Specifically, I am part of the Translational Psychiatry group at NORMENT - a group that focuses on the relationship between genetic risk, environmental factors and biological variables, and how these are related to clinical disease forms and brain organic relationship in serious mental illness. Most of our understanding of the spatial and temporal characteristics of these lifespan adaptations come from magnetic resonance imaging (MRI), in particular diffusion MRI (dMRI), which enables visualization and quantification of brain white matter … Brukernavn. For the main analyses, we used the residuals of each component's subject weight after regressing out age and sex, and additionally, phase encoding for IC4. This produces spatial maps based on the commonalities across features (e.g., GMD, DTI measures, DMN maps) and subjects, and corresponding subject weights (i.e., the degree to which a subject contributes to an LICA component). Model performance was slightly lower when regressing out phase encoding from all the IC features, and also suggested a different order of feature importance (see Results and Figure S9, Supporting Information). Dismiss. Dani Beck Beck to the Future Malta 131 connections. NOTE: Your email address is requested solely to identify you as the sender of this article. First, as illustrated by the large‐scale ENIGMA studies (Ho et al., 2019; Schmaal et al., 2016; Schmaal et al., 2017), the effect sizes in neuroimaging studies of mental disorders and depression are overall small (Paulus & Thompson, 2019). Age was negatively associated with IC1, indicating lower GMD and cortical surface area globally with increasing age, positively associated with IC2, indicating lower FA globally with increasing age, negatively associated with IC5, indicating lower DMN amplitude with increasing age, and negatively associated with IC7, indicating thinner cortex globally with increasing age. This is one possible explanation why other multimodal fusion studies of depression have reached higher prediction accuracies for classifying patients with depression from healthy controls (He et al., 2017; Ramezani et al., 2014; Yang et al., 2018), with patient groups consisting of no more than 60 individuals. Patients (n = 194) were primarily recruited from outpatient clinics, while healthy controls (n = 78) were recruited through posters, newspaper advertisements, and social media. In this mixed crosssectional and longitudinal study (mean interval: 15.2 months) including 702 multi-shell dMRI datasets, we combined complementary dMRI models to investigate age trajectories in healthy individuals aged 18 to 94 years (56.98% women). Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis. At Neuro Central, the online resource powered by Future Science Group, we unite all aspects of neurology and neuroscience to support synergistic progression through collaboration and learning. Dani Beck PhD Candidate in Clinical Neuroscience and Brain Imaging Oslo, Oslo, Norge 194 forbindelser. Dani Beck is a Clinical Neuroscience PhD candidate at the University of Oslo investigating the pathological brain changes associated with cardiovascular risk factors. Daniel Beck. To increase robustness and generalizability, we corrected for multiple comparisons across all univariate analyses and performed cross‐validation and robust model evaluation in the machine learning analyses. We included complete data from 70 patients and 171 controls in the decomposition. Exclusion criteria for all participants were MRI contraindications and a self‐reported history of neurological disorders. In line with the univariate analyses, the machine learning approach revealed overall low prediction accuracy for group classification and prediction of symptom loads for depression and anxiety, but high prediction accuracy for age. The low number of severely depressed patients relative to remitted patients may have influenced the sensitivity and specificity of the machine learning approach, in particular for predicting symptom loads of depression and anxiety. Dani Beckstrom Meteorologist at KTNV Channel 13 Action News. Name of supervisors: Professor Nils Inge Landrø and Luigi Maglanoc, … This included coregistration with T1 images, brain extraction, motion correction (MCFLIRT: Jenkinson, Bannister, Brady, & Smith, 2002), spatial smoothing (FWHM = 6 mm), high‐pass filtering (100 s), standard space registration (MNI‐152) with FLIRT, and single‐session independent component analysis (ICA; MELODIC). By decomposing the imaging data into a set of independent components, LICA enables an integrated perspective that may improve clinical sensitivity compared to unimodal analyses (Alnæs et al., 2018; Doan, Engvig, Persson, et al., 2017; Francx et al., 2016; Wu et al., 2019). P.O box 1094 Postadresse OUS Postboks 4956 Nydalen 0424 OSLO. (b) Feature importance based on CAR scores. In contrast, model performance was high when predicting age (RMSE = 6.764, p < .0001, MAE = 5.530, R2 = .7120, r = .861) using residualized features, with feature importance generally in line with the univariate results (see Figure 6). Secondly, and also related to the small effect sizes, mental disorders including depression are clinically highly heterogeneous.