Patterns of effective connectivity during memory encoding and retrieval differ between patients with mild cognitive impairment and healthy older adults

Abstract

Previous research has shown that there is considerable overlap in the neural networks mediating successful memory encoding and retrieval. However, little is known about how the relevant human brain regions interact during these distinct phases of memory or how such interactions are affected by memory deficits that characterize mild cognitive impairment (MCI), a condition that often precedes dementia due to Alzheimer’s disease. Here we employed multivariate Granger causality analysis using autoregressive modeling of inferred neuronal time series obtained by deconvolving the hemodynamic response function from measured blood oxygenation level- dependent (BOLD) time series data, in order to examine the effective connectivity between brain regions during successful encoding and/or retrieval of object location associations in MCI patients and comparable healthy older adults. During encoding, healthy older adults demonstrated a left hemisphere dominant pattern where the inferior frontal junction, anterior intraparietal sulcus (likely involving the parietal eye fields), and posterior cingulate cortex drove activation in most left hemisphere regions and virtually every right hemisphere region tested. These regions are part of a frontoparietal network that mediates top-down cognitive control and is implicated in successful memory formation. In contrast, in the MCI patients, the right frontal eye field drove activation in every left hemisphere region examined, suggesting reliance on more basic visual search processes. Retrieval in the healthy older adults was primarily driven by the right hippocampus with lesser contributions of the right anterior thalamic nuclei and right inferior frontal sulcus, consistent with theoretical models holding the hippocampus as critical for the successful retrieval of memories. The pattern differed in MCI patients, in whom the right inferior frontal junction and right anterior thalamus drove successful memory retrieval, reflecting the characteristic hippocampal dysfunction of these patients. These findings demonstrate that neural network interactions differ markedly between MCI patients and healthy older adults. Future efforts will investigate the impact of cognitive rehabilitation of memory on these connectivity patterns.

Cardiorespiratory fitness predicts effective connectivity between the hippocampus and default mode network nodes in young adults

“…Thus, effective connectivity methodology can be used to estimate the direction and magnitude of influence between brain regions (Friston, ), such as between the HC and other DMN nodes. Examining the link between CRF and hippocampal effective connectivity is of importance, given that previous studies employing effective connectivity techniques have demonstrated that activation of the HC influences the activation of other brain regions, specifically during tasks involving episodic (Hampstead, Khoshnoodi, Yan, Deshpande, & Sathian, ) and autobiographical memory (McCormick, St‐Laurent, Ty, Valiante, & McAndrews, ; St. Jacques, Rubin, & Cabeza, ). Investigating how CRF interacts with effective connectivity between the nodes of the DMN, including the HC, provides a unique opportunity to examine this question.…”

Dynamic brain connectivity is a better predictor of PTSD than static connectivity

“…Both studies were illustrated with application to task-based fMRI. Other studies have applied dynamic EC to task-based fMRI as well [Feng et al, 2015;Grant et al, 2014Grant et al, , 2015Hampstead et al, 2016;Hutcheson et al, 2015;Libero et al, 2015;Wheelock et al, 2014].…”

Multi-Level Clustering of Dynamic Directional Brain Network Patterns and Their Behavioral Relevance

“…This raises the possibility that dynamic alterations in resting state EC cannot be ignored and needs to be investigated. However, to the best of our knowledge, there has been scant literature on dynamic EC of resting state fMRI (but see Jin et al, 2017;Zhao et al, 2017;Rangaprakash et al, 2018), and most investigations of dynamic EC have focused on taskbased fMRI (Sato et al, 2006;Havlicek et al, 2010;Grant et al, 2014Grant et al, , 2015Lacey et al, 2014;Wheelock et al, 2014;Hutcheson et al, 2015;Feng et al, 2016Feng et al, , 2018Hampstead et al, 2016;Wang et al, 2017;Ramaihgari et al, 2018;Rao et al, 2018).…”