Functional Connectivity and Response Inhibition: A Secondary Analysis of an 8-Week Randomized Controlled Trial of Computerized Cognitive Training
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that computerized cognitive training (CCT) can improve cognitive function in older adults, particularly executive functions. However, the underlying mechanisms by which CCT may improve executive functions are not well established. OBJECTIVE: To determine: 1) inter-network functional connectivity correlates of changes in executive functions; and 2) the effect of CCT on these functional connectivity correlates. METHODS: This secondary analysis included a subset of 124 adults aged 65-85 years enrolled in an 8-week randomized controlled trial of CCT. Participants were randomized to either: 1) group-based CCT 3x/week for 1 hour plus 3x/week home-based training; 2) group-based CCT preceded by brisk walking (Ex+CCT) 3x/week for 1 hour plus 3x/week home-based training; or 3) group-based balanced and toned (BAT) classes 3x/week for 1 hour (control). At baseline and trial completion, 65 of the 124 participants completed resting-state functional magnetic resonance imaging and neuropsychological tests of executive functions, specifically the Stroop Colour-Word Test and Flanker Test. RESULTS: Improved performance on the Stroop Colour-Word Test and Flanker Test were associated with decreased correlation between the default mode network (DMN) and the fronto-parietal network (FPN) (p < 0.05). Compared with BAT, CCT alone significantly decreased correlation between the left dorsolateral prefrontal cortex and both the left and right medial temporal gyrus (-0.143, 95%CI [-0.256,-0.030], p = 0.014, and -0.123, 95%CI [-0.242,-0.004], p = 0.043, respectively). CONCLUSION: Decreased correlation between DMN and FPN, indicating less connection between these networks, may be an underlying mechanism by which CCT improves executive functions. Future studies are needed to replicate this finding.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 teacher head, 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".