Characterizing the distinct imaging phenotypes, clinical behavior, and genetic vulnerability of brain maturational subtypes in mood disorders.

BACKGROUND: Mood disorders are characterized by great heterogeneity in clinical manifestation. Uncovering such heterogeneity using neuroimaging-based individual biomarkers, clinical behaviors, and genetic risks, might contribute to elucidating the etiology of these diseases and support precision medicine. METHODS: We recruited 174 drug-naïve and drug-free patients with major depressive disorder and bipolar disorder, as well as 404 healthy controls. T1 MRI imaging data, clinical symptoms, and neurocognitive assessments, and genetics were obtained and analyzed. We applied regional gray matter volumes (GMV) and quantile normative modeling to create maturation curves, and then calculated individual deviations to identify subtypes within the patients using hierarchical clustering. We compared the between-subtype differences in GMV deviations, clinical behaviors, cell-specific transcriptomic associations, and polygenic risk scores. We also validated the GMV deviations based subtyping analysis in a replication cohort. RESULTS: Two subtypes emerged: subtype 1, characterized by increased GMV deviations in the frontal cortex, cognitive impairment, a higher genetic risk for Alzheimer's disease, and transcriptionally associated with Alzheimer's disease pathways, oligodendrocytes, and endothelial cells; and subtype 2, displaying globally decreased GMV deviations, more severe depressive symptoms, increased genetic vulnerability to major depressive disorder and transcriptionally related to microglia and inhibitory neurons. The distinct patterns of GMV deviations in the frontal, cingulate, and primary motor cortices between subtypes were shown to be replicable. CONCLUSIONS: Our current results provide vital links between MRI-derived phenotypes, spatial transcriptome, genetic vulnerability, and clinical manifestation, and uncover the heterogeneity of mood disorders in biological and behavioral terms.

Unmasking the mental health scars of COVID-19: A longitudinal investigation of children and adolescents in post-lockdown China.

The long-term mental health consequences of COVID-19 in children and adolescents remain unclear. We investigated the impact of COVID-19 infection on mental health after China's zero-COVID policy relaxation, focusing on symptom-specific and social-family risk factors for mental health issues in children and adolescents. In a longitudinal study, 8348 youths (aged 10-18) were assessed twice (T1: September to October 2022 and T2: April to May 2023). Mental health changes (Δ=T1-T2) were compared between COVID-19-infected (COVID+, n = 4108) and non-infected (COVID-, n = 4240). After balancing social-family confounding factors at T1 with propensity score-based inverse probability weights, multivariable logistic regression was employed to assess associations between COVID-19 infection and the onset/worsening of mental health symptoms. Multivariable logistic regression was conducted to explore specific acute COVID-19 symptoms and social-family risk factors associated with the onset/worsening of mental health symptoms in COVID + group. Compared to COVID- group, COVID + group exhibited lower overall mental health improvement (Δ). COVID + group was associated with increased risks of depression worsening (OR 1.20, 95 % CI 1.04-1.39), anxiety worsening (OR 1.30, 95 % CI 1.15-1.47), stress worsening (OR 1.23, 95 % CI 1.03-1.46), insomnia worsening (OR 1.21, 95 % CI 1.05-1.39), and emotional symptoms worsening (OR 1.72, 95 % CI 1.27-2.33). Moderate-to-severe difficulty thinking, breathlessness, and gastrointestinal symptoms were specific COVID-19 symptoms associated with worsening of various mental health outcomes. Furthermore, academic difficulties, economic disadvantages, family conflicts, food addiction, and alcohol consumption were identified as social-family risk factors for worsening mental health symptoms in COVID + youths. COVID-19 infection leaves lasting mental health scars in youths, extending beyond the acute phase. Specific symptoms, particularly cognitive dysfunction and respiratory/gastrointestinal distress play a significant role in this vulnerability. Social-family factors further modulate these effects, highlighting the need for comprehensive interventions that address both biological and psychosocial aspects. This study provides valuable insights for tailoring mental health support to youths navigating the consequences of the COVID-19 pandemic.

Applying dimensional psychopathology: transdiagnostic prediction of executive cognition using brain connectivity and inflammatory biomarkers.

BACKGROUND: The association between executive dysfunction, brain dysconnectivity, and inflammation is a prominent feature across major psychiatric disorders (MPDs), schizophrenia, bipolar disorder, and major depressive disorder. A dimensional approach is warranted to delineate their mechanistic interplay across MPDs. METHODS: This single site study included a total of 1543 participants (1058 patients and 485 controls). In total, 1169 participants underwent diffusion tensor and resting-state functional magnetic resonance imaging (745 patients and 379 controls completed the Wisconsin Card Sorting Test). Fractional anisotropy (FA) and regional homogeneity (ReHo) assessed structural and functional connectivity, respectively. Pro-inflammatory cytokine levels [interleukin (IL)-1ß, IL-6, and tumor necrosis factor-α] were obtained in 325 participants using blood samples collected with 24 h of scanning. Group differences were determined for main measures, and correlation and mediation analyses and machine learning prediction modeling were performed. RESULTS: Executive deficits were associated with decreased FA, increased ReHo, and elevated IL-1ß and IL-6 levels across MPDs, compared to controls. FA and ReHo alterations in fronto-limbic-striatal regions contributed to executive deficits. IL-1ß mediated the association between FA and cognition, and IL-6 mediated the relationship between ReHo and cognition. Executive cognition was better predicted by both brain connectivity and cytokine measures than either one alone for FA-IL-1ß and ReHo-IL-6. CONCLUSIONS: Transdiagnostic associations among brain connectivity, inflammation, and executive cognition exist across MPDs, implicating common neurobiological substrates and mechanisms for executive deficits in MPDs. Further, inflammation-related brain dysconnectivity within fronto-limbic-striatal regions may represent a transdiagnostic dimension underlying executive dysfunction that could be leveraged to advance treatment.

Neurodevelopmental trajectories, polygenic risk, and lipometabolism in vulnerability and resilience to schizophrenia.

BACKGROUND: Schizophrenia (SZ) arises from a complex interplay involving genetic and molecular factors. Early intervention of SZ hinges upon understanding its vulnerability and resiliency factors in study of SZ and genetic high risk for SZ (GHR). METHODS: Herein, using integrative and multimodal strategies, we first performed a longitudinal study of neural function as measured by amplitude of low frequency function (ALFF) in 21 SZ, 26 GHR, and 39 healthy controls to characterize neurodevelopmental trajectories of SZ and GHR. Then, we examined the relationship between polygenic risk score for SZ (SZ-PRS), lipid metabolism, and ALFF in 78 SZ, and 75 GHR in cross-sectional design to understand its genetic and molecular substrates. RESULTS: Across time, SZ and GHR diverge in ALFF alterations of the left medial orbital frontal cortex (MOF). At baseline, both SZ and GHR had increased left MOF ALFF compared to HC (P < 0.05). At follow-up, increased ALFF persisted in SZ, yet normalized in GHR. Further, membrane genes and lipid species for cell membranes predicted left MOF ALFF in SZ; whereas in GHR, fatty acids best predicted and were negatively correlated (r = -0.302, P < 0.05) with left MOF. CONCLUSIONS: Our findings implicate divergence in ALFF alteration in left MOF between SZ and GHR with disease progression, reflecting vulnerability and resiliency to SZ. They also indicate different influences of membrane genes and lipid metabolism on left MOF ALFF in SZ and GHR, which have important implications for understanding mechanisms underlying vulnerability and resiliency in SZ and contribute to translational efforts for early intervention.

Identifying and validating subtypes within major psychiatric disorders based on frontal-posterior functional imbalance via deep learning.

Converging evidence increasingly implicates shared etiologic and pathophysiological characteristics among major psychiatric disorders (MPDs), such as schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). Examining the neurobiology of the psychotic-affective spectrum may greatly advance biological determination of psychiatric diagnosis, which is critical for the development of more effective treatments. In this study, ensemble clustering was developed to identify subtypes within a trans-diagnostic sample of MPDs. Whole brain amplitude of low-frequency fluctuations (ALFF) was used to extract the low-dimensional features for clustering in a total of 944 participants: 581 psychiatric patients (193 with SZ, 171 with BD, and 217 with MDD) and 363 healthy controls (HC). We identified two subtypes with differentiating patterns of functional imbalance between frontal and posterior brain regions, as compared to HC: (1) Archetypal MPDs (60% of MPDs) had increased frontal and decreased posterior ALFF, and decreased cortical thickness and white matter integrity in multiple brain regions that were associated with increased polygenic risk scores and enriched risk gene expression in brain tissues; (2) Atypical MPDs (40% of MPDs) had decreased frontal and increased posterior ALFF with no associated alterations in validity measures. Medicated Archetypal MPDs had lower symptom severity than their unmedicated counterparts; whereas medicated and unmedicated Atypical MPDs had no differences in symptom scores. Our findings suggest that frontal versus posterior functional imbalance as measured by ALFF is a novel putative trans-diagnostic biomarker differentiating subtypes of MPDs that could have implications for precision medicine.

Combinatorial panel with endophenotypes from multilevel information of diffusion tensor imaging and lipid profile as predictors for depression.

OBJECTIVE: Clinical heterogeneity in major depressive disorder likely reflects the range of etiology and contributing factors in the disorder, such as genetic risk. Identification of more refined subgroups based on biomarkers such as white matter integrity and lipid-related metabolites could facilitate precision medicine in major depressive disorder. METHODS: A total of 148 participants (15 genetic high-risk participants, 57 patients with first-episode major depressive disorder and 76 healthy controls) underwent diffusion tensor imaging and plasma lipid profiling. Alterations in white matter integrity and lipid metabolites were identified in genetic high-risk participants and patients with first-episode major depressive disorder. Then, shared alterations between genetic high-risk and first-episode major depressive disorder were used to develop an imaging x metabolite diagnostic panel for genetically based major depressive disorder via factor analysis and logistic regression. A fivefold cross-validation test was performed to evaluate the diagnostic panel. RESULTS: Alterations of white matter integrity in corona radiata, superior longitudinal fasciculus and the body of corpus callosum and dysregulated unsaturated fatty acid metabolism were identified in both genetic high-risk participants and patients with first-episode major depressive disorder. An imaging x metabolite diagnostic panel, consisting of measures for white matter integrity and unsaturated fatty acid metabolism, was identified that achieved an area under the receiver operating characteristic curve of 0.86 and had a significantly higher diagnostic performance than that using either measure alone. And cross-validation confirmed the adequate reliability and accuracy of the diagnostic panel. CONCLUSION: Combining white matter integrity in corpus callosum, superior longitudinal fasciculus and corona radiata, and unsaturated fatty acid profile may improve the identification of genetically based endophenotypes in major depressive disorder to advance precision medicine strategies.

Transdiagnostic time-varying dysconnectivity across major psychiatric disorders.

Dynamic functional connectivity (DFC) analysis can capture time-varying properties of connectivity. However, studies on large samples using DFC to investigate transdiagnostic dysconnectivity across schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD) are rare. In this study, we used resting-state functional magnetic resonance imaging and a sliding-window method to study DFC in a total of 610 individuals (150 with SZ, 100 with BD, 150 with MDD, and 210 healthy controls [HC]) at a single site. Using k-means clustering, DFCs were clustered into three functional connectivity states: one was a more frequent state with moderate positive and negative connectivity (State 1), and the other two were less frequent states with stronger positive and negative connectivity (State 2 and State 3). Significant 4-group differences (SZ, BD, MDD, and HC groups; q < .05, false-discovery rate [FDR]-corrected) in DFC were nearly only in State 1. Post hoc analyses (q < .05, FDR-corrected) in State 1 showed that transdiagnostic dysconnectivity patterns among SZ, BD and MDD featured consistently decreased connectivity within most networks (the visual, somatomotor, salience and frontoparietal networks), which was most obvious in both range and extent for SZ. Our findings suggest that there is more common dysconnectivity across SZ, BD and MDD than we previously expected and that such dysconnectivity is state-dependent, which provides new insights into the pathophysiological mechanism of major psychiatric disorders.

Neurobiological substrates of major psychiatry disorders: transdiagnostic associations between white matter abnormalities, neuregulin 1 and clinical manifestation.

Background: Schizophrenia, bipolar disorder and major depressive disorder are increasingly being conceptualized as a transdiagnostic continuum. Disruption of white matter is a common alteration in these psychiatric disorders, but the molecular mechanisms underlying the disruption remain unclear. Neuregulin 1 (NRG1) is genetically linked with susceptibility to schizophrenia, bipolar disorder and major depressive disorder, and it is also related to white matter. Methods: Using a transdiagnostic approach, we aimed to identify white matter differences associated with NRG1 and their relationship to transdiagnostic symptoms and cognitive function. We examined the white matter of 1051 participants (318 healthy controls and 733 patients with major psychiatric disorders: 254 with schizophrenia, 212 with bipolar disorder and 267 with major depressive disorder) who underwent diffusion tensor imaging. We measured the plasma NRG1-ß1 levels of 331 participants. We also evaluated clinical symptoms and cognitive function. Results: In the patient group, abnormal white matter was negatively associated with NRG1-ß1 levels in the genu of the corpus callosum, right uncinate fasciculus, bilateral inferior fronto-occipital fasciculus, right external capsule, fornix, right optic tract, left straight gyrus white matter and left olfactory radiation. These NRG1-associated white matter abnormalities were also associated with depression and anxiety symptoms and executive function in patients with a major psychiatric disorder. Furthermore, across the 3 disorders we observed analogous alterations in white matter, NRG1-ß1 levels and clinical manifestations. Limitations: Medication status, the wide age range and our cross-sectional findings were limitations of this study. Conclusion: This study is the first to provide evidence for an association between NRG1, white matter abnormalities, clinical symptoms and cognition in a transdiagnostic psychiatric cohort. These findings provide further support for an understanding of the molecular mechanisms that underlie the neuroimaging substrates of major psychiatric disorders and their clinical implications.

Dynamic changes of functional segregation and integration in vulnerability and resilience to schizophrenia.

Schizophrenia (SZ) is a highly heritable disease with neurodevelopmental origins and significant functional brain network dysfunction. Functional network is heavily influenced by neurodevelopment processes and can be characterized by the degree of segregation and integration. This study examines functional segregation and integration in SZ and their first-degree relatives (high risk [HR]) to better understand the dynamic changes in vulnerability and resiliency, and disease markers. Resting-state functional magnetic resonance imaging data acquired from 137 SZ, 89 HR, and 210 healthy controls (HCs). Small-worldness σ was computed at voxel level to quantify balance between segregation and integration. Interregional functional associations were examined based on Euclidean distance between regions and reflect degree of segregation and integration. Distance strength maps were used to localize regions of altered distance-based functional connectivity. σ was significantly decreased in SZ compared to HC, with no differences in high risk (HR). In three-group comparison, significant differences were noted in short-range connectivity (primarily in the primary sensory, motor and their association cortices, and the thalamus) and medium/long-range connectivity (in the prefrontal cortices [PFCs]). Decreased short- and increased medium/long-range connectivity was found in SZ. Decreased short-range connectivity was seen in SZ and HR, while HR had decreased medium/long-range connectivity. We observed disrupted balance between segregation and integration in SZ, whereas relatively preserved in HR. Similarities and differences between SZ and HR, specific changes of SZ were found. These might reflect dynamic changes of segregation in primary cortices and integration in PFCs in vulnerability and resilience, and disease markers in SZ.

Spontaneous low-frequency fluctuations in the neural system for emotional perception in major psychiatric disorders: amplitude similarities and differences across frequency bands

Background: Growing evidence indicates both shared and distinct features of emotional perception in schizophrenia, bipolar disorder and major depressive disorder. In these disorders, alterations in spontaneous low-frequency fluctuations have been reported in the neural system for emotional perception, but the similarities and differences in the amplitude of low-frequency fluctuation (ALFF) across the 3 disorders are unknown. Methods: We compared ALFF and its signal balance in the neural system for emotional perception at 2 frequency bands (slow-5 and slow-4) in 119 participants with schizophrenia, 100 with bipolar disorder, 123 with major depressive disorder and 183 healthy controls. We performed exploratory Pearson partial correlation analyses to determine the relationship between ALFF signal balance and clinical variables. Results: We observed commonalities in ALFF change patterns across the 3 disorders for emotional perception neural substrates, such as increased ALFF in the anterior cerebrum (including subcortical, limbic, paralimbic and heteromodal cortical regions) and decreased ALFF in the posterior visual cortices. Schizophrenia, bipolar disorder and major depressive disorder showed significantly decreased ALFF signal balance in the neural system for emotional perception at both slow-5 and slow-4 frequency bands, with the greatest alterations for schizophrenia, followed by bipolar disorder and major depressive disorder. We found a negative correlation between ALFF signal balance and negative/disorganized symptoms in slow-4 across the 3 disorders. Limitations: The relatively broad age range in our sample and the cross-sectional study design may not account for our findings. Conclusion: The extent of the commonalities we observed further support the concept of core neurobiological disruptions shared among the 3 disorders; ALFF signal balance could be an important neuroimaging marker for the diagnosis and treatment of schizophrenia, bipolar disorder and major depressive disorder.

Alterations in amplitude of low frequency fluctuation in treatment-naïve major depressive disorder measured with resting-state fMRI.

There are limited resting-state functional magnetic resonance imaging (fMRI) studies in major depressive disorder (MDD). Of these studies, functional connectivity analyses are mostly used. However, a new method based on the magnitude of low frequency fluctuation (LFF) during resting-state fMRI may provide important insight into MDD. In this study, we examined the amplitude of LFF (ALFF) within the whole brain during resting-state fMRI in 30 treatment-naïve MDD subjects and 30 healthy control (HC) subjects. When compared with HC, MDD subjects showed increased ALFF in the frontal cortex (including the bilateral ventral/dorsal anterior cingulate cortex, orbitofrontal cortex, premotor cortex, ventral prefrontal cortex, left dorsal lateral frontal cortex, left superior frontal cortex), basal ganglia (including the right putamen and left caudate nucleus), left insular cortex, right anterior entorhinal cortex and left inferior parietal cortex, together with decreased ALFF in the bilateral occipital cortex, cerebellum hemisphere, and right superior temporal cortex. These findings may relate to characteristics of MDD, such as excessive self-referential processing and deficits in cognitive control of emotional processing, which may contribute to the persistent and recurrent nature of the disorder.

Integrative omics analysis reveals epigenomic and transcriptomic signatures underlying brain structural deficits in major depressive disorder.

Several lines of evidence support the involvement of transcriptomic and epigenetic mechanisms in the brain structural deficits of major depressive disorder (MDD) separately. However, research in these two areas has remained isolated. In this study, we proposed an integrative strategy that combined neuroimaging, brain-wide gene expression, and peripheral DNA methylation data to investigate the genetic basis of gray matter abnormalities in MDD. The MRI T1-weighted images and Illumina 850 K DNA methylation microarrays were obtained from 269 patients and 416 healthy controls, and brain-wide transcriptomic data were collected from Allen Human Brain Atlas. The between-group differences in gray matter volume (GMV) and differentially methylated CpG positions (DMPs) were examined. The genes with their expression patterns spatially related to GMV changes and genes with DMPs were overlapped and selected. Using principal component regression, the associations between DMPs in overlapped genes and GMV across individual patients were investigated, and the region-specific correlations between methylation status and gene expression were examined. We found significant associations between the decreased GMV and DMPs methylation status in the anterior cingulate cortex, inferior frontal cortex, and fusiform face cortex regions. These DMPs genes were primarily enriched in the neurodevelopmental and synaptic transmission process. There was a significant negative correlation between DNA methylation and gene expression in genes associated with GMV changes of the frontal cortex in MDD. Our findings suggest that GMV abnormalities in MDD may have a transcriptomic and epigenetic basis. This imaging-transcriptomic-epigenetic integrative analysis provides spatial and biological links between cortical morphological deficits and peripheral epigenetic signatures in MDD.

A neuroimaging-based precision medicine framework for depression.

BACKGROUND: Symptom-based diagnostic criteria of depression leads to notorious heterogeneity and subjectivity. METHODS: The study was conducted in two stages at two sites: development of a neuroimaging-based subtyping and precise repetitive transcranial magnetic stimulation (rTMS) strategy for depression at Center 1 and its clinical application at Center 2. Center 1 identified depression subtypes and subtype-specific rTMS targets based on amplitude of low frequency fluctuation (ALFF) in a cohort of 238 major depressive disorder patients and 66 healthy controls (HC). Subtypes were identified using a Gaussian Mixture Model, and subtype-specific rTMS targets were selected based on dominant brain regions prominently differentiating depression subtypes from HC. Subsequently, one classifier was employed and 72 hospitalized, depressed youths at Center 2 received two-week precise rTMS. MRI and clinical assessments were obtained at baseline, midpoint, and treatment completion for evaluation. RESULTS: Two neuroimaging subtypes of depression, archetypal and atypical depression, were identified based on distinct frontal-posterior functional imbalance patterns as measured by ALFF. The dorsomedial prefrontal cortex was identified as the rTMS target for archetypal depression, and the occipital cortex for atypical depression. Following precise rTMS, ALFF alterations were normalized in both archetypal and atypical depressed youths, corresponding with symptom response of 90.00% in archetypal depression and 70.73% in atypical depression. CONCLUSIONS: A precision medicine framework for depression was developed based on objective neurobiomarkers and implemented with promising results, actualizing a subtyping-treatment-evaluation closed loop in depression. Future randomized controlled trials are warranted.

Digital Dietary Behaviors in Individuals With Depression: Real-World Behavioral Observation.

BACKGROUND: Depression is often accompanied by changes in behavior, including dietary behaviors. The relationship between dietary behaviors and depression has been widely studied, yet previous research has relied on self-reported data which is subject to recall bias. Electronic device-based behavioral monitoring offers the potential for objective, real-time data collection of a large amount of continuous, long-term behavior data in naturalistic settings. OBJECTIVE: The study aims to characterize digital dietary behaviors in depression, and to determine whether these behaviors could be used to detect depression. METHODS: A total of 3310 students (2222 healthy controls [HCs], 916 with mild depression, and 172 with moderate-severe depression) were recruited for the study of their dietary behaviors via electronic records over a 1-month period, and depression severity was assessed in the middle of the month. The differences in dietary behaviors across the HCs, mild depression, and moderate-severe depression were determined by ANCOVA (analyses of covariance) with age, gender, BMI, and educational level as covariates. Multivariate logistic regression analyses were used to examine the association between dietary behaviors and depression severity. Support vector machine analysis was used to determine whether changes in dietary behaviors could detect mild and moderate-severe depression. RESULTS: The study found that individuals with moderate-severe depression had more irregular eating patterns, more fluctuated feeding times, spent more money on dinner, less diverse food choices, as well as eating breakfast less frequently, and preferred to eat only lunch and dinner, compared with HCs. Moderate-severe depression was found to be negatively associated with the daily 3 regular meals pattern (breakfast-lunch-dinner pattern; OR 0.467, 95% CI 0.239-0.912), and mild depression was positively associated with daily lunch and dinner pattern (OR 1.460, 95% CI 1.016-2.100). These changes in digital dietary behaviors were able to detect mild and moderate-severe depression (accuracy=0.53, precision=0.60), with better accuracy for detecting moderate-severe depression (accuracy=0.67, precision=0.64). CONCLUSIONS: This is the first study to develop a profile of changes in digital dietary behaviors in individuals with depression using real-world behavioral monitoring. The results suggest that digital markers may be a promising approach for detecting depression.

Visual cortex repetitive transcranial magnetic stimulation (rTMS) reversing neurodevelopmental impairments in adolescents with major psychiatric disorders (MPDs): A cross-species translational study.

AIMS: Neurodevelopmental impairments are closely linked to the basis of adolescent major psychiatric disorders (MPDs). The visual cortex can regulate neuroplasticity throughout the brain during critical periods of neurodevelopment, which may provide a promising target for neuromodulation therapy. This cross-species translational study examined the effects of visual cortex repetitive transcranial magnetic stimulation (rTMS) on neurodevelopmental impairments in MPDs. METHODS: Visual cortex rTMS was performed in both adolescent methylazoxymethanol acetate (MAM) rats and patients with MPDs. Functional magnetic resonance imaging (fMRI) and brain tissue proteomic data in rats and fMRI and clinical symptom data in patients were analyzed. RESULTS: The regional homogeneity (ReHo) analysis of fMRI data revealed an increase in the frontal cortex and a decrease in the posterior cortex in the MAM rats, representing the abnormal neurodevelopmental pattern in MPDs. In regard to the effects of rTMS, similar neuroimaging changes, particularly reduced frontal ReHo, were found both in MAM rats and adolescent patients, suggesting that rTMS may reverse the abnormal neurodevelopmental pattern. Proteomic analysis revealed that rTMS modulated frontal synapse-associated proteins, which may be the underpinnings of rTMS efficacy. Furthermore, a positive relationship was observed between frontal ReHo and clinical symptoms after rTMS in patients. CONCLUSION: Visual cortex rTMS was proven to be an effective treatment for adolescent MPDs, and the underlying neural and molecular mechanisms were uncovered. Our study provides translational evidence for therapeutics targeting the neurodevelopmental factor in MPDs.

Impaired interhemispheric connectivity in medication-naive patients with major depressive disorder.

BACKGROUND: Abnormalities in the anterior interhemispheric connections provided by the corpus callosum (CC) have long been implicated in major depressive disorder (MDD). The purpose of this study was to investigate interhemispheric connectivity in medication-naive patients with MDD by measuring fractional anisotropy in the CC with diffusion tensor imaging (DTI) techniques. METHODS: We obtained DTI scans from medication-naive patients with MDD and from matched healthy controls. Fractional anisotropy values were compared using semiautomatic region of interest methods to localize the regional CC differences between these 2 groups. RESULTS: We enrolled 27 patients and 27 controls in our study. Fractional anisotropy values were significantly lower in the anterior genu of the CC in the MDD group than in the control group (p = 0.009, corrected); results were not significantly different in any other CC subregions. LIMITATIONS: As patients with MDD were already experiencing acute episodes, future studies of individuals at risk for MDD are warranted to elucidate the interhemispheric connectivity abnormalities associated with the predisposition to MDD. CONCLUSION: The findings demonstrate abnormalities in the structural integrity of the anterior genu of the CC in medication-naive individuals with MDD, which may contribute to impairment of interhemispheric connectivity in patients with this disorder.

Functional connectivity between the amygdala and prefrontal cortex in medication-naive individuals with major depressive disorder.

BACKGROUND: Convergent evidence suggests dysfunction within the prefrontal cortex (PFC) and amygdala, important components of a neural system that subserves emotional processing, in individuals with major depressive disorder (MDD). Abnormalities in this system in the left hemisphere and during processing of negative emotional stimuli are especially implicated. In this study, we used functional magnetic resonance imaging (fMRI) to investigate amygdala-PFC functional connectivity during emotional face processing in medication-naive individuals with MDD. METHODS: Individuals with MDD and healthy controls underwent fMRI scanning while processing 3 types of emotional face stimuli. We compared the strength of functional connectivity from the amygdala between the MDD and control groups. RESULTS: Our study included 28 individuals with MDD and 30 controls. Decreased amygdala-left rostral PFC (rPFC) functional connectivity was observed in the MDD group compared with controls for the fear condition (p < 0.05, corrected). No significant differences were found in amygdala connectivity to any cerebral regions between the MDD and control groups for the happy or neutral conditions. LIMITATIONS: All participants with MDD were experiencing acute episodes, therefore the findings could not be generalized to the entire MDD population. CONCLUSION: Medication-naive individuals with MDD showed decreased amygdala-left rPFC functional connectivity in response to negative emotional stimuli, suggesting that abnormalities in amygdala-left rPFC neural circuitry responses to negative emotional stimuli might play an important role in the pathophysiology of MDD.

Towards a neuroimaging biomarker for predicting cognitive behavioural therapy outcomes in treatment-naive depression: Preliminary findings.

Clear prognostic indicators of cognitive behavioural therapy (CBT) are lacking for depression. This study aims to identify a biomarker that predicts CBT outcomes in depression. We developed a machine learning algorithm to predict post-CBT Hamilton Depression Rating Scale (HAMD) using pre-CBT regional homogeneity (ReHo). We examined transcriptomic signatures of regions with CBT-related ReHo changes. Twenty-five patients completed CBT and had increased ReHo in the dorsolateral prefrontal cortex (DLPFC) following CBT. Pre-CBT ReHo in left DLPFC was shown to be a predictor of post-HAMD scores. We identified left DLPFC ReHo as a neuroimaging biomarker for therapeutic effects of CBT in depression.