Biology subjects

Stringaris, A.

Publications and source records attributed to Stringaris, A..

9 recordsLinked to original sources

bioRxiv2025

Brain Diffusion Transformer for Personalized Neuroscience and Psychiatry

Task-fMRI analyses typically focus on localized activation contrasts between stimuli, neglecting the brains dynamic hierarchy. We introduce Brain Diffusion Transformer (Brain-DiT), a deep generative model capturing recurrent processing underlying individualized neurocognitive state transitions via functional networks. Without prior assumptions, Brain-DiT identifies canonical cognitive regions in the brain and reveals replicable subgroups with distinct neural circuits in large cohorts, offering critical clinical insights overlooked by traditional methods: individuals exhibiting negative emotion bias, linked to language-related regions, had a 12-fold higher likelihood of major depression, and those with maladaptive inhibition strategies, associated with overactive medial frontal regions, showed a 9-fold increased risk of alcohol abuse. By bridging cognitive theory and psychiatric applications, Brain-DiT provides a unified analytical paradigm, paving the way for operational personalized medicine in psychiatry.

neuroscience↗

bioRxiv2021

Integrating Task-Based Functional MRI Across Tasks Markedly Boosts Prediction and Reliability of Brain-Cognition Relationship

Capturing individual differences in cognition is central to human neuroscience. Yet our ability to estimate cognitive abilities via brain MRI is still poor in both prediction and reliability. Our study tested if this inability can be improved by integrating MRI signals across the whole brain and across modalities, including task-based functional MRI (tfMRI) of different tasks along with other non-task MRI modalities, such as structural MRI, resting-state functional connectivity. Using the Human Connectome Project (n=873, 473 females, after quality control), we directly compared predictive models comprising different sets of MRI modalities (e.g., seven tasks vs. non-task modalities). We applied two approaches to integrate multimodal MRI, stacked vs. flat models, and implemented 16 combinations of machine-learning algorithms. The stacked model integrating all modalities via stacking Elastic Net provided the best prediction (r=.57), relatively to other models tested, as well as excellent test-retest reliability (ICC=~.85) in capturing general cognitive abilities. Importantly, compared to the stacked model integrating across non-task modalities (r=.27), the stacked model integrating tfMRI across tasks led to significantly higher prediction (r=.56) while still providing excellent test-retest reliability (ICC=~.83). The stacked model integrating tfMRI across tasks was driven by frontal and parietal areas and by tasks that are cognition-related (working-memory, relational processing, and language). This result is consistent with the parieto-frontal integration theory of intelligence. Accordingly, our results contradict the recently popular notion that tfMRI is not reliable enough to capture individual differences in cognition. Instead, our study suggests that tfMRI, when used appropriately (i.e., by drawing information across the whole brain and across tasks and by integrating with other modalities), provides predictive and reliable sources of information for individual differences in cognitive abilities, more so than non-task modalities. HighlightsO_LINon-task MRI (sMRI, rs-fMRI) are often used for the brain-cognition relationship. C_LIO_LITask-based fMRI has been deemed unreliable for capturing individual differences. C_LIO_LIWe tested if drawing task-based fMRI information across regions/tasks improves prediction and reliability of the brain-cognition relationship. C_LIO_LIOur approach boosts prediction of task-based fMRI over non-task MRI. C_LIO_LIOur approach renders task-based fMRI reliable over time. C_LIO_LIOur approach shows the importance of the fronto-parietal areas in cognition. C_LI

neuroscience↗

bioRxiv2021

Mood and reward dynamics in human adolescent brain electrophysiology

Despite its omnipresence in everyday interactions and its importance for mental health, mood and its neuronal underpinnings are poorly understood. Computational models can help identify parameters affecting self-reported mood during mood induction tasks. Here we test if computationally modelled dynamics of self-reported mood during monetary gambling can be used to identify trial-by-trial variations in neuronal activity. To this end, we shifted mood in healthy (N=24) and depressed (N=30) adolescents by delivering individually tailored reward prediction errors whilst recording magnetoencephalography (MEG) data. Following a pre-registered analysis, we hypothesize that expectation (defined by previous reward outcomes) would be predictive of beta-gamma oscillatory power (25-40Hz), a frequency shown to modulate to reward feedback. We also hypothesize that trial variations in the evoked response to the presentation of gambling options and in source localized responses to reward feedback. Through our multilevel statistical analysis, we found confirmatory evidence that beta-gamma power is positively related to reward expectation during mood shifts, with possible localized sources in the posterior cingulate cortex. We also confirmed reward prediction error to be predictive of trial-level variations in the response of the paracentral lobule and expectation to have an effect on the cerebellum after presentation of gambling options. To our knowledge, this is the first study to relate fluctuations in mood on a minute timescale to variations in neural oscillations with noninvasive electrophysiology. Significance StatementBrain mechanisms underlying mood and its relationship with changes in reward contingencies in the environment are still elusive but could have a strong impact on our understanding and treatment of debilitating mood disorders. Building on a previously proposed computational mood model we use multilevel statistical models to find relationship between trial-by-trial variations in model components of mood and neural responses to rewards measured with non-invasive electrophysiology (MEG). Through confirmatory analysis we show that it is possible to observe relationships between trial variations in neural responses and computational parameters describing mood dynamics. Identifying the dynamics of mood and the neural processes it affects could pave the way for more effective neuromodulation treatments.

neuroscience↗

bioRxiv2021

Multimodal-Neural Predictive Models of Children's General Intelligence That Are Stable Across Two Years of Development

Cognitive abilities are one of the major transdiagnostic domains in the National Institute of Mental Healths Research Domain Criteria (RDoC). Following RDoCs integrative approach, we aimed to develop brain-based predictive models for cognitive abilities that a) are developmentally stable over years during adolescence and b) account for the relationships between cognitive abilities and socio-demographic, psychological and genetic factors. For this, we leveraged the unique power of the large-scale, longitudinal data from the Adolescent Brain Cognitive Development (ABCD) study (n [~]11k) and combined MRI data across modalities (task-fMRI from three tasks, resting-state fMRI, structural MRI, DTI) using machine-learning. Our brain-based, predictive models for cognitive abilities were stable across two years during young adolescence and generalisable to different sites, partially predicting childhood cognition at around 20% of the variance. Moreover, our use of opportunistic stacking allowed the model to handle missing values, reducing the exclusion from around 80% to around 5% of the data. We found fronto-parietal networks during a working-memory task to drive childhood-cognition prediction. The brain-based, predictive models significantly, albeit partially, accounted for variance in childhood cognition due to (1) key socio-demographic and psychological factors (proportion mediated=18.65% [17.29%-20.12%]) and (2) genetic variation, as reflected by the polygenic score of cognition (proportion mediated=15.6% [11%-20.7%]). Thus, our brain-based predictive models for cognitive abilities facilitate the development of a robust, transdiagnostic research tool for cognition at the neural level in keeping with the RDoCs integrative framework. Key PointsO_LIUsing opportunistic stacking and multimodal MRI, we developed brain-based predictive models for childrens cognitive abilities that were longitudinally stable, generalisable to different sites and robust against missing data. C_LIO_LIOur brain-based models were able to partially mediate the relationships of childhood cognitive abilities with the socio-demographic, psychological and genetic factors. C_LIO_LIOur approach should pave the way for future researchers to employ multimodal MRI as a tool for the brain-based indicator of cognitive abilities, according to the integrative RDoC framework. C_LI

neuroscience↗

bioRxiv2020

An Omics-Inspired Elastic Net Approach Drastically Improves Out-of-Sample Prediction and Regional Inference of Task-Based fMRI

Despite decades of costly research, we still cannot accurately predict individual differences in cognition from task-based fMRI. Moreover, aiming for methods with higher prediction is not sufficient. To understand brain-cognition relationships, we need to explain how these methods draw brain information to make the prediction. Here we applied an explainable machine-learning (ML) framework to predict cognition from task-based fMRI during the n-back working-memory task, using data from the Adolescent Brain Cognitive Development (n=3,989). We compared nine predictive algorithms in their ability to predict 12 cognitive abilities. We found better out-of-sample prediction from ML algorithms over the mass-univariate and OLS multiple regression. Among ML algorithms, Elastic Net, a linear and additive algorithm, performed either similar to or better than non-linear and interactive algorithms. We explained how these algorithms drew information, using SHapley Additive explanation, eNetXplorer, Accumulated Local Effects and Friedmans H-statistic. These explainers demonstrated benefits of ML over the OLS multiple regression. For example, ML provided some consistency in variable importance with a previous study (Sripada et al. 2020) and consistency with the mass-univariate approach in the directionality of brain-cognition relationships at different regions. Accordingly, our explainable-ML framework predicted cognition from task-based fMRI with boosted prediction and explainability over standard methodologies.

neuroscience↗

bioRxiv2020

Great Expectations: A Critical Review of and Recommendations for the study of Reward Processing as a Cause and Predictor of Depression

Both human and animal studies support the relationship between depression and reward processing abnormalities, giving rise to the expectation that neural signals of these processes may serve as biomarkers or mechanistic treatment targets. Given the great promise of this research line, we scrutinize those findings and the theoretical claims that underlie them. To achieve this, we apply the framework provided by classical work on causality as well as contemporary approaches to prediction. We identify a number of conceptual, practical, and analytical challenges to this line of research, and use a pre-registered meta-analysis to quantify the longitudinal associations between reward processing aberrations and depression. We also investigate the impact of measurement error on reported data. We find that reward processing abnormalities do not reach levels that would be useful for clinical prediction, yet the evidence thus far does not exclude their possible causal role in depression.

neuroscience↗

bioRxiv2019

Independent effects of socioeconomic status and genetics on adolescent cognition and brain development.

Genetic factors and socioeconomic (SES) inequalities play a large role in educational attainment, and both have been associated with variations in brain structure and cognition. However, genetics and SES are correlated, and no prior study has assessed their neural associations independently. Here we used polygenic score for educational attainment (EduYears-PGS) as well as SES, in a longitudinal study of 551 adolescents, to tease apart genetic and environmental associations with brain development and cognition. Subjects received a structural MRI scan at ages 14 and 19. At both time-points, they performed three working memory (WM) tasks. SES and EduYears-PGS were correlated (r = 0.27) and had both common and independent associations with brain structure and cognition. Specifically, lower SES was related to less total cortical surface area and lower WM. EduYears-PGS was also related to total cortical surface area, but in addition had a regional association with surface area in the right parietal lobe, a region related to non-verbal cognitive functions, including mathematics, spatial cognition, and WM. SES, but not EduYears-PGS, was related to a change in total cortical surface area from age 14 to 19. This is the first study demonstrating a regional association of EduYears-PGS and the independent prediction of SES on cognitive function and brain development. It suggests that the SES inequalities, in particular parental education, are related to global aspects of cortical development, and exert a persistent influence on brain development during adolescence. Significance statementThe influence of socioeconomic (SES) inequalities on brain and cognitive development is a hotly debated topic. However, previous studies have not considered that genetic factors overlap with SES. Here we showed, for the first time, that SES and EduYears-PGS (a score from thousands of genetic markers for educational attainment) have independent associations with both cognition and global cortical surface area in adolescents. EduYears-PGS also had a localized association in the brain: the intraparietal sulcus, a region related to non-verbal intelligence. In contrast, SES had global, but not regional, associations, and these persisted throughout adolescence. This suggests that the influence of SES inequalities is widespread - a result that opposes the current paradigm and can help inform policies in education.

neuroscience↗

bioRxiv2019

Timing matters: primacy effects of experience on mood revealed using a novel Mood-Machine-Interface

Humans refer to their own mood state regularly in day-to-day as well as in clinical interactions. Theoretical accounts suggest that when reporting on our mood we integrate over the history of our experiences; yet, the temporal structure of this integration remains unexamined. Here we use a computational approach to quantitatively answer this question and show that early events exert a stronger influence on the reported mood compared to recent events. We show that a Primacy model accounts better for mood reports compared to a range of alternative temporal representations, and replicate this result across random, consistent or dynamic structures of reward environments, age groups and both healthy and depressed participants. Moreover, we find evidence for neural encoding of the Primacy, but not the Recency, model in frontal brain regions related to mood regulation. These findings hold implications for the timing of events in experimental or clinical settings and suggest new directions for individualized mood interventions. SignificanceHow we rate our own mood at any given moment is shaped by our experiences; but are the most recent experiences the most influential, as assumed by current theories? Using several sources of experimental data and mathematical modeling, we show that earlier experiences within a context are more influential than recent events, and replicate this finding across task environments, age groups, and in healthy and depressed participants. Additionally, we present neural evidence supporting this primacy model. Our findings show that delineating a temporal structure is crucial in modeling mood and this has key implications for its measurement and definition in both clinical and everyday settings.

neuroscience↗

bioRxiv2018

Identifying novel subtypes of irritability using a developmental genetic approach

ObjectiveIrritability is a common reason for referral to services, strongly associated with impairment and negative outcomes, but is a nosological and treatment challenge. A major issue is how irritability should be conceptualized. This study used a developmental approach to test the hypothesis that there are several forms of irritability, including a neurodevelopmental/ADHD-like subtype with onset in childhood and a depression/mood subtype with onset in adolescence.\n\nMethodData were analyzed in the Avon Longitudinal Study of Parents and Children, a prospective UK population-based cohort. Irritability trajectory-classes were estimated for 7924 individuals with data at multiple time-points across childhood and adolescence (4 possible time-points from approximately ages 7 to 15 years). Psychiatric diagnoses were assessed at approximately ages 7 and 15 years. Psychiatric genetic risk was indexed by polygenic risk scores (PRS) for attention-deficit/hyperactivity disorder (ADHD) and major depressive disorder (MDD) derived using large genome-wide association study results.\n\nResultsFive irritability trajectory classes were identified: low (81.2%), decreasing (5.6%), increasing (5.5%), late-childhood limited (5.2%) and high-persistent (2.4%). The early-onset, high-persistent trajectory was associated with male preponderance, childhood ADHD (OR=108.64 (57.45-204.41), p<0.001) and ADHD PRS (OR=1.31 (1.09-1.58), p=0.005); the adolescent-onset, increasing trajectory was associated with female preponderance, adolescent MDD (OR=5.14 (2.47-10.73), p<0.001) and MDD PRS (OR=1.20, (1.05-1.38), p=0.009). Both trajectory classes were associated with MDD diagnosis and ADHD genetic risk.\n\nConclusionsThe developmental context of irritability may be important in its conceptualization: early-onset persistent irritability maybe more neurodevelopmental/ADHD-like and later-onset irritability more depression/mood-like. This has implications for treatment as well as nosology.

genetics↗