Understanding Cognitive Impairment in Early Psychosis through Functional Brain Dysconnectivity: A Whole-Brain Voxel-wise Analysis Approach
Bibliographic record
Abstract
Early psychosis (EP) is the early stage of onset of psychosis symptoms, characterized by a loss of touch with reality. Cognitive impairment is prominent and even precedes symptom onset in individuals with psychosis, which is commonly driven by alterations in functional brain connectivity. Previous work has focused on brain regions defined a priori and relied on the assumption of normal distributions, which can hinder result generalizability. This project aims to build upon previous findings in a data-driven way and share code following open science principles. We utilize the Human Connectome Project-Early Psychosis dataset of 183 participants, consisting of resting-state functional magnetic resonance imaging (rs-fMRI) scans and phenotypic data from a variety of cognitive domains, such as attention, memory, and processing speed. Functional connectivity is calculated in a voxel-wise manner to derive voxel-whole brain connectivity patterns. Multivariate Distance Matrix Regression (MDMR), a non-parametric technique, is then applied to assess relationships between functional connectivity differences and cognitive scores. Statistics for hypothesis testing follow an asymptotic null distribution, and theoretical p-values can be calculated such that the results are more robust. Further, scripts detailing the data analysis will be shared on GitHub, linked to an Open Science Framework project to aid replicability. We expect to identify some well-known brain areas implicating specific impairments, which will be externally validated in a follow-up study. This may provide a more holistic view on brain-cognition relationships for early psychosis and guide future applications in related fields of research.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".