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1. Intrinsic functional connectivity among memory networks does not predict individual differences in narrative recall

   https://doi.org/10.1162/imag_a_00169  

   Kurkela, Kyle; Ritchey, Maureen (May 2024, Imaging Neuroscience)

   Abstract Individuals differ greatly in their ability to remember the details of past events, yet little is known about the brain processes that explain such individual differences in a healthy young population. Previous research suggests that episodic memory relies on functional communication among ventral regions of the default mode network (“DMN-C”) that are strongly interconnected with the medial temporal lobes. In this study, we investigated whether the intrinsic functional connectivity of the DMN-C subnetwork is related to individual differences in memory ability, examining this relationship across 243 individuals (ages 18-50 years) from the openly available Cambridge Center for Aging and Neuroscience (Cam-CAN) dataset. We first estimated each participant’s whole-brain intrinsic functional brain connectivity by combining data from resting-state, movie-watching, and sensorimotor task scans to increase statistical power. We then examined whether intrinsic functional connectivity predicted performance on a narrative recall task. We found no evidence that functional connectivity of the DMN-C, with itself, with other related DMN subnetworks, or with the rest of the brain, was related to narrative recall. Exploratory connectome-based predictive modeling (CBPM) analyses of the entire connectome revealed a whole-brain multivariate pattern that predicted performance, although these changes were largely outside of known memory networks. These results add to emerging evidence suggesting that individual differences in memory cannot be easily explained by brain differences in areas typically associated with episodic memory function. 

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2. Uncertainty alters the balance between incremental learning and episodic memory

   https://doi.org/10.7554/eLife.81679  

   Nicholas, Jonathan; Daw, Nathaniel D; Shohamy, Daphna (December 2022, eLife)

   A key question in decision-making is how humans arbitrate between competing learning and memory systems to maximize reward. We address this question by probing the balance between the effects, on choice, of incremental trial-and-error learning versus episodic memories of individual events. Although a rich literature has studied incremental learning in isolation, the role of episodic memory in decision-making has only recently drawn focus, and little research disentangles their separate contributions. We hypothesized that the brain arbitrates rationally between these two systems, relying on each in circumstances to which it is most suited, as indicated by uncertainty. We tested this hypothesis by directly contrasting contributions of episodic and incremental influence to decisions, while manipulating the relative uncertainty of incremental learning using a well-established manipulation of reward volatility. Across two large, independent samples of young adults, participants traded these influences off rationally, depending more on episodic information when incremental summaries were more uncertain. These results support the proposal that the brain optimizes the balance between different forms of learning and memory according to their relative uncertainties and elucidate the circumstances under which episodic memory informs decisions. 

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3. Functional Realignment of Frontoparietal Subnetworks during Divergent Creative Thinking

   https://doi.org/10.1093/cercor/bhab100  

   Beaty, Roger E; Cortes, Robert A; Zeitlen, Daniel C; Weinberger, Adam B; Green, Adam E (April 2021, Cerebral Cortex)

   null (Ed.)

   Abstract Creative cognition has been consistently associated with functional connectivity between frontoparietal control and default networks. However, recent research identified distinct connectivity dynamics for subnetworks within the larger frontoparietal system—one subnetwork (FPCNa) shows positive coupling with the default network and another subnetwork (FPCNb) shows negative default coupling—raising questions about how these networks interact during creative cognition. Here we examine frontoparietal subnetwork functional connectivity in a large sample of participants (n = 171) who completed a divergent creative thinking task and a resting-state scan during fMRI. We replicated recent findings on functional connectivity of frontoparietal subnetworks at rest: FPCNa positively correlated with the default network and FPCNb negatively correlated with the default network. Critically, we found that divergent thinking evoked functional connectivity between both frontoparietal subnetworks and the default network, but in different ways. Using community detection, we found that FPCNa regions showed greater coassignment to a default network community. However, FPCNb showed overall stronger functional connectivity with the default network—reflecting a reversal of negative connectivity at rest—and the strength of FPCNb-default network connectivity correlated with individual creative ability. These findings provide novel evidence of a behavioral benefit to the cooperation of typically anticorrelated brain networks. 

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4. Supervised Graph Representation Learning for Modeling the Relationship between Structural and Functional Brain Connectivity

   https://doi.org/10.1109/ICASSP40776.2020.9053711  

   Li, Yang; Shafipour, Rasoul; Mateos, Gonzalo; Zhang, Zhengwu (May 2020, 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   null (Ed.)

   In this paper, we propose a supervised graph representation learning method to model the relationship between brain functional connectivity (FC) and structural connectivity (SC) through a graph encoder-decoder system. The graph convolutional network (GCN) model is leveraged in the encoder to learn lower-dimensional node representations (i.e. node embeddings) integrating information from both node attributes and network topology. In doing so, the encoder manages to capture both direct and indirect interactions between brain regions in the node embeddings which later help reconstruct empirical FC networks. From node embeddings, graph representations are learnt to embed the entire graphs into a vector space. Our end-to-end model utilizes a multi-objective loss function to simultaneously learn node representations for FC network reconstruction and graph representations for subject classification. The experiment on a large population of non-drinkers and heavy drinkers shows that our model can provide a characterization of the population pattern in the SC-FC relationship, while also learning features that capture individual uniqueness for subject classification. The identified key brain subnetworks show significant between-group difference and support the promising prospect of GCN-based graph representation learning on brain networks to model human brain activity and function. 

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5. Cortical Gradients Support Mental Time Travel into the Past and Future: Evidence from Activation Likelihood Estimation Meta-analysis

   https://doi.org/10.1007/s11065-025-09662-w  

   Teghil, Alice; Wiener, Martin; Boccia, Maddalena (May 2025, Neuropsychology Review)

   Abstract A longstanding issue concerns the extent to which episodic autobiographical memory (EAM) and episodic future thinking (EFT) are the expression of the same cognitive ability and may be dissociated at the neural level. Here, we provided an updated picture of overlaps and dissociations between brain networks supporting EAM and EFT, using Activation Likelihood Estimation. Moreover, we tested the hypothesis that spatial gradients characterize the transition between activations associated with the two domains, in line with accounts positing a transition in the relative predominance of their features and process components. We showed the involvement of a core network across EAM and EFT, including midline structures, the bilateral hippocampus/parahippocampus, angular gyrus and anterior middle temporal gyrus (aMTG) and the left superior frontal gyrus (SFG). Contrast analyses highlighted a cluster in the right aMTG significantly more activated during EFT compared with EAM. Finally, gradiental transitions were found in the ventromedial prefrontal cortex, left SFG, and bilateral aMTG. Results show that differences between EAM and EFT may arise at least partially through the organization of specific regions of common activation along functional gradients, and help to advocate between different theoretical accounts. 

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