We use mental models of the world-cognitive maps-to guide behavior. The lateral orbitofrontal cortex (lOFC) is typically thought to support behavior by deploying these maps to simulate outcomes, but recent evidence suggests that it may instead support behavior by underlying map creation. We tested between these two alternatives using outcome-specific devaluation and a high-potency chemogenetic approach. Selectively inactivating lOFC principal neurons when male rats learned distinct cue-outcome associations, but before outcome devaluation, disrupted subsequent inference, confirming a role for the lOFC in creating new maps. However, lOFC inactivation surprisingly led to generalized devaluation, a result that is inconsistent with a complete mapping failure. Using a reinforcement learning framework, we show that this effect is best explained by a circumscribed deficit in credit assignment precision during map construction, suggesting that the lOFC has a selective role in defining the specificity of associations that comprise cognitive maps.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9839657 | PMC |
http://dx.doi.org/10.1038/s41593-022-01216-0 | DOI Listing |
Biol Psychiatry
December 2024
Amsterdam UMC, Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam, The Netherlands; Amsterdam UMC, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands; Amsterdam UMC, Amsterdam Neuroscience, Amsterdam, The Netherlands; Amsterdam UMC, Compulsivity, Impulsivity and Attention, Amsterdam, The Netherlands.
Objective: Obsessive-compulsive disorder (OCD) is associated with altered brain function related to processing of negative emotions. To investigate neural correlates of negative valence in OCD, we pooled fMRI data of 633 individuals with OCD and 453 healthy controls from 16 studies using different negatively-valenced tasks across the ENIGMA-OCD Working-Group.
Methods: Participant data were processed uniformly using HALFpipe, to extract voxelwise participant-level statistical images of one common first-level contrast: negative vs.
Neuroimage
December 2024
Department of General Psychology, University of Padova, via Venezia 8, 35131 Padova, Italy; Padova Neuroscience Center, University of Padova, via Orus 2/B, 35129 Padova, Italy. Electronic address:
The impacting research on emotions of the last decades was carried out with different methods. The most popular was based on the use of a validated sample of slides, the International Affective Pictures System (IAPS), divided mainly into pleasant, neutral and unpleasant categories, and on fMRI as a measure of brain activation induced by these stimuli. With the present coordinate-based meta-analysis (CBMA) based on ALE approach, we aimed to unmask the main brain networks involved in the contrast of pleasant vs.
View Article and Find Full Text PDFBMC Geriatr
December 2024
College of Sports and Health, Shandong Sport University, Jinan, Shandong, China.
Background: This study aimed to investigate the modulatory role of prefrontal cortex (PFC) activity in older adults with mild cognitive impairment (MCI) when sensory cues were removed or presented inaccurately (i.e., increased sensory complexity) during sensory manipulation of a balance task.
View Article and Find Full Text PDFJ Clin Neurophysiol
December 2024
Department of Neurology, Mayo Clinic, Rochester, Minnesota, U.S.A.; and.
The lack of reliable seizure detection remains a significant challenge for epilepsy care. A clinical deep brain stimulation (DBS) system provides constrained ambulatory brain recordings; however, limited data exist on the use of DBS recordings for seizure detection and lateralization. We present the case of an 18-year-old patient with drug-resistant focal epilepsy, who had seizure detection and lateralization by DBS recordings.
View Article and Find Full Text PDFPeerJ
December 2024
Department of Fundamental and Applied Sciences, Faculty of Science and Information Technology, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia.
Background: Alzheimer's Disease (AD) poses a major challenge as a neurodegenerative disorder, and early detection is critical for effective intervention. Magnetic resonance imaging (MRI) is a critical tool in AD research due to its availability and cost-effectiveness in clinical settings.
Objective: This study aims to conduct a comprehensive analysis of machine learning (ML) methods for MRI-based biomarker selection and classification to investigate early cognitive decline in AD.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!