A Genetically Informed, Group fMRI Connectivity Modeling Approach: Application to Schizophrenia
Bibliographic record
Abstract
While neuroimaging data can provide valuable phenotypic information to inform genetic studies, the opposite is also true: known genotypes can be used to inform brain connectivity patterns from fMRI data. Here, we propose a framework for genetically informed group brain connectivity modeling. Subjects are first stratified according to their genotypes, and then a group regularized regression model is employed for brain connectivity modeling utilizing the time courses from a priori specified regions of interest (ROIs). With such an approach, each ROI time course is in turn predicted from all other ROI time courses at zero lag using a group regression framework which also incorporates a penalty based on genotypic similarity. Simulations supported such an approach when, as previously studies have indicated to be the case, genetic influences impart connectivity differences across subjects. The proposed method was applied to resting state fMRI data from Schizophrenia and normal control subjects. Genotypes were based on D-amino acid oxidase activator (DAOA) single-nucleotide polymorphisms (SNPs) information. With DAOA SNPs information integrated, the proposed approach was able to more accurately model the diversity in connectivity patterns. Specifically, connectivity with the left putamen, right posterior cingulate, and left middle frontal gyri were found to be jointly modulated by DAOA genotypes and the presence of Schizophrenia. We conclude that the proposed framework represents a multimodal analysis approach for incorporating genotypic variability into brain connectivity analysis directly.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".