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Shu Z, Wang J, Cheng Y, Lu J, Lin J, Wang Y, Zhang X, Yu Y, Zhu Z, Han J, Wu J, Yu N. fNIRS-based graph frequency analysis to identify mild cognitive impairment in Parkinson's disease. J Neurosci Methods 2024; 402:110031. [PMID: 38040127 DOI: 10.1016/j.jneumeth.2023.110031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 11/23/2023] [Accepted: 11/28/2023] [Indexed: 12/03/2023]
Abstract
BACKGROUND Early identification of mild cognitive impairment (MCI) is essential for its treatment and the prevention of dementia in Parkinson's disease (PD). Existing approaches are mostly based on neuropsychological assessments, while brain activation and connection have not been well considered. NEW METHOD This paper presents a neuroimaging-based graph frequency analysis method and the generated features to quantify the brain functional neurodegeneration and distinguish between PD-MCI patients and healthy controls. The Stroop color-word experiment was conducted with 20 PD-MCI patients and 34 healthy controls, and the brain activation was recorded with functional near-infrared spectroscopy (fNIRS). Then, the functional brain network was constructed based on Pearson's correlation coefficient calculation between every two fNIRS channels. Next, the functional brain network was represented as a graph and decomposed in the graph frequency domain through the graph Fourier transform (GFT) to obtain the eigenvector matrix. Total variation and weighted zero crossings of eigenvectors were defined and integrated to quantify functional interaction between brain regions and the spatial variability of the brain network in specific graph frequency ranges, respectively. After that, the features were employed in training a support vector machine (SVM) classifier. RESULTS The presented method achieved a classification accuracy of 0.833 and an F1 score of 0.877, significantly outperforming existing methods and features. COMPARISON WITH EXISTING METHODS Our method provided improved classification performance in the identification of PD-MCI. CONCLUSION The results suggest that the presented graph frequency analysis method well identify PD-MCI patients and the generated features promise functional brain biomarkers for PD-MCI diagnosis.
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Affiliation(s)
- Zhilin Shu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China
| | - Jin Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin 300350, China; Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin 300370, China
| | - Yuanyuan Cheng
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin 300370, China; Department of Rehabilitation Medicine, Tianjin Huanhu Hospital, Tianjin 300350, China
| | - Jiewei Lu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China
| | - Jianeng Lin
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China
| | - Yue Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin 300350, China
| | - Xinyuan Zhang
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin 300370, China
| | - Yang Yu
- Department of Rehabilitation Medicine, Tianjin Huanhu Hospital, Tianjin 300350, China
| | - Zhizhong Zhu
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin 300370, China; Department of Rehabilitation Medicine, Tianjin Huanhu Hospital, Tianjin 300350, China
| | - Jianda Han
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China; Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China.
| | - Jialing Wu
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin 300350, China; Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgical Institute, Tianjin 300350, China.
| | - Ningbo Yu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China; Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China.
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Zu Z, Choi S, Zhao Y, Gao Y, Li M, Schilling KG, Ding Z, Gore JC. The missing third dimension-Functional correlations of BOLD signals incorporating white matter. SCIENCE ADVANCES 2024; 10:eadi0616. [PMID: 38277462 PMCID: PMC10816695 DOI: 10.1126/sciadv.adi0616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 12/27/2023] [Indexed: 01/28/2024]
Abstract
Correlations between magnetic resonance imaging (MRI) blood oxygenation level-dependent (BOLD) signals from pairs of gray matter areas are used to infer their functional connectivity, but they are unable to describe how white matter is engaged in brain networks. Recently, evidence that BOLD signals in white matter are robustly detectable and are modulated by neural activities has accumulated. We introduce a three-way correlation between BOLD signals from pairs of gray matter volumes (nodes) and white matter bundles (edges) to define the communication connectivity through each white matter bundle. Using MRI images from publicly available databases, we show, for example, that the three-way connectivity is influenced by age. By integrating functional MRI signals from white matter as a third component in network analyses, more comprehensive descriptions of brain function may be obtained.
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Affiliation(s)
- Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
| | - Soyoung Choi
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Kurt G. Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37232, USA
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37232, USA
- Department of Computer Science, Vanderbilt University, Nashville, TN 37232, USA
| | - John C. Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37232, USA
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA
- Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA
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Sui J, Zhi D, Calhoun VD. Data-driven multimodal fusion: approaches and applications in psychiatric research. PSYCHORADIOLOGY 2023; 3:kkad026. [PMID: 38143530 PMCID: PMC10734907 DOI: 10.1093/psyrad/kkad026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/08/2023] [Accepted: 11/21/2023] [Indexed: 12/26/2023]
Abstract
In the era of big data, where vast amounts of information are being generated and collected at an unprecedented rate, there is a pressing demand for innovative data-driven multi-modal fusion methods. These methods aim to integrate diverse neuroimaging perspectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders. However, analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data. This is where data-driven multi-modal fusion techniques come into play. By combining information from multiple modalities in a synergistic manner, these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed. In this paper, we present an extensive overview of data-driven multimodal fusion approaches with or without prior information, with specific emphasis on canonical correlation analysis and independent component analysis. The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics, environment, cognition, and treatment outcomes across various brain disorders. After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications, we further discuss the emerging neuroimaging analyzing trends in big data, such as N-way multimodal fusion, deep learning approaches, and clinical translation. Overall, multimodal fusion emerges as an imperative approach providing valuable insights into the underlying neural basis of mental disorders, which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.
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Affiliation(s)
- Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, GA 30303, United States
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4
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Zhao R, Wang P, Liu L, Zhang F, Hu P, Wen J, Li H, Biswal BB. Whole-brain structure-function coupling abnormalities in mild cognitive impairment: a study combining amplitude of low-frequency fluctuations and voxel-based morphometry. Front Neurosci 2023; 17:1236221. [PMID: 37583417 PMCID: PMC10424122 DOI: 10.3389/fnins.2023.1236221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 07/12/2023] [Indexed: 08/17/2023] Open
Abstract
Alzheimer's disease (AD), one of the leading diseases of the nervous system, is accompanied by symptoms such as loss of memory, thinking and language skills. Both mild cognitive impairment (MCI) and very mild cognitive impairment (VMCI) are the transitional pathological stages between normal aging and AD. While the changes in whole-brain structural and functional information have been extensively investigated in AD, The impaired structure-function coupling remains unknown. The current study employed the OASIS-3 dataset, which includes 53 MCI, 90 VMCI, and 100 Age-, gender-, and education-matched normal controls (NC). Several structural and functional parameters, such as the amplitude of low-frequency fluctuations (ALFF), voxel-based morphometry (VBM), and The ALFF/VBM ratio, were used To estimate The whole-brain neuroimaging changes In MCI, VMCI, and NC. As disease symptoms became more severe, these regions, distributed in the frontal-inf-orb, putamen, and paracentral lobule in the white matter (WM), exhibited progressively increasing ALFF (ALFFNC < ALFFVMCI < ALFFMCI), which was similar to the tendency for The cerebellum and putamen in the gray matter (GM). Additionally, as symptoms worsened in AD, the cuneus/frontal lobe in the WM and the parahippocampal gyrus/hippocampus in the GM showed progressively decreasing structure-function coupling. As the typical focal areas in AD, The parahippocampal gyrus and hippocampus showed significant positive correlations with the severity of cognitive impairment, suggesting the important applications of the ALFF/VBM ratio in brain disorders. On the other hand, these findings from WM functional signals provided a novel perspective for understanding the pathophysiological mechanisms involved In cognitive decline in AD.
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Affiliation(s)
- Rong Zhao
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Pan Wang
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Lin Liu
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Fanyu Zhang
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Hu
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiaping Wen
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongyi Li
- The Fourth People’s Hospital of Chengdu, Chengdu, China
| | - Bharat B. Biswal
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
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5
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Zhao Y, Gao Y, Li M, Anderson AW, Ding Z, Gore JC. Functional Parcellation of Human Brain Using Localized Topo-Connectivity Mapping. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2670-2680. [PMID: 35442885 PMCID: PMC9844109 DOI: 10.1109/tmi.2022.3168888] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The analysis of connectivity between parcellated regions of cortex provides insights into the functional architecture of the brain at a systems level. However, the derivation of functional structures from voxel-wise analyses at finer scales remains a challenge. We propose a novel method, called localized topo-connectivity mapping with singular-value-decomposition-informed filtering (or filtered LTM), to identify and characterize voxel-wise functional structures in the human brain from resting-state fMRI data. Here we describe its mathematical formulation and provide a proof-of-concept using simulated data that allow an intuitive interpretation of the results of filtered LTM. The algorithm has also been applied to 7T fMRI data acquired as part of the Human Connectome Project to generate group-average LTM images. Generally, most of the functional structures revealed by LTM images agree in the boundaries with anatomical structures identified by T1-weighted images and fractional anisotropy maps derived from diffusion MRI. In addition, the LTM images also reveal subtle functional variations that are not apparent in the anatomical structures. To assess the performance of LTM images, the subcortical region and occipital white matter were separately parcellated. Statistical tests were performed to demonstrate that the synchronies of fMRI signals in LTM-derived functional parcels are significantly larger than those with geometric perturbations. Overall, the filtered LTM approach can serve as a tool to investigate the functional organization of the brain at the scale of individual voxels as measured in fMRI.
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Kirby ED, Frizzell TO, Grajauskas LA, Song X, Gawryluk JR, Lakhani B, Boyd L, D'Arcy RCN. Increased myelination plays a central role in white matter neuroplasticity. Neuroimage 2022; 263:119644. [PMID: 36170952 DOI: 10.1016/j.neuroimage.2022.119644] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/16/2022] [Accepted: 09/20/2022] [Indexed: 11/19/2022] Open
Abstract
White matter (WM) neuroplasticity in the human brain has been tracked non-invasively using advanced magnetic resonance imaging techniques, with increasing evidence for improved axonal transmission efficiency as a central mechanism. The current study is the culmination of a series of studies, which characterized the structure-function relationship of WM transmission efficiency in the cortico-spinal tract (CST) during motor learning. Here, we test the hypothesis that increased transmission efficiency is linked directly to increased myelination using myelin water imaging (MWI). MWI was used to evaluate neuroplasticity-related improvements in the CST. The MWI findings were then compared to diffusion tensor imaging (DTI) results, with the secondary hypothesis that radial diffusivity (RD) would have a stronger relationship than axial diffusivity (AD) if the changes were due to increased myelination. Both MWI and RD data showed the predicted pattern of significant results, strongly supporting that increased myelination plays a central role in WM neuroplasticity.
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Affiliation(s)
- Eric D Kirby
- BrainNET, Health and Technology District, Vancouver, Canada; Faculty of Individualized Interdisciplinary Studies, Simon Fraser University, Burnaby, Canada
| | - Tory O Frizzell
- BrainNET, Health and Technology District, Vancouver, Canada; Faculty of Applied Sciences, Simon Fraser University, Burnaby, Canada
| | - Lukas A Grajauskas
- Department of Biomedical, Physiology, and Kinesiology, Simon Fraser University, Burnaby, Canada; Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Xiaowei Song
- Department of Biomedical, Physiology, and Kinesiology, Simon Fraser University, Burnaby, Canada; Department of Research and Evaluation Services and Surrey Memorial Hospital, Fraser Health Authority, Surrey, Canada
| | - Jodie R Gawryluk
- Department of Psychology, University of Victoria, Victoria, Canada
| | - Bimal Lakhani
- BrainNET, Health and Technology District, Vancouver, Canada; Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
| | - Lara Boyd
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
| | - Ryan C N D'Arcy
- BrainNET, Health and Technology District, Vancouver, Canada; Faculty of Applied Sciences, Simon Fraser University, Burnaby, Canada; Department of Research and Evaluation Services and Surrey Memorial Hospital, Fraser Health Authority, Surrey, Canada; Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada.
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7
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Zhao Y, Gao Y, Zu Z, Li M, Schilling KG, Anderson AW, Ding Z, Gore JC. Detection of functional activity in brain white matter using fiber architecture informed synchrony mapping. Neuroimage 2022; 258:119399. [PMID: 35724855 PMCID: PMC9388229 DOI: 10.1016/j.neuroimage.2022.119399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 06/13/2022] [Accepted: 06/17/2022] [Indexed: 01/12/2023] Open
Abstract
A general linear model is widely used for analyzing fMRI data, in which the blood oxygenation-level dependent (BOLD) signals in gray matter (GM) evoked in response to neural stimulation are modeled by convolving the time course of the expected neural activity with a canonical hemodynamic response function (HRF) obtained a priori. The maps of brain activity produced reflect the magnitude of local BOLD responses. However, detecting BOLD signals in white matter (WM) is more challenging as the BOLD signals are weaker and the HRF is different, and may vary more across the brain. Here we propose a model-free approach to detect changes in BOLD signals in WM by measuring task-evoked increases of BOLD signal synchrony in WM fibers. The proposed approach relies on a simple assumption that, in response to a functional task, BOLD signals in relevant fibers are modulated by stimulus-evoked neural activity and thereby show greater synchrony than when measured in a resting state, even if their magnitudes do not change substantially. This approach is implemented in two technical stages. First, for each voxel a fiber-architecture-informed spatial window is created with orientation distribution functions constructed from diffusion imaging data. This provides the basis for defining neighborhoods in WM that share similar local fiber architectures. Second, a modified principal component analysis (PCA) is used to estimate the synchrony of BOLD signals in each spatial window. The proposed approach is validated using a 3T fMRI dataset from the Human Connectome Project (HCP) at a group level. The results demonstrate that neural activity can be reliably detected as increases in fMRI signal synchrony within WM fibers that are engaged in a task with high sensitivities and reproducibility.
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Affiliation(s)
- Yu Zhao
- Vanderbilt University Institute of Imaging Science, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, United States.
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, United States,Department of Biomedical Engineering, Vanderbilt University, United States
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, United States,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, United States
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, United States,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, United States
| | - Kurt G. Schilling
- Vanderbilt University Institute of Imaging Science, United States,Department of Biomedical Engineering, Vanderbilt University, United States
| | - Adam W. Anderson
- Vanderbilt University Institute of Imaging Science, United States,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, United States,Department of Biomedical Engineering, Vanderbilt University, United States
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, United States; Department of Biomedical Engineering, Vanderbilt University, United States; Department of Electrical and Computer Engineering, Vanderbilt University, United States.
| | - John C. Gore
- Vanderbilt University Institute of Imaging Science, United States,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, United States,Department of Biomedical Engineering, Vanderbilt University, United States,Department of Molecular Physiology and Biophysics, Vanderbilt University, United States,Department of Physics and Astronomy, Vanderbilt University, United States
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Behjat H, Westin CF, Aganj I. Cortical Surface-Informed Volumetric Spatial Smoothing of fMRI Data via Graph Signal Processing. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3804-3808. [PMID: 34892064 PMCID: PMC8669627 DOI: 10.1109/embc46164.2021.9629662] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Conventionally, as a preprocessing step, functional MRI (fMRI) data are spatially smoothed before further analysis, be it for activation mapping on task-based fMRI or functional connectivity analysis on resting-state fMRI data. When images are smoothed volumetrically, however, isotropic Gaussian kernels are generally used, which do not adapt to the underlying brain structure. Alternatively, cortical surface smoothing procedures provide the benefit of adapting the smoothing process to the underlying morphology, but require projecting volumetric data on to the surface. In this paper, leveraging principles from graph signal processing, we propose a volumetric spatial smoothing method that takes advantage of the gray-white and pial cortical surfaces, and as such, adapts the filtering process to the underlying morphological details at each point in the cortex.
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Behjat H, Aganj I, Abramian D, Eklund A, Westin CF. CHARACTERIZATION OF SPATIAL DYNAMICS OF FMRI DATA IN WHITE MATTER USING DIFFUSION-INFORMED WHITE MATTER HARMONICS. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2021; 2021:1586-1590. [PMID: 34084267 DOI: 10.1109/isbi48211.2021.9433958] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In this work, we leverage the Laplacian eigenbasis of voxel-wise white matter (WM) graphs derived from diffusion-weighted MRI data, dubbed WM harmonics, to characterize the spatial structure of WM fMRI data. Our motivation for such a characterization is based on studies that show WM fMRI data exhibit a spatial correlational anisotropy that coincides with underlying fiber patterns. By quantifying the energy content of WM fMRI data associated with subsets of WM harmonics across multiple spectral bands, we show that the data exhibits notable subtle spatial modulations under functional load that are not manifested during rest. WM harmonics provide a novel means to study the spatial dynamics of WM fMRI data, in such way that the analysis is informed by the underlying anatomical structure.
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Affiliation(s)
- Hamid Behjat
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.,Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA.,Department of Biomedical Engineering, Lund University, Lund, Sweden
| | - Iman Aganj
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA.,Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA
| | - David Abramian
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Anders Eklund
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.,Department of Computer and Information Science, Linköping University, Linköping, Sweden
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
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