Precise mapping of the neural landscape underlying recognition memory
Presented During:
Thursday, June 26, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
P2 (Plaza Level)
Poster No:
837
Submission Type:
Abstract Submission
Authors:
Joern Alexander Quent1, Kaixiang Zhuang1, Xinyu Liang1, Yun Wang1, Deniz Vatansever1
Institutions:
1Fudan University, Shanghai, Shanghai
First Author:
Co-Author(s):
Introduction:
Recognizing objects is fundamental to the construction of episodic memories and the interpretation of our daily experiences. However, studies investigating the neural basis of recognition memory have traditionally employed a limited number of exemplars or trials, failing to capture the complexity and richness of real-world cognition. Furthermore, while prior work has highlighted that large parts of the brain are sensitive to recognition memory1,2, the precise topographical contributions of these regions remain unclear. To address these gaps, here we conducted a continuous recognition memory experiment using dense scanning at ultra-high field 7T fMRI. Participants were tested on their ability to recognize repetitions of 1280 unique images allowing for high-resolution mapping of the neural mechanisms underlying recognition memory.
Methods:
A group of 20 healthy participants (20-29 years, mean = 24.56, SD = 2.42 years, F/M ratio: 14/6) were scanned daily using 7T fMRI (TR = 1.5 s, TE = 25 ms, voxel size = 1.5 mm isotropic) while performing 12 runs of a continuous recognition task across five consecutive days. During each run, participants viewed images of objects from the THINGS database3 and indicated whether the current image had been presented previously (64 trials per run; 3840 trials in total). Each image appeared three times according to a pre-defined trial sequence that ensured the presentation of new images even in the final session1. Participants' responses were categorized into four outcomes: hits, false alarms, misses and correct rejections. To identify neural signatures with high spatial precision, we employed the HCP pre-processing pipeline4,5 and estimated trial-wise beta maps using GLMSingle6 for enhanced signal modeling. Subject-level contrasts were computed by averaging beta estimates across conditions (e.g., all hits) and subtraction (e.g. hits - correct rejections). Statistical significance was determined using non-parametric permutation testing with PALM, controlling across vertices and voxels (cFDRp < .05).
Results:
Participants maintained consistent engagement with the task throughout the experiment, as evidenced by stable response rates and reaction times. However, the number of hits and correct rejections varied over the course of the experiment, while the number of false alarms remained steady. At the neural level, the canonical old/new effect was observed across extensive regions of the neocortex. In particular, bilateral Area PFm (i.e. angular gyrus), retrosplenial and dorsomedial prefrontal cortices exhibited greater activity for hits compared to correct rejections. In contrast, the correct identification of new images was linked to greater activity across visual cortices. Further analysis revealed a significant relationship between response speed and accuracy: when participants responded faster to old images, they showed a higher probability of being correct, β = -0.83 (95 % CI [-1.48, -0.16]), suggesting an influence on recognition memory performance. To further explore this effect, reaction times were discretized into six bins, enabling a comparison between strong and weak memory traces within images judged as old. For stronger memory traces, increased activation was observed in regions predominantly within the posterior parietal cortex, including the angular gyrus, precuneus, posterior cingulate and retrosplenial cortices. These findings suggest that some brain regions involved in recognition memory are modulated by memory retrieval but some by the strength of the memory trace.
Conclusions:
Our findings emphasize the involvement of a broad network of cortical regions in recognition memory. Novelty detection (correctly identifying images as new) predominantly engaged visual regions, reflecting the sensory-driven processing of unfamiliar stimuli. In contrast, posterior parietal regions were most associated with successful memory retrieval and modulated by memory strength, as indicated by faster and more accurate responses.
Learning and Memory:
Long-Term Memory (Episodic and Semantic) 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 2
Keywords:
Cognition
Cortex
Data analysis
FUNCTIONAL MRI
Learning
Memory
1|2Indicates the priority used for review
Abstract Information
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
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Provide references using APA citation style.
2. Wagner, A. D., Shannon, B. J., Kahn, I. & Buckner, R. L. Parietal lobe contributions to episodic memory retrieval. Trends Cogn. Sci. 9, 445–453 (2005).
3. Hebart, M. N. et al. THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images. PLOS ONE 14, e0223792 (2019).
4. Glasser, M. F. et al. The Human Connectome Project’s neuroimaging approach. Nat. Neurosci. 19, 1175–1187 (2016).
5. Ji, J. L. et al. QuNex—An integrative platform for reproducible neuroimaging analytics. Front. Neuroinformatics 17, 1104508 (2023).
6. Prince, J. S. et al. Improving the accuracy of single-trial fMRI response estimates using GLMsingle. eLife 11, e77599 (2022).