Human brain function during pattern separation follows hippocampal and neocortical connectivity gradients

Abstract

Episodic memory is our ability to remember past events accurately. Pattern separation, the process of of orthogonalizing similar aspects of external information into nonoverlapping representations, is one of its mechanisms. Converging evidence suggests a pivotal role of the hippocampus, in concert with neocortical areas, in this process. The current study aimed to identify principal dimensions of functional activation associated with pattern separation in hippocampal and neocortical areas, in both healthy individuals and patients with lesions to the hippocampus. Administering a pattern separation fMRI paradigm to a group of healthy adults, we detected task-related activation in bilateral hippocampal and distributed neocortical areas. Capitalizing on manifold learning techniques applied to parallel resting-state fMRI data, we could identify that hippocampal and neocortical activity patterns were efficiently captured by their principal gradients of intrinsic functional connectivity, which follows the hippocampal long axis and sensory-fugal cortical organization. Functional activation patterns and their alignment with these principal dimensions were altered in patients. Notably, inter-individual differences in the concordance between task-related activity and intrinsic functional gradients were correlated with pattern separation performance in both patients and controls. Our work outlines a parsimonious approach to capture the functional underpinnings of episodic memory processes at the systems level, and to decode functional reorganization in clinical populations.

Microstructural underpinnings and macroscale functional implications of temporal lobe connectivity gradients

“…Murphy et al, 2019). In the study of brain diseases associated with macroscale dysfunction, connectivity gradients have furthermore been used to contextualize changes in brain network architecture (Larivière et al, 2020; Li et al, 2020), supporting their utility to serve as coordinate systems of macroscale functional interactions in healthy and diseased brains.…”

Disorganization of language and working memory systems in frontal versus temporal lobe epilepsy

“…Across scales, we assessed correlations of task effects with cognitive performance in all participants 22, 43 using permutation-based analyses, each entailing 10000 permutations. Voxel-based regressions were conducted with SnPM13; age, sex and group were nuisance covariates.…”

“…As such, it recapitulates established models of cortical hierarchy, 39 and offers a formal framework to characterize organizational aspects of cognitive activity, as exemplified by recent work in healthy adults 40–42 and people with TLE engaged in a pattern separation task. 43 By conveying regional, systems-level and global viewpoints on the neural signatures of cognitive dysfunction in epilepsy, our innovative approach proves sensitive to both localized and higher-order abnormalities.…”

Heritability of hippocampal functional and microstructural organisation

“…Gradient techniques allow a continuous description of the functional connectome compared to discrete network representations of the brain [32][33][34] . Using these gradients, prior studies have found relationships of hippocampal functional connectivity with anatomical organization and microstructure 31 , as well as performance on memory recollection 30 , and pattern separation tasks 29 . It is possible such individual variations in function are due to genetic factors.…”

“…A second axis referred to as the transverse or medio-lateral axis emphasizes connectome differences in medial and lateral hippocampal portions 18,24,29,30 . In recent years, several groups have used dimension reduction techniques, commonly known as gradients, to provide a parsimonious account of hippocampal functional connectivity [31][32][33] . Gradient techniques allow a continuous description of the functional connectome compared to discrete network representations of the brain [34][35][36] .…”