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Hajebrahimi F, Sangoi A, Scheiman M, Santos E, Gohel S, Alvarez TL. From convergence insufficiency to functional reorganization: A longitudinal randomized controlled trial of treatment-induced connectivity plasticity. CNS Neurosci Ther 2024; 30:e70007. [PMID: 39185637 PMCID: PMC11345633 DOI: 10.1111/cns.70007] [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: 03/26/2024] [Revised: 07/11/2024] [Accepted: 08/08/2024] [Indexed: 08/27/2024] Open
Abstract
INTRODUCTION Convergence Insufficiency (CI) is the most prevalent oculomotor dysfunction of binocular vision that negatively impacts quality of life when performing visual near tasks. Decreased resting-state functional connectivity (RSFC) is reported in the CI participants compared to binocularly normal control participants. Studies report that therapeutic interventions such as office-based vergence and accommodative therapy (OBVAT) can improve CI participants' clinical signs, visual symptoms, and task-related functional activity. However, longitudinal studies investigating the RSFC changes after such treatments in participants with CI have not been conducted. This study aimed to investigate the neural basis of OBVAT using RSFC in CI participants compared to the placebo treatment to understand how OBVAT improves visual function and symptoms. METHODS A total of 51 CI participants between 18 and 35 years of age were included in the study and randomly allocated to receive either 12 one-hour sessions of OBVAT or placebo treatment for 6 to 8 weeks (1 to 2 sessions per week). Resting-state functional magnetic resonance imaging and clinical assessments were evaluated at baseline and outcome for each treatment group. Region of interest (ROI) analysis was conducted in nine ROIs of the oculomotor vergence network, including the following: cerebellar vermis (CV), frontal eye fields (FEF), supplementary eye fields (SEF), parietal eye fields (PEF), and primary visual cortices (V1). Paired t-tests assessed RSFC changes in each group. A linear regression analysis was conducted for significant ROI pairs in the group-level analysis for correlations with clinical measures. RESULTS Paired t-test results showed increased RSFC in 10 ROI pairs after the OBVAT but not placebo treatment (p < 0.05, false discovery rate corrected). These ROI pairs included the following: Left (L)-SEF-Right (R)-V1, L-SEF-CV, R-SEF-R-PEF, R-SEF-L-V1, R-SEF-R-V1, R-SEF-CV, R-PEF-CV, L-V1-CV, R-V1-CV, and L-V1-R-V1. Significant correlations were observed between the RSFC strength of the R-SEF-R-PEF ROI pair and the following clinical visual function parameters: positive fusional vergence and near point of convergence (p < 0.05). CONCLUSION OBVAT, but not placebo treatment, increased the RSFC in the ROIs of the oculomotor vergence network, which was correlated with the improvements in the clinical measures of the CI participants.
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Affiliation(s)
- Farzin Hajebrahimi
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNew JerseyUSA
| | - Ayushi Sangoi
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNew JerseyUSA
| | - Mitchell Scheiman
- Pennsylvania College of OptometrySalus UniversityPhiladelphiaPennsylvaniaUSA
| | - Elio Santos
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNew JerseyUSA
| | - Suril Gohel
- Department of Health InformaticsRutgers University School of Health ProfessionsNewarkNew JerseyUSA
| | - Tara L. Alvarez
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNew JerseyUSA
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Yüksel Dal D, Yıldırım Z, Gürvit H, Kabakçıoğlu A, Acar B. Reorganization of brain connectivity across the spectrum of clinical cognitive decline. Neurol Sci 2024:10.1007/s10072-024-07688-1. [PMID: 39078586 DOI: 10.1007/s10072-024-07688-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 07/08/2024] [Indexed: 07/31/2024]
Abstract
Clinical cognitive decline, leading to Alzheimer's Disease Dementia (ADD), has long been interpreted as a disconnection syndrome, hindering the information flow capacity of the brain, hence leading to the well-known symptoms of ADD. The structural and functional brain connectome analyses play a central role in studies of brain from this perspective. However, most current research implicitly assumes that the changes accompanying the progression of cognitive decline are monotonous in time, whether measured across the entire brain or in fixed cortical regions. We investigate the structural and functional connectivity-wise reorganization of the brain without such assumptions across the entire spectrum. We utilize nodal assortativity as a local topological measure of connectivity and follow a data-centric approach to identify and verify relevant local regions, as well as to understand the nature of underlying reorganization. The analysis of our preliminary experimental data points to statistically significant, hyper and hypo-assortativity regions that depend on the disease's stage, and differ for structural and functional connectomes. Our results suggest a new perspective into the dynamic, potentially a mix of degenerative and compensatory, topological alterations that occur in the brain as cognitive decline progresses.
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Affiliation(s)
- Demet Yüksel Dal
- Department of Electrical & Electronics Engineering, Boğaziçi University, 34342, İstanbul, Turkey.
| | - Zerrin Yıldırım
- Department of Neurology, Bağılar Training and Research Hospital, 34212, İstanbul, Turkey
- Neuroimaging Unit, Hulusi Behçet Life Sciences Research Lab, İstanbul University, 34093, İstanbul, Turkey
| | - Hakan Gürvit
- Department of Neurology, Faculty of Medicine, İstanbul University, 34093, İstanbul, Turkey
- Neuroimaging Unit, Hulusi Behçet Life Sciences Research Lab, İstanbul University, 34093, İstanbul, Turkey
| | | | - Burak Acar
- Department of Electrical & Electronics Engineering, Boğaziçi University, 34342, İstanbul, Turkey
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3
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Wang M, Hua Y, Bai Y. A review of the application of exercise intervention on improving cognition in patients with Alzheimer's disease: mechanisms and clinical studies. Rev Neurosci 2024; 0:revneuro-2024-0046. [PMID: 39029521 DOI: 10.1515/revneuro-2024-0046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 07/08/2024] [Indexed: 07/21/2024]
Abstract
Alzheimer's disease (AD) is the most common form of dementia, leading to sustained cognitive decline. An increasing number of studies suggest that exercise is an effective strategy to promote the improvement of cognition in AD. Mechanisms of the benefits of exercise intervention on cognitive function may include modulation of vascular factors by affecting cardiovascular risk factors, regulating cardiorespiratory health, and enhancing cerebral blood flow. Exercise also promotes neurogenesis by stimulating neurotrophic factors, affecting neuroplasticity in the brain. Additionally, regular exercise improves the neuropathological characteristics of AD by improving mitochondrial function, and the brain redox status. More and more attention has been paid to the effect of Aβ and tau pathology as well as sleep disorders on cognitive function in persons diagnosed with AD. Besides, there are various forms of exercise intervention in cognitive improvement in patients with AD, including aerobic exercise, resistance exercise, and multi-component exercise. Consequently, the purpose of this review is to summarize the findings of the mechanisms of exercise intervention on cognitive function in patients with AD, and also discuss the application of different exercise interventions in cognitive impairment in AD to provide a theoretical basis and reference for the selection of exercise intervention in cognitive rehabilitation in AD.
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Affiliation(s)
- Man Wang
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, No. 12 Middle Wulumuqi Road, Jing'an District, Shanghai 200040, China
- Department of Rehabilitation Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Yan Hua
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, No. 12 Middle Wulumuqi Road, Jing'an District, Shanghai 200040, China
| | - Yulong Bai
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, No. 12 Middle Wulumuqi Road, Jing'an District, Shanghai 200040, China
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Murai SA, Mano T, Sanes JN, Watanabe T. Atypical intrinsic neural timescale in the left angular gyrus in Alzheimer's disease. Brain Commun 2024; 6:fcae199. [PMID: 38993284 PMCID: PMC11227993 DOI: 10.1093/braincomms/fcae199] [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: 10/15/2023] [Revised: 04/18/2024] [Accepted: 06/07/2024] [Indexed: 07/13/2024] Open
Abstract
Alzheimer's disease is characterized by cognitive impairment and progressive brain atrophy. Recent human neuroimaging studies reported atypical anatomical and functional changes in some regions in the default mode network in patients with Alzheimer's disease, but which brain area of the default mode network is the key region whose atrophy disturbs the entire network activity and consequently contributes to the symptoms of the disease remains unidentified. Here, in this case-control study, we aimed to identify crucial neural regions that mediated the phenotype of Alzheimer's disease, and as such, we examined the intrinsic neural timescales-a functional metric to evaluate the capacity to integrate diverse neural information-and grey matter volume of the regions in the default mode network using resting-state functional MRI images and structural MRI data obtained from individuals with Alzheimer's disease and cognitively typical people. After confirming the atypically short neural timescale of the entire default mode network in Alzheimer's disease and its link with the symptoms of the disease, we found that the shortened neural timescale of the default mode network was associated with the aberrantly short neural timescale of the left angular gyrus. Moreover, we revealed that the shortened neural timescale of the angular gyrus was correlated with the atypically reduced grey matter volume of this parietal region. Furthermore, we identified an association between the neural structure, brain function and symptoms and proposed a model in which the reduced grey matter volume of the left angular gyrus shortened the intrinsic neural time of the region, which then destabilized the entire neural timescale of the default mode network and resultantly contributed to cognitive decline in Alzheimer's disease. These findings highlight the key role of the left angular gyrus in the anatomical and functional aetiology of Alzheimer's disease.
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Affiliation(s)
- Shota A Murai
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study, Bunkyo City, Tokyo 113-0033, Japan
| | - Tatsuo Mano
- Department of Degenerative Neurological Diseases, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Jerome N Sanes
- Department of Neuroscience, Brown University, Providence, RI 02912, USA
- Carney Institute for Brain Science, Brown University, Providence, RI 02912, USA
- Center for Neurorestoration and Neurotechnology, Veterans Affairs Providence Healthcare System, Providence, RI 02908, USA
| | - Takamitsu Watanabe
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study, Bunkyo City, Tokyo 113-0033, Japan
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Li J, Li X, Chen F, Li W, Chen J, Zhang B. Studying the Alzheimer's disease continuum using EEG and fMRI in single-modality and multi-modality settings. Rev Neurosci 2024; 35:373-386. [PMID: 38157429 DOI: 10.1515/revneuro-2023-0098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 12/01/2023] [Indexed: 01/03/2024]
Abstract
Alzheimer's disease (AD) is a biological, clinical continuum that covers the preclinical, prodromal, and clinical phases of the disease. Early diagnosis and identification of the stages of Alzheimer's disease (AD) are crucial in clinical practice. Ideally, biomarkers should reflect the underlying process (pathological or otherwise), be reproducible and non-invasive, and allow repeated measurements over time. However, the currently known biomarkers for AD are not suitable for differentiating the stages and predicting the trajectory of disease progression. Some objective parameters extracted using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are widely applied to diagnose the stages of the AD continuum. While electroencephalography (EEG) has a high temporal resolution, fMRI has a high spatial resolution. Combined EEG and fMRI (EEG-fMRI) can overcome single-modality drawbacks and obtain multi-dimensional information simultaneously, and it can help explore the hemodynamic changes associated with the neural oscillations that occur during information processing. This technique has been used in the cognitive field in recent years. This review focuses on the different techniques available for studying the AD continuum, including EEG and fMRI in single-modality and multi-modality settings, and the possible future directions of AD diagnosis using EEG-fMRI.
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Affiliation(s)
- Jing Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Xin Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Futao Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Weiping Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Jiu Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Bing Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
- Jiangsu Key Laboratory of Molecular Medicine, Nanjing, Jiangsu, 210008, China
- Institute of Brain Science, Nanjing University, Nanjing, Jiangsu, 210008, China
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Zhang A, Wengler K, Zhu X, Horga G, Goldberg TE, Lee S. Altered Hierarchical Gradients of Intrinsic Neural Timescales in Mild Cognitive Impairment and Alzheimer's Disease. J Neurosci 2024; 44:e2024232024. [PMID: 38658167 PMCID: PMC11209657 DOI: 10.1523/jneurosci.2024-23.2024] [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: 10/19/2023] [Revised: 03/08/2024] [Accepted: 04/12/2024] [Indexed: 04/26/2024] Open
Abstract
Alzheimer's disease (AD) is a devastating neurodegenerative disease that affects millions of seniors in the United States. Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to study neurophysiology in AD and its prodromal condition, mild cognitive impairment (MCI). The intrinsic neural timescale (INT), which can be estimated through the magnitude of the autocorrelation of neural signals from rs-fMRI, is thought to quantify the duration that neural information is stored in a local circuit. Such heterogeneity of the timescales forms a basis of the brain functional hierarchy and captures an aspect of circuit dynamics relevant to excitation/inhibition balance, which is broadly relevant for cognitive functions. Given that, we applied rs-fMRI to test whether distinct changes of INT at different hierarchies are present in people with MCI, those progressing to AD (called Converter), and AD patients of both sexes. Linear mixed-effect model was implemented to detect altered hierarchical gradients across populations followed by pairwise comparisons to identify regional differences. High similarities between AD and Converter were observed. Specifically, the inferior temporal, caudate, and pallidum areas exhibit significant alterations in both AD and Converter. Distinct INT-related pathological changes in MCI and AD were found. For AD/Converter, neural information is stored for a longer time in lower hierarchical areas, while higher levels of hierarchy seem to be preferentially impaired in MCI leading to a less pronounced hierarchical gradient. These results inform that the INT holds great potential as an additional measure for AD prediction, even a stable biomarker for clinical diagnosis.
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Affiliation(s)
- Aiying Zhang
- New York State Psychiatric Institute, New York, New York 10032
- Department of Psychiatry, Columbia University, New York, New York 10032
| | - Kenneth Wengler
- New York State Psychiatric Institute, New York, New York 10032
- Department of Psychiatry, Columbia University, New York, New York 10032
| | - Xi Zhu
- New York State Psychiatric Institute, New York, New York 10032
- Department of Psychiatry, Columbia University, New York, New York 10032
| | - Guillermo Horga
- New York State Psychiatric Institute, New York, New York 10032
- Department of Psychiatry, Columbia University, New York, New York 10032
| | - Terry E Goldberg
- New York State Psychiatric Institute, New York, New York 10032
- Department of Psychiatry, Columbia University, New York, New York 10032
- Department of Anesthesiology, Columbia University Irving Medical Center, New York, New York 10032
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, New York 10032
| | - Seonjoo Lee
- New York State Psychiatric Institute, New York, New York 10032
- Department of Psychiatry, Columbia University, New York, New York 10032
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York 10032
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Karim SMS, Fahad MS, Rathore RS. Identifying discriminative features of brain network for prediction of Alzheimer's disease using graph theory and machine learning. Front Neuroinform 2024; 18:1384720. [PMID: 38957548 PMCID: PMC11217540 DOI: 10.3389/fninf.2024.1384720] [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: 02/10/2024] [Accepted: 05/17/2024] [Indexed: 07/04/2024] Open
Abstract
Alzheimer's disease (AD) is a challenging neurodegenerative condition, necessitating early diagnosis and intervention. This research leverages machine learning (ML) and graph theory metrics, derived from resting-state functional magnetic resonance imaging (rs-fMRI) data to predict AD. Using Southwest University Adult Lifespan Dataset (SALD, age 21-76 years) and the Open Access Series of Imaging Studies (OASIS, age 64-95 years) dataset, containing 112 participants, various ML models were developed for the purpose of AD prediction. The study identifies key features for a comprehensive understanding of brain network topology and functional connectivity in AD. Through a 5-fold cross-validation, all models demonstrate substantial predictive capabilities (accuracy in 82-92% range), with the support vector machine model standing out as the best having an accuracy of 92%. Present study suggests that top 13 regions, identified based on most important discriminating features, have lost significant connections with thalamus. The functional connection strengths were consistently declined for substantia nigra, pars reticulata, substantia nigra, pars compacta, and nucleus accumbens among AD subjects as compared to healthy adults and aging individuals. The present finding corroborate with the earlier studies, employing various neuroimagining techniques. This research signifies the translational potential of a comprehensive approach integrating ML, graph theory and rs-fMRI analysis in AD prediction, offering potential biomarker for more accurate diagnostics and early prediction of AD.
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Affiliation(s)
- S. M. Shayez Karim
- Department of Bioinformatics, Central University of South Bihar, Bihar, India
| | - Md Shah Fahad
- Department of Computer Science and Engineering, Birla Institute of Technology, Ranchi, India
| | - R. S. Rathore
- Department of Bioinformatics, Central University of South Bihar, Bihar, India
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Rudroff T, Rainio O, Klén R. AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers - A narrative review of a growing field. Neurol Sci 2024:10.1007/s10072-024-07649-8. [PMID: 38866971 DOI: 10.1007/s10072-024-07649-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 06/10/2024] [Indexed: 06/14/2024]
Abstract
OBJECTIVES The objectives of this narrative review are to summarize the current state of AI applications in neuroimaging for early Alzheimer's disease (AD) prediction and to highlight the potential of AI techniques in improving early AD diagnosis, prognosis, and management. METHODS We conducted a narrative review of studies using AI techniques applied to neuroimaging data for early AD prediction. We examined single-modality studies using structural MRI and PET imaging, as well as multi-modality studies integrating multiple neuroimaging techniques and biomarkers. Furthermore, they reviewed longitudinal studies that model AD progression and identify individuals at risk of rapid decline. RESULTS Single-modality studies using structural MRI and PET imaging have demonstrated high accuracy in classifying AD and predicting progression from mild cognitive impairment (MCI) to AD. Multi-modality studies, integrating multiple neuroimaging techniques and biomarkers, have shown improved performance and robustness compared to single-modality approaches. Longitudinal studies have highlighted the value of AI in modeling AD progression and identifying individuals at risk of rapid decline. However, challenges remain in data standardization, model interpretability, generalizability, clinical integration, and ethical considerations. CONCLUSION AI techniques applied to neuroimaging data have the potential to improve early AD diagnosis, prognosis, and management. Addressing challenges related to data standardization, model interpretability, generalizability, clinical integration, and ethical considerations is crucial for realizing the full potential of AI in AD research and clinical practice. Collaborative efforts among researchers, clinicians, and regulatory agencies are needed to develop reliable, robust, and ethical AI tools that can benefit AD patients and society.
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Affiliation(s)
- Thorsten Rudroff
- Department of Health and Human Physiology, University of Iowa, Iowa City, IA, 52242, USA.
- Department of Neurology, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA.
| | - Oona Rainio
- Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland
| | - Riku Klén
- Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland
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Iordan AD, Ploutz-Snyder R, Ghosh B, Rahman-Filipiak A, Koeppe R, Peltier S, Giordani B, Albin RL, Hampstead BM. Salience Network Segregation Mediates the Effect of Tau Pathology on Mild Behavioral Impairment. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.26.24307943. [PMID: 38854100 PMCID: PMC11160832 DOI: 10.1101/2024.05.26.24307943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
INTRODUCTION A recently developed mild behavioral impairment (MBI) diagnostic framework standardizes the early characterization of neuropsychiatric symptoms in older adults. However, the links between MBI, brain function, and Alzheimer's disease (AD) biomarkers are unclear. METHODS Using data from 128 participants with diagnosis of amnestic mild cognitive impairment and mild dementia - Alzheimer's type, we test a novel model assessing direct relationships between AD biomarker status and MBI symptoms, as well as mediated effects through segregation of the salience and default-mode networks. RESULTS We identified a mediated effect of tau positivity on MBI through functional segregation of the salience network from the other high-level, association networks. There were no direct effects of AD biomarkers status on MBI. DISCUSSION Our findings suggest an indirect role of tau pathology in MBI through brain network dysfunction and emphasize the role of the salience network in mediating relationships between neuropathological changes and behavioral manifestations.
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Affiliation(s)
- Alexandru D. Iordan
- Research Program on Cognition and Neuromodulation Based Interventions (RP-CNBI), Department of Psychiatry, University of Michigan, 4251 Plymouth Rd., Ann Arbor, MI, 48105, USA
| | - Robert Ploutz-Snyder
- Applied Biostatistics Laboratory, School of Nursing, University of Michigan, 426 N Ingalls St, Ann Arbor, MI 48109, USA
| | - Bidisha Ghosh
- Applied Biostatistics Laboratory, School of Nursing, University of Michigan, 426 N Ingalls St, Ann Arbor, MI 48109, USA
| | - Annalise Rahman-Filipiak
- Research Program on Cognition and Neuromodulation Based Interventions (RP-CNBI), Department of Psychiatry, University of Michigan, 4251 Plymouth Rd., Ann Arbor, MI, 48105, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, 1500 E Medical Center Dr, Ann Arbor, MI 48109, USA
| | - Scott Peltier
- Functional MRI Laboratory, University of Michigan, 2360 Bonisteel Blvd, Ann Arbor, MI 48109, USA
- Department of Biomedical Engineering, University of Michigan, 2200 Bonisteel Blvd, Ann Arbor, MI 48109, USA
| | - Bruno Giordani
- Research Program on Cognition and Neuromodulation Based Interventions (RP-CNBI), Department of Psychiatry, University of Michigan, 4251 Plymouth Rd., Ann Arbor, MI, 48105, USA
- Department of Neurology, University of Michigan, 1500 E Medical Center Dr, Ann Arbor, MI 48109, USA
| | - Roger L. Albin
- Department of Neurology, University of Michigan, 1500 E Medical Center Dr, Ann Arbor, MI 48109, USA
- Neurology Service & GRECC, VAAAHS, 2215 Fuller Rd, Ann Arbor, MI 48105, USA
| | - Benjamin M. Hampstead
- Research Program on Cognition and Neuromodulation Based Interventions (RP-CNBI), Department of Psychiatry, University of Michigan, 4251 Plymouth Rd., Ann Arbor, MI, 48105, USA
- VA Ann Arbor Healthcare System, Neuropsychology Section, Mental Health Service, 2215 Fuller Rd, Ann Arbor, MI 48105, USA
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Xing L, Guo Z, Long Z. Energy landscape analysis of brain network dynamics in Alzheimer's disease. Front Aging Neurosci 2024; 16:1375091. [PMID: 38813531 PMCID: PMC11133694 DOI: 10.3389/fnagi.2024.1375091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 04/30/2024] [Indexed: 05/31/2024] Open
Abstract
Background Alzheimer's disease (AD) is a common neurodegenerative dementia, characterized by abnormal dynamic functional connectivity (DFC). Traditional DFC analysis, assuming linear brain dynamics, may neglect the complexity of the brain's nonlinear interactions. Energy landscape analysis offers a holistic, nonlinear perspective to investigate brain network attractor dynamics, which was applied to resting-state fMRI data for AD in this study. Methods This study utilized resting-state fMRI data from 60 individuals, comparing 30 Alzheimer's patients with 30 controls, from the Alzheimer's Disease Neuroimaging Initiative. Energy landscape analysis was applied to the data to characterize the aberrant brain network dynamics of AD patients. Results The AD group stayed in the co-activation state for less time than the healthy control (HC) group, and a positive correlation was identified between the transition frequency of the co-activation state and behavior performance. Furthermore, the AD group showed a higher occurrence frequency and transition frequency of the cognitive control state and sensory integration state than the HC group. The transition between the two states was positively correlated with behavior performance. Conclusion The results suggest that the co-activation state could be important to cognitive processing and that the AD group possibly raised cognitive ability by increasing the occurrence and transition between the impaired cognitive control and sensory integration states.
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Affiliation(s)
- Le Xing
- The State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Zhitao Guo
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Zhiying Long
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
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Kemik K, Ada E, Çavuşoğlu B, Aykaç C, Savaş DDE, Yener G. Detecting language network alterations in mild cognitive impairment using task-based fMRI and resting-state fMRI: A comparative study. Brain Behav 2024; 14:e3518. [PMID: 38698619 PMCID: PMC11066416 DOI: 10.1002/brb3.3518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 04/06/2024] [Accepted: 04/13/2024] [Indexed: 05/05/2024] Open
Abstract
OBJECTIVE The objective of this study was to investigate the functional changes associated with mild cognitive impairment (MCI) using independent component analysis (ICA) with the word generation task functional magnetic resonance imaging (fMRI) and resting-state fMRI. METHODS In this study 17 patients with MCI and age and education-matched 17 healthy individuals as control group are investigated. All participants underwent resting-state fMRI and task-based fMRI while performing the word generation task. ICA was used to identify the appropriate independent components (ICs) and their associated networks. The Dice Coefficient method was used to determine the relevance of the ICs to the networks of interest. RESULTS IC-14 was found relevant to language network in both resting-state and task-based fMRI, IC-4 to visual, and IC-28 to dorsal attention network (DAN) in word generation task-based fMRI by Sorento-Dice Coefficient. ICA showed increased activation in language network, which had a larger voxel size in resting-state functional MRI than word generation task-based fMRI in the bilateral lingual gyrus. Right temporo-occipital fusiform cortex, right hippocampus, and right thalamus were also activated in the task-based fMRI. Decreased activation was found in DAN and visual network MCI patients in word generation task-based fMRI. CONCLUSION Task-based fMRI and ICA are more sophisticated and reliable tools in evaluation cognitive impairments in language processing. Our findings support the neural mechanisms of the cognitive impairments in MCI.
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Affiliation(s)
- Kerem Kemik
- Department of Neuroscience, Institute of Health SciencesDokuz Eylül UniversityIzmirTurkey
| | - Emel Ada
- Department of RadiologyDokuz Eylül University Medicine FacultyIzmirTurkey
| | - Berrin Çavuşoğlu
- Department of Medical Physics, Institute of Health SciencesDokuz Eylül UniversityIzmirTurkey
| | - Cansu Aykaç
- Department of Neuroscience, Institute of Health SciencesDokuz Eylül UniversityIzmirTurkey
| | | | - Görsev Yener
- Department of Neurology, Faculty of MedicineIzmir University of EconomicsİzmirTurkey
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12
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Mannone M, Fazio P, Marwan N. Modeling a neurological disorder as the result of an operator acting on the brain: A first sketch based on network channel modeling. CHAOS (WOODBURY, N.Y.) 2024; 34:053133. [PMID: 38781106 DOI: 10.1063/5.0199988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 05/06/2024] [Indexed: 05/25/2024]
Abstract
The brain is a complex network, and diseases can alter its structures and connections between regions. Therefore, we can try to formalize the action of diseases by using operators acting on the brain network. Here, we propose a conceptual model of the brain, seen as a multilayer network, whose intra-lobe interactions are formalized as the diagonal blocks of an adjacency matrix. We propose a general and abstract definition of disease as an operator altering the weights of the connections between neural agglomerates, that is, the elements of the brain matrix. As models, we consider examples from three neurological disorders: epilepsy, Alzheimer-Perusini's disease, and schizophrenia. The alteration of neural connections can be seen as alterations of communication pathways, and thus, they can be described with a new channel model.
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Affiliation(s)
- Maria Mannone
- Institute of Physics and Astronomy, University of Potsdam, 14476 Potsdam, Germany
- DSMN, Ca' Foscari University of Venice, 30170 Venezia Mestre Italy
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, 14473 Potsdam, Germany
| | - Peppino Fazio
- DSMN, Ca' Foscari University of Venice, 30170 Venezia Mestre Italy
- VSB, Technical University of Ostrava, 708 00 Ostrava, Czechia
| | - Norbert Marwan
- Institute of Physics and Astronomy, University of Potsdam, 14476 Potsdam, Germany
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, 14473 Potsdam, Germany
- Institute of Geosciences Potsdam, University of Potsdam, 14473 Potsdam, Germany
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13
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Chen TY, Zhu JD, Tsai SJ, Yang AC. Exploring morphological similarity and randomness in Alzheimer's disease using adjacent grey matter voxel-based structural analysis. Alzheimers Res Ther 2024; 16:88. [PMID: 38654366 PMCID: PMC11036786 DOI: 10.1186/s13195-024-01448-1] [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: 10/27/2023] [Accepted: 04/01/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND Alzheimer's disease is characterized by large-scale structural changes in a specific pattern. Recent studies developed morphological similarity networks constructed by brain regions similar in structural features to represent brain structural organization. However, few studies have used local morphological properties to explore inter-regional structural similarity in Alzheimer's disease. METHODS Here, we sourced T1-weighted MRI images of 342 cognitively normal participants and 276 individuals with Alzheimer's disease from the Alzheimer's Disease Neuroimaging Initiative database. The relationships of grey matter intensity between adjacent voxels were defined and converted to the structural pattern indices. We conducted the information-based similarity method to evaluate the structural similarity of structural pattern organization between brain regions. Besides, we examined the structural randomness on brain regions. Finally, the relationship between the structural randomness and cognitive performance of individuals with Alzheimer's disease was assessed by stepwise regression. RESULTS Compared to cognitively normal participants, individuals with Alzheimer's disease showed significant structural pattern changes in the bilateral posterior cingulate gyrus, hippocampus, and olfactory cortex. Additionally, individuals with Alzheimer's disease showed that the bilateral insula had decreased inter-regional structural similarity with frontal regions, while the bilateral hippocampus had increased inter-regional structural similarity with temporal and subcortical regions. For the structural randomness, we found significant decreases in the temporal and subcortical areas and significant increases in the occipital and frontal regions. The regression analysis showed that the structural randomness of five brain regions was correlated with the Mini-Mental State Examination scores of individuals with Alzheimer's disease. CONCLUSIONS Our study suggested that individuals with Alzheimer's disease alter micro-structural patterns and morphological similarity with the insula and hippocampus. Structural randomness of individuals with Alzheimer's disease changed in temporal, frontal, and occipital brain regions. Morphological similarity and randomness provide valuable insight into brain structural organization in Alzheimer's disease.
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Affiliation(s)
- Ting-Yu Chen
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jun-Ding Zhu
- Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Shih-Jen Tsai
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Psychiatry, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Albert C Yang
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan.
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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14
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Seoane S, van den Heuvel M, Acebes Á, Janssen N. The subcortical default mode network and Alzheimer's disease: a systematic review and meta-analysis. Brain Commun 2024; 6:fcae128. [PMID: 38665961 PMCID: PMC11043657 DOI: 10.1093/braincomms/fcae128] [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: 10/09/2023] [Revised: 02/28/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
The default mode network is a central cortical brain network suggested to play a major role in several disorders and to be particularly vulnerable to the neuropathological hallmarks of Alzheimer's disease. Subcortical involvement in the default mode network and its alteration in Alzheimer's disease remains largely unknown. We performed a systematic review, meta-analysis and empirical validation of the subcortical default mode network in healthy adults, combined with a systematic review, meta-analysis and network analysis of the involvement of subcortical default mode areas in Alzheimer's disease. Our results show that, besides the well-known cortical default mode network brain regions, the default mode network consistently includes subcortical regions, namely the thalamus, lobule and vermis IX and right Crus I/II of the cerebellum and the amygdala. Network analysis also suggests the involvement of the caudate nucleus. In Alzheimer's disease, we observed a left-lateralized cluster of decrease in functional connectivity which covered the medial temporal lobe and amygdala and showed overlap with the default mode network in a portion covering parts of the left anterior hippocampus and left amygdala. We also found an increase in functional connectivity in the right anterior insula. These results confirm the consistency of subcortical contributions to the default mode network in healthy adults and highlight the relevance of the subcortical default mode network alteration in Alzheimer's disease.
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Affiliation(s)
- Sara Seoane
- Department of Complex Traits Genetics, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
- Institute of Biomedical Technologies (ITB), University of La Laguna, Tenerife 38200, Spain
- Instituto Universitario de Neurociencia (IUNE), University of La Laguna, Tenerife 38200, Spain
| | - Martijn van den Heuvel
- Department of Complex Traits Genetics, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
- Department of Child and Adolescent Psychiatry and Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam 1081 HV, The Netherlands
| | - Ángel Acebes
- Institute of Biomedical Technologies (ITB), University of La Laguna, Tenerife 38200, Spain
- Department of Basic Medical Sciences, University of La Laguna, Tenerife 38200, Spain
| | - Niels Janssen
- Institute of Biomedical Technologies (ITB), University of La Laguna, Tenerife 38200, Spain
- Instituto Universitario de Neurociencia (IUNE), University of La Laguna, Tenerife 38200, Spain
- Department of Cognitive, Social and Organizational Psychology, University of La Laguna, Tenerife 38200, Spain
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15
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De Waegenaere S, van den Berg M, Keliris GA, Adhikari MH, Verhoye M. Early altered directionality of resting brain network state transitions in the TgF344-AD rat model of Alzheimer's disease. Front Hum Neurosci 2024; 18:1379923. [PMID: 38646161 PMCID: PMC11026683 DOI: 10.3389/fnhum.2024.1379923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 03/18/2024] [Indexed: 04/23/2024] Open
Abstract
Introduction Alzheimer's disease (AD) is a progressive neurodegenerative disease resulting in memory loss and cognitive decline. Synaptic dysfunction is an early hallmark of the disease whose effects on whole-brain functional architecture can be identified using resting-state functional MRI (rsfMRI). Insights into mechanisms of early, whole-brain network alterations can help our understanding of the functional impact of AD's pathophysiology. Methods Here, we obtained rsfMRI data in the TgF344-AD rat model at the pre- and early-plaque stages. This model recapitulates the major pathological and behavioral hallmarks of AD. We used co-activation pattern (CAP) analysis to investigate if and how the dynamic organization of intrinsic brain functional networks states, undetectable by earlier methods, is altered at these early stages. Results We identified and characterized six intrinsic brain states as CAPs, their spatial and temporal features, and the transitions between the different states. At the pre-plaque stage, the TgF344-AD rats showed reduced co-activation of hub regions in the CAPs corresponding to the default mode-like and lateral cortical network. Default mode-like network activity segregated into two distinct brain states, with one state characterized by high co-activation of the basal forebrain. This basal forebrain co-activation was reduced in TgF344-AD animals mainly at the pre-plaque stage. Brain state transition probabilities were altered at the pre-plaque stage between states involving the default mode-like network, lateral cortical network, and basal forebrain regions. Additionally, while the directionality preference in the network-state transitions observed in the wild-type animals at the pre-plaque stage had diminished at the early-plaque stage, TgF344-AD animals continued to show directionality preference at both stages. Discussion Our study enhances the understanding of intrinsic brain state dynamics and how they are impacted at the early stages of AD, providing a nuanced characterization of the early, functional impact of the disease's neurodegenerative process.
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Affiliation(s)
- Sam De Waegenaere
- Department of Biomedical Sciences, Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Monica van den Berg
- Department of Biomedical Sciences, Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Georgios A. Keliris
- Institute of Computer Science, Foundation for Research and Technology – Hellas, Heraklion, Greece
| | - Mohit H. Adhikari
- Department of Biomedical Sciences, Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Marleen Verhoye
- Department of Biomedical Sciences, Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
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16
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Lee ATC, Luo Y, Huo Z, Shi L, Chu WCW, Lam LCW. Effect of increasing cognitive activity participation on default mode network in older adults with subjective cognitive decline: a randomised controlled trial. EBioMedicine 2024; 102:105082. [PMID: 38531174 PMCID: PMC10982549 DOI: 10.1016/j.ebiom.2024.105082] [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: 11/08/2023] [Revised: 03/07/2024] [Accepted: 03/09/2024] [Indexed: 03/28/2024] Open
Abstract
BACKGROUND Having more cognitive activities may prevent dementia, but its evidence of modulating the functional brain network is limited. This randomised controlled trial (RCT) investigated the effect of increased cognitive activity participation on the default mode network (DMN) in older adults who had already been having regular cognitive activity participation and experiencing subjective cognitive decline (SCD). METHODS Community-living Chinese individuals aged 55-75 years with regular practice of Chinese calligraphy and screened positive for SCD (but negative for mild cognitive impairment or dementia) were randomly allocated to either the intervention or control group. Over 6 months, the intervention group doubled their weekly calligraphy practice time, while the control group maintained their usual amount of practice. The primary outcome was functional connectivities (FCs) of DMN, with pre-specified regions of interest including medial prefrontal cortex (mPFC), inferior parietal lobe (IPL), hippocampal formation (HF), posterior cingulate cortex (PCC), and lateral temporal cortex (LTC). FC changes were compared using repeated measures multivariate analysis of variance (MANOVA). This study is registered at the Chinese Clinical Trial Registry, ChiCTR1900024433. FINDINGS Between 15 January 2020 and 31 December 2021, 112 individuals consented and completed the baseline assessment. The participants, who had a mean age of 66.3 (SD 4.3) years, with 83 (74%) being women, had been practising calligraphy for an average duration of 9.7 years before enrolment and, in the preceding six months, for an average of 3.1 hours per week. 96 (86%) completed the post-intervention fMRI scan. Significant between-group differences were observed in the FCs between mPFC and right LTC (group difference = 0.25 [95% CI = 0.06-0.44], p = 0.009), mPFC and right IPL (0.23 [0.06-0.39]; p = 0.007), left HF and right LTC (0.28 [0.002-0.57]; p = 0.04), and left HF and right IPL (0.34 [0.09-0.60]; p = 0.009). INTERPRETATION Our findings, which reveal positive neuromodulatory effects with increased calligraphy practice, highlight the importance of engaging more in cognitive activities in late life for better brain health. FUNDING Research Grants Council, Hong Kong (grant number 24114519).
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Affiliation(s)
- Allen Ting Chun Lee
- Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
| | - Yishan Luo
- BrainNow Medical Technology Limited, Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Zhaohua Huo
- Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Lin Shi
- BrainNow Medical Technology Limited, Hong Kong Science and Technology Park, Hong Kong SAR, China; Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Winnie Chiu Wing Chu
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Linda Chiu Wa Lam
- Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
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17
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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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18
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Huang W, Fang X, Li S, Mao R, Ye C, Liu W, Deng Y, Lin G. Abnormal characteristic static and dynamic functional network connectivity in idiopathic normal pressure hydrocephalus. CNS Neurosci Ther 2024; 30:e14178. [PMID: 36949617 PMCID: PMC10915979 DOI: 10.1111/cns.14178] [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: 11/22/2022] [Revised: 03/02/2023] [Accepted: 03/03/2023] [Indexed: 03/24/2023] Open
Abstract
AIMS Idiopathic Normal pressure hydrocephalus (iNPH) is a neurodegenerative disease characterized by gait disturbance, dementia, and urinary dysfunction. The neural network mechanisms underlying this phenomenon is currently unknown. METHODS To investigate the resting-state functional connectivity (rs-FC) abnormalities of iNPH-related brain connectivity from static and dynamic perspectives and the correlation of these abnormalities with clinical symptoms before and 3-month after shunt. We investigated both static and dynamic functional network connectivity (sFNC and dFNC, respectively) in 33 iNPH patients and 23 healthy controls (HCs). RESULTS The sFNC and dFNC of networks were generally decreased in iNPH patients. The reduction in sFNC within the default mode network (DMN) and between the somatomotor network (SMN) and visual network (VN) were related to symptoms. The temporal properties of dFNC and its temporal variability in state-4 were sensitive to the identification of iNPH and were correlated with symptoms. The temporal variability in the dorsal attention network (DAN) increased, and the average instantaneous FC was altered among networks in iNPH. These features were partially associated with clinical symptoms. CONCLUSION The dFNC may be a more sensitive biomarker for altered network function in iNPH, providing us with extra information on the mechanisms of iNPH.
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Affiliation(s)
- Wenjun Huang
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Xuhao Fang
- Department of NeurosurgeryHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Shihong Li
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Renling Mao
- Department of NeurosurgeryHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Chuntao Ye
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Wei Liu
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Yao Deng
- Department of NeurosurgeryHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Guangwu Lin
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
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19
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Wang Y, Li Q, Yao L, He N, Tang Y, Chen L, Long F, Chen Y, Kemp GJ, Lui S, Li F. Shared and differing functional connectivity abnormalities of the default mode network in mild cognitive impairment and Alzheimer's disease. Cereb Cortex 2024; 34:bhae094. [PMID: 38521993 DOI: 10.1093/cercor/bhae094] [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: 10/02/2023] [Revised: 02/19/2024] [Accepted: 02/21/2024] [Indexed: 03/25/2024] Open
Abstract
Alzheimer's disease (AD) and mild cognitive impairment (MCI) both show abnormal resting-state functional connectivity (rsFC) of default mode network (DMN), but it is unclear to what extent these abnormalities are shared. Therefore, we performed a comprehensive meta-analysis, including 31 MCI studies and 20 AD studies. MCI patients, compared to controls, showed decreased within-DMN rsFC in bilateral medial prefrontal cortex/anterior cingulate cortex (mPFC/ACC), precuneus/posterior cingulate cortex (PCC), right temporal lobes, and left angular gyrus and increased rsFC between DMN and left inferior temporal gyrus. AD patients, compared to controls, showed decreased rsFC within DMN in bilateral mPFC/ACC and precuneus/PCC and between DMN and left inferior occipital gyrus and increased rsFC between DMN and right dorsolateral prefrontal cortex. Conjunction analysis showed shared decreased rsFC in mPFC/ACC and precuneus/PCC. Compared to MCI, AD had decreased rsFC in left precuneus/PCC and between DMN and left inferior occipital gyrus and increased rsFC in right temporal lobes. MCI and AD share a decreased within-DMN rsFC likely underpinning episodic memory deficits and neuropsychiatric symptoms, but differ in DMN rsFC alterations likely related to impairments in other cognitive domains such as language, vision, and execution. This may throw light on neuropathological mechanisms in these two stages of dementia.
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Affiliation(s)
- Yaxuan Wang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
| | - Qian Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
| | - Li Yao
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
| | - Ning He
- Department of Psychiatry, West China Hospital of Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan, P.R. China
| | - Yingying Tang
- Department of Neurology, West China Hospital of Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan, P.R. China
| | - Lizhou Chen
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
| | - Fenghua Long
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
| | - Yufei Chen
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
| | - Graham J Kemp
- Institute of Life Course and Medical Sciences, University of Liverpool, 6 West Derby Street, Liverpool L7 8TX, United Kingdom
| | - Su Lui
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
| | - Fei Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
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20
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Drenth N, van Dijk SE, Foster-Dingley JC, Bertens AS, Rius Ottenheim N, van der Mast RC, Rombouts SARB, van Rooden S, van der Grond J. Distinct functional subnetworks of cognitive domains in older adults with minor cognitive deficits. Brain Commun 2024; 6:fcae048. [PMID: 38419735 PMCID: PMC10901264 DOI: 10.1093/braincomms/fcae048] [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: 04/28/2023] [Revised: 12/18/2023] [Accepted: 02/14/2024] [Indexed: 03/02/2024] Open
Abstract
Although past research has established a relationship between functional connectivity and cognitive function, less is known about which cognitive domains are associated with which specific functional networks. This study investigated associations between functional connectivity and global cognitive function and performance in the domains of memory, executive function and psychomotor speed in 166 older adults aged 75-91 years (mean = 80.3 ± 3.8) with minor cognitive deficits (Mini-Mental State Examination scores between 21 and 27). Functional connectivity was assessed within 10 standard large-scale resting-state networks and on a finer spatial resolution between 300 nodes in a functional connectivity matrix. No domain-specific associations with mean functional connectivity within large-scale resting-state networks were found. Node-level analysis revealed that associations between functional connectivity and cognitive performance differed across cognitive functions in strength, location and direction. Specific subnetworks of functional connections were found for each cognitive domain in which higher connectivity between some nodes but lower connectivity between other nodes were related to better cognitive performance. Our findings add to a growing body of literature showing differential sensitivity of functional connections to specific cognitive functions and may be a valuable resource for hypothesis generation of future studies aiming to investigate specific cognitive dysfunction with resting-state functional connectivity in people with beginning cognitive deficits.
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Affiliation(s)
- Nadieh Drenth
- Department of Radiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Suzanne E van Dijk
- Department of Radiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Jessica C Foster-Dingley
- Department of Radiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
- Department of Psychiatry, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Anne Suzanne Bertens
- Department of Radiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
- Department of Psychiatry, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Nathaly Rius Ottenheim
- Department of Psychiatry, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Roos C van der Mast
- Department of Psychiatry, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
- Department of Psychiatry, Collaborative Antwerp Psychiatric Research Institute (CAPRI)-University of Antwerp, Antwerp, Belgium
| | - Serge A R B Rombouts
- Department of Radiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
- Institute of Psychology, Leiden University, P.O. Box 9555, 2300 RB Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Sanneke van Rooden
- Department of Radiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
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21
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Zheng L, Rubinski A, Denecke J, Luan Y, Smith R, Strandberg O, Stomrud E, Ossenkoppele R, Svaldi DO, Higgins IA, Shcherbinin S, Pontecorvo MJ, Hansson O, Franzmeier N, Ewers M. Combined Connectomics, MAPT Gene Expression, and Amyloid Deposition to Explain Regional Tau Deposition in Alzheimer Disease. Ann Neurol 2024; 95:274-287. [PMID: 37837382 DOI: 10.1002/ana.26818] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 09/07/2023] [Accepted: 10/03/2023] [Indexed: 10/16/2023]
Abstract
OBJECTIVE We aimed to test whether region-specific factors, including spatial expression patterns of the tau-encoding gene MAPT and regional levels of amyloid positron emission tomography (PET), enhance connectivity-based modeling of the spatial variability in tau-PET deposition in the Alzheimer disease (AD) spectrum. METHODS We included 685 participants (395 amyloid-positive participants within AD spectrum and 290 amyloid-negative controls) with tau-PET and amyloid-PET from 3 studies (Alzheimer's Disease Neuroimaging Initiative, 18 F-AV-1451-A05, and BioFINDER-1). Resting-state functional magnetic resonance imaging was obtained in healthy controls (n = 1,000) from the Human Connectome Project, and MAPT gene expression from the Allen Human Brain Atlas. Based on a brain-parcellation atlas superimposed onto all modalities, we obtained region of interest (ROI)-to-ROI functional connectivity, ROI-level PET values, and MAPT gene expression. In stepwise regression analyses, we tested connectivity, MAPT gene expression, and amyloid-PET as predictors of group-averaged and individual tau-PET ROI values in amyloid-positive participants. RESULTS Connectivity alone explained 21.8 to 39.2% (range across 3 studies) of the variance in tau-PET ROI values averaged across amyloid-positive participants. Stepwise addition of MAPT gene expression and amyloid-PET increased the proportion of explained variance to 30.2 to 46.0% and 45.0 to 49.9%, respectively. Similarly, for the prediction of patient-level tau-PET ROI values, combining all 3 predictors significantly improved the variability explained (mean adjusted R2 range across studies = 0.118-0.148, 0.156-0.196, and 0.251-0.333 for connectivity alone, connectivity plus MAPT expression, and all 3 modalities combined, respectively). INTERPRETATION Across 3 study samples, combining the functional connectome and molecular properties substantially enhanced the explanatory power compared to single modalities, providing a valuable tool to explain regional susceptibility to tau deposition in AD. ANN NEUROL 2024;95:274-287.
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Affiliation(s)
- Lukai Zheng
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
| | - Anna Rubinski
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
| | - Jannis Denecke
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
| | - Ying Luan
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, the Netherlands
| | | | | | | | - Michael J Pontecorvo
- Eli Lilly and Company, Indianapolis, IN, USA
- Avid Radiopharmaceuticals, Philadelphia, PA, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
- Munich Cluster for Systems Neurology, Munich, Germany
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Michael Ewers
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
- German Center for Neurodegenerative Diseases, Munich, Germany
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22
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Guo Y, Ren J, Cui W, Dahmani L, Wang D, Fu X, Li M, Li S, Zhang Y, Lin X, Zhen Z, Xu Y, Xie D, Guan H, Yi F, Wang J, Shi Q, Liu H. Personalized brain MRI revealed distinct functional and anatomical disruptions in Creutzfeldt-Jakob disease and Alzheimer's disease. CNS Neurosci Ther 2024; 30:e14404. [PMID: 37577861 PMCID: PMC10848072 DOI: 10.1111/cns.14404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 07/01/2023] [Accepted: 07/31/2023] [Indexed: 08/15/2023] Open
Abstract
AIMS Creutzfeldt-Jakob disease (CJD) is a lethal neurodegenerative disorder, which leads to a rapidly progressive dementia. This study aimed to examine the cortical alterations in CJD, changes in these brain characteristics over time, and the differences between CJD and Alzheimer's disease (AD) that show similar clinical manifestations. METHODS To obtain reliable, subject-specific functional measures, we acquired 24 min of resting-state fMRI data from each subject. We applied an individual-based approach to characterize the functional brain organization of 10 patients with CJD, 8 matched patients with AD, and 8 normal controls. We measured cortical atrophy as well as disruption in resting-state functional connectivity (rsFC) and then investigated longitudinal brain changes in a subset of CJD patients. RESULTS CJD was associated with widespread cortical thinning and weakened rsFC. Compared with AD, CJD showed distinct atrophy patterns and greater disruptions in rsFC. Moreover, the longitudinal data demonstrated that the progressive cortical thinning and disruption in rsFC mainly affected the association rather than the primary cortex in CJD. CONCLUSIONS CJD shows unique anatomical and functional disruptions in the cerebral cortex, distinct from AD. Rapid progression of CJD affects both the cortical thickness and rsFC in the association cortex.
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Affiliation(s)
- Yanjun Guo
- Department of NeurologyBeijing Tongren Hospital, Capital Medical UniversityBeijingChina
| | | | - Weigang Cui
- School of Engineering MedicineBeihang UniversityBeijingChina
| | - Louisa Dahmani
- Department of RadiologyAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolCharlestownMassachusettsUSA
| | - Danhong Wang
- Department of RadiologyAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolCharlestownMassachusettsUSA
| | | | | | - Shiyi Li
- Changping LaboratoryBeijingChina
| | - Yi Zhang
- Department of RadiologyBeijing Friendship Hospital, Capital Medical UniversityBeijingChina
| | - Xue Lin
- Department of NeurologyBeijing Tongren Hospital, Capital Medical UniversityBeijingChina
| | - Zhen Zhen
- Department of NeurologyBeijing Tongren Hospital, Capital Medical UniversityBeijingChina
| | - Yichen Xu
- Department of NeurosurgeryBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina
| | - Dan Xie
- Department of NeurologyBeijing Friendship Hospital, Capital Medical UniversityBeijingChina
| | - Hongzhi Guan
- Department of NeurologyPeking Union Medical College Hospital, Chinese Academy of Medical SciencesBeijingChina
| | - Fang Yi
- Department of NeurologyLishilu Outpatient, Jingzhong Medical District, Chinese PLA General HospitalBeijingChina
| | - Jiawei Wang
- Department of NeurologyBeijing Tongren Hospital, Capital Medical UniversityBeijingChina
| | - Qi Shi
- State Key Laboratory for Infectious Disease Prevention and Control, National Institute for Viral Disease Control and PreventionChinese Center for Disease Control and PreventionBeijingChina
| | - Hesheng Liu
- Changping LaboratoryBeijingChina
- Biomedical Pioneering Innovation CenterPeking UniversityBeijingChina
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23
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Li W, Zhang M, Huang R, Hu J, Wang L, Ye G, Meng H, Lin X, Liu J, Li B, Zhang Y, Li Y. Topographic metabolism-function relationships in Alzheimer's disease: A simultaneous PET/MRI study. Hum Brain Mapp 2024; 45:e26604. [PMID: 38339890 DOI: 10.1002/hbm.26604] [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: 09/24/2023] [Revised: 12/20/2023] [Accepted: 01/10/2024] [Indexed: 02/12/2024] Open
Abstract
Disruptions of neural metabolism and function occur in parallel during Alzheimer's disease (AD). While many studies have shown diverse metabolic-functional relationships in specific brain regions, much less is known about how large-scale network-level functional activity is associated with the topology of metabolism in AD. In this study, we took the advantages of simultaneous PET/MRI and multivariate analyses to investigate the associations between AD-related stereotypical spatial patterns (topographies) of glucose metabolism, measured by fluorodeoxyglucose PET, and functional connectivity, measured by resting-state functional MRI. A total of 101 participants, including 37 patients with AD, 25 patients with mild cognitive impairment (MCI), and 39 cognitively normal controls, underwent PET/MRI scans and cognitive assessments. Three pairs of distinct but optimally correlated metabolic and functional topographies were identified, encompassing large-scale networks including the default-mode, executive and control, salience, attention, and subcortical networks. Importantly, the metabolic-functional associations were not only limited to one-to-one-corresponding regions, but also occur in remote and non-overlapping regions. Furthermore, both glucose metabolism and functional connectivity, as well as their linkages, exhibited various degrees of disruptions in patients with MCI and AD, and were correlated with cognitive decline. In conclusion, our results support distributed and heterogeneous topographic associations between metabolism and function, which are jeopardized by AD. Findings of this study may deepen our understanding of the pathological mechanism of AD through the perspectives of both local energy efficiency and long-term interactions between synaptic disruption and functional disconnection contributing to the clinical symptomatology in AD.
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Affiliation(s)
- Wenli Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Miao Zhang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ruodong Huang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jialin Hu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Lijun Wang
- Department of Neurovascular Center, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Guanyu Ye
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongping Meng
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaozhu Lin
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Liu
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Ruijin Center, Shanghai, China
| | - Yaoyu Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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24
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Roger K, Vannasing P, Tremblay J, Bringas Vega ML, Bryce CP, Rabinowitz A, Valdes-Sosa PA, Galler JR, Gallagher A. Early childhood malnutrition impairs adult resting brain function using near-infrared spectroscopy. Front Hum Neurosci 2024; 17:1287488. [PMID: 38298205 PMCID: PMC10827877 DOI: 10.3389/fnhum.2023.1287488] [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: 09/01/2023] [Accepted: 12/15/2023] [Indexed: 02/02/2024] Open
Abstract
Introduction Early childhood malnutrition affects 200+ million children under 5 years of age worldwide and is associated with persistent cognitive, behavioral and psychiatric impairments in adulthood. However, very few studies have investigated the long-term effects of childhood protein-energy malnutrition (PEM) on brain function using a functional hemodynamic brain imaging technique. Objective and methods This study aims to investigate functional brain network alterations using near infrared spectroscopy (NIRS) in adults, aged 45-51 years, from the Barbados Nutrition Study (BNS) who suffered from a single episode of malnutrition restricted to their first year of life (n = 26) and controls (n = 29). A total of 55 individuals from the BNS cohort underwent NIRS recording at rest. Results and discussion Using functional connectivity and permutation analysis, we found patterns of increased Pearson's correlation with a specific vulnerability of the frontal cortex in the PEM group (ps < 0.05). Using a graph theoretical approach, mixed ANCOVAs showed increased segregation (ps = 0.0303 and 0.0441) and decreased integration (p = 0.0498) in previously malnourished participants compared to healthy controls. These results can be interpreted as a compensatory mechanism to preserve cognitive functions, that could also be related to premature or pathological brain aging. To our knowledge, this study is the first NIRS neuroimaging study revealing brain function alterations in middle adulthood following early childhood malnutrition limited to the first year of life.
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Affiliation(s)
- Kassandra Roger
- LION Lab, Sainte-Justine University Hospital Research Center, University of Montreal, Montreal, QC, Canada
| | - Phetsamone Vannasing
- LION Lab, Sainte-Justine University Hospital Research Center, University of Montreal, Montreal, QC, Canada
| | - Julie Tremblay
- LION Lab, Sainte-Justine University Hospital Research Center, University of Montreal, Montreal, QC, Canada
| | - Maria L. Bringas Vega
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Arielle Rabinowitz
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Pedro Antonio Valdes-Sosa
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Janina R. Galler
- Division of Pediatric Gastroenterology and Nutrition, MassGeneral Hospital for Children, Boston, MA, United States
| | - Anne Gallagher
- LION Lab, Sainte-Justine University Hospital Research Center, University of Montreal, Montreal, QC, Canada
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25
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Chen Z, Chen K, Li Y, Geng D, Li X, Liang X, Lu H, Ding S, Xiao Z, Ma X, Zheng L, Ding D, Zhao Q, Yang L. Structural, static, and dynamic functional MRI predictors for conversion from mild cognitive impairment to Alzheimer's disease: Inter-cohort validation of Shanghai Memory Study and ADNI. Hum Brain Mapp 2024; 45:e26529. [PMID: 37991144 PMCID: PMC10789213 DOI: 10.1002/hbm.26529] [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: 09/30/2022] [Revised: 10/06/2023] [Accepted: 10/23/2023] [Indexed: 11/23/2023] Open
Abstract
Mild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer's disease (AD), and the mechanism underlying the conversion is not fully explored. Construction and inter-cohort validation of imaging biomarkers for predicting MCI conversion is of great challenge at present, due to lack of longitudinal cohorts and poor reproducibility of various study-specific imaging indices. We proposed a novel framework for inter-cohort MCI conversion prediction, involving comparison of structural, static, and dynamic functional brain features from structural magnetic resonance imaging (sMRI) and resting-state functional MRI (fMRI) between MCI converters (MCI_C) and non-converters (MCI_NC), and support vector machine for construction of prediction models. A total of 218 MCI patients with 3-year follow-up outcome were selected from two independent cohorts: Shanghai Memory Study cohort for internal cross-validation, and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for external validation. In comparison with MCI_NC, MCI_C were mainly characterized by atrophy, regional hyperactivity and inter-network hypo-connectivity, and dynamic alterations characterized by regional and connectional instability, involving medial temporal lobe (MTL), posterior parietal cortex (PPC), and occipital cortex. All imaging-based prediction models achieved an area under the curve (AUC) > 0.7 in both cohorts, with the multi-modality MRI models as the best with excellent performances of AUC > 0.85. Notably, the combination of static and dynamic fMRI resulted in overall better performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features. This inter-cohort validation study provides a new insight into the mechanisms of MCI conversion involving brain dynamics, and paves a way for clinical use of structural and functional MRI biomarkers in future.
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Affiliation(s)
- Zhihan Chen
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Academy for Engineering & TechnologyFudan UniversityShanghaiChina
| | - Keliang Chen
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Yuxin Li
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Institute of Functional and Molecular Medical ImagingFudan UniversityShanghaiChina
| | - Daoying Geng
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Academy for Engineering & TechnologyFudan UniversityShanghaiChina
- Institute of Functional and Molecular Medical ImagingFudan UniversityShanghaiChina
| | - Xiantao Li
- Department of Critical Care MedicineHuashan Hospital, Fudan UniversityShanghaiChina
| | - Xiaoniu Liang
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Huimeng Lu
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Saineng Ding
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Zhenxu Xiao
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Xiaoxi Ma
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Li Zheng
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Ding Ding
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Qianhua Zhao
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
- National Center for Neurological DisordersHuashan Hospital, Fudan UniversityShanghaiChina
- MOE Frontiers Center for Brain ScienceFudan UniversityShanghaiChina
- National Clinical Research Center for Aging and MedicineHuashan Hospital, Fudan UniversityShanghaiChina
| | - Liqin Yang
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Institute of Functional and Molecular Medical ImagingFudan UniversityShanghaiChina
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26
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Biswas R, Sripada S. Causal functional connectivity in Alzheimer's disease computed from time series fMRI data. Front Comput Neurosci 2023; 17:1251301. [PMID: 38169714 PMCID: PMC10758424 DOI: 10.3389/fncom.2023.1251301] [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: 07/01/2023] [Accepted: 11/28/2023] [Indexed: 01/05/2024] Open
Abstract
Functional connectivity between brain regions is known to be altered in Alzheimer's disease and promises to be a biomarker for early diagnosis. Several approaches for functional connectivity obtain an un-directed network representing stochastic associations (correlations) between brain regions. However, association does not necessarily imply causation. In contrast, Causal Functional Connectivity (CFC) is more informative, providing a directed network representing causal relationships between brain regions. In this paper, we obtained the causal functional connectome for the whole brain from resting-state functional magnetic resonance imaging (rs-fMRI) recordings of subjects from three clinical groups: cognitively normal, mild cognitive impairment, and Alzheimer's disease. We applied the recently developed Time-aware PC (TPC) algorithm to infer the causal functional connectome for the whole brain. TPC supports model-free estimation of whole brain CFC based on directed graphical modeling in a time series setting. We compared the CFC outcome of TPC with that of other related approaches in the literature. Then, we used the CFC outcomes of TPC and performed an exploratory analysis of the difference in strengths of CFC edges between Alzheimer's and cognitively normal groups, based on edge-wise p-values obtained by Welch's t-test. The brain regions thus identified are found to be in agreement with literature on brain regions impacted by Alzheimer's disease, published by researchers from clinical/medical institutions.
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Affiliation(s)
- Rahul Biswas
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
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27
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Lyu W, Wu Y, Huang H, Chen Y, Tan X, Liang Y, Ma X, Feng Y, Wu J, Kang S, Qiu S, Yap PT. Aberrant dynamic functional network connectivity in type 2 diabetes mellitus individuals. Cogn Neurodyn 2023; 17:1525-1539. [PMID: 37969945 PMCID: PMC10640562 DOI: 10.1007/s11571-022-09899-8] [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: 07/04/2022] [Revised: 09/11/2022] [Accepted: 10/09/2022] [Indexed: 11/24/2022] Open
Abstract
An increasing number of recent brain imaging studies are dedicated to understanding the neuro mechanism of cognitive impairment in type 2 diabetes mellitus (T2DM) individuals. In contrast to efforts to date that are limited to static functional connectivity, here we investigate abnormal connectivity in T2DM individuals by characterizing the time-varying properties of brain functional networks. Using group independent component analysis (GICA), sliding-window analysis, and k-means clustering, we extracted thirty-one intrinsic connectivity networks (ICNs) and estimated four recurring brain states. We observed significant group differences in fraction time (FT) and mean dwell time (MDT), and significant negative correlation between the Montreal Cognitive Assessment (MoCA) scores and FT/MDT. We found that in the T2DM group the inter- and intra-network connectivity decreases and increases respectively for the default mode network (DMN) and task-positive network (TPN). We also found alteration in the precuneus network (PCUN) and enhanced connectivity between the salience network (SN) and the TPN. Our study provides evidence of alterations of large-scale resting networks in T2DM individuals and shed light on the fundamental mechanisms of neurocognitive deficits in T2DM.
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Affiliation(s)
- Wenjiao Lyu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Ye Wu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC USA
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu China
| | - Haoming Huang
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Yuna Chen
- Department of Endocrinology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Xin Tan
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Yi Liang
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Xiaomeng Ma
- Department of Radiology, Jingzhou First People’s Hospital of Hubei Province, Jingzhou, Hubei China
| | - Yue Feng
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Jinjian Wu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Shangyu Kang
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC USA
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28
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Borchert RJ, Azevedo T, Badhwar A, Bernal J, Betts M, Bruffaerts R, Burkhart MC, Dewachter I, Gellersen HM, Low A, Lourida I, Machado L, Madan CR, Malpetti M, Mejia J, Michopoulou S, Muñoz-Neira C, Pepys J, Peres M, Phillips V, Ramanan S, Tamburin S, Tantiangco HM, Thakur L, Tomassini A, Vipin A, Tang E, Newby D, Ranson JM, Llewellyn DJ, Veldsman M, Rittman T. Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review. Alzheimers Dement 2023; 19:5885-5904. [PMID: 37563912 DOI: 10.1002/alz.13412] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 05/18/2023] [Accepted: 06/02/2023] [Indexed: 08/12/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. METHODS We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. RESULTS A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. DISCUSSION The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. HIGHLIGHTS There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.
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Affiliation(s)
- Robin J Borchert
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Tiago Azevedo
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - AmanPreet Badhwar
- Department of Pharmacology and Physiology, University of Montreal, Montreal, Canada
- Centre de recherche de l'Institut Universitaire de Gériatrie (CRIUGM), Montreal, Canada
| | - Jose Bernal
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Matthew Betts
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Center for Behavioral Brain Sciences, University of Magdeburg, Magdeburg, Germany
| | - Rose Bruffaerts
- Computational Neurology, Experimental Neurobiology Unit, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
| | | | - Ilse Dewachter
- Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Audrey Low
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | | | - Luiza Machado
- Department of Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | | | - Maura Malpetti
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Jhony Mejia
- Department of Biomedical Engineering, Universidad de Los Andes, Bogotá, Colombia
| | - Sofia Michopoulou
- Imaging Physics, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Carlos Muñoz-Neira
- Research into Memory, Brain sciences and dementia Group (ReMemBr Group), Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Artificial Intelligence & Computational Neuroscience Group (AICN Group), Sheffield Institute for Translational Neuroscience (SITraN), Department of Neuroscience, University of Sheffield, Sheffield, UK
| | - Jack Pepys
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Marion Peres
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | | | - Siddharth Ramanan
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | | | - Lokendra Thakur
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, UK
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Alessandro Tomassini
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | | | - Eugene Tang
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Danielle Newby
- Department of Psychiatry, University of Oxford, Oxford, UK
| | | | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- Alan Turing Institute, London, UK
| | - Michele Veldsman
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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Wang L, Xu H, Wang M, Brendel M, Rominger A, Shi K, Han Y, Jiang J. A metabolism-functional connectome sparse coupling method to reveal imaging markers for Alzheimer's disease based on simultaneous PET/MRI scans. Hum Brain Mapp 2023; 44:6020-6030. [PMID: 37740923 PMCID: PMC10619407 DOI: 10.1002/hbm.26493] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 09/01/2023] [Accepted: 09/08/2023] [Indexed: 09/25/2023] Open
Abstract
Abnormal glucose metabolism and hemodynamic changes in the brain are closely related to cognitive function, providing complementary information from distinct biochemical and physiological processes. However, it remains unclear how to effectively integrate these two modalities across distinct brain regions. In this study, we developed a connectome-based sparse coupling method for hybrid PET/MRI imaging, which could effectively extract imaging markers of Alzheimer's disease (AD) in the early stage. The FDG-PET and resting-state fMRI data of 56 healthy controls (HC), 54 subjective cognitive decline (SCD), and 27 cognitive impairment (CI) participants due to AD were obtained from SILCODE project (NCT03370744). For each participant, the metabolic connectome (MC) was constructed by Kullback-Leibler divergence similarity estimation, and the functional connectome (FC) was constructed by Pearson correlation. Subsequently, we measured the coupling strength between MC and FC at various sparse levels, assessed its stability, and explored the abnormal coupling strength along the AD continuum. Results showed that the sparse MC-FC coupling index was stable in each brain network and consistent across subjects. It was more normally distributed than other traditional indexes and captured more SCD-related brain areas, especially in the limbic and default mode networks. Compared to other traditional indices, this index demonstrated best classification performance. The AUC values reached 0.748 (SCD/HC) and 0.992 (CI/HC). Notably, we found a significant correlation between abnormal coupling strength and neuropsychological scales (p < .05). This study provides a clinically relevant tool for hybrid PET/MRI imaging, allowing for exploring imaging markers in early stage of AD and better understanding the pathophysiology along the AD continuum.
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Affiliation(s)
- Luyao Wang
- School of Life SciencesShanghai UniversityShanghaiChina
| | - Huanyu Xu
- School of Communication and Information EngineeringShanghai UniversityShanghaiChina
| | - Min Wang
- School of Life SciencesShanghai UniversityShanghaiChina
| | - Matthias Brendel
- Department of Nuclear MedicineUniversity Hospital of Munich, Ludwig Maximilian University of MunichMunichGermany
| | - Axel Rominger
- Department of Nuclear MedicineInselspital, University Hospital BernBernSwitzerland
| | - Kuangyu Shi
- Department of Nuclear MedicineInselspital, University Hospital BernBernSwitzerland
| | - Ying Han
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
- Center of Alzheimer's DiseaseBeijing Institute for Brain DisordersBeijingChina
- National Clinical Research Center for Geriatric DisordersBeijingChina
- Hainan UniversityHaikouChina
| | - Jiehui Jiang
- School of Life SciencesShanghai UniversityShanghaiChina
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30
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Bick A, McKyton A, Glick-Shames H, Rein N, Levin N. Abnormal network connections to early visual cortex in posterior cortical atrophy. J Neurol Sci 2023; 454:120826. [PMID: 37832379 DOI: 10.1016/j.jns.2023.120826] [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: 06/11/2023] [Revised: 10/02/2023] [Accepted: 10/04/2023] [Indexed: 10/15/2023]
Abstract
INTRODUCTION Posterior Cortical Atrophy (PCA), a visual variant of Alzheimer's disease, initially manifests with higher-order visual disorders and parieto/temporo-occipital atrophy. Recent studies have shown remote functional impairment in both distant brain networks and along the calcarine sulcus (V1). Functional alteration in the calcarine differs along its length, reflecting center to periphery visual space differences. Herein, we aim to connect between these two sets of findings by looking at the retinotopic patterns of functional connectivity between large-scale brain networks and V1, comparing patients with normally sighted subjects. METHODS Resting state functional magnetic resonance imaging (fMRI) and T1 anatomical scans were obtained from 11 PCA patients and 17 age-matched healthy volunteers. Default mode network (DMN) and fronto parietal network (FPN) were defined and differences between the networks in patients and healthy controls were evaluated at the whole brain level, specifically their connectivity to V1. RESULTS Connectivity patterns within the DMN and the FPN were similar between the groups, although differences were found in regions within and beyond the networks. Focusing on V1, in the control group we identified the expected pattern of a distributed connectivity along eccentricity, with foveal regions showing stronger connectivity to the FPN and peripheral regions showing stronger connectivity to the DMN. However, in PCA patients we could not identify a clear difference in connectivity along the eccentricities. CONCLUSION Lost specialization of function along the calcarine in PCA patients may have further implications on large-scale networks or vice versa. This impairment, distant from the core pathology, might explain patients' visual disabilities.
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Affiliation(s)
- Atira Bick
- fMRI unit, Neurology department, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Israel
| | - Ayelet McKyton
- fMRI unit, Neurology department, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Israel
| | - Haya Glick-Shames
- fMRI unit, Neurology department, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Israel
| | - Netaniel Rein
- fMRI unit, Neurology department, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Israel
| | - Netta Levin
- fMRI unit, Neurology department, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Israel.
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31
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Zhang Z, Chan MY, Han L, Carreno CA, Winter-Nelson E, Wig GS. Dissociable Effects of Alzheimer's Disease-Related Cognitive Dysfunction and Aging on Functional Brain Network Segregation. J Neurosci 2023; 43:7879-7892. [PMID: 37714710 PMCID: PMC10648516 DOI: 10.1523/jneurosci.0579-23.2023] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 09/03/2023] [Accepted: 09/11/2023] [Indexed: 09/17/2023] Open
Abstract
Alzheimer's disease (AD) is associated with changes in large-scale functional brain network organization. Individuals with AD exhibit less segregated resting-state brain networks compared with individuals without dementia. However, declines in brain network segregation are also evident as adult individuals grow older. Determining whether these observations reflect unique or overlapping alterations on the functional connectome of the brain is essential for understanding the impact of AD on network organization and incorporating measures of functional brain network organization toward AD characterization. Relationships between AD dementia severity and participant's age on resting-state brain system segregation were examined in 326 cognitively healthy and 275 cognitively impaired human individuals recruited through the Alzheimer's Disease Neuroimaging Initiative (ADNI) (N = 601; age range, 55-96 years; 320 females). Greater dementia severity and increasing age were independently associated with lower brain system segregation. Further, dementia versus age relationships with brain network organization varied according to the processing roles of brain systems and types of network interactions. Aging was associated with alterations to association systems, primarily among within-system relationships. Conversely, dementia severity was associated with alterations that included both association systems and sensory-motor systems and was most prominent among cross-system interactions. Dementia-related network alterations were evident regardless of the presence of cortical amyloid burden, revealing that the measures of functional network organization are unique from this marker of AD-related pathology. Collectively, these observations demonstrate the specific and widespread alterations in the topological organization of large-scale brain networks that accompany AD and highlight functionally dissociable brain network vulnerabilities associated with AD-related cognitive dysfunction versus aging.SIGNIFICANCE STATEMENT Alzheimer's disease (AD)-associated cognitive dysfunction is hypothesized to be a consequence of brain network damage. It is unclear exactly how brain network alterations vary with dementia severity and whether they are distinct from alterations associated with aging. We evaluated functional brain network organization measured at rest among individuals who varied in age and dementia status. AD and aging exerted dissociable impacts on the brain's functional connectome. AD-associated brain network alterations were widespread and involved systems that subserve not only higher-order cognitive operations, but also sensory and motor operations. Notably, AD-related network alterations were independent of amyloid pathology. The research furthers our understanding of AD-related brain dysfunction and motivates refining existing frameworks of dementia characterization with measures of functional network organization.
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Affiliation(s)
- Ziwei Zhang
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas 75235
| | - Micaela Y Chan
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas 75235
| | - Liang Han
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas 75235
| | - Claudia A Carreno
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas 75235
| | - Ezra Winter-Nelson
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas 75235
| | - Gagan S Wig
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas 75235
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas 75390
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32
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Khalilullah KMI, Agcaoglu O, Sui J, Adali T, Duda M, Calhoun VD. Multimodal fusion of multiple rest fMRI networks and MRI gray matter via parallel multilink joint ICA reveals highly significant function/structure coupling in Alzheimer's disease. Hum Brain Mapp 2023; 44:5167-5179. [PMID: 37605825 PMCID: PMC10502647 DOI: 10.1002/hbm.26456] [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: 01/09/2023] [Revised: 07/11/2023] [Accepted: 08/01/2023] [Indexed: 08/23/2023] Open
Abstract
In this article, we focus on estimating the joint relationship between structural magnetic resonance imaging (sMRI) gray matter (GM), and multiple functional MRI (fMRI) intrinsic connectivity networks (ICNs). To achieve this, we propose a multilink joint independent component analysis (ml-jICA) method using the same core algorithm as jICA. To relax the jICA assumption, we propose another extension called parallel multilink jICA (pml-jICA) that allows for a more balanced weight distribution over ml-jICA/jICA. We assume a shared mixing matrix for both the sMRI and fMRI modalities, while allowing for different mixing matrices linking the sMRI data to the different ICNs. We introduce the model and then apply this approach to study the differences in resting fMRI and sMRI data from patients with Alzheimer's disease (AD) versus controls. The results of the pml-jICA yield significant differences with large effect sizes that include regions in overlapping portions of default mode network, and also hippocampus and thalamus. Importantly, we identify two joint components with partially overlapping regions which show opposite effects for AD versus controls, but were able to be separated due to being linked to distinct functional and structural patterns. This highlights the unique strength of our approach and multimodal fusion approaches generally in revealing potentially biomarkers of brain disorders that would likely be missed by a unimodal approach. These results represent the first work linking multiple fMRI ICNs to GM components within a multimodal data fusion model and challenges the typical view that brain structure is more sensitive to AD than fMRI.
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Affiliation(s)
- K. M. Ibrahim Khalilullah
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Oktay Agcaoglu
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Jing Sui
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Tülay Adali
- Department of Electrical and Computer EngineeringUniversity of MarylandBaltimoreMarylandUSA
| | - Marlena Duda
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
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Kim H, Zhu X, Zhao Y, Bell SA, Gehrman PR, Cohen D, Devanand DP, Goldberg TE, Lee S. Resting-state functional connectivity changes in older adults with sleep disturbance and the role of amyloid burden. Mol Psychiatry 2023; 28:4399-4406. [PMID: 37596355 PMCID: PMC10842478 DOI: 10.1038/s41380-023-02214-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 07/19/2023] [Accepted: 08/02/2023] [Indexed: 08/20/2023]
Abstract
Sleep and related disorders could lead to changes in various brain networks, but little is known about the role of amyloid β (Aβ) burden-a key Alzheimer's disease (AD) biomarker-in the relationship between sleep disturbance and altered resting state functional connectivity (rsFC) in older adults. This cross-sectional study examined the association between sleep disturbance, Aβ burden, and rsFC using a large-scale dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Sample included 489 individuals (53.6% cognitively normal, 32.5% mild cognitive impairment, and 13.9% AD) who had completed sleep measures (Neuropsychiatric Inventory), PET Aβ data, and resting-state fMRI scans at baseline. Within and between rsFC of the Salience (SN), the Default Mode (DMN) and the Frontal Parietal network (FPN) were compared between participants with sleep disturbance versus without sleep disturbance. The interaction between Aβ positivity and sleep disturbance was evaluated using the linear regressions, controlling for age, diagnosis status, gender, sedatives and hypnotics use, and hypertension. Although no significant main effect of sleep disturbance was found on rsFC, a significant interaction term emerged between sleep disturbance and Aβ burden on rsFC of SN (β = 0.11, P = 0.006). Specifically, sleep disturbance was associated with SN hyperconnectivity, only with the presence of Aβ burden. Sleep disturbance may lead to altered connectivity in the SN when Aβ is accumulated in the brain. Individuals with AD pathology may be at increased risk for sleep-related aberrant rsFC; therefore, identifying and treating sleep problems in these individuals may help prevent further disease progression.
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Affiliation(s)
- Hyun Kim
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA.
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA.
| | - Xi Zhu
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Division of Anxiety, Mood, Eating, and Related Disorders, New York State Psychiatric Institute, New York, NY, USA
| | - Yiming Zhao
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Sophie A Bell
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA
- Department of Psychology, University of Virginia, Charlottesville, VA, USA
| | - Philip R Gehrman
- Department of Psychiatry, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, PA, USA
- Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Daniel Cohen
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA
| | - D P Devanand
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Terry E Goldberg
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA
- Department of Anesthesiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Seonjoo Lee
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
- Division of Mental Health Data Science, New York State Psychiatric Institute, New York, NY, USA
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34
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Gouilly D, Rafiq M, Nogueira L, Salabert AS, Payoux P, Péran P, Pariente J. Beyond the amyloid cascade: An update of Alzheimer's disease pathophysiology. Rev Neurol (Paris) 2023; 179:812-830. [PMID: 36906457 DOI: 10.1016/j.neurol.2022.12.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 10/02/2022] [Accepted: 12/02/2022] [Indexed: 03/13/2023]
Abstract
Alzheimer's disease (AD) is a multi-etiology disease. The biological system of AD is associated with multidomain genetic, molecular, cellular, and network brain dysfunctions, interacting with central and peripheral immunity. These dysfunctions have been primarily conceptualized according to the assumption that amyloid deposition in the brain, whether from a stochastic or a genetic accident, is the upstream pathological change. However, the arborescence of AD pathological changes suggests that a single amyloid pathway might be too restrictive or inconsistent with a cascading effect. In this review, we discuss the recent human studies of late-onset AD pathophysiology in an attempt to establish a general updated view focusing on the early stages. Several factors highlight heterogenous multi-cellular pathological changes in AD, which seem to work in a self-amplifying manner with amyloid and tau pathologies. Neuroinflammation has an increasing importance as a major pathological driver, and perhaps as a convergent biological basis of aging, genetic, lifestyle and environmental risk factors.
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Affiliation(s)
- D Gouilly
- Toulouse Neuroimaging Center, Toulouse, France.
| | - M Rafiq
- Toulouse Neuroimaging Center, Toulouse, France; Department of Cognitive Neurology, Epilepsy and Movement Disorders, CHU Toulouse Purpan, France
| | - L Nogueira
- Department of Cell Biology and Cytology, CHU Toulouse Purpan, France
| | - A-S Salabert
- Toulouse Neuroimaging Center, Toulouse, France; Department of Nuclear Medicine, CHU Toulouse Purpan, France
| | - P Payoux
- Toulouse Neuroimaging Center, Toulouse, France; Department of Nuclear Medicine, CHU Toulouse Purpan, France; Center of Clinical Investigation, CHU Toulouse Purpan (CIC1436), France
| | - P Péran
- Toulouse Neuroimaging Center, Toulouse, France
| | - J Pariente
- Toulouse Neuroimaging Center, Toulouse, France; Department of Cognitive Neurology, Epilepsy and Movement Disorders, CHU Toulouse Purpan, France; Center of Clinical Investigation, CHU Toulouse Purpan (CIC1436), France
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35
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Waschkies KF, Soch J, Darna M, Richter A, Altenstein S, Beyle A, Brosseron F, Buchholz F, Butryn M, Dobisch L, Ewers M, Fliessbach K, Gabelin T, Glanz W, Goerss D, Gref D, Janowitz D, Kilimann I, Lohse A, Munk MH, Rauchmann BS, Rostamzadeh A, Roy N, Spruth EJ, Dechent P, Heneka MT, Hetzer S, Ramirez A, Scheffler K, Buerger K, Laske C, Perneczky R, Peters O, Priller J, Schneider A, Spottke A, Teipel S, Düzel E, Jessen F, Wiltfang J, Schott BH, Kizilirmak JM. Machine learning-based classification of Alzheimer's disease and its at-risk states using personality traits, anxiety, and depression. Int J Geriatr Psychiatry 2023; 38:e6007. [PMID: 37800601 DOI: 10.1002/gps.6007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 09/07/2023] [Indexed: 10/07/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is often preceded by stages of cognitive impairment, namely subjective cognitive decline (SCD) and mild cognitive impairment (MCI). While cerebrospinal fluid (CSF) biomarkers are established predictors of AD, other non-invasive candidate predictors include personality traits, anxiety, and depression, among others. These predictors offer non-invasive assessment and exhibit changes during AD development and preclinical stages. METHODS In a cross-sectional design, we comparatively evaluated the predictive value of personality traits (Big Five), geriatric anxiety and depression scores, resting-state functional magnetic resonance imaging activity of the default mode network, apoliprotein E (ApoE) genotype, and CSF biomarkers (tTau, pTau181, Aβ42/40 ratio) in a multi-class support vector machine classification. Participants included 189 healthy controls (HC), 338 individuals with SCD, 132 with amnestic MCI, and 74 with mild AD from the multicenter DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE). RESULTS Mean predictive accuracy across all participant groups was highest when utilizing a combination of personality, depression, and anxiety scores. HC were best predicted by a feature set comprised of depression and anxiety scores and participants with AD were best predicted by a feature set containing CSF biomarkers. Classification of participants with SCD or aMCI was near chance level for all assessed feature sets. CONCLUSION Our results demonstrate predictive value of personality trait and state scores for AD. Importantly, CSF biomarkers, personality, depression, anxiety, and ApoE genotype show complementary value for classification of AD and its at-risk stages.
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Affiliation(s)
- Konrad F Waschkies
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
| | - Joram Soch
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Margarita Darna
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Anni Richter
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- German Center for Mental Health (DZPG), Munich, Germany
- Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C), Jena-Magdeburg-Halle, Germany
| | - Slawek Altenstein
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Aline Beyle
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University of Bonn, Bonn, Germany
| | | | - Friederike Buchholz
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin-Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Michaela Butryn
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- University of Bonn Medical Center, Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, Bonn, Germany
| | - Tatjana Gabelin
- Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin-Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Doreen Goerss
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Daria Gref
- Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin-Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Andrea Lohse
- Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Matthias H Munk
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
- Department of Neuroradiology, University Hospital LMU, Munich, Germany
| | - Ayda Rostamzadeh
- Department of Psychiatry, University of Cologne, Medical Faculty, Cologne, Germany
| | - Nina Roy
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Eike Jakob Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Peter Dechent
- MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University Goettingen, Göttingen, Germany
| | - Michael T Heneka
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Stefan Hetzer
- Berlin Center for Advanced Neuroimaging, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Alfredo Ramirez
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- University of Bonn Medical Center, Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, Bonn, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Department of Psychiatry & Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, San Antonio, Texas, USA
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Robert Perneczky
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
- Munich Cluster for Systems Neurology (SyNergy) Munich, Munich, Germany
- Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, UK
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin-Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
- School of Medicine, Technical University of Munich, Department of Psychiatry and Psychotherapy, Munich, Germany
- University of Edinburgh and UK DRI, Edinburgh, UK
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- University of Bonn Medical Center, Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, Bonn, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University of Bonn, Bonn, Germany
| | - Stefan Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Psychiatry, University of Cologne, Medical Faculty, Cologne, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Jens Wiltfang
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
- Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Björn H Schott
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
- Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Jasmin M Kizilirmak
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Neurodidactics and NeuroLab, Institute for Psychology, University of Hildesheim, Hildesheim, Germany
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Gil Ávila C, Bott FS, Tiemann L, Hohn VD, May ES, Nickel MM, Zebhauser PT, Gross J, Ploner M. DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience. Sci Data 2023; 10:613. [PMID: 37696851 PMCID: PMC10495446 DOI: 10.1038/s41597-023-02525-0] [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: 05/19/2023] [Accepted: 08/31/2023] [Indexed: 09/13/2023] Open
Abstract
Biomarker discovery in neurological and psychiatric disorders critically depends on reproducible and transparent methods applied to large-scale datasets. Electroencephalography (EEG) is a promising tool for identifying biomarkers. However, recording, preprocessing, and analysis of EEG data is time-consuming and researcher-dependent. Therefore, we developed DISCOVER-EEG, an open and fully automated pipeline that enables easy and fast preprocessing, analysis, and visualization of resting state EEG data. Data in the Brain Imaging Data Structure (BIDS) standard are automatically preprocessed, and physiologically meaningful features of brain function (including oscillatory power, connectivity, and network characteristics) are extracted and visualized using two open-source and widely used Matlab toolboxes (EEGLAB and FieldTrip). We tested the pipeline in two large, openly available datasets containing EEG recordings of healthy participants and patients with a psychiatric condition. Additionally, we performed an exploratory analysis that could inspire the development of biomarkers for healthy aging. Thus, the DISCOVER-EEG pipeline facilitates the aggregation, reuse, and analysis of large EEG datasets, promoting open and reproducible research on brain function.
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Affiliation(s)
- Cristina Gil Ávila
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München, München, Germany
| | - Felix S Bott
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Laura Tiemann
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Vanessa D Hohn
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Elisabeth S May
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Moritz M Nickel
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Paul Theo Zebhauser
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany
- Center for Interdisciplinary Pain Medicine, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Joachim Gross
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
| | - Markus Ploner
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany.
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany.
- Center for Interdisciplinary Pain Medicine, TUM School of Medicine, Technical University of Munich, München, Germany.
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Hari E, Kizilates-Evin G, Kurt E, Bayram A, Ulasoglu-Yildiz C, Gurvit H, Demiralp T. Functional and structural connectivity in the Papez circuit in different stages of Alzheimer's disease. Clin Neurophysiol 2023; 153:33-45. [PMID: 37451080 DOI: 10.1016/j.clinph.2023.06.008] [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: 02/23/2023] [Revised: 04/12/2023] [Accepted: 06/16/2023] [Indexed: 07/18/2023]
Abstract
OBJECTIVE Alzheimer's disease (AD) is a progressive neurodegenerative continuum with memory impairment. We aimed to examine the detailed functional (FC) and structural connectivity (SC) pattern of the Papez circuit, known as the memory circuit, along the AD. METHODS MRI data of 15 patients diagnosed with AD dementia (ADD), 15 patients with the amnestic mild cognitive impairment (MCI), and 15 patients with subjective cognitive impairment were analyzed. The FC analyses were performed between main nodes of the Papez circuit, and the SC was quantified as fractional anisotropy (FA) of the main white matter pathways of the Papez circuit. RESULTS The FC between the retrosplenial (RSC) and parahippocampal cortices (PHC) was the earliest affected FC, while a manifest SC change in the ventral cingulum and fornix was observed in the later ADD stage. The RSC-PHC FC and the ventral cingulum FA efficiently predicted the memory performance of the non-demented participants. CONCLUSIONS Our findings revealed the importance of the Papez circuit as target regions along the AD. SIGNIFICANCE The ventral cingulum connecting the RSC and PHC, a critical overlap area between the Papez circuit and the default mode network, seems to be a target region associated with the earliest objective memory findings in AD.
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Affiliation(s)
- Emre Hari
- Graduate School of Health Sciences, Istanbul University, 34216 Istanbul, Turkey; Department of Neuroscience, Aziz Sancar Institute of Experimental Medicine, Istanbul University, 34093 Istanbul, Turkey; Hulusi Behcet Life Sciences Research Laboratory, Neuroimaging Unit, Istanbul University, 34093 Istanbul, Turkey.
| | - Gozde Kizilates-Evin
- Department of Neuroscience, Aziz Sancar Institute of Experimental Medicine, Istanbul University, 34093 Istanbul, Turkey; Hulusi Behcet Life Sciences Research Laboratory, Neuroimaging Unit, Istanbul University, 34093 Istanbul, Turkey.
| | - Elif Kurt
- Department of Neuroscience, Aziz Sancar Institute of Experimental Medicine, Istanbul University, 34093 Istanbul, Turkey; Hulusi Behcet Life Sciences Research Laboratory, Neuroimaging Unit, Istanbul University, 34093 Istanbul, Turkey.
| | - Ali Bayram
- Department of Neuroscience, Aziz Sancar Institute of Experimental Medicine, Istanbul University, 34093 Istanbul, Turkey; Hulusi Behcet Life Sciences Research Laboratory, Neuroimaging Unit, Istanbul University, 34093 Istanbul, Turkey.
| | - Cigdem Ulasoglu-Yildiz
- Department of Neuroscience, Aziz Sancar Institute of Experimental Medicine, Istanbul University, 34093 Istanbul, Turkey; Hulusi Behcet Life Sciences Research Laboratory, Neuroimaging Unit, Istanbul University, 34093 Istanbul, Turkey.
| | - Hakan Gurvit
- Hulusi Behcet Life Sciences Research Laboratory, Neuroimaging Unit, Istanbul University, 34093 Istanbul, Turkey; Department of Neurology, Behavioral Neurology and Movement Disorders Unit, Istanbul Faculty of Medicine, Istanbul University, 34093 Istanbul, Turkey.
| | - Tamer Demiralp
- Hulusi Behcet Life Sciences Research Laboratory, Neuroimaging Unit, Istanbul University, 34093 Istanbul, Turkey; Department of Physiology, Istanbul Faculty of Medicine, Istanbul University, 34093 Istanbul, Turkey.
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Bakker ME, Djerourou I, Belanger S, Lesage F, Vanni MP. Alteration of functional connectivity despite preserved cerebral oxygenation during acute hypoxia. Sci Rep 2023; 13:13269. [PMID: 37582847 PMCID: PMC10427674 DOI: 10.1038/s41598-023-40321-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 08/08/2023] [Indexed: 08/17/2023] Open
Abstract
Resting state networks (RSN), which show the connectivity in the brain in the absence of any stimuli, are increasingly important to assess brain function. Here, we investigate the changes in RSN as well as the hemodynamic changes during acute, global hypoxia. Mice were imaged at different levels of oxygen (21, 12, 10 and 8%) over the course of 10 weeks, with hypoxia and normoxia acquisitions interspersed. Simultaneous GCaMP and intrinsic optical imaging allowed tracking of both neuronal and hemodynamic changes. During hypoxic conditions, we found a global increase of both HbO and HbR in the brain. The saturation levels of blood dropped after the onset of hypoxia, but surprisingly climbed back to levels similar to baseline within the 10-min hypoxia period. Neuronal activity also showed a peak at the onset of hypoxia, but dropped back to baseline as well. Despite regaining baseline sO2 levels, changes in neuronal RSN were observed. In particular, the connectivity as measured with GCaMP between anterior and posterior parts of the brain decreased. In contrast, when looking at these same connections with HbO measurements, an increase in connectivity in anterior-posterior brain areas was observed suggesting a potential neurovascular decoupling.
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Affiliation(s)
- Marleen E Bakker
- École d'Optométrie, Université de Montréal, 2500 Chem. De Polytechnique, Montréal, QC, H3T 1J4, Canada.
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montréal, Canada.
| | - Ismaël Djerourou
- École d'Optométrie, Université de Montréal, 2500 Chem. De Polytechnique, Montréal, QC, H3T 1J4, Canada
| | | | - Frédéric Lesage
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montréal, Canada
- Montréal Heart Institute, Montréal, Canada
| | - Matthieu P Vanni
- École d'Optométrie, Université de Montréal, 2500 Chem. De Polytechnique, Montréal, QC, H3T 1J4, Canada
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Chen X, Onur OA, Richter N, Fassbender R, Gramespacher H, Befahr Q, von Reutern B, Dillen K, Jacobs HIL, Kukolja J, Fink GR, Dronse J. Concordance of Intrinsic Brain Connectivity Measures Is Disrupted in Alzheimer's Disease. Brain Connect 2023; 13:344-355. [PMID: 34269605 DOI: 10.1089/brain.2020.0918] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Background: Recently, a new resting-state functional magnetic resonance imaging (rs-fMRI) measure to evaluate the concordance between different rs-fMRI metrics has been proposed and has not been investigated in Alzheimer's disease (AD). Methods: 3T rs-fMRI data were obtained from healthy young controls (YC, n = 26), healthy senior controls (SC, n = 29), and AD patients (n = 35). The fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and degree centrality (DC) were analyzed, followed by the calculation of their concordance using Kendall's W for each brain voxel across time. Group differences in the concordance were compared globally, within seven intrinsic brain networks, and on a voxel-by-voxel basis with covariates of age, sex, head motion, and gray matter volume. Results: The global concordance was lowest in AD among the three groups, with similar differences for the single metrics. When comparing AD to SC, reductions of concordance were detected in each of the investigated networks apart from the limbic network. For SC in comparison to YC, lower global concordance without any network-level difference was observed. Voxel-wise analyses revealed lower concordance in the right middle temporal gyrus in AD compared to SC and lower concordance in the left middle frontal gyrus in SC compared to YC. Lower fALFF were observed in the right angular gyrus in AD in comparison to SC, but ReHo and DC showed no group differences. Conclusions: The concordance of resting-state measures differentiates AD from healthy aging and may represent a novel imaging marker in AD.
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Affiliation(s)
- Xiangliang Chen
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Oezguer A Onur
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Nils Richter
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Ronja Fassbender
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Hannes Gramespacher
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Qumars Befahr
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Boris von Reutern
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Kim Dillen
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
| | - Heidi I L Jacobs
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Psychiatry and Neuropsychology, Alzheimer Centre, Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Juraj Kukolja
- Department of Neurology and Clinical Neurophysiology, Helios University Hospital Wuppertal, Wuppertal, Germany
- Department of Neurology, Faculty of Health, Witten/Herdecke University, Witten, Germany
| | - Gereon R Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Julian Dronse
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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40
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Wheelock MD, Strain JF, Mansfield P, Tu JC, Tanenbaum A, Preische O, Chhatwal JP, Cash DM, Cruchaga C, Fagan AM, Fox NC, Graff-Radford NR, Hassenstab J, Jack CR, Karch CM, Levin J, McDade EM, Perrin RJ, Schofield PR, Xiong C, Morris JC, Bateman RJ, Jucker M, Benzinger TLS, Ances BM, Eggebrecht AT, Gordon BA, Allegri R, Araki A, Barthelemy N, Bateman R, Bechara J, Benzinger T, Berman S, Bodge C, Brandon S, Brooks W, Brosch J, Buck J, Buckles V, Carter K, Cash D, Cash L, Chen C, Chhatwal J, Chrem P, Chua J, Chui H, Cruchaga C, Day GS, De La Cruz C, Denner D, Diffenbacher A, Dincer A, Donahue T, Douglas J, Duong D, Egido N, Esposito B, Fagan A, Farlow M, Feldman B, Fitzpatrick C, Flores S, Fox N, Franklin E, Friedrichsen N, Fujii H, Gardener S, Ghetti B, Goate A, Goldberg S, Goldman J, Gonzalez A, Gordon B, Gräber-Sultan S, Graff-Radford N, Graham M, Gray J, Gremminger E, Grilo M, Groves A, Haass C, Häsler L, Hassenstab J, Hellm C, Herries E, Hoechst-Swisher L, Hofmann A, Holtzman D, Hornbeck R, Igor Y, Ihara R, Ikeuchi T, Ikonomovic S, Ishii K, Jack C, Jerome G, Johnson E, Jucker M, Karch C, Käser S, Kasuga K, Keefe S, Klunk W, Koeppe R, Koudelis D, Kuder-Buletta E, Laske C, Lee JH, Levey A, Levin J, Li Y, Lopez O, Marsh J, Martinez R, Martins R, Mason NS, Masters C, Mawuenyega K, McCullough A, McDade E, Mejia A, Morenas-Rodriguez E, Mori H, Morris J, Mountz J, Mummery C, Nadkami N, Nagamatsu A, Neimeyer K, Niimi Y, Noble J, Norton J, Nuscher B, O'Connor A, Obermüller U, Patira R, Perrin R, Ping L, Preische O, Renton A, Ringman J, Salloway S, Sanchez-Valle R, Schofield P, Senda M, Seyfried N, Shady K, Shimada H, Sigurdson W, Smith J, Smith L, Snitz B, Sohrabi H, Stephens S, Taddei K, Thompson S, Vöglein J, Wang P, Wang Q, Weamer E, Xiong C, Xu J, Xu X. Brain network decoupling with increased serum neurofilament and reduced cognitive function in Alzheimer's disease. Brain 2023; 146:2928-2943. [PMID: 36625756 PMCID: PMC10316768 DOI: 10.1093/brain/awac498] [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: 01/20/2022] [Revised: 11/21/2022] [Accepted: 12/12/2022] [Indexed: 01/11/2023] Open
Abstract
Neurofilament light chain, a putative measure of neuronal damage, is measurable in blood and CSF and is predictive of cognitive function in individuals with Alzheimer's disease. There has been limited prior work linking neurofilament light and functional connectivity, and no prior work has investigated neurofilament light associations with functional connectivity in autosomal dominant Alzheimer's disease. Here, we assessed relationships between blood neurofilament light, cognition, and functional connectivity in a cross-sectional sample of 106 autosomal dominant Alzheimer's disease mutation carriers and 76 non-carriers. We employed an innovative network-level enrichment analysis approach to assess connectome-wide associations with neurofilament light. Neurofilament light was positively correlated with deterioration of functional connectivity within the default mode network and negatively correlated with connectivity between default mode network and executive control networks, including the cingulo-opercular, salience, and dorsal attention networks. Further, reduced connectivity within the default mode network and between the default mode network and executive control networks was associated with reduced cognitive function. Hierarchical regression analysis revealed that neurofilament levels and functional connectivity within the default mode network and between the default mode network and the dorsal attention network explained significant variance in cognitive composite scores when controlling for age, sex, and education. A mediation analysis demonstrated that functional connectivity within the default mode network and between the default mode network and dorsal attention network partially mediated the relationship between blood neurofilament light levels and cognitive function. Our novel results indicate that blood estimates of neurofilament levels correspond to direct measurements of brain dysfunction, shedding new light on the underlying biological processes of Alzheimer's disease. Further, we demonstrate how variation within key brain systems can partially mediate the negative effects of heightened total serum neurofilament levels, suggesting potential regions for targeted interventions. Finally, our results lend further evidence that low-cost and minimally invasive blood measurements of neurofilament may be a useful marker of brain functional connectivity and cognitive decline in Alzheimer's disease.
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Affiliation(s)
- Muriah D Wheelock
- Department of Radiology, Washington University in St. Louis, MO, USA
| | - Jeremy F Strain
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO, USA
| | | | - Jiaxin Cindy Tu
- Department of Radiology, Washington University in St. Louis, MO, USA
| | - Aaron Tanenbaum
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO, USA
| | - Oliver Preische
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Jasmeer P Chhatwal
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - David M Cash
- Dementia Research Center, UCL Queen Square, London, UK.,UK Dementia Research Institute, College London, London, UK
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St. Louis, MO, USA
| | - Anne M Fagan
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO, USA
| | - Nick C Fox
- Dementia Research Center, UCL Queen Square, London, UK.,UK Dementia Research Institute, College London, London, UK
| | | | - Jason Hassenstab
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO, USA
| | | | - Celeste M Karch
- Department of Psychiatry, Washington University in St. Louis, MO, USA
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany.,German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Eric M McDade
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO, USA
| | - Richard J Perrin
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO, USA.,Department of Pathology & Immunology, Washington University in St. Louis, MO, USA
| | - Peter R Schofield
- Neuroscience Research Australia, Sydney, NSW, Australia.,School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Chengjie Xiong
- Division of Biostatistics, Washington University in St. Louis, MO, USA
| | - John C Morris
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO, USA
| | - Randal J Bateman
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO, USA
| | - Mathias Jucker
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Tammie L S Benzinger
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO, USA
| | - Beau M Ances
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO, USA
| | - Adam T Eggebrecht
- Department of Radiology, Washington University in St. Louis, MO, USA
| | - Brian A Gordon
- Department of Radiology, Washington University in St. Louis, MO, USA
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Adhikari MH, Vasilkovska T, Cachope R, Tang H, Liu L, Keliris GA, Munoz-Sanjuan I, Pustina D, Van der Linden A, Verhoye M. Longitudinal investigation of changes in resting-state co-activation patterns and their predictive ability in the zQ175 DN mouse model of Huntington's disease. Sci Rep 2023; 13:10194. [PMID: 37353500 PMCID: PMC10290061 DOI: 10.1038/s41598-023-36812-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 06/10/2023] [Indexed: 06/25/2023] Open
Abstract
Huntington's disease (HD) is a neurodegenerative disorder caused by expanded (≥ 40) glutamine-encoding CAG repeats in the huntingtin gene, which leads to dysfunction and death of predominantly striatal and cortical neurons. While the genetic profile and clinical signs and symptoms of the disease are better known, changes in the functional architecture of the brain, especially before the clinical expression becomes apparent, are not fully and consistently characterized. In this study, we sought to uncover functional changes in the brain in the heterozygous (HET) zQ175 delta-neo (DN) mouse model at 3, 6, and 10 months of age, using resting-state functional magnetic resonance imaging (RS-fMRI). This mouse model shows molecular, cellular and circuitry alterations that worsen through age. Motor function disturbances are manifested in this model at 6 and 10 months of age. Specifically, we investigated, longitudinally, changes in co-activation patterns (CAPs) that are the transient states of brain activity constituting the resting-state networks (RSNs). Most robust changes in the temporal properties of CAPs occurred at the 10-months time point; the durations of two anti-correlated CAPs, characterized by simultaneous co-activation of default-mode like network (DMLN) and co-deactivation of lateral-cortical network (LCN) and vice-versa, were reduced in the zQ175 DN HET animals compared to the wild-type mice. Changes in the spatial properties, measured in terms of activation levels of different brain regions, during CAPs were found at all three ages and became progressively more pronounced at 6-, and 10 months of age. We then assessed the cross-validated predictive power of CAP metrics to distinguish HET animals from controls. Spatial properties of CAPs performed significantly better than the chance level at all three ages with 80% classification accuracy at 6 and 10 months of age.
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Affiliation(s)
- Mohit H Adhikari
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium.
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium.
| | - Tamara Vasilkovska
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Roger Cachope
- CHDI Management for CHDI Foundation, Princeton, NJ, USA
| | - Haiying Tang
- CHDI Management for CHDI Foundation, Princeton, NJ, USA
| | - Longbin Liu
- CHDI Management for CHDI Foundation, Princeton, NJ, USA
| | - Georgios A Keliris
- Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Crete, Greece
| | | | | | - Annemie Van der Linden
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Marleen Verhoye
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
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Martinez Villar G, Daneault V, Martineau-Dussault MÈ, Baril AA, Gagnon K, Lafond C, Gilbert D, Thompson C, Marchi NA, Lina JM, Montplaisir J, Carrier J, Gosselin N, André C. Altered resting-state functional connectivity patterns in late middle-aged and older adults with obstructive sleep apnea. Front Neurol 2023; 14:1215882. [PMID: 37470008 PMCID: PMC10353887 DOI: 10.3389/fneur.2023.1215882] [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: 05/02/2023] [Accepted: 06/05/2023] [Indexed: 07/21/2023] Open
Abstract
Introduction Obstructive sleep apnea (OSA) is increasingly recognized as a risk factor for cognitive decline, and has been associated with structural brain alterations in regions relevant to memory processes and Alzheimer's disease. However, it is unclear whether OSA is associated with disrupted functional connectivity (FC) patterns between these regions in late middle-aged and older populations. Thus, we characterized the associations between OSA severity and resting-state FC between the default mode network (DMN) and medial temporal lobe (MTL) regions. Second, we explored whether significant FC changes differed depending on cognitive status and were associated with cognitive performance. Methods Ninety-four participants [24 women, 65.7 ± 6.9 years old, 41% with Mild Cognitive Impairment (MCI)] underwent a polysomnography, a comprehensive neuropsychological assessment and a resting-state functional magnetic resonance imaging (MRI). General linear models were conducted between OSA severity markers (i.e., the apnea-hypopnea, oxygen desaturation and microarousal indices) and FC values between DMN and MTL regions using CONN toolbox. Partial correlations were then performed between OSA-related FC patterns and (i) OSA severity markers in subgroups stratified by cognitive status (i.e., cognitively unimpaired versus MCI) and (ii) cognitive scores in the whole sample. All analyzes were controlled for age, sex and education, and considered significant at a p < 0.05 threshold corrected for false discovery rate. Results In the whole sample, a higher apnea-hypopnea index was significantly associated with lower FC between (i) the medial prefrontal cortex and bilateral hippocampi, and (ii) the left hippocampus and both the posterior cingulate cortex and precuneus. FC patterns were not associated with the oxygen desaturation index, or micro-arousal index. When stratifying the sample according to cognitive status, all associations remained significant in cognitively unimpaired individuals but not in the MCI group. No significant associations were observed between cognition and OSA severity or OSA-related FC patterns. Discussion OSA severity was associated with patterns of lower FC in regions relevant to memory processes and Alzheimer's disease. Since no associations were found with cognitive performance, these FC changes could precede detectable cognitive deficits. Whether these FC patterns predict future cognitive decline over the long-term needs to be investigated.
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Affiliation(s)
- Guillermo Martinez Villar
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
- Department of Psychology, Université de Montréal, Montréal, QC, Canada
| | - Véronique Daneault
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
| | - Marie-Ève Martineau-Dussault
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
- Department of Psychology, Université de Montréal, Montréal, QC, Canada
| | - Andrée-Ann Baril
- Douglas Mental Health Institute, McGill University, Montréal, QC, Canada
| | - Katia Gagnon
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
- Laboratory and Sleep Clinic, Hôpital en Santé Mentale Rivière-des-Prairies, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
| | - Chantal Lafond
- Department of Pulmonology, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
- Department of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Danielle Gilbert
- Department of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, QC, Canada
- Department of Radiology, Hopital du Sacré-Coeur de Montréal, CIUSSS du Nord-de-l'Ile-de, Montréal, QC, Canada
| | - Cynthia Thompson
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
| | - Nicola Andrea Marchi
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
- Department of Psychology, Université de Montréal, Montréal, QC, Canada
- Center for Investigation and Research in Sleep, Department of Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jean-Marc Lina
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
- Département de Génie Electrique, École de Technologie Supérieure, Montréal, QC, Canada
| | - Jacques Montplaisir
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
- Department of Psychiatry, Université de Montréal, Montréal, QC, Canada
| | - Julie Carrier
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
- Department of Psychology, Université de Montréal, Montréal, QC, Canada
| | - Nadia Gosselin
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
- Department of Psychology, Université de Montréal, Montréal, QC, Canada
| | - Claire André
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord de l'Île-de-Montréal, Montréal, QC, Canada
- Department of Psychology, Université de Montréal, Montréal, QC, Canada
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43
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Jing R, Chen P, Wei Y, Si J, Zhou Y, Wang D, Song C, Yang H, Zhang Z, Yao H, Kang X, Fan L, Han T, Qin W, Zhou B, Jiang T, Lu J, Han Y, Zhang X, Liu B, Yu C, Wang P, Liu Y. Altered large-scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study. Hum Brain Mapp 2023; 44:3467-3480. [PMID: 36988434 PMCID: PMC10203807 DOI: 10.1002/hbm.26291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 02/27/2023] [Accepted: 03/15/2023] [Indexed: 03/30/2023] Open
Abstract
Alzheimer's disease (AD) is a common neurodegeneration disease associated with substantial disruptions in the brain network. However, most studies investigated static resting-state functional connections, while the alteration of dynamic functional connectivity in AD remains largely unknown. This study used group independent component analysis and the sliding-window method to estimate the subject-specific dynamic connectivity states in 1704 individuals from three data sets. Informative inherent states were identified by the multivariate pattern classification method, and classifiers were built to distinguish ADs from normal controls (NCs) and to classify mild cognitive impairment (MCI) patients with informative inherent states similar to ADs or not. In addition, MCI subgroups with heterogeneous functional states were examined in the context of different cognition decline trajectories. Five informative states were identified by feature selection, mainly involving functional connectivity belonging to the default mode network and working memory network. The classifiers discriminating AD and NC achieved the mean area under the receiver operating characteristic curve of 0.87 with leave-one-site-out cross-validation. Alterations in connectivity strength, fluctuation, and inter-synchronization were found in AD and MCIs. Moreover, individuals with MCI were clustered into two subgroups, which had different degrees of atrophy and different trajectories of cognition decline progression. The present study uncovered the alteration of dynamic functional connectivity in AD and highlighted that the dynamic states could be powerful features to discriminate patients from NCs. Furthermore, it demonstrated that these states help to identify MCIs with faster cognition decline and might contribute to the early prevention of AD.
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Affiliation(s)
- Rixing Jing
- School of Instrument Science and Opto‐Electronics EngineeringBeijing Information Science and Technology UniversityBeijingChina
| | - Pindong Chen
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Yongbin Wei
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijingChina
| | - Juanning Si
- School of Instrument Science and Opto‐Electronics EngineeringBeijing Information Science and Technology UniversityBeijingChina
| | - Yuying Zhou
- Department of NeurologyTianjin Huanhu Hospital, Tianjin UniversityTianjinChina
| | - Dawei Wang
- Department of RadiologyQilu Hospital of Shandong UniversityJi'nanChina
| | - Chengyuan Song
- Department of NeurologyQilu Hospital of Shandong UniversityJi'nanChina
| | - Hongwei Yang
- Department of RadiologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | | | - Hongxiang Yao
- Department of Radiology, the Second Medical CentreNational Clinical Research Centre for Geriatric Diseases, Chinese PLA General HospitalBeijingChina
| | - Xiaopeng Kang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Lingzhong Fan
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Tong Han
- Department of RadiologyTianjin Huanhu HospitalTianjinChina
| | - Wen Qin
- Department of RadiologyTianjin Medical University General HospitalTianjinChina
| | - Bo Zhou
- Department of Neurologythe Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General HospitalBeijingChina
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Jie Lu
- Department of RadiologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | - Ying Han
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
- Beijing Institute of GeriatricsBeijingChina
- National Clinical Research Center for Geriatric DisordersBeijingChina
| | - Xi Zhang
- Department of Neurologythe Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General HospitalBeijingChina
| | - Bing Liu
- State Key Laboratory of Cognition Neuroscience & LearningBeijing Normal UniversityBeijingChina
| | - Chunshui Yu
- Department of RadiologyTianjin Medical University General HospitalTianjinChina
| | - Pan Wang
- Department of NeurologyTianjin Huanhu Hospital, Tianjin UniversityTianjinChina
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijingChina
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44
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Singh NA, Martin PR, Graff-Radford J, Sintini I, Machulda MM, Duffy JR, Gunter JL, Botha H, Jones DT, Lowe VJ, Jack CR, Josephs KA, Whitwell JL. Altered within- and between-network functional connectivity in atypical Alzheimer's disease. Brain Commun 2023; 5:fcad184. [PMID: 37434879 PMCID: PMC10331277 DOI: 10.1093/braincomms/fcad184] [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: 09/03/2022] [Revised: 05/04/2023] [Accepted: 06/13/2023] [Indexed: 07/13/2023] Open
Abstract
Posterior cortical atrophy and logopenic progressive aphasia are atypical clinical presentations of Alzheimer's disease. Resting-state functional connectivity studies have shown functional network disruptions in both phenotypes, particularly involving the language network in logopenic progressive aphasia and the visual network in posterior cortical atrophy. However, little is known about how connectivity differs both within and between brain networks in these atypical Alzheimer's disease phenotypes. A cohort of 144 patients was recruited by the Neurodegenerative Research Group at Mayo Clinic, Rochester, MN, USA, and underwent structural and resting-state functional MRI. Spatially preprocessed data were analysed to explore the default mode network and the salience, sensorimotor, language, visual and memory networks. The data were analysed at the voxel and network levels. Bayesian hierarchical linear models adjusted for age and sex were used to analyse within- and between-network connectivity. Reduced within-network connectivity was observed in the language network in both phenotypes, with stronger evidence of reductions in logopenic progressive aphasia compared to controls. Only posterior cortical atrophy showed reduced within-network connectivity in the visual network compared to controls. Both phenotypes showed reduced within-network connectivity in the default mode and sensorimotor networks. No significant change was noted in the memory network, but a slight increase in the salience within-network connectivity was seen in both phenotypes compared to controls. Between-network analysis in posterior cortical atrophy showed evidence of reduced visual-to-language network connectivity, with reduced visual-to-salience network connectivity, compared to controls. An increase in visual-to-default mode network connectivity was noted in posterior cortical atrophy compared to controls. Between-network analysis in logopenic progressive aphasia showed evidence of reduced language-to-visual network connectivity and an increase in language-to-salience network connectivity compared to controls. Findings from the voxel-level and network-level analysis were in line with the Bayesian hierarchical linear model analysis, showing reduced connectivity in the dominant network based on diagnosis and more crosstalk between networks in general compared to controls. The atypical Alzheimer's disease phenotypes were associated with disruptions in connectivity, both within and between brain networks. Phenotype-specific differences in connectivity patterns were noted in the visual network for posterior cortical atrophy and the language network for logopenic progressive aphasia.
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Affiliation(s)
| | - Peter R Martin
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Irene Sintini
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Mary M Machulda
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN 55905, USA
| | - Joseph R Duffy
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Keith A Josephs
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
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45
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Chiari-Correia RD, Tumas V, Santos AC, Salmon CEG. Structural and functional differences in the brains of patients with MCI with and without depressive symptoms and their relations with Alzheimer's disease: an MRI study. PSYCHORADIOLOGY 2023; 3:kkad008. [PMID: 38666129 PMCID: PMC10917365 DOI: 10.1093/psyrad/kkad008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/19/2023] [Accepted: 06/12/2023] [Indexed: 04/28/2024]
Abstract
Background The mild cognitive impairment (MCI) stage among elderly individuals is very complex, and the level of diagnostic accuracy is far from ideal. Some studies have tried to improve the 'MCI due to Alzheimer's disease (AD)' classification by further stratifying these patients into subgroups. Depression-related symptoms may play an important role in helping to better define the MCI stage in elderly individuals. Objective In this work, we explored functional and structural differences in the brains of patients with nondepressed MCI (nDMCI) and patients with MCI with depressive symptoms (DMCI), and we examined how these groups relate to AD atrophy patterns and cognitive functioning. Methods Sixty-five participants underwent MRI exams and were divided into four groups: cognitively normal, nDMCI, DMCI, and AD. We compared the regional brain volumes, cortical thickness, and white matter microstructure measures using diffusion tensor imaging among groups. Additionally, we evaluated changes in functional connectivity using fMRI data. Results In comparison to the nDMCI group, the DMCI patients had more pronounced atrophy in the hippocampus and amygdala. Additionally, DMCI patients had asymmetric damage in the limbic-frontal white matter connection. Furthermore, two medial posterior regions, the isthmus of cingulate gyrus and especially the lingual gyrus, had high importance in the structural and functional differentiation between the two groups. Conclusion It is possible to differentiate nDMCI from DMCI patients using MRI techniques, which may contribute to a better characterization of subtypes of the MCI stage.
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Affiliation(s)
- Rodolfo Dias Chiari-Correia
- Department of Neurosciences and Behavioral Sciences, Ribeirao Preto Medical School, University of Sao Paulo, 3900 Bandeirantes Avenue, Ribeirao Preto SP, 14040-900, Brazil
| | - Vitor Tumas
- Department of Neurosciences and Behavioral Sciences, Ribeirao Preto Medical School, University of Sao Paulo, 3900 Bandeirantes Avenue, Ribeirao Preto SP, 14040-900, Brazil
| | - Antônio Carlos Santos
- Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirao Preto Medical School, University of Sao Paulo, 3900 Bandeirantes Avenue, Ribeirao Preto SP, 14040-900, Brazil
| | - Carlos Ernesto G Salmon
- Department of Physics, Faculty of Philosophy, Sciences and Letters, University of Sao Paulo, 3900 Bandeirantes Avenue, Ribeirao Preto SP, 14040-900, Brazil
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46
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Zhang J, Wang J, Xu X, You Z, Huang Q, Huang Y, Guo Q, Guan Y, Zhao J, Liu J, Xu W, Deng Y, Xie F, Li B. In vivo synaptic density loss correlates with impaired functional and related structural connectivity in Alzheimer's disease. J Cereb Blood Flow Metab 2023; 43:977-988. [PMID: 36718002 PMCID: PMC10196742 DOI: 10.1177/0271678x231153730] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 12/29/2022] [Accepted: 01/02/2023] [Indexed: 02/01/2023]
Abstract
Synapse loss has been considered as a major pathological change in Alzheimer's disease (AD). It remains unclear about whether and how synapse loss relates to functional and structural connectivity dysfunction in AD. We measured synaptic vesicle glycoprotein 2 A (SV2A) binding using 18F-SynVesT-1 PET to evaluate synaptic alterations in 33 participants with AD, 31 with mild cognitive impairment (MCI), and 30 controls. We examined the correlation between synaptic density and cognitive function. Functional MRI was performed to analyze functional connectivity in lower synaptic density regions. We tracked the white matter tracts between impaired functional connectivity regions using Diffusion MRI. In AD group, lower synaptic density in bilateral cortex and hippocampus was found when compared with controls. The synaptic density changes in right insular cortex and bilateral caudal middle frontal gyrus (MFG) were correlated with cognitive decline. Among them, right MFG synaptic density was positively associated with right MFG - bilateral superior frontal gyrus (SFG) functional connectivity. AD had lower probability of tract (POT) between right MFG and SFG than controls, which was significantly associated with global cognition. These findings provide evidence supporting synapse loss contributes to functional and related structural connectivity alterations underlying cognitive impairment of AD.
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Affiliation(s)
- Junfang Zhang
- Department of Neurology and
Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Jie Wang
- Department of Nuclear Medicine
& PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaomeng Xu
- Department of Neurology and
Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Zhiwen You
- Department of Nuclear Medicine,
Shanghai East Hospital, Tongji University School of Medicine, Shanghai,
China
| | - Qi Huang
- Department of Nuclear Medicine
& PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Yiyun Huang
- PET Center, Department of Radiology
and Biomedical Imaging, Yale University School of Medicine, New Haven,
Connecticut, USA
| | - Qihao Guo
- Department of Gerontology, Shanghai
Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Yihui Guan
- Department of Nuclear Medicine
& PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jun Zhao
- Department of Nuclear Medicine,
Shanghai East Hospital, Tongji University School of Medicine, Shanghai,
China
| | - Jun Liu
- Department of Neurology and
Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
- Clinical Neuroscience Center,
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai,
China
| | - Wei Xu
- Department of Neurology and
Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Yulei Deng
- Department of Neurology and
Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
- Clinical Neuroscience Center,
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai,
China
- Department of Neurology, Ruijin
Hospital LuWan Branch, Shanghai Jiao Tong University School of Medicine,
Shanghai, China
| | - Fang Xie
- Department of Nuclear Medicine
& PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Binyin Li
- Department of Neurology and
Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
- Clinical Neuroscience Center,
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai,
China
- Department of Neurology, Ruijin
Hospital LuWan Branch, Shanghai Jiao Tong University School of Medicine,
Shanghai, China
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47
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Ghanbari M, Li G, Hsu L, Yap P. Accumulation of network redundancy marks the early stage of Alzheimer's disease. Hum Brain Mapp 2023; 44:2993-3006. [PMID: 36896755 PMCID: PMC10171535 DOI: 10.1002/hbm.26257] [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: 08/25/2022] [Revised: 02/15/2023] [Accepted: 02/18/2023] [Indexed: 03/11/2023] Open
Abstract
Brain wiring redundancy counteracts aging-related cognitive decline by reserving additional communication channels as a neuroprotective mechanism. Such a mechanism plays a potentially important role in maintaining cognitive function during the early stages of neurodegenerative disorders such as Alzheimer's disease (AD). AD is characterized by severe cognitive decline and involves a long prodromal stage of mild cognitive impairment (MCI). Since MCI subjects are at high risk of converting to AD, identifying MCI individuals is essential for early intervention. To delineate the redundancy profile during AD progression and enable better MCI diagnosis, we define a metric that reflects redundant disjoint connections between brain regions and extract redundancy features in three high-order brain networks-medial frontal, frontoparietal, and default mode networks-based on dynamic functional connectivity (dFC) captured by resting-state functional magnetic resonance imaging (rs-fMRI). We show that redundancy increases significantly from normal control (NC) to MCI individuals and decreases slightly from MCI to AD individuals. We further demonstrate that statistical features of redundancy are highly discriminative and yield state-of-the-art accuracy of up to 96.8 ± 1.0% in support vector machine (SVM) classification between NC and MCI individuals. This study provides evidence supporting the notion that redundancy serves as a crucial neuroprotective mechanism in MCI.
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Affiliation(s)
- Maryam Ghanbari
- Department of RadiologyUniversity of North CarolinaChapel HillNorth CarolinaUSA
- Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Guoshi Li
- Department of RadiologyUniversity of North CarolinaChapel HillNorth CarolinaUSA
- Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Li‐Ming Hsu
- Department of RadiologyUniversity of North CarolinaChapel HillNorth CarolinaUSA
- Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Pew‐Thian Yap
- Department of RadiologyUniversity of North CarolinaChapel HillNorth CarolinaUSA
- Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillNorth CarolinaUSA
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48
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Yang M, Chen B, Zhou H, Mai N, Zhang M, Wu Z, Peng Q, Wang Q, Liu M, Zhang S, Lin G, Lao J, Zeng Y, Zhong X, Ning Y. Relationships Among Short Self-Reported Sleep Duration, Cognitive Impairment, and Insular Functional Connectivity in Late-Life Depression. J Alzheimers Dis 2023:JAD220968. [PMID: 37182865 DOI: 10.3233/jad-220968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
BACKGROUND Both late-life depression (LLD) and short sleep duration increase the risk of cognitive impairment. Increased insular resting-state functional connectivity (FC) has been reported in individuals with short sleep duration and dementia. OBJECTIVE This study aimed to investigate whether short sleep duration is associated with impaired cognition and higher insular FC in patients with LLD. METHODS This case- control study recruited 186 patients with LLD and 83 normal controls (NC), and comprehensive psychometric assessments, sleep duration reports and resting-state functional MRI scans (81 LLD patients and 54 NC) were conducted. RESULTS Patients with LLD and short sleep duration (LLD-SS patients) exhibited more severe depressive symptoms and worse cognitive function than those with normal sleep duration (LLD-NS patients) and NC. LLD-SS patients exhibited higher FC between the bilateral insula and inferior frontal gyrus (IFG) pars triangularis than LLD-NS patients and NC, while LLD-NS patients exhibited lower FC than NC. Increased insular FC was correlated with short sleep duration, severe depressive symptoms, and slower information processing speeds. Furthermore, an additive effect was found between sleep duration and LLD on global cognition and insular FC. CONCLUSION LLD-SS patients exhibited impaired cognition and increased insular FC. Abnormal FC in LLD-SS patients may be a therapeutic target for neuromodulation to improve sleep and cognitive performance and thus decrease the risk of dementia.
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Affiliation(s)
- Mingfeng Yang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
- The first School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Ben Chen
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Huarong Zhou
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Naikeng Mai
- Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Min Zhang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Zhangying Wu
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Qi Peng
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Qiang Wang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Meiling Liu
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Si Zhang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Gaohong Lin
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Jingyi Lao
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Yijie Zeng
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Xiaomei Zhong
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Yuping Ning
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
- The first School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong Province, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
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49
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Kizilirmak JM, Soch J, Schütze H, Düzel E, Feldhoff H, Fischer L, Knopf L, Maass A, Raschick M, Schult A, Yakupov R, Richter A, Schott BH. The relationship between resting-state amplitude fluctuations and memory-related deactivations of the default mode network in young and older adults. Hum Brain Mapp 2023; 44:3586-3609. [PMID: 37051727 DOI: 10.1002/hbm.26299] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 03/09/2023] [Accepted: 03/20/2023] [Indexed: 04/14/2023] Open
Abstract
The default mode network (DMN) typically exhibits deactivations during demanding tasks compared to periods of relative rest. In functional magnetic resonance imaging (fMRI) studies of episodic memory encoding, increased activity in DMN regions even predicts later forgetting in young healthy adults. This association is attenuated in older adults and, in some instances, increased DMN activity even predicts remembering rather than forgetting. It is yet unclear whether this phenomenon is due to a compensatory mechanism, such as self-referential or schema-dependent encoding, or whether it reflects overall reduced DMN activity modulation in older age. We approached this question by systematically comparing DMN activity during successful encoding and tonic, task-independent, DMN activity at rest in a sample of 106 young (18-35 years) and 111 older (60-80 years) healthy participants. Using voxel-wise multimodal analyses, we assessed the age-dependent relationship between DMN resting-state amplitude (mean percent amplitude of fluctuation, mPerAF) and DMN fMRI signals related to successful memory encoding, as well as their modulation by age-related hippocampal volume loss, while controlling for regional grey matter volume. Older adults showed lower resting-state DMN amplitudes and lower task-related deactivations. However, a negative relationship between resting-state mPerAF and subsequent memory effect within the precuneus was observed only in young, but not older adults. Hippocampal volumes showed no relationship with the DMN subsequent memory effect or mPerAF. Lastly, older adults with higher mPerAF in the DMN at rest tend to show higher memory performance, pointing towards the importance of a maintained ability to modulate DMN activity in old age.
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Affiliation(s)
- Jasmin M Kizilirmak
- Cognitive Geriatric Psychiatry, German Center for Neurodegenerative Diseases, Göttingen, Germany
- Neurodidactics and NeuroLab, Institute for Psychology, University of Hildesheim, Hildesheim, Germany
- German Centre for Higher Education Research and Science Studies, Hannover, Germany
| | - Joram Soch
- Cognitive Geriatric Psychiatry, German Center for Neurodegenerative Diseases, Göttingen, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Hartmut Schütze
- Medical Faculty, Institute for Cognitive Neurology and Dementia Research, Otto-von-Guericke-University, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
| | - Emrah Düzel
- Medical Faculty, Institute for Cognitive Neurology and Dementia Research, Otto-von-Guericke-University, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
- German Center for Neurodegenerative Diseases, Magdeburg, Germany
| | | | | | - Lea Knopf
- Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Anne Maass
- German Center for Neurodegenerative Diseases, Magdeburg, Germany
| | | | - Annika Schult
- Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Renat Yakupov
- German Center for Neurodegenerative Diseases, Magdeburg, Germany
| | - Anni Richter
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C), Jena-Magdeburg-Halle, Germany
| | - Björn H Schott
- Cognitive Geriatric Psychiatry, German Center for Neurodegenerative Diseases, Göttingen, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
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50
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Peterson A, Largent EA, Lynch HF, Karlawish J, Sisti D. Journeying to Ixtlan: Ethics of Psychedelic Medicine and Research for Alzheimer's Disease and Related Dementias. AJOB Neurosci 2023; 14:107-123. [PMID: 36476106 DOI: 10.1080/21507740.2022.2148771] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Abstract
In this paper, we examine the case of psychedelic medicine for Alzheimer's disease and related dementias (AD/ADRD). These "mind-altering" drugs are not currently offered as treatments to persons with AD/ADRD, though there is growing interest in their use to treat underlying causes and associated psychiatric symptoms. We present a research agenda for examining the ethics of psychedelic medicine and research involving persons living with AD/ADRD, and offer preliminary analyses of six ethical issues: the impact of psychedelics on autonomy and consent; the impact of "ego dissolution" on persons experiencing a pathology of self; how psychedelics might impact caregiving; the potential exploitation of patient desperation; institutional review boards' orientation to psychedelic research; and methods to mitigate inequity. These ethical issues are magnified for AD/ADRD but bear broader relevance to psychedelic medicine and research in other clinical populations.
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Affiliation(s)
| | | | | | | | - Dominic Sisti
- University of Pennsylvania Perelman School of Medicine
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