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Qi W, Zhu X, He D, Wang B, Cao S, Dong C, Li Y, Chen Y, Wang B, Shi Y, Jiang G, Liu F, Boots LMM, Li J, Lou X, Yao J, Lu X, Kang J. Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis. J Med Internet Res 2024; 26:e57830. [PMID: 39116438 PMCID: PMC11342017 DOI: 10.2196/57830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 05/04/2024] [Accepted: 06/25/2024] [Indexed: 08/10/2024] Open
Abstract
BACKGROUND With the rise of artificial intelligence (AI) in the field of dementia biomarker research, exploring its current developmental trends and research focuses has become increasingly important. This study, using literature data mining, analyzes and assesses the key contributions and development scale of AI in dementia biomarker research. OBJECTIVE The aim of this study was to comprehensively evaluate the current state, hot topics, and future trends of AI in dementia biomarker research globally. METHODS This study thoroughly analyzed the literature in the application of AI to dementia biomarkers across various dimensions, such as publication volume, authors, institutions, journals, and countries, based on the Web of Science Core Collection. In addition, scales, trends, and potential connections between AI and biomarkers were extracted and deeply analyzed through multiple expert panels. RESULTS To date, the field includes 1070 publications across 362 journals, involving 74 countries and 1793 major research institutions, with a total of 6455 researchers. Notably, 69.41% (994/1432) of the researchers ceased their studies before 2019. The most prevalent algorithms used are support vector machines, random forests, and neural networks. Current research frequently focuses on biomarkers such as imaging biomarkers, cerebrospinal fluid biomarkers, genetic biomarkers, and blood biomarkers. Recent advances have highlighted significant discoveries in biomarkers related to imaging, genetics, and blood, with growth in studies on digital and ophthalmic biomarkers. CONCLUSIONS The field is currently in a phase of stable development, receiving widespread attention from numerous countries, institutions, and researchers worldwide. Despite this, stable clusters of collaborative research have yet to be established, and there is a pressing need to enhance interdisciplinary collaboration. Algorithm development has shown prominence, especially the application of support vector machines and neural networks in imaging studies. Looking forward, newly discovered biomarkers are expected to undergo further validation, and new types, such as digital biomarkers, will garner increased research interest and attention.
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Affiliation(s)
- Wenhao Qi
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Xiaohong Zhu
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Danni He
- School of Nursing, Hangzhou Normal University, Hangzhou, China
- Nursing Department, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Bin Wang
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Shihua Cao
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Chaoqun Dong
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Yunhua Li
- College of Education, Chengdu College of Arts and Sciences, Sichuan, China
| | - Yanfei Chen
- School of Nursing, Hangzhou Normal University, Hangzhou, China
- Nursing Department, Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Bingsheng Wang
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Yankai Shi
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Guowei Jiang
- Department of Psychiatry and Neuropsychology and Alzheimer Center Limburg, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, Netherlands
| | - Fang Liu
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
| | - Lizzy M M Boots
- Department of Psychiatry and Neuropsychology and Alzheimer Center Limburg, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, Netherlands
| | - Jiaqi Li
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Xiajing Lou
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Jiani Yao
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Xiaodong Lu
- Department of Neurology, Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Junling Kang
- Department of Neurology, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
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Li W, Sun L, Yue L, Xiao S. Diagnostic and predictive power of plasma proteins in Alzheimer's disease: a cross-sectional and longitudinal study in China. Sci Rep 2024; 14:17557. [PMID: 39080359 PMCID: PMC11289122 DOI: 10.1038/s41598-024-66195-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 06/28/2024] [Indexed: 08/02/2024] Open
Abstract
Convenient and effective biomarkers are essential for the early diagnosis and treatment of Alzheimer's disease (AD). In the cross-sectional study, 103 patients with AD, 82 patients with aMCI and 508 normal controls (NC) were enrolled. The single-molecule array (Simoa) technique was used to assess the levels of plasma proteins, including NfL, T-tau, P-tau-181, Aβ40, Aβ42. Montreal Cognitive Assessment (MoCA) was used to assess the overall cognitive function of all subjects. Moreover, Amyloid PET and structural head MRI were also performed in a subset of the population. In the follow-up, the previous 508 normal older adults were followed up for two years, then COX regression analysis was used to investigate the association between baseline plasma proteins and future cognitive outcomes. NfL, T-tau, P-tau-181, Aβ40, Aβ42 and Aβ42/40 were altered in AD dementia, and NfL, Aβ42 and Aβ42/40 significantly outperformed all plasma proteins in differentiating AD dementia from NC, while NfL and Aβ42/40 could effectively distinguish between aMCI and NC. However, only plasma NfL was associated with future cognitive decline, and it was negatively correlated with MoCA (r = - 0.298, p < 0.001) and the volume of the left globus pallidus (r = - 0.278, p = 0.033). Plasma NfL can help distinguish between cognitively normal and cognitively impaired individuals (MCI/dementia) at the syndrome level. However, since we have not introduced other biomarkers for AD, such as PET CT or cerebrospinal fluid, and have not verified in other neurodegenerative diseases, whether plasma NFL can be used as a biomarker for AD needs to be further studied and explored.
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Affiliation(s)
- Wei Li
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
- Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Lin Sun
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
- Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Ling Yue
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
- Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China.
| | - Shifu Xiao
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
- Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China.
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3
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Upadhyay N, Spottke A, Schneider A, Hoffmann DC, Frommann I, Ballarini T, Fliessbach K, Bender B, Heekeren HR, Haynes JD, Ewers M, Düzel E, Glanz W, Dobisch L, Buerger K, Janowitz D, Levin J, Danek A, Teipel S, Kilimann I, Synofzik M, Wilke C, Peters O, Preis L, Priller J, Spruth EJ, Jessen F, Boecker H. Fronto-striatal alterations correlate with apathy severity in behavioral variant frontotemporal dementia. Brain Imaging Behav 2024; 18:66-72. [PMID: 37855956 PMCID: PMC10844138 DOI: 10.1007/s11682-023-00812-3] [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] [Accepted: 10/09/2023] [Indexed: 10/20/2023]
Abstract
Structural and functional changes in cortical and subcortical regions have been reported in behavioral variant frontotemporal dementia (bvFTD), however, a multimodal approach may provide deeper insights into the neural correlates of neuropsychiatric symptoms. In this multicenter study, we measured cortical thickness (CTh) and subcortical volumes to identify structural abnormalities in 37 bvFTD patients, and 37 age- and sex-matched healthy controls. For seed regions with significant structural changes, whole-brain functional connectivity (FC) was examined in a sub-cohort of N = 22 bvFTD and N = 22 matched control subjects to detect complementary alterations in brain network organization. To explore the functional significance of the observed structural and functional deviations, correlations with clinical and neuropsychological outcomes were tested where available. Significantly decreased CTh was observed in the bvFTD group in caudal middle frontal gyrus, left pars opercularis, bilateral superior frontal and bilateral middle temporal gyrus along with subcortical volume reductions in bilateral basal ganglia, thalamus, hippocampus, and amygdala. Resting-state functional magnetic resonance imaging showed decreased FC in bvFTD between: dorsal striatum and left caudal middle frontal gyrus; putamen and fronto-parietal regions; pallidum and cerebellum. Conversely, bvFTD showed increased FC between: left middle temporal gyrus and paracingulate gyrus; caudate nucleus and insula; amygdala and parahippocampal gyrus. Additionally, cortical thickness in caudal, lateral and superior frontal regions as well as caudate nucleus volume correlated negatively with apathy severity scores of the Neuropsychiatry Inventory Questionnaire. In conclusion, multimodal structural and functional imaging indicates that fronto-striatal regions have a considerable influence on the severity of apathy in bvFTD.
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Affiliation(s)
- Neeraj Upadhyay
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany.
- Clinical Functional Imaging Lab, Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany.
| | - Annika Spottke
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
- Department of Neurology, University Hospital Bonn, Bonn, Germany
| | - Anja Schneider
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Daniel C Hoffmann
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
| | - Ingo Frommann
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Tommaso Ballarini
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
| | - Klaus Fliessbach
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry/Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Benjamin Bender
- Department of Diagnostic and Interventional Neuroradiology, University of Tuebingen, Tuebingen, Germany
| | - Hauke R Heekeren
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
- Center for Cognitive Neuroscience Berlin, Freie Universität Berlin, Berlin, Germany
| | - John Dylan Haynes
- Bernstein Center for Computational Neuroscience, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Michael Ewers
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Munich, Germany
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Emrah Düzel
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Wenzel Glanz
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Magdeburg, Germany
| | - Laura Dobisch
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Magdeburg, Germany
| | - Katharina Buerger
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Munich, Germany
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Johannes Levin
- Department of Neurology, University Hospital of Munich, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Adrian Danek
- Department of Neurology, University Hospital of Munich, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Stefan Teipel
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Ingo Kilimann
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Matthis Synofzik
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Tübingen, Germany
- Division Translational Genomics of Neurodegenerative Diseases, Center for Neurology and Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Carlo Wilke
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Tübingen, Germany
- Division Translational Genomics of Neurodegenerative Diseases, Center for Neurology and Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Oliver Peters
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Berlin, Germany
- Department of Psychiatry, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Lukas Preis
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Berlin, Germany
- Department of Psychiatry, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Josef Priller
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
- Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
| | - Eike Jakob Spruth
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Frank Jessen
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Henning Boecker
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
- Clinical Functional Imaging Lab, Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
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4
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Ren X, Dong B, Luan Y, Wu Y, Huang Y. Alterations via inter-regional connective relationships in Alzheimer's disease. Front Hum Neurosci 2023; 17:1276994. [PMID: 38021241 PMCID: PMC10672243 DOI: 10.3389/fnhum.2023.1276994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Disruptions in the inter-regional connective correlation within the brain are believed to contribute to memory impairment. To detect these corresponding correlation networks in Alzheimer's disease (AD), we conducted three types of inter-regional correlation analysis, including structural covariance, functional connectivity and group-level independent component analysis (group-ICA). The analyzed data were obtained from the Alzheimer's Disease Neuroimaging Initiative, comprising 52 cognitively normal (CN) participants without subjective memory concerns, 52 individuals with late mild cognitive impairment (LMCI) and 52 patients with AD. We firstly performed vertex-wise cortical thickness analysis to identify brain regions with cortical thinning in AD and LMCI patients using structural MRI data. These regions served as seeds to construct both structural covariance networks and functional connectivity networks for each subject. Additionally, group-ICA was performed on the functional data to identify intrinsic brain networks at the cohort level. Through a comparison of the structural covariance and functional connectivity networks with ICA networks, we identified several inter-regional correlation networks that consistently exhibited abnormal connectivity patterns among AD and LMCI patients. Our findings suggest that reduced inter-regional connectivity is predominantly observed within a subnetwork of the default mode network, which includes the posterior cingulate and precuneus regions, in both AD and LMCI patients. This disruption of connectivity between key nodes within the default mode network provides evidence supporting the hypothesis that impairments in brain networks may contribute to memory deficits in AD and LMCI.
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Affiliation(s)
- Xiaomei Ren
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Bowen Dong
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Ying Luan
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ye Wu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Yunzhi Huang
- Institute for AI in Medicine, School of Artificial Intelligence (School of Future Technology), Nanjing University of Information Science and Technology, Nanjing, China
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5
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Eldaief MC, Brickhouse M, Katsumi Y, Rosen H, Carvalho N, Touroutoglou A, Dickerson BC. Atrophy in behavioural variant frontotemporal dementia spans multiple large-scale prefrontal and temporal networks. Brain 2023; 146:4476-4485. [PMID: 37201288 PMCID: PMC10629759 DOI: 10.1093/brain/awad167] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 04/10/2023] [Accepted: 04/16/2023] [Indexed: 05/20/2023] Open
Abstract
The identification of a neurodegenerative disorder's distributed pattern of atrophy-or atrophy 'signature'-can lend insights into the cortical networks that degenerate in individuals with specific constellations of symptoms. In addition, this signature can be used as a biomarker to support early diagnoses and to potentially reveal pathological changes associated with said disorder. Here, we characterized the cortical atrophy signature of behavioural variant frontotemporal dementia (bvFTD). We used a data-driven approach to estimate cortical thickness using surface-based analyses in two independent, sporadic bvFTD samples (n = 30 and n = 71, total n = 101), using age- and gender-matched cognitively and behaviourally normal individuals. We found highly similar patterns of cortical atrophy across the two independent samples, supporting the reliability of our bvFTD signature. Next, we investigated whether our bvFTD signature targets specific large-scale cortical networks, as is the case for other neurodegenerative disorders. We specifically asked whether the bvFTD signature topographically overlaps with the salience network, as previous reports have suggested. We hypothesized that because phenotypic presentations of bvFTD are diverse, this would not be the case, and that the signature would cross canonical network boundaries. Consistent with our hypothesis, the bvFTD signature spanned rostral portions of multiple networks, including the default mode, limbic, frontoparietal control and salience networks. We then tested whether the signature comprised multiple anatomical subtypes, which themselves overlapped with specific networks. To explore this, we performed a hierarchical clustering analysis. This yielded three clusters, only one of which extensively overlapped with a canonical network (the limbic network). Taken together, these findings argue against the hypothesis that the salience network is preferentially affected in bvFTD, but rather suggest that-at least in patients who meet diagnostic criteria for the full-blown syndrome-neurodegeneration in bvFTD encompasses a distributed set of prefrontal, insular and anterior temporal nodes of multiple large-scale brain networks, in keeping with the phenotypic diversity of this disorder.
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Affiliation(s)
- Mark C Eldaief
- Frontotemporal Disorders Unit and Alzheimer’s Disease Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Center for Brain Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Michael Brickhouse
- Frontotemporal Disorders Unit and Alzheimer’s Disease Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Yuta Katsumi
- Frontotemporal Disorders Unit and Alzheimer’s Disease Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Howard Rosen
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Nicole Carvalho
- Frontotemporal Disorders Unit and Alzheimer’s Disease Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Alexandra Touroutoglou
- Frontotemporal Disorders Unit and Alzheimer’s Disease Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Bradford C Dickerson
- Frontotemporal Disorders Unit and Alzheimer’s Disease Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
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6
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Macchitella L, Tosi G, Giaquinto F, Iaia M, Rizzi E, Chiarello Y, Bertoux M, Angelelli P, Romano DL. Genuine Memory Deficits as Assessed by the Free and Cued Selective Reminding Test (FCSRT) in the Behavioural Variant of Frontotemporal Dementia. A Systematic Review and Meta-analysis Study. Neuropsychol Rev 2023:10.1007/s11065-023-09613-3. [PMID: 37736861 DOI: 10.1007/s11065-023-09613-3] [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: 06/29/2022] [Accepted: 08/07/2023] [Indexed: 09/23/2023]
Abstract
The current diagnostic criteria for the behavioural variant of frontotemporal dementia (bvFTD) foresee a relative sparing of long-term memory. Although bvFTD patients were thought to report secondary memory deficits associated with prefrontal dysfunctions, some studies indicated the presence of a "genuine memory deficit" related to mesial temporal lobe dysfunctions. Among various neuropsychological tests, the Free and Cue Selective Reminding Test (FCSRT) has been recommended to distinguish genuine from apparent amnesia. We conducted a systematic review and a random effect Bayesian meta-analysis to evaluate the nature and severity of memory deficit in bvFTD. Our objective was to determine whether the existing literature offers evidence of genuine or apparent amnesia in patients with bvFTD, as assessed via the FCSRT. On 06/19/2021, we conducted a search across four databases (PMC, Scopus, Web of Science, and PubMed). We included all studies that evaluated memory performance using the FCSRT in patients with bvFTD, as long as they also included either cognitively unimpaired participants or AD groups. We tested publication bias through the Funnel plot and Egger's test. To assess the quality of studies, we used the Newcastle-Ottawa quality assessment scale adapted for cross-sectional studies. We included 16 studies in the meta-analysis. The results showed that bvFTD patients perform better than AD patients (pooled effects between 0.95 and 1.14), as their memory performance stands between AD and control groups (pooled effects between - 2.19 and - 1.25). Moreover, patients with bvFTD present both genuine and secondary memory disorders. As a major limitation of this study, due to our adoption of a rigorous methodology and stringent inclusion criteria, we ended up with just 16 studies. Nonetheless, our robust findings can contribute to the ongoing discussion on international consensus criteria for bvFTD and the selection of appropriate neuropsychological tools to facilitate the differential diagnosis between AD and bvFTD.
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Affiliation(s)
- Luigi Macchitella
- Department of Human and Social Sciences, University of Salento, Piazza Tancredi 7, 73100, Lecce, Italy
- Scientific Institute I.R.C.C.S. "E. Medea", Unit for Severe Disabilities in Developmental Age and Young Adults (Developmental Neurology and Neurorehabilitation), Piazza Di Summa, 72100, Brindisi, Italy
| | - Giorgia Tosi
- Department of Human and Social Sciences, University of Salento, Piazza Tancredi 7, 73100, Lecce, Italy.
- Department of Psychology, University of Milano-Bicocca, Piazza Ateneo Nuovo 1, 20126, Milan, Italy.
| | - Francesco Giaquinto
- Department of Human and Social Sciences, University of Salento, Piazza Tancredi 7, 73100, Lecce, Italy
| | - Marika Iaia
- Department of Human and Social Sciences, University of Salento, Piazza Tancredi 7, 73100, Lecce, Italy
| | - Ezia Rizzi
- Department of Human and Social Sciences, University of Salento, Piazza Tancredi 7, 73100, Lecce, Italy
| | - Ylenia Chiarello
- Department of Human and Social Sciences, University of Salento, Piazza Tancredi 7, 73100, Lecce, Italy
| | - Maxime Bertoux
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, LiCEND, DISTALZ, 42 rue Paul Duez, 59000, Lille, France
| | - Paola Angelelli
- Department of Human and Social Sciences, University of Salento, Piazza Tancredi 7, 73100, Lecce, Italy
| | - Daniele Luigi Romano
- Department of Human and Social Sciences, University of Salento, Piazza Tancredi 7, 73100, Lecce, Italy
- Department of Psychology, University of Milano-Bicocca, Piazza Ateneo Nuovo 1, 20126, Milan, Italy
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7
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Pérez-Millan A, Contador J, Juncà-Parella J, Bosch B, Borrell L, Tort-Merino A, Falgàs N, Borrego-Écija S, Bargalló N, Rami L, Balasa M, Lladó A, Sánchez-Valle R, Sala-Llonch R. Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data. Hum Brain Mapp 2023; 44:2234-2244. [PMID: 36661219 PMCID: PMC10028671 DOI: 10.1002/hbm.26205] [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: 07/22/2022] [Revised: 12/01/2022] [Accepted: 01/04/2023] [Indexed: 01/21/2023] Open
Abstract
Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnetic resonance imaging (MRI). We included baseline 3T-T1 MRI data from 339 subjects: 99 healthy controls (CTR), 153 AD and 87 FTD patients; and 2-year follow-up data from 114 subjects. We obtained subcortical gray matter volumes and cortical thickness measures using FreeSurfer. We used dimensionality reduction to obtain a single feature that was later used in a support vector machine for classification. Discrimination patterns were obtained with the contribution of each region to the single feature. Our algorithm differentiated CTR versus AD and CTR versus FTD at the cross-sectional level with 83.3% and 82.1% of accuracy. These increased up to 90.0% and 88.0% with longitudinal data. When we studied the classification between AD versus FTD we obtained an accuracy of 63.3% at the cross-sectional level and 75.0% for longitudinal data. The AD versus FTD versus CTR classification has reached an accuracy of 60.7%, and 71.3% for cross-sectional and longitudinal data respectively. Disease discrimination brain maps are in concordance with previous results obtained with classical approaches. By using a single feature, we were capable to classify CTR, AD, and FTD with good accuracy, considering the inherent overlap between diseases. Importantly, the algorithm can be used with cross-sectional and longitudinal data.
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Affiliation(s)
- Agnès Pérez-Millan
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
- Department of Biomedicine, Faculty of Medicine, Institute of Neurosciences, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - José Contador
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
| | - Jordi Juncà-Parella
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
| | - Beatriz Bosch
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
| | - Laia Borrell
- Department of Biomedicine, Faculty of Medicine, Institute of Neurosciences, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Adrià Tort-Merino
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
| | - Neus Falgàs
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
- Atlantic Fellow for Equity in Brain Health, Global Brain Heath Institute, University of California San Francisco, Trinity College Dublin, San Francisco, California, USA
| | - Sergi Borrego-Écija
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
| | - Nuria Bargalló
- Image Diagnostic Centre, Hospital Clínic de Barcelona, CIBER de Salud Mental, Instituto de Salud Carlos III. Magnetic Resonance Image Core Facility, IDIBAPS, Barcelona, Spain
| | - Lorena Rami
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
| | - Mircea Balasa
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
- Atlantic Fellow for Equity in Brain Health, Global Brain Heath Institute, University of California San Francisco, Trinity College Dublin, San Francisco, California, USA
| | - Albert Lladó
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
| | - Raquel Sánchez-Valle
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
| | - Roser Sala-Llonch
- Department of Biomedicine, Faculty of Medicine, Institute of Neurosciences, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain
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8
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Ferreira LK, Lindberg O, Santillo AF, Wahlund LO. Functional connectivity in behavioral variant frontotemporal dementia. Brain Behav 2022; 12:e2790. [PMID: 36306386 PMCID: PMC9759144 DOI: 10.1002/brb3.2790] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 09/13/2022] [Accepted: 09/24/2022] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION Functional connectivity (FC)-which reflects relationships between neural activity in different brain regions-has been used to explore the functional architecture of the brain in neurodegenerative disorders. Although an increasing number of studies have explored FC changes in behavioral variant frontotemporal dementia (bvFTD), there is no focused, in-depth review about FC in bvFTD. METHODS Comprehensive literature search and narrative review to summarize the current field of FC in bvFTD. RESULTS (1) Decreased FC within the salience network (SN) is the most consistent finding in bvFTD; (2) FC changes extend beyond the SN and affect the interplay between networks; (3) results within the Default Mode Network are mixed; (4) the brain as a network is less interconnected and less efficient in bvFTD; (5) symptoms, functional impairment, and cognition are associated with FC; and (6) the functional architecture resembles patterns of neuropathological spread. CONCLUSIONS FC has potential as a biomarker, and future studies are expected to advance the field with multicentric initiatives, longitudinal designs, and methodological advances.
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Affiliation(s)
- Luiz Kobuti Ferreira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Stockholm, Sweden
| | - Olof Lindberg
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Alexander F Santillo
- Clinical Memory Research Unit and Psychiatry, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Lars-Olof Wahlund
- 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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Functional Imaging for Neurodegenerative Diseases. Presse Med 2022; 51:104121. [PMID: 35490910 DOI: 10.1016/j.lpm.2022.104121] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 03/13/2022] [Accepted: 04/11/2022] [Indexed: 12/16/2022] Open
Abstract
Diagnosis and monitoring of neurodegenerative diseases has changed profoundly over the past twenty years. Biomarkers are now included in most diagnostic procedures as well as in clinical trials. Neuroimaging biomarkers provide access to brain structure and function over the course of neurodegenerative diseases. They have brought new insights into a wide range of neurodegenerative diseases and have made it possible to describe some of the imaging challenges in clinical populations. MRI mainly explores brain structure while molecular imaging, functional MRI and electro- and magnetoencephalography examine brain function. In this paper, we describe and analyse the current and potential contribution of MRI and molecular imaging in the field of neurodegenerative diseases.
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10
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Brier LM, Zhang X, Bice AR, Gaines SH, Landsness EC, Lee JM, Anastasio MA, Culver JP. A Multivariate Functional Connectivity Approach to Mapping Brain Networks and Imputing Neural Activity in Mice. Cereb Cortex 2022; 32:1593-1607. [PMID: 34541601 PMCID: PMC9016290 DOI: 10.1093/cercor/bhab282] [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: 03/12/2021] [Revised: 07/12/2021] [Accepted: 07/14/2021] [Indexed: 11/13/2022] Open
Abstract
Temporal correlation analysis of spontaneous brain activity (e.g., Pearson "functional connectivity," FC) has provided insights into the functional organization of the human brain. However, bivariate analysis techniques such as this are often susceptible to confounding physiological processes (e.g., sleep, Mayer-waves, breathing, motion), which makes it difficult to accurately map connectivity in health and disease as these physiological processes affect FC. In contrast, a multivariate approach to imputing individual neural networks from spontaneous neuroimaging data could be influential to our conceptual understanding of FC and provide performance advantages. Therefore, we analyzed neural calcium imaging data from Thy1-GCaMP6f mice while either awake, asleep, anesthetized, during low and high bouts of motion, or before and after photothrombotic stroke. A linear support vector regression approach was used to determine the optimal weights for integrating the signals from the remaining pixels to accurately predict neural activity in a region of interest (ROI). The resultant weight maps for each ROI were interpreted as multivariate functional connectivity (MFC), resembled anatomical connectivity, and demonstrated a sparser set of strong focused positive connections than traditional FC. While global variations in data have large effects on standard correlation FC analysis, the MFC mapping methods were mostly impervious. Lastly, MFC analysis provided a more powerful connectivity deficit detection following stroke compared to traditional FC.
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Affiliation(s)
- Lindsey M Brier
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Xiaohui Zhang
- Department of Bioengineering, University of Illinois, Urbana-Champaign, IL 61801, USA
| | - Annie R Bice
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Seana H Gaines
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Eric C Landsness
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Jin-Moo Lee
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Mark A Anastasio
- Department of Bioengineering, University of Illinois, Urbana-Champaign, IL 61801, USA
| | - Joseph P Culver
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Biomedical Engineering, Washington University School of Engineering, St. Louis, MO 63105, USA
- Department of Electrical and Systems Engineering, Washington University School of Engineering, St. Louis, MO 63112, USA
- Department of Physics, Washington University School of Arts and Science, St. Louis, MO 63130, USA
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11
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Manera AL, Dadar M, Collins DL, Ducharme S. Ventricular features as reliable differentiators between bvFTD and other dementias. Neuroimage Clin 2022; 33:102947. [PMID: 35134704 PMCID: PMC8856914 DOI: 10.1016/j.nicl.2022.102947] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 11/24/2021] [Accepted: 01/19/2022] [Indexed: 11/28/2022]
Abstract
Our results showed a consistent pattern of ventricle enlargement in the bvFTD patients, particularly in the anterior parts of the frontal and temporal horns of the lateral ventricles. The estimation of the proposed ventricular anteroposterior ratio (APR) resulted in statistically significant difference compared to all other groups. Our study proposes an easy to obtain and generalizable ventricle-based feature (APR) from T1-weighted structural MRI (routinely acquired and available in the clinic) that can be used not only to differentiate bvFTD from normal subjects, but also from other FTD variants (SV and PNFA), MCI, and AD patients. We have made our ventricle feature estimation and bvFTD diagnosis tool (VentRa) publicly available, allowing application of our model in other studies. If validated in a prospective study, VentRa has the potential to aid bvFTD diagnosis, particularly in settings where access to specialized FTD care is limited.
Introduction Lateral ventricles are reliable and sensitive indicators of brain atrophy and disease progression in behavioral variant frontotemporal dementia (bvFTD). We aimed to investigate whether an automated tool using ventricular features could improve diagnostic accuracy in bvFTD across neurodegenerative diseases. Methods Using 678 subjects −69 bvFTD, 38 semantic variant, 37 primary non-fluent aphasia, 218 amyloid + mild cognitive impairment, 74 amyloid + Alzheimer’s Dementia and 242 normal controls- with a total of 2750 timepoints, lateral ventricles were segmented and differences in ventricular features were assessed between bvFTD, normal controls and other dementia cohorts. Results Ventricular antero-posterior ratio (APR) was the only feature that was significantly different and increased faster in bvFTD compared to all other cohorts. We achieved a 10-fold cross-validation accuracy of 80% (77% sensitivity, 82% specificity) in differentiating bvFTD from all other cohorts with other ventricular features (i.e., total ventricular volume and left–right lateral ventricle ratios), and 76% accuracy using only the single APR feature. Discussion Ventricular features, particularly the APR, might be reliable and easy-to-implement markers for bvFTD diagnosis. We have made our ventricle feature estimation and bvFTD diagnostic tool publicly available, allowing application of our model in other studies.
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Affiliation(s)
- Ana L Manera
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec (QC), Canada.
| | - Mahsa Dadar
- Department of Psychiatry, Douglas Mental Health University Health Centre, McGill University, Montreal, Quebec (QC), Canada; Douglas Mental Health University Institute, Verdun, QC, Canada
| | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec (QC), Canada
| | - Simon Ducharme
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec (QC), Canada; Department of Psychiatry, Douglas Mental Health University Health Centre, McGill University, Montreal, Quebec (QC), Canada
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12
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Mi Y, Qin Q, Xing Y, Tang Y. Capgras Syndrome as the Core Manifestation of Early-Onset Alzheimer’s Disease. J Alzheimers Dis 2022; 87:155-160. [PMID: 35253758 DOI: 10.3233/jad-215565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Capgras syndrome (CS) was usually considered a symptom of a functional disorder in the young, most commonly schizophrenia, or an organic disorder in the elderly. The occurrence of CS among early-onset Alzheimer’s disease (EOAD) is extremely rare. We describe a case in which the unrecognition of CS as part of EOAD resulted in a wrong psychiatric diagnosis and inappropriate treatment. This paper aims to acknowledge CS as an early or core manifestation and highlight EOAD as a differential diagnosis of mental disorders in young people, even without a remarkable family history.
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Affiliation(s)
- Yingxin Mi
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Qi Qin
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Yi Xing
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Yi Tang
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
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13
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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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14
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McKenna MC, Tahedl M, Murad A, Lope J, Hardiman O, Hutchinson S, Bede P. White matter microstructure alterations in frontotemporal dementia: Phenotype-associated signatures and single-subject interpretation. Brain Behav 2022; 12:e2500. [PMID: 35072974 PMCID: PMC8865163 DOI: 10.1002/brb3.2500] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/22/2021] [Accepted: 01/01/2022] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Frontotemporal dementias (FTD) include a genetically heterogeneous group of conditions with distinctive molecular, radiological and clinical features. The majority of radiology studies in FTD compare FTD subgroups to healthy controls to describe phenotype- or genotype-associated imaging signatures. While the characterization of group-specific imaging traits is academically important, the priority of clinical imaging is the meaningful interpretation of individual datasets. METHODS To demonstrate the feasibility of single-subject magnetic resonance imaging (MRI) interpretation, we have evaluated the white matter profile of 60 patients across the clinical spectrum of FTD. A z-score-based approach was implemented, where the diffusivity metrics of individual patients were appraised with reference to demographically matched healthy controls. Fifty white matter tracts were systematically evaluated in each subject with reference to normative data. RESULTS The z-score-based approach successfully detected white matter pathology in single subjects, and group-level inferences were analogous to the outputs of standard track-based spatial statistics. CONCLUSIONS Our findings suggest that it is possible to meaningfully evaluate the diffusion profile of single FTD patients if large normative datasets are available. In contrast to the visual review of FLAIR and T2-weighted images, computational imaging offers objective, quantitative insights into white matter integrity changes even at single-subject level.
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Affiliation(s)
- Mary Clare McKenna
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
| | - Marlene Tahedl
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
| | - Aizuri Murad
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
| | - Jasmin Lope
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
| | | | - Peter Bede
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland.,Department of Neurology, St James's Hospital, Dublin, Ireland
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15
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Wong R, Luo Y, Mok VCT, Shi L. Advances in computerized MRI‐based biomarkers in Alzheimer’s disease. BRAIN SCIENCE ADVANCES 2021. [DOI: 10.26599/bsa.2021.9050005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
The use of neuroimaging examinations is crucial in Alzheimer’s disease (AD), in both research and clinical settings. Over the years, magnetic resonance imaging (MRI)–based computer‐aided diagnosis has been shown to be helpful for early screening and predicting cognitive decline. Meanwhile, an increasing number of studies have adopted machine learning for the classification of AD, with promising results. In this review article, we focus on computerized MRI‐based biomarkers of AD by reviewing representative studies that used computerized techniques to identify AD patients and predict cognitive progression. We categorized these studies based on the following applications: (1) identifying AD from normal control; (2) identifying AD from other dementia types, including vascular dementia, dementia with Lewy bodies, and frontotemporal dementia; and (3) predicting conversion from NC to mild cognitive impairment (MCI) and from MCI to AD. This systematic review could act as a state‐of‐the‐art overview of this emerging field as well as a basis for designing future studies.
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Affiliation(s)
- Raymond Wong
- BrainNow Research Institute, Shenzhen 518081, Guangdong, China
| | - Yishan Luo
- BrainNow Research Institute, Shenzhen 518081, Guangdong, China
| | - Vincent Chung-tong Mok
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong 999077, China
| | - Lin Shi
- BrainNow Research Institute, Shenzhen 518081, Guangdong, China
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong 999077, China
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16
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Cecchetti G, Agosta F, Basaia S, Cividini C, Cursi M, Santangelo R, Caso F, Minicucci F, Magnani G, Filippi M. Resting-state electroencephalographic biomarkers of Alzheimer's disease. NEUROIMAGE-CLINICAL 2021; 31:102711. [PMID: 34098525 PMCID: PMC8185302 DOI: 10.1016/j.nicl.2021.102711] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 04/21/2021] [Accepted: 05/26/2021] [Indexed: 10/29/2022]
Abstract
OBJECTIVE We evaluated the value of resting-state EEG source biomarkers to characterize mild cognitive impairment (MCI) subjects with an Alzheimer's disease (AD)-like cerebrospinal fluid (CSF) profile and to track neurodegeneration throughout the AD continuum. We further applied a resting-state functional MRI (fMRI)-driven model of source reconstruction and tested its advantage in terms of AD diagnostic accuracy. METHODS Thirty-nine consecutive patients with AD dementia (ADD), 86 amnestic MCI, and 33 healthy subjects enter the EEG study. All ADD subjects, 37 out of 86 MCI patients and a distinct group of 53 healthy controls further entered the fMRI study. MCI subjects were divided according to the CSF phosphorylated tau/β amyloid-42 ratio (MCIpos: ≥ 0.13, MCIneg: < 0.13). Using Exact low-resolution brain electromagnetic tomography (eLORETA), EEG lobar current densities were estimated at fixed frequencies and analyzed. To combine the two imaging techniques, networks mostly affected by AD pathology were identified using Independent Component Analysis applied to fMRI data of ADD subjects. Current density EEG analysis within ICA-based networks at selected frequency bands was performed. Afterwards, graph analysis was applied to EEG and fMRI data at ICA-based network level. RESULTS ADD patients showed a widespread slowing of spectral density. At a lobar level, MCIpos subjects showed a widespread higher theta density than MCIneg and healthy subjects; a lower beta2 density than healthy subjects was also found in parietal and occipital lobes. Evaluating EEG sources within the ICA-based networks, alpha2 band distinguished MCIpos from MCIneg, ADD and healthy subjects with good accuracy. Graph analysis on EEG data showed an alteration of connectome configuration at theta frequency in ADD and MCIpos patients and a progressive disruption of connectivity at alpha2 frequency throughout the AD continuum. CONCLUSIONS Theta frequency is the earliest and most sensitive EEG marker of AD pathology. Furthermore, EEG/fMRI integration highlighted the role of alpha2 band as potential neurodegeneration biomarker.
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Affiliation(s)
- Giordano Cecchetti
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Vita-Salute San Raffaele University, Via Olgettina 60, 20132 Milan, Italy
| | - Federica Agosta
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Vita-Salute San Raffaele University, Via Olgettina 60, 20132 Milan, Italy
| | - Silvia Basaia
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy
| | - Camilla Cividini
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Vita-Salute San Raffaele University, Via Olgettina 60, 20132 Milan, Italy
| | - Marco Cursi
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy
| | - Roberto Santangelo
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy
| | - Francesca Caso
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy
| | - Fabio Minicucci
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy
| | - Giuseppe Magnani
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy
| | - Massimo Filippi
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Vita-Salute San Raffaele University, Via Olgettina 60, 20132 Milan, Italy.
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17
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Manera AL, Dadar M, Van Swieten JC, Borroni B, Sanchez-Valle R, Moreno F, Laforce R, Graff C, Synofzik M, Galimberti D, Rowe JB, Masellis M, Tartaglia MC, Finger E, Vandenberghe R, de Mendonca A, Tagliavini F, Santana I, Butler CR, Gerhard A, Danek A, Levin J, Otto M, Frisoni G, Ghidoni R, Sorbi S, Rohrer JD, Ducharme S, Collins DL. MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia. J Neurol Neurosurg Psychiatry 2021; 92:jnnp-2020-324106. [PMID: 33722819 DOI: 10.1136/jnnp-2020-324106] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 11/24/2020] [Accepted: 12/22/2020] [Indexed: 01/24/2023]
Abstract
INTRODUCTION Structural brain imaging is paramount for the diagnosis of behavioural variant of frontotemporal dementia (bvFTD), but it has low sensitivity leading to erroneous or late diagnosis. METHODS A total of 515 subjects from two different bvFTD cohorts (training and independent validation cohorts) were used to perform voxel-wise morphometric analysis to identify regions with significant differences between bvFTD and controls. A random forest classifier was used to individually predict bvFTD from deformation-based morphometry differences in isolation and together with semantic fluency. Tenfold cross validation was used to assess the performance of the classifier within the training cohort. A second held-out cohort of genetically confirmed bvFTD cases was used for additional validation. RESULTS Average 10-fold cross-validation accuracy was 89% (82% sensitivity, 93% specificity) using only MRI and 94% (89% sensitivity, 98% specificity) with the addition of semantic fluency. In the separate validation cohort of definite bvFTD, accuracy was 88% (81% sensitivity, 92% specificity) with MRI and 91% (79% sensitivity, 96% specificity) with added semantic fluency scores. CONCLUSION Our results show that structural MRI and semantic fluency can accurately predict bvFTD at the individual subject level within a completely independent validation cohort coming from a different and independent database.
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Affiliation(s)
- Ana L Manera
- McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Mahsa Dadar
- McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Radiology and Nuclear Medicine, Laval University, Quebec City, Quebec, Canada
| | | | - Barbara Borroni
- Department of Clinical and Experimental Sciences, Centre for Ageing Brain and Neurodegenerative Disorders, University of Brescia, Brescia, Italy
| | - Raquel Sanchez-Valle
- Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Institut d'Investigacións Biomèdiques August Pi I Sunyer, University of Barcelona, Barcelona, Spain
| | - Fermin Moreno
- Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Spain
| | - Robert Laforce
- Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, and Faculté de Médecine, Université Laval, Quebec City, Quebec, Canada
| | - Caroline Graff
- Department of Geriatric Medicine, Karolinska University Hospital-Huddinge, Stockholm, Sweden
| | - Matthis Synofzik
- Department of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany
| | - Daniela Galimberti
- Neurodegenerative Diseases Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- LANE - Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - James Benedict Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Mario Masellis
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Maria Carmela Tartaglia
- Tanz Centre for Research in Neurodegenerative Disease, Toronto Western Hospital, Toronto, Ontario, Canada
| | - Elizabeth Finger
- Department of Clinical Neurological Sciences, University of Western Ontario, London, Ontario, Canada
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | | | - Fabrizio Tagliavini
- Neurology and Neuropathology, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milan, Italy
| | - Isabel Santana
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | | | - Alex Gerhard
- Institute of Brain, Behaviour and Mental Health, The University of Manchester, Manchester, UK
| | - Adrian Danek
- Neurologische Klinik und Poliklinik, Ludwig-Maximilians Universitat, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Johannes Levin
- Neurologische Klinik und Poliklinik, Ludwig-Maximilians Universitat, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Markus Otto
- Department of Neurology, University Hospital Ulm, Ulm, Germany
| | - Giovanni Frisoni
- LANE - Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Memory Clinic and LANVIE-Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Roberta Ghidoni
- Molecular Markers Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Sandro Sorbi
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy
| | | | - Simon Ducharme
- McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Department of Psychiatry, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - D Louis Collins
- McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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18
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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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19
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Ng ASL, Wang J, Ng KK, Chong JSX, Qian X, Lim JKW, Tan YJ, Yong ACW, Chander RJ, Hameed S, Ting SKS, Kandiah N, Zhou JH. Distinct network topology in Alzheimer's disease and behavioral variant frontotemporal dementia. ALZHEIMERS RESEARCH & THERAPY 2021; 13:13. [PMID: 33407913 PMCID: PMC7786961 DOI: 10.1186/s13195-020-00752-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 12/15/2020] [Indexed: 11/18/2022]
Abstract
Background Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD) cause distinct atrophy and functional disruptions within two major intrinsic brain networks, namely the default network and the salience network, respectively. It remains unclear if inter-network relationships and whole-brain network topology are also altered and underpin cognitive and social–emotional functional deficits. Methods In total, 111 participants (50 AD, 14 bvFTD, and 47 age- and gender-matched healthy controls) underwent resting-state functional magnetic resonance imaging (fMRI) and neuropsychological assessments. Functional connectivity was derived among 144 brain regions of interest. Graph theoretical analysis was applied to characterize network integration, segregation, and module distinctiveness (degree centrality, nodal efficiency, within-module degree, and participation coefficient) in AD, bvFTD, and healthy participants. Group differences in graph theoretical measures and empirically derived network community structures, as well as the associations between these indices and cognitive performance and neuropsychiatric symptoms, were subject to general linear models, with age, gender, education, motion, and scanner type controlled. Results Our results suggested that AD had lower integration in the default and control networks, while bvFTD exhibited disrupted integration in the salience network. Interestingly, AD and bvFTD had the highest and lowest degree of integration in the thalamus, respectively. Such divergence in topological aberration was recapitulated in network segregation and module distinctiveness loss, with AD showing poorer modular structure between the default and control networks, and bvFTD having more fragmented modules in the salience network and subcortical regions. Importantly, aberrations in network topology were related to worse attention deficits and greater severity in neuropsychiatric symptoms across syndromes. Conclusions Our findings underscore the reciprocal relationships between the default, control, and salience networks that may account for the cognitive decline and neuropsychiatric symptoms in dementia.
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Affiliation(s)
- Adeline Su Lyn Ng
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore, Singapore.,Neuroscience and Behavioral Disorders Program, Duke-NUS Medical School, Singapore, Singapore
| | - Juan Wang
- Centre for Sleep and Cognition, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Kwun Kei Ng
- Centre for Sleep and Cognition, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Joanna Su Xian Chong
- Centre for Sleep and Cognition, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Xing Qian
- Centre for Sleep and Cognition, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Joseph Kai Wei Lim
- Centre for Sleep and Cognition, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Yi Jayne Tan
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore, Singapore.,Neuroscience and Behavioral Disorders Program, Duke-NUS Medical School, Singapore, Singapore
| | - Alisa Cui Wen Yong
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore, Singapore
| | - Russell Jude Chander
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore, Singapore
| | - Shahul Hameed
- Neuroscience and Behavioral Disorders Program, Duke-NUS Medical School, Singapore, Singapore.,Department of Neurology, National Neuroscience Institute, Singapore General Hospital, Singapore, Singapore
| | - Simon Kang Seng Ting
- Neuroscience and Behavioral Disorders Program, Duke-NUS Medical School, Singapore, Singapore.,Department of Neurology, National Neuroscience Institute, Singapore General Hospital, Singapore, Singapore
| | - Nagaendran Kandiah
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore, Singapore.,Neuroscience and Behavioral Disorders Program, Duke-NUS Medical School, Singapore, Singapore
| | - Juan Helen Zhou
- Neuroscience and Behavioral Disorders Program, Duke-NUS Medical School, Singapore, Singapore. .,Centre for Sleep and Cognition, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. .,Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
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20
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Bogolepova A, Vasenina E, Gomzyakova N, Gusev E, Dudchenko N, Emelin A, Zalutskaya N, Isaev R, Kotovskaya Y, Levin O, Litvinenko I, Lobzin V, Martynov M, Mkhitaryan E, Nikolay G, Palchikova E, Tkacheva O, Cherdak M, Chimagomedova A, Yakhno N. Clinical Guidelines for Cognitive Disorders in Elderly and Older Patients. Zh Nevrol Psikhiatr Im S S Korsakova 2021. [DOI: 10.17116/jnevro20211211036] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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21
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Meysami S, Raji CA, Merrill DA, Porter VR, Mendez MF. Quantitative MRI Differences Between Early versus Late Onset Alzheimer's Disease. Am J Alzheimers Dis Other Demen 2021; 36:15333175211055325. [PMID: 34814740 PMCID: PMC10623969 DOI: 10.1177/15333175211055325] [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] [Indexed: 11/16/2022]
Abstract
Investigators report greater parietal tau deposition and alternate frontoparietal network involvement in early onset Alzheimer's Disease (EOAD) with onset <65 years as compared with typical late onset AD (LOAD). To determine whether clinical brain MRI volumes reflect these differences in EOAD compared with LOAD. This study investigated the clinical MRI scans of 45 persons with Clinically Probable AD with onset <65 years, and compared them to 32 with Clinically Probable AD with onset ≥65 years. Brain volumes on their T1 MRI scans were quantified with a volumetric program. Receiver operating curve analyses were performed. Persons with EOAD had significantly smaller parietal lobes (volumetric percentiles) than LOAD. Late onset Alzheimer's Disease had a smaller left putamen and hippocampus. Area Under the Curve was 96.5% with brain region delineation of EOAD compared to LOAD. This study indicates parietal atrophy less than 30% of normal on clinical MRI scans is suggestive of EOAD compared to LOAD.
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Affiliation(s)
- Somayeh Meysami
- Department of Neurology, David Geffen School of Medicine at University of California, Los Angeles, CA, USA
| | - Cyrus A. Raji
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St Louis, St Louis, MO, USA
| | - David A. Merrill
- Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA, USA
- Providence and St Johns Health Center, Pacific Neuroscience Institute, Santa Monica, CA, USA
| | - Verna R. Porter
- Department of Neurology, David Geffen School of Medicine at University of California, Los Angeles, CA, USA
- Providence and St Johns Health Center, Pacific Neuroscience Institute, Santa Monica, CA, USA
| | - Mario F. Mendez
- Department of Neurology, David Geffen School of Medicine at University of California, Los Angeles, CA, USA
- Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA, USA
- V.A. Greater Los Angeles Healthcare System, Los Angeles, CA, USA
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22
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Ducharme S, Dols A, Laforce R, Devenney E, Kumfor F, van den Stock J, Dallaire-Théroux C, Seelaar H, Gossink F, Vijverberg E, Huey E, Vandenbulcke M, Masellis M, Trieu C, Onyike C, Caramelli P, de Souza LC, Santillo A, Waldö ML, Landin-Romero R, Piguet O, Kelso W, Eratne D, Velakoulis D, Ikeda M, Perry D, Pressman P, Boeve B, Vandenberghe R, Mendez M, Azuar C, Levy R, Le Ber I, Baez S, Lerner A, Ellajosyula R, Pasquier F, Galimberti D, Scarpini E, van Swieten J, Hornberger M, Rosen H, Hodges J, Diehl-Schmid J, Pijnenburg Y. Recommendations to distinguish behavioural variant frontotemporal dementia from psychiatric disorders. Brain 2020; 143:1632-1650. [PMID: 32129844 PMCID: PMC7849953 DOI: 10.1093/brain/awaa018] [Citation(s) in RCA: 138] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Revised: 11/27/2019] [Accepted: 12/08/2019] [Indexed: 12/12/2022] Open
Abstract
The behavioural variant of frontotemporal dementia (bvFTD) is a frequent cause of early-onset dementia. The diagnosis of bvFTD remains challenging because of the limited accuracy of neuroimaging in the early disease stages and the absence of molecular biomarkers, and therefore relies predominantly on clinical assessment. BvFTD shows significant symptomatic overlap with non-degenerative primary psychiatric disorders including major depressive disorder, bipolar disorder, schizophrenia, obsessive-compulsive disorder, autism spectrum disorders and even personality disorders. To date, ∼50% of patients with bvFTD receive a prior psychiatric diagnosis, and average diagnostic delay is up to 5-6 years from symptom onset. It is also not uncommon for patients with primary psychiatric disorders to be wrongly diagnosed with bvFTD. The Neuropsychiatric International Consortium for Frontotemporal Dementia was recently established to determine the current best clinical practice and set up an international collaboration to share a common dataset for future research. The goal of the present paper was to review the existing literature on the diagnosis of bvFTD and its differential diagnosis with primary psychiatric disorders to provide consensus recommendations on the clinical assessment. A systematic literature search with a narrative review was performed to determine all bvFTD-related diagnostic evidence for the following topics: bvFTD history taking, psychiatric assessment, clinical scales, physical and neurological examination, bedside cognitive tests, neuropsychological assessment, social cognition, structural neuroimaging, functional neuroimaging, CSF and genetic testing. For each topic, responsible team members proposed a set of minimal requirements, optimal clinical recommendations, and tools requiring further research or those that should be developed. Recommendations were listed if they reached a ≥ 85% expert consensus based on an online survey among all consortium participants. New recommendations include performing at least one formal social cognition test in the standard neuropsychological battery for bvFTD. We emphasize the importance of 3D-T1 brain MRI with a standardized review protocol including validated visual atrophy rating scales, and to consider volumetric analyses if available. We clarify the role of 18F-fluorodeoxyglucose PET for the exclusion of bvFTD when normal, whereas non-specific regional metabolism abnormalities should not be over-interpreted in the case of a psychiatric differential diagnosis. We highlight the potential role of serum or CSF neurofilament light chain to differentiate bvFTD from primary psychiatric disorders. Finally, based on the increasing literature and clinical experience, the consortium determined that screening for C9orf72 mutation should be performed in all possible/probable bvFTD cases or suspected cases with strong psychiatric features.
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Affiliation(s)
- Simon Ducharme
- Department of Psychiatry, McGill University Health Centre, McGill University, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Str., Montreal, Quebec, H3A 2B4, Canada
| | - Annemiek Dols
- Department of Old Age Psychiatry, GGZ InGeest, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Robert Laforce
- Clinique Interdisciplinaire de Mémoire (CIME), Laval University, Quebec, Canada
| | - Emma Devenney
- Brain and Mind Centre, University of Sydney, Sydney, Australia
| | - Fiona Kumfor
- Brain and Mind Centre, University of Sydney, Sydney, Australia
| | - Jan van den Stock
- Laboratory for Translational Neuropsychiatry, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | | | - Harro Seelaar
- Department of Neurology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Flora Gossink
- Department of Old Age Psychiatry, GGZ InGeest, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Everard Vijverberg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Edward Huey
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Department of Psychiatry, Colombia University, New York, USA
| | - Mathieu Vandenbulcke
- Department of Geriatric Psychiatry, University Hospitals Leuven, Leuven, Belgium
| | - Mario Masellis
- Department of Neurology, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Calvin Trieu
- Department of Old Age Psychiatry, GGZ InGeest, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Chiadi Onyike
- Division of Geriatric Psychiatry and Neuropsychiatry, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Paulo Caramelli
- Behavioral and Cognitive Neurology Research Group, Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Leonardo Cruz de Souza
- Behavioral and Cognitive Neurology Research Group, Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Maria Landqvist Waldö
- Division of Clinical Sciences Helsingborg, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | | | - Olivier Piguet
- Division of Clinical Sciences Helsingborg, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Wendy Kelso
- Neuropsychiatry Unit, Royal Melbourne Hospital, Melbourne, Australia
| | - Dhamidhu Eratne
- Neuropsychiatry Unit, Royal Melbourne Hospital, Melbourne, Australia
| | - Dennis Velakoulis
- Neuropsychiatry Unit, Royal Melbourne Hospital, Melbourne, Australia
| | - Manabu Ikeda
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - David Perry
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, USA
| | - Peter Pressman
- Department of Neurology, University of Colorado Denver, Aurora, USA
| | - Bradley Boeve
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Rik Vandenberghe
- Department of Neurology, University Hospital Leuven, Leuven, Belgium
| | - Mario Mendez
- Department of Neurology, UCLA Medical Centre, University of California Los Angeles, Los Angeles, USA
| | - Carole Azuar
- Department of Neurology, Hôpital La Pitié Salpêtrière, Paris, France
| | - Richard Levy
- Department of Neurology, Hôpital La Pitié Salpêtrière, Paris, France
| | - Isabelle Le Ber
- Department of Neurology, Hôpital La Pitié Salpêtrière, Paris, France
| | - Sandra Baez
- Department of Psychology, Andes University, Bogota, Colombia
| | - Alan Lerner
- Department of Neurology, University Hospital Cleveland Medical Center, Cleveland, USA
| | - Ratnavalli Ellajosyula
- Department of Neurology, Manipal Hospital and Annasawmy Mudaliar Hospital, Bangalore, India
| | - Florence Pasquier
- Univ Lille, Inserm U1171, Memory Center, CHU Lille, DISTAlz, Lille, France
| | - Daniela Galimberti
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Centro Dino Ferrari, Milan, Italy
- Fondazione IRCCS Ca’ Granda, Ospedale Policlinico, Neurodegenerative Diseases Unit Milan, Italy
| | - Elio Scarpini
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Centro Dino Ferrari, Milan, Italy
- Fondazione IRCCS Ca’ Granda, Ospedale Policlinico, Neurodegenerative Diseases Unit Milan, Italy
| | - John van Swieten
- Department of Neurology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | | | - Howard Rosen
- Memory and Aging Center, University of California San Francisco, San Francisco, USA
| | - John Hodges
- Brain and Mind Centre, University of Sydney, Sydney, Australia
| | - Janine Diehl-Schmid
- Department of Psychiatry and Psychotherapy, Technical University of Munich, School of Medicine, Munich, Germany
| | - Yolande Pijnenburg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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23
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Falgàs N, Ruiz‐Peris M, Pérez‐Millan A, Sala‐Llonch R, Antonell A, Balasa M, Borrego‐Écija S, Ramos‐Campoy O, Augé JM, Castellví M, Tort‐Merino A, Olives J, Fernández‐Villullas G, Blennow K, Zetterberg H, Bargalló N, Lladó A, Sánchez‐Valle R. Contribution of CSF biomarkers to early-onset Alzheimer's disease and frontotemporal dementia neuroimaging signatures. Hum Brain Mapp 2020; 41:2004-2013. [PMID: 31944489 PMCID: PMC7267898 DOI: 10.1002/hbm.24925] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 12/11/2019] [Accepted: 01/04/2020] [Indexed: 12/19/2022] Open
Abstract
Prior studies have described distinct patterns of brain gray matter and white matter alterations in Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD), as well as differences in their cerebrospinal fluid (CSF) biomarkers profiles. We aim to investigate the relationship between early-onset AD (EOAD) and FTLD structural alterations and CSF biomarker levels. We included 138 subjects (64 EOAD, 26 FTLD, and 48 controls), all of them with a 3T MRI brain scan and CSF biomarkers available (the 42 amino acid-long form of the amyloid-beta protein [Aβ42], total-tau protein [T-tau], neurofilament light chain [NfL], neurogranin [Ng], and 14-3-3 levels). We used FreeSurfer and FSL to obtain cortical thickness (CTh) and fraction anisotropy (FA) maps. We studied group differences in CTh and FA and described the "AD signature" and "FTLD signature." We tested multiple regression models to find which CSF-biomarkers better explained each disease neuroimaging signature. CTh and FA maps corresponding to the AD and FTLD signatures were in accordance with previous literature. Multiple regression analyses showed that the biomarkers that better explained CTh values within the AD signature were Aβ and 14-3-3; whereas NfL and 14-3-3 levels explained CTh values within the FTLD signature. Similarly, NfL levels explained FA values in the FTLD signature. Ng levels were not predictive in any of the models. Biochemical markers contribute differently to structural (CTh and FA) changes typical of AD and FTLD.
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Affiliation(s)
- Neus Falgàs
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
- Atlantic Fellow for Equity in Brain HealthGlobal Brain Health Institute, University of CaliforniaSan FranciscoCalifornia
| | - Mariona Ruiz‐Peris
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
- Department of BiomedicineUniversity of BarcelonaBarcelonaSpain
| | - Agnès Pérez‐Millan
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
- Department of BiomedicineUniversity of BarcelonaBarcelonaSpain
| | | | - Anna Antonell
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
| | - Mircea Balasa
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
- Senior Atlantic Fellow for Equity inBrain Health, Global Brain Health InstituteTrinity College DublinIreland
| | - Sergi Borrego‐Écija
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
| | - Oscar Ramos‐Campoy
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
| | - Josep Maria Augé
- Biochemistry and Molecular Genetics Department, Hospital Clínic de BarcelonaBarcelonaSpain
| | - Magdalena Castellví
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
| | - Adrià Tort‐Merino
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
| | - Jaume Olives
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
| | - Guadalupe Fernández‐Villullas
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute ofNeuroscience and Physiology, The Sahlgrenska Academy at the University of GothenburgMölndalSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute ofNeuroscience and Physiology, The Sahlgrenska Academy at the University of GothenburgMölndalSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
- Department of Neurodegenerative DiseaseUniversity College LondonLondonUK
- UK Dementia Research Institute at UCLLondonUK
| | - Núria Bargalló
- Radiology Service, Hospital ClínicMRI imaging platform. Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)
| | - Albert Lladó
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
| | - Raquel Sánchez‐Valle
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, University of BarcelonaBarcelonaSpain
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Functional Connectivity in Neurodegenerative Disorders: Alzheimer's Disease and Frontotemporal Dementia. Top Magn Reson Imaging 2020; 28:317-324. [PMID: 31794504 DOI: 10.1097/rmr.0000000000000223] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Neurodegenerative disorders are a growing cause of morbidity and mortality worldwide. Onset is typically insidious and clinical symptoms of behavioral change, memory loss, or cognitive dysfunction may not be evident early in the disease process. Efforts have been made to discover biomarkers that allow for earlier diagnosis of neurodegenerative disorders, to initiate treatment that may slow the course of clinical deterioration. Neuronal dysfunction occurs earlier than clinical symptoms manifest. Thus, assessment of neuronal function using functional brain imaging has been examined as a potential biomarker. While most early studies used task-functional magnetic resonance imaging (fMRI), with the more recent technique of resting-state fMRI, "intrinsic" relationships between brain regions or brain networks have been studied in greater detail in neurodegenerative disorders. In Alzheimer's disease, the most common neurodegenerative disorder, and frontotemporal dementia, another of the common dementias, specific brain networks may be particularly susceptible to dysfunction. In this review, we highlight the major findings of functional connectivity assessed by resting state fMRI in Alzheimer's disease and frontotemporal dementia.
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25
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Plutino A, Camerucci E, Ranaldi V, Baldinelli S, Fiori C, Silvestrini M, Luzzi S. Insight in frontotemporal dementia and progressive supranuclear palsy. Neurol Sci 2020; 41:2135-2142. [PMID: 32128648 DOI: 10.1007/s10072-020-04290-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 02/10/2020] [Indexed: 12/27/2022]
Abstract
INTRODUCTION Progressive supranuclear palsy (PSP) and behavioural variant frontotemporal dementia - (bv-FTD) share common neuropsychological features except for online monitoring awareness. Therefore, the aim of our study is to explore if this assessment could be used in standard clinical practice. MATERIALS AND METHODS We retrospectively analyse 93 subjects (27 FTD, 25 PSP, 42 healthy controls). Neuropsychological and instrumental examinations were performed for each patient. RESULTS FTD patients made fewer self-corrections than PSP patients despite a similar number of total errors. We also performed ROC curves: the area under the curve (AUC) is 0.79. A model for a logistic regression was also developed: the only significant predictor is the number of self-corrections (p = 0.004 β = 1244). DISCUSSION AND CONCLUSIONS In conclusion, our findings show online awareness is more compromised in FTD patients than in PSP patients. This difference could be useful for making a differential diagnosis between the two diseases: for each extra point in number of self-corrections the probability of suffering from PSP increases by about three and a half times (OR 3.47).
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Affiliation(s)
- Andrea Plutino
- Neurological Clinic, Department of Experimental and Clinical Medicine, Università Politecnica delle Marche, Ancona, Italy.
| | - Emanuele Camerucci
- Neurological Clinic, Department of Experimental and Clinical Medicine, Università Politecnica delle Marche, Ancona, Italy
| | - Valentina Ranaldi
- Neurological Clinic, Department of Experimental and Clinical Medicine, Università Politecnica delle Marche, Ancona, Italy
| | - Sara Baldinelli
- Neurological Clinic, Department of Experimental and Clinical Medicine, Università Politecnica delle Marche, Ancona, Italy
| | - Chiara Fiori
- Neurological Clinic, Department of Experimental and Clinical Medicine, Università Politecnica delle Marche, Ancona, Italy
| | - Mauro Silvestrini
- Neurological Clinic, Department of Experimental and Clinical Medicine, Università Politecnica delle Marche, Ancona, Italy
| | - Simona Luzzi
- Neurological Clinic, Department of Experimental and Clinical Medicine, Università Politecnica delle Marche, Ancona, Italy
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26
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Bachli MB, Sedeño L, Ochab JK, Piguet O, Kumfor F, Reyes P, Torralva T, Roca M, Cardona JF, Campo CG, Herrera E, Slachevsky A, Matallana D, Manes F, García AM, Ibáñez A, Chialvo DR. Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach. Neuroimage 2019; 208:116456. [PMID: 31841681 PMCID: PMC7008715 DOI: 10.1016/j.neuroimage.2019.116456] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 10/29/2019] [Accepted: 12/09/2019] [Indexed: 12/12/2022] Open
Abstract
Accurate early diagnosis of neurodegenerative diseases represents a growing challenge for current clinical practice. Promisingly, current tools can be complemented by computational decision-support methods to objectively analyze multidimensional measures and increase diagnostic confidence. Yet, widespread application of these tools cannot be recommended unless they are proven to perform consistently and reproducibly across samples from different countries. We implemented machine-learning algorithms to evaluate the prediction power of neurocognitive biomarkers (behavioral and imaging measures) for classifying two neurodegenerative conditions –Alzheimer Disease (AD) and behavioral variant frontotemporal dementia (bvFTD)– across three different countries (>200 participants). We use machine-learning tools integrating multimodal measures such as cognitive scores (executive functions and cognitive screening) and brain atrophy volume (voxel based morphometry from fronto-temporo-insular regions in bvFTD, and temporo-parietal regions in AD) to identify the most relevant features in predicting the incidence of the diseases. In the Country-1 cohort, predictions of AD and bvFTD became maximally improved upon inclusion of cognitive screenings outcomes combined with atrophy levels. Multimodal training data from this cohort allowed predicting both AD and bvFTD in the other two novel datasets from other countries with high accuracy (>90%), demonstrating the robustness of the approach as well as the differential specificity and reliability of behavioral and neural markers for each condition. In sum, this is the first study, across centers and countries, to validate the predictive power of cognitive signatures combined with atrophy levels for contrastive neurodegenerative conditions, validating a benchmark for future assessments of reliability and reproducibility.
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Affiliation(s)
- M Belen Bachli
- Center for Complex Systems & Brain Sciences (CEMSC(3)), Escuela de Ciencia y Tecnologia (ECyT), Universidad Nacional de San Martín, 25 de Mayo 1169, San Martín, (1650), Buenos Aires, Argentina
| | - Lucas Sedeño
- Institute of Cognitive and Translational Neuroscience (INCYyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Godoy Cruz 2290, Buenos Aires, Argentina.
| | - Jeremi K Ochab
- Marian Smoluchowski Institute of Physics, Mark Kac Complex Systems Research Center Jagiellonian University, Ul. Łojasiewicza 11, PL30-348, Kraków, Poland
| | - Olivier Piguet
- ARC Centre of Excellence in Cognition and Its Disorders, Sydney, Australia; The University of Sydney, Brain and Mind Centre and School of Psychology, Sydney, Australia
| | - Fiona Kumfor
- ARC Centre of Excellence in Cognition and Its Disorders, Sydney, Australia; The University of Sydney, Brain and Mind Centre and School of Psychology, Sydney, Australia
| | - Pablo Reyes
- Radiology, Hospital Universitario San Ignacio (HUSI), Bogotá, Colombia; Medical School, Physiology Sciences, Psychiatry and Mental Health Pontificia Universidad Javeriana (PUJ) - Centro de Memoria y Cognición Intellectus, Hospital Universitario San Ignacio (HUSI), Bogotá, Colombia
| | - Teresa Torralva
- Institute of Cognitive and Translational Neuroscience (INCYyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina
| | - María Roca
- Institute of Cognitive and Translational Neuroscience (INCYyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina
| | | | - Cecilia Gonzalez Campo
- Institute of Cognitive and Translational Neuroscience (INCYyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Godoy Cruz 2290, Buenos Aires, Argentina
| | - Eduar Herrera
- Departamento de Estudios Psicológicos, Universidad Icesi, Cali, Colombia
| | - Andrea Slachevsky
- Gerosciences Center for Brain Health and Metabolism, Santiago, Chile; Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Department, ICBM, Neurosciences Department, East Neuroscience Department, Faculty of Medicine, University of Chile, Avenida Salvador 486, Providencia, Santiago, Chile; Memory and Neuropsychiatric Clinic (CMYN) Neurology Department- Hospital del Salvador & University of Chile, Av. Salvador 364, Providencia, Santiago, Chile; Servicio de Neurología, Departamento de Medicina, Clínica Alemana-Universidad del Desarrollo, Chile
| | - Diana Matallana
- Medical School, Aging Institute, Psychiatry and Mental Health, Pontificia Universidad Javeriana (PUJ) - Centro de Memoria y Cognición Intellectus. Hospital Universitario San Ignacio (HUSI), Bogotá, Colombia
| | - Facundo Manes
- Institute of Cognitive and Translational Neuroscience (INCYyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Godoy Cruz 2290, Buenos Aires, Argentina; ARC Centre of Excellence in Cognition and Its Disorders, Sydney, Australia
| | - Adolfo M García
- Institute of Cognitive and Translational Neuroscience (INCYyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Godoy Cruz 2290, Buenos Aires, Argentina; Faculty of Education, National University of Cuyo (UNCuyo), Sobremonte 74, C5500, Mendoza, Argentina
| | - Agustín Ibáñez
- Institute of Cognitive and Translational Neuroscience (INCYyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Godoy Cruz 2290, Buenos Aires, Argentina; ARC Centre of Excellence in Cognition and Its Disorders, Sydney, Australia; Universidad Autónoma del Caribe, Calle 90, No 46-112, C2754, Barranquilla, Colombia; Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, Diagonal Las Torres, 2640, Santiago, Chile
| | - Dante R Chialvo
- Center for Complex Systems & Brain Sciences (CEMSC(3)), Escuela de Ciencia y Tecnologia (ECyT), Universidad Nacional de San Martín, 25 de Mayo 1169, San Martín, (1650), Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Godoy Cruz 2290, Buenos Aires, Argentina
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27
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Feis RA, Bouts MJRJ, de Vos F, Schouten TM, Panman JL, Jiskoot LC, Dopper EGP, van der Grond J, van Swieten JC, Rombouts SARB. A multimodal MRI-based classification signature emerges just prior to symptom onset in frontotemporal dementia mutation carriers. J Neurol Neurosurg Psychiatry 2019; 90:1207-1214. [PMID: 31203211 DOI: 10.1136/jnnp-2019-320774] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 04/26/2019] [Accepted: 05/12/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND Multimodal MRI-based classification may aid early frontotemporal dementia (FTD) diagnosis. Recently, presymptomatic FTD mutation carriers, who have a high risk of developing FTD, were separated beyond chance level from controls using MRI-based classification. However, it is currently unknown how these scores from classification models progress as mutation carriers approach symptom onset. In this longitudinal study, we investigated multimodal MRI-based classification scores between presymptomatic FTD mutation carriers and controls. Furthermore, we contrasted carriers that converted during follow-up ('converters') and non-converting carriers ('non-converters'). METHODS We acquired anatomical MRI, diffusion tensor imaging and resting-state functional MRI in 55 presymptomatic FTD mutation carriers and 48 healthy controls at baseline, and at 2, 4, and 6 years of follow-up as available. At each time point, FTD classification scores were calculated using a behavioural variant FTD classification model. Classification scores were tested in a mixed-effects model for mean differences and differences over time. RESULTS Presymptomatic mutation carriers did not have higher classification score increase over time than controls (p=0.15), although carriers had higher FTD classification scores than controls on average (p=0.032). However, converters (n=6) showed a stronger classification score increase over time than non-converters (p<0.001). CONCLUSIONS Our findings imply that presymptomatic FTD mutation carriers may remain similar to controls in terms of MRI-based classification scores until they are close to symptom onset. This proof-of-concept study shows the promise of longitudinal MRI data acquisition in combination with machine learning to contribute to early FTD diagnosis.
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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.,Institute of Psychology, 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
| | - 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
| | - 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
| | - 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 Neurology, Erasmus Medical Centre, Rotterdam, 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, 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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28
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Lipid peroxidation biomarkers correlation with medial temporal atrophy in early Alzheimer Disease. Neurochem Int 2019; 129:104519. [DOI: 10.1016/j.neuint.2019.104519] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 07/10/2019] [Accepted: 08/05/2019] [Indexed: 12/11/2022]
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29
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Brzezicki MA, Kobetić MD, Neumann S, Pennington C. Diagnostic accuracy of frontotemporal dementia. An artificial intelligence-powered study of symptoms, imaging and clinical judgement. Adv Med Sci 2019; 64:292-302. [PMID: 30952029 DOI: 10.1016/j.advms.2019.03.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Revised: 01/12/2019] [Accepted: 03/19/2019] [Indexed: 01/28/2023]
Abstract
PURPOSE Frontotemporal dementia (FTD) is a neurodegenerative disorder associated with a poor prognosis and a substantial reduction in quality of life. The rate of misdiagnosis of FTD is very high, with patients often waiting for years without a firm diagnosis. This study investigates the current state of the misdiagnosis of FTD using a novel artificial intelligence-based algorithm. PATIENTS & METHODS An artificial intelligence algorithm has been developed to retrospectively analyse the patient journeys of 47 individuals diagnosed with FTD (age range 52-80). The algorithm analysed the efficiency of patient pathways by utilizing a reward signal of ‒1 to +1 to assess the symptoms, imaging techniques, and clinical judgement in both behavioural and language variants of the disease. RESULTS On average, every patient was subjected to 4.93 investigations, of which 67.4% were radiological scans. From first presentation it took on average 939 days for a firm diagnosis. The mean time between appointments was 204 days, and the average patient had their diagnosis altered 7.37 times during their journey. The algorithm proposed improvements by evaluating the interventions that resulted in a decreased reward signal to both the individual and the population as a whole. CONCLUSIONS The study proves that the algorithm can efficiently guide clinical practice and improve the accuracy of the diagnosis of FTD whilst making the process of auditing faster and more economically viable.
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30
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Hojjati SH, Ebrahimzadeh A, Babajani-Feremi A. Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI. Front Neurol 2019; 10:904. [PMID: 31543860 PMCID: PMC6730495 DOI: 10.3389/fneur.2019.00904] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 08/05/2019] [Indexed: 12/29/2022] Open
Abstract
Accurate prediction of the early stage of Alzheimer's disease (AD) is important but very challenging. The goal of this study was to utilize predictors for diagnosis conversion to AD based on integrating resting-state functional MRI (rs-fMRI) connectivity analysis and structural MRI (sMRI). We included 177 subjects in this study and aimed at identifying patients with mild cognitive impairment (MCI) who progress to AD, MCI converter (MCI-C), patients with MCI who do not progress to AD, MCI non-converter (MCI-NC), patients with AD, and healthy controls (HC). The graph theory was used to characterize different aspects of the rs-fMRI brain network by calculating measures of integration and segregation. The cortical and subcortical measurements, e.g., cortical thickness, were extracted from sMRI data. The rs-fMRI graph measures were combined with the sMRI measures to construct input features of a support vector machine (SVM) and classify different groups of subjects. Two feature selection algorithms [i.e., the discriminant correlation analysis (DCA) and sequential feature collection (SFC)] were used for feature reduction and selecting a subset of optimal features. Maximum accuracy of 67 and 56% for three-group (“AD, MCI-C, and MCI-NC” or “MCI-C, MCI-NC, and HC”) and four-group (“AD, MCI-C, MCI-NC, and HC”) classification, respectively, were obtained with the SFC feature selection algorithm. We also identified hub nodes in the rs-fMRI brain network which were associated with the early stage of AD. Our results demonstrated the potential of the proposed method based on integration of the functional and structural MRI for identification of the early stage of AD.
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Affiliation(s)
- Seyed Hani Hojjati
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, United States.,Department of Electrical Engineering, Babol University of Technology, Babol, Iran.,Neuroscience Institute and Children's Foundation Research Institute, Le Bonheur Children's Hospital, Memphis, TN, United States
| | - Ata Ebrahimzadeh
- Department of Electrical Engineering, Babol University of Technology, Babol, Iran
| | - Abbas Babajani-Feremi
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, United States.,Neuroscience Institute and Children's Foundation Research Institute, Le Bonheur Children's Hospital, Memphis, TN, United States.,Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, TN, United States
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31
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Ray PP, Dash D, De D. A Systematic Review and Implementation of IoT-Based Pervasive Sensor-Enabled Tracking System for Dementia Patients. J Med Syst 2019; 43:287. [PMID: 31317281 DOI: 10.1007/s10916-019-1417-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 07/08/2019] [Indexed: 01/06/2023]
Abstract
In today's world, 46.8 million people suffer from brain related diseases. Dementia is most prevalent of all. In general scenario, a dementia patient lacks proper guidance in searching out the way to return back at his/her home. Thus, increasing the risk of getting damaged at individual-health level. Therefore, it is important to track their movement in more sophisticated manner as possible. With emergence of wearables, GPS sensors and Internet of Things (IoT), such devices have become available in public domain. Smartphone apps support caregiver to locate the dementia patients in real-time. RF, GSM, 3G, Wi-Fi and 4G technology fill the communication gap between patient and caregiver to bring them closer. In this paper, we incorporated 7 most popular wearables for investigation to seek appropriateness for dementia tracking in recent times in systematic manners. We performed an in-depth review of these wearables as per the cost, technology wise and application wise characteristics. A case novel study i.e. IoT-based Force Sensor Resistance enabled System-FSRIoT, has been proposed and implemented to validate the effectiveness of IoT in the domain of smarter dementia patient tracking in wearable form factor. The results show promising aspect of a whole new notion to leverage efficient assistive physio-medical healthcare to the dementia patients and the affected family members to reduce life risks and achieve a better social life.
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Affiliation(s)
- Partha Pratim Ray
- Department of Computer Applications, Sikkim University, Gangtok, India.
| | - Dinesh Dash
- Department of Computer Science and Engineering, NIT Patna, Patna, India
| | - Debashis De
- Department of Computer Science and Engineering, MAKAUT, Kolkata, India
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32
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Bouts MJRJ, Möller C, Hafkemeijer A, van Swieten JC, Dopper E, van der Flier WM, Vrenken H, Wink AM, Pijnenburg YAL, Scheltens P, Barkhof F, Schouten TM, de Vos F, Feis RA, van der Grond J, de Rooij M, Rombouts SARB. Single Subject Classification of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging. J Alzheimers Dis 2019; 62:1827-1839. [PMID: 29614652 DOI: 10.3233/jad-170893] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND/OBJECTIVE Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to-on a subject-basis-differentiate these dementia-types is not yet known. METHODS Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier's ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC). RESULTS Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41). CONCLUSION Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI.
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Affiliation(s)
- Mark J R J Bouts
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Christiane Möller
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Anne Hafkemeijer
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - John C van Swieten
- Department of Clinical Genetics, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.,Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Elise Dopper
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands.,Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.,Department of Epidemiology and Biostatistics, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.,Department of Physics and Medical Technology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Alle Meije Wink
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Yolande A L Pijnenburg
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.,Institute of Neurology and Healthcare Engineering, University College London, London, UK
| | - Tijn M Schouten
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Frank de Vos
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Rogier A Feis
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Mark de Rooij
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Serge A R B Rombouts
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
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Forouzannezhad P, Abbaspour A, Fang C, Cabrerizo M, Loewenstein D, Duara R, Adjouadi M. A survey on applications and analysis methods of functional magnetic resonance imaging for Alzheimer’s disease. J Neurosci Methods 2019; 317:121-140. [DOI: 10.1016/j.jneumeth.2018.12.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 12/04/2018] [Accepted: 12/17/2018] [Indexed: 12/23/2022]
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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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Koikkalainen JR, Rhodius-Meester HFM, Frederiksen KS, Bruun M, Hasselbalch SG, Baroni M, Mecocci P, Vanninen R, Remes A, Soininen H, van Gils M, van der Flier WM, Scheltens P, Barkhof F, Erkinjuntti T, Lötjönen JMP. Automatically computed rating scales from MRI for patients with cognitive disorders. Eur Radiol 2019; 29:4937-4947. [DOI: 10.1007/s00330-019-06067-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 01/09/2019] [Accepted: 02/04/2019] [Indexed: 01/09/2023]
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Bruun M, Koikkalainen J, Rhodius-Meester HFM, Baroni M, Gjerum L, van Gils M, Soininen H, Remes AM, Hartikainen P, Waldemar G, Mecocci P, Barkhof F, Pijnenburg Y, van der Flier WM, Hasselbalch SG, Lötjönen J, Frederiksen KS. Detecting frontotemporal dementia syndromes using MRI biomarkers. NEUROIMAGE-CLINICAL 2019; 22:101711. [PMID: 30743135 PMCID: PMC6369219 DOI: 10.1016/j.nicl.2019.101711] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 02/01/2019] [Accepted: 02/03/2019] [Indexed: 12/20/2022]
Abstract
Background Diagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal dementia subtypes from other diagnostic groups, and from one another. Methods In this retrospective multicenter cohort study, we included 1213 patients (age 67 ± 9, 48% females) from two memory clinic cohorts: 116 frontotemporal dementia, 341 Alzheimer's disease, 66 Dementia with Lewy bodies, 40 vascular dementia, 104 other dementias, 229 mild cognitive impairment, and 317 subjective cognitive decline. Three MRI atrophy biomarkers were derived from the normalized volumes of automatically segmented cortical regions: 1) the anterior vs. posterior index, 2) the asymmetry index, and 3) the temporal pole left index. We used the following performance metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To account for the low prevalence of frontotemporal dementia we pursued a high specificity of 95%. Cross-validation was used in assessing the performance. The generalizability was assessed in an independent cohort (n = 200). Results The anterior vs. posterior index performed with an AUC of 83% for differentiation of frontotemporal dementia from all other diagnostic groups (Sensitivity = 59%, Specificity = 95%, positive likelihood ratio = 11.8, negative likelihood ratio = 0.4). The asymmetry index showed highest performance for separation of primary progressive aphasia and behavioral variant frontotemporal dementia (AUC = 85%, Sensitivity = 79%, Specificity = 92%, positive likelihood ratio = 9.9, negative likelihood ratio = 0.2), whereas the temporal pole left index was specific for detection of semantic variant primary progressive aphasia (AUC = 85%, Sensitivity = 82%, Specificity = 80%, positive likelihood ratio = 4.1, negative likelihood ratio = 0.2). The validation cohort provided corresponding results for the anterior vs. posterior index and temporal pole left index. Conclusion This study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia. Quantitative MRI biomarkers (API, ASI, and TPL) for detection of FTD and its subtypes. API differentiated FTD from other diagnostic groups with AUC of 83%. ASI and TPL showed highest performance for PPA subtypes. A subcortical bvFTD subtype resembling AD atrophy pattern seems undetectable for MRI.
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Affiliation(s)
- Marie Bruun
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark.
| | | | - Hanneke F M Rhodius-Meester
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Marta Baroni
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Le Gjerum
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
| | - Mark van Gils
- VTT Technical Research Center of Finland Ltd, Tampere, Finland
| | - Hilkka Soininen
- Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland; Neurocenter, neurology, Kuopio University Hospital, Kuopio, Finland
| | - Anne M Remes
- Unit of Clinical Neuroscience, Neurology, University of Oulu, Oulu, Finland; Medical Research Center, Oulu University Hospital, Oulu, Finland
| | | | - Gunhild Waldemar
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
| | - Patrizia Mecocci
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands; UCL institutes of Neurology and Healthcare Engineering, London, UK
| | - Yolande Pijnenburg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Steen G Hasselbalch
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
| | | | - Kristian S Frederiksen
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
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Ahmed MR, Zhang Y, Feng Z, Lo B, Inan OT, Liao H. Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects. IEEE Rev Biomed Eng 2018; 12:19-33. [PMID: 30561351 DOI: 10.1109/rbme.2018.2886237] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Dementia, a chronic and progressive cognitive declination of brain function caused by disease or impairment, is becoming more prevalent due to the aging population. A major challenge in dementia is achieving accurate and timely diagnosis. In recent years, neuroimaging with computer-aided algorithms have made remarkable advances in addressing this challenge. The success of these approaches is mostly attributed to the application of machine learning techniques for neuroimaging. In this review paper, we present a comprehensive survey of automated diagnostic approaches for dementia using medical image analysis and machine learning algorithms published in the recent years. Based on the rigorous review of the existing works, we have found that, while most of the studies focused on Alzheimer's disease, recent research has demonstrated reasonable performance in the identification of other types of dementia remains a major challenge. Multimodal imaging analysis deep learning approaches have shown promising results in the diagnosis of these other types of dementia. The main contributions of this review paper are as follows. 1) Based on the detailed analysis of the existing literature, this paper discusses neuroimaging procedures for dementia diagnosis. 2) It systematically explains the most recent machine learning techniques and, in particular, deep learning approaches for early detection of dementia.
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McCarthy J, Collins DL, Ducharme S. Morphometric MRI as a diagnostic biomarker of frontotemporal dementia: A systematic review to determine clinical applicability. Neuroimage Clin 2018; 20:685-696. [PMID: 30218900 PMCID: PMC6140291 DOI: 10.1016/j.nicl.2018.08.028] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 07/31/2018] [Accepted: 08/28/2018] [Indexed: 01/21/2023]
Abstract
Frontotemporal dementia (FTD) is difficult to diagnose, due to its heterogeneous nature and overlap in symptoms with primary psychiatric disorders. Brain MRI for atrophy is a key biomarker but lacks sensitivity in the early stage. Morphometric MRI-based measures and machine learning techniques are a promising tool to improve diagnostic accuracy. Our aim was to review the current state of the literature using morphometric MRI to classify FTD and assess its applicability for clinical practice. A search was completed using Pubmed and PsychInfo of studies which conducted a classification of subjects with FTD from non-FTD (controls or another disorder) using morphometric MRI metrics on an individual level, using single or combined approaches. 28 relevant articles were included and systematically reviewed following PRISMA guidelines. The studies were categorized based on the type of FTD subjects included and the group(s) against which they were classified. Studies varied considerably in subject selection, MRI methodology, and classification approach, and results are highly heterogeneous. Overall many studies indicate good diagnostic accuracy, with higher performance when differentiating FTD from controls (highest result was accuracy of 100%) than other dementias (highest result was AUC of 0.874). Very few machine learning algorithms have been tested in prospective replication. In conclusion, morphometric MRI with machine learning shows potential as an early diagnostic biomarker of FTD, however studies which use rigorous methodology and validate findings in an independent real-life cohort are necessary before this method can be recommended for use clinically.
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Affiliation(s)
- Jillian McCarthy
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Simon Ducharme
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.
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Bruun M, Rhodius-Meester HFM, Koikkalainen J, Baroni M, Gjerum L, Lemstra AW, Barkhof F, Remes AM, Urhemaa T, Tolonen A, Rueckert D, van Gils M, Frederiksen KS, Waldemar G, Scheltens P, Mecocci P, Soininen H, Lötjönen J, Hasselbalch SG, van der Flier WM. Evaluating combinations of diagnostic tests to discriminate different dementia types. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2018; 10:509-518. [PMID: 30320203 PMCID: PMC6180596 DOI: 10.1016/j.dadm.2018.07.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Introduction We studied, using a data-driven approach, how different combinations of diagnostic tests contribute to the differential diagnosis of dementia. Methods In this multicenter study, we included 356 patients with Alzheimer's disease, 87 frontotemporal dementia, 61 dementia with Lewy bodies, 38 vascular dementia, and 302 controls. We used a classifier to assess accuracy for individual performance and combinations of cognitive tests, cerebrospinal fluid biomarkers, and automated magnetic resonance imaging features for pairwise differentiation between dementia types. Results Cognitive tests had good performance in separating any type of dementia from controls. Cerebrospinal fluid optimally contributed to identifying Alzheimer's disease, whereas magnetic resonance imaging features aided in separating vascular dementia, dementia with Lewy bodies, and frontotemporal dementia. Combining diagnostic tests increased the accuracy, with balanced accuracies ranging from 78% to 97%. Discussion Different diagnostic tests have their distinct roles in differential diagnostics of dementias. Our results indicate that combining different diagnostic tests may increase the accuracy further.
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Affiliation(s)
- Marie Bruun
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Hanneke F M Rhodius-Meester
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | | | - Marta Baroni
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Le Gjerum
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Afina W Lemstra
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands.,UCL Institutes of Neurology and Healthcare Engineering, London, United Kingdom
| | - Anne M Remes
- Medical Research Center, Oulu University Hospital, Oulu, Finland.,Unit of Clinical Neuroscience, Neurology, University of Oulu, Oulu, Finland
| | - Timo Urhemaa
- VTT Technical Research Center of Finland Ltd, Tampere, Finland
| | - Antti Tolonen
- VTT Technical Research Center of Finland Ltd, Tampere, Finland
| | - Daniel Rueckert
- Department of Computing, Imperial College, London, United Kingdom
| | - Mark van Gils
- VTT Technical Research Center of Finland Ltd, Tampere, Finland
| | - Kristian S Frederiksen
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Gunhild Waldemar
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Philip Scheltens
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Patrizia Mecocci
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Hilkka Soininen
- Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland
| | | | - Steen G Hasselbalch
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Wiesje M van der Flier
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, the Netherlands.,Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
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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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Tolonen A, Rhodius-Meester HFM, Bruun M, Koikkalainen J, Barkhof F, Lemstra AW, Koene T, Scheltens P, Teunissen CE, Tong T, Guerrero R, Schuh A, Ledig C, Baroni M, Rueckert D, Soininen H, Remes AM, Waldemar G, Hasselbalch SG, Mecocci P, van der Flier WM, Lötjönen J. Data-Driven Differential Diagnosis of Dementia Using Multiclass Disease State Index Classifier. Front Aging Neurosci 2018; 10:111. [PMID: 29922145 PMCID: PMC5996907 DOI: 10.3389/fnagi.2018.00111] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Accepted: 04/03/2018] [Indexed: 01/18/2023] Open
Abstract
Clinical decision support systems (CDSSs) hold potential for the differential diagnosis of neurodegenerative diseases. We developed a novel CDSS, the PredictND tool, designed for differential diagnosis of different types of dementia. It combines information obtained from multiple diagnostic tests such as neuropsychological tests, MRI and cerebrospinal fluid samples. Here we evaluated how the classifier used in it performs in differentiating between controls with subjective cognitive decline, dementia due to Alzheimer's disease, vascular dementia, frontotemporal lobar degeneration and dementia with Lewy bodies. We used the multiclass Disease State Index classifier, which is the classifier used by the PredictND tool, to differentiate between controls and patients with the four different types of dementia. The multiclass Disease State Index classifier is an extension of a previously developed two-class Disease State Index classifier. As the two-class Disease State Index classifier, the multiclass Disease State Index classifier also offers a visualization of its decision making process, which makes it especially suitable for medical decision support where interpretability of the results is highly important. A subset of the Amsterdam Dementia cohort, consisting of 504 patients (age 65 ± 8 years, 44% females) with data from neuropsychological tests, cerebrospinal fluid samples and both automatic and visual MRI quantifications, was used for the evaluation. The Disease State Index classifier was highly accurate in separating the five classes from each other (balanced accuracy 82.3%). Accuracy was highest for vascular dementia and lowest for dementia with Lewy bodies. For the 50% of patients for which the classifier was most confident on the classification the balanced accuracy was 93.6%. Data-driven CDSSs can be of aid in differential diagnosis in clinical practice. The decision support system tested in this study was highly accurate in separating the different dementias and controls from each other. In addition to the predicted class, it also provides a confidence measure for the classification.
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Affiliation(s)
- Antti Tolonen
- VTT Technical Research Centre of Finland, Tampere, Finland
| | - Hanneke F M Rhodius-Meester
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Marie Bruun
- Danish Dementia Research Centre, Rigshospitalet, Copenhagen, Denmark
| | | | - Frederik Barkhof
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands.,Institutes of Neurology and Healthcare Engineering, University College London, London, United Kingdom
| | - Afina W Lemstra
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Teddy Koene
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Philip Scheltens
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Charlotte E Teunissen
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Tong Tong
- Imperial College London, London, United Kingdom
| | | | | | | | - Marta Baroni
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | | | - Hilkka Soininen
- Institute of Clinical Medicine and Department of Neurology, University of Eastern Finland, Kuopio, Finland.,Neurology, Neurocenter, Kuopio University Hospital, Kuopio, Finland
| | - Anne M Remes
- Institute of Clinical Medicine and Department of Neurology, University of Eastern Finland, Kuopio, Finland.,Neurology, Neurocenter, Kuopio University Hospital, Kuopio, Finland
| | - Gunhild Waldemar
- Danish Dementia Research Centre, Rigshospitalet, Copenhagen, Denmark
| | | | - Patrizia Mecocci
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Wiesje M van der Flier
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands.,Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, Netherlands
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Cajanus A, Hall A, Koikkalainen J, Solje E, Tolonen A, Urhemaa T, Liu Y, Haanpää RM, Hartikainen P, Helisalmi S, Korhonen V, Rueckert D, Hasselbalch S, Waldemar G, Mecocci P, Vanninen R, van Gils M, Soininen H, Lötjönen J, Remes AM. Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis. Dement Geriatr Cogn Dis Extra 2018; 8:51-59. [PMID: 29606954 PMCID: PMC5869565 DOI: 10.1159/000486849] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Accepted: 01/15/2018] [Indexed: 12/11/2022] Open
Abstract
Aims We assessed the value of automated MRI quantification methods in the differential diagnosis of behavioral-variant frontotemporal dementia (bvFTD) from Alzheimer disease (AD), Lewy body dementia (LBD), and subjective memory complaints (SMC). We also examined the role of the C9ORF72-related genetic status in the differentiation sensitivity. Methods The MRI scans of 50 patients with bvFTD (17 C9ORF72 expansion carriers) were analyzed using 6 quantification methods as follows: voxel-based morphometry (VBM), tensor-based morphometry, volumetry (VOL), manifold learning, grading, and white-matter hyperintensities. Each patient was then individually compared to an independent reference group in order to attain diagnostic suggestions. Results Only VBM and VOL showed utility in correctly identifying bvFTD from our set of data. The overall classification sensitivity of bvFTD with VOL + VBM achieved a total sensitivity of 60%. Using VOL + VBM, 32% were misclassified as having LBD. There was a trend of higher values for classification sensitivity of the C9ORF72 expansion carriers than noncarriers. Conclusion VOL, VBM, and their combination are effective in differential diagnostics between bvFTD and AD or SMC. However, MRI atrophy profiles for bvFTD and LBD are too similar for a reliable differentiation with the quantification methods tested in this study.
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Affiliation(s)
- Antti Cajanus
- Institute of Clinical Medicine - Neurology, University of Eastern Finland, Kuopio, Finland.,Neurocenter, Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Anette Hall
- Institute of Clinical Medicine - Neurology, University of Eastern Finland, Kuopio, Finland
| | | | - Eino Solje
- Institute of Clinical Medicine - Neurology, University of Eastern Finland, Kuopio, Finland.,Neurocenter, Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Antti Tolonen
- VTT Technical Research Centre of Finland Ltd., Tampere, Finland
| | - Timo Urhemaa
- VTT Technical Research Centre of Finland Ltd., Tampere, Finland
| | - Yawu Liu
- Institute of Clinical Medicine - Neurology, University of Eastern Finland, Kuopio, Finland.,Department of Radiology, Kuopio University Hospital, Kuopio, Finland
| | - Ramona M Haanpää
- Institute of Clinical Medicine - Neurology, University of Eastern Finland, Kuopio, Finland
| | - Päivi Hartikainen
- Institute of Clinical Medicine - Neurology, University of Eastern Finland, Kuopio, Finland.,Neurocenter, Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Seppo Helisalmi
- Institute of Clinical Medicine - Neurology, University of Eastern Finland, Kuopio, Finland
| | - Ville Korhonen
- Institute of Clinical Medicine - Neurology, University of Eastern Finland, Kuopio, Finland.,Neurocenter, Neurosurgery, Kuopio University Hospital, Kuopio, Finland
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London, United Kingdom
| | - Steen Hasselbalch
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Gunhild Waldemar
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Patrizia Mecocci
- Section of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Ritva Vanninen
- Department of Radiology, Kuopio University Hospital, Kuopio, Finland
| | - Mark van Gils
- VTT Technical Research Centre of Finland Ltd., Tampere, Finland
| | - Hilkka Soininen
- Institute of Clinical Medicine - Neurology, University of Eastern Finland, Kuopio, Finland.,Neurocenter, Neurology, Kuopio University Hospital, Kuopio, Finland
| | | | - Anne M Remes
- Institute of Clinical Medicine - Neurology, University of Eastern Finland, Kuopio, Finland.,Neurocenter, Neurology, Kuopio University Hospital, Kuopio, Finland.,Medical Research Center, Oulu University Hospital, Oulu, Finland.,Unit of Clinical Neuroscience, Neurology, University of Oulu, Oulu, Finland
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