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Markett S, Boeken OJ, Wudarczyk OA. Multimodal imaging investigation of structural rich club alterations in Alzheimer's disease and mild cognitive impairment: Amyloid deposition, structural atrophy, and functional activation differences. Eur J Neurosci 2024; 60:4169-4181. [PMID: 38779858 DOI: 10.1111/ejn.16384] [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: 09/13/2023] [Revised: 04/03/2024] [Accepted: 04/22/2024] [Indexed: 05/25/2024]
Abstract
Alzheimer's disease (AD) is characterized by significant cerebral dysfunction, including increased amyloid deposition, gray matter atrophy, and changes in brain function. The involvement of highly connected network hubs, known as the "rich club," in the pathology of the disease remains inconclusive despite previous research efforts. In this study, we aimed to systematically assess the link between the rich club and AD using a multimodal neuroimaging approach. We employed network analyses of diffusion magnetic resonance imaging (MRI), longitudinal assessments of gray matter atrophy, amyloid deposition measurements using positron emission tomography (PET) imaging, and meta-analytic data on functional activation differences. Our study focused on evaluating the role of both the structural brain network's core and extended rich club regions in individuals with mild cognitive impairment (MCI) and those diagnosed with AD. Our findings revealed that structural rich club regions exhibited accelerated gray matter atrophy and increased amyloid deposition in both MCI and AD. Importantly, these regions remained unaffected by altered functional activation patterns observed outside the core rich club regions. These results shed light on the connection between two major AD biomarkers and the rich club, providing valuable insights into AD as a potential disconnection syndrome.
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Affiliation(s)
| | - Ole J Boeken
- Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin, Berlin, Germany
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2
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Gutiérrez-de Pablo V, Poza J, Maturana-Candelas A, Rodríguez-González V, Tola-Arribas MÁ, Cano M, Hoshi H, Shigihara Y, Hornero R, Gómez C. Exploring the disruptions of the neurophysiological organization in Alzheimer's disease: An integrative approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108197. [PMID: 38688139 DOI: 10.1016/j.cmpb.2024.108197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 12/20/2023] [Accepted: 04/21/2024] [Indexed: 05/02/2024]
Abstract
BACKGROUND AND OBJECTIVE Alzheimer's disease (AD) is a neurological disorder that impairs brain functions associated with cognition, memory, and behavior. Noninvasive neurophysiological techniques like magnetoencephalography (MEG) and electroencephalography (EEG) have shown promise in reflecting brain changes related to AD. These techniques are usually assessed at two levels: local activation (spectral, nonlinear, and dynamic properties) and global synchronization (functional connectivity, frequency-dependent network, and multiplex network organization characteristics). Nonetheless, the understanding of the organization formed by the existing relationships between these levels, henceforth named neurophysiological organization, remains unexplored. This work aims to assess the alterations AD causes in the resting-state neurophysiological organization. METHODS To that end, three datasets from healthy controls (HC) and patients with dementia due to AD were considered: MEG database (55 HC and 87 patients with AD), EEG1 database (51 HC and 100 patients with AD), and EEG2 database (45 HC and 82 patients with AD). To explore the alterations induced by AD in the relationships between several features extracted from M/EEG data, association networks (ANs) were computed. ANs are graphs, useful to quantify and visualize the intricate relationships between multiple features. RESULTS Our results suggested a disruption in the neurophysiological organization of patients with AD, exhibiting a greater inclination towards the local activation level; and a significant decrease in the complexity and diversity of the ANs (p-value ¡ 0.05, Mann-Whitney U-test, Bonferroni correction). This effect might be due to a shift of the neurophysiological organization towards more regular configurations, which may increase its vulnerability. Moreover, our findings support the crucial role played by the local activation level in maintaining the stability of the neurophysiological organization. Classification performance exhibited accuracy values of 83.91%, 73.68%, and 72.65% for MEG, EEG1, and EEG2 databases, respectively. CONCLUSION This study introduces a novel, valuable methodology able to integrate parameters characterize different properties of the brain activity and to explore the intricate organization of the neurophysiological organization at different levels. It was noted that AD increases susceptibility to changes in functional neural organization, suggesting a greater ease in the development of severe impairments. Therefore, ANs could facilitate a deeper comprehension of the complex interactions in brain function from a global standpoint.
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Affiliation(s)
- Víctor Gutiérrez-de Pablo
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain.
| | - Jesús Poza
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
| | - Aarón Maturana-Candelas
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain
| | - Víctor Rodríguez-González
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain
| | - Miguel Ángel Tola-Arribas
- CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain; Department of Neurology, Río Hortega University Hospital, Valladolid, Spain
| | - Mónica Cano
- Department of Clinical Neurophysiology, Río Hortega University Hospital, Valladolid, Spain
| | - Hideyuki Hoshi
- Precision Medicine Centre, Hokuto Hospital, Obihiro, Japan
| | | | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
| | - Carlos Gómez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain
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3
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van Nifterick AM, Scheijbeler EP, Gouw AA, de Haan W, Stam CJ. Local signal variability and functional connectivity: Sensitive measures of the excitation-inhibition ratio? Cogn Neurodyn 2024; 18:519-537. [PMID: 38699618 PMCID: PMC11061092 DOI: 10.1007/s11571-023-10003-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/08/2023] [Accepted: 08/13/2023] [Indexed: 05/05/2024] Open
Abstract
A novel network version of permutation entropy, the inverted joint permutation entropy (JPEinv), holds potential as non-invasive biomarker of abnormal excitation-inhibition (E-I) ratio in Alzheimer's disease (AD). In this computational modelling study, we test the hypotheses that this metric, and related measures of signal variability and functional connectivity, are sensitive to altered E-I ratios. The E-I ratio in each neural mass of a whole-brain computational network model was systematically varied. We evaluated whether JPEinv, local signal variability (by permutation entropy) and functional connectivity (by weighted symbolic mutual information (wsMI)) were related to E-I ratio, on whole-brain and regional level. The hub disruption index can identify regions primarily affected in terms of functional connectivity strength (or: degree) by the altered E-I ratios. Analyses were performed for a range of coupling strengths, filter and time-delay settings. On whole-brain level, higher E-I ratios were associated with higher functional connectivity (by JPEinv and wsMI) and lower local signal variability. These relationships were nonlinear and depended on the coupling strength, filter and time-delay settings. On regional level, hub-like regions showed a selective decrease in functional degree (by JPEinv and wsMI) upon a lower E-I ratio, and non-hub-like regions showed a selective increase in degree upon a higher E-I ratio. These results suggest that abnormal functional connectivity and signal variability, as previously reported in patients across the AD continuum, can inform us about altered E-I ratios. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-023-10003-x.
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Affiliation(s)
- Anne M. van Nifterick
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Elliz P. Scheijbeler
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Alida A. Gouw
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Willem de Haan
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Cornelis J. Stam
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
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4
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Stam CJ. Hub overload and failure as a final common pathway in neurological brain network disorders. Netw Neurosci 2024; 8:1-23. [PMID: 38562292 PMCID: PMC10861166 DOI: 10.1162/netn_a_00339] [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: 06/21/2023] [Accepted: 09/26/2023] [Indexed: 04/04/2024] Open
Abstract
Understanding the concept of network hubs and their role in brain disease is now rapidly becoming important for clinical neurology. Hub nodes in brain networks are areas highly connected to the rest of the brain, which handle a large part of all the network traffic. They also show high levels of neural activity and metabolism, which makes them vulnerable to many different types of pathology. The present review examines recent evidence for the prevalence and nature of hub involvement in a variety of neurological disorders, emphasizing common themes across different types of pathology. In focal epilepsy, pathological hubs may play a role in spreading of seizure activity, and removal of such hub nodes is associated with improved outcome. In stroke, damage to hubs is associated with impaired cognitive recovery. Breakdown of optimal brain network organization in multiple sclerosis is accompanied by cognitive dysfunction. In Alzheimer's disease, hyperactive hub nodes are directly associated with amyloid-beta and tau pathology. Early and reliable detection of hub pathology and disturbed connectivity in Alzheimer's disease with imaging and neurophysiological techniques opens up opportunities to detect patients with a network hyperexcitability profile, who could benefit from treatment with anti-epileptic drugs.
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Affiliation(s)
- Cornelis Jan Stam
- Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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5
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Lan F, Roquet D, Dalton MA, El-Omar H, Ahmed RM, Piguet O, Irish M. Exploring graded profiles of hippocampal atrophy along the anterior-posterior axis in semantic dementia and Alzheimer's disease. Neurobiol Aging 2024; 135:70-78. [PMID: 38232501 DOI: 10.1016/j.neurobiolaging.2024.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 01/07/2024] [Accepted: 01/09/2024] [Indexed: 01/19/2024]
Abstract
Mounting evidence indicates marked hippocampal degeneration in semantic dementia (SD) however, the spatial distribution of hippocampal atrophy profiles in this syndrome remains unclear. Using a recently developed parcellation approach, we extracted hippocampal volumes from four distinct subregions running from anterior to posterior along the longitudinal axis (anterior, intermediate rostral, intermediate caudal, and posterior). Volumetric differences in hippocampal subregions were compared between 21 SD, 24 matched Alzheimer's disease (AD), and 27 healthy older Control participants. Despite comparable overall hippocampal volume loss, SD and AD groups diverged in terms of the magnitude of atrophy along the anterior-posterior axis of the hippocampus. Global hippocampal atrophy was observed in AD, with no discernible gradation or lateralisation. In contrast, SD patients displayed graded bilateral hippocampal atrophy, most pronounced on the left-hand side, and concentrated in anterior relative to posterior subregions. Finally, we found preliminary evidence that disease-specific vulnerability along the anterior-posterior axis of the hippocampus was associated with canonical clinical features of these syndromes.
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Affiliation(s)
- Fang Lan
- The University of Sydney, Brain and Mind Centre, Sydney, New South Wales, Australia; The University of Sydney, School of Psychology, Sydney, New South Wales, Australia
| | - Daniel Roquet
- The University of Sydney, Brain and Mind Centre, Sydney, New South Wales, Australia; The University of Sydney, School of Psychology, Sydney, New South Wales, Australia
| | - Marshall A Dalton
- The University of Sydney, Brain and Mind Centre, Sydney, New South Wales, Australia; The University of Sydney, School of Psychology, Sydney, New South Wales, Australia
| | - Hashim El-Omar
- The University of Sydney, Brain and Mind Centre, Sydney, New South Wales, Australia
| | - Rebekah M Ahmed
- The University of Sydney, Brain and Mind Centre, Sydney, New South Wales, Australia; Memory and Cognition Clinic, Department of Clinical Neurosciences, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
| | - Olivier Piguet
- The University of Sydney, Brain and Mind Centre, Sydney, New South Wales, Australia; The University of Sydney, School of Psychology, Sydney, New South Wales, Australia
| | - Muireann Irish
- The University of Sydney, Brain and Mind Centre, Sydney, New South Wales, Australia; The University of Sydney, School of Psychology, Sydney, New South Wales, Australia.
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6
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Jauny G, Mijalkov M, Canal-Garcia A, Volpe G, Pereira J, Eustache F, Hinault T. Linking structural and functional changes during aging using multilayer brain network analysis. Commun Biol 2024; 7:239. [PMID: 38418523 PMCID: PMC10902297 DOI: 10.1038/s42003-024-05927-x] [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: 08/05/2023] [Accepted: 02/16/2024] [Indexed: 03/01/2024] Open
Abstract
Brain structure and function are intimately linked, however this association remains poorly understood and the complexity of this relationship has remained understudied. Healthy aging is characterised by heterogenous levels of structural integrity changes that influence functional network dynamics. Here, we use the multilayer brain network analysis on structural (diffusion weighted imaging) and functional (magnetoencephalography) data from the Cam-CAN database. We found that the level of similarity of connectivity patterns between brain structure and function in the parietal and temporal regions (alpha frequency band) is associated with cognitive performance in healthy older individuals. These results highlight the impact of structural connectivity changes on the reorganisation of functional connectivity associated with the preservation of cognitive function, and provide a mechanistic understanding of the concepts of brain maintenance and compensation with aging. Investigation of the link between structure and function could thus represent a new marker of individual variability, and of pathological changes.
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Affiliation(s)
- Gwendolyn Jauny
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, Inserm, U1077, CHU de Caen, Centre Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Mite Mijalkov
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Anna Canal-Garcia
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Giovanni Volpe
- Department of Physics, Goteborg University, Goteborg, Sweden
| | - Joana Pereira
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Francis Eustache
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, Inserm, U1077, CHU de Caen, Centre Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Thomas Hinault
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, Inserm, U1077, CHU de Caen, Centre Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France.
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7
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Yu M, Risacher SL, Nho KT, Wen Q, Oblak AL, Unverzagt FW, Apostolova LG, Farlow MR, Brosch JR, Clark DG, Wang S, Deardorff R, Wu YC, Gao S, Sporns O, Saykin AJ. Spatial transcriptomic patterns underlying amyloid-β and tau pathology are associated with cognitive dysfunction in Alzheimer's disease. Cell Rep 2024; 43:113691. [PMID: 38244198 PMCID: PMC10926093 DOI: 10.1016/j.celrep.2024.113691] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 11/29/2023] [Accepted: 01/03/2024] [Indexed: 01/22/2024] Open
Abstract
Amyloid-β (Aβ) and tau proteins accumulate within distinct neuronal systems in Alzheimer's disease (AD). Although it is not clear why certain brain regions are more vulnerable to Aβ and tau pathologies than others, gene expression may play a role. We study the association between brain-wide gene expression profiles and regional vulnerability to Aβ (gene-to-Aβ associations) and tau (gene-to-tau associations) pathologies by leveraging two large independent AD cohorts. We identify AD susceptibility genes and gene modules in a gene co-expression network with expression profiles specifically related to regional vulnerability to Aβ and tau pathologies in AD. In addition, we identify distinct biochemical pathways associated with the gene-to-Aβ and the gene-to-tau associations. These findings may explain the discordance between regional Aβ and tau pathologies. Finally, we propose an analytic framework, linking the identified gene-to-pathology associations to cognitive dysfunction in AD at the individual level, suggesting potential clinical implication of the gene-to-pathology associations.
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Affiliation(s)
- Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA.
| | - Shannon L Risacher
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kwangsik T Nho
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA
| | - Qiuting Wen
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Adrian L Oblak
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Frederick W Unverzagt
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Liana G Apostolova
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Martin R Farlow
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jared R Brosch
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - David G Clark
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sophia Wang
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Rachael Deardorff
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Yu-Chien Wu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sujuan Gao
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Olaf Sporns
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Andrew J Saykin
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA.
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8
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Yan S, Hu Y, Zhang R, Qi D, Hu Y, Yao D, Shi L, Zhang L. Multilayer network-based channel selection for motor imagery brain-computer interface. J Neural Eng 2024; 21:016029. [PMID: 38295419 DOI: 10.1088/1741-2552/ad2496] [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: 08/17/2023] [Accepted: 01/31/2024] [Indexed: 02/02/2024]
Abstract
Objective. The number of electrode channels in a motor imagery-based brain-computer interface (MI-BCI) system influences not only its decoding performance, but also its convenience for use in applications. Although many channel selection methods have been proposed in the literature, they are usually based on the univariate features of a single channel. This leads to a loss of the interaction between channels and the exchange of information between networks operating at different frequency bands.Approach. We integrate brain networks containing four frequency bands into a multilayer network framework and propose a multilayer network-based channel selection (MNCS) method for MI-BCI systems. A graph learning-based method is used to estimate the multilayer network from electroencephalogram (EEG) data that are filtered by multiple frequency bands. The multilayer participation coefficient of the multilayer network is then computed to select EEG channels that do not contain redundant information. Furthermore, the common spatial pattern (CSP) method is used to extract effective features. Finally, a support vector machine classifier with a linear kernel is trained to accurately identify MI tasks.Main results. We used three publicly available datasets from the BCI Competition containing data on 12 healthy subjects and one dataset containing data on 15 stroke patients to validate the effectiveness of our proposed method. The results showed that the proposed MNCS method outperforms all channels (85.8% vs. 93.1%, 84.4% vs. 89.0%, 71.7% vs. 79.4%, and 72.7% vs. 84.0%). Moreover, it achieved significantly higher decoding accuracies on MI-BCI systems than state-of-the-art methods (pairedt-tests,p< 0.05).Significance. The experimental results showed that the proposed MNCS method can select appropriate channels to improve the decoding performance as well as the convenience of the application of MI-BCI systems.
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Affiliation(s)
- Shaoting Yan
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, People's Republic of China
| | - Yuxia Hu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, People's Republic of China
| | - Rui Zhang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, People's Republic of China
| | - Daowei Qi
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
| | - Yubo Hu
- The No.3 Provincial People's Hospital of Henan Province, Zhengzhou, People's Republic of China
| | - Dezhong Yao
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
| | - Li Shi
- Department of Automation, Tsinghua University, Beijing, People's Republic of China
- Beijing National Research Center for Information Science and Technology, Beijing, People's Republic of China
| | - Lipeng Zhang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, People's Republic of China
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9
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Al‐Sa'd M, Vanhatalo S, Tokariev A. Multiplex dynamic networks in the newborn brain disclose latent links with neurobehavioral phenotypes. Hum Brain Mapp 2024; 45:e26610. [PMID: 38339895 PMCID: PMC10839739 DOI: 10.1002/hbm.26610] [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: 07/21/2023] [Revised: 01/08/2024] [Accepted: 01/16/2024] [Indexed: 02/12/2024] Open
Abstract
The higher brain functions arise from coordinated neural activity between distinct brain regions, but the spatial, temporal, and spectral complexity of these functional connectivity networks (FCNs) has challenged the identification of correlates with neurobehavioral phenotypes. Characterizing behavioral correlates of early life FCNs is important to understand the activity dependent emergence of neurodevelopmental performance and for improving health outcomes. Here, we develop an analysis pipeline for identifying multiplex dynamic FCNs that combine spectral and spatiotemporal characteristics of the newborn cortical activity. This data-driven approach automatically uncovers latent networks that show robust neurobehavioral correlations and consistent effects by in utero drug exposure. Altogether, the proposed pipeline provides a robust end-to-end solution for an objective assessment and quantitation of neurobehaviorally meaningful network constellations in the highly dynamic cortical functions.
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Affiliation(s)
- Mohammad Al‐Sa'd
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's Hospital, HUS imaging, HUS Diagnostic CenterUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland
- Department of PhysiologyUniversity of HelsinkiHelsinkiFinland
- Faculty of Information Technology and Communication SciencesTampere UniversityTampereFinland
| | - Sampsa Vanhatalo
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's Hospital, HUS imaging, HUS Diagnostic CenterUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland
- Department of PhysiologyUniversity of HelsinkiHelsinkiFinland
| | - Anton Tokariev
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's Hospital, HUS imaging, HUS Diagnostic CenterUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland
- Department of PhysiologyUniversity of HelsinkiHelsinkiFinland
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Williamson J, James SA, Mukli P, Yabluchanskiy A, Wu DH, Sonntag W, Yang Y. Sex difference in brain functional connectivity of hippocampus in Alzheimer's disease. GeroScience 2024; 46:563-572. [PMID: 37743414 PMCID: PMC10828268 DOI: 10.1007/s11357-023-00943-x] [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: 05/05/2023] [Accepted: 09/11/2023] [Indexed: 09/26/2023] Open
Abstract
Alzheimer's disease (AD), affecting nearly 6.5 million people, is the fifth leading cause of death in individuals 65 years or older in the USA. Prior research has shown that AD disproportionality affects females; females have a greater incidence rate, perform worse on a variety of neuropsychological tasks, and have greater total brain atrophy. Recent research has linked these sex differences to neuroimaging markers of brain pathology, such as hippocampal volumes. Specifically, research from our lab found that functional connectivity from the hippocampus to the precuneus cortex and brain stem was significantly stronger in males than in females with mild cognitive impairment. The aim of this study was to extend our understanding to individuals with AD and to determine if these potential sex-specific functional connectivity biomarkers extend through different disease stages. The resting state fMRI and T2 MRI of cognitively normal individuals (n = 32, female = 16) and individuals with AD (n = 32, female = 16) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were analyzed using the Functional Connectivity Toolbox (CONN). Our results demonstrate that males had a significantly stronger interhemispheric functional connectivity between the left and right hippocampus compared to females. These results improve our current understanding of the role of the hippocampus in sex differences in AD. Understanding the contribution of impaired functional connectivity sex differences may aid in the development of sex-specific precision medicine for improved AD treatment.
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Affiliation(s)
- Jordan Williamson
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Shirley A James
- Department of Public Health, Health Science Center, University of Oklahoma, Oklahoma City, OK, USA
| | - Peter Mukli
- Vascular Cognitive Impairment and Neurodegeneration Program, Oklahoma Center for Geroscience and Healthy Brain Aging, Department of Neurosurgery, Health Sciences Center, University of Oklahoma, Oklahoma City, OK, USA
| | - Andriy Yabluchanskiy
- Vascular Cognitive Impairment and Neurodegeneration Program, Oklahoma Center for Geroscience and Healthy Brain Aging, Department of Neurosurgery, Health Sciences Center, University of Oklahoma, Oklahoma City, OK, USA
| | - Dee H Wu
- Department of Radiological Science and Medical Physics, Health Science Center, University of Oklahoma, Oklahoma City, OK, USA
- Data Institute for Societal Challenges, University of Oklahoma, Norman, OK, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - William Sonntag
- Vascular Cognitive Impairment and Neurodegeneration Program, Oklahoma Center for Geroscience and Healthy Brain Aging, Department of Neurosurgery, Health Sciences Center, University of Oklahoma, Oklahoma City, OK, USA
| | - Yuan Yang
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Department of Rehabilitation Sciences, Health Science Center, University of Oklahoma, Oklahoma City, OK, USA.
- Data Institute for Societal Challenges, University of Oklahoma, Norman, OK, USA.
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- SFCRI Clinical Imaging Research Center, Carle Foundation Hospital, Urbana, IL, USA.
- Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL, USA.
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11
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Stam CJ, de Haan W. Network Hyperexcitability in Early-Stage Alzheimer's Disease: Evaluation of Functional Connectivity Biomarkers in a Computational Disease Model. J Alzheimers Dis 2024; 99:1333-1348. [PMID: 38759000 PMCID: PMC11191539 DOI: 10.3233/jad-230825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/18/2024] [Indexed: 05/19/2024]
Abstract
Background There is increasing evidence from animal and clinical studies that network hyperexcitability (NH) may be an important pathophysiological process and potential target for treatment in early Alzheimer's disease (AD). Measures of functional connectivity (FC) have been proposed as promising biomarkers for NH, but it is unknown which measure has the highest sensitivity for early-stage changes in the excitation/inhibition balance. Objective We aim to test the performance of different FC measures in detecting NH at the earliest stage using a computational approach. Methods We use a whole brain computational model of activity dependent degeneration to simulate progressive AD pathology and NH. We investigate if and at what stage four measures of FC (amplitude envelope correlation corrected [AECc], phase lag index [PLI], joint permutation entropy [JPE] and a new measure: phase lag time [PLT]) can detect early-stage AD pathophysiology. Results The activity dependent degeneration model replicates spectral changes in line with clinical data and demonstrates increasing NH. Compared to relative theta power as a gold standard the AECc and PLI are shown to be less sensitive in detecting early-stage NH and AD-related neurophysiological abnormalities, while the JPE and the PLT show more sensitivity with excellent test characteristics. Conclusions Novel FC measures, which are better in detecting rapid fluctuations in neural activity and connectivity, may be superior to well-known measures such as the AECc and PLI in detecting early phase neurophysiological abnormalities and in particular NH in AD. These markers could improve early diagnosis and treatment target identification.
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Affiliation(s)
- Cornelis Jan Stam
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center (Amsterdam UMC), Amsterdam, The Netherlands
| | - Willem de Haan
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center (Amsterdam UMC), Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam University Medical Center (Amsterdam UMC), Amsterdam, The Netherlands
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12
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Tu JC, Millar PR, Strain JF, Eck A, Adeyemo B, Daniels A, Karch C, Huey ED, McDade E, Day GS, Yakushev I, Hassenstab J, Morris J, Llibre-Guerra JJ, Ibanez L, Jucker M, Mendez PC, Bateman RJ, Perrin RJ, Benzinger T, Jack CR, Betzel R, Ances BM, Eggebrecht AT, Gordon BA, Wheelock MD. Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.29.564633. [PMID: 37961586 PMCID: PMC10634945 DOI: 10.1101/2023.10.29.564633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer Disease (AD). Given their essential role in neural communication, disruptions to these hubs have profound implications for overall brain network integrity and functionality. Hub disruption, or targeted impairment of functional connectivity at the hubs, is recognized in AD patients. Computational models paired with evidence from animal experiments hint at a mechanistic explanation, suggesting that these hubs may be preferentially targeted in neurodegeneration, due to their high neuronal activity levels-a phenomenon termed "activity-dependent degeneration". Yet, two critical issues were unresolved. First, past research hasn't definitively shown whether hub regions face a higher likelihood of impairment (targeted attack) compared to other regions or if impairment likelihood is uniformly distributed (random attack). Second, human studies offering support for activity-dependent explanations remain scarce. We applied a refined hub disruption index to determine the presence of targeted attacks in AD. Furthermore, we explored potential evidence for activity-dependent degeneration by evaluating if hub vulnerability is better explained by global connectivity or connectivity variations across functional systems, as well as comparing its timing relative to amyloid beta deposition in the brain. Our unique cohort of participants with autosomal dominant Alzheimer Disease (ADAD) allowed us to probe into the preclinical stages of AD to determine the hub disruption timeline in relation to expected symptom emergence. Our findings reveal a hub disruption pattern in ADAD aligned with targeted attacks, detectable even in pre-clinical stages. Notably, the disruption's severity amplified alongside symptomatic progression. Moreover, since excessive local neuronal activity has been shown to increase amyloid deposition and high connectivity regions show high level of neuronal activity, our observation that hub disruption was primarily tied to regional differences in global connectivity and sequentially followed changes observed in Aβ PET cortical markers is consistent with the activity-dependent degeneration model. Intriguingly, these disruptions were discernible 8 years before the expected age of symptom onset. Taken together, our findings not only align with the targeted attack on hubs model but also suggest that activity-dependent degeneration might be the cause of hub vulnerability. This deepened understanding could be instrumental in refining diagnostic techniques and developing targeted therapeutic strategies for AD in the future.
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Affiliation(s)
- Jiaxin Cindy Tu
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Peter R Millar
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Jeremy F Strain
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Andrew Eck
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Babatunde Adeyemo
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Alisha Daniels
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Celeste Karch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Edward D Huey
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, 02912
| | - Eric McDade
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Gregory S Day
- Department of Neurology, Mayo Clinic College of Medicine, Jacksonville, FL, USA, 32224
| | - Igor Yakushev
- Department of Nuclear Medicine, Technical University of Munich, Munich, Germany, 81675
| | - Jason Hassenstab
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - John Morris
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Jorge J Llibre-Guerra
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Laura Ibanez
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA, 63108
- NeuroGenomics and Informatics Center, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Mathias Jucker
- Department of Cellular Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany, 72076
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany, 72076
| | | | - Randell J Bateman
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Richard J Perrin
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Tammie Benzinger
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, USA 55905
| | - Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN USA, 47405
| | - Beau M Ances
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Adam T Eggebrecht
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Brian A Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Muriah D Wheelock
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA, 63108
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13
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Scheijbeler EP, de Haan W, Stam CJ, Twisk JWR, Gouw AA. Longitudinal resting-state EEG in amyloid-positive patients along the Alzheimer's disease continuum: considerations for clinical trials. Alzheimers Res Ther 2023; 15:182. [PMID: 37858173 PMCID: PMC10585755 DOI: 10.1186/s13195-023-01327-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 10/06/2023] [Indexed: 10/21/2023]
Abstract
BACKGROUND To enable successful inclusion of electroencephalography (EEG) outcome measures in Alzheimer's disease (AD) clinical trials, we retrospectively mapped the progression of resting-state EEG measures over time in amyloid-positive patients with mild cognitive impairment (MCI) or dementia due to AD. METHODS Resting-state 21-channel EEG was recorded in 148 amyloid-positive AD patients (MCI, n = 88; dementia due to AD, n = 60). Two or more EEG recordings were available for all subjects. We computed whole-brain and regional relative power (i.e., theta (4-8 Hz), alpha1 (8-10 Hz), alpha2 (10-13 Hz), beta (13-30 Hz)), peak frequency, signal variability (i.e., theta permutation entropy), and functional connectivity values (i.e., alpha and beta corrected amplitude envelope correlation, theta phase lag index, weighted symbolic mutual information, inverted joint permutation entropy). Whole-group linear mixed effects models were used to model the development of EEG measures over time. Group-wise analysis was performed to investigate potential differences in change trajectories between the MCI and dementia subgroups. Finally, we estimated the minimum sample size required to detect different treatment effects (i.e., 50% less deterioration, stabilization, or 50% improvement) on the development of EEG measures over time, in hypothetical clinical trials of 1- or 2-year duration. RESULTS Whole-group analysis revealed significant regional and global oscillatory slowing over time (i.e., increased relative theta power, decreased beta power), with strongest effects for temporal and parieto-occipital regions. Disease severity at baseline influenced the EEG measures' rates of change, with fastest deterioration reported in MCI patients. Only AD dementia patients displayed a significant decrease of the parieto-occipital peak frequency and theta signal variability over time. We estimate that 2-year trials, focusing on amyloid-positive MCI patients, require 36 subjects per arm (2 arms, 1:1 randomization, 80% power) to detect a stabilizing treatment effect on temporal relative theta power. CONCLUSIONS Resting-state EEG measures could facilitate early detection of treatment effects on neuronal function in AD patients. Their sensitivity depends on the region-of-interest and disease severity of the study population. Conventional spectral measures, particularly recorded from temporal regions, present sensitive AD treatment monitoring markers.
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Affiliation(s)
- Elliz P Scheijbeler
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands.
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands.
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands.
| | - Willem de Haan
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
| | - Cornelis J Stam
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
| | - Jos W R Twisk
- Department of Epidemiology and Biostatistics, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
| | - Alida A Gouw
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
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14
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Zuo Q, Zhong N, Pan Y, Wu H, Lei B, Wang S. Brain Structure-Function Fusing Representation Learning Using Adversarial Decomposed-VAE for Analyzing MCI. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4017-4028. [PMID: 37815971 DOI: 10.1109/tnsre.2023.3323432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/12/2023]
Abstract
Integrating the brain structural and functional connectivity features is of great significance in both exploring brain science and analyzing cognitive impairment clinically. However, it remains a challenge to effectively fuse structural and functional features in exploring the complex brain network. In this paper, a novel brain structure-function fusing-representation learning (BSFL) model is proposed to effectively learn fused representation from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) for mild cognitive impairment (MCI) analysis. Specifically, the decomposition-fusion framework is developed to first decompose the feature space into the union of the uniform and unique spaces for each modality, and then adaptively fuse the decomposed features to learn MCI-related representation. Moreover, a knowledge-aware transformer module is designed to automatically capture local and global connectivity features throughout the brain. Also, a uniform-unique contrastive loss is further devised to make the decomposition more effective and enhance the complementarity of structural and functional features. The extensive experiments demonstrate that the proposed model achieves better performance than other competitive methods in predicting and analyzing MCI. More importantly, the proposed model could be a potential tool for reconstructing unified brain networks and predicting abnormal connections during the degenerative processes in MCI.
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15
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Bitra VR, Challa SR, Adiukwu PC, Rapaka D. Tau trajectory in Alzheimer's disease: Evidence from the connectome-based computational models. Brain Res Bull 2023; 203:110777. [PMID: 37813312 DOI: 10.1016/j.brainresbull.2023.110777] [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: 05/23/2023] [Revised: 07/08/2023] [Accepted: 10/06/2023] [Indexed: 10/11/2023]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an impairment of cognition and memory. Current research on connectomics have now related changes in the network organization in AD to the patterns of accumulation and spread of amyloid and tau, providing insights into the neurobiological mechanisms of the disease. In addition, network analysis and modeling focus on particular use of graphs to provide intuition into key organizational principles of brain structure, that stipulate how neural activity propagates along structural connections. The utility of connectome-based computational models aids in early predicting, tracking the progression of biomarker-directed AD neuropathology. In this article, we present a short review of tau trajectory, the connectome changes in tau pathology, and the dependent recent connectome-based computational modelling approaches for tau spreading, reproducing pragmatic findings, and developing significant novel tau targeted therapies.
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Affiliation(s)
- Veera Raghavulu Bitra
- School of Pharmacy, Faculty of Health Sciences, University of Botswana, P/Bag-0022, Gaborone, Botswana.
| | - Siva Reddy Challa
- Department of Cancer Biology and Pharmacology, University of Illinois College of Medicine, Peoria, IL 61614, USA; KVSR Siddartha College of Pharmaceutical Sciences, Vijayawada, Andhra Pradesh, India
| | - Paul C Adiukwu
- School of Pharmacy, Faculty of Health Sciences, University of Botswana, P/Bag-0022, Gaborone, Botswana
| | - Deepthi Rapaka
- Pharmacology Division, D.D.T. College of Medicine, Gaborone, Botswana.
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16
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Babiloni C, Lopez S, Noce G, Ferri R, Panerai S, Catania V, Soricelli A, Salvatore M, Nobili F, Arnaldi D, Famà F, Massa F, Buttinelli C, Giubilei F, Stocchi F, Vacca L, Marizzoni M, D'Antonio F, Bruno G, De Lena C, Güntekin B, Yıldırım E, Hanoğlu L, Yener G, Yerlikaya D, Taylor JP, Schumacher J, McKeith I, Bonanni L, Pantano P, Piervincenzi C, Petsas N, Frisoni GB, Del Percio C, Carducci F. Relationship between default mode network and resting-state electroencephalographic alpha rhythms in cognitively unimpaired seniors and patients with dementia due to Alzheimer's disease. Cereb Cortex 2023; 33:10514-10527. [PMID: 37615301 PMCID: PMC10588004 DOI: 10.1093/cercor/bhad300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 07/26/2023] [Accepted: 07/27/2023] [Indexed: 08/25/2023] Open
Abstract
Here we tested the hypothesis of a relationship between the cortical default mode network (DMN) structural integrity and the resting-state electroencephalographic (rsEEG) rhythms in patients with Alzheimer's disease with dementia (ADD). Clinical and instrumental datasets in 45 ADD patients and 40 normal elderly (Nold) persons originated from the PDWAVES Consortium (www.pdwaves.eu). Individual rsEEG delta, theta, alpha, and fixed beta and gamma bands were considered. Freeware platforms served to derive (1) the (gray matter) volume of the DMN, dorsal attention (DAN), and sensorimotor (SMN) cortical networks and (2) the rsEEG cortical eLORETA source activities. We found a significant positive association between the DMN gray matter volume, the rsEEG alpha source activity estimated in the posterior DMN nodes (parietal and posterior cingulate cortex), and the global cognitive status in the Nold and ADD participants. Compared with the Nold, the ADD group showed lower DMN gray matter, lower rsEEG alpha source activity in those nodes, and lower global cognitive status. This effect was not observed in the DAN and SMN. These results suggest that the DMN structural integrity and the rsEEG alpha source activities in the DMN posterior hubs may be related and predict the global cognitive status in ADD and Nold persons.
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Affiliation(s)
- Claudio Babiloni
- Department of Physiology and Pharmacology “Vittorio Erspamer,” Sapienza University of Rome, Rome, Italy
- Hospital San Raffaele Cassino, Cassino (FR), Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology “Vittorio Erspamer,” Sapienza University of Rome, Rome, Italy
| | | | | | | | | | - Andrea Soricelli
- IRCCS Synlab SDN, Naples, Italy
- Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | | | - Flavio Nobili
- Clinica neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Italy
| | - Dario Arnaldi
- Clinica neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Italy
| | - Francesco Famà
- Clinica neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Federico Massa
- Clinica neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Carla Buttinelli
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | | | | | - Moira Marizzoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Fabrizia D'Antonio
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Giuseppe Bruno
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Carlo De Lena
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Bahar Güntekin
- Department of Biophysics, International School of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Ebru Yıldırım
- Program of Electroneurophysiology, Vocational School, Istanbul Medipol University, Istanbul, Turkey
| | - Lutfu Hanoğlu
- Department of Neurology, School of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Görsev Yener
- Izmir School of Economics, Faculty of Medicine, Izmir, Turkey
| | - Deniz Yerlikaya
- Health Sciences Institute, Department of Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | - John Paul Taylor
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, United Kingdom
| | - Julia Schumacher
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, United Kingdom
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Ian McKeith
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, United Kingdom
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, University “G. d'Annunzio” of Chieti-Pescara, Chieti, Italy
| | - Patrizia Pantano
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- IRCCS Neuromed, Pozzilli (IS), Italy
| | | | - Nikolaos Petsas
- Scuola di Specializzazione in Statistica Medica e Biometria, Dipartimento di Sanità Pubblica e Malattie Infettive, Sapienza University of Rome, Rome, Italy
| | - Giovanni B Frisoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Claudio Del Percio
- Department of Physiology and Pharmacology “Vittorio Erspamer,” Sapienza University of Rome, Rome, Italy
| | - Filippo Carducci
- Department of Physiology and Pharmacology “Vittorio Erspamer,” Sapienza University of Rome, Rome, Italy
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17
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Zeng T, Li J, Xie L, Dong Z, Chen Q, Huang S, Xie S, Lai Y, Li J, Yan W, Wang Y, Xie Z, Hu C, Zhang J, Kuang S, Song Y, Gao L, Lv Z. Nrf2 regulates iron-dependent hippocampal synapses and functional connectivity damage in depression. J Neuroinflammation 2023; 20:212. [PMID: 37735410 PMCID: PMC10512501 DOI: 10.1186/s12974-023-02875-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 08/16/2023] [Indexed: 09/23/2023] Open
Abstract
Neuronal iron overload contributes to synaptic damage and neuropsychiatric disorders. However, the molecular mechanisms underlying iron deposition in depression remain largely unexplored. Our study aims to investigate how nuclear factor-erythroid 2 (NF-E2)-related factor 2 (Nrf2) ameliorates hippocampal synaptic dysfunction and reduces brain functional connectivity (FC) associated with excessive iron in depression. We treated mice with chronic unpredictable mild stress (CUMS) with the iron chelator deferoxamine mesylate (DFOM) and a high-iron diet (2.5% carbonyl iron) to examine the role of iron overload in synaptic plasticity. The involvement of Nrf2 in iron metabolism and brain function was assessed using molecular biological techniques and in vivo resting-state functional magnetic resonance imaging (rs-fMRI) through genetic deletion or pharmacologic activation of Nrf2. The results demonstrated a significant correlation between elevated serum iron levels and impaired hippocampal functional connectivity (FC), which contributed to the development of depression-induced CUMS. Iron overload plays a crucial role in CUMS-induced depression and synaptic dysfunction, as evidenced by the therapeutic effects of a high-iron diet and DFOM. The observed iron overload in this study was associated with decreased Nrf2 levels and increased expression of transferrin receptors (TfR). Notably, inhibition of iron accumulation effectively attenuated CUMS-induced synaptic damage mediated by downregulation of brain-derived neurotrophic factor (BDNF). Nrf2-/- mice exhibited compromised FC within the limbic system and the basal ganglia, particularly in the hippocampus, and inhibition of iron accumulation effectively attenuated CUMS-induced synaptic damage mediated by downregulation of brain-derived neurotrophic factor (BDNF). Activation of Nrf2 restored iron homeostasis and reversed vulnerability to depression. Mechanistically, we further identified that Nrf2 deletion promoted iron overload via upregulation of TfR and downregulation of ferritin light chain (FtL), leading to BDNF-mediated synapse damage in the hippocampus. Therefore, our findings unveil a novel role for Nrf2 in regulating iron homeostasis while providing mechanistic insights into poststress susceptibility to depression. Targeting Nrf2-mediated iron metabolism may offer promising strategies for developing more effective antidepressant therapies.
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Affiliation(s)
- Ting Zeng
- School of Traditional Chinese Medicine, Southern Medical University (SMU), Sha Tai Nan Road No. 1063, Guangzhou, 510515, Guangdong, China
- Department of Brain Diseases, The Affiliated TCM Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Junjie Li
- School of Traditional Chinese Medicine, Southern Medical University (SMU), Sha Tai Nan Road No. 1063, Guangzhou, 510515, Guangdong, China
| | - Lingpeng Xie
- Department of Hepatology, Cancer Center, Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, Guangzhou, 510315, China
| | - Zhaoyang Dong
- School of Nursing, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Qing Chen
- School of Traditional Chinese Medicine, Southern Medical University (SMU), Sha Tai Nan Road No. 1063, Guangzhou, 510515, Guangdong, China
| | - Sha Huang
- School of Traditional Chinese Medicine, Southern Medical University (SMU), Sha Tai Nan Road No. 1063, Guangzhou, 510515, Guangdong, China
| | - Shuwen Xie
- School of Traditional Chinese Medicine, Southern Medical University (SMU), Sha Tai Nan Road No. 1063, Guangzhou, 510515, Guangdong, China
| | - Yuqi Lai
- School of Traditional Chinese Medicine, Southern Medical University (SMU), Sha Tai Nan Road No. 1063, Guangzhou, 510515, Guangdong, China
| | - Jun Li
- School of Traditional Chinese Medicine, Southern Medical University (SMU), Sha Tai Nan Road No. 1063, Guangzhou, 510515, Guangdong, China
| | - Weixin Yan
- The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - YuHua Wang
- School of Traditional Chinese Medicine, Southern Medical University (SMU), Sha Tai Nan Road No. 1063, Guangzhou, 510515, Guangdong, China
| | - Zeping Xie
- School of Traditional Chinese Medicine, Southern Medical University (SMU), Sha Tai Nan Road No. 1063, Guangzhou, 510515, Guangdong, China
| | - Changlei Hu
- School of Traditional Chinese Medicine, Southern Medical University (SMU), Sha Tai Nan Road No. 1063, Guangzhou, 510515, Guangdong, China
| | - Jiayi Zhang
- School of Traditional Chinese Medicine, Southern Medical University (SMU), Sha Tai Nan Road No. 1063, Guangzhou, 510515, Guangdong, China
| | - Shanshan Kuang
- School of Traditional Chinese Medicine, Southern Medical University (SMU), Sha Tai Nan Road No. 1063, Guangzhou, 510515, Guangdong, China
| | - Yuhong Song
- Department of Traditional Chinese Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, Guangdong, China.
| | - Lei Gao
- School of Traditional Chinese Medicine, Southern Medical University (SMU), Sha Tai Nan Road No. 1063, Guangzhou, 510515, Guangdong, China.
| | - Zhiping Lv
- School of Traditional Chinese Medicine, Southern Medical University (SMU), Sha Tai Nan Road No. 1063, Guangzhou, 510515, Guangdong, China.
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18
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Chai Y, Sheline YI, Oathes DJ, Balderston NL, Rao H, Yu M. Functional connectomics in depression: insights into therapies. Trends Cogn Sci 2023; 27:814-832. [PMID: 37286432 PMCID: PMC10476530 DOI: 10.1016/j.tics.2023.05.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 05/09/2023] [Accepted: 05/09/2023] [Indexed: 06/09/2023]
Abstract
Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes.
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Affiliation(s)
- Ya Chai
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yvette I Sheline
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Desmond J Oathes
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn Brain Science, Translation, Innovation and Modulation Center (brainSTIM), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Nicholas L Balderston
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hengyi Rao
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA.
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19
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Amoroso N, Quarto S, La Rocca M, Tangaro S, Monaco A, Bellotti R. An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease. Front Aging Neurosci 2023; 15:1238065. [PMID: 37719873 PMCID: PMC10501457 DOI: 10.3389/fnagi.2023.1238065] [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: 06/10/2023] [Accepted: 08/08/2023] [Indexed: 09/19/2023] Open
Abstract
The advent of eXplainable Artificial Intelligence (XAI) has revolutionized the way human experts, especially from non-computational domains, approach artificial intelligence; this is particularly true for clinical applications where the transparency of the results is often compromised by the algorithmic complexity. Here, we investigate how Alzheimer's disease (AD) affects brain connectivity within a cohort of 432 subjects whose T1 brain Magnetic Resonance Imaging data (MRI) were acquired within the Alzheimer's Disease Neuroimaging Initiative (ADNI). In particular, the cohort included 92 patients with AD, 126 normal controls (NC) and 214 subjects with mild cognitive impairment (MCI). We show how graph theory-based models can accurately distinguish these clinical conditions and how Shapley values, borrowed from game theory, can be adopted to make these models intelligible and easy to interpret. Explainability analyses outline the role played by regions like putamen, middle and superior temporal gyrus; from a class-related perspective, it is possible to outline specific regions, such as hippocampus and amygdala for AD and posterior cingulate and precuneus for MCI. The approach is general and could be adopted to outline how brain connectivity affects specific brain regions.
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Affiliation(s)
- Nicola Amoroso
- Dipartimento di Farmacia-Scienze del Farmaco, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Silvano Quarto
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Marianna La Rocca
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
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20
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van Heusden FC, van Nifterick AM, Souza BC, França ASC, Nauta IM, Stam CJ, Scheltens P, Smit AB, Gouw AA, van Kesteren RE. Neurophysiological alterations in mice and humans carrying mutations in APP and PSEN1 genes. Alzheimers Res Ther 2023; 15:142. [PMID: 37608393 PMCID: PMC10464047 DOI: 10.1186/s13195-023-01287-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 08/11/2023] [Indexed: 08/24/2023]
Abstract
BACKGROUND Studies in animal models of Alzheimer's disease (AD) have provided valuable insights into the molecular and cellular processes underlying neuronal network dysfunction. Whether and how AD-related neurophysiological alterations translate between mice and humans remains however uncertain. METHODS We characterized neurophysiological alterations in mice and humans carrying AD mutations in the APP and/or PSEN1 genes, focusing on early pre-symptomatic changes. Longitudinal local field potential recordings were performed in APP/PS1 mice and cross-sectional magnetoencephalography recordings in human APP and/or PSEN1 mutation carriers. All recordings were acquired in the left frontal cortex, parietal cortex, and hippocampus. Spectral power and functional connectivity were analyzed and compared with wildtype control mice and healthy age-matched human subjects. RESULTS APP/PS1 mice showed increased absolute power, especially at higher frequencies (beta and gamma) and predominantly between 3 and 6 moa. Relative power showed an overall shift from lower to higher frequencies over almost the entire recording period and across all three brain regions. Human mutation carriers, on the other hand, did not show changes in power except for an increase in relative theta power in the hippocampus. Mouse parietal cortex and hippocampal power spectra showed a characteristic peak at around 8 Hz which was not significantly altered in transgenic mice. Human power spectra showed a characteristic peak at around 9 Hz, the frequency of which was significantly reduced in mutation carriers. Significant alterations in functional connectivity were detected in theta, alpha, beta, and gamma frequency bands, but the exact frequency range and direction of change differed for APP/PS1 mice and human mutation carriers. CONCLUSIONS Both mice and humans carrying APP and/or PSEN1 mutations show abnormal neurophysiological activity, but several measures do not translate one-to-one between species. Alterations in absolute and relative power in mice should be interpreted with care and may be due to overexpression of amyloid in combination with the absence of tau pathology and cholinergic degeneration. Future studies should explore whether changes in brain activity in other AD mouse models, for instance, those also including tau pathology, provide better translation to the human AD continuum.
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Affiliation(s)
- Fran C van Heusden
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, 1081HV, The Netherlands
| | - Anne M van Nifterick
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, 1081HV, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, 1081HV, The Netherlands
| | - Bryan C Souza
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, 6525AJ, The Netherlands
| | - Arthur S C França
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, 6525AJ, The Netherlands
- Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, 1105 BA, The Netherlands
| | - Ilse M Nauta
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, 1081HV, The Netherlands
| | - Cornelis J Stam
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, 1081HV, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, 1081HV, The Netherlands
| | - August B Smit
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, 1081HV, The Netherlands
| | - Alida A Gouw
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, 1081HV, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, 1081HV, The Netherlands
| | - Ronald E van Kesteren
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, 1081HV, The Netherlands.
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21
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Yu M, Risacher SL, Nho KT, Wen Q, Oblak AL, Unverzagt FW, Apostolova LG, Farlow MR, Brosch JR, Clark DG, Wang S, Deardorff R, Wu YC, Gao S, Sporns O, Saykin AJ. Spatial transcriptomic patterns underlying regional vulnerability to amyloid-β and tau pathologies and their relationships to cognitive dysfunction in Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.12.23294017. [PMID: 37645867 PMCID: PMC10462206 DOI: 10.1101/2023.08.12.23294017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Amyloid-β (Aβ) and tau proteins accumulate within distinct neuronal systems in Alzheimer's disease (AD). Although it is not clear why certain brain regions are more vulnerable to Aβ and tau pathologies than others, gene expression may play a role. We studied the association between brain-wide gene expression profiles and regional vulnerability to Aβ (gene-to-Aβ associations) and tau (gene-to-tau associations) pathologies leveraging two large independent cohorts (n = 715) of participants along the AD continuum. We identified several AD susceptibility genes and gene modules in a gene co-expression network with expression profiles related to regional vulnerability to Aβ and tau pathologies in AD. In particular, we found that the positive APOE -to-tau association was only seen in the AD cohort, whereas patients with AD and frontotemporal dementia shared similar positive MAPT -to-tau association. Some AD candidate genes showed sex-dependent negative gene-to-Aβ and gene-to-tau associations. In addition, we identified distinct biochemical pathways associated with the gene-to-Aβ and the gene-to-tau associations. Finally, we proposed a novel analytic framework, linking the identified gene-to-pathology associations to cognitive dysfunction in AD at the individual level, suggesting potential clinical implication of the gene-to-pathology associations. Taken together, our study identified distinct gene expression profiles and biochemical pathways that may explain the discordance between regional Aβ and tau pathologies, and filled the gap between gene-to-pathology associations and cognitive dysfunction in individual AD patients that may ultimately help identify novel personalized pathogenetic biomarkers and therapeutic targets. One Sentence Summary We identified replicable cognition-related associations between regional gene expression profiles and selectively regional vulnerability to amyloid-β and tau pathologies in AD.
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22
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van Lingen MR, Breedt LC, Geurts JJG, Hillebrand A, Klein M, Kouwenhoven MCM, Kulik SD, Reijneveld JC, Stam CJ, De Witt Hamer PC, Zimmermann MLM, Santos FAN, Douw L. The longitudinal relation between executive functioning and multilayer network topology in glioma patients. Brain Imaging Behav 2023; 17:425-435. [PMID: 37067658 PMCID: PMC10435610 DOI: 10.1007/s11682-023-00770-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/28/2023] [Indexed: 04/18/2023]
Abstract
Many patients with glioma, primary brain tumors, suffer from poorly understood executive functioning deficits before and/or after tumor resection. We aimed to test whether frontoparietal network centrality of multilayer networks, allowing for integration across multiple frequencies, relates to and predicts executive functioning in glioma. Patients with glioma (n = 37) underwent resting-state magnetoencephalography and neuropsychological tests assessing word fluency, inhibition, and set shifting before (T1) and one year after tumor resection (T2). We constructed binary multilayer networks comprising six layers, with each layer representing frequency-specific functional connectivity between source-localized time series of 78 cortical regions. Average frontoparietal network multilayer eigenvector centrality, a measure for network integration, was calculated at both time points. Regression analyses were used to investigate associations with executive functioning. At T1, lower multilayer integration (p = 0.017) and epilepsy (p = 0.006) associated with poorer set shifting (adj. R2 = 0.269). Decreasing multilayer integration (p = 0.022) and not undergoing chemotherapy at T2 (p = 0.004) related to deteriorating set shifting over time (adj. R2 = 0.283). No significant associations were found for word fluency or inhibition, nor did T1 multilayer integration predict changes in executive functioning. As expected, our results establish multilayer integration of the frontoparietal network as a cross-sectional and longitudinal correlate of executive functioning in glioma patients. However, multilayer integration did not predict postoperative changes in executive functioning, which together with the fact that this correlate is also found in health and other diseases, limits its specific clinical relevance in glioma.
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Affiliation(s)
- Marike R van Lingen
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands.
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands.
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands.
- Cancer Center Amsterdam, Amsterdam, the Netherlands.
| | - Lucas C Breedt
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
| | - Arjan Hillebrand
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Martin Klein
- Department of Medical Psychology, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Mathilde C M Kouwenhoven
- Department of Neurology, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Shanna D Kulik
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
| | - Jaap C Reijneveld
- Department of Neurology, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Cornelis J Stam
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Philip C De Witt Hamer
- Department of Neurosurgery, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Mona L M Zimmermann
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Fernando A N Santos
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
- Institute of Advanced Studies, University of Amsterdam, Amsterdam, the Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands.
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands.
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands.
- Cancer Center Amsterdam, Amsterdam, the Netherlands.
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23
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Klepl D, He F, Wu M, Blackburn DJ, Sarrigiannis PG. Cross-Frequency Multilayer Network Analysis with Bispectrum-based Functional Connectivity: A Study of Alzheimer's Disease. Neuroscience 2023; 521:77-88. [PMID: 37121381 DOI: 10.1016/j.neuroscience.2023.04.008] [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: 10/31/2022] [Revised: 02/08/2023] [Accepted: 04/04/2023] [Indexed: 05/02/2023]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder known to affect functional connectivity (FC) across many brain regions. Linear FC measures have been applied to study the differences in AD by splitting neurophysiological signals, such as electroencephalography (EEG) recordings, into discrete frequency bands and analysing them in isolation from each other. We address this limitation by quantifying cross-frequency FC in addition to the traditional within-band approach. Cross-bispectrum, a higher-order spectral analysis approach, is used to measure the nonlinear FC and is compared with the cross-spectrum, which only measures the linear FC within bands. This work reports the reconstruction of a cross-frequency FC network where each frequency band is treated as a layer in a multilayer network with both inter- and intra-layer edges. Cross-bispectrum detects cross-frequency differences, mainly increased FC in AD cases in δ-θ coupling. Overall, increased strength of low-frequency coupling and decreased level of high-frequency coupling is observed in AD cases in comparison to healthy controls (HC). We demonstrate that a graph-theoretic analysis of cross-frequency brain networks is crucial to obtain a more detailed insight into their structure and function. Vulnerability analysis reveals that the integration and segregation properties of networks are enabled by different frequency couplings in AD networks compared to HCs. Finally, we use the reconstructed networks for classification. The extra cross-frequency coupling information can improve the classification performance significantly, suggesting an important role of cross-frequency FC. The results highlight the importance of studying nonlinearity and including cross-frequency FC in characterising AD.
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Affiliation(s)
- Dominik Klepl
- Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry CV1 2JH, UK; Infocomm Research, A*STAR, Singapore
| | - Fei He
- Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry CV1 2JH, UK.
| | - Min Wu
- Infocomm Research, A*STAR, Singapore
| | - Daniel J Blackburn
- Department of Neuroscience, University of Sheffield, SheffieldS10 2HQ, UK
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24
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Karaaslanli A, Ortiz-Bouza M, Munia TTK, Aviyente S. Community detection in multi-frequency EEG networks. Sci Rep 2023; 13:8114. [PMID: 37208422 DOI: 10.1038/s41598-023-35232-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 05/15/2023] [Indexed: 05/21/2023] Open
Abstract
Functional connectivity networks of the human brain are commonly studied using tools from complex network theory. Existing methods focus on functional connectivity within a single frequency band. However, it is well-known that higher order brain functions rely on the integration of information across oscillations at different frequencies. Therefore, there is a need to study these cross-frequency interactions. In this paper, we use multilayer networks to model functional connectivity across multiple frequencies, where each layer corresponds to a different frequency band. We then introduce the multilayer modularity metric to develop a multilayer community detection algorithm. The proposed approach is applied to electroencephalogram (EEG) data collected during a study of error monitoring in the human brain. The differences between the community structures within and across different frequency bands for two response types, i.e. error and correct, are studied. The results indicate that following an error response, the brain organizes itself to form communities across frequencies, in particular between theta and gamma bands while a similar cross-frequency community formation is not observed following the correct response.
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Affiliation(s)
- Abdullah Karaaslanli
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, USA.
| | - Meiby Ortiz-Bouza
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Tamanna T K Munia
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Selin Aviyente
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, USA
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25
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van Nifterick AM, Mulder D, Duineveld DJ, Diachenko M, Scheltens P, Stam CJ, van Kesteren RE, Linkenkaer-Hansen K, Hillebrand A, Gouw AA. Resting-state oscillations reveal disturbed excitation-inhibition ratio in Alzheimer's disease patients. Sci Rep 2023; 13:7419. [PMID: 37150756 PMCID: PMC10164744 DOI: 10.1038/s41598-023-33973-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 04/21/2023] [Indexed: 05/09/2023] Open
Abstract
An early disruption of neuronal excitation-inhibition (E-I) balance in preclinical animal models of Alzheimer's disease (AD) has been frequently reported, but is difficult to measure directly and non-invasively in humans. Here, we examined known and novel neurophysiological measures sensitive to E-I in patients across the AD continuum. Resting-state magnetoencephalography (MEG) data of 86 amyloid-biomarker-confirmed subjects across the AD continuum (17 patients diagnosed with subjective cognitive decline, 18 with mild cognitive impairment (MCI) and 51 with dementia due to probable AD (AD dementia)), 46 healthy elderly and 20 young control subjects were reconstructed to source-space. E-I balance was investigated by detrended fluctuation analysis (DFA), a functional E/I (fE/I) algorithm, and the aperiodic exponent of the power spectrum. We found a disrupted E-I ratio in AD dementia patients specifically, by a lower DFA, and a shift towards higher excitation, by a higher fE/I and a lower aperiodic exponent. Healthy subjects showed lower fE/I ratios (< 1.0) than reported in previous literature, not explained by age or choice of an arbitrary threshold parameter, which warrants caution in interpretation of fE/I results. Correlation analyses showed that a lower DFA (E-I imbalance) and a lower aperiodic exponent (more excitation) was associated with a worse cognitive score in AD dementia patients. In contrast, a higher DFA in the hippocampi of MCI patients was associated with a worse cognitive score. This MEG-study showed E-I imbalance, likely due to increased excitation, in AD dementia, but not in early stage AD patients. To accurately determine the direction of shift in E-I balance, validations of the currently used markers and additional in vivo markers of E-I are required.
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Affiliation(s)
- Anne M van Nifterick
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands.
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands.
| | - Danique Mulder
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Denise J Duineveld
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Marina Diachenko
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
| | - Ronald E van Kesteren
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
| | - Klaus Linkenkaer-Hansen
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
| | - Alida A Gouw
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
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Dimitriadis SI. Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher's Choice Paths. Neuroinformatics 2023; 21:71-88. [PMID: 36372844 DOI: 10.1007/s12021-022-09610-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/05/2022] [Indexed: 11/15/2022]
Abstract
There is a growing interest in the neuroscience community on the advantages of multilayer functional brain networks. Researchers usually treated different frequencies separately at distinct functional brain networks. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. Here, Ι will focus on the multi-frequency multilayer functional connectivity analysis on resting-state fMRI (rs-fMRI) recordings. However, constructing a multilayer network depends on selecting multiple pre-processing steps that can affect the final network topology. Here, I analyzed the rs-fMRI dataset from a single human performing scanning over a period of 18 months (84 scans in total), and the rs-fMRI dataset containing 25 subjects with 3 repeat scans. I focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, I untangled specific combinations of researchers' choices that yield consistently brain networks with repeatable topologies, giving me the chance to recommend best practices over consistent topologies.
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Affiliation(s)
- Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, Faculty of Psychology, University of Barcelona, Passeig de la Vall d'Hebron, 171, 08035, Barcelona, Spain.
- Institut de Neurociències, University of Barcelona, Campus Mundet, Edifici de PonentPasseig de la Vall d'Hebron, 171, 08035, Barcelona, Spain.
- Integrative Neuroimaging Lab, 55133, Thessaloniki, Greece.
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Wales, CF24 4HQ, Cardiff, UK.
- Neuroinformatics Group, School of Psychology, College of Biomedical and Life Sciences, Cardiff University Brain Research Imaging Centre (CUBRIC), CF24 4HQ, Cardiff, Wales, UK.
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, CF24 4HQ, Wales, UK.
- Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, CF24 4HQ, Cardiff, Wales, UK.
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, CF24 4HQ, Wales, UK.
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Sriram S, Natiq H, Rajagopal K, Krejcar O, Krejcar O. Dynamics of a two-layer neuronal network with asymmetry in coupling. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:2908-2919. [PMID: 36899564 DOI: 10.3934/mbe.2023137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Investigating the effect of changes in neuronal connectivity on the brain's behavior is of interest in neuroscience studies. Complex network theory is one of the most capable tools to study the effects of these changes on collective brain behavior. By using complex networks, the neural structure, function, and dynamics can be analyzed. In this context, various frameworks can be used to mimic neural networks, among which multi-layer networks are a proper one. Compared to single-layer models, multi-layer networks can provide a more realistic model of the brain due to their high complexity and dimensionality. This paper examines the effect of changes in asymmetry coupling on the behaviors of a multi-layer neuronal network. To this aim, a two-layer network is considered as a minimum model of left and right cerebral hemispheres communicated with the corpus callosum. The chaotic model of Hindmarsh-Rose is taken as the dynamics of the nodes. Only two neurons of each layer connect two layers of the network. In this model, it is assumed that the layers have different coupling strengths, so the effect of each coupling change on network behavior can be analyzed. As a result, the projection of the nodes is plotted for several coupling strengths to investigate how the asymmetry coupling influences the network behaviors. It is observed that although no coexisting attractor is present in the Hindmarsh-Rose model, an asymmetry in couplings causes the emergence of different attractors. The bifurcation diagrams of one node of each layer are presented to show the variation of the dynamics due to coupling changes. For further analysis, the network synchronization is investigated by computing intra-layer and inter-layer errors. Calculating these errors shows that the network can be synchronized only for large enough symmetric coupling.
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Affiliation(s)
- Sridevi Sriram
- Centre for Computational Biology, Chennai Institute of Technology, Chennai 600069, India
| | - Hayder Natiq
- Department of Computer Technology Engineering, College of Information Technology, Imam Ja'afar Al-Sadiq University, Baghdad 10001, Iraq
| | - Karthikeyan Rajagopal
- Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai 600069, India
| | - Ondrej Krejcar
- Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czechia
- Institute of Technology and Business in Ceske Budejovice, Ceske Budejovice, Czechia
- Department of Biomedical Engineering and Measurement, Faculty of Mechanical Engineering Technical University of Kosice, Slovakia
| | - Ondrej Krejcar
- Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czechia
- School of Engineering, Monash University, Selangor, Malaysia
- College of Engineering and Science, Victoria University, Melbourne, Australia
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Breedt LC, Santos FAN, Hillebrand A, Reneman L, van Rootselaar AF, Schoonheim MM, Stam CJ, Ticheler A, Tijms BM, Veltman DJ, Vriend C, Wagenmakers MJ, van Wingen GA, Geurts JJG, Schrantee A, Douw L. Multimodal multilayer network centrality relates to executive functioning. Netw Neurosci 2023; 7:299-321. [PMID: 37339322 PMCID: PMC10275212 DOI: 10.1162/netn_a_00284] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 10/07/2022] [Indexed: 02/18/2024] Open
Abstract
Executive functioning (EF) is a higher order cognitive process that is thought to depend on a network organization facilitating integration across subnetworks, in the context of which the central role of the fronto-parietal network (FPN) has been described across imaging and neurophysiological modalities. However, the potentially complementary unimodal information on the relevance of the FPN for EF has not yet been integrated. We employ a multilayer framework to allow for integration of different modalities into one 'network of networks.' We used diffusion MRI, resting-state functional MRI, MEG, and neuropsychological data obtained from 33 healthy adults to construct modality-specific single-layer networks as well as a single multilayer network per participant. We computed single-layer and multilayer eigenvector centrality of the FPN as a measure of integration in this network and examined their associations with EF. We found that higher multilayer FPN centrality, but not single-layer FPN centrality, was related to better EF. We did not find a statistically significant change in explained variance in EF when using the multilayer approach as compared to the single-layer measures. Overall, our results show the importance of FPN integration for EF and underline the promise of the multilayer framework toward better understanding cognitive functioning.
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Affiliation(s)
- Lucas C. Breedt
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, The Netherlands
| | - Fernando A. N. Santos
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, The Netherlands
- Institute of Advanced Studies, University of Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, The Netherlands
| | - Liesbeth Reneman
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, The Netherlands
| | - Anne-Fleur van Rootselaar
- Department of Neurology and Clinical Neurophysiology, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, The Netherlands
| | - Menno M. Schoonheim
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, The Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, The Netherlands
- Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, The Netherlands
| | - Anouk Ticheler
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, The Netherlands
| | - Betty M. Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, The Netherlands
| | - Dick J. Veltman
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, The Netherlands
| | - Chris Vriend
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, The Netherlands
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, The Netherlands
| | - Margot J. Wagenmakers
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, The Netherlands
- GGZ in Geest Specialized Mental Health Care, Amsterdam, The Netherlands
| | - Guido A. van Wingen
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, The Netherlands
| | - Jeroen J. G. Geurts
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, The Netherlands
| | - Anouk Schrantee
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, The Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, The Netherlands
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Zuo Q, Lu L, Wang L, Zuo J, Ouyang T. Constructing brain functional network by Adversarial Temporal-Spatial Aligned Transformer for early AD analysis. Front Neurosci 2022; 16:1087176. [PMID: 36518529 PMCID: PMC9742604 DOI: 10.3389/fnins.2022.1087176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 11/10/2022] [Indexed: 09/19/2023] Open
Abstract
Introduction The brain functional network can describe the spontaneous activity of nerve cells and reveal the subtle abnormal changes associated with brain disease. It has been widely used for analyzing early Alzheimer's disease (AD) and exploring pathological mechanisms. However, the current methods of constructing functional connectivity networks from functional magnetic resonance imaging (fMRI) heavily depend on the software toolboxes, which may lead to errors in connection strength estimation and bad performance in disease analysis because of many subjective settings. Methods To solve this problem, in this paper, a novel Adversarial Temporal-Spatial Aligned Transformer (ATAT) model is proposed to automatically map 4D fMRI into functional connectivity network for early AD analysis. By incorporating the volume and location of anatomical brain regions, the region-guided feature learning network can roughly focus on local features for each brain region. Also, the spatial-temporal aligned transformer network is developed to adaptively adjust boundary features of adjacent regions and capture global functional connectivity patterns of distant regions. Furthermore, a multi-channel temporal discriminator is devised to distinguish the joint distributions of the multi-region time series from the generator and the real sample. Results Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) proved the effectiveness and superior performance of the proposed model in early AD prediction and progression analysis. Discussion To verify the reliability of the proposed model, the detected important ROIs are compared with clinical studies and show partial consistency. Furthermore, the most significant altered connectivity reflects the main characteristics associated with AD. Conclusion Generally, the proposed ATAT provides a new perspective in constructing functional connectivity networks and is able to evaluate the disease-related changing characteristics at different stages for neuroscience exploration and clinical disease analysis.
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Affiliation(s)
- Qiankun Zuo
- School of Information Engineering, Hubei University of Economics, Wuhan, China
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, and the SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China
| | - Libin Lu
- School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China
| | - Lin Wang
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, and the SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China
- Guangdong-Hong Kong-Macau Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, China
| | - Jiahui Zuo
- State Key Laboratory of Petroleum Resource and Prospecting, and Unconventional Petroleum Research Institute, China University of Petroleum, Beijing, China
| | - Tao Ouyang
- State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, China
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Cheron G, Ristori D, Marquez-Ruiz J, Cebolla AM, Ris L. Electrophysiological alterations of the Purkinje cells and deep cerebellar neurons in a mouse model of Alzheimer disease (electrophysiology on cerebellum of AD mice). Eur J Neurosci 2022; 56:5547-5563. [PMID: 35141975 DOI: 10.1111/ejn.15621] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 12/16/2021] [Accepted: 12/19/2021] [Indexed: 12/14/2022]
Abstract
Alzheimer's disease is histopathologically well defined by the presence of amyloid deposits and tau-related neurofibrillary tangles in crucial regions of the brain. Interest is growing in revealing and determining possible pathological markers also in the cerebellum as its involvement in cognitive functions is now well supported. Despite the central position of the Purkinje cell in the cerebellum, its electrophysiological behaviour in mouse models of Alzheimer's disease is scarce in the literature. Our first aim was here to focus on the electrophysiological behaviour of the cerebellum in awake mouse model of Alzheimer's disease (APPswe/PSEN1dE9) and the related performance on the water-maze test classically used in behavioural studies. We found prevalent signs of electrophysiological alterations in both Purkinje cells and deep cerebellar nuclei neurons which might explain the behavioural deficits reported during the water-maze test. The alterations of neurons firing were accompanied by a dual (~16 and ~228 Hz) local field potential's oscillation in the Purkinje cell layer of Alzheimer's disease mice which was concomitant to an important increase of both the simple and the complex spikes. In addition, β-amyloid deposits were present in the molecular layer of the cerebellum. These results highlight the importance of the output firing modification of the AD cerebellum that may indirectly impact the activity of its subcortical and cortical targets.
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Affiliation(s)
- Guy Cheron
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium.,ULB Neuroscience Institut, Université Libre de Bruxelles, Brussels, Belgium.,Laboratory of Neuroscience, Université de Mons, Mons, Belgium
| | - Dominique Ristori
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium
| | - Javier Marquez-Ruiz
- Department of Physiology, Anatomy and Cell Biology, Pablo de Olavide University, Seville, Spain
| | - Anna-Maria Cebolla
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium
| | - Laurence Ris
- Laboratory of Neuroscience, Université de Mons, Mons, Belgium.,UMONS Research Institut for health and technology, Université de Mons, Mons, Belgium
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31
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Dimitriadis SI. Universal Lifespan Trajectories of Source-Space Information Flow Extracted from Resting-State MEG Data. Brain Sci 2022; 12:1404. [PMID: 36291337 PMCID: PMC9599296 DOI: 10.3390/brainsci12101404] [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: 05/04/2022] [Revised: 07/15/2022] [Accepted: 07/22/2022] [Indexed: 02/15/2024] Open
Abstract
Source activity was extracted from resting-state magnetoencephalography data of 103 subjects aged 18-60 years. The directionality of information flow was computed from the regional time courses using delay symbolic transfer entropy and phase entropy. The analysis yielded a dynamic source connectivity profile, disentangling the direction, strength, and time delay of the underlying causal interactions, producing independent time delays for cross-frequency amplitude-to-amplitude and phase-to-phase coupling. The computation of the dominant intrinsic coupling mode (DoCM) allowed me to estimate the probability distribution of the DoCM independently of phase and amplitude. The results support earlier observations of a posterior-to-anterior information flow for phase dynamics in {α1, α2, β, γ} and an opposite flow (anterior to posterior) in θ. Amplitude dynamics reveal posterior-to-anterior information flow in {α1, α2, γ}, a sensory-motor β-oriented pattern, and an anterior-to-posterior pattern in {δ, θ}. The DoCM between intra- and cross-frequency couplings (CFC) are reported here for the first time and independently for amplitude and phase; in both domains {δ, θ, α1}, frequencies are the main contributors to DoCM. Finally, a novel brain age index (BAI) is introduced, defined as the ratio of the probability distribution of inter- over intra-frequency couplings. This ratio shows a universal age trajectory: a rapid rise from the end of adolescence, reaching a peak in adulthood, and declining slowly thereafter. The universal pattern is seen in the BAI of each frequency studied and for both amplitude and phase domains. No such universal age dependence was previously reported.
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Affiliation(s)
- Stavros I. Dimitriadis
- Neuroscience and Mental Health Research Institute (NMHI), College of Biomedical and Life Sciences, Cardiff University, Maindy Road, Cardiff CF24 4HQ, Wales, UK;
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road, Cardiff CF24 HQ, Wales, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Maindy Road, Cardiff CF24 4HQ, Wales, UK
- Neuroinformatics Group, School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road, Cardiff CF24 4HQ, Wales, UK
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Queens Road, Bristol BS8 1QU, Wales, UK
- Department of Clinical Psychology and Psychobiology, Faculty of Psychology, University of Barcelona, Passeig de la Vall d’Hebron, 171, 08035 Barcelona, Spain
- Institut de Neurociències, University of Barcelona, Campus Mundet, Edifici de Ponent, Passeig de la Vall d’Hebron, 171, 08035 Barcelona, Spain
- Integrative Neuroimaging Lab, 55133 Thessaloniki, Macedonia, Greece
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32
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Blevins AS, Bassett DS, Scott EK, Vanwalleghem GC. From calcium imaging to graph topology. Netw Neurosci 2022; 6:1125-1147. [PMID: 38800465 PMCID: PMC11117109 DOI: 10.1162/netn_a_00262] [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: 03/17/2022] [Accepted: 06/13/2022] [Indexed: 05/29/2024] Open
Abstract
Systems neuroscience is facing an ever-growing mountain of data. Recent advances in protein engineering and microscopy have together led to a paradigm shift in neuroscience; using fluorescence, we can now image the activity of every neuron through the whole brain of behaving animals. Even in larger organisms, the number of neurons that we can record simultaneously is increasing exponentially with time. This increase in the dimensionality of the data is being met with an explosion of computational and mathematical methods, each using disparate terminology, distinct approaches, and diverse mathematical concepts. Here we collect, organize, and explain multiple data analysis techniques that have been, or could be, applied to whole-brain imaging, using larval zebrafish as an example model. We begin with methods such as linear regression that are designed to detect relations between two variables. Next, we progress through network science and applied topological methods, which focus on the patterns of relations among many variables. Finally, we highlight the potential of generative models that could provide testable hypotheses on wiring rules and network progression through time, or disease progression. While we use examples of imaging from larval zebrafish, these approaches are suitable for any population-scale neural network modeling, and indeed, to applications beyond systems neuroscience. Computational approaches from network science and applied topology are not limited to larval zebrafish, or even to systems neuroscience, and we therefore conclude with a discussion of how such methods can be applied to diverse problems across the biological sciences.
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Affiliation(s)
- Ann S. Blevins
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S. Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - Ethan K. Scott
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
- Department of Anatomy and Physiology, School of Biomedical Sciences, University of Melbourne, Parkville, Australia
| | - Gilles C. Vanwalleghem
- Danish Research Institute of Translational Neuroscience (DANDRITE), Nordic EMBL Partnership for Molecular Medicine, Aarhus University, Aarhus, Denmark
- Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
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Xue J, Yao R, Cui X, Wang B, Wei J, Wu X, Sun J, Yang Y, Xiang J, Liu Y. Abnormal information interaction in multilayer directed network based on cross-frequency integration of mild cognitive impairment and Alzheimer’s disease. Cereb Cortex 2022; 33:4230-4247. [PMID: 36104855 DOI: 10.1093/cercor/bhac339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/14/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Mild cognitive impairment (MCI) and Alzheimer’s disease (AD) have been reported to result in abnormal cross-frequency integration. However, previous studies have failed to consider specific abnormalities in receiving and outputting information among frequency bands during integration. Here, we investigated heterogeneity in receiving and outputting information during cross-frequency integration in patients. The results showed that during cross-frequency integration, information interaction first increased and then decreased, manifesting in the heterogeneous distribution of inter-frequency nodes for receiving information. A possible explanation was that due to damage to some inter-frequency hub nodes, intra-frequency nodes gradually became new inter-frequency nodes, whereas original inter-frequency nodes gradually became new inter-frequency hub nodes. Notably, damage to the brain regions that receive information between layers was often accompanied by a strengthened ability to output information and the emergence of hub nodes for outputting information. Moreover, an important compensatory mechanism assisted in the reception of information in the cingulo-opercular and auditory networks and in the outputting of information in the visual network. This study revealed specific abnormalities in information interaction and compensatory mechanism during cross-frequency integration, providing important evidence for understanding cross-frequency integration in patients with MCI and AD.
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Affiliation(s)
- Jiayue Xue
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Rong Yao
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Xiaohong Cui
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Jing Wei
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Xubin Wu
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Jie Sun
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Yanli Yang
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Yi Liu
- Department of Anesthesiology, Shanxi Province Cancer Hospital , No. 3, Workers New Street, Taiyuan, Shanxi, 030013 , China
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Han C, Wang T, Wu Y, Li H, Wang E, Zhao X, Cao Q, Qian Q, Wang Y, Dou F, Liu JK, Sun L, Xing D. Compensatory mechanism of attention-deficit/hyperactivity disorder recovery in resting state alpha rhythms. Front Comput Neurosci 2022; 16:883065. [PMID: 36157841 PMCID: PMC9490822 DOI: 10.3389/fncom.2022.883065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 06/17/2022] [Indexed: 11/13/2022] Open
Abstract
Alpha rhythms in the human electroencephalogram (EEG), oscillating at 8-13 Hz, are located in parieto-occipital cortex and are strongest when awake people close their eyes. It has been suggested that alpha rhythms were related to attention-related functions and mental disorders (e.g., Attention-deficit/hyperactivity disorder (ADHD)). However, many studies have shown inconsistent results on the difference in alpha oscillation between ADHD and control groups. Hence it is essential to verify this difference. In this study, a dataset of EEG recording (128 channel EGI) from 87 healthy controls (HC) and 162 ADHD (141 persisters and 21 remitters) adults in a resting state with their eyes closed was used to address this question and a three-gauss model (summation of baseline and alpha components) was conducted to fit the data. To our surprise, the power of alpha components was not a significant difference among the three groups. Instead, the baseline power of remission and HC group in the alpha band is significantly stronger than that of persister groups. Our results suggest that ADHD recovery may have compensatory mechanisms and many abnormalities in EEG may be due to the influence of behavior rather than the difference in brain signals.
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Affiliation(s)
- Chuanliang Han
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Tian Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- College of Life Sciences, Beijing Normal University, Beijing, China
| | - Yujie Wu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Hui Li
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorder and Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing, China
| | - Encong Wang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorder and Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing, China
| | - Xixi Zhao
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorder and Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing, China
| | - Qingjiu Cao
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorder and Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing, China
| | - Qiujin Qian
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorder and Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing, China
| | - Yufeng Wang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorder and Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing, China
| | - Fei Dou
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- College of Life Sciences, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Genetic Engineering Drugs and Biotechnology, Beijing Normal University, Beijing, China
| | - Jian K. Liu
- School of Computing, University of Leeds, Leeds, United Kingdom
| | - Li Sun
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorder and Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing, China
- Li Sun,
| | - Dajun Xing
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- *Correspondence: Dajun Xing,
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Horsley JJ, Schroeder GM, Thomas RH, de Tisi J, Vos SB, Winston GP, Duncan JS, Wang Y, Taylor PN. Volumetric and structural connectivity abnormalities co-localise in TLE. Neuroimage Clin 2022; 35:103105. [PMID: 35863179 PMCID: PMC9421455 DOI: 10.1016/j.nicl.2022.103105] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 05/17/2022] [Accepted: 06/29/2022] [Indexed: 12/02/2022]
Abstract
Patients with temporal lobe epilepsy (TLE) exhibit both volumetric and structural connectivity abnormalities relative to healthy controls. How these abnormalities inter-relate and their mechanisms are unclear. We computed grey matter volumetric changes and white matter structural connectivity abnormalities in 144 patients with unilateral TLE and 96 healthy controls. Regional volumes were calculated using T1-weighted MRI, while structural connectivity was derived using white matter fibre tractography from diffusion-weighted MRI. For each regional volume and each connection strength, we calculated the effect size between patient and control groups in a group-level analysis. We then applied hierarchical regression to investigate the relationship between volumetric and structural connectivity abnormalities in individuals. Additionally, we quantified whether abnormalities co-localised within individual patients by computing Dice similarity scores. In TLE, white matter connectivity abnormalities were greater when joining two grey matter regions with abnormal volumes. Similarly, grey matter volumetric abnormalities were greater when joined by abnormal white matter connections. The extent of volumetric and connectivity abnormalities related to epilepsy duration, but co-localisation did not. Co-localisation was primarily driven by neighbouring abnormalities in the ipsilateral hemisphere. Overall, volumetric and structural connectivity abnormalities were related in TLE. Our results suggest that shared mechanisms may underlie changes in both volume and connectivity alterations in patients with TLE.
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Affiliation(s)
- Jonathan J Horsley
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Gabrielle M Schroeder
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Rhys H Thomas
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sjoerd B Vos
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Centre for Microscopy, Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia; Centre for Medical Image Computing, Computer Science Department, University College London, London, United Kingdom
| | - Gavin P Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Division of Neurology, Department of Medicine, Queen's University, Kingston, Canada
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.
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36
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Scheijbeler EP, van Nifterick AM, Stam CJ, Hillebrand A, Gouw AA, de Haan W. Network-level permutation entropy of resting-state MEG recordings: A novel biomarker for early-stage Alzheimer's disease? Netw Neurosci 2022; 6:382-400. [PMID: 35733433 PMCID: PMC9208018 DOI: 10.1162/netn_a_00224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/15/2021] [Indexed: 11/24/2022] Open
Abstract
Increasing evidence suggests that measures of signal variability and complexity could present promising biomarkers for Alzheimer's disease (AD). Earlier studies have however been limited to the characterization of local activity. Here, we investigate whether a network version of permutation entropy could serve as a novel biomarker for early-stage AD. Resting-state source-space magnetoencephalography was recorded in 18 subjects with subjective cognitive decline (SCD) and 18 subjects with mild cognitive impairment (MCI). Local activity was characterized by permutation entropy (PE). Network-level interactions were studied using the inverted joint permutation entropy (JPEinv), corrected for volume conduction. The JPEinv showed a reduction of nonlinear connectivity in MCI subjects in the theta and alpha band. Local PE showed increased theta band entropy. Between-group differences were widespread across brain regions. Receiver operating characteristic (ROC) analysis of classification of MCI versus SCD subjects revealed that a logistic regression model trained on JPEinv features (78.4% [62.5-93.3%]) slightly outperformed PE (76.9% [60.3-93.4%]) and relative theta power-based models (76.9% [60.4-93.3%]). Classification performance of theta JPEinv was at least as good as the relative theta power benchmark. The JPEinv is therefore a potential biomarker for early-stage AD that should be explored in larger studies.
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Affiliation(s)
- Elliz P. Scheijbeler
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Anne M. van Nifterick
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Alida A. Gouw
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Willem de Haan
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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37
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Song Y, Wu H, Chen S, Ge H, Yan Z, Xue C, Qi W, Yuan Q, Liang X, Lin X, Chen J. Differential Abnormality in Functional Connectivity Density in Preclinical and Early-Stage Alzheimer's Disease. Front Aging Neurosci 2022; 14:879836. [PMID: 35693335 PMCID: PMC9177137 DOI: 10.3389/fnagi.2022.879836] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 04/27/2022] [Indexed: 12/23/2022] Open
Abstract
Background Both subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) have a high risk of progression to Alzheimer's disease (AD). While most of the available evidence described changes in functional connectivity (FC) in SCD and aMCI, there was no confirmation of changes in functional connectivity density (FCD) that have not been confirmed. Therefore, the purpose of this study was to investigate the specific alterations in resting-state FCD in SCD and aMCI and further assess the extent to which these changes can distinguish the preclinical and early-stage AD. Methods A total of 57 patients with SCD, 59 patients with aMCI, and 78 healthy controls (HC) were included. The global FCD, local FCD, and long-range FCD were calculated for each voxel to identify brain regions with significant FCD alterations. The brain regions with abnormal FCD were then used as regions of interest for FC analysis. In addition, we calculated correlations between neuroimaging alterations and cognitive function and performed receiver-operating characteristic analyses to assess the diagnostic effect of the FCD and FC alterations on SCD and aMCI. Results FCD mapping revealed significantly increased global FCD in the left parahippocampal gyrus (PHG.L) and increased long-range FCD in the left hippocampus for patients with SCD when compared to HCs. However, when compared to SCD, patients with aMCI showed significantly decreased global FCD and long-range FCD in the PHG.L. The follow-up FC analysis further revealed significant variations between the PHG.L and the occipital lobe in patients with SCD and aMCI. In addition, patients with SCD also presented significant changes in FC between the left hippocampus, the left cerebellum anterior lobe, and the inferior temporal gyrus. Moreover, changes in abnormal indicators in the SCD and aMCI groups were significantly associated with cognitive function. Finally, combining FCD and FC abnormalities allowed for a more precise differentiation of the clinical stages. Conclusion To our knowledge, this study is the first to investigate specific alterations in FCD and FC for both patients with SCD and aMCI and confirms differential abnormalities that can serve as potential imaging markers for preclinical and early-stage Alzheimer's disease (AD). Also, it adds a new dimension of understanding to the diagnosis of SCD and aMCI as well as the evaluation of disease progression.
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Affiliation(s)
- Yu Song
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Huimin Wu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shanshan Chen
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Honglin Ge
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Zheng Yan
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenzhang Qi
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Qianqian Yuan
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xuhong Liang
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xingjian Lin
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Xingjian Lin
| | - Jiu Chen
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
- Jiu Chen
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Sorrentino P, Ambrosanio M, Rucco R, Cabral J, Gollo LL, Breakspear M, Baselice F. Detection of Cross-Frequency Coupling Between Brain Areas: An Extension of Phase Linearity Measurement. Front Neurosci 2022; 16:846623. [PMID: 35546895 PMCID: PMC9083011 DOI: 10.3389/fnins.2022.846623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 02/21/2022] [Indexed: 11/25/2022] Open
Abstract
The current paper proposes a method to estimate phase to phase cross-frequency coupling between brain areas, applied to broadband signals, without any a priori hypothesis about the frequency of the synchronized components. N:m synchronization is the only form of cross-frequency synchronization that allows the exchange of information at the time resolution of the faster signal, hence likely to play a fundamental role in large-scale coordination of brain activity. The proposed method, named cross-frequency phase linearity measurement (CF-PLM), builds and expands upon the phase linearity measurement, an iso-frequency connectivity metrics previously published by our group. The main idea lies in using the shape of the interferometric spectrum of the two analyzed signals in order to estimate the strength of cross-frequency coupling. We first provide a theoretical explanation of the metrics. Then, we test the proposed metric on simulated data from coupled oscillators synchronized in iso- and cross-frequency (using both Rössler and Kuramoto oscillator models), and subsequently apply it on real data from brain activity. Results show that the method is useful to estimate n:m synchronization, based solely on the phase of the signals (independently of the amplitude), and no a-priori hypothesis is available about the expected frequencies.
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Affiliation(s)
- Pierpaolo Sorrentino
- Systems Neuroscience Institute, Marseille, France.,Hermitage Capodimonte Hospital, Naples, Italy
| | | | | | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), University of Minho, Braga, Portugal.,Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Leonardo L Gollo
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia.,QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Michael Breakspear
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.,Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Fabio Baselice
- Egineering Department, University of Naples Parthenope, Naples, Italy
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Luppi JJ, Schoonhoven DN, van Nifterick AM, Gouw AA, Hillebrand A, Scheltens P, Stam CJ, de Haan W. Oscillatory Activity of the Hippocampus in Prodromal Alzheimer’s Disease: A Source-Space Magnetoencephalography Study. J Alzheimers Dis 2022; 87:317-333. [PMID: 35311705 PMCID: PMC9198749 DOI: 10.3233/jad-215464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: In Alzheimer’s disease (AD), oscillatory activity of the human brain slows down. However, oscillatory slowing varies between individuals, particularly in prodromal AD. Cortical oscillatory changes have shown suboptimal accuracy as diagnostic markers. We speculated that focusing on the hippocampus might prove more successful, particularly using magnetoencephalography (MEG) for capturing subcortical oscillatory activity. Objective: We explored MEG-based detection of hippocampal oscillatory abnormalities in prodromal AD patients. Methods: We acquired resting-state MEG data of 18 AD dementia patients, 18 amyloid-β-positive amnestic mild cognitive impairment (MCI, prodromal AD) patients, and 18 amyloid-β-negative persons with subjective cognitive decline (SCD). Oscillatory activity in 78 cortical regions and both hippocampi was reconstructed using beamforming. Between-group and hippocampal-cortical differences in spectral power were assessed. Classification accuracy was explored using ROC curves. Results: The MCI group showed intermediate power values between SCD and AD, except for the alpha range, where it was higher than both (p < 0.05 and p < 0.001). The largest differences between MCI and SCD were in the theta band, with higher power in MCI (p < 0.01). The hippocampi showed several unique group differences, such as higher power in the higher alpha band in MCI compared to SCD (p < 0.05). Classification accuracy (MCI versus SCD) was best for absolute theta band power in the right hippocampus (AUC = 0.87). Conclusion: In this MEG study, we detected oscillatory abnormalities of the hippocampi in prodromal AD patients. Moreover, hippocampus-based classification performed better than cortex-based classification. We conclude that a focus on hippocampal MEG may improve early detection of AD-related neuronal dysfunction.
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Affiliation(s)
- Janne J. Luppi
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Deborah N. Schoonhoven
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Anne M. van Nifterick
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Alida A. Gouw
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
- Department of Clinical Neurophysiology and MEG, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Philip Scheltens
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology and MEG, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Willem de Haan
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
- Department of Clinical Neurophysiology and MEG, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
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40
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Schoonhoven DN, Briels CT, Hillebrand A, Scheltens P, Stam CJ, Gouw AA. Sensitive and reproducible MEG resting-state metrics of functional connectivity in Alzheimer's disease. Alzheimers Res Ther 2022; 14:38. [PMID: 35219327 PMCID: PMC8881826 DOI: 10.1186/s13195-022-00970-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 01/30/2022] [Indexed: 01/08/2023]
Abstract
Background Analysis of functional brain networks in Alzheimer’s disease (AD) has been hampered by a lack of reproducible, yet valid metrics of functional connectivity (FC). This study aimed to assess both the sensitivity and reproducibility of the corrected amplitude envelope correlation (AEC-c) and phase lag index (PLI), two metrics of FC that are insensitive to the effects of volume conduction and field spread, in two separate cohorts of patients with dementia due to AD versus healthy elderly controls. Methods Subjects with a clinical diagnosis of AD dementia with biomarker proof, and a control group of subjective cognitive decline (SCD), underwent two 5-min resting-state MEG recordings. Data consisted of a test (AD = 28; SCD = 29) and validation (AD = 29; SCD = 27) cohort. Time-series were estimated for 90 regions of interest (ROIs) in the automated anatomical labelling (AAL) atlas. For each of five canonical frequency bands, the AEC-c and PLI were calculated between all 90 ROIs, and connections were averaged per ROI. General linear models were constructed to compare the global FC differences between the groups, assess the reproducibility, and evaluate the effects of age and relative power. Reproducibility of the regional FC differences was assessed using the Mann-Whitney U tests, with correction for multiple testing using the false discovery rate (FDR). Results The AEC-c showed significantly and reproducibly lower global FC for the AD group compared to SCD, in the alpha (8–13 Hz) and beta (13–30 Hz) bands, while the PLI revealed reproducibly lower FC for the AD group in the delta (0.5–4 Hz) band and higher FC for the theta (4–8 Hz) band. Regionally, the beta band AEC-c showed reproducibility for almost all ROIs (except for 13 ROIs in the frontal and temporal lobes). For the other bands, the AEC-c and PLI did not show regional reproducibility after FDR correction. The theta band PLI was susceptible to the effect of relative power. Conclusion For MEG, the AEC-c is a sensitive and reproducible metric, able to distinguish FC differences between patients with AD dementia and cognitively healthy controls. These two measures likely reflect different aspects of neural activity and show differential sensitivity to changes in neural dynamics. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-00970-4.
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Affiliation(s)
- Deborah N Schoonhoven
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands. .,Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
| | - Casper T Briels
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Alida A Gouw
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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Canal-Garcia A, Gómez-Ruiz E, Mijalkov M, Chang YW, Volpe G, Pereira JB. Multiplex Connectome Changes across the Alzheimer’s Disease Spectrum Using Gray Matter and Amyloid Data. Cereb Cortex 2022; 32:3501-3515. [PMID: 35059722 PMCID: PMC9376877 DOI: 10.1093/cercor/bhab429] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 10/27/2021] [Accepted: 10/28/2021] [Indexed: 11/25/2022] Open
Abstract
The organization of the Alzheimer’s disease (AD) connectome has been studied using graph theory using single neuroimaging modalities such as positron emission tomography (PET) or structural magnetic resonance imaging (MRI). Although these modalities measure distinct pathological processes that occur in different stages in AD, there is evidence that they are not independent from each other. Therefore, to capture their interaction, in this study we integrated amyloid PET and gray matter MRI data into a multiplex connectome and assessed the changes across different AD stages. We included 135 cognitively normal (CN) individuals without amyloid-β pathology (Aβ−) in addition to 67 CN, 179 patients with mild cognitive impairment (MCI) and 132 patients with AD dementia who all had Aβ pathology (Aβ+) from the Alzheimer’s Disease Neuroimaging Initiative. We found widespread changes in the overlapping connectivity strength and the overlapping connections across Aβ-positive groups. Moreover, there was a reorganization of the multiplex communities in MCI Aβ + patients and changes in multiplex brain hubs in both MCI Aβ + and AD Aβ + groups. These findings offer a new insight into the interplay between amyloid-β pathology and brain atrophy over the course of AD that moves beyond traditional graph theory analyses based on single brain networks.
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Affiliation(s)
- Anna Canal-Garcia
- Address correspondence to Department of NVS, Division of Clinical Geriatrics, NEO seventh floor, Blickagången 16, 141 52 Huddinge, Sweden. ; Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
| | | | - Mite Mijalkov
- Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
| | - Yu-Wei Chang
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Joana B Pereira
- Address correspondence to Department of NVS, Division of Clinical Geriatrics, NEO seventh floor, Blickagången 16, 141 52 Huddinge, Sweden. ; Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
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42
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Griffa A, Legdeur N, Badissi M, van den Heuvel MP, Stam CJ, Visser PJ, Hillebrand A. Magnetoencephalography Brain Signatures Relate to Cognition and Cognitive Reserve in the Oldest-Old: The EMIF-AD 90 + Study. Front Aging Neurosci 2021; 13:746373. [PMID: 34899269 PMCID: PMC8656941 DOI: 10.3389/fnagi.2021.746373] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/01/2021] [Indexed: 11/25/2022] Open
Abstract
The oldest-old subjects represent the fastest growing segment of society and are at high risk for dementia with a prevalence of up to 40%. Lifestyle factors, such as lifelong participation in cognitive and leisure activities, may contribute to individual cognitive reserve and reduce the risk for cognitive impairments. However, the neural bases underlying cognitive functioning and cognitive reserve in this age range are still poorly understood. Here, we investigate spectral and functional connectivity features obtained from resting-state MEG recordings in a cohort of 35 cognitively normal (92.2 ± 1.8 years old, 19 women) and 11 cognitively impaired (90.9 ± 1.9 years old, 1 woman) oldest-old participants, in relation to cognitive traits and cognitive reserve. The latter was approximated with a self-reported scale on lifelong engagement in cognitively demanding activities. Cognitively impaired oldest-old participants had slower cortical rhythms in frontal, parietal and default mode network regions compared to the cognitively normal subjects. These alterations mainly concerned the theta and beta band and partially explained inter-subject variability of episodic memory scores. Moreover, a distinct spectral pattern characterized by higher relative power in the alpha band was specifically associated with higher cognitive reserve while taking into account the effect of age and education level. Finally, stronger functional connectivity in the alpha and beta band were weakly associated with better cognitive performances in the whole group of subjects, although functional connectivity effects were less prominent than the spectral ones. Our results shed new light on the neural underpinnings of cognitive functioning in the oldest-old population and indicate that cognitive performance and cognitive reserve may have distinct spectral electrophysiological substrates.
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Affiliation(s)
- Alessandra Griffa
- Division of Neurology, Department of Clinical Neurosciences, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Center of Neuroprosthetics, Institute of Bioengineering, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland.,Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Nienke Legdeur
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Maryam Badissi
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Martijn P van den Heuvel
- Dutch Connectome Lab, Department of Complex Trait Genetics, Center for Neuroscience and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Pieter Jelle Visser
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands.,Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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Covantes-Osuna C, López JB, Paredes O, Vélez-Pérez H, Romo-Vázquez R. Multilayer Network Approach in EEG Motor Imagery with an Adaptive Threshold. SENSORS 2021; 21:s21248305. [PMID: 34960399 PMCID: PMC8704651 DOI: 10.3390/s21248305] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 12/05/2021] [Accepted: 12/06/2021] [Indexed: 12/15/2022]
Abstract
The brain has been understood as an interconnected neural network generally modeled as a graph to outline the functional topology and dynamics of brain processes. Classic graph modeling is based on single-layer models that constrain the traits conveyed to trace brain topologies. Multilayer modeling, in contrast, makes it possible to build whole-brain models by integrating features of various kinds. The aim of this work was to analyze EEG dynamics studies while gathering motor imagery data through single-layer and multilayer network modeling. The motor imagery database used consists of 18 EEG recordings of four motor imagery tasks: left hand, right hand, feet, and tongue. Brain connectivity was estimated by calculating the coherence adjacency matrices from each electrophysiological band (δ, θ, α and β) from brain areas and then embedding them by considering each band as a single-layer graph and a layer of the multilayer brain models. Constructing a reliable multilayer network topology requires a threshold that distinguishes effective connections from spurious ones. For this reason, two thresholds were implemented, the classic fixed (average) one and Otsu’s version. The latter is a new proposal for an adaptive threshold that offers reliable insight into brain topology and dynamics. Findings from the brain network models suggest that frontal and parietal brain regions are involved in motor imagery tasks.
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Relationship between Amyloid-β Deposition and the Coupling between Structural and Functional Brain Networks in Patients with Mild Cognitive Impairment and Alzheimer's Disease. Brain Sci 2021; 11:brainsci11111535. [PMID: 34827535 PMCID: PMC8615711 DOI: 10.3390/brainsci11111535] [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: 10/06/2021] [Revised: 11/17/2021] [Accepted: 11/17/2021] [Indexed: 01/02/2023] Open
Abstract
Previous studies have demonstrated that the accumulation of amyloid-β (Aβ) pathologies has distinctive stage-specific effects on the structural and functional brain networks along the Alzheimer's disease (AD) continuum. A more comprehensive account of both types of brain network may provide a better characterization of the stage-specific effects of Aβ pathologies. A potential candidate for this joint characterization is the coupling between the structural and functional brain networks (SC-FC coupling). We therefore investigated the effect of Aβ accumulation on global SC-FC coupling in patients with mild cognitive impairment (MCI), AD, and healthy controls. Patients with MCI were dichotomized according to their level of Aβ pathology seen in 18F-flutemetamol PET-CT scans-namely, Aβ-negative and Aβ-positive. Our results show that there was no difference in global SC-FC coupling between different cohorts. During the prodromal AD stage, there was a significant negative correlation between the level of Aβ pathology and the global SC-FC coupling of MCI patients with positive Aβ, but no significant correlation for MCI patients with negative Aβ. During the AD dementia stage, the correlation between Aβ pathology and global SC-FC coupling in patients with AD was positive. Our results suggest that Aβ pathology has distinctive stage-specific effects on global coupling between the structural and functional brain networks along the AD continuum.
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Franciotti R, Moretti DV, Benussi A, Ferri L, Russo M, Carrarini C, Barbone F, Arnaldi D, Falasca NW, Koch G, Cagnin A, Nobili FM, Babiloni C, Borroni B, Padovani A, Onofrj M, Bonanni L. Cortical network modularity changes along the course of frontotemporal and Alzheimer's dementing diseases. Neurobiol Aging 2021; 110:37-46. [PMID: 34847523 DOI: 10.1016/j.neurobiolaging.2021.10.016] [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: 06/18/2021] [Revised: 10/07/2021] [Accepted: 10/26/2021] [Indexed: 12/14/2022]
Abstract
Cortical network modularity underpins cognitive functions, so we hypothesized its progressive derangement along the course of frontotemporal (FTD) and Alzheimer's (AD) dementing diseases. EEG was recorded in 18 FTD, 18 AD, and 20 healthy controls (HC). In the FTD and AD patients, the EEG recordings were performed at the prodromal stage of dementia, at the onset of dementia, and three years after the onset of dementia. HC underwent three EEG recordings at 2-3-year time interval. Information flows underlying EEG activity recorded at electrode pairs were estimated by means of Mutual Information (MI) analysis. The functional organization of the cortical network was modelled by means of the Graph theory analysis on MI adjacency matrices. Graph theory analysis showed that the main hub of HC (Parietal area) was lost in FTD patients at onset of dementia, substituted by provincial hubs in frontal leads. No changes in global network organization were found in AD. Despite a progressive cognitive impairment during the FTD and AD progression, only the FTD patients showed a derangement in the cortical network modularity, possibly due to dysfunctions in frontal functional connectivity.
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Affiliation(s)
- Raffaella Franciotti
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Davide V Moretti
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Alberto Benussi
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Laura Ferri
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Mirella Russo
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Claudia Carrarini
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Filomena Barbone
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Dario Arnaldi
- Dipartimento di Neuroscienze (DINOGMI), University of Genova, Genoa, Italy; U.O. Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Nicola W Falasca
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Giacomo Koch
- Non Invasive Brain Stimulation Unit/Department of Behavioral and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy; Stroke Unit, Department of Neuroscience, Tor Vergata Policlinic, Rome, Italy
| | | | - Flavio M Nobili
- Dipartimento di Neuroscienze (DINOGMI), University of Genova, Genoa, Italy; U.O. Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "V. Erspamer," Sapienza University of Rome, Rome, Italy; Hospital San Raffaele Cassino (FR), Cassino, Italy
| | - Barbara Borroni
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Marco Onofrj
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy.
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Fodor Z, Horváth A, Hidasi Z, Gouw AA, Stam CJ, Csukly G. EEG Alpha and Beta Band Functional Connectivity and Network Structure Mark Hub Overload in Mild Cognitive Impairment During Memory Maintenance. Front Aging Neurosci 2021; 13:680200. [PMID: 34690735 PMCID: PMC8529331 DOI: 10.3389/fnagi.2021.680200] [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: 03/13/2021] [Accepted: 09/20/2021] [Indexed: 12/18/2022] Open
Abstract
Background: While decreased alpha and beta-band functional connectivity (FC) and changes in network topology have been reported in Alzheimer’s disease, it is not yet entirely known whether these differences can mark cognitive decline in the early stages of the disease. Our study aimed to analyze electroencephalography (EEG) FC and network differences in the alpha and beta frequency band during visuospatial memory maintenance between Mild Cognitive Impairment (MCI) patients and healthy elderly with subjective memory complaints. Methods: Functional connectivity and network structure of 17 MCI patients and 20 control participants were studied with 128-channel EEG during a visuospatial memory task with varying memory load. FC between EEG channels was measured by amplitude envelope correlation with leakage correction (AEC-c), while network analysis was performed by applying the Minimum Spanning Tree (MST) approach, which reconstructs the critical backbone of the original network. Results: Memory load (increasing number of to-be-learned items) enhanced the mean AEC-c in the control group in both frequency bands. In contrast to that, after an initial increase, the MCI group showed significantly (p < 0.05) diminished FC in the alpha band in the highest memory load condition, while in the beta band this modulation was absent. Moreover, mean alpha and beta AEC-c correlated significantly with the size of medial temporal lobe structures in the entire sample. The network analysis revealed increased maximum degree, betweenness centrality, and degree divergence, and decreased diameter and eccentricity in the MCI group compared to the control group in both frequency bands independently of the memory load. This suggests a rerouted network in the MCI group with a more centralized topology and a more unequal traffic load distribution. Conclusion: Alpha- and beta-band FC measured by AEC-c correlates with cognitive load-related modulation, with subtle medial temporal lobe atrophy, and with the disruption of hippocampal fiber integrity in the earliest stages of cognitive decline. The more integrated network topology of the MCI group is in line with the “hub overload and failure” framework and might be part of a compensatory mechanism or a consequence of neural disinhibition.
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Affiliation(s)
- Zsuzsanna Fodor
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - András Horváth
- Department of Neurology, National Institute of Clinical Neurosciences, Budapest, Hungary
| | - Zoltán Hidasi
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Alida A Gouw
- Department of Clinical Neurophysiology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands.,Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Gábor Csukly
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
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47
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Yrjölä P, Stjerna S, Palva JM, Vanhatalo S, Tokariev A. Phase-Based Cortical Synchrony Is Affected by Prematurity. Cereb Cortex 2021; 32:2265-2276. [PMID: 34668522 PMCID: PMC9113310 DOI: 10.1093/cercor/bhab357] [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: 05/31/2021] [Revised: 08/26/2021] [Accepted: 08/30/2021] [Indexed: 11/22/2022] Open
Abstract
Inter-areal synchronization by phase–phase correlations (PPCs) of cortical oscillations mediates many higher neurocognitive functions, which are often affected by prematurity, a globally prominent neurodevelopmental risk factor. Here, we used electroencephalography to examine brain-wide cortical PPC networks at term-equivalent age, comparing human infants after early prematurity to a cohort of healthy controls. We found that prematurity affected these networks in a sleep state-specific manner, and the differences between groups were also frequency-selective, involving brain-wide connections. The strength of synchronization in these networks was predictive of clinical outcomes in the preterm infants. These findings show that prematurity affects PPC networks in a clinically significant manner, suggesting early functional biomarkers of later neurodevelopmental compromise that may be used in clinical or translational studies after early neonatal adversity.
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Affiliation(s)
- Pauliina Yrjölä
- Department of Clinical Neurophysiology, BABA Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, 00029 HUS, Finland.,Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, 00076 AALTO, Finland.,Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
| | - Susanna Stjerna
- Department of Clinical Neurophysiology, BABA Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, 00029 HUS, Finland.,Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland.,Division of Neuropsychology, HUS Neurocenter, Helsinki University Hospital and University of Helsinki, PL 340, 00029 HUS, Finland
| | - J Matias Palva
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, 00076 AALTO, Finland.,Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland.,Centre for Cognitive Neuroimaging, Institute of Neuroscience and Psychology, University of Glasgow, Glasgow G12 8QB, UK
| | - Sampsa Vanhatalo
- Department of Clinical Neurophysiology, BABA Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, 00029 HUS, Finland.,Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
| | - Anton Tokariev
- Department of Clinical Neurophysiology, BABA Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, 00029 HUS, Finland.,Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
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48
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Douw L, Nissen IA, Fitzsimmons SMDD, Santos FAN, Hillebrand A, van Straaten ECW, Stam CJ, De Witt Hamer PC, Baayen JC, Klein M, Reijneveld JC, Heyer DB, Verhoog MB, Wilbers R, Hunt S, Mansvelder HD, Geurts JJG, de Kock CPJ, Goriounova NA. Cellular Substrates of Functional Network Integration and Memory in Temporal Lobe Epilepsy. Cereb Cortex 2021; 32:2424-2436. [PMID: 34564728 PMCID: PMC9157285 DOI: 10.1093/cercor/bhab349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 08/19/2021] [Accepted: 08/22/2021] [Indexed: 11/12/2022] Open
Abstract
Temporal lobe epilepsy (TLE) patients are at risk of memory deficits, which have been linked to functional network disturbances, particularly of integration of the default mode network (DMN). However, the cellular substrates of functional network integration are unknown. We leverage a unique cross-scale dataset of drug-resistant TLE patients (n = 31), who underwent pseudo resting-state functional magnetic resonance imaging (fMRI), resting-state magnetoencephalography (MEG) and/or neuropsychological testing before neurosurgery. fMRI and MEG underwent atlas-based connectivity analyses. Functional network centrality of the lateral middle temporal gyrus, part of the DMN, was used as a measure of local network integration. Subsequently, non-pathological cortical tissue from this region was used for single cell morphological and electrophysiological patch-clamp analysis, assessing integration in terms of total dendritic length and action potential rise speed. As could be hypothesized, greater network centrality related to better memory performance. Moreover, greater network centrality correlated with more integrative properties at the cellular level across patients. We conclude that individual differences in cognitively relevant functional network integration of a DMN region are mirrored by differences in cellular integrative properties of this region in TLE patients. These findings connect previously separate scales of investigation, increasing translational insight into focal pathology and large-scale network disturbances in TLE.
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Affiliation(s)
- Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands.,Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 02129 MA, Charlestown, USA
| | - Ida A Nissen
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Sophie M D D Fitzsimmons
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Fernando A N Santos
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Elisabeth C W van Straaten
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Philip C De Witt Hamer
- Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VUmc Cancer Center Amsterdam Brain Tumor Center Amsterdam, 1081 HV, Amsterdam, the Netherlands
| | - Johannes C Baayen
- Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VUmc Cancer Center Amsterdam Brain Tumor Center Amsterdam, 1081 HV, Amsterdam, the Netherlands
| | - Martin Klein
- Department of Medical Psychology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VUmc Cancer Center Amsterdam Brain Tumor Center Amsterdam, 1081 HV, Amsterdam, the Netherlands
| | - Jaap C Reijneveld
- Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VUmc Cancer Center Amsterdam Brain Tumor Center Amsterdam, 1081 HV, Amsterdam, the Netherlands.,Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede 2103 SW, Heemstede, the Netherlands
| | - Djai B Heyer
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Matthijs B Verhoog
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands.,Department of Human Biology, Division of Cell Biology, Neuroscience Institute, University of Cape Town, 7935, Cape Town, South Africa
| | - René Wilbers
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Sarah Hunt
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Huibert D Mansvelder
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Christiaan P J de Kock
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Natalia A Goriounova
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
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49
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Chen N, Liu G, Guo M, Li Y, Yao Z, Hu B. Calcarine as a bridge between brain function and structure in irritable bowel syndrome: A multiplex network analysis. J Gastroenterol Hepatol 2021; 36:2408-2415. [PMID: 33354807 DOI: 10.1111/jgh.15382] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 11/09/2020] [Accepted: 12/16/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIM Jointly analyzing structural and functional brain networks enables a better understanding of pathological underpinnings of irritable bowel syndrome (IBS). Multiplex network analysis provides a novel framework to study complex networks consisting of different types of connectivity patterns in multimodal data. METHODS In the present work, we integrated functional and structural networks to a multiplex network. Then, the multiplex metrics and the inner-layer/inter-layer hub nodes were investigated through 34 patients with IBS and 33 healthy controls. RESULTS Significantly differential multiplex degree in both left and right parts of calcarine was found, and meanwhile, IBS patients lost inner-layer hub properties in these regions. In addition, the left fusiform was no longer practicing as an inner-layer hub node, while the right median cingulate acted as a new inner-layer hub node in the IBS patients. Besides, the right calcarine, which lost its inner-layer hub identity, became a new inter-layer hub node, and the multiplex degree of the left hippocampus, which lost its inter-layer hub identity in IBS patients, was significantly positively correlated with the IBS Symptom Severity Score scores. CONCLUSIONS Inner-layer hub nodes of multiplex networks were preferentially vulnerable, and some inner-layer hub nodes would convert into inter-layer hub nodes in IBS patients. Besides, the inter-layer hub nodes might be influenced by IBS severity and therefore converted to general nodes.
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Affiliation(s)
- Nan Chen
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Guangyao Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
| | - Man Guo
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Yongchao Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.,Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University and Institute of Semiconductors, Chinese Academy of Sciences, Lanzhou, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.,Ministry of Education, Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University), Lanzhou, China
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50
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Yu M, Sporns O, Saykin AJ. The human connectome in Alzheimer disease - relationship to biomarkers and genetics. Nat Rev Neurol 2021; 17:545-563. [PMID: 34285392 PMCID: PMC8403643 DOI: 10.1038/s41582-021-00529-1] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/10/2021] [Indexed: 02/06/2023]
Abstract
The pathology of Alzheimer disease (AD) damages structural and functional brain networks, resulting in cognitive impairment. The results of recent connectomics studies have now linked changes in structural and functional network organization in AD to the patterns of amyloid-β and tau accumulation and spread, providing insights into the neurobiological mechanisms of the disease. In addition, the detection of gene-related connectome changes might aid in the early diagnosis of AD and facilitate the development of personalized therapeutic strategies that are effective at earlier stages of the disease spectrum. In this article, we review studies of the associations between connectome changes and amyloid-β and tau pathologies as well as molecular genetics in different subtypes and stages of AD. We also highlight the utility of connectome-derived computational models for replicating empirical findings and for tracking and predicting the progression of biomarker-indicated AD pathophysiology.
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Affiliation(s)
- Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana University Network Science Institute, Bloomington, IN, USA
| | - Olaf Sporns
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana University Network Science Institute, Bloomington, IN, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Andrew J Saykin
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA.
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
- Indiana University Network Science Institute, Bloomington, IN, USA.
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