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Casagrande CC, Rempe MP, Springer SD, Wilson TW. Comprehensive review of task-based neuroimaging studies of cognitive deficits in Alzheimer's disease using electrophysiological methods. Ageing Res Rev 2023; 88:101950. [PMID: 37156399 PMCID: PMC10261850 DOI: 10.1016/j.arr.2023.101950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 03/27/2023] [Accepted: 05/05/2023] [Indexed: 05/10/2023]
Abstract
With an aging population, cognitive decline and neurodegenerative disorders are an emerging public health crises with enormous, yet still under-recognized burdens. Alzheimer's disease (AD) is the most common type of dementia, and the number of cases is expected to dramatically rise in the upcoming decades. Substantial efforts have been placed into understanding the disease. One of the primary avenues of research is neuroimaging, and while positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) are most common, crucial recent advancements in electrophysiological methods such as magnetoencephalography (MEG) and electroencephalography (EEG) have provided novel insight into the aberrant neural dynamics at play in AD pathology. In this review, we outline task-based M/EEG studies published since 2010 using paradigms probing the cognitive domains most affected by AD, including memory, attention, and executive functioning. Furthermore, we provide important recommendations for adapting cognitive tasks for optimal use in this population and adjusting recruitment efforts to improve and expand future neuroimaging work.
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Affiliation(s)
- Chloe C Casagrande
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA
| | - Maggie P Rempe
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA; College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Seth D Springer
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA; College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA; Department of Pharmacology & Neuroscience, Creighton University, Omaha, NE 68178, USA.
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2
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Rai H, Gupta S, Kumar S, Yang J, Singh SK, Ran C, Modi G. Near-Infrared Fluorescent Probes as Imaging and Theranostic Modalities for Amyloid-Beta and Tau Aggregates in Alzheimer's Disease. J Med Chem 2022; 65:8550-8595. [PMID: 35759679 DOI: 10.1021/acs.jmedchem.1c01619] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A person suspected of having Alzheimer's disease (AD) is clinically diagnosed for the presence of principal biomarkers, especially misfolded amyloid-beta (Aβ) and tau proteins in the brain regions. Existing radiotracer diagnostic tools, such as PET imaging, are expensive and have limited availability for primary patient screening and pre-clinical animal studies. To change the status quo, small-molecular near-infrared (NIR) probes have been rapidly developed, which may serve as an inexpensive, handy imaging tool to comprehend the dynamics of pathogenic progression in AD and assess therapeutic efficacy in vivo. This Perspective summarizes the biochemistry of Aβ and tau proteins and then focuses on structurally diverse NIR probes with coverages of their spectroscopic properties, binding affinity toward Aβ and tau species, and theranostic effectiveness. With the summarized information and perspective discussions, we hope that this paper may serve as a guiding tool for designing novel in vivo imaging fluoroprobes with theranostic capabilities in the future.
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Affiliation(s)
- Himanshu Rai
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (BHU), Varanasi, U.P.-221005, India
| | - Sarika Gupta
- Molecular Science Laboratory, National Institute of Immunology, New Delhi-110067, India
| | - Saroj Kumar
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi-110029, India
| | - Jian Yang
- School of Medicine, Shanghai University, Shanghai 200444, China
| | - Sushil K Singh
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (BHU), Varanasi, U.P.-221005, India
| | - Chongzhao Ran
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts 02129, United States
| | - Gyan Modi
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (BHU), Varanasi, U.P.-221005, India
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3
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Núñez P, Poza J, Gómez C, Rodríguez-González V, Hillebrand A, Tewarie P, Tola-Arribas MÁ, Cano M, Hornero R. Abnormal meta-state activation of dynamic brain networks across the Alzheimer spectrum. Neuroimage 2021; 232:117898. [PMID: 33621696 DOI: 10.1016/j.neuroimage.2021.117898] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 01/19/2021] [Accepted: 02/16/2021] [Indexed: 02/06/2023] Open
Abstract
The characterization of the distinct dynamic functional connectivity (dFC) patterns that activate in the brain during rest can help to understand the underlying time-varying network organization. The presence and behavior of these patterns (known as meta-states) have been widely studied by means of functional magnetic resonance imaging (fMRI). However, modalities with high-temporal resolution, such as electroencephalography (EEG), enable the characterization of fast temporally evolving meta-state sequences. Mild cognitive impairment (MCI) and dementia due to Alzheimer's disease (AD) have been shown to disrupt spatially localized activation and dFC between different brain regions, but not much is known about how they affect meta-state network topologies and their network dynamics. The main hypothesis of the study was that MCI and dementia due to AD alter normal meta-state sequences by inducing a loss of structure in their patterns and a reduction of their dynamics. Moreover, we expected that patients with MCI would display more flexible behavior compared to patients with dementia due to AD. Thus, the aim of the current study was twofold: (i) to find repeating, distinctly organized network patterns (meta-states) in neural activity; and (ii) to extract information about meta-state fluctuations and how they are influenced by MCI and dementia due to AD. To accomplish these goals, we present a novel methodology to characterize dynamic meta-states and their temporal fluctuations by capturing aspects based on both their discrete activation and the continuous evolution of their individual strength. These properties were extracted from 60-s resting-state EEG recordings from 67 patients with MCI due to AD, 50 patients with dementia due to AD, and 43 cognitively healthy controls. First, the instantaneous amplitude correlation (IAC) was used to estimate instantaneous functional connectivity with a high temporal resolution. We then extracted meta-states by means of graph community detection based on recurrence plots (RPs), both at the individual- and group-level. Subsequently, a diverse set of properties of the continuous and discrete fluctuation patterns of the meta-states was extracted and analyzed. The main novelty of the methodology lies in the usage of Louvain GJA community detection to extract meta-states from IAC-derived RPs and the extended analysis of their discrete and continuous activation. Our findings showed that distinct dynamic functional connectivity meta-states can be found on the EEG time-scale, and that these were not affected by the oscillatory slowing induced by MCI or dementia due to AD. However, both conditions displayed a loss of meta-state modularity, coupled with shorter dwell times and higher complexity of the meta-state sequences. Furthermore, we found evidence that meta-state sequencing is not entirely random; it shows an underlying structure that is partially lost in MCI and dementia due to AD. These results show evidence that AD progression is associated with alterations in meta-state switching, and a degradation of dynamic brain flexibility.
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Affiliation(s)
- Pablo Núñez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.
| | - Jesús Poza
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid, Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
| | - Carlos Gómez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid, Spain
| | | | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Prejaas Tewarie
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Miguel Ángel Tola-Arribas
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid, Spain
| | - Mónica Cano
- Department of Clinical Neurophysiology, "Río Hortega" University Hospital, Valladolid, Spain
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid, Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
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4
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Koberskaya NN, Ostroumova TM. Near-moderate cognitive decline. NEUROLOGY, NEUROPSYCHIATRY, PSYCHOSOMATICS 2020. [DOI: 10.14412/2074-2711-2020-2-92-97] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- N. N. Koberskaya
- Department of Nervous System Diseases and Neurosurgery, Institute of Clinical Medicine, I.M. Sechenov First Moscow State Medical University (Sechenov University), Ministry of Health of Russia;
Center for Information Technologies in Design, Russian Academy of Sciences;
Russian Research and Clinical Center of Gerontology, N.I. Pirogov Russian National Research Medical University, Ministry of Health of Russia
| | - T. M. Ostroumova
- Department of Nervous System Diseases and Neurosurgery, Institute of Clinical Medicine, I.M. Sechenov First Moscow State Medical University (Sechenov University), Ministry of Health of Russia;
Center for Information Technologies in Design, Russian Academy of Sciences
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5
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Núñez P, Poza J, Gómez C, Barroso-García V, Maturana-Candelas A, Tola-Arribas MA, Cano M, Hornero R. Characterization of the dynamic behavior of neural activity in Alzheimer's disease: exploring the non-stationarity and recurrence structure of EEG resting-state activity. J Neural Eng 2020; 17:016071. [PMID: 32000144 DOI: 10.1088/1741-2552/ab71e9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
OBJECTIVE Mild cognitive impairment (MCI) and dementia due to Alzheimer's disease (AD) have been shown to induce perturbations to normal neuronal behavior and disrupt neuronal networks. Recent work suggests that the dynamic properties of resting-state neuronal activity could be affected by MCI and AD-induced neurodegeneration. The aim of the study was to characterize these properties from different perspectives: (i) using the Kullback-Leibler divergence (KLD), a measure of non-stationarity derived from the continuous wavelet transform; and (ii) using the entropy of the recurrence point density ([Formula: see text]) and the median of the recurrence point density ([Formula: see text]), two novel metrics based on recurrence quantification analysis. APPROACH KLD, [Formula: see text] and [Formula: see text] were computed for 49 patients with dementia due to AD, 66 patients with MCI due to AD and 43 cognitively healthy controls from 60 s electroencephalographic (EEG) recordings with a 10 s sliding window with no overlap. Afterwards, we tested whether the measures reflected alterations to normal neuronal activity induced by MCI and AD. MAIN RESULTS Our results showed that frequency-dependent alterations to normal dynamic behavior can be found in patients with MCI and AD, both in non-stationarity and recurrence structure. Patients with MCI showed signs of patterns of abnormal state recurrence in the theta (4-8 Hz) and beta (13-30 Hz) frequency bands that became more marked in AD. Moreover, abnormal non-stationarity patterns were found in MCI patients, but not in patients with AD in delta (1-4 Hz), alpha (8-13 Hz), and gamma (30-70 Hz). SIGNIFICANCE The alterations in normal levels of non-stationarity in patients with MCI suggest an initial increase in cortical activity during the development of AD. This increase could possibly be due to an impairment in neuronal inhibition that is not present during later stages. MCI and AD induce alterations to the recurrence structure of cortical activity, suggesting that normal state switching during rest may be affected by these pathologies.
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Affiliation(s)
- Pablo Núñez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain. Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina CIBER-BBN, Valladolid, Spain. Author to whom any correspondence should be addressed
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6
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López-Sanz D, Serrano N, Maestú F. The Role of Magnetoencephalography in the Early Stages of Alzheimer's Disease. Front Neurosci 2018; 12:572. [PMID: 30158852 PMCID: PMC6104188 DOI: 10.3389/fnins.2018.00572] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 07/30/2018] [Indexed: 01/01/2023] Open
Abstract
The ever increasing proportion of aged people in modern societies is leading to a substantial increase in the number of people affected by dementia, and Alzheimer’s Disease (AD) in particular, which is the most common cause for dementia. Throughout the course of the last decades several different compounds have been tested to stop or slow disease progression with limited success, which is giving rise to a strong interest toward the early stages of the disease. Alzheimer’s disease has an extended an insidious preclinical stage in which brain pathology accumulates slowly until clinical symptoms are observable in prodromal stages and in dementia. For this reason, the scientific community is focusing into investigating early signs of AD which could lead to the development of validated biomarkers. While some CSF and PET biomarkers have already been introduced in the clinical practice, the use of non-invasive measures of brain function as early biomarkers is still under investigation. However, the electrophysiological mechanisms and the early functional alterations underlying preclinical Alzheimer’s Disease is still scarcely studied. This work aims to briefly review the most relevant findings in the field of electrophysiological brain changes as measured by magnetoencephalography (MEG). MEG has proven its utility in some clinical areas. However, although its clinical relevance in dementia is still limited, a growing number of studies highlighted its sensitivity in these preclinical stages. Studies focusing on different analytical approaches will be reviewed. Furthermore, their potential applications to establish early diagnosis and determine subsequent progression to dementia are discussed.
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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, Madrid, Spain
| | - Noelia Serrano
- 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, Madrid, Spain
| | - 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, Madrid, Spain.,Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine, Zaragoza, Spain
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Klados MA, Styliadis C, Frantzidis CA, Paraskevopoulos E, Bamidis PD. Beta-Band Functional Connectivity is Reorganized in Mild Cognitive Impairment after Combined Computerized Physical and Cognitive Training. Front Neurosci 2016; 10:55. [PMID: 26973445 PMCID: PMC4770438 DOI: 10.3389/fnins.2016.00055] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2015] [Accepted: 02/05/2016] [Indexed: 01/12/2023] Open
Abstract
Physical and cognitive idleness constitute significant risk factors for the clinical manifestation of age-related neurodegenerative diseases. In contrast, a physically and cognitively active lifestyle may restructure age-declined neuronal networks enhancing neuroplasticity. The present study, investigated the changes of brain's functional network in a group of elderly individuals at risk for dementia that were induced by a combined cognitive and physical intervention scheme. Fifty seniors meeting Petersen's criteria of Mild Cognitive Impairment were equally divided into an experimental (LLM), and an active control (AC) group. Resting state electroencephalogram (EEG) was measured before and after the intervention. Functional networks were estimated by computing the magnitude square coherence between the time series of all available cortical sources as computed by standardized low resolution brain electromagnetic tomography (sLORETA). A statistical model was used to form groups' characteristic weighted graphs. The introduced modulation was assessed by networks' density and nodes' strength. Results focused on the beta band (12-30 Hz) in which the difference of the two networks' density is maximum, indicating that the structure of the LLM cortical network changes significantly due to the intervention, in contrast to the network of AC. The node strength of LLM participants in the beta band presents a higher number of bilateral connections in the occipital, parietal, temporal and prefrontal regions after the intervention. Our results show that the combined training scheme reorganizes the beta-band functional connectivity of MCI patients. ClinicalTrials.gov Identifier: NCT02313935 https://clinicaltrials.gov/ct2/show/NCT02313935.
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Affiliation(s)
- Manousos A Klados
- Medical Physics Laboratory, Faculty of Health Sciences, Medical School, Aristotle University of ThessalonikiThessaloniki, Greece; Research Group for Neuroanatomy and Connectivity, Max Planck Institute for Human Cognitive and Brain SciencesLeipzig, Germany
| | - Charis Styliadis
- Medical Physics Laboratory, Faculty of Health Sciences, Medical School, Aristotle University of Thessaloniki Thessaloniki, Greece
| | - Christos A Frantzidis
- Medical Physics Laboratory, Faculty of Health Sciences, Medical School, Aristotle University of Thessaloniki Thessaloniki, Greece
| | - Evangelos Paraskevopoulos
- Medical Physics Laboratory, Faculty of Health Sciences, Medical School, Aristotle University of Thessaloniki Thessaloniki, Greece
| | - Panagiotis D Bamidis
- Medical Physics Laboratory, Faculty of Health Sciences, Medical School, Aristotle University of Thessaloniki Thessaloniki, Greece
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8
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López ME, Bruña R, Aurtenetxe S, Pineda-Pardo JÁ, Marcos A, Arrazola J, Reinoso AI, Montejo P, Bajo R, Maestú F. Alpha-band hypersynchronization in progressive mild cognitive impairment: a magnetoencephalography study. J Neurosci 2014; 34:14551-9. [PMID: 25355209 PMCID: PMC6608420 DOI: 10.1523/jneurosci.0964-14.2014] [Citation(s) in RCA: 75] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2014] [Revised: 07/30/2014] [Accepted: 08/02/2014] [Indexed: 12/23/2022] Open
Abstract
People with mild cognitive impairment (MCI) show a high risk to develop Alzheimer's disease (AD; Petersen et al., 2001). Nonetheless, there is a lack of studies about how functional connectivity patterns may distinguish between progressive (pMCI) and stable (sMCI) MCI patients. To examine whether there were differences in functional connectivity between groups, MEG eyes-closed recordings from 30 sMCI and 19 pMCI subjects were compared. The average conversion time of pMCI was 1 year, so they were considered as fast converters. To this end, functional connectivity in different frequency bands was assessed with phase locking value in source space. Then the significant differences between both groups were correlated with neuropsychological scores and entorhinal, parahippocampal, and hippocampal volumes. Both groups did not differ in age, gender, or educational level. pMCI patients obtained lower scores in episodic and semantic memory and also in executive functioning. At the structural level, there were no differences in hippocampal volume, although some were found in left entorhinal volume between both groups. Additionally, pMCI patients exhibit a higher synchronization in the alpha band between the right anterior cingulate and temporo-occipital regions than sMCI subjects. This hypersynchronization was inversely correlated with cognitive performance, both hippocampal volumes, and left entorhinal volume. The increase in phase synchronization between the right anterior cingulate and temporo-occipital areas may be predictive of conversion from MCI to AD.
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Affiliation(s)
- María Eugenía López
- Laboratories of Cognitive and Computational Neuroscience (Complutense University of Madrid-Universidad Politécnica of Madrid) and Department of Basic Psychology II, Complutense University of Madrid (UCM), 28040 Madrid, Spain,
| | - Ricardo Bruña
- Laboratories of Cognitive and Computational Neuroscience (Complutense University of Madrid-Universidad Politécnica of Madrid) and
| | - Sara Aurtenetxe
- Laboratories of Cognitive and Computational Neuroscience (Complutense University of Madrid-Universidad Politécnica of Madrid) and Department of Basic Psychology II, Complutense University of Madrid (UCM), 28040 Madrid, Spain
| | - José Ángel Pineda-Pardo
- Laboratories of Cognitive and Computational Neuroscience (Complutense University of Madrid-Universidad Politécnica of Madrid) and Laboratory of Neuroimaging (Universidad Politécnica de Madrid) (National Pedagogic University), Centre for Biomedical Technology (CTB), 28223 Madrid, Spain
| | | | - Juan Arrazola
- Radiology, San Carlos University Hospital, 28040 Madrid, Spain
| | - Ana Isabel Reinoso
- Centre for Prevention of Cognitive Impairment, Madrid Health, 28006, Madrid, Spain, and
| | - Pedro Montejo
- Centre for Prevention of Cognitive Impairment, Madrid Health, 28006, Madrid, Spain, and
| | - Ricardo Bajo
- Laboratories of Cognitive and Computational Neuroscience (Complutense University of Madrid-Universidad Politécnica of Madrid) and Department of Mathematics, International University of La Rioja (UNIR), 26006 Logroño, Spain
| | - Fernando Maestú
- Laboratories of Cognitive and Computational Neuroscience (Complutense University of Madrid-Universidad Politécnica of Madrid) and Department of Basic Psychology II, Complutense University of Madrid (UCM), 28040 Madrid, Spain
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9
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Affiliation(s)
- Malcolm Proudfoot
- Nuffield Department of Clinical Neurosciences, University of Oxford, UK Oxford Centre for Human Brain Activity, University of Oxford, UK
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity, University of Oxford, UK
| | - Anna C Nobre
- Oxford Centre for Human Brain Activity, University of Oxford, UK
| | - Martin R Turner
- Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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10
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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.
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Affiliation(s)
- Leonides Canuet
- EuroEspes Biomedical Research Center, Institute for CNS Disorders and Genomic Medicine, Corunna, Spain.
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11
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de Haan W, Mott K, van Straaten ECW, Scheltens P, Stam CJ. Activity dependent degeneration explains hub vulnerability in Alzheimer's disease. PLoS Comput Biol 2012; 8:e1002582. [PMID: 22915996 PMCID: PMC3420961 DOI: 10.1371/journal.pcbi.1002582] [Citation(s) in RCA: 268] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2011] [Accepted: 05/07/2012] [Indexed: 11/18/2022] Open
Abstract
Brain connectivity studies have revealed that highly connected 'hub' regions are particularly vulnerable to Alzheimer pathology: they show marked amyloid-β deposition at an early stage. Recently, excessive local neuronal activity has been shown to increase amyloid deposition. In this study we use a computational model to test the hypothesis that hub regions possess the highest level of activity and that hub vulnerability in Alzheimer's disease is due to this feature. Cortical brain regions were modeled as neural masses, each describing the average activity (spike density and spectral power) of a large number of interconnected excitatory and inhibitory neurons. The large-scale network consisted of 78 neural masses, connected according to a human DTI-based cortical topology. Spike density and spectral power were positively correlated with structural and functional node degrees, confirming the high activity of hub regions, also offering a possible explanation for high resting state Default Mode Network activity. 'Activity dependent degeneration' (ADD) was simulated by lowering synaptic strength as a function of the spike density of the main excitatory neurons, and compared to random degeneration. Resulting structural and functional network changes were assessed with graph theoretical analysis. Effects of ADD included oscillatory slowing, loss of spectral power and long-range synchronization, hub vulnerability, and disrupted functional network topology. Observed transient increases in spike density and functional connectivity match reports in Mild Cognitive Impairment (MCI) patients, and may not be compensatory but pathological. In conclusion, the assumption of excessive neuronal activity leading to degeneration provides a possible explanation for hub vulnerability in Alzheimer's disease, supported by the observed relation between connectivity and activity and the reproduction of several neurophysiologic hallmarks. The insight that neuronal activity might play a causal role in Alzheimer's disease can have implications for early detection and interventional strategies.
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Affiliation(s)
- Willem de Haan
- Department of Clinical Neurophysiology and MEG, VU University Medical Center, Amsterdam, The Netherlands.
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12
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Rottschy C, Langner R, Dogan I, Reetz K, Laird AR, Schulz JB, Fox PT, Eickhoff SB. Modelling neural correlates of working memory: a coordinate-based meta-analysis. Neuroimage 2012; 60:830-46. [PMID: 22178808 PMCID: PMC3288533 DOI: 10.1016/j.neuroimage.2011.11.050] [Citation(s) in RCA: 656] [Impact Index Per Article: 54.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2011] [Revised: 11/10/2011] [Accepted: 11/17/2011] [Indexed: 11/28/2022] Open
Abstract
Working memory subsumes the capability to memorize, retrieve and utilize information for a limited period of time which is essential to many human behaviours. Moreover, impairments of working memory functions may be found in nearly all neurological and psychiatric diseases. To examine what brain regions are commonly and differently active during various working memory tasks, we performed a coordinate-based meta-analysis over 189 fMRI experiments on healthy subjects. The main effect yielded a widespread bilateral fronto-parietal network. Further meta-analyses revealed that several regions were sensitive to specific task components, e.g. Broca's region was selectively active during verbal tasks or ventral and dorsal premotor cortex were preferentially involved in memory for object identity and location, respectively. Moreover, the lateral prefrontal cortex showed a division in a rostral and a caudal part based on differential involvement in task set and load effects. Nevertheless, a consistent but more restricted "core" network emerged from conjunctions across analyses of specific task designs and contrasts. This "core" network appears to comprise the quintessence of regions, which are necessary during working memory tasks. It may be argued that the core regions form a distributed executive network with potentially generalized functions for focussing on competing representations in the brain. The present study demonstrates that meta-analyses are a powerful tool to integrate the data of functional imaging studies on a (broader) psychological construct, probing the consistency across various paradigms as well as the differential effects of different experimental implementations.
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Affiliation(s)
- C Rottschy
- Department of Neurology, RWTH Aachen University, Aachen, Germany
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13
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Bajo R, Castellanos NP, Cuesta P, Aurtenetxe S, Garcia-Prieto J, Gil-Gregorio P, del-Pozo F, Maestu F. Differential Patterns of Connectivity in Progressive Mild Cognitive Impairment. Brain Connect 2012; 2:21-4. [DOI: 10.1089/brain.2011.0069] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022] Open
Affiliation(s)
- Ricardo Bajo
- Laboratory of Cognitive and Computational Neuroscience, Centre of Biomedical Technology (CTB), Complutense University of Madrid (UCM) and Technological University of Madrid (UPM), Madrid, Spain
- Universidad Internacional de La Rioja (UNIR), Logroño, La Rioja, Spain
| | - Nazareth P. Castellanos
- Laboratory of Cognitive and Computational Neuroscience, Centre of Biomedical Technology (CTB), Complutense University of Madrid (UCM) and Technological University of Madrid (UPM), Madrid, Spain
| | - Pablo Cuesta
- Laboratory of Cognitive and Computational Neuroscience, Centre of Biomedical Technology (CTB), Complutense University of Madrid (UCM) and Technological University of Madrid (UPM), Madrid, Spain
| | - Sara Aurtenetxe
- Laboratory of Cognitive and Computational Neuroscience, Centre of Biomedical Technology (CTB), Complutense University of Madrid (UCM) and Technological University of Madrid (UPM), Madrid, Spain
| | - Juan Garcia-Prieto
- Laboratory of Cognitive and Computational Neuroscience, Centre of Biomedical Technology (CTB), Complutense University of Madrid (UCM) and Technological University of Madrid (UPM), Madrid, Spain
| | - Pedro Gil-Gregorio
- Department of Geriatrics (Memory Unit), San Carlos University Hospital, Madrid, Spain
| | - Francisco del-Pozo
- Laboratory of Cognitive and Computational Neuroscience, Centre of Biomedical Technology (CTB), Complutense University of Madrid (UCM) and Technological University of Madrid (UPM), Madrid, Spain
| | - Fernando Maestu
- Laboratory of Cognitive and Computational Neuroscience, Centre of Biomedical Technology (CTB), Complutense University of Madrid (UCM) and Technological University of Madrid (UPM), Madrid, Spain
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