MétaCan
Menu
← Back to cohort
Record W4312038645 · doi:10.1002/alz.069437

Electrophysiological markers of early Alzheimer’s Disease proteinopathy in the human brain

2022· article· en· W4312038645 on OpenAlexaff
Jonathan E Gallego Rudolf, Alex I. Wiesman, Alexa Pichet Binette, Sylvia Villeneuve, Sylvain Baillet

Bibliographic record

VenueAlzheimer s & Dementia · 2022
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsMcGill UniversityDouglas Mental Health University InstituteMcGill Genome CentreMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsMagnetoencephalographyNeurosciencePsychologyAlzheimer's diseaseNeurocognitiveDiseaseHuman brainNeuropsychologyAudiologyMedicineElectroencephalographyCognitionPathology

Abstract

fetched live from OpenAlex

Abstract Background Alzheimer's Disease (AD) is characterized by the pathological accumulation of amyloid‐beta (Aβ) and hyperphosphorylated tau proteins in the brain. Animal models have demonstrated that early Aβ accumulation induces neuronal hyperexcitability (Stargardt et al., 2015), whereas later additive effects of Aβ and tau lead to suppression of neuronal activity that parallels disease severity (Busche et al., 2020). Although neural hypo‐excitability has been reported in the later stages of AD, it remains unknow if such a shift from hyper‐ to hypo‐excitability exists at the macroscopic level in the human brain of asymptomatic individuals (Figure 1). Methods We used Positron Emission Tomography to measure the deposition of whole‐brain Aβ ([ 18 F] NAV4694) and medial temporal tau ([ 18 F] Flortaucipir) and resting‐state Magnetoencephalography (MEG) to capture the neurophysiological changes related to AD pathology in a group of clinically unimpaired older adults with family history of AD (PREVENT‐AD cohort; McSweeney et al., 2020). We used linear mixed effects models to test the association between MEG spectral power and Aβ across cortical regions, and the interactive effect of tau accumulation on this relationship. We then used linear regressions to test if the observed associations between MEG spectral power and AD pathology related to longitudinal cognitive performance, evaluated annually using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Results Aβ deposition was associated with neural hyper‐activity, as reflected by a positive association between Aβ SUVR values and MEG spectral power in higher frequencies (alpha [8 ‐ 12 Hz]) and a negative association in slow frequencies (delta [2 – 4 Hz]; Figure 2). The accumulation of medial temporal tau predicted a shift in these associations towards a hypo‐activation pattern (increased neural slowing), which was related to longitudinal decreases in attention scores (Figure 3). Conclusion Our results support the hypothesis that Aβ induces neural hyper‐activity in asymptomatic individuals, while the additive effects of Aβ and tau accumulation lead to a shift towards neural slowing that relates to cognitive deficits. These early detectable electrophysiological changes may represent novel non‐invasive biomarkers of the preclinical stages of AD and may potentially help predicting cognitive trajectories and disease progression in the AD continuum.

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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.048
GPT teacher head0.279
Teacher spread0.231 · 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

Explore more

Same venueAlzheimer s & Dementia→Same topicFunctional Brain Connectivity Studies→French-language works237,207→