Mild behavioral impairment and functional connectivity in dementia‐free elderly
Bibliographic record
Abstract
Abstract Background Mild Behavioral Impairment (MBI), characterized by de novo emergent and persistent neuropsychiatric symptoms in later life, may be used to improve early detection of neurodegenerative disease. While alterations in resting‐state networks have been demonstrated in early‐stage Alzheimer’s disease (AD), few studies have explored these networks in MBI. Here, we assessed activity in the default mode network (DMN) and the salience network (SN), using resting‐state functional magnetic resonance imaging (fMRI), in association with MBI. We hypothesized that the strength of functional connectivity (FC) within the DMN and SN would be reduced in dementia‐free persons with MBI (MBI+), relative to those without (MBI‐). Methods Data from dementia‐free participants in three prospective cohort studies were pooled. MBI+ status was determined using the MBI‐checklist. Imaging data were acquired using 3T MRI. Seed‐based connectivity analyses were performed using the CONN toolbox. The DMN included the posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC), and lateral parietal (LP) regions, with the PCC as the seed. The SN included the anterior cingulate cortex (ACC), anterior insula (AI), rostral prefrontal cortex (RPFC), and supramarginal gyrus (SMG), with the ACC as the seed. FC maps of the DMN and SN were computed for each MBI group; difference maps were extracted to identify regions with significantly different connectivity across MBI groups. A threshold of p<0.05 was used with false discovery rate correction for multiple comparisons at the cluster level; a p<0.001 uncorrected for voxel level was used for group comparisons. All analyses were covaried for age, sex, years of education, and Montreal Cognitive Assessment scores. Results Of the 95 participants, 32 were MBI+ and 63 MBI‐ (mean age 71.7; 54.7% female). Within the DMN, MBI+ individuals had lower functional connectivity between the PCC and MPFC, compared to MBI‐ (β=‐0.15, p=0.004). Within the SN, MBI+ was associated with lower functional connectivity between the ACC and left AI (β=‐0.12, p=0.028). Conclusion Our findings suggest that in dementia‐free individuals, MBI is associated with decreased functional connectivity in networks disrupted in AD. This result lends additional support to MBI as a potential early marker of 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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".