32 research outputs found
Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks
Brain extraction in magnetic resonance imaging (MRI) data is an important
segmentation step in many neuroimaging preprocessing pipelines. Image
segmentation is one of the research fields in which deep learning had the
biggest impact in recent years enabling high precision segmentation with
minimal compute. Consequently, traditional brain extraction methods are now
being replaced by deep learning-based methods. Here, we used a unique dataset
comprising 568 T1-weighted (T1w) MR images from 191 different studies in
combination with cutting edge deep learning methods to build a fast,
high-precision brain extraction tool called deepbet. deepbet uses LinkNet, a
modern UNet architecture, in a two stage prediction process. This increases its
segmentation performance, setting a novel state-of-the-art performance during
cross-validation with a median Dice score (DSC) of 99.0% on unseen datasets,
outperforming current state of the art models (DSC = 97.8% and DSC = 97.9%).
While current methods are more sensitive to outliers, resulting in Dice scores
as low as 76.5%, deepbet manages to achieve a Dice score of > 96.9% for all
samples. Finally, our model accelerates brain extraction by a factor of ~10
compared to current methods, enabling the processing of one image in ~2 seconds
on low level hardware
GateNet: A novel Neural Network Architecture for Automated Flow Cytometry Gating
Flow cytometry is widely used to identify cell populations in patient-derived
fluids such as peripheral blood (PB) or cerebrospinal fluid (CSF). While
ubiquitous in research and clinical practice, flow cytometry requires gating,
i.e. cell type identification which requires labor-intensive and error-prone
manual adjustments. To facilitate this process, we designed GateNet, the first
neural network architecture enabling full end-to-end automated gating without
the need to correct for batch effects. We train GateNet with over 8,000,000
events based on N=127 PB and CSF samples which were manually labeled
independently by four experts. We show that for novel, unseen samples, GateNet
achieves human-level performance (F1 score ranging from 0.910 to 0.997). In
addition we apply GateNet to a publicly available dataset confirming
generalization with an F1 score of 0.936. As our implementation utilizes
graphics processing units (GPU), gating only needs 15 microseconds per event.
Importantly, we also show that GateNet only requires ~10 samples to reach
human-level performance, rendering it widely applicable in all domains of flow
cytometry
Systematic Overestimation of Machine Learning Performance in Neuroimaging Studies of Depression
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. Drawing upon a
balanced sample of 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 ( to ) from the population and showed a strong
risk of overestimation. Specifically, for small sample sizes (), we
observe accuracies of up to 95%. For medium sample sizes () accuracies
up to 75% were found. Importantly, further investigation showed that
sufficiently large test sets effectively protect against performance
overestimation 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
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Evidence from comprehensive independent validation studies for smooth pursuit dysfunction as a sensorimotor biomarker for psychosis
Smooth pursuit eye movements are considered a well-established and quantifiable biomarker of sensorimotor function in psychosis research. Identifying psychotic syndromes on an individual level based on neurobiological markers is limited by heterogeneity and requires comprehensive external validation to avoid overestimation of prediction models. Here, we studied quantifiable sensorimotor measures derived from smooth pursuit eye movements in a large sample of psychosis probands (N = 674) and healthy controls (N = 305) using multivariate pattern analysis. Balanced accuracies of 64% for the prediction of psychosis status are in line with recent results from other large heterogenous psychiatric samples. They are confirmed by external validation in independent large samples including probands with (1) psychosis (N = 727) versus healthy controls (N = 292), (2) psychotic (N = 49) and non-psychotic bipolar disorder (N = 36), and (3) non-psychotic affective disorders (N = 119) and psychosis (N = 51) yielding accuracies of 65%, 66% and 58%, respectively, albeit slightly different psychosis syndromes. Our findings make a significant contribution to the identification of biologically defined profiles of heterogeneous psychosis syndromes on an individual level underlining the impact of sensorimotor dysfunction in psychosis
Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning
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
Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders: evidence through univariate and multivariate mega-analysis including 6420 participants from the ENIGMA MDD working group
Published online: 28 May 2020Emerging evidence suggests that obesity impacts brain physiology at multiple levels. Here we aimed to clarify the relationship between obesity and brain structure using structural MRI (n = 6420) and genetic data (n = 3907) from the ENIGMA Major Depressive Disorder (MDD) working group. Obesity (BMI > 30) was significantly associated with cortical and subcortical abnormalities in both mass-univariate and multivariate pattern recognition analyses independent of MDD diagnosis. The most pronounced effects were found for associations between obesity and lower temporo-frontal cortical thickness (maximum Cohen´s d (left fusiform gyrus) = −0.33). The observed regional distribution and effect size of cortical thickness reductions in obesity revealed considerable similarities with corresponding patterns of lower cortical thickness in previously published studies of neuropsychiatric disorders. A higher polygenic risk score for obesity significantly correlated with lower occipital surface area. In addition, a significant age-by-obesity interaction on cortical thickness emerged driven by lower thickness in older participants. Our findings suggest a neurobiological interaction between obesity and brain structure under physiological and pathological brain conditions.Nils Opel ... Bernhard T. Baune ... et al
DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features
Major depressive disorder (MDD) is a complex psychiatric disorder that
affects the lives of hundreds of millions of individuals around the globe. Even
today, researchers debate if morphological alterations in the brain are linked
to MDD, likely due to the heterogeneity of this disorder. The application of
deep learning tools to neuroimaging data, capable of capturing complex
non-linear patterns, has the potential to provide diagnostic and predictive
biomarkers for MDD. However, previous attempts to demarcate MDD patients and
healthy controls (HC) based on segmented cortical features via linear machine
learning approaches have reported low accuracies. In this study, we used
globally representative data from the ENIGMA-MDD working group containing an
extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a
comprehensive analysis with generalizable results. Based on the hypothesis that
integration of vertex-wise cortical features can improve classification
performance, we evaluated the classification of a DenseNet and a Support Vector
Machine (SVM), with the expectation that the former would outperform the
latter. As we analyzed a multi-site sample, we additionally applied the ComBat
harmonization tool to remove potential nuisance effects of site. We found that
both classifiers exhibited close to chance performance (balanced accuracy
DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher
classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was
found when the cross-validation folds contained subjects from all sites,
indicating site effect. In conclusion, the integration of vertex-wise
morphometric features and the use of the non-linear classifier did not lead to
the differentiability between MDD and HC. Our results support the notion that
MDD classification on this combination of features and classifiers is
unfeasible
From multivariate methods to an AI ecosystem
Depression constitutes a leading cause of disability worldwide. Despite extensive research on its interaction with psychobiological factors, associated pathways are far from being elucidated. Metabolomics, assessing the final products of complex biochemical reactions, has emerged as a valuable tool for exploring molecular pathways. We conducted a metabolome-wide association analysis to investigate the link between the serum metabolome and depressed mood (DM) in 1411 participants of the KORA (Cooperative Health Research in the Augsburg Region) F4 study (discovery cohort). Serum metabolomics data comprised 353 unique metabolites measured by Metabolon. We identified 72 (5.1%) KORA participants with DM. Linear regression tests were conducted modeling each metabolite value by DM status, adjusted for age, sex, body-mass index, antihypertensive, cardiovascular, antidiabetic, and thyroid gland hormone drugs, corticoids and antidepressants. Sensitivity analyses were performed in subcohorts stratified for sex, suicidal ideation, and use of antidepressants. We replicated our results in an independent sample of 968 participants of the SHIP-Trend (Study of Health in Pomerania) study including 52 (5.4%) individuals with DM (replication cohort). We found significantly lower laurylcarnitine levels in KORA F4 participants with DM after multiple testing correction according to Benjamini/Hochberg. This finding was replicated in the independent SHIP-Trend study. Laurylcarnitine remained significantly associated (p value < 0.05) with depression in samples stratified for sex, suicidal ideation, and antidepressant medication. Decreased blood laurylcarnitine levels in depressed individuals may point to impaired fatty acid oxidation and/or mitochondrial function in depressive disorders, possibly representing a novel therapeutic target