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Record W4210641702 · doi:10.3389/fnagi.2021.772136

Intrinsic Brain Activity of Inferior Temporal Region Increased in Prodromal Alzheimer's Disease With Hearing Loss

2022· article· en· W4210641702 on OpenAlexfundno aff
Luwei Hong, Qingze Zeng, Kaicheng Li, Xiao Luo, Xiaopei Xu, Xiaocao Liu, Zheyu Li, Yanv Fu, Yanbo Wang, Tianyi Zhang, Yanxing Chen, Zhirong Liu, Peiyu Huang, Minming Zhang

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2022
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsnot available
FundersJanssen Alzheimer Immunotherapy Research And DevelopmentCanadian Institutes of Health ResearchNational Institute of Biomedical Imaging and BioengineeringNational Key Research and Development Program of ChinaUniversity of California, San DiegoFundamental Research Funds for the Central UniversitiesNational Institutes of HealthH. Lundbeck A/SServierNational Natural Science Foundation of ChinaEisaiGenentechIXICOBiogenPfizerNovartis Pharmaceuticals CorporationNorthern California Institute for Research and EducationF. Hoffmann-La RocheUniversity of Southern CaliforniaU.S. Department of DefenseEli Lilly and CompanyBristol-Myers SquibbNational Institute on AgingAlzheimer's Association
KeywordsAudiologyNeuropsychologyCognitionResting state fMRIVoxelMedicinePsychologyCognitive declineVoxel-based morphometryBonferroni correctionHearing lossNeuroscienceInternal medicineDiseaseDementiaMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

Background and Objective Hearing loss (HL) is one of the modifiable risk factors for Alzheimer's disease (AD). However, the underlying mechanism behind HL in AD remains elusive. A possible mechanism is cognitive load hypothesis, which postulates that over-processing of degraded auditory signals in the auditory cortex leads to deficits in other cognitive functions. Given mild cognitive impairment (MCI) is a prodromal stage of AD, untangling the association between HL and MCI might provide insights for potential mechanism behind HL. Methods We included 85 cognitively normal (CN) subjects with no hearing loss (NHL), 24 CN with HL, 103 mild cognitive impairment (MCI) patients with NHL, and 23 MCI with HL from the ADNI database. All subjects underwent resting-state functional MRI and neuropsychological scale assessments. Fractional amplitude of low-frequency fluctuation (fALFF) was used to reflect spontaneous brain activity. The mixed-effects analysis was applied to explore the interactive effects between HL and cognitive status (GRF corrected, voxel p-value <0.005, cluster p-value < 0.05, two-tailed). Then, the FDG data was included to further reflect the regional neuronal abnormalities. Finally, Pearson correlation analysis was performed between imaging metrics and cognitive scores to explore the clinical significance (Bonferroni corrected, p < 0.05). Results The interactive effects primarily located in the left superior temporal gyrus (STG) and bilateral inferior temporal gyrus (ITG). Post-hoc analysis showed that NC with HL had lower fALFF in bilateral ITG compared to NC with NHL. NC with HL had higher fALFF in the left STG and decreased fALFF in bilateral ITG compared to MCI with HL. In addition, NC with HL had lower fALFF in the right ITG compared to MCI with NHL. Correlation analysis revealed that fALFF was associated with MMSE and ADNI-VS, while SUVR was associated with MMSE, MoCA, ADNI-EF and ADNI-Lan. Conclusion HL showed different effects on NC and MCI stages. NC had increased spontaneous brain activity in auditory cortex while decreased activity in the ITG. Such pattern altered with disease stage changing and manifested as decreased activity in auditory cortex along with increased activity in ITG in MCI. This suggested that the cognitive load hypothesis may be the underlying mechanism behind HL.

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 score0.003
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
Bibliometrics0.0000.000
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 head0.037
GPT teacher head0.268
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".

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Citations7
Published2022
Admission routes1
Has abstractyes

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