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Li J, Li X, Chen F, Li W, Chen J, Zhang B. Studying the Alzheimer's disease continuum using EEG and fMRI in single-modality and multi-modality settings. Rev Neurosci 2024; 35:373-386. [PMID: 38157429 DOI: 10.1515/revneuro-2023-0098] [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: 08/28/2023] [Accepted: 12/01/2023] [Indexed: 01/03/2024]
Abstract
Alzheimer's disease (AD) is a biological, clinical continuum that covers the preclinical, prodromal, and clinical phases of the disease. Early diagnosis and identification of the stages of Alzheimer's disease (AD) are crucial in clinical practice. Ideally, biomarkers should reflect the underlying process (pathological or otherwise), be reproducible and non-invasive, and allow repeated measurements over time. However, the currently known biomarkers for AD are not suitable for differentiating the stages and predicting the trajectory of disease progression. Some objective parameters extracted using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are widely applied to diagnose the stages of the AD continuum. While electroencephalography (EEG) has a high temporal resolution, fMRI has a high spatial resolution. Combined EEG and fMRI (EEG-fMRI) can overcome single-modality drawbacks and obtain multi-dimensional information simultaneously, and it can help explore the hemodynamic changes associated with the neural oscillations that occur during information processing. This technique has been used in the cognitive field in recent years. This review focuses on the different techniques available for studying the AD continuum, including EEG and fMRI in single-modality and multi-modality settings, and the possible future directions of AD diagnosis using EEG-fMRI.
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Affiliation(s)
- Jing Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Xin Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Futao Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Weiping Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Jiu Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Bing Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
- Jiangsu Key Laboratory of Molecular Medicine, Nanjing, Jiangsu, 210008, China
- Institute of Brain Science, Nanjing University, Nanjing, Jiangsu, 210008, China
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2
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Nabizadeh F. Disruption in functional networks mediated tau spreading in Alzheimer's disease. Brain Commun 2024; 6:fcae198. [PMID: 38978728 PMCID: PMC11227975 DOI: 10.1093/braincomms/fcae198] [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: 01/18/2024] [Revised: 04/27/2024] [Accepted: 06/07/2024] [Indexed: 07/10/2024] Open
Abstract
Alzheimer's disease may be conceptualized as a 'disconnection syndrome', characterized by the breakdown of neural connectivity within the brain as a result of amyloid-beta plaques, tau neurofibrillary tangles and other factors leading to progressive degeneration and shrinkage of neurons, along with synaptic dysfunction. It has been suggested that misfolded tau proteins spread through functional connections (known as 'prion-like' properties of tau). However, the local effect of tau spreading on the synaptic function and communication between regions is not well understood. I aimed to investigate how the spreading of tau aggregates through connections can locally influence functional connectivity. In total, the imaging data of 211 participants including 117 amyloid-beta-negative non-demented and 94 amyloid-beta-positive non-demented participants were recruited from the Alzheimer's Disease Neuroimaging Initiative. Furthermore, normative resting-state functional MRI connectomes were used to model tau spreading through functional connections, and functional MRI of the included participants was used to determine the effect of tau spreading on functional connectivity. I found that lower functional connectivity to tau epicentres is associated with tau spreading through functional connections in both amyloid-beta-negative and amyloid-beta-positive participants. Also, amyloid-beta-PET in tau epicentres mediated the association of tau spreading and functional connectivity to epicentres suggesting a partial mediating effect of amyloid-beta deposition in tau epicentres on the local effect of tau spreading on functional connectivity. My findings provide strong support for the notion that tau spreading through connection is locally associated with disrupted functional connectivity between tau epicentre and non-epicentre regions independent of amyloid-beta pathology. Also, I defined several groups based on the relationship between tau spreading and functional disconnection, which provides quantitative assessment to investigate susceptibility or resilience to functional disconnection related to tau spreading. I showed that amyloid-beta, other copathologies and the apolipoprotein E epsilon 4 allele can be a leading factor towards vulnerability to tau relative functional disconnection.
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Affiliation(s)
- Fardin Nabizadeh
- School of Medicine, Iran University of Medical Sciences, Tehran 441265421414, Iran
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3
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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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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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5
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Burnham SC, Iaccarino L, Pontecorvo MJ, Fleisher AS, Lu M, Collins EC, Devous MD. A review of the flortaucipir literature for positron emission tomography imaging of tau neurofibrillary tangles. Brain Commun 2023; 6:fcad305. [PMID: 38187878 PMCID: PMC10768888 DOI: 10.1093/braincomms/fcad305] [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: 05/04/2023] [Revised: 09/13/2023] [Accepted: 11/14/2023] [Indexed: 01/09/2024] Open
Abstract
Alzheimer's disease is defined by the presence of β-amyloid plaques and neurofibrillary tau tangles potentially preceding clinical symptoms by many years. Previously only detectable post-mortem, these pathological hallmarks are now identifiable using biomarkers, permitting an in vivo definitive diagnosis of Alzheimer's disease. 18F-flortaucipir (previously known as 18F-T807; 18F-AV-1451) was the first tau positron emission tomography tracer to be introduced and is the only Food and Drug Administration-approved tau positron emission tomography tracer (Tauvid™). It has been widely adopted and validated in a number of independent research and clinical settings. In this review, we present an overview of the published literature on flortaucipir for positron emission tomography imaging of neurofibrillary tau tangles. We considered all accessible peer-reviewed literature pertaining to flortaucipir through 30 April 2022. We found 474 relevant peer-reviewed publications, which were organized into the following categories based on their primary focus: typical Alzheimer's disease, mild cognitive impairment and pre-symptomatic populations; atypical Alzheimer's disease; non-Alzheimer's disease neurodegenerative conditions; head-to-head comparisons with other Tau positron emission tomography tracers; and technical considerations. The available flortaucipir literature provides substantial evidence for the use of this positron emission tomography tracer in assessing neurofibrillary tau tangles in Alzheimer's disease and limited support for its use in other neurodegenerative disorders. Visual interpretation and quantitation approaches, although heterogeneous, mostly converge and demonstrate the high diagnostic and prognostic value of flortaucipir in Alzheimer's disease.
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Affiliation(s)
| | | | | | | | - Ming Lu
- Avid, Eli Lilly and Company, Philadelphia, PA 19104, USA
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6
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Kucikova L, Zeng J, Muñoz-Neira C, Muniz-Terrera G, Huang W, Gregory S, Ritchie C, O'Brien J, Su L. Genetic risk factors of Alzheimer's Disease disrupt resting-state functional connectivity in cognitively intact young individuals. J Neurol 2023; 270:4949-4958. [PMID: 37358635 PMCID: PMC10511575 DOI: 10.1007/s00415-023-11809-9] [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: 04/28/2023] [Revised: 06/05/2023] [Accepted: 06/06/2023] [Indexed: 06/27/2023]
Abstract
BACKGROUND Past evidence shows that changes in functional brain connectivity in multiple resting-state networks occur in cognitively healthy individuals who have non-modifiable risk factors for Alzheimer's Disease. Here, we aimed to investigate how those changes differ in early adulthood and how they might relate to cognition. METHODS We investigated the effects of genetic risk factors of AD, namely APOEe4 and MAPTA alleles, on resting-state functional connectivity in a cohort of 129 cognitively intact young adults (aged 17-22 years). We used Independent Component Analysis to identify networks of interest, and Gaussian Random Field Theory to compare connectivity between groups. Seed-based analysis was used to quantify inter-regional connectivity strength from the clusters that exhibited significant between-group differences. To investigate the relationship with cognition, we correlated the connectivity and the performance on the Stroop task. RESULTS The analysis revealed a decrease in functional connectivity in the Default Mode Network (DMN) in both APOEe4 carriers and MAPTA carriers in comparison with non-carriers. APOEe4 carriers showed decreased connectivity in the right angular gyrus (size = 246, p-FDR = 0.0079), which was correlated with poorer performance on the Stroop task. MAPTA carriers showed decreased connectivity in the left middle temporal gyrus (size = 546, p-FDR = 0.0001). In addition, we found that only MAPTA carriers had a decreased connectivity between the DMN and multiple other brain regions. CONCLUSIONS Our findings indicate that APOEe4 and MAPTA alleles modulate brain functional connectivity in the brain regions within the DMN in cognitively intact young adults. APOEe4 carriers also showed a link between connectivity and cognition.
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Affiliation(s)
- Ludmila Kucikova
- Department of Neuroscience, Faculty of Medicine, Dentistry and Heath, Sheffield Institute for Translational Neuroscience, University of Sheffield, 385a Glossop Road, Sheffield, S10 2HQ, SY, UK
- Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
| | - Jianmin Zeng
- Sino-Britain Centre for Cognition and Ageing Research, Faculty of Psychology, Southwest University, Chongqing, China.
| | - Carlos Muñoz-Neira
- Department of Neuroscience, Faculty of Medicine, Dentistry and Heath, Sheffield Institute for Translational Neuroscience, University of Sheffield, 385a Glossop Road, Sheffield, S10 2HQ, SY, UK
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Graciela Muniz-Terrera
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Ohio University Heritage College of Osteopathic Medicine, Ohio University, Athens, OH, USA
| | - Weijie Huang
- Department of Neuroscience, Faculty of Medicine, Dentistry and Heath, Sheffield Institute for Translational Neuroscience, University of Sheffield, 385a Glossop Road, Sheffield, S10 2HQ, SY, UK
- Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Sarah Gregory
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Craig Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Scottish Brain Sciences, Edinburgh, UK
| | - John O'Brien
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Li Su
- Department of Neuroscience, Faculty of Medicine, Dentistry and Heath, Sheffield Institute for Translational Neuroscience, University of Sheffield, 385a Glossop Road, Sheffield, S10 2HQ, SY, UK.
- Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK.
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
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7
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Deery HA, Di Paolo R, Moran C, Egan GF, Jamadar SD. The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging. Psychophysiology 2023; 60:e14159. [PMID: 36106762 PMCID: PMC10909558 DOI: 10.1111/psyp.14159] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 12/23/2022]
Abstract
The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2938 records, 144 studies were included. Results on 11 network measures were summarized and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function, and decreased lateralization and a posterior to anterior shift at rest. Higher-order functional networks reliably showed age differences, whereas primary sensory and motor networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for within- and between-network altered patterns and speed of dynamic connectivity. Research on within-subject bold variability and connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.
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Affiliation(s)
- Hamish A. Deery
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Robert Di Paolo
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Chris Moran
- Peninsula Clinical School, Central Clinical SchoolMonash UniversityFrankstonVictoriaAustralia
- Department of Geriatric MedicinePeninsula HealthFrankstonVictoriaAustralia
| | - Gary F. Egan
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
| | - Sharna D. Jamadar
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
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8
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Moffat G, Zhukovsky P, Coughlan G, Voineskos AN. Unravelling the relationship between amyloid accumulation and brain network function in normal aging and very mild cognitive decline: a longitudinal analysis. Brain Commun 2022; 4:fcac282. [PMID: 36415665 PMCID: PMC9678202 DOI: 10.1093/braincomms/fcac282] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 07/29/2022] [Accepted: 10/31/2022] [Indexed: 06/27/2024] Open
Abstract
Pathological changes in the brain begin accumulating decades before the appearance of cognitive symptoms in Alzheimer's disease. The deposition of amyloid beta proteins and other neurotoxic changes occur, leading to disruption in functional connections between brain networks. Discrete characterization of the changes that take place in preclinical Alzheimer's disease has the potential to help treatment development by targeting the neuropathological mechanisms to prevent cognitive decline and dementia from occurring entirely. Previous research has focused on the cross-sectional differences in the brains of patients with mild cognitive impairment or Alzheimer's disease and healthy controls or has concentrated on the stages immediately preceding cognitive symptoms. The present study emphasizes the early preclinical phases of neurodegeneration. We use a longitudinal approach to examine the brain changes that take place during the early stages of cognitive decline in the Open Access Series of Imaging Studies-3 data set. Among 1098 participants, 274 passed the inclusion criteria (i.e. had at least two cognitive assessments and two amyloid scans). Over 90% of participants were healthy at baseline. Over 8-10 years, some participants progressed to very mild cognitive impairment (n = 48), while others stayed healthy (n = 226). Participants with cognitive decline show faster amyloid accumulation in the lateral temporal, motor and parts of the lateral prefrontal cortex. These changes in amyloid levels were linked to longitudinal increases in the functional connectivity of select networks, including default mode, frontoparietal and motor components. Our findings advance the understanding of amyloid staging and the corresponding changes in functional organization of large-scale brain networks during the progression of early preclinical Alzheimer's disease.
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Affiliation(s)
- Gemma Moffat
- Kimel Family Translational Imaging-Genetics Laboratory, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
| | - Peter Zhukovsky
- Kimel Family Translational Imaging-Genetics Laboratory, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
| | - Gillian Coughlan
- Rotman Research Institute, Baycrest Hospital, Toronto, ON, M6A 2E1, Canada
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02129, USA
| | - Aristotle N Voineskos
- Kimel Family Translational Imaging-Genetics Laboratory, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
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9
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Michalowska MM, Herholz K, Hinz R, Amadi C, McInnes L, Anton-Rodriguez JM, Karikari TK, Blennow K, Zetterberg H, Ashton NJ, Pendleton N, Carter SF. Evaluation of in vivo staging of amyloid deposition in cognitively unimpaired elderly aged 78-94. Mol Psychiatry 2022; 27:4335-4342. [PMID: 35858992 PMCID: PMC9718666 DOI: 10.1038/s41380-022-01685-6] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 05/27/2022] [Accepted: 06/27/2022] [Indexed: 02/07/2023]
Abstract
Amyloid-beta (Aβ) deposition is common in cognitively unimpaired (CU) elderly >85 years. This study investigated amyloid distribution and evaluated three published in vivo amyloid-PET staging schemes from a cognitively unimpaired (CU) cohort aged 84.9 ± 4.3 years (n = 75). SUV-based principal component analysis (PCA) was applied to 18F-flutemetamol PET data to determine an unbiased regional covariance pattern of tracer uptake across grey matter regions. PET staging schemes were applied to the data and compared to the PCA output. Concentration of p-tau181 was measured in blood plasma. The PCA revealed three distinct components accounting for 91.2% of total SUV variance. PC1 driven by the large common variance of uptake in neocortical and striatal regions was significantly positively correlated with global SUVRs, APOE4 status and p-tau181 concentration. PC2 represented mainly non-specific uptake in typical amyloid-PET reference regions, and PC3 the occipital lobe. Application of the staging schemes demonstrated that the majority of the CU cohort (up to 93%) were classified as having pathological amount and distribution of Aβ. Good correspondence existed between binary (+/-) classification and later amyloid stages, however, substantial differences existed between schemes for low stages with 8-17% of individuals being unstageable, i.e., not following the sequential progression of Aβ deposition. In spite of the difference in staging outcomes there was broad spatial overlap between earlier stages and PC1, most prominently in default mode network regions. This study critically evaluated the utility of in vivo amyloid staging from a single PET scan in CU elderly and found that early amyloid stages could not be consistently classified. The majority of the cohort had pathological Aβ, thus, it remains an open topic what constitutes abnormal brain Aβ in the oldest-old and what is the best method to determine that.
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Affiliation(s)
- Malgorzata M Michalowska
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
- Department of Neuropsychology and Psychopharmacology, Maastricht University, Maastricht, The Netherlands
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Karl Herholz
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
| | - Rainer Hinz
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
| | - Chinenye Amadi
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Lynn McInnes
- Department of Psychology, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Jose M Anton-Rodriguez
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK
| | - Thomas K Karikari
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- King's College London, Institute of Psychiatry, Psychology and Neuroscience Maurice Wohl Institute Clinical Neuroscience Institute, London, UK
- NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK
- Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Neil Pendleton
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Stephen F Carter
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK.
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
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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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Sendi MSE, Salat DH, Miller RL, Calhoun VD. Two-step clustering-based pipeline for big dynamic functional network connectivity data. Front Neurosci 2022; 16:895637. [PMID: 35958983 PMCID: PMC9358255 DOI: 10.3389/fnins.2022.895637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 06/29/2022] [Indexed: 11/13/2022] Open
Abstract
Background Dynamic functional network connectivity (dFNC) estimated from resting-state functional magnetic imaging (rs-fMRI) studies the temporally varying functional integration between brain networks. In a conventional dFNC pipeline, a clustering stage to summarize the connectivity patterns that are transiently but reliably realized over the course of a scanning session. However, identifying the right number of clusters (or states) through a conventional clustering criterion computed by running the algorithm repeatedly over a large range of cluster numbers is time-consuming and requires substantial computational power even for typical dFNC datasets, and the computational demands become prohibitive as datasets become larger and scans longer. Here we developed a new dFNC pipeline based on a two-step clustering approach to analyze large dFNC data without having access to huge computational power. Methods In the proposed dFNC pipeline, we implement two-step clustering. In the first step, we randomly use a sub-sample dFNC data and identify several sets of states at different model orders. In the second step, we aggregate all dFNC states estimated from all iterations in the first step and use this to identify the optimum number of clusters using the elbow criteria. Additionally, we use this new reduced dataset and estimate a final set of states by performing a second kmeans clustering on the aggregated dFNC states from the first k-means clustering. To validate the reproducibility of results in the new pipeline, we analyzed four dFNC datasets from the human connectome project (HCP). Results We found that both conventional and proposed dFNC pipelines generate similar brain dFNC states across all four sessions with more than 99% similarity. We found that the conventional dFNC pipeline evaluates the clustering order and finds the final dFNC state in 275 min, while this process takes only 11 min for the proposed dFNC pipeline. In other words, the new pipeline is 25 times faster than the traditional method in finding the optimum number of clusters and finding the final dFNC states. We also found that the new method results in better clustering quality than the conventional approach (p < 0.001). We show that the results are replicated across four different datasets from HCP. Conclusion We developed a new analytic pipeline that facilitates the analysis of large dFNC datasets without having access to a huge computational power source. We validated the reproducibility of the result across multiple datasets.
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Affiliation(s)
- Mohammad S. E. Sendi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- *Correspondence: Mohammad S. E. Sendi,
| | - David H. Salat
- Harvard Medical School, Boston, MA, United States
- Massachusetts General Hospital, Boston, MA, United States
| | - Robyn L. Miller
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Vince D. Calhoun
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
- Vince D. Calhoun,
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12
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Factors Influencing Alzheimer's Disease Risk: Whether and How They are Related to the APOE Genotype. Neurosci Bull 2022; 38:809-819. [PMID: 35149974 PMCID: PMC9276873 DOI: 10.1007/s12264-021-00814-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 11/25/2021] [Indexed: 11/21/2022] Open
Abstract
Alzheimer's disease (AD) is the most prevalent neurodegenerative disease featuring progressive cognitive impairment. Although the etiology of late-onset AD remains unclear, the close association of AD with apolipoprotein E (APOE), a gene that mainly regulates lipid metabolism, has been firmly established and may shed light on the exploration of AD pathogenesis and therapy. However, various confounding factors interfere with the APOE-related AD risk, raising questions about our comprehension of the clinical findings concerning APOE. In this review, we summarize the most debated factors interacting with the APOE genotype and AD pathogenesis, depict the extent to which these factors relate to APOE-dependent AD risk, and discuss the possible underlying mechanisms.
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13
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Cui L, Zhang Z, Zac Lo CY, Guo Q. Local Functional MR Change Pattern and Its Association With Cognitive Function in Objectively-Defined Subtle Cognitive Decline. Front Aging Neurosci 2021; 13:684918. [PMID: 34177559 PMCID: PMC8232526 DOI: 10.3389/fnagi.2021.684918] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 05/10/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: To identify individuals with preclinical cognitive impairment, researchers proposed the concept of objectively-defined subtle cognitive decline (Obj-SCD). However, it is not clear whether Obj-SCD has characteristic brain function changes. In this study, we aimed at exploring the changing pattern of brain function activity in Obj-SCD individuals and the similarities and differences with mild cognitive impairments (MCI). Method: 37 healthy control individuals, 25 Obj-SCD individuals (with the impairment in memory and language domain), and 28 aMCI individuals were included. Resting-state fMRI and neuropsychological tests were performed. fALFF was used to reflect the local functional activity and compared between groups. Finally, we analyzed the correlation between the fALFF values of significantly changed regions and neuropsychological performance. Results: We found similar functional activity enhancements in some local brain regions in the Obj-SCD and aMCI groups, including the left orbital part of the inferior frontal gyrus and the left median cingulate and paracingulate gyri. However, some changes in local functional activities of the Obj-SCD group showed different patterns from the aMCI group. Compared with healthy control (HC), the Obj-SCD group showed increased local functional activity in the right middle occipital gyrus, decreased local functional activity in the left precuneus and the left inferior temporal gyrus. In the Obj-SCD group, in normal band, the fALFF value of the right middle occipital gyrus was significantly negatively correlated with Mini-Mental State Examination (MMSE) score (r = -0.450, p = 0.024) and Animal Verbal Fluency Test (AFT) score (r = -0.402, p = 0.046); the left inferior temporal gyrus was significantly positively correlated with MMSE score (r = 0.588, p = 0.002). In slow-4 band, the fALFF value of the left precuneus was significantly positively correlated with MMSE score (r = 0.468, p = 0.018) and AFT score (r = 0.600, p = 0.002). In the aMCI group, the fALFF value of the left orbital part of the inferior frontal gyrus was significantly positively correlated with Auditory Verbal Learning Test (AVLT) long delay cued recall score (r = 0.506, p = 0.006). Conclusion: The Obj-SCD group showed a unique changing pattern; the functional changes of different brain regions have a close but different correlation with cognitive impairment, indicating that there may be a complex pathological basis inside. This suggests that Obj-SCD may be a separate stage of cognitive decline before aMCI and is helpful to the study of preclinical cognitive decline.
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Affiliation(s)
- Liang Cui
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Zhen Zhang
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Chun-Yi Zac Lo
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
| | - Qihao Guo
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
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14
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Wales RM, Leung HC. The Effects of Amyloid and Tau on Functional Network Connectivity in Older Populations. Brain Connect 2021; 11:599-612. [PMID: 33813858 DOI: 10.1089/brain.2020.0902] [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
Background: Neuroimaging studies suggest that aged brains show altered connectivity within and across functional networks. Similar changes in functional network integrity are also linked to the accumulation of pathological proteins in the brain, such as amyloid-beta plaques and neurofibrillary tau tangles seen in Alzheimer's disease. However, less is known about the specific impacts of amyloid and tau on functional network connectivity in cognitively normal older adults who harbor these proteins. Methods: We briefly summarize recent neuroimaging studies of aging and then thoroughly review positron emission tomography and functional magnetic resonance imaging studies measuring the relationship between amyloid-tau pathology and functional connectivity in cognitively normal older individuals. Results: The literature overall suggests that amyloid-positive older individuals show minor cognitive dysfunction and aberrant default mode network connectivity compared with amyloid-negative individuals. Tau, however, is more closely associated with network hypoconnectivity and poorer cognition. Those with substantial amyloid and tau experience even greater cognitive decline compared with those with primarily amyloid or tau, suggesting a potential interaction. Multimodal neuroimaging studies suggest that older adults with pathological protein deposits show amyloid-related hyperconnectivity and tau-related hypoconnectivity in multiple functional networks, including the default mode and frontoparietal networks. Discussion: We propose an updated model considering the effects of amyloid and tau on functional connectivity in older individuals. Large, longitudinal neuroimaging studies with multiple levels of analysis are required to obtain a deeper understanding of the dynamic relationship between pathological protein accumulation and functional connectivity changes, as amyloid- and tau-induced connectivity alterations may have critical and time-varying effects on neurodegeneration and cognitive decline. Impact statement Amyloid and tau accumulation have been linked with altered functional connectivity in cognitively normal older adults. This review synthesized recent functional imaging literatures in a discussion of how amyloid and tau can interactively affect functional connectivity in nonlinear ways, which can explain previous conflicting findings. Changes in connectivity strength may depend on the accumulation of both amyloid and tau, and their integrative effects seem to have critical consequences on cognition. Elucidating the effects of these pathological proteins on brain functioning is paramount to understand the etiology of Alzheimer's disease and the aging process overall.
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Affiliation(s)
- Ryan Michael Wales
- Integrative Neuroscience Program, Department of Psychology, Stony Brook University, Stony Brook, New York, USA
| | - Hoi-Chung Leung
- Integrative Neuroscience Program, Department of Psychology, Stony Brook University, Stony Brook, New York, USA
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15
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Hasani SA, Mayeli M, Salehi MA, Barzegar Parizi R. A Systematic Review of the Association between Amyloid-β and τ Pathology with Functional Connectivity Alterations in the Alzheimer Dementia Spectrum Utilizing PET Scan and rsfMRI. Dement Geriatr Cogn Dis Extra 2021; 11:78-90. [PMID: 34178011 PMCID: PMC8216015 DOI: 10.1159/000516164] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 03/25/2021] [Indexed: 11/19/2022] Open
Abstract
The association between functional connectivity (FC) alterations with amyloid-β (Aβ) and τ protein depositions in Alzheimer dementia is a subject of debate in the current literature. Although many studies have suggested a declining FC accompanying increased Aβ and τ concentrations, some investigations have contradicted this hypothesis. Therefore, this systematic review was conducted to sum up the current literature in this regard. The PROSPERO guideline for systematic reviews was applied for development of a research protocol, and this study was initiated after getting the protocol approval. Studies were screened, and those investigating FC measured by resting-state functional MRI and Aβ and τ protein depositions using amyloid and τ positron emission tomography were included. We categorized the included studies into 3 groups methodologically, addressing the question using global connectivity analysis (examining all regions of interest across the brain based on a functional atlas), seed-based connectivity analysis, or within-networks connectivity analysis. The quality of the studies was assessed using the Newcastle-Ottawa Scale. Among 31 included studies, 14 found both positive and negative correlations depending on the brain region and stage of the investigated disease, while 7 showed an overall negative correlation, 8 indicated an overall positive correlation, and 2 found a nonsignificant association between protein deposition and FC. The investigated regions were illustrated using tables. The posterior default mode network, one of the first regions of amyloid accumulation, and the temporal lobe, the early τ deposition region, are the 2 most investigated regions where inconsistencies exist. In conclusion, our study indicates that transneuronal spreading of τ and the amyloid hypothesis can justify higher FC related to higher protein depositions when global connectivity analysis is applied. However, the discrepancies observed when investigating the brain locally could be due to the varying manifestations of the amyloid and τ overload compensatory mechanisms in the brain at different stages of the disease with hyper- and hypoconnectivity cycles that can occur repeatedly. Nevertheless, further studies investigating both amyloid and τ deposition simultaneously while considering the stage of Alzheimer dementia are required to assess the accuracy of this hypothesis.
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Affiliation(s)
- Seyede Anis Hasani
- NeuroTRACT Association, Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahsa Mayeli
- NeuroTRACT Association, Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran.,School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Amin Salehi
- NeuroTRACT Association, Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran.,School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Rezvan Barzegar Parizi
- NeuroTRACT Association, Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
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16
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Michels L, Riese F, Meyer R, Kälin AM, Leh SE, Unschuld PG, Luechinger R, Hock C, O'Gorman R, Kollias S, Gietl A. EEG-fMRI Signal Coupling Is Modulated in Subjects With Mild Cognitive Impairment and Amyloid Deposition. Front Aging Neurosci 2021; 13:631172. [PMID: 33967737 PMCID: PMC8104007 DOI: 10.3389/fnagi.2021.631172] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 03/10/2021] [Indexed: 12/13/2022] Open
Abstract
Cognitive impairment indicates disturbed brain physiology which can be due to various mechanisms including Alzheimer's pathology. Combined functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) recordings (EEG-fMRI) can assess the interplay between complementary measures of brain activity and EEG changes to be localized to specific brain regions. We used a two-step approach, where we first examined changes related to a syndrome of mild cognitive impairment irrespective of pathology and then studied the specific impact of amyloid pathology. After detailed clinical and neuropsychological characterization as well as a positron emission tomography (PET) scans with the tracer 11-[C]-Pittsburgh Compound B to estimate cerebral amyloid deposition, 14 subjects with mild cognitive impairment (MCI) (mean age 75.6 SD: 8.9) according to standard criteria and 21 cognitively healthy controls (HCS) (mean age 71.8 SD: 4.2) were assessed with EEG-fMRI. Thalamo-cortical alpha-fMRI signal coupling was only observed in HCS. Additional EEG-fMRI signal coupling differences between HCS and MCI were observed in parts of the default mode network, salience network, fronto-parietal network, and thalamus. Individuals with significant cerebral amyloid deposition (amyloid-positive MCI and HCS combined compared to amyloid-negative HCS) displayed abnormal EEG-fMRI signal coupling in visual, fronto-parietal regions but also in the parahippocampus, brain stem, and cerebellum. This finding was paralleled by stronger absolute fMRI signal in the parahippocampus and weaker absolute fMRI signal in the inferior frontal gyrus in amyloid-positive subjects. We conclude that the thalamocortical coupling in the alpha band in HCS more closely reflects previous findings observed in younger adults, while in MCI there is a clearly aberrant coupling in several networks dominated by an anticorrelation in the posterior cingulate cortex. While these findings may broadly indicate physiological changes in MCI, amyloid pathology was specifically associated with abnormal fMRI signal responses and disrupted coupling between brain oscillations and fMRI signal responses, which especially involve core regions of memory: the hippocampus, para-hippocampus, and lateral prefrontal cortex.
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Affiliation(s)
- Lars Michels
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, Zurich, Switzerland
| | - Florian Riese
- Department of Geriatric Psychiatry, Psychiatric University Hospital Zurich (PUK), Zurich, Switzerland.,University Research Priority Programs (URPP) ≪Dynamics of Healthy Aging≫, University of Zurich, Zurich, Switzerland
| | - Rafael Meyer
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
| | - Andrea M Kälin
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
| | - Sandra E Leh
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
| | - Paul G Unschuld
- Department of Geriatric Psychiatry, Psychiatric University Hospital Zurich (PUK), Zurich, Switzerland.,Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland.,Geriatric Psychiatry, Geneva University Hospitals (HUG), Geneva, Switzerland
| | - Roger Luechinger
- Institute of Biomedical Engineering, University and Eidgenössische Technische Hochschule (ETH) Zurich, Zurich, Switzerland
| | - Christoph Hock
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland.,Neurimmune AG, Schlieren, Switzerland
| | - Ruth O'Gorman
- Center for Magnetic Resonance Research, University Children's Hospital Zurich, Zurich, Switzerland
| | - Spyros Kollias
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, Zurich, Switzerland
| | - Anton Gietl
- Department of Geriatric Psychiatry, Psychiatric University Hospital Zurich (PUK), Zurich, Switzerland.,Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
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17
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Karki HP, Jang Y, Jung J, Oh J. Advances in the development paradigm of biosample-based biosensors for early ultrasensitive detection of alzheimer's disease. J Nanobiotechnology 2021; 19:72. [PMID: 33750392 PMCID: PMC7945670 DOI: 10.1186/s12951-021-00814-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 02/25/2021] [Indexed: 02/07/2023] Open
Abstract
This review highlights current developments, challenges, and future directions for the use of invasive and noninvasive biosample-based small biosensors for early diagnosis of Alzheimer's disease (AD) with biomarkers to incite a conceptual idea from a broad number of readers in this field. We provide the most promising concept about biosensors on the basis of detection scale (from femto to micro) using invasive and noninvasive biosamples such as cerebrospinal fluid (CSF), blood, urine, sweat, and tear. It also summarizes sensor types and detailed analyzing techniques for ultrasensitive detection of multiple target biomarkers (i.e., amyloid beta (Aβ) peptide, tau protein, Acetylcholine (Ach), microRNA137, etc.) of AD in terms of detection ranges and limit of detections (LODs). As the most significant disadvantage of CSF and blood-based detection of AD is associated with the invasiveness of sample collection which limits future strategy with home-based early screening of AD, we extensively reviewed the future trend of new noninvasive detection techniques (such as optical screening and bio-imaging process). To overcome the limitation of non-invasive biosamples with low concentrations of AD biomarkers, current efforts to enhance the sensitivity of biosensors and discover new types of biomarkers using non-invasive body fluids are presented. We also introduced future trends facing an infection point in early diagnosis of AD with simultaneous emergence of addressable innovative technologies.
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Affiliation(s)
- Hem Prakash Karki
- Department of Mechanical Design Engineering, College of Engineering, Jeonbuk National University, Jeonju, 54896, South Korea
| | - Yeongseok Jang
- Department of Mechanical Design Engineering, College of Engineering, Jeonbuk National University, Jeonju, 54896, South Korea
| | - Jinmu Jung
- Department of Mechanical Design Engineering, College of Engineering, Jeonbuk National University, Jeonju, 54896, South Korea.
- Department of Nano-bio Mechanical System Engineering, College of Engineering, Jeonbuk National University, Jeonju, 54896, South Korea.
| | - Jonghyun Oh
- Department of Mechanical Design Engineering, College of Engineering, Jeonbuk National University, Jeonju, 54896, South Korea.
- Department of Nano-bio Mechanical System Engineering, College of Engineering, Jeonbuk National University, Jeonju, 54896, South Korea.
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18
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Wang SM, Kim NY, Kang DW, Um YH, Na HR, Woo YS, Lee CU, Bahk WM, Lim HK. A Comparative Study on the Predictive Value of Different Resting-State Functional Magnetic Resonance Imaging Parameters in Preclinical Alzheimer's Disease. Front Psychiatry 2021; 12:626332. [PMID: 34177638 PMCID: PMC8226028 DOI: 10.3389/fpsyt.2021.626332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 04/23/2021] [Indexed: 11/25/2022] Open
Abstract
Objective: Diverse resting-state functional magnetic resonance imaging (rs-fMRI) studies showed that rs-fMRI might be able to reflect the earliest detrimental effect of cerebral beta-amyloid (Aβ) pathology. However, no previous studies specifically compared the predictive value of different rs-fMRI parameters in preclinical AD. Methods: A total of 106 cognitively normal adults (Aβ+ group = 66 and Aβ- group = 40) were included. Three different rs-fMRI parameter maps including functional connectivity (FC), fractional amplitude of low-frequency fluctuations (fALFF), and regional homogeneity (ReHo) were calculated. Receiver operating characteristic (ROC) curve analyses were utilized to compare classification performance of the three rs-fMRI parameters. Results: FC maps showed the best classifying performance in ROC curve analysis (AUC, 0.915, p < 0.001). Good but weaker performance was achieved by using ReHo maps (AUC, 0.836, p < 0.001) and fALFF maps (AUC, 0.804, p < 0.001). The brain regions showing the greatest discriminative power included the left angular gyrus for FC, left anterior cingulate for ReHo, and left middle frontal gyrus for fALFF. However, among the three measurements, ROI-based FC was the only measure showing group difference in voxel-wise analysis. Conclusion: Our results strengthen the idea that rs-fMRI might be sensitive to earlier changes in spontaneous brain activity and FC in response to cerebral Aβ retention. However, further longitudinal studies with larger sample sizes are needed to confirm their utility in predicting the risk of AD.
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Affiliation(s)
- Sheng-Min Wang
- Department of Psychiatry, College of Medicine, Catholic University of Korea, Seoul, South Korea
| | - Nak-Young Kim
- Department of Psychiatry, Keyo Hospital, Uiwang, South Korea
| | - Dong Woo Kang
- Department of Psychiatry, College of Medicine, Catholic University of Korea, Seoul, South Korea
| | - Yoo Hyun Um
- Department of Psychiatry, College of Medicine, Catholic University of Korea, Seoul, South Korea
| | - Hae-Ran Na
- Department of Psychiatry, College of Medicine, Catholic University of Korea, Seoul, South Korea
| | - Young Sup Woo
- Department of Psychiatry, College of Medicine, Catholic University of Korea, Seoul, South Korea
| | - Chang Uk Lee
- Department of Psychiatry, College of Medicine, Catholic University of Korea, Seoul, South Korea
| | - Won-Myong Bahk
- Department of Psychiatry, College of Medicine, Catholic University of Korea, Seoul, South Korea
| | - Hyun Kook Lim
- Department of Psychiatry, College of Medicine, Catholic University of Korea, Seoul, South Korea
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