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Roshchupkina L, Wens V, Coquelet N, Urbain C, de Tiege X, Peigneux P. Motor learning- and consolidation-related resting state fast and slow brain dynamics across wake and sleep. Sci Rep 2024; 14:7531. [PMID: 38553500 PMCID: PMC10980824 DOI: 10.1038/s41598-024-58123-6] [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] [Received: 08/09/2023] [Accepted: 03/26/2024] [Indexed: 04/02/2024] Open
Abstract
Motor skills dynamically evolve during practice and after training. Using magnetoencephalography, we investigated the neural dynamics underpinning motor learning and its consolidation in relation to sleep during resting-state periods after the end of learning (boost window, within 30 min) and at delayed time scales (silent 4 h and next day 24 h windows) with intermediate daytime sleep or wakefulness. Resting-state neural dynamics were investigated at fast (sub-second) and slower (supra-second) timescales using Hidden Markov modelling (HMM) and functional connectivity (FC), respectively, and their relationship to motor performance. HMM results show that fast dynamic activities in a Temporal/Sensorimotor state network predict individual motor performance, suggesting a trait-like association between rapidly recurrent neural patterns and motor behaviour. Short, post-training task re-exposure modulated neural network characteristics during the boost but not the silent window. Re-exposure-related induction effects were observed on the next day, to a lesser extent than during the boost window. Daytime naps did not modulate memory consolidation at the behavioural and neural levels. These results emphasise the critical role of the transient boost window in motor learning and memory consolidation and provide further insights into the relationship between the multiscale neural dynamics of brain networks, motor learning, and consolidation.
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Affiliation(s)
- Liliia Roshchupkina
- UR2NF - Neuropsychology and Functional Neuroimaging Research Unit Affiliated at CRCN - Centre for Research in Cognition and Neurosciences, Université Libre de Bruxelles (ULB), Brussels, Belgium.
- UNI - ULB Neuroscience Institute, Brussels, Belgium.
- LN2T - Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, ULB, Brussels, Belgium.
- Faculté des Sciences Psychologiques et de l'Éducation, Campus du Solbosch - CP 191, Avenue F.D. Roosevelt, 50, 1050, Brussels, Belgium.
| | - Vincent Wens
- UNI - ULB Neuroscience Institute, Brussels, Belgium
- LN2T - Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, ULB, Brussels, Belgium
- Department of Functional Neuroimaging, Service of Nuclear Medicine, HUB - Hôpital Universitaire de Bruxelles, Hospital Erasme, Brussels, Belgium
| | - Nicolas Coquelet
- UNI - ULB Neuroscience Institute, Brussels, Belgium
- LN2T - Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, ULB, Brussels, Belgium
- Department of Functional Neuroimaging, Service of Nuclear Medicine, HUB - Hôpital Universitaire de Bruxelles, Hospital Erasme, Brussels, Belgium
| | - Charline Urbain
- UR2NF - Neuropsychology and Functional Neuroimaging Research Unit Affiliated at CRCN - Centre for Research in Cognition and Neurosciences, Université Libre de Bruxelles (ULB), Brussels, Belgium
- UNI - ULB Neuroscience Institute, Brussels, Belgium
- LN2T - Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, ULB, Brussels, Belgium
| | - Xavier de Tiege
- UNI - ULB Neuroscience Institute, Brussels, Belgium
- LN2T - Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, ULB, Brussels, Belgium
- Department of Functional Neuroimaging, Service of Nuclear Medicine, HUB - Hôpital Universitaire de Bruxelles, Hospital Erasme, Brussels, Belgium
| | - Philippe Peigneux
- UR2NF - Neuropsychology and Functional Neuroimaging Research Unit Affiliated at CRCN - Centre for Research in Cognition and Neurosciences, Université Libre de Bruxelles (ULB), Brussels, Belgium
- UNI - ULB Neuroscience Institute, Brussels, Belgium
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2
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Fernández-Martín R, Feys O, Juvené E, Aeby A, Urbain C, De Tiège X, Wens V. Towards the automated detection of interictal epileptiform discharges with magnetoencephalography. J Neurosci Methods 2024; 403:110052. [PMID: 38151188 DOI: 10.1016/j.jneumeth.2023.110052] [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: 09/14/2023] [Revised: 12/08/2023] [Accepted: 12/21/2023] [Indexed: 12/29/2023]
Abstract
BACKGROUND The analysis of clinical magnetoencephalography (MEG) in patients with epilepsy traditionally relies on visual identification of interictal epileptiform discharges (IEDs), which is time consuming and dependent on subjective criteria. NEW METHOD Here, we explore the ability of Independent Components Analysis (ICA) and Hidden Markov Modeling (HMM) to automatically detect and localize IEDs. We tested our pipelines on resting-state MEG recordings from 10 school-aged children with (multi)focal epilepsy. RESULTS In focal epilepsy patients, both pipelines successfully detected visually identified IEDs, but also revealed unidentified low-amplitude IEDs. Success was more mitigated in patients with multifocal epilepsy, as our automated pipeline missed IED activity associated with some foci-an issue that could be alleviated by post-hoc manual selection of epileptiform ICs or HMM states. COMPARISON WITH EXISTING METHODS We compared our results with visual IED detection by an experienced clinical magnetoencephalographer, getting heightened sensitivity and requiring minimal input from clinical practitioners. CONCLUSIONS IED detection based on ICA or HMM represents an efficient way to identify IED localization and timing. The development of these automatic IED detection algorithms provide a step forward in clinical MEG practice by decreasing the duration of MEG analysis and enhancing its sensitivity.
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Affiliation(s)
- Raquel Fernández-Martín
- Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LNbT), Brussels, Belgium.
| | - Odile Feys
- Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LNbT), Brussels, Belgium; Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B.), Hôpital Erasme, Department of Neurology, Brussels, Belgium
| | - Elodie Juvené
- Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B.), Department of Pediatric Neurology, Brussels, Belgium
| | - Alec Aeby
- Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B.), Department of Pediatric Neurology, Brussels, Belgium
| | - Charline Urbain
- Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LNbT), Brussels, Belgium; Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Centre for Research in Cognition and Neurosciences (CRCN), Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Brussels, Belgium
| | - Xavier De Tiège
- Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LNbT), Brussels, Belgium; Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B.), Hôpital Erasme, Service of translational Neuroimaging, Brussels, Belgium
| | - Vincent Wens
- Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LNbT), Brussels, Belgium; Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B.), Hôpital Erasme, Service of translational Neuroimaging, Brussels, Belgium
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3
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Kim S, Adams JN, Chappel-Farley MG, Keator D, Janecek J, Taylor L, Mikhail A, Hollearn M, McMillan L, Rapp P, Yassa MA. Examining the diagnostic value of the mnemonic discrimination task for classification of cognitive status and amyloid-beta burden. Neuropsychologia 2023; 191:108727. [PMID: 37939874 PMCID: PMC10764118 DOI: 10.1016/j.neuropsychologia.2023.108727] [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: 07/28/2023] [Revised: 10/20/2023] [Accepted: 11/03/2023] [Indexed: 11/10/2023]
Abstract
Alzheimer's disease (AD) is the most common type of dementia, characterized by early memory impairments and gradual worsening of daily functions. AD-related pathology, such as amyloid-beta (Aβ) plaques, begins to accumulate many years before the onset of clinical symptoms. Predicting risk for AD via related pathology is critical as the preclinical stage could serve as a therapeutic time window, allowing for early management of the disease and reducing health and economic costs. Current methods for detecting AD pathology, however, are often expensive and invasive, limiting wide and easy access to a clinical setting. A non-invasive, cost-efficient platform, such as computerized cognitive tests, could be potentially useful to identify at-risk individuals as early as possible. In this study, we examined the diagnostic value of an episodic memory task, the mnemonic discrimination task (MDT), for predicting risk of cognitive impairment or Aβ burden. We constructed a random forest classification algorithm, utilizing MDT performance metrics and various neuropsychological test scores as input features, and assessed model performance using area under the curve (AUC). Models based on MDT performance metrics achieved classification results with an AUC of 0.83 for cognitive status and an AUC of 0.64 for Aβ status. Our findings suggest that mnemonic discrimination function may be a useful predictor of progression to prodromal AD or increased risk of Aβ load, which could be a cost-efficient, noninvasive cognitive testing solution for potentially wide-scale assessment of AD pathological and cognitive risk.
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Affiliation(s)
- Soyun Kim
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA.
| | - Jenna N Adams
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA
| | - Miranda G Chappel-Farley
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA
| | - David Keator
- Department of Psychiatry and Behavioral Sciences, University of California, Irvine, CA, USA
| | - John Janecek
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA
| | - Lisa Taylor
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA
| | - Abanoub Mikhail
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA
| | - Martina Hollearn
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA
| | - Liv McMillan
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA
| | - Paul Rapp
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA; Department of Military & Emergency Medicine, Uniformed Services University, Bethesda, MD, USA
| | - Michael A Yassa
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA; Department of Psychiatry and Behavioral Sciences, University of California, Irvine, CA, USA.
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4
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Gao SL, Yue J, Li XL, Li A, Cao DN, Han SW, Wei ZY, Yang G, Zhang Q. Multimodal magnetic resonance imaging on brain network in amnestic mild cognitive impairment: A mini-review. Medicine (Baltimore) 2023; 102:e34994. [PMID: 37653770 PMCID: PMC10470781 DOI: 10.1097/md.0000000000034994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 09/02/2023] Open
Abstract
Amnestic mild cognitive impairment (aMCI) is a stage between normal aging and Alzheimer disease (AD) where individuals experience a noticeable decline in memory that is greater than what is expected with normal aging, but dose not meet the clinical criteria for AD. This stage is considered a transitional phase that puts individuals at a high risk for developing AD. It is crucial to intervene during this stage to reduce the changes of AD development. Recently, advanced multimodal magnetic resonance imaging techniques have been used to study the brain structure and functional networks in individuals with aMCI. Through the use of structural magnetic resonance imaging, diffusion tensor imaging, and functional magnetic resonance imaging, abnormalities in certain brain regions have been observed in individuals with aMCI. Specifically, the default mode network, salience network, and executive control network have been found to show abnormalities in both structure and function. This review aims to provide a comprehensive understanding of the brain structure and functional networks associated with aMCI. By analyzing the existing literature on multimodal magnetic resonance imaging and aMCI, this study seeks to uncover potential biomarkers and gain insight into the underlying pathogenesis of aMCI. This knowledge can then guide the development of future treatments and interventions to delay or prevent the progression of aMCI to AD.
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Affiliation(s)
- Sheng-Lan Gao
- Graduate School of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Jinhuan Yue
- Shenzhen Frontiers in Chinese Medicine Research Co., Ltd., Shenzhen, China
| | - Xiao-Ling Li
- Division of CT and MRI, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Ang Li
- Sanofi-Aventis China Investment Co., Ltd, Beijing, China
| | - Dan-Na Cao
- Division of CT and MRI, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Sheng-Wang Han
- Third Ward of Rehabilitation Department, Second Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Ze-Yi Wei
- Graduate School of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Guanhu Yang
- Department of Specialty Medicine, Ohio University, Athens, OH
| | - Qinhong Zhang
- Shenzhen Frontiers in Chinese Medicine Research Co., Ltd., Shenzhen, China
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5
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Tăuƫan AM, Casula EP, Pellicciari MC, Borghi I, Maiella M, Bonni S, Minei M, Assogna M, Palmisano A, Smeralda C, Romanella SM, Ionescu B, Koch G, Santarnecchi E. TMS-EEG perturbation biomarkers for Alzheimer's disease patients classification. Sci Rep 2023; 13:7667. [PMID: 37169900 PMCID: PMC10175269 DOI: 10.1038/s41598-022-22978-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 10/21/2022] [Indexed: 05/13/2023] Open
Abstract
The combination of TMS and EEG has the potential to capture relevant features of Alzheimer's disease (AD) pathophysiology. We used a machine learning framework to explore time-domain features characterizing AD patients compared to age-matched healthy controls (HC). More than 150 time-domain features including some related to local and distributed evoked activity were extracted from TMS-EEG data and fed into a Random Forest (RF) classifier using a leave-one-subject out validation approach. The best classification accuracy, sensitivity, specificity and F1 score were of 92.95%, 96.15%, 87.94% and 92.03% respectively when using a balanced dataset of features computed globally across the brain. The feature importance and statistical analysis revealed that the maximum amplitude of the post-TMS signal, its Hjorth complexity and the amplitude of the TEP calculated in the window 45-80 ms after the TMS-pulse were the most relevant features differentiating AD patients from HC. TMS-EEG metrics can be used as a non-invasive tool to further understand the AD pathophysiology and possibly contribute to patients' classification as well as longitudinal disease tracking.
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Affiliation(s)
- Alexandra-Maria Tăuƫan
- Precision Neuroscience and Neuromodulation Program & Network Control Laboratory, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- AI Multimedia Lab, Research Center CAMPUS, University Politehnica of Bucharest, 061344, Bucharest, Romania
| | - Elias P Casula
- Santa Lucia Foundation, 00179, Rome, Italy
- Department of Psychology, La Sapienza University, Via dei Marsi 78, 00185, Rome, Italy
| | | | | | | | | | | | | | - Annalisa Palmisano
- Precision Neuroscience and Neuromodulation Program & Network Control Laboratory, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Department of Education, Psychology and Communication, University of Bari Aldo Moro, Bari, Italy
| | - Carmelo Smeralda
- Siena Brain Investigation & Neuromodulation Lab (Si-BIN Lab), Department of Medicine, Surgery, Neurology and Clinical Neurophysiology Section, University of Siena, Siena, Italy
| | - Sara M Romanella
- Precision Neuroscience and Neuromodulation Program & Network Control Laboratory, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Siena Brain Investigation & Neuromodulation Lab (Si-BIN Lab), Department of Medicine, Surgery, Neurology and Clinical Neurophysiology Section, University of Siena, Siena, Italy
| | - Bogdan Ionescu
- AI Multimedia Lab, Research Center CAMPUS, University Politehnica of Bucharest, 061344, Bucharest, Romania
| | - Giacomo Koch
- Department of Neuroscience and Rehabilitation, Section of Human Physiology, University of Ferrara, 44121, Ferrara, Italy
- Santa Lucia Foundation, 00179, Rome, Italy
| | - Emiliano Santarnecchi
- Precision Neuroscience and Neuromodulation Program & Network Control Laboratory, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
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Bakhtiari A, Petersen J, Urdanibia-Centelles O, Ghazi MM, Fagerlund B, Mortensen EL, Osler M, Lauritzen M, Benedek K. Power and distribution of evoked gamma oscillations in brain aging and cognitive performance. GeroScience 2023:10.1007/s11357-023-00749-x. [PMID: 36763241 PMCID: PMC10400513 DOI: 10.1007/s11357-023-00749-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 01/31/2023] [Indexed: 02/11/2023] Open
Abstract
AIMS Gamma oscillations (≈25-100 Hz) are believed to play an essential role in cognition, and aberrant gamma oscillations occur in brain aging and neurodegeneration. This study examined age-related changes in visually evoked gamma oscillations at two different time points 5 years apart and tested the hypothesis that the power of gamma oscillations correlated to cognitive skills. METHODS The cohort consists of elderly males belonging to the Metropolit 1953 Danish Male Birth Cohort (first visit, N=124; second visit, N=88) over a 5-year period from 63 to 68 years of age. Cognitive functions were assessed using a neuropsychological test battery measuring global cognition, intelligence, memory, and processing speed. The power of steady-state visual evoked potentials (SSVEP) was measured at 8 Hz (alpha) and 36 Hz (gamma) frequencies using EEG scalp electrodes. RESULTS Over the 5-year period cognitive performance remained relatively stable while the power of visually evoked gamma oscillations shifted from posterior to anterior brain regions with increasing age. A higher-than-average cognitive score was correlated with higher gamma power in parieto-occipital areas and lower in frontocentral areas, i.e., preserved distribution of the evoked activity. CONCLUSIONS Our data reveal that the distribution of visually evoked gamma activity becomes distributed with age. Preserved posterior-occipital gamma power in participants with a high level of cognitive performance is consistent with a close association between the ability to produce gamma oscillations and cognition. The data may contribute to our understanding of the mechanisms that link evoked gamma activity and cognition in the aging brain.
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Affiliation(s)
- Aftab Bakhtiari
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark. .,Center for Healthy Aging, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. .,Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Jacob Petersen
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Olalla Urdanibia-Centelles
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Mostafa Mehdipour Ghazi
- Pioneer Centre for Artificial Intelligence, Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Birgitte Fagerlund
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark.,Child and Adolescent Mental Health Center, Copenhagen University Hospital - Mental Health Services CPH, Copenhagen, Denmark
| | | | - Merete Osler
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark.,Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Martin Lauritzen
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.,Center for Healthy Aging, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Krisztina Benedek
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
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7
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Dovorany N, Brannick S, Johnson N, Ratiu I, LaCroix AN. Happy and sad music acutely modulate different types of attention in older adults. Front Psychol 2023; 14:1029773. [PMID: 36777231 PMCID: PMC9909555 DOI: 10.3389/fpsyg.2023.1029773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 01/10/2023] [Indexed: 01/27/2023] Open
Abstract
Of the three subtypes of attention outlined by the attentional subsystems model, alerting (vigilance or arousal needed for task completion) and executive control (the ability to inhibit distracting information while completing a goal) are susceptible to age-related decline, while orienting remains relatively stable. Yet, few studies have investigated strategies that may acutely maintain or promote attention in typically aging older adults. Music listening may be one potential strategy for attentional maintenance as past research shows that listening to happy music characterized by a fast tempo and major mode increases cognitive task performance, likely by increasing cognitive arousal. The present study sought to investigate whether listening to happy music (fast tempo, major mode) impacts alerting, orienting, and executive control attention in 57 middle and older-aged adults (M = 61.09 years, SD = 7.16). Participants completed the Attention Network Test (ANT) before and after listening to music rated as happy or sad (slow tempo, minor mode), or no music (i.e., silence) for 10 min. Our results demonstrate that happy music increased alerting attention, particularly when relevant and irrelevant information conflicted within a trial. Contrary to what was predicted, sad music modulated executive control performance. Overall, our findings indicate that music written in the major mode with a fast tempo (happy) and minor mode with a slow tempo (sad) modulate different aspects of attention in the short-term.
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Affiliation(s)
- Nicholas Dovorany
- College of Graduate Studies, Midwestern University, Glendale, AZ, United States
| | - Schea Brannick
- College of Health Sciences, Midwestern University, Glendale, AZ, United States
| | - Nathan Johnson
- College of Graduate Studies, Midwestern University, Glendale, AZ, United States
| | - Ileana Ratiu
- College of Health Sciences, Midwestern University, Glendale, AZ, United States,College of Health Solutions, Arizona State University, Tempe, AZ, United States
| | - Arianna N. LaCroix
- Department of Speech, Language, and Hearing Sciences, College of Health and Human Sciences, Purdue University, West Lafayette, IN, United States,*Correspondence: Arianna N. LaCroix,
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8
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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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9
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Fathian A, Jamali Y, Raoufy MR. The trend of disruption in the functional brain network topology of Alzheimer's disease. Sci Rep 2022; 12:14998. [PMID: 36056059 PMCID: PMC9440254 DOI: 10.1038/s41598-022-18987-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/23/2022] [Indexed: 12/19/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain's functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study used resting state fMRI data to analyze the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer's disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression. There were network characteristics that have changed non-linearly regarding the disease progression, especially at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, the methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process.
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Affiliation(s)
- Alireza Fathian
- Biomathematics Laboratory, Department of Applied Mathematics, School of Mathematical Science, Tarbiat Modares University, Tehran, Iran
| | - Yousef Jamali
- Biomathematics Laboratory, Department of Applied Mathematics, School of Mathematical Science, Tarbiat Modares University, Tehran, Iran.
- Applied Systems Biology, Leibniz-Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Jena, Germany.
| | - Mohammad Reza Raoufy
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
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10
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Ponomareva NV, Andreeva TV, Protasova M, Konovalov RN, Krotenkova MV, Kolesnikova EP, Malina DD, Kanavets EV, Mitrofanov AA, Fokin VF, Illarioshkin SN, Rogaev EI. Genetic association of apolipoprotein E genotype with EEG alpha rhythm slowing and functional brain network alterations during normal aging. Front Neurosci 2022; 16:931173. [PMID: 35979332 PMCID: PMC9376365 DOI: 10.3389/fnins.2022.931173] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/27/2022] [Indexed: 12/02/2022] Open
Abstract
The ε4 allele of the apolipoprotein E (APOE4+) genotype is a major genetic risk factor for Alzheimer’s disease (AD), but the mechanisms underlying its influence remain incompletely understood. The study aimed to investigate the possible effect of the APOE genotype on spontaneous electroencephalogram (EEG) alpha characteristics, resting-state functional MRI (fMRI) connectivity (rsFC) in large brain networks and the interrelation of alpha rhythm and rsFC characteristics in non-demented adults during aging. We examined the EEG alpha subband’s relative power, individual alpha peak frequency (IAPF), and fMRI rsFC in non-demented volunteers (age range 26–79 years) stratified by the APOE genotype. The presence of the APOE4+ genotype was associated with lower IAPF and lower relative power of the 11–13 Hz alpha subbands. The age related decrease in EEG IAPF was more pronounced in the APOE4+ carriers than in the APOE4+ non-carriers (APOE4-). The APOE4+ carriers had a stronger fMRI positive rsFC of the interhemispheric regions of the frontoparietal, lateral visual and salience networks than the APOE4– individuals. In contrast, the negative rsFC in the network between the left hippocampus and the right posterior parietal cortex was reduced in the APOE4+ carriers compared to the non-carriers. Alpha rhythm slowing was associated with the dysfunction of hippocampal networks. Our results show that in adults without dementia APOE4+ genotype is associated with alpha rhythm slowing and that this slowing is age-dependent. Our data suggest predominant alterations of inhibitory processes in large-scale brain network of non-demented APOE4+ carriers. Moreover, dysfunction of large-scale hippocampal network can influence APOE-related alpha rhythm vulnerability.
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Affiliation(s)
- Natalya V. Ponomareva
- Research Center of Neurology, Moscow, Russia
- Center for Genetics and Life Science, Sirius University of Science and Technology, Sochi, Russia
- *Correspondence: Natalya V. Ponomareva,
| | - Tatiana V. Andreeva
- Center for Genetics and Life Science, Sirius University of Science and Technology, Sochi, Russia
- Vavilov Institute of General Genetics, Russian Academy of Sciences (RAS), Moscow, Russia
| | - Maria Protasova
- Center for Genetics and Life Science, Sirius University of Science and Technology, Sochi, Russia
- Vavilov Institute of General Genetics, Russian Academy of Sciences (RAS), Moscow, Russia
| | | | | | | | | | | | | | | | | | - Evgeny I. Rogaev
- Center for Genetics and Life Science, Sirius University of Science and Technology, Sochi, Russia
- Vavilov Institute of General Genetics, Russian Academy of Sciences (RAS), Moscow, Russia
- Brudnick Neuropsychiatric Research Institute (BNRI), University of Massachusetts Medical School, Worcester, MA, United States
- Evgeny I. Rogaev,
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11
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Liu L, Ren J, Li Z, Yang C. A review of MEG dynamic brain network research. Proc Inst Mech Eng H 2022; 236:763-774. [PMID: 35465768 DOI: 10.1177/09544119221092503] [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: 11/17/2022]
Abstract
The dynamic description of neural networks has attracted the attention of researchers for dynamic networks may carry more information compared with resting-state networks. As a non-invasive electrophysiological data with high temporal and spatial resolution, magnetoencephalogram (MEG) can provide rich information for the analysis of dynamic functional brain networks. In this review, the development of MEG brain network was summarized. Several analysis methods such as sliding window, Hidden Markov model, and time-frequency based methods used in MEG dynamic brain network studies were discussed. Finally, the current research about multi-modal brain network analysis and their applications with MEG neurophysiology, which are prospected to be one of the research directions in the future, were concluded.
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Affiliation(s)
- Lu Liu
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Jiechuan Ren
- Department of Internal Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhimei Li
- Department of Internal Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chunlan Yang
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
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12
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Roshchupkina L, Wens V, Coquelet N, de Tiege X, Peigneux P. Resting state fast brain dynamics predict interindividual variability in motor performance. Sci Rep 2022; 12:5340. [PMID: 35351907 PMCID: PMC8964712 DOI: 10.1038/s41598-022-08767-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 03/07/2022] [Indexed: 11/09/2022] Open
Abstract
Motor learning features rapid enhancement during practice then offline post-practice gains with the reorganization of related brain networks. We hypothesised that fast transient, sub-second variations in magnetoencephalographic (MEG) network activity during the resting-state (RS) reflect early learning-related plasticity mechanisms and/or interindividual motor variability in performance. MEG RS activity was recorded before and 20 min after motor learning. Hidden Markov modelling (HMM) of MEG power envelope signals highlighted 8 recurrent topographical states. For two states, motor performance levels were associated with HMM temporal parameters both in pre- and post-learning resting-state sessions. However, no association emerged with offline changes in performance. These results suggest a trait-like relationship between spontaneous transient neural dynamics at rest and interindividual variations in motor abilities. On the other hand, transient RS dynamics seem not to be state-dependent, i.e., modulated by learning experience and reflect neural plasticity, at least on the short timescale.
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Affiliation(s)
- Liliia Roshchupkina
- UR2NF-Neuropsychology and Functional Neuroimaging Research Unit affiliated at CRCN - Centre for Research in Cognition and Neurosciences, Avenue F.D. Roosevelt 50, 1050, Bruxelles, Belgium. .,UNI-ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Avenue F.D. Roosevelt 50, 1050, Bruxelles, Belgium. .,Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), Université Libre de Bruxelles (ULB), Brussels, Belgium.
| | - Vincent Wens
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), Université Libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicolas Coquelet
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), Université Libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Xavier de Tiege
- UR2NF-Neuropsychology and Functional Neuroimaging Research Unit affiliated at CRCN - Centre for Research in Cognition and Neurosciences, Avenue F.D. Roosevelt 50, 1050, Bruxelles, Belgium.,Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), Université Libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Philippe Peigneux
- UR2NF-Neuropsychology and Functional Neuroimaging Research Unit affiliated at CRCN - Centre for Research in Cognition and Neurosciences, Avenue F.D. Roosevelt 50, 1050, Bruxelles, Belgium.,UNI-ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Avenue F.D. Roosevelt 50, 1050, Bruxelles, Belgium
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13
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Xiao G, Wu Y, Yan Y, Gao L, Geng Z, Qiu B, Zhou S, Ji G, Wu X, Hu P, Wang K. Optimized Magnetic Stimulation Induced Hypoconnectivity Within the Executive Control Network Yields Cognition Improvements in Alzheimer’s Patients. Front Aging Neurosci 2022; 14:847223. [PMID: 35370614 PMCID: PMC8965584 DOI: 10.3389/fnagi.2022.847223] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Accepted: 02/07/2022] [Indexed: 11/13/2022] Open
Abstract
Alzheimer’s disease (AD) is a severe neurodegenerative disease, which mainly manifests as memory and progressive cognitive impairment. At present, there is no method to prevent the progression of AD or cure it, and effective intervention methods are urgently needed. Network-targeted intermittent theta burst stimulation (iTBS) may be effective in alleviating the cognitive symptoms of patients with mild AD. The abnormal function of the dorsolateral prefrontal cortex (DLPFC) within executive control network (ECN) may be the pathogenesis of AD. Here, we verify the abnormality of the ECN in the native AD data set, and build the relevant brain network. In addition, we also recruited AD patients to verify the clinical effects of DLPFC-targeted intervention, and explor the neuro-mechanism. Sixty clinically diagnosed AD patients and 62 normal controls were recruited to explore the ECN abnormalities. In addition, the researchers recruited 20 AD patients to explore the efficacy of 14-session iTBS treatments for targeted DLPFC interventions. Functional magnetic resonance imaging and neuropsychological assessment of resting state were performed before and after the intervention. Calculate the changes in the functional connectivity of related brain regions in the ECN, as well as the correlation between the baseline functional connectivity and the clinical scoring scale, to clarify the mechanism of the response of iTBS treatment to treatment. Our results showed that compared with normal control samples, the brain function connection between the left DLPFC and the left IPL within the ECN of AD patients was significantly enhanced (t = 2.687, p = 0.008, FDR-corrected p = 0.045). And we found that iTBS stimulation significantly reduced the functional magnetic resonance imaging signal between the left DLPFC and the left IPL in the ECN (t = 4.271, p < 0.001, FDR-corrected p = 0.006), and it was related to the improvement of the patient’s clinical symptoms (r = −0.470, p = 0.042). This work provides new insights for targeted brain area interventions. By targeted adjusting the functional connection of ECN to improve the clinical symptoms and cognitive function of AD patients.
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Affiliation(s)
- Guixian Xiao
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
| | - Yue Wu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
| | - Yibing Yan
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
| | - Liying Gao
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
| | - Zhi Geng
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Center for Neuropsychiatric Disorders and Mental Health, Hefei, China
- Department of Neurology, Second People’s Hospital of Hefei City, The Hefei Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Bensheng Qiu
- Center for Biomedical Imaging, University of Science and Technology of China, Hefei, China
| | - Shanshan Zhou
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
| | - Gongjun Ji
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
| | - Xingqi Wu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- *Correspondence: Xingqi Wu,
| | - Panpan Hu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- Panpan Hu,
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Center for Neuropsychiatric Disorders and Mental Health, Hefei, China
- Kai Wang,
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14
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Rayhan RU, Baraniuk JN. Submaximal Exercise Provokes Increased Activation of the Anterior Default Mode Network During the Resting State as a Biomarker of Postexertional Malaise in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Front Neurosci 2021; 15:748426. [PMID: 34975370 PMCID: PMC8714840 DOI: 10.3389/fnins.2021.748426] [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: 07/27/2021] [Accepted: 11/15/2021] [Indexed: 01/29/2023] Open
Abstract
Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is characterized by disabling fatigue and postexertional malaise. We developed a provocation paradigm with two submaximal bicycle exercise stress tests on consecutive days bracketed by magnetic resonance imaging, orthostatic intolerance, and symptom assessments before and after exercise in order to induce objective changes of exercise induced symptom exacerbation and cognitive dysfunction. Method: Blood oxygenation level dependent (BOLD) scans were performed while at rest on the preexercise and postexercise days in 34 ME/CFS and 24 control subjects. Seed regions from the FSL data library with significant BOLD signals were nodes that clustered into networks using independent component analysis. Differences in signal amplitudes between groups on pre- and post-exercise days were determined by general linear model and ANOVA. Results: The most striking exercise-induced effect in ME/CFS was the increased spontaneous activity in the medial prefrontal cortex that is the anterior node of the Default Mode Network (DMN). In contrast, this region had decreased activation for controls. Overall, controls had higher BOLD signals suggesting reduced global cerebral blood flow in ME/CFS. Conclusion: The dynamic increase in activation of the anterior DMN node after exercise may be a biomarker of postexertional malaise and symptom exacerbation in CFS. The specificity of this postexertional finding in ME/CFS can now be assessed by comparison to post-COVID fatigue, Gulf War Illness, fibromyalgia, chronic idiopathic fatigue, and fatigue in systemic medical and psychiatric diseases.
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Affiliation(s)
- Rakib U. Rayhan
- Department of Physiology and Biophysics, Howard University, Washington, DC, United States
| | - James N. Baraniuk
- Department of Medicine, Georgetown University, Washington, DC, United States,*Correspondence: James N. Baraniuk,
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15
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Naeije G, Coquelet N, Wens V, Goldman S, Pandolfo M, De Tiège X. Age of onset modulates resting-state brain network dynamics in Friedreich Ataxia. Hum Brain Mapp 2021; 42:5334-5344. [PMID: 34523778 PMCID: PMC8519851 DOI: 10.1002/hbm.25621] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 07/19/2021] [Accepted: 07/20/2021] [Indexed: 02/06/2023] Open
Abstract
This magnetoencephalography (MEG) study addresses (i) how Friedreich ataxia (FRDA) affects the sub‐second dynamics of resting‐state brain networks, (ii) the main determinants of their dynamic alterations, and (iii) how these alterations are linked with FRDA‐related changes in resting‐state functional brain connectivity (rsFC) over long timescales. For that purpose, 5 min of resting‐state MEG activity were recorded in 16 FRDA patients (mean age: 27 years, range: 12–51 years; 10 females) and matched healthy subjects. Transient brain network dynamics was assessed using hidden Markov modeling (HMM). Post hoc median‐split, nonparametric permutations and Spearman rank correlations were used for statistics. In FRDA patients, a positive correlation was found between the age of symptoms onset (ASO) and the temporal dynamics of two HMM states involving the posterior default mode network (DMN) and the temporo‐parietal junctions (TPJ). FRDA patients with an ASO <11 years presented altered temporal dynamics of those two HMM states compared with FRDA patients with an ASO > 11 years or healthy subjects. The temporal dynamics of the DMN state also correlated with minute‐long DMN rsFC. This study demonstrates that ASO is the main determinant of alterations in the sub‐second dynamics of posterior associative neocortices in FRDA patients and substantiates a direct link between sub‐second network activity and functional brain integration over long timescales.
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Affiliation(s)
- Gilles Naeije
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Neurology, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicolas Coquelet
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Vincent Wens
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Serge Goldman
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Massimo Pandolfo
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - Xavier De Tiège
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
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16
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Puttaert D, Wens V, Fery P, Rovai A, Trotta N, Coquelet N, De Breucker S, Sadeghi N, Coolen T, Goldman S, Peigneux P, Bier JC, De Tiège X. Decreased Alpha Peak Frequency Is Linked to Episodic Memory Impairment in Pathological Aging. Front Aging Neurosci 2021; 13:711375. [PMID: 34475819 PMCID: PMC8406997 DOI: 10.3389/fnagi.2021.711375] [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: 05/18/2021] [Accepted: 07/21/2021] [Indexed: 12/04/2022] Open
Abstract
The Free and Cued Selective Reminding Test (FCSRT) is a largely validated neuropsychological test for the identification of amnestic syndrome from the early stage of Alzheimer's disease (AD). Previous electrophysiological data suggested a slowing down of the alpha rhythm in the AD-continuum as well as a key role of this rhythmic brain activity for episodic memory processes. This study therefore investigates the link between alpha brain activity and alterations in episodic memory as assessed by the FCSRT. For that purpose, 37 patients with altered FCSRT performance underwent a comprehensive neuropsychological assessment, supplemented by 18F-fluorodeoxyglucose positron emission tomography/structural magnetic resonance imaging (18FDG-PET/MR), and 10 min of resting-state magnetoencephalography (MEG). The individual alpha peak frequency (APF) in MEG resting-state data was positively correlated with patients' encoding efficiency as well as with the efficacy of semantic cues in facilitating patients' retrieval of previous stored word. The APF also correlated positively with patients' hippocampal volume and their regional glucose consumption in the posterior cingulate cortex. Overall, this study demonstrates that alterations in the ability to learn and store new information for a relatively short-term period are related to a slowing down of alpha rhythmic activity, possibly due to altered interactions in the extended mnemonic system. As such, a decreased APF may be considered as an electrophysiological correlate of short-term episodic memory dysfunction accompanying pathological aging.
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Affiliation(s)
- Delphine Puttaert
- Laboratoire de Cartographie Fonctionnelle du Cerveau, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
- Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Center for Research in Cognition and Neurosciences, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
| | - Vincent Wens
- Laboratoire de Cartographie Fonctionnelle du Cerveau, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
- Clinic of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium
| | - Patrick Fery
- Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Center for Research in Cognition and Neurosciences, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
- Service of Neuropsychology and Speech Therapy, CUB Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium
| | - Antonin Rovai
- Laboratoire de Cartographie Fonctionnelle du Cerveau, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
- Clinic of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium
| | - Nicola Trotta
- Laboratoire de Cartographie Fonctionnelle du Cerveau, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
- Clinic of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium
| | - Nicolas Coquelet
- Laboratoire de Cartographie Fonctionnelle du Cerveau, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
| | - Sandra De Breucker
- Department of Geriatrics, CUB Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium
| | - Niloufar Sadeghi
- Department of Radiology, CUB Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium
| | - Tim Coolen
- Department of Radiology, CUB Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium
| | - Serge Goldman
- Laboratoire de Cartographie Fonctionnelle du Cerveau, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
- Clinic of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium
| | - Philippe Peigneux
- Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Center for Research in Cognition and Neurosciences, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
| | - Jean-Christophe Bier
- Department of Neurology, CUB Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium
| | - Xavier De Tiège
- Laboratoire de Cartographie Fonctionnelle du Cerveau, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
- Clinic of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium
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17
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Chen K, Li C, Sun W, Tao Y, Wang R, Hou W, Liu DQ. Hidden Markov Modeling Reveals Prolonged "Baseline" State and Shortened Antagonistic State across the Adult Lifespan. Cereb Cortex 2021; 32:439-453. [PMID: 34255827 DOI: 10.1093/cercor/bhab220] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 06/10/2021] [Accepted: 06/11/2021] [Indexed: 12/21/2022] Open
Abstract
The brain networks undergo functional reorganization across the whole lifespan, but the dynamic patterns behind the reorganization remain largely unclear. This study models the dynamics of spontaneous activity of large-scale networks using hidden Markov model (HMM), and investigates how it changes with age on two adult lifespan datasets of 176/157 subjects (aged 20-80 years). Results for both datasets showed that 1) older adults tended to spend less time on a state where default mode network (DMN) and attentional networks show antagonistic activity, 2) older adults spent more time on a "baseline" state with moderate-level activation of all networks, accompanied with lower transition probabilities from this state to the others and higher transition probabilities from the others to this state, and 3) HMM exhibited higher sensitivity in uncovering the age effects compared with temporal clustering method. Our results suggest that the aging brain is characterized by the shortening of the antagonistic instances between DMN and attention systems, as well as the prolongation of the inactive period of all networks, which might reflect the shift of the dynamical working point near criticality in older adults.
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Affiliation(s)
- Keyu Chen
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian 116029, China.,Key Laboratory of Brain and Cognitive Neuroscience, Dalian 116029, Liaoning Province, China
| | - Chaofan Li
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian 116029, China.,Key Laboratory of Brain and Cognitive Neuroscience, Dalian 116029, Liaoning Province, China
| | - Wei Sun
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian 116029, China.,Key Laboratory of Brain and Cognitive Neuroscience, Dalian 116029, Liaoning Province, China
| | - Yunyun Tao
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian 116029, China.,Key Laboratory of Brain and Cognitive Neuroscience, Dalian 116029, Liaoning Province, China
| | - Ruidi Wang
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian 116029, China.,Key Laboratory of Brain and Cognitive Neuroscience, Dalian 116029, Liaoning Province, China
| | - Wen Hou
- School of Mathematics, Liaoning Normal University, Dalian 116029, China
| | - Dong-Qiang Liu
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian 116029, China.,Key Laboratory of Brain and Cognitive Neuroscience, Dalian 116029, Liaoning Province, China
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18
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Shafer AT, Beason-Held L, An Y, Williams OA, Huo Y, Landman BA, Caffo BS, Resnick SM. Default mode network connectivity and cognition in the aging brain: the effects of age, sex, and APOE genotype. Neurobiol Aging 2021; 104:10-23. [PMID: 33957555 DOI: 10.1016/j.neurobiolaging.2021.03.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 03/04/2021] [Accepted: 03/24/2021] [Indexed: 01/18/2023]
Abstract
The default mode network (DMN) overlaps with regions showing early Alzheimer's Disease (AD) pathology. Age, sex, and apolipoprotein E ɛ4 are the predominant risk factors for developing AD. How these risk factors interact to influence DMN connectivity and connectivity-cognition relationships before the onset of impairment remains unknown. Here, we examined these issues in 475 cognitively normal adults, targeting total DMN connectivity, its anticorrelated network (acDMN), and the DMN-hippocampal component. There were four main findings. First, in the ɛ3 homozygous group, lower DMN and acDMN connectivity was observed with age. Second, sex and ɛ4 modified the relationship between age and connectivity for the DMN and hippocampus with ɛ4 vs. ɛ3 males showing sustained or higher connectivity with age. Third, in the ɛ3 group, age and sex modified connectivity-cognition relationships with the oldest participants having the most differential patterns due to sex. Fourth, ɛ4 carriers with lower connectivity had poorer cognitive performance. Taken together, our results show the three predominant risk factors for AD interact to influence brain function and function-cognition relationships.
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Affiliation(s)
- Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD.
| | - Lori Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD
| | - Owen A Williams
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD
| | - Yuankai Huo
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN
| | - Bennett A Landman
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD.
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19
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de Pasquale F, Spadone S, Betti V, Corbetta M, Della Penna S. Temporal modes of hub synchronization at rest. Neuroimage 2021; 235:118005. [PMID: 33819608 DOI: 10.1016/j.neuroimage.2021.118005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 03/12/2021] [Accepted: 03/17/2021] [Indexed: 10/21/2022] Open
Abstract
The brain is a dynamic system that generates a broad repertoire of perceptual, motor, and cognitive states by the integration and segregation of different functional domains represented in large-scale brain networks. However, the fundamental mechanisms underlying brain network integration remain elusive. Here, for the first time to our knowledge, we found that in the resting state the brain visits few synchronization modes defined as clusters of temporally aligned functional hubs. These modes alternate over time and their probability of switching leads to specific temporal loops among them. Notably, although each mode involves a small set of nodes, the brain integration seems highly vulnerable to a simulated attack on this temporal synchronization mechanism. In line with the hypothesis that the resting state represents a prior sculpted by the task activity, the observed synchronization modes might be interpreted as a temporal brain template needed to respond to task/environmental demands .
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Affiliation(s)
- F de Pasquale
- Faculty of Veterinary Medicine, University of Teramo, Teramo, Italy.
| | - S Spadone
- Department of Neuroscience, Imaging and Clinical Sciences, and Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - V Betti
- Department of Psychology, Sapienza University of Rome, 00185, Rome, Italy; IRCCS Fondazione Santa Lucia, 00142, Rome, Italy
| | - M Corbetta
- Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padua, Padua, Italy; Venetian Institute of Molecular Medicine (VIMM), Padua, Italy; Department of Neurology, Radiology, and Neuroscience, Washington University St. Louis
| | - S Della Penna
- Department of Neuroscience, Imaging and Clinical Sciences, and Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
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