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Record W2283033204 · doi:10.1016/j.jalz.2016.01.007

Pathoconnectomics of cognitive impairment in small vessel disease: A systematic review

2016· review· en· W2283033204 on OpenAlexafffund
Ayan Dey, Vess Stamenova, Gary R. Turner, Sandra E. Black, Brian Levine

Bibliographic record

VenueAlzheimer s & Dementia · 2016
Typereview
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsInstitute for Clinical Evaluative SciencesHealth Sciences CentreSunnybrook Health Science CentreBaycrest HospitalYork UniversityUniversity of Toronto
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsNeuroscienceCognitionNeuroimagingDiseaseDementiaPsychologyHyperintensityCognitive impairmentFunctional magnetic resonance imagingWhite matterMagnetic resonance imagingMedicinePathology

Abstract

fetched live from OpenAlex

INTRODUCTION: Cerebral small vessel disease (CSVD) is a highly prevalent condition associated with diffuse ischemic damage and cognitive dysfunction particularly in executive function and attention. Functional brain imaging studies can reveal mechanisms of cognitive impairment in CSVD, although findings are mixed. METHODS: A systematic review integrating findings from functional magnetic resonance imaging and electroencephalography in CSVD is involved. RESULTS: CSVD damages long-range white matter tracts connecting nodes within distributed brain networks. It also disrupts frontosubcortical circuits and cholinergic fiber tracts mediating attentional processes. These changes, illustrated within a model of network dynamics, synergistically relate to neurodegenerative pathology contributing to dementia. DISCUSSION: The effects of CSVD on attention and executive functioning are best understood within a network model of cognition as revealed by functional neuroimaging. Analysis of network function in CSVD can improve characterization of disease severity and treatment effects, and it can inform theoretical models of brain function.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.198
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.357
Teacher spread0.308 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations98
Published2016
Admission routes2
Has abstractyes

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