Enhanced Functional Relevance with High-Frequency fMRI via Normalized Dynamic Time Warping
Poster No:
551
Submission Type:
Abstract Submission
Authors:
Sir-Lord Wiafe1, Amritha Harikumar1, Najme Soleimani1, Nigar Khasayeva1, Vince Calhoun1
Institutions:
1Center for Translational Research in Neuroimaging and Data Science (TReNDS), GSU, GATech, Emory, Atlanta, GA
First Author:
Sir-Lord Wiafe
Center for Translational Research in Neuroimaging and Data Science (TReNDS), GSU, GATech, Emory
Atlanta, GA
Center for Translational Research in Neuroimaging and Data Science (TReNDS), GSU, GATech, Emory
Atlanta, GA
Co-Author(s):
Amritha Harikumar, M.A.
Center for Translational Research in Neuroimaging and Data Science (TReNDS), GSU, GATech, Emory
Atlanta, GA
Center for Translational Research in Neuroimaging and Data Science (TReNDS), GSU, GATech, Emory
Atlanta, GA
Najme Soleimani
Center for Translational Research in Neuroimaging and Data Science (TReNDS), GSU, GATech, Emory
Atlanta, GA
Center for Translational Research in Neuroimaging and Data Science (TReNDS), GSU, GATech, Emory
Atlanta, GA
Nigar Khasayeva
Center for Translational Research in Neuroimaging and Data Science (TReNDS), GSU, GATech, Emory
Atlanta, GA
Center for Translational Research in Neuroimaging and Data Science (TReNDS), GSU, GATech, Emory
Atlanta, GA
Vince Calhoun
Center for Translational Research in Neuroimaging and Data Science (TReNDS), GSU, GATech, Emory
Atlanta, GA
Center for Translational Research in Neuroimaging and Data Science (TReNDS), GSU, GATech, Emory
Atlanta, GA
Introduction:
Functional magnetic resonance imaging (fMRI) studies have utilized the 0.01–0.15Hz frequency range for analyzing the blood-oxygenated level-dependent (BOLD) signal(Yaesoubi, 2015). However, emerging research indicates that its functional significance may extend up to 0.198Hz across slow-5 to slow-3 bands(Gohel, 2015). Given Parseval's theorem, which links frequency and amplitude (time domain) through the signal's energy, we hypothesize that amplitude-sensitive methods can capture broader functional relevance with increased frequency bandwidth. Dynamic time warping (DTW), an amplitude-sensitive technique, has proven robust for analyzing fMRI data by accommodating temporal lags and complex signal deformations caused by variable neural timescales and hemodynamic delays(Wiafe, 2024). Compared to traditional correlation methods, DTW is more resilient to noise, less affected by global signal regression, more sensitive to group differences (e.g., sex, schizophrenia, autism), and offers higher test-retest reliability(Meszlényi, 2017). In this study, we demonstrate the functional relevance of extending the frequency range to 0.01–0.198Hz using the normalized DTW metric(Wiafe, 2024).
Methods:
fMRI Analysis
We utilized preprocessed Functional Biomedical Informatics Research Network (fBIRN) data and the NeuroMark independent component analysis pipeline(Du, 2020) to perform fully automated constrained ICA, yielding 53 intrinsic networks. The network time series were then detrended, de-spiked, band-pass filtered to 0.01–0.15 Hz and 0.01–0.198 Hz independently and finally z-scored.
Functional relevance
First, DTW was independently applied to each frequency range. To assess the impact of extended frequency ranges, we performed paired sample t-tests on normalized DTW distances across all network pairs within control and schizophrenia groups separately. Additionally, to investigate clinical associations with positive and negative syndrome scale (PANSS) scores specific to the broader frequency range, a generalized linear model was employed using a Poisson distribution, controlling for age, sex, site, and mean frame displacement. This model evaluated the relationships between DTW and both positive and negative PANSS scores exclusively within the broader frequency range.
We utilized preprocessed Functional Biomedical Informatics Research Network (fBIRN) data and the NeuroMark independent component analysis pipeline(Du, 2020) to perform fully automated constrained ICA, yielding 53 intrinsic networks. The network time series were then detrended, de-spiked, band-pass filtered to 0.01–0.15 Hz and 0.01–0.198 Hz independently and finally z-scored.
Functional relevance
First, DTW was independently applied to each frequency range. To assess the impact of extended frequency ranges, we performed paired sample t-tests on normalized DTW distances across all network pairs within control and schizophrenia groups separately. Additionally, to investigate clinical associations with positive and negative syndrome scale (PANSS) scores specific to the broader frequency range, a generalized linear model was employed using a Poisson distribution, controlling for age, sex, site, and mean frame displacement. This model evaluated the relationships between DTW and both positive and negative PANSS scores exclusively within the broader frequency range.
Results:
We observe that incorporating higher frequencies of the BOLD signal enhances connectivity among controls, while schizophrenia exhibits relatively more dysconnectivity (Fig. 1a). Although the effect sizes for these frequency-related differences are modest, the difference in effect sizes between controls and schizophrenia spans approximately 0.4 across brain network pairs, thereby increasing the specificity in distinguishing schizophrenia. Regarding clinical associations, associations with positive PANSS scores within the broader frequency range (0.01–0.198 Hz) revealed significant connections between cerebellar regions (Cb) and the default mode network (DMN), cognitive control (CC), and superior temporal gyrus (Aud). For negative PANSS scores, significant associations between CC and DMN regions were observed (Fig. 1b).
Conclusions:
The significant associations between the Cb and both the DMN and CC replicate findings that suggest the cerebellum plays a crucial role in driving positive symptoms in schizophrenia(Zhuo, 2018). Furthermore, the exclusive connections between the Cb and the superior temporal gyrus align with studies indicating disrupted cerebellar circuitry associated with auditory verbal hallucinations(Pinheiro, 2021). The observed associations between CC and DMN regions with negative symptoms are consistent with previous research linking these networks to negative symptoms in schizophrenia(Zhu, 2023).These findings underscore the previously overlooked functional significance of high-frequency BOLD signals in fMRI studies. Extending the frequency range enhances clinical specificity and uncovers critical neural associations in schizophrenia when utilizing amplitude-sensitive methods such as DTW.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Keywords:
Cerebellum
FUNCTIONAL MRI
Schizophrenia
1|2Indicates the priority used for review
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Provide references using APA citation style.
Gohel, S. R. (2015). Functional integration between brain regions at rest occurs in multiple-frequency bands. Brain Connect, 5(1), 23-34.
Meszlényi, R. J. (2017). Resting State fMRI Functional Connectivity Analysis Using Dynamic Time Warping. Frontiers in Neuroscience, 11.
Pinheiro, A. P. (2021). The Cerebellum Links to Positive Symptoms of Psychosis: A Systematic Review and Meta-analysis. Schizophrenia Bulletin Open, 2(1).
Wiafe, S.-L. (2024). The dynamics of dynamic time warping in fMRI data: a method to capture inter-network stretching and shrinking via warp elasticity. Imaging Neuroscience.
Wiafe, S.-L. (2024). Normalized Dynamic Time Warping Increases Sensitivity In Differentiating Functional Network Connectivity In Schizophrenia. bioRxiv, 2024.2010.2031.621415.
Yaesoubi, M. (2015). Dynamic coherence analysis of resting fMRI data to jointly capture state-based phase, frequency, and time-domain information. Neuroimage, 120, 133-142.
Zhu, T. (2023). Altered brain functional networks in schizophrenia with persistent negative symptoms: an activation likelihood estimation meta-analysis. Front Hum Neurosci, 17, 1204632.
Zhuo, C. (2018). Altered resting-state functional connectivity of the cerebellum in schizophrenia. Brain Imaging Behav, 12(2), 383-389.