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Record W2935022735 · doi:10.1016/j.nicl.2019.101816

Electroconvulsive therapy “corrects” the neural architecture of visuospatial memory: Implications for typical cognitive-affective functioning

2019· article· en· W2935022735 on OpenAlexafffund
Raluca Petrican, Hedvig Söderlund, Namita Kumar, Zafiris J. Daskalakis, Alastair J. Flint, Brian Levine

Bibliographic record

VenueNeuroImage Clinical · 2019
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsUniversity Health NetworkCentre for Addiction and Mental HealthUniversity of TorontoBaycrest Hospital
FundersNational Institute of Mental HealthNIH Blueprint for Neuroscience ResearchCanadian Institutes of Health ResearchMcDonnell Center for Systems NeuroscienceNational Institutes of Health
KeywordsPsychologyCognitionElectroconvulsive therapyMajor depressive disorderNeural correlates of consciousnessNeuropsychologyDepression (economics)NeuroscienceCognitive psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Although electroconvulsive therapy (ECT) is a widely used and effective treatment for refractory depression, the neural underpinnings of its therapeutic effects remain poorly understood. To address this issue, here, we focused on a core cognitive deficit associated with depression, which tends to be reliably ameliorated through ECT, specifically, the ability to learn visuospatial information. Thus, we pursued three goals. First, we tested whether ECT can "normalize" the functional brain organization patterns associated with visuospatial memory and whether such corrections would predict post-ECT improvements in learning visuospatial information. Second, we investigated whether, among healthy individuals, stronger expression of the neural pattern, susceptible to adjustments through ECT, would predict reduced incidence of depression-relevant cognition and affect. Third, we sought to quantify the heritability of the ECT-correctable neural profile. Thus, in a task fMRI study with a clinical and a healthy comparison sample, we characterized two functional connectome patterns: one that typifies trait depression (i.e., differentiates patients from healthy individuals) and another that is susceptible to "normalization" through ECT. Both before and after ECT, greater expression of the trait depression neural profile was associated with more frequent repetitive thinking about past personal events (affective persistence), a hallmark of depressogenic cognition. Complementarily, post-treatment, stronger expression of the ECT-corrected neural profile was linked to improvements in visuospatial learning, a mental ability which is markedly impaired in depression. Subsequently, using data from the Human Connectome Project (HCP) (N = 333), we demonstrated that the functional brain organization of healthy participants with greater levels of subclinical depression and higher incidence of its associated cognitive deficits (affective persistence, impaired learning) shows greater similarity to the trait depression neural profile and reduced similarity to the ECT-correctable neural profile, as identified in the patient sample. These results tended to be specific to learning-relevant task contexts (working memory, perceptual relational processing). Genetic analyses based on HCP twin data (N = 128 pairs) suggested that, among healthy individuals, a functional brain organization similar to the one normalized by ECT in the patient sample is endogenous to cognitive contexts that require visuospatial processing that extends beyond the here-and-now. Broadly, the present findings supported our hypothesis that some of the therapeutic effects of ECT may be due to its correcting the expression of a naturally occurring pattern of functional brain organization that facilitates integration of internal and external cognition beyond the immediate present. Given their substantial susceptibility to both genetic and environmental effects, such mechanisms may be useful both for identifying at risk individuals and for monitoring progress of interventions targeting mood-related pathology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.074
GPT teacher head0.368
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2019
Admission routes2
Has abstractyes

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