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Mao C, You H, Hou B, Chu S, Jin W, Huang X, Shang L, Feng F, Peng B, Gao J. Differentiation of Alzheimer’s Disease from Frontotemporal Dementia and Mild Cognitive Impairment Based on Arterial Spin Labeling Magnetic Resonance Imaging: A Pilot Cross-Sectional Study from PUMCH Dementia Cohort. J Alzheimers Dis 2023; 93:509-519. [PMID: 37038812 DOI: 10.3233/jad-221023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
Background: Arterial spin labeling (ASL) is helpful in early diagnosis and differential diagnosis of Alzheimer’s disease (AD), with advantages including no exposure to radioactivity, no injection of a contrast agent, more accessible, and relatively less expensive. Objective: To establish the perfusion pattern of different dementia in Chinese population and evaluate the effectiveness of ASL in differentiating AD from cognitive unimpaired (CU), mild cognitive impairment (MCI), and frontotemporal dementia (FTD). Methods: Four groups of participants were enrolled, including AD, FTD, MCI, and CU based on clinical diagnosis from PUMCH dementia cohort. ASL image was collected using 3D spiral fast spin echo–based pseudo-continuous ASL pulse sequence with background suppression and a high resolution T1-weighted scan covering the whole brain. Data processing was performed using Dr. Brain Platform to get cerebral blood flow (ml/100g/min) in every region of interest cortices. Results: Participants included 66 AD, 26 FTD, 21 MCI, and 21 CU. Statistically, widespread hypoperfusion neocortices, most significantly in temporal-parietal-occipital cortices, but not hippocampus and subcortical nucleus were found in AD. Hypoperfusion in parietal lobe was most significantly associated with cognitive decline in AD. Hypoperfusion in parietal lobe was found in MCI and extended to adjacent temporal, occipital and posterior cingulate cortices in AD. Significant reduced perfusion in frontal and temporal cortices, including subcortical nucleus and anterior cingulate cortex were found in FTD. Hypoperfusion regions were relatively symmetrical in AD and left predominant especially in FTD. Conclusion: Specific patterns of ASL hypoperfusion were helpful in differentiating AD from CU, MCI, and FTD.
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Affiliation(s)
- Chenhui Mao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Hui You
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of MedicalScience/ Peking Union Medical College, Beijing, China
| | - Bo Hou
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of MedicalScience/ Peking Union Medical College, Beijing, China
| | - Shanshan Chu
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Wei Jin
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Xinying Huang
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Li Shang
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of MedicalScience/ Peking Union Medical College, Beijing, China
| | - Bin Peng
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Jing Gao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
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Shea YF, Pan Y, Mak HKF, Bao Y, Lee SC, Chiu PKC, Chan HWF. A systematic review of atypical Alzheimer's disease including behavioural and psychological symptoms. Psychogeriatrics 2021; 21:396-406. [PMID: 33594793 DOI: 10.1111/psyg.12665] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 01/06/2021] [Accepted: 01/25/2021] [Indexed: 12/20/2022]
Abstract
Alzheimer's disease (AD) is the commonest cause of dementia, characterized by the clinical presentation of progressive anterograde episodic memory impairment. However, atypical presentation of patients is increasingly recognized. These atypical AD include logopenic aphasia, behavioural variant AD, posterior cortical atrophy, and corticobasal syndrome. These atypical AD are more common in patients with young onset AD before the age of 65 years old. Since medical needs (including the behavioural and psychological symptoms of dementia) of atypical AD patients could be different from typical AD patients, it is important for clinicians to be aware of these atypical forms of AD. In addition, disease modifying treatment may be available in the future. This review aims at providing an update on various important subtypes of atypical AD including behavioural and psychological symptoms.
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Affiliation(s)
- Yat-Fung Shea
- Department of Medicine, LKS Faculty of Medicine, University of Hong Kong, Queen Mary Hospital, Pok Fu Lam, Hong Kong
| | - Yining Pan
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Henry Ka-Fung Mak
- Department of Diagnostic Radiology, LKS Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Yiwen Bao
- Department of Diagnostic Radiology, LKS Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Shui-Ching Lee
- Department of Medicine, LKS Faculty of Medicine, University of Hong Kong, Queen Mary Hospital, Pok Fu Lam, Hong Kong
| | - Patrick Ka-Chun Chiu
- Department of Medicine, LKS Faculty of Medicine, University of Hong Kong, Queen Mary Hospital, Pok Fu Lam, Hong Kong
| | - Hon-Wai Felix Chan
- Department of Medicine, LKS Faculty of Medicine, University of Hong Kong, Queen Mary Hospital, Pok Fu Lam, Hong Kong
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Abstract
PURPOSE OF REVIEW This article presents an overview of the clinical syndrome of posterior cortical atrophy (PCA), including its pathologic underpinnings, clinical presentation, investigation findings, diagnostic criteria, and management. RECENT FINDINGS PCA is usually an atypical form of Alzheimer disease with relatively young age at onset. New diagnostic criteria allow patients to be diagnosed on a syndromic basis as having a primary visual (pure) form or more complex (plus) form of PCA and, when possible, on a disease-specific basis using biomarkers or underlying pathology. Imaging techniques have demonstrated that some pathologic processes are concordant (atrophy, hypometabolism, tau deposition) with clinical symptoms and some are discordant (widespread amyloid deposition). International efforts are under way to establish the genetic underpinnings of this typically sporadic form of Alzheimer disease. In the absence of specific disease-modifying therapies, a number of practical suggestions can be offered to patients and their families to facilitate reading and activities of daily living, promote independence, and improve quality of life SUMMARY: While rare, PCA is an important diagnostic entity for neurologists, ophthalmologists, and optometrists to recognize to allow for early accurate diagnosis and appropriate patient management. PCA provides an important opportunity to investigate the causes of selective vulnerability in Alzheimer disease.
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Firth NC, Primativo S, Marinescu RV, Shakespeare TJ, Suarez-Gonzalez A, Lehmann M, Carton A, Ocal D, Pavisic I, Paterson RW, Slattery CF, Foulkes AJM, Ridha BH, Gil-Néciga E, Oxtoby NP, Young AL, Modat M, Cardoso MJ, Ourselin S, Ryan NS, Miller BL, Rabinovici GD, Warrington EK, Rossor MN, Fox NC, Warren JD, Alexander DC, Schott JM, Yong KXX, Crutch SJ. Longitudinal neuroanatomical and cognitive progression of posterior cortical atrophy. Brain 2019; 142:2082-2095. [PMID: 31219516 PMCID: PMC6598737 DOI: 10.1093/brain/awz136] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 02/28/2019] [Accepted: 03/24/2019] [Indexed: 01/27/2023] Open
Abstract
Posterior cortical atrophy is a clinico-radiological syndrome characterized by progressive decline in visual processing and atrophy of posterior brain regions. With the majority of cases attributable to Alzheimer's disease and recent evidence for genetic risk factors specifically related to posterior cortical atrophy, the syndrome can provide important insights into selective vulnerability and phenotypic diversity. The present study describes the first major longitudinal investigation of posterior cortical atrophy disease progression. Three hundred and sixty-one individuals (117 posterior cortical atrophy, 106 typical Alzheimer's disease, 138 controls) fulfilling consensus criteria for posterior cortical atrophy-pure and typical Alzheimer's disease were recruited from three centres in the UK, Spain and USA. Participants underwent up to six annual assessments involving MRI scans and neuropsychological testing. We constructed longitudinal trajectories of regional brain volumes within posterior cortical atrophy and typical Alzheimer's disease using differential equation models. We compared and contrasted the order in which regional brain volumes become abnormal within posterior cortical atrophy and typical Alzheimer's disease using event-based models. We also examined trajectories of cognitive decline and the order in which different cognitive tests show abnormality using the same models. Temporally aligned trajectories for eight regions of interest revealed distinct (P < 0.002) patterns of progression in posterior cortical atrophy and typical Alzheimer's disease. Patients with posterior cortical atrophy showed early occipital and parietal atrophy, with subsequent higher rates of temporal atrophy and ventricular expansion leading to tissue loss of comparable extent later. Hippocampal, entorhinal and frontal regions underwent a lower rate of change and never approached the extent of posterior cortical involvement. Patients with typical Alzheimer's disease showed early hippocampal atrophy, with subsequent higher rates of temporal atrophy and ventricular expansion. Cognitive models showed tests sensitive to visuospatial dysfunction declined earlier in posterior cortical atrophy than typical Alzheimer's disease whilst tests sensitive to working memory impairment declined earlier in typical Alzheimer's disease than posterior cortical atrophy. These findings indicate that posterior cortical atrophy and typical Alzheimer's disease have distinct sites of onset and different profiles of spatial and temporal progression. The ordering of disease events both motivates investigation of biological factors underpinning phenotypic heterogeneity, and informs the selection of measures for clinical trials in posterior cortical atrophy.
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Affiliation(s)
- Nicholas C Firth
- Dementia Research Centre, Institute of Neurology, University College London, 8–11 Queen Square, London, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, Gower Street, London, UK
| | - Silvia Primativo
- Dementia Research Centre, Institute of Neurology, University College London, 8–11 Queen Square, London, UK
- Department of Human Science, LUMSA University, Via della Traspontina, 21, Rome, Italy
| | - Razvan-Valentin Marinescu
- Centre for Medical Image Computing, Department of Computer Science, University College London, Gower Street, London, UK
| | - Timothy J Shakespeare
- Dementia Research Centre, Institute of Neurology, University College London, 8–11 Queen Square, London, UK
| | - Aida Suarez-Gonzalez
- Dementia Research Centre, Institute of Neurology, University College London, 8–11 Queen Square, London, UK
- Department of Neurology, University Hospital Virgen del Rocio, Seville, Spain
| | - Manja Lehmann
- Dementia Research Centre, Institute of Neurology, University College London, 8–11 Queen Square, London, UK
- Memory and Aging Center, University of California San Francisco, San Francisco, California, USA
| | - Amelia Carton
- Dementia Research Centre, Institute of Neurology, University College London, 8–11 Queen Square, London, UK
| | - Dilek Ocal
- Dementia Research Centre, Institute of Neurology, University College London, 8–11 Queen Square, London, UK
| | - Ivanna Pavisic
- Dementia Research Centre, Institute of Neurology, University College London, 8–11 Queen Square, London, UK
| | - Ross W Paterson
- Dementia Research Centre, Institute of Neurology, University College London, 8–11 Queen Square, London, UK
| | - Catherine F Slattery
- Dementia Research Centre, Institute of Neurology, University College London, 8–11 Queen Square, London, UK
| | - Alexander J M Foulkes
- Dementia Research Centre, Institute of Neurology, University College London, 8–11 Queen Square, London, UK
| | - Basil H Ridha
- Dementia Research Centre, Institute of Neurology, University College London, 8–11 Queen Square, London, UK
| | - Eulogio Gil-Néciga
- Department of Neurology, University Hospital Virgen del Rocio, Seville, Spain
| | - Neil P Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, University College London, Gower Street, London, UK
| | - Alexandra L Young
- Centre for Medical Image Computing, Department of Computer Science, University College London, Gower Street, London, UK
| | - Marc Modat
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - M Jorge Cardoso
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Natalie S Ryan
- Dementia Research Centre, Institute of Neurology, University College London, 8–11 Queen Square, London, UK
| | - Bruce L Miller
- Memory and Aging Center, University of California San Francisco, San Francisco, California, USA
| | - Gil D Rabinovici
- Memory and Aging Center, University of California San Francisco, San Francisco, California, USA
| | - Elizabeth K Warrington
- Dementia Research Centre, Institute of Neurology, University College London, 8–11 Queen Square, London, UK
| | - Martin N Rossor
- Dementia Research Centre, Institute of Neurology, University College London, 8–11 Queen Square, London, UK
| | - Nick C Fox
- Dementia Research Centre, Institute of Neurology, University College London, 8–11 Queen Square, London, UK
| | - Jason D Warren
- Dementia Research Centre, Institute of Neurology, University College London, 8–11 Queen Square, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, Gower Street, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, Institute of Neurology, University College London, 8–11 Queen Square, London, UK
| | - Keir X X Yong
- Dementia Research Centre, Institute of Neurology, University College London, 8–11 Queen Square, London, UK
| | - Sebastian J Crutch
- Dementia Research Centre, Institute of Neurology, University College London, 8–11 Queen Square, London, UK
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Increased white matter metabolic rates in autism spectrum disorder and schizophrenia. Brain Imaging Behav 2019; 12:1290-1305. [PMID: 29168086 DOI: 10.1007/s11682-017-9785-9] [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] [Indexed: 12/16/2022]
Abstract
Both autism spectrum disorder (ASD) and schizophrenia are often characterized as disorders of white matter integrity. Multimodal investigations have reported elevated metabolic rates, cerebral perfusion and basal activity in various white matter regions in schizophrenia, but none of these functions has previously been studied in ASD. We used 18fluorodeoxyglucose positron emission tomography to compare white matter metabolic rates in subjects with ASD (n = 25) to those with schizophrenia (n = 41) and healthy controls (n = 55) across a wide range of stereotaxically placed regions-of-interest. Both subjects with ASD and schizophrenia showed increased metabolic rates across the white matter regions assessed, including internal capsule, corpus callosum, and white matter in the frontal and temporal lobes. These increases were more pronounced, more widespread and more asymmetrical in subjects with ASD than in those with schizophrenia. The highest metabolic increases in both disorders were seen in the prefrontal white matter and anterior limb of the internal capsule. Compared to normal controls, differences in gray matter metabolism were less prominent and differences in adjacent white matter metabolism were more prominent in subjects with ASD than in those with schizophrenia. Autism spectrum disorder and schizophrenia are associated with heightened metabolic activity throughout the white matter. Unlike in the gray matter, the vector of white matter metabolic abnormalities appears to be similar in ASD and schizophrenia, may reflect inefficient functional connectivity with compensatory hypermetabolism, and may be a common feature of neurodevelopmental disorders.
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Parker TD, Slattery CF, Yong KXX, Nicholas JM, Paterson RW, Foulkes AJM, Malone IB, Thomas DL, Cash DM, Crutch SJ, Fox NC, Schott JM. Differences in hippocampal subfield volume are seen in phenotypic variants of early onset Alzheimer's disease. Neuroimage Clin 2018; 21:101632. [PMID: 30558867 PMCID: PMC6411912 DOI: 10.1016/j.nicl.2018.101632] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 12/05/2018] [Accepted: 12/08/2018] [Indexed: 11/02/2022]
Abstract
The most common presentation of early onset Alzheimer's disease (EOAD - defined as symptom onset <65 years) is with progressive episodic memory impairment - amnestic or typical Alzheimer's disease (tAD). However, EOAD is notable for its phenotypic heterogeneity, with posterior cortical atrophy (PCA) - characterised by prominent higher-order visual processing deficits and relative sparing of episodic memory - the second most common canonical phenotype. The hippocampus, which comprises a number of interconnected anatomically and functionally distinct subfields, is centrally involved in Alzheimer's disease and is a crucial mediator of episodic memory. The extent to which volumes of individual hippocampal subfields differ between different phenotypes in EOAD is unclear. The aim of this analysis was to investigate the hypothesis that patients with a PCA phenotype will exhibit differences in specific hippocampal subfield volumes compared to tAD. We studied 63 participants with volumetric T1-weighted MRI performed on the same 3T scanner: 39 EOAD patients [27 with tAD and 12 with PCA] and 24 age-matched controls. Volumetric estimates of the following hippocampal subfields for each participant were obtained using Freesurfer version 6.0: CA1, CA2/3, CA4, presubiculum, subiculum, hippocampal tail, parasubiculum, the molecular and granule cell layers of the dentate gryus (GCMLDG), the molecular layer, and the hippocampal amygdala transition area (HATA). Linear regression analyses comparing mean hippocampal subfield volumes between groups, adjusting for age, sex and head size, were performed. Using a Bonferonni-corrected p-value of p < 0.0025, compared to controls, tAD was associated with atrophy in all hippocampal regions, except the parasubiculum. In PCA patients compared to controls, the strongest evidence for volume loss was in the left presubiclum, right subiculum, right GCMLDG, right molecular layer and the right HATA. Compared to PCA, patients with tAD had strong evidence for smaller volumes in left CA1 and left hippocampal tail. In conclusion, these data provide evidence that hippocampal subfield volumes differ in different phenotypes of EOAD.
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Affiliation(s)
- Thomas D Parker
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, UCL, London, UK.
| | - Catherine F Slattery
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, UCL, London, UK
| | - Keir X X Yong
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, UCL, London, UK
| | - Jennifer M Nicholas
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, UCL, London, UK; Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Ross W Paterson
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, UCL, London, UK
| | - Alexander J M Foulkes
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, UCL, London, UK
| | - Ian B Malone
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, UCL, London, UK
| | - David L Thomas
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, UK; Leonard Wolfson Experimental Neurology Centre, UCL Institute of Neurology, London, UK
| | - David M Cash
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, UCL, London, UK
| | - Sebastian J Crutch
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, UCL, London, UK
| | - Nick C Fox
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, UCL, London, UK
| | - Jonathan M Schott
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, UCL, London, UK
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