrus when compared with healthy subjects. Voxel signals in these regions during SVM analysis contributed to an accurate classification of the two diagnoses. The VBM and SVM results in the second cohort also supported these results. The current findings provide new evidence that gray matter volumes in the DLPFC and ACC are core regions in displaying shared and distinct neuroanatomical substrates and can shed light on elucidation of neural mechanism for depression within the bipolar/major depressive disorder continuum.

Altered Connectivity of the Anterior Cingulate and the Posterior Superior Temporal Gyrus in a Longitudinal Study of Later-life Depression.

Patients with later-life depression (LLD) show abnormal gray matter (GM) volume, white matter (WM) integrity and functional connectivity in the anterior cingulate cortex (ACC) and posterior superior temporal gyrus (pSTG), but it remains unclear whether these abnormalities persist over time. We examined whether structural and functional abnormalities in these two regions are present within the same subjects during depressed vs. remitted phases. Sixteen patients with LLD and 30 healthy subjects were studied over a period of 1.5 years. Brain images obtained with a 3-Tesla magnetic resonance imaging (MRI) system were analyzed by voxel-based morphometry of the GM volume, and diffusion tensor imaging (DTI) and resting-state functional MRI were used to assess ACC-pSTG connectivity. Patients with LLD in the depressed and remitted phases showed significantly smaller GM volume in the left ACC and left pSTG than healthy subjects. Both patients with LLD in the depressed and remitted phases had significantly higher diffusivities in the WM tract of the left ACC-pSTG than healthy subjects. Remitted patients with LLD showed lower functional ACC-pSTG connectivity compared to healthy subjects. No difference was found in the two regions between depressed and remitted patients in GM volume, structural or functional connectivity. Functional ACC-pSTG connectivity was positively correlated with lower global function during remission. Our preliminary data show that structural and functional abnormalities of the ACC and pSTG occur during LLD remission. Our findings tentatively reveal the brain pathophysiology involved in LLD and may aid in developing neuroanatomical biomarkers for this condition.

Differences in frontotemporal dysfunction during social and non-social cognition tasks between patients with autism spectrum disorder and schizophrenia.

Although literature evidence suggests deficits in social and non-social cognition in patients with autistic spectrum disorder (ASD) and schizophrenia (SCZ), the difference in neural correlates of the impairments between the two disorders has not been elucidated. We examined brain function in response to a non-social cognition and a social cognition task using functional near-infrared spectroscopy (fNIRS) in 13 patients with ASD, 15 patients with SCZ, and 18 healthy subjects. We assessed the brain function of participants using a verbal fluency task and an emotional facial recognition task. The patients with ASD showed significantly reduced brain activation in the left frontotemporal area during both tasks compared to healthy subjects. The patients with ASD with larger score in 'attention to detail' in the autism spectrum quotient showed lower activation of the left frontotemporal area during the two tasks. The patients with SCZ showed significantly reduced activation, compared to healthy subjects, and greater activation, compared to patients with ASD, in the area during the verbal fluency task. The patients with SCZ with more severe symptoms had lower brain activation during the task in this area. Our results suggest that two distinct areas are involved in the distinctive brain pathophysiology relevant to cognitive processing in patients with ASD and SCZ.

Altered plasma protein glycosylation in a mouse model of depression and in patients with major depression.

BACKGROUND: Glycosylation is a common posttranslational modification in protein biosynthesis that is implicated in several disease states. It has been reported that specific protein glycan structures are useful as biomarkers for cancer and some neuropsychiatric diseases; however, the relationship between plasma protein glycosylation and major depressive disorder (MDD) has not been investigated to date. The aim of this study was to determine whether plasma protein glycan structures are altered in depression using a stress-based mouse model and samples from patients with MDD. METHODS: We used chronic ultra-mildly stressed mice that were untreated or treated with imipramine as mouse models of depression and remission, respectively. We also made comparisons between samples from depressed and remitted patients with MDD. Protein glycosylation was analyzed using a lectin microarray that included 45 lectins with binding affinities for various glycan structures. RESULTS: Sia-alpha2-6Gal/GalNAc was a commonly altered glycan structure in both depression model mice and patients with MDD. Moreover, the expression of ST6GALNAC2 was decreased in leukocytes from patients with MDD. LIMITATIONS: Our study samples were small and we did not identify specific alpha2-6Gal/GalNAc-sialylated proteins. CONCLUSIONS: The glycan structure Sia-alpha2-6GalNAc in plasma protein and ST6GALNAC2 expression in peripheral leukocytes may have utility as candidate biomarkers for the clinical diagnosis and monitoring of MDD.

Effects of task language and second-language proficiency on the neural correlates of phonemic fluency in native Japanese speakers: a functional near-infrared spectroscopy study.

Data collected during a phonemic fluency task (or 'FAS test'), a standard component of neuropsychological batteries for assessment of cognitive deficits, may be language-dependent and may differ depending on second-language proficiency. The unique orthographic/phonological system of the task language, and the reported cognitive advantages inherent to bilinguals, may each influence the task's neural correlates. However, language background is not currently assessed in most studies testing phonemic fluency. Here, we used 52-channel functional near-infrared spectroscopy in college-aged native-Japanese subjects to examine functional changes in oxygenated hemoglobin elicited during a phonemic fluency task performed in Japanese and in English. We found activity differences that were related to task language and second-language proficiency. Besides loci activated in the Japanese test, bilateral precentral channels were specifically recruited in the English test. Furthermore, the higher-proficiency group showed almost no increase in oxygenated hemoglobin in either language context, whereas participants with lower proficiency showed widespread increases for both contexts. We interpret precentral increases as the consequence of additional articulatory resource recruitment in a second-language context. As for the lack of such variation in the higher-proficiency group, it may reflect an advantage in nonverbal executive control in this group. Together, our results point to language background and proficiency as confounding variables in neuroimaging studies of phonemic fluency and that the adequacy of such measures in populations with varying language backgrounds needs to be considered in future studies.

Disconnectivity between Dorsal Raphe Nucleus and Posterior Cingulate Cortex in Later Life Depression.

The dorsal raphe nucleus (DRN) has been repeatedly implicated as having a significant relationship with depression, along with its serotoninergic innervation. However, functional connectivity of the DRN in depression is not well understood. The current study aimed to isolate functional connectivity of the DRN distinct in later life depression (LLD) compared to a healthy age-matched population. Resting state functional magnetic resonance imaging (rsfMRI) data from 95 participants (33 LLD and 62 healthy) were collected to examine functional connectivity from the DRN to the whole brain in voxel-wise fashion. The posterior cingulate cortex (PCC) bilaterally showed significantly smaller connectivity in the LLD group than the control group. The DRN to PCC connectivity did not show any association with the depressive status. The findings implicate that the LLD involves disruption of serotoninergic input to the PCC, which has been suggested to be a part of the reduced default mode network in depression.

Identification of commonly altered genes between in major depressive disorder and a mouse model of depression.

The heterogeneity of depression (due to factors such as varying age of onset) may explain why biological markers of major depressive disorder (MDD) remain uncertain. We aimed to identify gene expression markers of MDD in leukocytes using microarray analysis. We analyzed gene expression profiles of patients with MDD (age ≥50, age of depression onset <50) (N = 10, depressed state; N = 13, remitted state). Seven-hundred and ninety-seven genes (558 upregulated, 239 downregulated when compared to those of 30 healthy subjects) were identified as potential markers for MDD. These genes were then cross-matched to microarray data obtained from a mouse model of depression (676 genes, 148 upregulated, 528 downregulated). Of the six common genes identified between patients and mice, five genes (SLC35A3, HIST1H2AL, YEATS4, ERLIN2, and PLPP5) were confirmed to be downregulated in patients with MDD by quantitative real-time polymerase chain reaction. Of these genes, HIST1H2AL was significantly decreased in a second set of independent subjects (age ≥20, age of onset <50) (N = 18, subjects with MDD in a depressed state; N = 19, healthy control participants). Taken together, our findings suggest that HIST1H2AL may be a biological marker of MDD.

Distinct and Shared Endophenotypes of Neural Substrates in Bipolar and Major Depressive Disorders.

Little is known about disorder-specific biomarkers of bipolar disorder (BD) and major depressive disorder (MDD). Our aim was to determine a neural substrate that could be used to distinguish BD from MDD. Our study included a BD group (10 patients with BD, 10 first-degree relatives (FDRs) of individuals with BD), MDD group (17 patients with MDD, 17 FDRs of individuals with MDD), and 27 healthy individuals. Structural and functional brain abnormalities were evaluated by voxel-based morphometry and a trail making test (TMT), respectively. The BD group showed a significant main effect of diagnosis in the gray matter (GM) volume of the anterior cingulate cortex (ACC; p = 0.01) and left insula (p < 0.01). FDRs of individuals with BD showed significantly smaller left ACC GM volume than healthy subjects (p < 0.01), and patients with BD showed significantly smaller ACC (p < 0.01) and left insular GM volume (p < 0.01) than healthy subjects. The MDD group showed a tendency toward a main effect of diagnosis in the right and left insular GM volume. The BD group showed a significantly inverse correlation between the left insular GM volume and TMT-A scores (p < 0.05). Our results suggest that the ACC volume could be a distinct endophenotype of BD, while the insular volume could be a shared BD and MDD endophenotype. Moreover, the insula could be associated with cognitive decline and poor outcome in BD.