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Record W2184230181 · doi:10.1016/j.tics.2015.10.007

The Neural Crossroads of Psychiatric Illness: An Emerging Target for Brain Stimulation

2015· review· en· W2184230181 on OpenAlexafffund
Jonathan Downar, Daniel M. Blumberger, Zafiris J. Daskalakis

Bibliographic record

VenueTrends in Cognitive Sciences · 2015
Typereview
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsCentre for Addiction and Mental HealthToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health Research
KeywordsNeuroscienceAnterior cingulate cortexPsychologySalience (neuroscience)NeuroimagingBrain stimulationFunctional neuroimagingInsulaStimulationFunctional connectivityDeep brain stimulationDiseaseCognitionMedicine

Abstract

fetched live from OpenAlex

Recent meta-analyses of structural and functional neuroimaging studies are converging on a collective core of brain regions affected across most psychiatric disorders, centered on the dorsal anterior cingulate cortex (dACC) and anterior insula. These nodes correspond well to an anterior cingulo-insular (aCIN) or 'salience' network, and stand at a crossroads within the functional architecture of the brain, acting as a switch to deploy other major functional networks according to motivational demands and environmental constraints. Therefore, disruption of these 'linchpin' areas may be disproportionately disabling, even when other networks remain intact. These regions may represent promising targets for a new generation of anatomically directed brain stimulation treatments. Here, we review the potential of the psychiatric core areas as targets for therapeutic brain stimulation in psychiatric disease.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.349
GPT teacher head0.498
Teacher spread0.149 · 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 designNot applicable
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

Citations166
Published2015
Admission routes2
Has abstractno

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