Thesis
Prediction of ADHD in adolescents utilizing fMRI-based individual cortical thickness measurements
Master of Science (M.S.), Drexel University
Jun 2023
DOI:
https://doi.org/10.17918/00001724
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is a neuropsychiatric disorder primarily affecting youth defined by symptoms of inattention and impulsivity. Current diagnosis tools consist of questionaries and tasks which are subject to biases. A quantitative and objective measurement for diagnosis does not exist. The use of fMRI-derived metrics, such as cortical thickness measurements, has gained popularity in diagnosing disease. Since cortical thickness differences exist in adolescents with ADHD, prediction algorithms can be written using this metric to diagnose ADHD. To determine regions most likely to predict ADHD, cortical thickness measurements were extracted in functional regions associated with symptoms of inattention and impulsivity, as well as for the whole brain. Four different types of classification models were created and tested across the three different functional conditions: inattention, impulsivity, and whole brain. 238 ADHD and 244 healthy adolescent participants from the ADHD 200 publicly available dataset were used to test and train the models. Across all models, the average accuracy measure was 63.83% (+/- 2.2%). There were no differences in accuracy values between the different functional networks associated with symptoms and the whole brain analysis. Support vector classification (SVC) predicted ADHD the most accurately across the different functional network conditions. Specificity was found to be slightly better than sensitivity across SVC models. Overall, cortical thickness provides a new avenue of possible prediction in diagnosing ADHD. Further work evaluating hemispheric cortical thickness differences and extracting the functional networks most responsible for accurate prediction needs to be completed to better understand how the models work.
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Details
- Title
- Prediction of ADHD in adolescents utilizing fMRI-based individual cortical thickness measurements
- Creators
- Julia Dengler
- Contributors
- John D. Medaglia (Advisor)Hasan Ayaz (Advisor)
- Awarding Institution
- Drexel University
- Degree Awarded
- Master of Science (M.S.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Number of pages
- xiii, 113 pages
- Resource Type
- Thesis
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems (1997-2026); Drexel University
- Other Identifier
- 991021113914604721