1
|
Zhen Y, Yang Y, Zheng Y, Wang X, Liu L, Zheng Z, Zheng H, Tang S. The heritability and structural correlates of resting-state fMRI complexity. Neuroimage 2024; 296:120657. [PMID: 38810892 DOI: 10.1016/j.neuroimage.2024.120657] [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: 03/09/2024] [Revised: 05/24/2024] [Accepted: 05/26/2024] [Indexed: 05/31/2024] Open
Abstract
The complexity of fMRI signals quantifies temporal dynamics of spontaneous neural activity, which has been increasingly recognized as providing important insights into cognitive functions and psychiatric disorders. However, its heritability and structural underpinnings are not well understood. Here, we utilize multi-scale sample entropy to extract resting-state fMRI complexity in a large healthy adult sample from the Human Connectome Project. We show that fMRI complexity at multiple time scales is heritable in broad brain regions. Heritability estimates are modest and regionally variable. We relate fMRI complexity to brain structure including surface area, cortical myelination, cortical thickness, subcortical volumes, and total brain volume. We find that surface area is negatively correlated with fine-scale complexity and positively correlated with coarse-scale complexity in most cortical regions, especially the association cortex. Most of these correlations are related to common genetic and environmental effects. We also find positive correlations between cortical myelination and fMRI complexity at fine scales and negative correlations at coarse scales in the prefrontal cortex, lateral temporal lobe, precuneus, lateral parietal cortex, and cingulate cortex, with these correlations mainly attributed to common environmental effects. We detect few significant associations between fMRI complexity and cortical thickness. Despite the non-significant association with total brain volume, fMRI complexity exhibits significant correlations with subcortical volumes in the hippocampus, cerebellum, putamen, and pallidum at certain scales. Collectively, our work establishes the genetic basis and structural correlates of resting-state fMRI complexity across multiple scales, supporting its potential application as an endophenotype for psychiatric disorders.
Collapse
Affiliation(s)
- Yi Zhen
- School of Mathematical Sciences, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China
| | - Yaqian Yang
- School of Mathematical Sciences, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China
| | - Yi Zheng
- School of Mathematical Sciences, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China
| | - Xin Wang
- Institute of Artificial Intelligence, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China; Zhongguancun Laboratory, Beijing 100094, China; Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China; PengCheng Laboratory, Shenzhen 518055, China
| | - Longzhao Liu
- Institute of Artificial Intelligence, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China; Zhongguancun Laboratory, Beijing 100094, China; Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China; PengCheng Laboratory, Shenzhen 518055, China
| | - Zhiming Zheng
- Institute of Artificial Intelligence, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China; Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai 264003, China; Zhongguancun Laboratory, Beijing 100094, China; Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China; PengCheng Laboratory, Shenzhen 518055, China; State Key Lab of Software Development Environment, Beihang University, Beijing 100191, China
| | - Hongwei Zheng
- Beijing Academy of Blockchain and Edge Computing, Beijing 100085, China.
| | - Shaoting Tang
- Institute of Artificial Intelligence, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China; Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai 264003, China; Zhongguancun Laboratory, Beijing 100094, China; Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China; PengCheng Laboratory, Shenzhen 518055, China; State Key Lab of Software Development Environment, Beihang University, Beijing 100191, China.
| |
Collapse
|
2
|
Santillo AF, Strandberg TO, Reislev NH, Nilsson M, Stomrud E, Spotorno N, van Westen D, Hansson O. Divergent functional connectivity changes associated with white matter hyperintensities. Neuroimage 2024; 296:120672. [PMID: 38851551 DOI: 10.1016/j.neuroimage.2024.120672] [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: 03/13/2024] [Revised: 05/17/2024] [Accepted: 06/06/2024] [Indexed: 06/10/2024] Open
Abstract
Age-related white matter hyperintensities are a common feature and are known to be negatively associated with structural integrity, functional connectivity, and cognitive performance. However, this has yet to be fully understood mechanistically. We analyzed multiple MRI modalities acquired in 465 non-demented individuals from the Swedish BioFINDER study including 334 cognitively normal and 131 participants with mild cognitive impairment. White matter hyperintensities were automatically quantified using fluid-attenuated inversion recovery MRI and parameters from diffusion tensor imaging were estimated in major white matter fibre tracts. We calculated fMRI resting state-derived functional connectivity within and between predefined cortical regions structurally linked by the white matter tracts. How change in functional connectivity is affected by white matter lesions and related to cognition (in the form of executive function and processing speed) was explored. We examined the functional changes using a measure of sample entropy. As expected hyperintensities were associated with disrupted structural white matter integrity and were linked to reduced functional interregional lobar connectivity, which was related to decreased processing speed and executive function. Simultaneously, hyperintensities were also associated with increased intraregional functional connectivity, but only within the frontal lobe. This phenomenon was also associated with reduced cognitive performance. The increased connectivity was linked to increased entropy (reduced predictability and increased complexity) of the involved voxels' blood oxygenation level-dependent signal. Our findings expand our previous understanding of the impact of white matter hyperintensities on cognition by indicating novel mechanisms that may be important beyond this particular type of brain lesions.
Collapse
Affiliation(s)
- Alexander F Santillo
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden. Postal address: Memory Clinic, Skåne University Hospital, SE-20502 Malmö, Sweden
| | - Tor O Strandberg
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden. Postal address: Memory Clinic, Skåne University Hospital, SE-20502 Malmö, Sweden
| | - Nina H Reislev
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden. Postal address: Memory Clinic, Skåne University Hospital, SE-20502 Malmö, Sweden; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Markus Nilsson
- Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Diagnostic Radiology, Lund. Diagnostic Radiology, Lunds Universitet/SUS/Lund, 221 85 Lund, Sweden, Sweden
| | - Erik Stomrud
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden. Postal address: Memory Clinic, Skåne University Hospital, SE-20502 Malmö, Sweden; Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Nicola Spotorno
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden. Postal address: Memory Clinic, Skåne University Hospital, SE-20502 Malmö, Sweden
| | - Danielle van Westen
- Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Diagnostic Radiology, Lund. Diagnostic Radiology, Lunds Universitet/SUS/Lund, 221 85 Lund, Sweden, Sweden
| | - Oskar Hansson
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden. Postal address: Memory Clinic, Skåne University Hospital, SE-20502 Malmö, Sweden; Memory Clinic, Skåne University Hospital, Malmö, Sweden.
| |
Collapse
|
3
|
Wehrheim MH, Faskowitz J, Schubert A, Fiebach CJ. Reliability of variability and complexity measures for task and task-free BOLD fMRI. Hum Brain Mapp 2024; 45:e26778. [PMID: 38980175 PMCID: PMC11232465 DOI: 10.1002/hbm.26778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 05/06/2024] [Accepted: 06/24/2024] [Indexed: 07/10/2024] Open
Abstract
Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures-which is an important precondition for robust individual differences as well as longitudinal research-is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. PRACTITIONER POINTS: Variability and complexity measures of BOLD activation show good split-half reliability and moderate test-retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability.
Collapse
Affiliation(s)
- Maren H. Wehrheim
- Department of PsychologyGoethe University FrankfurtFrankfurtGermany
- Department of Computer Science and MathematicsGoethe University FrankfurtFrankfurtGermany
- Frankfurt Institute for Advanced Studies (FIAS)FrankfurtGermany
| | - Joshua Faskowitz
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonUSA
| | - Anna‐Lena Schubert
- Department of PsychologyJohannes Gutenberg‐Universität MainzMainzGermany
| | - Christian J. Fiebach
- Department of PsychologyGoethe University FrankfurtFrankfurtGermany
- Brain Imaging CenterGoethe University FrankfurtFrankfurtGermany
| |
Collapse
|
4
|
Wang J, Zheng J, Pan H, Tong J, Liu Q. Refined composite multiscale slope entropy and its application in rolling bearing fault diagnosis. ISA TRANSACTIONS 2024:S0019-0578(24)00328-8. [PMID: 39095286 DOI: 10.1016/j.isatra.2024.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 07/03/2024] [Accepted: 07/03/2024] [Indexed: 08/04/2024]
Abstract
Rolling bearing is the key component of rotating machinery, and its vibration signal usually exhibits nonlinear and nonstationary characteristics when failure occurs. Multiscale permutation entropy (MPE) is an effective nonlinear dynamics analysis tool, which has been successfully applied to rolling bearing fault diagnosis in recent years. However, MPE ignores the deep amplitude information when measuring the complexity of the time series and the original multiscale coarse-graining is insufficient, which requires further research and improvement. In order to protect the integrity of information structure, a novel nonlinear dynamic analysis method termed refined composite multiscale slope entropy (RCMSlE) is proposed in this paper, which introduced the concept of refined composite to further boost the performance of MPE in nonlinear dynamical complexity analysis. Furthermore, RCMSlE utilizes a novel symbolic representation that takes full account of mode and amplitude information, which overcomes the weaknesses in describing the complexity and regularity of bearing signals. Based on this, a GWO-SVM multi-classifier is introduced to fulfill mode recognition, and then a new intelligent fault diagnosis method for rolling bearing based on RCMSlE and GWO-SVM is proposed. The experimental results show that the proposed method can not only accurately identify different fault types and degrees of rolling bearing, but also has a short computation time and better performance than other comparative methods.
Collapse
Affiliation(s)
- Junfeng Wang
- School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032, China
| | - Jinde Zheng
- School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032, China.
| | - Haiyang Pan
- School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032, China
| | - Jinyu Tong
- School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032, China
| | - Qingyun Liu
- School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032, China
| |
Collapse
|
5
|
Zhao CL, Hou W, Jia Y, Sahakian BJ, Luo Q. Sex differences of signal complexity at resting-state functional magnetic resonance imaging and their associations with the estrogen-signaling pathway in the brain. Cogn Neurodyn 2024; 18:973-986. [PMID: 38826661 PMCID: PMC11143120 DOI: 10.1007/s11571-023-09954-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 01/27/2023] [Accepted: 03/08/2023] [Indexed: 06/04/2024] Open
Abstract
Sex differences in the brain have been widely reported and may hold the key to elucidating sex differences in many medical conditions and drug response. However, the molecular correlates of these sex differences in structural and functional brain measures in the human brain remain unclear. Herein, we used sample entropy (SampEn) to quantify the signal complexity of resting-state functional magnetic resonance imaging (rsfMRI) in a large neuroimaging cohort (N = 1,642). The frontoparietal control network and the cingulo-opercular network had high signal complexity while the cerebellar and sensory motor networks had low signal complexity in both men and women. Compared with those in male brains, we found greater signal complexity in all functional brain networks in female brains with the default mode network exhibiting the largest sex difference. Using the gene expression data in brain tissues, we identified genes that were significantly associated with sex differences in brain signal complexity. The significant genes were enriched in the gene sets that were differentially expressed between the brain cortex and other tissues, the estrogen-signaling pathway, and the biological function of neural plasticity. In particular, the G-protein-coupled estrogen receptor 1 gene in the estrogen-signaling pathway was expressed more in brain regions with greater sex differences in SampEn. In conclusion, greater complexity in female brains may reflect the interactions between sex hormone fluctuations and neuromodulation of estrogen in women. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-023-09954-y.
Collapse
Affiliation(s)
- Cheng-li Zhao
- College of Science, National University of Defense Technology, Changsha, 410073 China
| | - Wenjie Hou
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433 China
- Center for Computational Psychiatry, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Human Phenome Institute, Fudan University, Shanghai, 200438 China
| | - Yanbing Jia
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, 471000 China
| | - Barbara J. Sahakian
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB UK
| | - the DIRECT Consortium
- College of Science, National University of Defense Technology, Changsha, 410073 China
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433 China
- Center for Computational Psychiatry, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Human Phenome Institute, Fudan University, Shanghai, 200438 China
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, 471000 China
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB UK
| | - Qiang Luo
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433 China
- Center for Computational Psychiatry, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Human Phenome Institute, Fudan University, Shanghai, 200438 China
| |
Collapse
|
6
|
Hirsch F, Bumanglag Â, Zhang Y, Wohlschlaeger A. Diverging functional connectivity timescales: Capturing distinct aspects of cognitive performance in early psychosis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.07.24306932. [PMID: 38766002 PMCID: PMC11100938 DOI: 10.1101/2024.05.07.24306932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Background Psychosis spectrum disorders (PSDs) are marked by cognitive impairments, the neurobiological correlates of which remain poorly understood. Here, we investigate the entropy of time-varying functional connectivity (TVFC) patterns from resting-state fMRI (rfMRI) as potential biomarker for cognitive performance in PSDs. By combining our results with multimodal reference data, we hope to generate new insights into the mechanisms underlying cognitive dysfunction in PSDs. We hypothesized that low-entropy TVFC patterns (LEN) would be more behaviorally informative than high-entropy TVFC patterns (HEN), especially for tasks that require extensive integration across diverse cognitive subdomains. Methods rfMRI and behavioral data from 97 patients in the early phases of psychosis and 53 controls were analyzed. Positron-Emission Tomography (PET) and magnetoencephalography (MEG) data were taken from a public repository (Hansen et al., 2022). Multivariate analyses were conducted to examine relationships between TVFC patterns at multiple spatial scales and cognitive performance in patients. Results Compared to HEN, LEN explained significantly more cognitive variance on average in PSD patients, driven by superior encoding of information on psychometrically more integrated tasks. HEN better captured information in specific subdomains of executive functioning. Nodal HEN-LEN transitions were spatially aligned with neurobiological gradients reflecting monoaminergic transporter densities and MEG beta power. Exploratory analyses revealed a close statistical relationship between LEN and positive PSD symptoms. Conclusion Our entropy-based analysis of TVFC patterns dissociates distinct aspects of cognition in PSDs. By linking topographies of neurotransmission and oscillatory dynamics with cognitive performance, it enhances our understanding of the mechanisms underlying cognitive deficits in PSDs. CRediT Authorship Contribution Statement Fabian Hirsch: Conceptualization, Methodology, Software, Formal analysis, Writing - Original Draft, Writing - Review & Editing, Visualization; Ângelo Bumanglag: Methodology, Software, Formal analysis, Writing - Review & Editing; Yifei Zhang: Methodology, Software, Formal analysis, Writing - Review & Editing; Afra Wohlschlaeger: Methodology, Writing - Review & Editing, Supervision, Project administration.
Collapse
|
7
|
Fan S, Zhang J, Wu Y, Yu Y, Zheng H, Guo YY, Ji Y, Pang X, Tian Y. Changed brain entropy and functional connectivity patterns induced by electroconvulsive therapy in majoy depression disorder. Psychiatry Res Neuroimaging 2024; 339:111788. [PMID: 38335560 DOI: 10.1016/j.pscychresns.2024.111788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 12/09/2023] [Accepted: 01/08/2024] [Indexed: 02/12/2024]
Abstract
OBJECTIVE Our objective is to innovatively integrate both linear and nonlinear characteristics of brain signals in Electroconvulsive Therapy (ECT) research, with the goal of uncovering deeper insights into the pathogenesis of Major Depressive Disorder (MDD) and identifying novel targets for other physical intervention therapies. METHODS We measured brain entropy (BEN) in 42 MDD patients and 42 matched healthy controls (HC) using rs-fMRI data. Brain regions that differed significantly in patients with MDD before and after ECT were extracted. Then, we use these brain regions as seed points to investigate the differences in whole-brain resting-state functional connectivity (RSFC) patterns before and after ECT. RESULTS Compared to HCs, patients had higher BEN levels in the right precuneus (PCUN.R) and right angular gyrus (ANG.R). After ECT, patients had lower BEN levels in the PCUN.R and ANG.R. Compared with before ECT, patients showed significantly increased RSFC after ECT between the PCUN.R and right middle temporal gyrus and ANG.R. Significantly increased RSFC was observed between the ANG.R and right middle frontal gyrus and right supramarginal gyrus after ECT. CONCLUSION Combining the linear and nonlinear characteristics of brain signals can effectively explore the pathogenesis of depression and provide new targets for ECT.
Collapse
Affiliation(s)
- Siyu Fan
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei. 230022, PR China
| | - Jiahua Zhang
- The College of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230032, PR China
| | - Yue Wu
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei,. 230601, PR China
| | - Yue Yu
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei. 230022, PR China
| | - Hao Zheng
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei. 230022, PR China
| | - Yuan Yuan Guo
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei. 230022, PR China
| | - Yang Ji
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei. 230022, PR China
| | - Xiaonan Pang
- Department of Neurology, the Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China.
| | - Yanghua Tian
- The College of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230032, PR China; Department of Neurology, the Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, 230032, PR China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, PR China; Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei,. 230601, PR China.
| |
Collapse
|
8
|
Xin X, Yu J, Gao X. The brain entropy dynamics in resting state. Front Neurosci 2024; 18:1352409. [PMID: 38595975 PMCID: PMC11002175 DOI: 10.3389/fnins.2024.1352409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/08/2024] [Indexed: 04/11/2024] Open
Abstract
As a novel measure for irregularity and complexity of the spontaneous fluctuations of brain activities, brain entropy (BEN) has attracted much attention in resting-state functional magnetic resonance imaging (rs-fMRI) studies during the last decade. Previous studies have shown its associations with cognitive and mental functions. While most previous research assumes BEN is approximately stationary during scan sessions, the brain, even at its resting state, is a highly dynamic system. Such dynamics could be characterized by a series of reoccurring whole-brain patterns related to cognitive and mental processes. The present study aims to explore the time-varying feature of BEN and its potential links with general cognitive ability. We adopted a sliding window approach to derive the dynamical brain entropy (dBEN) of the whole-brain functional networks from the HCP (Human Connectome Project) rs-fMRI dataset that includes 812 young healthy adults. The dBEN was further clustered into 4 reoccurring BEN states by the k-means clustering method. The fraction window (FW) and mean dwell time (MDT) of one BEN state, characterized by the extremely low overall BEN, were found to be negatively correlated with general cognitive abilities (i.e., cognitive flexibility, inhibitory control, and processing speed). Another BEN state, characterized by intermediate overall BEN and low within-state BEN located in DMN, ECN, and part of SAN, its FW, and MDT were positively correlated with the above cognitive abilities. The results of our study advance our understanding of the underlying mechanism of BEN dynamics and provide a potential framework for future investigations in clinical populations.
Collapse
Affiliation(s)
- Xiaoyang Xin
- Center for Psychological Sciences, Zhejiang University, Hangzhou, China
- Preschool College, Luoyang Normal University, Luoyang, China
| | - Jiaqian Yu
- Center for Psychological Sciences, Zhejiang University, Hangzhou, China
| | - Xiaoqing Gao
- Center for Psychological Sciences, Zhejiang University, Hangzhou, China
| |
Collapse
|
9
|
von Wegner F, Wiemers M, Hermann G, Tödt I, Tagliazucchi E, Laufs H. Complexity Measures for EEG Microstate Sequences: Concepts and Algorithms. Brain Topogr 2024; 37:296-311. [PMID: 37751054 PMCID: PMC10884068 DOI: 10.1007/s10548-023-01006-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 08/31/2023] [Indexed: 09/27/2023]
Abstract
EEG microstate sequence analysis quantifies properties of ongoing brain electrical activity which is known to exhibit complex dynamics across many time scales. In this report we review recent developments in quantifying microstate sequence complexity, we classify these approaches with regard to different complexity concepts, and we evaluate excess entropy as a yet unexplored quantity in microstate research. We determined the quantities entropy rate, excess entropy, Lempel-Ziv complexity (LZC), and Hurst exponents on Potts model data, a discrete statistical mechanics model with a temperature-controlled phase transition. We then applied the same techniques to EEG microstate sequences from wakefulness and non-REM sleep stages and used first-order Markov surrogate data to determine which time scales contributed to the different complexity measures. We demonstrate that entropy rate and LZC measure the Kolmogorov complexity (randomness) of microstate sequences, whereas excess entropy and Hurst exponents describe statistical complexity which attains its maximum at intermediate levels of randomness. We confirmed the equivalence of entropy rate and LZC when the LZ-76 algorithm is used, a result previously reported for neural spike train analysis (Amigó et al., Neural Comput 16:717-736, https://doi.org/10.1162/089976604322860677 , 2004). Surrogate data analyses prove that entropy-based quantities and LZC focus on short-range temporal correlations, whereas Hurst exponents include short and long time scales. Sleep data analysis reveals that deeper sleep stages are accompanied by a decrease in Kolmogorov complexity and an increase in statistical complexity. Microstate jump sequences, where duplicate states have been removed, show higher randomness, lower statistical complexity, and no long-range correlations. Regarding the practical use of these methods, we suggest that LZC can be used as an efficient entropy rate estimator that avoids the estimation of joint entropies, whereas entropy rate estimation via joint entropies has the advantage of providing excess entropy as the second parameter of the same linear fit. We conclude that metrics of statistical complexity are a useful addition to microstate analysis and address a complexity concept that is not yet covered by existing microstate algorithms while being actively explored in other areas of brain research.
Collapse
Affiliation(s)
- Frederic von Wegner
- School of Biomedical Sciences, University of New South Wales (UNSW), Wallace Wurth, Kensington, NSW, 2052, Australia.
| | - Milena Wiemers
- Department of Neurology and Clinical Neurophysiology, Lüneburg Hospital, Bögelstrasse 1, 21339, Lüneburg, Germany
| | - Gesine Hermann
- Department of Neurology, Christian-Albrechts University, Arnold-Heller-Strasse 3, 24105, Kiel, Germany
| | - Inken Tödt
- Institute of Sexual Medicine & Forensic Psychiatry and Psychotherapy, Christian-Albrechts University, Schwanenweg 24, 24105, Kiel, Germany
| | - Enzo Tagliazucchi
- Department of Physics, University of Buenos Aires, 1428, Buenos Aires, Argentina
| | - Helmut Laufs
- Department of Neurology, Christian-Albrechts University, Arnold-Heller-Strasse 3, 24105, Kiel, Germany
| |
Collapse
|
10
|
Roediger DJ, Butts J, Falke C, Fiecas MB, Klimes-Dougan B, Mueller BA, Cullen KR. Optimizing the measurement of sample entropy in resting-state fMRI data. Front Neurol 2024; 15:1331365. [PMID: 38426165 PMCID: PMC10902163 DOI: 10.3389/fneur.2024.1331365] [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: 11/01/2023] [Accepted: 01/31/2024] [Indexed: 03/02/2024] Open
Abstract
Introduction The complexity of brain signals may hold clues to understand brain-based disorders. Sample entropy, an index that captures the predictability of a signal, is a promising tool to measure signal complexity. However, measurement of sample entropy from fMRI signals has its challenges, and numerous questions regarding preprocessing and parameter selection require research to advance the potential impact of this method. For one example, entropy may be highly sensitive to the effects of motion, yet standard approaches to addressing motion (e.g., scrubbing) may be unsuitable for entropy measurement. For another, the parameters used to calculate entropy need to be defined by the properties of data being analyzed, an issue that has frequently been ignored in fMRI research. The current work sought to rigorously address these issues and to create methods that could be used to advance this field. Methods We developed and tested a novel windowing approach to select and concatenate (ignoring connecting volumes) low-motion windows in fMRI data to reduce the impact of motion on sample entropy estimates. We created utilities (implementing autoregressive models and a grid search function) to facilitate selection of the matching length m parameter and the error tolerance r parameter. We developed an approach to apply these methods at every grayordinate of the brain, creating a whole-brain dense entropy map. These methods and tools have been integrated into a publicly available R package ("powseR"). We demonstrate these methods using data from the ABCD study. After applying the windowing procedure to allow sample entropy calculation on the lowest-motion windows from runs 1 and 2 (combined) and those from runs 3 and 4 (combined), we identified the optimal m and r parameters for these data. To confirm the impact of the windowing procedure, we compared entropy values and their relationship with motion when entropy was calculated using the full set of data vs. those calculated using the windowing procedure. We then assessed reproducibility of sample entropy calculations using the windowed procedure by calculating the intraclass correlation between the earlier and later entropy measurements at every grayordinate. Results When applying these optimized methods to the ABCD data (from the subset of individuals who had enough windows of continuous "usable" volumes), we found that the novel windowing procedure successfully mitigated the large inverse correlation between entropy values and head motion seen when using a standard approach. Furthermore, using the windowed approach, entropy values calculated early in the scan (runs 1 and 2) are largely reproducible when measured later in the scan (runs 3 and 4), although there is some regional variability in reproducibility. Discussion We developed an optimized approach to measuring sample entropy that addresses concerns about motion and that can be applied across datasets through user-identified adaptations that allow the method to be tailored to the dataset at hand. We offer preliminary results regarding reproducibility. We also include recommendations for fMRI data acquisition to optimize sample entropy measurement and considerations for the field.
Collapse
Affiliation(s)
- Donovan J. Roediger
- Department of Psychiatry and Behavioral Sciences, Medical School, University of Minnesota (UMN), Minneapolis, MN, United States
| | - Jessica Butts
- Division of Biostatistics and Health Data Science, School of Public Health, UMN, Minneapolis, MN, United States
| | - Chloe Falke
- Division of Biostatistics and Health Data Science, School of Public Health, UMN, Minneapolis, MN, United States
| | - Mark B. Fiecas
- Division of Biostatistics and Health Data Science, School of Public Health, UMN, Minneapolis, MN, United States
| | - Bonnie Klimes-Dougan
- Psychology Department, College of Liberal Arts, UMN, Minneapolis, MN, United States
| | - Bryon A. Mueller
- Department of Psychiatry and Behavioral Sciences, Medical School, University of Minnesota (UMN), Minneapolis, MN, United States
| | - Kathryn R. Cullen
- Department of Psychiatry and Behavioral Sciences, Medical School, University of Minnesota (UMN), Minneapolis, MN, United States
| |
Collapse
|
11
|
Camargo A, Del Mauro G, Wang Z. Task-induced changes in brain entropy. J Neurosci Res 2024; 102:e25310. [PMID: 38400553 PMCID: PMC10947426 DOI: 10.1002/jnr.25310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 12/21/2023] [Accepted: 02/09/2024] [Indexed: 02/25/2024]
Abstract
Entropy indicates irregularity of a dynamic system, with higher entropy indicating higher irregularity and more transit states. In the human brain, regional brain entropy (BEN) has been increasingly assessed using resting state fMRI (rs-fMRI), while changes of regional BEN during task-based fMRI have been scarcely studied. The purpose of this study is to characterize task-induced regional BEN alterations using the large Human Connectome Project (HCP) data. To control the potential modulation by the block design, BEN of task-fMRI was calculated from the fMRI images acquired during the task conditions only (task BEN) and then compared to BEN of rs-fMRI (resting BEN). Moreover, BEN was separately calculated from the control blocks of the task-fMRI runs (control BEN) and compared to task BEN. Finally, control BEN was compared to resting BEN to test for residual task effects in the control condition. With respect to resting state, task performance unanimously induced BEN reduction in the peripheral cortical area and BEN increase in the centric part of the sensorimotor and perception networks. Control compared to resting BEN showed similar entropy alterations, suggesting large residual task effects. Task compared to control BEN was characterized by reduced entropy in occipital, orbitofrontal, and parietal regions.
Collapse
Affiliation(s)
- Aldo Camargo
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine
| | - Gianpaolo Del Mauro
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine
| | - Ze Wang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine
| |
Collapse
|
12
|
Morales-Rojas C, Panerai RB, Jara JL. Exploring Physiological Differences in Brain Areas Using Statistical Complexity Analysis of BOLD Signals. ENTROPY (BASEL, SWITZERLAND) 2024; 26:81. [PMID: 38248206 PMCID: PMC11154491 DOI: 10.3390/e26010081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 01/03/2024] [Accepted: 01/08/2024] [Indexed: 01/23/2024]
Abstract
The brain is a fundamental organ for the human body to function properly, for which it needs to receive a continuous flow of blood, which explains the existence of control mechanisms that act to maintain this flow as constant as possible in a process known as cerebral autoregulation. One way to obtain information on how the levels of oxygen supplied to the brain vary is through of BOLD (Magnetic Resonance) images, which have the advantage of greater spatial resolution than other forms of measurement, such as transcranial Doppler. However, they do not provide good temporal resolution nor allow for continuous prolonged examination. Thus, it is of great importance to find a method to detect regional differences from short BOLD signals. One of the existing alternatives is complexity measures that can detect changes in the variability and temporal organisation of a signal that could reflect different physiological states. The so-called statistical complexity, created to overcome the shortcomings of entropy alone to explain the concept of complexity, has shown potential with haemodynamic signals. The aim of this study is to determine by using statistical complexity whether it is possible to find differences between physiologically distinct brain areas in healthy individuals. The data set includes BOLD images of 10 people obtained at the University Hospital of Leicester NHS Trust with a 1.5 Tesla magnetic resonance imaging scanner. The data were captured for 180 s at a frequency of 1 Hz. Using various combinations of statistical complexities, no differences were found between hemispheres. However, differences were detected between grey matter and white matter, indicating that these measurements are sensitive to differences in brain tissues.
Collapse
Affiliation(s)
- Catalina Morales-Rojas
- Departamento de Ingeniería Informática, Facultad de Ingeniería, Universidad de Santiago de Chile, Santiago 9170022, Chile;
| | - Ronney B. Panerai
- Department of Cardiovascular Sciences, University of Leicester, Leicester LE1 7RH, UK;
- NIHR Leicester Biomedical Research Centre, British Heart Foundation Cardiovascular Research Centre, Glenfield Hospital, Leicester LE3 9QP, UK
| | - José Luis Jara
- Departamento de Ingeniería Informática, Facultad de Ingeniería, Universidad de Santiago de Chile, Santiago 9170022, Chile;
| |
Collapse
|
13
|
Lin C, Huang C, Chang W, Chang Y, Liu H, Ng S, Lin H, Lee TM, Lee S, Wu S. Predicting suicidality in late-life depression by 3D convolutional neural network and cross-sample entropy analysis of resting-state fMRI. Brain Behav 2024; 14:e3348. [PMID: 38376042 PMCID: PMC10790060 DOI: 10.1002/brb3.3348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 11/19/2023] [Accepted: 11/20/2023] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND Predicting suicide is a pressing issue among older adults; however, predicting its risk is difficult. Capitalizing on the recent development of machine learning, considerable progress has been made in predicting complex behavior such as suicide. As depression remained the strongest risk for suicide, we aimed to apply deep learning algorithms to identify suicidality in a group with late-life depression (LLD). METHODS We enrolled 83 patients with LLD, 35 of which were non-suicidal and 48 were suicidal, including 26 with only suicidal ideation and 22 with past suicide attempts, for resting-state functional magnetic resonance imaging (MRI). Cross-sample entropy (CSE) analysis was conducted to examine the complexity of MRI signals among brain regions. Three-dimensional (3D) convolutional neural networks (CNNs) were used, and the classification accuracy in each brain region was averaged to predict suicidality after sixfold cross-validation. RESULTS We found brain regions with a mean accuracy above 75% to predict suicidality located mostly in default mode, fronto-parietal, and cingulo-opercular resting-state networks. The models with right amygdala and left caudate provided the most reliable accuracy in all cross-validation folds, indicating their neurobiological importance in late-life suicide. CONCLUSION Combining CSE analysis and the 3D CNN, several brain regions were found to be associated with suicidality.
Collapse
Affiliation(s)
- Chemin Lin
- Department of PsychiatryKeelung Chang Gung Memorial HospitalKeelungTaiwan
- College of MedicineChang Gung UniversityTaoyuanTaiwan
- Community Medicine Research CenterChang Gung Memorial HospitalKeelungTaiwan
| | - Chih‐Mao Huang
- Department of Biological Science and TechnologyNational Yang Ming Chiao Tung UniversityHsinchuTaiwan
| | - Wei Chang
- Department of Engineering and System ScienceNational Tsing Hua UniversityHsinchuTaiwan
| | - You‐Xun Chang
- Department of Engineering and System ScienceNational Tsing Hua UniversityHsinchuTaiwan
| | - Ho‐Ling Liu
- Community Medicine Research CenterChang Gung Memorial HospitalKeelungTaiwan
- Department of Imaging PhysicsUniversity of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Shu‐Hang Ng
- Department of Head and Neck Oncology GroupLinkou Chang Gung Memorial Hospital and Chang Gung UniversityTaoyuanTaiwan
- Department of Diagnostic RadiologyLinkou Chang Gung Memorial Hospital and Chang Gung UniversityTaoyuanTaiwan
| | - Huang‐Li Lin
- Department of PsychiatryLinkou Chang Gung Memorial HospitalTaoyuanTaiwan
| | - Tatia Mei‐Chun Lee
- Community Medicine Research CenterChang Gung Memorial HospitalKeelungTaiwan
- Laboratory of Neuropsychology and Human NeuroscienceThe University of Hong KongPok Fu LamHong Kong
- State Key Laboratory of Brain and Cognitive ScienceThe University of Hong KongPok Fu LamHong Kong
| | - Shwu‐Hua Lee
- Department of PsychiatryLinkou Chang Gung Memorial HospitalTaoyuanTaiwan
| | - Shun‐Chi Wu
- Department of Engineering and System ScienceNational Tsing Hua UniversityHsinchuTaiwan
| |
Collapse
|
14
|
Reinwald JR, Schmitz CN, Skorodumov I, Kuchar M, Weber-Fahr W, Spanagel R, Meinhardt MW. Psilocybin-induced default mode network hypoconnectivity is blunted in alcohol-dependent rats. Transl Psychiatry 2023; 13:392. [PMID: 38097569 PMCID: PMC10721862 DOI: 10.1038/s41398-023-02690-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 11/15/2023] [Accepted: 11/27/2023] [Indexed: 12/17/2023] Open
Abstract
Alcohol Use Disorder (AUD) adversely affects the lives of millions of people, but still lacks effective treatment options. Recent advancements in psychedelic research suggest psilocybin to be potentially efficacious for AUD. However, major knowledge gaps remain regarding (1) psilocybin's general mode of action and (2) AUD-specific alterations of responsivity to psilocybin treatment in the brain that are crucial for treatment development. Here, we conducted a randomized, placebo-controlled crossover pharmaco-fMRI study on psilocybin effects using a translational approach with healthy rats and a rat model of alcohol relapse. Psilocybin effects were quantified with resting-state functional connectivity using data-driven whole-brain global brain connectivity, network-based statistics, graph theory, hypothesis-driven Default Mode Network (DMN)-specific connectivity, and entropy analyses. Results demonstrate that psilocybin induced an acute wide-spread decrease in different functional connectivity domains together with a distinct increase of connectivity between serotonergic core regions and cortical areas. We could further provide translational evidence for psilocybin-induced DMN hypoconnectivity reported in humans. Psilocybin showed an AUD-specific blunting of DMN hypoconnectivity, which strongly correlated to the alcohol relapse intensity and was mainly driven by medial prefrontal regions. In conclusion, our results provide translational validity for acute psilocybin-induced neural effects in the rodent brain. Furthermore, alcohol relapse severity was negatively correlated with neural responsivity to psilocybin treatment. Our data suggest that a clinical standard dose of psilocybin may not be sufficient to treat severe AUD cases; a finding that should be considered for future clinical trials.
Collapse
Affiliation(s)
- Jonathan R Reinwald
- Research Group Translational Imaging, Department of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
- Research Group Systems Neuroscience and Mental Health, Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Johannes Gutenberg University, Mainz, Germany
| | - Christian N Schmitz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
- Department of Molecular Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Ivan Skorodumov
- Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Martin Kuchar
- Forensic Laboratory of Biologically Active Substances, Department of Chemistry of Natural Compounds, University of Chemistry and Technology Prague, Prague, Czech Republic
- Psychedelics Research Centre, National Institute of Mental Health, Klecany, Czech Republic
| | - Wolfgang Weber-Fahr
- Research Group Translational Imaging, Department of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Rainer Spanagel
- Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany.
| | - Marcus W Meinhardt
- Department of Molecular Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany.
- Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany.
| |
Collapse
|
15
|
Lewandowska M, Tołpa K, Rogala J, Piotrowski T, Dreszer J. Multivariate multiscale entropy (mMSE) as a tool for understanding the resting-state EEG signal dynamics: the spatial distribution and sex/gender-related differences. BEHAVIORAL AND BRAIN FUNCTIONS : BBF 2023; 19:18. [PMID: 37798774 PMCID: PMC10552392 DOI: 10.1186/s12993-023-00218-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 09/18/2023] [Indexed: 10/07/2023]
Abstract
BACKGROUND The study aimed to determine how the resting-state EEG (rsEEG) complexity changes both over time and space (channels). The complexity of rsEEG and its sex/gender differences were examined using the multivariate Multiscale Entropy (mMSE) in 95 healthy adults. Following the probability maps (Giacometti et al. in J Neurosci Methods 229:84-96, 2014), channel sets have been identified that correspond to the functional networks. For each channel set the area under curve (AUC), which represents the total complexity, MaxSlope-the maximum complexity change of the EEG signal at thefine scales (1:4 timescales), and AvgEnt-to the average entropy level at coarse-grained scales (9:12 timescales), respectively, were extracted. To check dynamic changes between the entropy level at the fine and coarse-grained scales, the difference in mMSE between the #9 and #4 timescale (DiffEnt) was also calculated. RESULTS We found the highest AUC for the channel sets corresponding to the somatomotor (SMN), dorsolateral network (DAN) and default mode (DMN) whereas the visual network (VN), limbic (LN), and frontoparietal (FPN) network showed the lowest AUC. The largest MaxSlope were in the SMN, DMN, ventral attention network (VAN), LN and FPN, and the smallest in the VN. The SMN and DAN were characterized by the highest and the LN, FPN, and VN by the lowest AvgEnt. The most stable entropy were for the DAN and VN while the LN showed the greatest drop of entropy at the coarse scales. Women, compared to men, showed higher MaxSlope and DiffEnt but lower AvgEnt in all channel sets. CONCLUSIONS Novel results of the present study are: (1) an identification of the mMSE features that capture entropy at the fine and coarse timescales in the channel sets corresponding to the main resting-state networks; (2) the sex/gender differences in these features.
Collapse
Affiliation(s)
- Monika Lewandowska
- Department of Clinical Psychology and Neuropsychology, Institute of Psychology, Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University in Torun, Gagarina 39 Street, 87-100, Torun, Poland
| | - Krzysztof Tołpa
- Department of Clinical Psychology and Neuropsychology, Institute of Psychology, Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University in Torun, Gagarina 39 Street, 87-100, Torun, Poland
| | - Jacek Rogala
- Faculty of Physics, University of Warsaw, Pasteur 5 Street, 02-093, Warsaw, Poland
| | - Tomasz Piotrowski
- Institute of Engineering and Technology, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Torun, Grudziądzka 5 Street, 87-100, Torun, Poland
| | - Joanna Dreszer
- Department of Clinical Psychology and Neuropsychology, Institute of Psychology, Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University in Torun, Gagarina 39 Street, 87-100, Torun, Poland.
| |
Collapse
|
16
|
Camargo A, Mauro GD, Wang Z. Task-induced changes in brain entropy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.28.23289255. [PMID: 37205436 PMCID: PMC10187354 DOI: 10.1101/2023.04.28.23289255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Entropy indicates irregularity of a dynamic system with higher entropy indicating higher irregularity and more transit states. In the human brain, regional entropy has been increasingly assessed using resting state fMRI. Response of regional entropy to task has been scarcely studied. The purpose of this study is to characterize task-induced regional brain entropy (BEN) alterations using the large Human Connectome Project (HCP) data. To control the potential modulation by the block-design, BEN of task-fMRI was calculated from the fMRI images acquired during the task conditions only and then compared to BEN of rsfMRI. Compared to resting state, task-performance unanimously induced BEN reduction in the peripheral cortical area including both the task activated regions and task non-specific regions such as the task negative area and BEN increase in the centric part of the sensorimotor and perception networks. Task control condition showed large residual task effects. After controlling the task non-specific effects using the control BEN vs task BEN comparison, regional BEN showed task specific effects in target regions.
Collapse
Affiliation(s)
- Aldo Camargo
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine
| | - Gianpaolo Del Mauro
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine
| | - Ze Wang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine
| |
Collapse
|
17
|
Liu J, Lu H, Zhang X, Li X, Wang L, Yin S, Cui D. Which Multivariate Multi-Scale Entropy Algorithm Is More Suitable for Analyzing the EEG Characteristics of Mild Cognitive Impairment? ENTROPY (BASEL, SWITZERLAND) 2023; 25:396. [PMID: 36981285 PMCID: PMC10047945 DOI: 10.3390/e25030396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 02/10/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
So far, most articles using the multivariate multi-scale entropy algorithm mainly use algorithms to analyze the multivariable signal complexity without clearly describing what characteristics of signals these algorithms measure and what factors affect these algorithms. This paper analyzes six commonly used multivariate multi-scale entropy algorithms from a new perspective. It clarifies for the first time what characteristics of signals these algorithms measure and which factors affect them. It also studies which algorithm is more suitable for analyzing mild cognitive impairment (MCI) electroencephalograph (EEG) signals. The simulation results show that the multivariate multi-scale sample entropy (mvMSE), multivariate multi-scale fuzzy entropy (mvMFE), and refined composite multivariate multi-scale fuzzy entropy (RCmvMFE) algorithms can measure intra- and inter-channel correlation and multivariable signal complexity. In the joint analysis of coupling and complexity, they all decrease with the decrease in signal complexity and coupling strength, highlighting their advantages in processing related multi-channel signals, which is a discovery in the simulation. Among them, the RCmvMFE algorithm can better distinguish different complexity signals and correlations between channels. It also performs well in anti-noise and length analysis of multi-channel data simultaneously. Therefore, we use the RCmvMFE algorithm to analyze EEG signals from twenty subjects (eight control subjects and twelve MCI subjects). The results show that the MCI group had lower entropy than the control group on the short scale and the opposite on the long scale. Moreover, frontal entropy correlates significantly positively with the Montreal Cognitive Assessment score and Auditory Verbal Learning Test delayed recall score on the short scale.
Collapse
Affiliation(s)
- Jing Liu
- Hebei Key Laboratory of Information Transmission and Signal Processing, School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Huibin Lu
- Hebei Key Laboratory of Information Transmission and Signal Processing, School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Xiuru Zhang
- Hebei Key Laboratory of Information Transmission and Signal Processing, School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Xiaoli Li
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Lei Wang
- Neurology Department, Chinese People’s Liberation Army Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Shimin Yin
- Neurology Department, Chinese People’s Liberation Army Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Dong Cui
- Hebei Key Laboratory of Information Transmission and Signal Processing, School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
| |
Collapse
|
18
|
Zhao X, Jin F, Wang J, Zhao X, Wang L, Wei H. Entropy of left ventricular late gadolinium enhancement and its prognostic value in hypertrophic cardiomyopathy a new CMR assessment method. Int J Cardiol 2023; 373:134-141. [PMID: 36395920 DOI: 10.1016/j.ijcard.2022.11.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/04/2022] [Accepted: 11/08/2022] [Indexed: 11/16/2022]
Abstract
PURPOSE As a novel metric, entropy generated from late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) can be utilized to assess tissue heterogeneity. However, it is unknown if it can be utilized for risk stratification in hypertrophic cardiomyopathy (HCM). In addition, it is unknown if LGE entropy correlates with LGE mass%, which is commonly utilized for fibrosis assessment. This research was done to investigate these issues. MATERIALS AND METHODS Patients with HCM who underwent 3.0-T CMR between January 2015 and January 2020 were prospectively enrolled and classified into low- and high-risk groups according to the AHA/ACC risk stratification guideline for 2020. The LGE entropy was automatically estimated using a generic Python package algorithm. On CMR imaging, the LGE mass% was determined using the CVI 42 software. Endpoint events included sudden cardiac death (SCD), hospital readmission owing to heart failure, and implantable cardioverter defibrillator (ICD) treatment for ventricular arrhythmias. RESULTS A total of 109 HCM participants (70 males) were included. During the follow-up (23 ± 7 months), the patients in the high-risk group had higher LGE entropy (p < 0.001) and LGE mass% (p < 0.001) than those in the low-risk group, and patients with endpoint events had higher LGE entropy (p < 0.001) and LGE mass% (p < 0.001) than those without endpoint events. In all participants, there was a link between LGE entropy and LGE mass%, according to the Spearman rank correlation analysis (p < 0.001; r = 0.667). In ROC analysis, the area under the curve (AUC) of LGE entropy was 0.893 (95% CI, 0.794-0.993; P<0.001), AUC of LGE mass% was 0.826 (95% CI, 0.737-0.914; P<0.001), AUC of LVEF was 0.610 (95% CI, 0.473-0.748; P = 0.117) and AUC of 2020 AHA/ACC guideline for risk stratification was 0.716 (95% CI, 0.617-0.815; P = 0.002). According to Kaplan-Meier curves, HCM with a higher LGE entropy (≥cutoff value (<5.873) or ≥ thied tertile (5.540)) were more likely to experience the endpoint events. Following adjustment for the 2020 AHA/ACC guideline for risk categorization, LGE mass%, or decreased LVEF, Cox analysis showed that LGE entropy was independently linked with endpoint events. CONCLUSIONS The variability and extent of LGE pictures can be reflected by LGE entropy, which is a reliable, usable, and repeatable metric for risk classification in HCM. It is a prognostic indicator of endpoint events that is independent of other risk indicators.
Collapse
Affiliation(s)
- Xiaoying Zhao
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Dianmiandadao No. 374, Kunming, Yunnan 650000, China
| | - Fuwei Jin
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Dianmiandadao No. 374, Kunming, Yunnan 650000, China
| | - Jin Wang
- Department of Radiology, Yanan Hospital of Kunming City, Renmin Dong Lu No. 245, Kunming, Yunnan 650000, China.
| | - Xinxiang Zhao
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Dianmiandadao No. 374, Kunming, Yunnan 650000, China.
| | - Lujing Wang
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Dianmiandadao No. 374, Kunming, Yunnan 650000, China
| | - Hua Wei
- Department of Information, The Second Affiliated Hospital of Kunming Medical University,Dianmiandadao No. 374, Kunming, Yunnan 650000, China
| |
Collapse
|
19
|
Progressive brain abnormalities in schizophrenia across different illness periods: a structural and functional MRI study. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:2. [PMID: 36604437 PMCID: PMC9816110 DOI: 10.1038/s41537-022-00328-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 11/16/2022] [Indexed: 01/07/2023]
Abstract
Schizophrenia is a chronic brain disorder, and neuroimaging abnormalities have been reported in different stages of the illness for decades. However, when and how these brain abnormalities occur and evolve remains undetermined. We hypothesized structural and functional brain abnormalities progress throughout the illness course at different rates in schizophrenia. A total of 115 patients with schizophrenia were recruited and stratified into three groups of different illness periods: 5-year group (illness duration: ≤5 years), 15-year group (illness duration: 12-18 years), and 25-year group (illness duration: ≥25 years); 230 healthy controls were matched by age and sex to the three groups, respectively. All participants underwent resting-state MRI scanning. Each group of patients with schizophrenia was compared with the corresponding controls in terms of voxel-based morphometry (VBM), fractional anisotropy (FA), global functional connectivity density (gFCD), and sample entropy (SampEn) abnormalities. In the 5-year group we observed only SampEn abnormalities in the putamen. In the 15-year group, we observed VBM abnormalities in the insula and cingulate gyrus and gFCD abnormalities in the temporal cortex. In the 25-year group, we observed FA abnormalities in nearly all white matter tracts, and additional VBM and gFCD abnormalities in the frontal cortex and cerebellum. By using two structural and two functional MRI analysis methods, we demonstrated that individual functional abnormalities occur in limited brain areas initially, functional connectivity and gray matter density abnormalities ensue later in wider brain areas, and structural connectivity abnormalities involving almost all white matter tracts emerge in the third decade of the course in schizophrenia.
Collapse
|
20
|
Omidvarnia A, Liégeois R, Amico E, Preti MG, Zalesky A, Van De Ville D. On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1148. [PMID: 36010812 PMCID: PMC9407401 DOI: 10.3390/e24081148] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/08/2022] [Accepted: 08/10/2022] [Indexed: 06/15/2023]
Abstract
Measuring the temporal complexity of functional MRI (fMRI) time series is one approach to assess how brain activity changes over time. In fact, hemodynamic response of the brain is known to exhibit critical behaviour at the edge between order and disorder. In this study, we aimed to revisit the spatial distribution of temporal complexity in resting state and task fMRI of 100 unrelated subjects from the Human Connectome Project (HCP). First, we compared two common choices of complexity measures, i.e., Hurst exponent and multiscale entropy, and observed a high spatial similarity between them. Second, we considered four tasks in the HCP dataset (Language, Motor, Social, and Working Memory) and found high task-specific complexity, even when the task design was regressed out. For the significance thresholding of brain complexity maps, we used a statistical framework based on graph signal processing that incorporates the structural connectome to develop the null distributions of fMRI complexity. The results suggest that the frontoparietal, dorsal attention, visual, and default mode networks represent stronger complex behaviour than the rest of the brain, irrespective of the task engagement. In sum, the findings support the hypothesis of fMRI temporal complexity as a marker of cognition.
Collapse
Affiliation(s)
- Amir Omidvarnia
- Applied Machine Learning Group, Institute of Neuroscience and Medicine, Forschungszentrum Juelich, 52428 Juelich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, 40225 Duesseldorf, Germany
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, 1202 Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, 1211 Geneva, Switzerland
| | - Raphaël Liégeois
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, 1202 Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, 1211 Geneva, Switzerland
| | - Enrico Amico
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, 1202 Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, 1211 Geneva, Switzerland
| | - Maria Giulia Preti
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, 1202 Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, 1211 Geneva, Switzerland
- CIBM Center for Biomedical Imaging, 1015 Lausanne, Switzerland
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC 3010, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Dimitri Van De Ville
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, 1202 Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, 1211 Geneva, Switzerland
| |
Collapse
|
21
|
Lower resting brain entropy is associated with stronger task activation and deactivation. Neuroimage 2022; 249:118875. [PMID: 34998971 DOI: 10.1016/j.neuroimage.2022.118875] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 12/15/2021] [Accepted: 01/04/2022] [Indexed: 01/21/2023] Open
Abstract
Brain entropy (BEN) calculated from resting state fMRI has been the subject of increasing research interest in recent years. Previous studies have shown the correlations between rest BEN and neurocognition and task performance, but how this relates to task-evoked brain activations and deactivations remains unknown. The purpose of this study is to address this open question using large data (n = 862). Voxel wise correlations were calculated between rest BEN and task activations/deactivations of five different tasks. For most of the assessed tasks, lower rest BEN was found to be associated with stronger activations (negative correlations) and stronger deactivations (positive correlations) only in brain regions activated or deactivated by the tasks. Higher workload evoked spatially more extended negative correlations between rest BEN and task activations. These results not only confirm that resting brain activity can predict brain activity during task performance but also for the first time show that resting brain activity may facilitate both task activations and deactivations. In addition, the results provide a clue to understanding the individual differences of task performance and brain activations.
Collapse
|
22
|
Yin T, He Z, Ma P, Sun R, Xie K, Liu T, Chen L, Chen J, Hou L, Teng Y, Guo Y, Tian Z, Xiong J, Wang F, Li S, Yang S, Zeng F. Aberrant functional brain network dynamics in patients with functional constipation. Hum Brain Mapp 2021; 42:5985-5999. [PMID: 34533251 PMCID: PMC8596972 DOI: 10.1002/hbm.25663] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 08/25/2021] [Accepted: 09/06/2021] [Indexed: 12/17/2022] Open
Abstract
The aberrant static functional connectivity of brain network has been widely investigated in patients with functional constipation (FCon). However, the dynamics of brain functional connectivity in FCon patients remained unknown. This study aimed to detect the brain dynamics of functional connectivity states and network topological organizations of FCon patients and investigate the correlations of the aberrant brain dynamics with symptom severity. Eighty-three FCon patients and 80 healthy subjects (HS) were included in data analysis. The spatial group independent component analysis, sliding-window approach, k-means clustering, and graph-theoretic analysis were applied to investigate the dynamic temporal properties and coupling patterns of functional connectivity states, as well as the time-variation of network topological organizations in FCon patients. Four reoccurring functional connectivity states were identified in k-means clustering analysis. Compared to HS, FCon patients manifested the lower occurrence rate and mean dwell time in the state with a complex connection between default mode network and cognitive control network, as well as the aberrant anterior insula-cortical coupling patterns in this state, which were significantly correlated with the symptom severity. The graph-theoretic analysis demonstrated that FCon patients had higher sample entropy at the nodal efficiency of anterior insula than HS. The current findings provided dynamic perspectives for understanding the brain connectome of FCon and laid the foundation for the potential treatment of FCon based on brain connectomics.
Collapse
Affiliation(s)
- Tao Yin
- Acupuncture and Tuina School/The 3rd Teaching HospitalChengdu University of Traditional Chinese MedicineChengduSichuanChina
- Acupuncture and Brain Science Research CenterChengdu University of Traditional Chinese MedicineChengduSichuanChina
| | - Zhaoxuan He
- Acupuncture and Tuina School/The 3rd Teaching HospitalChengdu University of Traditional Chinese MedicineChengduSichuanChina
- Acupuncture and Brain Science Research CenterChengdu University of Traditional Chinese MedicineChengduSichuanChina
- Key Laboratory of Sichuan Province for Acupuncture and ChronobiologyChengduSichuanChina
| | - Peihong Ma
- Acupuncture and Tuina School/The 3rd Teaching HospitalChengdu University of Traditional Chinese MedicineChengduSichuanChina
- Acupuncture and Brain Science Research CenterChengdu University of Traditional Chinese MedicineChengduSichuanChina
| | - Ruirui Sun
- Acupuncture and Tuina School/The 3rd Teaching HospitalChengdu University of Traditional Chinese MedicineChengduSichuanChina
- Acupuncture and Brain Science Research CenterChengdu University of Traditional Chinese MedicineChengduSichuanChina
| | - Kunnan Xie
- Acupuncture and Tuina School/The 3rd Teaching HospitalChengdu University of Traditional Chinese MedicineChengduSichuanChina
| | - Tianyu Liu
- School of SportChengdu University of Traditional Chinese MedicineChengduSichuanChina
| | - Li Chen
- Acupuncture and Tuina School/The 3rd Teaching HospitalChengdu University of Traditional Chinese MedicineChengduSichuanChina
- Acupuncture and Brain Science Research CenterChengdu University of Traditional Chinese MedicineChengduSichuanChina
| | - Jingwen Chen
- Acupuncture and Tuina School/The 3rd Teaching HospitalChengdu University of Traditional Chinese MedicineChengduSichuanChina
| | - Likai Hou
- Sichuan Bayi Rehabilitation CenterChengduSichuanChina
| | - Yuke Teng
- Acupuncture and Tuina School/The 3rd Teaching HospitalChengdu University of Traditional Chinese MedicineChengduSichuanChina
- Acupuncture and Brain Science Research CenterChengdu University of Traditional Chinese MedicineChengduSichuanChina
| | - Yuyi Guo
- Acupuncture and Tuina School/The 3rd Teaching HospitalChengdu University of Traditional Chinese MedicineChengduSichuanChina
| | - Zilei Tian
- Acupuncture and Tuina School/The 3rd Teaching HospitalChengdu University of Traditional Chinese MedicineChengduSichuanChina
- Acupuncture and Brain Science Research CenterChengdu University of Traditional Chinese MedicineChengduSichuanChina
| | - Jing Xiong
- Acupuncture and Tuina School/The 3rd Teaching HospitalChengdu University of Traditional Chinese MedicineChengduSichuanChina
| | - Fumin Wang
- Acupuncture and Tuina School/The 3rd Teaching HospitalChengdu University of Traditional Chinese MedicineChengduSichuanChina
| | - Shenghong Li
- State Key Laboratory of Southwestern Chinese Medicine Resources/Innovative Institute of Chinese Medicine and PharmacyChengdu University of Traditional Chinese MedicineChengduSichuanChina
| | - Sha Yang
- Acupuncture and Tuina School/The 3rd Teaching HospitalChengdu University of Traditional Chinese MedicineChengduSichuanChina
- Acupuncture and Brain Science Research CenterChengdu University of Traditional Chinese MedicineChengduSichuanChina
- Key Laboratory of Sichuan Province for Acupuncture and ChronobiologyChengduSichuanChina
| | - Fang Zeng
- Acupuncture and Tuina School/The 3rd Teaching HospitalChengdu University of Traditional Chinese MedicineChengduSichuanChina
- Acupuncture and Brain Science Research CenterChengdu University of Traditional Chinese MedicineChengduSichuanChina
| |
Collapse
|
23
|
Gale MK, Nezafati M, Keilholz SD. Complexity Analysis of Resting-State and Task fMRI Using Multiscale Sample Entropy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2968-2971. [PMID: 34891868 DOI: 10.1109/embc46164.2021.9630607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Functional magnetic resonance imaging (fMRI) is a powerful tool that allows for analysis of neural activity via the measurement of blood-oxygenation-level-dependent (BOLD) signal. The BOLD fluctuations can exhibit different levels of complexity, depending upon the conditions under which they are measured. We examined the complexity of both resting-state and task-based fMRI using sample entropy (SampEn) as a surrogate for signal predictability. We found that within most tasks, regions of the brain that were deemed task-relevant displayed significantly low levels of SampEn, and there was a strong negative correlation between parcel entropy and amplitude.
Collapse
|
24
|
Soundirarajan M, Aghasian E, Krejcar O, Namazi H. Complexity-based analysis of the coupling between facial muscle and brain activities. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102511] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
25
|
Keilholz S, Maltbie E, Zhang X, Yousefi B, Pan WJ, Xu N, Nezafati M, LaGrow TJ, Guo Y. Relationship Between Basic Properties of BOLD Fluctuations and Calculated Metrics of Complexity in the Human Connectome Project. Front Neurosci 2020; 14:550923. [PMID: 33041756 PMCID: PMC7522447 DOI: 10.3389/fnins.2020.550923] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 08/13/2020] [Indexed: 12/14/2022] Open
Abstract
Resting state functional MRI (rs-fMRI) creates a rich four-dimensional data set that can be analyzed in a variety of ways. As more researchers come to view the brain as a complex dynamical system, tools are increasingly being drawn from other fields to characterize the complexity of the brain's activity. However, given that the signal measured with rs-fMRI arises from the hemodynamic response to neural activity, the extent to which complexity metrics reflect neural complexity as compared to signal properties related to image quality remains unknown. To provide some insight into this question, correlation dimension, approximate entropy and Lyapunov exponent were calculated for different rs-fMRI scans from the same subject to examine their reliability. The metrics of complexity were then compared to several properties of the rs-fMRI signal from each brain area to determine if basic signal features could explain differences in the complexity metrics. Differences in complexity across brain areas were highly reliable and were closely linked to differences in the frequency profiles of the rs-fMRI signal. The spatial distributions of the complexity and frequency metrics suggest that they are both influenced by location-dependent signal properties that can obscure changes related to neural activity.
Collapse
Affiliation(s)
- Shella Keilholz
- Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Eric Maltbie
- Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Xiaodi Zhang
- Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Behnaz Yousefi
- Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Wen-Ju Pan
- Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Nan Xu
- Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Maysam Nezafati
- Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Theodore J LaGrow
- Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Ying Guo
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, United States
| |
Collapse
|