34.4 EXAMINING SUBCORTICAL-CORTICAL CONNECTIVITY IN SCHIZOPHRENIA USING PERSONALIZED INTRINSIC NETWORK TOPOGRAPHY
Bibliographic record
Abstract
Spatial patterns of brain functional connectivity can vary substantially at the individual level. Applying individualized as opposed to group templates when mapping functional connectivity may allow for more meaningful biological markers of psychiatric disorders. That is, functional relationships between cortical regions can be localized more precisely within an individual using functional connectivity measures from fMRI than using brain anatomy alone. The implications of such individualized differences in schizophrenia spectrum disorders (SSDs) has not been previously reported. Resting State and anatomical MRI data was employed from n = 198 patients with an SSD and n= 284 healthy controls (HC) from four cohorts 1) Centre for Addiction and Mental Health 2) Zucker Hillside Hospital 3) The Center for Biomedical Research Excellence (Christensen et al 2014), and 4) UCLA Consortium for Neuropsychiatric Phenomics LA5c Study (Poldrack et al 2016). We used a newly developed method, Personalized Intrinsic Network Topography (PINT; Dickie et al., 2018) to identify 80 ROIs on the cortical surface from 6 intrinsic networks using the resting state connectivity in each participant. Functional timeseries were extracted from these 80 personalized cortical ROIs, as well as 80 initial template ROIs for comparison, and correlated with timeseries from striatal subregions defined by Choi et al. (2012) and cerebellum subregions defined by Buckner et al (2011). Using PINT compared to template ROIs, the correlation between cortical networks and the expected subregions of the striatum and cerebellum was increased in both patients and controls for all cortical networks (lowest t(418)=4.28, p=0.00002). In case-control comparisons, controlling for age, sex, scanner and in scanner motion, we observed robust patterns of cortical-subcortical hypo-connectivity and hyper-connectivity in the SSD compared to control group both before (127 hypo, 182 hyper) and after (136 hypo, 236 hyper) PINT (FDR corrected for multiple comparisons). Overall, connectivity from the frontal-parietal network and subcortical subregions was largely hypo-connected while connectivity between the sensory-motor and visual cortical networks and subcortical subregions was largely hyperconnected. Our results indicate that individual approaches may better characterize cortico-subcortical connectivity in controls and SSD. In agreement with previous studies, subcortical-cortical connectivity showed robust differences between patients and controls. Our individualized results increase confidence in impairment in subcortical-cortical connectivity as a biological marker for SSDs. These results also provide further support for the feasibility of personalized brain mapping using resting state data in participants with SSDs. Personalized brain mapping approaches have the potential to support more effective individually targeted therapy using interventions such as repetitive Transcranial Magnetic Stimulation (rTMS). Plenary Session:
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".