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García-Colomo A, Nebreda A, Carrasco-Gómez M, de Frutos-Lucas J, Ramirez-Toraño F, Spuch C, Comis-Tuche M, Bruña R, Alfonsín S, Maestú F. Longitudinal changes in the functional connectivity of individuals at risk of Alzheimer's disease. GeroScience 2024; 46:2989-3003. [PMID: 38172488 PMCID: PMC11009204 DOI: 10.1007/s11357-023-01036-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024] Open
Abstract
First-degree relatives of Alzheimer's disease patients constitute a key population in the search for early markers. Our group identified functional connectivity differences between cognitively unimpaired individuals with and without a family history. In this unprecedented follow-up study, we examine whether family history is associated with a longitudinal increase in the functional connectivity of those regions. Moreover, this is the first work to correlate electrophysiological measures with plasma p-tau231 levels, a known pathology marker, to interpret the nature of the change. We evaluated 69 cognitively unimpaired individuals with a family history of Alzheimer's disease and 28 without, at two different time points, approximately 3 years apart, including resting state magnetoencephalography recordings and plasma p-tau231 determinations. Functional connectivity changes in both precunei and left anterior cingulate cortex in the high-alpha band were studied using non-parametric cluster-based permutation tests. Connectivity values were correlated with p-tau231 levels. Three clusters emerged in individuals with family history, exhibiting a longitudinal increase of connectivity. Notably, the clusters for both precunei bore a striking resemblance to those found in previous cross-sectional studies. The connectivity values at follow-up and the change in connectivity in the left precuneus cluster showed significant positive correlations with p-tau231. This study consolidates the use of electrophysiology, in combination with plasma biomarkers, to monitor healthy individuals at risk of Alzheimer's disease and emphasizes the value of combining noninvasive markers to understand the underlying mechanisms and track disease progression. This could facilitate the design of more effective intervention strategies and accurate progression assessment tools.
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Affiliation(s)
- Alejandra García-Colomo
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, 28223, Madrid, Spain.
- Department of Experimental Psychology, Cognitive Psychology and Speech & Language Therapy, Complutense University of Madrid, 28223, Madrid, Spain.
| | - Alberto Nebreda
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, 28223, Madrid, Spain
- Department of Experimental Psychology, Cognitive Psychology and Speech & Language Therapy, Complutense University of Madrid, 28223, Madrid, Spain
| | - Martín Carrasco-Gómez
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, 28223, Madrid, Spain.
- Department of Electronic Engineering, Universidad Politécnica de Madrid, 28040, Madrid, Spain.
| | - Jaisalmer de Frutos-Lucas
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, 28223, Madrid, Spain
- Department of Experimental Psychology, Cognitive Psychology and Speech & Language Therapy, Complutense University of Madrid, 28223, Madrid, Spain
| | - Federico Ramirez-Toraño
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, 28223, Madrid, Spain
- Department of Experimental Psychology, Cognitive Psychology and Speech & Language Therapy, Complutense University of Madrid, 28223, Madrid, Spain
| | - Carlos Spuch
- Translational Neuroscience Research Group, Galicia Sur Health Research Institute (IIS-Galicia Sur), SERGAS-UVIGO, CIBERSAM, Vigo, Spain
| | - María Comis-Tuche
- Translational Neuroscience Research Group, Galicia Sur Health Research Institute (IIS-Galicia Sur), SERGAS-UVIGO, CIBERSAM, Vigo, Spain
| | - Ricardo Bruña
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, 28223, Madrid, Spain
- Health Research Institute of the Hospital Clínico San Carlo.s (IdISSC), 28240, Madrid, Spain
- Department of Radiology, Universidad Complutense de Madrid, 28240, Madrid, Spain
| | - Soraya Alfonsín
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, 28223, Madrid, Spain
- Department of Experimental Psychology, Cognitive Psychology and Speech & Language Therapy, Complutense University of Madrid, 28223, Madrid, Spain
| | - Fernando Maestú
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, 28223, Madrid, Spain
- Department of Experimental Psychology, Cognitive Psychology and Speech & Language Therapy, Complutense University of Madrid, 28223, Madrid, Spain
- Health Research Institute of the Hospital Clínico San Carlo.s (IdISSC), 28240, Madrid, Spain
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Świder K, Moratti S, Bruña R. How to make calibration less painful-A proposition for an automatic, reliable and time-efficient procedure. Psychophysiology 2024; 61:e14505. [PMID: 38229548 DOI: 10.1111/psyp.14505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/30/2023] [Accepted: 12/04/2023] [Indexed: 01/18/2024]
Abstract
In behavioral and neurophysiological pain studies, multiple types of calibration methods are used to quantify the individual pain sensation stimuli. Often, studies lack a detailed calibration procedure description, data linearity, and quality quantification and omit required control for sex pain differences. This hampers study repetition and interexperimental comparisons. Moreover, typical calibration procedures require a high number of stimulations, which may cause discomfort and stimuli habituation among participants. To overcome those shortcomings, we present an automatic calibration procedure with a novel stimuli estimation method for intraepidermal stimulation. We provide an in-depth data analysis of the collected self-reports from 70 healthy volunteers (37 males) and propose a method based on a dynamic truncated linear regression model (tLRM). We compare its estimates for the sensation (t) and pain (T) thresholds and mid-pain stimulation (MP), with those calculated using traditional estimation methods and standard linear regression models. Compared to the other methods, tLRM exhibits higher R2 and requires 36% fewer stimuli applications and has significantly higher t intensity and lower T and MP intensities. Regarding sex differences, t and T were found to be lower for females compared to males, regardless of the estimation method. The proposed tLRM method quantifies the calibration procedure quality, minimizes its duration and invasiveness, and provides validation of linearity between stimuli intensity and subjective scores, making it an enabling technique for further studies. Moreover, our results highlight the importance of control for sex in pain studies.
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Affiliation(s)
- Karolina Świder
- Department of Experimental Psychology, Psychology Faculty, Universidad Complutense de Madrid, Madrid, Spain
- Centre for Cognitive and Computational Neuroscience (C3N), Universidad Complutense de Madrid, Madrid, Spain
| | - Stephan Moratti
- Department of Experimental Psychology, Psychology Faculty, Universidad Complutense de Madrid, Madrid, Spain
- Centre for Cognitive and Computational Neuroscience (C3N), Universidad Complutense de Madrid, Madrid, Spain
| | - Ricardo Bruña
- Centre for Cognitive and Computational Neuroscience (C3N), Universidad Complutense de Madrid, Madrid, Spain
- Department of Radiology, Universidad Complutense de Madrid, IdISSC, Madrid, Spain
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3
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Fernández A, Cuesta P, Marcos A, Montenegro-Peña M, Yus M, Rodríguez-Rojo IC, Bruña R, Maestú F, López ME. Sex differences in the progression to Alzheimer's disease: a combination of functional and structural markers. GeroScience 2024; 46:2619-2640. [PMID: 38105400 PMCID: PMC10828170 DOI: 10.1007/s11357-023-01020-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 11/14/2023] [Indexed: 12/19/2023] Open
Abstract
Mild cognitive impairment (MCI) has been frequently interpreted as a transitional phase between healthy cognitive aging and dementia, particularly of the Alzheimer's disease (AD) type. Of note, few studies explored that transition from a multifactorial perspective, taking into consideration the effect of basic factors such as biological sex. In the present study 96 subjects with MCI (37 males and 59 females) were followed-up and divided into two subgroups according to their clinical outcome: "progressive" MCI (pMCI = 41), if they fulfilled the diagnostic criteria for AD at the end of follow-up; and "stable" MCI (sMCI = 55), if they remained with the initial diagnosis. Different markers were combined to characterize sex differences between groups, including magnetoencephalography recordings, cognitive performance, and brain volumes derived from magnetic resonance imaging. Results indicated that the pMCI group exhibited higher low-frequency activity, lower scores in neuropsychological tests and reduced brain volumes than the sMCI group, being these measures significantly correlated. When sex was considered, results revealed that this pattern was mainly due to the influence of the females' sample. Overall, females exhibited lower cognitive scores and reduced brain volumes. More interestingly, females in the pMCI group showed an increased theta activity that correlated with a more abrupt reduction of cognitive and volumetric scores as compared with females in the sMCI group and with males in the pMCI group. These findings suggest that females' brains might be more vulnerable to the effects of AD pathology, since regardless of age, they showed signs of more pronounced deterioration than males.
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Affiliation(s)
- Alberto Fernández
- Department of Legal Medicine, Psychiatry and Pathology, Universidad Complutense de Madrid, Madrid, Spain
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
| | - Pablo Cuesta
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Department of Radiology, Rehabilitation and Physiotherapy, Universidad Complutense de Madrid, Madrid, Spain
| | - Alberto Marcos
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Neurology Department, Hospital Clínico San Carlos, Madrid, Spain
| | - Mercedes Montenegro-Peña
- Centre for the Prevention of Cognitive Impairment, Madrid Salud, Madrid City Council, Madrid, Spain
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain
| | - Miguel Yus
- Radiology Department, Hospital Clínico San Carlos, Madrid, Spain
| | - Inmaculada Concepción Rodríguez-Rojo
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Department of Nursing and Psysiotherapy, Universidad de Alcalá, Madrid, Spain
| | - Ricardo Bruña
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Department of Radiology, Rehabilitation and Physiotherapy, Universidad Complutense de Madrid, Madrid, Spain
| | - Fernando Maestú
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain
| | - María Eugenia López
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain.
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain.
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain.
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4
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Haraldsen IH, Hatlestad-Hall C, Marra C, Renvall H, Maestú F, Acosta-Hernández J, Alfonsin S, Andersson V, Anand A, Ayllón V, Babic A, Belhadi A, Birck C, Bruña R, Caraglia N, Carrarini C, Christensen E, Cicchetti A, Daugbjerg S, Di Bidino R, Diaz-Ponce A, Drews A, Giuffrè GM, Georges J, Gil-Gregorio P, Gove D, Govers TM, Hallock H, Hietanen M, Holmen L, Hotta J, Kaski S, Khadka R, Kinnunen AS, Koivisto AM, Kulashekhar S, Larsen D, Liljeström M, Lind PG, Marcos Dolado A, Marshall S, Merz S, Miraglia F, Montonen J, Mäntynen V, Øksengård AR, Olazarán J, Paajanen T, Peña JM, Peña L, Peniche DL, Perez AS, Radwan M, Ramírez-Toraño F, Rodríguez-Pedrero A, Saarinen T, Salas-Carrillo M, Salmelin R, Sousa S, Suyuthi A, Toft M, Toharia P, Tveitstøl T, Tveter M, Upreti R, Vermeulen RJ, Vecchio F, Yazidi A, Rossini PM. Intelligent digital tools for screening of brain connectivity and dementia risk estimation in people affected by mild cognitive impairment: the AI-Mind clinical study protocol. Front Neurorobot 2024; 17:1289406. [PMID: 38250599 PMCID: PMC10796757 DOI: 10.3389/fnbot.2023.1289406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 12/12/2023] [Indexed: 01/23/2024] Open
Abstract
More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression. The European patient experience often involves a long journey from the initial signs of MCI to the eventual diagnosis of dementia. The trajectory is far from ideal. Here, we introduce the AI-Mind project, a pioneering initiative with an innovative approach to early risk assessment through the implementation of advanced artificial intelligence (AI) on multimodal data. The cutting-edge AI-based tools developed in the project aim not only to accelerate the diagnostic process but also to deliver highly accurate predictions regarding an individual's risk of developing dementia when prevention and intervention may still be possible. AI-Mind is a European Research and Innovation Action (RIA H2020-SC1-BHC-06-2020, No. 964220) financed between 2021 and 2026. First, the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. Second, the AI-Mind Predictor predicts dementia risk using data from the Connector, enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables. AI-Mind is integrated within a network of major European initiatives, including The Virtual Brain, The Virtual Epileptic Patient, and EBRAINS AISBL service for sensitive data, HealthDataCloud, where big patient data are generated for advancing digital and virtual twin technology development. AI-Mind's innovation lies not only in its early prediction of dementia risk, but it also enables a virtual laboratory scenario for hypothesis-driven personalized intervention research. This article introduces the background of the AI-Mind project and its clinical study protocol, setting the stage for future scientific contributions.
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Affiliation(s)
| | | | - Camillo Marra
- Memory Clinic, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Department of Neuroscience, Catholic University of the Sacred Heart, Rome, Italy
| | - Hanna Renvall
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - Fernando Maestú
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Department of Experimental Psychology, Cognitive Psychology and Speech and Language Therapy, Universidad Complutense de Madrid, Pozuelo de Alarcón, Spain
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
| | | | - Soraya Alfonsin
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Department of Experimental Psychology, Cognitive Psychology and Speech and Language Therapy, Universidad Complutense de Madrid, Pozuelo de Alarcón, Spain
| | | | - Abhilash Anand
- Performance and Assurance Solutions, Digital Solutions, DNV, Oslo, Norway
| | | | - Aleksandar Babic
- Healthcare Programme, Group Research and Development, DNV, Oslo, Norway
| | - Asma Belhadi
- Department of Computer Science, OsloMet—Oslo Metropolitan University, Oslo, Norway
- NordSTAR—Nordic Center for Sustainable and Trustworthy AI Research, Oslo, Norway
| | | | - Ricardo Bruña
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Department of Radiology, Universidad Complutense de Madrid, Madrid, Spain
| | - Naike Caraglia
- Memory Clinic, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Claudia Carrarini
- Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Rome, Italy
| | | | - Americo Cicchetti
- The Graduate School of Health Economics and Management (ALTEMS), Catholic University of the Sacred Heart, Rome, Italy
| | - Signe Daugbjerg
- The Graduate School of Health Economics and Management (ALTEMS), Catholic University of the Sacred Heart, Rome, Italy
| | - Rossella Di Bidino
- The Graduate School of Health Economics and Management (ALTEMS), Catholic University of the Sacred Heart, Rome, Italy
| | | | - Ainar Drews
- IT Department, University of Oslo, Oslo, Norway
| | - Guido Maria Giuffrè
- Memory Clinic, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Department of Neuroscience, Catholic University of the Sacred Heart, Rome, Italy
| | | | - Pedro Gil-Gregorio
- Department of Geriatric Medicine, Hospital Universitario Clínico San Carlos, Madrid, Spain
- Department of Geriatrics, Fundación para la Investigación Biomédica del Hospital Clínico San Carlos, Madrid, Spain
| | | | - Tim M. Govers
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands
| | - Harry Hallock
- Healthcare Programme, Group Research and Development, DNV, Oslo, Norway
| | - Marja Hietanen
- Division of Neuropsychology, HUS Neurocenter, Helsinki University Hospital and Helsinki University, Helsinki, Finland
| | - Lone Holmen
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Jaakko Hotta
- Department of Neurology, Helsinki University Hospital and Clinical Neurosciences, Neurology, University of Helsinki, Helsinki, Finland
| | - Samuel Kaski
- Department of Computer Science, Helsinki Institute of Information Technology, Aalto University, Helsinki, Finland
- Department of Computer Science, University of Manchester, Manchester, United Kingdom
| | - Rabindra Khadka
- Department of Computer Science, OsloMet—Oslo Metropolitan University, Oslo, Norway
- NordSTAR—Nordic Center for Sustainable and Trustworthy AI Research, Oslo, Norway
| | - Antti S. Kinnunen
- BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - Anne M. Koivisto
- Department of Neurology, Helsinki University Hospital and Clinical Neurosciences, Neurology, University of Helsinki, Helsinki, Finland
- Department of Neurosciences, University of Helsinki, Helsinki, Finland
- Neurocenter, Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Shrikanth Kulashekhar
- BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - Denis Larsen
- Department of Computer Science, OsloMet—Oslo Metropolitan University, Oslo, Norway
- NordSTAR—Nordic Center for Sustainable and Trustworthy AI Research, Oslo, Norway
| | - Mia Liljeström
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - Pedro G. Lind
- Department of Computer Science, OsloMet—Oslo Metropolitan University, Oslo, Norway
- NordSTAR—Nordic Center for Sustainable and Trustworthy AI Research, Oslo, Norway
| | - Alberto Marcos Dolado
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Neurology Department, Hospital Universitario Clínico San Carlos, Madrid, Spain
| | - Serena Marshall
- Healthcare Programme, Group Research and Development, DNV, Oslo, Norway
| | - Susanne Merz
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland
| | - Francesca Miraglia
- Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Rome, Italy
| | - Juha Montonen
- BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - Ville Mäntynen
- BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | | | - Javier Olazarán
- Neurology Service, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Teemu Paajanen
- Finnish Institute of Occupational Health, Helsinki, Finland
| | | | | | | | - Ana S. Perez
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Mohamed Radwan
- Department of Computer Science, OsloMet—Oslo Metropolitan University, Oslo, Norway
- NordSTAR—Nordic Center for Sustainable and Trustworthy AI Research, Oslo, Norway
| | - Federico Ramírez-Toraño
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Department of Experimental Psychology, Cognitive Psychology and Speech and Language Therapy, Universidad Complutense de Madrid, Pozuelo de Alarcón, Spain
| | - Andrea Rodríguez-Pedrero
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Department of Experimental Psychology, Cognitive Psychology and Speech and Language Therapy, Universidad Complutense de Madrid, Pozuelo de Alarcón, Spain
| | - Timo Saarinen
- BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - Mario Salas-Carrillo
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Memory Unit, Department of Geriatrics, Hospital Clínico San Carlos, Madrid, Spain
| | - Riitta Salmelin
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland
| | - Sonia Sousa
- School of Digital Technologies, Tallinn University, Tallinn, Estonia
| | - Abdillah Suyuthi
- Performance and Assurance Solutions, Digital Solutions, DNV, Oslo, Norway
| | - Mathias Toft
- Department of Neurology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Pablo Toharia
- Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
| | | | - Mats Tveter
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Ramesh Upreti
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Robin J. Vermeulen
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands
| | - Fabrizio Vecchio
- Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Como, Italy
| | - Anis Yazidi
- Department of Computer Science, OsloMet—Oslo Metropolitan University, Oslo, Norway
- NordSTAR—Nordic Center for Sustainable and Trustworthy AI Research, Oslo, Norway
| | - Paolo Maria Rossini
- Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Rome, Italy
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Tamburro G, Fiedler P, De Fano A, Raeisi K, Khazaei M, Vaquero L, Bruña R, Oppermann H, Bertollo M, Filho E, Zappasodi F, Comani S. An ecological study protocol for the multimodal investigation of the neurophysiological underpinnings of dyadic joint action. Front Hum Neurosci 2023; 17:1305331. [PMID: 38125713 PMCID: PMC10730734 DOI: 10.3389/fnhum.2023.1305331] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 11/15/2023] [Indexed: 12/23/2023] Open
Abstract
A novel multimodal experimental setup and dyadic study protocol were designed to investigate the neurophysiological underpinnings of joint action through the synchronous acquisition of EEG, ECG, EMG, respiration and kinematic data from two individuals engaged in ecologic and naturalistic cooperative and competitive joint actions involving face-to-face real-time and real-space coordinated full body movements. Such studies are still missing because of difficulties encountered in recording reliable neurophysiological signals during gross body movements, in synchronizing multiple devices, and in defining suitable study protocols. The multimodal experimental setup includes the synchronous recording of EEG, ECG, EMG, respiration and kinematic signals of both individuals via two EEG amplifiers and a motion capture system that are synchronized via a single-board microcomputer and custom Python scripts. EEG is recorded using new dry sports electrode caps. The novel study protocol is designed to best exploit the multimodal data acquisitions. Table tennis is the dyadic motor task: it allows naturalistic and face-to-face interpersonal interactions, free in-time and in-space full body movement coordination, cooperative and competitive joint actions, and two task difficulty levels to mimic changing external conditions. Recording conditions-including minimum table tennis rally duration, sampling rate of kinematic data, total duration of neurophysiological recordings-were defined according to the requirements of a multilevel analytical approach including a neural level (hyperbrain functional connectivity, Graph Theoretical measures and Microstate analysis), a cognitive-behavioral level (integrated analysis of neural and kinematic data), and a social level (extending Network Physiology to neurophysiological data recorded from two interacting individuals). Four practical tests for table tennis skills were defined to select the study population, permitting to skill-match the dyad members and to form two groups of higher and lower skilled dyads to explore the influence of skill level on joint action performance. Psychometric instruments are included to assess personality traits and support interpretation of results. Studying joint action with our proposed protocol can advance the understanding of the neurophysiological mechanisms sustaining daily life joint actions and could help defining systems to predict cooperative or competitive behaviors before being overtly expressed, particularly useful in real-life contexts where social behavior is a main feature.
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Affiliation(s)
- Gabriella Tamburro
- Department of Neuroscience Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
- Behavioral Imaging and Neural Dynamics Center, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
| | - Patrique Fiedler
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany
| | - Antonio De Fano
- Department of Neuroscience Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
- Behavioral Imaging and Neural Dynamics Center, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
| | - Khadijeh Raeisi
- Department of Neuroscience Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
| | - Mohammad Khazaei
- Department of Neuroscience Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
| | - Lucia Vaquero
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Department of Experimental Pschology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain
| | - Ricardo Bruña
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Department of Radiology, Universidad Complutense de Madrid, IdISSC, Madrid, Spain
| | - Hannes Oppermann
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany
| | - Maurizio Bertollo
- Behavioral Imaging and Neural Dynamics Center, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
- Department of Medicine and Sciences of Aging, “University G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
| | - Edson Filho
- Wheelock College of Education and Human Development, Boston University, Boston, MA, United States
| | - Filippo Zappasodi
- Department of Neuroscience Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
- Behavioral Imaging and Neural Dynamics Center, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
| | - Silvia Comani
- Department of Neuroscience Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
- Behavioral Imaging and Neural Dynamics Center, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
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6
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Aoki Y, Kazui H, Pascual-Marqui RD, Bruña R, Yoshiyama K, Wada T, Kanemoto H, Suzuki Y, Suehiro T, Satake Y, Yamakawa M, Hata M, Canuet L, Ishii R, Iwase M, Ikeda M. Normalized Power Variance: A new Field Orthogonal to Power in EEG Analysis. Clin EEG Neurosci 2023; 54:611-619. [PMID: 35345930 DOI: 10.1177/15500594221088736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
To date, electroencephalogram (EEG) has been used in the diagnosis of epilepsy, dementia, and disturbance of consciousness via the inspection of EEG waves and identification of abnormal electrical discharges and slowing of basic waves. In addition, EEG power analysis combined with a source estimation method like exact-low-resolution-brain-electromagnetic-tomography (eLORETA), which calculates the power of cortical electrical activity from EEG data, has been widely used to investigate cortical electrical activity in neuropsychiatric diseases. However, the recently developed field of mathematics "information geometry" indicates that EEG has another dimension orthogonal to power dimension - that of normalized power variance (NPV). In addition, by introducing the idea of information geometry, a significantly faster convergent estimator of NPV was obtained. Research into this NPV coordinate has been limited thus far. In this study, we applied this NPV analysis of eLORETA to idiopathic normal pressure hydrocephalus (iNPH) patients prior to a cerebrospinal fluid (CSF) shunt operation, where traditional power analysis could not detect any difference associated with CSF shunt operation outcome. Our NPV analysis of eLORETA detected significantly higher NPV values at the high convexity area in the beta frequency band between 17 shunt responders and 19 non-responders. Considering our present and past research findings about NPV, we also discuss the advantage of this application of NPV representing a sensitive early warning signal of cortical impairment. Overall, our findings demonstrated that EEG has another dimension - that of NPV, which contains a lot of information about cortical electrical activity that can be useful in clinical practice.
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Affiliation(s)
- Yasunori Aoki
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
- Department of Psychiatry, Nippon Life Hospital, Osaka, Japan
| | - Hiroaki Kazui
- Department of Neuropsychiatry, Kochi Medical School, Kochi University, Kochi, Japan
| | - Roberto D Pascual-Marqui
- The KEY Institute for Brain-Mind Research, University Hospital of Psychiatry, Zurich, Switzerland
| | - Ricardo Bruña
- UCM-UPM Centre for Biomedical Technology, Laboratory of Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Electrical Engineering, La Laguna University, Tenerife, Spain
| | - Kenji Yoshiyama
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Tamiki Wada
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Hideki Kanemoto
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Yukiko Suzuki
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Takashi Suehiro
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Yuto Satake
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Maki Yamakawa
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Masahiro Hata
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Leonides Canuet
- Neurology department, Nuestra Senora del Rosario hospital, Madrid, Spain
| | - Ryouhei Ishii
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
- Graduate School of Comprehensive Rehabilitation, Osaka Prefecture University, Osaka, Japan
| | - Masao Iwase
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Manabu Ikeda
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
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7
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Bruña R, Fuggetta G, Pereda E. One Definition to Join Them All: The N-Spherical Solution for the EEG Lead Field. Sensors (Basel) 2023; 23:8136. [PMID: 37836967 PMCID: PMC10575356 DOI: 10.3390/s23198136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 09/07/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023]
Abstract
Albeit its simplicity, the concentric spheres head model is widely used in EEG. The reason behind this is its simple mathematical definition, which allows for the calculation of lead fields with negligible computational cost, for example, for iterative approaches. Nevertheless, the literature shows contradictory formulations for the electrical solution of this head model. In this work, we study several different definitions for the electrical lead field of a four concentric spheres conduction model, finding that their results are contradictory. A thorough exploration of the mathematics used to build these formulations, provided in the original works, allowed for the identification of errors in some of the formulae, which proved to be the reason for the discrepancies. Moreover, this mathematical review revealed the iterative nature of some of these formulations, which allowed us to develop a formulation to solve the lead field in a head model built from an arbitrary number of concentric, homogeneous, and isotropic spheres.
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Affiliation(s)
- Ricardo Bruña
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid (UCM), 28040 Madrid, Spain;
- Department of Radiology, Rehabilitation and Physical Therapy, Universidad Complutense de Madrid (UCM), IdISSC, 28040 Madrid, Spain
| | - Giorgio Fuggetta
- School of Psychology, University of Roehampton, London SW15 4JD, UK;
| | - Ernesto Pereda
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid (UCM), 28040 Madrid, Spain;
- Department of Industrial Engineering, Institute of Neuroscience & Institute of Biomedical Technology, Universidad de La Laguna, 38200 Tenerife, Spain
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8
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Cuesta P, Bruña R, Shah E, Laohathai C, Garcia-Tarodo S, Funke M, Von Allmen G, Maestú F. An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity application. Brain Commun 2023; 5:fcad168. [PMID: 37274829 PMCID: PMC10236945 DOI: 10.1093/braincomms/fcad168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 01/24/2023] [Accepted: 05/23/2023] [Indexed: 06/07/2023] Open
Abstract
Epilepsy surgery continues to be a recommended treatment for intractable (medication-resistant) epilepsy; however, 30-70% of epilepsy surgery patients can continue to have seizures. Surgical failures are often associated with incomplete resection or inaccurate localization of the epileptogenic zone. This retrospective study aims to improve surgical outcome through in silico testing of surgical hypotheses through a personalized computational neurosurgery model created from individualized patient's magnetoencephalography recording and MRI. The framework assesses the extent of the epileptic network and evaluates underlying spike dynamics, resulting in identification of one single brain volume as a candidate for resection. Dynamic-locked networks were utilized for virtual cortical resection. This in silico protocol was tested in a cohort of 24 paediatric patients with focal drug-resistant epilepsy who underwent epilepsy surgery. Of 24 patients who were included in the analysis, 79% (19 of 24) of the models agreed with the patient's clinical surgery outcome and 21% (5 of 24) were considered as model failures (accuracy 0.79, sensitivity 0.77, specificity 0.82). Patients with unsuccessful surgery outcome typically showed a model cluster outside of the resected cavity, while those with successful surgery showed the cluster model within the cavity. Two of the model failures showed the cluster in the vicinity of the resected tissue and either a functional disconnection or lack of precision of the magnetoencephalography-MRI overlapping could explain the results. Two other cases were seizure free for 1 year but developed late recurrence. This is the first study that provides in silico personalized protocol for epilepsy surgery planning using magnetoencephalography spike network analysis. This model could provide complementary information to the traditional pre-surgical assessment methods and increase the proportion of patients achieving seizure-free outcome from surgery.
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Affiliation(s)
- Pablo Cuesta
- Correspondence to: Pablo Cuesta Pza. Ramón y Cajal, s/n. Ciudad Universitaria 28040 Madrid, Spain E-mail:
| | - Ricardo Bruña
- Department of Radiology, Rehabilitation and Physiotherapy, Universidad Complutense de Madrid, Madrid, 28040, Spain
- Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, 28040, Spain
| | - Ekta Shah
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | | | - Stephanie Garcia-Tarodo
- Département de la femme, de l'enfant et de l'adolescent, Hôpital des Enfants - Hôpitaux Universitaires de Genève, Geneva, 1211 Genève 14, Switzerland
| | - Michael Funke
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Gretchen Von Allmen
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Fernando Maestú
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, 28040, Spain
- Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, 28040, Spain
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, 28040, Spain
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9
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González D, Bruña R, Martínez-Castrillo JC, López JM, de Arcas G. First Longitudinal Study Using Binaural Beats on Parkinson Disease. Int J Neural Syst 2023; 33:2350027. [PMID: 37085963 DOI: 10.1142/s0129065723500272] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
This paper describes a longitudinal study to analyze the effects of acoustic stimulation with Binaural Beats (BBs) at 14[Formula: see text]Hz (beta band) in patients with Parkinson's Disease (PD). Participants ([Formula: see text], age [Formula: see text], stage [Formula: see text] Hoehn and Yahr scale) listened to binaural stimulation for 10[Formula: see text]min a day, 3 days a week, during six months and were assessed 3 times during this period using electroencephalography (EEG), cognitive (PD-CRS), quality of life (PDQ-39) and wearing-off (WOQ-19) tests. During each assessment (basal, and after 3 and 6 months), the relative power in theta band was analyzed before, during and after the stimulation. Focusing the analysis on the motor cortex, the results obtained have confirmed the initial hypothesis for the first session, but they have shown a habituation effect which decreases its efficiency with time. Also, different reactions have been detected among individuals, with some reacting as expected from the beginning, while others would react in an opposite way at the beginning but they have shown afterwards a tendency towards the expected outcome. Anyhow, the relative power of the theta band was reduced between the first and the last session for more than half of the participants, although with very different values. Subtle changes have also been observed in some items of the PD-CRS, PDQ-39 and WOQ-19 tests.
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Affiliation(s)
- David González
- Instrumentation and Applied Acoustic Research Group (I2A2), Universidad Politécnica de Madrid (UPM), Campus Sur UPM - Carretera de Valencia km 7, 28031 Madrid, Spain
| | - Ricardo Bruña
- Center for Cognitive and Computational Neuroscience (C3N), Facultad de Psicología, Universidad Complutense de Madrid, Campus de Somosaguas, 28223 Pozuelo de Alarcón, Madrid, Spain
- Department of Radiology, Rehabilitation and Physical Therapy, Facultad de Medicina, Universidad Complutense de Madrid, 28040 Madrid, Spain
- Instituto de Investigación Sanitaria San Carlos (IdISSC), Hospital Clínico San Carlos, Calle del Prof Martín Lagos, s/n, 28040 Madrid, Spain
| | - Juan Carlos Martínez-Castrillo
- Unidad de Trastornos del Movimiento-CSUR, Servicio de Neurología, IRYCIS. Hospital Universitario Ramón y Cajal. Carretera de Colmenar Viejo, Km 9,1 CP 28034 Madrid, Spain
| | - Juan Manuel López
- Instrumentation and Applied Acoustic Research Group (I2A2), Universidad Politécnica de Madrid (UPM), Campus Sur UPM - Carretera de Valencia km 7, 28031 Madrid, Spain
| | - Guillermo de Arcas
- Instrumentation and Applied Acoustic Research Group (I2A2), Universidad Politécnica de Madrid (UPM), Campus Sur UPM - Carretera de Valencia km 7, 28031 Madrid, Spain
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10
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Hatlestad-Hall C, Bruña R, Liljeström M, Renvall H, Heuser K, Taubøll E, Maestú F, Haraldsen IH. Reliable evaluation of functional connectivity and graph theory measures in source-level EEG: How many electrodes are enough? Clin Neurophysiol 2023; 150:1-16. [PMID: 36972647 DOI: 10.1016/j.clinph.2023.03.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 02/03/2023] [Accepted: 03/01/2023] [Indexed: 03/18/2023]
Abstract
OBJECTIVE Using EEG to characterise functional brain networks through graph theory has gained significant interest in clinical and basic research. However, the minimal requirements for reliable measures remain largely unaddressed. Here, we examined functional connectivity estimates and graph theory metrics obtained from EEG with varying electrode densities. METHODS EEG was recorded with 128 electrodes in 33 participants. The high-density EEG data were subsequently subsampled into three sparser montages (64, 32, and 19 electrodes). Four inverse solutions, four measures of functional connectivity, and five graph theory metrics were tested. RESULTS The correlation between the results obtained with 128-electrode and the subsampled montages decreased as a function of the number of electrodes. As a result of decreased electrode density, the network metrics became skewed: mean network strength and clustering coefficient were overestimated, while characteristic path length was underestimated. CONCLUSIONS Several graph theory metrics were altered when electrode density was reduced. Our results suggest that, for optimal balance between resource demand and result precision, a minimum of 64 electrodes should be utilised when graph theory metrics are used to characterise functional brain networks in source-reconstructed EEG data. SIGNIFICANCE Characterisation of functional brain networks derived from low-density EEG warrants careful consideration.
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Affiliation(s)
| | - Ricardo Bruña
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain; Department of Radiology, Universidad Complutense de Madrid, Madrid, Spain
| | - Mia Liljeström
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland; BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki, Finland
| | - Hanna Renvall
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland; BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki, Finland
| | - Kjell Heuser
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Erik Taubøll
- Department of Neurology, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Fernando Maestú
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain; Department of Experimental Psychology, Universidad Complutense de Madrid, Pozuelo de Alarcón, Spain
| | - Ira H Haraldsen
- Department of Neurology, Oslo University Hospital, Oslo, Norway; BrainSymph AS, Oslo, Norway
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11
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Antón-Toro LF, Shpakivska-Bilan D, Del Cerro-León A, Bruña R, Uceta M, García-Moreno LM, Maestú F. Longitudinal change of inhibitory control functional connectivity associated with the development of heavy alcohol drinking. Front Psychol 2023; 14:1069990. [PMID: 36818101 PMCID: PMC9935580 DOI: 10.3389/fpsyg.2023.1069990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 01/10/2023] [Indexed: 02/05/2023] Open
Abstract
Introduction Heavy drinking (HD) prevalent pattern of alcohol consumption among adolescents, particularly concerning because of their critical vulnerability to the neurotoxic effects of ethanol. Adolescent neurodevelopment is characterized by critical neurobiological changes of the prefrontal, temporal and parietal regions, important for the development of executive control processes, such as inhibitory control (IC). In the present Magnetoencephalography (MEG) study, we aimed to describe the relationship between electrophysiological Functional Connectivity (FC) during an IC task and HD development, as well as its impact on functional neuromaturation. Methods We performed a two-year longitudinal protocol with two stages. In the first stage, before the onset of HD, we recorded brain electrophysiological activity from a sample of 67 adolescents (mean age = 14.6 ± 0.7) during an IC task. Alcohol consumption was measured using the AUDIT test and a semi-structured interview. Two years later, in the second stage, 32 of the 67 participants (mean age 16.7 ± 0.7) completed a similar protocol. As for the analysis in the first stage, the source-space FC matrix was calculated, and then, using a cluster-based permutation test (CBPT) based on Spearman's correlation, we calculated the correlation between the FC of each cortical source and the number of standard alcohol units consumed two years later. For the analysis of longitudinal change, we followed a similar approach. We calculated the symmetrized percentage change (SPC) between FC at both stages and performed a CBPT analysis, analyzing the correlation between FC change and the level of alcohol consumed in a regular session. Results The results revealed an association between higher beta-band FC in the prefrontal and temporal regions and higher consumption years later. Longitudinal results showed that greater future alcohol consumption was associated with an exacerbated reduction in the FC of the same areas. Discussion These results underline the existence of several brain functional differences prior to alcohol misuse and their impact on functional neuromaturation.
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Affiliation(s)
- Luis F. Antón-Toro
- Center for Cognitive and Computational Neuroscience (C3N), Complutense University of Madrid (UCM), Madrid, Spain,Department of Psychology, University Camilo José Cela (UCJC), Madrid, Spain,*Correspondence: Luis F. Antón-Toro, ✉ ; ✉
| | - Danylyna Shpakivska-Bilan
- Center for Cognitive and Computational Neuroscience (C3N), Complutense University of Madrid (UCM), Madrid, Spain,Department of Experimental Psychology, Faculty of Psychology, Complutense University of Madrid (UCM), Madrid, Spain
| | - Alberto Del Cerro-León
- Center for Cognitive and Computational Neuroscience (C3N), Complutense University of Madrid (UCM), Madrid, Spain,Department of Experimental Psychology, Faculty of Psychology, Complutense University of Madrid (UCM), Madrid, Spain
| | - Ricardo Bruña
- Center for Cognitive and Computational Neuroscience (C3N), Complutense University of Madrid (UCM), Madrid, Spain,Department of Radiology, Faculty of Medicine, Complutense University of Madrid (UCM), Madrid, Spain
| | - Marcos Uceta
- Center for Cognitive and Computational Neuroscience (C3N), Complutense University of Madrid (UCM), Madrid, Spain,Department of Cellular Biology, Faculty of Biology, Complutense University of Madrid (UCM), Madrid, Spain
| | - Luis M. García-Moreno
- Department of Psychobiology and Methodology in Behavioral Science, Faculty of Psychology, Complutense University of Madrid (UCM), Madrid, Spain
| | - Fernando Maestú
- Center for Cognitive and Computational Neuroscience (C3N), Complutense University of Madrid (UCM), Madrid, Spain,Department of Experimental Psychology, Faculty of Psychology, Complutense University of Madrid (UCM), Madrid, Spain
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12
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Tarfa R, Yu SE, Ahmed OH, Moore JA, Bruña R, Velasquez N, Poplawsky AJ, Coffman BA, Lee SE. Neuromapping olfactory stimulation using magnetoencephalography - visualizing smell, a proof-of-concept study. Front Allergy 2023; 3:1019265. [PMID: 36698377 PMCID: PMC9869273 DOI: 10.3389/falgy.2022.1019265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 12/14/2022] [Indexed: 01/12/2023] Open
Abstract
Importance Currently, clinical assessment of olfaction is largely reliant on subjective methods that require patient participation. The objective method for measuring olfaction, using electroencephalogram (EEG) readings, can be supplemented with the improved temporal resolution of magnetoencephalography (MEG) for olfactory measurement that can delineate cortical and peripheral olfactory loss. MEG provides high temporal and spatial resolution which can enhance our understanding of central olfactory processing compared to using EEG alone. Objective To determine the feasibility of building an in-house portable olfactory stimulator paired with electrophysiological neuroimaging technique with MEG to assess olfaction in the clinical setting. Design setting and participants This proof-of-concept study utilized a paired MEG-olfactometer paradigm to assess olfaction in three normosmic participants. We used a two-channel olfactory stimulator to deliver odorants according to a programmed stimulus-rest paradigm. Two synthetic odorants: 2% phenethyl alcohol (rose) and 0.5% amyl acetate (banana) were delivered in increasing increments of time followed by periods of rest. Cortical activity was measured via a 306-channel MEG system. Main outcomes and measures Primary outcome measure was the relative spectral power for each frequency band, which was contrasted between rest and olfactory stimulation. Results Compared to rest, olfactory stimulation produced a 40% increase in relative alpha power within the olfactory cortex bilaterally with both odorants. A 25%-30% increase in relative alpha power occurred in the left orbitofrontal cortex and precentral gyrus with phenethyl alcohol stimulation but not amyl acetate. Conclusion and relevance In this proof-of-concept study, we demonstrate the feasibility of olfactory measurement via an olfactometer-MEG paradigm. We found that odorant-specific cortical signatures can be identified using this paradigm, setting the basis for further investigation of this system as a prognostic tool for olfactory loss.
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Affiliation(s)
- Rahilla Tarfa
- University of Pittsburgh School of Medicine, Pittsburgh, PA, United States,Department of Otolaryngology – Head and Neck Surgery, University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, United States
| | - Sophie E. Yu
- Department of Otolaryngology – Head & Neck Surgery, Harvard Medical School, Boston, MA, United States
| | - Omar H. Ahmed
- Penn Medicine Becker ENT & Allergy, Robbinsville, NJ, United States
| | - John A. Moore
- Department of Otolaryngology – Head and Neck Surgery, University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, United States
| | - Ricardo Bruña
- Department of Radiology, Universidad Complutense de Madrid (UCM), IdISSC, Madrid, Spain
| | - Nathalia Velasquez
- Department of Otolaryngology, Cleveland Clinic Florida, Weston, FL, United States
| | - Alexander J. Poplawsky
- Center for Neuroscience, McGowan Institute of Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Brian A. Coffman
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
| | - Stella E. Lee
- Division of Otolaryngology – Head & Neck Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States,Correspondence: Stella E. Lee
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13
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Bruña R, López-Sanz D, Maestú F, Cohen AD, Bagic A, Huppert T, Kim T, Roush RE, Snitz B, Becker JT. MEG Oscillatory Slowing in Cognitive Impairment is Associated with the Presence of Subjective Cognitive Decline. Clin EEG Neurosci 2023; 54:73-81. [PMID: 35188831 PMCID: PMC9392809 DOI: 10.1177/15500594221072708] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The mechanisms behind Alzheimer's disease are not yet fully described, and changes in the electrophysiology of patients across the continuum of the disease could help to understand them. In this work, we study the power spectral distribution of a set of 129 individuals from the Connectomics of Brian Aging and Dementia project.From this sample, we acquired task-free data, with eyes closed, and estimated the power spectral distribution in source space. We compared the spectral profiles of three groups of individuals: 70 healthy controls, 27 patients with amnestic MCI, and 32 individuals showing cognitive impairment without subjective complaints (IWOC).The results showed a slowing of the brain activity in the aMCI patients, when compared to both the healthy controls and the IWOC individuals. These differences appeared both as a decrease in power for high frequency oscillations and an increase in power in alpha oscillations. The slowing of the spectrum was significant mainly in parietal and medial frontal areas.We were able to validate the slowing of the brain activity in individuals with aMCI, appearing in our sample in areas related to the default mode network. However, this pattern did not appear in the IWOC individuals, suggesting that their condition is not part of the AD continuum. This work raises interesting questions about this group of individuals, and the underlying brain mechanisms behind their cognitive impairment.
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Affiliation(s)
- Ricardo Bruña
- Electrical Engineering, Universidad de La Laguna, La Laguna, Tenerife, Spain
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, Spain
| | - David López-Sanz
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, Spain
- Psicobiología y Metodología en Ciencias del Comportamiento, Universidad Complutense de Madrid, Madrid, Madrid, Spain
| | - Fernando Maestú
- Experimental Psychology, Universidad Complutense de Madrid, Pozuelo de Alarcón, Madrid, Spain
- Center for Biomedical Technology, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, Spain
| | - Ann D. Cohen
- Neurosurgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Anto Bagic
- Neurology, University of Pittsburgh Medical Center Health System, Pittsburgh, Pennsylvania, USA
| | - Ted Huppert
- Electrical Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Tae Kim
- Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Rebecca E. Roush
- Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Betz Snitz
- Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - James T. Becker
- Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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14
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López‐Cuenca I, Salobrar‐Garcia E, Sanchez‐Puebla L, de Hoz R, Nebreda A, García‐Colomo A, Bruña R, Ramírez‐Toraño F, Barabash A, Gil P, Mestú F, Ramirez JM, Ramirez AI, Salazar JJ. Value of ophthalmological psychophysical test and
MEG
in subjects at high risk for sporadic Alzheimer's disease. Acta Ophthalmol 2022. [DOI: 10.1111/j.1755-3768.2022.0266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Affiliation(s)
- Inés López‐Cuenca
- Ramon Castroviejo Institute of Ophthalmologic Research. Group UCM 920105, Health Research Institute of the Hospital Clínico San Carlos (IdISSC) Complutense University of Madrid Madrid Spain
| | - Elena Salobrar‐Garcia
- Ramon Castroviejo Institute of Ophthalmologic Research. Group UCM 920105, Health Research Institute of the Hospital Clínico San Carlos (IdISSC) Complutense University of Madrid Madrid Spain
- Department of Immunology, Ophthalmology and ENT, Faculty of Optics and Optometry Complutense University of Madrid Madrid Spain
| | - Lidia Sanchez‐Puebla
- Ramon Castroviejo Institute of Ophthalmologic Research. Group UCM 920105, Health Research Institute of the Hospital Clínico San Carlos (IdISSC) Complutense University of Madrid Madrid Spain
| | - Rosa de Hoz
- Ramon Castroviejo Institute of Ophthalmologic Research. Group UCM 920105, Health Research Institute of the Hospital Clínico San Carlos (IdISSC) Complutense University of Madrid Madrid Spain
- Department of Immunology, Ophthalmology and ENT, Faculty of Optics and Optometry Complutense University of Madrid Madrid Spain
| | - Alberto Nebreda
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology Technical University of Madrid Madrid Spain
- Department of Experimental Psychology Complutense University of Madrid Madrid Spain
| | - Alejandra García‐Colomo
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology Technical University of Madrid Madrid Spain
- Department of Experimental Psychology Complutense University of Madrid Madrid Spain
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology Technical University of Madrid Madrid Spain
- Department of Industrial Engineering & IUNE University of La Laguna Spain
| | - Federico Ramírez‐Toraño
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology Technical University of Madrid Madrid Spain
- Department of Experimental Psychology Complutense University of Madrid Madrid Spain
| | - Ana Barabash
- Department of Endocrinology and Nutrition IdISSC Madrid Spain
- Diabetes and Associated Metabolic Diseases Networking Biomedical Research Centre Carlos III Health Institute Madrid Spain
| | - Pedro Gil
- Department of Medicine II, School of Medicine Complutense University of Madrid Madrid Spain
- Memory Unit, Department of Geriatrics, Hospital Clínico San Carlos Madrid Spain
| | - Fernando Mestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology Technical University of Madrid Madrid Spain
- Department of Experimental Psychology Complutense University of Madrid Madrid Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine Madrid Spain
| | - Jose Manuel Ramirez
- Ramon Castroviejo Institute of Ophthalmologic Research. Group UCM 920105, Health Research Institute of the Hospital Clínico San Carlos (IdISSC) Complutense University of Madrid Madrid Spain
- Department of Immunology, Ophthalmology and ENT School of Medicine, Complutense University of Madrid Madrid Spain
| | - Ana Isabel Ramirez
- Ramon Castroviejo Institute of Ophthalmologic Research. Group UCM 920105, Health Research Institute of the Hospital Clínico San Carlos (IdISSC) Complutense University of Madrid Madrid Spain
- Department of Immunology, Ophthalmology and ENT, Faculty of Optics and Optometry Complutense University of Madrid Madrid Spain
| | - Juan Jose Salazar
- Ramon Castroviejo Institute of Ophthalmologic Research. Group UCM 920105, Health Research Institute of the Hospital Clínico San Carlos (IdISSC) Complutense University of Madrid Madrid Spain
- Department of Immunology, Ophthalmology and ENT, Faculty of Optics and Optometry Complutense University of Madrid Madrid Spain
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15
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Bruña R, Vaghari D, Greve A, Cooper E, Mada MO, Henson RN. Modified MRI Anonymization (De-Facing) for Improved MEG Coregistration. Bioengineering (Basel) 2022; 9:bioengineering9100591. [PMID: 36290559 PMCID: PMC9598466 DOI: 10.3390/bioengineering9100591] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 10/02/2022] [Accepted: 10/17/2022] [Indexed: 01/28/2023]
Abstract
Localising the sources of MEG/EEG signals often requires a structural MRI to create a head model, while ensuring reproducible scientific results requires sharing data and code. However, sharing structural MRI data often requires the face go be hidden to help protect the identity of the individuals concerned. While automated de-facing methods exist, they tend to remove the whole face, which can impair methods for coregistering the MRI data with the EEG/MEG data. We show that a new, automated de-facing method that retains the nose maintains good MRI-MEG/EEG coregistration. Importantly, behavioural data show that this "face-trimming" method does not increase levels of identification relative to a standard de-facing approach and has less effect on the automated segmentation and surface extraction sometimes used to create head models for MEG/EEG localisation. We suggest that this trimming approach could be employed for future sharing of structural MRI data, at least for those to be used in forward modelling (source reconstruction) of EEG/MEG data.
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Affiliation(s)
- Ricardo Bruña
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, 28040 Madrid, Spain
- Department of Radiology, Rehabilitation and Physical Therapy, Universidad Complutense de Madrid, IdISSC, 28040 Madrid, Spain
- Correspondence:
| | - Delshad Vaghari
- Department of Electrical & Computer Engineering, Tarbiat Modares University, Tehran P.O. Box 14115-111, Iran
| | - Andrea Greve
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Elisa Cooper
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Marius O. Mada
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Richard N. Henson
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
- Department of Psychiatry, University of Cambridge, Cambridge CB2 OSZ, UK
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16
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Fernández A, Ramírez-Toraño F, Bruña R, Zuluaga P, Esteba-Castillo S, Abásolo D, Moldenhauer F, Shumbayawonda E, Maestú F, García-Alba J. Brain signal complexity in adults with Down syndrome: Potential application in the detection of mild cognitive impairment. Front Aging Neurosci 2022; 14:988540. [PMID: 36337705 PMCID: PMC9631477 DOI: 10.3389/fnagi.2022.988540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 09/27/2022] [Indexed: 11/13/2022] Open
Abstract
Background Down syndrome (DS) is considered the most frequent cause of early-onset Alzheimer’s disease (AD), and the typical pathophysiological signs are present in almost all individuals with DS by the age of 40. Despite of this evidence, the investigation on the pre-dementia stages in DS is scarce. In the present study we analyzed the complexity of brain oscillatory patterns and neuropsychological performance for the characterization of mild cognitive impairment (MCI) in DS. Materials and methods Lempel-Ziv complexity (LZC) values from resting-state magnetoencephalography recordings and the neuropsychological performance in 28 patients with DS [control DS group (CN-DS) (n = 14), MCI group (MCI-DS) (n = 14)] and 14 individuals with typical neurodevelopment (CN-no-DS) were analyzed. Results Lempel-Ziv complexity was lowest in the frontal region within the MCI-DS group, while the CN-DS group showed reduced values in parietal areas when compared with the CN-no-DS group. Also, the CN-no-DS group exhibited the expected pattern of significant increase of LZC as a function of age, while MCI-DS cases showed a decrease. The combination of reduced LZC values and a divergent trajectory of complexity evolution with age, allowed the discrimination of CN-DS vs. MCI-DS patients with a 92.9% of sensitivity and 85.7% of specificity. Finally, a pattern of mnestic and praxic impairment was significantly associated in MCI-DS cases with the significant reduction of LZC values in frontal and parietal regions (p = 0.01). Conclusion Brain signal complexity measured with LZC is reduced in DS and its development with age is also disrupted. The combination of both features might assist in the detection of MCI within this population.
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Affiliation(s)
- Alberto Fernández
- Department of Legal Medicine, Psychiatry and Pathology, Universidad Complutense de Madrid, Madrid, Spain
- Institute of Sanitary Investigation (IdISSC), Hospital Universitario San Carlos, Madrid, Spain
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
| | - Federico Ramírez-Toraño
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain
| | - Ricardo Bruña
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Department of Radiology, Universidad Complutense de Madrid, Madrid, Spain
- Department of Industrial Engineering & IUNE & ITB, Universidad de La Laguna, San Cristóbal de La Laguna, Spain
| | - Pilar Zuluaga
- Statistics & Operations Research Department, Faculty of Medicine, Universidad Complutense de Madrid, Madrid, Spain
| | - Susanna Esteba-Castillo
- Neurodevelopmental Group, Girona Biomedical Research Institute-IDIBGI, Institute of Health Assistance (IAS), Parc Hospitalari Martí i Julià, Girona, Spain
| | - Daniel Abásolo
- Centre for Biomedical Engineering, School of Mechanical Engineering Sciences, University of Surrey, Guildford, United Kingdom
| | - Fernando Moldenhauer
- Adult Down Syndrome Unit, Internal Medicine Department, Health Research Institute, Hospital Universitario de La Princesa, Madrid, Spain
| | - Elizabeth Shumbayawonda
- Centre for Biomedical Engineering, School of Mechanical Engineering Sciences, University of Surrey, Guildford, United Kingdom
| | - Fernando Maestú
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain
| | - Javier García-Alba
- Department of Research and Psychology in Education, Universidad Complutense de Madrid, Madrid, Spain
- *Correspondence: Javier García-Alba,
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17
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Chino B, Cuesta P, Pacios J, de Frutos-Lucas J, Torres-Simón L, Doval S, Marcos A, Bruña R, Maestú F. Episodic memory dysfunction and hypersynchrony in brain functional networks in cognitively intact subjects and MCI: a study of 379 individuals. GeroScience 2022; 45:477-489. [PMID: 36109436 PMCID: PMC9886758 DOI: 10.1007/s11357-022-00656-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 09/01/2022] [Indexed: 02/03/2023] Open
Abstract
Delayed recall (DR) impairment is one of the most significant predictive factors in defining the progression to Alzheimer's disease (AD). Changes in brain functional connectivity (FC) could accompany this decline in the DR performance even in a resting state condition from the preclinical stages to the diagnosis of AD itself, so the characterization of the relationship between the two phenomena has attracted increasing interest. Another aspect to contemplate is the potential moderator role of the APOE genotype in this association, considering the evidence about their implication for the disease. 379 subjects (118 mild cognitive impairment (MCI) and 261 cognitively intact (CI) individuals) underwent an extensive evaluation, including MEG recording. Applying cluster-based permutation test, we identified a cluster of differences in FC and studied which connections drove such an effect in DR. The moderation effect of APOE genotype between FC results and delayed recall was evaluated too. Higher FC in beta band in the right occipital region is associated with lower DR scores in both groups. A significant anteroposterior link emerged in the seed-based analysis with higher values in MCI. Moreover, APOE genotype appeared as a moderator between beta FC and DR performance only in the CI group. An increased beta FC in the anteroposterior brain region appears to be associated with lower memory performance in MCI. This finding could help discriminate the pattern of the progression of healthy aging to MCI and the relation between resting state and memory performance.
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Affiliation(s)
- Brenda Chino
- Institute of Neuroscience, Autonomous University of Barcelona, Barcelona, Spain. .,Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain.
| | - Pablo Cuesta
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain ,Department of Radiology, Rehabilitation, and Physiotherapy, Complutense University of Madrid, Madrid, Spain ,Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain
| | - Javier Pacios
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain ,Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain ,Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | - Jaisalmer de Frutos-Lucas
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain ,Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain ,Centre for Precision Health, Edith Cowan University, Joondalup, WA 6027 Australia ,Centro de Investigación Nebrija en Cognición (CINC), Universidad de Nebrija, Madrid, Spain
| | - Lucía Torres-Simón
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain ,Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | - Sandra Doval
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain ,Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | - Alberto Marcos
- Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain ,Neurology Department, Hospital Clinico San Carlos, Madrid, Spain
| | - Ricardo Bruña
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain ,Department of Radiology, Rehabilitation, and Physiotherapy, Complutense University of Madrid, Madrid, Spain ,Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain
| | - Fernando Maestú
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain ,Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain ,Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
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18
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Antón-Toro LF, Bruña R, Del Cerro-León A, Shpakivska D, Mateos-Gordo P, Porras-Truque C, García-Gómez R, Maestú F, García-Moreno LM. Electrophysiological resting-state hyperconnectivity and poorer behavioural regulation as predisposing profiles of adolescent binge drinking. Addict Biol 2022; 27:e13199. [PMID: 35754100 PMCID: PMC9286401 DOI: 10.1111/adb.13199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 04/29/2022] [Accepted: 06/02/2022] [Indexed: 11/30/2022]
Abstract
Adolescent Binge Drinking (BD) has become an increasing health and social concern, with detrimental consequences for brain development and functional integrity. However, research on neurophysiological and neuropsychological traits predisposing to BD are limited at this time. In this work, we conducted a 2‐year longitudinal magnetoencephalography (MEG) study over a cohort of initially alcohol‐naïve adolescents with the purpose of exploring anomalies in resting‐state electrophysiological networks, impulsivity, sensation‐seeking, and dysexecutive behaviour able to predict future BD patterns. In a sample of 67 alcohol‐naïve adolescents (age = 14.5 ± 0.9), we measured resting‐state activity using MEG. Additionally, we evaluated their neuropsychological traits using self‐report ecological scales (BIS‐11, SSS‐V, BDEFS, BRIEF‐SR and DEX). In a second evaluation, 2 years later, we measured participant's alcohol consumption, sub‐dividing the original sample in two groups: future binge drinkers (22 individuals, age 14.6 ± 0.8; eight females) and future light/no drinkers (17 individuals, age 14.5 ± 0.8; eight females). Then, we searched for differences predating alcohol BD intake. We found abnormalities in MEG resting state, in a form of gamma band hyperconnectivity, in those adolescents who transitioned into BD years later. Furthermore, they showed higher impulsivity, dysexecutive behaviours and sensation seeking, positively correlated with functional connectivity (FC). Sensation seeking and impulsivity mainly predicted BD severity in the future, while the relationship between dysexecutive trait and FC with future BD was mediated by sensation seeking. These findings shed light to electrophysiological and neuropsychological traits of vulnerability towards alcohol consumption. We hypothesise that these differences may rely on divergent neurobiological development of inhibitory neurotransmission pathways and executive prefrontal circuits.
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Affiliation(s)
- Luis F Antón-Toro
- Department of Experimental Psychology, Complutense University of Madrid (UCM), Madrid, Spain
| | - Ricardo Bruña
- Department of Radiology, Complutense University of Madrid (UCM), Madrid, Spain
| | - Alberto Del Cerro-León
- Department of Experimental Psychology, Complutense University of Madrid (UCM), Madrid, Spain
| | - Danylyna Shpakivska
- Department of Experimental Psychology, Complutense University of Madrid (UCM), Madrid, Spain
| | - Patricia Mateos-Gordo
- Department of Psychobiology and Methodology in Behavioral Sciences, Complutense University of Madrid (UCM), Madrid, Spain
| | - Claudia Porras-Truque
- Department of Psychobiology and Methodology in Behavioral Sciences, Complutense University of Madrid (UCM), Madrid, Spain
| | - Raquel García-Gómez
- Department of Psychobiology and Methodology in Behavioral Sciences, Complutense University of Madrid (UCM), Madrid, Spain
| | - Fernando Maestú
- Department of Experimental Psychology, Complutense University of Madrid (UCM), Madrid, Spain
| | - Luis Miguel García-Moreno
- Department of Psychobiology and Methodology in Behavioral Sciences, Complutense University of Madrid (UCM), Madrid, Spain
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19
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Suárez-Méndez I, Bruña R, López-Sanz D, Montejo P, Montenegro-Peña M, Delgado-Losada ML, Marcos Dolado A, López-Higes R, Maestú F. Cognitive Training Modulates Brain Hypersynchrony in a Population at Risk for Alzheimer's Disease. J Alzheimers Dis 2022; 86:1185-1199. [PMID: 35180120 DOI: 10.3233/jad-215406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Recent studies demonstrated that brain hypersynchrony is an early sign of dysfunction in Alzheimer's disease (AD) that can represent a proxy for clinical progression. Conversely, non-pharmacological interventions, such as cognitive training (COGTR), are associated with cognitive gains that may be underpinned by a neuroprotective effect on brain synchrony. OBJECTIVE To study the potential of COGTR to modulate brain synchrony and to eventually revert the hypersynchrony phenomenon that characterizes preclinical AD. METHODS The effect of COGTR was examined in a sample of healthy controls (HC, n = 41, 22 trained) and individuals with subjective cognitive decline (SCD, n = 49, 24 trained). Magnetoencephalographic (MEG) activity and neuropsychological scores were acquired before and after a ten-week COGTR intervention aimed at improving cognitive function and daily living performance. Functional connectivity (FC) was analyzed using the phase-locking value. A mixed-effects ANOVA model with factors time (pre-intervention/post-intervention), training (trained/non-trained), and diagnosis (HC/SCD) was used to investigate significant changes in FC. RESULTS We found an average increase in alpha-band FC over time, but the effect was different in each group (trained and non-trained). In the trained group (HC and SCD), we report a reduction in the increase in FC within temporo-parietal and temporo-occipital connections. In the trained SCD group, this reduction was stronger and showed a tentative correlation with improved performance in different cognitive tests. CONCLUSION COGTR interventions could mitigate aberrant increases in FC in preclinical AD, promoting brain synchrony normalization in groups at a higher risk of developing dementia.
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Affiliation(s)
- Isabel Suárez-Méndez
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology (CTB), Universidad Politécnica de Madrid (UPM), Madrid, Spain.,Departamento de Estructura de la Materia, Física Térmica y Electrónica, Universidad Complutense de Madrid (UCM), Facultad de Ciencias Físicas, Madrid, Spain.,Department of Experimental Psychology, Faculty of Psychology, Complutense University of Madrid (UCM), Madrid, Spain
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology (CTB), Universidad Politécnica de Madrid (UPM), Madrid, Spain.,Department of Experimental Psychology, Faculty of Psychology, Complutense University of Madrid (UCM), Madrid, Spain.,Networking Research Center on Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - David López-Sanz
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology (CTB), Universidad Politécnica de Madrid (UPM), Madrid, Spain.,Department of Psychobiology, Faculty of Psychology, Complutense University of Madrid (UCM), Madrid, Spain
| | - Pedro Montejo
- Center for the Prevention of Cognitive Impairment (Madrid Salud), Madrid City Council, Spain
| | - Mercedes Montenegro-Peña
- Department of Experimental Psychology, Faculty of Psychology, Complutense University of Madrid (UCM), Madrid, Spain.,Center for the Prevention of Cognitive Impairment (Madrid Salud), Madrid City Council, Spain
| | - María Luisa Delgado-Losada
- Department of Experimental Psychology, Faculty of Psychology, Complutense University of Madrid (UCM), Madrid, Spain
| | | | - Ramón López-Higes
- Department of Experimental Psychology, Faculty of Psychology, Complutense University of Madrid (UCM), Madrid, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology (CTB), Universidad Politécnica de Madrid (UPM), Madrid, Spain.,Department of Experimental Psychology, Faculty of Psychology, Complutense University of Madrid (UCM), Madrid, Spain.,Networking Research Center on Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Madrid, Spain
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20
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Bruña R, Maestú F, López-Sanz D, Bagic A, Cohen AD, Chang YF, Cheng Y, Doman J, Huppert T, Kim T, Roush RE, Snitz BE, Becker JT. Sex Differences in Magnetoencephalography-Identified Functional Connectivity in the Human Connectome Project Connectomics of Brain Aging and Dementia Cohort. Brain Connect 2021; 12:561-570. [PMID: 34726478 PMCID: PMC9419974 DOI: 10.1089/brain.2021.0059] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Introduction: The human brain shows modest traits of sexual dimorphism, with the female brain, on average, 10% smaller than the male brain. These differences do not imply a lowered cognitive performance, but suggest a more optimal brain organization in women. Here we evaluate the patterns of functional connectivity (FC) in women and men from the Connectomics of Brain Aging and Dementia sample. Methods: We used phase locking values to calculate FC from the magnetoencephalography time series in a sample of 138 old adults (87 females and 51 males). We compared the FC patterns between sexes, with the intention of detecting regions with different levels of connectivity. Results: We found a frontal cluster, involving anterior cingulate and the medial frontal lobe, where women showed higher FC values than men. Involved connections included the following: (1) medial parietal areas, such as posterior cingulate cortices and precunei; (2) right insula; and (3) medium cingulate and paracingulate cortices. Moreover, these differences persisted when considering only cognitively intact individuals, but not when considering only cognitively impaired individuals. Discussion: Increased anteroposterior FC has been identified as a biomarker for increased risk of developing cognitive impairment or dementia. In our study, cognitively intact women showed higher levels of FC than their male counterparts. This result suggests that neurodegenerative processes could be taking place in these women, but the changes are undetected by current diagnosis tools. FC, as measured here, might be valuable for early identification of this neurodegeneration.
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Affiliation(s)
- Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain.,Department of Experimental Psychology, Universidad Complutense de Madrid, Pozuelo de Alarcón, Madrid, Spain.,Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain.,Department of Experimental Psychology, Universidad Complutense de Madrid, Pozuelo de Alarcón, Madrid, Spain.,Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - David López-Sanz
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain.,Department of Psychobiology, Universidad Complutense de Madrid, Madrid, Spain
| | - Anto Bagic
- Department of Psychiatry, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Statistics, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ann D Cohen
- Department of Neurosurgery, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Yue-Fang Chang
- Department of Neurosurgery, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Yu Cheng
- Department of Statistics, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Biostatistics, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Jack Doman
- Department of Neurosurgery, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ted Huppert
- Department of Electrical Engineering, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Tae Kim
- Department of Radiology, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Rebecca E Roush
- Department of Psychiatry, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Beth E Snitz
- Department of Psychiatry, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - James T Becker
- Department of Psychiatry, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Neurology, and The University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Psychology, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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21
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Cohen AD, Bruña R, Chang YF, Cheng Y, Doman J, Huppert T, Kim T, Maestu F, Roush RE, Snitz BE, Becker JT. Connectomics in Brain Aging and Dementia - The Background and Design of a Study of a Connectome Related to Human Disease. Front Aging Neurosci 2021; 13:669490. [PMID: 34690734 PMCID: PMC8530182 DOI: 10.3389/fnagi.2021.669490] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 09/06/2021] [Indexed: 11/20/2022] Open
Abstract
The natural history of Alzheimer’s Disease (AD) includes significant alterations in the human connectome, and this disconnection results in the dementia of AD. The organizing principle of our research project is the idea that the expression of cognitive dysfunction in the elderly is the result of two independent processes — the neuropathology associated with AD, and second the neuropathological changes of cerebrovascular disease. Synaptic loss, senile plaques, and neurofibrillary tangles are the functional and diagnostic hallmarks of AD, but it is the structural changes as a consequence of vascular disease that reduce brain reserve and compensation, resulting in an earlier expression of the clinical dementia syndrome. This work is being completed under the auspices of the Human Connectome Project (HCP). We have achieved an equal representation of Black individuals (vs. White individuals) and enrolled 60% Women. Each of the participants contributes demographic, behavioral and laboratory data. We acquire data relative to vascular risk, and the participants also undergo in vivo amyloid imaging, and magnetoencephalography (MEG). All of the data are publicly available under the HCP guidelines using the Connectome Coordinating Facility and the NIMH Data Archive. Locally, we use these data to address specific questions related to structure, function, AD, aging and vascular disease in multi-modality studies leveraging the differential advantages of magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), MEG, and in vivo beta amyloid imaging.
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Affiliation(s)
- Ann D Cohen
- Department of Psychiatry, The University of Pittsburgh, Pittsburgh, PA, United States
| | - Ricardo Bruña
- Department of Experimental Psychology, Universidad Complutense de Madrid, Pozuelo de Alarcón, Madrid, Spain
| | - Yue-Fang Chang
- Department of Neurosurgery, The University of Pittsburgh, Pittsburgh, PA, United States
| | - Yu Cheng
- Department of Statistics, The University of Pittsburgh, Pittsburgh, PA, United States.,Department of Biostatistics, The University of Pittsburgh, Pittsburgh, PA, United States
| | - Jack Doman
- Department of Psychiatry, The University of Pittsburgh, Pittsburgh, PA, United States
| | - Ted Huppert
- Department of Electrical Engineering, The University of Pittsburgh, Pittsburgh, PA, United States
| | - Tae Kim
- Department of Radiology, The University of Pittsburgh, Pittsburgh, PA, United States
| | - Fernando Maestu
- Department of Experimental Psychology, Universidad Complutense de Madrid, Pozuelo de Alarcón, Madrid, Spain
| | - Rebecca E Roush
- Department of Neurology, The University of Pittsburgh, Pittsburgh, PA, United States
| | - Beth E Snitz
- Department of Neurology, The University of Pittsburgh, Pittsburgh, PA, United States
| | - James T Becker
- Department of Psychiatry, The University of Pittsburgh, Pittsburgh, PA, United States.,Department of Neurology, The University of Pittsburgh, Pittsburgh, PA, United States.,Department of Psychology, The University of Pittsburgh, Pittsburgh, PA, United States
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22
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Ramírez-Toraño F, Abbas K, Bruña R, Marcos de Pedro S, Gómez-Ruiz N, Barabash A, Pereda E, Marcos A, López-Higes R, Maestu F, Goñi J. A Structural Connectivity Disruption One Decade before the Typical Age for Dementia: A Study in Healthy Subjects with Family History of Alzheimer's Disease. Cereb Cortex Commun 2021; 2:tgab051. [PMID: 34647029 PMCID: PMC8501268 DOI: 10.1093/texcom/tgab051] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/23/2021] [Accepted: 07/26/2021] [Indexed: 11/23/2022] Open
Abstract
The concept of the brain has shifted to a complex system where different subnetworks support the human cognitive functions. Neurodegenerative diseases would affect the interactions among these subnetworks and, the evolution of impairment and the subnetworks involved would be unique for each neurodegenerative disease. In this study, we seek for structural connectivity traits associated with the family history of Alzheimer's disease, that is, early signs of subnetworks impairment due to Alzheimer's disease. The sample in this study consisted of 123 first-degree Alzheimer's disease relatives and 61 nonrelatives. For each subject, structural connectomes were obtained using classical diffusion tensor imaging measures and different resolutions of cortical parcellation. For the whole sample, independent structural-connectome-traits were obtained under the framework of connICA. Finally, we tested the association of the structural-connectome-traits with different factors of relevance for Alzheimer's disease by means of a multiple linear regression. The analysis revealed a structural-connectome-trait obtained from fractional anisotropy associated with the family history of Alzheimer's disease. The structural-connectome-trait presents a reduced fractional anisotropy pattern in first-degree relatives in the tracts connecting posterior areas and temporal areas. The family history of Alzheimer's disease structural-connectome-trait presents a posterior-posterior and posterior-temporal pattern, supplying new evidences to the cascading network failure model.
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Affiliation(s)
- F Ramírez-Toraño
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid 28223, Comunidad de Madrid, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid 28223, Comunidad de Madrid, Spain
| | - Kausar Abbas
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN 46202, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN 46202, USA
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid 28223, Comunidad de Madrid, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid 28223, Comunidad de Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Madrid 28029, Comunidad de Madrid, Spain
| | - Silvia Marcos de Pedro
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid 28223, Comunidad de Madrid, Spain
- Facultad de Educación y Salud, Universidad Camilo José Cela, Madrid 28010, Comunidad de Madrid, Spain
| | - Natividad Gómez-Ruiz
- Sección Neurorradiología, Servicio de Diagnóstico por Imagen, Hospital Clínico San Carlos, Madrid 28040, Comunidad de Madrid, Spain
| | - Ana Barabash
- Endocrinology and Nutrition Department, Hospital Clinico San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, Madrid 28040, Comunidad de Madrid, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Madrid 28029, Comunidad de Madrid, Spain
| | - Ernesto Pereda
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid 28223, Comunidad de Madrid, Spain
- Electrical Engineering and Bioengineering Group, Department of Industrial Engineering & IUNE & ITB, Universidad de La Laguna, Santa Cruz de Tenerife 38205, Spain
| | - Alberto Marcos
- Neurology Department, Hospital Clinico San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, Madrid 28040, Comunidad de Madrid, Spain
| | - Ramón López-Higes
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid 28223, Comunidad de Madrid, Spain
| | - Fernando Maestu
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid 28223, Comunidad de Madrid, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid 28223, Comunidad de Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Madrid 28029, Comunidad de Madrid, Spain
| | - Joaquín Goñi
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN 46202, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN 46202, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 46202, USA
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Hatlestad-Hall C, Bruña R, Erichsen A, Andersson V, Syvertsen MR, Skogan AH, Renvall H, Marra C, Maestú F, Heuser K, Taubøll E, Solbakk AK, Haraldsen IH. The organization of functional neurocognitive networks in focal epilepsy correlates with domain-specific cognitive performance. J Neurosci Res 2021; 99:2669-2687. [PMID: 34173259 DOI: 10.1002/jnr.24896] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 04/28/2021] [Accepted: 05/15/2021] [Indexed: 11/10/2022]
Abstract
Understanding and diagnosing cognitive impairment in epilepsy remains a prominent challenge. New etiological models suggest that cognitive difficulties might not be directly linked to seizure activity, but are rather a manifestation of a broader brain pathology. Consequently, treating seizures is not sufficient to alleviate cognitive symptoms, highlighting the need for novel diagnostic tools. Here, we investigated whether the organization of three intrinsic, resting-state functional connectivity networks was correlated with domain-specific cognitive test performance. Using individualized EEG source reconstruction and graph theory, we examined the association between network small worldness and cognitive test performance in 23 patients with focal epilepsy and 17 healthy controls, who underwent a series of standardized pencil-and-paper and digital cognitive tests. We observed that the specific networks robustly correlated with test performance in distinct cognitive domains. Specifically, correlations were evident between the default mode network and memory in patients, the central-executive network and executive functioning in controls, and the salience network and social cognition in both groups. Interestingly, the correlations were evident in both groups, but in different domains, suggesting an alteration in these functional neurocognitive networks in focal epilepsy. The present findings highlight the potential clinical relevance of functional brain network dysfunction in cognitive impairment.
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Affiliation(s)
| | - Ricardo Bruña
- Center for Biomedical Technology, Technical University of Madrid, Pozuelo de Alarcón, Spain.,Department of Experimental Psychology, Complutense University of Madrid, Pozuelo de Alarcón, Spain.,Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Aksel Erichsen
- Department of Neurology, Oslo University Hospital, Oslo, Norway.,Department of Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | | | - Marte Roa Syvertsen
- Department of Neurology, Drammen Hospital, Vestre Viken Health Care Trust, Drammen, Norway
| | - Annette Holth Skogan
- Division of Clinical Neuroscience, National Centre for Epilepsy, Oslo University Hospital, Oslo, Norway
| | - Hanna Renvall
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland.,BioMag Laboratory, HUS Diagnostic Center, Helsinki University Hospital, University of Helsinki and Aalto, Helsinki, Finland
| | - Camillo Marra
- Department of Neuroscience, Fondazione Policlinico Agostino Gemelli, Rome, Italy
| | - Fernando Maestú
- Center for Biomedical Technology, Technical University of Madrid, Pozuelo de Alarcón, Spain.,Department of Experimental Psychology, Complutense University of Madrid, Pozuelo de Alarcón, Spain.,Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Kjell Heuser
- Department of Neurology, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Erik Taubøll
- Department of Neurology, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Anne-Kristin Solbakk
- Department of Psychology, Faculty of Social Sciences, University of Oslo, Oslo, Norway.,RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway.,Department of Neurosurgery, Oslo University Hospital, Oslo, Norway.,Department of Neuropsychology, Helgeland Hospital, Mosjøen, Norway
| | - Ira H Haraldsen
- Department of Neurology, Oslo University Hospital, Oslo, Norway
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Ramírez-Toraño F, García-Alba J, Bruña R, Esteba-Castillo S, Vaquero L, Pereda E, Maestú F, Fernández A. Hypersynchronized Magnetoencephalography Brain Networks in Patients with Mild Cognitive Impairment and Alzheimer's Disease in Down Syndrome. Brain Connect 2021; 11:725-733. [PMID: 33858203 DOI: 10.1089/brain.2020.0897] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Introduction: The majority of individuals with Down syndrome (DS) show signs of Alzheimer's disease (AD) neuropathology in their fourth decade. However, there is a lack of specific markers for characterizing the disease stages while considering this population's differential features. Methods: Forty-one DS individuals participated in the study, and were classified into three groups according to their clinical status: Alzheimer's disease (AD-DS), mild cognitive impairment (MCI-DS), and controls (CN-DS). We performed an exhaustive neuropsychological evaluation and assessed brain functional connectivity (FC) from magnetoencephalographic recordings. Results: Compared with CN-DS, both MCI-DS and AD-DS showed a pattern of increased FC within the high alpha band. The neuropsychological assessment showed a generalized cognitive impairment, especially affecting mnestic functions, in MCI-DS and, more pronouncedly, in AD-DS. Discussion: These findings might help to characterize the AD-continuum in DS. In addition, they support the role of the excitatory/inhibitory imbalance as a key pathophysiological factor in AD. Impact statement The pattern of functional connectivity (FC) hypersynchronization found in this study resembles the largely reported Alzheimer's disease (AD) FC evolution pattern in population with typical development. This study supports the hypothesis of the excitatory/inhibitory imbalance as a key pathophysiological factor in AD, and its conclusions could help in the characterization and prediction of Down syndrome individuals with a greater likelihood of converting to dementia.
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Affiliation(s)
- Federico Ramírez-Toraño
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain.,Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
| | - Javier García-Alba
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain.,Research and Psychology in Education Department, Complutense University of Madrid, Madrid, Spain
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain.,Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain.,Networking Research Center on Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Susanna Esteba-Castillo
- Specialized Department in Mental Health and Intellectual Disability, Parc Hospitalari Martí i Julià-Institut 'd'Assistència Sanitària, Institut 'd'Assistència Sanitària (IAS), Girona, Spain
| | - Lucía Vaquero
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain.,Department of Legal Medicine, Psychiatry and Pathology, Complutense University of Madrid, Spain
| | - Ernesto Pereda
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain.,Electrical Engineering and Bioengineering Group, Department of Industrial Engineering and IUNE and ITB Universidad de La Laguna, Santa Cruz de Tenerife, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain.,Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain.,Networking Research Center on Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Alberto Fernández
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain.,Department of Legal Medicine, Psychiatry and Pathology, Complutense University of Madrid, Spain.,Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
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25
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Hatlestad-Hall C, Bruña R, Syvertsen MR, Erichsen A, Andersson V, Vecchio F, Miraglia F, Rossini PM, Renvall H, Taubøll E, Maestú F, Haraldsen IH. Source-level EEG and graph theory reveal widespread functional network alterations in focal epilepsy. Clin Neurophysiol 2021; 132:1663-1676. [PMID: 34044189 DOI: 10.1016/j.clinph.2021.04.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 03/19/2021] [Accepted: 04/20/2021] [Indexed: 01/15/2023]
Abstract
OBJECTIVE The hypersynchronous neuronal activity associated with epilepsy causes widespread functional network disruptions extending beyond the epileptogenic zone. This altered network topology is considered a mediator for non-seizure symptoms, such as cognitive impairment. The aim of this study was to investigate functional network alterations in focal epilepsy patients with good seizure control and high quality of life. METHODS We compared twenty-two focal epilepsy patients and sixteen healthy controls on graph metrics derived from functional connectivity of source-level resting-state EEG. Graph metrics were calculated over a range of network densities in five frequency bands. RESULTS We observed a significantly increased small world index in patients relative to controls. On the local level, two left-hemisphere regions displayed a shift towards greater alpha band "hubness". The findings were not mediated by age, sex or education, nor by age of epilepsy onset, duration or focus lateralisation. CONCLUSIONS Widespread functional network alterations are evident in focal epilepsy, even in a cohort characterised by successful anti-seizure medication therapy and high quality of life. These findings might support the position that functional network analysis could hold clinical relevance for epilepsy. SIGNIFICANCE Focal epilepsy is accompanied by global and local functional network aberrancies which might be implied in the sustenance of non-seizure symptoms.
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Affiliation(s)
| | - Ricardo Bruña
- Center for Biomedical Technology, Technical University of Madrid, Pozuelo de Alarcón, Spain; Department of Experimental Psychology, Complutense University of Madrid, Pozuelo de Alarcón, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.
| | - Marte Roa Syvertsen
- Department of Neurology, Drammen Hospital, Vestre Viken Health Care Trust, Drammen, Norway.
| | - Aksel Erichsen
- Department of Neurology, Oslo University Hospital, Oslo, Norway; Department of Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
| | | | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy.
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy.
| | - Paolo M Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy.
| | - Hanna Renvall
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Diagnostic Center, Helsinki University Hospital, University of Helsinki and Aalto University School of Science, Helsinki, Finland.
| | - Erik Taubøll
- Department of Neurology, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.
| | - Fernando Maestú
- Center for Biomedical Technology, Technical University of Madrid, Pozuelo de Alarcón, Spain; Department of Experimental Psychology, Complutense University of Madrid, Pozuelo de Alarcón, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.
| | - Ira H Haraldsen
- Department of Neurology, Oslo University Hospital, Oslo, Norway.
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26
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Carrasco-Gómez M, Keijzer HM, Ruijter BJ, Bruña R, Tjepkema-Cloostermans MC, Hofmeijer J, van Putten MJAM. EEG functional connectivity contributes to outcome prediction of postanoxic coma. Clin Neurophysiol 2021; 132:1312-1320. [PMID: 33867260 DOI: 10.1016/j.clinph.2021.02.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 01/19/2021] [Accepted: 02/09/2021] [Indexed: 12/21/2022]
Abstract
OBJECTIVE To investigate the additional value of EEG functional connectivity features, in addition to non-coupling EEG features, for outcome prediction of comatose patients after cardiac arrest. METHODS Prospective, multicenter cohort study. Coherence, phase locking value, and mutual information were calculated in 19-channel EEGs at 12 h, 24 h and 48 h after cardiac arrest. Three sets of machine learning classification models were trained and validated with functional connectivity, EEG non-coupling features, and a combination of these. Neurological outcome was assessed at six months and categorized as "good" (Cerebral Performance Category [CPC] 1-2) or "poor" (CPC 3-5). RESULTS We included 594 patients (46% good outcome). A sensitivity of 51% (95% CI: 34-56%) at 100% specificity in predicting poor outcome was achieved by the best functional connectivity-based classifier at 12 h after cardiac arrest, while the best non-coupling-based model reached a sensitivity of 32% (0-54%) at 100% specificity using data at 12 h and 48 h. Combination of both sets of features achieved a sensitivity of 73% (50-77%) at 100% specificity. CONCLUSION Functional connectivity measures improve EEG based prediction models for poor outcome of postanoxic coma. SIGNIFICANCE Functional connectivity features derived from early EEG hold potential to improve outcome prediction of coma after cardiac arrest.
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Affiliation(s)
- Martín Carrasco-Gómez
- Laboratory of Cognitive and Computational Neuroscience (LNCyC), Centre for Biomedical Technology, Universidad Politécnica de Madrid, Spain; Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.
| | - Hanneke M Keijzer
- Department of Neurology, Rijnstate Hospital, Arnhem, the Netherlands; Department of Neurology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Barry J Ruijter
- Clinical Neurophysiology (CNPH), TechMed Centre, University of Twente, the Netherlands
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience (LNCyC), Centre for Biomedical Technology, Universidad Politécnica de Madrid, Spain; Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Marleen C Tjepkema-Cloostermans
- Clinical Neurophysiology (CNPH), TechMed Centre, University of Twente, the Netherlands; Neurocentrum, Medisch SpectrumTwente, Enschede, the Netherlands
| | - Jeannette Hofmeijer
- Clinical Neurophysiology (CNPH), TechMed Centre, University of Twente, the Netherlands; Department of Neurology, Rijnstate Hospital, Arnhem, the Netherlands
| | - Michel J A M van Putten
- Clinical Neurophysiology (CNPH), TechMed Centre, University of Twente, the Netherlands; Neurocentrum, Medisch SpectrumTwente, Enschede, the Netherlands
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Antón-Toro LF, Bruña R, Suárez-Méndez I, Correas Á, García-Moreno LM, Maestú F. Abnormal organization of inhibitory control functional networks in future binge drinkers. Drug Alcohol Depend 2021; 218:108401. [PMID: 33246710 DOI: 10.1016/j.drugalcdep.2020.108401] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 10/26/2020] [Accepted: 10/27/2020] [Indexed: 11/20/2022]
Abstract
BACKGROUND AND AIMS Adolescent Binge drinking has become an increasing health and social concern, which cause several detrimental consequences for brain integrity. However, research on neurophysiological traits of vulnerability for binge drinking predisposition is limited at this time. In this work, we conducted a two-year longitudinal study with magnetoencephalography (MEG) over a cohort of initially alcohol-naive adolescents with the purpose of characterize inhibitory cortical networks' anomalies prior to alcohol consumption onset in those youths who will transit into binge drinkers years later. METHODS Sixty-seven participant's inhibitory functional networks, and dysexecutive/impulsivity traits were measured by means of inhibitory task (go/no-go) and questionnaires battery. After a follow-up period of two years, we evaluated their alcohol consumption habits, sub-dividing them in two groups according to their alcohol intake patterns: future binge drinkers (fBD): n = 22; future Light/non-drinkers (fLD): n = 17. We evaluated whole-brain and seed-based functional connectivity profiles, as well as its correlation with impulsive and dysexecutive behaviours, searching for early abnormalities before consumption onset. RESULTS For the first time, abnormalities in MEG functional networks and higher dysexecutive and impulsivity profiles were detected in alcohol-naïve adolescents who two years later became binge drinkers. Concretely, fBD exhibit a distinctive pattern of beta band hyperconnectivity among crucial regions of inhibitory control networks, positively correlated with behavioral traits and future alcohol intake rate. CONCLUSIONS These findings strongly support the idea of early neurobiological vulnerabilities for substances consumption initiation, with inhibitory functional networks' abnormalities as a relevant neurophysiological marker of subjects at risk- we hypothesize this profile is due to neurodevelopmental and neurobiological differences involving cognitive control networks and neurotransmission pathways.
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Affiliation(s)
- Luis F Antón-Toro
- Department of Experimental Psychology, Complutense University of Madrid (UCM), 28223, Madrid, Spain; Laboratory for Cognitive and Computational Neuroscience (UCM - UPM), Center for Biomedical Technology (CBT), 28223, Madrid, Spain.
| | - Ricardo Bruña
- Department of Experimental Psychology, Complutense University of Madrid (UCM), 28223, Madrid, Spain; Laboratory for Cognitive and Computational Neuroscience (UCM - UPM), Center for Biomedical Technology (CBT), 28223, Madrid, Spain; Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN), 28029 Madrid, Spain
| | - Isabel Suárez-Méndez
- Department of Experimental Psychology, Complutense University of Madrid (UCM), 28223, Madrid, Spain; Laboratory for Cognitive and Computational Neuroscience (UCM - UPM), Center for Biomedical Technology (CBT), 28223, Madrid, Spain; Department of Structure of Matter, Thermal Physics and Electronics, Complutense University of Madrid (UCM), 28223, Madrid, Spain
| | - Ángeles Correas
- Laboratory for Cognitive and Computational Neuroscience (UCM - UPM), Center for Biomedical Technology (CBT), 28223, Madrid, Spain; Department of Psychology, San Diego State University, 5500 Campanile Drive San Diego, CA, 92182-4611, USA
| | - Luis M García-Moreno
- Department of Psychobiology and Methodology in Behavioral Sciences, Complutense University of Madrid (UCM), 28040, Madrid, Spain
| | - Fernando Maestú
- Department of Experimental Psychology, Complutense University of Madrid (UCM), 28223, Madrid, Spain; Laboratory for Cognitive and Computational Neuroscience (UCM - UPM), Center for Biomedical Technology (CBT), 28223, Madrid, Spain; Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN), 28029 Madrid, Spain
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28
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Bruña R, Pereda E. Multivariate extension of phase synchronization improves the estimation of region-to-region source space functional connectivity. Brain Multiphysics 2021. [DOI: 10.1016/j.brain.2021.100021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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29
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Ramírez-Toraño F, Bruña R, de Frutos-Lucas J, Rodríguez-Rojo IC, Marcos de Pedro S, Delgado-Losada ML, Gómez-Ruiz N, Barabash A, Marcos A, López Higes R, Maestú F. Functional Connectivity Hypersynchronization in Relatives of Alzheimer’s Disease Patients: An Early E/I Balance Dysfunction? Cereb Cortex 2020; 31:1201-1210. [DOI: 10.1093/cercor/bhaa286] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 08/05/2020] [Accepted: 09/01/2020] [Indexed: 12/13/2022] Open
Abstract
Abstract
Alzheimer’s disease (AD) studies on animal models, and humans showed a tendency of the brain tissue to become hyperexcitable and hypersynchronized, causing neurodegeneration. However, we know little about either the onset of this phenomenon or its early effects on functional brain networks. We studied functional connectivity (FC) on 127 participants (92 middle-age relatives of AD patients and 35 age-matched nonrelatives) using magnetoencephalography. FC was estimated in the alpha band in areas known both for early amyloid accumulation and disrupted FC in MCI converters to AD. We found a frontoparietal network (anterior cingulate cortex, dorsal frontal, and precuneus) where relatives of AD patients showed hypersynchronization in high alpha (not modulated by APOE-ε4 genotype) in comparison to age-matched nonrelatives. These results represent the first evidence of neurophysiological events causing early network disruption in humans, opening a new perspective for intervention on the excitation/inhibition unbalance.
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Affiliation(s)
- F Ramírez-Toraño
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Technical University of Madrid, Madrid, Comunidad de Madrid 28223, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Comunidad de Madrid 28223, Spain
| | - R Bruña
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Technical University of Madrid, Madrid, Comunidad de Madrid 28223, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Comunidad de Madrid 28223, Spain
- Networking Research Center on Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Madrid, Comunidad de Madrid 28029, Spain
| | - J de Frutos-Lucas
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Technical University of Madrid, Madrid, Comunidad de Madrid 28223, Spain
- Biological and Health Psychology Department, Universidad Autonoma de Madrid, Madrid, Comunidad de Madrid 28049, Spain
| | - I C Rodríguez-Rojo
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Technical University of Madrid, Madrid, Comunidad de Madrid 28223, Spain
- Facultad de Psicología, Centro Universitario Villanueva, Madrid, Comunidad de Madrid 28034, Spain
| | - S Marcos de Pedro
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Technical University of Madrid, Madrid, Comunidad de Madrid 28223, Spain
- Facultad de Educación y Salud, Universidad Camilo José Cela, Madrid, Comunidad de Madrid 28010, Spain
| | - M L Delgado-Losada
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Comunidad de Madrid 28223, Spain
| | - N Gómez-Ruiz
- Sección Neurorradiología, Servicio de Diagnóstico por Imagen, Hospital Clínico San Carlos, Madrid, Comunidad de Madrid 28040, Spain
| | - A Barabash
- Endocrinology and Nutrition Department, Hospital Clinico San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, Madrid, Comunidad de Madrid 28040, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Madrid, Comunidad de Madrid 28029, Spain
| | - A Marcos
- Neurology Department, Hospital Clinico San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, Madrid, Comunidad de Madrid 28040, Spain
| | - R López Higes
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Comunidad de Madrid 28223, Spain
| | - F Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Technical University of Madrid, Madrid, Comunidad de Madrid 28223, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Comunidad de Madrid 28223, Spain
- Networking Research Center on Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Madrid, Comunidad de Madrid 28029, Spain
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de Frutos-Lucas J, Cuesta P, Ramírez-Toraño F, Nebreda A, Cuadrado-Soto E, Peral-Suárez Á, Lopez-Sanz D, Bruña R, Marcos-de Pedro S, Delgado-Losada ML, López-Sobaler AM, Concepción Rodríguez-Rojo I, Barabash A, Serrano Rodriguez JM, Laws SM, Dolado AM, López-Higes R, Brown BM, Maestú F. Age and APOE genotype affect the relationship between objectively measured physical activity and power in the alpha band, a marker of brain disease. Alzheimers Res Ther 2020; 12:113. [PMID: 32962736 PMCID: PMC7507658 DOI: 10.1186/s13195-020-00681-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 09/10/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND Electrophysiological studies show that reductions in power within the alpha band are associated with the Alzheimer's disease (AD) continuum. Physical activity (PA) is a protective factor that has proved to reduce AD risk and pathological brain burden. Previous research has confirmed that exercise increases power in the alpha range. However, little is known regarding whether other non-modifiable risk factors for AD, such as increased age or APOE ε4 carriage, alter the association between PA and power in the alpha band. METHODS The relationship between PA and alpha band power was examined in a sample of 113 healthy adults using magnetoencephalography. Additionally, we explored whether ε4 carriage and age modulate this association. The correlations between alpha power and gray matter volumes and cognition were also investigated. RESULTS We detected a parieto-occipital cluster in which PA positively correlated with alpha power. The association between PA and alpha power remained following stratification of the cohort by genotype. Younger and older adults were investigated separately, and only younger adults exhibited a positive relationship between PA and alpha power. Interestingly, when four groups were created based on age (younger-older adult) and APOE (E3/E3-E3/E4), only younger E3/E3 (least predicted risk) and older E3/E4 (greatest predicted risk) had associations between greater alpha power and higher PA. Among older E3/E4, greater alpha power in these regions was associated with improved memory and preserved brain structure. CONCLUSION PA could protect against the slowing of brain activity that characterizes the AD continuum, where it is of benefit for all individuals, especially E3/E4 older adults.
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Affiliation(s)
- Jaisalmer de Frutos-Lucas
- Collaborative Genomics Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, 6027, Australia.
- Biological and Health Psychology Department, School of Psychology, Universidad Autonoma de Madrid, 28049, Madrid, Spain.
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223, Pozuelo de Alarcón, Madrid, Spain.
| | - Pablo Cuesta
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223, Pozuelo de Alarcón, Madrid, Spain
- Experimental Psychology Department, School of Psychology, Universidad Complutense de Madrid, 28223, Pozuelo de Alarcon, Spain
| | - Federico Ramírez-Toraño
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223, Pozuelo de Alarcón, Madrid, Spain
- Experimental Psychology Department, School of Psychology, Universidad Complutense de Madrid, 28223, Pozuelo de Alarcon, Spain
| | - Alberto Nebreda
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223, Pozuelo de Alarcón, Madrid, Spain
- Experimental Psychology Department, School of Psychology, Universidad Complutense de Madrid, 28223, Pozuelo de Alarcon, Spain
| | - Esther Cuadrado-Soto
- Departamento de Nutricion y Ciencia de los Alimentos, Facultad de Farmacia, Universidad Complutense de Madrid, 28040, Madrid, Spain
- IMDEA-Food, CEI UAM + CSIC, Madrid, 28049, Spain
| | - África Peral-Suárez
- Departamento de Nutricion y Ciencia de los Alimentos, Facultad de Farmacia, Universidad Complutense de Madrid, 28040, Madrid, Spain
| | - David Lopez-Sanz
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223, Pozuelo de Alarcón, Madrid, Spain
- Department of Psychobiology and Methodology in Behavioral Sciences, Universidad Complutense de Madrid (UCM), Pozuelo de Alarcón, 28223, Spain
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223, Pozuelo de Alarcón, Madrid, Spain
- Experimental Psychology Department, School of Psychology, Universidad Complutense de Madrid, 28223, Pozuelo de Alarcon, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28029, Madrid, Spain
| | - Silvia Marcos-de Pedro
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223, Pozuelo de Alarcón, Madrid, Spain
- Departamento de Especialidades Medicas y Salud Pública, Universidad Rey Juan Carlos, 28922, Alcorcon, Spain
| | - María Luisa Delgado-Losada
- Experimental Psychology Department, School of Psychology, Universidad Complutense de Madrid, 28223, Pozuelo de Alarcon, Spain
| | - Ana María López-Sobaler
- Departamento de Nutricion y Ciencia de los Alimentos, Facultad de Farmacia, Universidad Complutense de Madrid, 28040, Madrid, Spain
| | - Inmaculada Concepción Rodríguez-Rojo
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223, Pozuelo de Alarcón, Madrid, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, 28040, Madrid, Spain
- Physiotherapy and Nursing Faculty, University of Castilla-La Mancha, Toledo, 45004, Spain
| | - Ana Barabash
- Endocrinology and Nutrition Department, Hospital Clinico San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, 28040, Madrid, Spain
- Facultad de Psicología, Centro Universitario Villanueva, 28034, Madrid, Spain
| | - Juan Manuel Serrano Rodriguez
- Biological and Health Psychology Department, School of Psychology, Universidad Autonoma de Madrid, 28049, Madrid, Spain
| | - Simon M Laws
- Collaborative Genomics Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, 6027, Australia
- School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia, 6102, Australia
| | - Alberto Marcos Dolado
- Neurology Department, Hospital Clinico San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, 28040, Madrid, Spain
| | - Ramón López-Higes
- Experimental Psychology Department, School of Psychology, Universidad Complutense de Madrid, 28223, Pozuelo de Alarcon, Spain
| | - Belinda M Brown
- Discipline of Exercise Science, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, Western Australia, 6150, Australia
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223, Pozuelo de Alarcón, Madrid, Spain
- Experimental Psychology Department, School of Psychology, Universidad Complutense de Madrid, 28223, Pozuelo de Alarcon, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28029, Madrid, Spain
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de Frutos-Lucas J, Cuesta P, López-Sanz D, Peral-Suárez Á, Cuadrado-Soto E, Ramírez-Toraño F, Brown BM, Serrano JM, Laws SM, Rodríguez-Rojo IC, Verdejo-Román J, Bruña R, Delgado-Losada ML, Barabash A, López-Sobaler AM, López-Higes R, Marcos A, Maestú F. The relationship between physical activity, apolipoprotein E ε4 carriage, and brain health. Alzheimers Res Ther 2020; 12:48. [PMID: 32331531 PMCID: PMC7183121 DOI: 10.1186/s13195-020-00608-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Accepted: 03/30/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND Neuronal hyperexcitability and hypersynchrony have been described as key features of neurophysiological dysfunctions in the Alzheimer's disease (AD) continuum. Conversely, physical activity (PA) has been associated with improved brain health and reduced AD risk. However, there is controversy regarding whether AD genetic risk (in terms of APOE ε4 carriage) modulates these relationships. The utilization of multiple outcome measures within one sample may strengthen our understanding of this complex phenomenon. METHOD The relationship between PA and functional connectivity (FC) was examined in a sample of 107 healthy older adults using magnetoencephalography. Additionally, we explored whether ε4 carriage modulates this association. The correlation between FC and brain structural integrity, cognition, and mood was also investigated. RESULTS A relationship between higher PA and decreased FC (hyposynchrony) in the left temporal lobe was observed among all individuals (across the whole sample, in ε4 carriers, and in ε4 non-carriers), but its effects manifest differently according to genetic risk. In ε4 carriers, we report an association between this region-specific FC profile and preserved brain structure (greater gray matter volumes and higher integrity of white matter tracts). In this group, decreased FC also correlated with reduced anxiety levels. In ε4 non-carriers, this profile is associated with improved cognition (working and episodic memory). CONCLUSIONS PA could mitigate the increase in FC (hypersynchronization) that characterizes preclinical AD, being beneficial for all individuals, especially ε4 carriers.
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Affiliation(s)
- Jaisalmer de Frutos-Lucas
- Biological and Health Psychology Department, School of Psychology, Universidad Autonoma de Madrid, 28049, Madrid, Spain.
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for biomedical Technology, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223, Pozuelo de Alarcón, Madrid, Spain.
- Collaborative Genomics Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, 6027, Australia.
| | - Pablo Cuesta
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for biomedical Technology, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223, Pozuelo de Alarcón, Madrid, Spain
- Department of Industrial Engineering & IUNE, Universidad de La Laguna, 38200, San Cristobal de la Laguna, Tenerife, Spain
| | - David López-Sanz
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for biomedical Technology, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223, Pozuelo de Alarcón, Madrid, Spain
- Department of Psychobiology and Methodology in Behavioral Sciences, School of Education, Universidad Complutense de Madrid, 28040, Madrid, Spain
| | - África Peral-Suárez
- Departamento de Nutricion y Ciencia de los Alimentos, Facultad de Farmacia, Universidad Complutense de Madrid, 28040, Madrid, Spain
| | - Esther Cuadrado-Soto
- Departamento de Nutricion y Ciencia de los Alimentos, Facultad de Farmacia, Universidad Complutense de Madrid, 28040, Madrid, Spain
| | - Federico Ramírez-Toraño
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for biomedical Technology, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223, Pozuelo de Alarcón, Madrid, Spain
- Experimental Psychology Department, School of Psychology, Universidad Complutense de Madrid, 28223, Pozuelo de Alarcon, Spain
| | - Belinda M Brown
- Discipline of Exercise Science, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, Western Australia, 6150, Australia
| | - Juan M Serrano
- Biological and Health Psychology Department, School of Psychology, Universidad Autonoma de Madrid, 28049, Madrid, Spain
| | - Simon M Laws
- Collaborative Genomics Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, 6027, Australia
- School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia, 6102, Australia
| | - Inmaculada C Rodríguez-Rojo
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for biomedical Technology, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223, Pozuelo de Alarcón, Madrid, Spain
- Centro Universitario Villanueva, Facultad de Psicología, 28034, Madrid, Spain
| | - Juan Verdejo-Román
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for biomedical Technology, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223, Pozuelo de Alarcón, Madrid, Spain
- Experimental Psychology Department, School of Psychology, Universidad Complutense de Madrid, 28223, Pozuelo de Alarcon, Spain
- Mind, Brain and Behavior Research Center (CIMCYC), Universidad de Granada, 18071, Granada, Spain
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for biomedical Technology, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223, Pozuelo de Alarcón, Madrid, Spain
- Experimental Psychology Department, School of Psychology, Universidad Complutense de Madrid, 28223, Pozuelo de Alarcon, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28029, Madrid, Spain
| | - Maria L Delgado-Losada
- Experimental Psychology Department, School of Psychology, Universidad Complutense de Madrid, 28223, Pozuelo de Alarcon, Spain
| | - Ana Barabash
- Endocrinology and Nutrition Department, Hospital Clinico San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, 28040, Madrid, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, 28040, Madrid, Spain
| | - Ana M López-Sobaler
- Departamento de Nutricion y Ciencia de los Alimentos, Facultad de Farmacia, Universidad Complutense de Madrid, 28040, Madrid, Spain
| | - Ramón López-Higes
- Experimental Psychology Department, School of Psychology, Universidad Complutense de Madrid, 28223, Pozuelo de Alarcon, Spain
| | - Alberto Marcos
- Neurology Department, Hospital Clinico San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, 28040, Madrid, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for biomedical Technology, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223, Pozuelo de Alarcón, Madrid, Spain
- Experimental Psychology Department, School of Psychology, Universidad Complutense de Madrid, 28223, Pozuelo de Alarcon, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28029, Madrid, Spain
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Shumbayawonda E, López-Sanz D, Bruña R, Serrano N, Fernández A, Maestú F, Abasolo D. Complexity changes in preclinical Alzheimer’s disease: An MEG study of subjective cognitive decline and mild cognitive impairment. Clin Neurophysiol 2020; 131:437-445. [DOI: 10.1016/j.clinph.2019.11.023] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 09/25/2019] [Accepted: 11/11/2019] [Indexed: 12/15/2022]
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Pusil S, López ME, Cuesta P, Bruña R, Pereda E, Maestú F. Hypersynchronization in mild cognitive impairment: the ‘X’ model. Brain 2019; 142:3936-3950. [DOI: 10.1093/brain/awz320] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 08/06/2019] [Accepted: 08/13/2019] [Indexed: 12/21/2022] Open
Abstract
Hypersynchronization has been considered as a biomarker of synaptic dysfunction along the Alzheimeŕs disease continuum. In a longitudinal MEG study, Pusil et al. reveal changes in functional connectivity upon progression from MCI to Alzheimer’s disease. They propose the ‘X’ model to explain their findings, and suggest that hypersynchronization predicts conversion.
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Affiliation(s)
- Sandra Pusil
- Laboratory of Neuropsychology, University of the Balearic Islands, Spain
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
| | - María Eugenia López
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Pablo Cuesta
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Electrical Engineering and Bioengineering Lab, Department of Industrial Engineering and IUNE Universidad de La Laguna, Tenerife, Spain
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Ernesto Pereda
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Electrical Engineering and Bioengineering Lab, Department of Industrial Engineering and IUNE Universidad de La Laguna, Tenerife, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
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Serrano N, López-Sanz D, Bruña R, Garcés P, Rodríguez-Rojo IC, Marcos A, Crespo DP, Maestú F. Spatiotemporal Oscillatory Patterns During Working Memory Maintenance in Mild Cognitive Impairment and Subjective Cognitive Decline. Int J Neural Syst 2019; 30:1950019. [DOI: 10.1142/s0129065719500199] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Working memory (WM) is a crucial cognitive process and its disruption is among the earliest symptoms of Alzheimer’s disease. While alterations of the neuronal processes underlying WM have been evidenced in mild cognitive impairment (MCI), scarce literature is available in subjective cognitive decline (SCD). We used magnetoencephalography during a WM task performed by MCI [Formula: see text], SCD [Formula: see text] and healthy elders [Formula: see text] to examine group differences during the maintenance period (0–4000[Formula: see text]ms). Data were analyzed using time–frequency analysis and significant oscillatory differences were localized at the source level. Our results indicated significant differences between groups, mainly during the early maintenance (250–1250[Formula: see text]ms) in the theta, alpha and beta bands and in the late maintenance (2750–3750[Formula: see text]ms) in the theta band. MCI showed lower local synchronization in fronto-temporal cortical regions in the early theta–alpha window relative to controls [Formula: see text] and SCD [Formula: see text], and in the late theta window relative to controls [Formula: see text] and SCD [Formula: see text]. Early theta–alpha power was significantly correlated with memory scores [Formula: see text] and late theta power was correlated with task performance [Formula: see text] and functional activity scores [Formula: see text]. In the early beta window, MCI showed reduced power in temporo-posterior regions relative to controls [Formula: see text] and SCD [Formula: see text]. Our results may suggest that these alterations would reflect that memory-related networks are damaged.
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Affiliation(s)
- N. Serrano
- Department of Experimental Psychology, Complutense University of Madrid, Pozuelo de Alarcón, Madrid 28223, Spain
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology (CTB), Pozuelo de Alarcón, Madrid 28223, Spain
| | - D. López-Sanz
- Department of Experimental Psychology, Complutense University of Madrid, Pozuelo de Alarcón, Madrid 28223, Spain
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology (CTB), Pozuelo de Alarcón, Madrid 28223, Spain
| | - R. Bruña
- Department of Experimental Psychology, Complutense University of Madrid, Pozuelo de Alarcón, Madrid 28223, Spain
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology (CTB), Pozuelo de Alarcón, Madrid 28223, Spain
- CIBER’s Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Institute of Health Carlos III, Madrid, Spain
| | - P. Garcés
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology (CTB), Pozuelo de Alarcón, Madrid 28223, Spain
| | - I. C. Rodríguez-Rojo
- Department of Experimental Psychology, Complutense University of Madrid, Pozuelo de Alarcón, Madrid 28223, Spain
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology (CTB), Pozuelo de Alarcón, Madrid 28223, Spain
| | - A. Marcos
- Neurology Department, San Carlos Clinical Hospital, Madrid, Spain
| | - D. Prada Crespo
- Centro de Prevención del Deterioro Cognitivo del Ayuntamiento, de Madrid Madrid, Spain
| | - F. Maestú
- Department of Experimental Psychology, Complutense University of Madrid, Pozuelo de Alarcón, Madrid 28223, Spain
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology (CTB), Pozuelo de Alarcón, Madrid 28223, Spain
- CIBER’s Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Institute of Health Carlos III, Madrid, Spain
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García-Alba J, Ramírez-Toraño F, Esteba-Castillo S, Bruña R, Moldenhauer F, Novell R, Romero-Medina V, Maestú F, Fernández A. Neuropsychological and neurophysiological characterization of mild cognitive impairment and Alzheimer's disease in Down syndrome. Neurobiol Aging 2019; 84:70-79. [PMID: 31518951 DOI: 10.1016/j.neurobiolaging.2019.07.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 07/03/2019] [Accepted: 07/30/2019] [Indexed: 10/26/2022]
Abstract
Down syndrome (DS) has been considered a unique model for the investigation of Alzheimer's disease (AD) but intermediate stages in the continuum are poorly defined. Considering this, we investigated the neurophysiological (i.e., magnetoencephalography [MEG]) and neuropsychological patterns of mild cognitive impairment (MCI) and AD in middle-aged adults with DS. The sample was composed of four groups: Control-DS (n = 14, mean age 44.64 ± 3.30 years), MCI-DS (n = 14, 51.64 ± 3.95 years), AD-DS (n = 13, 53.54 ± 6.58 years), and Control-no-DS (healthy controls, n = 14, 45.21 ± 4.39 years). DS individuals were studied with neuropsychological tests and MEG, whereas the Control-no-DS group completed only the MEG session. Our results showed that the AD-DS group exhibited a significantly poorer performance as compared with the Control-DS group in all tests. Furthermore, this effect was crucially evident in AD-DS individuals when compared with the MCI-DS group in verbal and working memory abilities. In the neurophysiological domain, the Control-DS group showed a widespread increase of theta activity when compared with the Control-no-DS group. With disease progression, this increased theta was substituted by an augmented delta, accompanied with a reduction of alpha activity. Such spectral pattern-specifically observed in occipital, posterior temporal, cuneus, and precuneus regions-correlated with the performance in cognitive tests. This is the first MEG study in the field incorporating both neuropsychological and neurophysiological information, and demonstrating that this combination of markers is sensitive enough to characterize different stages along the AD continuum in DS.
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Affiliation(s)
- Javier García-Alba
- Research and Psychology in Education Department, Complutense University of Madrid, Madrid, Spain; Center for Biomedical Technology, Laboratory of Cognitive and Computational Neuroscience, Technical University of Madrid, Campus Montegancedo, Madrid, Spain.
| | - Federico Ramírez-Toraño
- Center for Biomedical Technology, Laboratory of Cognitive and Computational Neuroscience, Technical University of Madrid, Campus Montegancedo, Madrid, Spain; Department of Experimental Psychology, Complutense University of Madrid, Campus de Somosaguas, Pozuelo de Alarcón, Madrid, Spain
| | - Susanna Esteba-Castillo
- Specialized Department in Mental Health and Intellectual Disability, Parc Hospitalari Martí i Julià - Institut d'Assistència Sanitària, Institut d'Assistència Sanitària (IAS), Girona, Spain; Neurodevelopment group [Girona Biomedical Research Institute]-IDIBGI, Institute of Health Assistance (IAS), Parc Hospitalari Martí i Julià, Girona, Spain
| | - Ricardo Bruña
- Center for Biomedical Technology, Laboratory of Cognitive and Computational Neuroscience, Technical University of Madrid, Campus Montegancedo, Madrid, Spain; Department of Experimental Psychology, Complutense University of Madrid, Campus de Somosaguas, Pozuelo de Alarcón, Madrid, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Fernando Moldenhauer
- Internal Medicine Department, Adult Down Syndrome Unit, La Princesa University Hospital, Health Research Institute, Madrid, Spain
| | - Ramón Novell
- Specialized Department in Mental Health and Intellectual Disability, Parc Hospitalari Martí i Julià - Institut d'Assistència Sanitària, Institut d'Assistència Sanitària (IAS), Girona, Spain; Neurodevelopment group [Girona Biomedical Research Institute]-IDIBGI, Institute of Health Assistance (IAS), Parc Hospitalari Martí i Julià, Girona, Spain
| | - Verónica Romero-Medina
- Center for Biomedical Technology, Laboratory of Cognitive and Computational Neuroscience, Technical University of Madrid, Campus Montegancedo, Madrid, Spain
| | - Fernando Maestú
- Center for Biomedical Technology, Laboratory of Cognitive and Computational Neuroscience, Technical University of Madrid, Campus Montegancedo, Madrid, Spain; Department of Experimental Psychology, Complutense University of Madrid, Campus de Somosaguas, Pozuelo de Alarcón, Madrid, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Alberto Fernández
- Center for Biomedical Technology, Laboratory of Cognitive and Computational Neuroscience, Technical University of Madrid, Campus Montegancedo, Madrid, Spain; Department of Legal Medicine, Psychiatry and Pathology, Complutense University of Madrid, Spain, Madrid, Spain
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Hughes LE, Henson RN, Pereda E, Bruña R, López-Sanz D, Quinn AJ, Woolrich MW, Nobre AC, Rowe JB, Maestú F. Biomagnetic biomarkers for dementia: A pilot multicentre study with a recommended methodological framework for magnetoencephalography. Alzheimers Dement (Amst) 2019; 11:450-462. [PMID: 31431918 PMCID: PMC6579903 DOI: 10.1016/j.dadm.2019.04.009] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Introduction An increasing number of studies are using magnetoencephalography (MEG) to study dementia. Here we define a common methodological framework for MEG resting-state acquisition and analysis to facilitate the pooling of data from different sites. Methods Two groups of patients with mild cognitive impairment (MCI, n = 84) and healthy controls (n = 84) were combined from three sites, and site and group differences inspected in terms of power spectra and functional connectivity. Classification accuracy for MCI versus controls was compared across three different types of MEG analyses, and compared with classification based on structural MRI. Results The spectral analyses confirmed frequency-specific differences in patients with MCI, both in power and connectivity patterns, with highest classification accuracy from connectivity. Critically, site acquisition differences did not dominate the results. Discussion This work provides detailed protocols and analyses that are sensitive to cognitive impairment, and that will enable standardized data sharing to facilitate large-scale collaborative projects.
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Affiliation(s)
- Laura E Hughes
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.,MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Richard N Henson
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.,Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Ernesto Pereda
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Madrid, Spain.,Department of Industrial Engineering, Instituto Universitario de Neurociencia, Universidad de La Laguna, Tenerife, Spain
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Madrid, Spain.,Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.,Department of Experimental Psychology, Faculty of Psychology, Universidad Complutense de Madrid, Madrid, Spain
| | - David López-Sanz
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Madrid, Spain.,Department of Experimental Psychology, Faculty of Psychology, Universidad Complutense de Madrid, Madrid, Spain
| | - Andrew J Quinn
- Oxford Centre for Human Brain Activity, University of Oxford, Oxford, UK.,Wellcome Centre for Integrative Neuroscience, University of Oxford, Oxford, UK
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity, University of Oxford, Oxford, UK.,Wellcome Centre for Integrative Neuroscience, University of Oxford, Oxford, UK
| | - Anna C Nobre
- Oxford Centre for Human Brain Activity, University of Oxford, Oxford, UK.,Wellcome Centre for Integrative Neuroscience, University of Oxford, Oxford, UK.,Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.,MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Madrid, Spain.,Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.,Department of Experimental Psychology, Faculty of Psychology, Universidad Complutense de Madrid, Madrid, Spain
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López-Sanz D, Bruña R, Delgado-Losada ML, López-Higes R, Marcos-Dolado A, Maestú F, Walter S. Electrophysiological brain signatures for the classification of subjective cognitive decline: towards an individual detection in the preclinical stages of dementia. Alzheimers Res Ther 2019; 11:49. [PMID: 31151467 PMCID: PMC6544924 DOI: 10.1186/s13195-019-0502-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2018] [Accepted: 05/05/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) prevalence is rapidly growing as worldwide populations grow older. Available treatments have failed to slow down disease progression, thus increasing research focus towards early or preclinical stages of the disease. Subjective cognitive decline (SCD) is known to increase the risk of developing AD and several other negative outcomes. However, it is still very scarcely characterized and there is no neurophysiological study devoted to its individual classification which could improve targeted sample recruitment for clinical trials. METHODS Two hundred fifty-two older adults (70 healthy controls, 91 SCD, and 91 MCI) underwent a magnetoencephalography scan. Alpha relative power in the source space was employed to train a LASSO classifier and applied to distinguish between healthy controls and SCD. Moreover, MCI participants were used to further validate the previously trained algorithm. RESULTS The classifier was significantly associated to SCD with an AUC of 0.81 in the whole sample. After randomly splitting the sample in 2/3 for discovery and 1/3 for validation, the newly trained classifier was also able to correctly classify SCD individuals with an AUC of 0.75 in the validation sample. The regions selected by the algorithm included medial frontal, temporal, and occipital areas. The algorithm trained to select SCD individuals was also significantly associated to MCI diagnostic. CONCLUSIONS According to our results, magnetoencephalography could be a useful tool for distinguishing individuals with SCD and healthy older adults without cognitive concerns. Furthermore, our classifier showed good external validity, being not only successful for an unseen SCD sample, but also in a different population with MCI cases. This supports its utility in the context of preclinical dementia. These findings highlight the potential applications of electrophysiological techniques to improve sample recruitment at the individual level in the context of clinical trials.
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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 (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
- CIBER-BBN: Networking Research Center on Bioengineering, Biomaterials and Nanomedicine, Zaragoza, Spain
| | | | - Ramón López-Higes
- Department of Experimental Psychology, Complutense University of Madrid (UCM), 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 (UCM), Madrid, Spain
- CIBER-BBN: Networking Research Center on Bioengineering, Biomaterials and Nanomedicine, Zaragoza, Spain
| | - Stefan Walter
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, USA
- Dept. of Preventive Medicine and Public Health, University Rey Juan Carlos, Madrid, Spain
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López-Sanz D, Bruña R, de Frutos-Lucas J, Maestú F. Magnetoencephalography applied to the study of Alzheimer's disease. Prog Mol Biol Transl Sci 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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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 (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.
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Rodríguez-Rojo IC, Cuesta P, López ME, de Frutos-Lucas J, Bruña R, Pereda E, Barabash A, Montejo P, Montenegro-Peña M, Marcos A, López-Higes R, Fernández A, Maestú F. BDNF Val66Met Polymorphism and Gamma Band Disruption in Resting State Brain Functional Connectivity: A Magnetoencephalography Study in Cognitively Intact Older Females. Front Neurosci 2018; 12:684. [PMID: 30333719 PMCID: PMC6176075 DOI: 10.3389/fnins.2018.00684] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Accepted: 09/11/2018] [Indexed: 11/13/2022] Open
Abstract
The pathophysiological processes undermining brain functioning decades before the onset of the clinical symptoms associated with dementia are still not well understood. Several heritability studies have reported that the Brain Derived Neurotrophic Factor (BDNF) Val66Met genetic polymorphism could contribute to the acceleration of cognitive decline in aging. This mutation may affect brain functional connectivity (FC), especially in those who are carriers of the BDNF Met allele. The aim of this work was to explore the influence of the BDNF Val66Met polymorphism in whole brain eyes-closed, resting-state magnetoencephalography (MEG) FC in a sample of 36 cognitively intact (CI) older females. All of them were ε3ε3 homozygotes for the apolipoprotein E (APOE) gene and were divided into two subgroups according to the presence of the Met allele: Val/Met group (n = 16) and Val/Val group (n = 20). They did not differ in age, years of education, Mini-Mental State Examination scores, or normalized hippocampal volumes. Our results showed reduced antero-posterior gamma band FC within the Val/Met genetic risk group, which may be caused by a GABAergic network impairment. Despite the lack of cognitive decline, these results might suggest a selective brain network vulnerability due to the carriage of the BDNF Met allele, which is linked to a potential progression to dementia. This neurophysiological signature, as tracked with MEG FC, indicates that age-related brain functioning changes could be mediated by the influence of particular genetic risk factors.
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Affiliation(s)
- Inmaculada C Rodríguez-Rojo
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain.,Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain
| | - Pablo Cuesta
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain.,Electrical Engineering and Bioengineering Lab, Department of Industrial Engineering and IUNE, Universidad de La Laguna, Tenerife, Spain
| | - María Eugenia López
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain.,Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain
| | - Jaisalmer de Frutos-Lucas
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain.,Biological and Health Psychology Department, Universidad Autónoma de Madrid, Madrid, Spain
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain.,Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain.,Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Ernesto Pereda
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain.,Electrical Engineering and Bioengineering Lab, Department of Industrial Engineering and IUNE, Universidad de La Laguna, Tenerife, Spain
| | - Ana Barabash
- Laboratory of Psychoneuroendocrinology and Genetics, Hospital Clínico San Carlos, Madrid, Spain.,Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Pedro Montejo
- Center for the Prevention of Cognitive Impairment, Public Health Institute, Madrid-Salud, Madrid, Spain
| | - Mercedes Montenegro-Peña
- Center for the Prevention of Cognitive Impairment, Public Health Institute, Madrid-Salud, Madrid, Spain
| | - Alberto Marcos
- Neurology Department, Hospital Clínico San Carlos, Madrid, Spain
| | - Ramón López-Higes
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain
| | - Alberto Fernández
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain.,Department of Legal Medicine, Psychiatry, and Pathology, Universidad Complutense de Madrid, Madrid, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain.,Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain.,Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
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Abstract
OBJECTIVE Despite the increase in calculation power over the last few decades, the estimation of brain connectivity is still a tedious task. The high computational cost of the algorithms escalates with the square of the number of signals evaluated, usually in the range of thousands. In this work we propose a re-formulation of a widely used algorithm that allows the estimation of whole brain connectivity in much smaller times. APPROACH We start from the original implementation of phase locking value (PLV) and re-formulated it in a computationally very efficient way. What is more, this formulation stresses its strong similarity with coherence, which we used to introduce two new metrics insensitive to zero lag synchronization: the imaginary part of PLV (iPLV) and its corrected counterpart (ciPLV). MAIN RESULTS The new implementation of PLV avoids some highly CPU-expensive operations and achieves a 100-fold speedup over the original algorithm. The new derived metrics were highly robust in the presence of volume conduction. Moreover, ciPLV proved capable of ignoring zero-lag connectivity, while correctly estimating nonzero-lag connectivity. SIGNIFICANCE Our implementation of PLV makes it possible to calculate whole-brain connectivity in much shorter times. The results of the simulations using ciPLV suggest that this metric is ideal to measure synchronization in the presence of volume conduction or source leakage effects.
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Affiliation(s)
- Ricardo Bruña
- Laboratory for Cognitive and Computational Neuroscience, Canter for Biomedical Technology, Technical University of Madrid, Pozuelo de Alarcón, Madrid, Spain. Departamento de Psicologia Experimental, Procesos Psicologicos y Logopedia, Faculty of Psychology, Complutense University of Madrid, Pozuelo de Alarcón, Madrid, Spain
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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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López-Sanz D, Bruña R, Garcés P, Martín-Buro MC, Walter S, Delgado ML, Montenegro M, López Higes R, Marcos A, Maestú F. Functional Connectivity Disruption in Subjective Cognitive Decline and Mild Cognitive Impairment: A Common Pattern of Alterations. Front Aging Neurosci 2017; 9:109. [PMID: 28484387 PMCID: PMC5399035 DOI: 10.3389/fnagi.2017.00109] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2016] [Accepted: 04/04/2017] [Indexed: 11/28/2022] Open
Abstract
Functional connectivity (FC) alterations represent a key feature in Alzheimer's Disease (AD) and provide a useful tool to characterize and predict the course of the disease. Those alterations have been also described in Mild Cognitive Impairment (MCI), a prodromal stage of AD. There is a growing interest in detecting AD pathology in the brain in the very early stages of the disorder. Subjective Cognitive Decline (SCD) could represent a preclinical asymptomatic stage of AD but very little is known about this population. In the present work we assessed whether FC disruptions are already present in this stage, and if they share any spatial distribution properties with MCI alterations (a condition known to be highly related to AD). To this end, we measured electromagnetic spontaneous activity with MEG in 39 healthy control elders, 41 elders with SCD and 51 MCI patients. The results showed FC alterations in both SCD and MCI compared to the healthy control group. Interestingly, both groups exhibited a very similar spatial pattern of altered links: a hyper-synchronized anterior network and a posterior network characterized by a decrease in FC. This decrease was more pronounced in the MCI group. These results highlight that elders with SCD present FC alterations. More importantly, those disruptions affected AD typically related areas and showed great overlap with the alterations exhibited by MCI patients. These results support the consideration of SCD as a preclinical stage of AD and may indicate that FC alterations appear very early in the course of the disease.
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Affiliation(s)
- David López-Sanz
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of MadridPozuelo de Alarcón, Spain.,Department of Basic Psychology II, Complutense University of MadridPozuelo de Alarcón, Spain
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of MadridPozuelo de Alarcón, Spain
| | - Pilar Garcés
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of MadridPozuelo de Alarcón, Spain
| | - María Carmen Martín-Buro
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of MadridPozuelo de Alarcón, Spain.,Department of Basic Psychology II, Complutense University of MadridPozuelo de Alarcón, Spain
| | - Stefan Walter
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of MadridPozuelo de Alarcón, Spain.,Centro de investigación biomédica, Getafe HospitalGetafe, Spain
| | - María Luisa Delgado
- Department of Basic Psychology II, Complutense University of MadridPozuelo de Alarcón, Spain
| | - Mercedes Montenegro
- Memory Decline Prevention Center Madrid Salud, Ayuntamiento de MadridMadrid, Spain
| | - Ramón López Higes
- Department of Basic Psychology II, Complutense University of MadridPozuelo de Alarcón, Spain
| | - Alberto Marcos
- Neurology Department, San Carlos Clinical HospitalMadrid, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of MadridPozuelo de Alarcón, Spain.,Department of Basic Psychology II, Complutense University of MadridPozuelo de Alarcón, Spain
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López ME, Turrero A, Cuesta P, López-Sanz D, Bruña R, Marcos A, Gil P, Yus M, Barabash A, Cabranes JA, Maestú F, Fernández A. Searching for Primary Predictors of Conversion from Mild Cognitive Impairment to Alzheimer's Disease: A Multivariate Follow-Up Study. J Alzheimers Dis 2017; 52:133-43. [PMID: 27060953 DOI: 10.3233/jad-151034] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Recent proposals of diagnostic criteria within the healthy aging-Alzheimer's disease (AD) continuum stressed the role of biomarker information. More importantly, such information might be critical to predict those mild cognitive impairment (MCI) patients at a higher risk of conversion to AD. Usually, follow-up studies utilize a reduced number of potential markers although the conversion phenomenon may be deemed as multifactorial in essence. In addition, not only biological but also cognitive markers may play an important role. Considering this background, we investigated the role of cognitive reserve, cognitive performance in neuropsychological testing, hippocampal volumes, APOE genotype, and magnetoencephalography power sources to predict the conversion to AD in a sample of 33 MCI patients. MCIs were followed up during a 2-year period and divided into two subgroups according to their outcome: The "stable" MCI group (sMCI, 21 subjects) and the "progressive" MCI group (pMCI, 12 subjects). Baseline multifactorial information was submitted to a hierarchical logistic regression analysis to build a predictive model of conversion to AD. Results indicated that the combination of left hippocampal volume, occipital cortex theta power, and clock drawing copy subtest scores predicted conversion to AD with a 100% of sensitivity and 94.7% of specificity. According to these results it might be suggested that anatomical, cognitive, and neurophysiological markers may be considered as "first order" predictors of progression to AD, while APOE or cognitive reserve proxies might play a more secondary role.
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Affiliation(s)
- María Eugenia López
- Laboratory of Neuropsychology, Universitat de les Illes Balears, Palma de Mallorca, Spain.,Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain.,Institute of Sanitary Investigation [IdISSC], San Carlos University Hospital, Madrid, Spain
| | - Agustín Turrero
- Institute of Sanitary Investigation [IdISSC], San Carlos University Hospital, Madrid, Spain.,Department of Biostatistics and Operational Investigation, Complutense University of Madrid, Spain
| | - Pablo Cuesta
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain
| | - David López-Sanz
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain.,Department of Basic Psychology II, Complutense University of Madrid, Spain
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain
| | - Alberto Marcos
- Institute of Sanitary Investigation [IdISSC], San Carlos University Hospital, Madrid, Spain.,Neurology Department, San Carlos University Hospital, Madrid, Spain
| | - Pedro Gil
- Institute of Sanitary Investigation [IdISSC], San Carlos University Hospital, Madrid, Spain.,Geriatrics Department, San Carlos University Hospital, Madrid, Spain
| | - Miguel Yus
- Institute of Sanitary Investigation [IdISSC], San Carlos University Hospital, Madrid, Spain.,Radiology Department, San Carlos University Hospital, Madrid, Spain
| | - Ana Barabash
- Institute of Sanitary Investigation [IdISSC], San Carlos University Hospital, Madrid, Spain.,Laboratory of Psychoneuroendocrinology and Molecular Genetics, Biomedical Research Foundation, San Carlos University Hospital, Madrid, Spain
| | - José Antonio Cabranes
- Institute of Sanitary Investigation [IdISSC], San Carlos University Hospital, Madrid, Spain.,Laboratory of Psychoneuroendocrinology and Molecular Genetics, Biomedical Research Foundation, San Carlos University Hospital, Madrid, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain.,Institute of Sanitary Investigation [IdISSC], San Carlos University Hospital, Madrid, Spain.,Department of Basic Psychology II, Complutense University of Madrid, Spain
| | - Alberto Fernández
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain.,Institute of Sanitary Investigation [IdISSC], San Carlos University Hospital, Madrid, Spain.,Department of Psychiatry, Faculty of Medicine, Complutense University of Madrid, Spain
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López-Sanz D, Bruña R, Garcés P, Camara C, Serrano N, Rodríguez-Rojo IC, Delgado ML, Montenegro M, López-Higes R, Yus M, Maestú F. Alpha band disruption in the AD-continuum starts in the Subjective Cognitive Decline stage: a MEG study. Sci Rep 2016; 6:37685. [PMID: 27883082 PMCID: PMC5121589 DOI: 10.1038/srep37685] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Accepted: 11/01/2016] [Indexed: 11/09/2022] Open
Abstract
The consideration of Subjective Cognitive Decline (SCD) as a preclinical stage of AD remains still a matter of debate. Alpha band alterations represent one of the most significant changes in the electrophysiological profile of AD. In particular, AD patients exhibit reduced alpha relative power and frequency. We used alpha band activity measured with MEG to study whether SCD and MCI elders present these electrophysiological changes characteristic of AD, and to determine the evolution of the observed alterations across AD spectrum. The total sample consisted of 131 participants: 39 elders without SCD, 41 elders with SCD and 51 MCI patients. All of them underwent MEG and MRI scans and neuropsychological assessment. SCD and MCI patients exhibited a similar reduction in alpha band activity compared with the no SCD group. However, only MCI patients showed a slowing in their alpha peak frequency compared with both SCD and no SCD. These changes in alpha band were related to worse cognition. Our results suggest that AD-related alterations may start in the SCD stage, with a reduction in alpha relative power. It is later, in the MCI stage, where the slowing of the spectral profile takes place, giving rise to objective deficits in cognitive functioning.
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Affiliation(s)
- D López-Sanz
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain.,Department of Basic Psychology II, Complutense University of Madrid, Spain
| | - R Bruña
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain
| | - P Garcés
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain
| | - C Camara
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain
| | - N Serrano
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain.,Department of Basic Psychology II, Complutense University of Madrid, Spain
| | - I C Rodríguez-Rojo
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain.,Department of Basic Psychology II, Complutense University of Madrid, Spain
| | - M L Delgado
- Department of Basic Psychology II, Complutense University of Madrid, Spain
| | - M Montenegro
- Memory Decline Prevention Center Madrid Salud, Ayuntamiento de Madrid, Spain
| | - R López-Higes
- Department of Basic Psychology II, Complutense University of Madrid, Spain
| | - M Yus
- Radiology Department, San Carlos University Hospital, Madrid, Spain
| | - F Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain.,Department of Basic Psychology II, Complutense University of Madrid, Spain
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Camara C, Warwick K, Bruña R, Aziz T, del Pozo F, Maestú F. A Fuzzy Inference System for Closed-Loop Deep Brain Stimulation in Parkinson's Disease. J Med Syst 2015; 39:155. [PMID: 26385550 DOI: 10.1007/s10916-015-0328-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Accepted: 08/18/2015] [Indexed: 12/27/2022]
Abstract
Parkinsons disease is a complex neurodegenerative disorder for which patients present many symptoms, tremor being the main one. In advanced stages of the disease, Deep Brain Stimulation is a generalized therapy which can significantly improve the motor symptoms. However despite its beneficial effects on treating the symptomatology, the technique can be improved. One of its main limitations is that the parameters are fixed, and the stimulation is provided uninterruptedly, not taking into account any fluctuation in the patients state. A closed-loop system which provides stimulation by demand would adjust the stimulation to the variations in the state of the patient, stimulating only when it is necessary. It would not only perform a more intelligent stimulation, capable of adapting to the changes in real time, but also extending the devices battery life, thereby avoiding surgical interventions. In this work we design a tool that learns to recognize the principal symptom of Parkinsons disease and particularly the tremor. The goal of the designed system is to detect the moments the patient is suffering from a tremor episode and consequently to decide whether stimulation is needed or not. For that, local field potentials were recorded in the subthalamic nucleus of ten Parkinsonian patients, who were diagnosed with tremor-dominant Parkinsons disease and who underwent surgery for the implantation of a neurostimulator. Electromyographic activity in the forearm was simultaneously recorded, and the relation between both signals was evaluated using two different synchronization measures. The results of evaluating the synchronization indexes on each moment represent the inputs to the designed system. Finally, a fuzzy inference system was applied with the goal of identifying tremor episodes. Results are favourable, reaching accuracies of higher 98.7% in 70% of the patients.
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Affiliation(s)
- Carmen Camara
- Centre for Biomedical Technology, Technical University of Madrid, Madrid, Spain.
| | - Kevin Warwick
- Vice Chancellors Office, Coventry University, Coventry, UK
| | - Ricardo Bruña
- Centre for Biomedical Technology, Technical University of Madrid, Madrid, Spain
| | - Tipu Aziz
- Department of Surgery, John Radcliffe Hospital, Oxford, UK
| | - Francisco del Pozo
- Centre for Biomedical Technology, Technical University of Madrid, Madrid, Spain
| | - Fernando Maestú
- Centre for Biomedical Technology, Technical University of Madrid, Madrid, Spain
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Cuesta P, Garcés P, Castellanos NP, López ME, Aurtenetxe S, Bajo R, Pineda-Pardo JA, Bruña R, Marín AG, Delgado M, Barabash A, Ancín I, Cabranes JA, Fernandez A, del Pozo F, Sancho M, Marcos A, Nakamura A, Maestú F. Influence of the APOE ε4 Allele and Mild Cognitive Impairment Diagnosis in the Disruption of the MEG Resting State Functional Connectivity in Sources Space. ACTA ACUST UNITED AC 2015; 44:493-505. [DOI: 10.3233/jad-141872] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Pablo Cuesta
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain
- Department of Basic Psychology II, Complutense University of Madrid, Spain
- Department of Applied Physics III, Faculty of Physics, Complutense University of Madrid, Madrid, Spain
| | - Pilar Garcés
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain
- Department of Applied Physics III, Faculty of Physics, Complutense University of Madrid, Madrid, Spain
| | | | - Maria Eugenia López
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain
- Department of Basic Psychology II, Complutense University of Madrid, Spain
| | - Sara Aurtenetxe
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain
| | - Ricardo Bajo
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain
| | - José Angel Pineda-Pardo
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain
| | | | - Marisa Delgado
- Seniors Center of the District of Chamartin, Madrid, Spain
| | - Ana Barabash
- Laboratory of Psychoneuroendocrinology and Molecular Genetics, Biomedical Research Foundation, Clínico San Carlos Hospital, Madrid, Spain
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
| | - Inés Ancín
- Laboratory of Psychoneuroendocrinology and Molecular Genetics, Biomedical Research Foundation, Clínico San Carlos Hospital, Madrid, Spain
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
| | - Jose Antonio Cabranes
- Laboratory of Psychoneuroendocrinology and Molecular Genetics, Biomedical Research Foundation, Clínico San Carlos Hospital, Madrid, Spain
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
| | - Alberto Fernandez
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Department of Psychiatry, Faculty of Medicine, Complutense University of Madrid, Spain
| | - Francisco del Pozo
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain
| | - Miguel Sancho
- Department of Applied Physics III, Faculty of Physics, Complutense University of Madrid, Madrid, Spain
| | - Alberto Marcos
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Neurology Department, San Carlos University Hospital, Madrid, Spain
| | - Akinori Nakamura
- Department of Clinical and Experimental Neuroimaging, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain
- Department of Basic Psychology II, Complutense University of Madrid, Spain
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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48
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Pineda-Pardo JA, Bruña R, Woolrich M, Marcos A, Nobre AC, Maestú F, Vidaurre D. Guiding functional connectivity estimation by structural connectivity in MEG: an application to discrimination of conditions of mild cognitive impairment. Neuroimage 2014; 101:765-77. [PMID: 25111472 PMCID: PMC4312351 DOI: 10.1016/j.neuroimage.2014.08.002] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2014] [Revised: 07/30/2014] [Accepted: 08/01/2014] [Indexed: 01/18/2023] Open
Abstract
Whole brain resting state connectivity is a promising biomarker that might help to obtain an early diagnosis in many neurological diseases, such as dementia. Inferring resting-state connectivity is often based on correlations, which are sensitive to indirect connections, leading to an inaccurate representation of the real backbone of the network. The precision matrix is a better representation for whole brain connectivity, as it considers only direct connections. The network structure can be estimated using the graphical lasso (GL), which achieves sparsity through l1-regularization on the precision matrix. In this paper, we propose a structural connectivity adaptive version of the GL, where weaker anatomical connections are represented as stronger penalties on the corresponding functional connections. We applied beamformer source reconstruction to the resting state MEG recordings of 81 subjects, where 29 were healthy controls, 22 were single-domain amnestic Mild Cognitive Impaired (MCI), and 30 were multiple-domain amnestic MCI. An atlas-based anatomical parcellation of 66 regions was obtained for each subject, and time series were assigned to each of the regions. The fiber densities between the regions, obtained with deterministic tractography from diffusion-weighted MRI, were used to define the anatomical connectivity. Precision matrices were obtained with the region specific time series in five different frequency bands. We compared our method with the traditional GL and a functional adaptive version of the GL, in terms of log-likelihood and classification accuracies between the three groups. We conclude that introducing an anatomical prior improves the expressivity of the model and, in most cases, leads to a better classification between groups. We propose an anatomy-driven method for functional connectivity estimation in MEG. Structural prior contributes to a better representation of the functional connectivity. The proposed method is shown to be useful as a biomarker for classification of MCI.
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Affiliation(s)
- José Angel Pineda-Pardo
- Laboratory of Neuroimaging, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Campus de Montegancedo, 28223 Pozuelo de Alarcón, Spain; Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Campus de Montegancedo, 28223 Pozuelo de Alarcón, Spain.
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Campus de Montegancedo, 28223 Pozuelo de Alarcón, Spain.
| | - Mark Woolrich
- Oxford Center for Human Brain Activity (OHBA), University of Oxford, Oxford, United Kingdom; The Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom.
| | - Alberto Marcos
- The Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom; Department of Neurology, Hospital Clínico San Carlos, Madrid, Spain.
| | - Anna C Nobre
- Oxford Center for Human Brain Activity (OHBA), University of Oxford, Oxford, United Kingdom.
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Campus de Montegancedo, 28223 Pozuelo de Alarcón, Spain.
| | - Diego Vidaurre
- Oxford Center for Human Brain Activity (OHBA), University of Oxford, Oxford, United Kingdom; The Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom.
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López ME, Aurtenetxe S, Pereda E, Cuesta P, Castellanos NP, Bruña R, Niso G, Maestú F, Bajo R. Cognitive reserve is associated with the functional organization of the brain in healthy aging: a MEG study. Front Aging Neurosci 2014; 6:125. [PMID: 24982632 PMCID: PMC4056015 DOI: 10.3389/fnagi.2014.00125] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2014] [Accepted: 05/28/2014] [Indexed: 01/01/2023] Open
Abstract
The proportion of elderly people in the population has increased rapidly in the last century and consequently “healthy aging” is expected to become a critical area of research in neuroscience. Evidence reveals how healthy aging depends on three main behavioral factors: social lifestyle, cognitive activity, and physical activity. In this study, we focused on the role of cognitive activity, concentrating specifically on educational and occupational attainment factors, which were considered two of the main pillars of cognitive reserve (CR). Twenty-one subjects with similar rates of social lifestyle, physical and cognitive activity were selected from a sample of 55 healthy adults. These subjects were divided into two groups according to their level of CR; one group comprised subjects with high CR (9 members) and the other one contained those with low CR (12 members). To evaluate the cortical brain connectivity network, all participants were recorded by Magnetoencephalography (MEG) while they performed a memory task (modified version of the Sternberg's Task). We then applied two algorithms [Phase Locking Value (PLV) and Phase Lag Index (PLI)] to study the dynamics of functional connectivity. In response to the same task, the subjects with lower CR presented higher functional connectivity than those with higher CR. These results may indicate that participants with low CR needed a greater “effort” than those with high CR to achieve the same level of cognitive performance. Therefore, we conclude that CR contributes to the modulation of the functional connectivity patterns of the aging brain.
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Affiliation(s)
- María E López
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Universidad Politécnica de Madrid Madrid, Spain ; Department of Basic Psychology II, Complutense University of Madrid Spain
| | - Sara Aurtenetxe
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Universidad Politécnica de Madrid Madrid, Spain ; Department of Basic Psychology II, Complutense University of Madrid Spain
| | - Ernesto Pereda
- Grupo de Ingeniería Eléctrica y Bioingeniería, Department of Industrial Engineering and Institute of Biomedical Technology, Universidad de La Laguna La Laguna, Tenerife
| | - Pablo Cuesta
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Universidad Politécnica de Madrid Madrid, Spain ; Department of Basic Psychology II, Complutense University of Madrid Spain
| | - Nazareth P Castellanos
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Universidad Politécnica de Madrid Madrid, Spain
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Universidad Politécnica de Madrid Madrid, Spain
| | - Guiomar Niso
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Universidad Politécnica de Madrid Madrid, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Universidad Politécnica de Madrid Madrid, Spain ; Department of Basic Psychology II, Complutense University of Madrid Spain
| | - Ricardo Bajo
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Universidad Politécnica de Madrid Madrid, Spain ; Departamento de Matemáticas, Universidad Internacional de La Rioja (UNIR) Logroño, Spain
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50
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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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Affiliation(s)
- Ricardo Bruña
- Biomedical Engineering Group, Departmento T.S.C.I.T., E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Valladolid, Spain
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