White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group.

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) "OCD vs. healthy controls" (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) "unmedicated OCD vs. healthy controls" (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) "medicated OCD vs. unmedicated OCD" (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6-79.1 in adults; 35.9-63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research.

What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group.

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.

An overview of the first 5 years of the ENIGMA obsessive-compulsive disorder working group: The power of worldwide collaboration.

Neuroimaging has played an important part in advancing our understanding of the neurobiology of obsessive-compulsive disorder (OCD). At the same time, neuroimaging studies of OCD have had notable limitations, including reliance on relatively small samples. International collaborative efforts to increase statistical power by combining samples from across sites have been bolstered by the ENIGMA consortium; this provides specific technical expertise for conducting multi-site analyses, as well as access to a collaborative community of neuroimaging scientists. In this article, we outline the background to, development of, and initial findings from ENIGMA's OCD working group, which currently consists of 47 samples from 34 institutes in 15 countries on 5 continents, with a total sample of 2,323 OCD patients and 2,325 healthy controls. Initial work has focused on studies of cortical thickness and subcortical volumes, structural connectivity, and brain lateralization in children, adolescents and adults with OCD, also including the study on the commonalities and distinctions across different neurodevelopment disorders. Additional work is ongoing, employing machine learning techniques. Findings to date have contributed to the development of neurobiological models of OCD, have provided an important model of global scientific collaboration, and have had a number of clinical implications. Importantly, our work has shed new light on questions about whether structural and functional alterations found in OCD reflect neurodevelopmental changes, effects of the disease process, or medication impacts. We conclude with a summary of ongoing work by ENIGMA-OCD, and a consideration of future directions for neuroimaging research on OCD within and beyond ENIGMA.

Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium.

Brain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect common trajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsive disorder (OCD) may provide clues to the pathophysiology of this neurodevelopmental disorder. Using T1-weighted MRI scans acquired from 1616 individuals with OCD and 1463 healthy controls across 37 datasets participating in the ENIGMA-OCD Working Group, we calculated intra-individual brain structural covariance networks (using the bilaterally-averaged values of 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes), in which edge weights were proportional to the similarity between two brain morphological features in terms of deviation from healthy controls (i.e. z-score transformed). Global networks were characterized using measures of network segregation (clustering and modularity), network integration (global efficiency), and their balance (small-worldness), and their community membership was assessed. Hub profiling of regional networks was undertaken using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measures were integrated across the 37 datasets using a meta-analytical approach. These network measures were summated across the network density range of K = 0.10-0.25 per participant, and were integrated across the 37 datasets using a meta-analytical approach. Compared with healthy controls, at a global level, the structural covariance networks of OCD showed lower clustering (P < 0.0001), lower modularity (P < 0.0001), and lower small-worldness (P = 0.017). Detection of community membership emphasized lower network segregation in OCD compared to healthy controls. At the regional level, there were lower (rank-transformed) centrality values in OCD for volume of caudate nucleus and thalamus, and surface area of paracentral cortex, indicative of altered distribution of brain hubs. Centrality of cingulate and orbito-frontal as well as other brain areas was associated with OCD illness duration, suggesting greater involvement of these brain areas with illness chronicity. In summary, the findings of this study, the largest brain structural covariance study of OCD to date, point to a less segregated organization of structural covariance networks in OCD, and reorganization of brain hubs. The segregation findings suggest a possible signature of altered brain morphometry in OCD, while the hub findings point to OCD-related alterations in trajectories of brain development and maturation, particularly in cingulate and orbitofrontal regions.

Influence of electroconvulsive therapy on white matter structure in a diffusion tensor imaging study.

BACKGROUND: Electroconvulsive therapy (ECT) is a fast-acting intervention for major depressive disorder. Previous studies indicated neurotrophic effects following ECT that might contribute to changes in white matter brain structure. We investigated the influence of ECT in a non-randomized prospective study focusing on white matter changes over time. METHODS: Twenty-nine severely depressed patients receiving ECT in addition to inpatient treatment, 69 severely depressed patients with inpatient treatment (NON-ECT) and 52 healthy controls (HC) took part in a non-randomized prospective study. Participants were scanned twice, approximately 6 weeks apart, using diffusion tensor imaging, applying tract-based spatial statistics. Additional correlational analyses were conducted in the ECT subsample to investigate the effects of seizure duration and therapeutic response. RESULTS: Mean diffusivity (MD) increased after ECT in the right hemisphere, which was an ECT-group-specific effect. Seizure duration was associated with decreased fractional anisotropy (FA) following ECT. Longitudinal changes in ECT were not associated with therapy response. However, within the ECT group only, baseline FA was positively and MD negatively associated with post-ECT symptomatology. CONCLUSION: Our data suggest that ECT changes white matter integrity, possibly reflecting increased permeability of the blood-brain barrier, resulting in disturbed communication of fibers. Further, baseline diffusion metrics were associated with therapy response. Coherent fiber structure could be a prerequisite for a generalized seizure and inhibitory brain signaling necessary to successfully inhibit increased seizure activity.

Anxiety and depression in COVID-19 survivors: Role of inflammatory and clinical predictors.

Infection-triggered perturbation of the immune system could induce psychopathology, and psychiatric sequelae were observed after previous coronavirus outbreaks. The spreading of the Severe Acute Respiratory Syndrome Coronavirus (COVID-19) pandemic could be associated with psychiatric implications. We investigated the psychopathological impact of COVID-19 in survivors, also considering the effect of clinical and inflammatory predictors. We screened for psychiatric symptoms 402 adults surviving COVID-19 (265 male, mean age 58), at one month follow-up after hospital treatment. A clinical interview and a battery of self-report questionnaires were used to investigate post-traumatic stress disorder (PTSD), depression, anxiety, insomnia, and obsessive-compulsive (OC) symptomatology. We collected sociodemographic information, clinical data, baseline inflammatory markers and follow-up oxygen saturation levels. A significant proportion of patients self-rated in the psychopathological range: 28% for PTSD, 31% for depression, 42% for anxiety, 20% for OC symptoms, and 40% for insomnia. Overall, 56% scored in the pathological range in at least one clinical dimension. Despite significantly lower levels of baseline inflammatory markers, females suffered more for both anxiety and depression. Patients with a positive previous psychiatric diagnosis showed increased scores on most psychopathological measures, with similar baseline inflammation. Baseline systemic immune-inflammation index (SII), which reflects the immune response and systemic inflammation based on peripheral lymphocyte, neutrophil, and platelet counts, positively associated with scores of depression and anxiety at follow-up. PTSD, major depression, and anxiety, are all high-burden non-communicable conditions associated with years of life lived with disability. Considering the alarming impact of COVID-19 infection on mental health, the current insights on inflammation in psychiatry, and the present observation of worse inflammation leading to worse depression, we recommend to assess psychopathology of COVID-19 survivors and to deepen research on inflammatory biomarkers, in order to diagnose and treat emergent psychiatric conditions.

Mild adverse childhood experiences increase neural efficacy during affective theory of mind.

Adverse childhood experiences (ACEs) affect the development of social cognition (and identify a risk factor for several physical and mental disorders). Theory of Mind (ToM) is a key predictor of social functioning, mental health, and quality of life. No previous study explored the effect of mild ACEs on the neural correlates of ToM in healthy humans. In 23 healthy participants, we used brain blood oxygen level-dependent fMRI to study the effect of ACEs on the neural responses to tasks targeting affective and cognitive ToM. Results pointed out an association between ACEs and a lower neural response in the vermis of the cerebellum (r = -.85), precentral gyrus, and inferior frontal operculum (putative Mirror Neural System, r = -.78) during affective ToM. A lower recruitment of these brain regions, paralleled by the same performance, could express an increased neural efficacy in inferring affective mental states driven by previous experience, in this case, ACEs.

Kynurenine pathway and white matter microstructure in bipolar disorder.

Decreased availability of serotonin in the central nervous system has been suggested to be a central factor in the pathogenesis of depression. Activation of indoleamine 2-3 dioxygenase following a pro-inflammatory state could reduce the amount of tryptophan converted to serotonin and increase the production of tryptophan catabolites such as kynurenic acid, an antagonist of ionotropic excitatory aminoacid receptors, whose levels are reduced in bipolar disorder. Abnormalities in white matter (WM) integrity have been widely reported in BD. We then hypothesized that metabolites involved in serotoninergic turnover in BD could influence DTI measures of WM microstructure. Peripheral levels of tryptophan, kynurenine, kynurenic acid, 3-hydroxy-kynurenine, and 5-HIAA were analysed in 22 patients affected by BD and 15 healthy controls. WM microstructure was evaluated using diffusion tensor imaging and tract-based spatial statistics with threshold-free cluster enhancement only in bipolar patients. We observed that kynurenic acid and 5-HIAA were reduced in BD and associated with DTI measures of WM integrity in several association fibres: inferior and superior longitudinal fasciculus, cingulum bundle, corpus callosum, uncus, anterior thalamic radiation and corona radiata. Our results seem to suggest that higher levels of 5-HIAA, a measure of serotonin levels, and higher levels of kynurenic acid, which protects from glutamate excitotoxicity, could exert a protective effect on WM microstructure. Reduced levels of these metabolites in BD thus seem to confirm a crucial role of serotonin turnover in BD pathophysiology.

White matter alterations associate with onset symptom dimension in obsessive-compulsive disorder.

AIM: The high heterogeneity of obsessive-compulsive disorder (OCD) is best described by a multidimensional model involving symptom dimensions. We aimed to investigate white matter alterations associated with OCD, focusing on the impact of long-lasting effect of symptom dimensions assessed at onset of illness. Furthermore, we investigated white matter alterations associated with this disorder, controlling for the impact of medications and for the prevailing current symptom dimension. METHODS: We studied 58 patients affected by OCD and 58 age- and sex-matched healthy controls. We divided patients according to symptom dimension at onset of illness, assessed with the five-factor model. T-tests were performed in order to investigate differences between subgroups. Similar analyses were performed considering the prevailing current symptom dimension. Analyses were conducted with tract-based spatial statistics on diffusion tensor imaging. RESULTS: Doubt/checking and rituals/superstition symptom dimensions at onset and symmetry/perfectionism current symptom dimensions were characterized by significant alterations in diffusion tensor imaging measures. An association of white matter alterations and symmetry/perfectionism current dimension was found only when controlling for the effect of doubt/checking dimension at onset. Finally, results pointed out that the observed differences between patients and healthy controls were carried by the effect of previous and current medications. CONCLUSION: Our findings evidenced that onset symptom dimensions are associated with enduring alterations of white matter microstructure. Onset symptom dimensions may reflect underlying endophenotypes. In addition, present results confirm the effect of medications on white matter in OCD, showing a large effect of current treatment on myelination.

Body mass index associates with white matter microstructure in bipolar depression.

OBJECTIVES: Obesity has been reported in over 60% of bipolar disorder (BD) patients. It worsens the severity of illness, and influences cognition and functional outcomes. White matter (WM) abnormalities are one of the most consistently reported findings in neuroimaging studies of BD. We hypothesized that body mass index (BMI) could correlate with WM integrity in bipolar patients. METHODS: We evaluated BMI in a sample of 164 depressed patients affected by BD. We performed whole-brain tract-based spatial statistics with threshold-free cluster enhancement for the diffusion tensor imaging (DTI) measures of WM integrity: fractional anisotropy; axial, radial, and mean diffusivity. RESULTS: We observed that BMI was associated with DTI measures of WM integrity in several fiber tracts: anterior corona radiata, anterior thalamic radiation, inferior fronto-occipital fasciculus and corpus callosum. CONCLUSIONS: The association of BMI in key WM tracts that are crucial to mood regulation and neurocognitive functioning suggests that BMI might contribute to the pathophysiology of BD through a detrimental action on structural connectivity in critical cortico-limbic networks.

Corticolimbic Connectivity Mediates the Relationship between Adverse Childhood Experiences and Symptom Severity in Borderline Personality Disorder.

The interaction between biological and environmental factors (especially adverse childhood experiences, ACEs) plays a crucial role in the development and maintenance of borderline personality disorder (BPD). These factors act influencing BPD core features such as pervasive instability in affect regulation, impulse control, social cognition, and interpersonal relationships. In line with this perspective, abnormalities in social cognition and related neurobiological underpinnings could mediate the relationship between ACEs and psychopathological manifestations in adulthood. In a sample of 14 females, functional connectivity (FC) analyses were performed modeling the interaction between ACEs and corticolimbic dysregulation during emotional processing and its relationship with BPD symptom severity. ACEs were associated with a dampening of the negative FC between (1) the right amygdala (Amy) and right dorsolateral prefrontal cortex (DLPFC) and between (2) the left Amy and bilateral DLPFC, right precuneus, left cerebellum and left dorsomedial prefrontal cortex during emotional processing. The connectivity between right Amy and DLPFC mediates the relationship between childhood adversities and BPD symptomatology. Furthermore, the negative FC between Amy and DLPFC, postcentral gyrus, the vermis of cerebellum and precuneus was also associated with BPD symptom severity, with a weaker negative coupling between Amy and these regions being related to a worse BPD psychopathology. Our results confirm the role of ACEs in contributing to social cognition impairments in BPD and related symptomatology from a neurobiological perspective.

Sterol Regulatory Element Binding Transcription Factor-1 Gene Variation and Medication Load Influence White Matter Structure in Schizophrenia.

BACKGROUND: Diffusion tensor imaging (DTI) studies have shown a widespread disruption of white matter (WM) microstructure in schizophrenia. Furthermore, higher fractional anisotropy (FA) has been consistently correlated with the severity of psychotic symptoms. Antipsychotic drugs (APDs) affect lipid homeostasis. Gene polymorphisms in sterol regulatory element binding transcription factor (SREBF)-1 and SREBF-2 have been associated with schizophrenia. METHODS: In a sample of 65 patients affected by chronic schizophrenia, we investigated the effect of ongoing APD medication, SREBF-1 rs11868035 polymorphism and SREBF-2 rs1052717 polymorphism on the WM microstructure, using tract-based spatial statistics with threshold-free cluster enhancement. RESULTS: We reported increased FA associated with the risk rs11868035 G/G genotype in several WM tracts, mainly located in the left hemisphere, and opposite effects of the APD medication load, with reduced FA and generally increased diffusivity. These opposite effects overlapped in the forceps minor, cingulum, uncinate fasciculus, the superior and inferior longitudinal fasciculi, the corticospinal tract, inferior fronto-occipital fasciculus and the anterior thalamic radiation. CONCLUSION: We suggest that changes of WM structure could be an as yet poorly explored biomarker of the effects of APDs, to be further investigated in prospective studies correlating long-term clinical effects with changes of DTI measures in specific WM tracts contributing to the functional integrity of the brain. © 2015 S. Karger AG, Basel.

Multimodal brain-derived subtypes of Major depressive disorder differentiate patients for anergic symptoms, immune-inflammatory markers, history of childhood trauma and treatment-resistance.

An estimated 30 % of Major Depressive Disorder (MDD) patients exhibit resistance to conventional antidepressant treatments. Identifying reliable biomarkers of treatment-resistant depression (TRD) represents a major goal of precision psychiatry, which is hampered by the clinical and biological heterogeneity. To uncover biologically-driven subtypes of MDD, we applied an unsupervised data-driven framework to stratify 102 MDD patients on their neuroimaging signature, including extracted measures of cortical thickness, grey matter volumes, and white matter fractional anisotropy. Our novel analytical pipeline integrated different machine learning algorithms to harmonize data, perform data dimensionality reduction, and provide a stability-based relative clustering validation. The obtained clusters were characterized for immune-inflammatory peripheral biomarkers, TRD, history of childhood trauma and depressive symptoms. Our results indicated two different clusters of patients, differentiable with 67 % of accuracy: one cluster (n = 59) was associated with a higher proportion of TRD, and higher scores of energy-related depressive symptoms, history of childhood abuse and emotional neglect; this cluster showed a widespread reduction in cortical thickness (d = 0.43-1.80) and volumes (d = 0.45-1.05), along with fractional anisotropy in the fronto-occipital fasciculus, stria terminalis, and corpus callosum (d = 0.46-0.52); the second cluster (n = 43) was associated with cognitive and affective depressive symptoms, thicker cortices and wider volumes. Multivariate analyses revealed distinct brain-inflammation relationships between the two clusters, with increase in pro-inflammatory markers being associated with decreased cortical thickness and volumes. Our stratification of MDD patients based on structural neuroimaging identified clinically-relevant subgroups of MDD with specific symptomatic and immune-inflammatory profiles, which can contribute to the development of tailored personalized interventions for MDD.

Concurrent validity and reliability of suicide risk assessment instruments: A meta-analysis of 20 instruments across 27 international cohorts.

OBJECTIVE: A major limitation of current suicide research is the lack of power to identify robust correlates of suicidal thoughts or behavior. Variation in suicide risk assessment instruments used across cohorts may represent a limitation to pooling data in international consortia. METHOD: Here, we examine this issue through two approaches: (a) an extensive literature search on the reliability and concurrent validity of the most commonly used instruments and (b) by pooling data (N ∼ 6,000 participants) from cohorts from the Enhancing NeuroImaging Genetics Through Meta-Analysis (ENIGMA) Major Depressive Disorder and ENIGMA-Suicidal Thoughts and Behaviour working groups, to assess the concurrent validity of instruments currently used for assessing suicidal thoughts or behavior. RESULTS: We observed moderate-to-high correlations between measures, consistent with the wide range (κ range: 0.15-0.97; r range: 0.21-0.94) reported in the literature. Two common multi-item instruments, the Columbia Suicide Severity Rating Scale and the Beck Scale for Suicidal Ideation were highly correlated with each other (r = 0.83). Sensitivity analyses identified sources of heterogeneity such as the time frame of the instrument and whether it relies on self-report or a clinical interview. Finally, construct-specific analyses suggest that suicide ideation items from common psychiatric questionnaires are most concordant with the suicide ideation construct of multi-item instruments. CONCLUSIONS: Our findings suggest that multi-item instruments provide valuable information on different aspects of suicidal thoughts or behavior but share a modest core factor with single suicidal ideation items. Retrospective, multisite collaborations including distinct instruments should be feasible provided they harmonize across instruments or focus on specific constructs of suicidality. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: A systematic review and meta-analysis.

Applying machine learning (ML) to objective markers may overcome prognosis uncertainty due to the subjective nature of the diagnosis of bipolar disorder (BD). This PRISMA-compliant meta-analysis provides new systematic evidence of the BD classification accuracy reached by different markers and ML algorithms. We focused on neuroimaging, electrophysiological techniques, peripheral biomarkers, genetic data, neuropsychological or clinical measures, and multimodal approaches. PubMed, Embase and Scopus were searched through 3rd December 2020. Meta-analyses were performed using random-effect models. Overall, 81 studies were included in this systematic review and 65 in the meta-analysis (11,336 participants, 3903 BD). The overall pooled classification accuracy was 0.77 (95%CI[0.75;0.80]). Despite subgroup analyses for diagnostic comparison group, psychiatric disorders, marker, ML algorithm, and validation procedure were not significant, linear discriminant analysis significantly outperformed support vector machine for peripheral biomarkers (p = 0.03). Sample size was inversely related to accuracy. Evidence of publication bias was detected. Ultimately, although ML reached a high accuracy in differentiating BD from other psychiatric disorders, best practices in methodology are needed for the advancement of future studies.

Selective association of cytokine levels and kynurenine/tryptophan ratio with alterations in white matter microstructure in bipolar but not in unipolar depression.

Bipolar (BD) and major depression (MDD) disorders are severe mental illnesses characterised by altered levels of immune/inflammatory markers and disrupted white matter (WM) microstructure. A pro-inflammatory state was suggested to activate indoleamine 2,3-dioxygenase which, in turn, increases the amount of tryptophan (Trp) converted into kynurenine (Kyn). We investigated whether plasma levels of Trp, Kyn and Kyn/Trp ratio are associated with peripheral levels of immune/inflammatory markers and whether they are related to WM integrity in 100 MDD and 66 BD patients. Patients also underwent MRI, and fractional anisotropy (FA) was estimated as a measure of WM microstructure. BD patients showed higher Kyn levels and Kyn/Trp ratio than MDD patients, and lower FA in several WM tracts, including the corpus callosum and the inferior fronto-occipital fasciculus (IFO). Lower Trp levels associated with a more severe depressive symptomatology irrespective of diagnosis and with lower FA in the corpus callosum (CC) and external capsule (EC). We found an association of immune/inflammatory markers with Kyn/Trp ratio selectively in BD patients: IL-1ß and TNF-α showed a positive relationship and IL-2 and IL-9 a negative relationship; in addition, higher IL-4 correlated with lower Kyn levels; higher Kyn/Trp ratio and IL-1ß correlated with lower FA in the CC and IFO. Notably, the detrimental effect of IL-1ß on the IFO was moderated by the Kyn/Trp ratio. These data suggest that in BD, cytokines and the conversion of Trp into Kyn may affect WM microstructure and support the idea that distinct mechanisms underlie the pathophysiology of BD and MDD.

Brain spectroscopic measures of glutamatergic and neuronal metabolism and glial activation influence white matter integrity in bipolar depression.

Bipolar disorder (BD) is associated with alterations in white matter (WM) microstructure, glutamatergic neurotransmission, and glia activity. Previous studies showed higher concentrations of glutamate (Glu), glutamate+glutamine (Glx), and reduced N-acetyl-aspartate (NAA) in BD. We investigated brain concentrations of Glu, Glx, NAA, mI as indirect marker of microglia activation, and Glx/NAA ratio as index of neuronal damage through 1H-MR, and WM integrity with Tract-Based Spatial Statistics in 93 depressed BD patients and 58 healthy controls (HC). We tested for linear effects of cited spectroscopic metabolites on DTI measures of WM integrity with general linear models for each group, then performing a conjunction analysis of Glx/NAA and mI concentration on the same measures. Statistical analyses (whole sample) revealed higher concentration of Glx/NAA, Glx and mI in BD patients compared to HC, and a positive association between mI and the ratio. DTI analyses (87 BD and 35 HC) showed a significant association of Glx/NAA ratio, and mI with WM microstructure. Conjunction analysis revealed a joint negative association between Glx/NAA and mI with fractional anisotropy. This is the first study showing an association between brain metabolites involved in neuronal damage, and glial activation and the alterations in WM consistently reported in BD.

Long-term effect of childhood trauma: Role of inflammation and white matter in mood disorders.

Bipolar disorder (BD) and major depressive disorder (MDD) are severe psychiatric illnesses that share among their environmental risk factors the exposure to adverse childhood experiences (ACE). Exposure to ACE has been associated with long-term changes in brain structure and the immune response. In the lasts decades, brain abnormalities including alterations of white matter (WM) microstructure and higher levels of peripheral immune/inflammatory markers have been reported in BD and MDD and an association between inflammation and WM microstructure has been shown. However, differences in these measures have been reported by comparing the two diagnostic groups. The aim of the present study was to investigate the interplay between ACE, inflammation, and WM in BD and MDD. We hypothesize that inflammation will mediate the association between ACE and WM and that this will be different in the two groups. A sample of 200 patients (100 BD, 100 MDD) underwent 3T MRI scan and ACE assessment through Childhood Trauma Questionnaire. A subgroup of 130 patients (75 MDD and 55 BD) underwent blood sampling for the assessment of immune/inflammatory markers. We observed that ACE associated with higher peripheral levels of IL-2, IL-17, bFGF, IFN-γ, TNF-α, CCL3, CCL4, CCL5, and PDGF-BB only in the BD group. Further, higher levels of CCL3 and IL-2 associated with lower FA in BD. ACE were found to differently affect WM microstructure in the two diagnostic groups and to be negatively associated with FA and AD in BD patients. Mediation analyses showed a significant indirect effect of ACE on WM microstructure mediated by IL-2. Our findings suggest that inflammation may mediate the detrimental effect of early experiences on brain structure and different mechanisms underlying brain alterations in BD and MDD.

The thalamus and its subnuclei-a gateway to obsessive-compulsive disorder.

Larger thalamic volume has been found in children with obsessive-compulsive disorder (OCD) and children with clinical-level symptoms within the general population. Particular thalamic subregions may drive these differences. The ENIGMA-OCD working group conducted mega- and meta-analyses to study thalamic subregional volume in OCD across the lifespan. Structural T1-weighted brain magnetic resonance imaging (MRI) scans from 2649 OCD patients and 2774 healthy controls across 29 sites (50 datasets) were processed using the FreeSurfer built-in ThalamicNuclei pipeline to extract five thalamic subregions. Volume measures were harmonized for site effects using ComBat before running separate multiple linear regression models for children, adolescents, and adults to estimate volumetric group differences. All analyses were pre-registered ( https://osf.io/73dvy ) and adjusted for age, sex and intracranial volume. Unmedicated pediatric OCD patients (<12 years) had larger lateral (d = 0.46), pulvinar (d = 0.33), ventral (d = 0.35) and whole thalamus (d = 0.40) volumes at unadjusted p-values <0.05. Adolescent patients showed no volumetric differences. Adult OCD patients compared with controls had smaller volumes across all subregions (anterior, lateral, pulvinar, medial, and ventral) and smaller whole thalamic volume (d = -0.15 to -0.07) after multiple comparisons correction, mostly driven by medicated patients and associated with symptom severity. The anterior thalamus was also significantly smaller in patients after adjusting for thalamus size. Our results suggest that OCD-related thalamic volume differences are global and not driven by particular subregions and that the direction of effects are driven by both age and medication status.