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Record W4391344072 · doi:10.1213/ane.0000000000006799

Changes in Intrinsic Connectivity Networks Topology Across Levels of Dexmedetomidine-Induced Alteration of Consciousness

2024· article· en· W4391344072 on OpenAlexaff
Cécile Staquet, Audrey Vanhaudenhuyse, Sivayini Kandeepan, Robert D. Sanders, Demetrius Ribeiro de Paula, Jean François Brichant, Steven Laureys, Vincent Bonhomme, Andrea Soddu

Bibliographic record

VenueAnesthesia & Analgesia · 2024
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsWestern University
FundersStichting Tegen Kanker
KeywordsDexmedetomidineUnconsciousnessSedationConsciousnessWakefulnessPsychologyNeural correlates of consciousnessNeuroscienceFunctional magnetic resonance imagingAnesthesiaMedicineElectroencephalographyCognition

Abstract

fetched live from OpenAlex

BACKGROUND: Human consciousness is generally thought to emerge from the activity of intrinsic connectivity networks (resting-state networks [RSNs]) of the brain, which have topological characteristics including, among others, graph strength and efficiency. So far, most functional brain imaging studies in anesthetized subjects have compared wakefulness and unresponsiveness, a state considered as corresponding to unconsciousness. Sedation and general anesthesia not only produce unconsciousness but also phenomenological states of preserved mental content and perception of the environment (connected consciousness), and preserved mental content but no perception of the environment (disconnected consciousness). Unresponsiveness may be seen during unconsciousness, but also during disconnectedness. Deep dexmedetomidine sedation is frequently a state of disconnected consciousness. In this study, we were interested in characterizing the RSN topology changes across 4 different and steady-state levels of dexmedetomidine-induced alteration of consciousness, namely baseline (Awake, drug-free state), Mild sedation (drowsy, still responding), Deep sedation (unresponsive), and Recovery, with a focus on changes occurring between a connected consciousness state and an unresponsiveness state. METHODS: A functional magnetic resonance imaging database acquired in 14 healthy volunteers receiving dexmedetomidine sedation was analyzed using a method combining independent component analysis and graph theory, specifically looking at changes in connectivity strength and efficiency occurring during the 4 above-mentioned dexmedetomidine-induced altered consciousness states. RESULTS: Dexmedetomidine sedation preserves RSN architecture. Unresponsiveness during dexmedetomidine sedation is mainly characterized by a between-networks graph strength alteration and within-network efficiency alteration of lower-order sensory RSNs, while graph strength and efficiency in higher-order RSNs are relatively preserved. CONCLUSIONS: The differential dexmedetomidine-induced RSN topological changes evidenced in this study may be the signature of inadequate processing of sensory information by lower-order RSNs, and of altered communication between lower-order and higher-order networks, while the latter remain functional. If replicated in an experimental paradigm distinguishing, in unresponsive subjects, disconnected consciousness from unconsciousness, such changes would sustain the hypothesis that disconnected consciousness arises from altered information handling by lower-order sensory networks and altered communication between lower-order and higher-order networks, while the preservation of higher-order networks functioning allows for an internally generated mental content (or dream).

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)0.002
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 integrity0.0000.000
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 head0.044
GPT teacher head0.305
Teacher spread0.261 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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