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Mirzaei G, Adeli H. Resting state functional magnetic resonance imaging processing techniques in stroke studies. Rev Neurosci 2016; 27:871-885. [DOI: 10.1515/revneuro-2016-0052] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Accepted: 10/01/2016] [Indexed: 01/15/2023]
Abstract
AbstractIn recent years, there has been considerable research interest in the study of brain connectivity using the resting state functional magnetic resonance imaging (rsfMRI). Studies have explored the brain networks and connection between different brain regions. These studies have revealed interesting new findings about the brain mapping as well as important new insights in the overall organization of functional communication in the brain network. In this paper, after a general discussion of brain networks and connectivity imaging, the brain connectivity and resting state networks are described with a focus on rsfMRI imaging in stroke studies. Then, techniques for preprocessing of the rsfMRI for stroke patients are reviewed, followed by brain connectivity processing techniques. Recent research on brain connectivity using rsfMRI is reviewed with an emphasis on stroke studies. The authors hope this paper generates further interest in this emerging area of computational neuroscience with potential applications in rehabilitation of stroke patients.
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Affiliation(s)
- Golrokh Mirzaei
- 1Department of Computer Science and Engineering, The Ohio State University, Marion, OH 43302, United States of America
| | - Hojjat Adeli
- 2Department of Biomedical Engineering, Biomedical Informatics, Neurology, Neuroscience, Electrical and Computer Engineering, Civil and Environmental Engineering, The Ohio State University, Columbus, OH 43210, United States of America
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Beheshti I, Demirel H, Farokhian F, Yang C, Matsuda H. Structural MRI-based detection of Alzheimer's disease using feature ranking and classification error. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 137:177-193. [PMID: 28110723 DOI: 10.1016/j.cmpb.2016.09.019] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Revised: 09/02/2016] [Accepted: 09/22/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE This paper presents an automatic computer-aided diagnosis (CAD) system based on feature ranking for detection of Alzheimer's disease (AD) using structural magnetic resonance imaging (sMRI) data. METHODS The proposed CAD system is composed of four systematic stages. First, global and local differences in the gray matter (GM) of AD patients compared to the GM of healthy controls (HCs) are analyzed using a voxel-based morphometry technique. The aim is to identify significant local differences in the volume of GM as volumes of interests (VOIs). Second, the voxel intensity values of the VOIs are extracted as raw features. Third, the raw features are ranked using a seven-feature ranking method, namely, statistical dependency (SD), mutual information (MI), information gain (IG), Pearson's correlation coefficient (PCC), t-test score (TS), Fisher's criterion (FC), and the Gini index (GI). The features with higher scores are more discriminative. To determine the number of top features, the estimated classification error based on training set made up of the AD and HC groups is calculated, with the vector size that minimized this error selected as the top discriminative feature. Fourth, the classification is performed using a support vector machine (SVM). In addition, a data fusion approach among feature ranking methods is introduced to improve the classification performance. RESULTS The proposed method is evaluated using a data-set from ADNI (130 AD and 130 HC) with 10-fold cross-validation. The classification accuracy of the proposed automatic system for the diagnosis of AD is up to 92.48% using the sMRI data. CONCLUSIONS An automatic CAD system for the classification of AD based on feature-ranking method and classification errors is proposed. In this regard, seven-feature ranking methods (i.e., SD, MI, IG, PCC, TS, FC, and GI) are evaluated. The optimal size of top discriminative features is determined by the classification error estimation in the training phase. The experimental results indicate that the performance of the proposed system is comparative to that of state-of-the-art classification models.
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Affiliation(s)
- Iman Beheshti
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo 187-8551, Japan.
| | - Hasan Demirel
- Biomedical Image Processing Lab, Department of Electrical & Electronic Engineering, Eastern Mediterranean University, Famagusta, Mersin 10, Turkey
| | - Farnaz Farokhian
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100022, China
| | - Chunlan Yang
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100022, China
| | - Hiroshi Matsuda
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo 187-8551, Japan
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Alderson-Day B, Diederen K, Fernyhough C, Ford JM, Horga G, Margulies DS, McCarthy-Jones S, Northoff G, Shine JM, Turner J, van de Ven V, van Lutterveld R, Waters F, Jardri R. Auditory Hallucinations and the Brain's Resting-State Networks: Findings and Methodological Observations. Schizophr Bull 2016; 42:1110-23. [PMID: 27280452 PMCID: PMC4988751 DOI: 10.1093/schbul/sbw078] [Citation(s) in RCA: 93] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
In recent years, there has been increasing interest in the potential for alterations to the brain's resting-state networks (RSNs) to explain various kinds of psychopathology. RSNs provide an intriguing new explanatory framework for hallucinations, which can occur in different modalities and population groups, but which remain poorly understood. This collaboration from the International Consortium on Hallucination Research (ICHR) reports on the evidence linking resting-state alterations to auditory hallucinations (AH) and provides a critical appraisal of the methodological approaches used in this area. In the report, we describe findings from resting connectivity fMRI in AH (in schizophrenia and nonclinical individuals) and compare them with findings from neurophysiological research, structural MRI, and research on visual hallucinations (VH). In AH, various studies show resting connectivity differences in left-hemisphere auditory and language regions, as well as atypical interaction of the default mode network and RSNs linked to cognitive control and salience. As the latter are also evident in studies of VH, this points to a domain-general mechanism for hallucinations alongside modality-specific changes to RSNs in different sensory regions. However, we also observed high methodological heterogeneity in the current literature, affecting the ability to make clear comparisons between studies. To address this, we provide some methodological recommendations and options for future research on the resting state and hallucinations.
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Affiliation(s)
| | - Kelly Diederen
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | | | - Judith M. Ford
- Department of Psychiatry, School of Medicine, University of California, San Francisco, San Francisco, CA
| | - Guillermo Horga
- New York State Psychiatric Institute, Columbia University Medical Center, New York, NY
| | - Daniel S. Margulies
- Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | | | - Georg Northoff
- Mind, Brain Imaging and Neuroethics Research Unit, The Royal’s Institute of Mental Health Research, Ottawa, ON, Canada
| | - James M. Shine
- Department of Psychology, Stanford University, Stanford, CA
| | - Jessica Turner
- Department of Psychology, Neuroscience Institute, Georgia State University, Atlanta, GA
| | - Vincent van de Ven
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Remko van Lutterveld
- Center for Mindfulness, University of Massachusetts Medical School, Worcester, MA
| | - Flavie Waters
- North Metro Health Service Mental Health, Graylands Health Campus, School of Psychiatry and Clinical Neurosciences, University of Western Australia, Crawley, WA, Australia
| | - Renaud Jardri
- Univ Lille, CNRS (UMR 9193), SCALab & CHU Lille, Psychiatry dept. (CURE), Lille, France
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54
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Organization of the intrinsic functional network in the cervical spinal cord: A resting state functional MRI study. Neuroscience 2016; 336:30-38. [PMID: 27590264 DOI: 10.1016/j.neuroscience.2016.08.042] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2016] [Revised: 08/22/2016] [Accepted: 08/24/2016] [Indexed: 12/29/2022]
Abstract
Resting state functional magnetic resonance imaging (rsfMRI) has been extensively applied to investigate the organization of functional networks in the brain. As an essential part of the central nervous system (CNS), the spinal cord has not been well explored about its intrinsic functional network. In this study, we aim to thoroughly investigate the characteristics of the intrinsic functional network in the spinal cord using rsfMRI. Functional connectivity and graph theory analysis were employed to evaluate the organization of the functional network, including its topology and network communication properties. Furthermore, the reproducibility of rsfMRI analysis on the spinal cord was also examined by intra-class correlation (ICC). Comprehensive evaluation of the intrinsic functional organization presented a non-uniform distribution of topological characteristics of the functional network, in which the upper levels (C2 and C3 vertebral levels) of the cervical spinal cord showed high levels of connectivity. The present results revealed the significance of the upper cervical cord in the intrinsic functional network of the human cervical spinal cord. In addition, this study demonstrated the efficiency of the cervical spinal cord functional network and the reproducibility of rsfMRI analysis on the spinal cord was also confirmed. As knowledge expansion of intrinsic functional network from the brain to the spinal cord, this study shed light on the organization of the spinal cord functional network in both normal development and clinical disorders.
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Martinez-Murcia FJ, Górriz JM, Ramírez J, Ortiz A. A Structural Parametrization of the Brain Using Hidden Markov Models-Based Paths in Alzheimer’s Disease. Int J Neural Syst 2016; 26:1650024. [DOI: 10.1142/s0129065716500246] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The usage of biomedical imaging in the diagnosis of dementia is increasingly widespread. A number of works explore the possibilities of computational techniques and algorithms in what is called computed aided diagnosis. Our work presents an automatic parametrization of the brain structure by means of a path generation algorithm based on hidden Markov models (HMMs). The path is traced using information of intensity and spatial orientation in each node, adapting to the structure of the brain. Each path is itself a useful way to characterize the distribution of the tissue inside the magnetic resonance imaging (MRI) image by, for example, extracting the intensity levels at each node or generating statistical information of the tissue distribution. Additionally, a further processing consisting of a modification of the grey level co-occurrence matrix (GLCM) can be used to characterize the textural changes that occur throughout the path, yielding more meaningful values that could be associated to Alzheimer’s disease (AD), as well as providing a significant feature reduction. This methodology achieves moderate performance, up to 80.3% of accuracy using a single path in differential diagnosis involving Alzheimer-affected subjects versus controls belonging to the Alzheimer’s disease neuroimaging initiative (ADNI).
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Affiliation(s)
| | - Juan M. Górriz
- Department of Signal Theory, Networking and Communications, University of Granada, Granada 18071, Spain
| | - Javier Ramírez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada 18071, Spain
| | - Andres Ortiz
- Department of Communications Engineering, University of Malaga, Malaga, Spain
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Iliya S, Neri F. Towards Artificial Speech Therapy: A Neural System for Impaired Speech Segmentation. Int J Neural Syst 2016; 26:1650023. [DOI: 10.1142/s0129065716500234] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This paper presents a neural system-based technique for segmenting short impaired speech utterances into silent, unvoiced, and voiced sections. Moreover, the proposed technique identifies those points of the (voiced) speech where the spectrum becomes steady. The resulting technique thus aims at detecting that limited section of the speech which contains the information about the potential impairment of the speech. This section is of interest to the speech therapist as it corresponds to the possibly incorrect movements of speech organs (lower lip and tongue with respect to the vocal tract). Two segmentation models to detect and identify the various sections of the disordered (impaired) speech signals have been developed and compared. The first makes use of a combination of four artificial neural networks. The second is based on a support vector machine (SVM). The SVM has been trained by means of an ad hoc nested algorithm whose outer layer is a metaheuristic while the inner layer is a convex optimization algorithm. Several metaheuristics have been tested and compared leading to the conclusion that some variants of the compact differential evolution (CDE) algorithm appears to be well-suited to address this problem. Numerical results show that the SVM model with a radial basis function is capable of effective detection of the portion of speech that is of interest to a therapist. The best performance has been achieved when the system is trained by the nested algorithm whose outer layer is hybrid-population-based/CDE. A population-based approach displays the best performance for the isolation of silence/noise sections, and the detection of unvoiced sections. On the other hand, a compact approach appears to be clearly well-suited to detect the beginning of the steady state of the voiced signal. Both the proposed segmentation models display outperformed two modern segmentation techniques based on Gaussian mixture model and deep learning.
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Affiliation(s)
- Sunday Iliya
- Centre for Computational Intelligence, School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester LE1 9BH, England, UK
| | - Ferrante Neri
- Centre for Computational Intelligence, School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester LE1 9BH, England, UK
- Department of Mathematical Information Technology, University of Jyväskylä Jyväskylä, Finland
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Cui LB, Liu K, Li C, Wang LX, Guo F, Tian P, Wu YJ, Guo L, Liu WM, Xi YB, Wang HN, Yin H. Putamen-related regional and network functional deficits in first-episode schizophrenia with auditory verbal hallucinations. Schizophr Res 2016; 173:13-22. [PMID: 26995674 DOI: 10.1016/j.schres.2016.02.039] [Citation(s) in RCA: 101] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2015] [Revised: 11/27/2015] [Accepted: 02/29/2016] [Indexed: 10/22/2022]
Abstract
OBJECTIVE Auditory verbal hallucinations (AVHs) are one of the cardinal symptoms of schizophrenia (SZ). Cerebral dysfunction may represent pathophysiological underpinnings behind AVHs in SZ. However, regional and network functional deficits for AVHs in SZ remain to be identified. METHODS Seventeen medication-naïve first-episode SZ patients with AVHs, 15 without AVHs, and 19 healthy controls (HCs) were studied using resting-state functional magnetic resonance imaging. We compared the amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) among these subjects. Areas with both ALFF and ReHo alterations were used as seeds in functional connectivity (FC) analysis. Then we performed correlation analysis between image measures and symptoms and receiver operating characteristic analysis. RESULTS One-way analysis of variance showed significant differences of ALFF and ReHo in the bilateral putamen, thereby being used as seeds. SZ patients with AVHs showed decreased ALFF in the left putamen, increased ReHo in the right dorsolateral prefrontal cortex (DLPFC), and increased right putamen-seeded FC with the left DLPFC and Broca's area relative to those without AVHs. Furthermore, the increased strength of the connectivity between the right putamen and left Broca's area correlated with the severity of SZ symptoms. Both patient groups demonstrated hypoconnectivity within frontal/parietal/temporal cortico-striatal-cerebellar networks compared with HCs. CONCLUSION AVHs in SZ may be caused by abnormal regional function in the putamen and prefrontal cortex, as well as hyperconnectivity between them. The putamen-related regional and network functional deficits may reflect imbalance in neuromodulation of AVHs in SZ. Furthermore, dysconnectivity within cortico-striatal-cerebellar networks might subserve the pathogenesis of SZ.
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Affiliation(s)
- Long-Biao Cui
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Kang Liu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Chen Li
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Liu-Xian Wang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Fan Guo
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Ping Tian
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Yu-Jing Wu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Li Guo
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Wen-Ming Liu
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Yi-Bin Xi
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Hua-Ning Wang
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
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Buoli M, Caldiroli A, Altamura AC. Psychiatric Conditions in Parkinson Disease: A Comparison With Classical Psychiatric Disorders. J Geriatr Psychiatry Neurol 2016; 29:72-91. [PMID: 26377851 DOI: 10.1177/0891988715606233] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Psychiatric conditions often complicate the outcome of patients affected by Parkinson disease (PD), but they differ from classical psychiatric disorders in terms of underlying biological mechanisms, clinical presentation, and treatment response. The purpose of the present review is to illustrate the biological and clinical aspects of psychiatric conditions associated with PD, with particular reference to the differences with respect to classical psychiatric disorders. A careful search of articles on main databases was performed in order to obtain a comprehensive review about the main psychiatric conditions associated with PD. A manual selection of the articles was then performed in order to consider only those articles that concerned with the topic of the review. Psychiatric conditions in patients with PD present substantial differences with respect to classical psychiatric disorders. Their clinical presentation does not align with the symptom profiles represented by Diagnostic and Statistical Manual for Mental Disorders and International Classification of Diseases. Furthermore, psychiatry treatment guidelines are of poor help in managing psychiatric symptoms of patients with PD. Specific diagnostic tools and treatment guidelines are needed to allow early diagnosis and adequate treatment of psychiatric conditions in comorbidity with PD.
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Affiliation(s)
- Massimiliano Buoli
- Department of Psychiatry, University of Milan, Fondazione IRCCS Ca'Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Alice Caldiroli
- Department of Psychiatry, University of Milan, Fondazione IRCCS Ca'Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Alfredo Carlo Altamura
- Department of Psychiatry, University of Milan, Fondazione IRCCS Ca'Granda Ospedale Maggiore Policlinico, Milan, Italy
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Akar SA, Kara S, Latifoğlu F, Bilgiç V. Analysis of the Complexity Measures in the EEG of Schizophrenia Patients. Int J Neural Syst 2015; 26:1650008. [PMID: 26762866 DOI: 10.1142/s0129065716500088] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Complexity measures have been enormously used in schizophrenia patients to estimate brain dynamics. However, the conflicting results in terms of both increased and reduced complexity values have been reported in these studies depending on the patients' clinical status or symptom severity or medication and age status. The objective of this study is to investigate the nonlinear brain dynamics of chronic and medicated schizophrenia patients using distinct complexity estimators. EEG data were collected from 22 relaxed eyes-closed patients and age-matched healthy controls. A single-trial EEG series of 2 min was partitioned into identical epochs of 20 s intervals. The EEG complexity of participants were investigated and compared using approximate entropy (ApEn), Shannon entropy (ShEn), Kolmogorov complexity (KC) and Lempel-Ziv complexity (LZC). Lower complexity values were obtained in schizophrenia patients. The most significant complexity differences between patients and controls were obtained in especially left frontal (F3) and parietal (P3) regions of the brain when all complexity measures were applied individually. Significantly, we found that KC was more sensitive for detecting EEG complexity of patients than other estimators in all investigated brain regions. Moreover, significant inter-hemispheric complexity differences were found in the frontal and parietal areas of schizophrenics' brain. Our findings demonstrate that the utilizing of sensitive complexity estimators to analyze brain dynamics of patients might be a useful discriminative tool for diagnostic purposes. Therefore, we expect that nonlinear analysis will give us deeper understanding of schizophrenics' brain.
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Affiliation(s)
- S. Akdemir Akar
- Institute of Biomedical Engineering, Fatih University, Buyukcekmece, İstanbul 34500, Turkey
| | - S. Kara
- Institute of Biomedical Engineering, Fatih University, Buyukcekmece, İstanbul 34500, Turkey
| | - F. Latifoğlu
- Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey
| | - V. Bilgiç
- Psychiatry Department, Faculty of Medicine, Fatih University, İstanbul 34500, Turkey
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