Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 63,112 bioRxiv papers from 279,939 authors.
Machine Learning Classification of Attention-Deficit/Hyperactivity Disorder Using Structural MRI Data
Background Clinical symptoms-based ADHD diagnosis is considered “subjective”. Machine learning (ML) classifiers have been explored to develop objective diagnosis of ADHD using magnetic resonance imaging (MRI) biomarkers. Methods We reviewed previous literature and developed ensemble classifiers using the ENIGMA-ADHD dataset, with the implementation of data balancing to control for age, sex, diagnostic groups, and sample sites and a held-out test set for independent evaluation. Results Our review showed that classification accuracies reported previously using cross-validation (CV) samples were inflated and did not generalize well to independent test samples. Our results showed a significant discrimination between ADHD and control samples for both adult and children, but the accuracies were modest (the area under the receiver operating characteristic curve (AUC): 66% and 67% respectively). We found that child samples were informative for predicting adult ADHD, and vice versa. The most important brain MRI structures for prediction were intracranial volume (ICV), followed by surface area and some subcortical volumes. The cortical thickness measurements were the least useful. Conclusions Although previous ML classification studies reported overly optimistic accuracies and suffered methodological limitations, our results suggest that clinically useful classification of ADHD may be possible with larger samples. In contrast to prior reports of ENIGMA-ADHD studies, our work finds ADHD-related sMRI differences in adults and shows that the brain differences between cases and controls seen in youth can be useful in discriminating adults with and without ADHD. This provides additional evidence for the continuity of ADHD’s pathophysiology from childhood to adulthood.
- Downloaded 678 times
- Download rankings, all-time:
- Site-wide: 12,814 out of 63,112
- In bioinformatics: 2,098 out of 6,276
- Year to date:
- Site-wide: 2,947 out of 63,112
- Since beginning of last month:
- Site-wide: 7,591 out of 63,112
Downloads over time
Distribution of downloads per paper, site-wide
- Top preprints of 2018
- Paper search
- Author leaderboards
- Overall metrics
- The API
- Email newsletter
- 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
- 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
- 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
- 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
- 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
- 22 Jan 2019: Nature just published an article about Rxivist and our data.
- 13 Jan 2019: The Rxivist preprint is live!