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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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Deng J, Sun B, Scheel N, Renli AB, Zhu DC, Zhu D, Ren J, Li T, Zhang R. Causalized convergent cross-mapping and its approximate equivalence with directed information in causality analysis. PNAS NEXUS 2024; 3:pgad422. [PMID: 38169910 PMCID: PMC10758925 DOI: 10.1093/pnasnexus/pgad422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 11/27/2023] [Indexed: 01/05/2024]
Abstract
Convergent cross-mapping (CCM) has attracted increased attention recently due to its capability to detect causality in nonseparable systems under deterministic settings, which may not be covered by the traditional Granger causality. From an information-theoretic perspective, causality is often characterized as the directed information (DI) flowing from one side to the other. As information is essentially nondeterministic, a natural question is: does CCM measure DI flow? Here, we first causalize CCM so that it aligns with the presumption in causality analysis-the future values of one process cannot influence the past of the other, and then establish and validate the approximate equivalence of causalized CCM (cCCM) and DI under Gaussian variables through both theoretical derivations and fMRI-based brain network causality analysis. Our simulation result indicates that, in general, cCCM tends to be more robust than DI in causality detection. The underlying argument is that DI relies heavily on probability estimation, which is sensitive to data size as well as digitization procedures; cCCM, on the other hand, gets around this problem through geometric cross-mapping between the manifolds involved. Overall, our analysis demonstrates that cross-mapping provides an alternative way to evaluate DI and is potentially an effective technique for identifying both linear and nonlinear causal coupling in brain neural networks and other settings, either random or deterministic, or both.
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Affiliation(s)
- Jinxian Deng
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Boxin Sun
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Norman Scheel
- Department of Radiology, Michigan State University, East Lansing, MI 48824, USA
| | - Alina B Renli
- Department of Neuroscience, Michigan State University, East Lansing, MI 48824, USA
| | - David C Zhu
- Department of Radiology, Michigan State University, East Lansing, MI 48824, USA
| | - Dajiang Zhu
- Department of Computer Science and Engineering, University of Texas, Arlington, TX 76010, USA
| | - Jian Ren
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Tongtong Li
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Rong Zhang
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital Dallas, Dallas, TX 75231, USA
- Departments of Neurology and Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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Luo D, Liu Y, Zhang N, Wang T. Differences in the distribution of triggers among resting state networks in patients with juvenile myoclonic epilepsy explained by network analysis. Front Neurosci 2023; 17:1214687. [PMID: 37859762 PMCID: PMC10582565 DOI: 10.3389/fnins.2023.1214687] [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: 04/30/2023] [Accepted: 08/25/2023] [Indexed: 10/21/2023] Open
Abstract
Background Juvenile myoclonus epilepsy (JME) is an idiopathic generalized epilepsy syndrome. Functional connectivity studies based on graph theory have demonstrated changes in functional connectivity among different brain regions in patients with JME and healthy controls. However, previous studies have not been able to clarify why visual stimulation or increased cognitive load induces epilepsy symptoms in only some patients with JME. Methods This study constructed a small-world network for the visualization of functional connectivity of brain regions in patients with JME, based on system mapping. We used the node reduction method repeatedly to identify the core nodes of the resting brain network of patients with JME. Thereafter, a functional connectivity network of the core brain regions in patients with JME was established, and it was analyzed manually with white matter tracks restriction to explain the differences in symptom distribution in patients with JME. Results Patients with JME had 21 different functional connections in their resting state, and no significant differences in their distribution were noted. The thalamus, cerebellum, basal ganglia, supplementary motor area, visual cortex, and prefrontal lobe were the core brain regions that comprised the functional connectivity network in patients with JME during their resting state. The betweenness centrality of the prefrontal lobe and the visual cortex in the core functional connectivity network of patients with JME was lower than that of the other brain regions. Conclusion The functional connectivity and node importance of brain regions of patients with JME changed dynamically in the resting state. Abnormal discharges originating from the thalamus, cerebellum, basal ganglia, supplementary motor area, visual cortex, and prefrontal cortex are most likely to lead to seizures in patients with JME. Further, the low average value of betweenness centrality of the prefrontal and visual cortices explains why visual stimulation or increased cognitive load can induce epileptic symptoms in only some patients with JME.
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Affiliation(s)
- Dadong Luo
- Department of Neurology, Lanzhou University Second Hospital, Lanzhou, China
- Second School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Yaqing Liu
- Department of Neurology, Lanzhou University Second Hospital, Lanzhou, China
- Second School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Ningning Zhang
- Department of Neurology, Lanzhou University Second Hospital, Lanzhou, China
- Second School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Tiancheng Wang
- Department of Neurology, Lanzhou University Second Hospital, Lanzhou, China
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