The neural signature of psychomotor disturbance in depression.

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

Impact of review method on the conclusions of clinical reviews: A systematic review on dietary interventions in depression as a case in point.

BackgroundThe recommendations of experts who write review articles are a critical determinant of the adaptation of new treatments by clinicians. Several types of reviews exist (narrative, systematic, meta-analytic), and some of these are more vulnerable to researcher bias than others. Recently, the interest in nutritional interventions in psychiatry has increased and many experts, who are often active researchers on this topic, have come to strong conclusions about the benefits of a healthy diet on depression. In a young and active field of study, we aimed to investigate whether the strength of an author's conclusion is associated with the type of review article they wrote.MethodsSystematic searches were performed in PubMed, Web of Science, Cochrane Database of Systematic Reviews, and Google Scholar for narrative reviews and systematic reviews with and without meta-analyses on the effects of diet on depression (final search date: May 30th, 2020). Conclusions were extracted from the abstract and discussion section and rated as strong, moderate, or weak by independent raters who were blind to study type. A benchmark on legitimate conclusion strength was based on a GRADE assessment of the highest level of evidence. This systematic review was registered with PROSPERO, number CRD42020141372.Findings24 narrative reviews, 12 systematic reviews, and 14 meta-analyses were included. In the abstract, 33% of narrative reviews and 8% of systematic reviews came to strong conclusions, whereas no meta-analysis did. Narrative reviews were 8.94 (95% CI: 2.17, 36.84) times more likely to report stronger conclusions in the abstract than systematic reviews with and without meta-analyses. These findings were similar for conclusions in the discussion section. Narrative reviews used 45.6% fewer input studies and were more likely to be written by authors with potential conflicts of interest. A study limitation is the subjective nature of the conclusion classification system despite high inter-rater agreements and its confirmation outside of the review team.ConclusionsWe have shown that narrative reviews come to stronger conclusions about the benefits of a healthy diet on depression despite inconclusive evidence. This finding empirically underscores the importance of a systematic method for summarizing the evidence of a field of study. Journal editors may want to reconsider publishing narrative reviews before meta-analytic reviews are available

Relative importance of speech and voice features in the classification of schizophrenia and depression

Abstract 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

Data-driven multivariate identification of gyrification patterns in a transdiagnostic patient cohort: A cluster analysis approach

Background: 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. Methods: 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. Results: 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. Conclusions: 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

Altered brain dynamic in major depressive disorder: state and trait features

Abstract 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

Structural brain network connectivity mediates the association between polygenic score for tumor necrosis factor-α and processing speed in acute depression

Introduction: 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. 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. Methods: Processing speed of N=1402 acutely depressed or (partially) remitted patients and 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. A mediation analysis was performed to investigate whether network connectivity mediates the association between TNF-α PGS and processing speed. Results: 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 (partially) 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. Discussion: 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

Brain structural network connectivity of formal thought disorder dimensions in affective and psychotic disorders

BACKGROUND: The psychopathological syndrome of formal thought disorder (FTD) is present in schizophrenia (SZ) but is also highly prevalent in major depression (MDD) and bipolar disorder (BD). 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 SAPS and SANS, we performed exploratory and confirmatory factor analyses in N=864 patients with MDD (n=689), BD (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 sub-dimensions and global structural connectome measures, we employed linear regression models. We used network-based statistic (NBS) 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. NBS identified subnetworks associated with FTD dimensions "disorganization" and "emptiness" but not with "incoherence". Post-hoc analyses on subnetworks did not reveal diagnosis x 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 MDD, BD and SZ associated with FTD dimensions that comprise predominantly brain regions implicated in speech. Results open an avenue for transdiagnostic, psychopathology informed, dimensional studies in pathogenetic research

Shared and Specific Patterns of Structural Brain Connectivity Across Affective and Psychotic Disorders

BACKGROUND: Altered brain structural connectivity has been implicated in the pathophysiology of psychiatric disorders including schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). However, it is unknown which part of these connectivity abnormalities are disorder specific and which are shared across the spectrum of psychotic and affective disorders. We investigated common and distinct brain connectivity alterations in a large sample (N = 1743) of patients with SZ, BD, or MDD and healthy control (HC) subjects. METHODS: This study examined diffusion-weighted imaging-based structural connectome topology in 720 patients with MDD, 112 patients with BD, 69 patients with SZ, and 842 HC subjects (mean age of all subjects: 35.7 years). Graph theory-based network analysis was used to investigate connectome organization. Machine learning algorithms were trained to classify groups based on their structural connectivity matrices. RESULTS: Groups differed significantly in the network metrics global efficiency, clustering, present edges, and global connectivity strength with a converging pattern of alterations between diagnoses (e.g., efficiency: HC > MDD > BD > SZ, false discovery rate-corrected p = .028). Subnetwork analysis revealed a common core of edges that were affected across all 3 disorders, but also revealed differences between disorders. Machine learning algorithms could not discriminate between disorders but could discriminate each diagnosis from HC. Furthermore, dysconnectivity patterns were found most pronounced in patients with an early disease onset irrespective of diagnosis. CONCLUSIONS: We found shared and specific signatures of structural white matter dysconnectivity in SZ, BD, and MDD, leading to commonly reduced network efficiency. These results showed a compromised brain communication across a spectrum of major psychiatric disorders

Shared and Specific Patterns of Structural Brain Connectivity Across Affective and Psychotic Disorders

Background: Altered brain structural connectivity has been implicated in the pathophysiology of psychiatric disorders including schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). However, it is unknown which part of these connectivity abnormalities are disorder specific and which are shared across the spectrum of psychotic and affective disorders. We investigated common and distinct brain connectivity alterations in a large sample (N = 1743) of patients with SZ, BD, or MDD and healthy control (HC) subjects. Methods: This study examined diffusion-weighted imaging-based structural connectome topology in 720 patients with MDD, 112 patients with BD, 69 patients with SZ, and 842 HC subjects (mean age of all subjects: 35.7 years). Graph theory–based network analysis was used to investigate connectome organization. Machine learning algorithms were trained to classify groups based on their structural connectivity matrices. Results: Groups differed significantly in the network metrics global efficiency, clustering, present edges, and global connectivity strength with a converging pattern of alterations between diagnoses (e.g., efficiency: HC > MDD > BD > SZ, false discovery rate–corrected p = .028). Subnetwork analysis revealed a common core of edges that were affected across all 3 disorders, but also revealed differences between disorders. Machine learning algorithms could not discriminate between disorders but could discriminate each diagnosis from HC. Furthermore, dysconnectivity patterns were found most pronounced in patients with an early disease onset irrespective of diagnosis. Conclusions: We found shared and specific signatures of structural white matter dysconnectivity in SZ, BD, and MDD, leading to commonly reduced network efficiency. These results showed a compromised brain communication across a spectrum of major psychiatric disorders

A systematic evaluation of machine learning-based biomarkers for major depressive disorder across modalities

Background: 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), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy. Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression. Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification. Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research