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Hadad R, Mandelli ML, Rankin KP, Toohey C, Sturm VE, Javandel S, Milicic A, Knudtson M, Allen IE, Hoffmann N, Friedberg A, Possin K, Valcour V, Miller BL. Itching Frequency and Neuroanatomic Correlates in Frontotemporal Lobar Degeneration. JAMA Neurol 2024:2821069. [PMID: 39037825 PMCID: PMC11264090 DOI: 10.1001/jamaneurol.2024.2213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 05/24/2024] [Indexed: 07/24/2024]
Abstract
Importance Itching is common in geriatric populations and is frequently linked to dermatological or systemic conditions. Itching engages specific brain regions that are implicated in the pathogenesis of frontotemporal lobar degeneration spectrum disorders (FTLD-SD). Thus, itching of undetermined origin (IUO) may indicate the presence of a neurodegenerative process. Objective To compare the frequency of itching in FTLD-SD and Alzheimer disease (AD) and to determine the neuroanatomical underpinnings of IUO. Design, Setting, and Participants This case-control study evaluated data and brain magnetic resonance images (MRIs) for participants with FTLD-SD or AD. Participants of a research study on FTLD-SD at the University of California, San Francisco, Memory and Aging Center were evaluated from May 1, 2002, to December 31, 2021. The exposure group underwent structural brain MRI within 6 months of initial diagnosis. Research visit summaries were reviewed to validate qualitative details and accurately identify itching with undetermined origin (IUO). Exposures Symptoms suggestive of FTLD-SD or AD. Main Outcomes and Measures Frequency of itching in FTLD-SD and AD and neuroanatomic correlates. Results A total of 2091 research visit summaries were reviewed for 1112 patients exhibiting symptoms indicative of FTLD-SD or AD. From 795 records where itching or a related phrase was endorsed, 137 had IUO. A total of 454 participants were included in the study: 137 in the itching group (mean [SD] age, 62.7 [9.9] years; 74 [54%] females and 63 males [46%]) and 317 in the nonitching group (mean [SD] age, 60.7 [10.8] years; 154 [49%] females and 163 males [51%]). Groups were similar in age, sex, and disease severity. More frequent itching was found in FTLD-SD (95/248 patients [38%], of which 44 [46%] had behavioral variant frontotemporal dementia [bvFTD]) compared with the AD group (14/77 patients [18%]; P = .001). The odds of itching were 2.4 (95% CI, 1.48-3.97) times higher for FTLD-SD compared with all other cases of dementia. Compared with healthy controls, the group with IUO exhibited greater gray matter atrophy bilaterally in the amygdala, insula, precentral gyrus, and cingulum, as well as in the right frontal superior gyrus and thalamus. Among patients with bvFTD and itching vs bvFTD without itching, itching was associated with right-lateralized gray matter atrophy affecting the insula, thalamus, superior frontal gyrus, and cingulum. Conclusions and Relevance Among individuals with IUO, FTLD-SD was disproportionately represented compared with AD. In FTLD-SD, dysfunction in the right anterior insula and its connected regions, including the right precentral gyrus, cingulum, and bilateral amygdala, contribute to dysregulation of the itching-scratching networks, resulting in uncontrollable itching or skin picking. Awareness among physicians about the relationship between neurodegeneration and itching may help in the management of itch in older individuals. Further studies are needed to determine the best treatments for these symptoms in patients with neurodegenerative disorders.
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Affiliation(s)
- Rafi Hadad
- Stroke and Cognition Institute, Department of Neurology, Rambam Health Care Campus, Haifa, Israel
- Global Brain Health Institute, Department of Neurology, University of California, San Francisco
| | - Maria Luisa Mandelli
- Global Brain Health Institute, Department of Neurology, University of California, San Francisco
- Memory and Aging Center, Department of Neurology, University of California, San Francisco
| | - Katherine P. Rankin
- Global Brain Health Institute, Department of Neurology, University of California, San Francisco
- Memory and Aging Center, Department of Neurology, University of California, San Francisco
| | - Charlie Toohey
- Memory and Aging Center, Department of Neurology, University of California, San Francisco
| | - Virginia E. Sturm
- Global Brain Health Institute, Department of Neurology, University of California, San Francisco
- Memory and Aging Center, Department of Neurology, University of California, San Francisco
- Department of Psychiatry and Behavioral Sciences University of California, San Francisco
| | - Shireen Javandel
- Global Brain Health Institute, Department of Neurology, University of California, San Francisco
- Memory and Aging Center, Department of Neurology, University of California, San Francisco
| | - Andjelika Milicic
- Global Brain Health Institute, Department of Neurology, University of California, San Francisco
- Memory and Aging Center, Department of Neurology, University of California, San Francisco
| | - Marguerite Knudtson
- Memory and Aging Center, Department of Neurology, University of California, San Francisco
| | - Isabel Elaine Allen
- Global Brain Health Institute, Department of Neurology, University of California, San Francisco
- Department of Epidemiology and Biostatistics, University of California, San Francisco
| | - Nathalia Hoffmann
- Federal University of Health Sciences of Porto Alegre (UFCSPA), Porto Alegre, Brazil
| | - Adit Friedberg
- Global Brain Health Institute, Department of Neurology, University of California, San Francisco
- Memory and Aging Center, Department of Neurology, University of California, San Francisco
| | - Katherine Possin
- Global Brain Health Institute, Department of Neurology, University of California, San Francisco
- Memory and Aging Center, Department of Neurology, University of California, San Francisco
| | - Victor Valcour
- Global Brain Health Institute, Department of Neurology, University of California, San Francisco
- Memory and Aging Center, Department of Neurology, University of California, San Francisco
| | - Bruce L. Miller
- Global Brain Health Institute, Department of Neurology, University of California, San Francisco
- Memory and Aging Center, Department of Neurology, University of California, San Francisco
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Chen Y, Spina S, Callahan P, Grinberg LT, Seeley WW, Rosen HJ, Kramer JH, Miller BL, Rankin KP. Pathology-specific patterns of cerebellar atrophy in neurodegenerative disorders. Alzheimers Dement 2024; 20:1771-1783. [PMID: 38109286 PMCID: PMC10984510 DOI: 10.1002/alz.13551] [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: 06/27/2023] [Revised: 10/20/2023] [Accepted: 10/23/2023] [Indexed: 12/20/2023]
Abstract
INTRODUCTION Associations of cerebellar atrophy with specific neuropathologies in Alzheimer's disease and related dementias (ADRD) have not been systematically analyzed. This study examined cerebellar gray matter volume across major pathological subtypes of ADRD. METHODS Cerebellar gray matter volume was examined using voxel-based morphometry in 309 autopsy-proven ADRD cases and 80 healthy controls. ADRD subtypes included AD, mixed Lewy body disease and AD (LBD-AD), and frontotemporal lobar degeneration (FTLD). Clinical function was assessed using the Clinical Dementia Rating (CDR) scale. RESULTS Distinct patterns of cerebellar atrophy were observed in all ADRD subtypes. Significant cerebellar gray matter changes appeared in the early stages of most subtypes and the very early stages of AD, LBD-AD, FTLD-TDP type A, and progressive supranuclear palsy. Cortical atrophy positively predicted cerebellar atrophy across all subtypes. DISCUSSION Our findings establish pathology-specific profiles of cerebellar atrophy in ADRD and propose cerebellar neuroimaging as a non-invasive biomarker for differential diagnosis and disease monitoring. HIGHLIGHTS Cerebellar atrophy was examined in 309 patients with autopsy-proven neurodegeneration. Distinct patterns of cerebellar atrophy are found in all pathological subtypes of Alzheimer's disease and related dementias (ADRD). Cerebellar atrophy is seen in early-stage (Clinical Dementia Rating [CDR] ≤1) AD, Lewy body dementia (LBD), frontotemporal lobar degeneration with tau-positive inclusion (FTLD-tau), and FTLD-transactive response DNA binding protein (FTLD-TDP). Cortical atrophy positively predicts cerebellar atrophy across all neuropathologies.
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Affiliation(s)
- Yu Chen
- Department of NeurologyMemory and Aging CenterWeill Institute for NeurosciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Salvatore Spina
- Department of NeurologyMemory and Aging CenterWeill Institute for NeurosciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Patrick Callahan
- Department of NeurologyMemory and Aging CenterWeill Institute for NeurosciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Lea T. Grinberg
- Department of NeurologyMemory and Aging CenterWeill Institute for NeurosciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Department of PathologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - William W. Seeley
- Department of NeurologyMemory and Aging CenterWeill Institute for NeurosciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Department of PathologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Howard J. Rosen
- Department of NeurologyMemory and Aging CenterWeill Institute for NeurosciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Joel H. Kramer
- Department of NeurologyMemory and Aging CenterWeill Institute for NeurosciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Bruce L. Miller
- Department of NeurologyMemory and Aging CenterWeill Institute for NeurosciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Katherine P. Rankin
- Department of NeurologyMemory and Aging CenterWeill Institute for NeurosciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
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Chelban V, Nikram E, Perez-Soriano A, Wilke C, Foubert-Samier A, Vijiaratnam N, Guo T, Jabbari E, Olufodun S, Gonzalez M, Senkevich K, Laurens B, Péran P, Rascol O, Le Traon AP, Todd EG, Costantini AA, Alikhwan S, Tariq A, Ng BL, Muñoz E, Painous C, Compta Y, Junque C, Segura B, Zhelcheska K, Wellington H, Schöls L, Jaunmuktane Z, Kobylecki C, Church A, Hu MTM, Rowe JB, Leigh PN, Massey L, Burn DJ, Pavese N, Foltynie T, Pchelina S, Wood N, Heslegrave AJ, Zetterberg H, Bocchetta M, Rohrer JD, Marti MJ, Synofzik M, Morris HR, Meissner WG, Houlden H. Neurofilament light levels predict clinical progression and death in multiple system atrophy. Brain 2022; 145:4398-4408. [PMID: 35903017 PMCID: PMC9762941 DOI: 10.1093/brain/awac253] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 06/06/2022] [Accepted: 06/17/2022] [Indexed: 11/12/2022] Open
Abstract
Disease-modifying treatments are currently being trialled in multiple system atrophy. Approaches based solely on clinical measures are challenged by heterogeneity of phenotype and pathogenic complexity. Neurofilament light chain protein has been explored as a reliable biomarker in several neurodegenerative disorders but data on multiple system atrophy have been limited. Therefore, neurofilament light chain is not yet routinely used as an outcome measure in multiple system atrophy. We aimed to comprehensively investigate the role and dynamics of neurofilament light chain in multiple system atrophy combined with cross-sectional and longitudinal clinical and imaging scales and for subject trial selection. In this cohort study, we recruited cross-sectional and longitudinal cases in a multicentre European set-up. Plasma and CSF neurofilament light chain concentrations were measured at baseline from 212 multiple system atrophy cases, annually for a mean period of 2 years in 44 multiple system atrophy patients in conjunction with clinical, neuropsychological and MRI brain assessments. Baseline neurofilament light chain characteristics were compared between groups. Cox regression was used to assess survival; receiver operating characteristic analysis to assess the ability of neurofilament light chain to distinguish between multiple system atrophy patients and healthy controls. Multivariate linear mixed-effects models were used to analyse longitudinal neurofilament light chain changes and correlated with clinical and imaging parameters. Polynomial models were used to determine the differential trajectories of neurofilament light chain in multiple system atrophy. We estimated sample sizes for trials aiming to decrease neurofilament light chain levels. We show that in multiple system atrophy, baseline plasma neurofilament light chain levels were better predictors of clinical progression, survival and degree of brain atrophy than the neurofilament light chain rate of change. Comparative analysis of multiple system atrophy progression over the course of disease, using plasma neurofilament light chain and clinical rating scales, indicated that neurofilament light chain levels rise as the motor symptoms progress, followed by deceleration in advanced stages. Sample size prediction suggested that significantly lower trial participant numbers would be needed to demonstrate treatment effects when incorporating plasma neurofilament light chain values into multiple system atrophy clinical trials in comparison to clinical measures alone. In conclusion, neurofilament light chain correlates with clinical disease severity, progression and prognosis in multiple system atrophy. Combined with clinical and imaging analysis, neurofilament light chain can inform patient stratification and serve as a reliable biomarker of treatment response in future multiple system atrophy trials of putative disease-modifying agents.
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Affiliation(s)
- Viorica Chelban
- Correspondence to: Dr Viorica Chelban Department of Neuromuscular Diseases UCL Queen Square Institute of Neurology London WC1N 3BG, UK E-mail:
| | - Elham Nikram
- Peninsula Technology Assessment Group (PenTAG), University of Exeter, Exeter EX 2LU, UK
| | - Alexandra Perez-Soriano
- Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Barcelona 08036, Spain
- Parkinson's Disease and Movement Disorders Unit, Neurology Department, Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain
- Parkinson's Disease and Movement Disorders Unit, Neurology Department, Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid 28029, Spain
| | - Carlo Wilke
- Division Translational Genomics of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, 72074 Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), 72074 Tübingen, Germany
| | - Alexandra Foubert-Samier
- CRMR AMS, Service de Neurologie – Maladies Neurodégénératives, CHU de Bordeaux, F-33000 Bordeaux, France
- Université de Bordeaux, CNRS, IMN, UMR 5293, F-33000 Bordeaux, France
- Université de Bordeaux, INSERM, BPH, U1219, F-33000 Bordeaux, France
- Inserm, CIC 1401 Bordeaux, Clinical Epidemiology Unit, F-33000 Bordeaux, France
| | - Nirosen Vijiaratnam
- Department Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Tong Guo
- Department Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Edwin Jabbari
- Department Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Simisola Olufodun
- Department of Neuromuscular Diseases, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Mariel Gonzalez
- Department of Neuromuscular Diseases, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Konstantin Senkevich
- Neurogenomics and Precision Medicine (NAP-Med) Laboratory, The Neuro (Montreal Neurological Institute-Hospital), Montreal, QC H3A 2B4, Canada
- Department of Neurology & Neurosurgery, McGill University, Montreal, QC H3A 2B4, Canada
- Laboratory of Human Genetics, Petersburg Nuclear Physics Institute named by B.P. Konstantinov of National Research Centre 'Kurchatov Institute', Gatchina 188300, Russia
- Laboratory of Medical Genetics, Pavlov First Saint-Petersburg State Medical University, St. Petersburg 197022, Russia
| | - Brice Laurens
- CRMR AMS, Service de Neurologie – Maladies Neurodégénératives, CHU de Bordeaux, F-33000 Bordeaux, France
- Université de Bordeaux, CNRS, IMN, UMR 5293, F-33000 Bordeaux, France
| | - Patrice Péran
- ToNIC, Toulouse NeuroImaging Center, UMR 1214, Université de Toulouse, 31024 Toulouse, France
| | - Olivier Rascol
- CRMR AMS, CHU de Toulouse, 31300 Toulouse, France
- Clinical Investigation Center CIC 1436, NS-Park/F-CRIN Network and NeuroToul COEN Center; Inserm, University of Toulouse 3 and CHU of Toulouse, F-31000 Toulouse, France
- Departments of Neurosciences and Clinical Pharmacology, CHU Toulouse and University of Toulouse 3, F-31000 Toulouse, France
| | - Anne Pavy Le Traon
- CRMR AMS, CHU de Toulouse, 31300 Toulouse, France
- Institut des Maladies Métaboliques et Cardiovasculaires, Inserm U 1297, Toulouse University, F-31000 Toulouse, France
| | - Emily G Todd
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, WC1N 3BG London, UK
| | - Alyssa A Costantini
- Department Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Sondos Alikhwan
- Department of Neuromuscular Diseases, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Ambreen Tariq
- Department of Neuromuscular Diseases, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Bai Lin Ng
- Department of Economics, University College London, London WC1N 3BG, UK
| | - Esteban Muñoz
- Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Barcelona 08036, Spain
- Parkinson's Disease and Movement Disorders Unit, Neurology Department, Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain
- Parkinson's Disease and Movement Disorders Unit, Neurology Department, Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid 28029, Spain
| | - Celia Painous
- Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Barcelona 08036, Spain
- Parkinson's Disease and Movement Disorders Unit, Neurology Department, Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain
- Parkinson's Disease and Movement Disorders Unit, Neurology Department, Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid 28029, Spain
| | - Yaroslau Compta
- Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Barcelona 08036, Spain
- Parkinson's Disease and Movement Disorders Unit, Neurology Department, Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain
- Parkinson's Disease and Movement Disorders Unit, Neurology Department, Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid 28029, Spain
| | - Carme Junque
- Parkinson's Disease and Movement Disorders Unit, Neurology Department, Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain
- Parkinson's Disease and Movement Disorders Unit, Neurology Department, Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid 28029, Spain
- Medical Psychology Unit, Department of Medicine, Institute of Neuroscience, University of Barcelona, 08035 Barcelona, Spain
| | - Barbara Segura
- Parkinson's Disease and Movement Disorders Unit, Neurology Department, Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain
- Parkinson's Disease and Movement Disorders Unit, Neurology Department, Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid 28029, Spain
- Medical Psychology Unit, Department of Medicine, Institute of Neuroscience, University of Barcelona, 08035 Barcelona, Spain
| | - Kristina Zhelcheska
- Department of Neuromuscular Diseases, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Henny Wellington
- Biomarkers Factory Laboratory, UK Dementia Research Institute, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Ludger Schöls
- Division Translational Genomics of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, 72074 Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), 72074 Tübingen, Germany
| | - Zane Jaunmuktane
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, WC1N 3BG London, UK
| | - Christopher Kobylecki
- Department of Neurology, Manchester Academic Health Science Centre, Northern Care Alliance NHS Foundation Trust, Stott Lane, Salford M6 8HD, UK
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, University of Manchester, Oxford Road, Manchester M13 9PT, UK
| | - Alistair Church
- Department of Neurology, Royal Gwent Hospital, Newport NP20 2UB, UK
| | - Michele T M Hu
- Division of Neurology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - James B Rowe
- Department of Clinical Neurosciences, Cambridge University, Cambridge CB3 0SZ, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, CB3 0SZ Cambridge, UK
- Neurology Department, Cambridge University Hospitals NHS Trust, Cambridge CB2 0QQ, UK
| | - P Nigel Leigh
- Department of Neuroscience, Brighton and Sussex Medical School, Brighton BN1 9PX, UK
| | - Luke Massey
- Neurology Department, University Hospitals Dorset, Poole BH15 2JB, UK
| | - David J Burn
- Faculty of Medical Sciences, Clinical Ageing Research Unit, Newcastle University, NE4 5PL Newcastle, UK
| | - Nicola Pavese
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, WC1N 3BG London, UK
| | - Tom Foltynie
- Department Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Sofya Pchelina
- Laboratory of Human Genetics, Petersburg Nuclear Physics Institute named by B.P. Konstantinov of National Research Centre 'Kurchatov Institute', Gatchina 188300, Russia
- Laboratory of Medical Genetics, Pavlov First Saint-Petersburg State Medical University, St. Petersburg 197022, Russia
| | - Nicholas Wood
- Department Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Amanda J Heslegrave
- Biomarkers Factory Laboratory, UK Dementia Research Institute, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Henrik Zetterberg
- Biomarkers Factory Laboratory, UK Dementia Research Institute, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, WC1N 3BG London, UK
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, 405 30 Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 405 30 Mölndal, Sweden
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong 1512-1518, China
| | - Martina Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, WC1N 3BG London, UK
| | - Jonathan D Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, WC1N 3BG London, UK
| | - Maria J Marti
- Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Barcelona 08036, Spain
- Parkinson's Disease and Movement Disorders Unit, Neurology Department, Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain
- Parkinson's Disease and Movement Disorders Unit, Neurology Department, Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid 28029, Spain
| | - Matthis Synofzik
- Division Translational Genomics of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, 72074 Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), 72074 Tübingen, Germany
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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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Lin N, Gao J, Mao C, Sun H, Lu Q, Cui L. Differences in Multimodal Electroencephalogram and Clinical Correlations Between Early-Onset Alzheimer's Disease and Frontotemporal Dementia. Front Neurosci 2021; 15:687053. [PMID: 34421518 PMCID: PMC8374312 DOI: 10.3389/fnins.2021.687053] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 07/02/2021] [Indexed: 11/24/2022] Open
Abstract
Background Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are the two main types of dementia. We investigated the electroencephalogram (EEG) difference and clinical correlation in early-onset Alzheimer’s disease (EOAD), and FTD using multimodal EEG analyses. EOAD had more severe EEG abnormalities than late-onset AD (LOAD). Group comparisons between EOAD and LOAD were also performed. Methods Thirty patients diagnosed with EOAD, nine patients with LOAD, and 14 patients with FTD (≤65 y) were recruited (2008.1–2020.2), along with 24 healthy controls (≤65 y, n = 18; >65 y, n = 6). Clinical data were reviewed. Visual EEG, EEG microstate, and spectral analyses were performed. Results Compared to controls, markedly increased mean microstate duration, reduced mean occurrence, and reduced global field power (GFP) peaks per second were observed in EOAD and FTD. We found increased durations of class B in EOAD and class A in FTD. EOAD had reduced occurrences in classes A, B, and C, while only class C occurrence was reduced in FTD. The visual EEG results did not differ between AD and FTD. Microstate B showed correlations with activities of daily living score (r = 0.780, p = 0.008) and cerebrospinal fluid (CSF) Aβ42 (r = −0.833, p = 0.010) in EOAD. Microstate D occurrence was correlated with the CSF Aβ42 level in FTD (r = 0.786, p = 0.021). Spectral analysis revealed a general slowing EEG, which may contribute to microstate dynamic loss. Power in delta was significantly higher in EOAD than in FTD all over the head. In addition, EOAD had a marked increased duration and decreased occurrence than late-onset AD (LOAD), with no group differences in visual EEG results. Conclusion The current study found that EOAD and FTD had different EEG changes, and microstate had an association with clinical severity and CSF biomarkers. EEG microstate is more sensitive than visual EEG and may be useful for the differentiation between AD and FTD. The observations support that EEG can be a potential biomarker for the diagnosis and assessment of early-onset dementias.
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Affiliation(s)
- Nan Lin
- Department of Neurology, Peking Union Medical College Hospital, Beijing, China
| | - Jing Gao
- Department of Neurology, Peking Union Medical College Hospital, Beijing, China
| | - Chenhui Mao
- Department of Neurology, Peking Union Medical College Hospital, Beijing, China
| | - Heyang Sun
- Department of Neurology, Peking Union Medical College Hospital, Beijing, China
| | - Qiang Lu
- Department of Neurology, Peking Union Medical College Hospital, Beijing, China
| | - Liying Cui
- Department of Neurology, Peking Union Medical College Hospital, Beijing, China
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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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7
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Leocadi M, Canu E, Calderaro D, Corbetta D, Filippi M, Agosta F. An update on magnetic resonance imaging markers in AD. Ther Adv Neurol Disord 2020; 13:1756286420947986. [PMID: 33747128 PMCID: PMC7903819 DOI: 10.1177/1756286420947986] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 07/09/2020] [Indexed: 12/22/2022] Open
Abstract
The purpose of the present review is to provide an update of the available recent scientific literature on the use of magnetic resonance imaging (MRI) in Alzheimer's disease (AD). MRI is playing an increasingly important role in the characterization of the AD signatures, which can be useful in both the diagnostic process and monitoring of disease progression. Furthermore, this technique is unique in assessing brain structure and function and provides a deep understanding of in vivo evolution of cerebral pathology. In the reviewing process, we established a priori criteria and we thoroughly searched the very recent scientific literature (January 2018-March 2020) for relevant articles on this topic. In summary, we selected 73 articles out of 1654 publications retrieved from PubMed. Based on this selection, this review summarizes the recent application of MRI in clinical trials, defining the predementia stages of AD, the clinical utility of MRI, proposal of novel biomarkers and brain regions of interest, and assessing the relationship between MRI and cognitive features, risk and protective factors of AD. Finally, the value of a multiparametric approach in clinical and preclinical stages of AD is discussed.
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Affiliation(s)
- Michela Leocadi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elisa Canu
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Davide Calderaro
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Davide Corbetta
- Laboratory of Movement Analysis, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, Neurology and Neurophysiology Units, IRCCS San Raffaele Scientific Institute, and Vita-Salute San Raffaele University, Milan, Italy
| | - Federica Agosta
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, and Vita-Salute San Raffaele University, Via Olgettina 60, Milan 20132, Italy
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8
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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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9
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Santos-Santos MA, Rabinovici GD, Iaccarino L, Ayakta N, Tammewar G, Lobach I, Henry ML, Hubbard I, Mandelli ML, Spinelli E, Miller ZA, Pressman PS, O'Neil JP, Ghosh P, Lazaris A, Meyer M, Watson C, Yoon SJ, Rosen HJ, Grinberg L, Seeley WW, Miller BL, Jagust WJ, Gorno-Tempini ML. Rates of Amyloid Imaging Positivity in Patients With Primary Progressive Aphasia. JAMA Neurol 2019; 75:342-352. [PMID: 29309493 DOI: 10.1001/jamaneurol.2017.4309] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Importance The ability to predict the pathology underlying different neurodegenerative syndromes is of critical importance owing to the advent of molecule-specific therapies. Objective To determine the rates of positron emission tomography (PET) amyloid positivity in the main clinical variants of primary progressive aphasia (PPA). Design, Setting, and Participants This prospective clinical-pathologic case series was conducted at a tertiary research clinic specialized in cognitive disorders. Patients were evaluated as part of a prospective, longitudinal research study between January 2002 and December 2015. Inclusion criteria included clinical diagnosis of PPA; availability of complete speech, language, and cognitive testing; magnetic resonance imaging performed within 6 months of the cognitive evaluation; and PET carbon 11-labeled Pittsburgh Compound-B or florbetapir F 18 brain scan results. Of 109 patients referred for evaluation of language symptoms who underwent amyloid brain imaging, 3 were excluded because of incomplete language evaluations, 5 for absence of significant aphasia, and 12 for presenting with significant initial symptoms outside of the language domain, leaving a cohort of 89 patients with PPA. Main Outcomes and Measures Clinical, cognitive, neuroimaging, and pathology results. Results Twenty-eight cases were classified as imaging-supported semantic variant PPA (11 women [39.3%]; mean [SD] age, 64 [7] years), 31 nonfluent/agrammatic variant PPA (22 women [71.0%]; mean [SD] age, 68 [7] years), 26 logopenic variant PPA (17 women [65.4%]; mean [SD] age, 63 [8] years), and 4 mixed PPA cases. Twenty-four of 28 patients with semantic variant PPA (86%) and 28 of 31 patients with nonfluent/agrammatic variant PPA (90%) had negative amyloid PET scan results, while 25 of 26 patients with logopenic variant PPA (96%) and 3 of 4 mixed PPA cases (75%) had positive scan results. The amyloid positive semantic variant PPA and nonfluent/agrammatic variant PPA cases with available autopsy data (2 of 4 and 2 of 3, respectively) all had a primary frontotemporal lobar degeneration and secondary Alzheimer disease pathologic diagnoses, whereas autopsy of 2 patients with amyloid PET-positive logopenic variant PPA confirmed Alzheimer disease. One mixed PPA patient with a negative amyloid PET scan had Pick disease at autopsy. Conclusions and Relevance Primary progressive aphasia variant diagnosis according to the current classification scheme is associated with Alzheimer disease biomarker status, with the logopenic variant being associated with carbon 11-labeled Pittsburgh Compound-B positivity in more than 95% of cases. Furthermore, in the presence of a clinical syndrome highly predictive of frontotemporal lobar degeneration pathology, biomarker positivity for Alzheimer disease may be associated more with mixed pathology rather than primary Alzheimer disease.
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Affiliation(s)
- Miguel A Santos-Santos
- Department of Neurology, Memory and Aging Center, University of California San Francisco.,Autonomous University of Barcelona, Cerdanyola del Valles, Spain.,Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute, L'Hospitalet de Llobregat, Barcelona, Spain.,Fundació Alzheimer Memory Clinic and Research Center, Institut Catalá de Neurociències Aplicades, Barcelona, Spain
| | - Gil D Rabinovici
- Department of Neurology, Memory and Aging Center, University of California San Francisco.,Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley
| | - Leonardo Iaccarino
- Department of Neurology, Memory and Aging Center, University of California San Francisco.,Vita-Salute San Raffaele University, Milan, Italy
| | - Nagehan Ayakta
- Department of Neurology, Memory and Aging Center, University of California San Francisco.,Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley
| | - Gautam Tammewar
- Department of Neurology, Memory and Aging Center, University of California San Francisco.,Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley
| | - Iryna Lobach
- Department of Epidemiology and Biostatistics, University of California San Francisco
| | - Maya L Henry
- Department of Communication Sciences and Disorders, University of Texas, Austin
| | - Isabel Hubbard
- Department of Neurology, Memory and Aging Center, University of California San Francisco
| | - Maria Luisa Mandelli
- Department of Neurology, Memory and Aging Center, University of California San Francisco
| | - Edoardo Spinelli
- Department of Neurology, Memory and Aging Center, University of California San Francisco.,Vita-Salute San Raffaele University, Milan, Italy
| | - Zachary A Miller
- Department of Neurology, Memory and Aging Center, University of California San Francisco
| | - Peter S Pressman
- Department of Neurology, Memory and Aging Center, University of California San Francisco.,University of Colorado Denver, Denver
| | - James P O'Neil
- Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California
| | - Pia Ghosh
- Department of Neurology, Memory and Aging Center, University of California San Francisco
| | - Andreas Lazaris
- Department of Neurology, Memory and Aging Center, University of California San Francisco
| | - Marita Meyer
- Department of Neurology, Memory and Aging Center, University of California San Francisco
| | - Christa Watson
- Department of Neurology, Memory and Aging Center, University of California San Francisco
| | - Soo Jin Yoon
- Department of Neurology, Memory and Aging Center, University of California San Francisco.,Department of Neurology, Eulji University Hospital, Daejeon, South Korea
| | - Howard J Rosen
- Department of Neurology, Memory and Aging Center, University of California San Francisco
| | - Lea Grinberg
- Department of Neurology, Memory and Aging Center, University of California San Francisco.,Department of Pathology, University of California San Francisco, California
| | - William W Seeley
- Department of Neurology, Memory and Aging Center, University of California San Francisco.,Department of Pathology, University of California San Francisco, California
| | - Bruce L Miller
- Department of Neurology, Memory and Aging Center, University of California San Francisco
| | - William J Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley.,Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California
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Chelban V, Bocchetta M, Hassanein S, Haridy NA, Houlden H, Rohrer JD. An update on advances in magnetic resonance imaging of multiple system atrophy. J Neurol 2019; 266:1036-1045. [PMID: 30460448 PMCID: PMC6420901 DOI: 10.1007/s00415-018-9121-3] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 11/11/2018] [Indexed: 02/08/2023]
Abstract
In this review, we describe how different neuroimaging tools have been used to identify novel MSA biomarkers, highlighting their advantages and limitations. First, we describe the main structural MRI changes frequently associated with MSA including the 'hot cross-bun' and 'putaminal rim' signs as well as putaminal, pontine, and middle cerebellar peduncle (MCP) atrophy. We discuss the sensitivity and specificity of different supra- and infratentorial changes in differentiating MSA from other disorders, highlighting those that can improve diagnostic accuracy, including the MCP width and MCP/superior cerebellar peduncle (SCP) ratio on T1-weighted imaging, raised putaminal diffusivity on diffusion-weighted imaging, and increased T2* signal in the putamen, striatum, and substantia nigra on susceptibility-weighted imaging. Second, we focus on recent advances in structural and functional MRI techniques including diffusion tensor imaging (DTI), resting-state functional MRI (fMRI), and arterial spin labelling (ASL) imaging. Finally, we discuss new approaches for MSA research such as multimodal neuroimaging strategies and how such markers may be applied in clinical trials to provide crucial data for accurately selecting patients and to act as secondary outcome measures.
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Affiliation(s)
- Viorica Chelban
- Department of Neuromuscular Diseases, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
- Department of Neurology and Neurosurgery, Institute of Emergency Medicine, Toma Ciorbă 1, 2052, Chisinau, Moldova
| | - Martina Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, WC1N 3BG, London, UK
| | - Sara Hassanein
- Diagnostic Radiology department, Faculty of Medicine Assiut University, Assiut, Egypt
- Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, WC1N 3BG, London, UK
| | - Nourelhoda A Haridy
- Department of Neuromuscular Diseases, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
- Department of Neurology and Psychiatry, Faculty of Medicine, Assiut University Hospital, Assiut, Egypt
| | - Henry Houlden
- Department of Neuromuscular Diseases, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
| | - Jonathan D Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, WC1N 3BG, London, UK.
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11
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Meijboom R, Steketee RME, Ham LS, Mantini D, Bron EE, van der Lugt A, van Swieten JC, Smits M. Exploring quantitative group-wise differentiation of Alzheimer's disease and behavioural variant frontotemporal dementia using tract-specific microstructural white matter and functional connectivity measures at multiple time points. Eur Radiol 2019; 29:5148-5159. [PMID: 30859283 PMCID: PMC6719324 DOI: 10.1007/s00330-019-06061-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 01/07/2019] [Accepted: 02/01/2019] [Indexed: 12/13/2022]
Abstract
Objectives This study explored group-wise quantitative measures of tract-specific white matter (WM) microstructure and functional default mode network (DMN) connectivity to establish an initial indication of their clinical applicability for early-stage and follow-up differential diagnosis of Alzheimer’s disease (AD) and behavioural variant frontotemporal dementia (bvFTD). Methods Eleven AD and 12 bvFTD early-stage patients and 18 controls underwent diffusion tensor imaging and resting state functional magnetic resonance imaging at 3 T. All AD and 6 bvFTD patients underwent the same protocol at 1-year follow-up. Functional connectivity measures of DMN and WM tract-specific diffusivity measures were determined for all groups. Exploratory analyses were performed to compare all measures between the three groups at baseline and between patients at follow-up. Additionally, the difference between baseline and follow-up diffusivity measures in AD and bvFTD patients was compared. Results Functional connectivity of the DMN was not different between groups at baseline and at follow-up. Diffusion abnormalities were observed widely in bvFTD and regionally in the hippocampal cingulum in AD. The extent of the differences between bvFTD and AD was diminished at follow-up, yet abnormalities were still more pronounced in bvFTD. The rate of change was similar in bvFTD and AD. Conclusions This study provides a tentative indication that quantitative tract-specific microstructural WM abnormalities, but not quantitative functional connectivity of the DMN, may aid early-stage and follow-up differential diagnosis of bvFTD and AD. Specifically, pronounced microstructural changes in anterior WM tracts may characterise bvFTD, whereas microstructural abnormalities of the hippocampal cingulum may characterise AD. Key Points • The clinical applicability of quantitative brain imaging measures for early-stage and follow-up differential diagnosis of dementia subtypes was explored using a group-wise approach. • Quantitative tract-specific microstructural white matter abnormalities, but not quantitative functional connectivity of the default mode network, may aid early-stage and follow-up differential diagnosis of behavioural variant frontotemporal dementia and Alzheimer’s disease. • Pronounced microstructural white matter (WM) changes in anterior WM tracts characterise behavioural variant frontotemporal dementia, whereas microstructural WM abnormalities of the hippocampal cingulum in the absence of other WM changes characterise Alzheimer’s disease. Electronic supplementary material The online version of this article (10.1007/s00330-019-06061-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- R Meijboom
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - R M E Steketee
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | - L S Ham
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | - D Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium.,Functional Neuroimaging Laboratory, IRCCS San Camillo Hospital Foundation, Lido, Italy
| | - E E Bron
- Biomedical Imaging Group Rotterdam - Departments of Medical Informatics and Radiology, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | - A van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | - J C van Swieten
- Department of Neurology, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | - M Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands.
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12
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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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Vinceti G, Olney N, Mandelli ML, Spina S, Hubbard HI, Santos-Santos MA, Watson C, Miller ZA, Lomen-Hoerth C, Nichelli P, Miller BL, Grinberg LT, Seeley WW, Gorno-Tempini ML. Primary progressive aphasia and the FTD-MND spectrum disorders: clinical, pathological, and neuroimaging correlates. Amyotroph Lateral Scler Frontotemporal Degener 2019; 20:146-158. [PMID: 30668155 DOI: 10.1080/21678421.2018.1556695] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Objective: Behavioral variant frontotemporal dementia (bvFTD), is commonly considered the cognitive presentation of the frontotemporal dementia-motor neuron disease (FTD-MND) spectrum disorder. We evaluated the prevalence of primary progressive aphasia in a series of pathologically confirmed cases of FTD-MND spectrum. Methods: Pathologically confirmed cases of frontotemporal lobar degeneration-motor neuron disease (FTLD-MND) were obtained from the UCSF brain bank. Cases were analyzed for presence of language impairment via retrospective chart review of research visits that include neurologic exam, in-depth cognitive testing and magnetic resonance imaging (MRI) imaging. Forty one cases were included. Thirty two were diagnosed with FTD-MND, while nine cases were diagnosed as MND-only from clinical evaluation. Results: Ten FTLD-MND cases (31%) presented with prominent or isolated language involvement consistent with a diagnosis of primary progressive aphasia (PPA), which we called progressive aphasia with motor neuron disease (PA-MND). Of these, three cases that mirrored the non-fluent variant of PPA (nfvPPA) were named nfvPA-MND. The imaging pattern of these nfvPA-MND showed atrophy strictly confined to the frontal and anterior temporal language cortical areas. Another group of seven cases that resembled patients with the semantic variant PPA (svPPA) were named svPA-MND. The group of svPPA-MND on imaging analysis showed selective atrophy of the temporal lobe and orbitofrontal cortex. Conclusions: Language impairment was a frequent phenotype of FTD-MND associated with focal atrophy patterns within the language networks. This data suggest patients with FTD-MND can present quite often with language phenotype of nfvPPA and svPPA, as opposed to exclusive bvFTD symptoms.
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Affiliation(s)
- Giulia Vinceti
- a Department of Neurology, Memory and Aging Center , University of California , San Francisco , CA , USA.,b Department of Biomedical, Metabolic and Neural Science , University of Modena and Reggio Emilia , Modena , Italy
| | - Nicholas Olney
- a Department of Neurology, Memory and Aging Center , University of California , San Francisco , CA , USA.,d UCSF ALS Center , University of California , San Francisco , CA , USA
| | - Maria Luisa Mandelli
- a Department of Neurology, Memory and Aging Center , University of California , San Francisco , CA , USA
| | - Salvatore Spina
- a Department of Neurology, Memory and Aging Center , University of California , San Francisco , CA , USA
| | - H Isabel Hubbard
- a Department of Neurology, Memory and Aging Center , University of California , San Francisco , CA , USA.,c Department of Communication Sciences and Disorders , University of Texas , Austin , TX, USA
| | - Miguel A Santos-Santos
- a Department of Neurology, Memory and Aging Center , University of California , San Francisco , CA , USA
| | - Christa Watson
- a Department of Neurology, Memory and Aging Center , University of California , San Francisco , CA , USA
| | - Zachary A Miller
- a Department of Neurology, Memory and Aging Center , University of California , San Francisco , CA , USA
| | | | - Paolo Nichelli
- b Department of Biomedical, Metabolic and Neural Science , University of Modena and Reggio Emilia , Modena , Italy
| | - Bruce L Miller
- a Department of Neurology, Memory and Aging Center , University of California , San Francisco , CA , USA
| | - Lea T Grinberg
- a Department of Neurology, Memory and Aging Center , University of California , San Francisco , CA , USA.,e Department of Pathology , University of California , San Francisco , CA , USA
| | - William W Seeley
- a Department of Neurology, Memory and Aging Center , University of California , San Francisco , CA , USA.,e Department of Pathology , University of California , San Francisco , CA , USA
| | - Maria Luisa Gorno-Tempini
- a Department of Neurology, Memory and Aging Center , University of California , San Francisco , CA , USA
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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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15
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Risacher SL, Saykin AJ. Neuroimaging in aging and neurologic diseases. HANDBOOK OF CLINICAL NEUROLOGY 2019; 167:191-227. [PMID: 31753134 DOI: 10.1016/b978-0-12-804766-8.00012-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Neuroimaging biomarkers for neurologic diseases are important tools, both for understanding pathology associated with cognitive and clinical symptoms and for differential diagnosis. This chapter explores neuroimaging measures, including structural and functional measures from magnetic resonance imaging (MRI) and molecular measures primarily from positron emission tomography (PET), in healthy aging adults and in a number of neurologic diseases. The spectrum covers neuroimaging measures from normal aging to a variety of dementias: late-onset Alzheimer's disease [AD; including mild cognitive impairment (MCI)], familial and nonfamilial early-onset AD, atypical AD syndromes, posterior cortical atrophy (PCA), logopenic aphasia (lvPPA), cerebral amyloid angiopathy (CAA), vascular dementia (VaD), sporadic and familial behavioral-variant frontotemporal dementia (bvFTD), semantic dementia (SD), progressive nonfluent aphasia (PNFA), frontotemporal dementia with motor neuron disease (FTD-MND), frontotemporal dementia with amyotrophic lateral sclerosis (FTD-ALS), corticobasal degeneration (CBD), progressive supranuclear palsy (PSP), dementia with Lewy bodies (DLB), Parkinson's disease (PD) with and without dementia, and multiple systems atrophy (MSA). We also include a discussion of the appropriate use criteria (AUC) for amyloid imaging and conclude with a discussion of differential diagnosis of neurologic dementia disorders in the context of neuroimaging.
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Affiliation(s)
- Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States.
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16
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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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17
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Anazodo UC, Finger E, Kwan BYM, Pavlosky W, Warrington JC, Günther M, Prato FS, Thiessen JD, St Lawrence KS. Using simultaneous PET/MRI to compare the accuracy of diagnosing frontotemporal dementia by arterial spin labelling MRI and FDG-PET. NEUROIMAGE-CLINICAL 2017; 17:405-414. [PMID: 29159053 PMCID: PMC5683801 DOI: 10.1016/j.nicl.2017.10.033] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 10/24/2017] [Accepted: 10/28/2017] [Indexed: 11/30/2022]
Abstract
Purpose The clinical utility of FDG-PET in diagnosing frontotemporal dementia (FTD) has been well demonstrated over the past decades. On the contrary, the diagnostic value of arterial spin labelling (ASL) MRI - a relatively new technique - in clinical diagnosis of FTD has yet to be confirmed. Using simultaneous PET/MRI, we evaluated the diagnostic performance of ASL in identifying pathological abnormalities in FTD (FTD) to determine whether ASL can provide similar diagnostic value as FDG-PET. Methods ASL and FDG-PET images were compared in 10 patients with FTD and 10 healthy older adults. Qualitative and quantitative measures of diagnostic equivalency were used to determine the diagnostic utility of ASL compared to FDG-PET. Sensitivity, specificity, and inter-rater reliability were calculated for each modality from scores of subjective visual ratings and from analysis of regional mean values in thirteen a priori regions of interest (ROI). To determine the extent of concordance between modalities in each patient, individual statistical maps generated from comparison of each patient to controls were compared between modalities using the Jaccard similarity index (JI). Results Visual assessments revealed lower sensitivity, specificity and inter-rater reliability for ASL (66.67%/62.12%/0.2) compared to FDG-PET (88.43%/90.91%/0.61). Across all regions, ASL performed lower than FDG-PET in discriminating patients from controls (areas under the receiver operating curve: ASL = 0.75 and FDG-PET = 0.87). In all patients, ASL identified patterns of reduced perfusion consistent with FTD, but areas of hypometabolism exceeded hypoperfused areas (group-mean JI = 0.30 ± 0.22). Conclusion This pilot study demonstrated that ASL can detect similar spatial patterns of abnormalities in individual FTD patients compared to FDG-PET, but its sensitivity and specificity for discriminant diagnosis of a patient from healthy individuals remained unmatched to FDG-PET. Further studies at the individual level are required to confirm the clinical role of ASL in FTD management.
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Affiliation(s)
- Udunna C Anazodo
- Lawson Health Research Institute, St Joseph's Health Care, 268 Grosvenor St., London, Ontario N6A 4V2, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, Medical Sciences Building, Rm M407, London, Ontario N6A 5C1, Canada.
| | - Elizabeth Finger
- Department of Clinical Neurological Sciences, Western University, 339 Windermere Road, London, Ontario N6A 5A5, Canada.
| | - Benjamin Yin Ming Kwan
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario N6A 5W9, Canada
| | - William Pavlosky
- Lawson Health Research Institute, St Joseph's Health Care, 268 Grosvenor St., London, Ontario N6A 4V2, Canada; Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario N6A 5W9, Canada.
| | - James Claude Warrington
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario N6A 5W9, Canada.
| | - Matthias Günther
- Fraunhofer Institute for Medical Image Computing MEVIS, Am Fallturm 1, 28359 Bremen, Germany.; University Bremen, Faculty 1, Otto-Hahn-Allee 1, 28359 Bremen, Germany.
| | - Frank S Prato
- Lawson Health Research Institute, St Joseph's Health Care, 268 Grosvenor St., London, Ontario N6A 4V2, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, Medical Sciences Building, Rm M407, London, Ontario N6A 5C1, Canada.
| | - Jonathan D Thiessen
- Lawson Health Research Institute, St Joseph's Health Care, 268 Grosvenor St., London, Ontario N6A 4V2, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, Medical Sciences Building, Rm M407, London, Ontario N6A 5C1, Canada.
| | - Keith S St Lawrence
- Lawson Health Research Institute, St Joseph's Health Care, 268 Grosvenor St., London, Ontario N6A 4V2, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, Medical Sciences Building, Rm M407, London, Ontario N6A 5C1, Canada.
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18
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Ma HR, Pan PL, Sheng LQ, Dai ZY, Wang GD, Luo R, Chen JH, Xiao PR, Zhong JG, Shi HC. Aberrant pattern of regional cerebral blood flow in Alzheimer's disease: a voxel-wise meta-analysis of arterial spin labeling MR imaging studies. Oncotarget 2017; 8:93196-93208. [PMID: 29190989 PMCID: PMC5696255 DOI: 10.18632/oncotarget.21475] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Accepted: 09/20/2017] [Indexed: 12/20/2022] Open
Abstract
Many studies have applied arterial spin labeling (ASL) to characterize cerebral perfusion patterns of Alzheimer's disease (AD). However, findings across studies are not conclusive. A quantitatively voxel-wise meta-analysis to pool the resting-state ASL studies that measure regional cerebral blood flow (rCBF) alterations in AD was conducted to identify the most consistent and replicable perfusion pattern using seed-based d mapping. The meta-analysis, including 17 ASL studies encompassing 327 AD patients and 357 healthy controls, demonstrated that decreased rCBF in AD patients relative to healthy controls were consistently identified in the bilateral posterior cingulate cortices (PCC)/precuneus, bilateral inferior parietal lobules (IPLs), and left dorsolateral prefrontal cortex. The meta-regression analysis showed that more severe cognitive impairment in the AD samples correlated with greater decreases of rCBF in the bilateral PCC and left IPL. This study characterizes an aberrant ASL-rCBF perfusion pattern of AD involving the posterior default mode network and executive network, which are implicated in its pathophysiology and hold promise for developing imaging biomarkers.
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Affiliation(s)
- Hai Rong Ma
- Department of Neurology, Traditional Chinese Medicine Hospital of Kunshan, Kunshan, PR China
| | - Ping Lei Pan
- Department of Neurology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, Yancheng, PR China
| | - Li Qin Sheng
- Department of Neurology, Traditional Chinese Medicine Hospital of Kunshan, Kunshan, PR China
| | - Zhen Yu Dai
- Department of Radiology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, Yancheng, PR China
| | - Gen Di Wang
- Department of Neurology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, Yancheng, PR China
| | - Rong Luo
- Department of Neurology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, Yancheng, PR China
| | - Jia Hui Chen
- Department of Neurology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, Yancheng, PR China
| | - Pei Rong Xiao
- Department of Neurology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, Yancheng, PR China
| | - Jian Guo Zhong
- Department of Neurology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, Yancheng, PR China
| | - Hai Cun Shi
- Department of Neurology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, Yancheng, PR China
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Tsao S, Gajawelli N, Zhou J, Shi J, Ye J, Wang Y, Leporé N. Feature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry. Brain Behav 2017; 7:e00733. [PMID: 28729939 PMCID: PMC5516607 DOI: 10.1002/brb3.733] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 04/10/2017] [Accepted: 04/14/2017] [Indexed: 12/14/2022] Open
Abstract
INTRODUCTION Prediction of Alzheimer's disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi-task machine learning method (cFSGL) with a novel MR-based multivariate morphometric surface map of the hippocampus (mTBM) to predict future cognitive scores of patients. METHODS Previous work has shown that a multi-task learning framework that performs prediction of all future time points simultaneously (cFSGL) can be used to encode both sparsity as well as temporal smoothness. The authors showed that this method is able to predict cognitive outcomes of ADNI subjects using FreeSurfer-based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied a multivariate tensor-based parametric surface analysis method (mTBM) to extract features from the hippocampal surfaces. RESULTS We combined mTBM features with traditional surface features such as middle axis distance, the Jacobian determinant as well as 2 of the Jacobian principal eigenvalues to yield 7 normalized hippocampal surface maps of 300 points each. By combining these 7 × 300 = 2100 features together with the previous ~350 features, we illustrate how this type of sparsifying method can be applied to an entire surface map of the hippocampus that yields a feature space that is 2 orders of magnitude larger than what was previously attempted. CONCLUSIONS By combining the power of the cFSGL multi-task machine learning framework with the addition of AD sensitive mTBM feature maps of the hippocampus surface, we are able to improve the predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.
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Affiliation(s)
- Sinchai Tsao
- CIBORG Children's Hospital Los Angeles and University of Southern California Los Angeles CA USA
| | - Niharika Gajawelli
- CIBORG Children's Hospital Los Angeles and University of Southern California Los Angeles CA USA
| | - Jiayu Zhou
- Department of Computer Science and Engineering Michigan State University East Lansing MI USA
| | - Jie Shi
- School of Computing, Informatics and Decision Systems Engineering Arizona State University Phoenix AZ USA
| | - Jieping Ye
- Department of Computational Medicine and Bioinformatics & Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA
| | - Yalin Wang
- School of Computing, Informatics and Decision Systems Engineering Arizona State University Phoenix AZ USA
| | - Natasha Leporé
- CIBORG Children's Hospital Los Angeles and University of Southern California Los Angeles CA USA
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20
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Gordon E, Rohrer JD, Fox NC. Advances in neuroimaging in frontotemporal dementia. J Neurochem 2017; 138 Suppl 1:193-210. [PMID: 27502125 DOI: 10.1111/jnc.13656] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Revised: 05/02/2016] [Accepted: 05/03/2016] [Indexed: 12/12/2022]
Abstract
Frontotemporal dementia (FTD) is a clinically and neuroanatomically heterogeneous neurodegenerative disorder with multiple underlying genetic and pathological causes. Whilst initial neuroimaging studies highlighted the presence of frontal and temporal lobe atrophy or hypometabolism as the unifying feature in patients with FTD, more detailed studies have revealed diverse patterns across individuals, with variable frontal or temporal predominance, differing degrees of asymmetry, and the involvement of other cortical areas including the insula and cingulate, as well as subcortical structures such as the basal ganglia and thalamus. Recent advances in novel imaging modalities including diffusion tensor imaging, resting-state functional magnetic resonance imaging and molecular positron emission tomography imaging allow the possibility of investigating alterations in structural and functional connectivity and the visualisation of pathological protein deposition. This review will cover the major imaging modalities currently used in research and clinical practice, focusing on the key insights they have provided into FTD, including the onset and evolution of pathological changes and also importantly their utility as biomarkers for disease detection and staging, differential diagnosis and measurement of disease progression. Validating neuroimaging biomarkers that are able to accomplish these tasks will be crucial for the ultimate goal of powering upcoming clinical trials by correctly stratifying patient enrolment and providing sensitive markers for evaluating the effects and efficacy of disease-modifying therapies. This review describes the key insights provided by research into the major neuroimaging modalities currently used in research and clinical practice, including what they tell us about the onset and evolution of FTD and how they may be used as biomarkers for disease detection and staging, differential diagnosis and measurement of disease progression. This article is part of the Frontotemporal Dementia special issue.
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Affiliation(s)
- Elizabeth Gordon
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | - Jonathan D Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | - Nick C Fox
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
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21
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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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22
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Santos-Santos MA, Mandelli ML, Binney RJ, Ogar J, Wilson SM, Henry ML, Hubbard HI, Meese M, Attygalle S, Rosenberg L, Pakvasa M, Trojanowski JQ, Grinberg LT, Rosen H, Boxer AL, Miller BL, Seeley WW, Gorno-Tempini ML. Features of Patients With Nonfluent/Agrammatic Primary Progressive Aphasia With Underlying Progressive Supranuclear Palsy Pathology or Corticobasal Degeneration. JAMA Neurol 2017; 73:733-42. [PMID: 27111692 DOI: 10.1001/jamaneurol.2016.0412] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
IMPORTANCE We provide novel evidence of specific clinical and neuroimaging features that may help for the in vivo prediction of underlying pathology in patients with nonfluent/agrammatic primary progressive aphasia (nfvPPA) and progressive supranuclear palsy (PSP) or corticobasal degeneration (CBD) proved by autopsy. OBJECTIVE To characterize the neurological, cognitive, and neuroimaging features of patients with nfvPPA-in whom either PSP or CBD was eventually confirmed at autopsy-at initial presentation and at 1-year follow-up. DESIGN, SETTING, AND PARTICIPANTS A prospective longitudinal clinical-pathological study was conducted in a tertiary research clinic that specialized in cognitive disorders. Fourteen patients were evaluated between January 2002 and December 2014. Inclusion criteria for the study were a clinical diagnosis of nfvPPA; the availability of speech, language, and cognitive testing for at least 1 evaluation; magnetic resonance imaging within 6 months of initial evaluation; and a postmortem pathological diagnosis of PSP or CBD. Ten matched healthy control participants were also included. MAIN OUTCOMES AND MEASURES Clinical, cognitive, and neuroimaging longitudinal data were analyzed to characterize the whole nfvPPA-4-repeat-tau group and identify differences between nfvPPA-PSP and nfvPPA-CBD both at presentation and longitudinally. RESULTS Patient groups did not differ significantly in age, sex, or handedness (nfvPPA-PSP group: median [interquartile range (IQR)] age, 74 [67-76] years; 1 of 5 male [20%]; 1 of 5 left-handed [20%]; and nfvPPA-CBD group: mean [IQR] age, 65 [54-81] years; 3 of 9 male [33%]; 0 left-handed). Motor speech impairment and left frontal white matter atrophy were the most prominent common features. At presentation, dysarthria (Motor Speech Examination median [IQR] score: nfvPPA-PSP, 4 [2-7]; nfvPPA-CBD, 0 [0-4]; P = .02), depression (Geriatric Depression Scale median [IQR] score: nfvPPA-PSP, 19 [3-28]; nfvPPA-CBD, 4 [0-16]; P = .04), and relatively selective white matter atrophy were typical of the nfvPPA-PSP group, while greater gray matter atrophy and a trend toward greater sentence comprehension deficits (median [IQR] sentence comprehension correct: nfvPPA-PSP, 98% [80-100]; nfvPPA-CBD, 81% [65-98]; P = .08) were found in the nfvPPA-CBD group. At follow-up after 1 year, we observed no significant differences in any speech or language measures. Furthermore, atrophy in patients with PSP progressed within the subcortical/brainstem motor system generating greater oculomotors deficits and swallowing difficulty; atrophy in patients with CBD spread anteriorly in prefrontal regions consistent with their greater working memory impairment and development of behavioral symptoms. CONCLUSIONS AND RELEVANCE In patients presenting with nfvPPA, presence of early severe dysarthria, relatively selective white matter atrophy at presentation, and a greater rate of change in the brainstem measured by longitudinal imaging may be useful for differentiating underlying PSP from CBD pathology during life.
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Affiliation(s)
| | - Maria Luisa Mandelli
- Department of Neurology, Memory Aging Center, University of California, San Francisco
| | - Richard J Binney
- Department of Communication Sciences and Disorders, Temple University, Philadelphia, Pennsylvania
| | - Jennifer Ogar
- Department of Neurology, Memory Aging Center, University of California, San Francisco
| | - Stephen M Wilson
- Department of Speech, Language, and Hearing Sciences, University of Arizona, Tucson
| | - Maya L Henry
- Department of Communication Sciences and Disorders, University of Texas, Austin
| | - H Isabel Hubbard
- Department of Neurology, Memory Aging Center, University of California, San Francisco
| | - Minerva Meese
- Department of Neurology, Memory Aging Center, University of California, San Francisco
| | - Suneth Attygalle
- Department of Neurology, Memory Aging Center, University of California, San Francisco
| | - Lynne Rosenberg
- Department of Neurology, Memory Aging Center, University of California, San Francisco
| | - Mikhail Pakvasa
- Department of Neurology, Memory Aging Center, University of California, San Francisco
| | - John Q Trojanowski
- Center for Neurodegenerative Disease Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Lea T Grinberg
- Department of Neurology, Memory Aging Center, University of California, San Francisco6Department of Pathology, University of California, San Francisco
| | - Howie Rosen
- Department of Neurology, Memory Aging Center, University of California, San Francisco
| | - Adam L Boxer
- Department of Neurology, Memory Aging Center, University of California, San Francisco
| | - Bruce L Miller
- Department of Neurology, Memory Aging Center, University of California, San Francisco
| | - William W Seeley
- Department of Neurology, Memory Aging Center, University of California, San Francisco6Department of Pathology, University of California, San Francisco
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23
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Spinelli EG, Mandelli ML, Miller ZA, Santos-Santos MA, Wilson SM, Agosta F, Grinberg LT, Huang EJ, Trojanowski JQ, Meyer M, Henry ML, Comi G, Rabinovici G, Rosen HJ, Filippi M, Miller BL, Seeley WW, Gorno-Tempini ML. Typical and atypical pathology in primary progressive aphasia variants. Ann Neurol 2017; 81:430-443. [PMID: 28133816 DOI: 10.1002/ana.24885] [Citation(s) in RCA: 234] [Impact Index Per Article: 33.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Revised: 01/26/2017] [Accepted: 01/26/2017] [Indexed: 12/11/2022]
Abstract
OBJECTIVE To characterize in vivo signatures of pathological diagnosis in a large cohort of patients with primary progressive aphasia (PPA) variants defined by current diagnostic classification. METHODS Extensive clinical, cognitive, neuroimaging, and neuropathological data were collected from 69 patients with sporadic PPA, divided into 29 semantic (svPPA), 25 nonfluent (nfvPPA), 11 logopenic (lvPPA), and 4 mixed PPA. Patterns of gray matter (GM) and white matter (WM) atrophy at presentation were assessed and tested as predictors of pathological diagnosis using support vector machine (SVM) algorithms. RESULTS A clinical diagnosis of PPA was associated with frontotemporal lobar degeneration (FTLD) with transactive response DNA-binding protein (TDP) inclusions in 40.5%, FTLD-tau in 40.5%, and Alzheimer disease (AD) pathology in 19% of cases. Each variant was associated with 1 typical pathology; 24 of 29 (83%) svPPA showed FTLD-TDP type C, 22 of 25 (88%) nfvPPA showed FTLD-tau, and all 11 lvPPA had AD. Within FTLD-tau, 4R-tau pathology was commonly associated with nfvPPA, whereas Pick disease was observed in a minority of subjects across all variants except for lvPPA. Compared with pathologically typical cases, svPPA-tau showed significant extrapyramidal signs, greater executive impairment, and severe striatal and frontal GM and WM atrophy. nfvPPA-TDP patients lacked general motor symptoms or significant WM atrophy. Combining GM and WM volumes, SVM analysis showed 92.7% accuracy to distinguish FTLD-tau and FTLD-TDP pathologies across variants. INTERPRETATION Each PPA clinical variant is associated with a typical and most frequent cognitive, neuroimaging, and neuropathological profile. Specific clinical and early anatomical features may suggest rare and atypical pathological diagnosis in vivo. Ann Neurol 2017;81:430-443.
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Affiliation(s)
- Edoardo G Spinelli
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA.,Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Maria Luisa Mandelli
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA
| | - Zachary A Miller
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA
| | | | - Stephen M Wilson
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA.,Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Federica Agosta
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Lea T Grinberg
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA
| | - Eric J Huang
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA
| | - Marita Meyer
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA
| | - Maya L Henry
- Department of Communication Sciences and Disorders, University of Texas, Austin, TX
| | - Giancarlo Comi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Gil Rabinovici
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA
| | - Howard J Rosen
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Bruce L Miller
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA
| | - William W Seeley
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA
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24
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Multiparametric computer-aided differential diagnosis of Alzheimer's disease and frontotemporal dementia using structural and advanced MRI. Eur Radiol 2016; 27:3372-3382. [PMID: 27986990 PMCID: PMC5491625 DOI: 10.1007/s00330-016-4691-x] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Accepted: 11/29/2016] [Indexed: 01/24/2023]
Abstract
Objectives To investigate the added diagnostic value of arterial spin labelling (ASL) and diffusion tensor imaging (DTI) to structural MRI for computer-aided classification of Alzheimer's disease (AD), frontotemporal dementia (FTD), and controls. Methods This retrospective study used MRI data from 24 early-onset AD and 33 early-onset FTD patients and 34 controls (CN). Classification was based on voxel-wise feature maps derived from structural MRI, ASL, and DTI. Support vector machines (SVMs) were trained to classify AD versus CN (AD-CN), FTD-CN, AD-FTD, and AD-FTD-CN (multi-class). Classification performance was assessed by the area under the receiver-operating-characteristic curve (AUC) and accuracy. Using SVM significance maps, we analysed contributions of brain regions. Results Combining ASL and DTI with structural MRI resulted in higher classification performance for differential diagnosis of AD and FTD (AUC = 84%; p = 0.05) than using structural MRI by itself (AUC = 72%). The performance of ASL and DTI themselves did not improve over structural MRI. The classifications were driven by different brain regions for ASL and DTI than for structural MRI, suggesting complementary information. Conclusions ASL and DTI are promising additions to structural MRI for classification of early-onset AD, early-onset FTD, and controls, and may improve the computer-aided differential diagnosis on a single-subject level. Key points • Multiparametric MRI is promising for computer-aided diagnosis of early-onset AD and FTD. • Diagnosis is driven by different brain regions when using different MRI methods. • Combining structural MRI, ASL, and DTI may improve differential diagnosis of dementia. Electronic supplementary material The online version of this article (doi:10.1007/s00330-016-4691-x) contains supplementary material, which is available to authorized users.
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25
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Tosun D, Schuff N, Rabinovici GD, Ayakta N, Miller BL, Jagust W, Kramer J, Weiner MM, Rosen HJ. Diagnostic utility of ASL-MRI and FDG-PET in the behavioral variant of FTD and AD. Ann Clin Transl Neurol 2016; 3:740-751. [PMID: 27752510 PMCID: PMC5048385 DOI: 10.1002/acn3.330] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Revised: 06/06/2016] [Accepted: 06/08/2016] [Indexed: 12/12/2022] Open
Abstract
Objective To compare the values of arterial spin‐labeled (ASL) MRI and fluorodeoxyglucose (FDG) PET in the diagnosis of behavioral variant of frontotemporal dementia (bvFTD) and Alzheimer's disease (AD). Methods Partial least squares logistic regression was used to identify voxels with diagnostic value in cerebral blood flow (CBF) and cerebral metabolic rate of glucose (CMRgl) maps from patients with bvFTD (n = 32) and AD (n = 28), who were compared with each other and with cognitively normal controls (CN, n = 15). Diagnostic values of these maps were compared with each other. Results Regions that differentiated each disorder from controls were similar for CBF and CMRgl. For differentiating AD from CN, the areas under the curve (AUC) for CBF (0.89) and CMRgl (0.91) were similar, with similar sensitivity (CBF: 86%, CMRgl: 78%) and specificity (CBF: 92%, CMRgl: 100%). Likewise, for differentiating bvFTD from CN performances of CBF (AUC = 0.83) and CMRgl (AUC = 0.85) were equivalent, with similar sensitivity (CBF: 78%, CMRgl: 79%) and specificity (CBF: 92%, CMRgl: 100%). In differentiating bvFTD from AD, classification was again similar for CBF (AUC = 0.87) and CMRgl (AUC = 0.79), as were sensitivity (CBF: 83%, CMRgl: 89%) and specificity (CBF: 93%, CMRgl: 78%). None of the differences in any performance measure were statistically significant. Interpretation ASL‐MRI has similar diagnostic utility as FDG‐PET in the diagnosis of AD and bvFTD. Continued development of ASL‐MRI as a diagnostic tool for neurodegenerative dementias is warranted.
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Affiliation(s)
- Duygu Tosun
- Department of Radiology and Biomedical Imaging University of California San Francisco California
| | - Norbert Schuff
- Department of Radiology and Biomedical Imaging University of California San Francisco California
| | - Gil D Rabinovici
- Memory and Aging Center Department of Neurology University of California San Francisco California
| | - Nagehan Ayakta
- Memory and Aging Center Department of Neurology University of California San Francisco California; School of Public Health University of California Berkeley California
| | - Bruce L Miller
- Memory and Aging Center Department of Neurology University of California San Francisco California
| | - William Jagust
- School of Public Health University of California Berkeley California
| | - Joel Kramer
- Memory and Aging Center Department of Neurology University of California San Francisco California
| | - Michael M Weiner
- Department of Radiology and Biomedical Imaging University of California San Francisco California
| | - Howard J Rosen
- Memory and Aging Center Department of Neurology University of California San Francisco California
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26
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Dopper EGP, Chalos V, Ghariq E, den Heijer T, Hafkemeijer A, Jiskoot LC, de Koning I, Seelaar H, van Minkelen R, van Osch MJP, Rombouts SARB, van Swieten JC. Cerebral blood flow in presymptomatic MAPT and GRN mutation carriers: A longitudinal arterial spin labeling study. NEUROIMAGE-CLINICAL 2016; 12:460-5. [PMID: 27625986 PMCID: PMC5011170 DOI: 10.1016/j.nicl.2016.08.001] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Revised: 06/25/2016] [Accepted: 08/01/2016] [Indexed: 12/11/2022]
Abstract
Objective Frontotemporal dementia (FTD) is characterized by behavioral disturbances and language problems. Familial forms can be caused by genetic defects in microtubule-associated protein tau (MAPT), progranulin (GRN), and C9orf72. In light of upcoming clinical trials with potential disease-modifying agents, the development of sensitive biomarkers to evaluate such agents in the earliest stage of FTD is crucial. In the current longitudinal study we used arterial spin labeling MRI (ASL) in presymptomatic carriers of MAPT and GRN mutations to investigate early changes in cerebral blood flow (CBF). Methods Healthy first-degree relatives of patients with a MAPT or GRN mutation underwent ASL at baseline and follow-up after two years. We investigated cross-sectional and longitudinal differences in CBF between mutation carriers (n = 34) and controls without a mutation (n = 31). Results GRN mutation carriers showed significant frontoparietal hypoperfusion compared with controls at follow-up, whereas we found no cross-sectional group differences in the total study group or the MAPT subgroup. Longitudinal analyses revealed a significantly stronger decrease in CBF in frontal, temporal, parietal, and subcortical areas in the total group of mutation carriers and the GRN subgroup, with the strongest decrease in two mutation carriers who converted to clinical FTD during follow-up. Interpretation We demonstrated longitudinal alterations in CBF in presymptomatic FTD independent of grey matter atrophy, with the strongest decrease in individuals that developed symptoms during follow-up. Therefore, ASL could have the potential to serve as a sensitive biomarker of disease progression in the presymptomatic stage of FTD in future clinical trials. Longitudinal alterations in cerebral blood flow in presymptomatic FTD Larger decline in cerebral blood flow during conversion to symptomatic FTD Arterial spin labeling might provide a useful biomarker for therapeutic trials.
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Key Words
- AD, Alzheimer's disease
- ASL, arterial spin labeling
- Arterial spin labeling
- BDI-II, Beck Depression inventory II (BDI-II)
- BNT, Boston Naming Test
- CBF, cerebral blood flow
- Cerebral blood flow
- FDG-PET, positron emission tomography with 18F-fluorodeoxyglucose
- FTD, frontotemporal dementia
- Frontotemporal dementia
- GRN, progranulin
- LDST, Letter Digit Substitution Test
- MAPT, microtubule-associated protein tau
- MMSE, Mini-Mental State Examination
- Presymptomatic
- RAVLT, Rey Auditory Verbal Learning Test
- SAT, Semantic Association Test
- TMT, Trailmaking Test
- VAT, Visual Association Test
- WCST, Wisconsin Card Sorting Test
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Affiliation(s)
- 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
| | - Vicky Chalos
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Eidrees Ghariq
- C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Tom den Heijer
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Neurology, Sint Franciscus Gasthuis, Rotterdam, The Netherlands
| | - Anne Hafkemeijer
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands; Department of Methodology and Statistics, Institute of Psychology, Leiden University, Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Lize C Jiskoot
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands; Department of Neuropsychology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Inge de Koning
- Department of Neuropsychology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Harro Seelaar
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Rick van Minkelen
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Matthias J P van Osch
- C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Serge A R B Rombouts
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands; Department of Methodology and Statistics, Institute of Psychology, Leiden University, Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - John C van Swieten
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Clinical Genetics, VU Medical Center, Amsterdam, The Netherlands
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27
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Structural and functional brain abnormalities place phenocopy frontotemporal dementia (FTD) in the FTD spectrum. NEUROIMAGE-CLINICAL 2016; 11:595-605. [PMID: 27222795 PMCID: PMC4856342 DOI: 10.1016/j.nicl.2016.03.019] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2015] [Revised: 02/20/2016] [Accepted: 03/25/2016] [Indexed: 11/26/2022]
Abstract
Purpose ‘Phenocopy’ frontotemporal dementia (phFTD) patients may clinically mimic the behavioral variant of FTD (bvFTD), but do not show functional decline or abnormalities upon visual inspection of routine neuroimaging. We aimed to identify abnormalities in gray matter (GM) volume and perfusion in phFTD and to assess whether phFTD belongs to the FTD spectrum. We compared phFTD patients with both healthy controls and bvFTD patients. Materials & methods Seven phFTD and 11 bvFTD patients, and 20 age-matched controls underwent structural T1-weighted magnetic resonance imaging (MRI) and 3D pseudo-continuous arterial spin labeling (pCASL) at 3T. Normalized GM (nGM) volumes and perfusion, corrected for partial volume effects, were quantified regionally as well as in the entire supratentorial cortex, and compared between groups taking into account potential confounding effects of gender and scanner. Results PhFTD patients showed cortical atrophy, most prominently in the right temporal lobe. Apart from this regional atrophy, GM volume was generally not different from either controls or from bvFTD. BvFTD however showed extensive frontotemporal atrophy. Perfusion was increased in the left prefrontal cortex compared to bvFTD and to a lesser extent to controls. Conclusion PhFTD and bvFTD show overlapping cortical structural abnormalities indicating a continuum of changes especially in the frontotemporal regions. Together with functional changes suggestive of a compensatory response to incipient pathology in the left prefrontal regions, these findings are the first to support a possible neuropathological etiology of phFTD and suggest that phFTD may be a neurodegenerative disease on the FTD spectrum. Both phFTD and bvFTD show frontotemporal cortical structural abnormalities. PhFTD shows left frontal hyperperfusion, suggestive of functional compensation. Overlapping findings in phFTD and bvFTD findings suggest a common disease spectrum.
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28
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Xie L, Dolui S, Das SR, Stockbower GE, Daffner M, Rao H, Yushkevich PA, Detre JA, Wolk DA. A brain stress test: Cerebral perfusion during memory encoding in mild cognitive impairment. Neuroimage Clin 2016; 11:388-397. [PMID: 27222794 PMCID: PMC4821452 DOI: 10.1016/j.nicl.2016.03.002] [Citation(s) in RCA: 26] [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: 07/21/2015] [Revised: 02/16/2016] [Accepted: 03/01/2016] [Indexed: 11/24/2022]
Abstract
Arterial spin labeled perfusion magnetic resonance imaging (ASL MRI) provides non-invasive quantification of cerebral blood flow, which can be used as a biomarker of brain function due to the tight coupling between cerebral blood flow (CBF) and brain metabolism. A growing body of literature suggests that regional CBF is altered in neurodegenerative diseases. Here we examined ASL MRI CBF in subjects with amnestic mild cognitive impairment (n = 65) and cognitively normal healthy controls (n = 62), both at rest and during performance of a memory-encoding task. As compared to rest, task-enhanced ASL MRI improved group discrimination, which supports the notion that physiologic measures during a cognitive challenge, or "stress test", may increase the ability to detect subtle functional changes in early disease stages. Further, logistic regression analysis demonstrated that ASL MRI and concomitantly acquired structural MRI provide complementary information of disease status. The current findings support the potential utility of task-enhanced ASL MRI as a biomarker in early Alzheimer's disease.
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Key Words
- AAL, Anatomical Automatic Labeling
- ASL, arterial spin labeled
- Alzheimer's disease
- Arterial spin labeling
- BOLD, blood oxygen level dependent
- Biomarker
- CBF, cerebral blood flow
- CSF, cerebrospinal fluid
- FDG PET, flourodeoyglucose positron emission tomography
- FWER, familywise error rate
- HC, health control
- MCI, mild cognitive impairment
- MMSE, mini-mental status exam
- MNI, Montreal Neurological Institute
- MTL, medial temporal lobe
- Medial temporal lobe
- PASL, pulsed ASL
- PCC, posterior cingulate cortex
- ROI, region of interest
- SCORE, structural correlation based outlier rejection
- Scene-encoding memory task
- a-MCI, amnestic mild cognitive impairment
- aCBF, absolute cerebral blood flow
- pCASL, pseudo-continuous ASL
- rCBF, relative cerebral blood flow
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Affiliation(s)
- Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
| | - Sudipto Dolui
- Center for Functional Neuroimaging, Department of Neurology, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Grace E Stockbower
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Molly Daffner
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Hengyi Rao
- Center for Functional Neuroimaging, Department of Neurology, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - John A Detre
- Center for Functional Neuroimaging, Department of Neurology, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
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Möller C, Hafkemeijer A, Pijnenburg YA, Rombouts SA, van der Grond J, Dopper E, van Swieten J, Versteeg A, Pouwels PJ, Barkhof F, Scheltens P, Vrenken H, van der Flier WM. Joint assessment of white matter integrity, cortical and subcortical atrophy to distinguish AD from behavioral variant FTD: A two-center study. Neuroimage Clin 2015; 9:418-29. [PMID: 26594624 PMCID: PMC4600847 DOI: 10.1016/j.nicl.2015.08.022] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2014] [Revised: 08/25/2015] [Accepted: 08/31/2015] [Indexed: 12/03/2022]
Abstract
We investigated the ability of cortical and subcortical gray matter (GM) atrophy in combination with white matter (WM) integrity to distinguish behavioral variant frontotemporal dementia (bvFTD) from Alzheimer's disease (AD) and from controls using voxel-based morphometry, subcortical structure segmentation, and tract-based spatial statistics. To determine which combination of MR markers differentiated the three groups with the highest accuracy, we conducted discriminant function analyses. Adjusted for age, sex and center, both types of dementia had more GM atrophy, lower fractional anisotropy (FA) and higher mean (MD), axial (L1) and radial diffusivity (L23) values than controls. BvFTD patients had more GM atrophy in orbitofrontal and inferior frontal areas than AD patients. In addition, caudate nucleus and nucleus accumbens were smaller in bvFTD than in AD. FA values were lower; MD, L1 and L23 values were higher, especially in frontal areas of the brain for bvFTD compared to AD patients. The combination of cortical GM, hippocampal volume and WM integrity measurements, classified 97-100% of controls, 81-100% of AD and 67-75% of bvFTD patients correctly. Our results suggest that WM integrity measures add complementary information to measures of GM atrophy, thereby improving the classification between AD and bvFTD.
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Affiliation(s)
- Christiane Möller
- Department of Neurology & Alzheimer Center, Neuroscience Campus, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Anne Hafkemeijer
- Institute of Psychology, Leiden University, Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Yolande A.L. Pijnenburg
- Department of Neurology & Alzheimer Center, Neuroscience Campus, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Serge A.R.B. Rombouts
- Institute of Psychology, Leiden University, 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
| | - Elise Dopper
- Department of Neurology & Alzheimer Center, Neuroscience Campus, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Clinical Genetics, Neuroscience Campus, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - John van Swieten
- Department of Clinical Genetics, Neuroscience Campus, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Adriaan Versteeg
- Department of Radiology & Nuclear Medicine, Neuroscience Campus, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Petra J.W. Pouwels
- Department of Physics & Medical Technology, Neuroscience Campus, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, Neuroscience Campus, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Philip Scheltens
- Department of Neurology & Alzheimer Center, Neuroscience Campus, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Hugo Vrenken
- Department of Radiology & Nuclear Medicine, Neuroscience Campus, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
- Department of Physics & Medical Technology, Neuroscience Campus, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Wiesje M. van der Flier
- Department of Neurology & Alzheimer Center, Neuroscience Campus, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
- Department of Epidemiology & Biostatistics, Neuroscience Campus, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
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Early-stage differentiation between presenile Alzheimer's disease and frontotemporal dementia using arterial spin labeling MRI. Eur Radiol 2015; 26:244-53. [PMID: 26024845 PMCID: PMC4666273 DOI: 10.1007/s00330-015-3789-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2014] [Revised: 04/01/2015] [Accepted: 04/09/2015] [Indexed: 11/16/2022]
Abstract
Objective To investigate arterial spin labeling (ASL)-MRI for the early diagnosis of and differentiation between the two most common types of presenile dementia: Alzheimer’s disease (AD) and frontotemporal dementia (FTD), and for distinguishing age-related from pathological perfusion changes. Methods Thirteen AD and 19 FTD patients, and 25 age-matched older and 22 younger controls underwent 3D pseudo-continuous ASL-MRI at 3 T. Gray matter (GM) volume and cerebral blood flow (CBF), corrected for partial volume effects, were quantified in the entire supratentorial cortex and in 10 GM regions. Sensitivity, specificity and diagnostic performance were evaluated in regions showing significant CBF differences between patient groups or between patients and older controls. Results AD compared with FTD patients had hypoperfusion in the posterior cingulate cortex, differentiating these with a diagnostic performance of 74 %. Compared to older controls, FTD patients showed hypoperfusion in the anterior cingulate cortex, whereas AD patients showed a more widespread regional hypoperfusion as well as atrophy. Regional atrophy was not different between AD and FTD. Diagnostic performance of ASL to differentiate AD or FTD from controls was good (78-85 %). Older controls showed global hypoperfusion compared to young controls. Conclusion ASL-MRI contributes to early diagnosis of and differentiation between presenile AD and FTD. Key Points • ASL-MRI facilitates differentiation of early Alzheimer’s disease and frontotemporal dementia. • Posterior cingulate perfusion is lower in Alzheimer’s disease than frontotemporal dementia. • Compared to controls, Alzheimer’s disease patients show hypoperfusion in multiple regions. • Compared to controls, frontotemporal dementia patients show focal anterior cingulate hypoperfusion. • Global decreased perfusion in older adults differs from hypoperfusion in dementia.
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White matter disease contributes to apathy and disinhibition in behavioral variant frontotemporal dementia. Cogn Behav Neurol 2015; 27:206-14. [PMID: 25539040 DOI: 10.1097/wnn.0000000000000044] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVE To relate changes in fractional anisotropy associated with behavioral variant frontotemporal dementia to measures of apathy and disinhibition. BACKGROUND Apathy and disinhibition are the 2 most common behavioral features of behavioral variant frontotemporal dementia, and these symptoms are associated with accelerated patient decline and caregiver stress. However, little is known about how white matter disease contributes to these symptoms. METHODS We collected neuropsychiatric data, volumetric magnetic resonance imaging, and diffusion-weighted imaging in 11 patients who met published criteria for behavioral variant frontotemporal dementia and had an autopsy-validated cerebrospinal fluid profile consistent with frontotemporal lobar degeneration. We also collected imaging data on 34 healthy seniors for analyses defining regions of disease in the patients. We calculated and analyzed fractional anisotropy with a white matter tract-specific method. This approach uses anatomically guided data reduction to increase sensitivity, and localizes results within canonically defined tracts. We used nonparametric, cluster-based statistical analysis to relate fractional anisotropy to neuropsychiatric measures of apathy and disinhibition. RESULTS The patients with behavioral variant frontotemporal dementia had widespread reductions in fractional anisotropy in anterior portions of frontal and temporal white matter, compared to the controls. Fractional anisotropy correlated with apathy in the left uncinate fasciculus and with disinhibition in the right corona radiata. CONCLUSIONS In patients with behavioral variant frontotemporal dementia, apathy and disinhibition are associated with distinct regions of white matter disease. The implicated fiber tracts likely support frontotemporal networks that are involved in goal-directed behavior.
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Suri S, Topiwala A, Mackay CE, Ebmeier KP, Filippini N. Using structural and diffusion magnetic resonance imaging to differentiate the dementias. Curr Neurol Neurosci Rep 2015; 14:475. [PMID: 25030502 DOI: 10.1007/s11910-014-0475-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Dementia is one of the major causes of personal, societal and financial dependence in older people and in today's ageing society there is a pressing need for early and accurate markers of cognitive decline. There are several subtypes of dementia but the four most common are Alzheimer's disease, Lewy body dementia, vascular dementia and frontotemporal dementia. These disorders can only be diagnosed at autopsy, and ante-mortem assessments of "probable dementia (e.g. of Alzheimer type)" are traditionally driven by clinical symptoms of cognitive or behavioural deficits. However, owing to the overlapping nature of symptoms and age of onset, a significant proportion of dementia cases remain incorrectly diagnosed. Misdiagnosis can have an extensive impact, both at the level of the individual, who may not be offered the appropriate treatment, and on a wider scale, by influencing the entry of patients into relevant clinical trials. Magnetic resonance imaging (MRI) may help to improve diagnosis by providing non-invasive and detailed disease-specific markers of cognitive decline. MRI-derived measurements of grey and white matter structural integrity are potential surrogate markers of disease progression, and may also provide valuable diagnostic information. This review summarises the latest evidence on the use of structural and diffusion MRI in differentiating between the four major dementia subtypes.
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Affiliation(s)
- Sana Suri
- Department of Psychiatry, Warneford Hospital, Warneford Lane, University of Oxford, Oxford, OX3 7JX, UK
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Sha SJ, Ghosh PM, Lee SE, Corbetta-Rastelli C, Jagust WJ, Kornak J, Rankin KP, Grinberg LT, Vinters HV, Mendez MF, Dickson DW, Seeley WW, Gorno-Tempini M, Kramer J, Miller BL, Boxer AL, Rabinovici GD. Predicting amyloid status in corticobasal syndrome using modified clinical criteria, magnetic resonance imaging and fluorodeoxyglucose positron emission tomography. ALZHEIMERS RESEARCH & THERAPY 2015; 7:8. [PMID: 25733984 PMCID: PMC4346122 DOI: 10.1186/s13195-014-0093-y] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2014] [Accepted: 12/22/2014] [Indexed: 12/11/2022]
Abstract
Introduction Group comparisons demonstrate greater visuospatial and memory deficits and temporoparietal-predominant degeneration on neuroimaging in patients with corticobasal syndrome (CBS) found to have Alzheimer’s disease (AD) pathology versus those with underlying frontotemporal lobar degeneration (FTLD). The value of these features in predicting underlying AD pathology in individual patients is unknown. The goal of this study is to evaluate the utility of modified clinical criteria and visual interpretations of magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) for predicting amyloid deposition (as a surrogate of Alzheimer’s disease neuropathology) in patients presenting with CBS. Methods In total, 25 patients meeting CBS core criteria underwent amyloid (Pittsburgh compound B; PIB) PET scans. Clinical records, MRI, and FDG scans were reviewed blinded to PIB results. Modified clinical criteria were used to classify CBS patients as temporoparietal variant CBS (tpvCBS) or frontal variant CBS (fvCBS). MRI and FDG-PET were classified based on the predominant atrophy/hypometabolism pattern (frontal or temporoparietal). Results A total of 9 out of 13 patients classified as tpvCBS were PIB+, compared to 2out of 12 patients classified as fvCBS (P < 0.01, sensitivity 82%, specificity 71% for PIB+ status). Visual MRI reads had 73% sensitivity and 46% specificity for PIB+ status with moderate intra-rater reliability (Cohen’s kappa = 0.42). Visual FDG reads had higher sensitivity (91%) for PIB+ status with perfect intra-rater reliability (kappa = 1.00), though specificity was low (50%). PIB results were confirmed in all 8 patients with available histopathology (3 PIB+ with confirmed AD, 5 PIB- with FTLD). Conclusions Splitting CBS patients into frontal or temporoparietal clinical variants can help predict the likelihood of underlying AD, but criteria require further refinement. Temporoparietal-predominant neuroimaging patterns are sensitive but not specific for AD. Electronic supplementary material The online version of this article (doi:10.1186/s13195-014-0093-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sharon J Sha
- Department of Neurology and Neurological Sciences, Stanford University, 300 Pasteur Drive, Rm A343, Stanford, CA 94305 USA
| | - Pia M Ghosh
- Department of Neurology, University of California, San Francisco, San Francisco, CA USA ; Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA USA
| | - Suzee E Lee
- Department of Neurology, University of California, San Francisco, San Francisco, CA USA
| | - Chiara Corbetta-Rastelli
- Department of Neurology, University of California, San Francisco, San Francisco, CA USA ; Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA USA
| | - Willian J Jagust
- Department of Neurology, University of California, San Francisco, San Francisco, CA USA ; Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA USA ; Lawrence Berkeley National Laboratory, Berkeley, CA USA
| | - John Kornak
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA USA
| | - Katherine P Rankin
- Department of Neurology, University of California, San Francisco, San Francisco, CA USA
| | - Lea T Grinberg
- Department of Neurology, University of California, San Francisco, San Francisco, CA USA
| | - Harry V Vinters
- Department of Neurology, University of California, Los Angeles, CA USA ; Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA USA
| | - Mario F Mendez
- Department of Neurology, University of California, Los Angeles, CA USA
| | - Dennis W Dickson
- Department of Laboratory Medicine & Pathology, Mayo Clinic, Jacksonville, FL USA
| | - William W Seeley
- Department of Neurology, University of California, San Francisco, San Francisco, CA USA
| | - Marilu Gorno-Tempini
- Department of Neurology, University of California, San Francisco, San Francisco, CA USA
| | - Joel Kramer
- Department of Neurology, University of California, San Francisco, San Francisco, CA USA
| | - Bruce L Miller
- Department of Neurology, University of California, San Francisco, San Francisco, CA USA
| | - Adam L Boxer
- Department of Neurology, University of California, San Francisco, San Francisco, CA USA
| | - Gil D Rabinovici
- Department of Neurology, University of California, San Francisco, San Francisco, CA USA ; Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA USA ; Lawrence Berkeley National Laboratory, Berkeley, CA USA
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Abstract
Dementia, whether secondary to Alzheimer disease or another process, is a significant cause of morbidity and mortality worldwide. Although dementia remains a clinical diagnosis, for many years imaging has served as a key component in the assessment of patients with cognitive impairment. There have been tremendous advancements in the neuroimaging of dementia over the past decade, moving the field past the rule-out dogma toward ruling in specific pathophysiologic processes. This article is written for the practicing clinician, to provide a review of neuroimaging findings associated with selected degenerative and nondegenerative forms of dementia.
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35
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Mahoney CJ, Ridgway GR, Malone IB, Downey LE, Beck J, Kinnunen KM, Schmitz N, Golden HL, Rohrer JD, Schott JM, Rossor MN, Ourselin S, Mead S, Fox NC, Warren JD. Profiles of white matter tract pathology in frontotemporal dementia. Hum Brain Mapp 2014; 35:4163-79. [PMID: 24510641 PMCID: PMC4312919 DOI: 10.1002/hbm.22468] [Citation(s) in RCA: 88] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2013] [Revised: 12/13/2013] [Accepted: 01/07/2014] [Indexed: 12/11/2022] Open
Abstract
Despite considerable interest in improving clinical and neurobiological characterisation of frontotemporal dementia and in defining the role of brain network disintegration in its pathogenesis, information about white matter pathway alterations in frontotemporal dementia remains limited. Here we investigated white matter tract damage using an unbiased, template-based diffusion tensor imaging (DTI) protocol in a cohort of 27 patients with the behavioral variant of frontotemporal dementia (bvFTD) representing both major genetic and sporadic forms, in relation both to healthy individuals and to patients with Alzheimer's disease. Widespread white matter tract pathology was identified in the bvFTD group compared with both healthy controls and Alzheimer's disease group, with prominent involvement of uncinate fasciculus, cingulum bundle and corpus callosum. Relatively discrete and distinctive white matter profiles were associated with genetic subgroups of bvFTD associated with MAPT and C9ORF72 mutations. Comparing diffusivity metrics, optimal overall separation of the bvFTD group from the healthy control group was signalled using radial diffusivity, whereas optimal overall separation of the bvFTD group from the Alzheimer's disease group was signalled using fractional anisotropy. Comparing white matter changes with regional grey matter atrophy (delineated using voxel based morphometry) in the bvFTD cohort revealed co-localisation between modalities particularly in the anterior temporal lobe, however white matter changes extended widely beyond the zones of grey matter atrophy. Our findings demonstrate a distributed signature of white matter alterations that is likely to be core to the pathophysiology of bvFTD and further suggest that this signature is modulated by underlying molecular pathologies.
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Affiliation(s)
- Colin J Mahoney
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, United Kingdom
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Raamana PR, Rosen H, Miller B, Weiner MW, Wang L, Beg MF. Three-Class Differential Diagnosis among Alzheimer Disease, Frontotemporal Dementia, and Controls. Front Neurol 2014; 5:71. [PMID: 24860545 PMCID: PMC4026692 DOI: 10.3389/fneur.2014.00071] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2014] [Accepted: 04/26/2014] [Indexed: 01/18/2023] Open
Abstract
Biomarkers derived from brain magnetic resonance (MR) imaging have promise in being able to assist in the clinical diagnosis of brain pathologies. These have been used in many studies in which the goal has been to distinguish between pathologies such as Alzheimer's disease and healthy aging. However, other dementias, in particular, frontotemporal dementia, also present overlapping pathological brain morphometry patterns. Hence, a classifier that can discriminate morphometric features from a brain MRI from the three classes of normal aging, Alzheimer's disease (AD), and frontotemporal dementia (FTD) would offer considerable utility in aiding in correct group identification. Compared to the conventional use of multiple pair-wise binary classifiers that learn to discriminate between two classes at each stage, we propose a single three-way classification system that can discriminate between three classes at the same time. We present a novel classifier that is able to perform a three-class discrimination test for discriminating among AD, FTD, and normal controls (NC) using volumes, shape invariants, and local displacements (three features) of hippocampi and lateral ventricles (two structures times two hemispheres individually) obtained from brain MR images. In order to quantify its utility in correct discrimination, we optimize the three-class classifier on a training set and evaluate its performance using a separate test set. This is a novel, first-of-its-kind comparative study of multiple individual biomarkers in a three-class setting. Our results demonstrate that local atrophy features in lateral ventricles offer the potential to be a biomarker in discriminating among AD, FTD, and NC in a three-class setting for individual patient classification.
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Affiliation(s)
| | - Howard Rosen
- Memory and Aging Center at University of California, San Francisco, CA, USA
| | - Bruce Miller
- Memory and Aging Center at University of California, San Francisco, CA, USA
| | - Michael W. Weiner
- Department of Radiology, VA Medical Center at University of California, San Francisco, CA, USA
| | - Lei Wang
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Mirza Faisal Beg
- School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada
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McMillan CT, Avants BB, Cook P, Ungar L, Trojanowski JQ, Grossman M. The power of neuroimaging biomarkers for screening frontotemporal dementia. Hum Brain Mapp 2014; 35:4827-40. [PMID: 24687814 DOI: 10.1002/hbm.22515] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2014] [Accepted: 03/17/2014] [Indexed: 11/09/2022] Open
Abstract
Frontotemporal dementia (FTD) is a clinically and pathologically heterogeneous neurodegenerative disease that can result from either frontotemporal lobar degeneration (FTLD) or Alzheimer's disease (AD) pathology. It is critical to establish statistically powerful biomarkers that can achieve substantial cost-savings and increase the feasibility of clinical trials. We assessed three broad categories of neuroimaging methods to screen underlying FTLD and AD pathology in a clinical FTD series: global measures (e.g., ventricular volume), anatomical volumes of interest (VOIs) (e.g., hippocampus) using a standard atlas, and data-driven VOIs using Eigenanatomy. We evaluated clinical FTD patients (N = 93) with cerebrospinal fluid, gray matter (GM) magnetic resonance imaging (MRI), and diffusion tensor imaging (DTI) to assess whether they had underlying FTLD or AD pathology. Linear regression was performed to identify the optimal VOIs for each method in a training dataset and then we evaluated classification sensitivity and specificity in an independent test cohort. Power was evaluated by calculating minimum sample sizes required in the test classification analyses for each model. The data-driven VOI analysis using a multimodal combination of GM MRI and DTI achieved the greatest classification accuracy (89% sensitive and 89% specific) and required a lower minimum sample size (N = 26) relative to anatomical VOI and global measures. We conclude that a data-driven VOI approach using Eigenanatomy provides more accurate classification, benefits from increased statistical power in unseen datasets, and therefore provides a robust method for screening underlying pathology in FTD patients for entry into clinical trials.
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Affiliation(s)
- Corey T McMillan
- Department of Neurology, Penn Frontotemporal Degeneration Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
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38
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Miller ZA, Mandelli ML, Rankin KP, Henry ML, Babiak MC, Frazier DT, Lobach IV, Bettcher BM, Wu TQ, Rabinovici GD, Graff-Radford NR, Miller BL, Gorno-Tempini ML. Handedness and language learning disability differentially distribute in progressive aphasia variants. Brain 2013; 136:3461-73. [PMID: 24056533 PMCID: PMC3808687 DOI: 10.1093/brain/awt242] [Citation(s) in RCA: 92] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2013] [Revised: 06/27/2013] [Accepted: 07/05/2013] [Indexed: 01/12/2023] Open
Abstract
Primary progressive aphasia is a neurodegenerative clinical syndrome that presents in adulthood with an isolated, progressive language disorder. Three main clinical/anatomical variants have been described, each associated with distinctive pathology. A high frequency of neurodevelopmental learning disability in primary progressive aphasia has been reported. Because the disorder is heterogeneous with different patterns of cognitive, anatomical and biological involvement, we sought to identify whether learning disability had a predilection for one or more of the primary progressive aphasia subtypes. We screened the University of California San Francisco Memory and Aging Center's primary progressive aphasia cohort (n = 198) for history of language-related learning disability as well as hand preference, which has associations with learning disability. The study included logopenic (n = 48), non-fluent (n = 54) and semantic (n = 96) variant primary progressive aphasias. We investigated whether the presence of learning disability or non-right-handedness was associated with differential effects on demographic, neuropsychological and neuroimaging features of primary progressive aphasia. We showed that a high frequency of learning disability was present only in the logopenic group (χ(2) = 15.17, P < 0.001) and (χ(2) = 11.51, P < 0.001) compared with semantic and non-fluent populations. In this group, learning disability was associated with earlier onset of disease, more isolated language symptoms, and more focal pattern of left posterior temporoparietal atrophy. Non-right-handedness was instead over-represented in the semantic group, at nearly twice the prevalence of the general population (χ(2) = 6.34, P = 0.01). Within semantic variant primary progressive aphasia the right-handed and non-right-handed cohorts appeared homogeneous on imaging, cognitive profile, and structural analysis of brain symmetry. Lastly, the non-fluent group showed no increase in learning disability or non-right-handedness. Logopenic variant primary progressive aphasia and developmental dyslexia both manifest with phonological disturbances and posterior temporal involvement. Learning disability might confer vulnerability of this network to early-onset, focal Alzheimer's pathology. Left-handedness has been described as a proxy for atypical brain hemispheric lateralization. As non-right-handedness was increased only in the semantic group, anomalous lateralization mechanisms might instead be related to frontotemporal lobar degeneration with abnormal TARDBP. Taken together, this study suggests that neurodevelopmental signatures impart differential trajectories towards neurodegenerative disease.
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Affiliation(s)
- Zachary A. Miller
- 1 Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94158, USA
- 2 Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Maria Luisa Mandelli
- 1 Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94158, USA
- 2 Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Katherine P. Rankin
- 1 Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94158, USA
- 2 Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Maya L. Henry
- 1 Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94158, USA
- 2 Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Miranda C. Babiak
- 1 Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94158, USA
- 2 Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Darvis T. Frazier
- 1 Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94158, USA
- 2 Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Iryna V. Lobach
- 1 Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94158, USA
- 2 Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Brianne M. Bettcher
- 1 Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94158, USA
- 2 Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Teresa Q. Wu
- 1 Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94158, USA
- 2 Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Gil D. Rabinovici
- 1 Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94158, USA
- 2 Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | | | - Bruce L. Miller
- 1 Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94158, USA
- 2 Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Maria Luisa Gorno-Tempini
- 1 Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94158, USA
- 2 Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
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Abstract
Neurodegenerative disorders leading to dementia are common diseases that affect many older and some young adults. Neuroimaging methods are important tools for assessing and monitoring pathological brain changes associated with progressive neurodegenerative conditions. In this review, the authors describe key findings from neuroimaging studies (magnetic resonance imaging and radionucleotide imaging) in neurodegenerative disorders, including Alzheimer's disease (AD) and prodromal stages, familial and atypical AD syndromes, frontotemporal dementia, amyotrophic lateral sclerosis with and without dementia, Parkinson's disease with and without dementia, dementia with Lewy bodies, Huntington's disease, multiple sclerosis, HIV-associated neurocognitive disorder, and prion protein associated diseases (i.e., Creutzfeldt-Jakob disease). The authors focus on neuroimaging findings of in vivo pathology in these disorders, as well as the potential for neuroimaging to provide useful information for differential diagnosis of neurodegenerative disorders.
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Affiliation(s)
- Shannon L. Risacher
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, and Indiana Alzheimer Disease Center Indiana University School of Medicine, Indianapolis, Indiana
| | - Andrew J. Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, and Indiana Alzheimer Disease Center Indiana University School of Medicine, Indianapolis, Indiana
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Chen G, Zhang HY, Xie C, Chen G, Zhang ZJ, Teng GJ, Li SJ. Modular reorganization of brain resting state networks and its independent validation in Alzheimer's disease patients. Front Hum Neurosci 2013; 7:456. [PMID: 23950743 PMCID: PMC3739061 DOI: 10.3389/fnhum.2013.00456] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2013] [Accepted: 07/22/2013] [Indexed: 12/05/2022] Open
Abstract
Previous studies have demonstrated disruption in structural and functional connectivity occurring in the Alzheimer's Disease (AD). However, it is not known how these disruptions alter brain network reorganization. With the modular analysis method of graph theory, and datasets acquired by the resting-state functional connectivity MRI (R-fMRI) method, we investigated and compared the brain organization patterns between the AD group and the cognitively normal control (CN) group. Our main finding is that the largest homotopic module (defined as the insula module) in the CN group was broken down to the pieces in the AD group. Specifically, it was discovered that the eight pairs of the bilateral regions (the opercular part of inferior frontal gyrus, area triangularis, insula, putamen, globus pallidus, transverse temporal gyri, superior temporal gyrus, and superior temporal pole) of the insula module had lost symmetric functional connection properties, and the corresponding gray matter concentration (GMC) was significant lower in AD group. We further quantified the functional connectivity changes with an index (index A) and structural changes with the GMC index in the insula module to demonstrate their great potential as AD biomarkers. We further validated these results with six additional independent datasets (271 subjects in six groups). Our results demonstrated specific underlying structural and functional reorganization from young to old, and for diseased subjects. Further, it is suggested that by combining the structural GMC analysis and functional modular analysis in the insula module, a new biomarker can be developed at the single-subject level.
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Affiliation(s)
- Guangyu Chen
- Department of Biophysics, Medical College of Wisconsin Milwaukee, WI, USA
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41
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Diffusion tensor tractography versus volumetric imaging in the diagnosis of behavioral variant frontotemporal dementia. PLoS One 2013; 8:e66932. [PMID: 23874403 PMCID: PMC3715470 DOI: 10.1371/journal.pone.0066932] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2013] [Accepted: 05/13/2013] [Indexed: 12/02/2022] Open
Abstract
MRI diffusion tensor imaging (DTI) studies of white matter integrity in behavioral variant frontotemporal dementia have consistently shown involvement of frontal and temporal white matter, corresponding to regional loss of cortical volume. Volumetric imaging has a suboptimal sensitivity as a diagnostic tool and thus we wanted to explore if DTI is a better method to discriminate patients and controls than volumetric imaging. We examined the anterior cingulum bundle in 14 patients with behavioral variant frontotemporal dementia and 22 healthy controls using deterministic manual diffusion tensor tractography, and compared DTI parameters with two measures of cortical atrophy, VBM and cortical thickness, of the anterior cingulate cortex (ACC). Statistically significant changes between patients and controls were detected in all DTI parameters, with large effect sizes. ROC-AUC was for the best DTI parameters: 0.92 (fractional anisotropy) to 0.97 (radial diffusivity), 0.82 for the best cortical parameter, VBM of the ACC. Results from the AUC were confirmed with binary logistic regression analysis including demographic variables, but only for fractional anisotropy and mean diffusivity. Ability to classify patient/nonpatient status was significantly better for mean diffusivity vs. VBM (p=0.031), and borderline significant for fractional anisotropy vs. VBM (p=0.062). The results indicate that DTI could offer advantages in comparison with the assessment of cortical volume in differentiating patients with behavioral variant frontotemporal dementia and controls.
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42
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White Matter Lesion Assessment in Patients with Cognitive Impairment and Healthy Controls: Reliability Comparisons between Visual Rating, a Manual, and an Automatic Volumetrical MRI Method-The Gothenburg MCI Study. J Aging Res 2013; 2013:198471. [PMID: 23401776 PMCID: PMC3562671 DOI: 10.1155/2013/198471] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2012] [Revised: 11/13/2012] [Accepted: 11/13/2012] [Indexed: 11/23/2022] Open
Abstract
Age-related white matter lesions (WML) are a risk factor for stroke, cognitive decline, and dementia. Different requirements are imposed on methods for the assessment of WML in clinical settings and for research purposes, but reliability analysis is of major importance. In this study, WML assessment with three different methods was evaluated. In the Gothenburg mild cognitive impairment study, MRI scans from 152 participants were used to assess WML with the Fazekas visual rating scale on T2 images, a manual volumetric method on FLAIR images, and FreeSurfer volumetry on T1 images. Reliability was acceptable for all three methods. For low WML volumes (2/3 of the patients), reliability was overall lower and nonsignificant for the manual volumetric method. Unreliability in the assessment of patients with low WML with manual volumetry may mainly be due to intensity variation in the FLAIR sequence used; hence, intensity standardization and normalization methods must be used for more accurate assessments. The FreeSurfer segmentations resulted in smaller WML volumes than the volumes acquired with the manual method and showed deviations from visible hypointensities in the T1 images, which quite likely reduces validity.
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McMillan CT, Avants B, Irwin DJ, Toledo JB, Wolk DA, Van Deerlin VM, Shaw LM, Trojanoswki JQ, Grossman M. Can MRI screen for CSF biomarkers in neurodegenerative disease? Neurology 2012; 80:132-8. [PMID: 23269595 DOI: 10.1212/wnl.0b013e31827b9147] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE Alzheimer disease (AD) and frontotemporal lobar degeneration (FTLD) may have overlapping clinical presentations despite distinct underlying neuropathologies, thus making in vivo diagnosis challenging. In this study, we evaluate the utility of MRI as a noninvasive screening procedure for the differential diagnosis of AD and FTLD. METHODS We recruited 185 patients with a clinically diagnosed neurodegenerative disease consistent with AD or FTLD who had a lumbar puncture and a volumetric MRI. A subset of 32 patients had genetic or autopsy-confirmed AD or FTLD. We used singular value decomposition to decompose MRI volumes and linear regression and cross-validation to predict CSF total tau (tt) and β-amyloid (Aβ(1-42)) ratio (tt/Aβ) in patients with AD and patients with FTLD. We then evaluated accuracy of MRI-based predicted tt/Aβ using 4 converging sources including neuroanatomic visualization and categorization of a subset of patients with genetic or autopsy-confirmed AD or FTLD. RESULTS Regression analyses showed that MRI-predicted tt/Aβ is highly related to actual CSF tt/Aβ. In each group, both predicted and actual CSF tt/Aβ have extensively overlapping neuroanatomic correlates: low tt/Aβ consistent with FTLD is related to ventromedial prefrontal regions while high tt/Aβ consistent with AD is related to posterior cortical regions. MRI-predicted tt/Aβ is 75% accurate at identifying underlying diagnosis in patients with known pathology and in clinically diagnosed patients with known CSF tt/Aβ levels. CONCLUSION MRI may serve as a noninvasive procedure that can screen for AD and FTLD pathology as a surrogate for CSF biomarkers.
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Affiliation(s)
- Corey T McMillan
- Department of Neurology, University of Pennsylvania, Philadelphia, USA.
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Sha SJ, Takada LT, Rankin KP, Yokoyama JS, Rutherford NJ, Fong JC, Khan B, Karydas A, Baker MC, DeJesus-Hernandez M, Pribadi M, Coppola G, Geschwind DH, Rademakers R, Lee SE, Seeley W, Miller BL, Boxer AL. Frontotemporal dementia due to C9ORF72 mutations: clinical and imaging features. Neurology 2012; 79:1002-11. [PMID: 22875087 DOI: 10.1212/wnl.0b013e318268452e] [Citation(s) in RCA: 144] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
OBJECTIVE To describe the phenotype of patients with C9FTD/ALS (C9ORF72) hexanucleotide repeat expansion. METHODS A total of 648 patients with frontotemporal dementia (FTD)-related clinical diagnoses and Alzheimer disease (AD) dementia were tested for C9ORF72 expansion and 31 carried expanded repeats (C9+). Clinical and neuroimaging data were compared between C9+ (15 behavioral variant FTD [bvFTD], 11 FTD-motor neuron disease [MND], 5 amyotrophic lateral sclerosis [ALS]) and sporadic noncarriers (48 bvFTD, 19 FTD-MND, 6 ALS). RESULTS All C9+ patients displayed clinical syndromes of bvFTD, ALS, or FTD-MND. At first evaluation, C9+ bvFTD patients had more delusions and greater impairment of working memory, but milder eating dysregulation compared to bvFTD noncarriers. C9+FTD-MND patients had a trend for longer survival and had an earlier age at onset than FTD-MND noncarriers. Voxel-based morphometry demonstrated more thalamic atrophy in FTD and FTD-MND carriers than in noncarriers. CONCLUSIONS Patients with the C9ORF72 hexanucleotide repeat expansion develop bvFTD, ALS, or FTD-MND with similar clinical and imaging features to sporadic cases. Other FTD spectrum diagnoses and AD dementia appear rare or absent among C9+ individuals. Longer survival in C9+ FTD-MND suggests slower disease progression and thalamic atrophy represents a novel and unexpected feature.
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Affiliation(s)
- Sharon J Sha
- Department of Neurology, University of California, San Francisco, USA.
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McMillan CT, Brun C, Siddiqui S, Churgin M, Libon D, Yushkevich P, Zhang H, Boller A, Gee J, Grossman M. White matter imaging contributes to the multimodal diagnosis of frontotemporal lobar degeneration. Neurology 2012; 78:1761-8. [PMID: 22592372 DOI: 10.1212/wnl.0b013e31825830bd] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVE To evaluate the distribution of white matter (WM) disease in frontotemporal lobar degeneration (FTLD) and Alzheimer disease (AD) and to evaluate the relative usefulness of WM and gray matter (GM) for distinguishing these conditions in vivo. METHODS Patients were classified as having FTLD (n = 50) or AD (n = 42) using autopsy-validated CSF values of total-tau:β-amyloid (t-tau:Aβ(1-42)) ratios. Patients underwent WM diffusion tensor imaging (DTI) and volumetric MRI of GM. We employed tract-specific analyses of WM fractional anisotropy (FA) and whole-brain GM density analyses. Individual patient classification was performed using receiver operator characteristic (ROC) curves with FA, GM, and a combination of the 2 modalities. RESULTS Regional FA and GM were significantly reduced in FTLD and AD relative to healthy seniors. Direct comparisons revealed significantly reduced FA in the corpus callosum in FTLD relative to AD. GM analyses revealed reductions in anterior temporal cortex for FTLD relative to AD, and in posterior cingulate and precuneus for AD relative to FTLD. ROC curves revealed that a multimodal combination of WM and GM provide optimal classification (area under the curve = 0.938), with 87% sensitivity and 83% specificity. CONCLUSIONS FTLD and AD have significant WM and GM defects. A combination of DTI and volumetric MRI modalities provides a quantitative method for distinguishing FTLD and AD in vivo.
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Affiliation(s)
- C T McMillan
- Department of Neurology, University of Pennsylvania, PA, USA.
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Kuceyeski A, Zhang Y, Raj A. Linking white matter integrity loss to associated cortical regions using structural connectivity information in Alzheimer's disease and fronto-temporal dementia: the Loss in Connectivity (LoCo) score. Neuroimage 2012; 61:1311-23. [PMID: 22484307 DOI: 10.1016/j.neuroimage.2012.03.039] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2011] [Revised: 03/09/2012] [Accepted: 03/13/2012] [Indexed: 12/12/2022] Open
Abstract
It is well known that gray matter changes occur in neurodegenerative diseases like Alzheimer's (AD) and fronto-temporal dementia (FTD), and several studies have investigated their respective patterns of atrophy progression. Recent work, however, has revealed that diffusion MRI that is able to detect white matter integrity changes may be an earlier or more sensitive biomarker in both diseases. However, studies that examine white matter changes only are limited in that they do not provide the functional specificity of GM region-based analysis. In this study, we develop a new metric called the Loss in Connectivity (LoCo) score that gives the amount of structural network disruption incurred by a gray matter region for a particular pattern of white matter integrity loss. Leveraging the relative strengths of WM and GM markers, this metric links areas of WM integrity loss to their connected GM regions as a first step in understanding their functional implications. The LoCo score is calculated for three groups: 18AD, 18 FTD, and 19 age-matched normal controls. We show significant correlations of the LoCo with the respective atrophy patterns in AD (R=0.51, p=2.2 × 10(-9)) and FTD (R=0.49, p=2.5 × 10(-8)) for a standard 116 region gray matter atlas. In addition, we demonstrate that the LoCo outperforms a measure of gray matter atrophy when classifying individuals into AD, FTD, and normal groups.
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Affiliation(s)
- Amy Kuceyeski
- Imaging and Data Evaluation and Analysis Laboratory (IDEAL), Dept. of Radiology, Weill Cornell Medical College, 515 E. 71st St. New York, NY 10065, USA.
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