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Zhang P, Wan X, Jiang J, Liu Y, Wang D, Ai K, Liu G, Zhang X, Zhang J. A causal effect study of cortical morphology and related covariate networks in classical trigeminal neuralgia patients. Cereb Cortex 2024; 34:bhae337. [PMID: 39123310 DOI: 10.1093/cercor/bhae337] [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: 06/18/2024] [Revised: 07/17/2024] [Accepted: 07/27/2024] [Indexed: 08/12/2024] Open
Abstract
Structural covariance networks and causal effects within can provide critical information on gray matter reorganization and disease-related hierarchical changes. Based on the T1WI data of 43 classical trigeminal neuralgia patients and 45 controls, we constructed morphological similarity networks of cortical thickness, sulcal depth, fractal dimension, and gyrification index. Moreover, causal structural covariance network analyses were conducted in regions with morphological abnormalities or altered nodal properties, respectively. We found that patients showed reduced sulcal depth, gyrification index, and fractal dimension, especially in the salience network and the default mode network. Additionally, the integration of the fractal dimension and sulcal depth networks was significantly reduced, accompanied by decreased nodal efficiency of the bilateral temporal poles, and right pericalcarine cortex within the sulcal depth network. Negative causal effects existed from the left insula to the right caudal anterior cingulate cortex in the gyrification index map, also from bilateral temporal poles to right pericalcarine cortex within the sulcal depth network. Collectively, patients exhibited impaired integrity of the covariance networks in addition to the abnormal gray matter morphology in the salience network and default mode network. Furthermore, the patients may experience progressive impairment in the salience network and from the limbic system to the sensory system in network topology, respectively.
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Affiliation(s)
- Pengfei Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, Sichuan 610041, China
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Xinyue Wan
- Department of Radiology, Huashan Hospital, Fudan University, No. 12, Urumqi Middle Road, Jingan District, Shanghai 200040, China
| | - Jingqi Jiang
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Yang Liu
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Danyang Wang
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Kai Ai
- Department of Clinical and Technical Supports, Philips Healthcare, No. 64 West Section, South 2nd Ring Road, Yanta District, Xi'an 710000, China
| | - Guangyao Liu
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Xinding Zhang
- Department of Neurosurgery and Laboratory of Neurosurgery, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
| | - Jing Zhang
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
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Chung MK, Huang SG, Carroll IC, Calhoun VD, Goldsmith HH. Topological state-space estimation of functional human brain networks. PLoS Comput Biol 2024; 20:e1011869. [PMID: 38739671 PMCID: PMC11115255 DOI: 10.1371/journal.pcbi.1011869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 05/23/2024] [Accepted: 01/29/2024] [Indexed: 05/16/2024] Open
Abstract
We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest. Our method utilizes the Wasserstein distance to measure topological differences, enabling the clustering of brain networks into distinct topological states. This technique outperforms the commonly used k-means clustering in identifying brain network state spaces by effectively incorporating the temporal dynamics of the data without the need for explicit model specification. We further investigate the genetic underpinnings of these topological features using a twin study design, examining the heritability of such state changes. Our findings suggest that the topology of brain networks, particularly in their dynamic state changes, may hold significant hidden genetic information.
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Affiliation(s)
- Moo K. Chung
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin, United States of America
| | | | - Ian C. Carroll
- Department of Child and Adolescent Psychiatry, New York University Grossman School of Medicine, New York, United States of America
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States of America
| | - H. Hill Goldsmith
- Department of Psychology & Waisman Center, University of Wisconsin, Madison, Wisconsin, United States of America
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3
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Chung MK, Azizi T, Hanson JL, Alexander AL, Pollak SD, Davidson RJ. Altered topological structure of the brain white matter in maltreated children through topological data analysis. Netw Neurosci 2024; 8:355-376. [PMID: 38711544 PMCID: PMC11073548 DOI: 10.1162/netn_a_00355] [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: 04/07/2023] [Accepted: 11/30/2023] [Indexed: 05/08/2024] Open
Abstract
Childhood maltreatment may adversely affect brain development and consequently influence behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter in maltreated and typically developing children. We perform topological data analysis (TDA) to assess the alteration in the global topology of the brain white matter structural covariance network among children. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging and diffusion tensor imaging. We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children with a typically developing control group, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly suggest that TDA can be a valuable framework to model altered topological structures of the brain. The MATLAB codes and processed data used in this study can be found at https://github.com/laplcebeltrami/maltreated.
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Affiliation(s)
- Moo K. Chung
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, WI, USA
| | - Tahmineh Azizi
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, WI, USA
| | - Jamie L. Hanson
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Andrew L. Alexander
- Department of Medical Physics, University of Wisconsin–Madison, Madison, WI, USA
| | - Seth D. Pollak
- Department of Psychology, University of Wisconsin–Madison, Madison, WI, USA
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4
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Guzmán-Vargas L, Zabaleta-Ortega A, Guzmán-Sáenz A. Simplicial complex entropy for time series analysis. Sci Rep 2023; 13:22696. [PMID: 38123652 PMCID: PMC10733285 DOI: 10.1038/s41598-023-49958-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023] Open
Abstract
The complex behavior of many systems in nature requires the application of robust methodologies capable of identifying changes in their dynamics. In the case of time series (which are sensed values of a system during a time interval), several methods have been proposed to evaluate their irregularity. However, for some types of dynamics such as stochastic and chaotic, new approaches are required that can provide a better characterization of them. In this paper we present the simplicial complex approximate entropy, which is based on the conditional probability of the occurrence of elements of a simplicial complex. Our results show that this entropy measure provides a wide range of values with details not easily identifiable with standard methods. In particular, we show that our method is able to quantify the irregularity in simulated random sequences and those from low-dimensional chaotic dynamics. Furthermore, it is possible to consistently differentiate cardiac interbeat sequences from healthy subjects and from patients with heart failure, as well as to identify changes between dynamical states of coupled chaotic maps. Our results highlight the importance of the structures revealed by the simplicial complexes, which holds promise for applications of this approach in various contexts.
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Affiliation(s)
- Lev Guzmán-Vargas
- Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas, Instituto Politécnico Nacional, 07340, Mexico City, Mexico.
| | - Alvaro Zabaleta-Ortega
- Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas, Instituto Politécnico Nacional, 07340, Mexico City, Mexico
| | - Aldo Guzmán-Sáenz
- Topological Data Analysis in Genomics, Thomas J. Watson Research Center, Yorktown Heights, NY, USA
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El-Yaagoubi AB, Chung MK, Ombao H. Topological Data Analysis for Multivariate Time Series Data. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1509. [PMID: 37998201 PMCID: PMC10669999 DOI: 10.3390/e25111509] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 10/23/2023] [Accepted: 10/23/2023] [Indexed: 11/25/2023]
Abstract
Over the last two decades, topological data analysis (TDA) has emerged as a very powerful data analytic approach that can deal with various data modalities of varying complexities. One of the most commonly used tools in TDA is persistent homology (PH), which can extract topological properties from data at various scales. The aim of this article is to introduce TDA concepts to a statistical audience and provide an approach to analyzing multivariate time series data. The application's focus will be on multivariate brain signals and brain connectivity networks. Finally, this paper concludes with an overview of some open problems and potential application of TDA to modeling directionality in a brain network, as well as the casting of TDA in the context of mixed effect models to capture variations in the topological properties of data collected from multiple subjects.
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Affiliation(s)
- Anass B. El-Yaagoubi
- Statistics Program, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia;
| | - Moo K. Chung
- Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA;
| | - Hernando Ombao
- Statistics Program, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia;
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6
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Argiris G, Stern Y, Lee S, Ryu H, Habeck C. Simple topological task-based functional connectivity features predict longitudinal behavioral change of fluid reasoning in the RANN cohort. Neuroimage 2023; 277:120237. [PMID: 37343735 PMCID: PMC10999229 DOI: 10.1016/j.neuroimage.2023.120237] [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: 05/12/2023] [Accepted: 06/18/2023] [Indexed: 06/23/2023] Open
Abstract
Recent attention has been given to topological data analysis (TDA), and more specifically persistent homology (PH), to identify the underlying shape of brain network connectivity beyond simple edge pairings by computing connective components across different connectivity thresholds (see Sizemore et al., 2019). In the present study, we applied PH to task-based functional connectivity, computing 0-dimension Betti (B0) curves and calculating the area under these curves (AUC); AUC indicates how quickly a single connected component is formed across correlation filtration thresholds, with lower values interpreted as potentially analogous to lower whole-brain system segregation (e.g., Gracia-Tabuenca et al., 2020). One hundred sixty-three participants from the Reference Ability Neural Network (RANN) longitudinal lifespan cohort (age 20-80 years) were tested in-scanner at baseline and five-year follow-up on a battery of tests comprising four domains of cognition (i.e., Stern et al., 2014). We tested for 1.) age-related change in the AUC of the B0 curve over time, 2.) the predictive utility of AUC in accounting for longitudinal change in behavioral performance and 3.) compared system segregation to the PH approach. Results demonstrated longitudinal age-related decreases in AUC for Fluid Reasoning, with these decreases predicting longitudinal declines in cognition, even after controlling for demographic and brain integrity factors; moreover, change in AUC partially mediated the effect of age on change in cognitive performance. System segregation also significantly decreased with age in three of the four cognitive domains but did not predict change in cognition. These results argue for greater application of TDA to the study of aging.
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Affiliation(s)
- Georgette Argiris
- Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, 710 West 168th Street, 3rd floor, New York, NY 10032, United States
| | - Yaakov Stern
- Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, 710 West 168th Street, 3rd floor, New York, NY 10032, United States
| | - Seonjoo Lee
- Mental Health Data Science, New York State Psychiatric Institute, New York, NY, United States; Department of Biostatistics, Mailman School of Public Health, New York, NY, United States; Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States
| | - Hyunnam Ryu
- Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, 710 West 168th Street, 3rd floor, New York, NY 10032, United States; Taub Institute, Columbia University, New York, NY, United States; Mental Health Data Science, New York State Psychiatric Institute, New York, NY, United States
| | - Christian Habeck
- Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, 710 West 168th Street, 3rd floor, New York, NY 10032, United States; Taub Institute, Columbia University, New York, NY, United States.
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Yang X, Ren Y, Hong B, He A, Wang J, Wang Z. Epileptic detection in single and multi-lead EEG signals using persistent homology based on bi-directional weighted visibility graphs. CHAOS (WOODBURY, N.Y.) 2023; 33:2894484. [PMID: 37276567 DOI: 10.1063/5.0140579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 05/03/2023] [Indexed: 06/07/2023]
Abstract
Epilepsy is a widespread neurological disorder, and its recurrence and suddenness are making automatic detection of seizure an urgent necessity. For this purpose, this paper performs topological data analysis (TDA) of electroencephalographic (EEG) signals by the medium of graphs to explore the potential brain activity information they contain. Through our innovative method, we first map the time series of epileptic EEGs into bi-directional weighted visibility graphs (BWVGs), which give more comprehensive reflections of the signals compared to previous existing structures. Traditional graph-theoretic measurements are generally partial and mainly consider differences or correlations in vertices or edges, whereas persistent homology (PH), the essential part of TDA, provides an alternative way of thinking by quantifying the topology structure of the graphs and analyzing the evolution of these topological properties with scale changes. Therefore, we analyze the PH for BWVGs and then obtain the two indicators of persistence and birth-death for homology groups to reflect the topology of the mapping graphs of EEG signals and reveal the discrepancies in brain dynamics. Furthermore, we adopt neural networks (NNs) for the automatic detection of epileptic signals and successfully achieve a classification accuracy of 99.67% when distinguishing among three different sets of EEG signals from seizure, seizure-free, and healthy subjects. In addition, to accommodate multi-leads, we propose a classifier that incorporates graph structure to distinguish seizure and seizure-free EEG signals. The classification accuracies of the two subjects used in the classifier are as high as 99.23% and 94.76%, respectively, indicating that our proposed model is useful for the analysis of EEG signals.
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Affiliation(s)
- Xiaodong Yang
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
| | - Yanlin Ren
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
| | - Binyi Hong
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
| | - Aijun He
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China
| | - Jun Wang
- School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Zhixiao Wang
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
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Gracia-Tabuenca Z, Díaz-Patiño JC, Arelio-Ríos I, Moreno-García MB, Barrios FA, Alcauter S. Development of the Functional Connectome Topology in Adolescence: Evidence from Topological Data Analysis. eNeuro 2023; 10:ENEURO.0296-21.2022. [PMID: 36717266 PMCID: PMC9933932 DOI: 10.1523/eneuro.0296-21.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 11/09/2022] [Accepted: 12/01/2022] [Indexed: 01/31/2023] Open
Abstract
Adolescence is a crucial developmental period in terms of behavior and mental health. Therefore, understanding how the brain develops during this stage is a fundamental challenge for neuroscience. Recent studies have modeled the brain as a network or connectome, mainly applying measures from graph theory, showing a change in its functional organization, such as an increase in its segregation and integration. Topological Data Analysis (TDA) complements such modeling by extracting high-dimensional features across the whole range of connectivity values instead of exploring a fixed set of connections. This study inquires into the developmental trajectories of such properties using a longitudinal sample of typically developing human participants (N = 98; 53/45 female/male; 6.7-18.1 years), applying TDA to their functional connectomes. In addition, we explore the effect of puberty on individual developmental trajectories. Results showed that the adolescent brain has a more distributed topology structure compared with random networks but is more densely connected at the local level. Furthermore, developmental effects showed nonlinear trajectories for the topology of the whole brain and fronto-parietal networks, with an inflection point and increasing trajectories after puberty onset. These results add to the insights into the development of the functional organization of the adolescent brain.
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Affiliation(s)
- Zeus Gracia-Tabuenca
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro 76230, México
| | - Juan Carlos Díaz-Patiño
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro 76230, México
- Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada 22860, México
| | - Isaac Arelio-Ríos
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro 76230, México
| | | | - Fernando A Barrios
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro 76230, México
| | - Sarael Alcauter
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro 76230, México
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Yoon N, Huh Y, Lee H, Kim JI, Lee J, Yang CM, Jang S, Ahn YD, Oh MR, Lee DS, Kang H, Kim BN. Alterations in Social Brain Network Topology at Rest in Children With Autism Spectrum Disorder. Psychiatry Investig 2022; 19:1055-1068. [PMID: 36588440 PMCID: PMC9806512 DOI: 10.30773/pi.2022.0174] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 11/24/2022] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE Underconnectivity in the resting brain is not consistent in autism spectrum disorder (ASD). However, it is known that the functional connectivity of the default mode network is mainly decreased in childhood ASD. This study investigated the brain network topology as the changes in the connection strength and network efficiency in childhood ASD, including the early developmental stages. METHODS In this study, 31 ASD children aged 2-11 years were compared with 31 age and sex-matched children showing typical development. We explored the functional connectivity based on graph filtration by assessing the single linkage distance and global and nodal efficiencies using resting-state functional magnetic resonance imaging. The relationship between functional connectivity and clinical scores was also analyzed. RESULTS Underconnectivities within the posterior default mode network subregions and between the inferior parietal lobule and inferior frontal/superior temporal regions were observed in the ASD group. These areas significantly correlated with the clinical phenotypes. The global, local, and nodal network efficiencies were lower in children with ASD than in those with typical development. In the preschool-age children (2-6 years) with ASD, the anterior-posterior connectivity of the default mode network and cerebellar connectivity were reduced. CONCLUSION The observed topological reorganization, underconnectivity, and disrupted efficiency in the default mode network subregions and social function-related regions could be significant biomarkers of childhood ASD.
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Affiliation(s)
- Narae Yoon
- Division of Children and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Youngmin Huh
- Medical Research Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hyekyoung Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.,Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Johanna Inhyang Kim
- Department of Psychiatry, Hanyang University Medical Center, Seoul, Republic of Korea
| | - Jung Lee
- Division of Children and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.,Integrative Care Hub, Seoul National University Children's Hospital, Seoul, Republic of Korea
| | - Chan-Mo Yang
- Division of Children and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Soomin Jang
- Division of Children and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yebin D Ahn
- Division of Children and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Mee Rim Oh
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Dong Soo Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Molecular Medicine and Biopharmaceutical Science, Seoul National University, Seoul, Republic of Korea
| | - Hyejin Kang
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.,Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Bung-Nyun Kim
- Division of Children and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
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10
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A Multiview Deep Learning Method for Brain Functional Connectivity Classification. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5782569. [PMID: 36254204 PMCID: PMC9569226 DOI: 10.1155/2022/5782569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/24/2022] [Indexed: 12/01/2022]
Abstract
The brain functional connectivity classification based on deep learning is a research hotspot nowadays. However, the classification performance is far behind the demand of clinical applications. To alleviate the problem, this paper proposes a multiview deep learning method for brain functional connectivity classification. Firstly, the proposed method adopts multiple brain atlases to identify brain regions and thereby builds different brain functional connectivity of different views. Secondly, it uses a multiview feature selection strategy to select out the most discriminative features of each view with the assistance of other views. Then, it trains a stacked autoencoder to extract deep features of the brain functional connectivity of each view. At last, it utilizes a multiview fusion strategy to take full advantage of complementary information of different views for brain functional connectivity classification. The proposed method has been compared with several deep learning-based brain functional connectivity classification methods on three public datasets of neuropsychiatric disorders. The experimental results have validated the superior performance of the proposed method.
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11
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Guo G, Zhao Y, Liu C, Fu Y, Xi X, Jin L, Shi D, Wang L, Duan Y, Huang J, Tan S, Yin G. Method for persistent topological features extraction of schizophrenia patients' electroencephalography signal based on persistent homology. Front Comput Neurosci 2022; 16:1024205. [PMID: 36277610 PMCID: PMC9579369 DOI: 10.3389/fncom.2022.1024205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 09/21/2022] [Indexed: 11/13/2022] Open
Abstract
With the development of network science and graph theory, brain network research has unique advantages in explaining those mental diseases, the neural mechanism of which is unclear. Additionally, it can provide a new perspective in revealing the pathophysiological mechanism of brain diseases from the system level. The selection of threshold plays an important role in brain networks construction. There are no generally accepted criteria for determining the proper threshold. Therefore, based on the topological data analysis of persistent homology theory, this study developed a multi-scale brain network modeling analysis method, which enables us to quantify various persistent topological features at different scales in a coherent manner. In this method, the Vietoris-Rips filtering algorithm is used to extract dynamic persistent topological features by gradually increasing the threshold in the range of full-scale distances. Subsequently, the persistent topological features are visualized using barcodes and persistence diagrams. Finally, the stability of persistent topological features is analyzed by calculating the Bottleneck distances and Wasserstein distances between the persistence diagrams. Experimental results show that compared with the existing methods, this method can extract the topological features of brain networks more accurately and improves the accuracy of diagnostic and classification. This work not only lays a foundation for exploring the higher-order topology of brain functional networks in schizophrenia patients, but also enhances the modeling ability of complex brain systems to better understand, analyze, and predict their dynamic behaviors.
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Affiliation(s)
- Guangxing Guo
- College of Geography Science, Taiyuan Normal University, Jinzhong, China
- Institute of Big Data Analysis Technology and Application, Taiyuan Normal University, Jinzhong, China
- College of Resource and Environment, Shanxi Agricultural University, Taigu, China
| | - Yanli Zhao
- Psychiatry Research Center, Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Chenxu Liu
- Laboratory of Data Mining and Machine Learning, College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, China
| | - Yongcan Fu
- Laboratory of Data Mining and Machine Learning, College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, China
| | - Xinhua Xi
- Laboratory of Data Mining and Machine Learning, College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, China
| | - Lizhong Jin
- College of Applied Science, Taiyuan University of Science and Technology, Taiyuan, China
| | - Dongli Shi
- Laboratory of Data Mining and Machine Learning, College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, China
| | - Lin Wang
- Laboratory of Data Mining and Machine Learning, College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, China
| | - Yonghong Duan
- College of Resource and Environment, Shanxi Agricultural University, Taigu, China
| | - Jie Huang
- Psychiatry Research Center, Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Shuping Tan
- Psychiatry Research Center, Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Guimei Yin
- Laboratory of Data Mining and Machine Learning, College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, China
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Branco S, Carvalho JG, Reis MS, Lopes NV, Cabral J. 0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems. SENSORS (BASEL, SWITZERLAND) 2022; 22:3657. [PMID: 35632064 PMCID: PMC9144123 DOI: 10.3390/s22103657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 05/02/2022] [Accepted: 05/06/2022] [Indexed: 06/15/2023]
Abstract
Persistent Homology (PH) analysis is a powerful tool for understanding many relevant topological features from a given dataset. PH allows finding clusters, noise, and relevant connections in the dataset. Therefore, it can provide a better view of the problem and a way of perceiving if a given dataset is equal to another, if a given sample is relevant, and how the samples occupy the feature space. However, PH involves reducing the problem to its simplicial complex space, which is computationally expensive and implementing PH in such Resource-Scarce Embedded Systems (RSES) is an essential add-on for them. However, due to its complexity, implementing PH in such tiny devices is considerably complicated due to the lack of memory and processing power. The following paper shows the implementation of 0-Dimensional Persistent Homology Analysis in a set of well-known RSES, using a technique that reduces the memory footprint and processing power needs of the 0-Dimensional PH algorithm. The results are positive and show that RSES can be equipped with this real-time data analysis tool.
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Affiliation(s)
- Sérgio Branco
- Algoritmi Center, University of Minho, 4800-058 Guimarães, Portugal; (S.B.); (J.G.C.)
- CEiiA—Centro de Engenharia, Av. D. Afonso Henriques 1825, 4450-017 Matosinhos, Portugal
| | - João G. Carvalho
- Algoritmi Center, University of Minho, 4800-058 Guimarães, Portugal; (S.B.); (J.G.C.)
- DTx—Digital Transformation CoLab, University of Minho, 4800-058 Guimarães, Portugal;
| | - Marco S. Reis
- CIEPQPF, Department of Chemical Engineering, University of Coimbra, Rua Sílvio Lima, Pólo II—Pinhal de Marrocos, 3030-790 Coimbra, Portugal;
| | - Nuno V. Lopes
- DTx—Digital Transformation CoLab, University of Minho, 4800-058 Guimarães, Portugal;
| | - Jorge Cabral
- Algoritmi Center, University of Minho, 4800-058 Guimarães, Portugal; (S.B.); (J.G.C.)
- CEiiA—Centro de Engenharia, Av. D. Afonso Henriques 1825, 4450-017 Matosinhos, Portugal
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