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

A03 – Funktionelle Rolle und dynamische Änderung der Netzwerkkonnektivität im Rahmen von Extinktionslernen

Erhan Genç, Nikolai Axmacher

Unter Verwendung fortgeschrittener Bildgebungsverfahren wird getestet, inwieweit dynamische Netzwerkinteraktionen innerhalb des Extinktionsnetzwerks das Lernen widerspiegeln. Hierbei wird untersucht, ob der Lernfortschritt auf dynamischen Änderungen der funktionellen Netzwerkkonnektivität beruht. Simultane EEG/fMRT-Messungen liefern die elektrophysiologischen Grundlagen dieser Interaktionen. Zudem wird untersucht, ob Lernunterschiede von Individuen durch grundlegende strukturelle Netzwerkunterschiede erklärt werden können. Schließlich werden hochaufgelöste 7T Messungen der Amygdalakerne durchgeführt, um aufzudecken, inwieweit deren Interaktionen mit dem restlichen Netzwerk das Extinktionslernen beeinflusst.

Leitfragen des Projekts A03:

  • Wie verändern sich rsfMRI und die aufgabenbezogene Konnektivität des Extinktionsnetzwerks während und nach dem Lernen?
  • Was ist beim Menschen die elektrophysiologische Grundlage der Interaktionen im Extinktionsnetzwerk?
  • Können inter-individuelle Lernunterschiede durch Unterschiede in der Hirnkonnektivität vorhergesagt werden?
  • Wie interagieren spezifische Amygdala-Kerne während des Lernens mit dem verbleibenden Extinktionsnetzwerk?
Zurück zur Projektübersicht

Erhan Genç

Projektleiter A03

IfADo

Nikolai Axmacher

Projektleiter A02, A03, F02

Ruhr-Universität Bochum

Christoph Fraenz

Postdoc A03

IfADo

Justinas Narbutas

Postdoc A03

IfADo

Arslan Gabdulkhakov

Doktorand A03

IfADo

George Jacob

Doktorand A03

Ruhr-Universität Bochum

10 projektrelevante Publikationen

Bouyeure A, Pacheco D, Fellner MC, Jacob G, Kobelt M, Rose J, Axmacher N (2025) Distinct representational properties of cues and contexts shape fear learning and extinction. bioRxiv. 2024.12.16.628638. https://doi.org/10.1101/2024.12.16.628638

Chen S, Tan Z, Xia W, Gomes CA, Zhang X, Zhou W, Liang S, Axmacher N, Wang L (2021) Theta oscillations synchronize human medial prefrontal cortex and amygdala during fear learning. Science advances 7:eabf4198. https://doi.org/10.1126/sciadv.abf4198

Chhabra H, Ma Y, Genç E, Nitsche MA, Yavari F (2025) The physiological foundation of extinction improvement by tDCS over the ventromedial prefrontal cortex (vmPFC): An fMRI study. SSRN: abstract=5180197. https://doi.org/10.2139/ssrn.5180197 

Fraenz C., Metzen D., Packheiser J., Merz C. J., Selpien H., Axmacher N., Genç E. 2020. Fear learning sculpts functional brain connectivity at rest beyond the traditional fear network in humans. bioRxiv 2020.05.26.115840. https://doi.org/10.1101/2020.05.26.115840

Fraenz C., Metzen D., Merz C. J., Selpien H., Friedrich P., Ocklenburg S., Axmacher N., Genç E. 2020. Multi-Modal Brain Properties are Associated with Interindividual Differences in Fear Acquisition and Extinction. bioRxiv. doi:10.1101/2025.04.05.647350. https://doi.org/10.1101/2025.04.05.647350

Genç E, Fraenz C, Schlüter C, Friedrich P, Hossiep R, Voelkle MC, Ling JM, Güntürkün O, Jung RE (2018) Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence. Nat Commun 9. https://doi.org/10.1038/s41467-018-04268-8

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. Human brain mapping, 44(8), 3359–3376. https://doi.org/10.1002/hbm.26286

Ocklenburg S, Friedrich P, Fraenz C, Schlueter C, Beste C, Güntürkün O, Genç E (2018) Neurite architecture of the planum temporale predicts neurophysiological processing of auditory speech. Science Advances 4. https://doi.org/10.1126/sciadv.aar6830

Schlüter C, Fraenz C, Friedrich P, Güntürkün O, Genç E (2022) Neurite density imaging in amygdala nuclei reveals interindividual differences in neuroticism. Human brain mapping 43. https://doi.org/10.1002/hbm.25775

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 

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.