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Du Y, Fang S, He X, Calhoun VD. A survey of brain functional network extraction methods using fMRI data. Trends Neurosci 2024:S0166-2236(24)00091-2. [PMID: 38906797 DOI: 10.1016/j.tins.2024.05.011] [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: 02/20/2024] [Revised: 05/04/2024] [Accepted: 05/23/2024] [Indexed: 06/23/2024]
Abstract
Functional network (FN) analyses play a pivotal role in uncovering insights into brain function and understanding the pathophysiology of various brain disorders. This paper focuses on classical and advanced methods for deriving brain FNs from functional magnetic resonance imaging (fMRI) data. We systematically review their foundational principles, advantages, shortcomings, and interrelations, encompassing both static and dynamic FN extraction approaches. In the context of static FN extraction, we present hypothesis-driven methods such as region of interest (ROI)-based approaches as well as data-driven methods including matrix decomposition, clustering, and deep learning. For dynamic FN extraction, both window-based and windowless methods are surveyed with respect to the estimation of time-varying FN and the subsequent computation of FN states. We also discuss the scope of application of the various methods and avenues for future improvements.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, China.
| | - Songke Fang
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Xingyu He
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
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2
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Warren SL, Khan DM, Moustafa AA. Assistive tools for classifying neurological disorders using fMRI and deep learning: A guide and example. Brain Behav 2024; 14:e3554. [PMID: 38841732 PMCID: PMC11154821 DOI: 10.1002/brb3.3554] [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: 01/15/2024] [Revised: 05/02/2024] [Accepted: 05/03/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND Deep-learning (DL) methods are rapidly changing the way researchers classify neurological disorders. For example, combining functional magnetic resonance imaging (fMRI) and DL has helped researchers identify functional biomarkers of neurological disorders (e.g., brain activation and connectivity) and pilot innovative diagnostic models. However, the knowledge required to perform DL analyses is often domain-specific and is not widely taught in the brain sciences (e.g., psychology, neuroscience, and cognitive science). Conversely, neurological diagnoses and neuroimaging training (e.g., fMRI) are largely restricted to the brain and medical sciences. In turn, these disciplinary knowledge barriers and distinct specializations can act as hurdles that prevent the combination of fMRI and DL pipelines. The complexity of fMRI and DL methods also hinders their clinical adoption and generalization to real-world diagnoses. For example, most current models are not designed for clinical settings or use by nonspecialized populations such as students, clinicians, and healthcare workers. Accordingly, there is a growing area of assistive tools (e.g., software and programming packages) that aim to streamline and increase the accessibility of fMRI and DL pipelines for the diagnoses of neurological disorders. OBJECTIVES AND METHODS In this study, we present an introductory guide to some popular DL and fMRI assistive tools. We also create an example autism spectrum disorder (ASD) classification model using assistive tools (e.g., Optuna, GIFT, and the ABIDE preprocessed repository), fMRI, and a convolutional neural network. RESULTS In turn, we provide researchers with a guide to assistive tools and give an example of a streamlined fMRI and DL pipeline. CONCLUSIONS We are confident that this study can help more researchers enter the field and create accessible fMRI and deep-learning diagnostic models for neurological disorders.
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Affiliation(s)
- Samuel L. Warren
- Faculty of Society and Design, School of PsychologyBond UniversityGold CoastQueenslandAustralia
| | - Danish M. Khan
- Department of Electronic EngineeringNED University of Engineering & TechnologyKarachiSindhPakistan
| | - Ahmed A. Moustafa
- Faculty of Society and Design, School of PsychologyBond UniversityGold CoastQueenslandAustralia
- The Faculty of Health Sciences, Department of Human Anatomy and PhysiologyUniversity of JohannesburgAuckland ParkSouth Africa
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3
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Wein S, Riebel M, Seidel P, Brunner LM, Wagner V, Nothdurfter C, Rupprecht R, Schwarzbach JV. Local and global effects of sedation in resting-state fMRI: a randomized, placebo-controlled comparison between etifoxine and alprazolam. Neuropsychopharmacology 2024:10.1038/s41386-024-01884-5. [PMID: 38822128 DOI: 10.1038/s41386-024-01884-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 04/29/2024] [Accepted: 05/02/2024] [Indexed: 06/02/2024]
Abstract
TSPO ligands are promising alternatives to benzodiazepines in the treatment of anxiety, as they display less pronounced side effects such as sedation, cognitive impairment, tolerance development and abuse potential. In a randomized double-blind repeated-measures study we compare a benzodiazepine (alprazolam) to a TSPO ligand (etifoxine) by assessing side effects and acquiring resting-state fMRI data from 34 healthy participants after 5 days of taking alprazolam, etifoxine or a placebo. To study the effects of the pharmacological interventions in fMRI in detail and across different scales, we combine in our study complementary analysis strategies related to whole-brain functional network connectivity, local connectivity analysis expressed in regional homogeneity, fluctuations in low-frequency BOLD amplitudes and coherency of independent resting-state networks. Participants reported considerable adverse effects such as fatigue, sleepiness and concentration impairments, related to the administration of alprazolam compared to placebo. In resting-state fMRI we found a significant decrease in functional connection density, network efficiency and a decrease in the networks rich-club coefficient related to alprazolam. While observing a general decrease in regional homogeneity in high-level brain networks in the alprazolam condition, we simultaneously could detect an increase in regional homogeneity and resting-state network coherence in low-level sensory regions. Further we found a general increase in the low-frequency compartment of the BOLD signal. In the etifoxine condition, participants did not report any significant side effects compared to the placebo, and we did not observe any corresponding modulations in our fMRI metrics. Our results are consistent with the idea that sedation globally disconnects low-level functional networks, but simultaneously increases their within-connectivity. Further, our results point towards the potential of TSPO ligands in the treatment of anxiety and depression.
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Affiliation(s)
- Simon Wein
- Department of Psychiatry and Psychotherapy, University of Regensburg, Universitätsstrasse 84, Regensburg, 93053, Germany
| | - Marco Riebel
- Department of Psychiatry and Psychotherapy, University of Regensburg, Universitätsstrasse 84, Regensburg, 93053, Germany
| | - Philipp Seidel
- Department of Psychiatry and Psychotherapy, University of Regensburg, Universitätsstrasse 84, Regensburg, 93053, Germany
| | - Lisa-Marie Brunner
- Department of Psychiatry and Psychotherapy, University of Regensburg, Universitätsstrasse 84, Regensburg, 93053, Germany
| | - Viola Wagner
- Department of Psychiatry and Psychotherapy, University of Regensburg, Universitätsstrasse 84, Regensburg, 93053, Germany
| | - Caroline Nothdurfter
- Department of Psychiatry and Psychotherapy, University of Regensburg, Universitätsstrasse 84, Regensburg, 93053, Germany
| | - Rainer Rupprecht
- Department of Psychiatry and Psychotherapy, University of Regensburg, Universitätsstrasse 84, Regensburg, 93053, Germany
| | - Jens V Schwarzbach
- Department of Psychiatry and Psychotherapy, University of Regensburg, Universitätsstrasse 84, Regensburg, 93053, Germany.
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4
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Yang H, Vu T, Long Q, Calhoun V, Adali T. Identification of Homogeneous Subgroups from Resting-State fMRI Data. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23063264. [PMID: 36991975 PMCID: PMC10051904 DOI: 10.3390/s23063264] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 03/04/2023] [Accepted: 03/14/2023] [Indexed: 06/12/2023]
Abstract
The identification of homogeneous subgroups of patients with psychiatric disorders can play an important role in achieving personalized medicine and is essential to provide insights for understanding neuropsychological mechanisms of various mental disorders. The functional connectivity profiles obtained from functional magnetic resonance imaging (fMRI) data have been shown to be unique to each individual, similar to fingerprints; however, their use in characterizing psychiatric disorders in a clinically useful way is still being studied. In this work, we propose a framework that makes use of functional activity maps for subgroup identification using the Gershgorin disc theorem. The proposed pipeline is designed to analyze a large-scale multi-subject fMRI dataset with a fully data-driven method, a new constrained independent component analysis algorithm based on entropy bound minimization (c-EBM), followed by an eigenspectrum analysis approach. A set of resting-state network (RSN) templates is generated from an independent dataset and used as constraints for c-EBM. The constraints present a foundation for subgroup identification by establishing a connection across the subjects and aligning subject-wise separate ICA analyses. The proposed pipeline was applied to a dataset comprising 464 psychiatric patients and discovered meaningful subgroups. Subjects within the identified subgroups share similar activation patterns in certain brain areas. The identified subgroups show significant group differences in multiple meaningful brain areas including dorsolateral prefrontal cortex and anterior cingulate cortex. Three sets of cognitive test scores were used to verify the identified subgroups, and most of them showed significant differences across subgroups, which provides further confirmation of the identified subgroups. In summary, this work represents an important step forward in using neuroimaging data to characterize mental disorders.
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Affiliation(s)
- Hanlu Yang
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA
| | - Trung Vu
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA
| | - Qunfang Long
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA
| | - Vince Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Tülay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA
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5
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An EEG Classification-Based Method for Single-Trial N170 Latency Detection and Estimation. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:6331956. [PMID: 35222689 PMCID: PMC8881175 DOI: 10.1155/2022/6331956] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/11/2022] [Accepted: 02/03/2022] [Indexed: 11/17/2022]
Abstract
Event-related potentials (ERPs) can reflect the high-level thinking activities of the brain. In ERP analysis, the superposition and averaging method is often used to estimate ERPs. However, the single-trial ERP estimation can provide researchers with more information on cognitive activities. In recent years, more and more researchers try to find an effective method to extract single-trial ERPs, because most of the existing methods have poor generalization ability or suffer from strong assumptions about the characteristics of ERPs, resulting in unsatisfactory results under the condition of a very low signal-to-noise ratio. In this paper, an EEG classification-based method for single-trial ERP detection and estimation was proposed. This study used a linear generated EEG model containing templates of ERP local descriptors which include amplitude and latency, and this model can avoid the invalid assumption about ERPs taken by other methods. The purpose of this method is not to recover the whole ERP waveform but to model the amplitude and latency of ERP components. This method afterwards examined the three machine learning models including logistic regression, neural network, and support vector machine in the EEG signal classification for ERP detection and selected the best performed MLPNN model for detection. To get the utmost out of information produced in the classification process, this study also used extra information to propose a new optimization model, with which outperformed detection results were obtained. Performance of the proposed method is evaluated on simulated N170 and real P50 data sets, and the results show that the model is more effective than the Woody filter and the SingleTrialEM algorithm. These results are also consistent with the conclusion of sensory gating, which demonstrated good generalization ability.
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6
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Zhou Y, Song Z, Han X, Li H, Tang X. Prediction of Alzheimer's Disease Progression Based on Magnetic Resonance Imaging. ACS Chem Neurosci 2021; 12:4209-4223. [PMID: 34723463 DOI: 10.1021/acschemneuro.1c00472] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The neuroimaging method of multimodal magnetic resonance imaging (MRI) can identify the changes in brain structure and function caused by Alzheimer's disease (AD) at different stages, and it is a practical method to study the mechanism of AD progression. This paper reviews the studies of methods and biomarkers for predicting AD progression based on multimodal MRI. First, different approaches for predicting AD progression are analyzed and summarized, including machine learning, deep learning, regression, and other MRI analysis methods. Then, the effective biomarkers of AD progression under structural magnetic resonance imaging, diffusion tensor imaging, functional magnetic resonance imaging, and arterial spin labeling modes of MRI are summarized. It is believed that the brain changes shown on MRI may be related to the cognitive decline in different prodrome stages of AD, which is conducive to the further realization of early intervention and prevention of AD. Finally, the deficiencies of the existing studies are analyzed in terms of data set size, data heterogeneity, processing methods, and research depth. More importantly, future research directions are proposed, including enriching data sets, simplifying biomarkers, utilizing multimodal magnetic resonance, etc. In the future, the study of AD progression by multimodal MRI will still be a challenge but also a significant research hotspot.
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Affiliation(s)
- Ying Zhou
- School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, P.R. China
| | - Zeyu Song
- School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, P.R. China
| | - Xiao Han
- School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, P.R. China
| | - Hanjun Li
- School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, P.R. China
| | - Xiaoying Tang
- School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, P.R. China
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7
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Wein S, Deco G, Tomé AM, Goldhacker M, Malloni WM, Greenlee MW, Lang EW. Brain Connectivity Studies on Structure-Function Relationships: A Short Survey with an Emphasis on Machine Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:5573740. [PMID: 34135951 PMCID: PMC8177997 DOI: 10.1155/2021/5573740] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 05/06/2021] [Indexed: 12/12/2022]
Abstract
This short survey reviews the recent literature on the relationship between the brain structure and its functional dynamics. Imaging techniques such as diffusion tensor imaging (DTI) make it possible to reconstruct axonal fiber tracks and describe the structural connectivity (SC) between brain regions. By measuring fluctuations in neuronal activity, functional magnetic resonance imaging (fMRI) provides insights into the dynamics within this structural network. One key for a better understanding of brain mechanisms is to investigate how these fast dynamics emerge on a relatively stable structural backbone. So far, computational simulations and methods from graph theory have been mainly used for modeling this relationship. Machine learning techniques have already been established in neuroimaging for identifying functionally independent brain networks and classifying pathological brain states. This survey focuses on methods from machine learning, which contribute to our understanding of functional interactions between brain regions and their relation to the underlying anatomical substrate.
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Affiliation(s)
- Simon Wein
- CIML, Biophysics, University of Regensburg, Regensburg 93040, Germany
- Experimental Psychology, University of Regensburg, Regensburg 93040, Germany
| | - Gustavo Deco
- Center for Brain and Cognition, Department of Technology and Information, University Pompeu Fabra, Carrer Tanger, 122-140, Barcelona 08018, Spain
- Institució Catalana de la Recerca i Estudis Avançats, University Barcelona, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Ana Maria Tomé
- IEETA/DETI, University de Aveiro, Aveiro 3810-193, Portugal
| | - Markus Goldhacker
- CIML, Biophysics, University of Regensburg, Regensburg 93040, Germany
- Experimental Psychology, University of Regensburg, Regensburg 93040, Germany
| | - Wilhelm M. Malloni
- Experimental Psychology, University of Regensburg, Regensburg 93040, Germany
| | - Mark W. Greenlee
- Experimental Psychology, University of Regensburg, Regensburg 93040, Germany
| | - Elmar W. Lang
- CIML, Biophysics, University of Regensburg, Regensburg 93040, Germany
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8
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3D-Deep Learning Based Automatic Diagnosis of Alzheimer's Disease with Joint MMSE Prediction Using Resting-State fMRI. Neuroinformatics 2020; 18:71-86. [PMID: 31093956 DOI: 10.1007/s12021-019-09419-w] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
We performed this research to 1) evaluate a novel deep learning method for the diagnosis of Alzheimer's disease (AD) and 2) jointly predict the Mini Mental State Examination (MMSE) scores of South Korean patients with AD. Using resting-state functional Magnetic Resonance Imaging (rs-fMRI) scans of 331 participants, we obtained functional 3-dimensional (3-D) independent component spatial maps for use as features in classification and regression tasks. A 3-D convolutional neural network (CNN) architecture was developed for the classification task. MMSE scores were predicted using: linear least square regression (LLSR), support vector regression, bagging-based ensemble regression, and tree regression with group independent component analysis (gICA) features. To improve MMSE regression performance, we applied feature optimization methods including least absolute shrinkage and selection operator and support vector machine-based recursive feature elimination (SVM-RFE). The mean balanced test accuracy was 85.27% for the classification of AD versus healthy controls. The medial visual, default mode, dorsal attention, executive, and auditory related networks were mainly associated with AD. The maximum clinical MMSE score prediction accuracy with the LLSR method applied on gICA combined with SVM-RFE features had the lowest root mean square error (3.27 ± 0.58) and the highest R2 value (0.63 ± 0.02). Classification of AD and healthy controls can be successfully achieved using only rs-fMRI and MMSE scores can be accurately predicted using functional independent component features. In the absence of trained clinicians, AD disease status and clinical MMSE scores can be jointly predicted using 3-D deep learning and regression learning approaches with rs-fMRI data.
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9
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Wein S, Tomé AM, Goldhacker M, Greenlee MW, Lang EW. A Constrained ICA-EMD Model for Group Level fMRI Analysis. Front Neurosci 2020; 14:221. [PMID: 32351349 PMCID: PMC7175031 DOI: 10.3389/fnins.2020.00221] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 02/28/2020] [Indexed: 11/13/2022] Open
Abstract
Independent component analysis (ICA), being a data-driven method, has been shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is that it is not, in general, compatible with the analysis of group data. Various techniques have been proposed to overcome this limitation of ICA. In this paper, a novel ICA-based workflow for extracting resting-state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used, in a data-driven manner, to generate reference signals that can be incorporated into a constrained version of ICA (cICA), thereby eliminating the inherent ambiguities of ICA. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach for fMRI analysis. In this study, we demonstrate that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA. This approach yields typical resting-state patterns that are consistent over subjects. By introducing these reference signals into the ICA, our processing pipeline yields comparable activity patterns across subjects in a mathematically transparent manner. Our approach provides a user-friendly tool to adjust the trade-off between a high similarity across subjects and preserving individual subject features of the independent components.
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Affiliation(s)
- Simon Wein
- CIML, Biophysics, University of Regensburg, Regensburg, Germany.,Experimental Psychology, University of Regensburg, Regensburg, Germany
| | - Ana M Tomé
- IEETA/DETI, Universidade de Aveiro, Aveiro, Portugal
| | - Markus Goldhacker
- CIML, Biophysics, University of Regensburg, Regensburg, Germany.,Experimental Psychology, University of Regensburg, Regensburg, Germany
| | - Mark W Greenlee
- Experimental Psychology, University of Regensburg, Regensburg, Germany
| | - Elmar W Lang
- CIML, Biophysics, University of Regensburg, Regensburg, Germany
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10
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Wein S, Tome AM, Goldhacker M, Greenlee MW, Lang EW. Hybridizing EMD with cICA for fMRI Analysis of Patient Groups. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:194-197. [PMID: 31945876 DOI: 10.1109/embc.2019.8856355] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Independent component analysis (ICA), as a data driven method, has shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is, that it is naturally not convenient for analysis of group studies. Therefore various techniques have been proposed in order to overcome this limitation of ICA. In this paper a novel ICA based work-flow for extracting resting state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used to generate reference signals in a data driven manner, which can be incorporated into a constrained version of ICA (cICA), what helps to overcome the inherent ambiguities. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach. It is demonstrated that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA to obtain typical resting state patterns, which are consistent over subjects. This novel processing pipeline makes it transparent for the user, how comparable activity patterns across subjects emerge, and also the trade-off between similarity across subjects and preserving individual features can be well adjusted and adapted for different requirements in the new work-flow.
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11
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Khoshnevis SA, Sankar R. Applications of Higher Order Statistics in Electroencephalography Signal Processing: A Comprehensive Survey. IEEE Rev Biomed Eng 2019; 13:169-183. [PMID: 31689211 DOI: 10.1109/rbme.2019.2951328] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Electroencephalography (EEG) is a noninvasive electrophysiological monitoring technique that records the electrical activities of the brain from the scalp using electrodes. EEG is not only an essential tool for diagnosing diseases and disorders affecting the brain, but also helps us to achieve a better understanding of brain's activities and structures. EEG recordings are weak, nonlinear, and nonstationary signals that contain various noise and artifacts. Therefore, for analyzing them, advanced signal processing techniques are required. Second order statistical features are usually sufficient for analyzing most basic signals. However, higher order statistical features possess characteristics that are missing in the second order; characteristics that can be highly beneficial for analysis of more complex signals, such as EEG. The primary goal of this article is to provide a comprehensive survey of the applications of higher order statistics or spectra (HOS) in EEG signal processing. Therefore, we start the survey with a summary of previous studies in EEG analysis followed by a brief mathematical description of HOS. Then, HOS related features and their applications in EEG analysis are presented. These applications are then grouped into three categories, each of which are further explored thoroughly with examples of prior studies. Finally, we provide some specific recommendations based on the literature survey and discuss possible future directions of this field.
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12
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Waser M, Lauritzen MJ, Fagerlund B, Osler M, Mortensen EL, Sørensen HBD, Jennum P. Sleep efficiency and neurophysiological patterns in middle-aged men are associated with cognitive change over their adult life course. J Sleep Res 2019; 28:e12793. [PMID: 30417544 PMCID: PMC6383751 DOI: 10.1111/jsr.12793] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Revised: 06/22/2018] [Accepted: 10/18/2018] [Indexed: 12/29/2022]
Abstract
Disrupted sleep is a contributing factor to cognitive ageing, while also being associated with neurodegenerative disorders. Little is known, however, about the relation of sleep and the gradual cognitive changes over the adult life course. Sleep electroencephalogram (EEG) patterns are potential markers of the cognitive progress. To test this hypothesis, we assessed sleep architecture and EEG of 167 men born in the Copenhagen Metropolitan Area in 1953, who, based on individual cognitive testing from early (~18 years) to late adulthood (~58 years), were divided into 85 subjects with negative and 82 with positive cognitive change over their adult life. Participants underwent standard polysomnography, including manual sleep scoring at age ~58 years. Features of sleep macrostructure were combined with a number of EEG features to distinguish between the two groups. EEG rhythmicity was assessed by spectral power analysis in frontal, central and occipital sites. Functional connectivity was measured by inter-hemispheric EEG coherence. Group differences were assessed by analysis of covariance (p < 0.05), including education and severity of depression as potential covariates. Subjects with cognitive decline exhibited lower sleep efficiency, reduced inter-hemispheric connectivity during rapid eye movement (REM) sleep, and slower EEG rhythms during stage 2 non-REM sleep. Individually, none of these tendencies remained significant after multiple test correction; however, by combining them in a machine learning approach, the groups were separated with 72% accuracy (75% sensitivity, 67% specificity). Ongoing medical screenings are required to confirm the potential of sleep efficiency and sleep EEG patterns as signs of individual cognitive progress.
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Affiliation(s)
- Markus Waser
- Biomedical Engineering, Department of Electrical Engineering, Technical University of Denmark, Lyngby, Denmark
- Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet Glostrup, Glostrup, Denmark
- AIT Austrian Institute of Technology GmbH, Center for Digital Safety and Security, Sensing and Vision Solutions, Vienna, Austria
| | - Martin J. Lauritzen
- Center for Healthy Aging and Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Neurophysiology, Rigshospitalet Glostrup, Glostrup, Denmark
| | - Birgitte Fagerlund
- Clinical Intervention and Neuropsychiatric Schizophrenia Research/Center for Neuropsychiatric Schizophrenia Research, Psychiatric Center Glostrup, Glostrup, Denmark
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Merete Osler
- Center for Clinical Research and Prevention, Frederiksberg Hospital, Frederiksberg, Denmark
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Erik L. Mortensen
- Center for Healthy Aging and Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Helge B. D. Sørensen
- Biomedical Engineering, Department of Electrical Engineering, Technical University of Denmark, Lyngby, Denmark
| | - Poul Jennum
- Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet Glostrup, Glostrup, Denmark
- Center for Healthy Aging and Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
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13
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Macwan R, Benezeth Y, Mansouri A. Heart rate estimation using remote photoplethysmography with multi-objective optimization. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.10.012] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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14
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Qureshi MNI, Ryu S, Song J, Lee KH, Lee B. Evaluation of Functional Decline in Alzheimer's Dementia Using 3D Deep Learning and Group ICA for rs-fMRI Measurements. Front Aging Neurosci 2019; 11:8. [PMID: 30804774 PMCID: PMC6378312 DOI: 10.3389/fnagi.2019.00008] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Accepted: 01/10/2019] [Indexed: 12/21/2022] Open
Abstract
Purpose: To perform automatic assessment of dementia severity using a deep learning framework applied to resting-state functional magnetic resonance imaging (rs-fMRI) data. Method: We divided 133 Alzheimer’s disease (AD) patients with clinical dementia rating (CDR) scores from 0.5 to 3 into two groups based on dementia severity; the groups with very mild/mild (CDR: 0.5–1) and moderate to severe (CDR: 2–3) dementia consisted of 77 and 56 subjects, respectively. We used rs-fMRI to extract functional connectivity features, calculated using independent component analysis (ICA), and performed automated severity classification with three-dimensional convolutional neural networks (3D-CNNs) based on deep learning. Results: The mean balanced classification accuracy was 0.923 ± 0.042 (p < 0.001) with a specificity of 0.946 ± 0.019 and sensitivity of 0.896 ± 0.077. The rs-fMRI data indicated that the medial frontal, sensorimotor, executive control, dorsal attention, and visual related networks mainly correlated with dementia severity. Conclusions: Our CDR-based novel classification using rs-fMRI is an acceptable objective severity indicator. In the absence of trained neuropsychologists, dementia severity can be objectively and accurately classified using a 3D-deep learning framework with rs-fMRI independent components.
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Affiliation(s)
- Muhammad Naveed Iqbal Qureshi
- Department of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea.,Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada.,Alzheimer's Disease Research Unit, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.,Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Seungjun Ryu
- Department of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea
| | - Joonyoung Song
- Department of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea
| | - Kun Ho Lee
- National Research Center for Dementia, Chosun University, Gwangju, South Korea.,Department of Biomedical Science, Chosun University, Gwangju, South Korea
| | - Boreom Lee
- Department of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea
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Waser M, Benke T, Dal‐Bianco P, Garn H, Mosbacher JA, Ransmayr G, Schmidt R, Seiler S, Sorensen HBD, Jennum PJ. Neuroimaging markers of global cognition in early Alzheimer's disease: A magnetic resonance imaging-electroencephalography study. Brain Behav 2019; 9:e01197. [PMID: 30592179 PMCID: PMC6346656 DOI: 10.1002/brb3.1197] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 11/26/2018] [Accepted: 12/05/2018] [Indexed: 12/16/2022] Open
Abstract
INTRODUCTION Magnetic resonance imaging (MRI) and electroencephalography (EEG) are a promising means to an objectified assessment of cognitive impairment in Alzheimer's disease (AD). Individually, however, these modalities tend to lack precision in both AD diagnosis and AD staging. A joint MRI-EEG approach that combines structural with functional information has the potential to overcome these limitations. MATERIALS AND METHODS This cross-sectional study systematically investigated the link between MRI and EEG markers and the global cognitive status in early AD. We hypothesized that the joint modalities would identify cognitive deficits with higher accuracy than the individual modalities. In a cohort of 111 AD patients, we combined MRI measures of cortical thickness and regional brain volume with EEG measures of rhythmic activity, information processing and functional coupling in a generalized multiple regression model. Machine learning classification was used to evaluate the markers' utility in accurately separating the subjects according to their cognitive score. RESULTS We found that joint measures of temporal volume, cortical thickness, and EEG slowing were well associated with the cognitive status and explained 38.2% of ifs variation. The inclusion of the covariates age, sex, and education considerably improved the model. The joint markers separated the subjects with an accuracy of 84.7%, which was considerably higher than by using individual modalities. CONCLUSIONS These results suggest that including joint MRI-EEG markers may be beneficial in the diagnostic workup, thus allowing for adequate treatment. Further studies in larger populations, with a longitudinal design and validated against functional-metabolic imaging are warranted to confirm the results.
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Affiliation(s)
- Markus Waser
- Biomedical EngineeringDepartment of Electrical EngineeringTechnical University of DenmarkLyngbyDenmark
- Danish Center for Sleep MedicineDepartment of Clinical NeurophysiologyRigshospitalet GlostrupGlostrupDenmark
- AIT Austrian Institute of Technology GmbHCenter for Digital Safety & SecuritySensing and Vision SolutionsViennaAustria
| | - Thomas Benke
- Department of NeurologyMedical University of InnsbruckInnsbruckAustria
| | - Peter Dal‐Bianco
- Department of NeurologyMedical University of ViennaViennaAustria
| | - Heinrich Garn
- AIT Austrian Institute of Technology GmbHCenter for Digital Safety & SecuritySensing and Vision SolutionsViennaAustria
| | | | - Gerhard Ransmayr
- Clinic for Neurology IIKepler University HospitalMed Campus IIILinzAustria
| | | | - Stephan Seiler
- Department of NeurologyMedical University of GrazGrazAustria
| | - Helge B. D. Sorensen
- Biomedical EngineeringDepartment of Electrical EngineeringTechnical University of DenmarkLyngbyDenmark
| | - Poul J. Jennum
- Danish Center for Sleep MedicineDepartment of Clinical NeurophysiologyRigshospitalet GlostrupGlostrupDenmark
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Shi Y, Zeng W. SCTICA: Sub-packet constrained temporal ICA method for fMRI data analysis. Comput Biol Med 2018; 102:75-85. [PMID: 30248514 DOI: 10.1016/j.compbiomed.2018.09.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 09/14/2018] [Accepted: 09/15/2018] [Indexed: 01/04/2023]
Abstract
Independent component analysis (ICA) has become a widely used method for functional magnetic resonance imaging (fMRI) data analysis. However, spatial ICA usually performs better than temporal ICA with regard to the stability and accuracy of functional connectivity detection, and temporal ICA is often not feasible when it is applied to the analysis of real fMRI data of the whole brain because of the excessive spatial dimensions. In this paper, to overcome these problems, we propose a sub-packet constrained temporal ICA (SCTICA) method to take advantage of the a priori information using a multi-objective optimization framework with the Newton iterative algorithm. Moreover, a splitting strategy is presented to improve the feasibility of the temporal ICA for whole brain fMRI data analysis. The experimental results of real data show that the splitting strategy improved the ability of the temporal ICA to analyze whole brain fMRI data. Furthermore, the experimental results also demonstrated that the proposed SCTICA method can not only improve the stability of the temporal ICA, but can also improve the functional connectivity detection ability compared with the classical ICA and ICA with a priori information methods. In brief, the proposed SCTICA method overcomes the problem that prevents temporal ICA from being applied to fMRI data of the whole brain, and the functional connectivity detection performance is greatly improved compared with that of traditional methods.
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Affiliation(s)
- Yuhu Shi
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China
| | - Weiming Zeng
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China.
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17
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Shi Y, Zeng W, Wang N, Zhao L. A New Constrained Spatiotemporal ICA Method Based on Multi-Objective Optimization for fMRI Data Analysis. IEEE Trans Neural Syst Rehabil Eng 2018; 26:1690-1699. [PMID: 30028710 DOI: 10.1109/tnsre.2018.2857501] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Compared with independent component analysis (ICA), constrained ICA (CICA) has unique advantages in functional magnetic resonance image (fMRI) data analysis by introducing some priori information into the estimation process. However, there are still some controversies in the current CICA methods, such as how to choose the threshold parameter to restrain the similarity, and how to reduce the accuracy requirements for a priori information. In this paper, we propose a new constrained spatiotemporal ICA (CSTICA) method based on the framework of multi-objective optimization, where the inequality constraint of the traditional CICA method is transformed into the objective optimization function of the CSTICA, and both temporal and spatial a priori information are included simultaneously. The simulated, hybrid, and real fMRI data experiments are designed to evaluate the performance of the proposed CSTICA method in comparison with the classical ICA and CICA methods. Compared with the traditional CICA methods, the CSTICA has circumvented the problem of threshold parameter selection. Furthermore, the experimental results demonstrate that the source recovery ability of the CSTICA has been improved to a certain extent especially in the cases of a priori information with low accuracies. Meanwhile, the results also indicate that the CSTICA reduces dependency on the accuracy of a priori information.
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Yang C, Tavassolian N. An Independent Component Analysis Approach to Motion Noise Cancelation of Cardio-Mechanical Signals. IEEE Trans Biomed Eng 2018; 66:784-793. [PMID: 30028685 DOI: 10.1109/tbme.2018.2856700] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This paper proposes a new framework for measuring sternal cardio-mechanical signals from moving subjects using multiple sensors. An array of inertial measurement units are attached to the chest wall of subjects to measure the seismocardiogram (SCG) from accelerometers and the gyrocardiogram (GCG) from gyroscopes. A digital signal processing method based on constrained independent component analysis is applied to extract the desired cardio-mechanical signals from the mixture of vibration observations. Electrocardiogram and photoplethysmography modalities are evaluated as reference sources for the constrained independent component analysis algorithm. Experimental studies with 14 young, healthy adult subjects demonstrate the feasibility of extracting seismo- and gyrocardiogram signals from walking and jogging subjects, with speeds of 3.0 mi/h and 4.6 mi/h, respectively. Beat-to-beat and ensemble-averaged features are extracted from the outputs of the algorithm. The beat-to-beat cardiac interval results demonstrate average detection rates of 91.44% during walking and 86.06% during jogging from SCG, and 87.32% during walking and 76.30% during jogging from GCG. The ensemble-averaged pre-ejection period (PEP) calculation results attained overall squared correlation coefficients of 0.9048 from SCG and 0.8350 from GCG with reference PEP from impedance cardiogram. Our results indicate that the proposed framework can improve the motion tolerance of cardio-mechanical signals in moving subjects. The effective number of recordings during day time could be potentially increased by the proposed framework, which will push forward the implementation of cardio-mechanical monitoring devices in mobile healthcare.
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Wang N, Chang C, Zeng W, Shi Y, Yan H. A Novel Feature-Map Based ICA Model for Identifying the Individual, Intra/Inter-Group Brain Networks across Multiple fMRI Datasets. Front Neurosci 2017; 11:510. [PMID: 28943838 PMCID: PMC5596109 DOI: 10.3389/fnins.2017.00510] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Accepted: 08/28/2017] [Indexed: 11/17/2022] Open
Abstract
Independent component analysis (ICA) has been widely used in functional magnetic resonance imaging (fMRI) data analysis to evaluate functional connectivity of the brain; however, there are still some limitations on ICA simultaneously handling neuroimaging datasets with diverse acquisition parameters, e.g., different repetition time, different scanner, etc. Therefore, it is difficult for the traditional ICA framework to effectively handle ever-increasingly big neuroimaging datasets. In this research, a novel feature-map based ICA framework (FMICA) was proposed to address the aforementioned deficiencies, which aimed at exploring brain functional networks (BFNs) at different scales, e.g., the first level (individual subject level), second level (intragroup level of subjects within a certain dataset) and third level (intergroup level of subjects across different datasets), based only on the feature maps extracted from the fMRI datasets. The FMICA was presented as a hierarchical framework, which effectively made ICA and constrained ICA as a whole to identify the BFNs from the feature maps. The simulated and real experimental results demonstrated that FMICA had the excellent ability to identify the intergroup BFNs and to characterize subject-specific and group-specific difference of BFNs from the independent component feature maps, which sharply reduced the size of fMRI datasets. Compared with traditional ICAs, FMICA as a more generalized framework could efficiently and simultaneously identify the variant BFNs at the subject-specific, intragroup, intragroup-specific and intergroup levels, implying that FMICA was able to handle big neuroimaging datasets in neuroscience research.
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Affiliation(s)
- Nizhuan Wang
- Neuroimaging Lab, School of Biomedical Engineering, Health Science Center, Shenzhen UniversityShenzhen, China
- Guangdong Key Laboratory of Biomedical Information Detection and Ultrasound ImagingShenzhen, China
| | - Chunqi Chang
- Neuroimaging Lab, School of Biomedical Engineering, Health Science Center, Shenzhen UniversityShenzhen, China
- Guangdong Key Laboratory of Biomedical Information Detection and Ultrasound ImagingShenzhen, China
- Center for Neuroimaging, Shenzhen Institute of NeuroscienceShenzhen, China
| | - Weiming Zeng
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime UniversityShanghai, China
| | - Yuhu Shi
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime UniversityShanghai, China
| | - Hongjie Yan
- Department of Neurology, Affiliated Lianyungang Hospital of Xuzhou Medical UniversityLianyungang, China
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20
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Shi Y, Zeng W, Tang X, Kong W, Yin J. An improved multi-objective optimization-based CICA method with data-driver temporal reference for group fMRI data analysis. Med Biol Eng Comput 2017; 56:683-694. [PMID: 28864838 DOI: 10.1007/s11517-017-1716-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2017] [Accepted: 08/17/2017] [Indexed: 11/26/2022]
Abstract
Group independent component analysis (GICA) has been successfully applied to study multi-subject functional magnetic resonance imaging (fMRI) data, and the group independent component (GIC) represents the commonality of all subjects in the group. However, some studies show that the performance of GICA can be improved by incorporating a priori information, which is not always considered when looking for GICs in existing GICA methods. In this paper, we propose an improved multi-objective optimization-based constrained independent component analysis (CICA) method to take advantage of the temporal a priori information extracted from all subjects in the group by incorporating it into the computational process of GICA for group fMRI data analysis. The experimental results of simulated and real data show that the activated regions and the time course detected by the improved CICA method are more accurate in some sense. Moreover, the GIC computed by the improved CICA method has a higher correlation with the corresponding independent component of each subject in the group, which means that the improved CICA method with the temporal a priori information extracted from the group can better reflect the commonality of the subjects. These results demonstrate that the improved CICA method has its own advantages in fMRI data analysis.
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Affiliation(s)
- Yuhu Shi
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China
| | - Weiming Zeng
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China.
- Information Engineering College, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China.
| | - Xiaoyan Tang
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China
| | - Wei Kong
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China
| | - Jun Yin
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China
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21
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Shi Y, Zeng W, Wang N. SCGICAR: Spatial concatenation based group ICA with reference for fMRI data analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 148:137-151. [PMID: 28774436 DOI: 10.1016/j.cmpb.2017.07.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Revised: 05/21/2017] [Accepted: 07/03/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE With the rapid development of big data, the functional magnetic resonance imaging (fMRI) data analysis of multi-subject is becoming more and more important. As a kind of blind source separation technique, group independent component analysis (GICA) has been widely applied for the multi-subject fMRI data analysis. However, spatial concatenated GICA is rarely used compared with temporal concatenated GICA due to its disadvantages. METHODS In this paper, in order to overcome these issues and to consider that the ability of GICA for fMRI data analysis can be improved by adding a priori information, we propose a novel spatial concatenation based GICA with reference (SCGICAR) method to take advantage of the priori information extracted from the group subjects, and then the multi-objective optimization strategy is used to implement this method. Finally, the post-processing means of principal component analysis and anti-reconstruction are used to obtain group spatial component and individual temporal component in the group, respectively. RESULTS The experimental results show that the proposed SCGICAR method has a better performance on both single-subject and multi-subject fMRI data analysis compared with classical methods. It not only can detect more accurate spatial and temporal component for each subject of the group, but also can obtain a better group component on both temporal and spatial domains. CONCLUSIONS These results demonstrate that the proposed SCGICAR method has its own advantages in comparison with classical methods, and it can better reflect the commonness of subjects in the group.
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Affiliation(s)
- Yuhu Shi
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China
| | - Weiming Zeng
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China.
| | - Nizhuan Wang
- Neuroimaging Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China; Guangdong Key Laboratory of Biomedical Information Detection and Ultrasound Imaging, Shenzhen 518060, China
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22
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Karimi F, Kofman J, Mrachacz-Kersting N, Farina D, Jiang N. Detection of Movement Related Cortical Potentials from EEG Using Constrained ICA for Brain-Computer Interface Applications. Front Neurosci 2017; 11:356. [PMID: 28713232 PMCID: PMC5492875 DOI: 10.3389/fnins.2017.00356] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Accepted: 06/07/2017] [Indexed: 11/13/2022] Open
Abstract
The movement related cortical potential (MRCP), a slow cortical potential from the scalp electroencephalogram (EEG), has been used in real-time brain-computer-interface (BCI) systems designed for neurorehabilitation. Detecting MPCPs in real time with high accuracy and low latency is essential in these applications. In this study, we propose a new MRCP detection method based on constrained independent component analysis (cICA). The method was tested for MRCP detection during executed and imagined ankle dorsiflexion of 24 healthy participants, and compared with four commonly used spatial filters for MRCP detection in an offline experiment. The effect of cICA and the compared spatial filters on the morphology of the extracted MRCP was evaluated by two indices quantifying the signal-to-noise ratio and variability of the extracted MRCP. The performance of the filters for detection was then directly compared for accuracy and latency. The latency obtained with cICA (-34 ± 29 ms motor execution (ME) and 28 ± 16 ms for motor imagery (MI) dataset) was significantly smaller than with all other spatial filters. Moreover, cICA resulted in greater true positive rates (87.11 ± 11.73 for ME and 86.66 ± 6.96 for MI dataset) and lower false positive rates (20.69 ± 13.68 for ME and 19.31 ± 12.60 for MI dataset) compared to the other methods. These results confirm the superiority of cICA in MRCP detection with respect to previously proposed EEG filtering approaches.
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Affiliation(s)
- Fatemeh Karimi
- Department of Systems Design Engineering, Faculty of Engineering, University of WaterlooWaterloo, ON, Canada
| | - Jonathan Kofman
- Department of Systems Design Engineering, Faculty of Engineering, University of WaterlooWaterloo, ON, Canada
| | | | - Dario Farina
- Neurorehabilitation Engineering Department of Bioengineering, Imperial College LondonLondon, United Kingdom
| | - Ning Jiang
- Department of Systems Design Engineering, Faculty of Engineering, University of WaterlooWaterloo, ON, Canada
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23
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A new method for independent component analysis with priori information based on multi-objective optimization. J Neurosci Methods 2017; 283:72-82. [DOI: 10.1016/j.jneumeth.2017.03.018] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Revised: 03/26/2017] [Accepted: 03/26/2017] [Indexed: 11/23/2022]
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Chi Z, Li H, Lu H, Yang M. Dual Deep Network for Visual Tracking. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2017; 26:2005-2015. [PMID: 28212087 DOI: 10.1109/tip.2017.2669880] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Visual tracking addresses the problem of identifying and localizing an unknown target in a video given the target specified by a bounding box in the first frame. In this paper, we propose a dual network to better utilize features among layers for visual tracking. It is observed that features in higher layers encode semantic context while its counterparts in lower layers are sensitive to discriminative appearance. Thus we exploit the hierarchical features in different layers of a deep model and design a dual structure to obtain better feature representation from various streams, which is rarely investigated in previous work. To highlight geometric contours of the target, we integrate the hierarchical feature maps with an edge detector as the coarse prior maps to further embed local details around the target. To leverage the robustness of our dual network, we train it with random patches measuring the similarities between the network activation and target appearance, which serves as a regularization to enforce the dual network to focus on target object. The proposed dual network is updated online in a unique manner based on the observation that the target being tracked in consecutive frames should share more similar feature representations than those in the surrounding background. It is also found that for a target object, the prior maps can help further enhance performance by passing message into the output maps of the dual network. Therefore, an independent component analysis with reference algorithm (ICA-R) is employed to extract target context using prior maps as guidance. Online tracking is conducted by maximizing the posterior estimate on the final maps with stochastic and periodic update. Quantitative and qualitative evaluations on two large-scale benchmark data sets show that the proposed algorithm performs favourably against the stateof- the-arts.
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26
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Zhao D, Sun Y, Wan S, Wang F. SFST: A robust framework for heart rate monitoring from photoplethysmography signals during physical activities. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.12.005] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Lin W, Wu H, Liu Y, Lv D, Yang L. A CCA and ICA-Based Mixture Model for Identifying Major Depression Disorder. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:745-756. [PMID: 27893387 DOI: 10.1109/tmi.2016.2631001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The fMRI signals are usually filtered before processing and analyzing. This process can result in the loss of information carried by the higher frequency in the low frequency fluctuation. ICA and CCA are two classical methods in fMRI. ICA finds the statistically independent components of the observed data, however these components are usually physiologically uninterpretable without auxiliary procedures. CCA decomposes two sets of data into component pairs in some order, however these components may be mixtures of real signals and noise. In order to obtain statistically independent components and avoid the loss of information in the process of filtering, we propose a mixed model based on ICA and CCA, which does not need to filter the data. It is shown by the experiments that the new model has some advantages compared with the classical ICA and CCA. The components obtained by the new model is statistically independent. The useful information included in the low frequency fluctuation can be preserved. Experiments on synthetic data show satisfying results. As an application, this new model is used to design an algorithm to discriminate the major depressions from normal controls, with encouraging experimental results.
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Götz T, Stadler L, Fraunhofer G, Tomé AM, Hausner H, Lang EW. A combined cICA-EEMD analysis of EEG recordings from depressed or schizophrenic patients during olfactory stimulation. J Neural Eng 2016; 14:016011. [PMID: 27991435 DOI: 10.1088/1741-2552/14/1/016011] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE We propose a combination of a constrained independent component analysis (cICA) with an ensemble empirical mode decomposition (EEMD) to analyze electroencephalographic recordings from depressed or schizophrenic subjects during olfactory stimulation. APPROACH EEMD serves to extract intrinsic modes (IMFs) underlying the recorded EEG time. The latter then serve as reference signals to extract the most similar underlying independent component within a constrained ICA. The extracted modes are further analyzed considering their power spectra. MAIN RESULTS The analysis of the extracted modes reveals clear differences in the related power spectra between the disease characteristics of depressed and schizophrenic patients. Such differences appear in the high frequency γ-band in the intrinsic modes, but also in much more detail in the low frequency range in the α-, θ- and δ-bands. SIGNIFICANCE The proposed method provides various means to discriminate both disease pictures in a clinical environment.
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Affiliation(s)
- Th Götz
- CIML Group, Biophysics, University of Regensburg, 93040 Regensburg, Germany
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29
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Lee WL, Tan T, Falkmer T, Leung YH. Single-trial event-related potential extraction through one-unit ICA-with-reference. J Neural Eng 2016; 13:066010. [PMID: 27739404 DOI: 10.1088/1741-2560/13/6/066010] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE In recent years, ICA has been one of the more popular methods for extracting event-related potential (ERP) at the single-trial level. It is a blind source separation technique that allows the extraction of an ERP without making strong assumptions on the temporal and spatial characteristics of an ERP. However, the problem with traditional ICA is that the extraction is not direct and is time-consuming due to the need for source selection processing. In this paper, the application of an one-unit ICA-with-Reference (ICA-R), a constrained ICA method, is proposed. APPROACH In cases where the time-region of the desired ERP is known a priori, this time information is utilized to generate a reference signal, which is then used for guiding the one-unit ICA-R to extract the source signal of the desired ERP directly. MAIN RESULTS Our results showed that, as compared to traditional ICA, ICA-R is a more effective method for analysing ERP because it avoids manual source selection and it requires less computation thus resulting in faster ERP extraction. SIGNIFICANCE In addition to that, since the method is automated, it reduces the risks of any subjective bias in the ERP analysis. It is also a potential tool for extracting the ERP in online application.
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Affiliation(s)
- Wee Lih Lee
- Department of Electrical and Computer Engineering, Curtin University, Perth, Australia
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Silva RF, Plis SM, Sui J, Pattichis MS, Adalı T, Calhoun VD. Blind Source Separation for Unimodal and Multimodal Brain Networks: A Unifying Framework for Subspace Modeling. IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2016; 10:1134-1149. [PMID: 28461840 PMCID: PMC5409135 DOI: 10.1109/jstsp.2016.2594945] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
In the past decade, numerous advances in the study of the human brain were fostered by successful applications of blind source separation (BSS) methods to a wide range of imaging modalities. The main focus has been on extracting "networks" represented as the underlying latent sources. While the broad success in learning latent representations from multiple datasets has promoted the wide presence of BSS in modern neuroscience, it also introduced a wide variety of objective functions, underlying graphical structures, and parameter constraints for each method. Such diversity, combined with a host of datatype-specific know-how, can cause a sense of disorder and confusion, hampering a practitioner's judgment and impeding further development. We organize the diverse landscape of BSS models by exposing its key features and combining them to establish a novel unifying view of the area. In the process, we unveil important connections among models according to their properties and subspace structures. Consequently, a high-level descriptive structure is exposed, ultimately helping practitioners select the right model for their applications. Equipped with that knowledge, we review the current state of BSS applications to neuroimaging. The gained insight into model connections elicits a broader sense of generalization, highlighting several directions for model development. In light of that, we discuss emerging multi-dataset multidimensional (MDM) models and summarize their benefits for the study of the healthy brain and disease-related changes.
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Affiliation(s)
- Rogers F. Silva
- Dept. of ECE at The University of New Mexico, NM USA
- The Mind Research Network, LBERI, Albuquerque, New Mexico USA
| | - Sergey M. Plis
- The Mind Research Network, LBERI, Albuquerque, New Mexico USA
| | - Jing Sui
- Brainnetome Center & NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing China
- The Mind Research Network, LBERI, Albuquerque, New Mexico USA
| | | | - Tülay Adalı
- Dept. of CSEE, University of Maryland Baltimore County, Baltimore, Maryland USA
| | - Vince D. Calhoun
- Dept. of ECE at The University of New Mexico, NM USAThe Mind Research Network, LBERI, Albuquerque, New Mexico USA
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A Fetal Electrocardiogram Signal Extraction Algorithm Based on Fast One-Unit Independent Component Analysis with Reference. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2016; 2016:5127978. [PMID: 27703492 PMCID: PMC5040836 DOI: 10.1155/2016/5127978] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2016] [Accepted: 08/08/2016] [Indexed: 11/24/2022]
Abstract
Fetal electrocardiogram (FECG) extraction is very important procedure for fetal health assessment. In this article, we propose a fast one-unit independent component analysis with reference (ICA-R) that is suitable to extract the FECG. Most previous ICA-R algorithms only focused on how to optimize the cost function of the ICA-R and payed little attention to the improvement of cost function. They did not fully take advantage of the prior information about the desired signal to improve the ICA-R. In this paper, we first use the kurtosis information of the desired FECG signal to simplify the non-Gaussian measurement function and then construct a new cost function by directly using a nonquadratic function of the extracted signal to measure its non-Gaussianity. The new cost function does not involve the computation of the difference between the function of the Gaussian random vector and that of the extracted signal, which is time consuming. Centering and whitening are also used to preprocess the observed signal to further reduce the computation complexity. While the proposed method has the same error performance as other improved one-unit ICA-R methods, it actually has lower computation complexity than those other methods. Simulations are performed separately on artificial and real-world electrocardiogram signals.
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Shi Y, Zeng W, Wang N, Chen D. A novel fMRI group data analysis method based on data-driven reference extracting from group subjects. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015; 122:362-371. [PMID: 26387634 DOI: 10.1016/j.cmpb.2015.09.002] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2015] [Revised: 08/09/2015] [Accepted: 09/01/2015] [Indexed: 06/05/2023]
Abstract
Group-independent component analysis (GICA) is a well-established blind source separation technique that has been widely applied to study multi-subject functional magnetic resonance imaging (fMRI) data. The group-independent components (GICs) represent the commonness of all of the subjects in the group. Similar to independent component analysis on the single-subject level, the performance of GICA can be improved for multi-subject fMRI data analysis by incorporating a priori information; however, a priori information is not always considered while looking for GICs in existing GICA methods, especially when no obvious or specific knowledge about an unknown group is available. In this paper, we present a novel method to extract the group intrinsic reference from all of the subjects of the group and then incorporate it into the GICA extraction procedure. Comparison experiments between FastICA and GICA with intrinsic reference (GICA-IR) are implemented on the group level with regard to the simulated, hybrid and real fMRI data. The experimental results show that the GICs computed by GICA-IR have a higher correlation with the corresponding independent component of each subject in the group, and the accuracy of activation regions detected by GICA-IR was also improved. These results have demonstrated the advantages of the GICA-IR method, which can better reflect the commonness of the subjects in the group.
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Affiliation(s)
- Yuhu Shi
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai 201306, China
| | - Weiming Zeng
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai 201306, China.
| | - Nizhuan Wang
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai 201306, China
| | - Dongtailang Chen
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai 201306, China
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Lu N, Li T, Pan J, Ren X, Feng Z, Miao H. Structure constrained semi-nonnegative matrix factorization for EEG-based motor imagery classification. Comput Biol Med 2015; 60:32-9. [PMID: 25747342 DOI: 10.1016/j.compbiomed.2015.02.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2014] [Revised: 01/28/2015] [Accepted: 02/15/2015] [Indexed: 11/17/2022]
Abstract
BACKGROUND Electroencephalogram (EEG) provides a non-invasive approach to measure the electrical activities of brain neurons and has long been employed for the development of brain-computer interface (BCI). For this purpose, various patterns/features of EEG data need to be extracted and associated with specific events like cue-paced motor imagery. However, this is a challenging task since EEG data are usually non-stationary time series with a low signal-to-noise ratio. NEW METHOD In this study, we propose a novel method, called structure constrained semi-nonnegative matrix factorization (SCS-NMF), to extract the key patterns of EEG data in time domain by imposing the mean envelopes of event-related potentials (ERPs) as constraints on the semi-NMF procedure. The proposed method is applicable to general EEG time series, and the extracted temporal features by SCS-NMF can also be combined with other features in frequency domain to improve the performance of motor imagery classification. RESULTS Real data experiments have been performed using the SCS-NMF approach for motor imagery classification, and the results clearly suggest the superiority of the proposed method. COMPARISON WITH EXISTING METHODS Comparison experiments have also been conducted. The compared methods include ICA, PCA, Semi-NMF, Wavelets, EMD and CSP, which further verified the effectivity of SCS-NMF. CONCLUSIONS The SCS-NMF method could obtain better or competitive performance over the state of the art methods, which provides a novel solution for brain pattern analysis from the perspective of structure constraint.
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Affiliation(s)
- Na Lu
- State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
| | - Tengfei Li
- State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Jinjin Pan
- State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xiaodong Ren
- State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Zuren Feng
- State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Hongyu Miao
- Department of Biostatistics and Computational Biology, School of Medicine and Dentistry, University of Rochester, Rochester, NY, USA.
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Yan H, Liu H, Huang X, Zhao Y, Si J, Liu T. Invariant heart beat span versus variant heart beat intervals and its application to fetal ECG extraction. Biomed Eng Online 2014; 13:163. [PMID: 25494711 PMCID: PMC4320593 DOI: 10.1186/1475-925x-13-163] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2014] [Accepted: 12/05/2014] [Indexed: 11/11/2022] Open
Abstract
Background The fundamental assumptions for various kinds of fetal electrocardiogram (fECG) extraction methods are not consistent with each other, which is a very important problem needed to be ascertained. Methods Based on two public databases, the regularity on ECG wave durations for normal sinus rhythm is investigated statistically. Taking the ascertained regularity as an assumption, a new fECG extraction algorithm is proposed, called Partial R-R interval Resampling (PRR). Results Both synthetic and real abdominal ECG signals are used to test the algorithm. The results indicate that the PRR algorithm has better performance over the whole R-R interval resampling based comb filtering method (RR) and linear template method (LP), which takes advantages of both LP and RR. Conclusions The final drawn conclusion is: (1) the proposition should be true that the individual’s heart beat span is invariable for normal sinus rhythm; (2) the proposed PRR fetal ECG extraction algorithm can estimate the maternal ECG (mECG) more accurately and stably even in the condition of large HRV, finally resulting in better fetal ECG extraction.
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Affiliation(s)
| | - Hongxing Liu
- School of Electronic Science and Engineering, Nanjing University, Xianlin Campus, Nanjing 210023, China.
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Rodriguez PA, Anderson M, Calhoun VD, Adali T. General nonunitary constrained ICA and its application to complex-valued fMRI data. IEEE Trans Biomed Eng 2014; 62:922-9. [PMID: 25420255 DOI: 10.1109/tbme.2014.2371791] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Constrained independent component analysis (C-ICA) algorithms provide an effective way to introduce prior information into the complex- and real-valued ICA framework. The work in this area has focus on adding constraints to the objective function of algorithms that assume a unitary demixing matrix. The unitary condition is required in order to decouple-isolate-the constraints applied for each individual source. This assumption limits the optimization space and, therefore, the separation performance of C-ICA algorithms. We generalize the existing C-ICA framework by using a novel decoupling method that preserves the larger optimization space for the demixing matrix. This framework allows for the constraining of either the sources or the mixing coefficients. A constrained version of the nonunitary entropy bound minimization algorithm is introduced and applied to actual complex-valued fMRI data. We show that constraining the mixing parameters using a temporal constraint improves the estimation of the spatial map and timecourses of task-related components.
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Fault feature extraction using independent component analysis with reference and its application on fault diagnosis of rotating machinery. Neural Comput Appl 2014. [DOI: 10.1007/s00521-014-1726-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Shafiq G, Veluvolu KC. Surface chest motion decomposition for cardiovascular monitoring. Sci Rep 2014; 4:5093. [PMID: 24865183 PMCID: PMC4035586 DOI: 10.1038/srep05093] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2014] [Accepted: 05/06/2014] [Indexed: 11/09/2022] Open
Abstract
Surface chest motion can be easily monitored with a wide variety of sensors such as pressure belts, fiber Bragg gratings and inertial sensors, etc. The current applications of these sensors are mainly restricted to respiratory motion monitoring/analysis due to the technical challenges involved in separation of the cardiac motion from the dominant respiratory motion. The contribution of heart to the surface chest motion is relatively very small as compared to the respiratory motion. Further, the heart motion spectrally overlaps with the respiratory harmonics and their separation becomes even more challenging. In this paper, we approach this source separation problem with independent component analysis (ICA) framework. ICA with reference (ICA-R) yields only desired component with improved separation, but the method is highly sensitive to the reference generation. Several reference generation approaches are developed to solve the problem. Experimental validation of these proposed approaches is performed with chest displacement data and ECG obtained from healthy subjects under normal breathing and post-exercise conditions. The extracted component morphologically matches well with the collected ECG. Results show that the proposed methods perform better than conventional band pass filtering.
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Affiliation(s)
- Ghufran Shafiq
- School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, South Korea 702-701
| | - Kalyana C Veluvolu
- School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, South Korea 702-701
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Mi JX. A novel algorithm for independent component analysis with reference and methods for its applications. PLoS One 2014; 9:e93984. [PMID: 24826986 PMCID: PMC4020756 DOI: 10.1371/journal.pone.0093984] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2013] [Accepted: 03/12/2014] [Indexed: 11/18/2022] Open
Abstract
This paper presents a stable and fast algorithm for independent component analysis with reference (ICA-R). This is a technique for incorporating available reference signals into the ICA contrast function so as to form an augmented Lagrangian function under the framework of constrained ICA (cICA). The previous ICA-R algorithm was constructed by solving the optimization problem via a Newton-like learning style. Unfortunately, the slow convergence and potential misconvergence limit the capability of ICA-R. This paper first investigates and probes the flaws of the previous algorithm and then introduces a new stable algorithm with a faster convergence speed. There are two other highlights in this paper: first, new approaches, including the reference deflation technique and a direct way of obtaining references, are introduced to facilitate the application of ICA-R; second, a new method is proposed that the new ICA-R is used to recover the complete underlying sources with new advantages compared with other classical ICA methods. Finally, the experiments on both synthetic and real-world data verify the better performance of the new algorithm over both previous ICA-R and other well-known methods.
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Affiliation(s)
- Jian-Xun Mi
- Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China
- College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
- * E-mail:
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Peng F, Zhang Z, Gou X, Liu H, Wang W. Motion artifact removal from photoplethysmographic signals by combining temporally constrained independent component analysis and adaptive filter. Biomed Eng Online 2014; 13:50. [PMID: 24761769 PMCID: PMC4021027 DOI: 10.1186/1475-925x-13-50] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2014] [Accepted: 04/01/2014] [Indexed: 11/25/2022] Open
Abstract
Background The calculation of arterial oxygen saturation (SpO2) relies heavily on the amplitude information of the high-quality photoplethysmographic (PPG) signals, which could be contaminated by motion artifacts (MA) during monitoring. Methods A new method combining temporally constrained independent component analysis (cICA) and adaptive filters is presented here to extract the clean PPG signals from the MA corrupted PPG signals with the amplitude information reserved. The underlying PPG signal could be extracted from the MA contaminated PPG signals automatically by using cICA algorithm. Then the amplitude information of the PPG signals could be recovered by using adaptive filters. Results Compared with conventional ICA algorithms, the proposed approach is permutation and scale ambiguity-free. Numerical examples with both synthetic datasets and real-world MA corrupted PPG signals demonstrate that the proposed method could remove the MA from MA contaminated PPG signals more effectively than the two existing FFT-LMS and moving average filter (MAF) methods. Conclusions This paper presents a new method which combines the cICA algorithm and adaptive filter to extract the underlying PPG signals from the MA contaminated PPG signals with the amplitude information reserved. The new method could be used in the situations where one wants to extract the interested source automatically from the mixed observed signals with the amplitude information reserved. The results of study demonstrated the efficacy of this proposed method.
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Affiliation(s)
| | | | | | | | - Weidong Wang
- Department of Biomedical Engineering, Chinese PLA General Hospital, Beijing, China.
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Ma X, Zhang H, Zhao X, Yao L, Long Z. Semi-Blind Independent Component Analysis of fMRI Based on Real-Time fMRI System. IEEE Trans Neural Syst Rehabil Eng 2013; 21:416-26. [DOI: 10.1109/tnsre.2012.2184303] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Approaches and applications of semi-blind signal extraction for communication signals based on constrained independent component analysis: The complex case. Neurocomputing 2013. [DOI: 10.1016/j.neucom.2012.09.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Lee WL, Tan T, Leung YH. An improved P300 extraction using ICA-R for P300-BCI speller. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:7064-7067. [PMID: 24111372 DOI: 10.1109/embc.2013.6611185] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
In this study, a new P300 extraction method is investigated by using a form of constrained independent component analysis (cICA) algorithm called one-unit ICA-with-reference (ICA-R) which extracts the P300 signal based on its temporal information. The main advantage of this method compared to the existing ICA-based method is that the desired P300 signal is extracted directly without requiring partial or full signal decomposition and any post-processing on the outcome of the ICA before the P300 signal can be obtained. Since only one IC is extracted, the method is computationally more efficient for real-time P300 BCI applications. In our study, when tested on the BCI competition 2003 dataset IIb, the current state-of-the-art performance is maintained by using the one-unit ICA-R. Besides that, the ability of the method to visualize P300 signals at the single-trial level also suggests it has potential applications in other types of ERP studies.
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Wang X, Huang Z, Zhou Y. Semi-blind signal extraction for communication signals by combining independent component analysis and spatial constraints. SENSORS 2012; 12:9024-45. [PMID: 23012531 PMCID: PMC3444089 DOI: 10.3390/s120709024] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2012] [Revised: 06/15/2012] [Accepted: 06/28/2012] [Indexed: 11/16/2022]
Abstract
Signal of interest (SOI) extraction is a vital issue in communication signal processing. In this paper, we propose two novel iterative algorithms for extracting SOIs from instantaneous mixtures, which explores the spatial constraint corresponding to the Directions of Arrival (DOAs) of the SOIs as a priori information into the constrained Independent Component Analysis (cICA) framework. The first algorithm utilizes the spatial constraint to form a new constrained optimization problem under the previous cICA framework which requires various user parameters, i.e., Lagrange parameter and threshold measuring the accuracy degree of the spatial constraint, while the second algorithm incorporates the spatial constraints to select specific initialization of extracting vectors. The major difference between the two novel algorithms is that the former incorporates the prior information into the learning process of the iterative algorithm and the latter utilizes the prior information to select the specific initialization vector. Therefore, no extra parameters are necessary in the learning process, which makes the algorithm simpler and more reliable and helps to improve the speed of extraction. Meanwhile, the convergence condition for the spatial constraints is analyzed. Compared with the conventional techniques, i.e., MVDR, numerical simulation results demonstrate the effectiveness, robustness and higher performance of the proposed algorithms.
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Affiliation(s)
- Xiang Wang
- College of Electronic Science and Engineering, National University of Defense Technology (NUDT), Changsha 410073, China.
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A constrained independent component analysis technique for artery–vein separation of two-photon laser scanning microscopy images of the cerebral microvasculature. Med Image Anal 2012; 16:239-51. [DOI: 10.1016/j.media.2011.08.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2010] [Revised: 04/08/2011] [Accepted: 08/12/2011] [Indexed: 11/20/2022]
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48
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Extraction of microsaccade-related signal from single-trial local field potential by ICA with reference. Neural Comput Appl 2011. [DOI: 10.1007/s00521-010-0469-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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