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Sorrentino P, Rabuffo G, Baselice F, Troisi Lopez E, Liparoti M, Quarantelli M, Sorrentino G, Bernard C, Jirsa V. Dynamical interactions reconfigure the gradient of cortical timescales. Netw Neurosci 2023; 7:73-85. [PMID: 37334007 PMCID: PMC10270712 DOI: 10.1162/netn_a_00270] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 07/14/2022] [Indexed: 09/18/2023] Open
Abstract
The functional organization of the brain is usually presented with a back-to-front gradient of timescales, reflecting regional specialization with sensory areas (back) processing information faster than associative areas (front), which perform information integration. However, cognitive processes require not only local information processing but also coordinated activity across regions. Using magnetoencephalography recordings, we find that the functional connectivity at the edge level (between two regions) is also characterized by a back-to-front gradient of timescales following that of the regional gradient. Unexpectedly, we demonstrate a reverse front-to-back gradient when nonlocal interactions are prominent. Thus, the timescales are dynamic and can switch between back-to-front and front-to-back patterns.
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Affiliation(s)
- P. Sorrentino
- Institut de Neurosciences des Systèmes, Aix-Marseille University, Marseille, France
- Institute of Applied Sciences and Intelligent Systems, National Research Council, Pozzuoli, Italy
| | - G. Rabuffo
- Institut de Neurosciences des Systèmes, Aix-Marseille University, Marseille, France
| | - F. Baselice
- Department of Engineering, Parthenope University of Naples, Naples, Italy
| | - E. Troisi Lopez
- Department of Motor Sciences and Wellness, Parthenope University of Naples, Naples, Italy
- Institute for Diagnosis and Cure Hermitage Capodimonte, Naples, Italy
| | - M. Liparoti
- Department of Motor Sciences and Wellness, Parthenope University of Naples, Naples, Italy
- Institute for Diagnosis and Cure Hermitage Capodimonte, Naples, Italy
| | - M. Quarantelli
- Biostructure and Bioimaging Institute, National Research Council, Naples, Italy
| | - G. Sorrentino
- Institute of Applied Sciences and Intelligent Systems, National Research Council, Pozzuoli, Italy
- Institute for Diagnosis and Cure Hermitage Capodimonte, Naples, Italy
| | - C. Bernard
- Institut de Neurosciences des Systèmes, Aix-Marseille University, Marseille, France
| | - V. Jirsa
- Institut de Neurosciences des Systèmes, Aix-Marseille University, Marseille, France
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2
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Echegoyen I, López-Sanz D, Martínez JH, Maestú F, Buldú JM. Permutation Entropy and Statistical Complexity in Mild Cognitive Impairment and Alzheimer's Disease: An Analysis Based on Frequency Bands. ENTROPY 2020; 22:e22010116. [PMID: 33285891 PMCID: PMC7516422 DOI: 10.3390/e22010116] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 01/15/2020] [Accepted: 01/16/2020] [Indexed: 12/14/2022]
Abstract
We present one of the first applications of Permutation Entropy (PE) and Statistical Complexity (SC) (measured as the product of PE and Jensen-Shanon Divergence) on Magnetoencephalography (MEG) recordings of 46 subjects suffering from Mild Cognitive Impairment (MCI), 17 individuals diagnosed with Alzheimer's Disease (AD) and 48 healthy controls. We studied the differences in PE and SC in broadband signals and their decomposition into frequency bands ( δ , θ , α and β ), considering two modalities: (i) raw time series obtained from the magnetometers and (ii) a reconstruction into cortical sources or regions of interest (ROIs). We conducted our analyses at three levels: (i) at the group level we compared SC in each frequency band and modality between groups; (ii) at the individual level we compared how the [PE, SC] plane differs in each modality; and (iii) at the local level we explored differences in scalp and cortical space. We recovered classical results that considered only broadband signals and found a nontrivial pattern of alterations in each frequency band, showing that SC does not necessarily decrease in AD or MCI.
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Affiliation(s)
- Ignacio Echegoyen
- Laboratory of Biological Networks, Centre for Biomedical Technology, Universidad Politécnica de Madrid (UPM), 28223 Madrid, Spain;
- Complex Systems Group, Rey Juan Carlos University, 28933 Madrid, Spain
- Grupo Interdisciplinar de Sistemas Complejos (GISC), 28911 Madrid, Spain;
- Correspondence:
| | - David López-Sanz
- Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid (UPM), 28223 Madrid, Spain; (D.L.-S.); (F.M.)
- Department of Experimental Psychology, Complutense University of Madrid, 28223 Madrid, Spain
| | - Johann H. Martínez
- Grupo Interdisciplinar de Sistemas Complejos (GISC), 28911 Madrid, Spain;
- Biomedical Engineering Department, Universidad de los Andes, Bogotá 111711, Colombia
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid (UPM), 28223 Madrid, Spain; (D.L.-S.); (F.M.)
- Department of Experimental Psychology, Complutense University of Madrid, 28223 Madrid, Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine, 28029 Zaragoza, Spain
| | - Javier M. Buldú
- Laboratory of Biological Networks, Centre for Biomedical Technology, Universidad Politécnica de Madrid (UPM), 28223 Madrid, Spain;
- Complex Systems Group, Rey Juan Carlos University, 28933 Madrid, Spain
- Grupo Interdisciplinar de Sistemas Complejos (GISC), 28911 Madrid, Spain;
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3
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Noguchi Y, Xia Y, Kakigi R. Desynchronizing to be faster? Perceptual- and attentional-modulation of brain rhythms at the sub-millisecond scale. Neuroimage 2019; 191:225-233. [PMID: 30772401 DOI: 10.1016/j.neuroimage.2019.02.027] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2018] [Revised: 01/14/2019] [Accepted: 02/11/2019] [Indexed: 01/15/2023] Open
Abstract
Neural oscillatory signals has been associated with many high-level functions (e.g. attention and working memory), because they reflect correlated behaviors of neural population that would facilitate the information transfer in the brain. On the other hand, a decreased power of oscillation (event-related desynchronization, ERD) has been associated with an irregular state in which many neurons behave in an uncorrelated manner. In contrast to this view, here we show that the human ERD is linked to the increased regularity of oscillatory signals. Using magnetoencephalography, we found that presenting a visual stimulus not only induced a decrease in power of alpha (8-12 Hz) to beta (13-30 Hz) rhythms in the contralateral visual cortex but also reduced the mean and variance of their inter-peak intervals (IPIs). This indicates that the suppressed alpha/beta rhythms became faster (reduced mean) and more regular (reduced variance) during visual stimulation. The same changes in IPIs, especially those of beta rhythm, were observed when subjects allocated their attention to a contralateral visual field. Those results revealed a new role of the event-related decrease in alpha/beta power and further suggested that our brain regulates and accelerates a clock for neural computations by actively suppressing the oscillation amplitude in task-relevant regions.
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Affiliation(s)
- Yasuki Noguchi
- Department of Psychology, Graduate School of Humanities, Kobe University, 1-1 Rokkodai-cho, Nada, Kobe, 657-8501, Japan.
| | - Yi Xia
- Department of Psychology, Graduate School of Humanities, Kobe University, 1-1 Rokkodai-cho, Nada, Kobe, 657-8501, Japan
| | - Ryusuke Kakigi
- Department of Integrative Physiology, National Institute for Physiological Sciences, 38 Nishigonaka, Myodaiji, Okazaki, 444-8585, Japan
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Mandal PK, Banerjee A, Tripathi M, Sharma A. A Comprehensive Review of Magnetoencephalography (MEG) Studies for Brain Functionality in Healthy Aging and Alzheimer's Disease (AD). Front Comput Neurosci 2018; 12:60. [PMID: 30190674 PMCID: PMC6115612 DOI: 10.3389/fncom.2018.00060] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Accepted: 07/09/2018] [Indexed: 12/16/2022] Open
Abstract
Neural oscillations were established with their association with neurophysiological activities and the altered rhythmic patterns are believed to be linked directly to the progression of cognitive decline. Magnetoencephalography (MEG) is a non-invasive technique to record such neuronal activity due to excellent temporal and fair amount of spatial resolution. Single channel, connectivity as well as brain network analysis using MEG data in resting state and task-based experiments were analyzed from existing literature. Single channel analysis studies reported a less complex, more regular and predictable oscillations in Alzheimer's disease (AD) primarily in the left parietal, temporal and occipital regions. Investigations on both functional connectivity (FC) and effective (EC) connectivity analysis demonstrated a loss of connectivity in AD compared to healthy control (HC) subjects found in higher frequency bands. It has been reported from multiplex network of MEG study in AD in the affected regions of hippocampus, posterior default mode network (DMN) and occipital areas, however, conclusions cannot be drawn due to limited availability of clinical literature. Potential utilization of high spatial resolution in MEG likely to provide information related to in-depth brain functioning and underlying factors responsible for changes in neuronal waves in AD. This review is a comprehensive report to investigate diagnostic biomarkers for AD may be identified by from MEG data. It is also important to note that MEG data can also be utilized for the same pursuit in combination with other imaging modalities.
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Affiliation(s)
- Pravat K. Mandal
- Neuroimaging and Neurospectroscopy Lab, National Brain Research Centre, Gurgaon, India
- Department of Neurodegeneration, Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
| | - Anwesha Banerjee
- Neuroimaging and Neurospectroscopy Lab, National Brain Research Centre, Gurgaon, India
| | - Manjari Tripathi
- Department of Neurology, All Indian Institute of Medical Sciences, New Delhi, India
| | - Ankita Sharma
- Neuroimaging and Neurospectroscopy Lab, National Brain Research Centre, Gurgaon, India
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5
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Abstract
Alzheimer's disease (AD), a cognitive disability is analysed using a long range dependence parameter, hurst exponent (HE), calculated based on the time domain analysis of the measured electrical activity of brain. The electroencephalogram (EEG) signals of controls and mild cognitive impairment (MCI)-AD patients are evaluated under normal resting and mental arithmetic conditions. Simultaneous low pass filtering and total variation denoising algorithm is employed for preprocessing. Larger values of HE observed in the right hemisphere of the brain for AD patients indicated a decrease in irregularity of the EEG signal under cognitive task conditions. Correlations between HE and the neuropsychological indices are analysed using bivariate correlation analysis. The observed reduction in the values of Auto mutual information and cross mutual information in the local antero-frontal and distant regions in the brain hemisphere indicates the loss of information transmission in MCI-AD patients.
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Automated Multiclass Classification of Spontaneous EEG Activity in Alzheimer's Disease and Mild Cognitive Impairment. ENTROPY 2018; 20:e20010035. [PMID: 33265122 PMCID: PMC7512207 DOI: 10.3390/e20010035] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 01/04/2018] [Accepted: 01/05/2018] [Indexed: 12/24/2022]
Abstract
The discrimination of early Alzheimer’s disease (AD) and its prodromal form (i.e., mild cognitive impairment, MCI) from cognitively healthy control (HC) subjects is crucial since the treatment is more effective in the first stages of the dementia. The aim of our study is to evaluate the usefulness of a methodology based on electroencephalography (EEG) to detect AD and MCI. EEG rhythms were recorded from 37 AD patients, 37 MCI subjects and 37 HC subjects. Artifact-free trials were analyzed by means of several spectral and nonlinear features: relative power in the conventional frequency bands, median frequency, individual alpha frequency, spectral entropy, Lempel–Ziv complexity, central tendency measure, sample entropy, fuzzy entropy, and auto-mutual information. Relevance and redundancy analyses were also conducted through the fast correlation-based filter (FCBF) to derive an optimal set of them. The selected features were used to train three different models aimed at classifying the trials: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and multi-layer perceptron artificial neural network (MLP). Afterwards, each subject was automatically allocated in a particular group by applying a trial-based majority vote procedure. After feature extraction, the FCBF method selected the optimal set of features: individual alpha frequency, relative power at delta frequency band, and sample entropy. Using the aforementioned set of features, MLP showed the highest diagnostic performance in determining whether a subject is not healthy (sensitivity of 82.35% and positive predictive value of 84.85% for HC vs. all classification task) and whether a subject does not suffer from AD (specificity of 79.41% and negative predictive value of 84.38% for AD vs. all comparison). Our findings suggest that our methodology can help physicians to discriminate AD, MCI and HC.
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7
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Nimmy John T, D Puthankattil S, Menon R. Analysis of long range dependence in the EEG signals of Alzheimer patients. Cogn Neurodyn 2018; 12:183-199. [PMID: 29564027 DOI: 10.1007/s11571-017-9467-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Revised: 11/14/2017] [Accepted: 12/19/2017] [Indexed: 11/28/2022] Open
Abstract
Alzheimer's disease (AD), a cognitive disability is analysed using a long range dependence parameter, hurst exponent (HE), calculated based on the time domain analysis of the measured electrical activity of brain. The electroencephalogram (EEG) signals of controls and mild cognitive impairment (MCI)-AD patients are evaluated under normal resting and mental arithmetic conditions. Simultaneous low pass filtering and total variation denoising algorithm is employed for preprocessing. Larger values of HE observed in the right hemisphere of the brain for AD patients indicated a decrease in irregularity of the EEG signal under cognitive task conditions. Correlations between HE and the neuropsychological indices are analysed using bivariate correlation analysis. The observed reduction in the values of Auto mutual information and cross mutual information in the local antero-frontal and distant regions in the brain hemisphere indicates the loss of information transmission in MCI-AD patients.
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Affiliation(s)
- T Nimmy John
- 1Department of Electrical Engineering, National Institute of Technology Calicut, Kozhikode, India
| | - Subha D Puthankattil
- 1Department of Electrical Engineering, National Institute of Technology Calicut, Kozhikode, India
| | - Ramshekhar Menon
- 2Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
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8
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Haun AM, Oizumi M, Kovach CK, Kawasaki H, Oya H, Howard MA, Adolphs R, Tsuchiya N. Conscious Perception as Integrated Information Patterns in Human Electrocorticography. eNeuro 2017; 4:ENEURO.0085-17.2017. [PMID: 29085895 PMCID: PMC5659238 DOI: 10.1523/eneuro.0085-17.2017] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Revised: 07/16/2017] [Accepted: 07/28/2017] [Indexed: 01/24/2023] Open
Abstract
A significant problem in neuroscience concerns the distinction between neural processing that is correlated with conscious percepts from processing that is not. Here, we tested if a hierarchical structure of causal interactions between neuronal populations correlates with conscious perception. We derived the hierarchical causal structure as a pattern of integrated information, inspired by the integrated information theory (IIT) of consciousness. We computed integrated information patterns from intracranial electrocorticography (ECoG) from six human neurosurgical patients with electrodes implanted over lateral and ventral cortices. During recording, subjects viewed continuous flash suppression (CFS) and backward masking (BM) stimuli intended to dissociate conscious percept from stimulus, and unmasked suprathreshold stimuli. Object-sensitive areas revealed correspondence between conscious percepts and integrated information patterns. We quantified this correspondence using unsupervised classification methods that revealed clustering of visual experiences with integrated information, but not with broader information measures including mutual information and entropy. Our findings point to a significant role of locally integrated information for understanding the neural substrate of conscious object perception.
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Affiliation(s)
- Andrew M. Haun
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI
- School of Psychological Sciences, Monash University, Clayton, Australia
| | - Masafumi Oizumi
- School of Psychological Sciences, Monash University, Clayton, Australia
- RIKEN Brain Science Institute, Wako, Japan
| | | | | | - Hiroyuki Oya
- Department of Neurosurgery, University of Iowa, IA
| | | | - Ralph Adolphs
- Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA
| | - Naotsugu Tsuchiya
- School of Psychological Sciences, Monash University, Clayton, Australia
- Monash Institute of Cognitive and Clinical Neuroscience, Monash University, Clayton, Australia
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9
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Engels MMA, van der Flier WM, Stam CJ, Hillebrand A, Scheltens P, van Straaten ECW. Alzheimer's disease: The state of the art in resting-state magnetoencephalography. Clin Neurophysiol 2017. [PMID: 28622527 DOI: 10.1016/j.clinph.2017.05.012] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Alzheimer's disease (AD) is accompanied by functional brain changes that can be detected in imaging studies, including electromagnetic activity recorded with magnetoencephalography (MEG). Here, we systematically review the studies that have examined resting-state MEG changes in AD and identify areas that lack scientific or clinical progress. Three levels of MEG analysis will be covered: (i) single-channel signal analysis, (ii) pairwise analyses over time series, which includes the study of interdependencies between two time series and (iii) global network analyses. We discuss the findings in the light of other functional modalities, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Overall, single-channel MEG results show consistent changes in AD that are in line with EEG studies, but the full potential of the high spatial resolution of MEG and advanced functional connectivity and network analysis has yet to be fully exploited. Adding these features to the current knowledge will potentially aid in uncovering organizational patterns of brain function in AD and thereby aid the understanding of neuronal mechanisms leading to cognitive deficits.
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Affiliation(s)
- M M A Engels
- Alzheimer Centrum and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
| | - W M van der Flier
- Alzheimer Centrum and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands; Department of Epidemiology and Biostatistics, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - C J Stam
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - A Hillebrand
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - Ph Scheltens
- Alzheimer Centrum and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - E C W van Straaten
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
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10
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Liu X, Zhang C, Ji Z, Ma Y, Shang X, Zhang Q, Zheng W, Li X, Gao J, Wang R, Wang J, Yu H. Multiple characteristics analysis of Alzheimer's electroencephalogram by power spectral density and Lempel-Ziv complexity. Cogn Neurodyn 2016; 10:121-33. [PMID: 27066150 PMCID: PMC4805689 DOI: 10.1007/s11571-015-9367-8] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2015] [Revised: 10/26/2015] [Accepted: 11/05/2015] [Indexed: 10/22/2022] Open
Abstract
To investigate the electroencephalograph (EEG) background activity in patients with Alzheimer's disease (AD), power spectrum density (PSD) and Lempel-Ziv (LZ) complexity analysis are proposed to extract multiple effective features of EEG signals from AD patients and further applied to distinguish AD patients from the normal controls. Spectral analysis based on autoregressive Burg method is first used to quantify the power distribution of EEG series in the frequency domain. Compared with the control group, the relative PSD of AD group is significantly higher in the theta frequency band while lower in the alpha frequency bands. In order to explore the nonlinear information, Lempel-Ziv complexity (LZC) and multi-scale LZC is further applied to all electrodes for the four frequency bands. Analysis results demonstrate that the group difference is significant in the alpha frequency band by LZC and multi-scale LZC analysis. However, the group difference of multi-scale LZC is much more remarkable, manifesting as more channels undergo notable changes, particularly in electrodes O1 and O2 in the occipital area. Moreover, the multi-scale LZC value provided a better classification between the two groups with an accuracy of 85.7 %. In addition, we combine both features of the relative PSD and multi-scale LZC to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature, reaching 91.4 %. The obtained results show that analysis of PSD and multi-scale LZC can be taken as a potential comprehensive measure to distinguish AD patients from the normal controls, which may benefit our understanding of the disease.
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Affiliation(s)
- Xiaokun Liu
- />Department of Cardiology, Tangshan Gongren Hospital, Hebei Medical University, Tangshan, 063000 Hebei People’s Republic of China
| | - Chunlai Zhang
- />Department of Cardiology, Tangshan Gongren Hospital, Hebei Medical University, Tangshan, 063000 Hebei People’s Republic of China
| | - Zheng Ji
- />Department of Cardiology, Tangshan Gongren Hospital, Hebei Medical University, Tangshan, 063000 Hebei People’s Republic of China
| | - Yi Ma
- />Department of Cardiology, Tangshan Gongren Hospital, Hebei Medical University, Tangshan, 063000 Hebei People’s Republic of China
| | - Xiaoming Shang
- />Department of Cardiology, Tangshan Gongren Hospital, Hebei Medical University, Tangshan, 063000 Hebei People’s Republic of China
| | - Qi Zhang
- />Department of Cardiology, Tangshan Gongren Hospital, Hebei Medical University, Tangshan, 063000 Hebei People’s Republic of China
| | - Wencheng Zheng
- />Department of Cardiology, Tangshan Gongren Hospital, Hebei Medical University, Tangshan, 063000 Hebei People’s Republic of China
| | - Xia Li
- />Department of Cardiology, Tangshan Gongren Hospital, Hebei Medical University, Tangshan, 063000 Hebei People’s Republic of China
| | - Jun Gao
- />Department of Cardiology, Tangshan Gongren Hospital, Hebei Medical University, Tangshan, 063000 Hebei People’s Republic of China
| | - Ruofan Wang
- />School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072 People’s Republic of China
| | - Jiang Wang
- />School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072 People’s Republic of China
| | - Haitao Yu
- />School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072 People’s Republic of China
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Wang R, Wang J, Li S, Yu H, Deng B, Wei X. Multiple feature extraction and classification of electroencephalograph signal for Alzheimers' with spectrum and bispectrum. CHAOS (WOODBURY, N.Y.) 2015; 25:013110. [PMID: 25637921 DOI: 10.1063/1.4906038] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
In this paper, we have combined experimental neurophysiologic recording and statistical analysis to investigate the nonlinear characteristic and the cognitive function of the brain. Spectrum and bispectrum analyses are proposed to extract multiple effective features of electroencephalograph (EEG) signals from Alzheimer's disease (AD) patients and further applied to distinguish AD patients from the normal controls. Spectral analysis based on autoregressive Burg method is first used to quantify the power distribution of EEG series in the frequency domain. Compared to the control group, the relative power spectral density of AD group is significantly higher in the theta frequency band, while lower in the alpha frequency bands. In addition, median frequency of spectrum is decreased, and spectral entropy ratio of these two frequency bands undergoes drastic changes at the P3 electrode in the central-parietal brain region, implying that the electrophysiological behavior in AD brain is much slower and less irregular. In order to explore the nonlinear high order information, bispectral analysis which measures the complexity of phase-coupling is further applied to P3 electrode in the whole frequency band. It is demonstrated that less bispectral peaks appear and the amplitudes of peaks fall, suggesting a decrease of non-Gaussianity and nonlinearity of EEG in ADs. Notably, the application of this method to five brain regions shows higher concentration of the weighted center of bispectrum and lower complexity reflecting phase-coupling by bispectral entropy. Based on spectrum and bispectrum analyses, six efficient features are extracted and then applied to discriminate AD from the normal in the five brain regions. The classification results indicate that all these features could differentiate AD patients from the normal controls with a maximum accuracy of 90.2%. Particularly, different brain regions are sensitive to different features. Moreover, the optimal combination of features obtained by discriminant analysis may improve the classification accuracy. These results demonstrate the great promise for scape EEG spectral and bispectral features as a potential effective method for detection of AD, which may facilitate our understanding of the pathological mechanism of the disease.
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Affiliation(s)
- Ruofan Wang
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Jiang Wang
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Shunan Li
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Haitao Yu
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Bin Deng
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Xile Wei
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
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12
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McBride JC, Zhao X, Munro NB, Smith CD, Jicha GA, Hively L, Broster LS, Schmitt FA, Kryscio RJ, Jiang Y. Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 114:153-63. [PMID: 24598317 PMCID: PMC4021716 DOI: 10.1016/j.cmpb.2014.01.019] [Citation(s) in RCA: 92] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2013] [Revised: 01/17/2014] [Accepted: 01/28/2014] [Indexed: 06/03/2023]
Abstract
Amnestic mild cognitive impairment (aMCI) often is an early stage of Alzheimer's disease (AD). MCI is characterized by cognitive decline departing from normal cognitive aging but that does not significantly interfere with daily activities. This study explores the potential of scalp EEG for early detection of alterations from cognitively normal status of older adults signifying MCI and AD. Resting 32-channel EEG records from 48 age-matched participants (mean age 75.7 years)-15 normal controls (NC), 16 early MCI, and 17 early stage AD-are examined. Regional spectral and complexity features are computed and used in a support vector machine model to discriminate between groups. Analyses based on three-way classifications demonstrate overall discrimination accuracies of 83.3%, 85.4%, and 79.2% for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. These results demonstrate the great promise for scalp EEG spectral and complexity features as noninvasive biomarkers for detection of MCI and early AD.
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Affiliation(s)
- Joseph C McBride
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN 37996, United States
| | - Xiaopeng Zhao
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN 37996, United States; National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, Knoxville, TN 37996, United States.
| | - Nancy B Munro
- Oak Ridge National Laboratory, Oak Ridge, TN 37831-6418, United States
| | - Charles D Smith
- Department of Neurology, University of Kentucky, Lexington, KY 40356, United States; Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40356, United States
| | - Gregory A Jicha
- Department of Neurology, University of Kentucky, Lexington, KY 40356, United States; Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40356, United States
| | - Lee Hively
- Oak Ridge National Laboratory, Oak Ridge, TN 37831-6418, United States
| | - Lucas S Broster
- Department of Behavioral Science, University of Kentucky, Lexington, KY 40356, United States; Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40356, United States
| | - Frederick A Schmitt
- Department of Neurology, University of Kentucky, Lexington, KY 40356, United States; Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40356, United States
| | - Richard J Kryscio
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40356, United States; Department of Statistics, University of Kentucky, Lexington, KY 40356, United States
| | - Yang Jiang
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40356, United States; Department of Behavioral Science, University of Kentucky, Lexington, KY 40356, United States
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13
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Poza J, Gómez C, Bachiller A, Hornero R. Spectral and Non-Linear Analyses of Spontaneous Magnetoencephalographic Activity in Alzheimer's Disease. JOURNAL OF HEALTHCARE ENGINEERING 2012. [DOI: 10.1260/2040-2295.3.2.299] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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14
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Gómez C, Hornero R. Entropy and Complexity Analyses in Alzheimer's Disease: An MEG Study. Open Biomed Eng J 2010; 4:223-35. [PMID: 21625647 PMCID: PMC3044892 DOI: 10.2174/1874120701004010223] [Citation(s) in RCA: 54] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2010] [Revised: 07/27/2010] [Accepted: 07/29/2010] [Indexed: 11/22/2022] Open
Abstract
Alzheimer’s disease (AD) is one of the most frequent disorders among elderly population and it is considered the main cause of dementia in western countries. This irreversible brain disorder is characterized by neural loss and the appearance of neurofibrillary tangles and senile plaques. The aim of the present study was the analysis of the magnetoencephalogram (MEG) background activity from AD patients and elderly control subjects. MEG recordings from 36 AD patients and 26 controls were analyzed by means of six entropy and complexity measures: Shannon spectral entropy (SSE), approximate entropy (ApEn), sample entropy (SampEn), Higuchi’s fractal dimension (HFD), Maragos and Sun’s fractal dimension (MSFD), and Lempel-Ziv complexity (LZC). SSE is an irregularity estimator in terms of the flatness of the spectrum, whereas ApEn and SampEn are embbeding entropies that quantify the signal regularity. The complexity measures HFD and MSFD were applied to MEG signals to estimate their fractal dimension. Finally, LZC measures the number of different substrings and the rate of their recurrence along the original time series. Our results show that MEG recordings are less complex and more regular in AD patients than in control subjects. Significant differences between both groups were found in several brain regions using all these methods, with the exception of MSFD (p-value < 0.05, Welch’s t-test with Bonferroni’s correction). Using receiver operating characteristic curves with a leave-one-out cross-validation procedure, the highest accuracy was achieved with SSE: 77.42%. We conclude that entropy and complexity analyses from MEG background activity could be useful to help in AD diagnosis.
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Affiliation(s)
- Carlos Gómez
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Spain
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15
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Alonso JF, Poza J, Mañanas MA, Romero S, Fernández A, Hornero R. MEG connectivity analysis in patients with Alzheimer's disease using cross mutual information and spectral coherence. Ann Biomed Eng 2010; 39:524-36. [PMID: 20824340 DOI: 10.1007/s10439-010-0155-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2010] [Accepted: 08/24/2010] [Indexed: 11/24/2022]
Abstract
Alzheimer's disease (AD) is an irreversible brain disorder which represents the most common form of dementia in western countries. An early and accurate diagnosis of AD would enable to develop new strategies for managing the disease; however, nowadays there is no single test that can accurately predict the development of AD. In this sense, only a few studies have focused on the magnetoencephalographic (MEG) AD connectivity patterns. This study compares brain connectivity in terms of linear and nonlinear couplings by means of spectral coherence and cross mutual information function (CMIF), respectively. The variables defined from these functions provide statistically significant differences (p < 0.05) between AD patients and control subjects, especially the variables obtained from CMIF. The results suggest that AD is characterized by both decreases and increases of functional couplings in different frequency bands as well as by an increase in regularity, that is, more evident statistical deterministic relationships in AD patients' MEG connectivity. The significant differences obtained indicate that AD could disturb brain interactions causing abnormal brain connectivity and operation. Furthermore, the combination of coherence and CMIF features to perform a diagnostic test based on logistic regression improved the tests based on individual variables for its robustness.
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Affiliation(s)
- Joan Francesc Alonso
- Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Carrer Pau Gargallo 5, 08028, Barcelona, Spain.
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16
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Complexity analysis of spontaneous brain activity in Alzheimer disease and mild cognitive impairment: an MEG study. Alzheimer Dis Assoc Disord 2010; 24:182-9. [PMID: 20505435 DOI: 10.1097/wad.0b013e3181c727f7] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Nonlinear analyses have shown that Alzheimer disease (AD) patients' brain activity is characterized by a reduced complexity and connectivity. The aim of this study is to define complexity patterns of mild cognitive impairment (MCI) patients. Whole-head magnetoencephalography recordings were obtained from 18 diagnosed AD patients, 18 MCI patients, and 18 healthy controls during resting conditions. Lempel-Ziv complexity (LZC) values were calculated. MCI patients exhibited intermediary LZC scores between AD patients and controls. A combination of age and posterior LZC scores allowed ADs-MCIs discrimination with 94.4% sensitivity and specificity, whereas no LZC score allowed MCIs---controls discrimination. AD patients and controls showed a parallel tendency to diminished LZC scores as a function of age, but MCI patients did not exhibit such "normal" tendency. Accordingly, anterior LZC scores allowed MCIs-controls discrimination for subjects below 75 years. MCIs exhibited a qualitatively distinct relationship between aging and complexity reduction, with scores higher than controls in older individuals. This fact might be considered a new example of compensatory mechanism in MCI before fully established dementia.
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Gomez C, Hornero R, Abasolo D, Fernandez A, Poza J. Study of the MEG background activity in Alzheimer's disease patients with scaling analysis methods. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2010; 2009:3485-8. [PMID: 19964992 DOI: 10.1109/iembs.2009.5334569] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Alzheimer's disease (AD) is one of the most prominent neurodegenerative disorders. The aim of this research work is to study the magnetoencephalogram (MEG) background activity in AD patients using two scaling analysis methods: detrended fluctuation analysis (DFA) and backward detrended moving average (BDMA). Both measures have been designed to quantify correlations in noisy and non-stationary signals. Five minutes of recording were acquired with a 148-channel whole-head magnetometer in 15 patients with probable AD and 15 control subjects. Both DFA and BDMA exhibited two scaling regions with different slopes. Significant differences between both groups were found in the second region of DFA and in the first region of BDMA (p < 0.01, Student's t-test). Using receiver operating characteristic curves, accuracies of 83.33% with DFA and of 80% with BDMA were reached. Our findings show the usefulness of these scaling analysis methods to increase our insight into AD.
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Affiliation(s)
- Carlos Gomez
- Biomedical Engineering Group at Department of Signal Theory and Communications, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Campus Miguel Delibes, 47011 - Valladolid, Spain.
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18
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Stam CJ. Use of magnetoencephalography (MEG) to study functional brain networks in neurodegenerative disorders. J Neurol Sci 2009; 289:128-34. [PMID: 19729174 DOI: 10.1016/j.jns.2009.08.028] [Citation(s) in RCA: 170] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The pathophysiological mechanisms underlying clinical symptoms in neurodegenerative disorders such as Parkinson's disease (PD) and Alzheimer's disease (AD) are incompletely understood. Magnetoencephalography (MEG) is a relatively new functional neuroimaging technique, which allows the simultaneous recording of the brain's magnetic activity from large arrays of sensors covering the whole head. MEG studies in PD and AD have identified characteristic patterns of abnormal oscillatory activity in different frequency bands. Furthermore, MEG studies aimed at the characterization of distributed functional networks have demonstrated distinct patterns of abnormal connectivity in demented and non-demented PD, as well as in AD. In PD abnormal oscillatory activity and disturbed connectivity may respond differently to dopaminergic treatment. Further studies in this field could benefit from new technological developments such as ultra low field MRI and from the application of a well-defined theoretical framework such as graph theory to the study of disturbed brain networks.
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Affiliation(s)
- C J Stam
- Department of Clinical Neurophysiology, VU University Medical Center, Amsterdam, The Netherlands.
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19
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Fernández A, Quintero J, Hornero R, Zuluaga P, Navas M, Gómez C, Escudero J, García-Campos N, Biederman J, Ortiz T. Complexity analysis of spontaneous brain activity in attention-deficit/hyperactivity disorder: diagnostic implications. Biol Psychiatry 2009; 65:571-7. [PMID: 19103438 DOI: 10.1016/j.biopsych.2008.10.046] [Citation(s) in RCA: 67] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2008] [Revised: 10/27/2008] [Accepted: 10/27/2008] [Indexed: 11/18/2022]
Abstract
BACKGROUND Attention-deficit/hyperactivity disorder (ADHD) is defined as the most common neurobehavioral disorder of childhood, but an objective diagnostic test is not available yet to date. Neurophychological, neuroimaging, and neurophysiological research offer ample evidence of brain and behavioral dysfunctions in ADHD, but these findings have not been useful as a diagnostic test. METHODS Whole-head magnetoencephalographic recordings were obtained from 14 diagnosed ADHD patients and 14 healthy children during resting conditions. Lempel-Ziv complexity (LZC) values were obtained for each channel and child and averaged in five sensor groups: anterior, central, left lateral, right lateral, and posterior. RESULTS Lempel-Ziv complexity scores were significantly higher in control subjects, with the maximum value in anterior region. Combining age and anterior complexity values allowed the correct classification of ADHD patients and control subjects with a 93% sensitivity and 79% specificity. Control subjects showed an age-related monotonic increase of LZC scores in all sensor groups, while children with ADHD exhibited a nonsignificant tendency toward decreased LZC scores. The age-related divergence resulted in a 100% specificity in children older than 9 years. CONCLUSIONS Results support the role of a frontal hypoactivity in the diagnosis of ADHD. Moreover, the age-related divergence of complexity scores between ADHD patients and control subjects might reflect distinctive developmental trajectories. This interpretation of our results is in agreement with recent investigations reporting a delay of cortical maturation in the prefrontal cortex.
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Affiliation(s)
- Alberto Fernández
- Departamento de Psiquiatría, Universidad Complutense de Madrid, Madrid, Spain.
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20
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Gómez C, Mediavilla Á, Hornero R, Abásolo D, Fernández A. Use of the Higuchi's fractal dimension for the analysis of MEG recordings from Alzheimer's disease patients. Med Eng Phys 2009; 31:306-13. [DOI: 10.1016/j.medengphy.2008.06.010] [Citation(s) in RCA: 111] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2008] [Revised: 06/24/2008] [Accepted: 06/25/2008] [Indexed: 11/26/2022]
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Hornero R, Abásolo D, Escudero J, Gómez C. Nonlinear analysis of electroencephalogram and magnetoencephalogram recordings in patients with Alzheimer's disease. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2009; 367:317-336. [PMID: 18940776 DOI: 10.1098/rsta.2008.0197] [Citation(s) in RCA: 92] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
The aim of the present study is to show the usefulness of nonlinear methods to analyse the electroencephalogram (EEG) and magnetoencephalogram (MEG) in patients with Alzheimer's disease (AD). The following nonlinear methods have been applied to study the EEG and MEG background activity in AD patients and control subjects: approximate entropy, sample entropy, multiscale entropy, auto-mutual information and Lempel-Ziv complexity. We discuss why these nonlinear methods are appropriate to analyse the EEG and MEG. Furthermore, the performance of all these methods has been compared when applied to the same databases of EEG and MEG recordings. Our results show that EEG and MEG background activities in AD patients are less complex and more regular than in healthy control subjects. In line with previous studies, our work suggests that nonlinear analysis techniques could be useful in AD diagnosis.
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Affiliation(s)
- Roberto Hornero
- Biomedical Engineering Group, E.T.S.I. Telecomunicación, University of Valladolid, Camino del Cementerio s/n, 47011 Valladolid, Spain.
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Gomez C, Hornero R, Mediavilla A, Fernandez A, Abasolo D. Nonlinear forecasting measurement of magnetoencephalogram recordings from Alzheimer's disease patients. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2009; 2008:2153-6. [PMID: 19163123 DOI: 10.1109/iembs.2008.4649620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The goal of this study was to analyze the magnetoencephalogram (MEG) background activity in patients with Alzheimer's disease (AD) using a nonlinear forecasting measure. It is a nonparametric method to quantify the predictability of time series. Five minutes of recording were acquired with a 148-channel whole-head magnetometer in 15 patients with probable AD and 15 elderly control subjects. Stationary epochs of 5 seconds (848 points, sample frequency of 169.55 Hz) were selected. Our results showed that AD patients' MEGs were more predictable than controls' recordings. Additionally, an accuracy of 76.7% (80.0% sensitivity; 73.3% specificity) was reached using a receiver operating characteristic curve. These preliminary results suggest the usefulness of nonlinear forecasting to gain a better understanding of dynamical processes underlying the MEG recording.
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Affiliation(s)
- Carlos Gomez
- Department of Signal Theory and Communications, University of Valladolid, Campus Miguel Delibes, 47011, Spain.
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Escudero J, Hornero R, Abásolo D. Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis. Application to electroencephalogram recordings. Physiol Meas 2009; 30:187-99. [PMID: 19147896 DOI: 10.1088/0967-3334/30/2/006] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The mutual information (MI) is a measure of both linear and nonlinear dependences. It can be applied to a time series and a time-delayed version of the same sequence to compute the auto-mutual information function (AMIF). Moreover, the AMIF rate of decrease (AMIFRD) with increasing time delay in a signal is correlated with its entropy and has been used to characterize biomedical data. In this paper, we aimed at gaining insight into the dependence of the AMIFRD on several signal processing concepts and at illustrating its application to biomedical time series analysis. Thus, we have analysed a set of synthetic sequences with the AMIFRD. The results show that the AMIF decreases more quickly as bandwidth increases and that the AMIFRD becomes more negative as there is more white noise contaminating the time series. Additionally, this metric detected changes in the nonlinear dynamics of a signal. Finally, in order to illustrate the analysis of real biomedical signals with the AMIFRD, this metric was applied to electroencephalogram (EEG) signals acquired with eyes open and closed and to ictal and non-ictal intracranial EEG recordings.
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Affiliation(s)
- Javier Escudero
- Biomedical Engineering Group, E.T.S.I. Telecomunicación, University of Valladolid, Camino del Cementerio s/n, 47011, Valladolid, Spain.
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Gómez C, Hornero R, Abásolo D, Fernández A, Escudero J. Analysis of MEG Background Activity in Alzheimer’s Disease Using Nonlinear Methods and ANFIS. Ann Biomed Eng 2009; 37:586-94. [DOI: 10.1007/s10439-008-9633-6] [Citation(s) in RCA: 56] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2008] [Accepted: 12/23/2008] [Indexed: 11/24/2022]
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Rosso OA, Mendes A, Rostas JA, Hunter M, Moscato P. Distinguishing childhood absence epilepsy patients from controls by the analysis of their background brain electrical activity. J Neurosci Methods 2008; 177:461-8. [PMID: 19013193 DOI: 10.1016/j.jneumeth.2008.10.017] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2008] [Revised: 10/10/2008] [Accepted: 10/13/2008] [Indexed: 10/21/2022]
Abstract
Background electroencephalography (EEG), recorded with scalp electrodes, in children with childhood absence epilepsy (CAE) and control individuals has been analyzed. We considered 5 CAE patients, all right-handed females and aged 6-8 years. The 15 control individuals had the same characteristics of the CAE ones, but presented a normal EEG. The EEG was obtained using bipolar connections from a standard 10-20 electrode placement (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1 and O2). Recordings were undertaken in the resting state with eyes closed. EEG hallmarks of absence seizure activity are widely accepted, but there is a recognition that the bulk of interictal EEG in CAE appears normal to visual inspection. The functional activity between electrodes was evaluated using a wavelet decomposition in conjunction with the Wootters distance. Then, pairs of electrodes with differentiated behavior between CAE and controls were identified using a test statistic-based feature selection technique. This approach identified clear differences between CAE and healthy control background EEG in the frontocentral electrodes, as measured by Principal Component Analysis. The findings of this pilot study can have strong implications in future clinical practice.
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Affiliation(s)
- Osvaldo A Rosso
- Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine and Hunter Medical Research Institute, School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan, NSW 2308, Australia.
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Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer's disease patients. Med Biol Eng Comput 2008; 46:1019-28. [PMID: 18784948 DOI: 10.1007/s11517-008-0392-1] [Citation(s) in RCA: 69] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2007] [Accepted: 08/19/2008] [Indexed: 10/21/2022]
Abstract
We analysed the electroencephalogram (EEG) from Alzheimer's disease (AD) patients with two nonlinear methods: approximate entropy (ApEn) and auto mutual information (AMI). ApEn quantifies regularity in data, while AMI detects linear and nonlinear dependencies in time series. EEGs from 11 AD patients and 11 age-matched controls were analysed. ApEn was significantly lower in AD patients at electrodes O1, O2, P3 and P4 (p < 0.01). The EEG AMI decreased more slowly with time delays in patients than in controls, with significant differences at electrodes T5, T6, O1, O2, P3 and P4 (p < 0.01). The strong correlation between results from both methods shows that the AMI rate of decrease can be used to estimate the regularity in time series. Our work suggests that nonlinear EEG analysis may contribute to increase the insight into brain dysfunction in AD, especially when different time scales are inspected, as is the case with AMI.
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Assessment of classification improvement in patients with Alzheimer's disease based on magnetoencephalogram blind source separation. Artif Intell Med 2008; 43:75-85. [PMID: 18329868 DOI: 10.1016/j.artmed.2008.01.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2007] [Revised: 12/31/2007] [Accepted: 01/20/2008] [Indexed: 11/20/2022]
Abstract
OBJECTIVES In this pilot study, we intended to assess whether a procedure based on blind source separation (BSS) and subsequent partial reconstruction of magnetoencephalogram (MEG) recordings might enhance the differences between MEGs from Alzheimer's disease (AD) patients and elderly control subjects. MATERIALS AND METHODS We analysed MEG background activity recordings acquired with a 148-channel whole-head magnetometer from 21 AD patients and 21 control subjects. Artefact-free epochs of 20 s were blindly decomposed using the algorithm for multiple unknown signals extraction (AMUSE), which arranges the extracted components by decreasing linear predictability. Thus, the components of diverse epochs and subjects could be easily compared. Every component was characterised with its median frequency and spectral entropy (denoted by fmedian and SpecEn, respectively). The differences between subject groups in these variables were statistically evaluated to find out which components could improve the subject classification. Then, these significant components were used to partially reconstruct the MEG recordings. RESULTS The statistical analysis showed that the AMUSE components which provided the largest differences between demented patients and control subjects were ordered together. Considering this analysis, we defined two subsets, denoted by BSS-{15,35} and BSS-{20,30}, which included 21 components (15-35) and 11 components (20-30), respectively. We partially reconstructed the MEGs with these subsets. Then, the classification performance was computed with a leave-one-out cross-validation procedure for the case where no BSS was applied and for the partial reconstructions BSS-{15,35} and BSS-{20,30}. The BSS and component selection procedure improved the classification accuracy from 69.05% to 83.33% using f(median) with BSS-{15,35} and from 61.91% to 73.81% using SpecEn with BSS-{20,30}. CONCLUSION These preliminary results lead us to think that the proposed procedure based on BSS and selection of significant components may improve the classification of AD patients using straightforward features from MEG recordings.
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Escudero J, Hornero R, Abasolo D, Lopez M. On the application of the auto mutual information rate of decrease to biomedical signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2008; 2008:2137-2140. [PMID: 19163119 DOI: 10.1109/iembs.2008.4649616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
The auto mutual information function (AMIF) evaluates the signal predictability by assessing linear and non-linear dependencies between two measurements taken from a single time series. Furthermore, the AMIF rate of decrease (AMIFRD) is correlated with signal entropy. This metric has been used to analyze biomedical data, including cardiac and brain activity recordings. Hence, the AMIFRD can be a relevant parameter in the context of biomedical signal analysis. Thus, in this pilot study, we have analyzed a synthetic sequence (a Lorenz system) and real biosignals (electroencephalograms recorded with eyes open and closed) with the AMIFRD. We aimed at illustrating the application of this parameter to biomedical time series. Our results show that the AMIFRD can detect changes in the non-linear dynamics of a sequence and that it can distinguish different physiological conditions.
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Affiliation(s)
- Javier Escudero
- University of Valladolid, Camino del Cementerio s/n, 47011, Spain.
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29
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Poza J, Hornero R, Escudero J, Fernandez A, Gomez C. Analysis of spontaneous MEG activity in Alzheimer's disease using time-frequency parameters. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2008; 2008:5712-5715. [PMID: 19164014 DOI: 10.1109/iembs.2008.4650511] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
The aim of this study was to explore the ability of several time-frequency parameters to discriminate between spontaneous magnetoencephalographic (MEG) oscillations from 20 Alzheimer's disease (AD) patients and 21 controls. The spectral crest factor (SCF) and the spectral turbulence (ST) were calculated from the time-frequency distribution of the normalized power spectral density averaged over all MEG sensors. Results revealed statistically significant higher SCF and ST mean values for AD patients than controls (p 0.05). This fact suggests a significant decrease in irregularity of AD patients' MEG activity. The standard deviation of SCF also provided significant differences (p 0.05). This result indicates that AD patients showed a significantly higher variability than controls. The highest accuracy of 85.4% (90.5% sensitivity, 80.0% specificity) was achieved using simultaneously the mean value and the standard deviation of the SCF. We conclude that the variability of the spectral parameters can yield complementary information to the mean values, useful to help in AD detection.
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Affiliation(s)
- Jesus Poza
- Biomedical Engineering Group (GIB), Dpt. TSCIT, University of Valladolid, Camino del Cementerio s/n, 47011, Spain.
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