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Record W4415381268 · doi:10.1186/s13195-025-01874-9

Neurobiological correlates of Mild Behavioral Impairment: a systematic review and meta-analysis

2025· review· en· W4415381268 on OpenAlexaboutno aff
Francesca Remelli, Maria Giorgia Barbieri, Elena Ferrighi, Federico Triolo, Giulia Grande, Davide Liborio Vetrano, Caterina Trevisan, Stefano Volpato

Bibliographic record

VenueAlzheimer s Research & Therapy · 2025
Typereview
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersKarolinska Institutet
KeywordsNeurologyBiomarkerGeriatric psychiatryNeurochemistryNeuroimagingNeurogeneticsBrain aging

Abstract

fetched live from OpenAlex

BACKGROUND: Although individuals with Mild Behavioral Impairment (MBI) show an increased rate of developing dementia, it remains uncertain whether MBI should be considered a risk factor or an actual early sign of neurocognitive disease. OBJECTIVES: This systematic review and meta-analysis aimed to explore the association between MBI and neurobiological correlates of dementia. METHODS: The study protocol followed PRISMA guidelines and was registered in PROSPERO (CRD42024589059). Five databases and gray literature were systematically searched from inception to January 31, 2025 to identify studies that explored the relationship between MBI and brain imaging findings or neurodegenerative and neuroinflammatory fluid biomarker levels. When studies employed comparable methodologies, a random-effects meta-analysis was performed to summarize the results; conversely, a qualitative synthesis was conducted. The Newcastle-Ottawa Quality Assessment Scale was used to assess the study quality. RESULTS: Of the 834 records, 27 studies were included. Most studies were cross-sectional and examined the presence of structural or functional abnormalities through brain imaging in individuals with MBI. Six studies, 4 of which were longitudinal, focused on MBI and cerebrospinal fluid or plasma biomarkers of neurodegeneration and neuroinflammation. Due to the high methodological heterogeneity across studies, five random-effects meta-analyses were conducted, each including two studies. These analyses reported a positive, cross-sectional correlation between MBI burden and brain deposition of amyloid-beta (Aβ) or tau. Conversely, MBI was not significantly associated with either plasma phosphorylated-tau181 levels or Magnetic Resonance Imaging (MRI) brain atrophy markers. Nevertheless, based on the qualitative synthesis of the 27 included studies, MBI was frequently linked to Alzheimer's disease (AD) abnormalities - both in biomarkers and brain imaging studies. CONCLUSIONS: Across studies, MBI appears to be linked to specific neurobiological markers of AD, including Aβ and tau brain deposition, as well as alterations in the mesolimbic pathway and neurodegenerative and neuroinflammatory fluid biomarker levels. Although emerging evidence supports MBI as a potential early clinical sign of AD, heterogeneity across studies precludes definitive conclusions regarding its precise role in the onset and progression of the disease.

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)0.019
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.042
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0190.037
Bibliometrics0.0080.009
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.340
GPT teacher head0.512
Teacher spread0.172 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations1
Published2025
Admission routes1
Has abstractyes

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