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Pérez-Millan A, Thirion B, Falgàs N, Borrego-Écija S, Bosch B, Juncà-Parella J, Tort-Merino A, Sarto J, Augé JM, Antonell A, Bargalló N, Balasa M, Lladó A, Sánchez-Valle R, Sala-Llonch R. Beyond group classification: Probabilistic differential diagnosis of frontotemporal dementia and Alzheimer's disease with MRI and CSF biomarkers. Neurobiol Aging 2024; 144:1-11. [PMID: 39232438 DOI: 10.1016/j.neurobiolaging.2024.08.008] [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: 02/23/2024] [Revised: 08/27/2024] [Accepted: 08/28/2024] [Indexed: 09/06/2024]
Abstract
Neuroimaging and fluid biomarkers are used to differentiate frontotemporal dementia (FTD) from Alzheimer's disease (AD). We implemented a machine learning algorithm that provides individual probabilistic scores based on magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data. We investigated whether combining MRI and CSF levels could improve the diagnosis confidence. 215 AD patients, 103 FTD patients, and 173 healthy controls (CTR) were studied. With MRI data, we obtained an accuracy of 82 % for AD vs. FTD. A total of 74 % of FTD and 73 % of AD participants have a high probability of accurate diagnosis. Adding CSF-NfL and 14-3-3 levels improved the accuracy and the number of patients in the confidence group for differentiating FTD from AD. We obtain individual diagnostic probabilities with high precision to address the problem of confidence in the diagnosis. We suggest when MRI, CSF, or the combination are necessary to improve the FTD and AD diagnosis. This algorithm holds promise towards clinical applications as support to clinical findings or in settings with limited access to expert diagnoses.
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Affiliation(s)
- Agnès Pérez-Millan
- Alzheimer's disease and other cognitive disorders unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Barcelona, Spain; Institut de Neurociències, University of Barcelona, Barcelona, Spain; Department of Biomedicine, Faculty of Medicine, University of Barcelona, Barcelona, Spain; Inria, CEA, Université Paris-Saclay, Paris, France
| | | | - Neus Falgàs
- Alzheimer's disease and other cognitive disorders unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Barcelona, Spain
| | - Sergi Borrego-Écija
- Alzheimer's disease and other cognitive disorders unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Barcelona, Spain
| | - Beatriz Bosch
- Alzheimer's disease and other cognitive disorders unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Barcelona, Spain
| | - Jordi Juncà-Parella
- Alzheimer's disease and other cognitive disorders unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Barcelona, Spain
| | - Adrià Tort-Merino
- Alzheimer's disease and other cognitive disorders unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Barcelona, Spain
| | - Jordi Sarto
- Alzheimer's disease and other cognitive disorders unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Barcelona, Spain
| | - Josep Maria Augé
- Biochemistry and Molecular Genetics Department, Hospital Clínic de Barcelona, Barcelona, Spain
| | - Anna Antonell
- Alzheimer's disease and other cognitive disorders unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Barcelona, Spain
| | - Nuria Bargalló
- Image Diagnostic Centre, Hospital Clínic de Barcelona, CIBER de Salud Mental, Instituto de Salud Carlos III.Magnetic Resonance Image Core Facility, IDIBAPS, Barcelona, Spain
| | - Mircea Balasa
- Alzheimer's disease and other cognitive disorders unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Barcelona, Spain
| | - Albert Lladó
- Alzheimer's disease and other cognitive disorders unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Barcelona, Spain; Institut de Neurociències, University of Barcelona, Barcelona, Spain
| | - Raquel Sánchez-Valle
- Alzheimer's disease and other cognitive disorders unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Barcelona, Spain; Institut de Neurociències, University of Barcelona, Barcelona, Spain
| | - Roser Sala-Llonch
- Institut de Neurociències, University of Barcelona, Barcelona, Spain; Department of Biomedicine, Faculty of Medicine, University of Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain; Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi I Sunyer (FRCB-IDIBAPS), Barcelona, Spain.
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2
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Li W, Sun L, Yue L, Xiao S. Diagnostic and predictive power of plasma proteins in Alzheimer's disease: a cross-sectional and longitudinal study in China. Sci Rep 2024; 14:17557. [PMID: 39080359 PMCID: PMC11289122 DOI: 10.1038/s41598-024-66195-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 06/28/2024] [Indexed: 08/02/2024] Open
Abstract
Convenient and effective biomarkers are essential for the early diagnosis and treatment of Alzheimer's disease (AD). In the cross-sectional study, 103 patients with AD, 82 patients with aMCI and 508 normal controls (NC) were enrolled. The single-molecule array (Simoa) technique was used to assess the levels of plasma proteins, including NfL, T-tau, P-tau-181, Aβ40, Aβ42. Montreal Cognitive Assessment (MoCA) was used to assess the overall cognitive function of all subjects. Moreover, Amyloid PET and structural head MRI were also performed in a subset of the population. In the follow-up, the previous 508 normal older adults were followed up for two years, then COX regression analysis was used to investigate the association between baseline plasma proteins and future cognitive outcomes. NfL, T-tau, P-tau-181, Aβ40, Aβ42 and Aβ42/40 were altered in AD dementia, and NfL, Aβ42 and Aβ42/40 significantly outperformed all plasma proteins in differentiating AD dementia from NC, while NfL and Aβ42/40 could effectively distinguish between aMCI and NC. However, only plasma NfL was associated with future cognitive decline, and it was negatively correlated with MoCA (r = - 0.298, p < 0.001) and the volume of the left globus pallidus (r = - 0.278, p = 0.033). Plasma NfL can help distinguish between cognitively normal and cognitively impaired individuals (MCI/dementia) at the syndrome level. However, since we have not introduced other biomarkers for AD, such as PET CT or cerebrospinal fluid, and have not verified in other neurodegenerative diseases, whether plasma NFL can be used as a biomarker for AD needs to be further studied and explored.
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Affiliation(s)
- Wei Li
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
- Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Lin Sun
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
- Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Ling Yue
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
- Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China.
| | - Shifu Xiao
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
- Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China.
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3
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Huang M, Landin-Romero R, Matis S, Dalton MA, Piguet O. Longitudinal volumetric changes in amygdala subregions in frontotemporal dementia. J Neurol 2024; 271:2509-2520. [PMID: 38265470 PMCID: PMC11055736 DOI: 10.1007/s00415-023-12172-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 12/20/2023] [Accepted: 12/23/2023] [Indexed: 01/25/2024]
Abstract
Amygdala atrophy has been found in frontotemporal dementia (FTD), yet the specific changes of its subregions across different FTD phenotypes remain unclear. The aim of this study was to investigate the volumetric alterations of the amygdala subregions in FTD phenotypes and how they evolve with disease progression. Patients clinically diagnosed with behavioral variant FTD (bvFTD) (n = 20), semantic dementia (SD) (n = 20), primary nonfluent aphasia (PNFA) (n = 20), Alzheimer's disease (AD) (n = 20), and 20 matched healthy controls underwent whole brain structural MRI. The patient groups were followed up annually for up to 3.5 years. Amygdala nuclei were segmented using FreeSurfer, corrected by total intracranial volumes, and grouped into the basolateral, superficial, and centromedial subregions. Linear mixed effects models were applied to identify changes in amygdala subregional volumes over time. At baseline, bvFTD, SD, and AD displayed global amygdala volume reduction, whereas amygdala volume appeared to be preserved in PNFA. Asymmetrical amygdala atrophy (left > right) was most pronounced in SD. Longitudinally, SD and PNFA showed greater rates of annual decline in the right basolateral and superficial subregions compared to bvFTD and AD. The findings provide comprehensive insights into the differential impact of FTD pathology on amygdala subregions, revealing distinct atrophy patterns that evolve over disease progression. The characterization of amygdala subregional involvement in FTD and their potential role as biomarkers carry substantial clinical implications.
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Affiliation(s)
- Mengjie Huang
- School of Psychology, The University of Sydney, Camperdown, NSW, 2050, Australia
- Brain and Mind Centre, The University of Sydney, 94 Mallett Street, Camperdown, NSW, 2050, Australia
| | - Ramon Landin-Romero
- Brain and Mind Centre, The University of Sydney, 94 Mallett Street, Camperdown, NSW, 2050, Australia
- School of Health Sciences, The University of Sydney, Camperdown, NSW, 2050, Australia
| | - Sophie Matis
- Brain and Mind Centre, The University of Sydney, 94 Mallett Street, Camperdown, NSW, 2050, Australia
- School of Health Sciences, The University of Sydney, Camperdown, NSW, 2050, Australia
| | - Marshall A Dalton
- School of Psychology, The University of Sydney, Camperdown, NSW, 2050, Australia
- Brain and Mind Centre, The University of Sydney, 94 Mallett Street, Camperdown, NSW, 2050, Australia
| | - Olivier Piguet
- School of Psychology, The University of Sydney, Camperdown, NSW, 2050, Australia.
- Brain and Mind Centre, The University of Sydney, 94 Mallett Street, Camperdown, NSW, 2050, Australia.
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4
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Pérez-Millan A, Borrego-Écija S, Falgàs N, Juncà-Parella J, Bosch B, Tort-Merino A, Antonell A, Bargalló N, Rami L, Balasa M, Lladó A, Sala-Llonch R, Sánchez-Valle R. Cortical thickness modeling and variability in Alzheimer's disease and frontotemporal dementia. J Neurol 2024; 271:1428-1438. [PMID: 38012398 PMCID: PMC10896866 DOI: 10.1007/s00415-023-12087-1] [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: 06/09/2023] [Revised: 09/29/2023] [Accepted: 10/31/2023] [Indexed: 11/29/2023]
Abstract
BACKGROUND AND OBJECTIVE Alzheimer's disease (AD) and frontotemporal dementia (FTD) show different patterns of cortical thickness (CTh) loss compared with healthy controls (HC), even though there is relevant heterogeneity between individuals suffering from each of these diseases. Thus, we developed CTh models to study individual variability in AD, FTD, and HC. METHODS We used the baseline CTh measures of 379 participants obtained from the structural MRI processed with FreeSurfer. A total of 169 AD patients (63 ± 9 years, 65 men), 88 FTD patients (64 ± 9 years, 43 men), and 122 HC (62 ± 10 years, 47 men) were studied. We fitted region-wise temporal models of CTh using Support Vector Regression. Then, we studied associations of individual deviations from the model with cerebrospinal fluid levels of neurofilament light chain (NfL) and 14-3-3 protein and Mini-Mental State Examination (MMSE). Furthermore, we used real longitudinal data from 144 participants to test model predictivity. RESULTS We defined CTh spatiotemporal models for each group with a reliable fit. Individual deviation correlated with MMSE for AD and with NfL for FTD. AD patients with higher deviations from the trend presented higher MMSE values. In FTD, lower NfL levels were associated with higher deviations from the CTh prediction. For AD and HC, we could predict longitudinal visits with the presented model trained with baseline data. For FTD, the longitudinal visits had more variability. CONCLUSION We highlight the value of CTh models for studying AD and FTD longitudinal changes and variability and their relationships with cognitive features and biomarkers.
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Affiliation(s)
- Agnès Pérez-Millan
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain
- Institut de Neurociències, University of Barcelona, 08036, Barcelona, Spain
- Department of Biomedicine, Faculty of Medicine, University of Barcelona, 08036, Barcelona, Spain
| | - Sergi Borrego-Écija
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain
| | - Neus Falgàs
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain
- Atlantic Fellow for Equity in Brain Health, Global Brain Health Institute, University of California San Francisco, San Francisco, 94143, USA
| | - Jordi Juncà-Parella
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain
| | - Beatriz Bosch
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain
| | - Adrià Tort-Merino
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain
| | - Anna Antonell
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain
| | - Nuria Bargalló
- Image Diagnostic Centre, CIBER de Salud Mental, Instituto de Salud Carlos III, Magnetic Resonance Image Core Facility, IDIBAPS, Hospital Clínic de Barcelona, Barcelona, Spain
| | - Lorena Rami
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain
| | - Mircea Balasa
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain
- Atlantic Fellow for Equity in Brain Health, Global Brain Health Institute, University of California San Francisco, San Francisco, 94143, USA
| | - Albert Lladó
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain
- Institut de Neurociències, University of Barcelona, 08036, Barcelona, Spain
| | - Roser Sala-Llonch
- Institut de Neurociències, University of Barcelona, 08036, Barcelona, Spain
- Department of Biomedicine, Faculty of Medicine, University of Barcelona, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 08036, Barcelona, Spain
| | - Raquel Sánchez-Valle
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain.
- Institut de Neurociències, University of Barcelona, 08036, Barcelona, Spain.
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, 08036, Barcelona, Spain.
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5
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Rodríguez JJ, Zallo F, Gardenal E, Cabot J, Busquets X. Entorhinal cortex astrocytic atrophy in human frontotemporal dementia. Brain Struct Funct 2024:10.1007/s00429-024-02763-x. [PMID: 38308043 DOI: 10.1007/s00429-024-02763-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 01/11/2024] [Indexed: 02/04/2024]
Abstract
The pathophysiology of Fronto Temporal Dementia (FTD) remains poorly understood, specifically the role of astroglia. Our aim was to explore the hypothesis of astrocytic alterations as a component for FTD pathophysiology. We performed an in-depth tri-dimensional (3-D) anatomical and morphometric study of glial fibrillary acidic protein (GFAP)-positive and glutamine synthetase (GS)-positive astrocytes in the human entorhinal cortex (EC) of FTD patients. The studies at this level in the different types of human dementia are scarce. We observed a prominent astrocyte atrophy of GFAP-positive astrocytes and co-expressing GFAP/GS astrocytes, characterised by a decrease in area and volume, whilst minor changes in GS-positive astrocytes in FTD compared to non-dementia controls (ND) samples. This study evidences the importance of astrocyte atrophy and dysfunction in human EC. We hypothesise that FTD is not only a neuropathological disease, but also a gliopathological disease having a major relevance in the understanding the astrocyte role in FTD pathological processes and development.
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Affiliation(s)
- J J Rodríguez
- Functional Neuroanatomy Group; IKERBASQUE, Basque Foundation for Science, 48009, Bilbao, Spain.
- Dept. of Neurosciences, Medical Faculty, University of the Basque Country (UPV/EHU), 48940, Leioa, Spain.
| | - F Zallo
- Functional Neuroanatomy Group; IKERBASQUE, Basque Foundation for Science, 48009, Bilbao, Spain
- Dept. of Neurosciences, Medical Faculty, University of the Basque Country (UPV/EHU), 48940, Leioa, Spain
| | - E Gardenal
- Functional Neuroanatomy Group; IKERBASQUE, Basque Foundation for Science, 48009, Bilbao, Spain
- Dept. of Neurosciences, Medical Faculty, University of the Basque Country (UPV/EHU), 48940, Leioa, Spain
| | - J Cabot
- Laboratory of Molecular Cell Biomedicine, Department of Biology, University of the Balearic Islands, 07122, Palma, Spain
| | - X Busquets
- Laboratory of Molecular Cell Biomedicine, Department of Biology, University of the Balearic Islands, 07122, Palma, Spain
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Antonioni A, Raho EM, Lopriore P, Pace AP, Latino RR, Assogna M, Mancuso M, Gragnaniello D, Granieri E, Pugliatti M, Di Lorenzo F, Koch G. Frontotemporal Dementia, Where Do We Stand? A Narrative Review. Int J Mol Sci 2023; 24:11732. [PMID: 37511491 PMCID: PMC10380352 DOI: 10.3390/ijms241411732] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/18/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023] Open
Abstract
Frontotemporal dementia (FTD) is a neurodegenerative disease of growing interest, since it accounts for up to 10% of middle-age-onset dementias and entails a social, economic, and emotional burden for the patients and caregivers. It is characterised by a (at least initially) selective degeneration of the frontal and/or temporal lobe, generally leading to behavioural alterations, speech disorders, and psychiatric symptoms. Despite the recent advances, given its extreme heterogeneity, an overview that can bring together all the data currently available is still lacking. Here, we aim to provide a state of the art on the pathogenesis of this disease, starting with established findings and integrating them with more recent ones. In particular, advances in the genetics field will be examined, assessing them in relation to both the clinical manifestations and histopathological findings, as well as considering the link with other diseases, such as amyotrophic lateral sclerosis (ALS). Furthermore, the current diagnostic criteria will be explored, including neuroimaging methods, nuclear medicine investigations, and biomarkers on biological fluids. Of note, the promising information provided by neurophysiological investigations, i.e., electroencephalography and non-invasive brain stimulation techniques, concerning the alterations in brain networks and neurotransmitter systems will be reviewed. Finally, current and experimental therapies will be considered.
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Affiliation(s)
- Annibale Antonioni
- Unit of Clinical Neurology, Neurosciences and Rehabilitation Department, University of Ferrara, 44121 Ferrara, Italy
- Doctoral Program in Translational Neurosciences and Neurotechnologies, University of Ferrara, 44121 Ferrara, Italy
| | - Emanuela Maria Raho
- Unit of Clinical Neurology, Neurosciences and Rehabilitation Department, University of Ferrara, 44121 Ferrara, Italy
| | - Piervito Lopriore
- Neurological Institute, Department of Clinical and Experimental Medicine, University of Pisa, 56126 Pisa, Italy
| | - Antonia Pia Pace
- Institute of Radiology, Department of Medicine, University of Udine, University Hospital S. Maria della Misericordia, Azienda Sanitaria-Universitaria Friuli Centrale, 33100 Udine, Italy
| | - Raffaela Rita Latino
- Complex Structure of Neurology, Emergency Department, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Casa Sollievo della Sofferenza, 71013 San Giovanni Rotondo, Italy
| | - Martina Assogna
- Centro Demenze, Policlinico Tor Vergata, University of Rome 'Tor Vergata', 00133 Rome, Italy
- Non Invasive Brain Stimulation Unit, Istituto di Ricovero e Cura a Carattere Scientifico Santa Lucia, 00179 Rome, Italy
| | - Michelangelo Mancuso
- Neurological Institute, Department of Clinical and Experimental Medicine, University of Pisa, 56126 Pisa, Italy
| | - Daniela Gragnaniello
- Nuerology Unit, Neurosciences and Rehabilitation Department, Ferrara University Hospital, 44124 Ferrara, Italy
| | - Enrico Granieri
- Unit of Clinical Neurology, Neurosciences and Rehabilitation Department, University of Ferrara, 44121 Ferrara, Italy
| | - Maura Pugliatti
- Unit of Clinical Neurology, Neurosciences and Rehabilitation Department, University of Ferrara, 44121 Ferrara, Italy
| | - Francesco Di Lorenzo
- Non Invasive Brain Stimulation Unit, Istituto di Ricovero e Cura a Carattere Scientifico Santa Lucia, 00179 Rome, Italy
| | - Giacomo Koch
- Non Invasive Brain Stimulation Unit, Istituto di Ricovero e Cura a Carattere Scientifico Santa Lucia, 00179 Rome, Italy
- Iit@Unife Center for Translational Neurophysiology, Istituto Italiano di Tecnologia, 44121 Ferrara, Italy
- Section of Human Physiology, Neurosciences and Rehabilitation Department, University of Ferrara, 44121 Ferrara, Italy
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7
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Jakabek D, Power BD, Spotorno N, Macfarlane MD, Walterfang M, Velakoulis D, Nilsson C, Waldö ML, Lätt J, Nilsson M, van Westen D, Lindberg O, Looi JCL, Santillo AF. Structural and microstructural thalamocortical network disruption in sporadic behavioural variant frontotemporal dementia. Neuroimage Clin 2023; 39:103471. [PMID: 37473493 PMCID: PMC10371821 DOI: 10.1016/j.nicl.2023.103471] [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: 02/06/2023] [Revised: 06/09/2023] [Accepted: 07/06/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Using multi-block methods we combined multimodal neuroimaging metrics of thalamic morphology, thalamic white matter tract diffusion metrics, and cortical thickness to examine changes in behavioural variant frontotemporal dementia. (bvFTD). METHOD Twenty-three patients with sporadic bvFTD and 24 healthy controls underwent structural and diffusion MRI scans. Clinical severity was assessed using the Clinical Dementia Rating scale and behavioural severity using the Frontal Behaviour Inventory by patient caregivers. Thalamic volumes were manually segmented. Anterior and posterior thalamic radiation fractional anisotropy and mean diffusivity were extracted using Tract-Based Spatial Statistics. Finally, cortical thickness was assessed using Freesurfer. We used shape analyses, diffusion measures, and cortical thickness as features in sparse multi-block partial least squares (PLS) discriminatory analyses to classify participants within bvFTD or healthy control groups. Sparsity was tuned with five-fold cross-validation repeated 10 times. Final model fit was assessed using permutation testing. Additionally, sparse multi-block PLS was used to examine associations between imaging features and measures of dementia severity. RESULTS Bilateral anterior-dorsal thalamic atrophy, reduction in mean diffusivity of thalamic projections, and frontotemporal cortical thinning, were the main features predicting bvFTD group membership. The model had a sensitivity of 96%, specificity of 68%, and was statistically significant using permutation testing (p = 0.012). For measures of dementia severity, we found similar involvement of regional thalamic and cortical areas as in discrimination analyses, although more extensive thalamo-cortical white matter metric changes. CONCLUSIONS Using multimodal neuroimaging, we demonstrate combined structural network dysfunction of anterior cortical regions, cortical-thalamic projections, and anterior thalamic regions in sporadic bvFTD.
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Affiliation(s)
| | - Brian D Power
- School of Medicine, The University of Notre Dame Australia, Fremantle, Australia
| | - Nicola Spotorno
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden
| | | | - Mark Walterfang
- Neuropsychiatry Unit, Royal Melbourne Hospital, Melbourne, Australia; Department of Psychiatry, University of Melbourne, Melbourne, Australia
| | - Dennis Velakoulis
- Neuropsychiatry Unit, Royal Melbourne Hospital, Melbourne, Australia; Department of Psychiatry, University of Melbourne, Melbourne, Australia
| | - Christer Nilsson
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden
| | - Maria Landqvist Waldö
- Clinical Sciences Helsingborg, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Jimmy Lätt
- Diagnostic Radiology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Markus Nilsson
- Diagnostic Radiology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Danielle van Westen
- Imaging and Function, Skane University Hospital, Lund, Sweden; Diagnostic Radiology, Institution for Clinical Sciences, Lund University, Lund, Sweden
| | - Olof Lindberg
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden
| | - Jeffrey C L Looi
- Academic Unit of Psychiatry and Addiction Medicine, The Australian National University School of Medicine and Psychology, Canberra Hospital, Canberra, Australian Capital Territory, Australia
| | - Alexander F Santillo
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden.
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8
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Piervincenzi C, Suppa A, Petsas N, Fabbrini A, Trebbastoni A, Asci F, Giannì C, Berardelli A, Pantano P. Parkinsonism Is Associated with Altered SMA-Basal Ganglia Structural and Functional Connectivity in Frontotemporal Degeneration. Biomedicines 2023; 11:522. [PMID: 36831058 PMCID: PMC9953061 DOI: 10.3390/biomedicines11020522] [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: 01/27/2023] [Revised: 02/07/2023] [Accepted: 02/09/2023] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND Patients with frontotemporal degeneration (FTD) often manifest parkinsonism, which likely results from cortical and subcortical degeneration of brain structures involved in motor control. We used a multimodal magnetic resonance imaging (MRI) approach to investigate possible structural and/or functional alterations in FTD patients with and without parkinsonism (Park+ and Park-). METHODS Thirty FTD patients (12 Park+, 18 Park-) and 30 healthy controls were enrolled and underwent 3T MRI scanning. MRI analyses included: (1) surface-based morphometry; (2) basal ganglia and thalamic volumetry; (3) diffusion-based probabilistic tractography of fiber tracts connecting the supplementary motor area (SMA) and primary motor cortex (M1) to the putamen, globus pallidus, and thalamus; and (4) resting-state functional connectivity (RSFC) between the aforementioned regions. RESULTS Patients in Park+ and Park- groups showed comparable patterns of cortical thinning in frontotemporal regions and reduced thalamic volume with respect to controls. Only Park+ patients showed reduced putaminal volume and reduced fractional anisotropy of the fibers connecting the SMA to the globus pallidus, putamen, and thalamus, with respect to controls. Park+ patients also showed decreased RSFC between the SMA and putamen with respect to both Park- patients and controls. CONCLUSIONS The present findings support the hypothesis that FTD patients with parkinsonism are characterized by neurodegenerative processes in specific corticobasal ganglia-thalamocortical motor loops.
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Affiliation(s)
- Claudia Piervincenzi
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Antonio Suppa
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
- IRCCS NEUROMED, 86077 Pozzilli, Italy
| | - Nikolaos Petsas
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Andrea Fabbrini
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
| | | | - Francesco Asci
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
- IRCCS NEUROMED, 86077 Pozzilli, Italy
| | - Costanza Giannì
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
- IRCCS NEUROMED, 86077 Pozzilli, Italy
| | - Alfredo Berardelli
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
- IRCCS NEUROMED, 86077 Pozzilli, Italy
| | - Patrizia Pantano
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
- IRCCS NEUROMED, 86077 Pozzilli, Italy
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9
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McKenna MC, Lope J, Bede P, Tan EL. Thalamic pathology in frontotemporal dementia: Predilection for specific nuclei, phenotype-specific signatures, clinical correlates, and practical relevance. Brain Behav 2023; 13:e2881. [PMID: 36609810 PMCID: PMC9927864 DOI: 10.1002/brb3.2881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/17/2022] [Accepted: 12/18/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Frontotemporal dementia (FTD) phenotypes are classically associated with distinctive cortical atrophy patterns and regional hypometabolism. However, the spectrum of cognitive and behavioral manifestations in FTD arises from multisynaptic network dysfunction. The thalamus is a key hub of several corticobasal and corticocortical circuits. The main circuits relayed via the thalamic nuclei include the dorsolateral prefrontal circuit, the anterior cingulate circuit, and the orbitofrontal circuit. METHODS In this paper, we have reviewed evidence for thalamic pathology in FTD based on radiological and postmortem studies. Original research papers were systematically reviewed for preferential involvement of specific thalamic regions, for phenotype-associated thalamic disease burden patterns, characteristic longitudinal changes, and genotype-associated thalamic signatures. Moreover, evidence for presymptomatic thalamic pathology was also reviewed. Identified papers were systematically scrutinized for imaging methods, cohort sizes, clinical profiles, clinicoradiological associations, and main anatomical findings. The findings of individual research papers were amalgamated for consensus observations and their study designs further evaluated for stereotyped shortcomings. Based on the limitations of existing studies and conflicting reports in low-incidence FTD variants, we sought to outline future research directions and pressing research priorities. RESULTS FTD is associated with focal thalamic degeneration. Phenotype-specific thalamic traits mirror established cortical vulnerability patterns. Thalamic nuclei mediating behavioral and language functions are preferentially involved. Given the compelling evidence for considerable thalamic disease burden early in the course of most FTD subtypes, we also reflect on the practical relevance, diagnostic role, prognostic significance, and monitoring potential of thalamic metrics in FTD. CONCLUSIONS Cardinal manifestations of FTD phenotypes are likely to stem from thalamocortical circuitry dysfunction and are not exclusively driven by focal cortical changes.
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Affiliation(s)
- Mary Clare McKenna
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland.,Department of Neurology, St James's Hospital, Dublin, Ireland
| | - Jasmin Lope
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland.,Department of Neurology, St James's Hospital, Dublin, Ireland
| | - Ee Ling Tan
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
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10
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Gonzalez-Gomez R, Ibañez A, Moguilner S. Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference. Netw Neurosci 2023; 7:322-350. [PMID: 37333999 PMCID: PMC10270711 DOI: 10.1162/netn_a_00285] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 10/03/2022] [Indexed: 04/03/2024] Open
Abstract
Characterizing a particular neurodegenerative condition against others possible diseases remains a challenge along clinical, biomarker, and neuroscientific levels. This is the particular case of frontotemporal dementia (FTD) variants, where their specific characterization requires high levels of expertise and multidisciplinary teams to subtly distinguish among similar physiopathological processes. Here, we used a computational approach of multimodal brain networks to address simultaneous multiclass classification of 298 subjects (one group against all others), including five FTD variants: behavioral variant FTD, corticobasal syndrome, nonfluent variant primary progressive aphasia, progressive supranuclear palsy, and semantic variant primary progressive aphasia, with healthy controls. Fourteen machine learning classifiers were trained with functional and structural connectivity metrics calculated through different methods. Due to the large number of variables, dimensionality was reduced, employing statistical comparisons and progressive elimination to assess feature stability under nested cross-validation. The machine learning performance was measured through the area under the receiver operating characteristic curves, reaching 0.81 on average, with a standard deviation of 0.09. Furthermore, the contributions of demographic and cognitive data were also assessed via multifeatured classifiers. An accurate simultaneous multiclass classification of each FTD variant against other variants and controls was obtained based on the selection of an optimum set of features. The classifiers incorporating the brain's network and cognitive assessment increased performance metrics. Multimodal classifiers evidenced specific variants' compromise, across modalities and methods through feature importance analysis. If replicated and validated, this approach may help to support clinical decision tools aimed to detect specific affectations in the context of overlapping diseases.
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Affiliation(s)
- Raul Gonzalez-Gomez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile
- Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Agustín Ibañez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile
- Cognitive Neuroscience Center, Universidad de San Andres, Buenos Aires, Argentina
- Global Brain Health Institute, University of California San Francisco, San Francisco, CA, USA
- Trinity College Dublin, Dublin, Ireland
| | - Sebastian Moguilner
- Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibañez, Santiago de Chile, Chile
- Cognitive Neuroscience Center, Universidad de San Andres, Buenos Aires, Argentina
- Global Brain Health Institute, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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11
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Yamamoto Y, Hirano J, Ueda R, Yoshitake H, Yamagishi M, Kimura M, Kamiya K, Shino M, Mimura M, Yamagata B. White matter alterations in the dorsal attention network contribute to a high risk of unsafe driving in healthy older people. PCN REPORTS : PSYCHIATRY AND CLINICAL NEUROSCIENCES 2022; 1:e45. [PMID: 38868688 PMCID: PMC11114439 DOI: 10.1002/pcn5.45] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 08/21/2022] [Indexed: 06/14/2024]
Abstract
Aim Healthy older drivers may be at high risk of fatal traffic accidents. Our recent study showed that volumetric alterations in gray matter in the brain regions within the dorsal attention network (DAN) were strongly related to the risk of unsafe driving in healthy older people. However, the relationship between white matter (WM) structural connectivity and driving ability in healthy older people is still unclear. Methods We used diffusion tensor imaging to examine the association between microstructural alterations in the DAN and the risk of unsafe driving among healthy older people. We enrolled 32 healthy older individuals aged over 65 years and screened unsafe drivers using an on-road driving test. We then determined the pattern of WM aberrations in unsafe drivers using tract-based spatial statistics. Results The analysis demonstrated that unsafe drivers had significantly higher axial diffusivity values in nine WM clusters compared with safe drivers. These results were primarily observed bilaterally in the dorsal superior longitudinal fasciculus, which is involved in the DAN. Furthermore, correlation analyses showed that higher axial diffusivity values in the superior longitudinal fasciculus were associated with lower Trail Making Test A scores within unsafe drivers. This result suggests that functionally, WM microstructural alterations in the DAN are associated with attention problems, which may contribute to the risk of unsafe driving among healthy older people. Conclusion Our findings may elucidate the neurobiological mechanisms underlying the increased risk of unsafe driving in healthy older people, potentially facilitating the development of new interventions to prevent fatal accidents.
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Affiliation(s)
- Yasuharu Yamamoto
- Department of NeuropsychiatryKeio University School of MedicineTokyoJapan
| | - Jinichi Hirano
- Department of NeuropsychiatryKeio University School of MedicineTokyoJapan
| | - Ryo Ueda
- Office of Radiation TechnologyKeio University HospitalTokyoJapan
| | - Hiroshi Yoshitake
- Department of Human and Engineered Environmental StudiesThe University of TokyoTokyoJapan
| | - Mika Yamagishi
- Department of NeuropsychiatryKeio University School of MedicineTokyoJapan
| | - Mariko Kimura
- Department of NeuropsychiatryKeio University School of MedicineTokyoJapan
- Graduate School of PsychologyRissho UniversityTokyoJapan
| | - Kei Kamiya
- Department of NeuropsychiatryKeio University School of MedicineTokyoJapan
| | - Motoki Shino
- Department of Human and Engineered Environmental StudiesThe University of TokyoTokyoJapan
| | - Masaru Mimura
- Department of NeuropsychiatryKeio University School of MedicineTokyoJapan
| | - Bun Yamagata
- Department of NeuropsychiatryKeio University School of MedicineTokyoJapan
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12
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McKenna MC, Murad A, Huynh W, Lope J, Bede P. The changing landscape of neuroimaging in frontotemporal lobar degeneration: from group-level observations to single-subject data interpretation. Expert Rev Neurother 2022; 22:179-207. [PMID: 35227146 DOI: 10.1080/14737175.2022.2048648] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION While the imaging signatures of frontotemporal lobar degeneration (FTLD) phenotypes and genotypes are well-characterised based on group-level descriptive analyses, the meaningful interpretation of single MRI scans remains challenging. Single-subject MRI classification frameworks rely on complex computational models and large training datasets to categorise individual patients into diagnostic subgroups based on distinguishing imaging features. Reliable individual subject data interpretation is hugely important in the clinical setting to expedite the diagnosis and classify individuals into relevant prognostic categories. AREAS COVERED This article reviews (1) the neuroimaging studies that propose single-subject MRI classification strategies in symptomatic and pre-symptomatic FTLD, (2) potential practical implications and (3) the limitations of current single-subject data interpretation models. EXPERT OPINION Classification studies in FTLD have demonstrated the feasibility of categorising individual subjects into diagnostic groups based on multiparametric imaging data. Preliminary data indicate that pre-symptomatic FTLD mutation carriers may also be reliably distinguished from controls. Despite momentous advances in the field, significant further improvements are needed before these models can be developed into viable clinical applications.
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Affiliation(s)
| | - Aizuri Murad
- Computational Neuroimaging Group, Trinity College Dublin, Ireland
| | - William Huynh
- Brain and Mind Centre, University of Sydney, Australia
| | - Jasmin Lope
- Computational Neuroimaging Group, Trinity College Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Trinity College Dublin, Ireland.,Pitié-Salpêtrière University Hospital, Sorbonne University, France
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13
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Pathak N, Vimal SK, Tandon I, Agrawal L, Hongyi C, Bhattacharyya S. Neurodegenerative Disorders of Alzheimer, Parkinsonism, Amyotrophic Lateral Sclerosis and Multiple Sclerosis: An Early Diagnostic Approach for Precision Treatment. Metab Brain Dis 2022; 37:67-104. [PMID: 34719771 DOI: 10.1007/s11011-021-00800-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 07/11/2021] [Indexed: 12/21/2022]
Abstract
Neurodegenerative diseases (NDs) are characterised by progressive dysfunction of synapses, neurons, glial cells and their networks. Neurodegenerative diseases can be classified according to primary clinical features (e.g., dementia, parkinsonism, or motor neuron disease), anatomic distribution of neurodegeneration (e.g., frontotemporal degenerations, extrapyramidal disorders, or spinocerebellar degenerations), or principal molecular abnormalities. The most common neurodegenerative disorders are amyloidosis, tauopathies, a-synucleinopathy, and TAR DNA-binding protein 43 (TDP-43) proteopathy. The protein abnormalities in these disorders have abnormal conformational properties along with altered cellular mechanisms, and they exhibit motor deficit, mitochondrial malfunction, dysfunctions in autophagic-lysosomal pathways, synaptic toxicity, and more emerging mechanisms such as the roles of stress granule pathways and liquid-phase transitions. Finally, for each ND, microglial cells have been reported to be implicated in neurodegeneration, in particular, because the microglial responses can shift from neuroprotective to a deleterious role. Growing experimental evidence suggests that abnormal protein conformers act as seed material for oligomerization, spreading from cell to cell through anatomically connected neuronal pathways, which may in part explain the specific anatomical patterns observed in brain autopsy sample. In this review, we mention the human pathology of select neurodegenerative disorders, focusing on how neurodegenerative disorders (i.e., Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and multiple sclerosis) represent a great healthcare problem worldwide and are becoming prevalent because of the increasing aged population. Despite many studies have focused on their etiopathology, the exact cause of these diseases is still largely unknown and until now with the only available option of symptomatic treatments. In this review, we aim to report the systematic and clinically correlated potential biomarker candidates. Although future studies are necessary for their use in early detection and progression in humans affected by NDs, the promising results obtained by several groups leads us to this idea that biomarkers could be used to design a potential therapeutic approach and preclinical clinical trials for the treatments of NDs.
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Affiliation(s)
- Nishit Pathak
- Department of Pharmaceutical Sciences and Chinese Traditional Medicine, Southwest University, Beibei, Chongqing, 400715, People's Republic of China
| | - Sunil Kumar Vimal
- Department of Pharmaceutical Sciences and Chinese Traditional Medicine, Southwest University, Beibei, Chongqing, 400715, People's Republic of China
| | - Ishi Tandon
- Amity University Jaipur, Rajasthan, Jaipur, Rajasthan, India
| | - Lokesh Agrawal
- Graduate School of Comprehensive Human Sciences, Kansei Behavioural and Brain Sciences, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki, 305-8577, Japan
| | - Cao Hongyi
- Department of Pharmaceutical Sciences and Chinese Traditional Medicine, Southwest University, Beibei, Chongqing, 400715, People's Republic of China
| | - Sanjib Bhattacharyya
- Department of Pharmaceutical Sciences and Chinese Traditional Medicine, Southwest University, Beibei, Chongqing, 400715, People's Republic of China.
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14
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Bocchetta M, Malpetti M, Todd EG, Rowe JB, Rohrer JD. Looking beneath the surface: the importance of subcortical structures in frontotemporal dementia. Brain Commun 2021; 3:fcab158. [PMID: 34458729 PMCID: PMC8390477 DOI: 10.1093/braincomms/fcab158] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2021] [Indexed: 12/15/2022] Open
Abstract
Whilst initial anatomical studies of frontotemporal dementia focussed on cortical involvement, the relevance of subcortical structures to the pathophysiology of frontotemporal dementia has been increasingly recognized over recent years. Key structures affected include the caudate, putamen, nucleus accumbens, and globus pallidus within the basal ganglia, the hippocampus and amygdala within the medial temporal lobe, the basal forebrain, and the diencephalon structures of the thalamus, hypothalamus and habenula. At the most posterior aspect of the brain, focal involvement of brainstem and cerebellum has recently also been shown in certain subtypes of frontotemporal dementia. Many of the neuroimaging studies on subcortical structures in frontotemporal dementia have been performed in clinically defined sporadic cases. However, investigations of genetically- and pathologically-confirmed forms of frontotemporal dementia are increasingly common and provide molecular specificity to the changes observed. Furthermore, detailed analyses of sub-nuclei and subregions within each subcortical structure are being added to the literature, allowing refinement of the patterns of subcortical involvement. This review focuses on the existing literature on structural imaging and neuropathological studies of subcortical anatomy across the spectrum of frontotemporal dementia, along with investigations of brain–behaviour correlates that examine the cognitive sequelae of specific subcortical involvement: it aims to ‘look beneath the surface’ and summarize the patterns of subcortical involvement have been described in frontotemporal dementia.
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Affiliation(s)
- Martina Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Maura Malpetti
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
| | - Emily G Todd
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - James B Rowe
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK.,Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Jonathan D Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
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15
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Feis RA, van der Grond J, Bouts MJRJ, Panman JL, Poos JM, Schouten TM, de Vos F, Jiskoot LC, Dopper EGP, van Buchem MA, van Swieten JC, Rombouts SARB. Classification using fractional anisotropy predicts conversion in genetic frontotemporal dementia, a proof of concept. Brain Commun 2021; 2:fcaa079. [PMID: 33543126 PMCID: PMC7846185 DOI: 10.1093/braincomms/fcaa079] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 04/29/2020] [Accepted: 05/11/2020] [Indexed: 11/14/2022] Open
Abstract
Frontotemporal dementia is a highly heritable and devastating neurodegenerative disease. About 10–20% of all frontotemporal dementia is caused by known pathogenic mutations, but a reliable tool to predict clinical conversion in mutation carriers is lacking. In this retrospective proof-of-concept case-control study, we investigate whether MRI-based and cognition-based classifiers can predict which mutation carriers from genetic frontotemporal dementia families will develop symptoms (‘convert’) within 4 years. From genetic frontotemporal dementia families, we included 42 presymptomatic frontotemporal dementia mutation carriers. We acquired anatomical, diffusion-weighted imaging, and resting-state functional MRI, as well as neuropsychological data. After 4 years, seven mutation carriers had converted to frontotemporal dementia (‘converters’), while 35 had not (‘non-converters’). We trained regularized logistic regression models on baseline MRI and cognitive data to predict conversion to frontotemporal dementia within 4 years, and quantified prediction performance using area under the receiver operating characteristic curves. The prediction model based on fractional anisotropy, with highest contribution of the forceps minor, predicted conversion to frontotemporal dementia beyond chance level (0.81 area under the curve, family-wise error corrected P = 0.025 versus chance level). Other MRI-based and cognitive features did not outperform chance level. Even in a small sample, fractional anisotropy predicted conversion in presymptomatic frontotemporal dementia mutation carriers beyond chance level. After validation in larger data sets, conversion prediction in genetic frontotemporal dementia may facilitate early recruitment into clinical trials.
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Affiliation(s)
- Rogier A Feis
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2333 ZA, Leiden, the Netherlands.,Institute of Psychology, Leiden University, 2333 AK, Leiden, the Netherlands
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands
| | - Mark J R J Bouts
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2333 ZA, Leiden, the Netherlands.,Institute of Psychology, Leiden University, 2333 AK, Leiden, the Netherlands
| | - Jessica L Panman
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Department of Neurology, Erasmus Medical Centre, 3015 GD, Rotterdam, the Netherlands
| | - Jackie M Poos
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Department of Neurology, Erasmus Medical Centre, 3015 GD, Rotterdam, the Netherlands
| | - Tijn M Schouten
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2333 ZA, Leiden, the Netherlands.,Institute of Psychology, Leiden University, 2333 AK, Leiden, the Netherlands
| | - Frank de Vos
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2333 ZA, Leiden, the Netherlands.,Institute of Psychology, Leiden University, 2333 AK, Leiden, the Netherlands
| | - Lize C Jiskoot
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Department of Neurology, Erasmus Medical Centre, 3015 GD, Rotterdam, the Netherlands.,Dementia Research Centre, University College London, London, WC1N 3AR, UK
| | - Elise G P Dopper
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Department of Neurology, Erasmus Medical Centre, 3015 GD, Rotterdam, the Netherlands
| | - Mark A van Buchem
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2333 ZA, Leiden, the Netherlands
| | - John C van Swieten
- Department of Neurology, Erasmus Medical Centre, 3015 GD, Rotterdam, the Netherlands
| | - Serge A R B Rombouts
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2333 ZA, Leiden, the Netherlands.,Institute of Psychology, Leiden University, 2333 AK, Leiden, the Netherlands
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16
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Yu Q, Mai Y, Ruan Y, Luo Y, Zhao L, Fang W, Cao Z, Li Y, Liao W, Xiao S, Mok VCT, Shi L, Liu J. An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer's disease. Alzheimers Res Ther 2021; 13:23. [PMID: 33436059 PMCID: PMC7805212 DOI: 10.1186/s13195-020-00757-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 12/21/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND The differential diagnosis of frontotemporal dementia (FTD) and Alzheimer's disease (AD) is difficult due to the overlaps of clinical symptoms. Structural magnetic resonance imaging (sMRI) presents distinct brain atrophy and potentially helps in their differentiation. In this study, we aim at deriving a novel integrated index by leveraging the volumetric measures in brain regions with significant difference between AD and FTD and developing an MRI-based strategy for the differentiation of FTD and AD. METHODS In this study, the data were acquired from three different databases, including 47 subjects with FTD, 47 subjects with AD, and 47 normal controls in the NACC database; 50 subjects with AD in the ADNI database; and 50 subjects with FTD in the FTLDNI database. The MR images of all subjects were automatically segmented, and the brain atrophy, including the AD resemblance atrophy index (AD-RAI), was quantified using AccuBrain®. A novel MRI index, named the frontotemporal dementia index (FTDI), was derived as the ratio between the weighted sum of the volumetric indexes in "FTD dominant" structures over that obtained from "AD dominant" structures. The weights and the identification of "FTD/AD dominant" structures were acquired from the statistical analysis of NACC data. The differentiation performance of FTDI was validated using independent data from ADNI and FTLDNI databases. RESULTS AD-RAI is a proven imaging biomarker to identify AD and FTD from NC with significantly higher values (p < 0.001 and AUC = 0.88) as we reported before, while no significant difference was found between AD and FTD (p = 0.647). FTDI showed excellent accuracy in identifying FTD from AD (AUC = 0.90; SEN = 89%, SPE = 75% with threshold value = 1.08). The validation using independent data from ADNI and FTLDNI datasets also confirmed the efficacy of FTDI (AUC = 0.93; SEN = 96%, SPE = 70% with threshold value = 1.08). CONCLUSIONS Brain atrophy in AD, FTD, and normal elderly shows distinct patterns. In addition to AD-RAI that is designed to detect abnormal brain atrophy in dementia, a novel index specific to FTD is proposed and validated. By combining AD-RAI and FTDI, an MRI-based decision strategy was further proposed as a promising solution for the differential diagnosis of AD and FTD in clinical practice.
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Affiliation(s)
- Qun Yu
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang West Road, Guangzhou, Guangdong, China
| | - Yingren Mai
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang West Road, Guangzhou, Guangdong, China
| | - Yuting Ruan
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang West Road, Guangzhou, Guangdong, China
| | - Yishan Luo
- BrainNow Research Institute, Shenzhen, China
| | - Lei Zhao
- BrainNow Research Institute, Shenzhen, China
| | - Wenli Fang
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang West Road, Guangzhou, Guangdong, China
| | - Zhiyu Cao
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang West Road, Guangzhou, Guangdong, China
| | - Yi Li
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang West Road, Guangzhou, Guangdong, China
| | - Wang Liao
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang West Road, Guangzhou, Guangdong, China
| | - Songhua Xiao
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang West Road, Guangzhou, Guangdong, China
| | - Vincent C T Mok
- BrainNow Research Institute, Shenzhen, China
- Gerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative Medicine, Division of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Lin Shi
- BrainNow Research Institute, Shenzhen, China.
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China.
| | - Jun Liu
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang West Road, Guangzhou, Guangdong, China.
- Guangdong Province Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
- Laboratory of RNA and Major Diseases of Brain and Heart, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
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Falgàs N, Ruiz‐Peris M, Pérez‐Millan A, Sala‐Llonch R, Antonell A, Balasa M, Borrego‐Écija S, Ramos‐Campoy O, Augé JM, Castellví M, Tort‐Merino A, Olives J, Fernández‐Villullas G, Blennow K, Zetterberg H, Bargalló N, Lladó A, Sánchez‐Valle R. Contribution of CSF biomarkers to early-onset Alzheimer's disease and frontotemporal dementia neuroimaging signatures. Hum Brain Mapp 2020; 41:2004-2013. [PMID: 31944489 PMCID: PMC7267898 DOI: 10.1002/hbm.24925] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 12/11/2019] [Accepted: 01/04/2020] [Indexed: 12/19/2022] Open
Abstract
Prior studies have described distinct patterns of brain gray matter and white matter alterations in Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD), as well as differences in their cerebrospinal fluid (CSF) biomarkers profiles. We aim to investigate the relationship between early-onset AD (EOAD) and FTLD structural alterations and CSF biomarker levels. We included 138 subjects (64 EOAD, 26 FTLD, and 48 controls), all of them with a 3T MRI brain scan and CSF biomarkers available (the 42 amino acid-long form of the amyloid-beta protein [Aβ42], total-tau protein [T-tau], neurofilament light chain [NfL], neurogranin [Ng], and 14-3-3 levels). We used FreeSurfer and FSL to obtain cortical thickness (CTh) and fraction anisotropy (FA) maps. We studied group differences in CTh and FA and described the "AD signature" and "FTLD signature." We tested multiple regression models to find which CSF-biomarkers better explained each disease neuroimaging signature. CTh and FA maps corresponding to the AD and FTLD signatures were in accordance with previous literature. Multiple regression analyses showed that the biomarkers that better explained CTh values within the AD signature were Aβ and 14-3-3; whereas NfL and 14-3-3 levels explained CTh values within the FTLD signature. Similarly, NfL levels explained FA values in the FTLD signature. Ng levels were not predictive in any of the models. Biochemical markers contribute differently to structural (CTh and FA) changes typical of AD and FTLD.
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Affiliation(s)
- Neus Falgàs
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
- Atlantic Fellow for Equity in Brain HealthGlobal Brain Health Institute, University of CaliforniaSan FranciscoCalifornia
| | - Mariona Ruiz‐Peris
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
- Department of BiomedicineUniversity of BarcelonaBarcelonaSpain
| | - Agnès Pérez‐Millan
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
- Department of BiomedicineUniversity of BarcelonaBarcelonaSpain
| | | | - Anna Antonell
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
| | - Mircea Balasa
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
- Senior Atlantic Fellow for Equity inBrain Health, Global Brain Health InstituteTrinity College DublinIreland
| | - Sergi Borrego‐Écija
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
| | - Oscar Ramos‐Campoy
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
| | - Josep Maria Augé
- Biochemistry and Molecular Genetics Department, Hospital Clínic de BarcelonaBarcelonaSpain
| | - Magdalena Castellví
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
| | - Adrià Tort‐Merino
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
| | - Jaume Olives
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
| | - Guadalupe Fernández‐Villullas
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute ofNeuroscience and Physiology, The Sahlgrenska Academy at the University of GothenburgMölndalSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute ofNeuroscience and Physiology, The Sahlgrenska Academy at the University of GothenburgMölndalSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
- Department of Neurodegenerative DiseaseUniversity College LondonLondonUK
- UK Dementia Research Institute at UCLLondonUK
| | - Núria Bargalló
- Radiology Service, Hospital ClínicMRI imaging platform. Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)
| | - Albert Lladó
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
| | - Raquel Sánchez‐Valle
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
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Subcortical gray matter changes in pediatric patients with new-onset juvenile myoclonic epilepsy. Epilepsy Behav 2020; 104:106860. [PMID: 31935646 DOI: 10.1016/j.yebeh.2019.106860] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 11/16/2019] [Accepted: 12/13/2019] [Indexed: 11/23/2022]
Abstract
OBJECTIVE The objective of the study was to identify the relationship between subcortical gray matter (GM) volumes and juvenile myoclonic epilepsy (JME). METHODS We analyzed the brain magnetic resonance imaging (MRI) scans that were performed during the time of the diagnosis of epilepsy by using voxel-based morphometry (VBM) method. The volumetric three-dimensional sequence was used for structural investigation. The volumes of the thalamus, caudate nucleus, pallidum, and putamen were measured in both hemispheres of patients with JME, patients with generalized tonic-clonic seizures alone (GTCS) (as a disease control group) and healthy controls (HCs). All patients were drug-naïve, and treatment had been started after evaluating MRI results. RESULTS Fifteen patients with JME (9 females, mean age = 16.1 ± 3.2), 18 patients with GTCS (10 females, mean age = 15.5 ± 2.9), and 43 HCs (24 females, mean age = 15.9 ± 2.8) were included in the analysis. No significant difference was found for relative globus pallidus, caudate, and putamen volumes among the groups with JME, GTCS, and the HC group. The relative left and right thalamic volumes were significantly different between groups (Kruskal-Wallis rank test, p = 0.007, p = 0.001). In pairwise comparisons, both right and left relative thalamic volumes were lower in patients with JME than in HCs (right thalamus: means: 0.521 ± 0.066 vs. 0.597 ± 0.058, p < 0.001; left thalamus: means: 0.526 ± 0.088 vs. 0.605 ± 0.057, p < 0.001, Bonferroni post hoc corrections) and in patients with JME than in patients with GTCS (right thalamus: means: 0.521 ± 0.066 vs. 0.578 ± 0.066, p = 0.03; left thalamus: means: 0.526 ± 0.088 vs. 0.592 ± 0.068, p = 0.01, Bonferroni post hoc corrections), whereas there was no significant difference between the HCs and patients with GTCS (right thalamus: means: 0.597 ± 0.058 vs. 0.578 ± 0.066, p = 0.8; left thalamus: means: 0.605 ± 0.057 vs. 0.592 ± 0.068, p = 0.999, Bonferroni post hoc corrections). CONCLUSION This study allowed us to know that microstructural abnormalities exist from the disease onset, and the thalamus might play a critical role in the pathogenesis of JME.
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Feis RA, Bouts MJRJ, Dopper EGP, Filippini N, Heise V, Trachtenberg AJ, van Swieten JC, van Buchem MA, van der Grond J, Mackay CE, Rombouts SARB. Multimodal MRI of grey matter, white matter, and functional connectivity in cognitively healthy mutation carriers at risk for frontotemporal dementia and Alzheimer's disease. BMC Neurol 2019; 19:343. [PMID: 31881858 PMCID: PMC6933911 DOI: 10.1186/s12883-019-1567-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 12/11/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Frontotemporal dementia (FTD) and Alzheimer's disease (AD) are associated with divergent differences in grey matter volume, white matter diffusion, and functional connectivity. However, it is unknown at what disease stage these differences emerge. Here, we investigate whether divergent differences in grey matter volume, white matter diffusion, and functional connectivity are already apparent between cognitively healthy carriers of pathogenic FTD mutations, and cognitively healthy carriers at increased AD risk. METHODS We acquired multimodal magnetic resonance imaging (MRI) brain scans in cognitively healthy subjects with (n=39) and without (n=36) microtubule-associated protein Tau (MAPT) or progranulin (GRN) mutations, and with (n=37) and without (n=38) apolipoprotein E ε4 (APOE4) allele. We evaluated grey matter volume using voxel-based morphometry, white matter diffusion using tract-based spatial statistics (TBSS), and region-to-network functional connectivity using dual regression in the default mode network and salience network. We tested for differences between the respective carriers and controls, as well as for divergence of those differences. For the divergence contrast, we additionally performed region-of-interest TBSS analyses in known areas of white matter diffusion differences between FTD and AD (i.e., uncinate fasciculus, forceps minor, and anterior thalamic radiation). RESULTS MAPT/GRN carriers did not differ from controls in any modality. APOE4 carriers had lower fractional anisotropy than controls in the callosal splenium and right inferior fronto-occipital fasciculus, but did not show grey matter volume or functional connectivity differences. We found no divergent differences between both carrier-control contrasts in any modality, even in region-of-interest analyses. CONCLUSIONS Concluding, we could not find differences suggestive of divergent pathways of underlying FTD and AD pathology in asymptomatic risk mutation carriers. Future studies should focus on asymptomatic mutation carriers that are closer to symptom onset to capture the first specific signs that may differentiate between FTD and AD.
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Affiliation(s)
- Rogier A. Feis
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands
- FMRIB, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- LIBC, Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Mark J. R. J. Bouts
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands
- LIBC, Leiden Institute for Brain and Cognition, Leiden, The Netherlands
- Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Elise G. P. Dopper
- Department of Neurology, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Nicola Filippini
- FMRIB, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Verena Heise
- FMRIB, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Aaron J. Trachtenberg
- FMRIB, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | - Mark A. van Buchem
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands
- LIBC, Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Clare E. Mackay
- FMRIB, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Serge A. R. B. Rombouts
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands
- LIBC, Leiden Institute for Brain and Cognition, Leiden, The Netherlands
- Institute of Psychology, Leiden University, Leiden, The Netherlands
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20
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Marino S, Bonanno L, Lo Buono V, Ciurleo R, Corallo F, Morabito R, Chirico G, Marra A, Bramanti P. Longitudinal analysis of brain atrophy in Alzheimer's disease and frontotemporal dementia. J Int Med Res 2019; 47:5019-5027. [PMID: 31524019 PMCID: PMC6833431 DOI: 10.1177/0300060519830830] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Objectives Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are among the leading causes of early-onset dementia. This study aimed to assess the rate of whole brain atrophy by comparing bvFTD and AD. Methods Two patients (one man with AD, and one woman with bvFTD) had neuropsychological and neuroimaging assessment by using automated techniques for cross-sectional and longitudinal atrophy measurements. Results In the patient with AD, magnetic resonance imaging (MRI) showed decreased bilateral hippocampal and mesial-temporal volume. However, conventional images showed no difference between baseline (T0) and after 1 year (T1). In the patient with bvFTD, MRI showed bilateral frontotemporal lobe atrophy and a moderate increase in atrophy between T0 and T1, particularly in the temporal lobes. A cross-sectional cerebral volume examination showed a considerable reduction in brain volume in the patient with bvFDT and a moderate reduction in the patient with AD. A longitudinal cerebral volume examination showed a lower percentage brain volume change in the patient with bvFTS compared with the patient with AD. Conclusions Our results suggest that bvFTD has more neurodegenerative progression. MRI findings should be considered as a reliable marker of disease progression in the brain. Our findings offer potential for monitoring treatment outcomes.
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Affiliation(s)
- Silvia Marino
- IRCCS Centro Neurolesi "Bonino Pulejo", Messina, Italy.,Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy
| | - Lilla Bonanno
- IRCCS Centro Neurolesi "Bonino Pulejo", Messina, Italy
| | | | | | | | - Rosa Morabito
- IRCCS Centro Neurolesi "Bonino Pulejo", Messina, Italy
| | | | - Angela Marra
- IRCCS Centro Neurolesi "Bonino Pulejo", Messina, Italy
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21
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Pagnozzi AM, Fripp J, Rose SE. Quantifying deep grey matter atrophy using automated segmentation approaches: A systematic review of structural MRI studies. Neuroimage 2019; 201:116018. [PMID: 31319182 DOI: 10.1016/j.neuroimage.2019.116018] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 07/01/2019] [Accepted: 07/12/2019] [Indexed: 12/13/2022] Open
Abstract
The deep grey matter (DGM) nuclei of the brain play a crucial role in learning, behaviour, cognition, movement and memory. Although automated segmentation strategies can provide insight into the impact of multiple neurological conditions affecting these structures, such as Multiple Sclerosis (MS), Huntington's disease (HD), Alzheimer's disease (AD), Parkinson's disease (PD) and Cerebral Palsy (CP), there are a number of technical challenges limiting an accurate automated segmentation of the DGM. Namely, the insufficient contrast of T1 sequences to completely identify the boundaries of these structures, as well as the presence of iso-intense white matter lesions or extensive tissue loss caused by brain injury. Therefore in this systematic review, 269 eligible studies were analysed and compared to determine the optimal approaches for addressing these technical challenges. The automated approaches used among the reviewed studies fall into three broad categories, atlas-based approaches focusing on the accurate alignment of atlas priors, algorithmic approaches which utilise intensity information to a greater extent, and learning-based approaches that require an annotated training set. Studies that utilise freely available software packages such as FIRST, FreeSurfer and LesionTOADS were also eligible, and their performance compared. Overall, deep learning approaches achieved the best overall performance, however these strategies are currently hampered by the lack of large-scale annotated data. Improving model generalisability to new datasets could be achieved in future studies with data augmentation and transfer learning. Multi-atlas approaches provided the second-best performance overall, and may be utilised to construct a "silver standard" annotated training set for deep learning. To address the technical challenges, providing robustness to injury can be improved by using multiple channels, highly elastic diffeomorphic transformations such as LDDMM, and by following atlas-based approaches with an intensity driven refinement of the segmentation, which has been done with the Expectation Maximisation (EM) and level sets methods. Accounting for potential lesions should be achieved with a separate lesion segmentation approach, as in LesionTOADS. Finally, to address the issue of limited contrast, R2*, T2* and QSM sequences could be used to better highlight the DGM due to its higher iron content. Future studies could look to additionally acquire these sequences by retaining the phase information from standard structural scans, or alternatively acquiring these sequences for only a training set, allowing models to learn the "improved" segmentation from T1-sequences alone.
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Affiliation(s)
- Alex M Pagnozzi
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia.
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
| | - Stephen E Rose
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
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Whitwell JL. FTD spectrum: Neuroimaging across the FTD spectrum. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2019; 165:187-223. [PMID: 31481163 DOI: 10.1016/bs.pmbts.2019.05.009] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Frontotemporal dementia is a complex and heterogeneous neurodegenerative disease that encompasses many clinical syndromes, pathological diseases, and genetic mutations. Neuroimaging has played a critical role in our understanding of the underlying pathophysiology of frontotemporal dementia and provided biomarkers to aid diagnosis. Early studies defined patterns of neurodegeneration and hypometabolism associated with the clinical, pathological and genetic aspects of frontotemporal dementia, with more recent studies highlighting how the breakdown of structural and functional brain networks define frontotemporal dementia. Molecular positron emission tomography ligands allowing the in vivo imaging of tau proteins have also provided important insights, although more work is needed to understand the biology of the currently available ligands.
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Bouts MJRJ, Möller C, Hafkemeijer A, van Swieten JC, Dopper E, van der Flier WM, Vrenken H, Wink AM, Pijnenburg YAL, Scheltens P, Barkhof F, Schouten TM, de Vos F, Feis RA, van der Grond J, de Rooij M, Rombouts SARB. Single Subject Classification of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging. J Alzheimers Dis 2019; 62:1827-1839. [PMID: 29614652 DOI: 10.3233/jad-170893] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND/OBJECTIVE Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to-on a subject-basis-differentiate these dementia-types is not yet known. METHODS Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier's ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC). RESULTS Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41). CONCLUSION Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI.
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Affiliation(s)
- Mark J R J Bouts
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Christiane Möller
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Anne Hafkemeijer
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - John C van Swieten
- Department of Clinical Genetics, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.,Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Elise Dopper
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands.,Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.,Department of Epidemiology and Biostatistics, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.,Department of Physics and Medical Technology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Alle Meije Wink
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Yolande A L Pijnenburg
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.,Institute of Neurology and Healthcare Engineering, University College London, London, UK
| | - Tijn M Schouten
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Frank de Vos
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Rogier A Feis
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Mark de Rooij
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Serge A R B Rombouts
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
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24
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Yu J, Lee TM. The longitudinal decline of white matter microstructural integrity in behavioral variant frontotemporal dementia and its association with executive function. Neurobiol Aging 2019; 76:62-70. [DOI: 10.1016/j.neurobiolaging.2018.12.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 11/23/2018] [Accepted: 12/15/2018] [Indexed: 12/13/2022]
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25
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Feis RA, Bouts MJRJ, Panman JL, Jiskoot LC, Dopper EGP, Schouten TM, de Vos F, van der Grond J, van Swieten JC, Rombouts SARB. Single-subject classification of presymptomatic frontotemporal dementia mutation carriers using multimodal MRI. Neuroimage Clin 2019; 22:101718. [PMID: 30827922 PMCID: PMC6543025 DOI: 10.1016/j.nicl.2019.101718] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
BACKGROUND Classification models based on magnetic resonance imaging (MRI) may aid early diagnosis of frontotemporal dementia (FTD) but have only been applied in established FTD cases. Detection of FTD patients in earlier disease stages, such as presymptomatic mutation carriers, may further advance early diagnosis and treatment. In this study, we aim to distinguish presymptomatic FTD mutation carriers from controls on an individual level using multimodal MRI-based classification. METHODS Anatomical MRI, diffusion tensor imaging (DTI) and resting-state functional MRI data were collected in 55 presymptomatic FTD mutation carriers (8 microtubule-associated protein Tau, 35 progranulin, and 12 chromosome 9 open reading frame 72) and 48 familial controls. We calculated grey and white matter density features from anatomical MRI scans, diffusivity features from DTI, and functional connectivity features from resting-state functional MRI. These features were applied in a recently introduced multimodal behavioural variant FTD (bvFTD) classification model, and were subsequently used to train and test unimodal and multimodal carrier-control models. Classification performance was quantified using area under the receiver operator characteristic curves (AUC). RESULTS The bvFTD model was not able to separate presymptomatic carriers from controls beyond chance level (AUC = 0.582, p = 0.078). In contrast, one unimodal and several multimodal carrier-control models performed significantly better than chance level. The unimodal model included the radial diffusivity feature and had an AUC of 0.642 (p = 0.032). The best multimodal model combined radial diffusivity and white matter density features (AUC = 0.684, p = 0.004). CONCLUSIONS FTD mutation carriers can be separated from controls with a modest AUC even before symptom-onset, using a newly created carrier-control classification model, while this was not possible using a recent bvFTD classification model. A multimodal MRI-based classification score may therefore be a useful biomarker to aid earlier FTD diagnosis. The exclusive selection of white matter features in the best performing model suggests that the earliest FTD-related pathological processes occur in white matter.
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Affiliation(s)
- Rogier A Feis
- Department of Radiology, Leiden University Medical Centre, Leiden, Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands.
| | - Mark J R J Bouts
- Department of Radiology, Leiden University Medical Centre, Leiden, Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands; Institute of Psychology, Leiden University, Leiden, Netherlands.
| | - Jessica L Panman
- Department of Radiology, Leiden University Medical Centre, Leiden, Netherlands; Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands.
| | - Lize C Jiskoot
- Department of Radiology, Leiden University Medical Centre, Leiden, Netherlands; Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands.
| | - Elise G P Dopper
- Department of Radiology, Leiden University Medical Centre, Leiden, Netherlands; Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands; Alzheimer Centre & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Centre, Amsterdam, Netherlands.
| | - Tijn M Schouten
- Department of Radiology, Leiden University Medical Centre, Leiden, Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands; Institute of Psychology, Leiden University, Leiden, Netherlands.
| | - Frank de Vos
- Department of Radiology, Leiden University Medical Centre, Leiden, Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands; Institute of Psychology, Leiden University, Leiden, Netherlands.
| | | | - John C van Swieten
- Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands; Department of Clinical Genetics, Neuroscience Campus Amsterdam, VU University Medical Centre, Amsterdam, Netherlands.
| | - Serge A R B Rombouts
- Department of Radiology, Leiden University Medical Centre, Leiden, Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands; Institute of Psychology, Leiden University, Leiden, Netherlands.
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26
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Bruun M, Koikkalainen J, Rhodius-Meester HFM, Baroni M, Gjerum L, van Gils M, Soininen H, Remes AM, Hartikainen P, Waldemar G, Mecocci P, Barkhof F, Pijnenburg Y, van der Flier WM, Hasselbalch SG, Lötjönen J, Frederiksen KS. Detecting frontotemporal dementia syndromes using MRI biomarkers. NEUROIMAGE-CLINICAL 2019; 22:101711. [PMID: 30743135 PMCID: PMC6369219 DOI: 10.1016/j.nicl.2019.101711] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 02/01/2019] [Accepted: 02/03/2019] [Indexed: 12/20/2022]
Abstract
Background Diagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal dementia subtypes from other diagnostic groups, and from one another. Methods In this retrospective multicenter cohort study, we included 1213 patients (age 67 ± 9, 48% females) from two memory clinic cohorts: 116 frontotemporal dementia, 341 Alzheimer's disease, 66 Dementia with Lewy bodies, 40 vascular dementia, 104 other dementias, 229 mild cognitive impairment, and 317 subjective cognitive decline. Three MRI atrophy biomarkers were derived from the normalized volumes of automatically segmented cortical regions: 1) the anterior vs. posterior index, 2) the asymmetry index, and 3) the temporal pole left index. We used the following performance metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To account for the low prevalence of frontotemporal dementia we pursued a high specificity of 95%. Cross-validation was used in assessing the performance. The generalizability was assessed in an independent cohort (n = 200). Results The anterior vs. posterior index performed with an AUC of 83% for differentiation of frontotemporal dementia from all other diagnostic groups (Sensitivity = 59%, Specificity = 95%, positive likelihood ratio = 11.8, negative likelihood ratio = 0.4). The asymmetry index showed highest performance for separation of primary progressive aphasia and behavioral variant frontotemporal dementia (AUC = 85%, Sensitivity = 79%, Specificity = 92%, positive likelihood ratio = 9.9, negative likelihood ratio = 0.2), whereas the temporal pole left index was specific for detection of semantic variant primary progressive aphasia (AUC = 85%, Sensitivity = 82%, Specificity = 80%, positive likelihood ratio = 4.1, negative likelihood ratio = 0.2). The validation cohort provided corresponding results for the anterior vs. posterior index and temporal pole left index. Conclusion This study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia. Quantitative MRI biomarkers (API, ASI, and TPL) for detection of FTD and its subtypes. API differentiated FTD from other diagnostic groups with AUC of 83%. ASI and TPL showed highest performance for PPA subtypes. A subcortical bvFTD subtype resembling AD atrophy pattern seems undetectable for MRI.
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Affiliation(s)
- Marie Bruun
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark.
| | | | - Hanneke F M Rhodius-Meester
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Marta Baroni
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Le Gjerum
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
| | - Mark van Gils
- VTT Technical Research Center of Finland Ltd, Tampere, Finland
| | - Hilkka Soininen
- Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland; Neurocenter, neurology, Kuopio University Hospital, Kuopio, Finland
| | - Anne M Remes
- Unit of Clinical Neuroscience, Neurology, University of Oulu, Oulu, Finland; Medical Research Center, Oulu University Hospital, Oulu, Finland
| | | | - Gunhild Waldemar
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
| | - Patrizia Mecocci
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands; UCL institutes of Neurology and Healthcare Engineering, London, UK
| | - Yolande Pijnenburg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Steen G Hasselbalch
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
| | | | - Kristian S Frederiksen
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
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27
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Jiskoot LC, Panman JL, Meeter LH, Dopper EGP, Donker Kaat L, Franzen S, van der Ende EL, van Minkelen R, Rombouts SARB, Papma JM, van Swieten JC. Longitudinal multimodal MRI as prognostic and diagnostic biomarker in presymptomatic familial frontotemporal dementia. Brain 2019; 142:193-208. [PMID: 30508042 PMCID: PMC6308313 DOI: 10.1093/brain/awy288] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 09/26/2018] [Accepted: 10/02/2018] [Indexed: 12/12/2022] Open
Abstract
Developing and validating sensitive biomarkers for the presymptomatic stage of familial frontotemporal dementia is an important step in early diagnosis and for the design of future therapeutic trials. In the longitudinal Frontotemporal Dementia Risk Cohort, presymptomatic mutation carriers and non-carriers from families with familial frontotemporal dementia due to microtubule-associated protein tau (MAPT) and progranulin (GRN) mutations underwent a clinical assessment and multimodal MRI at baseline, 2-, and 4-year follow-up. Of the cohort of 73 participants, eight mutation carriers (three GRN, five MAPT) developed clinical features of frontotemporal dementia ('converters'). Longitudinal whole-brain measures of white matter integrity (fractional anisotropy) and grey matter volume in these converters (n = 8) were compared with healthy mutation carriers ('non-converters'; n = 35) and non-carriers (n = 30) from the same families. We also assessed the prognostic performance of decline within white matter and grey matter regions of interest by means of receiver operating characteristic analyses followed by stepwise logistic regression. Longitudinal whole-brain analyses demonstrated lower fractional anisotropy values in extensive white matter regions (genu corpus callosum, forceps minor, uncinate fasciculus, and superior longitudinal fasciculus) and smaller grey matter volumes (prefrontal, temporal, cingulate, and insular cortex) over time in converters, present from 2 years before symptom onset. White matter integrity loss of the right uncinate fasciculus and genu corpus callosum provided significant classifiers between converters, non-converters, and non-carriers. Converters' within-individual disease trajectories showed a relatively gradual onset of clinical features in MAPT, whereas GRN mutations had more rapid changes around symptom onset. MAPT converters showed more decline in the uncinate fasciculus than GRN converters, and more decline in the genu corpus callosum in GRN than MAPT converters. Our study confirms the presence of spreading predominant frontotemporal pathology towards symptom onset and highlights the value of multimodal MRI as a prognostic biomarker in familial frontotemporal dementia.
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Affiliation(s)
- Lize C Jiskoot
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jessica L Panman
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Lieke H Meeter
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Elise G P Dopper
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Neurology, VU Medical Center, Amsterdam, The Netherlands
| | - Laura Donker Kaat
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Sanne Franzen
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | - Rick van Minkelen
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Serge A R B Rombouts
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Institute of Psychology, Leiden University, Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Janne M Papma
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - John C van Swieten
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
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28
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Vuksanović V, Staff RT, Ahearn T, Murray AD, Wischik CM. Cortical Thickness and Surface Area Networks in Healthy Aging, Alzheimer's Disease and Behavioral Variant Fronto-Temporal Dementia. Int J Neural Syst 2018; 29:1850055. [PMID: 30638083 DOI: 10.1142/s0129065718500557] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Models of the human brain as a complex network of inter-connected sub-units are important in helping to understand the structural basis of the clinical features of neurodegenerative disorders. The aim of this study was to characterize in a systematic manner the differences in the structural correlation networks in cortical thickness (CT) and surface area (SA) in Alzheimer's disease (AD) and behavioral variant Fronto-Temporal Dementia (bvFTD). We have used the baseline magnetic resonance imaging (MRI) data available from a large population of patients from three clinical trials in mild to moderate AD and mild bvFTD and compared this to a well-characterized healthy aging cohort. The study population comprised 202 healthy elderly subjects, 213 with bvFTD and 213 with AD. We report that both CT and SA network architecture can be described in terms of highly correlated networks whose positive and inverse links map onto the intrinsic modular organization of the four cortical lobes. The topology of the disturbance in structural network is different in the two disease conditions, and both are different from normal aging. The changes from normal are global in character and are not restricted to fronto-temporal and temporo-parietal lobes, respectively, in bvFTD and AD, and indicate an increase in both global correlational strength and in particular nonhomologous inter-lobar connectivity defined by inverse correlations. These inverse correlations appear to be adaptive in character, reflecting coordinated increases in CT and SA that may compensate for corresponding impairment in functionally linked nodes. The effects were more pronounced in the cortical thickness atrophy network in bvFTD and in the surface area network in AD. Although lobar modularity is preserved in the context of neurodegenerative disease, the hub-like organization of networks differs both from normal and between the two forms of dementia. This implies that hubs may be secondary features of the connectivity adaptation to neurodegeneration and may not be an intrinsic property of the brain. However, analysis of the topological differences in hub-like organization CT and SA networks, and their underlying positive and negative correlations, may provide a basis for assisting in the differential diagnosis of bvFTD and AD.
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Affiliation(s)
- Vesna Vuksanović
- 1Aberdeen Biomedical Imaging Center, University of Aberdeen, Aberdeen, AB25 2ZD, UK
| | - Roger T Staff
- 2Imaging Physics, National Health Service Grampian, Aberdeen, AB25 2ZD, UK
| | - Trevor Ahearn
- 2Imaging Physics, National Health Service Grampian, Aberdeen, AB25 2ZD, UK
| | - Alison D Murray
- 1Aberdeen Biomedical Imaging Center, University of Aberdeen, Aberdeen, AB25 2ZD, UK
| | - Claude M Wischik
- 3TauRx, Therapeutics, Aberdeen, AB24 5RP, UK.,4School of Medicine and Dentistry, University of Aberdeen, Aberdeen, AB25 2ZD, UK
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29
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McCarthy J, Collins DL, Ducharme S. Morphometric MRI as a diagnostic biomarker of frontotemporal dementia: A systematic review to determine clinical applicability. Neuroimage Clin 2018; 20:685-696. [PMID: 30218900 PMCID: PMC6140291 DOI: 10.1016/j.nicl.2018.08.028] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 07/31/2018] [Accepted: 08/28/2018] [Indexed: 01/21/2023]
Abstract
Frontotemporal dementia (FTD) is difficult to diagnose, due to its heterogeneous nature and overlap in symptoms with primary psychiatric disorders. Brain MRI for atrophy is a key biomarker but lacks sensitivity in the early stage. Morphometric MRI-based measures and machine learning techniques are a promising tool to improve diagnostic accuracy. Our aim was to review the current state of the literature using morphometric MRI to classify FTD and assess its applicability for clinical practice. A search was completed using Pubmed and PsychInfo of studies which conducted a classification of subjects with FTD from non-FTD (controls or another disorder) using morphometric MRI metrics on an individual level, using single or combined approaches. 28 relevant articles were included and systematically reviewed following PRISMA guidelines. The studies were categorized based on the type of FTD subjects included and the group(s) against which they were classified. Studies varied considerably in subject selection, MRI methodology, and classification approach, and results are highly heterogeneous. Overall many studies indicate good diagnostic accuracy, with higher performance when differentiating FTD from controls (highest result was accuracy of 100%) than other dementias (highest result was AUC of 0.874). Very few machine learning algorithms have been tested in prospective replication. In conclusion, morphometric MRI with machine learning shows potential as an early diagnostic biomarker of FTD, however studies which use rigorous methodology and validate findings in an independent real-life cohort are necessary before this method can be recommended for use clinically.
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Affiliation(s)
- Jillian McCarthy
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Simon Ducharme
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.
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30
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Bruun M, Rhodius-Meester HFM, Koikkalainen J, Baroni M, Gjerum L, Lemstra AW, Barkhof F, Remes AM, Urhemaa T, Tolonen A, Rueckert D, van Gils M, Frederiksen KS, Waldemar G, Scheltens P, Mecocci P, Soininen H, Lötjönen J, Hasselbalch SG, van der Flier WM. Evaluating combinations of diagnostic tests to discriminate different dementia types. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2018; 10:509-518. [PMID: 30320203 PMCID: PMC6180596 DOI: 10.1016/j.dadm.2018.07.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Introduction We studied, using a data-driven approach, how different combinations of diagnostic tests contribute to the differential diagnosis of dementia. Methods In this multicenter study, we included 356 patients with Alzheimer's disease, 87 frontotemporal dementia, 61 dementia with Lewy bodies, 38 vascular dementia, and 302 controls. We used a classifier to assess accuracy for individual performance and combinations of cognitive tests, cerebrospinal fluid biomarkers, and automated magnetic resonance imaging features for pairwise differentiation between dementia types. Results Cognitive tests had good performance in separating any type of dementia from controls. Cerebrospinal fluid optimally contributed to identifying Alzheimer's disease, whereas magnetic resonance imaging features aided in separating vascular dementia, dementia with Lewy bodies, and frontotemporal dementia. Combining diagnostic tests increased the accuracy, with balanced accuracies ranging from 78% to 97%. Discussion Different diagnostic tests have their distinct roles in differential diagnostics of dementias. Our results indicate that combining different diagnostic tests may increase the accuracy further.
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Affiliation(s)
- Marie Bruun
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Hanneke F M Rhodius-Meester
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | | | - Marta Baroni
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Le Gjerum
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Afina W Lemstra
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands.,UCL Institutes of Neurology and Healthcare Engineering, London, United Kingdom
| | - Anne M Remes
- Medical Research Center, Oulu University Hospital, Oulu, Finland.,Unit of Clinical Neuroscience, Neurology, University of Oulu, Oulu, Finland
| | - Timo Urhemaa
- VTT Technical Research Center of Finland Ltd, Tampere, Finland
| | - Antti Tolonen
- VTT Technical Research Center of Finland Ltd, Tampere, Finland
| | - Daniel Rueckert
- Department of Computing, Imperial College, London, United Kingdom
| | - Mark van Gils
- VTT Technical Research Center of Finland Ltd, Tampere, Finland
| | - Kristian S Frederiksen
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Gunhild Waldemar
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Philip Scheltens
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Patrizia Mecocci
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Hilkka Soininen
- Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland
| | | | - Steen G Hasselbalch
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Wiesje M van der Flier
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, the Netherlands.,Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
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31
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Feis RA, Bouts MJRJ, Panman JL, Jiskoot LC, Dopper EGP, Schouten TM, de Vos F, van der Grond J, van Swieten JC, Rombouts SARB. Single-subject classification of presymptomatic frontotemporal dementia mutation carriers using multimodal MRI. Neuroimage Clin 2018; 20:188-196. [PMID: 30094168 PMCID: PMC6072645 DOI: 10.1016/j.nicl.2018.07.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 06/29/2018] [Accepted: 07/15/2018] [Indexed: 11/30/2022]
Abstract
Background Classification models based on magnetic resonance imaging (MRI) may aid early diagnosis of frontotemporal dementia (FTD) but have only been applied in established FTD cases. Detection of FTD patients in earlier disease stages, such as presymptomatic mutation carriers, may further advance early diagnosis and treatment. In this study, we aim to distinguish presymptomatic FTD mutation carriers from controls on an individual level using multimodal MRI-based classification. Methods Anatomical MRI, diffusion tensor imaging (DTI) and resting-state functional MRI data were collected in 55 presymptomatic FTD mutation carriers (8 microtubule-associated protein Tau, 35 progranulin, and 12 chromosome 9 open reading frame 72) and 48 familial controls. We calculated grey and white matter density features from anatomical MRI scans, diffusivity features from DTI, and functional connectivity features from resting-state functional MRI. These features were applied in a recently introduced multimodal behavioural variant FTD (bvFTD) classification model, and were subsequently used to train and test unimodal and multimodal carrier-control models. Classification performance was quantified using area under the receiver operator characteristic curves (AUC). Results The bvFTD model was not able to separate presymptomatic carriers from controls beyond chance level (AUC = 0.570, p = 0.11). In contrast, one unimodal and several multimodal carrier-control models performed significantly better than chance level. The unimodal model included the radial diffusivity feature and had an AUC of 0.646 (p = 0.021). The best multimodal model combined radial diffusivity and white matter density features (AUC = 0.680, p = 0.005). Conclusions FTD mutation carriers can be separated from controls with a modest AUC even before symptom-onset, using a newly created carrier-control classification model, while this was not possible using a recent bvFTD classification model. A multimodal MRI-based classification score may therefore be a useful biomarker to aid earlier FTD diagnosis. The exclusive selection of white matter features in the best performing model suggests that the earliest FTD-related pathological processes occur in white matter.
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Key Words
- (bv)FTD, (behavioural variant) Frontotemporal dementia
- (rs-f)MRI, (resting-state functional) Magnetic resonance imaging
- 3DT1w, 3-dimensional T1-weighted
- AUC, Area under the receiver operating characteristics curve
- AxD, Axial diffusivity
- C9orf72, Chromosome 9 open reading frame 72
- C9orf72, human
- DTI, Diffusion tensor imaging
- DWI, Diffusion-weighted imaging
- Diffusion Tensor Imaging
- FA, Fractional anisotropy
- FCor, Full correlations
- Frontotemporal dementia
- GM, Grey matter
- GMD, Grey matter density
- GRN protein, human
- GRN, Progranulin
- ICA, Independent component analysis
- MAPT protein, human
- MAPT, Microtubule-associated protein Tau
- MD, Mean diffusivity
- MMSE, Mini-mental state examination
- Multimodal MRI
- Pcor, Sparse L1-regularised partial correlations
- RD, Radial diffusivity
- ROC, Receiver operating characteristics
- Resting-state functional MRI
- TBSS, Tract-based spatial statistics
- WM, White matter
- WMD, White matter density
- classification
- machine learning
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Affiliation(s)
- Rogier A Feis
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, the Netherlands.
| | - Mark J R J Bouts
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, the Netherlands; Institute of Psychology, Leiden University, Leiden, the Netherlands.
| | - Jessica L Panman
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Department of Neurology, Erasmus Medical Centre, Rotterdam, the Netherlands.
| | - Lize C Jiskoot
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Department of Neurology, Erasmus Medical Centre, Rotterdam, the Netherlands.
| | - Elise G P Dopper
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Department of Neurology, Erasmus Medical Centre, Rotterdam, the Netherlands; Alzheimer Centre & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Centre, Amsterdam, the Netherlands.
| | - Tijn M Schouten
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, the Netherlands; Institute of Psychology, Leiden University, Leiden, the Netherlands.
| | - Frank de Vos
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, the Netherlands; Institute of Psychology, Leiden University, Leiden, the Netherlands.
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands.
| | - John C van Swieten
- Department of Neurology, Erasmus Medical Centre, Rotterdam, the Netherlands; Department of Clinical Genetics, Neuroscience Campus Amsterdam, VU University Medical Centre, Amsterdam, the Netherlands.
| | - Serge A R B Rombouts
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, the Netherlands; Institute of Psychology, Leiden University, Leiden, the Netherlands.
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32
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Jakabek D, Power BD, Macfarlane MD, Walterfang M, Velakoulis D, van Westen D, Lätt J, Nilsson M, Looi JCL, Santillo AF. Regional structural hypo- and hyperconnectivity of frontal-striatal and frontal-thalamic pathways in behavioral variant frontotemporal dementia. Hum Brain Mapp 2018; 39:4083-4093. [PMID: 29923666 DOI: 10.1002/hbm.24233] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 04/09/2018] [Accepted: 05/15/2018] [Indexed: 12/14/2022] Open
Abstract
Behavioral variant frontotemporal dementia (bvFTD) has been predominantly considered as a frontotemporal cortical disease, with limited direct investigation of frontal-subcortical connections. We aim to characterize the grey and white matter components of frontal-thalamic and frontal-striatal circuits in bvFTD. Twenty-four patients with bvFTD and 24 healthy controls underwent morphological and diffusion imaging. Subcortical structures were manually segmented according to published protocols. Probabilistic pathways were reconstructed separately from the dorsolateral, orbitofrontal and medial prefrontal cortex to the striatum and thalamus. Patients with bvFTD had smaller cortical and subcortical volumes, lower fractional anisotropy, and higher mean diffusivity metrics, which is consistent with disruptions in frontal-striatal-thalamic pathways. Unexpectedly, regional volumes of the striatum and thalamus connected to the medial prefrontal cortex were significantly larger in bvFTD (by 135% in the striatum, p = .032, and 217% in the thalamus, p = .004), despite smaller dorsolateral prefrontal cortex connected regional volumes (by 67% in the striatum, p = .002, and 65% in the thalamus, p = .020), and inconsistent changes in orbitofrontal cortex connected regions. These unanticipated findings may represent compensatory or maladaptive remodeling in bvFTD networks. Comparisons are made to other neuropsychiatric disorders suggesting a common mechanism of changes in frontal-subcortical networks; however, longitudinal studies are necessary to test this hypothesis.
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Affiliation(s)
- David Jakabek
- Graduate School of Medicine, University of Wollongong, Wollongong, Australia
| | - Brian D Power
- School of Medicine, The University of Notre Dame Australia, Fremantle, Australia; Clinical Research Centre, North Metropolitan Health Service - Mental Health, Perth, Australia
| | - Matthew D Macfarlane
- Graduate School of Medicine, University of Wollongong, Wollongong, Australia.,Illawarra Shoalhaven Local Health District, Wollongong, Australia
| | - Mark Walterfang
- Neuropsychiatry Unit, Royal Melbourne Hospital, Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, University of Melbourne, Melbourne, Australia
| | - Dennis Velakoulis
- Neuropsychiatry Unit, Royal Melbourne Hospital, Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, University of Melbourne, Melbourne, Australia
| | - Danielle van Westen
- Centre for Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Jimmy Lätt
- Centre for Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden.,Department of Radiology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Markus Nilsson
- Department of Radiology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Jeffrey C L Looi
- Neuropsychiatry Unit, Royal Melbourne Hospital, Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, University of Melbourne, Melbourne, Australia.,Research Centre for the Neurosciences of Ageing, Academic Unit of Psychiatry and Addiction Medicine, Australian National University Medical School, Canberra Hospital, Canberra, Australia
| | - Alexander F Santillo
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
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33
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Wang Q, Guo L, Thompson PM, Jack CR, Dodge H, Zhan L, Zhou J. The Added Value of Diffusion-Weighted MRI-Derived Structural Connectome in Evaluating Mild Cognitive Impairment: A Multi-Cohort Validation1. J Alzheimers Dis 2018; 64:149-169. [PMID: 29865049 PMCID: PMC6272125 DOI: 10.3233/jad-171048] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
T1-weighted MRI has been extensively used to extract imaging biomarkers and build classification models for differentiating Alzheimer's disease (AD) patients from healthy controls, but only recently have brain connectome networks derived from diffusion-weighted MRI been used to model AD progression and various stages of disease such as mild cognitive impairment (MCI). MCI, as a possible prodromal stage of AD, has gained intense interest recently, since it may be used to assess risk factors for AD. Little work has been done to combine information from both white matter and gray matter, and it is unknown how much classification power the diffusion-weighted MRI-derived structural connectome could provide beyond information available from T1-weighted MRI. In this paper, we focused on investigating whether diffusion-weighted MRI-derived structural connectome can improve differentiating healthy controls subjects from those with MCI. Specifically, we proposed a novel feature-ranking method to build classification models using the most highly ranked feature variables to classify MCI with healthy controls. We verified our method on two independent cohorts including the second stage of Alzheimer's Disease Neuroimaging Initiative (ADNI2) database and the National Alzheimer's Coordinating Center (NACC) database. Our results indicated that 1) diffusion-weighted MRI-derived structural connectome can complement T1-weighted MRI in the classification task; 2) the feature-rank method is effective because of the identified consistent T1-weighted MRI and network feature variables on ADNI2 and NACC. Furthermore, by comparing the top-ranked feature variables from ADNI2, NACC, and combined dataset, we concluded that cross-validation using independent cohorts is necessary and highly recommended.
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Affiliation(s)
- Qi Wang
- Computer Science and Engineering, Michigan State University, East Lansing, MI
| | - Lei Guo
- Mathematics, Statistics & Computer Science Department, University of Wisconsin-Stout, Menomonie, WI
| | - Paul M. Thompson
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA
| | | | - Hiroko Dodge
- Michigan Alzheimer's Disease Center and Department of Neurology, University of Michigan, Ann Arbor, MI
- Layton Aging and Alzheimer's Disease Center and Department of Neurology, Oregon Health & Science University, Portland, OR
| | - Liang Zhan
- Computer Engineering Program, University of Wisconsin-Stout, Menomonie, WI
| | - Jiayu Zhou
- Computer Science and Engineering, Michigan State University, East Lansing, MI
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van der Flier WM, Scheltens P. Amsterdam Dementia Cohort: Performing Research to Optimize Care. J Alzheimers Dis 2018; 62:1091-1111. [PMID: 29562540 PMCID: PMC5870023 DOI: 10.3233/jad-170850] [Citation(s) in RCA: 203] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/04/2017] [Indexed: 01/01/2023]
Abstract
The Alzheimer center of the VU University Medical Center opened in 2000 and was initiated to combine both patient care and research. Together, to date, all patients forming the Amsterdam Dementia Cohort number almost 6,000 individuals. In this cohort profile, we provide an overview of the results produced based on the Amsterdam Dementia Cohort. We describe the main results over the years in each of these research lines: 1) early diagnosis, 2) heterogeneity, and 3) vascular factors. Among the most important research efforts that have also impacted patients' lives and/or the research field, we count the development of novel, easy to use diagnostic measures such as visual rating scales for MRI and the Amsterdam IADL Questionnaire, insight in different subgroups of AD, and findings on incidence and clinical sequelae of microbleeds. Finally, we describe in the outlook how our research endeavors have improved the lives of our patients.
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Affiliation(s)
- Wiesje M. van der Flier
- Department of Neurology, Alzheimer Center, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands
| | - Philip Scheltens
- Department of Neurology, Alzheimer Center, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands
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Staffaroni AM, Elahi FM, McDermott D, Marton K, Karageorgiou E, Sacco S, Paoletti M, Caverzasi E, Hess CP, Rosen HJ, Geschwind MD. Neuroimaging in Dementia. Semin Neurol 2017; 37:510-537. [PMID: 29207412 PMCID: PMC5823524 DOI: 10.1055/s-0037-1608808] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Although the diagnosis of dementia still is primarily based on clinical criteria, neuroimaging is playing an increasingly important role. This is in large part due to advances in techniques that can assist with discriminating between different syndromes. Magnetic resonance imaging remains at the core of differential diagnosis, with specific patterns of cortical and subcortical changes having diagnostic significance. Recent developments in molecular PET imaging techniques have opened the door for not only antemortem but early, even preclinical, diagnosis of underlying pathology. This is vital, as treatment trials are underway for pharmacological agents with specific molecular targets, and numerous failed trials suggest that earlier treatment is needed. This article provides an overview of classic neuroimaging findings as well as new and cutting-edge research techniques that assist with clinical diagnosis of a range of dementia syndromes, with an emphasis on studies using pathologically proven cases.
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Affiliation(s)
- Adam M. Staffaroni
- Department of Neurology, Memory and Aging Center, University of California, San Francisco (UCSF), San Francisco, California
| | - Fanny M. Elahi
- Department of Neurology, Memory and Aging Center, University of California, San Francisco (UCSF), San Francisco, California
| | - Dana McDermott
- Department of Neurology, Memory and Aging Center, University of California, San Francisco (UCSF), San Francisco, California
| | - Kacey Marton
- Department of Neurology, Memory and Aging Center, University of California, San Francisco (UCSF), San Francisco, California
| | - Elissaios Karageorgiou
- Department of Neurology, Memory and Aging Center, University of California, San Francisco (UCSF), San Francisco, California
- Neurological Institute of Athens, Athens, Greece
| | - Simone Sacco
- Department of Neurology, Memory and Aging Center, University of California, San Francisco (UCSF), San Francisco, California
- Institute of Radiology, Department of Clinical Surgical Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy
| | - Matteo Paoletti
- Department of Neurology, Memory and Aging Center, University of California, San Francisco (UCSF), San Francisco, California
- Institute of Radiology, Department of Clinical Surgical Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy
| | - Eduardo Caverzasi
- Department of Neurology, Memory and Aging Center, University of California, San Francisco (UCSF), San Francisco, California
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Christopher P. Hess
- Division of Neuroradiology, Department of Radiology, University of California, San Francisco (UCSF), California
| | - Howard J. Rosen
- Department of Neurology, Memory and Aging Center, University of California, San Francisco (UCSF), San Francisco, California
| | - Michael D. Geschwind
- Department of Neurology, Memory and Aging Center, University of California, San Francisco (UCSF), San Francisco, California
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Canu E, Agosta F, Mandic-Stojmenovic G, Stojković T, Stefanova E, Inuggi A, Imperiale F, Copetti M, Kostic VS, Filippi M. Multiparametric MRI to distinguish early onset Alzheimer's disease and behavioural variant of frontotemporal dementia. NEUROIMAGE-CLINICAL 2017; 15:428-438. [PMID: 28616383 PMCID: PMC5458769 DOI: 10.1016/j.nicl.2017.05.018] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Revised: 05/12/2017] [Accepted: 05/25/2017] [Indexed: 12/11/2022]
Abstract
This prospective study explored whether an approach combining structural [cortical thickness and white matter (WM) microstructure] and resting state functional MRI can aid differentiation between 62 early onset Alzheimer's disease (EOAD) and 27 behavioural variant of frontotemporal dementia (bvFTD) patients. Random forest and receiver operator characteristic curve analyses assessed the ability of MRI in classifying the two clinical syndromes. All patients showed a distributed pattern of brain alterations relative to controls. Compared to bvFTD, EOAD patients showed bilateral inferior parietal cortical thinning and decreased default mode network functional connectivity. Compared to EOAD, bvFTD patients showed bilateral orbitofrontal and temporal cortical thinning, and WM damage of the corpus callosum, bilateral uncinate fasciculus, and left superior longitudinal fasciculus. Random forest analysis revealed that left inferior parietal cortical thickness (accuracy 0.78, specificity 0.76, sensitivity 0.83) and WM integrity of the right uncinate fasciculus (accuracy 0.81, specificity 0.96, sensitivity 0.43) were the best predictors of clinical diagnosis. The combination of cortical thickness and DT MRI measures was able to distinguish patients with EOAD and bvFTD with accuracy 0.82, specificity 0.76, and sensitivity 0.96. The diagnostic ability of MRI models was confirmed in a subsample of patients with biomarker-based clinical diagnosis. Multiparametric MRI is useful to identify brain alterations which are specific to EOAD and bvFTD. A severe cortical involvement is suggestive of EOAD, while a prominent WM damage is indicative of bvFTD. Multimodal MRI distinguishes in vivo EOAD and bvFTD patients EOAD and bvFTD show a distributed pattern of structural brain alterations A severe cortical involvement is suggestive of EOAD relative to bvFTD A prominent WM damage is indicative of bvFTD relative to EOAD
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Key Words
- ACE-R, Addenbrooke's Cognitive Examination-revised
- Behavioural variant of frontotemporal dementia
- CC, corpus callosum
- CSF, cerebrospinal fluid
- Cortical thickness
- DMN, default mode network
- DT, diffusion tensor
- Diagnosis
- EOAD, early onset Alzheimer's disease
- Early onset Alzheimer's disease
- GM, grey matter
- IC, independent component
- ILF, inferior longitudinal fasciculus
- LOAD, late onset Alzheimer's disease
- MNI, Montreal Neurological Institute
- NVI, Normalized Variable Importance
- RS fMRI, resting state functional MRI
- RSN, resting state network
- Resting state functional MRI
- SLF, superior longitudinal fasciculus
- TFCE, threshold-free cluster enhancement
- WM, white matter
- White matter (WM) damage
- bvFTD, behavioural variant frontotemporal dementia
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Affiliation(s)
- Elisa Canu
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, via Olgettina 60, 20132 Milan, Italy
| | - Federica Agosta
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, via Olgettina 60, 20132 Milan, Italy
| | - Gorana Mandic-Stojmenovic
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, via Olgettina 60, 20132 Milan, Italy; Clinic of Neurology, Faculty of Medicine, University of Belgrade, Dr Subotića 6, PO Box 12, 11129 Belgrade 102, Serbia
| | - Tanja Stojković
- Clinic of Neurology, Faculty of Medicine, University of Belgrade, Dr Subotića 6, PO Box 12, 11129 Belgrade 102, Serbia
| | - Elka Stefanova
- Clinic of Neurology, Faculty of Medicine, University of Belgrade, Dr Subotića 6, PO Box 12, 11129 Belgrade 102, Serbia
| | - Alberto Inuggi
- Unit of Robotics, Brain and Cognitive Sciences, Istituto Italiano di Tecnologia, Via Morego, 30, 16163 Genoa, Italy
| | - Francesca Imperiale
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, via Olgettina 60, 20132 Milan, Italy
| | - Massimiliano Copetti
- Biostatistics Unit, IRCCS-Ospedale Casa Sollievo della Sofferenza, Viale Cappuccini, San Giovanni Rotondo, 71013 Foggia, Italy
| | - Vladimir S Kostic
- Clinic of Neurology, Faculty of Medicine, University of Belgrade, Dr Subotića 6, PO Box 12, 11129 Belgrade 102, Serbia
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, via Olgettina 60, 20132 Milan, Italy; Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, via Olgettina 60, 20132 Milan, Italy.
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