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Taddei M, Cuesta P, Annunziata S, Bulgheroni S, Esposito S, Visani E, Granvillano A, Dotta S, Rossi DS, Panzica F, Franceschetti S, Varotto G, Riva D. Correlation between autistic traits and brain functional connectivity in preschoolers with autism spectrum disorder: a resting state MEG study. Neurol Sci 2024; 45:4549-4561. [PMID: 38639894 DOI: 10.1007/s10072-024-07528-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/09/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND Neurophysiological studies recognized that Autism Spectrum Disorder (ASD) is associated with altered patterns of over- and under-connectivity. However, little is known about network organization in children with ASD in the early phases of development and its correlation with the severity of core autistic features. METHODS The present study aimed at investigating the association between brain connectivity derived from MEG signals and severity of ASD traits measured with different diagnostic clinical scales, in a sample of 16 children with ASD aged 2 to 6 years. RESULTS A significant correlation emerged between connectivity strength in cortical brain areas implicated in several resting state networks (Default mode, Central executive, Salience, Visual and Sensorimotor) and the severity of communication anomalies, social interaction problems, social affect problems, and repetitive behaviors. Seed analysis revealed that this pattern of correlation was mainly caused by global rather than local effects. CONCLUSIONS The present evidence suggests that altered connectivity strength in several resting state networks is related to clinical features and may contribute to neurofunctional correlates of ASD. Future studies implementing the same method on a wider and stratified sample may further support functional connectivity as a possible biomarker of the condition.
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Affiliation(s)
- Matilde Taddei
- Unit for Neurogenetic Syndromes With Intellectual Disabilities and Autism Spectrum Disorders, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy
| | - Pablo Cuesta
- Department of Radiology, Rehabilitation, and Physiotherapy, Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
| | - Silvia Annunziata
- Unit for Neurogenetic Syndromes With Intellectual Disabilities and Autism Spectrum Disorders, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy
- Fondazione Don Carlo Gnocchi Onlus-IRCCS S. Maria Nascente, Via Capecelatro 66, 20148, Milan, Italy
| | - Sara Bulgheroni
- Unit for Neurogenetic Syndromes With Intellectual Disabilities and Autism Spectrum Disorders, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy
| | - Silvia Esposito
- Unit for Neurogenetic Syndromes With Intellectual Disabilities and Autism Spectrum Disorders, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy
| | - Elisa Visani
- Neurophysiopathology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy
| | - Alice Granvillano
- Neurophysiopathology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy
| | - Sara Dotta
- Neurophysiopathology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy
| | - Davide Sebastiano Rossi
- Neurophysiopathology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy
| | - Ferruccio Panzica
- Clinical Engineering Service, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Silvana Franceschetti
- Neurophysiopathology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy
| | - Giulia Varotto
- Neurophysiopathology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy.
- Epilepsy Unit, Bioengineering Group, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy.
- Laboratory for Clinical Neuroscience, Center for Biomedical Technology, University Politécnica de Madrid, Madrid, Spain.
| | - Daria Riva
- Unit for Neurogenetic Syndromes With Intellectual Disabilities and Autism Spectrum Disorders, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy
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García-Colomo A, López-Sanz D, Stam CJ, Hillebrand A, Carrasco-Gómez M, Spuch C, Comis-Tuche M, Maestú F. Minimum spanning tree analysis of unimpaired individuals at risk of Alzheimer's disease. Brain Commun 2024; 6:fcae283. [PMID: 39229485 PMCID: PMC11369931 DOI: 10.1093/braincomms/fcae283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 06/20/2024] [Accepted: 08/16/2024] [Indexed: 09/05/2024] Open
Abstract
Identifying early and non-invasive biomarkers to detect individuals in the earliest stages of the Alzheimer's disease continuum is crucial. As a result, electrophysiology and plasma biomarkers are emerging as great candidates in this pursuit due to their low invasiveness. This is the first magnetoencephalography study to assess the relationship between minimum spanning tree parameters, an alternative to overcome the comparability and thresholding problem issues characteristic of conventional brain network analyses, and plasma phosphorylated tau231 levels in unimpaired individuals, with different risk levels of Alzheimer's disease. Seventy-six individuals with available magnetoencephalography recordings and phosphorylated tau231 plasma determination were included. The minimum spanning tree for the theta, alpha and beta bands for each subject was obtained, and the leaf fraction, tree hierarchy and diameter were calculated. To study the relationship between these topological parameters and phosphorylated tau231, we performed correlation analyses, for the whole sample and considering the two risk sub-groups separately. Increasing concentrations of phosphorylated tau231 were associated with greater leaf fraction and tree hierarchy values, along with lower diameter values, for the alpha and theta frequency bands. These results emerged for the whole sample and the higher risk group, but not for the lower risk group. Our results indicate that the network topology of cognitively unimpaired individuals with elevated plasma phosphorylated tau231 levels, a marker of Alzheimer's disease pathology and amyloid-β accumulation, is already altered, shifting towards a more integrated network increasing its vulnerability and hub-dependency, mostly in the alpha band. This is indicated by increases in leaf fraction and tree hierarchy, along with reductions in diameter. These results match the initial trajectory proposed by theoretical models of disease progression and network disruption and suggest that changes in brain function and organization begin early on.
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Affiliation(s)
- Alejandra García-Colomo
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, 28223 Pozuelo de Alarcón, Spain
- Department of Experimental Psychology, Cognitive Psychology and Speech and Language Therapy, Complutense University of Madrid, 28223 Pozuelo de Alarcón, Spain
| | - David López-Sanz
- Department of Experimental Psychology, Cognitive Psychology and Speech and Language Therapy, Complutense University of Madrid, 28223 Pozuelo de Alarcón, Spain
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and Magnetoencephalography Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, 1081 HV Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and Magnetoencephalography Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, 1081 HV Amsterdam, The Netherlands
| | - Martín Carrasco-Gómez
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, 28223 Pozuelo de Alarcón, Spain
- Department of Electronic Engineering, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Carlos Spuch
- Translational Neuroscience Research Group, Galicia Sur Health Research Institute (IIS-Galicia Sur), SERGAS-UVIGO, CIBERSAM, 36312 Vigo, Spain
| | - María Comis-Tuche
- Translational Neuroscience Research Group, Galicia Sur Health Research Institute (IIS-Galicia Sur), SERGAS-UVIGO, CIBERSAM, 36312 Vigo, Spain
| | - Fernando Maestú
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, 28223 Pozuelo de Alarcón, Spain
- Department of Experimental Psychology, Cognitive Psychology and Speech and Language Therapy, Complutense University of Madrid, 28223 Pozuelo de Alarcón, Spain
- Health Research Institute of the Hospital Clínico San Carlos (IdISSC), 28040 Madrid, Spain
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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] [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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Carrasco-Gómez M, García-Colomo A, Nebreda A, Bruña R, Santos A, Maestú F. Dynamic functional connectivity is modulated by the amount of p-Tau231 in blood in cognitively intact participants. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.29.596323. [PMID: 38854147 PMCID: PMC11160744 DOI: 10.1101/2024.05.29.596323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
INTRODUCTION Electrophysiology and plasma biomarkers are early and non-invasive candidates for Alzheimer's disease detection. The purpose of this paper is to evaluate changes in dynamic functional connectivity measured with magnetoencephalography, associated with the plasma pathology marker p-tau231 in unimpaired adults. METHODS 73 individuals were included. Static and dynamic functional connectivity were calculated using leakage corrected amplitude envelope correlation. Each source's strength entropy across trials was calculated. A data-driven statistical analysis was performed to find the association between functional connectivity and plasma p-tau231 levels. Regression models were used to assess the influence of other variables over the clusters' connectivity. RESULTS Frontotemporal dynamic connectivity positively associated with p-tau231 levels. Linear regression models identified pathological, functional and structural factors that influence dynamic functional connectivity. DISCUSSION These results expand previous literature on dynamic functional connectivity in healthy individuals at risk of AD, highlighting its usefulness as an early, non-invasive and more sensitive biomarker.
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Affiliation(s)
- Martín Carrasco-Gómez
- Department of Electronic Engineering, ETSIT, Universidad Politécnica de Madrid, 28040, Madrid, Spain
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, 28223, Madrid, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - 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
| | - Ricardo Bruña
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, 28223, Madrid, Spain
- Health Research Institute of the Hospital Clínico San Carlos (IdISSC), 28240, Madrid, Spain
- Department of Radiology, Universidad Complutense de Madrid, 28240, Madrid, Spain
| | - Andrés Santos
- Department of Electronic Engineering, ETSIT, Universidad Politécnica de Madrid, 28040, Madrid, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, 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 Carlos (IdISSC), 28240, Madrid, Spain
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5
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Prabhu P, Morise H, Kudo K, Beagle A, Mizuiri D, Syed F, Kotegar KA, Findlay A, Miller BL, Kramer JH, Rankin KP, Garcia PA, Kirsch HE, Vossel K, Nagarajan SS, Ranasinghe KG. Abnormal gamma phase-amplitude coupling in the parahippocampal cortex is associated with network hyperexcitability in Alzheimer's disease. Brain Commun 2024; 6:fcae121. [PMID: 38665964 PMCID: PMC11043655 DOI: 10.1093/braincomms/fcae121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 03/08/2024] [Accepted: 04/07/2024] [Indexed: 04/28/2024] Open
Abstract
While animal models of Alzheimer's disease (AD) have shown altered gamma oscillations (∼40 Hz) in local neural circuits, the low signal-to-noise ratio of gamma in the resting human brain precludes its quantification via conventional spectral estimates. Phase-amplitude coupling (PAC) indicating the dynamic integration between the gamma amplitude and the phase of low-frequency (4-12 Hz) oscillations is a useful alternative to capture local gamma activity. In addition, PAC is also an index of neuronal excitability as the phase of low-frequency oscillations that modulate gamma amplitude, effectively regulates the excitability of local neuronal firing. In this study, we sought to examine the local neuronal activity and excitability using gamma PAC, within brain regions vulnerable to early AD pathophysiology-entorhinal cortex and parahippocampus, in a clinical population of patients with AD and age-matched controls. Our clinical cohorts consisted of a well-characterized cohort of AD patients (n = 50; age, 60 ± 8 years) with positive AD biomarkers, and age-matched, cognitively unimpaired controls (n = 35; age, 63 ± 5.8 years). We identified the presence or the absence of epileptiform activity in AD patients (AD patients with epileptiform activity, AD-EPI+, n = 20; AD patients without epileptiform activity, AD-EPI-, n = 30) using long-term electroencephalography (LTM-EEG) and 1-hour long magnetoencephalography (MEG) with simultaneous EEG. Using the source reconstructed MEG data, we computed gamma PAC as the coupling between amplitude of the gamma frequency (30-40 Hz) with phase of the theta (4-8 Hz) and alpha (8-12 Hz) frequency oscillations, within entorhinal and parahippocampal cortices. We found that patients with AD have reduced gamma PAC in the left parahippocampal cortex, compared to age-matched controls. Furthermore, AD-EPI+ patients showed greater reductions in gamma PAC than AD-EPI- in bilateral parahippocampal cortices. In contrast, entorhinal cortices did not show gamma PAC abnormalities in patients with AD. Our findings demonstrate the spatial patterns of altered gamma oscillations indicating possible region-specific manifestations of network hyperexcitability within medial temporal lobe regions vulnerable to AD pathophysiology. Greater deficits in AD-EPI+ suggests that reduced gamma PAC is a sensitive index of network hyperexcitability in AD patients. Collectively, the current results emphasize the importance of investigating the role of neural circuit hyperexcitability in early AD pathophysiology and explore its potential as a modifiable contributor to AD pathobiology.
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Affiliation(s)
- Pooja Prabhu
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Data science and Computer Applications, Manipal Institute of Technology, Manipal 576104, India
| | - Hirofumi Morise
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
- Medical Imaging Business Center, Ricoh Company Ltd., Kanazawa 920-0177, Japan
| | - Kiwamu Kudo
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
- Medical Imaging Business Center, Ricoh Company Ltd., Kanazawa 920-0177, Japan
| | - Alexander Beagle
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Danielle Mizuiri
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Faatimah Syed
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Karunakar A Kotegar
- Department of Data science and Computer Applications, Manipal Institute of Technology, Manipal 576104, India
| | - Anne Findlay
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Bruce L Miller
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Joel H Kramer
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Katherine P Rankin
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Paul A Garcia
- Epilepsy Center, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Heidi E Kirsch
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
- Epilepsy Center, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Keith Vossel
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
- Mary S. Easton Center for Alzheimer’s Research and Care, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Kamalini G Ranasinghe
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
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Cabrera-Álvarez J, Stefanovski L, Martin L, Susi G, Maestú F, Ritter P. A Multiscale Closed-Loop Neurotoxicity Model of Alzheimer's Disease Progression Explains Functional Connectivity Alterations. eNeuro 2024; 11:ENEURO.0345-23.2023. [PMID: 38565295 PMCID: PMC11026343 DOI: 10.1523/eneuro.0345-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 12/05/2023] [Accepted: 12/22/2023] [Indexed: 04/04/2024] Open
Abstract
The accumulation of amyloid-β (Aβ) and hyperphosphorylated-tau (hp-tau) are two classical histopathological biomarkers in Alzheimer's disease (AD). However, their detailed interactions with the electrophysiological changes at the meso- and macroscale are not yet fully understood. We developed a mechanistic multiscale model of AD progression, linking proteinopathy to its effects on neural activity and vice-versa. We integrated a heterodimer model of prion-like protein propagation and a brain network model of Jansen-Rit neural masses derived from human neuroimaging data whose parameters varied due to neurotoxicity. Results showed that changes in inhibition guided the electrophysiological alterations found in AD, and these changes were mainly attributed to Aβ effects. Additionally, we found a causal disconnection between cellular hyperactivity and interregional hypersynchrony contrary to previous beliefs. Finally, we demonstrated that early Aβ and hp-tau depositions' location determine the spatiotemporal profile of the proteinopathy. The presented model combines the molecular effects of both Aβ and hp-tau together with a mechanistic protein propagation model and network effects within a closed-loop model. This holds the potential to enlighten the interplay between AD mechanisms on various scales, aiming to develop and test novel hypotheses on the contribution of different AD-related variables to the disease evolution.
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Affiliation(s)
- Jesús Cabrera-Álvarez
- Department of Experimental Psychology, Complutense University of Madrid, Pozuelo de Alarcón 28223, Spain
- Centre for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid 28040, Spain
| | - Leon Stefanovski
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
- Department of Neurology with Experimental Neurology, Brain Simulation Section, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Leon Martin
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
- Department of Neurology with Experimental Neurology, Brain Simulation Section, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Gianluca Susi
- Centre for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid 28040, Spain
- Department of Structure of Matter, Thermal Physics and Electronics, Complutense University of Madrid, Madrid 28040, Spain
| | - Fernando Maestú
- Department of Experimental Psychology, Complutense University of Madrid, Pozuelo de Alarcón 28223, Spain
- Centre for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid 28040, Spain
| | - Petra Ritter
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
- Department of Neurology with Experimental Neurology, Brain Simulation Section, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin 10115, Germany
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7
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van Nifterick AM, Scheijbeler EP, Gouw AA, de Haan W, Stam CJ. Local signal variability and functional connectivity: Sensitive measures of the excitation-inhibition ratio? Cogn Neurodyn 2024; 18:519-537. [PMID: 38699618 PMCID: PMC11061092 DOI: 10.1007/s11571-023-10003-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/08/2023] [Accepted: 08/13/2023] [Indexed: 05/05/2024] Open
Abstract
A novel network version of permutation entropy, the inverted joint permutation entropy (JPEinv), holds potential as non-invasive biomarker of abnormal excitation-inhibition (E-I) ratio in Alzheimer's disease (AD). In this computational modelling study, we test the hypotheses that this metric, and related measures of signal variability and functional connectivity, are sensitive to altered E-I ratios. The E-I ratio in each neural mass of a whole-brain computational network model was systematically varied. We evaluated whether JPEinv, local signal variability (by permutation entropy) and functional connectivity (by weighted symbolic mutual information (wsMI)) were related to E-I ratio, on whole-brain and regional level. The hub disruption index can identify regions primarily affected in terms of functional connectivity strength (or: degree) by the altered E-I ratios. Analyses were performed for a range of coupling strengths, filter and time-delay settings. On whole-brain level, higher E-I ratios were associated with higher functional connectivity (by JPEinv and wsMI) and lower local signal variability. These relationships were nonlinear and depended on the coupling strength, filter and time-delay settings. On regional level, hub-like regions showed a selective decrease in functional degree (by JPEinv and wsMI) upon a lower E-I ratio, and non-hub-like regions showed a selective increase in degree upon a higher E-I ratio. These results suggest that abnormal functional connectivity and signal variability, as previously reported in patients across the AD continuum, can inform us about altered E-I ratios. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-023-10003-x.
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Affiliation(s)
- Anne M. van Nifterick
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Elliz P. Scheijbeler
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Alida A. Gouw
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Willem de Haan
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Cornelis J. Stam
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
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8
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Kudo K, Ranasinghe KG, Morise H, Syed F, Sekihara K, Rankin KP, Miller BL, Kramer JH, Rabinovici GD, Vossel K, Kirsch HE, Nagarajan SS. Neurophysiological trajectories in Alzheimer's disease progression. eLife 2024; 12:RP91044. [PMID: 38546337 PMCID: PMC10977971 DOI: 10.7554/elife.91044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/01/2024] Open
Abstract
Alzheimer's disease (AD) is characterized by the accumulation of amyloid-β and misfolded tau proteins causing synaptic dysfunction, and progressive neurodegeneration and cognitive decline. Altered neural oscillations have been consistently demonstrated in AD. However, the trajectories of abnormal neural oscillations in AD progression and their relationship to neurodegeneration and cognitive decline are unknown. Here, we deployed robust event-based sequencing models (EBMs) to investigate the trajectories of long-range and local neural synchrony across AD stages, estimated from resting-state magnetoencephalography. The increases in neural synchrony in the delta-theta band and the decreases in the alpha and beta bands showed progressive changes throughout the stages of the EBM. Decreases in alpha and beta band synchrony preceded both neurodegeneration and cognitive decline, indicating that frequency-specific neuronal synchrony abnormalities are early manifestations of AD pathophysiology. The long-range synchrony effects were greater than the local synchrony, indicating a greater sensitivity of connectivity metrics involving multiple regions of the brain. These results demonstrate the evolution of functional neuronal deficits along the sequence of AD progression.
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Affiliation(s)
- Kiwamu Kudo
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San FranciscoSan FranciscoUnited States
- Medical Imaging Business Center, Ricoh Company LtdKanazawaJapan
| | - Kamalini G Ranasinghe
- Memory and Aging Center,UCSF Weill Institute for Neurosciences, University of California, San FranciscoSan FranciscoUnited States
| | - Hirofumi Morise
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San FranciscoSan FranciscoUnited States
- Medical Imaging Business Center, Ricoh Company LtdKanazawaJapan
| | - Faatimah Syed
- Memory and Aging Center,UCSF Weill Institute for Neurosciences, University of California, San FranciscoSan FranciscoUnited States
| | | | - Katherine P Rankin
- Memory and Aging Center,UCSF Weill Institute for Neurosciences, University of California, San FranciscoSan FranciscoUnited States
| | - Bruce L Miller
- Memory and Aging Center,UCSF Weill Institute for Neurosciences, University of California, San FranciscoSan FranciscoUnited States
| | - Joel H Kramer
- Memory and Aging Center,UCSF Weill Institute for Neurosciences, University of California, San FranciscoSan FranciscoUnited States
| | - Gil D Rabinovici
- Memory and Aging Center,UCSF Weill Institute for Neurosciences, University of California, San FranciscoSan FranciscoUnited States
- Department of Radiology and Biomedical Imaging, University of California, San FranciscoSan FranciscoUnited States
| | - Keith Vossel
- Memory and Aging Center,UCSF Weill Institute for Neurosciences, University of California, San FranciscoSan FranciscoUnited States
- Mary S. Easton Center for Alzheimer’s Research and Care, Department of Neurology, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Heidi E Kirsch
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San FranciscoSan FranciscoUnited States
| | - Srikantan S Nagarajan
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San FranciscoSan FranciscoUnited States
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Kudo K, Ranasinghe KG, Morise H, Syed F, Sekihara K, Rankin KP, Miller BL, Kramer JH, Rabinovici GD, Vossel K, Kirsch HE, Nagarajan SS. Neurophysiological trajectories in Alzheimer's disease progression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.18.541379. [PMID: 37293044 PMCID: PMC10245777 DOI: 10.1101/2023.05.18.541379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Alzheimer's disease (AD) is characterized by the accumulation of amyloid-β and misfolded tau proteins causing synaptic dysfunction, and progressive neurodegeneration and cognitive decline. Altered neural oscillations have been consistently demonstrated in AD. However, the trajectories of abnormal neural oscillations in AD progression and their relationship to neurodegeneration and cognitive decline are unknown. Here, we deployed robust event-based sequencing models (EBMs) to investigate the trajectories of long-range and local neural synchrony across AD stages, estimated from resting-state magnetoencephalography. The increases in neural synchrony in the delta-theta band and the decreases in the alpha and beta bands showed progressive changes throughout the stages of the EBM. Decreases in alpha and beta band synchrony preceded both neurodegeneration and cognitive decline, indicating that frequency-specific neuronal synchrony abnormalities are early manifestations of AD pathophysiology. The long-range synchrony effects were greater than the local synchrony, indicating a greater sensitivity of connectivity metrics involving multiple regions of the brain. These results demonstrate the evolution of functional neuronal deficits along the sequence of AD progression.
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Affiliation(s)
- Kiwamu Kudo
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, 94143, USA
- Medical Imaging Business Center, Ricoh Company, Ltd., Kanazawa, 920-0177, Japan
| | - Kamalini G Ranasinghe
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, 94158, USA
| | - Hirofumi Morise
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, 94143, USA
- Medical Imaging Business Center, Ricoh Company, Ltd., Kanazawa, 920-0177, Japan
| | - Faatimah Syed
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, 94158, USA
| | | | - Katherine P Rankin
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, 94158, USA
| | - Bruce L Miller
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, 94158, USA
| | - Joel H Kramer
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, 94158, USA
| | - Gil D Rabinovici
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, 94158, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, 94143, USA
| | - Keith Vossel
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, 94158, USA
- Mary S. Easton Center for Alzheimer’s Research and Care, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Heidi E Kirsch
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, 94143, USA
| | - Srikantan S Nagarajan
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, 94143, USA
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Ferrante FJ, Migeot J, Birba A, Amoruso L, Pérez G, Hesse E, Tagliazucchi E, Estienne C, Serrano C, Slachevsky A, Matallana D, Reyes P, Ibáñez A, Fittipaldi S, Campo CG, García AM. Multivariate word properties in fluency tasks reveal markers of Alzheimer's dementia. Alzheimers Dement 2024; 20:925-940. [PMID: 37823470 PMCID: PMC10916979 DOI: 10.1002/alz.13472] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 08/15/2023] [Accepted: 08/20/2023] [Indexed: 10/13/2023]
Abstract
INTRODUCTION Verbal fluency tasks are common in Alzheimer's disease (AD) assessments. Yet, standard valid response counts fail to reveal disease-specific semantic memory patterns. Here, we leveraged automated word-property analysis to capture neurocognitive markers of AD vis-à-vis behavioral variant frontotemporal dementia (bvFTD). METHODS Patients and healthy controls completed two fluency tasks. We counted valid responses and computed each word's frequency, granularity, neighborhood, length, familiarity, and imageability. These features were used for group-level discrimination, patient-level identification, and correlations with executive and neural (magnetic resonanance imaging [MRI], functional MRI [fMRI], electroencephalography [EEG]) patterns. RESULTS Valid responses revealed deficits in both disorders. Conversely, frequency, granularity, and neighborhood yielded robust group- and subject-level discrimination only in AD, also predicting executive outcomes. Disease-specific cortical thickness patterns were predicted by frequency in both disorders. Default-mode and salience network hypoconnectivity, and EEG beta hypoconnectivity, were predicted by frequency and granularity only in AD. DISCUSSION Word-property analysis of fluency can boost AD characterization and diagnosis. HIGHLIGHTS We report novel word-property analyses of verbal fluency in AD and bvFTD. Standard valid response counts captured deficits and brain patterns in both groups. Specific word properties (e.g., frequency, granularity) were altered only in AD. Such properties predicted cognitive and neural (MRI, fMRI, EEG) patterns in AD. Word-property analysis of fluency can boost AD characterization and diagnosis.
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Affiliation(s)
- Franco J. Ferrante
- Centro de Neurociencias CognitivasUniversidad de San AndrésVictoriaProvincia de Buenos AiresArgentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)Ciudad Autónoma de Buenos AiresArgentina
- Facultad de IngenieríaUniversidad de Buenos Aires (FIUBA)CABAArgentina
| | - Joaquín Migeot
- Latin American Brain Health (BrainLat) InstituteUniversidad Adolfo IbáñezPeñalolénRegión MetropolitanaChile
- Center for Social and Cognitive Neuroscience (CSCN)School of PsychologyUniversidad Adolfo IbáñezLas CondesChile
| | - Agustina Birba
- Centro de Neurociencias CognitivasUniversidad de San AndrésVictoriaProvincia de Buenos AiresArgentina
- Instituto Universitario de NeurocienciaUniversidad de La LagunaLa LagunaTenerifeEspaña
- Cognitive Department of PsychologyUniversidad de La LagunaLa LagunaTenerifeEspaña
| | - Lucía Amoruso
- Centro de Neurociencias CognitivasUniversidad de San AndrésVictoriaProvincia de Buenos AiresArgentina
- Basque Center on Cognition Brain and Language (BCBL)San SebastiánGipuzkoaEspaña
- IkerbasqueBasque Foundation for ScienceBilbaoSpain
| | - Gonzalo Pérez
- Centro de Neurociencias CognitivasUniversidad de San AndrésVictoriaProvincia de Buenos AiresArgentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)Ciudad Autónoma de Buenos AiresArgentina
- Facultad de IngenieríaUniversidad de Buenos Aires (FIUBA)CABAArgentina
| | - Eugenia Hesse
- Centro de Neurociencias CognitivasUniversidad de San AndrésVictoriaProvincia de Buenos AiresArgentina
- Departamento de Matemática y CienciasUniversidad de San AndrésVictoriaProvincia de Buenos AiresArgentina
| | - Enzo Tagliazucchi
- Latin American Brain Health (BrainLat) InstituteUniversidad Adolfo IbáñezPeñalolénRegión MetropolitanaChile
- Departamento de FísicaUniversidad de Buenos Aires and Instituto de Física de Buenos Aires (IFIBA‐CONICET)CABAArgentina
| | - Claudio Estienne
- Instituto de Ingeniería BiomédicaUniversidad de Buenos AiresBuenos AiresArgentina
| | - Cecilia Serrano
- Unidad de Neurología CognitivaHospital César MilsteinCABAArgentina
| | - Andrea Slachevsky
- Neuropsychology and Clinical Neuroscience Laboratory (LANNEC)Physiopathology Department ‐ ICBMNeurocience and East Neuroscience DepartmentsFaculty of MedicineUniversity of ChileProvidenciaSantiagoChile
- Geroscience Center for Brain Health and Metabolism (GERO)Faculty of MedicineUniversity of ChileProvidenciaSantiagoChile
- Memory and Neuropsychiatric Clinic (CMYN) Neurology DepartmentHospital del Salvador and Faculty of MedicineUniversity of ChileProvidenciaSantiagoChile
- Servicio de NeurologíaDepartamento de MedicinaClínica Alemana‐Universidad del DesarrolloLas CondesRegión MetropolitanaChile
| | - Diana Matallana
- Instituto de EnvejecimientoDepartment of PsychiatrySchool of MedicinePontifical Xaverian UniversityBogotáColombia
- Department of Mental HealthHospital Universitario Santa Fe de BogotáBogotáColombia
| | - Pablo Reyes
- Centro de Memoria y CogniciónIntellectus‐Hospital Universitario San IgnacioBogotáColombia
- Pontificia Universidad JaverianaDepartments of PhysiologyPsychiatry and Aging InstituteBogotáColombia
| | - Agustín Ibáñez
- Centro de Neurociencias CognitivasUniversidad de San AndrésVictoriaProvincia de Buenos AiresArgentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)Ciudad Autónoma de Buenos AiresArgentina
- Latin American Brain Health (BrainLat) InstituteUniversidad Adolfo IbáñezPeñalolénRegión MetropolitanaChile
- Global Brain Health Institute, University of California San Francisco, San Francisco, California, USATrinity College DublinDublinIreland
| | - Sol Fittipaldi
- Centro de Neurociencias CognitivasUniversidad de San AndrésVictoriaProvincia de Buenos AiresArgentina
- Latin American Brain Health (BrainLat) InstituteUniversidad Adolfo IbáñezPeñalolénRegión MetropolitanaChile
- Global Brain Health Institute, University of California San Francisco, San Francisco, California, USATrinity College DublinDublinIreland
| | - Cecilia Gonzalez Campo
- Centro de Neurociencias CognitivasUniversidad de San AndrésVictoriaProvincia de Buenos AiresArgentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)Ciudad Autónoma de Buenos AiresArgentina
| | - Adolfo M. García
- Centro de Neurociencias CognitivasUniversidad de San AndrésVictoriaProvincia de Buenos AiresArgentina
- Latin American Brain Health (BrainLat) InstituteUniversidad Adolfo IbáñezPeñalolénRegión MetropolitanaChile
- Global Brain Health Institute, University of California San Francisco, San Francisco, California, USATrinity College DublinDublinIreland
- Departamento de Lingüística y LiteraturaFacultad de HumanidadesUniversidad de Santiago de ChileEstación CentralSantiagoChile
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Portal B, Södergren M, Parés i Borrell T, Giraud R, Metzendorf NG, Hultqvist G, Nilsson P, Lindskog M. Early Astrocytic Dysfunction Is Associated with Mistuned Synapses as well as Anxiety and Depressive-Like Behavior in the AppNL-F Mouse Model of Alzheimer's Disease. J Alzheimers Dis 2024; 100:1017-1037. [PMID: 38995780 PMCID: PMC11307019 DOI: 10.3233/jad-231461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/31/2024] [Indexed: 07/14/2024]
Abstract
Background Alzheimer's disease (AD) is the most common neurodegenerative disease. Unfortunately, efficient and affordable treatments are still lacking for this neurodegenerative disorder, it is therefore urgent to identify new pharmacological targets. Astrocytes are playing a crucial role in the tuning of synaptic transmission and several studies have pointed out severe astrocyte reactivity in AD. Reactive astrocytes show altered physiology and function, suggesting they could have a role in the early pathophysiology of AD. Objective We aimed to characterize early synaptic impairments in the AppNL-F knock-in mouse model of AD, especially to understand the contribution of astrocytes to early brain dysfunctions. Methods The AppNL-F mouse model carries two disease-causing mutations inserted in the amyloid precursor protein gene. This strain does not start to develop amyloid-β plaques until 9 months of age. Thanks to electrophysiology, we investigated synaptic function, at both neuronal and astrocytic levels, in 6-month-old animals and correlate the synaptic activity with emotional behavior. Results Electrophysiological recordings in the hippocampus revealed an overall synaptic mistuning at a pre-plaque stage of the pathology, associated to an intact social memory but a stronger depressive-like behavior. Astrocytes displayed a reactive-like morphology and a higher tonic GABA current compared to control mice. Interestingly, we here show that the synaptic impairments in hippocampal slices are partially corrected by a pre-treatment with the monoamine oxidase B blocker deprenyl or the fast-acting antidepressant ketamine (5 mg/kg). Conclusions We propose that reactive astrocytes can induce synaptic mistuning early in AD, before plaques deposition, and that these changes are associated with emotional symptoms.
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Affiliation(s)
- Benjamin Portal
- Department for Medical Cell Biology, Uppsala University, Uppsala, Sweden
| | - Moa Södergren
- Department for Medical Cell Biology, Uppsala University, Uppsala, Sweden
| | | | - Romain Giraud
- Department for Medical Cell Biology, Uppsala University, Uppsala, Sweden
| | - Nicole G. Metzendorf
- Department of Pharmacy, Division of Protein Drug Design, Uppsala University, Uppsala, Sweden
| | - Greta Hultqvist
- Department of Pharmacy, Division of Protein Drug Design, Uppsala University, Uppsala, Sweden
| | - Per Nilsson
- Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden
| | - Maria Lindskog
- Department for Medical Cell Biology, Uppsala University, Uppsala, Sweden
- Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden
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12
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Dimitriadis SI, Routley B, Linden DEJ, Singh KD. Multiplexity of human brain oscillations as a personal brain signature. Hum Brain Mapp 2023; 44:5624-5640. [PMID: 37668332 PMCID: PMC10619372 DOI: 10.1002/hbm.26466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 07/11/2023] [Accepted: 08/08/2023] [Indexed: 09/06/2023] Open
Abstract
Human individuality is likely underpinned by the constitution of functional brain networks that ensure consistency of each person's cognitive and behavioral profile. These functional networks should, in principle, be detectable by noninvasive neurophysiology. We use a method that enables the detection of dominant frequencies of the interaction between every pair of brain areas at every temporal segment of the recording period, the dominant coupling modes (DoCM). We apply this method to brain oscillations, measured with magnetoencephalography (MEG) at rest in two independent datasets, and show that the spatiotemporal evolution of DoCMs constitutes an individualized brain fingerprint. Based on this successful fingerprinting we suggest that DoCMs are important targets for the investigation of neural correlates of individual psychological parameters and can provide mechanistic insight into the underlying neurophysiological processes, as well as their disturbance in brain diseases.
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Affiliation(s)
- Stavros I. Dimitriadis
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffWalesUK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of MedicineCardiff UniversityCardiffWalesUK
- Department of Clinical Psychology and PsychobiologyUniversity of BarcelonaBarcelonaSpain
| | - B. Routley
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffWalesUK
| | - David E. J. Linden
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffWalesUK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of MedicineCardiff UniversityCardiffWalesUK
- School for Mental Health and Neuroscience, Faculty of Health Medicine and Life SciencesMaastricht UniversityMaastrichtThe Netherlands
| | - Krish D. Singh
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffWalesUK
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13
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Hijazi S, Smit AB, van Kesteren RE. Fast-spiking parvalbumin-positive interneurons in brain physiology and Alzheimer's disease. Mol Psychiatry 2023; 28:4954-4967. [PMID: 37419975 PMCID: PMC11041664 DOI: 10.1038/s41380-023-02168-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 06/26/2023] [Accepted: 06/26/2023] [Indexed: 07/09/2023]
Abstract
Fast-spiking parvalbumin (PV) interneurons are inhibitory interneurons with unique morphological and functional properties that allow them to precisely control local circuitry, brain networks and memory processing. Since the discovery in 1987 that PV is expressed in a subset of fast-spiking GABAergic inhibitory neurons, our knowledge of the complex molecular and physiological properties of these cells has been expanding. In this review, we highlight the specific properties of PV neurons that allow them to fire at high frequency and with high reliability, enabling them to control network oscillations and shape the encoding, consolidation and retrieval of memories. We next discuss multiple studies reporting PV neuron impairment as a critical step in neuronal network dysfunction and cognitive decline in mouse models of Alzheimer's disease (AD). Finally, we propose potential mechanisms underlying PV neuron dysfunction in AD and we argue that early changes in PV neuron activity could be a causal step in AD-associated network and memory impairment and a significant contributor to disease pathogenesis.
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Affiliation(s)
- Sara Hijazi
- Department of Pharmacology, University of Oxford, Oxford, OX1 3QT, UK
| | - August B Smit
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
| | - Ronald E van Kesteren
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands.
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14
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Chang KH, Wang C, Nester CO, Katz MJ, Byrd DA, Lipton RB, Rabin LA. Examining the role of participant and study partner report in widely-used classification approaches of mild cognitive impairment in demographically-diverse community dwelling individuals: results from the Einstein aging study. Front Aging Neurosci 2023; 15:1221768. [PMID: 38076542 PMCID: PMC10702963 DOI: 10.3389/fnagi.2023.1221768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 09/29/2023] [Indexed: 01/28/2024] Open
Abstract
Objective The role of subjective cognitive concerns (SCC) as a diagnostic criterion for MCI remains uncertain and limits the development of a universally (or widely)-accepted MCI definition. The optimal MCI definition should define an at-risk state and accurately predict the development of incident dementia. Questions remain about operationalization of definitions of self- and informant-reported SCCs and their individual and joint associations with incident dementia. Methods The present study included Einstein Aging Study participants who were non-Hispanic White or Black, free of dementia at enrollment, had follow-up, and completed neuropsychological tests and self-reported SCC at enrollment to determine MCI status. Informant-reported SCC at baseline were assessed via the CERAD clinical history questionnaire. Self-reported SCC were measured using the CERAD, items from the EAS Health Self-Assessment, and the single memory item from the Geriatric Depression Scale. Cox proportional hazards models examined the association of different operationalizations of SCC with Petersen and Jak/Bondi MCI definitions on the risk of dementia, further controlling for age, sex, education, and race/ethnicity. Time-dependent sensitivity and specificity at specific time points for each definition, and Youden's index were calculated as an accuracy measure. Cox proportional hazards models were also used to evaluate the associations of combinations of self- and informant-reported SCC with the risk of incident dementia. Results 91% of the sample endorsed at least one SCC. Youden's index showed that not including SCC in either Jak/Bondi or Petersen classifications had the best balance between sensitivity and specificity across follow-up. A subset of individuals with informants, on average, had a lower proportion of non-Hispanic Blacks and 94% endorsed at least one self-reported SCC. Both informant-reported and self-reported SCC were significantly associated with incident dementia. Conclusion Our findings suggest that the SCC criterion may not improve the predictive validity for dementia when included in widely-employed definitions of MCI. Consistent with some prior research, informant-reported SCC was more related to risk of incident dementia than self-reported SCC. Given that requiring informant report as a diagnostic criterion may unintentionally exclude health disparate groups, additional consideration is needed to determine how best to utilize informant-report in MCI diagnosis.
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Affiliation(s)
- Katherine H. Chang
- Department of Psychology, Queens College, City University of New York (CUNY), Queens, NY, United States
- Department of Psychology, The Graduate Center, City University of New York (CUNY), New York, NY, United States
| | - Cuiling Wang
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States
- Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Caroline O. Nester
- Department of Psychology, Queens College, City University of New York (CUNY), Queens, NY, United States
- Department of Psychology, The Graduate Center, City University of New York (CUNY), New York, NY, United States
| | - Mindy J. Katz
- Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Desiree A. Byrd
- Department of Psychology, Queens College, City University of New York (CUNY), Queens, NY, United States
- Department of Psychology, The Graduate Center, City University of New York (CUNY), New York, NY, United States
| | - Richard B. Lipton
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States
- Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, United States
- Department of Psychiatry and Behavioral Medicine, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Laura A. Rabin
- Department of Psychology, Queens College, City University of New York (CUNY), Queens, NY, United States
- Department of Psychology, The Graduate Center, City University of New York (CUNY), New York, NY, United States
- Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, United States
- Department of Psychology, Brooklyn College, City University of New York (CUNY), Brooklyn, NY, United States
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Antón Toro LF, Salto F, Requena C, Maestú F. Electrophysiological connectivity of logical deduction: Early cortical MEG study. Cortex 2023; 166:365-376. [PMID: 37499565 DOI: 10.1016/j.cortex.2023.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 04/14/2023] [Accepted: 06/15/2023] [Indexed: 07/29/2023]
Abstract
Complex human reasoning involves minimal abilities to extract conclusions implied in the available information. These abilities are considered "deductive" because they exemplify certain abstract relations among propositions or probabilities called deductive arguments. However, the electrophysiological dynamics which supports such complex cognitive processes has not been addressed yet. In this work we consider typically deductive logico-probabilistically valid inferences and aim to verify or refute their electrophysiological functional connectivity differences from invalid inferences with the same content (same relational variables, same stimuli, same relevant and salient features). We recorded the brain electrophysiological activity of 20 participants (age = 20.35 ± 3.23) by means of an MEG system during two consecutive reasoning tasks: a search task (invalid condition) without any specific deductive rules to follow, and a logically valid deductive task (valid condition) with explicit deductive rules as instructions. We calculated the functional connectivity (FC) for each condition and conducted a seed-based analysis in a set of cortical regions of interest. Finally, we used a cluster-based permutation test to compare the differences between logically valid and invalid conditions in terms of FC. As a first novel result we found higher FC for valid condition in beta band between regions of interest and left prefrontal, temporal, parietal, and cingulate structures. FC analysis allows a second novel result which is the definition of a propositional network with operculo-cingular, parietal and medial nodes, specifically including disputed medial deductive "core" areas. The experiment discloses measurable cortical processes which do not depend on content but on truth-functional propositional operators. These experimental novelties may contribute to understand the cortical bases of deductive processes.
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Affiliation(s)
- Luis F Antón Toro
- Research Group on Aging, Neuroscience and Applied Logic, Department of Psychology, Sociology and Philosophy, University of León, Campus Vegazana S/n 24171, León, Spain; Center for Cognitive and Computational Neuroscience (C3N), Complutense University of Madrid, Campus Somosaguas, 28223 Pozuelo, Madrid, Spain; Department of Psychology, Health Faculty, Camilo José Cela University (UCJC), C. Castillo de Alarcón, 49, 28692 Villafranca Del Castillo, Madrid, Spain.
| | - Francisco Salto
- Research Group on Aging, Neuroscience and Applied Logic, Department of Psychology, Sociology and Philosophy, University of León, Campus Vegazana S/n 24171, León, Spain.
| | - Carmen Requena
- Research Group on Aging, Neuroscience and Applied Logic, Department of Psychology, Sociology and Philosophy, University of León, Campus Vegazana S/n 24171, León, Spain.
| | - Fernando Maestú
- Center for Cognitive and Computational Neuroscience (C3N), Complutense University of Madrid, Campus Somosaguas, 28223 Pozuelo, Madrid, Spain; Department of Experimental Psychology, Complutense University of Madrid (UCM), Campus Somosaguas, 28223 Pozuelo, Madrid, Spain.
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Fuscà M, Siebenhühner F, Wang SH, Myrov V, Arnulfo G, Nobili L, Palva JM, Palva S. Brain criticality predicts individual levels of inter-areal synchronization in human electrophysiological data. Nat Commun 2023; 14:4736. [PMID: 37550300 PMCID: PMC10406818 DOI: 10.1038/s41467-023-40056-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 07/10/2023] [Indexed: 08/09/2023] Open
Abstract
Neuronal oscillations and their synchronization between brain areas are fundamental for healthy brain function. Yet, synchronization levels exhibit large inter-individual variability that is associated with behavioral variability. We test whether individual synchronization levels are predicted by individual brain states along an extended regime of critical-like dynamics - the Griffiths phase (GP). We use computational modelling to assess how synchronization is dependent on brain criticality indexed by long-range temporal correlations (LRTCs). We analyze LRTCs and synchronization of oscillations from resting-state magnetoencephalography and stereo-electroencephalography data. Synchronization and LRTCs are both positively linearly and quadratically correlated among healthy subjects, while in epileptogenic areas they are negatively linearly correlated. These results show that variability in synchronization levels is explained by the individual position along the GP with healthy brain areas operating in its subcritical and epileptogenic areas in its supercritical side. We suggest that the GP is fundamental for brain function allowing individual variability while retaining functional advantages of criticality.
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Affiliation(s)
- Marco Fuscà
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Felix Siebenhühner
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University, and Helsinki University Hospital, Helsinki, Finland
| | - Sheng H Wang
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- CEA, NeuroSpin, Gif-sur-Yvette, France
- MIND team, Inria, Université Paris-Saclay, Bures-sur-Yvette, France
| | - Vladislav Myrov
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Gabriele Arnulfo
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Dept. of Informatics, Bioengineering, Robotics and System engineering, University of Genoa, Genoa, Italy
| | - Lino Nobili
- Child Neuropsychiatry Unit, IRCCS, Istituto G. Gaslini, Department of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy
- "Claudio Munari" Epilepsy Surgery Centre, Niguarda Hospital, Milan, Italy
| | - J Matias Palva
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Satu Palva
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK.
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.
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17
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van Nifterick AM, Mulder D, Duineveld DJ, Diachenko M, Scheltens P, Stam CJ, van Kesteren RE, Linkenkaer-Hansen K, Hillebrand A, Gouw AA. Resting-state oscillations reveal disturbed excitation-inhibition ratio in Alzheimer's disease patients. Sci Rep 2023; 13:7419. [PMID: 37150756 PMCID: PMC10164744 DOI: 10.1038/s41598-023-33973-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 04/21/2023] [Indexed: 05/09/2023] Open
Abstract
An early disruption of neuronal excitation-inhibition (E-I) balance in preclinical animal models of Alzheimer's disease (AD) has been frequently reported, but is difficult to measure directly and non-invasively in humans. Here, we examined known and novel neurophysiological measures sensitive to E-I in patients across the AD continuum. Resting-state magnetoencephalography (MEG) data of 86 amyloid-biomarker-confirmed subjects across the AD continuum (17 patients diagnosed with subjective cognitive decline, 18 with mild cognitive impairment (MCI) and 51 with dementia due to probable AD (AD dementia)), 46 healthy elderly and 20 young control subjects were reconstructed to source-space. E-I balance was investigated by detrended fluctuation analysis (DFA), a functional E/I (fE/I) algorithm, and the aperiodic exponent of the power spectrum. We found a disrupted E-I ratio in AD dementia patients specifically, by a lower DFA, and a shift towards higher excitation, by a higher fE/I and a lower aperiodic exponent. Healthy subjects showed lower fE/I ratios (< 1.0) than reported in previous literature, not explained by age or choice of an arbitrary threshold parameter, which warrants caution in interpretation of fE/I results. Correlation analyses showed that a lower DFA (E-I imbalance) and a lower aperiodic exponent (more excitation) was associated with a worse cognitive score in AD dementia patients. In contrast, a higher DFA in the hippocampi of MCI patients was associated with a worse cognitive score. This MEG-study showed E-I imbalance, likely due to increased excitation, in AD dementia, but not in early stage AD patients. To accurately determine the direction of shift in E-I balance, validations of the currently used markers and additional in vivo markers of E-I are required.
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Affiliation(s)
- Anne M van Nifterick
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands.
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands.
| | - Danique Mulder
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Denise J Duineveld
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Marina Diachenko
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
| | - Ronald E van Kesteren
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
| | - Klaus Linkenkaer-Hansen
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
| | - Alida A Gouw
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
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18
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Brown J, Camporesi E, Lantero-Rodriguez J, Olsson M, Wang A, Medem B, Zetterberg H, Blennow K, Karikari TK, Wall M, Hill E. Tau in cerebrospinal fluid induces neuronal hyperexcitability and alters hippocampal theta oscillations. Acta Neuropathol Commun 2023; 11:67. [PMID: 37095572 PMCID: PMC10127378 DOI: 10.1186/s40478-023-01562-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 04/03/2023] [Indexed: 04/26/2023] Open
Abstract
Alzheimer's disease (AD) and other tauopathies are characterized by the aggregation of tau into soluble and insoluble forms (including tangles and neuropil threads). In humans, a fraction of both phosphorylated and non-phosphorylated N-terminal to mid-domain tau species, are secreted into cerebrospinal fluid (CSF). Some of these CSF tau species can be measured as diagnostic and prognostic biomarkers, starting from early stages of disease. While in animal models of AD pathology, soluble tau aggregates have been shown to disrupt neuronal function, it is unclear whether the tau species present in CSF will modulate neural activity. Here, we have developed and applied a novel approach to examine the electrophysiological effects of CSF from patients with a tau-positive biomarker profile. The method involves incubation of acutely-isolated wild-type mouse hippocampal brain slices with small volumes of diluted human CSF, followed by a suite of electrophysiological recording methods to evaluate their effects on neuronal function, from single cells through to the network level. Comparison of the toxicity profiles of the same CSF samples, with and without immuno-depletion for tau, has enabled a pioneering demonstration that CSF-tau potently modulates neuronal function. We demonstrate that CSF-tau mediates an increase in neuronal excitability in single cells. We then observed, at the network level, increased input-output responses and enhanced paired-pulse facilitation as well as an increase in long-term potentiation. Finally, we show that CSF-tau modifies the generation and maintenance of hippocampal theta oscillations, which have important roles in learning and memory and are known to be altered in AD patients. Together, we describe a novel method for screening human CSF-tau to understand functional effects on neuron and network activity, which could have far-reaching benefits in understanding tau pathology, thus allowing for the development of better targeted treatments for tauopathies in the future.
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Affiliation(s)
- Jessica Brown
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK
- Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PL, UK
| | - Elena Camporesi
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, 43180, Mölndal, Sweden
| | - Juan Lantero-Rodriguez
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, 43180, Mölndal, Sweden
| | - Maria Olsson
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, 43180, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 43180, Mölndal, Sweden
| | - Alice Wang
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK
| | - Blanca Medem
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, 43180, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 43180, Mölndal, Sweden
- UK Dementia Research Institute at UCL, London, WC1E 6BT, UK
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, WC1E 6BT, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin, Madison, WI, 53792, USA
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53792, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, 43180, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 43180, Mölndal, Sweden
| | - Thomas K Karikari
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, 43180, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 43180, Mölndal, Sweden
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Mark Wall
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK
| | - Emily Hill
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK.
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19
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Torok J, Anand C, Verma P, Raj A. Connectome-based biophysics models of Alzheimer's disease diagnosis and prognosis. Transl Res 2023; 254:13-23. [PMID: 36031051 PMCID: PMC11019890 DOI: 10.1016/j.trsl.2022.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 08/08/2022] [Indexed: 11/22/2022]
Abstract
With the increasing prevalence of Alzheimer's disease (AD) among aging populations and the limited therapeutic options available to slow or reverse its progression, the need has never been greater for improved diagnostic tools for identifying patients in the preclinical and prodomal phases of AD. Biophysics models of the connectome-based spread of amyloid-beta (Aβ) and microtubule-associated protein tau (τ) have enjoyed recent success as tools for predicting the time course of AD-related pathological changes. However, given the complex etiology of AD, which involves not only connectome-based spread of protein pathology but also the interactions of many molecular and cellular players over multiple spatiotemporal scales, more robust, complete biophysics models are needed to better understand AD pathophysiology and ultimately provide accurate patient-specific diagnoses and prognoses. Here we discuss several areas of active research in AD whose insights can be used to enhance the mathematical modeling of AD pathology as well as recent attempts at developing improved connectome-based biophysics models. These efforts toward a comprehensive yet parsimonious mathematical description of AD hold great promise for improving both the diagnosis of patients at risk for AD and our mechanistic understanding of how AD progresses.
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Affiliation(s)
- Justin Torok
- Department of Radiology, University of California, San Francisco, San Francisco, California.
| | - Chaitali Anand
- Department of Radiology, University of California, San Francisco, San Francisco, California
| | - Parul Verma
- Department of Radiology, University of California, San Francisco, San Francisco, California
| | - Ashish Raj
- Department of Radiology, University of California, San Francisco, San Francisco, California; Department of Bioengineering, University of California, Berkeley and University of California, San Francisco, Berkeley, California; Department of Radiology, Weill Cornell Medicine, New York, New York.
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20
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Dimitriadis SI. Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher's Choice Paths. Neuroinformatics 2023; 21:71-88. [PMID: 36372844 DOI: 10.1007/s12021-022-09610-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/05/2022] [Indexed: 11/15/2022]
Abstract
There is a growing interest in the neuroscience community on the advantages of multilayer functional brain networks. Researchers usually treated different frequencies separately at distinct functional brain networks. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. Here, Ι will focus on the multi-frequency multilayer functional connectivity analysis on resting-state fMRI (rs-fMRI) recordings. However, constructing a multilayer network depends on selecting multiple pre-processing steps that can affect the final network topology. Here, I analyzed the rs-fMRI dataset from a single human performing scanning over a period of 18 months (84 scans in total), and the rs-fMRI dataset containing 25 subjects with 3 repeat scans. I focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, I untangled specific combinations of researchers' choices that yield consistently brain networks with repeatable topologies, giving me the chance to recommend best practices over consistent topologies.
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Affiliation(s)
- Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, Faculty of Psychology, University of Barcelona, Passeig de la Vall d'Hebron, 171, 08035, Barcelona, Spain.
- Institut de Neurociències, University of Barcelona, Campus Mundet, Edifici de PonentPasseig de la Vall d'Hebron, 171, 08035, Barcelona, Spain.
- Integrative Neuroimaging Lab, 55133, Thessaloniki, Greece.
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Wales, CF24 4HQ, Cardiff, UK.
- Neuroinformatics Group, School of Psychology, College of Biomedical and Life Sciences, Cardiff University Brain Research Imaging Centre (CUBRIC), CF24 4HQ, Cardiff, Wales, UK.
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, CF24 4HQ, Wales, UK.
- Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, CF24 4HQ, Cardiff, Wales, UK.
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, CF24 4HQ, Wales, UK.
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21
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Lanskey JH, Kocagoncu E, Quinn AJ, Cheng YJ, Karadag M, Pitt J, Lowe S, Perkinton M, Raymont V, Singh KD, Woolrich M, Nobre AC, Henson RN, Rowe JB. New Therapeutics in Alzheimer's Disease Longitudinal Cohort study (NTAD): study protocol. BMJ Open 2022; 12:e055135. [PMID: 36521898 PMCID: PMC9756184 DOI: 10.1136/bmjopen-2021-055135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 11/01/2022] [Indexed: 12/23/2022] Open
Abstract
INTRODUCTION With the pressing need to develop treatments that slow or stop the progression of Alzheimer's disease, new tools are needed to reduce clinical trial duration and validate new targets for human therapeutics. Such tools could be derived from neurophysiological measurements of disease. METHODS AND ANALYSIS The New Therapeutics in Alzheimer's Disease study (NTAD) aims to identify a biomarker set from magneto/electroencephalography that is sensitive to disease and progression over 1 year. The study will recruit 100 people with amyloid-positive mild cognitive impairment or early-stage Alzheimer's disease and 30 healthy controls aged between 50 and 85 years. Measurements of the clinical, cognitive and imaging data (magnetoencephalography, electroencephalography and MRI) of all participants will be taken at baseline. These measurements will be repeated after approximately 1 year on participants with Alzheimer's disease or mild cognitive impairment, and clinical and cognitive assessment of these participants will be repeated again after approximately 2 years. To assess reliability of magneto/electroencephalographic changes, a subset of 30 participants with mild cognitive impairment or early-stage Alzheimer's disease will also undergo repeat magneto/electroencephalography 2 weeks after baseline. Baseline and longitudinal changes in neurophysiology are the primary analyses of interest. Additional outputs will include atrophy and cognitive change and estimated numbers needed to treat each arm of simulated clinical trials of a future disease-modifying therapy. ETHICS AND DATA STATEMENT The study has received a favourable opinion from the East of England Cambridge Central Research Ethics Committee (REC reference 18/EE/0042). Results will be disseminated through internal reports, peer-reviewed scientific journals, conference presentations, website publication, submission to regulatory authorities and other publications. Data will be made available via the Dementias Platform UK Data Portal on completion of initial analyses by the NTAD study group.
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Affiliation(s)
| | - Ece Kocagoncu
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Andrew J Quinn
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Yun-Ju Cheng
- Lilly Corporate Center, Indianapolis, Indiana, USA
| | - Melek Karadag
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Jemma Pitt
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Stephen Lowe
- Lilly Centre for Clinical Pharmacology, Singapore
| | | | | | - Krish D Singh
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK
| | - Mark Woolrich
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Anna C Nobre
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Richard N Henson
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - James B Rowe
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
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22
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Maldjian JA, Lee R, Jordan J, Davenport EM, Proskovec AL, Wintermark M, Stufflebeam S, Anderson J, Mukherjee P, Nagarajan SS, Ferrari P, Gaetz W, Schwartz E, Roberts TPL. ACR White Paper on Magnetoencephalography and Magnetic Source Imaging: A Report from the ACR Commission on Neuroradiology. AJNR Am J Neuroradiol 2022; 43:E46-E53. [PMID: 36456085 DOI: 10.3174/ajnr.a7714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 10/04/2022] [Indexed: 12/04/2022]
Abstract
Magnetoencephalography, the extracranial detection of tiny magnetic fields emanating from intracranial electrical activity of neurons, and its source modeling relation, magnetic source imaging, represent a powerful functional neuroimaging technique, able to detect and localize both spontaneous and evoked activity of the brain in health and disease. Recent years have seen an increased utilization of this technique for both clinical practice and research, in the United States and worldwide. This report summarizes current thinking, presents recommendations for clinical implementation, and offers an outlook for emerging new clinical indications.
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Affiliation(s)
- J A Maldjian
- From the Advanced Neuroscience Imaging Research Laboratory (J.A.M., E.M.D., A.L.P.) .,MEG Center of Excellence (J.A.M., E.M.D., A.L.P.).,Department of Radiology (J.A.M., E.M.D., A.L.P.), University of Texas Southwestern Medical Center, Dallas, Texas
| | - R Lee
- Department of Neuroradiology (R.L.), University of California San Diego, San Diego, California
| | - J Jordan
- ACR Commission on Neuroradiology (J.J.), American College of Radiology, Reston, Virginia.,Stanford University School of Medicine (J.J.), Stanford, California
| | - E M Davenport
- From the Advanced Neuroscience Imaging Research Laboratory (J.A.M., E.M.D., A.L.P.).,MEG Center of Excellence (J.A.M., E.M.D., A.L.P.).,Department of Radiology (J.A.M., E.M.D., A.L.P.), University of Texas Southwestern Medical Center, Dallas, Texas
| | - A L Proskovec
- From the Advanced Neuroscience Imaging Research Laboratory (J.A.M., E.M.D., A.L.P.).,MEG Center of Excellence (J.A.M., E.M.D., A.L.P.).,Department of Radiology (J.A.M., E.M.D., A.L.P.), University of Texas Southwestern Medical Center, Dallas, Texas
| | - M Wintermark
- Department of Neuroradiology (M.W.), University of Texas MD Anderson Center, Houston, Texas
| | - S Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging (S.S.), Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts
| | - J Anderson
- Department of Radiology and Imaging Sciences (J.A.), University of Utah School of Medicine, Salt Lake City, Utah
| | - P Mukherjee
- Department of Radiology and Biomedical Imaging (P.M., S.S.N.), University of California, San Francisco, San Francisco, California
| | - S S Nagarajan
- Department of Radiology and Biomedical Imaging (P.M., S.S.N.), University of California, San Francisco, San Francisco, California
| | - P Ferrari
- Pediatric Neurosciences (P.F.), Helen DeVos Children's Hospital, Grand Rapids, Michigan.,Department of Pediatrics and Human Development (P.F.), College of Human Medicine, Michigan State University, Grand Rapids, Michigan
| | - W Gaetz
- Department of Radiology (W.G., E.S., T.P.L.R.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - E Schwartz
- Department of Radiology (W.G., E.S., T.P.L.R.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - T P L Roberts
- Department of Radiology (W.G., E.S., T.P.L.R.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
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23
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van Nifterick AM, Gouw AA, van Kesteren RE, Scheltens P, Stam CJ, de Haan W. A multiscale brain network model links Alzheimer’s disease-mediated neuronal hyperactivity to large-scale oscillatory slowing. Alzheimers Res Ther 2022; 14:101. [PMID: 35879779 PMCID: PMC9310500 DOI: 10.1186/s13195-022-01041-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 07/02/2022] [Indexed: 01/30/2023]
Abstract
Background Neuronal hyperexcitability and inhibitory interneuron dysfunction are frequently observed in preclinical animal models of Alzheimer’s disease (AD). This study investigates whether these microscale abnormalities explain characteristic large-scale magnetoencephalography (MEG) activity in human early-stage AD patients. Methods To simulate spontaneous electrophysiological activity, we used a whole-brain computational network model comprised of 78 neural masses coupled according to human structural brain topology. We modified relevant model parameters to simulate six literature-based cellular scenarios of AD and compare them to one healthy and six contrast (non-AD-like) scenarios. The parameters include excitability, postsynaptic potentials, and coupling strength of excitatory and inhibitory neuronal populations. Whole-brain spike density and spectral power analyses of the simulated data reveal mechanisms of neuronal hyperactivity that lead to oscillatory changes similar to those observed in MEG data of 18 human prodromal AD patients compared to 18 age-matched subjects with subjective cognitive decline. Results All but one of the AD-like scenarios showed higher spike density levels, and all but one of these scenarios had a lower peak frequency, higher spectral power in slower (theta, 4–8Hz) frequencies, and greater total power. Non-AD-like scenarios showed opposite patterns mainly, including reduced spike density and faster oscillatory activity. Human AD patients showed oscillatory slowing (i.e., higher relative power in the theta band mainly), a trend for lower peak frequency and higher total power compared to controls. Combining model and human data, the findings indicate that neuronal hyperactivity can lead to oscillatory slowing, likely due to hyperexcitation (by hyperexcitability of pyramidal neurons or greater long-range excitatory coupling) and/or disinhibition (by reduced excitability of inhibitory interneurons or weaker local inhibitory coupling strength) in early AD. Conclusions Using a computational brain network model, we link findings from different scales and models and support the hypothesis of early-stage neuronal hyperactivity underlying E/I imbalance and whole-brain network dysfunction in prodromal AD. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-01041-4.
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24
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Simola J, Siebenhühner F, Myrov V, Kantojärvi K, Paunio T, Palva JM, Brattico E, Palva S. Genetic polymorphisms in COMT and BDNF influence synchronization dynamics of human neuronal oscillations. iScience 2022; 25:104985. [PMID: 36093050 PMCID: PMC9460523 DOI: 10.1016/j.isci.2022.104985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 06/15/2022] [Accepted: 08/16/2022] [Indexed: 11/01/2022] Open
Abstract
Neuronal oscillations, their inter-areal synchronization, and scale-free dynamics constitute fundamental mechanisms for cognition by regulating communication in neuronal networks. These oscillatory dynamics have large inter-individual variability that is partly heritable. We hypothesized that this variability could be partially explained by genetic polymorphisms in neuromodulatory genes. We recorded resting-state magnetoencephalography (MEG) from 82 healthy participants and investigated whether oscillation dynamics were influenced by genetic polymorphisms in catechol-O-methyltransferase (COMT) Val158Met and brain-derived neurotrophic factor (BDNF) Val66Met. Both COMT and BDNF polymorphisms influenced local oscillation amplitudes and their long-range temporal correlations (LRTCs), while only BDNF polymorphism affected the strength of large-scale synchronization. Our findings demonstrate that COMT and BDNF genetic polymorphisms contribute to inter-individual variability in neuronal oscillation dynamics. Comparison of these results to computational modeling of near-critical synchronization dynamics further suggested that COMT and BDNF polymorphisms influenced local oscillations by modulating the excitation-inhibition balance according to the brain criticality framework.
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Affiliation(s)
- Jaana Simola
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Haartmaninkatu 3, 00014 Helsinki, Finland
- Helsinki Collegium for Advanced Studies (HCAS), University of Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Centre, 00029 HUS, Finland
| | - Felix Siebenhühner
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Haartmaninkatu 3, 00014 Helsinki, Finland
| | - Vladislav Myrov
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Haartmaninkatu 3, 00014 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering (NBE), Aalto University, 02150 Espoo, Finland
| | - Katri Kantojärvi
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, 00271 Helsinki, Finland
- Department of Psychiatry and SleepWell Research Program, Faculty of Medicine, University of Helsinki and Helsinki University Central Hospital, Helsinki, 00014 Helsinki, Finland
| | - Tiina Paunio
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, 00271 Helsinki, Finland
- Department of Psychiatry and SleepWell Research Program, Faculty of Medicine, University of Helsinki and Helsinki University Central Hospital, Helsinki, 00014 Helsinki, Finland
| | - J. Matias Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Haartmaninkatu 3, 00014 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering (NBE), Aalto University, 02150 Espoo, Finland
- Centre for Cognitive Neuroimaging (CCNi), Institute of Neuroscience and Psychology, University of Glasgow, Glasgow G12 8QB, UK
| | - Elvira Brattico
- Center for Music in the Brain (MIB), Department of Clinical Medicine, Aarhus University &The Royal Academy of Music Aarhus/Aalborg, 8000 Aarhus C, Denmark
- Department of Education, Psychology, Communication, University of Bari Aldo Moro, 70121 Bari, Italy
| | - Satu Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Haartmaninkatu 3, 00014 Helsinki, Finland
- Centre for Cognitive Neuroimaging (CCNi), Institute of Neuroscience and Psychology, University of Glasgow, Glasgow G12 8QB, UK
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25
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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] [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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26
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Silberstein RB, Pipingas A, Scholey AB. Homocysteine Modulates Brain Functional Connectivity in a Memory Retrieval Task. J Alzheimers Dis 2022; 90:199-209. [DOI: 10.3233/jad-220612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Homocysteine, a methionine metabolite, is a recognized risk factor for accelerated age-related cognitive decline and dementia. Objective: In the light of studies indicating increases in brain activity and brain functional connectivity in the early stages of age-related cognitive decline, we undertook a study to examine the relationship between plasma homocysteine levels and brain functional connectivity in a group of late middle-aged males at risk of cognitive decline due to high body mass index and a sedentary lifestyle. Methods: Brain functional connectivity was measured using the steady state visual evoked potential event related partial coherence while 38 participants performed a memory task where each trial comprised an object recognition task followed by a location memory task. Results: We observed a significant transient peak in the correlation between plasma homocysteine levels and fronto-parietal brain functional connectivity immediately before the presentation of the memory location component of the task. Significantly, this correlation was only apparent if the participant pool included individuals with homocysteine concentrations above 11μmole/L. Conclusion: Our findings suggest that the increased brain functional connectivity observed in the earlier stages of age-related cognitive decline reflects pathognomonic changes in brain function and not compensatory changes engaged to enhance task performance. Our findings also suggest that homocysteine interferes with the inhibition of cortical networks where this inhibition is necessary for optimum task performance. Finally, we observed that the effect of homocysteine on brain functional connectivity is only apparent at concentrations above 11μmol/L.
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Affiliation(s)
- Richard B. Silberstein
- Centre for Human Psychopharmacology, Swinburne University of Technology, Hawthorn, VIC, Australia
- Neuro-Insight Pty Ltd, Hawthorn, VIC, Australia
| | - Andrew Pipingas
- Centre for Human Psychopharmacology, Swinburne University of Technology, Hawthorn, VIC, Australia
| | - Andrew B. Scholey
- Centre for Human Psychopharmacology, Swinburne University of Technology, Hawthorn, VIC, Australia
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27
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Vaghari D, Bruna R, Hughes LE, Nesbitt D, Tibon R, Rowe JB, Maestu F, Henson RN. A multi-site, multi-participant magnetoencephalography resting-state dataset to study dementia: The BioFIND dataset. Neuroimage 2022; 258:119344. [PMID: 35660461 PMCID: PMC7613066 DOI: 10.1016/j.neuroimage.2022.119344] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 04/26/2022] [Accepted: 05/30/2022] [Indexed: 01/04/2023] Open
Abstract
Early detection of Alzheimer's Disease (AD) is vital to reduce the burden of dementia and for developing effective treatments. Neuroimaging can detect early brain changes, such as hippocampal atrophy in Mild Cognitive Impairment (MCI), a prodromal state of AD. However, selecting the most informative imaging features by machine-learning requires many cases. While large publically-available datasets of people with dementia or prodromal disease exist for Magnetic Resonance Imaging (MRI), comparable datasets are missing for Magnetoencephalography (MEG). MEG offers advantages in its millisecond resolution, revealing physiological changes in brain oscillations or connectivity before structural changes are evident with MRI. We introduce a MEG dataset with 324 individuals: patients with MCI and healthy controls. Their brain activity was recorded while resting with eyes closed, using a 306-channel MEG scanner at one of two sites (Madrid or Cambridge), enabling tests of generalization across sites. A T1-weighted MRI is provided to assist source localisation. The MEG and MRI data are formatted according to international BIDS standards and analysed freely on the DPUK platform (https://portal.dementiasplatform.uk/Apply). Here, we describe this dataset in detail, report some example (benchmark) analyses, and consider its limitations and future directions.
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Affiliation(s)
- Delshad Vaghari
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK; Department of Electrical and Computer Engineering, Tarbiat Modares University, Iran
| | - Ricardo Bruna
- Department of Experimental Psychology, Complutense University of Madrid, Spain; Center for Biomedical Technology, Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Spain
| | - Laura E Hughes
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK
| | - David Nesbitt
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK
| | - Roni Tibon
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK
| | - James B Rowe
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK; Cambridge University Hospitals NHS Trust and Department of Clinical Neurosciences, University of Cambridge, UK
| | - Fernando Maestu
- Department of Experimental Psychology, Complutense University of Madrid, Spain; Center for Biomedical Technology, Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Spain
| | - Richard N Henson
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK; Department of Psychiatry, University of Cambridge, UK.
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28
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Network synchronization deficits caused by dementia and Alzheimer's disease serve as topographical biomarkers: a pilot study. Brain Struct Funct 2022; 227:2957-2969. [PMID: 35997832 PMCID: PMC9396580 DOI: 10.1007/s00429-022-02554-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 08/09/2022] [Indexed: 11/24/2022]
Abstract
Mild cognitive impairment (MCI) is known as an early stage of cognitive decline. Amnestic MCI (aMCI) is considered as the preliminary stage of dementia which may progress to Alzheimer’s disease (AD). While some aMCI patients may stay in this condition for years, others might develop dementia associated with AD. Early detection of MCI allows for potential treatments to prevent or decelerate the process of developing dementia. Standard methods of diagnosing MCI and AD employ structural (imaging), behavioral (cognitive tests), and genetic or molecular (blood or CSF tests) techniques. Our study proposes network-level neural synchronization parameters as topographical markers for diagnosing aMCI and AD. We conducted a pilot study based on EEG data recorded during an olfactory task from a group of elderly participants consisting of healthy individuals and patients of aMCI and AD to assess the value of different indicators of network-level phase and amplitude synchronization in differentiating the three groups. Significant differences were observed in the percent phase locking value, theta-gamma phase-amplitude coupling, and amplitude coherence between the groups, and classifiers were developed to differentiate the three groups based on these parameters. The observed differences in these indicators of network-level functionality of the brain can help explain the underlying processes involved in aMCI and AD.
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29
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Wiesman AI, Murman DL, Losh RA, Schantell M, Christopher-Hayes NJ, Johnson HJ, Willett MP, Wolfson SL, Losh KL, Johnson CM, May PE, Wilson TW. Spatially resolved neural slowing predicts impairment and amyloid burden in Alzheimer's disease. Brain 2022; 145:2177-2189. [PMID: 35088842 PMCID: PMC9246709 DOI: 10.1093/brain/awab430] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 10/05/2021] [Accepted: 10/24/2021] [Indexed: 11/28/2022] Open
Abstract
An extensive electrophysiological literature has proposed a pathological 'slowing' of neuronal activity in patients on the Alzheimer's disease spectrum. Supported by numerous studies reporting increases in low-frequency and decreases in high-frequency neural oscillations, this pattern has been suggested as a stable biomarker with potential clinical utility. However, no spatially resolved metric of such slowing exists, stymieing efforts to understand its relation to proteinopathy and clinical outcomes. Further, the assumption that this slowing is occurring in spatially overlapping populations of neurons has not been empirically validated. In the current study, we collected cross-sectional resting state measures of neuronal activity using magnetoencephalography from 38 biomarker-confirmed patients on the Alzheimer's disease spectrum and 20 cognitively normal biomarker-negative older adults. From these data, we compute and validate a new metric of spatially resolved oscillatory deviations from healthy ageing for each patient on the Alzheimer's disease spectrum. Using this Pathological Oscillatory Slowing Index, we show that patients on the Alzheimer's disease spectrum exhibit robust neuronal slowing across a network of temporal, parietal, cerebellar and prefrontal cortices. This slowing effect is shown to be directly relevant to clinical outcomes, as oscillatory slowing in temporal and parietal cortices significantly predicted both general (i.e. Montreal Cognitive Assessment scores) and domain-specific (i.e. attention, language and processing speed) cognitive function. Further, regional amyloid-β accumulation, as measured by quantitative 18F florbetapir PET, robustly predicted the magnitude of this pathological neural slowing effect, and the strength of this relationship between amyloid-β burden and neural slowing also predicted attentional impairments across patients. These findings provide empirical support for a spatially overlapping effect of oscillatory neural slowing in biomarker-confirmed patients on the Alzheimer's disease spectrum, and link this effect to both regional proteinopathy and cognitive outcomes in a spatially resolved manner. The Pathological Oscillatory Slowing Index also represents a novel metric that is of potentially high utility across a number of clinical neuroimaging applications, as oscillatory slowing has also been extensively documented in other patient populations, most notably Parkinson's disease, with divergent spectral and spatial features.
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Affiliation(s)
- Alex I Wiesman
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Department of Neurological Sciences, University of Nebraska Medical Center (UNMC), Omaha, NE, USA
| | - Daniel L Murman
- Department of Neurological Sciences, University of Nebraska Medical Center (UNMC), Omaha, NE, USA
- Memory Disorders & Behavioral Neurology Program, UNMC, Omaha, NE, USA
| | - Rebecca A Losh
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Mikki Schantell
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
| | | | - Hallie J Johnson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Madelyn P Willett
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
| | | | - Kathryn L Losh
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
| | | | - Pamela E May
- Department of Neurological Sciences, University of Nebraska Medical Center (UNMC), Omaha, NE, USA
| | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
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Scheijbeler EP, van Nifterick AM, Stam CJ, Hillebrand A, Gouw AA, de Haan W. Network-level permutation entropy of resting-state MEG recordings: A novel biomarker for early-stage Alzheimer's disease? Netw Neurosci 2022; 6:382-400. [PMID: 35733433 PMCID: PMC9208018 DOI: 10.1162/netn_a_00224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/15/2021] [Indexed: 11/24/2022] Open
Abstract
Increasing evidence suggests that measures of signal variability and complexity could present promising biomarkers for Alzheimer's disease (AD). Earlier studies have however been limited to the characterization of local activity. Here, we investigate whether a network version of permutation entropy could serve as a novel biomarker for early-stage AD. Resting-state source-space magnetoencephalography was recorded in 18 subjects with subjective cognitive decline (SCD) and 18 subjects with mild cognitive impairment (MCI). Local activity was characterized by permutation entropy (PE). Network-level interactions were studied using the inverted joint permutation entropy (JPEinv), corrected for volume conduction. The JPEinv showed a reduction of nonlinear connectivity in MCI subjects in the theta and alpha band. Local PE showed increased theta band entropy. Between-group differences were widespread across brain regions. Receiver operating characteristic (ROC) analysis of classification of MCI versus SCD subjects revealed that a logistic regression model trained on JPEinv features (78.4% [62.5-93.3%]) slightly outperformed PE (76.9% [60.3-93.4%]) and relative theta power-based models (76.9% [60.4-93.3%]). Classification performance of theta JPEinv was at least as good as the relative theta power benchmark. The JPEinv is therefore a potential biomarker for early-stage AD that should be explored in larger studies.
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Affiliation(s)
- Elliz P. Scheijbeler
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Anne M. van Nifterick
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Alida A. Gouw
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Willem de Haan
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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31
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Dobrushina OR, Dobrynina LA, Arina GA, Kremneva EI, Novikova ES, Gubanova MV, Pechenkova EV, Suslina AD, Aristova VV, Trubitsyna VV, Krotenkova MV. Enhancing Brain Connectivity With Infra-Low Frequency Neurofeedback During Aging: A Pilot Study. Front Hum Neurosci 2022; 16:891547. [PMID: 35712529 PMCID: PMC9195620 DOI: 10.3389/fnhum.2022.891547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Aging is associated with decreased functional connectivity in the main brain networks, which can underlie changes in cognitive and emotional processing. Neurofeedback is a promising non-pharmacological approach for the enhancement of brain connectivity. Previously, we showed that a single session of infra-low frequency neurofeedback results in increased connectivity between sensory processing networks in healthy young adults. In the current pilot study, we aimed to evaluate the possibility of enhancing brain connectivity during aging with the use of infra-low frequency neurofeedback. Nine females aged 52 ± 7 years with subclinical signs of emotional dysregulation, including anxiety, mild depression, and somatoform symptoms, underwent 15 sessions of training. A resting-state functional MRI scan was acquired before and after the training. A hypothesis-free intrinsic connectivity analysis showed increased connectivity in regions in the bilateral temporal fusiform cortex, right supplementary motor area, left amygdala, left temporal pole, and cerebellum. Next, a seed-to-voxel analysis for the revealed regions was performed using the post- vs. pre-neurofeedback contrast. Finally, to explore the whole network of neurofeedback-related connectivity changes, the regions revealed by the intrinsic connectivity and seed-to-voxel analyses were entered into a network-based statistical analysis. An extended network was revealed, including the temporal and occipital fusiform cortex, multiple areas from the visual cortex, the right posterior superior temporal sulcus, the amygdala, the temporal poles, the superior parietal lobule, and the supplementary motor cortex. Clinically, decreases in alexithymia, depression, and anxiety levels were observed. Thus, infra-low frequency neurofeedback appears to be a promising method for enhancing brain connectivity during aging, and subsequent sham-controlled studies utilizing larger samples are feasible.
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Affiliation(s)
- Olga R. Dobrushina
- Third Neurological Department, Research Center of Neurology, Moscow, Russia
- *Correspondence: Olga R. Dobrushina
| | | | - Galina A. Arina
- Faculty of Psychology, M.V. Lomonosov Moscow State University, Moscow, Russia
| | - Elena I. Kremneva
- Department of Radiology, Research Center of Neurology, Moscow, Russia
| | | | - Mariia V. Gubanova
- Third Neurological Department, Research Center of Neurology, Moscow, Russia
| | | | | | - Vlada V. Aristova
- Third Neurological Department, Research Center of Neurology, Moscow, Russia
- Faculty of Psychology, M.V. Lomonosov Moscow State University, Moscow, Russia
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32
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Ranasinghe KG, Verma P, Cai C, Xie X, Kudo K, Gao X, Lerner H, Mizuiri D, Strom A, Iaccarino L, La Joie R, Miller BL, Gorno-Tempini ML, Rankin KP, Jagust WJ, Vossel K, Rabinovici GD, Raj A, Nagarajan SS. Altered excitatory and inhibitory neuronal subpopulation parameters are distinctly associated with tau and amyloid in Alzheimer's disease. eLife 2022; 11:e77850. [PMID: 35616532 PMCID: PMC9217132 DOI: 10.7554/elife.77850] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 05/19/2022] [Indexed: 11/13/2022] Open
Abstract
Background Neuronal- and circuit-level abnormalities of excitation and inhibition are shown to be associated with tau and amyloid-beta (Aβ) in preclinical models of Alzheimer's disease (AD). These relationships remain poorly understood in patients with AD. Methods Using empirical spectra from magnetoencephalography and computational modeling (neural mass model), we examined excitatory and inhibitory parameters of neuronal subpopulations and investigated their specific associations to regional tau and Aβ, measured by positron emission tomography, in patients with AD. Results Patients with AD showed abnormal excitatory and inhibitory time-constants and neural gains compared to age-matched controls. Increased excitatory time-constants distinctly correlated with higher tau depositions while increased inhibitory time-constants distinctly correlated with higher Aβ depositions. Conclusions Our results provide critical insights about potential mechanistic links between abnormal neural oscillations and cellular correlates of impaired excitatory and inhibitory synaptic functions associated with tau and Aβ in patients with AD. Funding This study was supported by the National Institutes of Health grants: K08AG058749 (KGR), F32AG050434-01A1 (KGR), K23 AG038357 (KAV), P50 AG023501, P01 AG19724 (BLM), P50-AG023501 (BLM and GDR), R01 AG045611 (GDR); AG034570, AG062542 (WJ); NS100440 (SSN), DC176960 (SSN), DC017091 (SSN), AG062196 (SSN); a grant from John Douglas French Alzheimer's Foundation (KAV); grants from Larry L. Hillblom Foundation: 2015-A-034-FEL (KGR), 2019-A-013-SUP (KGR); grants from the Alzheimer's Association: AARG-21-849773 (KGR); PCTRB-13-288476 (KAV), and made possible by Part the CloudTM (ETAC-09-133596); a grant from Tau Consortium (GDR and WJJ), and a gift from the S. D. Bechtel Jr. Foundation.
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Affiliation(s)
- Kamalini G Ranasinghe
- Memory and Aging Center, Department of Neurology, University of California, San FranciscoSan FranciscoUnited States
| | - Parul Verma
- Department of Radiology and Biomedical Imaging, University of California, San FranciscoSan FranciscoUnited States
| | - Chang Cai
- Department of Radiology and Biomedical Imaging, University of California, San FranciscoSan FranciscoUnited States
| | - Xihe Xie
- Department of Radiology and Biomedical Imaging, University of California, San FranciscoSan FranciscoUnited States
| | - Kiwamu Kudo
- Department of Radiology and Biomedical Imaging, University of California, San FranciscoSan FranciscoUnited States
- Medical Imaging Business Center, Ricoh CompanyKanazawaJapan
| | - Xiao Gao
- Department of Radiology and Biomedical Imaging, University of California, San FranciscoSan FranciscoUnited States
| | - Hannah Lerner
- Memory and Aging Center, Department of Neurology, University of California, San FranciscoSan FranciscoUnited States
| | - Danielle Mizuiri
- Department of Radiology and Biomedical Imaging, University of California, San FranciscoSan FranciscoUnited States
| | - Amelia Strom
- Memory and Aging Center, Department of Neurology, University of California, San FranciscoSan FranciscoUnited States
| | - Leonardo Iaccarino
- Memory and Aging Center, Department of Neurology, University of California, San FranciscoSan FranciscoUnited States
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, University of California, San FranciscoSan FranciscoUnited States
| | - Bruce L Miller
- Memory and Aging Center, Department of Neurology, University of California, San FranciscoSan FranciscoUnited States
| | - Maria Luisa Gorno-Tempini
- Memory and Aging Center, Department of Neurology, University of California, San FranciscoSan FranciscoUnited States
| | - Katherine P Rankin
- Memory and Aging Center, Department of Neurology, University of California, San FranciscoSan FranciscoUnited States
| | - William J Jagust
- Helen Wills Neuroscience Institute, University of California, BerkeleyBerkeleyUnited States
| | - Keith Vossel
- Memory and Aging Center, Department of Neurology, University of California, San FranciscoSan FranciscoUnited States
- Mary S. Easton Center for Alzheimer’s Disease Research, Department of Neurology, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Gil D Rabinovici
- Memory and Aging Center, Department of Neurology, University of California, San FranciscoSan FranciscoUnited States
- Department of Radiology and Biomedical Imaging, University of California, San FranciscoSan FranciscoUnited States
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California, San FranciscoSan FranciscoUnited States
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging, University of California, San FranciscoSan FranciscoUnited States
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Torres-Simón L, Doval S, Nebreda A, Llinas SJ, Marsh EB, Maestú F. Understanding brain function in vascular cognitive impairment and dementia with EEG and MEG: A systematic review. Neuroimage Clin 2022; 35:103040. [PMID: 35653914 PMCID: PMC9163840 DOI: 10.1016/j.nicl.2022.103040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 04/09/2022] [Accepted: 05/06/2022] [Indexed: 11/22/2022]
Abstract
Vascular Cognitive Impairment (VCI) is the second most prevalent dementia after Alzheimer's Disease (AD), and cerebrovascular disease (CBVD) is a major comorbid contributor to the progression of most neurodegenerative diseases. Early differentiation of cognitive impairment is critical given both the high prevalence of CBVD, and that its risk factors are modifiable. The ability for electroencephalogram (EEG) and magnetoencephalogram (MEG) to detect changes in brain functioning for other dementias suggests that they may also be promising biomarkers for early VCI. The present systematic review aims to summarize the literature regarding electrophysiological patterns of mild and major VCI. Despite considerable heterogeneity in clinical definition and electrophysiological methodology, common patterns exist when comparing patients with VCI to healthy controls (HC) and patients with AD, though there is a low specificity when comparing between VCI subgroups. Similar to other dementias, slowed frequency patterns and disrupted inter- and intra-hemispheric connectivity are repeatedly reported for VCI patients, as well as longer latencies and smaller amplitudes in evoked responses. Further study is needed to fully establish MEG and EEG as clinically useful biomarkers, including a clear definition of VCI and standardized methodology, allowing for comparison across groups and consolidation of multicenter efforts.
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Affiliation(s)
- Lucía Torres-Simón
- Center of Cognitive and Computational Neuroscience; Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain.
| | - Sandra Doval
- Center of Cognitive and Computational Neuroscience; Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain
| | - Alberto Nebreda
- Center of Cognitive and Computational Neuroscience; Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain
| | - Sophia J Llinas
- Department of Neurology, the Johns Hopkins School of Medicine, Baltimore, MD USA
| | - Elisabeth B Marsh
- Department of Neurology, the Johns Hopkins School of Medicine, Baltimore, MD USA
| | - Fernando Maestú
- Center of Cognitive and Computational Neuroscience; Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain
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Luppi JJ, Schoonhoven DN, van Nifterick AM, Gouw AA, Hillebrand A, Scheltens P, Stam CJ, de Haan W. Oscillatory Activity of the Hippocampus in Prodromal Alzheimer’s Disease: A Source-Space Magnetoencephalography Study. J Alzheimers Dis 2022; 87:317-333. [PMID: 35311705 PMCID: PMC9198749 DOI: 10.3233/jad-215464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: In Alzheimer’s disease (AD), oscillatory activity of the human brain slows down. However, oscillatory slowing varies between individuals, particularly in prodromal AD. Cortical oscillatory changes have shown suboptimal accuracy as diagnostic markers. We speculated that focusing on the hippocampus might prove more successful, particularly using magnetoencephalography (MEG) for capturing subcortical oscillatory activity. Objective: We explored MEG-based detection of hippocampal oscillatory abnormalities in prodromal AD patients. Methods: We acquired resting-state MEG data of 18 AD dementia patients, 18 amyloid-β-positive amnestic mild cognitive impairment (MCI, prodromal AD) patients, and 18 amyloid-β-negative persons with subjective cognitive decline (SCD). Oscillatory activity in 78 cortical regions and both hippocampi was reconstructed using beamforming. Between-group and hippocampal-cortical differences in spectral power were assessed. Classification accuracy was explored using ROC curves. Results: The MCI group showed intermediate power values between SCD and AD, except for the alpha range, where it was higher than both (p < 0.05 and p < 0.001). The largest differences between MCI and SCD were in the theta band, with higher power in MCI (p < 0.01). The hippocampi showed several unique group differences, such as higher power in the higher alpha band in MCI compared to SCD (p < 0.05). Classification accuracy (MCI versus SCD) was best for absolute theta band power in the right hippocampus (AUC = 0.87). Conclusion: In this MEG study, we detected oscillatory abnormalities of the hippocampi in prodromal AD patients. Moreover, hippocampus-based classification performed better than cortex-based classification. We conclude that a focus on hippocampal MEG may improve early detection of AD-related neuronal dysfunction.
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Affiliation(s)
- Janne J. Luppi
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Deborah N. Schoonhoven
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Anne M. van Nifterick
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Alida A. Gouw
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
- Department of Clinical Neurophysiology and MEG, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Philip Scheltens
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology and MEG, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Willem de Haan
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
- Department of Clinical Neurophysiology and MEG, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
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35
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Pusil S, Torres-Simon L, Chino B, López ME, Canuet L, Bilbao Á, Maestú F, Paúl N. Resting-State Beta-Band Recovery Network Related to Cognitive Improvement After Stroke. Front Neurol 2022; 13:838170. [PMID: 35280290 PMCID: PMC8914082 DOI: 10.3389/fneur.2022.838170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 02/03/2022] [Indexed: 11/29/2022] Open
Abstract
Background Stroke is the second leading cause of death worldwide and it causes important long-term cognitive and physical deficits that hamper patients' daily activity. Neuropsychological rehabilitation (NR) has increasingly become more important to recover from cognitive disability and to improve the functionality and quality of life of these patients. Since in most stroke cases, restoration of functional connectivity (FC) precedes or accompanies cognitive and behavioral recovery, understanding the electrophysiological signatures underlying stroke recovery mechanisms is a crucial scientific and clinical goal. Methods For this purpose, a longitudinal study was carried out with a sample of 10 stroke patients, who underwent two neuropsychological assessments and two resting-state magnetoencephalographic (MEG) recordings, before and after undergoing a NR program. Moreover, to understand the degree of cognitive and neurophysiological impairment after stroke and the mechanisms of recovery after cognitive rehabilitation, stroke patients were compared to 10 healthy controls matched for age, sex, and educational level. Findings After intra and inter group comparisons, we found the following results: (1) Within the stroke group who received cognitive rehabilitation, almost all cognitive domains improved relatively or totally; (2) They exhibit a pattern of widespread increased in FC within the beta band that was related to the recovery process (there were no significant differences between patients who underwent rehabilitation and controls); (3) These FC recovery changes were related with the enhanced of cognitive performance. Furthermore, we explored the capacity of the neuropsychological scores before rehabilitation, to predict the FC changes in the brain network. Significant correlations were found in global indexes from the WAIS-III: Performance IQ (PIQ) and Perceptual Organization index (POI) (i.e., Picture Completion, Matrix Reasoning, and Block Design).
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Affiliation(s)
- Sandra Pusil
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
| | - Lucía Torres-Simon
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
| | - Brenda Chino
- Institute of Neuroscience, Autonomous University of Barcelona, Barcelona, Spain
| | - María Eugenia López
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
| | - Leonides Canuet
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
| | - Álvaro Bilbao
- National Centre for Brain Injury Treatment, Centro de Referencia Estatal de Atención Al Daño Cerebral (CEADAC), Madrid, Spain
| | - Fernando Maestú
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
| | - Nuria Paúl
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
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36
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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] [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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37
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Chen S, Song Y, Wu H, Ge H, Qi W, Xi Y, Wu J, Ji Y, Chen K, Lin X, Chen J. Hyperconnectivity Associated with Anosognosia Accelerating Clinical Progression in Amnestic Mild Cognitive Impairment. ACS Chem Neurosci 2022; 13:120-133. [PMID: 34923823 DOI: 10.1021/acschemneuro.1c00595] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The incidence and prevalence of anosognosia are highly variable in amnestic mild cognitive impairment (aMCI) patients. The study aims to explore the neuropathological mechanism of anosognosia in aMCI patients using two different but complementary technologies, including 18F-flortaucipir positron emission tomography and resting state functional magnetic resonance imaging. The study found that anosognosia was related to higher tau accumulation in the left medial orbitofrontal cortex (OFC), left posterior cingulate cortex, and right precuneus in aMCI patients. Intrinsic functional connectivity analyses found significant correlations between anosognosia index and hypoconnectivity between the left medial OFC and left middle temporal gyrus (MTG), right precuneus and left lingual gyrus. Longitudinally, the connectivity of these brain regions as well as the right precuneus and right cuneus showed hyperconnectivity in aMCI patients with anosognosia. The anosognosia index was also correlated with AD pathological markers (i.e., Aβ, t-tau, and p-tau) and brain glucose metabolism in aMCI patients. In conclusion, anosognosia in aMCI patients is associated with the dysfunction of medial OFC-MTG circuit and the precuneus-visual cortex circuit and accelerates clinical progression to AD dementia.
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Affiliation(s)
- Shanshan Chen
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Yu Song
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Huimin Wu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Honglin Ge
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Wenzhang Qi
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Yue Xi
- Fourth Clinical College of Nanjing Medical University, Nanjing 211166, China
| | - Jiayi Wu
- Fourth Clinical College of Nanjing Medical University, Nanjing 211166, China
| | - Yuxiang Ji
- Fourth Clinical College of Nanjing Medical University, Nanjing 211166, China
| | - Kexin Chen
- Fourth Clinical College of Nanjing Medical University, Nanjing 211166, China
| | - Xingjian Lin
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Jiu Chen
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu 210029, China
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38
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Cuesta P, Ochoa-Urrea M, Funke M, Hasan O, Zhu P, Marcos A, López ME, Schulz PE, Lhatoo S, Pantazis D, Mosher JC, Maestu F. OUP accepted manuscript. Brain Commun 2022; 4:fcac012. [PMID: 35282163 PMCID: PMC8914494 DOI: 10.1093/braincomms/fcac012] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 11/29/2021] [Accepted: 02/01/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Pablo Cuesta
- Department of Radiology, Rehabilitation and Physiotherapy, Complutense University of Madrid, Madrid, Spain
- Correspondence to: Pablo Cuesta Prieto, Associate professor Department of Radiology, Rehabilitation and Physiotherapy, Medicine School Complutense University of Madrid Plaza, Ramón y Cajal, s/n. Ciudad Universitaria 28040 Madrid, Spain E-mail:
| | - Manuela Ochoa-Urrea
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Michael Funke
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Omar Hasan
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ping Zhu
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX, USA
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Alberto Marcos
- Neurology Department, Hospital Clinico San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, Madrid, Spain
| | - Maria Eugenia López
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Complutense University of Madrid, Madrid, Spain
| | - Paul E. Schulz
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Samden Lhatoo
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, USA
| | - John C. Mosher
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Fernando Maestu
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Complutense University of Madrid, Madrid, Spain
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39
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Ranasinghe KG, Petersen C, Kudo K, Mizuiri D, Rankin KP, Rabinovici GD, Gorno-Tempini ML, Seeley WW, Spina S, Miller BL, Vossel K, Grinberg LT, Nagarajan SS. Reduced synchrony in alpha oscillations during life predicts post mortem neurofibrillary tangle density in early-onset and atypical Alzheimer's disease. Alzheimers Dement 2021; 17:2009-2019. [PMID: 33884753 PMCID: PMC8528895 DOI: 10.1002/alz.12349] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 03/04/2021] [Accepted: 03/19/2021] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Neurophysiological manifestations selectively associated with amyloid beta and tau depositions in Alzheimer's disease (AD) are useful network biomarkers to identify peptide specific pathological processes. The objective of this study was to validate the associations between reduced neuronal synchrony within alpha oscillations and neurofibrillary tangle (NFT) density in autopsy examination, in patients with AD. METHODS In a well-characterized clinicopathological cohort of AD patients (n = 13), we quantified neuronal synchrony within alpha (8-12 Hz) and delta-theta (2-8 Hz) oscillations, using magnetoencephalography during the disease course, within six selected neocortical and hippocampal regions, including angular gyrus, superior temporal gurus, middle frontal gyrus, primary motor cortex, CA1, and subiculum, and correlated these with regional NFT density quantified at histopathological examination. RESULTS Abnormal synchrony in alpha, but not in delta-theta, significantly predicted the NFT density at post mortem neuropathological examination. DISCUSSION Reduced alpha synchrony is a sensitive neurophysiological index associated with pathological tau, and a potential network biomarker for clinical trials, to gauge the extent of network dysfunction and the degree of rescue in treatments targeting tau pathways in AD.
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Affiliation(s)
- Kamalini G. Ranasinghe
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Cathrine Petersen
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Kiwamu Kudo
- Department Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, California, USA,Medical Imaging Business Center, Ricoh Company, Ltd., Kanazawa, Japan
| | - Danielle Mizuiri
- Department Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Katherine P. Rankin
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Gil D. Rabinovici
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, California, USA,Department Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Maria Luisa Gorno-Tempini
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - William W. Seeley
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, California, USA,Department of Pathology, University of California, San Francisco, San Francisco, California, USA
| | - Salvatore Spina
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Bruce L. Miller
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Keith Vossel
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, California, USA,Mary S. Easton Center for Alzheimer’s Disease Research, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Lea T. Grinberg
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, California, USA,Department of Pathology, University of California, San Francisco, San Francisco, California, USA,Department of Pathology, LIM22, University of Sao Paulo, Sao Paulo, Brazil
| | - Srikantan S. Nagarajan
- Department Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
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40
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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] [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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41
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Roascio M, Canessa A, Trò RD, Mattioli P, Famà F, Giorgetti L, Girtler N, Orso B, Morbelli S, Nobili FM, Arnaldi D, Arnulfo G. Phase and amplitude EEG correlations change with disease progression in people with idiopathic rapid eye-movement sleep behavior disorder. Sleep 2021; 45:6374127. [PMID: 34551110 PMCID: PMC8754497 DOI: 10.1093/sleep/zsab232] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 08/31/2021] [Indexed: 11/21/2022] Open
Abstract
Study Objectives Increased phase synchronization in electroencephalography (EEG) bands might reflect the activation of compensatory mechanisms of cognitive decline in people with neurodegenerative diseases. Here, we investigated whether altered large-scale couplings of brain oscillations could be linked to the balancing of cognitive decline in a longitudinal cohort of people with idiopathic rapid eye-movement sleep behavior disorder (iRBD). Methods We analyzed 18 patients (17 males, 69.7 ± 7.5 years) with iRBD undergoing high-density EEG (HD-EEG), presynaptic dopaminergic imaging, and clinical and neuropsychological (NPS) assessments at two time points (time interval 24.2 ± 5.9 months). We thus quantified the HD-EEG power distribution, orthogonalized amplitude correlation, and weighted phase-lag index at both time points and correlated them with clinical, NPS, and imaging data. Results Four patients phenoconverted at follow-up (three cases of parkinsonism and one of dementia). At the group level, NPS scores decreased over time, without reaching statistical significance. However, alpha phase synchronization increased and delta amplitude correlations decreased significantly at follow-up compared to baseline. Both large-scale network connectivity metrics were significantly correlated with NPS scores but not with sleep quality indices or presynaptic dopaminergic imaging data. Conclusions These results suggest that increased alpha phase synchronization and reduced delta amplitude correlation may be considered electrophysiological signs of an active compensatory mechanism of cognitive impairment in people with iRBD. Large-scale functional modifications may be helpful biomarkers in the characterization of prodromal stages of alpha-synucleinopathies.
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Affiliation(s)
- M Roascio
- Department of Informatics, Bioengineering, Robotics and System engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - A Canessa
- Department of Informatics, Bioengineering, Robotics and System engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - R D Trò
- Department of Informatics, Bioengineering, Robotics and System engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - P Mattioli
- Clinical Neurology, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Children's Sciences (DINOGMI), University of Genoa, Genoa, Italy.,IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - F Famà
- Clinical Neurology, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Children's Sciences (DINOGMI), University of Genoa, Genoa, Italy.,IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - L Giorgetti
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - N Girtler
- Clinical Neurology, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Children's Sciences (DINOGMI), University of Genoa, Genoa, Italy.,IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - B Orso
- Clinical Neurology, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Children's Sciences (DINOGMI), University of Genoa, Genoa, Italy
| | - S Morbelli
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Nuclear Medicine, Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - F M Nobili
- Clinical Neurology, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Children's Sciences (DINOGMI), University of Genoa, Genoa, Italy.,IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - D Arnaldi
- Clinical Neurology, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Children's Sciences (DINOGMI), University of Genoa, Genoa, Italy.,IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - G Arnulfo
- Department of Informatics, Bioengineering, Robotics and System engineering (DIBRIS), University of Genoa, Genoa, Italy.,Neuroscience Center, Helsinki Institute of Life Science (HiLife), University of Helsinki, Helsinki, Finland
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Yang S, Bornot JMS, Fernandez RB, Deravi F, Hoque S, Wong-Lin K, Prasad G. Detection of Mild Cognitive Impairment with MEG Functional Connectivity Using Wavelet-Based Neuromarkers. SENSORS (BASEL, SWITZERLAND) 2021; 21:6210. [PMID: 34577423 PMCID: PMC8473237 DOI: 10.3390/s21186210] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 09/08/2021] [Accepted: 09/10/2021] [Indexed: 11/23/2022]
Abstract
Studies on developing effective neuromarkers based on magnetoencephalographic (MEG) signals have been drawing increasing attention in the neuroscience community. This study explores the idea of using source-based magnitude-squared spectral coherence as a spatial indicator for effective regions of interest (ROIs) localization, subsequently discriminating the participants with mild cognitive impairment (MCI) from a group of age-matched healthy control (HC) elderly participants. We found that the cortical regions could be divided into two distinctive groups based on their coherence indices. Compared to HC, some ROIs showed increased connectivity (hyper-connected ROIs) for MCI participants, whereas the remaining ROIs demonstrated reduced connectivity (hypo-connected ROIs). Based on these findings, a series of wavelet-based source-level neuromarkers for MCI detection are proposed and explored, with respect to the two distinctive ROI groups. It was found that the neuromarkers extracted from the hyper-connected ROIs performed significantly better for MCI detection than those from the hypo-connected ROIs. The neuromarkers were classified using support vector machine (SVM) and k-NN classifiers and evaluated through Monte Carlo cross-validation. An average recognition rate of 93.83% was obtained using source-reconstructed signals from the hyper-connected ROI group. To better conform to clinical practice settings, a leave-one-out cross-validation (LOOCV) approach was also employed to ensure that the data for testing was from a participant that the classifier has never seen. Using LOOCV, we found the best average classification accuracy was reduced to 83.80% using the same set of neuromarkers obtained from the ROI group with functional hyper-connections. This performance surpassed the results reported using wavelet-based features by approximately 15%. Overall, our work suggests that (1) certain ROIs are particularly effective for MCI detection, especially when multi-resolution wavelet biomarkers are employed for such diagnosis; (2) there exists a significant performance difference in system evaluation between research-based experimental design and clinically accepted evaluation standards.
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Affiliation(s)
- Su Yang
- Department of Computer Science, Swansea University, Swansea SA1 8EN, UK
| | - Jose Miguel Sanchez Bornot
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry BT48 7JL, Ireland; (J.M.S.B.); (K.W.-L.); (G.P.)
| | | | - Farzin Deravi
- School of Engineering, University of Kent, Canterbury CT2 7NZ, UK; (F.D.); (S.H.)
| | - Sanaul Hoque
- School of Engineering, University of Kent, Canterbury CT2 7NZ, UK; (F.D.); (S.H.)
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry BT48 7JL, Ireland; (J.M.S.B.); (K.W.-L.); (G.P.)
| | - Girijesh Prasad
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry BT48 7JL, Ireland; (J.M.S.B.); (K.W.-L.); (G.P.)
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43
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Soto-Añari M, López N, Rivera-Fernández C, Belón-Hercilla V, Fernández-Guinea S. Literacy Level and Executive Control in Healthy Older Peruvian Adults. Front Neurol 2021; 12:629048. [PMID: 34512496 PMCID: PMC8426511 DOI: 10.3389/fneur.2021.629048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 07/30/2021] [Indexed: 11/16/2022] Open
Abstract
Introduction: Early-life educational experiences are associated with cognitive performance in aging. Early literacy seems to improve executive control mechanisms, however, it is not clear whether early education would still be an advantage in countries like Peru, where access to and quality of education is highly variable. Aim: Our objective was to analyze the association of literacy level with executive control factors. Method: We evaluated 93 healthy older adults with a clinical protocol that included the Mini-Mental State Examination, the Geriatric Depression Scale and Global Dementia Staging. We also used a neuropsychological executive function battery which included the Trail-Making Test parts A and B, the Stroop Test, phonological and semantic verbal fluency tasks, Forward and Backward Digits, Numbers and Letters of the Wechsler Scale, and the Go/No-Go task. We used a principal component analysis for the dimensional reduction of the variables. To measure the level of literacy we used the word accentuation test (WAT). Results: We observed statistically significant correlations between the principal components (PCs) of working memory, cognitive flexibility and inhibitory control with the WAT scores. Furthermore, we observed that processing speed and WAT predict the scores on PCs factors better than years of education and age. Conclusions: Literacy level correlates more closely with better cognitive performance than years of education and thus, might improve executive control factors that could compensate and protect against brain changes in cognitive decline and dementia.
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Affiliation(s)
- Marcio Soto-Añari
- Laboratorio de Neurociencia, Departamento de Psicología, Universidad Católica San Pablo, Arequipa, Peru
| | | | | | - Verónica Belón-Hercilla
- Laboratorio de Neurociencia, Departamento de Psicología, Universidad Católica San Pablo, Arequipa, Peru
| | - Sara Fernández-Guinea
- Departamento de Psicología Experimental, Facultad de Psicología, Universidad Complutense de Madrid, Madrid, Spain
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44
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Maestú F, de Haan W, Busche MA, DeFelipe J. Neuronal excitation/inhibition imbalance: core element of a translational perspective on Alzheimer pathophysiology. Ageing Res Rev 2021; 69:101372. [PMID: 34029743 DOI: 10.1016/j.arr.2021.101372] [Citation(s) in RCA: 75] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 04/16/2021] [Accepted: 05/19/2021] [Indexed: 02/08/2023]
Abstract
Our incomplete understanding of the link between Alzheimer's Disease pathology and symptomatology is a crucial obstacle for therapeutic success. Recently, translational studies have begun to connect the dots between protein alterations and deposition, brain network dysfunction and cognitive deficits. Disturbance of neuronal activity, and in particular an imbalance in underlying excitation/inhibition (E/I), appears early in AD, and can be regarded as forming a central link between structural brain pathology and cognitive dysfunction. While there are emerging (non-)pharmacological options to influence this imbalance, the complexity of human brain dynamics has hindered identification of an optimal approach. We suggest that focusing on the integration of neurophysiological aspects of AD at the micro-, meso- and macroscale, with the support of computational network modeling, can unite fundamental and clinical knowledge, provide a general framework, and suggest rational therapeutic targets.
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45
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Zhang T, Liao Q, Zhang D, Zhang C, Yan J, Ngetich R, Zhang J, Jin Z, Li L. Predicting MCI to AD Conversation Using Integrated sMRI and rs-fMRI: Machine Learning and Graph Theory Approach. Front Aging Neurosci 2021; 13:688926. [PMID: 34421570 PMCID: PMC8375594 DOI: 10.3389/fnagi.2021.688926] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 06/23/2021] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Graph theory and machine learning have been shown to be effective ways of classifying different stages of Alzheimer's disease (AD). Most previous studies have only focused on inter-subject classification with single-mode neuroimaging data. However, whether this classification can truly reflect the changes in the structure and function of the brain region in disease progression remains unverified. In the current study, we aimed to evaluate the classification framework, which combines structural Magnetic Resonance Imaging (sMRI) and resting-state functional Magnetic Resonance Imaging (rs-fMRI) metrics, to distinguish mild cognitive impairment non-converters (MCInc)/AD from MCI converters (MCIc) by using graph theory and machine learning. METHODS With the intra-subject (MCInc vs. MCIc) and inter-subject (MCIc vs. AD) design, we employed cortical thickness features, structural brain network features, and sub-frequency (full-band, slow-4, slow-5) functional brain network features for classification. Three feature selection methods [random subset feature selection algorithm (RSFS), minimal redundancy maximal relevance (mRMR), and sparse linear regression feature selection algorithm based on stationary selection (SS-LR)] were used respectively to select discriminative features in the iterative combinations of MRI and network measures. Then support vector machine (SVM) classifier with nested cross-validation was employed for classification. We also compared the performance of multiple classifiers (Random Forest, K-nearest neighbor, Adaboost, SVM) and verified the reliability of our results by upsampling. RESULTS We found that in the classifications of MCIc vs. MCInc, and MCIc vs. AD, the proposed RSFS algorithm achieved the best accuracies (84.71, 89.80%) than the other algorithms. And the high-sensitivity brain regions found with the two classification groups were inconsistent. Specifically, in MCIc vs. MCInc, the high-sensitivity brain regions associated with both structural and functional features included frontal, temporal, caudate, entorhinal, parahippocampal, and calcarine fissure and surrounding cortex. While in MCIc vs. AD, the high-sensitivity brain regions associated only with functional features included frontal, temporal, thalamus, olfactory, and angular. CONCLUSIONS These results suggest that our proposed method could effectively predict the conversion of MCI to AD, and the inconsistency of specific brain regions provides a novel insight for clinical AD diagnosis.
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Affiliation(s)
| | | | | | | | | | | | | | - Zhenlan Jin
- Key Laboratory for NeuroInformation of Ministry of Education, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ling Li
- Key Laboratory for NeuroInformation of Ministry of Education, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
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46
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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] [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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47
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Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06105-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
AbstractIn this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble classifiers based on deep convolutional neural networks. For the scope of predicting the early signs of Alzheimer’s disease (AD), functional connectivity (FC) measures between the brain bio-magnetic signals originated from spatially separated brain regions are used as MEG data representations for the analysis. After stacking the FC indicators relative to different frequency bands into multiple images, a deep transfer learning model is used to extract different sets of deep features and to derive improved classification ensembles. The proposed Deep-MEG architectures were tested on a set of resting-state MEG recordings and their corresponding magnetic resonance imaging scans, from a longitudinal study involving 87 subjects. Accuracy values of 89% and 87% were obtained, respectively, for the early prediction of AD conversion in a sample of 54 mild cognitive impairment subjects and in a sample of 87 subjects, including 33 healthy controls. These results indicate that the proposed Deep-MEG approach is a powerful tool for detecting early alterations in the spectral–temporal connectivity profiles and in their spatial relationships.
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48
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Xu M, Sanz DL, Garces P, Maestu F, Li Q, Pantazis D. A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression With MEG Brain Networks. IEEE Trans Biomed Eng 2021; 68:1579-1588. [PMID: 33400645 PMCID: PMC8162933 DOI: 10.1109/tbme.2021.3049199] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Characterizing the subtle changes of functional brain networks associated with the pathological cascade of Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression prior to clinical symptoms. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G), which can learn highly informative network features by mapping high-dimensional resting-state brain networks into a low-dimensional latent space. These latent distribution-based embeddings enable a quantitative characterization of subtle and heterogeneous brain connectivity patterns at different regions, and can be used as input to traditional classifiers for various downstream graph analytic tasks, such as AD early stage prediction, and statistical evaluation of between-group significant alterations across brain regions. We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.
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49
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Perez-Valero E, Lopez-Gordo MA, Morillas C, Pelayo F, Vaquero-Blasco MA. A Review of Automated Techniques for Assisting the Early Detection of Alzheimer's Disease with a Focus on EEG. J Alzheimers Dis 2021; 80:1363-1376. [PMID: 33682717 DOI: 10.3233/jad-201455] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In this paper, we review state-of-the-art approaches that apply signal processing (SP) and machine learning (ML) to automate the detection of Alzheimer's disease (AD) and its prodromal stages. In the first part of the document, we describe the economic and social implications of the disease, traditional diagnosis techniques, and the fundaments of automated AD detection. Then, we present electroencephalography (EEG) as an appropriate alternative for the early detection of AD, owing to its reduced cost, portability, and non-invasiveness. We also describe the main time and frequency domain EEG features that are employed in AD detection. Subsequently, we examine some of the main studies of the last decade that aim to provide an automatic detection of AD and its previous stages by means of SP and ML. In these studies, brain data was acquired using multiple medical techniques such as magnetic resonance imaging, positron emission tomography, and EEG. The main aspects of each approach, namely feature extraction, classification model, validation approach, and performance metrics, are compiled and discussed. Lastly, a set of conclusions and recommendations for future research on AD automatic detection are drawn in the final section of the paper.
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Affiliation(s)
- Eduardo Perez-Valero
- Research Centre for Information and Communications Technologies (CITIC), University of Granada, Granada, Spain.,Department of Computer Architecture and Technology, University of Granada, Granada, Spain
| | - Miguel A Lopez-Gordo
- Department of Signal Theory, Telematics and Communications, University of Granada, Granada,Spain.,Nicolo Association, Churriana de la Vega, Spain
| | - Christian Morillas
- Research Centre for Information and Communications Technologies (CITIC), University of Granada, Granada, Spain.,Department of Computer Architecture and Technology, University of Granada, Granada, Spain
| | - Francisco Pelayo
- Research Centre for Information and Communications Technologies (CITIC), University of Granada, Granada, Spain.,Department of Computer Architecture and Technology, University of Granada, Granada, Spain
| | - Miguel A Vaquero-Blasco
- Research Centre for Information and Communications Technologies (CITIC), University of Granada, Granada, Spain.,Department of Signal Theory, Telematics and Communications, University of Granada, Granada,Spain
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50
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Han SH, Pyun JM, Yeo S, Kang DW, Jeong HT, Kang SW, Kim S, Youn YC. Differences between memory encoding and retrieval failure in mild cognitive impairment: results from quantitative electroencephalography and magnetic resonance volumetry. Alzheimers Res Ther 2021; 13:3. [PMID: 33397486 PMCID: PMC7784298 DOI: 10.1186/s13195-020-00739-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 12/04/2020] [Indexed: 02/17/2023]
Abstract
BACKGROUND The memory impairments in mild cognitive impairment (MCI) can be classified into encoding (EF) and retrieval (RF) failure, which can be affected by underlying pathomechanism. We explored the differences structurally and functionally. METHODS We compared quantitative electroencephalography (qEEG) power spectra and connectivity between 87 MCI patients with EF and 78 MCI with RF using iSyncBrain® (iMediSync Inc., Republic of Korea) ( https://isyncbrain.com/ ). Voxel-based morphometric analysis of the gray matter (GM) in the MCI groups and 71 cognitive normal controls was also done using the Computational Anatomy Toolbox 12 ( http://www.neuro.uni-jena.de/cat/ ). RESULTS qEEG showed higher frontal theta and lower beta2 band power, and higher theta connectivity in the EF. There was no statistically significant difference in GM volume between the EF and RF. However, when compared to normal control, GM volume reductions due to EF in the left thalamus and bilateral hippocampi and reductions due to RF in the left thalamus, right superior frontal lobe, right superior temporal lobe, and right middle cingulum were observed (p < 0.05, family-wise error correction). CONCLUSIONS MCI differs functionally and structurally according to their specific memory impairments. The EF findings are structurally and functionally more consistent with the prodromal Alzheimer's disease stage than the RF findings. Since this study is a cross-sectional study, prospective follow-up studies are needed to investigate whether different types of memory impairments can predict the underlying pathology of amnestic MCI. Additionally, insufficient sample size may lead to ambiguous statistical findings in direct comparisons, and a larger patient cohort could more robustly identify differences in GM volume reductions between the EF and the RF group.
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Affiliation(s)
- Su-Hyun Han
- Department of Neurology, Chung-Ang University College of Medicine, 102, Heukseok-ro, Dongjak-gu, Seoul, 06973, Republic of Korea
| | - Jung-Min Pyun
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Soeun Yeo
- Department of Neurology, Chung-Ang University College of Medicine, 102, Heukseok-ro, Dongjak-gu, Seoul, 06973, Republic of Korea
| | | | - Ho Tae Jeong
- Department of Neurology, Chung-Ang University College of Medicine, 102, Heukseok-ro, Dongjak-gu, Seoul, 06973, Republic of Korea
| | - Seung Wan Kang
- iMediSync Inc., Seoul, Republic of Korea.
- Data Center for Korean EEG, College of Nursing, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
| | - SangYun Kim
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Young Chul Youn
- Department of Neurology, Chung-Ang University College of Medicine, 102, Heukseok-ro, Dongjak-gu, Seoul, 06973, Republic of Korea.
- Department of Medical Informatics, Chung-Ang University College of Medicine, Seoul, Republic of Korea.
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