Mild Behavioral Impairment and Cortical Thinning: Biomarkers of Early Neurodegeneration
Bibliographic record
Abstract
Abstract Background Mild Behavioral Impairment(MBI) is increasingly recognized as an early phenotypic marker of neurodegeneration, with neuropsychiatric symptoms(NPS) frequently emerging prior to cognitive deficits. While structural neuroimaging studies suggest a link between cortical thinning and NPS, the link between MBI and cortical morphology remains underexplored in diverse community-based cohorts. This study investigated whether early behavioral alterations, assessed via the Mild Behavioral Impairment Checklist(MBI-C), correlate with region-specific cortical thinning in a Southeast Asian cohort. Methods We conducted a cross-sectional analysis of 969 participants (mean age 61.99±10.19years;39.6% male;87.2%Chinese) enrolled in the Biomarkers and Cognition Study in Singapore(BIOCIS), ranging from cognitively normal, subjective cognitive decline(SCD), to mild cognitive impairment(MCI). MBI was quantified using the self-reported MBI-C. T1-weighted MRI scans were processed with FreeSurfer to measure cortical thickness. Associations between MBI-C scores(total/subdomains) and cortical thinning were examined. Results Higher MBI-C Belief scores were significantly associated with cortical thinning in the right hemisphere(β=−0.0177;95%CI:−0.0342to−0.0012;P=0.035). Region-specific analyses showed significant thinning in posterior banks of the superior temporal sulcus, fusiform gyrus, superior temporal gyrus, temporal pole, and transverse temporal gyrus, which remained significant after false discovery rate correction(FDR P=0.042–0.045). Additional thinning was noted in right postcentral and supramarginal gyri and right insula(FDR P≤0.039). Conclusions Elevated MBI, particularly abnormal beliefs, is linked to cortical thinning in regions subserving memory, sensory integration, and emotional regulation, especially within the right hemisphere. These findings highlight the potential of MBI-C as an early neurodegenerative marker and underscore the need for longitudinal studies to clarify temporal dynamics and mechanisms underlying behavioral symptoms and neurodegenerative processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".