BACKGROUND: Individuals at risk for bipolar disorder (BD) have a wide range of genetic and non-genetic risk factors, like a positive family history of BD or (sub)threshold affective symptoms. Yet, it is unclear whether these individuals at risk and those diagnosed with BD share similar gray matter brain alterations. METHODS: In 410 male and female participants aged 17-35 years, we compared gray matter volume (3T MRI) between individuals at risk for BD (as assessed using the EPIbipolar scale; n = 208), patients with a DSM-IV-TR diagnosis of BD (n = 87), and healthy controls (n = 115) using voxel-based morphometry in SPM12/CAT12. We applied conjunction analyses to identify similarities in gray matter volume alterations in individuals at risk and BD patients, relative to healthy controls. We also performed exploratory whole-brain analyses to identify differences in gray matter volume among groups. ComBat was used to harmonize imaging data from seven sites. RESULTS: Both individuals at risk and BD patients showed larger volumes in the right putamen than healthy controls. Furthermore, individuals at risk had smaller volumes in the right inferior occipital gyrus, and BD patients had larger volumes in the left precuneus, compared to healthy controls. These findings were independent of course of illness (number of lifetime manic and depressive episodes, number of hospitalizations), comorbid diagnoses (major depressive disorder, attention-deficit hyperactivity disorder, anxiety disorder, eating disorder), familial risk, current disease severity (global functioning, remission status), and current medication intake. CONCLUSIONS: Our findings indicate that alterations in the right putamen might constitute a vulnerability marker for BD.
Reduced processing speed is a core deficit in major depressive disorder (MDD) and has been linked to altered structural brain network connectivity. Ample evidence highlights the involvement of genetic-immunological processes in MDD and specific depressive symptoms. Here, we extended these findings by examining associations between polygenic scores for tumor necrosis factor-α blood levels (TNF-α PGS), structural brain connectivity, and processing speed in a large sample of MDD patients. Processing speed performance of n = 284 acutely depressed, n = 177 partially and n = 198 fully remitted patients, and n = 743 healthy controls (HC) was estimated based on five neuropsychological tests. Network-based statistic was used to identify a brain network associated with processing speed. We employed general linear models to examine the association between TNF-α PGS and processing speed. We investigated whether network connectivity mediates the association between TNF-α PGS and processing speed. We identified a structural network positively associated with processing speed in the whole sample. We observed a significant negative association between TNF-α PGS and processing speed in acutely depressed patients, whereas no association was found in remitted patients and HC. The mediation analysis revealed that brain connectivity partially mediated the association between TNF-α PGS and processing speed in acute MDD. The present study provides evidence that TNF-α PGS is associated with decreased processing speed exclusively in patients with acute depression. This association was partially mediated by structural brain connectivity. Using multimodal data, the current findings advance our understanding of cognitive dysfunction in MDD and highlight the involvement of genetic-immunological processes in its pathomechanisms.
BACKGROUND: Neuroinflammation affects brain tissue integrity in multiple sclerosis (MS) and may have a role in major depressive disorder (MDD). Whether advanced magnetic resonance imaging characteristics of the gray-to-white matter border serve as proxy of neuroinflammatory activity in MDD and MS remain unknown. METHODS: We included 684 participants (132 MDD patients with recurrent depressive episodes (RDE), 70 MDD patients with a single depressive episode (SDE), 222 MS patients without depressive symptoms (nMS), 58 MS patients with depressive symptoms (dMS), and 202 healthy controls (HC)). 3 T-T1w MRI-derived gray-to-white matter contrast (GWc) was used to reconstruct and characterize connectivity alterations of GWc-covariance networks by means of modularity, clustering coefficient, and degree. A cross-validated support vector machine was used to test the ability of GWc to stratify groups according to their depression symptoms, measured with BDI, at the single-subject level in MS and MDD independently. FINDINGS: MS and MDD patients showed increased modularity (ANOVA partial-η2 = 0.3) and clustering (partial-η2 = 0.1) compared to HC. In the subgroups, a linear trend analysis attested a gradient of modularity increases in the form: HC, dMS, nMS, SDE, and RDE (ANOVA partial-η2 = 0.28, p < 0.001) while this trend was less evident for clustering coefficient. Reduced morphological integrity (GWc) was seen in patients with increased depressive symptoms (partial-η2 = 0.42, P < 0.001) and was associated with depression scores across patient groups (r = -0.2, P < 0.001). Depressive symptoms in MS were robustly classified (88 %). CONCLUSIONS: Similar structural network alterations in MDD and MS exist, suggesting possible common inflammatory events like demyelination, neuroinflammation that are caught by GWc analyses. These alterations may vary depending on the severity of symptoms and in the case of MS may elucidate the occurrence of comorbid depression.
Formal thought disorder (FTD) is a clinical key factor in schizophrenia, but the neurobiological underpinnings remain unclear. In particular, the relationship between FTD symptom dimensions and patterns of regional brain volume loss in schizophrenia remains to be established in large cohorts. Even less is known about the cellular basis of FTD. Our study addresses these major obstacles by enrolling a large multi-site cohort acquired by the ENIGMA Schizophrenia Working Group (752 schizophrenia patients and 1256 controls), to unravel the neuroanatomy of FTD in schizophrenia and using virtual histology tools on implicated brain regions to investigate the cellular basis. Based on the findings of previous clinical and neuroimaging studies, we decided to separately explore positive, negative and total formal thought disorder. We used virtual histology tools to relate brain structural changes associated with FTD to cellular distributions in cortical regions. We identified distinct neural networks positive and negative FTD. Both networks encompassed fronto-occipito-amygdalar brain regions, but positive and negative FTD demonstrated a dissociation: negative FTD showed a relative sparing of orbitofrontal cortical thickness, while positive FTD also affected lateral temporal cortices. Virtual histology identified distinct transcriptomic fingerprints associated for both symptom dimensions. Negative FTD was linked to neuronal and astrocyte fingerprints, while positive FTD also showed associations with microglial cell types. These results provide an important step towards linking FTD to brain structural changes and their cellular underpinnings, providing an avenue for a better mechanistic understanding of this syndrome.
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.
BACKGROUND: Depressive symptoms seem to be interrelated in a complex and self-reinforcing way. To gain a better understanding of this complexity, the inclusion of theoretically relevant constructs (such as risk and protective factors) offers a comprehensive view into the complex mechanisms underlying depression. METHODS: Cross-sectional data from individuals diagnosed with a major depressive disorder (N = 986) and healthy controls (N = 1049) were analyzed. Participants self-reported their depressive symptoms, as well as several risk factors and protective factors. Regularized partial correlation networks were estimated for each group and compared using a network comparison test. RESULTS: Symptoms of depression were more strongly connected in the network of depressed patients than in healthy controls. Among the risk factors, perceived stress, the experience of negative life events, emotional neglect, and emotional abuse were the most centrally embedded in both networks. However, the centrality of risk factors did not significantly differ between the two groups. Among the protective factors, social support, personal competence, and acceptance were the most central in both networks, where the latter was significantly more strongly associated with the symptom of self-hate in depressed patients. CONCLUSION: The network analysis revealed that key symptoms of depression were more strongly connected for depressed patients than for healthy controls, and that risk and protective factors play an important role, particularly perceived stress in both groups and an accepting attitude for depressed patients. However, the purpose of this study is hypothesis generating and assisting in the potential selection of non-symptom nodes for future research.
Depression , Depressive Disorder, Major , Humans , Depression/etiology , Depressive Disorder, Major/epidemiology , Protective Factors , Cross-Sectional Studies , Self Report
Borderline personality disorder (BPD) is characterised by structural and functional brain alterations. Yet, there is little data on functional connectivity (FC) across different levels of brain networks and parameters. In this study, we applied a multi-level approach to analyse abnormal functional connectivity. We analysed resting-state functional magnetic resonance imaging (fMRI) data sets of 69 subjects: 17 female BPD patients and 51 age-matched psychiatrically healthy female controls. fMRI was analysed using CONN toolbox including: a) seed-based FC analysis of amygdala connectivity, b) independent component analysis (ICA) based network analysis of intra- and inter-network FC of selected resting-state networks (DMN, SN, FPN), as well as c) graph-theory based measures of network-level characteristics. We show group-level seed FC differences with higher amygdala to contralateral (superior) occipital cortex connectivity in BPD, which correlated with schema-therapy derived measures of symptoms/traits across the entire cohort. While there was no significant group effect on DMN, SN, or FPN intra-network or inter-network FC, we show a significant group difference for local efficiency and cluster coefficient for a DMN-linked cerebellum cluster. Our findings demonstrate BPD-linked changes in FC across multiple levels of observation, which supports a multi-level analysis for future studies to consider different aspects of functional connectome alterations.
Borderline Personality Disorder , Connectome , Humans , Female , Borderline Personality Disorder/diagnostic imaging , Brain , Amygdala/diagnostic imaging , Connectome/methods , Occipital Lobe
Patients with bipolar disorder (BD) show alterations in both gray matter volume (GMV) and white matter (WM) integrity compared with healthy controls (HC). However, it remains unclear whether the phenotypically distinct BD subtypes (BD-I and BD-II) also exhibit brain structural differences. This study investigated GMV and WM differences between HC, BD-I, and BD-II, along with clinical and genetic associations. N = 73 BD-I, n = 63 BD-II patients and n = 136 matched HC were included. Using voxel-based morphometry and tract-based spatial statistics, main effects of group in GMV and fractional anisotropy (FA) were analyzed. Associations between clinical and genetic features and GMV or FA were calculated using regression models. For FA but not GMV, we found significant differences between groups. BD-I patients showed lower FA compared with BD-II patients (ptfce-FWE = 0.006), primarily in the anterior corpus callosum. Compared with HC, BD-I patients exhibited lower FA in widespread clusters (ptfce-FWE < 0.001), including almost all major projection, association, and commissural fiber tracts. BD-II patients also demonstrated lower FA compared with HC, although less pronounced (ptfce-FWE = 0.049). The results remained unchanged after controlling for clinical and genetic features, for which no independent associations with FA or GMV emerged. Our findings suggest that, at a neurobiological level, BD subtypes may reflect distinct degrees of disease expression, with increasing WM microstructure disruption from BD-II to BD-I. This differential magnitude of microstructural alterations was not clearly linked to clinical and genetic variables. These findings should be considered when discussing the classification of BD subtypes within the spectrum of affective disorders.
Bipolar Disorder , White Matter , Humans , Bipolar Disorder/diagnostic imaging , Bipolar Disorder/genetics , Gray Matter/diagnostic imaging , Brain , White Matter/diagnostic imaging , Cerebral Cortex , Anisotropy
BACKGROUND: Case-control studies in major depression have established numerous regional grey and white matter effects in fronto-limbic brain regions. Yet, brain structural studies of dimensional depressive psychopathology within the subclinical spectrum are still limited, in particular for multi-modal imaging approaches. METHODS: Using voxel-based and surface-based morphometry (cortical thickness) in combination with diffusion tensor imaging (DTI) in a large non-clinical sample (N = 300), we correlated grey and white matter structural variation with subclinical depressive symptoms assessed with Beck's Depression inventory (BDI). RESULTS: We found a significant decrease of axial diffusivity associated with higher BDI scores in the left hippocampal part of the cingulum bundle (p < 0.05, threshold free cluster enhanced [TFCE] p-value) and some grey matter trend results e.g., a non-linear negative correlation of cortical thickness with depressive symptom load in the right pre/postcentral cortex (pFWE = 0.054, family wise error [FWE] peak level corrected) and a trend in grey matter volume decrease in women in the inferior frontal gyrus (pFWE = 0.054). LIMITATIONS: Since all grey matter effects disappear after FWE correction, we assume more stable effects in a larger, less homogenous sample enriched by help-seeking subjects covering a wider range of subclinical psychopathology. CONCLUSION: Our study adds correlations between single depressive symptoms and brain structure to a growing literature. Since subclinical depression is increasingly recognised to be relevant in our understanding of manifest depression, early detection and identification of potential brain correlates of minor depressive symptoms has the potential to expand and reveal possible biomarkers and early psychological treatment.
Diffusion Tensor Imaging , White Matter , Humans , Female , Diffusion Tensor Imaging/methods , Depression/diagnostic imaging , Depression/pathology , Brain/pathology , Gray Matter/diagnostic imaging , Gray Matter/pathology , White Matter/diagnostic imaging , White Matter/pathology
Schizophrenia is a prototypical network disorder with widespread brain-morphological alterations, yet it remains unclear whether these distributed alterations robustly reflect the underlying network layout. We tested whether large-scale structural alterations in schizophrenia relate to normative structural and functional connectome architecture, and systematically evaluated robustness and generalizability of these network-level alterations. Leveraging anatomical MRI scans from 2439 adults with schizophrenia and 2867 healthy controls from 26 ENIGMA sites and normative data from the Human Connectome Project (n = 207), we evaluated structural alterations of schizophrenia against two network susceptibility models: (i) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; (ii) epicenter mapping, which identifies regions whose typical connectivity profile most closely resembles the disease-related morphological alterations. To assess generalizability and specificity, we contextualized the influence of site, disease stages, and individual clinical factors and compared network associations of schizophrenia with that found in affective disorders. Our findings show schizophrenia-related cortical thinning is spatially associated with functional and structural hubs, suggesting that highly interconnected regions are more vulnerable to morphological alterations. Predominantly temporo-paralimbic and frontal regions emerged as epicenters with connectivity profiles linked to schizophrenia's alteration patterns. Findings were robust across sites, disease stages, and related to individual symptoms. Moreover, transdiagnostic comparisons revealed overlapping epicenters in schizophrenia and bipolar, but not major depressive disorder, suggestive of a pathophysiological continuity within the schizophrenia-bipolar-spectrum. In sum, cortical alterations over the course of schizophrenia robustly follow brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters at both the level of the group and the individual. Subtle variations of epicenters across disease stages suggest interacting pathological processes, while associations with patient-specific symptoms support additional inter-individual variability of hub vulnerability and epicenters in schizophrenia. Our work outlines potential pathways to better understand macroscale structural alterations, and inter- individual variability in schizophrenia.
Background: Schizophrenia is associated with an increased risk of aggressive behaviour, which may partly be explained by illness-related changes in brain structure. However, previous studies have been limited by group-level analyses, small and selective samples of inpatients and long time lags between exposure and outcome. Methods: This cross-sectional study pooled data from 20 sites participating in the international ENIGMA-Schizophrenia Working Group. Sites acquired T1-weighted and diffusion-weighted magnetic resonance imaging scans in a total of 2095 patients with schizophrenia and 2861 healthy controls. Measures of grey matter volume and white matter microstructural integrity were extracted from the scans using harmonised protocols. For each measure, normative modelling was used to calculate how much patients deviated (in z-scores) from healthy controls at the individual level. Ordinal regression models were used to estimate the associations of these deviations with concurrent aggressive behaviour (as odds ratios [ORs] with 99% confidence intervals [CIs]). Mediation analyses were performed for positive symptoms (i.e., delusions, hallucinations and disorganised thinking), impulse control and illness insight. Aggression and potential mediators were assessed with the Positive and Negative Syndrome Scale, Scale for the Assessment of Positive Symptoms or Brief Psychiatric Rating Scale. Results: Aggressive behaviour was significantly associated with reductions in total cortical volume (OR [99% CI] = 0.88 [0.78, 0.98], p = .003) and global white matter integrity (OR [99% CI] = 0.72 [0.59, 0.88], p = 3.50 × 10-5) and additional reductions in dorsolateral prefrontal cortex volume (OR [99% CI] = 0.85 [0.74, 0.97], p =.002), inferior parietal lobule volume (OR [99% CI] = 0.76 [0.66, 0.87], p = 2.20 × 10-7) and internal capsule integrity (OR [99% CI] = 0.76 [0.63, 0.92], p = 2.90 × 10-4). Except for inferior parietal lobule volume, these associations were largely mediated by increased severity of positive symptoms and reduced impulse control. Conclusions: This study provides evidence that the co-occurrence of positive symptoms, poor impulse control and aggressive behaviour in schizophrenia has a neurobiological basis, which may inform the development of therapeutic interventions.
Schizophrenia (SCZ) is a genetically heterogenous psychiatric disorder of highly polygenic nature. Correlative evidence from genetic studies indicate that the aggregated effects of distinct genetic risk factor combinations found in each patient converge onto common molecular mechanisms. To prove this on a functional level, we employed a reductionistic cellular model system for polygenic risk by differentiating induced pluripotent stem cells (iPSCs) from 104 individuals with high polygenic risk load and controls into cortical glutamatergic neurons (iNs). Multi-omics profiling identified widespread differences in alternative polyadenylation (APA) in the 3' untranslated region of many synaptic transcripts between iNs from SCZ patients and healthy donors. On the cellular level, 3'APA was associated with a reduction in synaptic density of iNs. Importantly, differential APA was largely conserved between postmortem human prefrontal cortex from SCZ patients and healthy donors, and strongly enriched for transcripts related to synapse biology. 3'APA was highly correlated with SCZ polygenic risk and affected genes were significantly enriched for SCZ associated common genetic variation. Integrative functional genomic analysis identified the RNA binding protein and SCZ GWAS risk gene PTBP2 as a critical trans-acting factor mediating 3'APA of synaptic genes in SCZ subjects. Functional characterization of PTBP2 in iNs confirmed its key role in 3'APA of synaptic transcripts and regulation of synapse density. Jointly, our findings show that the aggregated effects of polygenic risk converge on 3'APA as one common molecular mechanism that underlies synaptic impairments in SCZ.
Importance: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, major depressive disorder (MDD), no informative biomarkers have been identified. Objective: To evaluate whether machine learning (ML) can identify a multivariate biomarker for MDD. Design, Setting, and Participants: This study used data from the Marburg-Münster Affective Disorders Cohort Study, a case-control clinical neuroimaging study. Patients with acute or lifetime MDD and healthy controls aged 18 to 65 years were recruited from primary care and the general population in Münster and Marburg, Germany, from September 11, 2014, to September 26, 2018. The Münster Neuroimaging Cohort (MNC) was used as an independent partial replication sample. Data were analyzed from April 2022 to June 2023. Exposure: Patients with MDD and healthy controls. Main Outcome and Measure: Diagnostic classification accuracy was quantified on an individual level using an extensive ML-based multivariate approach across a comprehensive range of neuroimaging modalities, including structural and functional magnetic resonance imaging and diffusion tensor imaging as well as a polygenic risk score for depression. Results: Of 1801 included participants, 1162 (64.5%) were female, and the mean (SD) age was 36.1 (13.1) years. There were a total of 856 patients with MDD (47.5%) and 945 healthy controls (52.5%). The MNC replication sample included 1198 individuals (362 with MDD [30.1%] and 836 healthy controls [69.9%]). Training and testing a total of 4 million ML models, mean (SD) accuracies for diagnostic classification ranged between 48.1% (3.6%) and 62.0% (4.8%). Integrating neuroimaging modalities and stratifying individuals based on age, sex, treatment, or remission status does not enhance model performance. Findings were replicated within study sites and also observed in structural magnetic resonance imaging within MNC. Under simulated conditions of perfect reliability, performance did not significantly improve. Analyzing model errors suggests that symptom severity could be a potential focus for identifying MDD subgroups. Conclusion and Relevance: Despite the improved predictive capability of multivariate compared with univariate neuroimaging markers, no informative individual-level MDD biomarker-even under extensive ML optimization in a large sample of diagnosed patients-could be identified.
Depressive Disorder, Major , Humans , Female , Male , Depressive Disorder, Major/diagnostic imaging , Depressive Disorder, Major/pathology , Diffusion Tensor Imaging , Cohort Studies , Reproducibility of Results , Magnetic Resonance Imaging , Biomarkers
BACKGROUND: The psychopathological syndrome of formal thought disorder (FTD) is not only present in schizophrenia (SZ), but also highly prevalent in major depressive disorder and bipolar disorder. It remains unknown how alterations in the structural white matter connectome of the brain correlate with psychopathological FTD dimensions across affective and psychotic disorders. METHODS: Using FTD items of the Scale for the Assessment of Positive Symptoms and Scale for the Assessment of Negative Symptoms, we performed exploratory and confirmatory factor analyses in 864 patients with major depressive disorder (n= 689), bipolar disorder (n = 108), or SZ (n = 67) to identify psychopathological FTD dimensions. We used T1- and diffusion-weighted magnetic resonance imaging to reconstruct the structural connectome of the brain. To investigate the association of FTD subdimensions and global structural connectome measures, we employed linear regression models. We used network-based statistic to identify subnetworks of white matter fiber tracts associated with FTD symptomatology. RESULTS: Three psychopathological FTD dimensions were delineated, i.e., disorganization, emptiness, and incoherence. Disorganization and incoherence were associated with global dysconnectivity. Network-based statistics identified subnetworks associated with the FTD dimensions disorganization and emptiness but not with the FTD dimension incoherence. Post hoc analyses on subnetworks did not reveal diagnosis × FTD dimension interaction effects. Results remained stable after correcting for medication and disease severity. Confirmatory analyses showed a substantial overlap of nodes from both subnetworks with cortical brain regions previously associated with FTD in SZ. CONCLUSIONS: We demonstrated white matter subnetwork dysconnectivity in major depressive disorder, bipolar disorder, and SZ associated with FTD dimensions that predominantly comprise brain regions implicated in speech. Results open an avenue for transdiagnostic, psychopathology-informed, dimensional studies in pathogenetic research.
Depressive Disorder, Major , Frontotemporal Dementia , Psychotic Disorders , Schizophrenia , Humans , Depressive Disorder, Major/diagnostic imaging , Depressive Disorder, Major/complications , Frontotemporal Dementia/complications , Psychotic Disorders/psychology , Brain/diagnostic imaging , Brain/pathology , Schizophrenia/pathology , Magnetic Resonance Imaging
BACKGROUND: Schizotypy represents an index of psychosis-proneness in the general population, often associated with childhood trauma exposure. Both schizotypy and childhood trauma are linked to structural brain alterations, and it is possible that trauma exposure moderates the extent of brain morphological differences associated with schizotypy. METHODS: We addressed this question using data from a total of 1182 healthy adults (age range: 18-65 years old, 647 females/535 males), pooled from nine sites worldwide, contributing to the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Schizotypy working group. All participants completed both the Schizotypal Personality Questionnaire Brief version (SPQ-B), and the Childhood Trauma Questionnaire (CTQ), and underwent a 3D T1-weighted brain MRI scan from which regional indices of subcortical gray matter volume and cortical thickness were determined. RESULTS: A series of multiple linear regressions revealed that differences in cortical thickness in four regions-of-interest were significantly associated with interactions between schizotypy and trauma; subsequent moderation analyses indicated that increasing levels of schizotypy were associated with thicker left caudal anterior cingulate gyrus, right middle temporal gyrus and insula, and thinner left caudal middle frontal gyrus, in people exposed to higher (but not low or average) levels of childhood trauma. This was found in the context of morphological changes directly associated with increasing levels of schizotypy or increasing levels of childhood trauma exposure. CONCLUSIONS: These results suggest that alterations in brain regions critical for higher cognitive and integrative processes that are associated with schizotypy may be enhanced in individuals exposed to high levels of trauma.
Adverse Childhood Experiences , Psychological Tests , Schizotypal Personality Disorder , Self Report , Adult , Male , Female , Humans , Adolescent , Young Adult , Middle Aged , Aged , Schizotypal Personality Disorder/diagnostic imaging , Schizotypal Personality Disorder/psychology , Brain/diagnostic imaging , Gray Matter , Magnetic Resonance Imaging/methods
Up to 70% of patients with major depressive disorder present with psychomotor disturbance (PmD), but at the present time understanding of its pathophysiology is limited. In this study, we capitalized on a large sample of patients to examine the neural correlates of PmD in depression. This study included 820 healthy participants and 699 patients with remitted (n = 402) or current (n = 297) depression. Patients were further categorized as having psychomotor retardation, agitation, or no PmD. We compared resting-state functional connectivity (ROI-to-ROI) between nodes of the cerebral motor network between the groups, including primary motor cortex, supplementary motor area, sensory cortex, superior parietal lobe, caudate, putamen, pallidum, thalamus, and cerebellum. Additionally, we examined network topology of the motor network using graph theory. Among the currently depressed 55% had PmD (15% agitation, 29% retardation, and 11% concurrent agitation and retardation), while 16% of the remitted patients had PmD (8% retardation and 8% agitation). When compared with controls, currently depressed patients with PmD showed higher thalamo-cortical and pallido-cortical connectivity, but no network topology alterations. Currently depressed patients with retardation only had higher thalamo-cortical connectivity, while those with agitation had predominant higher pallido-cortical connectivity. Currently depressed patients without PmD showed higher thalamo-cortical, pallido-cortical, and cortico-cortical connectivity, as well as altered network topology compared to healthy controls. Remitted patients with PmD showed no differences in single connections but altered network topology, while remitted patients without PmD did not differ from healthy controls in any measure. We found evidence for compensatory increased cortico-cortical resting-state functional connectivity that may prevent psychomotor disturbance in current depression, but may perturb network topology. Agitation and retardation show specific connectivity signatures. Motor network topology is slightly altered in remitted patients arguing for persistent changes in depression. These alterations in functional connectivity may be addressed with non-invasive brain stimulation.
Background: Controllability is a measure of the brain's ability to orchestrate neural activity which can be quantified in terms of properties of the brain's network connectivity. Evidence from the literature suggests that aging can exert a general effect on whole-brain controllability. Mounting evidence, on the other hand, suggests that parenthood and motherhood in particular lead to long-lasting changes in brain architecture that effectively slow down brain aging. We hypothesize that parenthood might preserve brain controllability properties from aging. Methods: In a sample of 814 healthy individuals (aged 33.9 ± 12.7 years, 522 females), we estimate whole-brain controllability and compare the aging effects in subjects with vs. those without children. We use diffusion tensor imaging (DTI) to estimate the brain structural connectome. The level of brain control is then calculated from the connectomic properties of the brain structure. Specifically, we measure the network control over many low-energy state transitions (average controllability) and the network control over difficult-to-reach states (modal controllability). Results and conclusion: In nulliparous females, whole-brain average controllability increases, and modal controllability decreases with age, a trend that we do not observe in parous females. Statistical comparison of the controllability metrics shows that modal controllability is higher and average controllability is lower in parous females compared to nulliparous females. In men, we observed the same trend, but the difference between nulliparous and parous males do not reach statistical significance. Our results provide strong evidence that parenthood contradicts aging effects on brain controllability and the effect is stronger in mothers.
Formal thought disorder (FTD) is a key clinical factor in schizophrenia, but the neurobiological underpinnings remain unclear. In particular, relationship between FTD symptom dimensions and patterns of regional brain volume deficiencies in schizophrenia remain to be established in large cohorts. Even less is known about the cellular basis of FTD. Our study addresses these major obstacles based on a large multi-site cohort through the ENIGMA Schizophrenia Working Group (752 individuals with schizophrenia and 1256 controls), to unravel the neuroanatomy of positive, negative and total FTD in schizophrenia and their cellular bases. We used virtual histology tools to relate brain structural changes associated with FTD to cellular distributions in cortical regions. We identified distinct neural networks for positive and negative FTD. Both networks encompassed fronto-occipito-amygdalar brain regions, but negative FTD showed a relative sparing of orbitofrontal cortical thickness, while positive FTD also affected lateral temporal cortices. Virtual histology identified distinct transcriptomic fingerprints associated for both symptom dimensions. Negative FTD was linked to neuronal and astrocyte fingerprints, while positive FTD was also linked to microglial cell types. These findings relate different dimensions of FTD to distinct brain structural changes and their cellular underpinnings, improve our mechanistic understanding of these key psychotic symptoms.
Psychiatric disorders show high co-morbidity, including co-morbid expressions of subclinical psychopathology across multiple disease spectra. Given the limitations of classical case-control designs in elucidating this overlap, new approaches are needed to identify biological underpinnings of spectra and their interaction. We assessed autistic-like traits (using the Autism Quotient, AQ) and schizotypy - as models of subclinical expressions of disease phenotypes and examined their association with volumes and regional cerebral blood flow (rCBF) of anterior, mid- and posterior hippocampus segments from structural MRI scans in 318 and arterial spin labelling (ASL) in 346 nonclinical subjects, which overlapped with the structural imaging sample (N = 298). We demonstrate significant interactive effects of positive schizotypy and AQ social skills as well as of positive schizotypy and AQ imagination on hippocampal subfield volume variation. Moreover, we show that AQ attention switching modulated hippocampal head rCBF, while positive schizotypy by AQ attention to detail interactions modulated hippocampal tail rCBF. In addition, we show significant correlation of hippocampal volume and rCBF in both region-of-interest and voxel-wise analyses, which were robust after removal of variance related to schizotypy and autistic traits. These findings provide empirical evidence for both the modulation of hippocampal subfield structure and function through subclinical traits, and in particular how only the interaction of phenotype facets leads to significant reductions or variations in these parameters. This makes a case for considering the synergistic impact of different (subclinical) disease spectra on transdiagnostic biological parameters in psychiatry.
