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      Combining aperiodic 1/f slopes and brain simulation: An EEG/MEG proxy marker of excitation/inhibition imbalance in Alzheimer's disease

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          Abstract

          INTRODUCTION

          Accumulation and interaction of amyloid‐beta (Aβ) and tau proteins during progression of Alzheimer's disease (AD) are shown to tilt neuronal circuits away from balanced excitation/inhibition (E/I). Current available techniques for noninvasive interrogation of E/I in the intact human brain, for example, magnetic resonance spectroscopy (MRS), are highly restrictive (i.e., limited spatial extent), have low temporal and spatial resolution and suffer from the limited ability to distinguish accurately between different neurotransmitters complicating its interpretation. As such, these methods alone offer an incomplete explanation of E/I. Recently, the aperiodic component of neural power spectrum, often referred to in the literature as the ‘1/f slope’, has been described as a promising and scalable biomarker that can track disruptions in E/I potentially underlying a spectrum of clinical conditions, such as autism, schizophrenia, or epilepsy, as well as developmental E/I changes as seen in aging.

          METHODS

          Using 1/f slopes from resting‐state spectral data and computational modeling, we developed a new method for inferring E/I alterations in AD.

          RESULTS

          We tested our method on recent freely and publicly available electroencephalography (EEG) and magnetoencephalography (MEG) datasets of patients with AD or prodromal disease and demonstrated the method's potential for uncovering regional patterns of abnormal excitatory and inhibitory parameters.

          DISCUSSION

          Our results provide a general framework for investigating circuit‐level disorders in AD and developing therapeutic interventions that aim to restore the balance between excitation and inhibition.

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          Most cited references84

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          The diagnosis of mild cognitive impairment due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease

          The National Institute on Aging and the Alzheimer's Association charged a workgroup with the task of developing criteria for the symptomatic predementia phase of Alzheimer's disease (AD), referred to in this article as mild cognitive impairment due to AD. The workgroup developed the following two sets of criteria: (1) core clinical criteria that could be used by healthcare providers without access to advanced imaging techniques or cerebrospinal fluid analysis, and (2) research criteria that could be used in clinical research settings, including clinical trials. The second set of criteria incorporate the use of biomarkers based on imaging and cerebrospinal fluid measures. The final set of criteria for mild cognitive impairment due to AD has four levels of certainty, depending on the presence and nature of the biomarker findings. Considerable work is needed to validate the criteria that use biomarkers and to standardize biomarker analysis for use in community settings. Copyright © 2011 The Alzheimer's Association. All rights reserved.
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            Clinical diagnosis of Alzheimer's disease: Report of the NINCDS-ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease

            Neurology, 34(7), 939-939
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              Parameterizing neural power spectra into periodic and aperiodic components

              Electrophysiological signals exhibit both periodic and aperiodic properties. Periodic oscillations have been linked to numerous physiological, cognitive, behavioral and disease states. Emerging evidence demonstrates that the aperiodic component has putative physiological interpretations and that it dynamically changes with age, task demands and cognitive states. Electrophysiological neural activity is typically analyzed using canonically defined frequency bands, without consideration of the aperiodic (1/f-like) component. We show that standard analytic approaches can conflate periodic parameters (center frequency, power, bandwidth) with aperiodic ones (offset, exponent), compromising physiological interpretations. To overcome these limitations, we introduce an algorithm to parameterize neural power spectra as a combination of an aperiodic component and putative periodic oscillatory peaks. This algorithm requires no a priori specification of frequency bands. We validate this algorithm on simulated data, and demonstrate how it can be used in applications ranging from analyzing age-related changes in working memory to large-scale data exploration and analysis.
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                Author and article information

                Contributors
                pablomc@ugr.es
                Journal
                Alzheimers Dement (Amst)
                Alzheimers Dement (Amst)
                10.1002/(ISSN)2352-8729
                DAD2
                Alzheimer's & Dementia : Diagnosis, Assessment & Disease Monitoring
                John Wiley and Sons Inc. (Hoboken )
                2352-8729
                01 September 2023
                Jul-Sep 2023
                : 15
                : 3 ( doiID: 10.1002/dad2.v15.3 )
                : e12477
                Affiliations
                [ 1 ] Department of Computer Engineering Automation and Robotics University of Granada Granada Spain
                [ 2 ] Research Centre for Information and Communications Technologies (CITIC) University of Granada Granada Spain
                [ 3 ] Department of Signal Theory Telematics and Communications University of Granada Granada Spain
                Author notes
                [*] [* ] Correspondence

                Pablo Martínez‐Cañada, Research Centre for Information and Communications Technologies (CITIC), University of Granada, Periodista Rafael Gómez Montero 2, Granada, 18014, Spain.

                Email: pablomc@ 123456ugr.es

                Author information
                https://orcid.org/0000-0003-2634-5229
                Article
                DAD212477
                10.1002/dad2.12477
                10474329
                37662693
                f22b1506-e8cd-4b75-939f-6f017811201e
                © 2023 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 27 July 2023
                : 30 January 2023
                : 08 August 2023
                Page count
                Figures: 3, Tables: 0, Pages: 12, Words: 8354
                Funding
                Funded by: Ministerio de Ciencia e Innovación, Gobierno de España/Agencia Estatal de Investigación/European Regional Development Fund
                Award ID: PID2022‐137461NB‐C31
                Award ID: PID2022‐139055OA‐I00
                Award ID: PID2021‐128529OA‐I00
                Funded by: Consejería de Universidad, Investigación e Innovación, Junta de Andalucía
                Award ID: PROYEXCEL_00084
                Funded by: Universidad de Granada , doi 10.13039/501100006393;
                Award ID: PPJIA2022.33
                Award ID: PP2022.PP.33
                Award ID: PP2021.PP‐28
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                July‐September 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.3 mode:remove_FC converted:02.09.2023

                1/f slope,alzheimer's disease,eeg,excitation‐inhibition,meg,network of spiking neurons

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