Biology subjects

Koutsouleris, N.

Publications and source records attributed to Koutsouleris, N..

5 recordsLinked to original sources

bioRxiv2024

Molecular, physiological and functional features underlying cortical thinning related to antipsychotic medication use

Use of antipsychotic medication is related to thinning of the cerebral cortex, but the underlying mechanisms of this effect remain largely unknown. Here, we investigated potential mechanisms across multiple levels of description, from molecular and physiological factors to whole-brain functional patterns. We first analyzed a single site discovery sample of patients (N=131) with early psychosis for whom antipsychotic related cortical thinning was estimated based on lifetime exposure to antipsychotics. Findings were replicated using data from a large (N[≥]2168) ENIGMA meta-analysis. We discovered that antipsychotic related cortical thinning is associated with a number of neurotransmitter systems, most notably the serotonin system, physiological measures, and functional networks and neural oscillatory power distributions typical for regions subserving higher cognition. At the functional level, antipsychotic related cortical thinning affects regions involved in executive function and motivation, but not perception. These results show how molecular, physiological, and large-scale functional patterns underlie antipsychotic related cortical thinning.

neuroscience↗

bioRxiv2021

dsMTL - a computational framework for privacy-preserving, distributed multi-task machine learning

Multitask learning allows the simultaneous learning of multiple communicating algorithms. It is increasingly adopted for biomedical applications, such as the modeling of disease progression. As data protection regulations limit data sharing for such analyses, an implementation of multitask learning on geographically distributed data sources would be highly desirable. Here, we describe the development of dsMTL, a computational framework for privacy-preserving, distributed multi-task machine learning that includes three supervised and one unsupervised algorithms. dsMTL is implemented as a library for the R programming language and builds on the DataSHIELD platform that supports the federated analysis of sensitive individual-level data. We provide a comparative evaluation of dsMTL for the identification of biological signatures in distributed datasets using two case studies, and evaluate the computational performance of the supervised and unsupervised algorithms. dsMTL provides an easy- to-use framework for privacy-preserving, federated analysis of geographically distributed datasets, and has several application areas, including comorbidity modeling and translational research focused on the simultaneous prediction of different outcomes across datasets. dsMTL is available at https://github.com/transbioZI/dsMTLBase (server-side package) and https://github.com/transbioZI/dsMTLClient (client-side package).

bioinformatics↗

bioRxiv2020

Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions

BackgroundCognitive gains following cognitive training interventions (CT) are associated with improved functioning in people with schizophrenia (SCZ). However, considerable inter-individual variability is observed. Here, we evaluate the sensitivity of brain structural features to predict functional response to auditory-based cognitive training (ABCT) at a single subject level. MethodsWe employed whole-brain multivariate pattern analysis (MVPA) with support vector machine (SVM) modeling to identify grey matter (GM) patterns that predicted higher vs. lower functioning after 40 hours of ABCT at the single subject level in SCZ patients. The generalization capacity of the SVM model was evaluated by applying the original model through an Out-Of-Sample Cross Validation analysis (OOCV) to unseen SCZ patients from an independent sample that underwent 50 hours of ABCT. ResultsThe whole-brain GM volume-based pattern classification predicted higher vs. lower functioning at follow-up with a balanced accuracy (BAC) of 69.4% (sensitivity 72.2%, specificity 66.7%) as determined by nested cross-validation. The neuroanatomical model was generalizable to an independent cohort with a BAC of 62.1% (sensitivity 90.9%, specificity 33.3%). ConclusionsIn particular, greater baseline GM volume in regions within superior temporal gyrus, thalamus, anterior cingulate and cerebellum -- predicted improved functioning at the single-subject level following ABCT in SCZ participants.

neuroscience↗

bioRxiv2019

Cortico-thalamic dysconnectivity links with aberrant striatal dopamine in schizophrenia. A simultaneous 18F-DOPA-PET/resting-state fMRI study

In schizophrenia, among the most consistent brain changes are both aberrant dopamine function in the dorsal striatum and aberrant intrinsic functional connectivity (iFC) between distinct cortical networks and thalamic nuclei; however, it is unknown whether these changes are pathophysiologically related. Such a relationship is expected because cortico-thalamic-connectivity is modulated by striatal dopamine within topographically distinct, parallel but interacting cortico-basal-ganglia-thalamic circuits. We hypothesized: (1) Within-circuits, aberrant striatal dopamine contributes to aberrant cortico-thalamic-iFC, specifically, associative-striatum dopamine contributes to salience-network-thalamic-iFC, and sensorimotor-striatum dopamine to auditory-sensorimotor-network-thalamic-iFC. (2) Due to between-circuits interactions following an anterior-to-posterior gradient, salience-network-centered-system changes contribute to auditory-sensorimotor-network-centered-system changes. To test these hypotheses, 19 patients with schizophrenia during symptomatic remission of positive symptoms and 19 age- and sex-comparable controls underwent simultaneous fluorodihydroxyphenyl-L-alanine positron emission tomography (18F-DOPA-PET) and resting-state functional magnetic resonance imaging (rs-fMRI). The influx constant kicer based on 18F-DOPA-PET was used to measure dopamine synthesis capacity (DSC), indicating striatal dopamine function; correlation coefficients between rs-fMRI time-series of cortical networks and thalamic regions-of-interest were used to measure iFC. In the salience-network(SAL)-centered-system, patients had reduced associative-striatum-DSC, which correlated positively with SAL-mediodorsal-thalamus-iFC and mediated the reduction of SAL-thalamic-iFC in patients. In the auditory-sensorimotor-network(ASM)-centered-system, patients had reduced sensorimotor-striatum-DSC, which correlated positively with ASM-ventrolateral-thalamus-iFC, but did not mediate increased ASM-thalamic-iFC in patients. Finally, aberrant DSC and iFC of the SAL-centered-system mediated corresponding changes in the ASM-centered-system. Results demonstrate that cortico-thalamic-dysconnectivity links with aberrant striatal dopamine in schizophrenia - in a topographically distinct way, with an anterior-to-posterior gradient, and primary changes in the SAL-centered system.

neuroscience↗

bioRxiv2019

Harmonization of large multi-site imaging datasets: Application to 10,232 MRIs for the analysis of imaging patterns of structural brain change throughout the lifespan

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,232 structural brain MRI scans from participants without known neuropsychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive normative age trends of brain structure through the lifespan (3 to 96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this normative reference of brain development and aging, and to examine deviations from normative ranges, potentially related to disease.

bioinformatics↗