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Record W4408102223 · doi:10.1016/j.isci.2025.112136

Spine loss in depression impairs dendritic signal integration in human cortical microcircuit models

2025· article· en· W4408102223 on OpenAlexafffund
Heng Kang Yao, Frank Mazza, Thomas D. Prévot, Etienne Sibille, Etay Hay

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersKrembil Foundation
KeywordsDendritic spineDepression (economics)NeuroscienceCortical spreading depressionSIGNAL (programming language)SPINE (molecular biology)ChemistryBiologyPsychologyComputer scienceCell biologyPsychiatryEconomics

Abstract

fetched live from OpenAlex

Major depressive disorder (depression) is associated with altered dendritic structure and function of cortical pyramidal neurons, due to decreased inhibition from somatostatin (SST) interneurons and loss of spines and associated synapses, as indicated in postmortem human studies. Dendrites mediate signal processing through synaptic integration and nonlinear properties including backpropagating action potentials and dendritic Na + spikes that enhance the neuron's computational power. However, it is currently unclear how depression-related dendritic changes impact signal integration. Here, we integrated human neuronal data of active dendritic properties and spine loss in depression into detailed computational models of human cortical microcircuits. We show that spine loss dampens signal response, worsening signal detection impairment than due to reduced SST interneuron inhibition alone. Furthermore, altered intrinsic properties due to spine loss abolished nonlinear dendritic signal integration and impaired recurrent microcircuit activity. Our study mechanistically links cellular changes in depression to impaired dendritic processing in human cortical microcircuits.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.304
Teacher spread0.267 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations9
Published2025
Admission routes2
Has abstractyes

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