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Biology subjects

Venegas, G.

Publications and source records attributed to Venegas, G..

2 recordsLinked to original sources

Transdiagnostic Symptom Domains are Associated with Head Motion During Multimodal Imaging in Children

BackgroundHead motion is a challenge for neuroimaging research in developmental populations. However, it is unclear how transdiagnostic symptom domains including attention, disruptive behavior (e.g., externalizing behavior), and internalizing problems are linked to scanner motion in children, particularly across structural and functional MRI. The current study examined whether transdiagnostic domains of attention, disruptive behavior, and internalizing symptoms are associated with scanner motion in children during multimodal imaging. MethodsIn a sample of 9,045 children aged 9-10 years in the Adolescent Brain Cognitive Development (ABCD) Study, logistic regression and linear mixed-effects models were used to examine associations between motion and behavior. Motion was indexed using ABCD Study quality control metrics and mean framewise displacement for the following: T1-weighted structural, resting-state fMRI, diffusion MRI, Stop-Signal Task, Monetary Incentive Delay task, and Emotional n-Back task. The Child Behavior Checklist was used as a continuous measure of symptom severity. ResultsGreater attention and disruptive behavior problem severity was associated with a lower likelihood of passing motion quality control across several imaging modalities. In contrast, increased internalizing severity was associated with a higher likelihood of passing motion quality control. Increased attention and disruptive behavior problem severity was also associated with increased mean motion, whereas increased internalizing problem severity was associated with decreased mean motion. ConclusionTransdiagnostic domains emerged as predictors of motion in youths. These findings have implications for advancing development of generalizable and robust brain-based biomarkers, computational approaches for mitigating motion effects, and enhancing accessibility of imaging protocols for children with varying symptom severities.

neuroscience↗

Development of algorithms for automated detection of cervical pre-cancers with a low-cost, point-of-care, Pocket Colposcope

GoalIn this work, we propose methods for (1) automatic feature extraction and classification for acetic acid and Lugols iodine cervigrams and (2) methods for combining features/diagnosis of different contrasts in cervigrams for improved performance.\n\nMethodsWe developed algorithms to pre-process pathology-labeled cervigrams and to extract simple but powerful color and textural-based features. The features were used to train a support vector machine model to classify cervigrams based on corresponding pathology for visual inspection with acetic acid, visual inspection with Lugols iodine, and a combination of the two contrasts.\n\nResultsThe proposed framework achieved a sensitivity, specificity, and accuracy of 81.3%, 78.6%, and 80.0%, respectively when used to distinguish cervical intraepithelial neoplasia (CIN+) relative to normal and benign tissues. This is superior to the average values achieved by three expert physicians on the same data set for discriminating normal/benign cases from CIN+ (77% sensitivity, 51% specificity, 63% accuracy).\n\nConclusionThe results suggest that utilizing simple color- and textural-based features from visual inspection with acetic acid and visual inspection with Lugols iodine images may provide unbiased automation of cervigrams.\n\nSignificanceThis would enable automated, expert-level diagnosis of cervical pre-cancer at the point-of-care.

bioinformatics↗