Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics
NeuroImage, ISSN: 1053-8119, Vol: 206, Page: 116276
2020
- 156Citations
- 245Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations156
- Citation Indexes156
- 156
- CrossRef97
- Captures245
- Readers245
- 245
Article Description
There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts in a variety of neuroimaging applications, yet there are few direct comparisons of relative utility. Here, we compared the performance of three DNN architectures and a classical machine learning algorithm (kernel regression) in predicting individual phenotypes from whole-brain resting-state functional connectivity (RSFC) patterns. One of the DNNs was a generic fully-connected feedforward neural network, while the other two DNNs were recently published approaches specifically designed to exploit the structure of connectome data. By using a combined sample of almost 10,000 participants from the Human Connectome Project (HCP) and UK Biobank, we showed that the three DNNs and kernel regression achieved similar performance across a wide range of behavioral and demographic measures. Furthermore, the generic feedforward neural network exhibited similar performance to the two state-of-the-art connectome-specific DNNs. When predicting fluid intelligence in the UK Biobank, performance of all algorithms dramatically improved when sample size increased from 100 to 1000 subjects. Improvement was smaller, but still significant, when sample size increased from 1000 to 5000 subjects. Importantly, kernel regression was competitive across all sample sizes. Overall, our study suggests that kernel regression is as effective as DNNs for RSFC-based behavioral prediction, while incurring significantly lower computational costs. Therefore, kernel regression might serve as a useful baseline algorithm for future studies.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1053811919308675; http://dx.doi.org/10.1016/j.neuroimage.2019.116276; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85074456548&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/31610298; https://linkinghub.elsevier.com/retrieve/pii/S1053811919308675; https://dx.doi.org/10.1016/j.neuroimage.2019.116276
Elsevier BV
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