Template-driven dynamic functional network connectivity predicts medication response for major depression and bipolar disorders
Bibliographic record
Abstract
ABSTRACT The process of finding reliable treatment for major depression and bipolar disorder can be arduous. The myriad behavioral symptoms presented by patients and resistance to treatment from particular medication classes complicate standard diagnostic and prescription methodologies, often requiring multiple attempted treatments during which symptoms may still be present. Physiological information such as neuroimaging scans may help to alleviate some of the uncertainty surrounding diagnosis and treatment when incorporated into a clinical setting. Changes in functional magnetic resonance imaging show particular promise, as the incorporation of dynamical information may provide insights into physiological changes prior to static, structural changes. In this work, we present a novel method for generating robust and replicable dynamic functional network connectivity (dFNC) features from neuroimaging data using a template of dynamic states derived from a large, non-affected data set. We demonstrate that this template-driven dFNC approach expands on standard dFNC approaches by allowing for the derivation of a continuous state-contribution time series. We demonstrate that the derived biomarkers can support high predictive performance for the identification of medication class and non-responders while also expanding the set of biomarkers available for studying differences in mood disorder medication response.
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 | 0.001 | 0.006 |
| 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 | 0.000 |
| 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".