A Multi-Model Framework to Explore ADHD Diagnosis from Neuroimaging Data
Volume 22, Issue 2 (2024): Special Issue: 2023 Symposium on Data Science and Statistics (SDSS): “Inquire, Investigate, Implement, Innovate”, pp. 191–207
Pub. online: 2 May 2024
Type: Data Science In Action
Open Access
Open Access
Received
31 July 2023
31 July 2023
Accepted
3 April 2024
3 April 2024
Published
2 May 2024
2 May 2024
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is a frequent neurodevelopmental disorder in children that is commonly diagnosed subjectively. The objective detection of ADHD based on neuroimaging data has been a complex problem with low ranges of accuracy, possibly due to (among others) complex diagnostic processes, the high number of features considered and imperfect measurements in data collection. Hence, reliable neuroimaging biomarkers for detecting ADHD have been elusive. To address this problem we consider a recently proposed multi-model selection method called Sparse Wrapper AlGorithm (SWAG), which is a greedy algorithm that combines screening and wrapper approaches to create a set of low-dimensional models with good predictive power. While preserving the previous levels of accuracy, SWAG provides a measure of importance of brain regions for identifying ADHD. Our approach also provides a set of equally-performing and simple models which highlight the main feature combinations to be analyzed and the interactions between them. Taking advantage of the network of models resulting from this approach, we confirm the relevance of the frontal and temporal lobes as well as highlight how the different regions interact to detect the presence of ADHD. In particular, these results are fairly consistent across different learning mechanisms employed within the SWAG (i.e. logistic regression, linear and radial-kernel support vector machines) thereby providing population-level insights, as well as delivering feature combinations that are smaller and often perform better than those that would be used if employing their original versions directly.
Supplementary material
Supplementary MaterialAll of our code is open source in the following GitHub repository https://github.com/yagmuryavuzozdemir/SDSS_SWAG_ADHD. One can find the necessary codes and the datasets used in the analysis of our work in this folder.
References
Arbabshirani MR, Plis S, Sui J, Calhoun VD (2017). Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. NeuroImage, 145: 137–165. https://doi.org/10.1016/j.neuroimage.2016.02.079
Bellec P, Chu C, Chouinard-Decorte F, Benhajali Y, Margulies DS, Craddock RC (2017). The neuro bureau adhd-200 preprocessed repository. NeuroImage, 144: 275–286. https://doi.org/10.1016/j.neuroimage.2016.06.034
Branca M, Orso S, Molinari RC, Xu H, Guerrier S, Zhang Y, et al. (2018). Is nonmetastatic cutaneous melanoma predictable through genomic biomarkers? Melanoma Research, 28(1): 21–29. https://doi.org/10.1097/CMR.0000000000000412
Breiman L (2001). Random forests. Machine Learning, 45: 5–32. https://doi.org/10.1023/A:1010933404324MR3874153
Carmona S, Vilarroya O, Bielsa A, Tremols V, Soliva J, Rovira M, et al. (2005). Global and regional gray matter reductions in adhd: A voxel-based morphometric study. Neuroscience Letters, 389(2): 88–93. https://doi.org/10.1016/j.neulet.2005.07.020
Chandrashekar G, Sahin F (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1): 16–28. https://doi.org/10.1016/j.compeleceng.2013.11.024
Craddock RC, James GA, Holtzheimer III PE, Hu XP, Mayberg HS (2012). A whole brain fmri atlas generated via spatially constrained spectral clustering. Human Brain Mapping, 33(8): 1914–1928. https://doi.org/10.1002/hbm.21333
Curatolo P, D’Agati E, Moavero R (2010). The neurobiological basis of adhd. Italian Journal of Pediatrics, 36(1): 1–7. https://doi.org/10.1186/1824-7288-36-79
Deshpande G, Wang P, Rangaprakash D, Wilamowski B (2015). Fully connected cascade artificial neural network architecture for attention deficit hyperactivity disorder classification from functional magnetic resonance imaging data. IEEE Transactions on Cybernetics, 45(12): 2668–2679. https://doi.org/10.1109/TCYB.2014.2379621
Draghici S, Khatri P, Eklund AC, Szallasi Z (2006). Reliability and reproducibility issues in dna microarray measurements. Trends in Genetics, 22(2): 101–109. https://doi.org/10.1016/j.tig.2005.12.005
Fisher A, Rudin C, Dominici F (2019). All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously. Journal of Machine Learning Research, 20(177): 1–81.MR4048988
Friedman J, Hastie T, Tibshirani R (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1): 1. https://doi.org/10.18637/jss.v033.i01
Kelly C, Biswal BB, Craddock RC, Castellanos FX, Milham MP (2012). Characterizing variation in the functional connectome: Promise and pitfalls. Trends in Cognitive Sciences, 16(3): 181–188. https://doi.org/10.1016/j.tics.2012.02.001
Kobel M, Bechtel N, Specht K, Klarhöfer M, Weber P, Scheffler K, et al. (2010). Structural and functional imaging approaches in attention deficit/hyperactivity disorder: Does the temporal lobe play a key role? Psychiatry Research: Neuroimaging, 183(3): 230–236. https://doi.org/10.1016/j.pscychresns.2010.03.010
Lanka P, Rangaprakash D, Dretsch MN, Katz JS, Denney TS, Deshpande G (2020). Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets. Brain Imaging and Behavior, 14: 2378–2416. https://doi.org/10.1007/s11682-019-00191-8
Lin H, Haider SP, Kaltenhauser S, Mozayan A, Malhotra A, Constable RT, et al. (2023). Population level multimodal neuroimaging correlates of attention-deficit hyperactivity disorder among children. Frontiers in Neuroscience, 17: 1138670. https://doi.org/10.3389/fnins.2023.1138670MR4644006
Loh HW, Ooi CP, Barua PD, Palmer EE, Molinari F, Acharya UR (2022). Automated detection of adhd: Current trends and future perspective. Computers in Biology and Medicine, 146: 105525. https://doi.org/10.1016/j.compbiomed.2022.105525
Meinshausen N, Yu B (2009). Lasso-type recovery of sparse representations for high-dimensional data.MR2488351
Miglioli C, Bakalli G, Orso S, Karemera M, Molinari R, Guerrier S, et al. (2022). Evidence of antagonistic predictive effects of mirnas in breast cancer cohorts through data-driven networks. Scientific Reports, 12(1): 5166. https://doi.org/10.1038/s41598-022-08737-5
Molinari R, Bakalli G, Guerrier S, Miglioli C, Orso S, Karemera M, et al. (2020). Swag: A wrapper method for sparse learning. arXiv preprint: https://arxiv.org/abs/2006.12837.
Olivetti E, Greiner S, Avesani P (2012). Adhd diagnosis from multiple data sources with batch effects. Frontiers in Systems Neuroscience, 6: 70. https://doi.org/10.3389/fnsys.2012.00070
Rubia K, Criaud M, Wulff M, Alegria A, Brinson H, Barker G, et al. (2019). Functional connectivity changes associated with fmri neurofeedback of right inferior frontal cortex in adolescents with adhd. NeuroImage, 188: 43–58. https://doi.org/10.1016/j.neuroimage.2018.11.055
Rudin C (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5): 206–215. https://doi.org/10.1038/s42256-019-0048-x
Sayal K, Prasad V, Daley D, Ford T, Coghill D (2018). Adhd in children and young people: Prevalence, care pathways, and service provision. The Lancet Psychiatry, 5(2): 175–186. https://doi.org/10.1016/S2215-0366(17)30167-0
Sowell ER, Thompson PM, Welcome SE, Henkenius AL, Toga AW, Peterson BS (2003). Cortical abnormalities in children and adolescents with attention-deficit hyperactivity disorder. The Lancet, 362(9397): 1699–1707. https://doi.org/10.1016/S0140-6736(03)14842-8
van der Ploeg T, Austin PC, Steyerberg EW (2014). Modern modelling techniques are data hungry: A simulation study for predicting dichotomous endpoints. BMC Medical Research Methodology, 14(1): 1–13. https://doi.org/10.1186/1471-2288-14-1
Vaughan L, Chen Y (2015). Data mining from web search queries: A comparison of Google trends and baidu index. The Journal of the Association for Information Science and Technology, 66(1): 13–22. https://doi.org/10.1002/asi.23201
Visser SN, Danielson ML, Bitsko RH, Holbrook JR, Kogan MD, Ghandour RM, et al. (2014). Trends in the parent-report of health care provider-diagnosed and medicated attention-deficit/hyperactivity disorder: United States, 2003–2011. Journal of the American Academy of Child and Adolescent Psychiatry, 53(1): 34–46. https://doi.org/10.1016/j.jaac.2013.09.001
Wang G, Li W, Zuluaga MA, Pratt R, Patel PA, Aertsen M, et al. (2018). Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Transactions on Medical Imaging, 37(7): 1562–1573. https://doi.org/10.1109/TMI.2018.2791721
Wu GR, Liao W, Stramaglia S, Ding JR, Chen H, Marinazzo D (2013). A blind deconvolution approach to recover effective connectivity brain networks from resting state fmri data. Medical Image Analysis, 17(3): 365–374. https://doi.org/10.1016/j.media.2013.01.003
Zhang Z, Xu Y, Yang J, Li X, Zhang D (2015). A survey of sparse representation: Algorithms and applications. IEEE Access, 3: 490–530. https://doi.org/10.1109/ACCESS.2015.2430359
