Identifying texture features from structural magnetic resonance imaging scans associated with Tourette’s syndrome using machine learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose: Tourette syndrome (TS) is a neurodevelopmental disorder characterized by neurophysiological and neuroanatomical changes, primarily affecting individuals aged 2 to 18. Involuntary motor and vocal tics are common features of this syndrome. Currently, there is no curative therapy for TS, only psychological treatments or medications that temporarily manage the tics. The absence of a definitive diagnostic tool complicates the differentiation of TS from other neurological and psychological conditions. Approach: We aim to enhance the diagnosis of TS through the classification of structural magnetic resonance scans. Our methodology comprises four sequential steps: (1) image acquisition, data were generated for the National Taiwan University, composing images of pediatric magnetic resonance imaging (MRI); (2) pre-processing, involving anatomical structural segmentation using reesurfer software; (3) feature extraction, where texture features in volumetric images are obtained; and (4) image classification, employing support vector machine and naive Bayes classifiers to determine the presence of TS. Results: The analysis indicated significant changes in the regions of the limbic system, such as the thalamus and amygdala, and regions outside the limbic system such as medial orbitofrontal cortex and insula, which are strongly associated with TS. Conclusions: Our findings suggest that texture features derived from sMRI scans can aid in the diagnosis of TS by highlighting critical brain regions involved in the disorder. The proposed method holds promise for improving diagnostic accuracy and understanding the neuroanatomical underpinnings of TS.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it