1
|
Shirzadi S, Dadgostar M, Einalou Z, Erdoğan SB, Akin A. Sex based differences in functional connectivity during a working memory task: an fNIRS study. Front Psychol 2024; 15:1207202. [PMID: 38390414 PMCID: PMC10881810 DOI: 10.3389/fpsyg.2024.1207202] [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: 04/18/2023] [Accepted: 01/17/2024] [Indexed: 02/24/2024] Open
Abstract
Differences in corticocerebral structure and function between males and females and their effects on behavior and the prevalence of various neuropsychiatric disorders have been considered as a fundamental topic in various fields of neuroscience. Recent studies on working memory (WM) reported the impact of sex on brain connectivity patterns, which reflect the important role of functional connectivity in the sex topic. Working memory, one of the most important cognitive tasks performed by regions of the PFC, can provide evidence regarding the presence of a difference between males and females. The present study aimed to assess sex differences in brain functional connectivity during working memory-related tasks by using functional near-infrared spectroscopy (fNIRS). In this regard, nine males and nine females completed a dual n-back working memory task with two target inputs of color and location stimuli in three difficulty levels (n = 0, 1, 2). Functional connectivity matrices were extracted for each subject for each memory load level. Females made less errors than males while spending more time performing the task for all workload levels except in 0-back related to the color stimulus, where the reaction time of females was shorter than males. The results of functional connectivity reveal the inverse behavior of two hemispheres at different memory workload levels between males and females. In the left hemisphere, males exhibited stronger connectivity compared to the females, while stronger connectivity was observed in the females' right hemisphere. Furthermore, an inverse trend was detected in the channel pairs with significant connectivity in the right hemisphere of males (falling) and females (rising) by enhancing working memory load level. Considering both behavioral and functional results for two sexes demonstrated a better performance in females due to the more effective use of the brain. The results indicate that sex affects functional connectivity between different areas in both hemispheres of the brain during cognitive tasks of varying difficulty levels although the general impression is that spatial capabilities are considered as a performance of the brain's right hemisphere. These results reinforce the presence of a sex effect in the functional imaging studies of hemodynamic function and emphasize the importance of evaluating brain network connectivity for achieving a better scientific understanding of sex differences.
Collapse
Affiliation(s)
- Sima Shirzadi
- Department of Biomedical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Mehrdad Dadgostar
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Zahra Einalou
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Sinem Burcu Erdoğan
- Department of Biomedical Engineering, Acibadem Mehmet Ali Aydinlar University, Istanbul, Türkiye
| | - Ata Akin
- Department of Biomedical Engineering, Acibadem Mehmet Ali Aydinlar University, Istanbul, Türkiye
| |
Collapse
|
2
|
Prado P, Moguilner S, Mejía JA, Sainz-Ballesteros A, Otero M, Birba A, Santamaria-Garcia H, Legaz A, Fittipaldi S, Cruzat J, Tagliazucchi E, Parra M, Herzog R, Ibáñez A. Source space connectomics of neurodegeneration: One-metric approach does not fit all. Neurobiol Dis 2023; 179:106047. [PMID: 36841423 PMCID: PMC11170467 DOI: 10.1016/j.nbd.2023.106047] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 02/05/2023] [Accepted: 02/15/2023] [Indexed: 02/25/2023] Open
Abstract
Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patientsHCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration.
Collapse
Affiliation(s)
- Pavel Prado
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago, Chile
| | - Sebastian Moguilner
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina
| | - Jhony A Mejía
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Departamento de Ingeniería Biomédica, Universidad de Los Andes, Bogotá, Colombia
| | | | - Mónica Otero
- Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Santiago, Chile; Centro BASAL Ciencia & Vida, Universidad San Sebastián, Santiago, Chile
| | - Agustina Birba
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina
| | - Hernando Santamaria-Garcia
- PhD Neuroscience Program, Physiology and Psychiatry Departments, Pontificia Universidad Javeriana, Bogotá, Colombia; Memory and Cognition Center Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia; Global Brain Health Institute, University of California San Francisco, San Francisco, California; Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Agustina Legaz
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina; National Scientific and Technical Research Council, Buenos Aires, Argentina
| | - Sol Fittipaldi
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina; Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland; National Scientific and Technical Research Council, Buenos Aires, Argentina
| | - Josephine Cruzat
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Departamento de Física, Universidad de Buenos Aires and Instituto de Física de Buenos Aires (IFIBA -CONICET), Buenos Aires, Argentina
| | - Mario Parra
- School of Psychological Sciences and Health, University of Strathclyde, Glasgow, United Kingdom
| | - Rubén Herzog
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Fundación para el Estudio de la Conciencia Humana (EcoH), Chile
| | - Agustín Ibáñez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina; PhD Neuroscience Program, Physiology and Psychiatry Departments, Pontificia Universidad Javeriana, Bogotá, Colombia; Memory and Cognition Center Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia; Trinity College Dublin (TCD), Dublin, Ireland.
| |
Collapse
|
3
|
O'Keeffe R, Shirazi SY, Bilaloglu S, Jahed S, Bighamian R, Raghavan P, Atashzar SF. Nonlinear functional muscle network based on information theory tracks sensorimotor integration post stroke. Sci Rep 2022; 12:13029. [PMID: 35906239 PMCID: PMC9338017 DOI: 10.1038/s41598-022-16483-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 07/11/2022] [Indexed: 11/28/2022] Open
Abstract
Sensory information is critical for motor coordination. However, understanding sensorimotor integration is complicated, especially in individuals with impairment due to injury to the central nervous system. This research presents a novel functional biomarker, based on a nonlinear network graph of muscle connectivity, called InfoMuNet, to quantify the role of sensory information on motor performance. Thirty-two individuals with post-stroke hemiparesis performed a grasp-and-lift task, while their muscle activity from 8 muscles in each arm was measured using surface electromyography. Subjects performed the task with their affected hand before and after sensory exposure to the task performed with the less-affected hand. For the first time, this work shows that InfoMuNet robustly quantifies changes in functional muscle connectivity in the affected hand after exposure to sensory information from the less-affected side. > 90% of the subjects conformed with the improvement resulting from this sensory exposure. InfoMuNet also shows high sensitivity to tactile, kinesthetic, and visual input alterations at the subject level, highlighting its potential use in precision rehabilitation interventions.
Collapse
Affiliation(s)
- Rory O'Keeffe
- Department of Electrical and Computer Engineering, New York University, New York, NY, USA
| | - Seyed Yahya Shirazi
- Department of Electrical and Computer Engineering, New York University, New York, NY, USA
| | - Seda Bilaloglu
- Department of Medicine, New York University Langone Health, New York, NY, USA
| | - Shayan Jahed
- Department of Electrical and Computer Engineering, New York University, New York, NY, USA
| | - Ramin Bighamian
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration, Silver Spring, MD, USA
| | - Preeti Raghavan
- Departments of Physical Medicine and Rehabilitation and Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - S Farokh Atashzar
- Department of Electrical and Computer Engineering, New York University, New York, NY, USA.
- Department of Mechanical and Aerospace Engineering, New York University, New York, NY, USA.
| |
Collapse
|
4
|
Fred AL, Kumar SN, Kumar Haridhas A, Ghosh S, Purushothaman Bhuvana H, Sim WKJ, Vimalan V, Givo FAS, Jousmäki V, Padmanabhan P, Gulyás B. A Brief Introduction to Magnetoencephalography (MEG) and Its Clinical Applications. Brain Sci 2022; 12:brainsci12060788. [PMID: 35741673 PMCID: PMC9221302 DOI: 10.3390/brainsci12060788] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/01/2022] [Accepted: 06/08/2022] [Indexed: 11/30/2022] Open
Abstract
Magnetoencephalography (MEG) plays a pivotal role in the diagnosis of brain disorders. In this review, we have investigated potential MEG applications for analysing brain disorders. The signal-to-noise ratio (SNRMEG = 2.2 db, SNREEG < 1 db) and spatial resolution (SRMEG = 2−3 mm, SREEG = 7−10 mm) is higher for MEG than EEG, thus MEG potentially facilitates accurate monitoring of cortical activity. We found that the direct electrophysiological MEG signals reflected the physiological status of neurological disorders and play a vital role in disease diagnosis. Single-channel connectivity, as well as brain network analysis, using MEG data acquired during resting state and a given task has been used for the diagnosis of neurological disorders such as epilepsy, Alzheimer’s, Parkinsonism, autism, and schizophrenia. The workflow of MEG and its potential applications in the diagnosis of disease and therapeutic planning are also discussed. We forecast that computer-aided algorithms will play a prominent role in the diagnosis and prediction of neurological diseases in the future. The outcome of this narrative review will aid researchers to utilise MEG in diagnostics.
Collapse
Affiliation(s)
- Alfred Lenin Fred
- Department of CSE, Mar Ephraem College of Engineering and Technology, Marthandam 629171, Tamil Nadu, India; (A.L.F.); (F.A.S.G.)
| | | | - Ajay Kumar Haridhas
- Department of ECE, Mar Ephraem College of Engineering and Technology, Marthandam 629171, Tamil Nadu, India;
| | - Sayantan Ghosh
- Department of Integrative Biology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India;
| | - Harishita Purushothaman Bhuvana
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore; (H.P.B.); (W.K.J.S.); (V.V.); (V.J.)
| | - Wei Khang Jeremy Sim
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore; (H.P.B.); (W.K.J.S.); (V.V.); (V.J.)
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore
| | - Vijayaragavan Vimalan
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore; (H.P.B.); (W.K.J.S.); (V.V.); (V.J.)
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore
| | - Fredin Arun Sedly Givo
- Department of CSE, Mar Ephraem College of Engineering and Technology, Marthandam 629171, Tamil Nadu, India; (A.L.F.); (F.A.S.G.)
| | - Veikko Jousmäki
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore; (H.P.B.); (W.K.J.S.); (V.V.); (V.J.)
- Aalto NeuroImaging, Department of Neuroscience and Biomedical Engineering, Aalto University, 12200 Espoo, Finland
| | - Parasuraman Padmanabhan
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore; (H.P.B.); (W.K.J.S.); (V.V.); (V.J.)
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore
- Correspondence: (P.P.); (B.G.)
| | - Balázs Gulyás
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore; (H.P.B.); (W.K.J.S.); (V.V.); (V.J.)
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore
- Department of Clinical Neuroscience, Karolinska Institute, 17176 Stockholm, Sweden
- Correspondence: (P.P.); (B.G.)
| |
Collapse
|
5
|
Verma RK, Pandey M, Chawla P, Choudhury H, Mayuren J, Bhattamisra SK, Gorain B, Raja MAG, Amjad MW, Obaidur Rahman S. An insight into the role of Artificial Intelligence in the early diagnosis of Alzheimer's disease. CNS & NEUROLOGICAL DISORDERS-DRUG TARGETS 2021; 21:901-912. [PMID: 33982657 DOI: 10.2174/1871527320666210512014505] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 01/12/2021] [Accepted: 02/17/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND The complication of Alzheimer's disease (AD) has made the development of its therapeutic a challenging task. Even after decades of research, we have achieved no more than a few years of symptomatic relief. The inability to diagnose the disease early is the foremost hurdle behind its treatment. Several studies have aimed to identify potential biomarkers that can be detected in body fluids (CSF, blood, urine, etc) or assessed by neuroimaging (i.e., PET and MRI). However, the clinical implementation of these biomarkers is incomplete as they cannot be validated. METHOD To overcome the limitation, the use of artificial intelligence along with technical tools has been extensively investigated for AD diagnosis. For developing a promising artificial intelligence strategy that can diagnose AD early, it is critical to supervise neuropsychological outcomes and imaging-based readouts with a proper clinical review. CONCLUSION Profound knowledge, a large data pool, and detailed investigations are required for the successful implementation of this tool. This review will enlighten various aspects of early diagnosis of AD using artificial intelligence.
Collapse
Affiliation(s)
- Rohit Kumar Verma
- International Medical University Department of Pharmacy Practice, School of Pharmacy, Malaysia
| | - Manisha Pandey
- Department of Pharmaceutical Technology, School of Pharmacy, International Medical University-Bukit Jalil 57000, Kuala Lumpur, Malaysia School of Pharmacy,, Malaysia
| | - Pooja Chawla
- ISF College of Pharmacy, Moga Pharmaceutical Chemistry, India
| | - Hira Choudhury
- International Medical University Pharmaceutical Technology, Malaysia
| | - Jayashree Mayuren
- School of Pharmacy, International Medical University Department of Pharmaceutical Technology,, Malaysia
| | | | - Bapi Gorain
- Lincoln University College Faculty of Pharmacy, Malaysia
| | | | | | - Syed Obaidur Rahman
- Department of Pharmaceutical Medicine, School of Pharmaceutical Education and Research, Jamia Humdard, New Delhi India Pharmacology, India
| |
Collapse
|
6
|
López-Sanz D, Bruña R, de Frutos-Lucas J, Maestú F. Magnetoencephalography applied to the study of Alzheimer's disease. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2019; 165:25-61. [PMID: 31481165 DOI: 10.1016/bs.pmbts.2019.04.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Magnetoencephalography (MEG) is a relatively modern neuroimaging technique able to study normal and pathological brain functioning with temporal resolution in the order of milliseconds and adequate spatial resolution. Although its clinical applications are still relatively limited, great advances have been made in recent years in the field of dementia and Alzheimer's disease (AD) in particular. In this chapter, we briefly describe the physiological phenomena underlying MEG brain signals and the different metrics that can be computed from these data in order to study the alterations disrupting brain activity not only in demented patients, but also in the preclinical and prodromal stages of the disease. Changes in non-linear brain dynamics, power spectral properties, functional connectivity and network topological changes observed in AD are narratively summarized in the context of the pathophysiology of the disease. Furthermore, the potential of MEG as a potential biomarker to identify AD pathology before dementia onset is discussed in the light of current knowledge and the relationship between potential MEG biomarkers and current established hallmarks of the disease is also reviewed. To this aim, findings from different approaches such as resting state or during the performance of different cognitive paradigms are discussed.Lastly, there is an increasing interest in current scientific literature in promoting interventions aimed at modifying certain lifestyles, such as nutrition or physical activity among others, thought to reduce or delay AD risk. We discuss the utility of MEG as a potential marker of the success of such interventions from the available literature.
Collapse
Affiliation(s)
- David López-Sanz
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Technical University of Madrid (UPM), Madrid, Spain; Department of Experimental Psychology, Complutense University of Madrid (UCM), Madrid, Spain
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Technical University of Madrid (UPM), Madrid, Spain; Department of Experimental Psychology, Complutense University of Madrid (UCM), Madrid, Spain; Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain
| | - Jaisalmer de Frutos-Lucas
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Technical University of Madrid (UPM), Madrid, Spain; Biological and Health Psychology Department, Universidad Autonoma de Madrid, Madrid, Spain; School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Technical University of Madrid (UPM), Madrid, Spain; Department of Experimental Psychology, Complutense University of Madrid (UCM), Madrid, Spain; Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain.
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
Dimitriadis SI, López ME, Bruña R, Cuesta P, Marcos A, Maestú F, Pereda E. How to Build a Functional Connectomic Biomarker for Mild Cognitive Impairment From Source Reconstructed MEG Resting-State Activity: The Combination of ROI Representation and Connectivity Estimator Matters. Front Neurosci 2018; 12:306. [PMID: 29910704 PMCID: PMC5992286 DOI: 10.3389/fnins.2018.00306] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 04/20/2018] [Indexed: 11/24/2022] Open
Abstract
Our work aimed to demonstrate the combination of machine learning and graph theory for the designing of a connectomic biomarker for mild cognitive impairment (MCI) subjects using eyes-closed neuromagnetic recordings. The whole analysis based on source-reconstructed neuromagnetic activity. As ROI representation, we employed the principal component analysis (PCA) and centroid approaches. As representative bi-variate connectivity estimators for the estimation of intra and cross-frequency interactions, we adopted the phase locking value (PLV), the imaginary part (iPLV) and the correlation of the envelope (CorrEnv). Both intra and cross-frequency interactions (CFC) have been estimated with the three connectivity estimators within the seven frequency bands (intra-frequency) and in pairs (CFC), correspondingly. We demonstrated how different versions of functional connectivity graphs single-layer (SL-FCG) and multi-layer (ML-FCG) can give us a different view of the functional interactions across the brain areas. Finally, we applied machine learning techniques with main scope to build a reliable connectomic biomarker by analyzing both SL-FCG and ML-FCG in two different options: as a whole unit using a tensorial extraction algorithm and as single pair-wise coupling estimations. We concluded that edge-weighed feature selection strategy outperformed the tensorial treatment of SL-FCG and ML-FCG. The highest classification performance was obtained with the centroid ROI representation and edge-weighted analysis of the SL-FCG reaching the 98% for the CorrEnv in α1:α2 and 94% for the iPLV in α2. Classification performance based on the multi-layer participation coefficient, a multiplexity index reached 52% for iPLV and 52% for CorrEnv. Selected functional connections that build the multivariate connectomic biomarker in the edge-weighted scenario are located in default-mode, fronto-parietal, and cingulo-opercular network. Our analysis supports the notion of analyzing FCG simultaneously in intra and cross-frequency whole brain interactions with various connectivity estimators in beamformed recordings.
Collapse
Affiliation(s)
- Stavros I. Dimitriadis
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
- School of Psychology, Cardiff University, Cardiff, United Kingdom
- Neuroinformatics Group, Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
| | - María E. López
- Department of Basic Psychology II, Complutense University of Madrid, Madrid, Spain
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain
| | - Ricardo Bruña
- Department of Basic Psychology II, Complutense University of Madrid, Madrid, Spain
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain
| | - Pablo Cuesta
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Madrid, Spain
- Electrical Engineering and Bioengineering Group, Department of Industrial Engineering and IUNE, Universidad de La Laguna, Tenerife, Spain
| | - Alberto Marcos
- Department of Neurology, San Carlos University Hospital, Madrid, Spain
| | - Fernando Maestú
- Department of Basic Psychology II, Complutense University of Madrid, Madrid, Spain
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain
| | - Ernesto Pereda
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Madrid, Spain
- Electrical Engineering and Bioengineering Group, Department of Industrial Engineering and IUNE, Universidad de La Laguna, Tenerife, Spain
| |
Collapse
|
9
|
Sitnikova TA, Hughes JW, Ahlfors SP, Woolrich MW, Salat DH. Short timescale abnormalities in the states of spontaneous synchrony in the functional neural networks in Alzheimer's disease. NEUROIMAGE-CLINICAL 2018; 20:128-152. [PMID: 30094163 PMCID: PMC6077178 DOI: 10.1016/j.nicl.2018.05.028] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 04/20/2018] [Accepted: 05/20/2018] [Indexed: 10/28/2022]
Abstract
Alzheimer's disease (AD) is a prevalent neurodegenerative condition that can lead to severe cognitive and functional deterioration. Functional magnetic resonance imaging (fMRI) revealed abnormalities in AD in intrinsic synchronization between spatially separate regions in the so-called default mode network (DMN) of the brain. To understand the relationship between this disruption in large-scale synchrony and the cognitive impairment in AD, it is critical to determine whether and how the deficit in the low frequency hemodynamic fluctuations recorded by fMRI translates to much faster timescales of memory and other cognitive processes. The present study employed magnetoencephalography (MEG) and a Hidden Markov Model (HMM) approach to estimate spontaneous synchrony variations in the functional neural networks with high temporal resolution. In a group of cognitively healthy (CH) older adults, we found transient (mean duration of 150-250 ms) network activity states, which were visited in a rapid succession, and were characterized by spatially coordinated changes in the amplitude of source-localized electrophysiological oscillations. The inferred states were similar to those previously observed in younger participants using MEG, and the estimated cortical source distributions of the state-specific activity resembled the classic functional neural networks, such as the DMN. In patients with AD, inferred network states were different from those of the CH group in short-scale timing and oscillatory features. The state of increased oscillatory amplitudes in the regions overlapping the DMN was visited less often in AD and for shorter periods of time, suggesting that spontaneous synchronization in this network was less likely and less stable in the patients. During the visits to this state, in some DMN nodes, the amplitude change in the higher-frequency (8-30 Hz) oscillations was less robust in the AD than CH group. These findings highlight relevance of studying short-scale temporal evolution of spontaneous activity in functional neural networks to understanding the AD pathophysiology. Capacity of flexible intrinsic synchronization in the DMN may be crucial for memory and other higher cognitive functions. Our analysis yielded metrics that quantify distinct features of the neural synchrony disorder in AD and may offer sensitive indicators of the neural network health for future investigations.
Collapse
Affiliation(s)
- Tatiana A Sitnikova
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Harvard Medical School, Boston, MA 02115, USA.
| | - Jeremy W Hughes
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA.
| | - Seppo P Ahlfors
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Harvard Medical School, Boston, MA 02115, USA.
| | - Mark W Woolrich
- Oxford Center for Human Brain Activity, University of Oxford, Oxford OX3 7JX, UK.
| | - David H Salat
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Harvard Medical School, Boston, MA 02115, USA.
| |
Collapse
|
10
|
Verfaillie SCJ, Pichet Binette A, Vachon-Presseau E, Tabrizi S, Savard M, Bellec P, Ossenkoppele R, Scheltens P, van der Flier WM, Breitner JCS, Villeneuve S. Subjective Cognitive Decline Is Associated With Altered Default Mode Network Connectivity in Individuals With a Family History of Alzheimer's Disease. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2017; 3:463-472. [PMID: 29735156 DOI: 10.1016/j.bpsc.2017.11.012] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2017] [Revised: 11/20/2017] [Accepted: 11/22/2017] [Indexed: 01/03/2023]
Abstract
BACKGROUND Both subjective cognitive decline (SCD) and a family history of Alzheimer's disease (AD) portend risk of brain abnormalities and progression to dementia. Posterior default mode network (pDMN) connectivity is altered early in the course of AD. It is unclear whether SCD predicts similar outcomes in cognitively normal individuals with a family history of AD. METHODS We studied 124 asymptomatic individuals with a family history of AD (age 64 ± 5 years). Participants were categorized as having SCD if they reported that their memory was becoming worse (SCD+). We used extensive neuropsychological assessment to investigate five different cognitive domain performances at baseline (n = 124) and 1 year later (n = 59). We assessed interconnectivity among three a priori defined ROIs: pDMN, anterior ventral DMN, medial temporal memory system (MTMS), and the connectivity of each with the rest of brain. RESULTS Sixty-eight (55%) participants reported SCD. Baseline cognitive performance was comparable between groups (all false discovery rate-adjusted p values > .05). At follow-up, immediate and delayed memory improved across groups, but the improvement in immediate memory was reduced in SCD+ compared with SCD- (all false discovery rate-adjusted p values < .05). When compared with SCD-, SCD+ subjects showed increased pDMN-MTMS connectivity (false discovery rate-adjusted p < .05). Higher connectivity between the MTMS and the rest of the brain was associated with better baseline immediate memory, attention, and global cognition, whereas higher MTMS and pDMN-MTMS connectivity were associated with lower immediate memory over time (all false discovery rate-adjusted p values < .05). CONCLUSIONS SCD in cognitively normal individuals is associated with diminished immediate memory practice effects and a brain connectivity pattern that mirrors early AD-related connectivity failure.
Collapse
Affiliation(s)
- Sander C J Verfaillie
- Montreal Neurological Institute, Montreal, Quebec, Canada; Centre for the Studies on Prevention of Alzheimer's Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Alzheimer Center and Department of Neurology, Vrije Universiteit Medical Centre, Amsterdam, The Netherlands; Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Alexa Pichet Binette
- Centre for the Studies on Prevention of Alzheimer's Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | | | - Shirin Tabrizi
- Centre for the Studies on Prevention of Alzheimer's Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
| | - Mélissa Savard
- Centre for the Studies on Prevention of Alzheimer's Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
| | - Pierre Bellec
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Montreal, Quebec, Canada; Department of Computer Science and Operations Research, University of Montreal, Montreal, Quebec, Canada
| | - Rik Ossenkoppele
- Alzheimer Center and Department of Neurology, Vrije Universiteit Medical Centre, Amsterdam, The Netherlands; Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center and Department of Neurology, Vrije Universiteit Medical Centre, Amsterdam, The Netherlands; Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center and Department of Neurology, Vrije Universiteit Medical Centre, Amsterdam, The Netherlands; Department of Epidemiology and Biostatistics, Vrije Universiteit Medical Centre, Amsterdam, The Netherlands; Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - John C S Breitner
- Montreal Neurological Institute, Montreal, Quebec, Canada; Centre for the Studies on Prevention of Alzheimer's Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Sylvia Villeneuve
- Montreal Neurological Institute, Montreal, Quebec, Canada; Centre for the Studies on Prevention of Alzheimer's Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada.
| | | |
Collapse
|
11
|
Einalou Z, Maghooli K, Setarehdan SK, Akin A. Graph theoretical approach to functional connectivity in prefrontal cortex via fNIRS. NEUROPHOTONICS 2017; 4:041407. [PMID: 28840159 PMCID: PMC5565675 DOI: 10.1117/1.nph.4.4.041407] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Accepted: 07/19/2017] [Indexed: 05/20/2023]
Abstract
Functional near-infrared spectroscopy (fNIRS) has been proposed as an affordable, fast, and robust alternative to many neuroimaging modalities yet it still has long way to go to be adapted in the clinic. One request from the clinicians has been the delivery of a simple and straightforward metric (a so-called biomarker) from the vast amount of data a multichannel fNIRS system provides. We propose a simple-straightforward signal processing algorithm derived from [Formula: see text] data collected during a modified version of the color-word matching Stroop task that consists of three different conditions. The algorithm starts with a wavelet-transform-based preprocessing, then uses partial correlation analysis to compute the functional connectivity matrices at each condition and then computes the global efficiency values. To this end, a continuous wave 16 channels fNIRS device (ARGES Cerebro, Hemosoft Inc., Turkey) was used to measure the changes in [Formula: see text] concentrations from 12 healthy volunteers. We have considered 10% of strongest connections in each network. A strong Stroop interference effect was found between the incongruent against neutral condition ([Formula: see text]) while a similar significance was observed for the global efficiency values decreased from neutral to congruent to incongruent conditions [[Formula: see text], [Formula: see text]]. The findings bring us closer to delivering a biomarker derived from fNIRS data that can be reliably and easily adopted by the clinicians.
Collapse
Affiliation(s)
- Zahra Einalou
- Department of Biomedical Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Keivan Maghooli
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
- Address all correspondence to: Keivan Maghooli, E-mail:
| | - Seyaed Kamaledin Setarehdan
- University of Tehran, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, Tehran, Iran
| | - Ata Akin
- Acibadem University, Department of Medical Engineering, Istanbul, Turkey
| |
Collapse
|
12
|
Wrosch JK, Einem VV, Breininger K, Dahlmanns M, Maier A, Kornhuber J, Groemer TW. Rewiring of neuronal networks during synaptic silencing. Sci Rep 2017; 7:11724. [PMID: 28916806 PMCID: PMC5601899 DOI: 10.1038/s41598-017-11729-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Accepted: 08/29/2017] [Indexed: 12/14/2022] Open
Abstract
Analyzing the connectivity of neuronal networks, based on functional brain imaging data, has yielded new insight into brain circuitry, bringing functional and effective networks into the focus of interest for understanding complex neurological and psychiatric disorders. However, the analysis of network changes, based on the activity of individual neurons, is hindered by the lack of suitable meaningful and reproducible methodologies. Here, we used calcium imaging, statistical spike time analysis and a powerful classification model to reconstruct effective networks of primary rat hippocampal neurons in vitro. This method enables the calculation of network parameters, such as propagation probability, path length, and clustering behavior through the measurement of synaptic activity at the single-cell level, thus providing a fuller understanding of how changes at single synapses translate to an entire population of neurons. We demonstrate that our methodology can detect the known effects of drug-induced neuronal inactivity and can be used to investigate the extensive rewiring processes affecting population-wide connectivity patterns after periods of induced neuronal inactivity.
Collapse
Affiliation(s)
- Jana Katharina Wrosch
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nuremberg, 91054, Erlangen, Germany.
| | - Vicky von Einem
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nuremberg, 91054, Erlangen, Germany.,Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nürnberg, 91058, Erlangen, Germany
| | - Katharina Breininger
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nürnberg, 91058, Erlangen, Germany
| | - Marc Dahlmanns
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nuremberg, 91054, Erlangen, Germany
| | - Andreas Maier
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nürnberg, 91058, Erlangen, Germany
| | - Johannes Kornhuber
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nuremberg, 91054, Erlangen, Germany
| | - Teja Wolfgang Groemer
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nuremberg, 91054, Erlangen, Germany
| |
Collapse
|
13
|
Engels MMA, Yu M, Stam CJ, Gouw AA, van der Flier WM, Scheltens P, van Straaten ECW, Hillebrand A. Directional information flow in patients with Alzheimer's disease. A source-space resting-state MEG study. Neuroimage Clin 2017; 15:673-681. [PMID: 28702344 PMCID: PMC5486371 DOI: 10.1016/j.nicl.2017.06.025] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Revised: 05/10/2017] [Accepted: 06/16/2017] [Indexed: 01/24/2023]
Abstract
In a recent magnetoencephalography (MEG) study, we found posterior-to-anterior information flow over the cortex in higher frequency bands in healthy subjects, with a reversed pattern in the theta band. A disruption of information flow may underlie clinical symptoms in Alzheimer's disease (AD). In AD, highly connected regions (hubs) in posterior areas are mostly disrupted. We therefore hypothesized that in AD the information flow from these hub regions would be disturbed. We used resting-state MEG recordings from 27 early-onset AD patients and 26 healthy controls. Using beamformer-based virtual electrodes, we estimated neuronal oscillatory activity for 78 cortical regions of interest (ROIs) and 12 subcortical ROIs of the AAL atlas, and calculated the directed phase transfer entropy (dPTE) as a measure of information flow between these ROIs. Group differences were evaluated using permutation tests and, for the AD group, associations between dPTE and general cognition or CSF biomarkers were determined using Spearman correlation coefficients. We confirmed the previously reported posterior-to-anterior information flow in the higher frequency bands in the healthy controls, and found it to be disturbed in the beta band in AD. Most prominently, the information flow from the precuneus and the visual cortex, towards frontal and subcortical structures, was decreased in AD. These disruptions did not correlate with cognitive impairment or CSF biomarkers. We conclude that AD pathology may affect the flow of information between brain regions, particularly from posterior hub regions, and that changes in the information flow in the beta band indicate an aspect of the pathophysiological process in AD.
Collapse
Affiliation(s)
- M M A Engels
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
| | - M Yu
- Department of Clinical Neurophysiology and MEG Center, 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 A Gouw
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - W M van der Flier
- Alzheimer Center 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
| | - Ph Scheltens
- Alzheimer Center 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
| | - A Hillebrand
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| |
Collapse
|
14
|
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.
Collapse
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
| |
Collapse
|
15
|
López ME, Engels MMA, van Straaten ECW, Bajo R, Delgado ML, Scheltens P, Hillebrand A, Stam CJ, Maestú F. MEG Beamformer-Based Reconstructions of Functional Networks in Mild Cognitive Impairment. Front Aging Neurosci 2017; 9:107. [PMID: 28487647 PMCID: PMC5403893 DOI: 10.3389/fnagi.2017.00107] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Accepted: 04/04/2017] [Indexed: 11/20/2022] Open
Abstract
Subjects with mild cognitive impairment (MCI) have an increased risk of developing Alzheimer’s disease (AD), and their functional brain networks are presumably already altered. To test this hypothesis, we compared magnetoencephalography (MEG) eyes-closed resting-state recordings from 29 MCI subjects and 29 healthy elderly subjects in the present exploratory study. Functional connectivity in different frequency bands was assessed with the phase lag index (PLI) in source space. Normalized weighted clustering coefficient (normalized Cw) and path length (normalized Lw), as well as network measures derived from the minimum spanning tree [MST; i.e., betweenness centrality (BC) and node degree], were calculated. First, we found altered PLI values in the lower and upper alpha bands in MCI patients compared to controls. Thereafter, we explored network differences in these frequency bands. Normalized Cw and Lw did not differ between the groups, whereas BC and node degree of the MST differed, although these differences did not survive correction for multiple testing using the False Discovery Rate (FDR). As an exploratory study, we may conclude that: (1) the increases and decreases observed in PLI values in lower and upper alpha bands in MCI patients may be interpreted as a dual pattern of disconnection and aberrant functioning; (2) network measures are in line with connectivity findings, indicating a lower efficiency of the brain networks in MCI patients; (3) the MST centrality measures are more sensitive to detect subtle differences in the functional brain networks in MCI than traditional graph theoretical metrics.
Collapse
Affiliation(s)
- Maria E López
- Laboratory of Neuropsychology, Universitat de les Illes BalearsPalma de Mallorca, Spain.,Networking Research Center on Bioengineering, Biomaterials and NanomedicineMadrid, Spain
| | - Marjolein M A Engels
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical CenterAmsterdam, Netherlands
| | - Elisabeth C W van Straaten
- Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical CenterAmsterdam, Netherlands.,Nutricia Advanced Medical Nutrition, Nutricia ResearchUtrecht, Netherlands
| | - Ricardo Bajo
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of MadridMadrid, Spain
| | - María L Delgado
- Seniors Center of the District of ChamartínMadrid, Spain.,Department of Basic Psychology II, Complutense University of MadridMadrid, Spain
| | - Philip Scheltens
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical CenterAmsterdam, Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical CenterAmsterdam, Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical CenterAmsterdam, Netherlands
| | - Fernando Maestú
- Networking Research Center on Bioengineering, Biomaterials and NanomedicineMadrid, Spain.,Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of MadridMadrid, Spain.,Department of Basic Psychology II, Complutense University of MadridMadrid, Spain
| |
Collapse
|
16
|
van Straaten ECW, de Waal H, Lansbergen MM, Scheltens P, Maestu F, Nowak R, Hillebrand A, Stam CJ. Magnetoencephalography for the Detection of Intervention Effects of a Specific Nutrient Combination in Patients with Mild Alzheimer's Disease: Results from an Exploratory Double-Blind, Randomized, Controlled Study. Front Neurol 2016; 7:161. [PMID: 27799918 PMCID: PMC5065957 DOI: 10.3389/fneur.2016.00161] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Accepted: 09/13/2016] [Indexed: 01/01/2023] Open
Abstract
Synaptic loss is an early pathological finding in Alzheimer’s disease (AD) and correlates with memory impairment. Changes in macroscopic brain activity measured with electro- and magnetoencephalography (EEG and MEG) in AD indicate synaptic changes and may therefore serve as markers of intervention effects in clinical trials. EEG peak frequency and functional networks have shown, in addition to improved memory performance, to be sensitive to detect an intervention effect in mild AD patients of the medical food Souvenaid containing the specific nutrient combination Fortasyn® Connect, which is designed to enhance synapse formation and function. Here, we explore the value of MEG, with higher spatial resolution than EEG, in identifying intervention effects of the nutrient combination by comparing MEG spectral measures, functional connectivity, and networks between an intervention and a control group. Quantitative markers describing spectral properties, functional connectivity, and graph theoretical aspects of MEG from the exploratory 24-week, double-blind, randomized, controlled Souvenir II MEG sub-study (NTR1975, http://www.trialregister.nl) in drug naïve patients with mild AD were compared between a test group (n = 27), receiving Souvenaid, and a control group (n = 28), receiving an isocaloric control product. The groups were unbalanced at screening with respect to Mini-Mental State Examination. Peak frequencies of MEG were compared with EEG peak frequencies, recorded in the same patients at similar time points, were compared with respect to sensitivity to intervention effects. No consistent statistically significant intervention effects were detected. In addition, we found no difference in sensitivity between MEG and EEG peak frequency. This exploratory study could not unequivocally establish the value of MEG in detecting interventional effects on brain activity, possibly due to small sample size and unbalanced study groups. We found no indication that the difference could be attributed to a lack of sensitivity of MEG compared with EEG. MEG in randomized controlled trials is feasible but its value to disclose intervention effects of Souvenaid in mild AD patients needs to be studied further.
Collapse
Affiliation(s)
- Elisabeth C W van Straaten
- Department of Clinical Neurophysiology, MEG Center, VU Medical Center, Amsterdam, Netherlands; Nutricia Advanced Medical Nutrition, Nutricia Research, Utrecht, Netherlands
| | - Hanneke de Waal
- Department of Neurology, Alzheimer Center, VU Medical Center , Amsterdam , Netherlands
| | | | - Philip Scheltens
- Department of Neurology, Alzheimer Center, VU Medical Center , Amsterdam , Netherlands
| | - Fernando Maestu
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology , Madrid , Spain
| | - Rafal Nowak
- Magnetoencephalography Unit, Centro Medico Teknon , Barcelona , Spain
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology, MEG Center, VU Medical Center , Amsterdam , Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology, MEG Center, VU Medical Center , Amsterdam , Netherlands
| |
Collapse
|
17
|
Juan-Cruz C, Gomez C, Poza J, Fernandez A, Hornero R. Analysis of magnetoencephalography signals from Alzheimer's disease patients using granger causality. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2016:724-727. [PMID: 28268430 DOI: 10.1109/embc.2016.7590804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The aim of this study was to analyze resting-state magnetoencephalography (MEG) activity in Alzheimer's disease (AD) by means of Granger Causality (GC), an effective connectivity measure that provides an estimation of the information flow between brain regions. For this task, five minutes of MEG recordings were acquired with a 148-channel whole-head magnetometer from 36 AD patients and 26 healthy controls. Abnormalities in AD connectivity were found in the five typical frequency bands: delta (δ, 1-4 Hz), theta (θ, 4-8 Hz), alpha (α, 8-13 Hz), beta (β, 13-30 Hz), and gamma (γ, 30-65 Hz). Noteworthy increments in delta band and decrements in beta and gamma bands revealed disrupted connections in AD brain activity. Our analyses suggest that GC may be useful to characterize the brain impairment in AD.
Collapse
|
18
|
Alberdi A, Aztiria A, Basarab A. On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey. Artif Intell Med 2016; 71:1-29. [PMID: 27506128 DOI: 10.1016/j.artmed.2016.06.003] [Citation(s) in RCA: 83] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Revised: 05/23/2016] [Accepted: 06/07/2016] [Indexed: 11/15/2022]
Abstract
INTRODUCTION The number of Alzheimer's Disease (AD) patients is increasing with increased life expectancy and 115.4 million people are expected to be affected in 2050. Unfortunately, AD is commonly diagnosed too late, when irreversible damages have been caused in the patient. OBJECTIVE An automatic, continuous and unobtrusive early AD detection method would be required to improve patients' life quality and avoid big healthcare costs. Thus, the objective of this survey is to review the multimodal signals that could be used in the development of such a system, emphasizing on the accuracy that they have shown up to date for AD detection. Some useful tools and specific issues towards this goal will also have to be reviewed. METHODS An extensive literature review was performed following a specific search strategy, inclusion criteria, data extraction and quality assessment in the Inspec, Compendex and PubMed databases. RESULTS This work reviews the extensive list of psychological, physiological, behavioural and cognitive measurements that could be used for AD detection. The most promising measurements seem to be magnetic resonance imaging (MRI) for AD vs control (CTL) discrimination with an 98.95% accuracy, while electroencephalogram (EEG) shows the best results for mild cognitive impairment (MCI) vs CTL (97.88%) and MCI vs AD distinction (94.05%). Available physiological and behavioural AD datasets are listed, as well as medical imaging analysis steps and neuroimaging processing toolboxes. Some issues such as "label noise" and multi-site data are discussed. CONCLUSIONS The development of an unobtrusive and transparent AD detection system should be based on a multimodal system in order to take full advantage of all kinds of symptoms, detect even the smallest changes and combine them, so as to detect AD as early as possible. Such a multimodal system might probably be based on physiological monitoring of MRI or EEG, as well as behavioural measurements like the ones proposed along the article. The mentioned AD datasets and image processing toolboxes are available for their use towards this goal. Issues like "label noise" and multi-site neuroimaging incompatibilities may also have to be overcome, but methods for this purpose are already available.
Collapse
Affiliation(s)
- Ane Alberdi
- Mondragon University, Electronics and Computing Department, Goiru Kalea, 2, Arrasate 20500, Spain.
| | - Asier Aztiria
- Mondragon University, Electronics and Computing Department, Goiru Kalea, 2, Arrasate 20500, Spain.
| | - Adrian Basarab
- Université de Toulouse, Institut de Recherche en Informatique de Toulouse, Centre National de la Recherche Scientifique, Unité Mixte de Recherche 5505, Université Paul Sabatier, 118 Route de Narbonne, 31062 Toulouse, France.
| |
Collapse
|
19
|
The application of information theory for the research of aging and aging-related diseases. Prog Neurobiol 2016; 157:158-173. [PMID: 27004830 DOI: 10.1016/j.pneurobio.2016.03.005] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2015] [Revised: 03/13/2016] [Accepted: 03/19/2016] [Indexed: 11/23/2022]
Abstract
This article reviews the application of information-theoretical analysis, employing measures of entropy and mutual information, for the study of aging and aging-related diseases. The research of aging and aging-related diseases is particularly suitable for the application of information theory methods, as aging processes and related diseases are multi-parametric, with continuous parameters coexisting alongside discrete parameters, and with the relations between the parameters being as a rule non-linear. Information theory provides unique analytical capabilities for the solution of such problems, with unique advantages over common linear biostatistics. Among the age-related diseases, information theory has been used in the study of neurodegenerative diseases (particularly using EEG time series for diagnosis and prediction), cancer (particularly for establishing individual and combined cancer biomarkers), diabetes (mainly utilizing mutual information to characterize the diseased and aging states), and heart disease (mainly for the analysis of heart rate variability). Few works have employed information theory for the analysis of general aging processes and frailty, as underlying determinants and possible early preclinical diagnostic measures for aging-related diseases. Generally, the use of information-theoretical analysis permits not only establishing the (non-linear) correlations between diagnostic or therapeutic parameters of interest, but may also provide a theoretical insight into the nature of aging and related diseases by establishing the measures of variability, adaptation, regulation or homeostasis, within a system of interest. It may be hoped that the increased use of such measures in research may considerably increase diagnostic and therapeutic capabilities and the fundamental theoretical mathematical understanding of aging and disease.
Collapse
|
20
|
Dubois B, Hampel H, Feldman HH, Scheltens P, Aisen P, Andrieu S, Bakardjian H, Benali H, Bertram L, Blennow K, Broich K, Cavedo E, Crutch S, Dartigues JF, Duyckaerts C, Epelbaum S, Frisoni GB, Gauthier S, Genthon R, Gouw AA, Habert MO, Holtzman DM, Kivipelto M, Lista S, Molinuevo JL, O'Bryant SE, Rabinovici GD, Rowe C, Salloway S, Schneider LS, Sperling R, Teichmann M, Carrillo MC, Cummings J, Jack CR. Preclinical Alzheimer's disease: Definition, natural history, and diagnostic criteria. Alzheimers Dement 2016; 12:292-323. [PMID: 27012484 PMCID: PMC6417794 DOI: 10.1016/j.jalz.2016.02.002] [Citation(s) in RCA: 1162] [Impact Index Per Article: 145.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
During the past decade, a conceptual shift occurred in the field of Alzheimer's disease (AD) considering the disease as a continuum. Thanks to evolving biomarker research and substantial discoveries, it is now possible to identify the disease even at the preclinical stage before the occurrence of the first clinical symptoms. This preclinical stage of AD has become a major research focus as the field postulates that early intervention may offer the best chance of therapeutic success. To date, very little evidence is established on this "silent" stage of the disease. A clarification is needed about the definitions and lexicon, the limits, the natural history, the markers of progression, and the ethical consequence of detecting the disease at this asymptomatic stage. This article is aimed at addressing all the different issues by providing for each of them an updated review of the literature and evidence, with practical recommendations.
Collapse
Affiliation(s)
- Bruno Dubois
- Institute of Memory and Alzheimer's Disease (IM2A) and Brain and Spine Institute (ICM) UMR S 1127 Frontlab, Department of Neurology, AP_HP, Pitié-Salpêtrière University Hospital, Sorbonne Universities, Pierre et Marie Curie University, Paris 06, Paris, France.
| | - Harald Hampel
- Institute of Memory and Alzheimer's Disease (IM2A) and Brain and Spine Institute (ICM) UMR S 1127 Frontlab, Department of Neurology, AP_HP, Pitié-Salpêtrière University Hospital, Sorbonne Universities, Pierre et Marie Curie University, Paris 06, Paris, France; AXA Research Fund & UPMC Chair, Paris, France
| | | | - Philip Scheltens
- Department of Neurology and Alzheimer Center, VU University Medical Center and Neuroscience Campus, Amsterdam, The Netherlands
| | - Paul Aisen
- University of Southern California San Diego, CA, USA
| | - Sandrine Andrieu
- UMR1027, INSERM, Université Toulouse III, Toulouse University Hospital, France
| | - Hovagim Bakardjian
- IHU-A-ICM-Institut des Neurosciences translationnelles de Paris, Paris, France
| | - Habib Benali
- INSERM U1146-CNRS UMR 7371-UPMC UM CR2, Site Pitié-Salpêtrière, Paris, France
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), Institutes of Neurogenetics and Integrative and Experimental Genomics, University of Lübeck, Lübeck, Germany; School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Kaj Blennow
- Clinical Neurochemistry Lab, Department of Neuroscience and Physiology, University of Gothenburg, Mölndal Hospital, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Karl Broich
- Federal Institute for Drugs and Medical Devices, Bonn, Germany
| | - Enrica Cavedo
- AXA Research Fund & UPMC Chair, Paris, France; Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Sebastian Crutch
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, London, UK
| | | | - Charles Duyckaerts
- University Pierre et Marie Curie, Assistance Publique des Hôpitaux de Paris, Alzheimer-Prion Team Institut du Cerveau et de la Moelle (ICM), Paris, France
| | - Stéphane Epelbaum
- Institute of Memory and Alzheimer's Disease (IM2A) and Brain and Spine Institute (ICM) UMR S 1127 Frontlab, Department of Neurology, AP_HP, Pitié-Salpêtrière University Hospital, Sorbonne Universities, Pierre et Marie Curie University, Paris 06, Paris, France
| | - Giovanni B Frisoni
- University Hospitals and University of Geneva, Geneva, Switzerland; IRCCS Fatebenefratelli, Brescia, Italy
| | - Serge Gauthier
- McGill Center for Studies in Aging, Douglas Mental Health Research Institute, Montreal, Canada
| | - Remy Genthon
- Fondation pour la Recherche sur Alzheimer, Hôpital Pitié-Salpêtrière, Paris, France
| | - Alida A Gouw
- UMR1027, INSERM, Université Toulouse III, Toulouse University Hospital, France; Department of Clinical Neurophysiology/MEG Center, VU University Medical Center, Amsterdam
| | - Marie-Odile Habert
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, Paris, France; AP-HP, Hôpital Pitié-Salpêtrière, Département de Médecine Nucléaire, Paris, France
| | - David M Holtzman
- Department of Neurology, Washington University, Hope Center for Neurological Disorders, St. Louis, MO, USA; Department of Neurology, Washington University, Knight Alzheimer's Disease Research Center, St. Louis, MO, USA
| | - Miia Kivipelto
- Center for Alzheimer Research, Karolinska Institutet, Department of Geriatric Medicine, Karolinska University Hospital, Stockholm, Sweden; Institute of Clinical Medicine/ Neurology, University of Eastern Finland, Kuopio, Finland
| | | | - José-Luis Molinuevo
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | - Sid E O'Bryant
- Center for Alzheimer's & Neurodegenerative Disease Research, University of North Texas Health Science Center, TX, USA
| | - Gil D Rabinovici
- Memory & Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Christopher Rowe
- Department of Molecular Imaging, Austin Health, University of Melbourne, Australia
| | - Stephen Salloway
- Memory and Aging Program, Butler Hospital, Alpert Medical School of Brown University, USA; Department of Neurology, Alpert Medical School of Brown University, USA; Department of Psychiatry, Alpert Medical School of Brown University, USA
| | - Lon S Schneider
- Keck School of Medicine of the University of Southern California, Los Angeles, CA, USA
| | - Reisa Sperling
- Harvard Medical School, Memory Disorders Unit, Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Boston, USA; Harvard Medical School, Memory Disorders Unit, Center for Alzheimer Research and Treatment, Massachusetts General Hospital, Boston, USA
| | - Marc Teichmann
- Institute of Memory and Alzheimer's Disease (IM2A) and Brain and Spine Institute (ICM) UMR S 1127 Frontlab, Department of Neurology, AP_HP, Pitié-Salpêtrière University Hospital, Sorbonne Universities, Pierre et Marie Curie University, Paris 06, Paris, France
| | - Maria C Carrillo
- The Alzheimer's Association Division of Medical & Scientific Relations, Chicago, USA
| | - Jeffrey Cummings
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
| | - Cliff R Jack
- Department of Radiology, Mayo Clinic, Rochester MN, USA
| |
Collapse
|
21
|
Morabito FC, Campolo M, Labate D, Morabito G, Bonanno L, Bramanti A, de Salvo S, Marra A, Bramanti P. A Longitudinal EEG Study of Alzheimer's Disease Progression Based on A Complex Network Approach. Int J Neural Syst 2015; 25:1550005. [DOI: 10.1142/s0129065715500057] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
A complex network approach is combined with time dynamics in order to conduct a space–time analysis applicable to longitudinal studies aimed to characterize the progression of Alzheimer's disease (AD) in individual patients. The network analysis reveals how patient-specific patterns are associated with disease progression, also capturing the widespread effect of local disruptions. This longitudinal study is carried out on resting electroence phalography (EEGs) of seven AD patients. The test is repeated after a three months' period. The proposed methodology allows to extract some averaged information and regularities on the patients' cohort and to quantify concisely the disease evolution. From the functional viewpoint, the progression of AD is shown to be characterized by a loss of connected areas here measured in terms of network parameters (characteristic path length, clustering coefficient, global efficiency, degree of connectivity and connectivity density). The differences found between baseline and at follow-up are statistically significant. Finally, an original topographic multiscale approach is proposed that yields additional results.
Collapse
Affiliation(s)
| | | | | | | | - Lilla Bonanno
- IRCCS Centro Neurolesi "Bonino Pulejo", Messina, Italy
| | | | | | - Angela Marra
- IRCCS Centro Neurolesi "Bonino Pulejo", Messina, Italy
| | | |
Collapse
|
22
|
Innovative diagnostic tools for early detection of Alzheimer's disease. Alzheimers Dement 2014; 11:561-78. [PMID: 25443858 DOI: 10.1016/j.jalz.2014.06.004] [Citation(s) in RCA: 157] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2014] [Revised: 04/21/2014] [Accepted: 06/16/2014] [Indexed: 02/06/2023]
|
23
|
Khadem A, Hossein-Zadeh GA. Quantification of the effects of volume conduction on the EEG/MEG connectivity estimates: an index of sensitivity to brain interactions. Physiol Meas 2014; 35:2149-64. [PMID: 25243864 DOI: 10.1088/0967-3334/35/10/2149] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
In the context of EEG/MEG, the term 'volume conduction (VC) effects' refers to the recording of an instantaneous linear mixture of multiple brain source activities by each EEG/MEG channel. VC effects may lead to the detection of spurious functional/effective couplings among EEG/MEG channels that are not caused by brain interactions. It is of importance to determine which detected couplings are indicators of brain interactions and which originate from the VC artefacts. In this paper, a quantitative framework is proposed to explore the origin of detected channel couplings by using two types of surrogate datasets. Also, a sensitivity index (called SI) is proposed to compare the power of different connectivity measures to discriminate between the brain interactions and the instantaneous linear mixing effects. We use seven different functional connectivity estimators to evaluate our method on simulation models and resting state EEG data. The error rate of the proposed framework for simulation data by using each of the connectivity estimators is less than 5.2%. Also, SI ranks these connectivity estimators according to their sensitivity to brain interactions in the presence of VC artefacts. As expected, the connectivity measures which are theoretically robust to VC artefacts yield high SI in simulation models and EEG data. In addition, for EEG data in the alpha frequency band the reproducible functional couplings which are indicators of brain interactions are in the back-front directions. This is consistent with the previous studies in this field.
Collapse
Affiliation(s)
- Ali Khadem
- Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | | |
Collapse
|
24
|
Rutter L, Nadar SR, Holroyd T, Carver FW, Apud J, Weinberger DR, Coppola R. Graph theoretical analysis of resting magnetoencephalographic functional connectivity networks. Front Comput Neurosci 2013; 7:93. [PMID: 23874288 PMCID: PMC3709101 DOI: 10.3389/fncom.2013.00093] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2013] [Accepted: 06/21/2013] [Indexed: 11/13/2022] Open
Abstract
Complex networks have been observed to comprise small-world properties, believed to represent an optimal organization of local specialization and global integration of information processing at reduced wiring cost. Here, we applied magnitude squared coherence to resting magnetoencephalographic time series in reconstructed source space, acquired from controls and patients with schizophrenia, and generated frequency-dependent adjacency matrices modeling functional connectivity between virtual channels. After configuring undirected binary and weighted graphs, we found that all human networks demonstrated highly localized clustering and short characteristic path lengths. The most conservatively thresholded networks showed efficient wiring, with topographical distance between connected vertices amounting to one-third as observed in surrogate randomized topologies. Nodal degrees of the human networks conformed to a heavy-tailed exponentially truncated power-law, compatible with the existence of hubs, which included theta and alpha bilateral cerebellar tonsil, beta and gamma bilateral posterior cingulate, and bilateral thalamus across all frequencies. We conclude that all networks showed small-worldness, minimal physical connection distance, and skewed degree distributions characteristic of physically-embedded networks, and that these calculations derived from graph theoretical mathematics did not quantifiably distinguish between subject populations, independent of bandwidth. However, post-hoc measurements of edge computations at the scale of the individual vertex revealed trends of reduced gamma connectivity across the posterior medial parietal cortex in patients, an observation consistent with our prior resting activation study that found significant reduction of synthetic aperture magnetometry gamma power across similar regions. The basis of these small differences remains unclear.
Collapse
Affiliation(s)
- Lindsay Rutter
- MEG Core Facility, National Institute of Mental HealthBethesda, MD, USA
| | | | - Tom Holroyd
- MEG Core Facility, National Institute of Mental HealthBethesda, MD, USA
| | | | - Jose Apud
- Clinical Brain Disorders Branch, National Institute of Mental HealthBethesda, MD, USA
| | | | - Richard Coppola
- MEG Core Facility, National Institute of Mental HealthBethesda, MD, USA
- Clinical Brain Disorders Branch, National Institute of Mental HealthBethesda, MD, USA
| |
Collapse
|
25
|
Noh K, Shin KS, Shin D, Hwang JY, Kim JS, Jang JH, Chung CK, Kwon JS, Cho KH. Impaired coupling of local and global functional feedbacks underlies abnormal synchronization and negative symptoms of schizophrenia. BMC SYSTEMS BIOLOGY 2013; 7:30. [PMID: 23575114 PMCID: PMC3639871 DOI: 10.1186/1752-0509-7-30] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2012] [Accepted: 03/14/2013] [Indexed: 02/02/2023]
Abstract
Background Abnormal synchronization of brain oscillations is found to be associated with various core symptoms of schizophrenia. However, the underlying mechanism of this association remains yet to be elucidated. Results In this study, we found that coupled local and global feedback (CLGF) circuits in the cortical functional network are related to the abnormal synchronization and also correlated to the negative symptom of schizophrenia. Analysis of the magnetoencephalography data obtained from patients with chronic schizophrenia during rest revealed an increase in beta band synchronization and a reduction in gamma band power compared to healthy controls. Using a feedback identification method based on non-causal impulse responses, we constructed functional feedback networks and found that CLGF circuits were significantly reduced in schizophrenia. From computational analysis on the basis of the Wilson-Cowan model, we unraveled that the CLGF circuits are critically involved in the abnormal synchronization and the dynamical switching between beta and gamma bands power in schizophrenia. Moreover, we found that the abundance of CLGF circuits was negatively correlated with the development of negative symptoms of schizophrenia, suggesting that the negative symptom is closely related to the impairment of this circuit. Conclusions Our study implicates that patients with schizophrenia might have the impaired coupling of inter- and intra-regional functional feedbacks and that the CLGF circuit might serve as a critical bridge between abnormal synchronization and the negative symptoms of schizophrenia.
Collapse
Affiliation(s)
- Kyungchul Noh
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | | | | | | | | | | | | | | | | |
Collapse
|
26
|
Chan HL, Chu JH, Fung HC, Tsai YT, Meng LF, Huang CC, Hsu WC, Chao PK, Wang JJ, Lee JD, Wai YY, Tsai MT. Brain connectivity of patients with Alzheimer's disease by coherence and cross mutual information of electroencephalograms during photic stimulation. Med Eng Phys 2013; 35:241-52. [DOI: 10.1016/j.medengphy.2011.10.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2011] [Revised: 10/02/2011] [Accepted: 10/12/2011] [Indexed: 10/15/2022]
|
27
|
Adaptive reorganization of cortical networks in Alzheimer’s disease. Clin Neurophysiol 2013; 124:35-43. [DOI: 10.1016/j.clinph.2012.05.028] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2012] [Revised: 05/16/2012] [Accepted: 05/31/2012] [Indexed: 11/24/2022]
|
28
|
Canuet L, Tellado I, Couceiro V, Fraile C, Fernandez-Novoa L, Ishii R, Takeda M, Cacabelos R. Resting-state network disruption and APOE genotype in Alzheimer's disease: a lagged functional connectivity study. PLoS One 2012; 7:e46289. [PMID: 23050006 PMCID: PMC3457973 DOI: 10.1371/journal.pone.0046289] [Citation(s) in RCA: 105] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2012] [Accepted: 08/28/2012] [Indexed: 01/07/2023] Open
Abstract
Background The apolipoprotein E epsilon 4 (APOE-4) is associated with a genetic vulnerability to Alzheimer's disease (AD) and with AD-related abnormalities in cortical rhythms. However, it is unclear whether APOE-4 is linked to a specific pattern of intrinsic functional disintegration of the brain after the development of the disease or during its different stages. This study aimed at identifying spatial patterns and effects of APOE genotype on resting-state oscillations and functional connectivity in patients with AD, using a physiological connectivity index called “lagged phase synchronization”. Methodology/Principal Findings Resting EEG was recorded during awake, eyes-closed state in 125 patients with AD and 60 elderly controls. Source current density and functional connectivity were determined using eLORETA. Patients with AD exhibited reduced parieto-occipital alpha oscillations compared with controls, and those carrying the APOE-4 allele had reduced alpha activity in the left inferior parietal and temporo-occipital cortex relative to noncarriers. There was a decreased alpha2 connectivity pattern in AD, involving the left temporal and bilateral parietal cortex. Several brain regions exhibited increased lagged phase synchronization in low frequencies, specifically in the theta band, across and within hemispheres, where temporal lobe connections were particularly compromised. Areas with abnormal theta connectivity correlated with cognitive scores. In patients with early AD, we found an APOE-4-related decrease in interhemispheric alpha connectivity in frontal and parieto-temporal regions. Conclusions/Significance In addition to regional cortical dysfunction, as indicated by abnormal alpha oscillations, there are patterns of functional network disruption affecting theta and alpha bands in AD that associate with the level of cognitive disturbance or with the APOE genotype. These functional patterns of nonlinear connectivity may potentially represent neurophysiological or phenotypic markers of AD, and aid in early detection of the disorder.
Collapse
Affiliation(s)
- Leonides Canuet
- EuroEspes Biomedical Research Center, Institute for CNS Disorders and Genomic Medicine, Corunna, Spain.
| | | | | | | | | | | | | | | |
Collapse
|
29
|
Tahaei MS, Jalili M, Knyazeva MG. Synchronizability of EEG-Based Functional Networks in Early Alzheimer's Disease. IEEE Trans Neural Syst Rehabil Eng 2012; 20:636-41. [DOI: 10.1109/tnsre.2012.2202127] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
30
|
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]
|
31
|
Cross-conditional entropy and coherence analysis of pharmaco-EEG changes induced by alprazolam. Psychopharmacology (Berl) 2012; 221:397-406. [PMID: 22127555 DOI: 10.1007/s00213-011-2587-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2011] [Accepted: 11/09/2011] [Indexed: 10/15/2022]
Abstract
RATIONALE Quantitative analysis of electroencephalographic signals (EEG) and their interpretation constitute a helpful tool in the assessment of the bioavailability of psychoactive drugs in the brain. Furthermore, psychotropic drug groups have typical signatures which relate biochemical mechanisms with specific EEG changes. OBJECTIVES To analyze the pharmacological effect of a dose of alprazolam on the connectivity of the brain during wakefulness by means of linear and nonlinear approaches. METHODS EEG signals were recorded after alprazolam administration in a placebo-controlled crossover clinical trial. Nonlinear couplings assessed by means of corrected cross-conditional entropy were compared to linear couplings measured with the classical magnitude squared coherence. RESULTS Linear variables evidenced a statistically significant drug-induced decrease, whereas nonlinear variables showed significant increases. All changes were highly correlated to drug plasma concentrations. The spatial distribution of the observed connectivity changes clearly differed from a previous study: changes before and after the maximum drug effect were mainly observed over the anterior half of the scalp. Additionally, a new variable with very low computational cost was defined to evaluate nonlinear coupling. This is particularly interesting when all pairs of EEG channels are assessed as in this study. CONCLUSIONS Results showed that alprazolam induced changes in terms of uncoupling between regions of the scalp, with opposite trends depending on the variables: decrease in linear ones and increase in nonlinear features. Maps provided consistent information about the way brain changed in terms of connectivity being definitely necessary to evaluate separately linear and nonlinear interactions.
Collapse
|
32
|
Bruña R, Poza J, Gómez C, García M, Fernández A, Hornero R. Analysis of spontaneous MEG activity in mild cognitive impairment and Alzheimer's disease using spectral entropies and statistical complexity measures. J Neural Eng 2012; 9:036007. [PMID: 22571870 DOI: 10.1088/1741-2560/9/3/036007] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Alzheimer's disease (AD) is the most common cause of dementia. Over the last few years, a considerable effort has been devoted to exploring new biomarkers. Nevertheless, a better understanding of brain dynamics is still required to optimize therapeutic strategies. In this regard, the characterization of mild cognitive impairment (MCI) is crucial, due to the high conversion rate from MCI to AD. However, only a few studies have focused on the analysis of magnetoencephalographic (MEG) rhythms to characterize AD and MCI. In this study, we assess the ability of several parameters derived from information theory to describe spontaneous MEG activity from 36 AD patients, 18 MCI subjects and 26 controls. Three entropies (Shannon, Tsallis and Rényi entropies), one disequilibrium measure (based on Euclidean distance ED) and three statistical complexities (based on Lopez Ruiz-Mancini-Calbet complexity LMC) were used to estimate the irregularity and statistical complexity of MEG activity. Statistically significant differences between AD patients and controls were obtained with all parameters (p < 0.01). In addition, statistically significant differences between MCI subjects and controls were achieved by ED and LMC (p < 0.05). In order to assess the diagnostic ability of the parameters, a linear discriminant analysis with a leave-one-out cross-validation procedure was applied. The accuracies reached 83.9% and 65.9% to discriminate AD and MCI subjects from controls, respectively. Our findings suggest that MCI subjects exhibit an intermediate pattern of abnormalities between normal aging and AD. Furthermore, the proposed parameters provide a new description of brain dynamics in AD and MCI.
Collapse
Affiliation(s)
- Ricardo Bruña
- Biomedical Engineering Group, Departmento T.S.C.I.T., E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Valladolid, Spain
| | | | | | | | | | | |
Collapse
|
33
|
Escudero J, Sanei S, Jarchi D, Abásolo D, Hornero R. Regional coherence evaluation in mild cognitive impairment and Alzheimer's disease based on adaptively extracted magnetoencephalogram rhythms. Physiol Meas 2011; 32:1163-80. [PMID: 21709337 DOI: 10.1088/0967-3334/32/8/011] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
|
34
|
Hartman D, Hlinka J, Palus M, Mantini D, Corbetta M. The role of nonlinearity in computing graph-theoretical properties of resting-state functional magnetic resonance imaging brain networks. CHAOS (WOODBURY, N.Y.) 2011; 21:013119. [PMID: 21456833 PMCID: PMC4108645 DOI: 10.1063/1.3553181] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2010] [Accepted: 01/18/2011] [Indexed: 05/13/2023]
Abstract
In recent years, there has been an increasing interest in the study of large-scale brain activity interaction structure from the perspective of complex networks, based on functional magnetic resonance imaging (fMRI) measurements. To assess the strength of interaction (functional connectivity, FC) between two brain regions, the linear (Pearson) correlation coefficient of the respective time series is most commonly used. Since a potential use of nonlinear FC measures has recently been discussed in this and other fields, the question arises whether particular nonlinear FC measures would be more informative for the graph analysis than linear ones. We present a comparison of network analysis results obtained from the brain connectivity graphs capturing either full (both linear and nonlinear) or only linear connectivity using 24 sessions of human resting-state fMRI. For each session, a matrix of full connectivity between 90 anatomical parcel time series is computed using mutual information. For comparison, connectivity matrices obtained for multivariate linear Gaussian surrogate data that preserve the correlations, but remove any nonlinearity are generated. Binarizing these matrices using multiple thresholds, we generate graphs corresponding to linear and full nonlinear interaction structures. The effect of neglecting nonlinearity is then assessed by comparing the values of a range of graph-theoretical measures evaluated for both types of graphs. Statistical comparisons suggest a potential effect of nonlinearity on the local measures-clustering coefficient and betweenness centrality. Nevertheless, subsequent quantitative comparison shows that the nonlinearity effect is practically negligible when compared to the intersubject variability of the graph measures. Further, on the group-average graph level, the nonlinearity effect is unnoticeable.
Collapse
Affiliation(s)
- D Hartman
- Institute of Computer Science AS CR, Pod vodárenskou věží 2, 18207 Prague 8, Czech Republic
| | | | | | | | | |
Collapse
|