Predicting drinking behavior from functional brain connectomics by harvesting information from multiple sources (TRR 265 - A09)
Project Summary:
The implicit assumption that intra-individual changes in cognitive control and decision-making are reflected by changes in real-life drinking behavior remains largely untested. Alcohol use disorder (AUD) is characterized by maladaptive control that includes an inability to stop alcohol intake in the face of detrimental consequences. Continuing alcohol use, despite an intentional aim to reduce intake or remain abstinent, points to a failure in cognitive control that is often presumed to reflect reduced goal-directed and increased habitual decision-making. Thus far, there remains a gap in measuring cognitive control constructs in the lab and real-life data that captures drinking behavior and self-control. Therefore, we aim to identify differential cognitive trajectories related to (1) losing and (2) regaining control over everyday drinking behavior, and (3) to better understand how cognitive control modifies the impact of known triggers for alcohol intake (exposure to stress, drug cues, priming doses) as well as the impact of subjective states (craving, mood) on control over drinking behavior.
We will investigate these issues by using a smartphone application for the sparse longitudinal ambulatory assessment of cognitive control and decision-making, once per month over one year, in an age-stratified cohort of 900 individuals with AUD (WP1 & 2). In WP1, we will use four already established short app-based and gamified experiments to assess inhibitory control, risk taking, working memory, and Pavlovian bias. In WP2, we will develop app-based experiments in a computational framework to assess goal-directed decision-making and add both to longitudinal data collection. Drinking patterns, environmental factors and subjective states will be assessed longitudinally with ambulatory assessments including ecological momentary assessment (EMA) in close collaboration with Project A01 (Rapp, Banaschewski, Heinz). In WP3, we will develop a novel intense cognitive and physiological ambulatory assessment to predict shifts in drinking behavior, both within hours and across days, for application in further funding periods.
Project Members
Principle Investigators
Michael Marxen, Ph.D.
Leader of Brain Dynamics and Imaging Methods, Section of Systems Neuroscience
Prof. Dr. med. Dr. phil. Henrik Walter
Leader of Research Division of Mind and Brain, Charité, Universitätsmedizin Berlin
Staff
M.Sc. Justin Böhmer, Ph.D. cand., Charité, Universitätsmedizin Berlin
M.Sc. Marco Bottino, Ph.D. cand., TU Dresden
M.Sc. Garvit Joshi, Ph.D. cand., TU Dresden
Collaborators
Prof. Thomas Yeo, Ph.D.
Leader of Computational Brain Imaging Group, National University Singapore
Funding
Key Publications
- Mikolas P, Marxen M, Riedel P, Brockel K, Martini J, Huth F, Berndt C, Vogelbacher C, Jansen A, Kircher T, Falkenberg I, Lambert M, Kraft V, Leicht G, Mulert C, Fallgatter AJ, Ethofer T, Rau A, Leopold K, Bechdolf A, Reif A, Matura S, Bermpohl F, Fiebig J, Stamm T, Correll CU, Juckel G, Flasbeck V, Ritter P, Bauer M & Pfennig A (2024) Prediction of estimated risk for bipolar disorder using machine learning and structural mri features. Psychol Med 54(2):278-88. https://doi.org/10.1017/S0033291723001319
- Spanagel R, Bach P, Banaschewski T, Beck A, Bermpohl F, Bernardi RE, Beste C, Deserno L, Durstewitz D, Ebner-Priemer U, Endrass T, Ersche KD, Feld G, Gerchen MF, Gerlach B, Goschke T, Hansson AC, Heim C, Kiebel S, Kiefer F, Kirsch P, Kirschbaum C, Koppe G, Lenz B, Liu S, Marxen M, Meinhardt MW, Meyer-Lindenberg A, Montag C, Muller CP, Nagel WE, Oliveria AMM, Owald D, Pilhatsch M, Priller J, Rapp MA, Reichert M, Ripke S, Ritter K, Romanczuk-Seiferth N, Schlagenhauf F, Schwarz E, Schwobel S, Smolka MN, Soekadar SR, Sommer WH, Stock AK, Strohle A, Tost H, Vollstadt-Klein S, Walter H, Waschke T, Witt SH, Heinz A & Other members of the ReCoDe C (2024) The recode addiction research consortium: Losing and regaining control over drug intake-findings and future perspectives. Addict Biol 29(7):e13419. https://doi.org/10.1111/adb.13419
- Bohmer J, Reinhardt P, Garbusow M, Marxen M, Smolka MN, Zimmermann US, Heinz A, Bzdok D, Friedel E, Kruschwitz JD & Walter H (2023) Aberrant functional brain network organization is associated with relapse during 1-year follow-up in alcohol-dependent patients. Addict Biol 28(11):e13339. https://doi.org/10.1111/adb.13339
- Huth F, Tozzi L, Marxen M, Riedel P, Brockel K, Martini J, Berndt C, Sauer C, Vogelbacher C, Jansen A, Kircher T, Falkenberg I, Thomas-Odenthal F, Lambert M, Kraft V, Leicht G, Mulert C, Fallgatter AJ, Ethofer T, Rau A, Leopold K, Bechdolf A, Reif A, Matura S, Biere S, Bermpohl F, Fiebig J, Stamm T, Correll CU, Juckel G, Flasbeck V, Ritter P, Bauer M, Pfennig A & Mikolas P (2023) Machine learning prediction of estimated risk for bipolar disorders using hippocampal subfield and amygdala nuclei volumes. Brain Sci 13(6). https://doi.org/10.3390/brainsci13060870
- Fang XM, M. (2022) Test-retest reliability of dynamic functional connectivity parameters for a two-state model. BioRXiv epub ahead of print. https://doi.org/10.1101/2022.11.15.51655
- Fang X, Deza-Araujo YI, Petzold J, Spreer M, Riedel P, Marxen M, O'Connor SJ, Zimmermann US & Smolka MN (2021) Effects of moderate alcohol levels on default mode network connectivity in heavy drinkers. Alcohol Clin Exp Res 45(5):1039-50. https://doi.org/10.1111/acer.14602
Links
FP2 - Domain A: Collaborative Research Centre TRR 265: Losing and Regaining Control over Drug Intake