Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
331-03-003 / 331-03-003 ISMRM Abstract

Localized Frequency Differences between Functional Brain Networks in Autism through Wavelet Analysis

Accepted
Sjir Schielen 1, Danny Ruijters1,2, Albert P Aldenkamp1,3, Svitlana Zinger1
1Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
2Image Guided Therapy, Philips Healthcare, Best, Netherlands
3Epilepsy Center Kempenhaeghe, Heeze, The Netherlands, Heeze, Netherlands
Presenting Author: Sjir Schielen

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. Schielen, S. J., Pilmeyer, J., Aldenkamp, A. P., & Zinger, S. (2024). The diagnosis of ASD with MRI: a systematic review and meta-analysis. Translational psychiatry, 14(1), 318. DOI: https://doi.org/10.1038/s41398-024-03024-5 [doi]
2. Bernas, A., Aldenkamp, A. P., & Zinger, S. (2018). Wavelet coherence-based classifier: A resting-state functional MRI study on neurodynamics in adolescents with high-functioning autism. Computer methods and programs in biomedicine, 154, 143-151. DOI: 10.1016/j.cmpb.2017.11.017 [doi]
3. Al-Hiyali, M. I., Yahya, N., Faye, I., & Hussein, A. F. (2021). Identification of autism subtypes based on wavelet coherence of BOLD FMRI signals using convolutional neural network. Sensors, 21(16), 5256. DOI: https://doi.org/10.3390/s21165256 [doi]
4. Beckmann CF, Mackay CE, Filippini N, et al. Group comparison of resting-state FMRI data using multi-subject ICA and dual regression. OHBM. 2009. DOI: https://doi.org/10.1016/S1053-8119(09)71511-3 [doi]
5. Cîrstian, R., Pilmeyer, J., Bernas, A., Jansen, J. F., Breeuwer, M., Aldenkamp, A. P., & Zinger, S. (2023). Objective biomarkers of depression: A study of Granger causality and wavelet coherence in resting‐state fMRI. Journal of Neuroimaging, 33(3), 404-414. DOI: https://doi.org/10.1111/jon.13085 [doi]
6. Di Martino A, Yan CG, Li Q, Denio E, et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry. 2014;19(6):659-667. DOI: https://doi.org/10.1038/mp.2013.78 [doi]
7. Di Martino A, O’Connor D, Chen B, et al. Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Scientific data. 2017;4(1):1-15. DOI: https://doi.org/10.1038/sdata.2017.10 [doi]
8. Schielen, S. J., Pilmeyer, J., Aldenkamp, A. P., Ruijters, D., & Zinger, S. (2025). ICA-Based Resting-State Networks Obtained on Large Autism fMRI Dataset ABIDE. Data, 10(7), 109. DOI: https://doi.org/10.3390/data10070109 [doi]
9. Welch, B. L. (1947). The generalization of ‘STUDENT'S’ problem when several different population variances are involved. Biometrika, 34(1-2), 28-35. DOI: https://doi.org/10.2307/2332510 [doi]
10. Kwak, S. G., & Kim, J. H. (2017). Central limit theorem: the cornerstone of modern statistics. Korean journal of anesthesiology, 70(2), 144. DOI: https://doi.org/10.4097/kjae.2017.70.2.144 [doi]
11. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological). 1995;57(1):289-300. DOI: https://doi.org/10.1111/j.2517-6161.1995.tb02031.x [doi]
12. Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. The annals of mathematical statistics, 50-60. DOI: https://www.jstor.org/stable/2236101
13. Coll SM, Foster, NE, Meilleur A, et al. Sensorimotor skills in autism spectrum disorder: A meta-analysis. Research in Autism Spectrum Disorders. 2020;76:101570. DOI: https://doi.org/10.1016/j.rasd.2020.101570 [doi]
14. Padmanabhan, A., Lynch, C. J., Schaer, M., & Menon, V. (2017). The default mode network in autism. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2(6), 476-486. DOI: https://doi.org/10.1016/j.bpsc.2017.04.004 [doi]

Cite this abstract