Differences in functional connectivity amongst older adults with mild cognitive impairment, subjective cognitive decline or normal cognition
Bibliographic record
Abstract
Abstract Background Subjective cognitive decline (SCD), hypothesized as a preclinical stage of Alzheimer’s disease (AD), describes individuals with subjective cognitive concerns despite normal performance on cognitive tests (Jessen et al., 2014). The reduced ability of the salience network (SN) to control switching between the central executive network (CEN) and the default mode network (DMN) has been linked to deterioration of cognitive functioning in normal aging, mild cognitive impairment (MCI) and AD dementia (He et al., 2014). Since alterations in functional connectivity between these networks have been implicated in MCI and AD, we compared connectivity of the SN with CEN and DMN amongst SCD, MCI and older adults who were cognitively unimpaired (CU). We predicted that SCD would show reduced SN connectivity between CEN and DMN networks compared to CU, but greater connectivity compared to MCI. Method Participants were 72 older adults [39 females, mean age=71.5] who were classified as either CU (n=26), SCD (n=29), or MCI (n=17). CU reported no memory concerns and performed within normal limits on neuropsychological (NP) assessments. SCD was established by affirmative responses to “Do you feel like your memory is becoming worse?” “If so, are you worried?” and normal NP test performance. MCI was based on the presence of subjective cognitive concerns and impairment on ≥ 2 NP measures within a cognitive domain, and functional independence. Participants underwent resting state functional magnetic resonance imaging (rs‐fMRI) for 6 minutes in an awake state with their eyes closed using gradient‐echo EPI BOLD at 3T (TR=2000ms, TE=30ms). Imaging data were processed using CONN toolbox. Seed‐based analysis using left and right frontoinsular cortices as seeds, which are key nodes in the SN network (MNI coordinates: ‐34,24‐,6 and 38,16,6, respectively), was conducted to measure connectivity between SN and the other two networks, CEN and DMN, within each group. Result Groups did not differ in age, years of education, or sex distribution [F(2,69)=0.415, p=0.66;F(2,69)=2.20, p=0.12;X2(2, N=72)=1.89, p=0.39, respectively]. Results from imaging data analyses will be presented. Conclusion Examination of functional connectivity in SCD can be conducted using rs‐fMRI and may provide a window to the neurobiology of SCD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".