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Record W4393075161 · doi:10.1113/jp285568

Multispectral brain connectivity during visual attention distinguishes controlled from uncontrolled hypertension

2024· article· en· W4393075161 on OpenAlexaff
Jake J. Son, Yasra Arif, Davina B. Oludipe, Lucas Weyrich, Abraham D. Killanin, Alex I. Wiesman, Hannah J. Okelberry, Madelyn P. Willett, Hallie J. Johnson, Tony W. Wilson

Bibliographic record

VenueThe Journal of Physiology · 2024
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institute of General Medical SciencesNational Institutes of HealthNational Institute of Neurological Disorders and StrokeNational Institute of Mental HealthAmerican Heart Association
KeywordsMagnetoencephalographyNeuroimagingNeuroscienceCognitionFunctional magnetic resonance imagingBrain activity and meditationFunctional connectivityNeurovascular bundlePsychologyFunctional neuroimagingMedicineElectroencephalographyPathology

Abstract

fetched live from OpenAlex

Hypertension-related changes in brain function place individuals at higher risk for cognitive impairment and Alzheimer's disease. The existing functional neuroimaging literature has identified important neural and behavioural differences between normotensive and hypertensive individuals. However, previously-used methods (i.e. magnetic resonance imaging, functional near-infrared spectroscopy) rely on neurovascular coupling, which is a useful but indirect measure of neuronal activity. Furthermore, most studies fail to distinguish between controlled and uncontrolled hypertensive individuals, who exhibit significant behavioural and clinical differences. To partially remedy this gap in the literature, we used magnetoencephalography (MEG) to directly examine neuronal activity that is invariant to neurovascular coupling changes induced by hypertension. Our study included 52 participants (19 healthy controls, 15 controlled hypertensives, 18 uncontrolled hypertensives) who completed a modified flanker attention task during MEG. We identified significant oscillatory neural responses in two frequencies (alpha: 8-14 Hz, gamma: 48-60 Hz) for imaging and used grand-averaged images to determine seeds for whole-brain connectivity analysis. We then conducted Fisher-z tests for each pair of groups, using the relationship between the neural connectivity and behavioural attention effects. This highlighted a distributed network of regions associated with cognitive control and selective attention, including frontal-occipital and interhemispheric occipital connections. Importantly, the inferior frontal cortex exhibited a unique neurobehavioural relationship that distinguished the uncontrolled hypertensive group from the controlled hypertensive and normotensive groups. This is the first investigation of hypertension using MEG and identifies critical whole-brain connectivity differences based on hypertension profiles. KEY POINTS: Structural and functional changes in brain circuitry scale with hypertension severity and increase the risk of cognitive impairment and Alzheimer's disease. We harness the excellent spatiotemporal precision of magnetoencephalography (MEG) to directly quantify dynamic functional connectivity in healthy control, controlled hypertensive and uncontrolled hypertensive groups during a flanker task. In the first MEG study of hypertension, we show that there are neurobehavioural relationships that distinguish the uncontrolled hypertensive group from healthy and controlled hypertensive group in the prefrontal cortex. These results provide novel insights into the differential impact of hypertension on brain dynamics underlying selective attention.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.022
GPT teacher head0.272
Teacher spread0.250 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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