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Han F, Liu X, Mailman RB, Huang X, Liu X. Resting-state global brain activity affects early β-amyloid accumulation in default mode network. Nat Commun 2023; 14:7788. [PMID: 38012153 PMCID: PMC10682457 DOI: 10.1038/s41467-023-43627-y] [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: 09/20/2022] [Accepted: 11/14/2023] [Indexed: 11/29/2023] Open
Abstract
It remains unclear why β-amyloid (Aβ) plaque, a hallmark pathology of Alzheimer's disease (AD), first accumulates cortically in the default mode network (DMN), years before AD diagnosis. Resting-state low-frequency ( < 0.1 Hz) global brain activity recently was linked to AD, presumably due to its role in glymphatic clearance. Here we show that the preferential Aβ accumulation in the DMN at the early stage of Aβ pathology was associated with the preferential reduction of global brain activity in the same regions. This can be partly explained by its failure to reach these regions as propagating waves. Together, these findings highlight the important role of resting-state global brain activity in early preferential Aβ deposition in the DMN.
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Affiliation(s)
- Feng Han
- Department of Biomedical Engineering, The Pennsylvania State University, State College, PA, USA
| | - Xufu Liu
- Department of Biomedical Engineering, The Pennsylvania State University, State College, PA, USA
| | - Richard B Mailman
- Departments of Neurology and Pharmacology, Translational Brain Research Center, Pennsylvania State University College of Medicine and Milton S. Hershey Medical Center, Hershey, PA, USA
| | - Xuemei Huang
- Departments of Neurology and Pharmacology, Translational Brain Research Center, Pennsylvania State University College of Medicine and Milton S. Hershey Medical Center, Hershey, PA, USA
- Departments of Radiology, Neurosurgery, and Kinesiology, Translational Brain Research Center, Pennsylvania State University and Milton S. Hershey Medical Center, Hershey, PA, USA
- Institute for Computational and Data Sciences, The Pennsylvania State University, State College, PA, USA
| | - Xiao Liu
- Department of Biomedical Engineering, The Pennsylvania State University, State College, PA, USA.
- Institute for Computational and Data Sciences, The Pennsylvania State University, State College, PA, USA.
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van Balkom TD, van den Heuvel OA, Berendse HW, van der Werf YD, Hagen RH, Berk T, Vriend C. Long-term effects of cognitive training in Parkinson's disease: A randomized, controlled trial. Clin Park Relat Disord 2023; 9:100204. [PMID: 38107671 PMCID: PMC10724826 DOI: 10.1016/j.prdoa.2023.100204] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 05/12/2023] [Accepted: 06/02/2023] [Indexed: 12/19/2023] Open
Abstract
Background Computerized cognitive training may be promising to improve cognitive impairment in Parkinson's disease and has even been suggested to delay cognitive decline. However, evidence to date is limited. The aim of this study was to assess the durability of eight-week cognitive training effects at up to two years follow-up. Methods One hundred and thirty-six (1 3 6) individuals with Parkinson's disease, subjective cognitive complaints but without severe cognitive impairment (Montreal Cognitive Assessment ≥ 22) participated in this double-blind RCT. Participants underwent an eight-week home-based intervention of either adaptive, computerized cognitive training with BrainGymmer (n = 68) or an active control (n = 68). They underwent extensive neuropsychological assessment, psychiatric questionnaires and motor symptom assessment at baseline and one and two years after the intervention. We used mixed-model analyses to assess changes in cognitive function at follow-up and performed Fisher's exact tests to assess conversion of cognitive status. Results There were no group differences on any neuropsychological assessment outcome at one- and two-year follow-up. Groups were equally likely to show conversion of cognitive status at follow-up. A considerable amount of assessments was missed (1y: n = 27; 2y: n = 33), most notably due to COVID-19 regulations. Conclusions Eight-week cognitive training did not affect long-term cognitive function in Parkinson's disease. Future studies may focus on one cognitive subgroup to enhance reliability of study results. Intervention improvements are needed to work towards effective, lasting treatment options.
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Affiliation(s)
- Tim D. van Balkom
- Amsterdam UMC, Vrije Universiteit Amsterdam, Psychiatry, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, Anatomy & Neurosciences, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Odile A. van den Heuvel
- Amsterdam UMC, Vrije Universiteit Amsterdam, Psychiatry, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, Anatomy & Neurosciences, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Compulsivity, Impulsivity & Attention, Amsterdam, The Netherlands
| | - Henk W. Berendse
- Amsterdam UMC, Vrije Universiteit Amsterdam, Neurology, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Ysbrand D. van der Werf
- Amsterdam UMC, Vrije Universiteit Amsterdam, Anatomy & Neurosciences, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Compulsivity, Impulsivity & Attention, Amsterdam, The Netherlands
| | - Rob H. Hagen
- Dutch Parkinson’s Disease Association, PO Box 46, 3980 CA Bunnik, The Netherlands
| | - Tanja Berk
- Dutch Parkinson’s Disease Association, PO Box 46, 3980 CA Bunnik, The Netherlands
| | - Chris Vriend
- Amsterdam UMC, Vrije Universiteit Amsterdam, Psychiatry, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, Anatomy & Neurosciences, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Compulsivity, Impulsivity & Attention, Amsterdam, The Netherlands
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Filippi M, Spinelli EG, Cividini C, Ghirelli A, Basaia S, Agosta F. The human functional connectome in neurodegenerative diseases: relationship to pathology and clinical progression. Expert Rev Neurother 2023; 23:59-73. [PMID: 36710600 DOI: 10.1080/14737175.2023.2174016] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
INTRODUCTION Neurodegenerative diseases can be considered as 'disconnection syndromes,' in which a communication breakdown prompts cognitive or motor dysfunction. Mathematical models applied to functional resting-state MRI allow for the organization of the brain into nodes and edges, which interact to form the functional brain connectome. AREAS COVERED The authors discuss the recent applications of functional connectomics to neurodegenerative diseases, from preclinical diagnosis, to follow up along with the progressive changes in network organization, to the prediction of the progressive spread of neurodegeneration, to stratification of patients into prognostic groups, and to record responses to treatment. The authors searched PubMed using the terms 'neurodegenerative diseases' AND 'fMRI' AND 'functional connectome' OR 'functional connectivity' AND 'connectomics' OR 'graph metrics' OR 'graph analysis.' The time range covered the past 20 years. EXPERT OPINION Considering the great pathological and phenotypical heterogeneity of neurodegenerative diseases, identifying a common framework to diagnose, monitor and elaborate prognostic models is challenging. Graph analysis can describe the complexity of brain architectural rearrangements supporting the network-based hypothesis as unifying pathogenetic mechanism. Although a multidisciplinary team is needed to overcome the limit of methodologic complexity in clinical application, advanced methodologies are valuable tools to better characterize functional disconnection in neurodegeneration.
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Affiliation(s)
- Massimo Filippi
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy.,Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Edoardo Gioele Spinelli
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Camilla Cividini
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alma Ghirelli
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Silvia Basaia
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Federica Agosta
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
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Imaging the Limbic System in Parkinson's Disease-A Review of Limbic Pathology and Clinical Symptoms. Brain Sci 2022; 12:brainsci12091248. [PMID: 36138984 PMCID: PMC9496800 DOI: 10.3390/brainsci12091248] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/05/2022] [Accepted: 09/13/2022] [Indexed: 01/09/2023] Open
Abstract
The limbic system describes a complex of brain structures central for memory, learning, as well as goal directed and emotional behavior. In addition to pathological studies, recent findings using in vivo structural and functional imaging of the brain pinpoint the vulnerability of limbic structures to neurodegeneration in Parkinson's disease (PD) throughout the disease course. Accordingly, dysfunction of the limbic system is critically related to the symptom complex which characterizes PD, including neuropsychiatric, vegetative, and motor symptoms, and their heterogeneity in patients with PD. The aim of this systematic review was to put the spotlight on neuroimaging of the limbic system in PD and to give an overview of the most important structures affected by the disease, their function, disease related alterations, and corresponding clinical manifestations. PubMed was searched in order to identify the most recent studies that investigate the limbic system in PD with the help of neuroimaging methods. First, PD related neuropathological changes and corresponding clinical symptoms of each limbic system region are reviewed, and, finally, a network integration of the limbic system within the complex of PD pathology is discussed.
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