Intergenerational Transmission of Functional Connectivity Profiles in Isolated Reading and Math Networks: A Scoping Review and Study Proposal
Bibliographic record
Abstract
The scoping review surveyed the existing literature on the topic of resting-state functional connectivity (rsFC) and mathematical cognition. The review revealed that rsFC is indicative of distinct long-term developmental trends in mathematical processing, alluding to individual differences in math abilities. Though there have been multiple studies that investigate individual differences in functional connectivity patterns related to math development and math learning disorders, no study has directly investigated to what degree these neurobiological factors are heritable. To address this topic, the following intergenerational transmission (IT) study is proposed. IT is the transfer of personal values, abilities, behaviours, and traits, from parents to children (Durlauf & Blume, 2016). A recent study conducted by Takagi et al. (2021) investigated the effects of IT via neurobiological substrates. The investigation was primarily concerned with whether identification of a parent-child dyad was possible based on brain similarity, using both structural and functional information. Using a similar method as Takagi et al., we plan to use data from the Parents and Children: Measuring Academic skills using Neuroimaging (PACMAN) project to investigate whether parent-child dyads are identifiable based on brain similarity - specifically using the reading- and math-related networks. Similar to the Takagi et al. (2021) study, we predict that parent-child dyads will be identifiable based on functional connectivity profiles localized in reading- and math-related brain networks.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".