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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. Hierarchical Modular Structure of the Drosophila Connectome

   https://doi.org/10.1523/JNEUROSCI.0134-23.2023  

   Kunin, Alexander B.; Guo, Jiahao; Bassler, Kevin E.; Pitkow, Xaq; Josić, Krešimir (August 2023, The Journal of Neuroscience)

   The structure of neural circuitry plays a crucial role in brain function. Previous studies of brain organization generally had to trade off between coarse descriptions at a large scale and fine descriptions on a small scale. Researchers have now reconstructed tens to hundreds of thousands of neurons at synaptic resolution, enabling investigations into the interplay between global, modular organization, and cell type-specific wiring. Analyzing data of this scale, however, presents unique challenges. To address this problem, we applied novel community detection methods to analyze the synapse-level reconstruction of an adult femaleDrosophila melanogasterbrain containing >20,000 neurons and 10 million synapses. Using a machine-learning algorithm, we find the most densely connected communities of neurons by maximizing a generalized modularity density measure. We resolve the community structure at a range of scales, from large (on the order of thousands of neurons) to small (on the order of tens of neurons). We find that the network is organized hierarchically, and larger-scale communities are composed of smaller-scale structures. Our methods identify well-known features of the fly brain, including its sensory pathways. Moreover, focusing on specific brain regions, we are able to identify subnetworks with distinct connectivity types. For example, manual efforts have identified layered structures in the fan-shaped body. Our methods not only automatically recover this layered structure, but also resolve finer connectivity patterns to downstream and upstream areas. We also find a novel modular organization of the superior neuropil, with distinct clusters of upstream and downstream brain regions dividing the neuropil into several pathways. These methods show that the fine-scale, local network reconstruction made possible by modern experimental methods are sufficiently detailed to identify the organization of the brain across scales, and enable novel predictions about the structure and function of its parts. Significance StatementThe Hemibrain is a partial connectome of an adult femaleDrosophila melanogasterbrain containing >20,000 neurons and 10 million synapses. Analyzing the structure of a network of this size requires novel and efficient computational tools. We applied a new community detection method to automatically uncover the modular structure in the Hemibrain dataset by maximizing a generalized modularity measure. This allowed us to resolve the community structure of the fly hemibrain at a range of spatial scales revealing a hierarchical organization of the network, where larger-scale modules are composed of smaller-scale structures. The method also allowed us to identify subnetworks with distinct cell and connectivity structures, such as the layered structures in the fan-shaped body, and the modular organization of the superior neuropil. Thus, network analysis methods can be adopted to the connectomes being reconstructed using modern experimental methods to reveal the organization of the brain across scales. This supports the view that such connectomes will allow us to uncover the organizational structure of the brain, which can ultimately lead to a better understanding of its function. 

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