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Diverse language models (LMs), including large language models (LLMs) based on deep neural networks, allow us to chart how people organize meanings in speech and how this process breaks down in conditions. Recent evidence has pointed to higher mean semantic similarities between words in people with psychosis, conceptualized as a ‘shrunk’ (more compressed) semantic space. Based on this, we hypothesized that the dimensionality of the vector spaces as defined by the embeddings of speech samples from LMs would also be easier to reduce in psychosis. To test this, we used principal component analysis (PCA) to calculate different metrics serving as proxies for reducibility, including the number of components needed to reach 90% of variance, and the cumulative variance explained by the first two components. For further exploration, intrinsic dimensionality (ID) was also estimated. Results consistent over datasets in three languages confirmed significantly higher reducibility of the semantic space in psychosis. This result points to the existence of an underlying intrinsic geometry of the space of semantic associations in speech, which may underlie more surface-level measurements such as semantic similarity. It also offers a new foundational approach to speech in mental disorders.

Recurrences of depressive episodes in major depressive disorder (MDD) can be explained by the diathesis-stress model, suggesting that stressful life events (SLEs) can trigger MDD episodes in individuals with pre-existing vulnerabilities. However, the longitudinal neurobiological impact of SLEs on gray matter volume (GMV) in MDD and its interaction with early-life adversity remains unresolved. In 754 participants aged 18–65 years (362 MDD patients; 392 healthy controls; HCs), we assessed longitudinal associations between SLEs (Life Events Questionnaire) and whole-brain GMV changes (3 Tesla MRI) during a 2-year interval, using voxel-based morphometry in SPM12/CAT12. We also explored the potential moderating role of childhood maltreatment (Childhood Trauma Questionnaire) on these associations. Over the 2-year interval, HCs demonstrated significant GMV reductions in the middle frontal, precentral, and postcentral gyri in response to higher levels of SLEs, while MDD patients showed no such GMV changes. Childhood maltreatment did not moderate these associations in either group. However, MDD patients who had at least one depressive episode during the 2-year interval, compared to those who did not, or HCs, showed GMV increases in the middle frontal, precentral, and postcentral gyri associated with an increase in SLEs and childhood maltreatment. Our findings indicate distinct GMV changes in response to SLEs between MDD patients and HCs. GMV decreases in HCs may represent adaptive responses to stress, whereas GMV increases in MDD patients with both childhood maltreatment and a depressive episode during the 2-year interval may indicate maladaptive changes, suggesting a neural foundation for the diathesis-stress model in MDD recurrences.

Speech is a promising biomarker for schizophrenia spectrum disorder (SSD) and major depressive disorder (MDD). This proof of principle study investigates previously studied speech acoustics in combination with a novel application of voice pathology features as objective and reproducible classifiers for depression, schizophrenia, and healthy controls (HC). Speech and voice features for classification were calculated from recordings of picture descriptions from 240 speech samples (20 participants with SSD, 20 with MDD, and 20 HC each with 4 samples). Binary classification support vector machine (SVM) models classified the disorder groups and HC. For each feature, the permutation feature importance was calculated, and the top 25% most important features were used to compare differences between the disorder groups and HC including correlations between the important features and symptom severity scores. Multiple kernels for SVM were tested and the pairwise models with the best performing kernel (3-degree polynomial) were highly accurate for each classification: 0.947 for HC vs. SSD, 0.920 for HC vs. MDD, and 0.932 for SSD vs. MDD. The relatively most important features were measures of articulation coordination, number of pauses per minute, and speech variability. There were moderate correlations between important features and positive symptoms for SSD. The important features suggest that speech characteristics relating to psychomotor slowing, alogia, and flat affect differ between HC, SSD, and MDD.

Temporal neural synchrony disruption can be linked to a variety of symptoms of major depressive disorder (MDD), including mood rigidity and the inability to break the cycle of negative emotion or attention biases. This might imply that altered dynamic neural synchrony may play a role in the persistence and exacerbation of MDD symptoms. Our study aimed to investigate the changes in whole-brain dynamic patterns of the brain functional connectivity and activity related to depression using the hidden Markov model (HMM) on resting-state functional magnetic resonance imaging (rs-fMRI) data. We compared the patterns of brain functional dynamics in a large sample of 314 patients with MDD (65.9% female; age (mean ± standard deviation): 35.9 ± 13.4) and 498 healthy controls (59.4% female; age: 34.0 ± 12.8). The HMM model was used to explain variations in rs-fMRI functional connectivity and averaged functional activity across the whole-brain by using a set of six unique recurring states. This study compared the proportion of time spent in each state and the average duration of visits to each state to assess stability between different groups. Compared to healthy controls, patients with MDD showed significantly higher proportional time spent and temporal stability in a state characterized by weak functional connectivity within and between all brain networks and relatively strong averaged functional activity of regions located in the somatosensory motor (SMN), salience (SN), and dorsal attention (DAN) networks. Both proportional time spent and temporal stability of this brain state was significantly associated with depression severity. Healthy controls, in contrast to the MDD group, showed proportional time spent and temporal stability in a state with relatively strong functional connectivity within and between all brain networks but weak averaged functional activity across the whole brain. These findings suggest that disrupted brain functional synchrony across time is present in MDD and associated with current depression severity.

While the need for personalised treatment approaches grows in recognition, predicting treatment outcomes for adults with Attention-Deficit/Hyperactivity Disorder (ADHD) remains underexplored. Recent interest has turned to the brain’s surface and its association with treatment response. Although the precise interplay between cortical gyrification and ADHD treatment outcomes remains to be elucidated, preliminary investigations suggest a promising avenue for diagnostic innovation. Expanding upon the Comparison of Methylphenidate and Psychotherapy in Adult ADHD Study (COMPAS), we investigated the prognostic value of cortical gyrification in predicting treatment response. Specifically, we explored how pre-treatment cortical gyrification might predict response to psychotherapy or clinical management in combination with either methylphenidate or placebo following a 12-week intensive treatment period. Cortical gyrification was assessed using 121 T1-weighted anatomical scans. Linear regression models investigated the predictive value of cortical gyrification, regressing baseline cortical structure against post-treatment severity. All brain structural analyses were conducted using the threshold-free cluster enhancement (TFCE) approach and the Computational Anatomy Toolbox (CAT12) within the Statistical Parametric Mapping Software (Matlab Version R2021a). Results revealed significant positive region-specific associations between cortical gyrification and treatment response across three symptom dimensions, with significant associations localised predominantly in frontal regions of the left hemisphere. Our findings emphasise that increased cortical gyrification in frontal cortical regions signifies enhanced treatment efficacy following a 12-week intervention. Further research in this area is imperative to verify the reliability of biological markers in view of treatment success to potentially reduce unnecessary drug-related side-effects, minimising delay from receiving more effective treatments, and increase treatment adherence.

•Data-driven, multivariate statistical approach for structural MRI data.•Identification of gyrification cluster patterns beyond diagnostic categories.•Data-driven subgroups are discriminative in transdiagnostic disease risk factors.•Using DSM diagnoses had little power in discriminating global gyrification patterns. Multivariate data-driven statistical approaches offer the opportunity to study multi-dimensional interdependences between a large set of biological parameters, such as high-dimensional brain imaging data. For gyrification, a putative marker of early neurodevelopment, direct comparisons of patterns among multiple psychiatric disorders and investigations of potential heterogeneity of gyrification within one disorder and a transdiagnostic characterization of neuroanatomical features are lacking. In this study we used a data-driven, multivariate statistical approach to analyze cortical gyrification in a large cohort of N = 1028 patients with major psychiatric disorders (Major depressive disorder: n = 783, bipolar disorder: n = 129, schizoaffective disorder: n = 44, schizophrenia: n = 72) to identify cluster patterns of gyrification beyond diagnostic categories. Cluster analysis applied on gyrification data of 68 brain regions (DK-40 atlas) identified three clusters showing difference in overall (global) gyrification and minor regional variation (regions). Newly, data-driven subgroups are further discriminative in cognition and transdiagnostic disease risk factors. Results indicate that gyrification is associated with transdiagnostic risk factors rather than diagnostic categories and further imply a more global role of gyrification related to mental health than a disorder specific one. Our findings support previous studies highlighting the importance of association cortices involved in psychopathology. Explorative, data-driven approaches like ours can help to elucidate if the brain imaging data on hand and its a priori applied grouping actually has the potential to find meaningful effects or if previous hypotheses about the phenotype as well as its grouping have to be revisited.

Stressful life events (SLEs) in adulthood are a risk factor for various disorders such as depression, cancer or infections. Part of this risk is mediated through pathways altering brain physiology and structure. There is a lack of longitudinal studies examining associations between SLEs and brain structural changes. High‐resolution structural magnetic resonance imaging data of 212 healthy subjects were acquired at baseline and after 2 years. Voxel‐based morphometry was used to identify associations between SLEs using the Life Events Questionnaire and grey matter volume (GMV) changes during the 2‐year period in an ROI approach. Furthermore, we assessed adverse childhood experiences as a possible moderator of SLEs‐GMV change associations. SLEs were negatively associated with GMV changes in the left medial prefrontal cortex. This association was stronger when subjects had experienced adverse childhood experiences. The medial prefrontal cortex has previously been associated with stress‐related disorders. The present findings represent a potential neural basis of the diathesis‐stress model of various disorders.

Alterations in DNA methylation (DNAm) profiles have been implicated in affective and psychotic disorders. However, no comprehensive understanding of peripheral DNAm profiles associated with diagnostic groups, course of illness, and other clinical variables has emerged yet. In particular, studies exploring commonalities and differences across diagnoses are lacking. Here we conducted a systematic epigenetic characterization of the transdiagnostic German FOR2107 cohort, including individuals with major depressive disorder (MDD, n = 342), bipolar disorder (BD, n = 99), or a schizophrenia spectrum disorder (SSD, n = 101) and healthy controls (HC, n = 339). For 183 MDD cases and 178 HC, we assessed additional DNAm data from the two-year follow-up study visit. To explore DNAm differences between and across diagnostic groups, case-control and case-case methylome-wide association studies were performed. Our sample was further characterized using methylation risk scores (MRS) for MDD and SSD. Finally, epigenetic age acceleration was examined and compared to a measure of brain age acceleration. We identified few methylome-wide significant associations with diagnostic groups. MRS for MDD did not differ between diagnostic groups, and an increase in MRS for SSD in SSD compared to HC did not remain significant when adjusting for smoking behavior and BMI. An increase in epigenetic age acceleration was most evident for SSD compared to HC, which did not remain significant when adjusting for covariates. No correlation between epigenetic and brain age acceleration was observed. Our findings emphasize the relevance of potential confounding factors in epigenetics research in psychiatry and contribute to a growing body of studies on DNAm profiles across affective and psychotic disorders. •New blood-based DNA methylation dataset across affective and psychotic disorders.•Strong effect of mental health-linked lifestyle factors on methylation profiles.•Low variance explained by existing disorder-specific methylation risk scores.•Epigenetic age acceleration in schizophrenia spectrum disorder compared to controls.

Childhood maltreatment has been associated with reduced hippocampal volume in healthy individuals, whereas social support, a protective factor, has been positively associated with hippocampal volumes. In this study, we investigated how social support is associated with hippocampal volume in healthy people with previous experience of childhood maltreatment. We separated a sample of 446 healthy participants into 2 groups using the Childhood Trauma Questionnaire: 265 people without maltreatment and 181 people with maltreatment. We measured perceived social support using a short version of the Social Support Questionnaire. We examined hippocampal volume using automated segmentation (Freesurfer). We conducted a social support × group analysis of covariance on hippocampal volumes controlling for age, sex, total intracranial volume, site and verbal intelligence. Our analysis revealed significantly lower left hippocampal volume in people with maltreatment (left F1,432 = 5.686, p = 0.018; right F1,433 = 3.371, p = 0.07), but no main effect of social support emerged. However, we did find a significant social support × group interaction for left hippocampal volume (left F1,432 = 5.712, p = 0.017; right F1,433 = 3.480, p = 0.06). In people without maltreatment, we observed a trend toward a positive association between social support and hippocampal volume. In contrast, social support was negatively associated with hippocampal volume in people with maltreatment. Because of the correlative nature of our study, we could not infer causal relationships between social support, maltreatment and hippocampal volume. Our results point to a complex dynamic between environmental risk, protective factors and brain structure — in line with previous evidence — suggesting a detrimental effect of maltreatment on hippocampal development.

Large language models provide high-dimensional representations (embeddings) of word meaning, which allow quantifying changes in the geometry of the semantic space in mental disorders. A pattern of a more condensed (‘shrinking’) semantic space marked by an increase in mean semantic similarity between words has been recently documented in psychosis across several languages. We aimed to explore this pattern further in picture descriptions provided by a transdiagnostic German sample of patients with schizophrenia spectrum disorders (SSD) (n = 42), major depression (MDD, n = 43), and healthy controls (n = 44). Compared to controls, both clinical groups showed more restricted dynamic navigational patterns as captured by the time series of semantic distances crossed, while also showing differential patterns in the total distances and trajectories navigated. These findings demonstrate alterations centred on the dynamics of the flow of meaning across the semantic space in SSD and MDD, preserving previous indications towards a shrinking semantic space in both cases.