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Record W3110635404 · doi:10.3389/fnagi.2020.550664

Distinct Brain Functional Impairment Patterns Between Suspected Non-Alzheimer Disease Pathophysiology and Alzheimer’s Disease: A Study Combining Static and Dynamic Functional Magnetic Resonance Imaging

2020· article· en· W3110635404 on OpenAlexfundno aff
Zheyu Li, Kaicheng Li, Xiao Luo, Qingze Zeng, Shuai Zhao, Baorong Zhang, Minming Zhang, Yanxing Chen

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2020
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsnot available
FundersNatural Science Foundation of Zhejiang ProvinceJohnson and Johnson Pharmaceutical Research and DevelopmentCanadian Institutes of Health ResearchNational Institutes of HealthH. Lundbeck A/SServierEisaiGenentechIXICONational Natural Science Foundation of ChinaNorthern California Institute for Research and EducationPfizerNovartis Pharmaceuticals CorporationUniversity of Southern CaliforniaBiogenEli Lilly and CompanyBristol-Myers SquibbBioClinicaU.S. Department of DefenseAlzheimer's Disease Neuroimaging InitiativeMeso Scale DiagnosticsNational Institute on AgingAlzheimer's Association
KeywordsSuperior frontal gyrusPrecuneusAlzheimer's diseaseNeurodegenerationNeurosciencePathophysiologyPsychologyInsulaMedicineMedial frontal gyrusCognitive declineSnapPopulationFunctional magnetic resonance imagingDiseaseInternal medicineDementia

Abstract

fetched live from OpenAlex

Background : Suspected non-Alzheimer disease pathophysiology (SNAP) refers to the subjects who feature negative β-amyloid (Aβ) but positive tau or neurodegeneration biomarkers. It accounts for a quarter of the elderly population and is associated with cognitive decline. However, the underlying pathophysiology is still unclear. Methods : We included 111 non-demented subjects, then classified them into three groups using cerebrospinal fluid (CSF) Aβ 1–42 (A), phosphorylated tau 181 (T), and total tau (N). Specifically, we identified the normal control (NC; subjects with normal biomarkers, A-T-N-), SNAP (subjects with normal amyloid but abnormal tau, A−T+), and predementia Alzheimer’s disease (AD; subjects with abnormal amyloid and tau, A+T+). Then, we used the static amplitude of low-frequency fluctuation (sALFF) and dynamic ALFF (dALFF) variance to reflect the intrinsic functional network strength and stability, respectively. Further, we performed a correlation analysis to explore the possible relationship between intrinsic brain activity changes and cognition. Results : SNAP showed decreased sALFF in left superior frontal gyrus (SFG) while increased sALFF in left insula as compared to NC. Regarding the dynamic metric, SNAP showed a similarly decreased dALFF in the left SFG and left paracentral lobule as compared to NC. By contrast, when compared to NC, predementia AD showed decreased sALFF in left inferior parietal gyrus (IPG) and right precuneus, while increased sALFF in the left insula, with more widely distributed decreased dALFF variance across the frontal, parietal and occipital lobe. When directly compared to SNAP, predementia AD showed decreased sALFF in left middle occipital gyrus and IPG, while showing decreased dALFF variance in the left temporal pole. Further correlation analysis showed that increased sALFF in the insula had a negative correlation with the general cognition in the SNAP group. Besides, sALFF and dALFF variance in the right precuneus negatively correlated with attention in the predementia AD group. Conclusion : SNAP and predementia AD show distinct functional impairment patterns. Specifically, SNAP has functional impairments that are confined to the frontal region, which is usually spared in early-stage AD, while predementia AD exhibits widely distributed functional damage involving the frontal, parietal and occipital cortex.

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)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.263
Teacher spread0.233 · 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 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

Citations16
Published2020
Admission routes1
Has abstractyes

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