Publications by authors named "Tim Hahn"

109 Publications

Genetic risk for psychiatric illness is associated with the number of hospitalizations of bipolar disorder patients.

J Affect Disord 2021 Sep 28;296:532-540. Epub 2021 Sep 28.

Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany; Center for Mind, Brain and Behavior (CMBB), Marburg, Germany.

Objectives: Bipolar disorder (BD) has a highly heterogeneous clinical course that is characterized by relapses and increased health care utilization in a significant fraction of patients. A thorough understanding of factors influencing illness course is essential for predicting disorder severity and developing targeted therapies.

Methods: We performed polygenic score analyses in four cohorts (N = 954) to test whether the genetic risk for BD, schizophrenia, or major depression is associated with a severe course of BD. We analyzed BD patients with a minimum illness duration of five years. The severity of the disease course was assessed by using the number of hospitalizations in a mental health facility and a composite measure of longitudinal illness severity (OPCRIT item 90).

Results: Our analyses showed that higher polygenic scores for BD (β = 0.11, SE = 0.03, p = 1.17 × 10) and schizophrenia (β = 0.09, SE = 0.03, p = 4.24 × 10), but not for major depression, were associated with more hospitalizations. None of the investigated polygenic scores was associated with the composite measure of longitudinal illness severity (OPCRIT item 90).

Limitations: We could not account for non-genetic influences on disease course. Our clinical sample contained more severe cases.

Conclusions: This study demonstrates that the genetic risk burden for psychiatric illness is associated with increased health care utilization, a proxy for disease severity, in BD patients. The findings are in line with previous observations made for patients diagnosed with schizophrenia or major depression. Therefore, in the future psychiatric disorder polygenic scores might become helpful for stratifying patients with high risk of a chronic manifestation and predicting disease course.
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http://dx.doi.org/10.1016/j.jad.2021.09.073DOI Listing
September 2021

Altered resting-state functional connectome in major depressive disorder: a mega-analysis from the PsyMRI consortium.

Transl Psychiatry 2021 10 7;11(1):511. Epub 2021 Oct 7.

Department of Psychology, Friedrich Schiller University Jena, Jena, Germany.

Major depressive disorder (MDD) is associated with abnormal neural circuitry. It can be measured by assessing functional connectivity (FC) at resting-state functional MRI, that may help identifying neural markers of MDD and provide further efficient diagnosis and monitor treatment outcomes. The main aim of the present study is to investigate, in an unbiased way, functional alterations in patients with MDD using a large multi-center dataset from the PsyMRI consortium including 1546 participants from 19 centers ( www.psymri.com ). After applying strict exclusion criteria, the final sample consisted of 606 MDD patients (age: 35.8 ± 11.9 y.o.; females: 60.7%) and 476 healthy participants (age: 33.3 ± 11.0 y.o.; females: 56.7%). We found significant relative hypoconnectivity within somatosensory motor (SMN), salience (SN) networks and between SMN, SN, dorsal attention (DAN), and visual (VN) networks in MDD patients. No significant differences were detected within the default mode (DMN) and frontoparietal networks (FPN). In addition, alterations in network organization were observed in terms of significantly lower network segregation of SMN in MDD patients. Although medicated patients showed significantly lower FC within DMN, FPN, and SN than unmedicated patients, there were no differences between medicated and unmedicated groups in terms of network organization in SMN. We conclude that the network organization of cortical networks, involved in processing of sensory information, might be a more stable neuroimaging marker for MDD than previously assumed alterations in higher-order neural networks like DMN and FPN.
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http://dx.doi.org/10.1038/s41398-021-01619-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8497531PMC
October 2021

Cerebrospinal fluid flow cytometry distinguishes psychosis spectrum disorders from differential diagnoses.

Mol Psychiatry 2021 Aug 6. Epub 2021 Aug 6.

Department of Neurology with Institute of Translational Neurology, University of Münster, Münster, Germany.

Psychotic disorders are common and disabling mental conditions. The relative importance of immune-related mechanisms in psychotic disorders remains subject of debate. Here, we present a large-scale retrospective study of blood and cerebrospinal fluid (CSF) immune cell profiles of psychosis spectrum patients. We performed basic CSF analysis and multi-dimensional flow cytometry of CSF and blood cells from 59 patients with primary psychotic disorders (F20, F22, F23, and F25) in comparison to inflammatory (49 RRMS and 16 NMDARE patients) and non-inflammatory controls (52 IIH patients). We replicated the known expansion of monocytes in the blood of psychosis spectrum patients, that we identified to preferentially affect classical monocytes. In the CSF, we found a relative shift from lymphocytes to monocytes, increased protein levels, and evidence of blood-brain barrier disruption in psychosis. In fact, these CSF features confidently distinguished autoimmune encephalitis from psychosis despite similar (initial) clinical features. We then constructed machine learning models incorporating blood and CSF parameters and demonstrated their superior ability to differentiate psychosis from non-inflammatory controls compared to individual parameters. Multi-dimensional and multi-compartment immune cell signatures can thus support the diagnosis of psychosis spectrum disorders with the potential to accelerate diagnosis and initiation of therapy.
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http://dx.doi.org/10.1038/s41380-021-01244-5DOI Listing
August 2021

Technical feasibility and adherence of the Remote Monitoring Application in Psychiatry (ReMAP) for the assessment of affective symptoms.

J Affect Disord 2021 Nov 16;294:652-660. Epub 2021 Jul 16.

Institute for Translational Psychiatry, University of Münster, Germany; Interdisciplinary Centre for Clinical Research (IZKF) Münster, University of Münster, Germany. Electronic address:

Background: Smartphone-based monitoring constitutes a cost-effective instrument to assess and predict affective symptom trajectories. Large-scale transdiagnostic studies utilizing this methodology are yet lacking in psychiatric research. Thus, we introduce the Remote Monitoring Application in Psychiatry (ReMAP) and evaluate its feasibility and adherence in a large transdiagnostic sample.

Methods: The ReMAP app was distributed among n = 997 healthy control participants and psychiatric patients, including affective, anxiety, and psychotic disorders. Passive sensor data (acceleration, geolocation, walking distance, steps), optional standardized self-reports on mood and sleep, and voice samples were assessed. Feasibility and adherence were evaluated based on frequency of transferred data, and participation duration. Preliminary results are presented while data collection is ongoing.

Results: Retention rates of 90.25% for the minimum study duration of two weeks and 33.09% for one year were achieved (median participation 135 days, IQR=111). Participants transferred an average of 51.83 passive events per day. An average of 34.59 self-report events were transferred per user, with a considerable range across participants (0-552 events). Clinical and non-clinical subgroups did not differ in participation duration or rate of data transfer. The mean rate of days with passive data was higher and less heterogeneous in iOS (91.85%, SD=21.25) as compared to Android users (63.04%, SD=35.09).

Limitations: Subjective user experience was not assessed limiting conclusions about app acceptance.

Conclusions: ReMAP is a technically feasible tool to assess affective symptoms with high temporal resolution in large-scale transdiagnostic samples with good adherence. Future studies should account for differences between operating systems.
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http://dx.doi.org/10.1016/j.jad.2021.07.030DOI Listing
November 2021

Brain structural connectivity, anhedonia, and phenotypes of major depressive disorder: A structural equation model approach.

Hum Brain Mapp 2021 Oct 24;42(15):5063-5074. Epub 2021 Jul 24.

Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany.

Aberrant brain structural connectivity in major depressive disorder (MDD) has been repeatedly reported, yet many previous studies lack integration of different features of MDD with structural connectivity in multivariate modeling approaches. In n = 595 MDD patients, we used structural equation modeling (SEM) to test the intercorrelations between anhedonia, anxiety, neuroticism, and cognitive control in one comprehensive model. We then separately analyzed diffusion tensor imaging (DTI) connectivity measures in association with those clinical variables, and finally integrated brain connectivity associations, clinical/cognitive variables into a multivariate SEM. We first confirmed our clinical/cognitive SEM. DTI analyses (FWE-corrected) showed a positive correlation of anhedonia with fractional anisotropy (FA) in the right anterior thalamic radiation (ATR) and forceps minor/corpus callosum, while neuroticism was negatively correlated with axial diffusivity (AD) in the left uncinate fasciculus (UF) and inferior fronto-occipital fasciculus (IFOF). An extended SEM confirmed the associations of ATR FA with anhedonia and UF/IFOF AD with neuroticism impacting on cognitive control. Our findings provide evidence for a differential impact of state and trait variables of MDD on brain connectivity and cognition. The multivariate approach shows feasibility of explaining heterogeneity within MDD and tracks this to specific brain circuits, thus adding to better understanding of heterogeneity on the biological level.
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http://dx.doi.org/10.1002/hbm.25600DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8449111PMC
October 2021

PHOTONAI-A Python API for rapid machine learning model development.

PLoS One 2021 21;16(7):e0254062. Epub 2021 Jul 21.

Institute for Translational Psychiatry, University of Münster, Münster, Germany.

PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm sequences. It is especially designed to support the iterative model development process and automates the repetitive training, hyperparameter optimization and evaluation tasks. Importantly, the workflow ensures unbiased performance estimates while still allowing the user to fully customize the machine learning analysis. PHOTONAI extends existing solutions with a novel pipeline implementation supporting more complex data streams, feature combinations, and algorithm selection. Metrics and results can be conveniently visualized using the PHOTONAI Explorer and predictive models are shareable in a standardized format for further external validation or application. A growing add-on ecosystem allows researchers to offer data modality specific algorithms to the community and enhance machine learning in the areas of the life sciences. Its practical utility is demonstrated on an exemplary medical machine learning problem, achieving a state-of-the-art solution in few lines of code. Source code is publicly available on Github, while examples and documentation can be found at www.photon-ai.com.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0254062PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8294542PMC
July 2021

Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning.

Neuropsychopharmacology 2021 10 14;46(11):1895-1905. Epub 2021 Jun 14.

Max Planck Institute of Psychiatry, Munich, Germany.

Psychiatric disorders show heterogeneous symptoms and trajectories, with current nosology not accurately reflecting their molecular etiology and the variability and symptomatic overlap within and between diagnostic classes. This heterogeneity impedes timely and targeted treatment. Our study aimed to identify psychiatric patient clusters that share clinical and genetic features and may profit from similar therapies. We used high-dimensional data clustering on deep clinical data to identify transdiagnostic groups in a discovery sample (N = 1250) of healthy controls and patients diagnosed with depression, bipolar disorder, schizophrenia, schizoaffective disorder, and other psychiatric disorders. We observed five diagnostically mixed clusters and ordered them based on severity. The least impaired cluster 0, containing most healthy controls, showed general well-being. Clusters 1-3 differed predominantly regarding levels of maltreatment, depression, daily functioning, and parental bonding. Cluster 4 contained most patients diagnosed with psychotic disorders and exhibited the highest severity in many dimensions, including medication load. Depressed patients were present in all clusters, indicating that we captured different disease stages or subtypes. We replicated all but the smallest cluster 1 in an independent sample (N = 622). Next, we analyzed genetic differences between clusters using polygenic scores (PGS) and the psychiatric family history. These genetic variables differed mainly between clusters 0 and 4 (prediction area under the receiver operating characteristic curve (AUC) = 81%; significant PGS: cross-disorder psychiatric risk, schizophrenia, and educational attainment). Our results confirm that psychiatric disorders consist of heterogeneous subtypes sharing molecular factors and symptoms. The identification of transdiagnostic clusters advances our understanding of the heterogeneity of psychiatric disorders and may support the development of personalized treatments.
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http://dx.doi.org/10.1038/s41386-021-01051-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8429672PMC
October 2021

The progression of disorder-specific brain pattern expression in schizophrenia over 9 years.

NPJ Schizophr 2021 Jun 14;7(1):32. Epub 2021 Jun 14.

Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.

Age plays a crucial role in the performance of schizophrenia vs. controls (SZ-HC) neuroimaging-based machine learning (ML) models as the accuracy of identifying first-episode psychosis from controls is poor compared to chronic patients. Resolving whether this finding reflects longitudinal progression in a disorder-specific brain pattern or a systematic but non-disorder-specific deviation from a normal brain aging (BA) trajectory in schizophrenia would help the clinical translation of diagnostic ML models. We trained two ML models on structural MRI data: an SZ-HC model based on 70 schizophrenia patients and 74 controls and a BA model (based on 561 healthy individuals, age range = 66 years). We then investigated the two models' predictions in the naturalistic longitudinal Northern Finland Birth Cohort 1966 (NFBC1966) following 29 schizophrenia and 61 controls for nine years. The SZ-HC model's schizophrenia-specificity was further assessed by utilizing independent validation (62 schizophrenia, 95 controls) and depression samples (203 depression, 203 controls). We found better performance at the NFBC1966 follow-up (sensitivity = 75.9%, specificity = 83.6%) compared to the baseline (sensitivity = 58.6%, specificity = 86.9%). This finding resulted from progression in disorder-specific pattern expression in schizophrenia and was not explained by concomitant acceleration of brain aging. The disorder-specific pattern's progression reflected longitudinal changes in cognition, outcomes, and local brain changes, while BA captured treatment-related and global brain alterations. The SZ-HC model was also generalizable to independent schizophrenia validation samples but classified depression as control subjects. Our research underlines the importance of taking account of longitudinal progression in a disorder-specific pattern in schizophrenia when developing ML classifiers for different age groups.
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http://dx.doi.org/10.1038/s41537-021-00157-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8203625PMC
June 2021

The Course of Disease in Major Depressive Disorder Is Associated With Altered Activity of the Limbic System During Negative Emotion Processing.

Biol Psychiatry Cogn Neurosci Neuroimaging 2021 Jun 5. Epub 2021 Jun 5.

Institute for Translational Psychiatry, University of Münster, Münster, Germany. Electronic address:

Background: Brain functional alterations during emotion processing in patients with major depressive disorder (MDD) compared with healthy control subjects (HCs) are frequently reported. However, evidence for functional correlates of emotion processing with regard to MDD trajectories is scarce. This study investigates the role of lifetime disease course for limbic brain activation during negative emotional face processing in patients with MDD.

Methods: In a large sample of patients with MDD (n = 333; 58.55% female) and HCs (n = 333; 60.06% female), brain activation was investigated during a negative emotional face-processing task within a cross-sectional design. Differences between HC and MDD groups were analyzed. Previous disease course, characterized by 2 components, namely hospitalization and duration of illness, was regressed on brain activation of the amygdala, (para-)hippocampus, and insula in patients with MDD.

Results: Patients with MDD showed increased activation in the amygdala, insula, and hippocampus compared with HCs (all p values corrected for familywise error [p] < .045). The hospitalization component showed negative associations with brain activation in the bilateral insula (right: p = .026, left: p = .019) and (para-)hippocampus (right: p = .038, left: p = .031). No significant association was found for the duration of illness component (all p > .057).

Conclusions: This study investigated negative emotion processing in a large sample of patients with MDD and HCs. Our results confirm limbic hyperactivation in patients with MDD during negative emotion processing; however, this hyperactivation may resolve with a more severe lifetime disease course in the insula and (para-)hippocampus-brain regions involved in emotion processing and regulation. These findings need further replication in longitudinal studies.
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http://dx.doi.org/10.1016/j.bpsc.2021.05.008DOI Listing
June 2021

Interpreting weights of multimodal machine learning models-problems and pitfalls.

Neuropsychopharmacology 2021 10 20;46(11):1861-1862. Epub 2021 May 20.

Institute for Translational Psychiatry, University of Münster, Münster, Germany.

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http://dx.doi.org/10.1038/s41386-021-01030-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8429467PMC
October 2021

From multivariate methods to an AI ecosystem.

Mol Psychiatry 2021 May 12. Epub 2021 May 12.

Department of Psychiatry, University of Muenster, Münster, Germany.

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http://dx.doi.org/10.1038/s41380-021-01116-yDOI Listing
May 2021

Systematic misestimation of machine learning performance in neuroimaging studies of depression.

Neuropsychopharmacology 2021 07 6;46(8):1510-1517. Epub 2021 May 6.

Department of Psychiatry, University of Münster, Münster, Germany.

We currently observe a disconcerting phenomenon in machine learning studies in psychiatry: While we would expect larger samples to yield better results due to the availability of more data, larger machine learning studies consistently show much weaker performance than the numerous small-scale studies. Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy controls based on neuroimaging data. Drawing upon structural MRI data from a balanced sample of N = 1868 MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61%. Next, we mimicked the process by which researchers would draw samples of various sizes (N = 4 to N = 150) from the population and showed a strong risk of misestimation. Specifically, for small sample sizes (N = 20), we observe accuracies of up to 95%. For medium sample sizes (N = 100) accuracies up to 75% were found. Importantly, further investigation showed that sufficiently large test sets effectively protect against performance misestimation whereas larger datasets per se do not. While these results question the validity of a substantial part of the current literature, we outline the relatively low-cost remedy of larger test sets, which is readily available in most cases.
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http://dx.doi.org/10.1038/s41386-021-01020-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8209109PMC
July 2021

Social support and hippocampal volume are negatively associated in adults with previous experience of childhood maltreatment.

J Psychiatry Neurosci 2021 Apr 27;46(3):E328-E336. Epub 2021 Apr 27.

From the Department of Psychiatry, University of Münster, Münster, Germany (Förster, Danzer, Redlich, Opel, Grotegerd, Leehr, Dohm, Enneking, Meinert, Goltermann, Lemke, Waltemate, Thiel, Behnert, Hahn, Repple, Dannlowski); the Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, TU Dresden, Dresden, Germany (Förster); the Department of Clinical Psychology, University of Halle, Halle, Germany (Redlich); the Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany (Brosch, Stein, Meller, Ringwald, Schmitt, Steinsträter, Jansen, Krug, Nenadic, Kircher); the Core-Unit Brain Imaging, Faculty of Medicine, University of Marburg, Marburg, Germany (Jansen); the Department of Psychiatry, University of Bonn, Bonn, Germany (Krug); and the University Clinic for Clinical Radiology, University of Münster, Münster, Germany (Kugel, Heindel).

Background: 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.

Methods: 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.

Results: 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.

Limitations: Because of the correlative nature of our study, we could not infer causal relationships between social support, maltreatment and hippocampal volume.

Conclusion: 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.
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http://dx.doi.org/10.1503/jpn.200162DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8327979PMC
April 2021

Association between body mass index and subcortical brain volumes in bipolar disorders-ENIGMA study in 2735 individuals.

Mol Psychiatry 2021 Apr 16. Epub 2021 Apr 16.

Unit for Psychosomatics / CL Outpatient Clinic for Adults, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.

Individuals with bipolar disorders (BD) frequently suffer from obesity, which is often associated with neurostructural alterations. Yet, the effects of obesity on brain structure in BD are under-researched. We obtained MRI-derived brain subcortical volumes and body mass index (BMI) from 1134 BD and 1601 control individuals from 17 independent research sites within the ENIGMA-BD Working Group. We jointly modeled the effects of BD and BMI on subcortical volumes using mixed-effects modeling and tested for mediation of group differences by obesity using nonparametric bootstrapping. All models controlled for age, sex, hemisphere, total intracranial volume, and data collection site. Relative to controls, individuals with BD had significantly higher BMI, larger lateral ventricular volume, and smaller volumes of amygdala, hippocampus, pallidum, caudate, and thalamus. BMI was positively associated with ventricular and amygdala and negatively with pallidal volumes. When analyzed jointly, both BD and BMI remained associated with volumes of lateral ventricles  and amygdala. Adjusting for BMI decreased the BD vs control differences in ventricular volume. Specifically, 18.41% of the association between BD and ventricular volume was mediated by BMI (Z = 2.73, p = 0.006). BMI was associated with similar regional brain volumes as BD, including lateral ventricles, amygdala, and pallidum. Higher BMI may in part account for larger ventricles, one of the most replicated findings in BD. Comorbidity with obesity could explain why neurostructural alterations are more pronounced in some individuals with BD. Future prospective brain imaging studies should investigate whether obesity could be a modifiable risk factor for neuroprogression.
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http://dx.doi.org/10.1038/s41380-021-01098-xDOI Listing
April 2021

Classification of neurological diseases using multi-dimensional CSF analysis.

Brain 2021 Oct;144(9):2625-2634

Department of Neurology with Institute of Translational Neurology, University and University Hospital Münster, 48149 Münster, Germany.

Although CSF analysis routinely enables the diagnosis of neurological diseases, it is mainly used for the gross distinction between infectious, autoimmune inflammatory, and degenerative disorders of the CNS. To investigate, whether a multi-dimensional cellular blood and CSF characterization can support the diagnosis of clinically similar neurological diseases, we analysed 546 patients with autoimmune neuroinflammatory, degenerative, or vascular conditions in a cross-sectional retrospective study. By combining feature selection with dimensionality reduction and machine learning approaches we identified pan-disease parameters that were altered across all autoimmune neuroinflammatory CNS diseases and differentiated them from other neurological conditions and inter-autoimmunity classifiers that subdifferentiate variants of CNS-directed autoimmunity. Pan-disease as well as diseases-specific changes formed a continuum, reflecting clinical disease evolution. A validation cohort of 231 independent patients confirmed that combining multiple parameters into composite scores can assist the classification of neurological patients. Overall, we showed that the integrated analysis of blood and CSF parameters improves the differential diagnosis of neurological diseases, thereby facilitating early treatment decisions.
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http://dx.doi.org/10.1093/brain/awab147DOI Listing
October 2021

Effects of polygenic risk for major mental disorders and cross-disorder on cortical complexity.

Psychol Med 2021 Apr 8:1-12. Epub 2021 Apr 8.

Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039Marburg, Germany.

Background: MRI-derived cortical folding measures are an indicator of largely genetically driven early developmental processes. However, the effects of genetic risk for major mental disorders on early brain development are not well understood.

Methods: We extracted cortical complexity values from structural MRI data of 580 healthy participants using the CAT12 toolbox. Polygenic risk scores (PRS) for schizophrenia, bipolar disorder, major depression, and cross-disorder (incorporating cumulative genetic risk for depression, schizophrenia, bipolar disorder, autism spectrum disorder, and attention-deficit hyperactivity disorder) were computed and used in separate general linear models with cortical complexity as the regressand. In brain regions that showed a significant association between polygenic risk for mental disorders and cortical complexity, volume of interest (VOI)/region of interest (ROI) analyses were conducted to investigate additional changes in their volume and cortical thickness.

Results: The PRS for depression was associated with cortical complexity in the right orbitofrontal cortex (right hemisphere: p = 0.006). A subsequent VOI/ROI analysis showed no association between polygenic risk for depression and either grey matter volume or cortical thickness. We found no associations between cortical complexity and polygenic risk for either schizophrenia, bipolar disorder or psychiatric cross-disorder when correcting for multiple testing.

Conclusions: Changes in cortical complexity associated with polygenic risk for depression might facilitate well-established volume changes in orbitofrontal cortices in depression. Despite the absence of psychopathology, changed cortical complexity that parallels polygenic risk for depression might also change reward systems, which are also structurally affected in patients with depressive syndrome.
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http://dx.doi.org/10.1017/S0033291721001082DOI Listing
April 2021

Association Between Genetic Risk for Type 2 Diabetes and Structural Brain Connectivity in Major Depressive Disorder.

Biol Psychiatry Cogn Neurosci Neuroimaging 2021 Mar 5. Epub 2021 Mar 5.

Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany.

Background: Major depressive disorder (MDD) and type 2 diabetes mellitus (T2D) are known to share clinical comorbidity and to have genetic overlap. Besides their shared genetics, both diseases seem to be associated with alterations in brain structural connectivity and impaired cognitive performance, but little is known about the mechanisms by which genetic risk of T2D might affect brain structure and function and if they do, how these effects could contribute to the disease course of MDD.

Methods: This study explores the association of polygenic risk for T2D with structural brain connectome topology and cognitive performance in 434 nondiabetic patients with MDD and 539 healthy control subjects.

Results: Polygenic risk score for T2D across MDD patients and healthy control subjects was found to be associated with reduced global fractional anisotropy, a marker of white matter microstructure, an effect found to be predominantly present in MDD-related fronto-temporo-parietal connections. A mediation analysis further suggests that this fractional anisotropy variation may mediate the association between polygenic risk score and cognitive performance.

Conclusions: Our findings provide preliminary evidence of a polygenic risk for T2D to be linked to brain structural connectivity and cognition in patients with MDD and healthy control subjects, even in the absence of a direct T2D diagnosis. This suggests an effect of T2D genetic risk on white matter integrity, which may mediate an association of genetic risk for diabetes and cognitive impairments.
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http://dx.doi.org/10.1016/j.bpsc.2021.02.010DOI Listing
March 2021

Apolipoprotein E homozygous ε4 allele status: Effects on cortical structure and white matter integrity in a young to mid-age sample.

Eur Neuropsychopharmacol 2021 May 27;46:93-104. Epub 2021 Feb 27.

Department of Psychiatry, University of Münster, Münster, Germany. Electronic address:

Apolipoprotein E (APOE) genotype is the strongest single gene predictor of Alzheimer's disease (AD) and has been frequently associated with AD-related brain structural alterations before the onset of dementia. While previous research has primarily focused on hippocampal morphometry in relation to APOE, sporadic recent findings have questioned the specificity of the hippocampus and instead suggested more global effects on the brain. With the present study we aimed to investigate associations between homozygous APOE ε4 status and cortical gray matter structure as well as white matter microstructure. In our study, we contrasted n = 31 homozygous APOE ε4 carriers (age=34.47 years, including a subsample of n = 12 subjects with depression) with a demographically matched sample without an ε4 allele (resulting total sample: N = 62). Morphometry analyses included a) Freesurfer based cortical segmentations of thickness and surface area measures and b) tract based spatial statistics of DTI measures. We found pronounced and widespread reductions in cortical surface area of ε4 homozygotes in 57 out of 68 cortical brain regions. In contrast, no differences in cortical thickness were observed. Furthermore, APOE ε4 homozygous carriers showed significantly lower fractional anisotropy in the corpus callosum, the right internal and external capsule, the left corona radiata and the right fornix. The present findings support a global rather than regionally specific effect of homozygous APOE ε4 allele status on cortical surface area and white matter microstructure. Future studies should aim to delineate the clinical implications of these findings.
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http://dx.doi.org/10.1016/j.euroneuro.2021.02.006DOI Listing
May 2021

Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years.

Hum Brain Mapp 2021 Feb 17. Epub 2021 Feb 17.

Laboratory of Psychiatric Neuroimaging, Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil.

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.
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http://dx.doi.org/10.1002/hbm.25364DOI Listing
February 2021

Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years.

Hum Brain Mapp 2021 Feb 11. Epub 2021 Feb 11.

Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.
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http://dx.doi.org/10.1002/hbm.25320DOI Listing
February 2021

Novelty seeking is associated with increased body weight and orbitofrontal grey matter volume reduction.

Psychoneuroendocrinology 2021 04 19;126:105148. Epub 2021 Jan 19.

Department of Psychiatry, University of Münster, Germany. Electronic address:

Novelty seeking (NS) has previously been identified as a personality trait that is associated with elevated body mass index (BMI) and obesity. Of note, both obesity and reduced impulse control - a core feature of NS - have previously been associated with grey matter volume (GMV) reductions in the orbitofrontal cortex (OFC). Yet, it remains unknown, if body weight-related grey matter decline in the OFC might be explained by higher levels of NS. To address this question, we studied associations between NS, BMI and brain structure in 355 healthy subjects. Brain images were pre-processed using voxel-based morphometry (VBM). BMI was calculated from self-reported height and weight. The Tridimensional Personality Questionnaire (TPQ) was used to assess NS. NS and BMI were associated positively (r = .137, p = .01) with NS being a significant predictor of BMI (B = 0.172; SE B = 0.05; ß = 0.184; p = 0.001). Significant associations between BMI and GMV specifically in the OFC (x = -44, y = 56, z = -2, t(350) = 4.34, k = 5, p = 0.011) did not uphold when correcting for NS in the model. In turn, a significant negative association between NS and OFC GMV was found independent of BMI (x = -2, y = 48, z = -10, t(349) = 4.42, k = 88, p = 0.008). Body mass-related grey matter decrease outside the OFC could not be attributed to NS. Our results suggest that body-weight-related orbitofrontal grey matter reduction can at least partly be linked to higher levels of NS. Given the pivotal role of the OFC in overweight as well as cognitive domains such as impulse inhibition, executive control and reward processing, its association with NS seems to provide a tenable neurobiological correlate for future research.
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http://dx.doi.org/10.1016/j.psyneuen.2021.105148DOI Listing
April 2021

Smartphone-Based Self-Reports of Depressive Symptoms Using the Remote Monitoring Application in Psychiatry (ReMAP): Interformat Validation Study.

JMIR Ment Health 2021 Jan 12;8(1):e24333. Epub 2021 Jan 12.

Department of Psychiatry, University of Münster, Münster, Germany.

Background: Smartphone-based symptom monitoring has gained increased attention in psychiatric research as a cost-efficient tool for prospective and ecologically valid assessments based on participants' self-reports. However, a meaningful interpretation of smartphone-based assessments requires knowledge about their psychometric properties, especially their validity.

Objective: The goal of this study is to systematically investigate the validity of smartphone-administered assessments of self-reported affective symptoms using the Remote Monitoring Application in Psychiatry (ReMAP).

Methods: The ReMAP app was distributed to 173 adult participants of ongoing, longitudinal psychiatric phenotyping studies, including healthy control participants, as well as patients with affective disorders and anxiety disorders; the mean age of the sample was 30.14 years (SD 11.92). The Beck Depression Inventory (BDI) and single-item mood and sleep information were assessed via the ReMAP app and validated with non-smartphone-based BDI scores and clinician-rated depression severity using the Hamilton Depression Rating Scale (HDRS).

Results: We found overall high comparability between smartphone-based and non-smartphone-based BDI scores (intraclass correlation coefficient=0.921; P<.001). Smartphone-based BDI scores further correlated with non-smartphone-based HDRS ratings of depression severity in a subsample (r=0.783; P<.001; n=51). Higher agreement between smartphone-based and non-smartphone-based assessments was found among affective disorder patients as compared to healthy controls and anxiety disorder patients. Highly comparable agreement between delivery formats was found across age and gender groups. Similarly, smartphone-based single-item self-ratings of mood correlated with BDI sum scores (r=-0.538; P<.001; n=168), while smartphone-based single-item sleep duration correlated with the sleep item of the BDI (r=-0.310; P<.001; n=166).

Conclusions: These findings demonstrate that smartphone-based monitoring of depressive symptoms via the ReMAP app provides valid assessments of depressive symptomatology and, therefore, represents a useful tool for prospective digital phenotyping in affective disorder patients in clinical and research applications.
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http://dx.doi.org/10.2196/24333DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7837996PMC
January 2021

[Big Data, AI and Machine Learning for Precision Psychiatry: How are they changing the clinical practice?]

Fortschr Neurol Psychiatr 2020 Nov 30;88(12):786-793. Epub 2020 Sep 30.

Universitätsklinikum Münster Klinik für Psychiatrie und Psychotherapie.

Currently, we are witnessing an increasing interest in predictive models and personalized diagnosis and treatment choice in psychiatric research. Against this background, the emerging field of Precision Psychiatry is trying to establish precise diagnostics and personalized therapy through Big Data. Electronic Health Records (EHR), smartphone-based data collection and advances in genotyping and imaging allow for a detailed clinical and neurobiological characterization of numerous patients. In order to revolutionize the treatment of psychiatric disorders, a personalization of psychiatry through machine learning (ML) and artificial intelligence (AI) is needed. We must therefore establish an AI ecosystem to develop and strictly validate custom-tailored AI and ML solutions. Furthermore, personalized predictions and detailed patient information must be integrated in AI-based Clinical Decision Support systems. Only in this way can Big Data, ML and AI support the clinician most effectively and help personalize treatment in psychiatry.
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http://dx.doi.org/10.1055/a-1234-6247DOI Listing
November 2020

White matter fiber microstructure is associated with prior hospitalizations rather than acute symptomatology in major depressive disorder.

Psychol Med 2020 Sep 14:1-9. Epub 2020 Sep 14.

Department of Psychiatry, University of Münster, Münster, Germany.

Background: Eighty percent of all patients suffering from major depressive disorder (MDD) relapse at least once in their lifetime. Thus, understanding the neurobiological underpinnings of the course of MDD is of utmost importance. A detrimental course of illness in MDD was most consistently associated with superior longitudinal fasciculus (SLF) fiber integrity. As similar associations were, however, found between SLF fiber integrity and acute symptomatology, this study attempts to disentangle associations attributed to current depression from long-term course of illness.

Methods: A total of 531 patients suffering from acute (N = 250) or remitted (N = 281) MDD from the FOR2107-cohort were analyzed in this cross-sectional study using tract-based spatial statistics for diffusion tensor imaging. First, the effects of disease state (acute v. remitted), current symptom severity (BDI-score) and course of illness (number of hospitalizations) on fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity were analyzed separately. Second, disease state and BDI-scores were analyzed in conjunction with the number of hospitalizations to disentangle their effects.

Results: Disease state (pFWE < 0.042) and number of hospitalizations (pFWE< 0.032) were associated with decreased FA and increased MD and RD in the bilateral SLF. A trend was found for the BDI-score (pFWE > 0.067). When analyzed simultaneously only the effect of course of illness remained significant (pFWE < 0.040) mapping to the right SLF.

Conclusions: Decreased FA and increased MD and RD values in the SLF are associated with more hospitalizations when controlling for current psychopathology. SLF fiber integrity could reflect cumulative illness burden at a neurobiological level and should be targeted in future longitudinal analyses.
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http://dx.doi.org/10.1017/S0033291720002950DOI Listing
September 2020

Virtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders.

JAMA Psychiatry 2021 Jan;78(1):47-63

Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University, the Netherlands.

Importance: Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood.

Objective: To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia.

Design, Setting, And Participants: Profiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Analysis for gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The numbers of cases and controls in each of the 6 disorders were as follows: ADHD: 1814 and 1602; ASD: 1748 and 1770; BD: 1547 and 3405; MDD: 2658 and 3572; OCD: 2266 and 2007; and schizophrenia: 2688 and 3244.

Main Outcomes And Measures: Interregional profiles of group difference in cortical thickness between cases and controls.

Results: A total of 12 721 cases and 15 600 controls, ranging from ages 2 to 89 years, were included in this study. Interregional profiles of group differences in cortical thickness for each of the 6 psychiatric disorders were associated with profiles of gene expression specific to pyramidal (CA1) cells, astrocytes (except for BD), and microglia (except for OCD); collectively, gene-expression profiles of the 3 cell types explain between 25% and 54% of variance in interregional profiles of group differences in cortical thickness. Principal component analysis revealed a shared profile of difference in cortical thickness across the 6 disorders (48% variance explained); interregional profile of this principal component 1 was associated with that of the pyramidal-cell gene expression (explaining 56% of interregional variation). Coexpression analyses of these genes revealed 2 clusters: (1) a prenatal cluster enriched with genes involved in neurodevelopmental (axon guidance) processes and (2) a postnatal cluster enriched with genes involved in synaptic activity and plasticity-related processes. These clusters were enriched with genes associated with all 6 psychiatric disorders.

Conclusions And Relevance: In this study, shared neurobiologic processes were associated with differences in cortical thickness across multiple psychiatric disorders. These processes implicate a common role of prenatal development and postnatal functioning of the cerebral cortex in these disorders.
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http://dx.doi.org/10.1001/jamapsychiatry.2020.2694DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7450410PMC
January 2021

Polygenic risk for schizophrenia and schizotypal traits in non-clinical subjects.

Psychol Med 2020 Aug 6:1-11. Epub 2020 Aug 6.

Department of Psychiatry and Psychotherapy, Philipps-University and University Hospital Marburg, UKGM, Rudolf-Bultmann-Str. 8, 35039Marburg, Germany.

Background: Schizotypy is a putative risk phenotype for psychosis liability, but the overlap of its genetic architecture with schizophrenia is poorly understood.

Methods: We tested the hypothesis that dimensions of schizotypy (assessed with the SPQ-B) are associated with a polygenic risk score (PRS) for schizophrenia in a sample of 623 psychiatrically healthy, non-clinical subjects from the FOR2107 multi-centre study and a second sample of 1133 blood donors.

Results: We did not find correlations of schizophrenia PRS with either overall SPQ or specific dimension scores, nor with adjusted schizotypy scores derived from the SPQ (addressing inter-scale variance). Also, PRS for affective disorders (bipolar disorder and major depression) were not significantly associated with schizotypy.

Conclusions: This important negative finding demonstrates that despite the hypothesised continuum of schizotypy and schizophrenia, schizotypy might share less genetic risk with schizophrenia than previously assumed (and possibly less compared to psychotic-like experiences).
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http://dx.doi.org/10.1017/S0033291720002822DOI Listing
August 2020

Predicting intelligence from brain gray matter volume.

Brain Struct Funct 2020 Sep 21;225(7):2111-2129. Epub 2020 Jul 21.

Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany.

A positive association between brain size and intelligence is firmly established, but whether region-specific anatomical differences contribute to general intelligence remains an open question. Results from voxel-based morphometry (VBM) - one of the most widely used morphometric methods - have remained inconclusive so far. Here, we applied cross-validated machine learning-based predictive modeling to test whether out-of-sample prediction of individual intelligence scores is possible on the basis of voxel-wise gray matter volume. Features were derived from structural magnetic resonance imaging data (N = 308) using (a) a purely data-driven method (principal component analysis) and (b) a domain knowledge-based approach (atlas parcellation). When using relative gray matter (corrected for total brain size), only the atlas-based approach provided significant prediction, while absolute gray matter (uncorrected) allowed for above-chance prediction with both approaches. Importantly, in all significant predictions, the absolute error was relatively high, i.e., greater than ten IQ points, and in the atlas-based models, the predicted IQ scores varied closely around the sample mean. This renders the practical value even of statistically significant prediction results questionable. Analyses based on the gray matter of functional brain networks yielded significant predictions for the fronto-parietal network and the cerebellum. However, the mean absolute errors were not reduced in contrast to the global models, suggesting that general intelligence may be related more to global than region-specific differences in gray matter volume. More generally, our study highlights the importance of predictive statistical analysis approaches for clarifying the neurobiological bases of intelligence and provides important suggestions for future research using predictive modeling.
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http://dx.doi.org/10.1007/s00429-020-02113-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7473979PMC
September 2020
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