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Piramide N, De Micco R, Siciliano M, Silvestro M, Tessitore A. Resting-State Functional MRI Approaches to Parkinsonisms and Related Dementia. Curr Neurol Neurosci Rep 2024:10.1007/s11910-024-01365-8. [PMID: 39046642 DOI: 10.1007/s11910-024-01365-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/12/2024] [Indexed: 07/25/2024]
Abstract
PURPOSE OF THE REVIEW In this review, we attempt to summarize the most updated studies that applied resting-state functional magnetic resonance imaging (rs-fMRI) in the field of Parkinsonisms and related dementia. RECENT FINDINGS Over the past decades, increasing interest has emerged on investigating the presence and pathophysiology of cognitive symptoms in Parkinsonisms and their possible role as predictive biomarkers of neurodegenerative brain processes. In recent years, evidence has been provided, applying mainly three methodological approaches (i.e. seed-based, network-based and graph-analysis) on rs-fMRI data, with promising results. Neural correlates of cognitive impairment and dementia have been detected in patients with Parkinsonisms along the diseases course. Interestingly, early functional connectivity signatures were proposed to track and predict future progression of neurodegenerative processes. However, longitudinal studies are still sparce and further investigations are needed to overcome this knowledge gap.
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Affiliation(s)
- Noemi Piramide
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Napoli, Italy
| | - Rosa De Micco
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Napoli, Italy
| | - Mattia Siciliano
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Napoli, Italy
- Neuropsychology Laboratory, Department of Psychology, University of Campania "Luigi Vanvitelli", Caserta, Italy
| | - Marcello Silvestro
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Napoli, Italy
| | - Alessandro Tessitore
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Napoli, Italy.
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Matsushima T, Yoshinaga K, Wakasugi N, Togo H, Hanakawa T. Functional connectivity-based classification of rapid eye movement sleep behavior disorder. Sleep Med 2024; 115:5-13. [PMID: 38295625 DOI: 10.1016/j.sleep.2024.01.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 01/13/2024] [Accepted: 01/16/2024] [Indexed: 02/02/2024]
Abstract
BACKGROUND Isolated rapid eye movement sleep behavior disorder (iRBD) is a clinically important parasomnia syndrome preceding α-synucleinopathies, thereby prompting us to develop methods for evaluating latent brain states in iRBD. Resting-state functional magnetic resonance imaging combined with a machine learning-based classification technology may help us achieve this purpose. METHODS We developed a machine learning-based classifier using functional connectivity to classify 55 patients with iRBD and 97 healthy elderly controls (HC). Selecting 55 HCs randomly from the HC dataset 100 times, we conducted a classification of iRBD and HC for each sampling, using functional connectivity. Random forest ranked the importance of functional connectivity, which was subsequently used for classification with logistic regression and a support vector machine. We also conducted correlation analysis of the selected functional connectivity with subclinical variations in motor and non-motor functions in the iRBDs. RESULTS Mean classification performance using logistic regression was 0.649 for accuracy, 0.659 for precision, 0.662 for recall, 0.645 for f1 score, and 0.707 for the area under the receiver operating characteristic curve (p < 0.001 for all). The result was similar in the support vector machine. The classifier used functional connectivity information from nine connectivities across the motor and somatosensory areas, parietal cortex, temporal cortex, thalamus, and cerebellum. Inter-individual variations in functional connectivity were correlated with the subclinical motor and non-motor symptoms of iRBD patients. CONCLUSIONS Machine learning-based classifiers using functional connectivity may be useful to evaluate latent brain states in iRBD.
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Affiliation(s)
- Toma Matsushima
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, 187-8501, Japan; Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, Koganei, Tokyo, 184-8588, Japan
| | - Kenji Yoshinaga
- Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, 606-8501, Japan
| | - Noritaka Wakasugi
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, 187-8501, Japan
| | - Hiroki Togo
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, 187-8501, Japan; Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, 606-8501, Japan
| | - Takashi Hanakawa
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, 187-8501, Japan; Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, 606-8501, Japan.
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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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