Biology subjects

Marxen, M.

Publications and source records attributed to Marxen, M..

8 recordsLinked to original sources

bioRxiv2026

Predicting alcohol consumption from resting-state functional connectivity: Feature relevance and the potential of meta-matching.

Alcohol use disorder (AUD) is among the most prevalent mental disorders. Associations between resting-state fMRI functional connectivity (FC) and AUD have been reported but rarely replicated. In this preregistered study, we investigated the extent to which FC predicts alcohol consumption, as measured by the AUDIT-C score, in a large sample of non-treatment-seeking alcohol consumers (N = 739), including 528 participants with varying degrees of AUD severity. To optimize performance, we compared kernel ridge regression (KRR) trained directly on this sample with transfer-learning models based on multilayer meta-matching embeddings, from which we derived data-driven relevance maps at the connection, region, and network levels. We tested split-half reliability, reproducibility across cohorts, and outcome specificity of the relevance maps. Both models predicted AUDIT-C scores moderately but significantly (r {approx} 0.15). Meta-matching did not outperform KRR in prediction. Relevance maps showed limited split-half reliability, consistent with the modest predictive performance, but reproduced moderately well across two independent cohorts. These maps indicate reduced connectivity among limbic regions, the anterior middle temporal gyrus, the dorsomedial prefrontal cortex, and dorsal attention network regions. In contrast, hypothesized hyperconnectivity patterns involving the striatum, anterior cingulate cortex, and precuneus were less reliable and mostly non-significant. Overall, our findings suggest that resting-state FC contains reproducible but weak signals related to alcohol consumption. Transfer learning improved relevance-map reliability but not prediction performance and reduced outcome specificity. These results support the utility of interpretable machine learning for identifying connectivity patterns related to drinking behavior, while also highlighting current limitations in its predictive power.

neuroscience↗

bioRxiv2026

Impaired Control Symptoms in Alcohol Use Disorder Show Convergent Associations Across Behavioral and Neural Markers of Inhibitory Control and Drinking Behavior

Background: Deficits in inhibitory control are considered a central feature of alcohol use disorder (AUD), yet behavioral and neuroimaging findings have been inconsistent. One potential explanation is the marked heterogeneity of AUD, which encompasses multiple symptom dimensions that may differ in their underlying neurocognitive mechanisms. In this study, we investigated whether behavioral and neural markers of inhibitory control are associated with specific DSM-5 AUD symptom domains. Methods: A total of 589 participants (432 with non-treatment seeking AUD, 157 controls) completed a Stop-Signal Task (SST) during fMRI. Associations between DSM-5 symptom domains (impaired control over alcohol use, risky use, social problems, physical dependence) and SST performance, inhibition-related brain activation, and alcohol consumption were examined. Real-life drinking behavior was additionally assessed over 12 months using ecological momentary assessment (EMA). Results: Greater impaired control symptom severity was associated with poorer performance on go trials and diminished post-error slowing during SST. At the neural level, higher impaired control symptom severity was associated with reduced activation across fronto-insular, parietal, cingulate, supplementary motor, and cerebellar regions. In contrast, other symptom domains showed little to no associations with inhibition-related behavioral or neural measures. Higher impaired control symptom severity was associated with greater alcohol consumption across questionnaire-based and EMA-based assessments. Conclusions: In this large cohort, altered behavioral performance and recruitment of inhibition-related brain regions in the SST was selectively linked to impaired control symptoms. These findings highlight the importance of symptom-based approaches for understanding heterogeneity in addiction and may inform the development of more targeted interventions.

neuroscience↗

bioRxiv2026

Reduced Control Over Pavlovian-to-Instrumental Transfer in Human Addiction: A Systematic Investigation Across Alcohol Use Disorder and Smoking Status

Pavlovian conditioned stimuli can compromise goal-directed behavior, particularly when they conflict with the required instrumental response. Previous research linked an increased impact of such Pavlovian-to-instrumental transfer (PIT) interference to risky drinking and alcohol dependence. However, it remained unclear whether the observed behavioral and neural alterations generalize to the substantially larger population of non-treatment-seeking individuals with predominantly mild-to-moderate alcohol use disorder (AUD) and to other substances like nicotine. We therefore studied 200 participants from the general population who were stratified by AUD and smoking status before completing a single-lever PIT task during functional imaging. PIT interference was measured as the difference in error rates between incongruent PIT trials, characterized by conflicts of Pavlovian and instrumental stimuli, and congruent trials. Associations with AUD and smoking status, as well as dimensional associations of behavioral PIT interference and neural responses to PIT conflicts with substance-related measures, were examined. As expected, participants with AUD showed greater PIT interference and reduced conflict-related activation in the lateral prefrontal, dorsomedial prefrontal, and anterior cingulate cortex than participants without AUD. The magnitude of these alterations also correlated with higher AUD severity and alcohol consumption, while being independent of smoking status and other smoking-related measures. These findings suggest that enhanced PIT interference generalizes to the broader AUD population but may not extend to other substances like nicotine. They further implicate a reduced recruitment of prefrontal control networks during PIT conflicts as a potential mechanism underlying enhanced PIT interference in AUD and its association with clinically relevant outcome measures.

neuroscience↗

bioRxiv2026

Enhanced Alcohol-Specific but not General Pavlovian-to-Instrumental Transfer in Alcohol Use Disorder

Background: Pavlovian-to-instrumental transfer (PIT) paradigms assess how environmental cues influence instrumental behaviour. Our previous research linked PIT to alcohol use disorder (AUD) and high-risk drinking using monetary rewards during learning. To better capture alcohol-related cue effects, we developed a PIT paradigm delivering trial-by-trial alcohol and juice rewards alongside monetary rewards, to examine alcohol-specific and general PIT effects in parallel. Methods: Seventy-five participants with AUD and ninety-five controls completed the task during functional magnetic resonance imaging. Alcohol-specific PIT was defined as increased alcohol choices during presentation of alcohol-associated versus juice-associated cues. General PIT was defined as increased response vigor during gain- versus loss-associated cues. Behavioural and neural associations with AUD status and AUDIT scores from baseline through one-year follow-up were examined. Results: Participants with AUD showed stronger alcohol-specific PIT effect than controls. Alcohol-specific PIT was positively associated with baseline AUDIT scores and with AUDIT scores across the one-year follow-up. Alcohol cues elicited stronger anterior insula responses, and region-of-interest analysis showed greater left amygdala responses to alcohol versus juice cues in AUD compared with controls. In contrast, general PIT showed a negative association with AUDIT but no association with AUD status. In the general PIT, no AUD-related neural differences were observed. Conclusions: These findings suggest that alcohol-specific PIT captures a clinically meaningful mechanism of cue-driven alcohol seeking that is distinct from generalized motivational transfer, supporting its potential utility as a mechanistic marker in AUD.

neuroscience↗

bioRxiv2023

Aberrant functional brain network organization is associated with relapse during 1-year follow-up in alcohol-dependent patients

Alcohol dependence (AD) is a debilitating disease associated with high relapse rates even after long periods of abstinence. Thus, elucidating neurobiological substrates of relapse risk is fundamental for the development of novel targeted interventions that could promote long-lasting abstinence. In the present study, we analyzed resting-state functional magnetic resonance imaging (rsfMRI) data from a sample of recently detoxified AD patients (n = 93) who were followed-up for 12 months after rsfMRI assessment. Specifically, we employed graph theoretic analyses to compare functional brain network topology and functional connectivity between future relapsers (REL, n = 59), future abstainers (ABS, n = 28) and age and gender matched controls (CON, n = 83). Our results suggest increased whole-brain network segregation, decreased global network integration and overall blunted connectivity strength in REL compared to CON. Conversely, we found evidence for a comparable network architecture in ABS relative to CON. At the nodal level, REL exhibited decreased integration and decoupling between multiple brain systems compared to CON, encompassing regions associated with higher-order executive functions, sensory and reward processing. Among AD patients, increased coupling between nodes implicated in reward valuation and salience attribution constitutes a particular risk factor for future relapse. Importantly, aberrant network organization in REL was consistently associated with shorter abstinence duration during follow-up, portending to a putative neural signature of relapse risk in AD. Future research should further evaluate the potential diagnostic value of the identified changes in network topology and functional connectivity for relapse prediction at the individual subject level.

neuroscience↗

bioRxiv2022

Test-Retest Reliability of Dynamic Functional Connectivity Parameters for a Two-State Model

Dynamic functional connectivity (DFC) metrics derived from resting-state functional magnetic resonance imaging (rs-fMRI) that measure dynamic features of brain communications that change over time are gaining more and more attention. A key concern is the reliability of DFC parameters, especially given the many methodological options. The DFC parameters mean dwell time (MDT), prevalence (Prev.), inter-transition interval (ITI) and state variability (Var.) were computed in 23 participants using a two-state model, sliding window analysis, and two imaging sessions. Reliability of the connectivity states, test-retest reliability of the parameters as well as correlations between DFC parameters were investigated for different scan lengths (i.e., 16 vs 8 min.), atlases (i.e., 116 vs. 442 regions of interest) and with/without within-subject centering of DFC matrices. The results showed an integrated (I) and a segregated (S) brain state with high intra-class correlation (ICC) values of the states between sessions (0.67 [≥] ICC [≥] 0.99). Reliability of the DFC parameters is, at best, intermediate with an ICC = 0.51 for prevalence for not-centered, 16 min. data and the coarse atlas. Moreover, MDTs of both states were positively correlated for centered data. Prevalence of one state was positively correlated with MDT of the same state and frequently negatively correlated with MDT of the other state. Var was positively correlated with MDT and Prev for the same state and negatively correlated for the other state for not-centered data. This study suggests that prevalence is the most reliable DFC parameter with lower reliability for shorter scans, more atlas regions and centering. Thus, it is advantageous to formulate hypotheses related to brain dynamics in terms of prevalence, especially in small-scale studies.

neuroscience↗

bioRxiv2020

Determinants of Real-Time fMRI Neurofeedback Performance and Improvement: a Machine Learning Mega-Analysis

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in Open Science and data sharing.

neuroscience↗

bioRxiv2019

A Comparison of fMRI and Behavioral Models for Predicting Inter-Temporal Choices

In an inter-temporal choice (IteCh) task, subjects are offered a smaller amount of money immediately or a larger amount at a later time point. Here, we are using trial-by-trial fMRI data from 363 recording sessions and machine learning in an attempt to build a classifier that would ideally outperform established behavioral model given that it has access to brain activity specific to a single trial. Such methods could allow for future investigations of state-like factors that influence IteCh choices. To investigate this, coefficients of a GLM with one regressor per trial were used as features for a support vector machine (SVM) in combination with a searchlight approach for feature selection and cross-validation. We then compare the results to the performance of four different behavioral models. We found that the behavioral models reached mean accuracies of 90% and above, while the fMRI model only reached 54.84% at the best location in the brain with a spatial distribution similar to the well-known value-tracking network. This low, though significant, accuracy is in line with simulations showing that classifying based on signals with realistic correlations with subjective value produces comparable, low accuracies. These results emphasize the limitations of fMRI recordings from single events to predict human choices, especially when compared to conventional behavioral models. Better performance may be obtained with paradigms that allow the construction of miniblocks to improve the available signal-to-noise ratio.

neuroscience↗