Intrinsic functional connectivity among memory networks does not predict individual differences in narrative recall.
Kyle Kurkela1, Maureen Ritchey1
1Department of Psychology and Neuroscience, Boston College.
Individual memory performance is not explained by connectivity within the default mode network (DMN-C). Instead, whole-brain patterns outside known memory networks predict recall ability.
Area of Science:
- Neuroscience
- Cognitive Neuroscience
- Neuroimaging
Background:
- Episodic memory varies significantly across individuals, but the underlying neural mechanisms in healthy young adults remain unclear.
- Prior research links episodic memory to functional connectivity within the ventral default mode network (DMN-C) and medial temporal lobes.
- Understanding individual differences in memory is crucial for cognitive neuroscience.
Approach:
- Investigated the relationship between intrinsic functional connectivity of the DMN-C and narrative recall performance in 243 healthy adults (ages 18-50) using the Cam-CAN dataset.
- Combined resting-state, movie-watching, and sensorimotor task fMRI data to enhance statistical power for estimating whole-brain functional connectivity.
- Utilized connectome-based predictive modeling (CBPM) for exploratory analyses of the entire brain connectome.
Key Points:
- No significant relationship was found between DMN-C functional connectivity (within itself, other DMN subnetworks, or the whole brain) and narrative recall performance.
- Exploratory CBPM analyses identified a whole-brain multivariate pattern associated with memory performance.
- This predictive pattern largely involved brain regions outside of canonical episodic memory networks.
Conclusions:
- Intrinsic functional connectivity of the DMN-C does not explain individual differences in narrative recall ability in healthy young adults.
- Memory performance may be influenced by broader, distributed brain network interactions rather than localized connectivity within established memory circuits.
- These findings challenge traditional models and suggest a more complex neural basis for individual memory variations.
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