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Cotta Ramusino M, Massa F, Festari C, Gandolfo F, Nicolosi V, Orini S, Nobili F, Frisoni GB, Morbelli S, Garibotto V. Diagnostic performance of molecular imaging methods in predicting the progression from mild cognitive impairment to dementia: an updated systematic review. Eur J Nucl Med Mol Imaging 2024; 51:1876-1890. [PMID: 38355740 DOI: 10.1007/s00259-024-06631-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 01/27/2024] [Indexed: 02/16/2024]
Abstract
PURPOSE Epidemiological and logistical reasons are slowing the clinical validation of the molecular imaging biomarkers in the initial stages of neurocognitive disorders. We provide an updated systematic review of the recent advances (2017-2022), highlighting methodological shortcomings. METHODS Studies reporting the diagnostic accuracy values of the molecular imaging techniques (i.e., amyloid-, tau-, [18F]FDG-PETs, DaT-SPECT, and cardiac [123I]-MIBG scintigraphy) in predicting progression from mild cognitive impairment (MCI) to dementia were selected according to the Preferred Reporting Items for a Systematic Review and Meta-Analysis (PRISMA) method and evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Main eligibility criteria were as follows: (1) ≥ 50 subjects with MCI, (2) follow-up ≥ 3 years, (3) gold standard: progression to dementia or diagnosis on pathology, and (4) measures of prospective accuracy. RESULTS Sensitivity (SE) and specificity (SP) in predicting progression to dementia, mainly to Alzheimer's dementia were 43-100% and 63-94% for [18F]FDG-PET and 64-94% and 48-93% for amyloid-PET. Longitudinal studies were lacking for less common disorders (Dementia with Lewy bodies-DLB and Frontotemporal lobe degeneration-FTLD) and for tau-PET, DaT-SPECT, and [123I]-MIBG scintigraphy. Therefore, the accuracy values from cross-sectional studies in a smaller sample of subjects (n > 20, also including mild dementia stage) were chosen as surrogate outcomes. DaT-SPECT showed 47-100% SE and 71-100% SP in differentiating Lewy body disease (LBD) from non-LBD conditions; tau-PET: 88% SE and 100% SP in differentiating DLB from Posterior Cortical Atrophy. [123I]-MIBG scintigraphy differentiated LBD from non-LBD conditions with 47-100% SE and 71-100% SP. CONCLUSION Molecular imaging has a moderate-to-good accuracy in predicting the progression of MCI to Alzheimer's dementia. Longitudinal studies are sparse in non-AD conditions, requiring additional efforts in these settings.
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Affiliation(s)
- Matteo Cotta Ramusino
- Unit of Behavior Neurology and Dementia Research Center, IRCCS Mondino Foundation, via Mondino 2, 27100, Pavia, Italy.
| | - Federico Massa
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Cristina Festari
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni Di Dio Fatebenefratelli, Brescia, Italy
| | - Federica Gandolfo
- Department of Geriatric Care, Orthogeriatrics and Rehabilitation, E.O. Galliera Hospital, Genoa, Italy
| | - Valentina Nicolosi
- UOC Neurologia Ospedale Magalini Di Villafranca Di Verona (VR) ULSS 9, Verona, Italy
| | - Stefania Orini
- Alzheimer's Unit-Memory Clinic, IRCCS Istituto Centro San Giovanni Di Dio Fatebenefratelli, Brescia, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Flavio Nobili
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University and University Hospitals, Geneva, Switzerland
| | - Silvia Morbelli
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, University Hospitals of Geneva, Geneva, Switzerland
- NIMTLab, Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Geneva, Switzerland
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Stockbauer A, Beyer L, Huber M, Kreuzer A, Palleis C, Katzdobler S, Rauchmann BS, Morbelli S, Chincarini A, Bruffaerts R, Vandenberghe R, Kramberger MG, Trost M, Garibotto V, Nicastro N, Lathuilière A, Lemstra AW, van Berckel BNM, Pilotto A, Padovani A, Ochoa-Figueroa MA, Davidsson A, Camacho V, Peira E, Bauckneht M, Pardini M, Sambuceti G, Aarsland D, Nobili F, Gross M, Vöglein J, Perneczky R, Pogarell O, Buerger K, Franzmeier N, Danek A, Levin J, Höglinger GU, Bartenstein P, Cumming P, Rominger A, Brendel M. Metabolic network alterations as a supportive biomarker in dementia with Lewy bodies with preserved dopamine transmission. Eur J Nucl Med Mol Imaging 2024; 51:1023-1034. [PMID: 37971501 PMCID: PMC10881642 DOI: 10.1007/s00259-023-06493-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 10/26/2023] [Indexed: 11/19/2023]
Abstract
PURPOSE Metabolic network analysis of FDG-PET utilizes an index of inter-regional correlation of resting state glucose metabolism and has been proven to provide complementary information regarding the disease process in parkinsonian syndromes. The goals of this study were (i) to evaluate pattern similarities of glucose metabolism and network connectivity in dementia with Lewy bodies (DLB) subjects with subthreshold dopaminergic loss compared to advanced disease stages and to (ii) investigate metabolic network alterations of FDG-PET for discrimination of patients with early DLB from other neurodegenerative disorders (Alzheimer's disease, Parkinson's disease, multiple system atrophy) at individual patient level via principal component analysis (PCA). METHODS FDG-PETs of subjects with probable or possible DLB (n = 22) without significant dopamine deficiency (z-score < 2 in putamen binding loss on DaT-SPECT compared to healthy controls (HC)) were scaled by global-mean, prior to volume-of-interest-based analyses of relative glucose metabolism. Single region metabolic changes and network connectivity changes were compared against HC (n = 23) and against DLB subjects with significant dopamine deficiency (n = 86). PCA was applied to test discrimination of patients with DLB from disease controls (n = 101) at individual patient level. RESULTS Similar patterns of hypo- (parietal- and occipital cortex) and hypermetabolism (basal ganglia, limbic system, motor cortices) were observed in DLB patients with and without significant dopamine deficiency when compared to HC. Metabolic connectivity alterations correlated between DLB patients with and without significant dopamine deficiency (R2 = 0.597, p < 0.01). A PCA trained by DLB patients with dopamine deficiency and HC discriminated DLB patients without significant dopaminergic loss from other neurodegenerative parkinsonian disorders at individual patient level (area-under-the-curve (AUC): 0.912). CONCLUSION Disease-specific patterns of altered glucose metabolism and altered metabolic networks are present in DLB subjects without significant dopaminergic loss. Metabolic network alterations in FDG-PET can act as a supporting biomarker in the subgroup of DLB patients without significant dopaminergic loss at symptoms onset.
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Affiliation(s)
- Anna Stockbauer
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
- Department of Neurology, University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Leonie Beyer
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Maria Huber
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Annika Kreuzer
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Carla Palleis
- Department of Neurology, University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Sabrina Katzdobler
- Department of Neurology, University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Department of Neuroradiology, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Silvia Morbelli
- Nuclear Medicine Uni, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Health Sciences, University of Genoa, Genoa, Italy
| | - Andrea Chincarini
- National Institute of Nuclear Physics (INFN), Genoa Section, Genoa, Italy
| | - Rose Bruffaerts
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Louvain, Belgium
- Neurology Department, University Hospitals Leuven, Louvain, Belgium
- Biomedical Research Institute, Hasselt University, Hasselt, Belgium
- Experimental Neurobiology Unit, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Louvain, Belgium
- Neurology Department, University Hospitals Leuven, Louvain, Belgium
| | - Milica G Kramberger
- Department of Neurology and Department for Nuclear Medicine, University Medical Centre, Ljubljana, Slovenia
| | - Maja Trost
- Department of Neurology and Department for Nuclear Medicine, University Medical Centre, Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals and NIMTLab, Geneva University, Geneva, Switzerland
| | - Nicolas Nicastro
- Department of Clinical Neurosciences, Geneva University Hospitals, Geneva, Switzerland
| | - Aurélien Lathuilière
- LANVIE (Laboratoire de Neuroimagerie du Vieillissement), Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | - Afina W Lemstra
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Bart N M van Berckel
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Andrea Pilotto
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
- Parkinson's Disease Rehabilitation Centre, FERB ONLUS - S. Isidoro Hospital, Trescore Balneario, BG, Italy
| | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Miguel A Ochoa-Figueroa
- Department of Clinical Physiology in Linköping, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Department of Diagnostic Radiology, Linköping University Hospital, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Anette Davidsson
- Department of Clinical Physiology in Linköping, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Valle Camacho
- Servicio de Medicina Nuclear, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Enrico Peira
- National Institute of Nuclear Physics (INFN), Genoa Section, Genoa, Italy
- Department of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy
| | - Matteo Bauckneht
- Nuclear Medicine Uni, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Matteo Pardini
- Department of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy
- Clinical Neurology, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Gianmario Sambuceti
- Nuclear Medicine Uni, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Health Sciences, University of Genoa, Genoa, Italy
| | - Dag Aarsland
- Centre of Age-Related Medicine (SESAM), Stavanger University Hospital, Stavanger, Norway
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology, and Neuroscience, King's College, London, UK
| | - Flavio Nobili
- Department of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy
- Clinical Neurology, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Mattes Gross
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Jonathan Vöglein
- Department of Neurology, University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Robert Perneczky
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, S10 2HQ, UK
- Ageing Epidemiology (AGE) Research Unit, School of Public Health, Imperial College, London, UK
| | - Oliver Pogarell
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institut for Stroke and Dementia Research, University of Munich, Munich, Germany
| | | | - Adrian Danek
- Department of Neurology, University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Johannes Levin
- Department of Neurology, University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Günter U Höglinger
- Department of Neurology, University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Peter Bartenstein
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Paul Cumming
- Department of Nuclear Medicine, University of Bern, Inselspital Bern, Bern, Switzerland
- School of Psychology and Counselling and IHBI, Queensland University of Technology, Brisbane, Australia
| | - Axel Rominger
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
- Department of Nuclear Medicine, University of Bern, Inselspital Bern, Bern, Switzerland
| | - Matthias Brendel
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany.
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
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Kameyama M, Umeda-Kameyama Y. Applications of artificial intelligence in dementia. Geriatr Gerontol Int 2024; 24 Suppl 1:25-30. [PMID: 37916614 DOI: 10.1111/ggi.14709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/29/2023] [Accepted: 10/04/2023] [Indexed: 11/03/2023]
Abstract
The recent evolution of artificial intelligence (AI) can be considered life-changing. In particular, there is great interest in emerging hot topics in AI such as image classification and natural language processing. Our world has been revolutionized by convolutional neural networks and transformer for image classification and natural language processing, respectively. Moreover, these techniques can be used in the field of dementia. We introduce some applications of AI systems for treating and diagnosing dementia, including image-classification AI for recognizing facial features associated with dementia, image-classification AI for classifying leukoaraiosis in MRI images, object-detection AI for detecting microbleeding in MRI images, object-detection AI for support care, natural language-processing AI for detecting dementia within conversations, and natural language-processing AI for chatbots. Such AI technologies can significantly transform the future of dementia diagnosis and treatment. Geriatr Gerontol Int 2024; 24: 25-30.
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Affiliation(s)
- Masashi Kameyama
- AI and Theoretical Image Processing, Research Team for Neuroimaging, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
| | - Yumi Umeda-Kameyama
- Dementia Center, The University of Tokyo, Tokyo, Japan
- Department of Geriatric Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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Mattoli MV, Cocciolillo F, Chiacchiaretta P, Dotta F, Trevisi G, Carrarini C, Thomas A, Sensi S, Pizzi AD, Nicola ADD, Crosta AD, Mammarella N, Padovani A, Pilotto A, Moda F, Tiraboschi P, Martino G, Bonanni L. Combined 18F-FDG PET-CT markers in dementia with Lewy bodies. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12515. [PMID: 38145190 PMCID: PMC10746864 DOI: 10.1002/dad2.12515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/10/2023] [Accepted: 11/20/2023] [Indexed: 12/26/2023]
Abstract
INTRODUCTION 18F-Fluoro-deoxyglucose-positron emission tomography (FDG-PET) is a supportive biomarker in dementia with Lewy bodies (DLB) diagnosis and its advanced analysis methods, including radiomics and machine learning (ML), were developed recently. The aim of this study was to evaluate the FDG-PET diagnostic performance in predicting a DLB versus Alzheimer's disease (AD) diagnosis. METHODS FDG-PET scans were visually and semi-quantitatively analyzed in 61 patients. Radiomics and ML analyses were performed, building five ML models: (1) clinical features; (2) visual and semi-quantitative PET features; (3) radiomic features; (4) all PET features; and (5) overall features. RESULTS At follow-up, 34 patients had DLB and 27 had AD. At visual analysis, DLB PET signs were significantly more frequent in DLB, having the highest diagnostic accuracy (86.9%). At semi-quantitative analysis, the right precuneus, superior parietal, lateral occipital, and primary visual cortices showed significantly reduced uptake in DLB. The ML model 2 had the highest diagnostic accuracy (84.3%). DISCUSSION FDG-PET is a valuable tool in DLB diagnosis, having visual and semi-quantitative analyses with the highest diagnostic accuracy at ML analyses.
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Affiliation(s)
- Maria Vittoria Mattoli
- Department of NeuroscienceImaging and Clinical SciencesUniversity G. d'Annunzio of Chieti‐PescaraChietiItaly
- Nuclear Medicine UnitPresidio Ospedaliero Santo SpiritoPescaraItaly
| | - Fabrizio Cocciolillo
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed EmatologiaUOC di Medicina Nucleare, Fondazione Policlinico Universitario Agostino Gemelli IRCCSRomeItaly
| | - Piero Chiacchiaretta
- Department of Innovative Technologies in Medicine and DentistryUniversity G. d'Annunzio of Chieti – PescaraChietiItaly
- Advanced Computing Core, Center for Advanced Studies and Technology ‐ C.A.S.TUniversity G. d'Annunzio of Chieti – PescaraChietiItaly
| | - Francesco Dotta
- Department of Innovative Technologies in Medicine and DentistryUniversity G. d'Annunzio of Chieti – PescaraChietiItaly
| | - Gianluca Trevisi
- Department of NeuroscienceImaging and Clinical SciencesUniversity G. d'Annunzio of Chieti‐PescaraChietiItaly
| | - Claudia Carrarini
- Department of NeuroscienceCatholic University of Sacred HeartRomeItaly
- IRCCS San RaffaeleRomeItaly
| | - Astrid Thomas
- Department of NeuroscienceImaging and Clinical SciencesUniversity G. d'Annunzio of Chieti‐PescaraChietiItaly
| | - Stefano Sensi
- Department of NeuroscienceImaging and Clinical SciencesUniversity G. d'Annunzio of Chieti‐PescaraChietiItaly
| | - Andrea Delli Pizzi
- Department of Innovative Technologies in Medicine and DentistryUniversity G. d'Annunzio of Chieti – PescaraChietiItaly
| | | | - Adolfo Di Crosta
- Department of Psychological ScienceHumanities and TerritoryUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Department of Medicine and Aging SciencesUniversity G d'Annunzio of Chieti‐PescaraChietiItaly
| | - Nicola Mammarella
- Department of Psychological ScienceHumanities and TerritoryUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
| | - Andrea Pilotto
- Neurology Unit, Department of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
- Parkinson's Disease Rehabilitation CentreFERB ONLUS‐S. Isidoro HospitalTrescore BalnearioBergamoItaly
| | - Fabio Moda
- Division of Neurology 5 and NeuropathologyFondazione IRCCS Istituto Neurologico Carlo BestaMilanItaly
| | - Pietro Tiraboschi
- Division of Neurology 5 and NeuropathologyFondazione IRCCS Istituto Neurologico Carlo BestaMilanItaly
| | - Gianluigi Martino
- Department of Radiological Sciences, Nuclear Medicine UniteSS. Annunziata HospitalVia dei Vestini 31ChietiItaly
| | - Laura Bonanni
- Department of Medicine and Aging SciencesUniversity G d'Annunzio of Chieti‐PescaraChietiItaly
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Štokelj E, Tomše P, Tomanič T, Dhawan V, Eidelberg D, Trošt M, Simončič U. Effect of the identification group size and image resolution on the diagnostic performance of metabolic Alzheimer's disease-related pattern. EJNMMI Res 2023; 13:47. [PMID: 37222957 DOI: 10.1186/s13550-023-01001-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Accepted: 05/16/2023] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND Alzheimer's disease-related pattern (ADRP) is a metabolic brain biomarker of Alzheimer's disease (AD). While ADRP is being introduced into research, the effect of the size of the identification cohort and the effect of the resolution of identification and validation images on ADRP's performance need to be clarified. METHODS 240 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography images [120 AD/120 cognitive normals (CN)] were selected from the Alzheimer's disease neuroimaging initiative database. A total of 200 images (100 AD/100 CN) were used to identify different versions of ADRP using a scaled subprofile model/principal component analysis. For this purpose, five identification groups were randomly selected 25 times. The identification groups differed in the number of images (20 AD/20 CN, 30 AD/30 CN, 40 AD/40 CN, 60 AD/60 CN, and 80 AD/80 CN) and image resolutions (6, 8, 10, 12, 15 and 20 mm). A total of 750 ADRPs were identified and validated through the area under the curve (AUC) values on the remaining 20 AD/20 CN with six different image resolutions. RESULTS ADRP's performance for the differentiation between AD patients and CN demonstrated only a marginal average AUC increase, when the number of subjects in the identification group increases (AUC increase for about 0.03 from 20 AD/20 CN to 80 AD/80 CN). However, the average of the lowest five AUC values increased with the increasing number of participants (AUC increase for about 0.07 from 20 AD/20 CN to 30 AD/30 CN and for an additional 0.02 from 30 AD/30 CN to 40 AD/40 CN). The resolution of the identification images affects ADRP's diagnostic performance only marginally in the range from 8 to 15 mm. ADRP's performance stayed optimal even when applied to validation images of resolution differing from the identification images. CONCLUSIONS While small (20 AD/20 CN images) identification cohorts may be adequate in a favorable selection of cases, larger cohorts (at least 30 AD/30 CN images) shall be preferred to overcome possible/random biological differences and improve ADRP's diagnostic performance. ADRP's performance stays stable even when applied to the validation images with a resolution different than the resolution of the identification ones.
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Affiliation(s)
- Eva Štokelj
- Faculty of Mathematics and Physics, University of Ljubljana, Jadranska ulica 19, 1000, Ljubljana, Slovenia.
| | - Petra Tomše
- Department of Nuclear Medicine, University Medical Centre Ljubljana, Zaloška cesta 7, 1000, Ljubljana, Slovenia
| | - Tadej Tomanič
- Faculty of Mathematics and Physics, University of Ljubljana, Jadranska ulica 19, 1000, Ljubljana, Slovenia
| | - Vijay Dhawan
- Center for Neurosciences, The Feinstein Institute for Medical Research, 350 Community Dr, Manhasset, NY, 11030, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institute for Medical Research, 350 Community Dr, Manhasset, NY, 11030, USA
| | - Maja Trošt
- Department of Nuclear Medicine, University Medical Centre Ljubljana, Zaloška cesta 7, 1000, Ljubljana, Slovenia
- Department of Neurology, University Medical Centre Ljubljana, Zaloška cesta 2, 1000, Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000, Ljubljana, Slovenia
| | - Urban Simončič
- Faculty of Mathematics and Physics, University of Ljubljana, Jadranska ulica 19, 1000, Ljubljana, Slovenia
- Jožef Stefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia
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Meng M, Liu F, Ma Y, Qin W, Guo L, Peng S, Gordon ML, Wang Y, Zhang N. The identification and cognitive correlation of perfusion patterns measured with arterial spin labeling MRI in Alzheimer's disease. Alzheimers Res Ther 2023; 15:75. [PMID: 37038198 PMCID: PMC10088108 DOI: 10.1186/s13195-023-01222-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 03/28/2023] [Indexed: 04/12/2023]
Abstract
BACKGROUND Vascular dysfunction, including cerebral hypoperfusion, plays an important role in the pathogenesis and progression of Alzheimer's disease (AD), independent of amyloid and tau pathology. We established an AD-related perfusion pattern (ADRP) measured with arterial spin labeling (ASL) MRI using multivariate spatial covariance analysis. METHODS We obtained multimodal MRI including pseudo-continuous ASL and neurocognitive testing in a total of 55 patients with a diagnosis of mild to moderate AD supported by amyloid PET and 46 normal controls (NCs). An ADRP was established from an identification cohort of 32 patients with AD and 32 NCs using a multivariate analysis method based on scaled subprofile model/principal component analysis, and pattern expression in individual subjects was quantified for both the identification cohort and a validation cohort (23 patients with AD and 14 NCs). Subject expression score of the ADRP was then used to assess diagnostic accuracy and cognitive correlations in AD patients and compared with global and regional cerebral blood flow (CBF) in specific areas identified from voxel-based univariate analysis. RESULTS The ADRP featured negative loading in the bilateral middle and posterior cingulate and precuneus, inferior parietal lobule, and frontal areas, and positive loading in the right cerebellum and bilateral basal areas. Subject expression score of the ADRP was significantly elevated in AD patients compared with NCs (P < 0.001) and showed good diagnostic accuracy for AD with area under receiver-operator curve of 0.87 [95% CI (0.78-0.96)] in the identification cohort and 0.85 in the validation cohort. Moreover, there were negative correlations between subject expression score and global cognitive function and performance in various cognitive domains in patients with AD. The characteristics of the ADRP topography and subject expression scores were supported by analogous findings obtained with regional CBF. CONCLUSIONS We have reported a characteristic perfusion pattern associated with AD using ASL MRI. Subject expression score of this spatial covariance pattern is a promising MRI biomarker for the identification and monitoring of AD.
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Affiliation(s)
- Meng Meng
- Department of Neurology, Tianjin Medical University General Hospital Airport Site, Tianjin, China
| | - Fang Liu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, 154, Anshan Road, Tianjin, 300052, China
| | - Yilong Ma
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
- Department of Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra-Northwell, Hofstra University, Hempstead, NY, USA
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Lining Guo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Shichun Peng
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Marc L Gordon
- The Litwin-Zucker Research Center, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
- Departments of Neurology and Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra-Northwell, Hofstra University, Hempstead, NY, USA
| | - Yue Wang
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, 154, Anshan Road, Tianjin, 300052, China
| | - Nan Zhang
- Department of Neurology, Tianjin Medical University General Hospital Airport Site, Tianjin, China.
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, 154, Anshan Road, Tianjin, 300052, China.
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7
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Perovnik M, Rus T, Schindlbeck KA, Eidelberg D. Functional brain networks in the evaluation of patients with neurodegenerative disorders. Nat Rev Neurol 2023; 19:73-90. [PMID: 36539533 DOI: 10.1038/s41582-022-00753-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/21/2022] [Indexed: 12/24/2022]
Abstract
Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as a marker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient's scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations.
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Affiliation(s)
- Matej Perovnik
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia.,Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Tomaž Rus
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia.,Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | | | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, USA.
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8
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Habich A, Wahlund LO, Westman E, Dierks T, Ferreira D. (Dis-)Connected Dots in Dementia with Lewy Bodies-A Systematic Review of Connectivity Studies. Mov Disord 2023; 38:4-15. [PMID: 36253921 PMCID: PMC10092805 DOI: 10.1002/mds.29248] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/04/2022] [Accepted: 09/12/2022] [Indexed: 01/21/2023] Open
Abstract
Studies on dementia with Lewy bodies (DLB) have mainly focused on the degeneration of distinct cortical and subcortical regions related to the deposition of Lewy bodies. In view of the proposed trans-synaptic spread of the α-synuclein pathology, investigating the disease only in this segregated fashion would be detrimental to our understanding of its progression. In this systematic review, we summarize findings on structural and functional brain connectivity in DLB, as connectivity measures may offer better insights on how the brain is affected by the spread of the pathology. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched Web of Science, PubMed, and SCOPUS for relevant articles published up to November 1, 2021. Of 1215 identified records, we selected and systematically reviewed 53 articles that compared connectivity features between patients with DLB and healthy controls. Structural and functional magnetic resonance imaging, positron emission tomography, single-positron emission computer tomography, and electroencephalography assessments of patients revealed widespread abnormalities within and across brain networks in DLB. Frontoparietal, default mode, and visual networks and their connections to other brain regions featured the most consistent disruptions, which were also associated with core clinical features and cognitive impairments. Furthermore, graph theoretical measures revealed disease-related decreases in local and global network efficiency. This systematic review shows that structural and functional connectivity characteristics in DLB may be particularly valuable at early stages, before overt brain atrophy can be observed. This knowledge may help improve the diagnosis and prognosis in DLB as well as pinpoint targets for future disease-modifying treatments. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Annegret Habich
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Lars-Olof Wahlund
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Eric Westman
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Thomas Dierks
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Daniel Ferreira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
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9
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van Veen R, Meles SK, Renken RJ, Reesink FE, Oertel WH, Janzen A, de Vries GJ, Leenders KL, Biehl M. FDG-PET combined with learning vector quantization allows classification of neurodegenerative diseases and reveals the trajectory of idiopathic REM sleep behavior disorder. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 225:107042. [PMID: 35970056 DOI: 10.1016/j.cmpb.2022.107042] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 07/11/2022] [Accepted: 07/27/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVES 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with principal component analysis (PCA) has been applied to identify disease-related brain patterns in neurodegenerative disorders such as Parkinson's disease (PD), Dementia with Lewy Bodies (DLB) and Alzheimer's disease (AD). These patterns are used to quantify functional brain changes at the single subject level. This is especially relevant in determining disease progression in idiopathic REM sleep behavior disorder (iRBD), a prodromal stage of PD and DLB. However, the PCA method is limited in discriminating between neurodegenerative conditions. More advanced machine learning algorithms may provide a solution. In this study, we apply Generalized Matrix Learning Vector Quantization (GMLVQ) to FDG-PET scans of healthy controls, and patients with AD, PD and DLB. Scans of iRBD patients, scanned twice with an approximate 4 year interval, were projected into GMLVQ space to visualize their trajectory. METHODS We applied a combination of SSM/PCA and GMLVQ as a classifier on FDG-PET data of healthy controls, AD, DLB, and PD patients. We determined the diagnostic performance by performing a ten times repeated ten fold cross validation. We analyzed the validity of the classification system by inspecting the GMLVQ space. First by the projection of the patients into this space. Second by representing the axis, that span this decision space, into a voxel map. Furthermore, we projected a cohort of RBD patients, whom have been scanned twice (approximately 4 years apart), into the same decision space and visualized their trajectories. RESULTS The GMLVQ prototypes, relevance diagonal, and decision space voxel maps showed metabolic patterns that agree with previously identified disease-related brain patterns. The GMLVQ decision space showed a plausible quantification of FDG-PET data. Distance traveled by iRBD subjects through GMLVQ space per year (i.e. velocity) was correlated with the change in motor symptoms per year (Spearman's rho =0.62, P=0.004). CONCLUSION In this proof-of-concept study, we show that GMLVQ provides a classification of patients with neurodegenerative disorders, and may be useful in future studies investigating speed of progression in prodromal disease stages.
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Affiliation(s)
- Rick van Veen
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, the Netherlands; Data Science Department, Software Competence Center Hagenberg, Hagenberg, Austria.
| | - Sanne K Meles
- Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Remco J Renken
- Department of Biomedical Sciences of Cells & Systems, University of Groningen, University Medical Center Groningen, Cognitive Neuroscience Center, Groningen, the Netherlands
| | - Fransje E Reesink
- Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Wolfgang H Oertel
- Department of Neurology, Philipps-Universität Marburg, Marburg, Germany; Institute for Neurogenomics, Helmholtz Center for Health and Environment, Munich, Germany
| | - Annette Janzen
- Department of Neurology, Philipps-Universität Marburg, Marburg, Germany
| | | | - Klaus L Leenders
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Michael Biehl
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, the Netherlands; SMQB, Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, Birmingham, United Kingdom
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10
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Jiang J, Sheng C, Chen G, Liu C, Jin S, Li L, Jiang X, Han Y. Glucose metabolism patterns: A potential index to characterize brain ageing and predict high conversion risk into cognitive impairment. GeroScience 2022; 44:2319-2336. [PMID: 35581512 PMCID: PMC9616982 DOI: 10.1007/s11357-022-00588-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 05/07/2022] [Indexed: 12/28/2022] Open
Abstract
Exploring individual hallmarks of brain ageing is important. Here, we propose the age-related glucose metabolism pattern (ARGMP) as a potential index to characterize brain ageing in cognitively normal (CN) elderly people. We collected 18F-fluorodeoxyglucose (18F-FDG) PET brain images from two independent cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 127) and the Xuanwu Hospital of Capital Medical University, Beijing, China (N = 84). During follow-up (mean 80.60 months), 23 participants in the ADNI cohort converted to cognitive impairment. ARGMPs were identified using the scaled subprofile model/principal component analysis method, and cross-validations were conducted in both independent cohorts. A survival analysis was further conducted to calculate the predictive effect of conversion risk by using ARGMPs. The results showed that ARGMPs were characterized by hypometabolism with increasing age primarily in the bilateral medial superior frontal gyrus, anterior cingulate and paracingulate gyri, caudate nucleus, and left supplementary motor area and hypermetabolism in part of the left inferior cerebellum. The expression network scores of ARGMPs were significantly associated with chronological age (R = 0.808, p < 0.001), which was validated in both the ADNI and Xuanwu cohorts. Individuals with higher network scores exhibited a better predictive effect (HR: 0.30, 95% CI: 0.1340 ~ 0.6904, p = 0.0068). These findings indicate that ARGMPs derived from CN participants may represent a novel index for characterizing brain ageing and predicting high conversion risk into cognitive impairment.
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Affiliation(s)
- Jiehui Jiang
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, 200444, China.
| | - Can Sheng
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Guanqun Chen
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Chunhua Liu
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, 200444, China
| | - Shichen Jin
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, 200444, China
| | - Lanlan Li
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, 200444, China
| | - Xueyan Jiang
- School of Biomedical Engineering, Hainan University, Haikou, 570228, China
- German Centre for Neurodegenerative Disease, Clinical Research Group, Venusberg Campus 1, 53121, Bonn, Germany
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
- School of Biomedical Engineering, Hainan University, Haikou, 570228, China.
- Centre of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China.
- National Clinical Research Centre for Geriatric Disorders, Beijing, 100053, China.
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11
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Perovnik M, Tomše P, Jamšek J, Tang C, Eidelberg D, Trošt M. Metabolic brain pattern in dementia with Lewy bodies: Relationship to Alzheimer's disease topography. Neuroimage Clin 2022; 35:103080. [PMID: 35709556 PMCID: PMC9207351 DOI: 10.1016/j.nicl.2022.103080] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/26/2022] [Accepted: 06/05/2022] [Indexed: 10/28/2022]
Abstract
PURPOSE Dementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia, that shares clinical and metabolic similarities with both Alzheimer's and Parkinson's disease. In this study we aimed to identify a DLB-related pattern (DLBRP), study its relationship with other metabolic brain patterns and explore its diagnostic and prognostic value. METHODS A cohort of 79 participants with DLB, 63 with dementia due to Alzheimer's disease (AD) and 41 normal controls (NCs) and their 2-[18F]FDG PET scans were analysed for identification and validation of DLBRP. Voxel-wise correlation and multiple linear regression were used to study the relation between DLBRP and Alzheimer's disease-related pattern (ADRP), Parkinson's disease-related pattern (PDRP) and PD-related cognitive pattern (PDCP). Diagnostic and prognostic value of DLBRP and of modified DLBRP after accounting for ADRP overlap (DLBRP ⊥ ADRP), were explored. RESULTS The newly identified DLBRP shared topographic similarities with ADRP (R2 = 24%) and PDRP (R2 = 37%), but not with PDCP. We could accurately discriminate between DLB and NC (AUC = 0.99) based on DLBRP expression, and between DLB and AD (AUC = 0.87) based on DLBRP ⊥ ADRP expression. DLBRP expression correlated with cognitive impairment, but the correlation was lost after accounting for ADRP overlap. DLBRP and DLBRP ⊥ ADRP correlated with patients' survival time. CONCLUSION DLBRP has proven to be a specific metabolic brain biomarker of DLB, sharing similarities with ADRP and PDRP, but not PDCP. We observed a similar metabolic mechanism underlying cognitive impairment in DLB and AD. DLB-specific metabolic changes were more detrimental for overall survival.
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Affiliation(s)
- Matej Perovnik
- Department of Neurology, University Medical Center Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia; Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia.
| | - Petra Tomše
- Department of Nuclear Medicine, University Medical Center Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
| | - Jan Jamšek
- Department of Nuclear Medicine, University Medical Center Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
| | - Chris Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY 11030, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY 11030, USA
| | - Maja Trošt
- Department of Neurology, University Medical Center Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia; Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia; Department of Nuclear Medicine, University Medical Center Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
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12
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AIM in Neurodegenerative Diseases: Parkinson and Alzheimer. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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13
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Ma WY, Tian MJ, Yao Q, Li Q, Tang FY, Xiao CY, Shi JP, Chen J. Neuroimaging alterations in dementia with Lewy bodies and neuroimaging differences between dementia with Lewy bodies and Alzheimer's disease: An activation likelihood estimation meta-analysis. CNS Neurosci Ther 2021; 28:183-205. [PMID: 34873859 PMCID: PMC8739049 DOI: 10.1111/cns.13775] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 11/07/2021] [Accepted: 11/21/2021] [Indexed: 12/11/2022] Open
Abstract
Aims The aim of this study was to identify brain regions with local, structural, and functional abnormalities in dementia with Lewy bodies (DLB) and uncover the differences between DLB and Alzheimer's disease (AD). The neural networks involved in the identified abnormal brain regions were further described. Methods PubMed, Web of Science, OVID, Science Direct, and Cochrane Library databases were used to identify neuroimaging studies that included DLB versus healthy controls (HCs) or DLB versus AD. The coordinate‐based meta‐analysis and functional meta‐analytic connectivity modeling were performed using the activation likelihood estimation algorithm. Results Eleven structural studies and fourteen functional studies were included in this quantitative meta‐analysis. DLB patients showed a dysfunction in the bilateral inferior parietal lobule and right lingual gyrus compared with HC patients. DLB patients showed a relative preservation of the medial temporal lobe and a tendency of lower metabolism in the right lingual gyrus compared with AD. The frontal‐parietal, salience, and visual networks were all abnormally co‐activated in DLB, but the default mode network remained normally co‐activated compared with AD. Conclusions The convergence of local brain regions and co‐activation neural networks might be potential specific imaging markers in the diagnosis of DLB. This might provide a pathway for the neural regulation in DLB patients, and it might contribute to the development of specific interventions for DLB and AD.
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Affiliation(s)
- Wen-Ying Ma
- Department of Neurology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Min-Jie Tian
- Department of Neurology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Qun Yao
- Department of Neurology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Qian Li
- Department of Neurology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Fan-Yu Tang
- Department of Neurology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Chao-Yong Xiao
- Department of Radiology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jing-Ping Shi
- Department of Neurology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.,Institute of Brain Functional Imaging, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jiu Chen
- Institute of Neuropsychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.,Institute of Brain Functional Imaging, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
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14
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Update on neuroimaging in non-Alzheimer's disease dementia: a focus on the Lewy body disease spectrum. Curr Opin Neurol 2021; 34:532-538. [PMID: 34227573 DOI: 10.1097/wco.0000000000000958] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
PURPOSE OF REVIEW An accurate differential diagnosis between Alzheimer's disease (AD) and non-AD dementia is of paramount importance to study disease mechanisms, define prognosis, and select patients for disease-specific treatments. The purpose of the present review is to describe the most recent neuroimaging studies in Lewy body disease spectrum (LBDS), focusing on differences with AD. RECENT FINDINGS Different neuroimaging methods are used to investigate patterns of alterations, which can be helpful to distinguish LBDS from AD. Positron emission tomography radiotracers and advanced MRI structural and functional methods discriminate these two conditions with increasing accuracy. Prodromal disease stages can be identified, allowing an increasingly earlier diagnosis. SUMMARY Neuroimaging biomarkers can aid in obtaining the best diagnostic accuracy in LBDS. Despite the main role of neuroimaging in clinical setting is to exclude secondary causes of dementia, structural and metabolic imaging techniques give an essential help to study in-vivo pathophysiological mechanisms of diseases. The importance of neuroimaging in LBDS is given by the increasing number of imaging biomarker developed and studied in the last years.
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15
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Davids J, Ashrafian H. AIM in Neurodegenerative Diseases: Parkinson and Alzheimer. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_190-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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