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Record W7132947250

An Integrated Assessment of Changes in Brain Structure and Function among Older Adults with Major Depressive Disorder or Mild Cognitive Impairment

2022· dissertation· W7132947250 on OpenAlexaff
Neda Rashidi-Ranjbar

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBrain Structure and FunctionDepression (economics)Major depressive disorderCognitionDepressive symptomsHippocampal formationDiseaseAffect (linguistics)Psychological resilience
DOInot available

Abstract

fetched live from OpenAlex

Major Depressive Disorder (MDD) has been associated with an increased risk of developing dementia. In 2008 Butters et al. proposed a mechanistic model by which late-life depression (LLD) may increase the risk for Alzheimer’s disease (AD) consisting of: I) a vascular hypothesis, i.e., structural damage to frontal-executive circuit, II) an inflammation hypothesis, i.e., a high level of stress hormones in depression can lead to hippocampal volume loss (i.e., corticolimbic circuit).In study 1, we conducted a systematic review between 2008 and October 2018 to evaluate the evidence for the conceptual mechanistic model, focusing on frontal-executive and corticolimbic circuits in LLD. Findings from this study revealed inconsistent evidence of alterations in these circuits in LLD (compared to healthy controls; HC). In study 2 and study 3, we assessed structural and functional brain alterations of frontal-executive (i.e., ECN) and corticolimbic (i.e., DMN) circuits in five groups of older adults putatively at-risk for developing dementia: remitted depression with normal cognition (MDD-NC), non-amnestic MCI (naMCI), remitted MDD+naMCI, amnestic MCI (aMCI), and remitted MDD+aMCI. We also examined non-psychiatric HC and individuals with AD. We hypothesized that structural (via T1-weighted imaging & diffusion-weighted imaging) and functional (via resting-state-fMRI) alterations of frontal-executive (i.e., ECN) and corticolimbic (i.e., DMN) circuits would be present among these seven groups of older individuals, with the degree of alterations ranked according to the expected degree of risk for AD. Findings from these studies were contrary to our hypotheses: older individuals with remitted MDD did not show early signs of structural or functional neural alterations in the frontal-executive (i.e., ECN) or corticolimbic (i.e., DMN) circuits associated with preclinical AD. Therefore, remission from depression may protect from the risk of developing AD conferred by depression Taken together, our findings did not fully support the mechanistic ‘multiple pathways model’ linking depression and dementia. In contrast, our findings suggested that treating depression to remission in older adults could potentially reduce the likelihood of developing AD. Future large longitudinal studies should investigate differences in age of onset of depression, treatment-responsiveness, and the effects of antidepressant treatment in relation to risk of developing AD or dementia.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.314
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2022
Admission routes1
Has abstractyes

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