A01 A02 A03 A04 A05 A06 A07 A09 A10 A11 A12 A13 A14 A18 A19 A21 F01 F02 INF Ö

F02 – Focus Group Neuroimaging and Genetics

Nikolai Axmacher, Dagmar Timmann-Braun, Robert Kumsta

In year 1, the Focus Group Neuroimaging staff will analyze selected existing data sets of FOR 1581, including re-analyses of fMRI data using normalization methods optimized for the cerebellum. They will also coordinate, prepare and optimize the acquisition of human neuroimaging data (rsfMRI and DTI). In years 2–4, meta-analytic approaches will be used to analyze the differential relationship between functional and structural connectivity and extinction learning across subprojects. State-of-the-art analysis procedures for structural and functional connectivity analysis as well as optimized normalization protocols of the cerebellum will be made accessible to all neuroimaging subprojects throughout the entire funding period.

Hypothesis of F02:

  • Functional and structural connectivity of the extinction network allow predicting inter-individual differences in the efficacy of extinction learning across paradigms.
  • Extinction of appetitive and aversive learning relies on partly distinct functional and structural connectivity patterns.
  • The cerebellum shows pronounced functional and structural connectivity with other areas of the extinction network. Connectivity patterns of different cerebellar subregions play specific roles for different aspects of extinction.
  • Functional and structural connectivity of the extinction network is systematically altered in patients with disturbed extinction.
  • Genetic variability predicts inter-individual differences of functional and structural connectivity of
    the extinction network.
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Nikolai Axmacher

Project Lead A02, A03, F02

Ruhr University Bochum

Dagmar Timmann

Project Lead A05, F02

University of Duisburg-Essen

Robert Kumsta

Project Lead F02

University of Luxembourg

Tamás Spisák

Project Lead F02 (Associated)

Predictive NeuroImaging Lab

Carlos A. Gomes

Postdoc F02

Ruhr University Bochum

Javier Schneider Penate

PhD Student F02

Ruhr University Bochum

10 project-relevant publications

Bouyeure A, Pacheco D, Jacob G, Kobelt M, Fellner MC, Rose J, Axmacher N (2025) Distinct representational properties of cues and contexts shape fear learning and extinction. Elife:14:RP105126. https://doi.org/10.7554/eLife.105126 

Englert R, Kincses B, Kotikalapudi R, Gallitto G, Li J, Hoffschlag K, Woo C-W, Wager TD, Timmann D, Bingel U, Spisák T (2024) Connectome-Based Attractor Dynamics Underlie Brain Activity in Rest, Task, and Disease. eLife. 13: RP98725. https://doi.org/10.7554/eLife.98725 

Ernst TM, Brol AE, Gratz M, Ritter C, Bingel U, Schlamann M, Maderwald S, Quick HH, Merz CJ, Timmann D (2019) The cerebellum is involved in processing of predictions and prediction errors in a fear conditioning paradigm. Elife. 8: e46831. https://doi.org/10.7554/eLife.46831 

Genç E, Metzen D, Fraenz C, Schlüter C, Voelkle MC, Arning L, Streit F, Nguyen HP, Güntürkün O, Ocklenburg S, Kumsta R (2023) Structural architecture and brain network efficiency link polygenic scores to intelligence. Hum Brain Mapp. 44(8): 3359-3376. https://doi.org/10.1002/hbm.26286 

Kincses B, Forkmann K, Schlitt F, Jan Pawlik R, Schmidt K, Timmann D, Elsenbruch S, Wiech K, Bingel U, Spisák T (2024) An externally validated resting-state brain connectivity signature of pain-related learning. Commun Biol. 7(1): 875. https://doi.org/10.1038/s42003-024-06574-y 

Labrenz F, Spisák T, Ernst TM, Gomes CA, Quick HH, Axmacher N, Elsenbruch S, Timmann D (2022) Temporal dynamics of fMRI signal changes during conditioned interoceptive pain-related fear and safety acquisition and extinction. Behav Brain Res. 427: 113868. https://doi.org/10.1016/j.bbr.2022.113868 

Nio E, Pais Pereira P, Diekmann N, Petrenko M, Doubliez A, Ernst TM, Batsikadze G, Maderwald S, Deuschl C, Üngör M, Cheng S, Merz CJ, Quick HH, Timmann D (2025) Human cerebellum and ventral tegmental area interact during extinction of learned fear. eLife. 14: RP105399. https://doi.org/10.7554/eLife.105399 

Pfaffenrot V, Bouyeure A, Gomes CA, Kashyap S, Axmacher N, Norris DG (2025) Characterizing BOLD activation patterns in the human hippocampus with laminar fMRI. bioRxiv. 2024.07.04.602065 https://doi.org/10.1162/imag_a_00532 

Schneider Penate JE, Gomes CA, Spisák T, Genç E, Merz CJ, Wolf OT, Quick HH, Elsenbruch S, Engler H, Fraenz C, Metzen D, Ernst TM, Thieme A, Batsikadze G, Hagedorn B, Timmann D, Güntürkün O, Axmacher N, Kumsta R (2025) Polygenic prediction of fear learning is mediated by brain connectivity. medRxiv 2025.03.12.25323754 https://doi.org/10.1101/2025.03.12.25323754 

Spisák T, Bingel U, Wager TD (2023) Multivariate BWAS can be replicable with moderate sample sizes. Nature. 615(7951): E4-7. https://doi.org/10.1038/s41586-023-05745-x

New Year, New Me: The Facts

As the calendar turns to a new year, millions of people around the world commit to New Year’s resolutions, making promises to use the new year as a fresh beginning and an opportunity for transformation. In 2024, almost three-quarters of the British population set themselves New Year’s resolutions — that’s around 40 million people (or the entire population of Canada). This tradition was particularly strong among younger generations, with 96% of Generation Z (aged 18-27) planning resolutions, compared to just 35% of the Silent Generation (aged 79+).

Most common new years resolutions:

  1. Saving more money (52%)
  2. Eat healthier (50%)
  3. Exercise more (48%)
  4. Lose weight (37%)
  5. Spend more time with family/friends (35%)

How long do most resolutions normally last before being broken?

  • Data from America (2016) shows that 75% of individuals maintain their resolutions through the first week. 
  • 64% of individuals maintain their resolutions through the first month. 
  • 46% of individuals in America keep their resolutions past the 6-month mark.

What makes resolutions stick?

Oscarsson et al. (2020) conducted research into what makes New Year’s resolutions stick. Biggest success rates depended on how people phrased their goals. Participants who set approach-oriented goals (trying to move toward or maintain a desirable outcome or state) than those with avoidance-oriented goals (trying to move toward or maintain a desirable outcome or state) were significantly more successful (58.9% vs. 47.1%) at sticking to their goals.

The study also investigates the effects of outside support. These participants received monthly follow-ups and emails with information and exercises for coping with hurdles when striving toward personal goals, and were also encouraged to set goals using the SMART technique and to set interim goals. The group that received some support was exclusively and significantly more successful compared to the groups who received a lot of support or no support at all. 

Additionally, you might feel more successful if you set goals that are measurable in numbers. While success for a person striving to quit smoking or lose weight could easily be measured in the number of cigarettes smoked or body mass index, the success for a person striving to “take better care of themselves” could be highly subjective and possibly impossible to measure.

So as we enter 2026, let’s remember to work with our brain’s natural learning system: Frame your goals positively, break them into manageable steps, and celebrate small wins along the way.