Print Close

Spatiotemporal characterization of the latency structure in functional MRI: an autism study

Poster No:

293

Submission Type:

Abstract Submission

Authors:

Hyoungshin Choi¹^,2, Yeongjun Park¹^,2, Bo-yong Park³^,2, Hyunjin Park¹^,2

Institutions:

¹Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea, ²Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea, ³Department of Brain and Cognitive Engineering, Korea University, Suwon, Republic of Korea

First Author:

Hyoungshin Choi
Department of Electrical and Computer Engineering, Sungkyunkwan University|Center for Neuroscience Imaging Research, Institute for Basic Science
Suwon, Republic of Korea|Suwon, Republic of Korea

Co-Author(s):

Yeongjun Park
Department of Electrical and Computer Engineering, Sungkyunkwan University|Center for Neuroscience Imaging Research, Institute for Basic Science
Suwon, Republic of Korea|Suwon, Republic of Korea

Bo-yong Park
Department of Brain and Cognitive Engineering, Korea University|Center for Neuroscience Imaging Research, Institute for Basic Science
Suwon, Republic of Korea|Suwon, Republic of Korea

Hyunjin Park
Department of Electrical and Computer Engineering, Sungkyunkwan University|Center for Neuroscience Imaging Research, Institute for Basic Science
Suwon, Republic of Korea|Suwon, Republic of Korea

Introduction:

The discovery that BOLD signals fluctuations on fMRI reflect intrinsic brain activity has spurred research into spatial and temporal neural dynamics [1]. Functional connectivity (FC) assesses the synchronicity across brain regions, defining large-scale resting-state networks (RSNs) [2]. While effective in mapping spatial patterns in both healthy and pathological brains, FC neglects temporal characteristics. To address this, the temporal lag or delay approach explores the latency structure of brain activity by measuring time shifts between regions [3, 4]. This reveals how segregated networks exchange information through the macroscopic propagation of brain signals. Manifold learning represents these latency structures succinctly, identifying core components of signal propagation that overlap spatially with RSNs but remain distinct in topography [3]. Understanding these dynamics offer new insights into cognitive processes and brain physiology in healthy and disease [5]. This study examines differences in the latency structure between autism spectrum disorder (ASD) and neurotypical controls (TD).

Methods:

We analyzed MRI data from the ABIDE 1 database [6], 449 participants were selected (208 ASD; 241 TD). A time delay matrix was constructed from the preprocessed rs‐fMRI data, incorporating 300 cortical areas from the Schaefer atlas [7], and 14 subcortical regions from the Desikan–Killiany atlas [8]. Matrix elements were derived from the latency τ that maximized lagged cross-covariance between time series pairs, with a 5-second threshold for hemodynamic delays. [3, 4]. We upsampled the time series when constructing the time delay matrix [5]. Principal component analysis provides low-dimensional latency eigenvectors, aligned to group-level templates defined by averaging matrices across participants. Individual eigenvectors were aligned to the group template using Procrustes alignment, controlling for age and sex. ASD and TD group differences in eigenvectors were tested with Hotelling's T² test, with 1,000 permutation tests and false discovery rate (FDR) correction for multiple comparisons. Groups differences were analyzed within the Yeo seven networks. Meta-analytic cognitive decoding linked to the spatial patterns of group differences to associated cognitive terms. [9, 10].

Results:

We analyzed the changes in latency eigenvectors in the ASD group compared with the TD group (Fig 1), revealing significant differences (pspin-FDR < 0.05) in the precuneus and medial prefrontal cortex, with strong effects in the default mode and sensory/motor regions, as well as in the thalamus, putamen, pallidum, accumbens, and amygdala (Fig 2A). To determine the cognitive associations underlying the differences in cortical and subcortical latency eigenvectors between the groups, we performed meta-analysis decoding [9, 10]. We found correlations with the cognitive and sensory‐related terms, such as "recognition," "perception," "autobiographical," "social," "motor," and "task" (Fig 2B).

·Figure 1. Computation of latency eigenvectors.

·Figure 2. Between‐group differences in the cortical and subcortical latency eigenvectors and cognitive decoding analysis.

Conclusions:

We explored the clinical implications of latency eigenvectors in a neuropsychiatric condition. Our findings offer valuable insights into latency structures and hold substantial potential for the development of markers for psychiatric conditions.

ACKNOWLEDGEMENTS
Bo-yong Park received funding from the Institute for Information and Communications Technology Planning and Evaluation (IITP), funded by the Korean Government (MSIT) (No. 2022-0-00448/RS-2022-II220448, Deep Total Recall: Continual Learning for Human-Like Recall of Artificial Neural Networks). Hyunjin Park was supported by the National Research Foundation (RS-2024-00408040), AI Graduate School Support Program(Sungkyunkwan University) (RS-2019-II190421), and the ICT Creative Consilience program (IITP-2024-2020-0-01821). Bo-Yong Park and Hyunjin Park were jointly supported by the IITP funded by the Korean Government (MSIT) (RS-2021-II212068, Artificial Intelligence Innovation Hub), and the Institute for Basic Science (IBS-R015-D1).

Disorders of the Nervous System:

Neurodevelopmental/ Early Life (eg. ADHD, autism) ¹

Modeling and Analysis Methods:

fMRI Connectivity and Network Modeling

Novel Imaging Acquisition Methods:

BOLD fMRI ²

Keywords:

Autism

Computational Neuroscience

FUNCTIONAL MRI

^1|2Indicates the priority used for review

Abstract Information

By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.

I accept

The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:

I do not want to participate in the reproducibility challenge.

Please indicate below if your study was a "resting state" or "task-activation” study.

Resting state

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Patients

Was this research conducted in the United States?

Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Yes

Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Functional MRI

For human MRI, what field strength scanner do you use?

3.0T

Which processing packages did you use for your study?

AFNI

FSL

Free Surfer

Provide references using APA citation style.

1. M. D. Fox and M. E. Raichle, "Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging," Nature reviews neuroscience, vol. 8, no. 9, pp. 700-711, 2007.
2. S. M. Smith et al., "Correspondence of the brain's functional architecture during activation and rest," Proceedings of the national academy of sciences, vol. 106, no. 31, pp. 13040-13045, 2009.
3. A. Mitra, A. Z. Snyder, T. Blazey, and M. E. Raichle, "Lag threads organize the brain’s intrinsic activity," Proceedings of the National Academy of Sciences, vol. 112, no. 17, pp. E2235-E2244, 2015.
4. A. Mitra, A. Z. Snyder, C. D. Hacker, and M. E. Raichle, "Lag structure in resting-state fMRI," Journal of neurophysiology, vol. 111, no. 11, pp. 2374-2391, 2014.
5. A. Mitra, A. Z. Snyder, J. N. Constantino, and M. E. Raichle, "The lag structure of intrinsic activity is focally altered in high functioning adults with autism," Cerebral cortex, vol. 27, no. 2, p. bhv294, 2015.
6. A. Di Martino et al., "The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism," Molecular psychiatry, vol. 19, no. 6, pp. 659-667, 2014.
7. A. Schaefer et al., "Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI," Cerebral Cortex, vol. 28, no. 9, pp. 3095-3114, 2017, doi: 10.1093/cercor/bhx179.
8. R. S. Desikan et al., "An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest," NeuroImage, vol. 31, no. 3, pp. 968-980, 2006/07/01/ 2006, doi: https://doi.org/10.1016/j.neuroimage.2006.01.021.
9. T. N. Rubin, O. Koyejo, K. J. Gorgolewski, M. N. Jones, R. A. Poldrack, and T. Yarkoni, "Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition," PLOS Computational Biology, vol. 13, no. 10, p. e1005649, 2017, doi: 10.1371/journal.pcbi.1005649.
10. T. Yarkoni, R. A. Poldrack, T. E. Nichols, D. C. Van Essen, and T. D. Wager, "Large-scale automated synthesis of human functional neuroimaging data," Nature Methods, vol. 8, no. 8, pp. 665-670, 2011/08/01 2011, doi: 10.1038/nmeth.1635.

UNESCO Institute of Statistics and World Bank Waiver Form

I attest that I currently live, work, or study in a country on the UNESCO Institute of Statistics and World Bank List of Low and Middle Income Countries list provided.