Functional connectivity underpinnings of electroconvulsive therapy-induced memory impairments in patients with depression

Abstract

Electroconvulsive therapy (ECT) is an effective treatment for severe medication-resistant depression. However, ECT frequently results in episodic memory impairments, causing many patients to discontinue treatment. The objective of this study was to explore the functional connectivity underpinnings of ECT-induced episodic memory impairments. We investigated verbal episodic memory and intrinsic functional connectivity in 24 patients with depression (13F, 11M) before and after ECT, and 1 month after treatment. We used a novel individual-oriented approach to examine functional connectivity, and trained a linear support vector regression model to estimate verbal memory performance based on connectivity. The model identified a set of brain connections that can predict baseline verbal memory performance (r = 0.535, p = 0.026). Importantly, we found a nonoverlapping set of brain connections whose changes after ECT can track patients' verbal memory impairments (r = 0.613, p = 0.008). These connections mainly involve the frontoparietal control, default mode, and hippocampal networks, suggesting that ECT affects broad functional networks that are involved in memory performance. In contrast, functional connectivity defined using traditional group-level analyses was unable to estimate either baseline memory performance or post-ECT verbal memory impairments. A parallel analysis using the same strategy did not identify a connectivity marker for overall mood improvement, suggesting that functional connectivity changes related to depressive symptoms may be highly heterogenous. Our findings shed light on the mechanism through which ECT impairs episodic memory, and additionally underline the importance of accounting for interindividual variability in the investigation of functional brain organization in patients with depression.

The Neurobiological Basis of Cognitive Side Effects of Electroconvulsive Therapy: A Systematic Review

“…Interestingly, the impaired memory was closely associated with changes in functional and effective connectivity within the salience network. Similarly, Wang et al [39] showed a significant reduction in delayed recall Rey AVLT (RAVLT) scores following ECT. Specifically, functional connectivity within the Frontoparietal Network (FPN), the Default Mode Network (DMN), and subcortical structures involving the hippocampus were significantly able to predict these RAVLT changes.…”

Recent developments in representations of the connectome

“…It also provides a cortical areal classifier that enables mapping cortical areas in individual subjects, even when those areas are atypical in layouts and not aligned with the best available surface registration methods (see Section 5 ). Furthermore, the characterization of the parcellated connectome as a fingerprint has been a valuable catalyst for efforts to predict behavior and clinical symptomatology ( Brennan et al, 2019 ; Finn et al, 2015 ; Lebois et al, 2021 ; Li et al, 2019 ; Wang et al, 2020a ; Wang et al, 2020b ). Related to these efforts, recent work has shown that transformations of parcellated connectivity estimates (such as tangent space projections) can improve performance when using subsequent machine learning methods for behavioral prediction ( Dadi et al, 2019 ; Pervaiz et al, 2020 ).…”

“…Moreover, similar spatial distribution of inter-individual variability may be present in macaque monkeys and humans which differentiates the multimodal association areas from primary areas ( Ren et al, 2020 ), suggesting that this phenomenon has an evolutionary history. In line with this group-to-subject shift in applied scientific findings, methodological efforts are slowly shifting away from the creation of group-based functional atlases ( Craddock et al, 2012 ; Power et al, 2011 ; Yeo et al, 2011 ), towards methods that capture unbiased individualized connectome variation in healthy subjects as well as in patients ( Bijsterbosch et al, 2019 ; Brennan et al, 2019 ; Glasser et al, 2016a ; Hacker et al, 2013 ; Harrison et al, 2020 ; Haxby et al, 2020 ; Lebois et al, 2021 ; Li et al, 2019 ; Wang et al, 2020a ; Wang et al, 2020b ). In parallel with this appreciation of between-subject differences, the field has also started to move beyond focusing only on the view of the brain as a modular set of regions/networks with clear boundaries to also study smooth gradients of organization ( Huntenburg et al, 2018 ; Margulies et al, 2016 ; Valk et al, 2020 ), and complex spatio-temporal modes of function ( Abbas et al, 2019 ; Vidaurre et al, 2018 ).…”

Unrevealing Reliable Cortical Parcellation of Individual Brains Using Resting-State Functional Magnetic Resonance Imaging and Masked Graph Convolutions

“…The majority of current approaches focused on the group representative functional mapping of the cerebral cortex, which may provide useful insights into the intrinsic organizational principles of the human brain ( Buckner et al, 2013 ; Wig, 2017 ), but ignore the variability of individual brains in areal size, location, spatial arrangement, and connectivity patterns ( Mueller et al, 2013 ; Zuo and Xing, 2014 ). The precise mapping of individualized functional areas is a critical step toward better understanding of the structural–functional relationship of the human brain that underlying cognition and behavior ( Wang et al, 2015 ; Kong et al, 2019 , 2021 ) as well as for personalized localization diagnosis and treatment of neurological disorders ( Mueller et al, 2015 ; Wang et al, 2020 ).…”