Neuroimaging Biomarkers for Youth At-Risk for Serious Mental Illness
Bibliographic record
Abstract
Background: Serious mental illness (SMI) is a constellation of psychiatric illnesses, commonly referring to major depressive disorder (MDD), bipolar disorder(s) (BD), and schizophrenia (SCZ). The diagnosis of SMI relies on a descriptive collection of behaviors with no objective measurements. Young individuals with early signs of developing mental illness are often misdiagnosed or diagnosed at later stages of illness when the opportunity for effective early intervention has passed. Therefore, there is a need for biomarkers that can identify youth at risk for SMI and stratify individuals into clinical stages so that appropriate interventions can be offered. Objective: The overreaching goal of my research is to enhance our understanding of the neurobiological changes underlying the early brain pathophysiology of (SMI). The ultimate aim of this dissertation is to identify neurobiological biomarkers for SMI that will have diagnostic or informative value in the classification of risk stages. Methods: By making use of data generated from two large Canadian studies, the Canadian Psychiatric Risk and Outcome Study (PROCAN), and the Canadian Biomarker Integration Network in Depression (CAN-BIND), I investigate specific structural and functional brain changes accompanying various stages risk and evolution of SMI. Results: This manuscript-based thesis presents structural and functional neuroimaging findings that parallel clinical presentation in youth-at-risk and adults with an existing mood disorder. Subtle structural changes within the hippocampus, amygdala, and thalamus were found in symptomatic at-risk youth with either attenuated or distress syndromes. Volume deficits within the body of the hippocampus were detected even in asymptomatic youth at risk due to the family history of an SMI. Moreover, in at-risk youth with a history of childhood abuse, structural deficits within the basal nuclei of the amygdala mediated the severity of depressive and anxiety symptoms at present. In adult patients diagnosed with MDD, specific patterns of hippocampal disproportions were not only indicative of depression status but also informed about the rate and the likelihood of successful antidepressant treatment. While deviations in subcortical limbic structures supported the transdiagnostic clinical staging model, functional connectivity changes within cerebello-limbic pathways were nearly exclusively indicative of high clinical risk for psychosis. Conclusions: Distributed limbic brain changes can precede the clinical onset of the mental illness, possibly, reflecting the cumulative impact of pernicious effects of stress. Together, these results can be offered in support of a transdiagnostic clinical staging for youth at risk of SMI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".