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Tanabe J, Lim MF, Dash S, Pattee J, Steach B, Pressman P, Bettcher BM, Honce JM, Potigailo VA, Colantoni W, Zander D, Thaker AA. Automated Volumetric Software in Dementia: Help or Hindrance to the Neuroradiologist? AJNR Am J Neuroradiol 2024:ajnr.A8406. [PMID: 39362700 DOI: 10.3174/ajnr.a8406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Accepted: 06/15/2024] [Indexed: 10/05/2024]
Abstract
BACKGROUND AND PURPOSE Brain atrophy occurs in the late stage of dementia, yet structural MRI is widely used in the work-up. Atrophy patterns can suggest a diagnosis of Alzheimer disease (AD) or frontotemporal dementia (FTD) but are difficult to assess visually. We hypothesized that the availability of a quantitative volumetric brain MRI report would increase neuroradiologists' accuracy in diagnosing AD, FTD, or healthy controls compared with visual assessment. MATERIALS AND METHODS Twenty-two patients with AD, 17 with FTD, and 21 cognitively healthy patients were identified from the electronic health systems record and a behavioral neurology clinic. Four neuroradiologists evaluated T1-weighted anatomic MRI studies with and without a volumetric report. Outcome measures were the proportion of correct diagnoses of neurodegenerative disease versus normal aging ("rough accuracy") and AD versus FTD ("exact accuracy"). Generalized linear mixed models were fit to assess whether the use of a volumetric report was associated with higher accuracy, accounting for random effects of within-rater and within-subject variability. Post hoc within-group analysis was performed with multiple comparisons correction. Residualized volumes were tested for an association with the diagnosis using ANOVA. RESULTS There was no statistically significant effect of the report on overall correct diagnoses. The proportion of "exact" correct diagnoses was higher with the report versus without the report for AD (0.52 versus 0.38) and FTD (0.49 versus 0.32) and lower for cognitively healthy (0.75 versus 0.89). The proportion of "rough" correct diagnoses of neurodegenerative disease was higher with the report than without the report within the AD group (0.59 versus 0.41), and it was similar within the FTD group (0.66 versus 0.63). Post hoc within-group analysis suggested that the report increased the accuracy in AD (OR = 2.77) and decreased the accuracy in cognitively healthy (OR = 0.25). Residualized hippocampal volumes were smaller in AD (mean difference -1.8; multiple comparisons correction, -2.8 to -0.8; P < .001) and FTD (mean difference -1.2; multiple comparisons correction, -2.2 to -0.1; P = .02) compared with cognitively healthy. CONCLUSIONS The availability of a brain volumetric report did not improve neuroradiologists' accuracy over visual assessment in diagnosing AD or FTD in this limited sample. Post hoc analysis suggested that the report may have biased readers incorrectly toward a diagnosis of neurodegeneration in cognitively healthy adults.
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Affiliation(s)
- Jody Tanabe
- From the Department of Radiology (J.T., M.F.L., S.D., J.M.H., V.A.P., W.C., D.Z., A.A.T.), University of Colorado School of Medicine, Aurora, Colorado
| | - Maili F Lim
- From the Department of Radiology (J.T., M.F.L., S.D., J.M.H., V.A.P., W.C., D.Z., A.A.T.), University of Colorado School of Medicine, Aurora, Colorado
| | - Siddhant Dash
- From the Department of Radiology (J.T., M.F.L., S.D., J.M.H., V.A.P., W.C., D.Z., A.A.T.), University of Colorado School of Medicine, Aurora, Colorado
| | - Jack Pattee
- Center for Innovative Design and Analysis (J.P.), University of Colorado School of Public Health, Aurora, Colorado
| | | | - Peter Pressman
- Department of Neurology (P.P., B.M.B.), University of Colorado School of Medicine, Aurora, Colorado
| | - Brianne M Bettcher
- Department of Neurology (P.P., B.M.B.), University of Colorado School of Medicine, Aurora, Colorado
| | - Justin M Honce
- From the Department of Radiology (J.T., M.F.L., S.D., J.M.H., V.A.P., W.C., D.Z., A.A.T.), University of Colorado School of Medicine, Aurora, Colorado
| | - Valeria A Potigailo
- From the Department of Radiology (J.T., M.F.L., S.D., J.M.H., V.A.P., W.C., D.Z., A.A.T.), University of Colorado School of Medicine, Aurora, Colorado
| | - William Colantoni
- From the Department of Radiology (J.T., M.F.L., S.D., J.M.H., V.A.P., W.C., D.Z., A.A.T.), University of Colorado School of Medicine, Aurora, Colorado
| | - David Zander
- From the Department of Radiology (J.T., M.F.L., S.D., J.M.H., V.A.P., W.C., D.Z., A.A.T.), University of Colorado School of Medicine, Aurora, Colorado
| | - Ashesh A Thaker
- From the Department of Radiology (J.T., M.F.L., S.D., J.M.H., V.A.P., W.C., D.Z., A.A.T.), University of Colorado School of Medicine, Aurora, Colorado
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Raji CA, Meysami S, Porter VR, Merrill DA, Mendez MF. Diagnostic utility of brain MRI volumetry in comparing traumatic brain injury, Alzheimer disease and behavioral variant frontotemporal dementia. BMC Neurol 2024; 24:337. [PMID: 39261753 PMCID: PMC11389120 DOI: 10.1186/s12883-024-03844-4] [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: 05/24/2024] [Accepted: 09/03/2024] [Indexed: 09/13/2024] Open
Abstract
BACKGROUND Brain MRI with volumetric quantification, MRI volumetry, can improve diagnostic delineation of patients with neurocognitive disorders by identifying brain atrophy that may not be evident on visual assessments. OBJECTIVE To investigate diagnostic utility of MRI volumetry in traumatic brain injury (TBI), early-onset Alzheimer disease (EOAD), late-onset Alzheimer disease, and behavioral variant frontotemporal dementia (bvFTD). METHOD We utilized 137 participants of TBI (n = 40), EOAD (n = 45), LOAD (n = 32), and bvFTD (n = 20). Participants had 3D T1 brain MRI imaging amendable to MRI volumetry. Scan volumes were analyzed with Neuroreader. One-way ANOVA compared brain volumes across diagnostic groups. Discriminant analysis was done with leave-one-out cross validation on Neuroreader metrics to determine diagnostic delineation across groups. RESULT LOAD was the oldest compared to other groups (F = 27.5, p < .001). There were no statistically significant differences in sex (p = .58) with women comprising 54.7% of the entire cohort. EOAD and LOAD had the lowest Mini-Mental State Exam (MMSE) scores compared to TBI (p = .04 for EOAD and p = .01 for LOAD). LOAD had lowest hippocampal volumes (Left Hippocampus F = 13.1, Right Hippocampus F = 7.3, p < .001), low white matter volume in TBI (F = 5.9, p < .001), lower left parietal lobe volume in EOAD (F = 9.4, p < .001), and lower total gray matter volume in bvFTD (F = 32.8, p < .001) and caudate atrophy (F = 1737.5, p < .001). Areas under the curve ranged from 92.3 to 100%, sensitivity between 82.2 and 100%, specificity of 78.1-100%. TBI was the most accurately delineated diagnosis. Predictive features included caudate, frontal, parietal, temporal lobar and total white matter volumes. CONCLUSION We identified the diagnostic utility of regional volumetric differences across multiple neurocognitive disorders. Brain MRI volumetry is widely available and can be applied in distinguishing these disorders.
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Affiliation(s)
- Cyrus A Raji
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA.
- Department of Neurology, Washington University, St. Louis, MO, USA.
| | - Somayeh Meysami
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, USA
| | - Verna R Porter
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, USA
- Providence Saint John's Health Center, Santa Monica, CA, USA
| | - David A Merrill
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, USA
- Providence Saint John's Health Center, Santa Monica, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Mario F Mendez
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Greater Los Angeles VA Healthcare System, Los Angeles, CA, USA
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Wang LX, Wang YZ, Han CG, Zhao L, He L, Li J. Revolutionizing early Alzheimer's disease and mild cognitive impairment diagnosis: a deep learning MRI meta-analysis. ARQUIVOS DE NEURO-PSIQUIATRIA 2024; 82:1-10. [PMID: 39146974 DOI: 10.1055/s-0044-1788657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
BACKGROUND The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains a significant challenge in neurology, with conventional methods often limited by subjectivity and variability in interpretation. Integrating deep learning with artificial intelligence (AI) in magnetic resonance imaging (MRI) analysis emerges as a transformative approach, offering the potential for unbiased, highly accurate diagnostic insights. OBJECTIVE A meta-analysis was designed to analyze the diagnostic accuracy of deep learning of MRI images on AD and MCI models. METHODS A meta-analysis was performed across PubMed, Embase, and Cochrane library databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, focusing on the diagnostic accuracy of deep learning. Subsequently, methodological quality was assessed using the QUADAS-2 checklist. Diagnostic measures, including sensitivity, specificity, likelihood ratios, diagnostic odds ratio, and area under the receiver operating characteristic curve (AUROC) were analyzed, alongside subgroup analyses for T1-weighted and non-T1-weighted MRI. RESULTS A total of 18 eligible studies were identified. The Spearman correlation coefficient was -0.6506. Meta-analysis showed that the combined sensitivity and specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.84, 0.86, 6.0, 0.19, and 32, respectively. The AUROC was 0.92. The quiescent point of hierarchical summary of receiver operating characteristic (HSROC) was 3.463. Notably, the images of 12 studies were acquired by T1-weighted MRI alone, and those of the other 6 were gathered by non-T1-weighted MRI alone. CONCLUSION Overall, deep learning of MRI for the diagnosis of AD and MCI showed good sensitivity and specificity and contributed to improving diagnostic accuracy.
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Affiliation(s)
- Li-Xue Wang
- Beijing Tsinghua Changgung Hospital, Department of Radiology, Beijing, China
- Tsinghua University, School of Clinical Medicine, Beijing, China
| | - Yi-Zhe Wang
- Beijing Tsinghua Changgung Hospital, Department of Radiology, Beijing, China
- Tsinghua University, School of Clinical Medicine, Beijing, China
| | - Chen-Guang Han
- Tsinghua University, School of Clinical Medicine, Beijing, China
- Beijing Tsinghua Changgung Hospital, Department of Information Administration, Beijing, China
| | - Lei Zhao
- Beijing Tsinghua Changgung Hospital, Department of Radiology, Beijing, China
- Tsinghua University, School of Clinical Medicine, Beijing, China
| | - Li He
- Beijing Tsinghua Changgung Hospital, Department of Radiology, Beijing, China
- Tsinghua University, School of Clinical Medicine, Beijing, China
| | - Jie Li
- Beijing Tsinghua Changgung Hospital, Department of Radiology, Beijing, China
- Tsinghua University, School of Clinical Medicine, Beijing, China
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Gerlach LR, Prabhakaran V, Antuono PG, Granadillo E. The use of an anterior-posterior atrophy index to distinguish Alzheimer's disease from frontotemporal disorders: an automated volumetric MRI Study. Acta Radiol 2024; 65:808-816. [PMID: 38803154 DOI: 10.1177/02841851241254746] [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] [Indexed: 05/29/2024]
Abstract
BACKGROUND Alzheimer's disease (AD) and frontotemporal dementia (FTD) require different treatments. Since clinical presentation can be nuanced, imaging biomarkers aid in diagnosis. Automated software such as Neuroreader (NR) provides volumetric imaging data, and indices between anterior and posterior brain areas have proven useful in distinguishing dementia subtypes in research cohorts. Existing indices are complex and require further validation in clinical settings. PURPOSE To provide initial validation for a simplified anterior-posterior index (API) from NR in distinguishing FTD and AD in a clinical cohort. MATERIAL AND METHODS A retrospective chart review was completed. We derived a simplified API: API = (logVA/VP-μ)/σ where V A is weighted volume of frontal and temporal lobes and V P of parietal and occipital lobes. μ and σ are the mean and standard deviation of logVA/VP computed for AD participants. Receiver operating characteristic (ROC) curves and regression analyses assessed the efficacy of the API versus brain areas in predicting diagnosis of AD versus FTD. RESULTS A total of 39 participants with FTD and 78 participants with AD were included. The API had an excellent performance in distinguishing AD from FTD with an area under the ROC curve of 0.82 and a positive association with diagnostic classification on logistic regression analysis (B = 1.491, P < 0.001). CONCLUSION The API successfully distinguished AD and FTD with excellent performance. The results provide preliminary validation of the API in a clinical setting.
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Affiliation(s)
- Leah R Gerlach
- Medical School, Medical College of Wisconsin, Milwaukee WI, USA
| | - Vivek Prabhakaran
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison WI, USA
| | - Piero G Antuono
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Elias Granadillo
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
- Institute for Clinical and Translational Research, University of Wisconsin - Madison, Madison WI, USA
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Huang M, Landin-Romero R, Matis S, Dalton MA, Piguet O. Longitudinal volumetric changes in amygdala subregions in frontotemporal dementia. J Neurol 2024; 271:2509-2520. [PMID: 38265470 PMCID: PMC11055736 DOI: 10.1007/s00415-023-12172-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 12/20/2023] [Accepted: 12/23/2023] [Indexed: 01/25/2024]
Abstract
Amygdala atrophy has been found in frontotemporal dementia (FTD), yet the specific changes of its subregions across different FTD phenotypes remain unclear. The aim of this study was to investigate the volumetric alterations of the amygdala subregions in FTD phenotypes and how they evolve with disease progression. Patients clinically diagnosed with behavioral variant FTD (bvFTD) (n = 20), semantic dementia (SD) (n = 20), primary nonfluent aphasia (PNFA) (n = 20), Alzheimer's disease (AD) (n = 20), and 20 matched healthy controls underwent whole brain structural MRI. The patient groups were followed up annually for up to 3.5 years. Amygdala nuclei were segmented using FreeSurfer, corrected by total intracranial volumes, and grouped into the basolateral, superficial, and centromedial subregions. Linear mixed effects models were applied to identify changes in amygdala subregional volumes over time. At baseline, bvFTD, SD, and AD displayed global amygdala volume reduction, whereas amygdala volume appeared to be preserved in PNFA. Asymmetrical amygdala atrophy (left > right) was most pronounced in SD. Longitudinally, SD and PNFA showed greater rates of annual decline in the right basolateral and superficial subregions compared to bvFTD and AD. The findings provide comprehensive insights into the differential impact of FTD pathology on amygdala subregions, revealing distinct atrophy patterns that evolve over disease progression. The characterization of amygdala subregional involvement in FTD and their potential role as biomarkers carry substantial clinical implications.
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Affiliation(s)
- Mengjie Huang
- School of Psychology, The University of Sydney, Camperdown, NSW, 2050, Australia
- Brain and Mind Centre, The University of Sydney, 94 Mallett Street, Camperdown, NSW, 2050, Australia
| | - Ramon Landin-Romero
- Brain and Mind Centre, The University of Sydney, 94 Mallett Street, Camperdown, NSW, 2050, Australia
- School of Health Sciences, The University of Sydney, Camperdown, NSW, 2050, Australia
| | - Sophie Matis
- Brain and Mind Centre, The University of Sydney, 94 Mallett Street, Camperdown, NSW, 2050, Australia
- School of Health Sciences, The University of Sydney, Camperdown, NSW, 2050, Australia
| | - Marshall A Dalton
- School of Psychology, The University of Sydney, Camperdown, NSW, 2050, Australia
- Brain and Mind Centre, The University of Sydney, 94 Mallett Street, Camperdown, NSW, 2050, Australia
| | - Olivier Piguet
- School of Psychology, The University of Sydney, Camperdown, NSW, 2050, Australia.
- Brain and Mind Centre, The University of Sydney, 94 Mallett Street, Camperdown, NSW, 2050, Australia.
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Qian Z, Lin J, Jiang R, Jean S, Dai Y, Deng D, Tagu PT, Shi L, Song S. Evaluation of MRI post-processing methods combined with PET in detecting focal cortical dysplasia lesions for patients with MRI-negative epilepsy. Seizure 2024; 117:275-283. [PMID: 38579502 DOI: 10.1016/j.seizure.2024.03.011] [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: 08/30/2023] [Revised: 03/15/2024] [Accepted: 03/25/2024] [Indexed: 04/07/2024] Open
Abstract
OBJECTIVE Accurate detection of focal cortical dysplasia (FCD) through magnetic resonance imaging (MRI) plays a pivotal role in the preoperative assessment of epilepsy. The integration of multimodal imaging has demonstrated substantial value in both diagnosing FCD and devising effective surgical strategies. This study aimed to enhance MRI post-processing by incorporating positron emission tomography (PET) analysis. We sought to compare the diagnostic efficacy of diverse image post-processing methodologies in patients presenting MRI-negative FCD. METHODS In this retrospective investigation, we assembled a cohort of patients with negative preoperative MRI results. T1-weighted volumetric sequences were subjected to morphometric analysis program (MAP) and composite parametric map (CPM) post-processing techniques. We independently co-registered images derived from various methods with PET scans. The alignment was subsequently evaluated, and its correlation was correlated with postoperative seizure outcomes. RESULTS A total of 41 patients were enrolled in the study. In the PET-MAP(p = 0.0189) and PET-CPM(p = 0.00041) groups, compared with the non-overlap group, the overlap group significantly associated with better postoperative outcomes. In PET(p = 0.234), CPM(p = 0.686) and MAP(p = 0.672), there is no statistical significance between overlap and seizure-free outcomes. The sensitivity of using the CPM alone outperformed the MAP (0.65 vs 0.46). The use of PET-CPM demonstrated superior sensitivity (0.96), positive predictive value (0.83), and negative predictive value (0.91), whereas the MAP displayed superior specificity (0.71). CONCLUSIONS Our findings suggested a superiority in sensitivity of CPM in detecting potential FCD lesions compared to MAP, especially when it is used in combination with PET for diagnosis of MRI-negative epilepsy patients. Moreover, we confirmed the superiority of synergizing metabolic imaging (PET) with quantitative maps derived from structural imaging (MAP or CPM) to enhance the identification of subtle epileptogenic zones (EZs). This study serves to illuminate the potential of integrated multimodal techniques in advancing our capability to pinpoint elusive pathological features in epilepsy cases.
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Affiliation(s)
- Zhe Qian
- Fujian Medical University, Fuzhou, China.
| | - Jiuluan Lin
- Department of Neurosurgery, Tsinghua University Yuquan Hospital, Fuzhou, China.
| | - Rifeng Jiang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China.
| | - Stéphane Jean
- Department of Neurosurgery, Fuzhou Children's Hospital, Fuzhou, China
| | - Yihai Dai
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Donghuo Deng
- Fujian Medical University Union Hospital, Fuzhou, China.
| | | | - Lin Shi
- BrainNow Research Institute, Guangdong, China.
| | - Shiwei Song
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, China.
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Bottani S, Thibeau-Sutre E, Maire A, Ströer S, Dormont D, Colliot O, Burgos N. Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI. BMC Med Imaging 2024; 24:67. [PMID: 38504179 PMCID: PMC10953143 DOI: 10.1186/s12880-024-01242-3] [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] [Received: 05/22/2023] [Accepted: 03/07/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Clinical data warehouses provide access to massive amounts of medical images, but these images are often heterogeneous. They can for instance include images acquired both with or without the injection of a gadolinium-based contrast agent. Harmonizing such data sets is thus fundamental to guarantee unbiased results, for example when performing differential diagnosis. Furthermore, classical neuroimaging software tools for feature extraction are typically applied only to images without gadolinium. The objective of this work is to evaluate how image translation can be useful to exploit a highly heterogeneous data set containing both contrast-enhanced and non-contrast-enhanced images from a clinical data warehouse. METHODS We propose and compare different 3D U-Net and conditional GAN models to convert contrast-enhanced T1-weighted (T1ce) into non-contrast-enhanced (T1nce) brain MRI. These models were trained using 230 image pairs and tested on 77 image pairs from the clinical data warehouse of the Greater Paris area. RESULTS Validation using standard image similarity measures demonstrated that the similarity between real and synthetic T1nce images was higher than between real T1nce and T1ce images for all the models compared. The best performing models were further validated on a segmentation task. We showed that tissue volumes extracted from synthetic T1nce images were closer to those of real T1nce images than volumes extracted from T1ce images. CONCLUSION We showed that deep learning models initially developed with research quality data could synthesize T1nce from T1ce images of clinical quality and that reliable features could be extracted from the synthetic images, thus demonstrating the ability of such methods to help exploit a data set coming from a clinical data warehouse.
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Affiliation(s)
- Simona Bottani
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, 75013, France
| | - Elina Thibeau-Sutre
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, 75013, France
| | - Aurélien Maire
- Innovation & Données - Département des Services Numériques, AP-HP, Paris, 75013, France
| | - Sebastian Ströer
- Hôpital Pitié Salpêtrière, Department of Neuroradiology, AP-HP, Paris, 75012, France
| | - Didier Dormont
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, DMU DIAMENT, Paris, 75013, France
| | - Olivier Colliot
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, 75013, France
| | - Ninon Burgos
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, 75013, France.
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Rodríguez JJ, Zallo F, Gardenal E, Cabot J, Busquets X. Entorhinal cortex astrocytic atrophy in human frontotemporal dementia. Brain Struct Funct 2024:10.1007/s00429-024-02763-x. [PMID: 38308043 DOI: 10.1007/s00429-024-02763-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 01/11/2024] [Indexed: 02/04/2024]
Abstract
The pathophysiology of Fronto Temporal Dementia (FTD) remains poorly understood, specifically the role of astroglia. Our aim was to explore the hypothesis of astrocytic alterations as a component for FTD pathophysiology. We performed an in-depth tri-dimensional (3-D) anatomical and morphometric study of glial fibrillary acidic protein (GFAP)-positive and glutamine synthetase (GS)-positive astrocytes in the human entorhinal cortex (EC) of FTD patients. The studies at this level in the different types of human dementia are scarce. We observed a prominent astrocyte atrophy of GFAP-positive astrocytes and co-expressing GFAP/GS astrocytes, characterised by a decrease in area and volume, whilst minor changes in GS-positive astrocytes in FTD compared to non-dementia controls (ND) samples. This study evidences the importance of astrocyte atrophy and dysfunction in human EC. We hypothesise that FTD is not only a neuropathological disease, but also a gliopathological disease having a major relevance in the understanding the astrocyte role in FTD pathological processes and development.
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Affiliation(s)
- J J Rodríguez
- Functional Neuroanatomy Group; IKERBASQUE, Basque Foundation for Science, 48009, Bilbao, Spain.
- Dept. of Neurosciences, Medical Faculty, University of the Basque Country (UPV/EHU), 48940, Leioa, Spain.
| | - F Zallo
- Functional Neuroanatomy Group; IKERBASQUE, Basque Foundation for Science, 48009, Bilbao, Spain
- Dept. of Neurosciences, Medical Faculty, University of the Basque Country (UPV/EHU), 48940, Leioa, Spain
| | - E Gardenal
- Functional Neuroanatomy Group; IKERBASQUE, Basque Foundation for Science, 48009, Bilbao, Spain
- Dept. of Neurosciences, Medical Faculty, University of the Basque Country (UPV/EHU), 48940, Leioa, Spain
| | - J Cabot
- Laboratory of Molecular Cell Biomedicine, Department of Biology, University of the Balearic Islands, 07122, Palma, Spain
| | - X Busquets
- Laboratory of Molecular Cell Biomedicine, Department of Biology, University of the Balearic Islands, 07122, Palma, Spain
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Wang Z, Liu A, Yu J, Wang P, Bi Y, Xue S, Zhang J, Guo H, Zhang W. The effect of aperiodic components in distinguishing Alzheimer's disease from frontotemporal dementia. GeroScience 2024; 46:751-768. [PMID: 38110590 PMCID: PMC10828513 DOI: 10.1007/s11357-023-01041-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 12/07/2023] [Indexed: 12/20/2023] Open
Abstract
Distinguishing between Alzheimer's disease (AD) and frontotemporal dementia (FTD) presents a clinical challenge. Inexpensive and accessible techniques such as electroencephalography (EEG) are increasingly being used to address this challenge. In particular, the potential relevance between aperiodic components of EEG activity and these disorders has gained interest as our understanding evolves. This study aims to determine the differences in aperiodic activity between AD and FTD and evaluate its potential for distinguishing between the two disorders. A total of 88 participants, including 36 patients with AD, 23 patients with FTD, and 29 healthy controls (CN) underwent cognitive assessment and scalp EEG acquisition. Neuronal power spectra were parameterized to decompose the EEG spectrum, enabling comparison of group differences in different components. A support vector machine was employed to assess the impact of aperiodic parameters on the differential diagnosis. Compared with the CN group, both the AD and FTD groups showed varying degrees of increased alpha power (both periodic and raw power) and theta alpha power ratio. At the channel level, theta power (both periodic and raw power) in the frontal regions was higher in the AD group compared to the FTD group, and aperiodic parameters (both exponents and offsets) in the frontal, temporal, central, and parietal regions were higher in the AD group than in the FTD group. Importantly, the inclusion of aperiodic parameters led to improved performance in distinguishing between the two disorders. These findings highlight the significance of aperiodic components in discriminating dementia-related diseases.
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Affiliation(s)
- Zhuyong Wang
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China
| | - Anyang Liu
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China
| | - Jianshen Yu
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China
| | - Pengfei Wang
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China
| | - Yuewei Bi
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China
| | - Sha Xue
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China
| | - Jiajun Zhang
- Guangdong Province Key Laboratory of Computational Science, School of Mathematics, Sun Yat-Sen University, No. 135, Xingang Xi Road, Guangzhou, People's Republic of China.
| | - Hongbo Guo
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China.
| | - Wangming Zhang
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China.
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10
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Halder A, Drummond E. Strategies for translating proteomics discoveries into drug discovery for dementia. Neural Regen Res 2024; 19:132-139. [PMID: 37488854 PMCID: PMC10479849 DOI: 10.4103/1673-5374.373681] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/25/2023] [Accepted: 04/06/2023] [Indexed: 07/26/2023] Open
Abstract
Tauopathies, diseases characterized by neuropathological aggregates of tau including Alzheimer's disease and subtypes of frontotemporal dementia, make up the vast majority of dementia cases. Although there have been recent developments in tauopathy biomarkers and disease-modifying treatments, ongoing progress is required to ensure these are effective, economical, and accessible for the globally ageing population. As such, continued identification of new potential drug targets and biomarkers is critical. "Big data" studies, such as proteomics, can generate information on thousands of possible new targets for dementia diagnostics and therapeutics, but currently remain underutilized due to the lack of a clear process by which targets are selected for future drug development. In this review, we discuss current tauopathy biomarkers and therapeutics, and highlight areas in need of improvement, particularly when addressing the needs of frail, comorbid and cognitively impaired populations. We highlight biomarkers which have been developed from proteomic data, and outline possible future directions in this field. We propose new criteria by which potential targets in proteomics studies can be objectively ranked as favorable for drug development, and demonstrate its application to our group's recent tau interactome dataset as an example.
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Affiliation(s)
- Aditi Halder
- School of Medical Sciences and Brain & Mind Center, University of Sydney, NSW, Sydney, Australia
- Department of Aged Care, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Eleanor Drummond
- School of Medical Sciences and Brain & Mind Center, University of Sydney, NSW, Sydney, Australia
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11
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Fan X, Cai Y, Zhao L, Liu W, Luo Y, Au LWC, Shi L, Mok VCT. Machine Learning-Derived MRI-Based Neurodegeneration Biomarker for Alzheimer's Disease: A Multi-Database Validation Study. J Alzheimers Dis 2024; 97:883-893. [PMID: 38189749 DOI: 10.3233/jad-230574] [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: 01/09/2024]
Abstract
BACKGROUND Pilot study showed that Alzheimer's disease resemblance atrophy index (AD-RAI), a machine learning-derived MRI-based neurodegeneration biomarker of AD, achieved excellent diagnostic performance in diagnosing AD with moderate to severe dementia. OBJECTIVE The primary objective was to validate and compare the performance of AD-RAI with conventional volumetric hippocampal measures in diagnosing AD with mild dementia. The secondary objectives were 1) to investigate the association between imaging biomarkers with age and gender among cognitively unimpaired (CU) participants; 2) to analyze whether the performance of differentiating AD with mild dementia from CU will improve after adjustment for age/gender. METHODS AD with mild dementia (n = 218) and CU (n = 1,060) participants from 4 databases were included. We investigated the area under curve (AUC), sensitivity, specificity, and balanced accuracy of AD-RAI, hippocampal volume (HV), and hippocampal fraction (HF) in differentiating between AD and CU participants. Among amyloid-negative CU participants, we further analyzed correlation between the biomarkers with age/gender. We also investigated whether adjustment for age/gender will affect performance. RESULTS The AUC of AD-RAI (0.93) was significantly higher than that of HV (0.89) and HF (0.89). Subgroup analysis among A + AD and A- CU showed that AUC of AD-RAI (0.97) was also higher than HV (0.94) and HF (0.93). Diagnostic performance of AD-RAI and HF was not affected by age/gender while that of HV improved after age adjustment. CONCLUSIONS AD-RAI achieves excellent clinical validity and outperforms conventional volumetric hippocampal measures in aiding the diagnosis of AD mild dementia without the need for age adjustment.
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Affiliation(s)
- Xiang Fan
- Department of Medical Imaging, Peking University Shenzhen Hospital, Shenzhen, China
- Department of Medicine and Therapeutics, Faculty of Medicine, Division of Neurology, Gerald Choa Neuroscience Institute, Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - Yuan Cai
- Department of Medicine and Therapeutics, Faculty of Medicine, Division of Neurology, Gerald Choa Neuroscience Institute, Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - Lei Zhao
- BrainNow Research Institute, Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Wanting Liu
- Department of Medicine and Therapeutics, Faculty of Medicine, Division of Neurology, Gerald Choa Neuroscience Institute, Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - Yishan Luo
- BrainNow Research Institute, Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Lisa Wing Chi Au
- Department of Medicine and Therapeutics, Faculty of Medicine, Division of Neurology, Gerald Choa Neuroscience Institute, Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - Lin Shi
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
- BrainNow Research Institute, Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Vincent Chung Tong Mok
- Department of Medicine and Therapeutics, Faculty of Medicine, Division of Neurology, Gerald Choa Neuroscience Institute, Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
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12
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Du C, Dang M, Chen K, Chen Y, Zhang Z. Divergent brain regional atrophy and associated fiber disruption in amnestic and non-amnestic MCI. Alzheimers Res Ther 2023; 15:199. [PMID: 37957768 PMCID: PMC10642051 DOI: 10.1186/s13195-023-01335-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 10/17/2023] [Indexed: 11/15/2023]
Abstract
BACKGROUND Understanding the pathological characteristics of various mild cognitive impairment (MCI) subtypes is crucial for the differential diagnosis of dementia. The purpose of this study was to feature divergent symptom-deficit profiles in amnestic MCI (aMCI) and non-amnestic MCI (naMCI). METHODS T1 and DTI MRI data from a total of 158 older adults with 50 normal controls, 56 aMCI, and 52 naMCI were included. The voxel-wise gray matter volumes and the number of seed-based white matter fiber bundles were compared among these three groups. Furthermore, correlation and mediation analyses between the neuroimaging indices and cognitive measures were performed. RESULTS The aMCI with specific memory abnormalities was characterized by volumetric atrophy of the left hippocampus but not by damage in the linked white matter fiber bundles. Conversely, naMCI was characterized by both the altered volume of the right inferior frontal gyrus and the significant damage to fiber bundles traversing the region in all three directions, not only affecting fibers around the atrophied area but also distant fibers. Mediation analyses of gray matter-white matter-cognition showed that gray matter atrophy affects the number of fiber bundles and further affects attention and executive function. Meanwhile, fiber bundle damage also affects gray matter volume, which further affects visual processing and language. CONCLUSIONS The divergent structural damage patterns of the MCI subtypes and cognitive dysfunctions highlight the importance of detailed differential diagnoses in the early stages of pathological neurodegenerative diseases to deepen the understanding of dementia subtypes and inform targeted early clinical interventions.
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Affiliation(s)
- Chao Du
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing, 100875, China
- Research Institute of Intelligent and Complex Systems, Fudan University, Shanghai, 200433, China
| | - Mingxi Dang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing, 100875, China
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, 85006, USA
- Arizona State University, Temple, AZ, 85281, USA
| | - Yaojing Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing, 100875, China.
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing, 100875, China.
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13
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Wang H, Ma LZ, Sheng ZH, Liu JY, Yuan WY, Guo F, Zhang W, Tan L. Association between cerebrospinal fluid clusterin and biomarkers of Alzheimer's disease pathology in mild cognitive impairment: a longitudinal cohort study. Front Aging Neurosci 2023; 15:1256389. [PMID: 37941999 PMCID: PMC10629112 DOI: 10.3389/fnagi.2023.1256389] [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: 07/10/2023] [Accepted: 10/10/2023] [Indexed: 11/10/2023] Open
Abstract
Background Clusterin, a glycoprotein implicated in Alzheimer's disease (AD), remains unclear. The objective of this study was to analyze the effect of cerebrospinal fluid (CSF) clusterin in relation to AD biomarkers using a longitudinal cohort of non-demented individuals. Methods We gathered a sample comprising 86 individuals under cognition normal (CN) and 134 patients diagnosed with MCI via the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. To investigate the correlation of CSF clusterin with cognitive function and markers of key physiological changes, we employed multiple linear regression and mixed-effect models. We undertook a causal mediation analysis to inspect the mediating influence of CSF clusterin on cognitive abilities. Results Pathological characteristics associated with baseline Aβ42, Tau, brain volume, exhibited a correlation with initial CSF clusterin in the general population, Specifically, these correlations were especially prominent in the MCI population; CSF Aβ42 (PCN = 0.001; PMCI = 0.007), T-tau (PCN < 0.001; PMCI < 0.001), and Mid temporal (PCN = 0.033; PMCI = 0.005). Baseline CSF clusterin level was predictive of measurable cognitive shifts in the MCI population, as indicated by MMSE (β = 0.202, p = 0.029), MEM (β = 0.186, p = 0.036), RAVLT immediate recall (β = 0.182, p = 0.038), and EF scores (β = 0.221, p = 0.013). In MCI population, the alterations in brain regions (17.87% of the total effect) mediated the effect of clusterin on cognition. It was found that variables such as age, gender, and presence of APOE ε4 carrier status, influenced some of these connections. Conclusion Our investigation underscored a correlation between CSF clusterin concentrations and pivotal AD indicators, while also highlighting clusterin's potential role as a protective factor for cognitive abilities in MCI patients.
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Affiliation(s)
- Hao Wang
- Department of Neurology, Affiliated Hospital of Weifang Medical University, School of Clinical Medicine, Weifang Medical University, Weifang, China
| | - Ling-Zhi Ma
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Ze-Hu Sheng
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Jia-Yao Liu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei-Yu Yuan
- Department of Neurology, Affiliated Hospital of Weifang Medical University, School of Clinical Medicine, Weifang Medical University, Weifang, China
| | - Fan Guo
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei Zhang
- Department of Neurology, Affiliated Hospital of Weifang Medical University, School of Clinical Medicine, Weifang Medical University, Weifang, China
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
- Department of Neurology, Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences, Qingdao, China
| | - Lan Tan
- Department of Neurology, Affiliated Hospital of Weifang Medical University, School of Clinical Medicine, Weifang Medical University, Weifang, China
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
- Department of Neurology, Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences, Qingdao, China
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Chouliaras L, O'Brien JT. The use of neuroimaging techniques in the early and differential diagnosis of dementia. Mol Psychiatry 2023; 28:4084-4097. [PMID: 37608222 PMCID: PMC10827668 DOI: 10.1038/s41380-023-02215-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 07/27/2023] [Accepted: 08/03/2023] [Indexed: 08/24/2023]
Abstract
Dementia is a leading cause of disability and death worldwide. At present there is no disease modifying treatment for any of the most common types of dementia such as Alzheimer's disease (AD), Vascular dementia, Lewy Body Dementia (LBD) and Frontotemporal dementia (FTD). Early and accurate diagnosis of dementia subtype is critical to improving clinical care and developing better treatments. Structural and molecular imaging has contributed to a better understanding of the pathophysiology of neurodegenerative dementias and is increasingly being adopted into clinical practice for early and accurate diagnosis. In this review we summarise the contribution imaging has made with particular focus on multimodal magnetic resonance imaging (MRI) and positron emission tomography imaging (PET). Structural MRI is widely used in clinical practice and can help exclude reversible causes of memory problems but has relatively low sensitivity for the early and differential diagnosis of dementia subtypes. 18F-fluorodeoxyglucose PET has high sensitivity and specificity for AD and FTD, while PET with ligands for amyloid and tau can improve the differential diagnosis of AD and non-AD dementias, including recognition at prodromal stages. Dopaminergic imaging can assist with the diagnosis of LBD. The lack of a validated tracer for α-synuclein or TAR DNA-binding protein 43 (TDP-43) imaging remain notable gaps, though work is ongoing. Emerging PET tracers such as 11C-UCB-J for synaptic imaging may be sensitive early markers but overall larger longitudinal multi-centre cross diagnostic imaging studies are needed.
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Affiliation(s)
- Leonidas Chouliaras
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Specialist Dementia and Frailty Service, Essex Partnership University NHS Foundation Trust, St Margaret's Hospital, Epping, UK
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, UK.
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
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15
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Huang L, Cui L, Chen K, Han Z, Guo Q. Functional and structural network changes related with cognition in semantic dementia longitudinally. Hum Brain Mapp 2023; 44:4287-4298. [PMID: 37209400 PMCID: PMC10318263 DOI: 10.1002/hbm.26345] [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: 01/02/2023] [Revised: 04/06/2023] [Accepted: 05/01/2023] [Indexed: 05/22/2023] Open
Abstract
Longitudinal changes in the white matter/functional brain networks of semantic dementia (SD), as well as their relations with cognition remain unclear. Using a graph-theoretic method, we examined the neuroimaging (T1, diffusion tensor imaging, functional MRI) network properties and cognitive performance in processing semantic knowledge of general and six modalities (i.e., object form, color, motion, sound, manipulation and function) from 31 patients (at two time points with an interval of 2 years) and 20 controls (only at baseline). Partial correlation analyses were carried out to explore the relationships between the network changes and the declines of semantic performance. SD exhibited aberrant general and modality-specific semantic impairment, and gradually worsened over time. Overall, the brain networks showed a decreased global and local efficiency in the functional network organization but a preserved structural network organization with a 2-year follow-up. With disease progression, both structural and functional alterations were found to be extended to the temporal and frontal lobes. The regional topological alteration in the left inferior temporal gyrus (ITG.L) was significantly correlated with general semantic processing. Meanwhile, the right superior temporal gyrus and right supplementary motor area were identified to be associated with color and motor-related semantic attributes. SD manifested disrupted structural and functional network pattern longitudinally. We proposed a hub region (i.e., ITG.L) of semantic network and distributed modality-specific semantic-related regions. These findings support the hub-and-spoke semantic theory and provide targets for future therapy.
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Affiliation(s)
- Lin Huang
- Department of GerontologyShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Liang Cui
- Department of GerontologyShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Keliang Chen
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Zaizhu Han
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
| | - Qihao Guo
- Department of GerontologyShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina
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16
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Diaz-Torres S, Ogonowski N, García-Marín LM, Bonham LW, Duran-Aniotz C, Yokoyama JS, Rentería ME. Genetic overlap between cortical brain morphometry and frontotemporal dementia risk. Cereb Cortex 2023; 33:7428-7435. [PMID: 36813468 PMCID: PMC10267623 DOI: 10.1093/cercor/bhad049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 02/01/2023] [Accepted: 02/02/2023] [Indexed: 02/24/2023] Open
Abstract
Frontotemporal dementia (FTD) has a complex genetic etiology, where the precise mechanisms underlying the selective vulnerability of brain regions remain unknown. We leveraged summary-based data from genome-wide association studies (GWAS) and performed LD score regression to estimate pairwise genetic correlations between FTD risk and cortical brain imaging. Then, we isolated specific genomic loci with a shared etiology between FTD and brain structure. We also performed functional annotation, summary-data-based Mendelian randomization for eQTL using human peripheral blood and brain tissue data, and evaluated the gene expression in mice targeted brain regions to better understand the dynamics of the FTD candidate genes. Pairwise genetic correlation estimates between FTD and brain morphology measures were high but not statistically significant. We identified 5 brain regions with a strong genetic correlation (rg > 0.45) with FTD risk. Functional annotation identified 8 protein-coding genes. Building upon these findings, we show in a mouse model of FTD that cortical N-ethylmaleimide sensitive factor (NSF) expression decreases with age. Our results highlight the molecular and genetic overlap between brain morphology and higher risk for FTD, specifically for the right inferior parietal surface area and right medial orbitofrontal cortical thickness. In addition, our findings implicate NSF gene expression in the etiology of FTD.
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Affiliation(s)
- Santiago Diaz-Torres
- Mental Health & Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Natalia Ogonowski
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Centro de Neurociencias Cognitivas (CNC), Universidad de San Andrés, Buenos Aires, Argentina
| | - Luis M García-Marín
- Mental Health & Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Luke W Bonham
- Memory and Aging Center, University of California, San Francisco, CA, United States
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States
| | - Claudia Duran-Aniotz
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- School of Psychology, Center for Social and Cognitive Neuroscience (CSCN), Universidad Adolfo Ibanez, Santiago, Chile
| | - Jennifer S Yokoyama
- Memory and Aging Center, University of California, San Francisco, CA, United States
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States
- Department of Neurology, Weill Institute of Neurosciences, University of California, San Francisco, CA, United States
| | - Miguel E Rentería
- Mental Health & Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
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Massett R, Maher A, Imms P, Amgalan A, Chaudhari N, Chowdhury N, Irimia A. Regional Neuroanatomic Effects on Brain Age Inferred Using Magnetic Resonance Imaging and Ridge Regression. J Gerontol A Biol Sci Med Sci 2023; 78:872-881. [PMID: 36183259 PMCID: PMC10235198 DOI: 10.1093/gerona/glac209] [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: 03/11/2022] [Indexed: 11/14/2022] Open
Abstract
The biological age of the brain differs from its chronological age (CA) and can be used as biomarker of neural/cognitive disease processes and as predictor of mortality. Brain age (BA) is often estimated from magnetic resonance images (MRIs) using machine learning (ML) that rarely indicates how regional brain features contribute to BA. Leveraging an aggregate training sample of 3 418 healthy controls (HCs), we describe a ridge regression model that quantifies each region's contribution to BA. After model testing on an independent sample of 651 HCs, we compute the coefficient of partial determination R¯p2 for each regional brain volume to quantify its contribution to BA. Model performance is also evaluated using the correlation r between chronological and biological ages, the mean absolute error (MAE ) and mean squared error (MSE) of BA estimates. On training data, r=0.92, MSE=70.94 years, MAE=6.57 years, and R¯2=0.81; on test data, r=0.90, MSE=81.96 years, MAE=7.00 years, and R¯2=0.79. The regions whose volumes contribute most to BA are the nucleus accumbens (R¯p2=7.27%), inferior temporal gyrus (R¯p2=4.03%), thalamus (R¯p2=3.61%), brainstem (R¯p2=3.29%), posterior lateral sulcus (R¯p2=3.22%), caudate nucleus (R¯p2=3.05%), orbital gyrus (R¯p2=2.96%), and precentral gyrus (R¯p2=2.80%). Our ridge regression, although outperformed by the most sophisticated ML approaches, identifies the importance and relative contribution of each brain structure to overall BA. Aside from its interpretability and quasi-mechanistic insights, our model can be used to validate future ML approaches for BA estimation.
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Affiliation(s)
- Roy J Massett
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
| | - Alexander S Maher
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
| | - Phoebe E Imms
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
| | - Anar Amgalan
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
| | - Nikhil N Chaudhari
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Nahian F Chowdhury
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
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18
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Combining semi-quantitative rating and automated brain volumetry in MRI evaluation of patients with probable behavioural variant of fronto-temporal dementia: an added value for clinical practise? Neuroradiology 2023; 65:1025-1035. [PMID: 36867204 DOI: 10.1007/s00234-023-03133-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 02/06/2023] [Indexed: 03/04/2023]
Abstract
PURPOSE To evaluate the diagnostic value of combined semiquantitative and quantitative assessment of brain atrophy in the diagnostic workup of the behavioural-variant of frontotemporal dementia (bvFTD). METHODS Three neuroradiologists defined brain atrophy grading and identified atrophy pattern suggestive of bvFTD on 3D-T1 brain MRI of 112 subjects using a semiquantitative rating scale (Kipps'). A quantitative atrophy assessment was performed using two different automated software (Quantib® ND and Icometrix®). A combined semiquantitative and quantitative assessment of brain atrophy was made to evaluate the improvement in brain atrophy grading to identify probable bvFTD patients. RESULTS Observers' performances in the diagnosis of bvFTD were very good for Observer 1 (k value = 0.881) and 2 (k value = 0.867), substantial for Observer 3 (k value = 0.741). Semiquantitative atrophy grading of all the observers showed a moderate and a poor correlation with the volume values calculated by Icometrix® and by Quantib® ND, respectively. For the definition of neuroradiological signs presumptive of bvFTD, the use of Icometrix® software improved the diagnostic accuracy for Observer 1 resulting in an AUC of 0.974, and for Observer 3 resulting in a AUC of 0.971 (p-value < 0.001). The use of Quantib® ND software improved the diagnostic accuracy for Observer 1 resulting in an AUC of 0.974, and for Observer 3 resulting in a AUC of 0.977 (p-value < 0.001). No improvement was observed for Observer 2. CONCLUSION Combining semiquantitative and quantitative brain imaging evaluation allows to reduce discrepancies in the neuroradiological diagnostic workup of bvFTD by different readers.
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Kandiah N, Choi SH, Hu CJ, Ishii K, Kasuga K, Mok VC. Current and Future Trends in Biomarkers for the Early Detection of Alzheimer's Disease in Asia: Expert Opinion. J Alzheimers Dis Rep 2022; 6:699-710. [PMID: 36606209 PMCID: PMC9741748 DOI: 10.3233/adr-220059] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 10/23/2022] [Indexed: 11/16/2022] Open
Abstract
Alzheimer's disease (AD) poses a substantial healthcare burden in the rapidly aging Asian population. Early diagnosis of AD, by means of biomarkers, can lead to interventions that might alter the course of the disease. The amyloid, tau, and neurodegeneration (AT[N]) framework, which classifies biomarkers by their core pathophysiological features, is a biomarker measure of amyloid plaques and neurofibrillary tangles. Our current AD biomarker armamentarium, comprising neuroimaging biomarkers and cerebrospinal fluid biomarkers, while clinically useful, may be invasive and expensive and hence not readily available to patients. Several studies have also investigated the use of blood-based measures of established core markers for detection of AD, such as amyloid-β and phosphorylated tau. Furthermore, novel non-invasive peripheral biomarkers and digital biomarkers could potentially expand access to early AD diagnosis to patients in Asia. Despite the multiplicity of established and potential biomarkers in AD, a regional framework for their optimal use to guide early AD diagnosis remains lacking. A group of experts from five regions in Asia gathered at a meeting in March 2021 to review the current evidence on biomarkers in AD diagnosis and discuss best practice around their use, with the goal of developing practical guidance that can be implemented easily by clinicians in Asia to support the early diagnosis of AD. This article summarizes recent key evidence on AD biomarkers and consolidates the experts' insights into the current and future use of these biomarkers for the screening and early diagnosis of AD in Asia.
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Affiliation(s)
- Nagaendran Kandiah
- Dementia Research Centre (Singapore), Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore,Correspondence to: Nagaendran Kandiah, Dementia Research Centre, Lee Kong Chian School of Medicine, Nanyang Technological University, 11 Mandalay Road, Singapore 308232. Tel.: +65 6592 2653; Fax: +65 6339 2889; E-mail: ; ORCID: 0000-0001-9244-4298
| | - Seong Hye Choi
- Department of Neurology, Inha University School of Medicine, Incheon, Republic of Korea
| | - Chaur-Jong Hu
- Department of Neurology, Dementia Center, Shuang Ho Hospital, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Kenji Ishii
- Team for Neuroimaging Research, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Kensaku Kasuga
- Department of Molecular Genetics, Center for Bioresources, Brain Research Institute, Niigata University, Niigata, Japan
| | - Vincent C.T. Mok
- Division of Neurology, Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China,Li Ka Shing Institute of Health Sciences, Gerald Choa Neuroscience Institute, Lui Che Woo Institute of Innovative Medicine, Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Hong Kong, China
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20
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Kim JS, Han JW, Bae JB, Moon DG, Shin J, Kong JE, Lee H, Yang HW, Lim E, Kim JY, Sunwoo L, Cho SJ, Lee D, Kim I, Ha SW, Kang MJ, Suh CH, Shim WH, Kim SJ, Kim KW. Deep learning-based diagnosis of Alzheimer's disease using brain magnetic resonance images: an empirical study. Sci Rep 2022; 12:18007. [PMID: 36289390 PMCID: PMC9606115 DOI: 10.1038/s41598-022-22917-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 10/20/2022] [Indexed: 01/24/2023] Open
Abstract
The limited accessibility of medical specialists for Alzheimer's disease (AD) can make obtaining an accurate diagnosis in a timely manner challenging and may influence prognosis. We investigated whether VUNO Med-DeepBrain AD (DBAD) using a deep learning algorithm can be employed as a decision support service for the diagnosis of AD. This study included 98 elderly participants aged 60 years or older who visited the Seoul Asan Medical Center and the Korea Veterans Health Service. We administered a standard diagnostic assessment for diagnosing AD. DBAD and three panels of medical experts (ME) diagnosed participants with normal cognition (NC) or AD using T1-weighted magnetic resonance imaging. The accuracy (87.1% for DBAD and 84.3% for ME), sensitivity (93.3% for DBAD and 80.0% for ME), and specificity (85.5% for DBAD and 85.5% for ME) of both DBAD and ME for diagnosing AD were comparable; however, DBAD showed a higher trend in every analysis than ME diagnosis. DBAD may support the clinical decisions of physicians who are not specialized in AD; this may enhance the accessibility of AD diagnosis and treatment.
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Affiliation(s)
- Jun Sung Kim
- grid.412484.f0000 0001 0302 820XInstitute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea ,grid.412480.b0000 0004 0647 3378Department of Neuropsychiatry, Seoul National University Bundang Hospital, 82, Gumi-Ro 173 Beon-Gil, Bundang-Gu, Seongnam-Si, Gyeonggi-Do 13620 Republic of Korea
| | - Ji Won Han
- grid.412480.b0000 0004 0647 3378Department of Neuropsychiatry, Seoul National University Bundang Hospital, 82, Gumi-Ro 173 Beon-Gil, Bundang-Gu, Seongnam-Si, Gyeonggi-Do 13620 Republic of Korea ,grid.31501.360000 0004 0470 5905Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jong Bin Bae
- grid.412480.b0000 0004 0647 3378Department of Neuropsychiatry, Seoul National University Bundang Hospital, 82, Gumi-Ro 173 Beon-Gil, Bundang-Gu, Seongnam-Si, Gyeonggi-Do 13620 Republic of Korea
| | - Dong Gyu Moon
- grid.412480.b0000 0004 0647 3378Department of Neuropsychiatry, Seoul National University Bundang Hospital, 82, Gumi-Ro 173 Beon-Gil, Bundang-Gu, Seongnam-Si, Gyeonggi-Do 13620 Republic of Korea
| | - Jin Shin
- grid.412480.b0000 0004 0647 3378Department of Neuropsychiatry, Seoul National University Bundang Hospital, 82, Gumi-Ro 173 Beon-Gil, Bundang-Gu, Seongnam-Si, Gyeonggi-Do 13620 Republic of Korea
| | - Juhee Eliana Kong
- grid.412480.b0000 0004 0647 3378Department of Neuropsychiatry, Seoul National University Bundang Hospital, 82, Gumi-Ro 173 Beon-Gil, Bundang-Gu, Seongnam-Si, Gyeonggi-Do 13620 Republic of Korea
| | - Hyungji Lee
- grid.412480.b0000 0004 0647 3378Department of Neuropsychiatry, Seoul National University Bundang Hospital, 82, Gumi-Ro 173 Beon-Gil, Bundang-Gu, Seongnam-Si, Gyeonggi-Do 13620 Republic of Korea
| | - Hee Won Yang
- grid.411665.10000 0004 0647 2279Department of Psychiatry, Chungnam National University Hospital, Daejeon, Republic of Korea
| | - Eunji Lim
- grid.256681.e0000 0001 0661 1492Department of Neuropsychiatry, Gyeongsang National University Changwon Hospital, Changwon, Republic of Korea
| | - Jun Yup Kim
- grid.412480.b0000 0004 0647 3378Department of Neurology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Leonard Sunwoo
- grid.412480.b0000 0004 0647 3378Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea ,grid.31501.360000 0004 0470 5905Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Se Jin Cho
- grid.412480.b0000 0004 0647 3378Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea ,grid.31501.360000 0004 0470 5905Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | | | - Injoong Kim
- Department of Radiology, Veterans Health Service Medical Center, Seoul, Republic of Korea
| | - Sang Won Ha
- Department of Neurology, Veterans Health Service Medical Center, Seoul, Republic of Korea
| | - Min Ju Kang
- Department of Neurology, Veterans Health Service Medical Center, Seoul, Republic of Korea
| | - Chong Hyun Suh
- grid.267370.70000 0004 0533 4667Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Woo Hyun Shim
- grid.267370.70000 0004 0533 4667Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sang Joon Kim
- grid.267370.70000 0004 0533 4667Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ki Woong Kim
- grid.412484.f0000 0001 0302 820XInstitute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea ,grid.412480.b0000 0004 0647 3378Department of Neuropsychiatry, Seoul National University Bundang Hospital, 82, Gumi-Ro 173 Beon-Gil, Bundang-Gu, Seongnam-Si, Gyeonggi-Do 13620 Republic of Korea ,grid.31501.360000 0004 0470 5905Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea ,grid.31501.360000 0004 0470 5905Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Republic of Korea
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21
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He Q, Shi L, Luo Y, Wan C, Malone IB, Mok VCT, Cole JH, Anatürk M. Validation of the Alzheimer's disease-resemblance atrophy index in classifying and predicting progression in Alzheimer's disease. Front Aging Neurosci 2022; 14:932125. [PMID: 36062150 PMCID: PMC9435378 DOI: 10.3389/fnagi.2022.932125] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 07/14/2022] [Indexed: 12/01/2022] Open
Abstract
Background Automated tools for characterising dementia risk have the potential to aid in the diagnosis, prognosis, and treatment of Alzheimer's disease (AD). Here, we examined a novel machine learning-based brain atrophy marker, the AD-resemblance atrophy index (AD-RAI), to assess its test-retest reliability and further validate its use in disease classification and prediction. Methods Age- and sex-matched 44 probable AD (Age: 69.13 ± 7.13; MMSE: 27-30) and 22 non-demented control (Age: 69.38 ± 7.21; MMSE: 27-30) participants were obtained from the Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) dataset. Serial T1-weighted images (n = 678) from up to nine time points over a 2-year period, including 179 pairs of back-to-back scans acquired on same participants on the same day and 40 pairs of scans acquired at 2-week intervals were included. All images were automatically processed with AccuBrain® to calculate the AD-RAI. Its same-day repeatability and 2-week reproducibility were first assessed. The discriminative performance of AD-RAI was evaluated using the receiver operating characteristic curve, where DeLong's test was used to evaluate its performance against quantitative medial temporal lobe atrophy (QMTA) and hippocampal volume adjusted by intracranial volume (ICV)-proportions and ICV-residuals methods, respectively (HVR and HRV). Linear mixed-effects modelling was used to investigate longitudinal trajectories of AD-RAI and baseline AD-RAI prediction of cognitive decline. Finally, the longitudinal associations between AD-RAI and MMSE scores were assessed. Results AD-RAI had excellent same-day repeatability and excellent 2-week reproducibility. AD-RAI's AUC (99.8%; 95%CI = [99.3%, 100%]) was equivalent to that of QMTA (96.8%; 95%CI = [92.9%, 100%]), and better than that of HVR (86.8%; 95%CI = [78.2%, 95.4%]) or HRV (90.3%; 95%CI = [83.0%, 97.6%]). While baseline AD-RAI was significantly higher in the AD group, it did not show detectable changes over 2 years. Baseline AD-RAI was negatively associated with MMSE scores and the rate of the change in MMSE scores over time. A negative longitudinal association was also found between AD-RAI values and the MMSE scores among AD patients. Conclusions The AD-RAI represents a potential biomarker that may support AD diagnosis and be used to predict the rate of future cognitive decline in AD patients.
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Affiliation(s)
- Qiling He
- UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Lin Shi
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Yishan Luo
- BrainNow Research Institute, Shenzhen, China
| | - Chao Wan
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Ian B. Malone
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Vincent C. T. Mok
- Gerald Choa Neuroscience Centre, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Division of Neurology, Department of Medicine and Therapeutics, Prince of Wales Hospital, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - James H. Cole
- Department of Computer Science, Faculty of Engineering Science, University College London, London, United Kingdom
- Dementia Research Centre, Institute of Neurology, University College London, London, United Kingdom
| | - Melis Anatürk
- Department of Computer Science, Faculty of Engineering Science, University College London, London, United Kingdom
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
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22
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Zhao L, Luo Y, Mok V, Shi L. Automated brain volumetric measures with AccuBrain: version comparison in accuracy, reproducibility and application for diagnosis. BMC Med Imaging 2022; 22:117. [PMID: 35787256 PMCID: PMC9252062 DOI: 10.1186/s12880-022-00841-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 06/02/2022] [Indexed: 11/29/2022] Open
Abstract
Background Automated brain volumetry has been widely used to assess brain volumetric changes that may indicate clinical states and progression. Among the tools that implement automated brain volumetry, AccuBrain has been validated for its accuracy, reliability and clinical applications for the older version (IV1.2). Here, we aim to investigate the performance of an updated version (IV2.0) of AccuBrain for future use from several aspects. Methods Public datasets with 3D T1-weighted scans were included for version comparisons, each with Alzheimer’s disease (AD) patients and normal control (NC) subjects that were matched in age and gender. For the comparisons of the brain volumetric measures quantified from the same scans, we investigated the difference of hippocampal segmentation accuracy (using Dice similarity coefficient [DSC] as the major measurement). As AccuBrain generates a composite index (AD resemblance atrophy index, AD-RAI) that indicates similarity with AD-like brain atrophy pattern, we also compared the two versions for the diagnostic accuracy of AD versus NC with AD-RAI. Also, we examined the intra-scanner reproducibility of the two versions for the scans acquired with short-intervals using intraclass correlation coefficient. Results AccuBrain IV2.0 presented significantly higher accuracy of hippocampal segmentation (DSC: 0.91 vs. 0.89, p < 0.001) and diagnostic accuracy of AD (AUC: 0.977 vs. 0.921, p < 0.001) than IV1.2. The results of intra-scanner reproducibility did not favor one version over the other. Conclusions AccuBrain IV2.0 presented better segmentation accuracy and diagnostic accuracy of AD, and similar intra-scanner reproducibility compared with IV1.2. Both versions should be feasible for use due to the small magnitude of differences.
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Affiliation(s)
- Lei Zhao
- BrainNow Research Institute, Shenzhen, Guangdong Province, China
| | - Yishan Luo
- BrainNow Research Institute, Shenzhen, Guangdong Province, China
| | - Vincent Mok
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.,Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.,Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.,Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Lin Shi
- BrainNow Research Institute, Shenzhen, Guangdong Province, China. .,Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
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23
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Sun W, Huang L, Cheng Y, Qin R, Xu H, Shao P, Ma J, Yao Z, Shi L, Xu Y. Medial Temporal Atrophy Contributes to Cognitive Impairment in Cerebral Small Vessel Disease. Front Neurol 2022; 13:858171. [PMID: 35665031 PMCID: PMC9159509 DOI: 10.3389/fneur.2022.858171] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 04/14/2022] [Indexed: 11/18/2022] Open
Abstract
Background The role of brain atrophy in cognitive decline related to cerebral small vessel disease (CSVD) remains unclear. This study used AccuBrain™ to identify major CSVD-related brain changes and verified the relationship between brain atrophy and different cognition domains in CSVD patients. Methods All enrolled 242 CSVD patients and 76 healthy participants underwent magnetic resonance imaging examinations and detailed neuropsychological scale assessments were collected at the same time. The AccuBrain™ technology was applied to fully automated image segmentation, measurement, and calculation of the acquired imaging results to obtain the volumes of different brain partitions and the volume of WMH for quantitative analysis. Correlation analyses were used to estimate the relationship between MRI features and different cognitive domains. Multifactor linear regression models were performed to analyze independent predictors of MTA and cognitive decline. Results CSVD patients exhibited multiple gray matter nucleus volume decreases in the basal ganglia regions and brain lobes, including the temporal lobe (P = 0.019), especially in the medial temporal lobe (p < 0.001), parietal lobe (p = 0.013), and cingulate lobe (p = 0.036) compare to HC. The volume of PWMH was an independent predictor of MTA for CSVD patients. Both medial temporal atrophy (MTA) and PWMH were associated with cognition impairment in CSVD-CI patients. MTA mediated the effect of PWMH on executive function in CSVD-CI patients. Conclusions Our results showed that MTA was related to cognition impairment in CSVD patients, which might become a potential imaging marker for CSVD-CI.
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Affiliation(s)
- Wenshan Sun
- Department of Neurology, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China
- Department of Neurology, Nanjing Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Jiangsu Key Laboratory for Molecular Medicine, Nanjing University, Nanjing, China
- Department of Neurology, Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, China
| | - Lili Huang
- Department of Neurology, Nanjing Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Jiangsu Key Laboratory for Molecular Medicine, Nanjing University, Nanjing, China
| | - Yue Cheng
- Department of Neurology, Nanjing Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Jiangsu Key Laboratory for Molecular Medicine, Nanjing University, Nanjing, China
| | - Ruomeng Qin
- Department of Neurology, Nanjing Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Jiangsu Key Laboratory for Molecular Medicine, Nanjing University, Nanjing, China
| | - Hengheng Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Jiangsu Key Laboratory for Molecular Medicine, Nanjing University, Nanjing, China
| | - Pengfei Shao
- Department of Neurology, Nanjing Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Jiangsu Key Laboratory for Molecular Medicine, Nanjing University, Nanjing, China
| | - Junyi Ma
- Department of Neurology, Nanjing Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Jiangsu Key Laboratory for Molecular Medicine, Nanjing University, Nanjing, China
| | - Zhelv Yao
- Department of Neurology, Nanjing Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Jiangsu Key Laboratory for Molecular Medicine, Nanjing University, Nanjing, China
| | - Lin Shi
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
- BrainNow Research Institute, Hong Kong Science and Technology Park, Hong Kong, Hong Kong SAR, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China
- Department of Neurology, Nanjing Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Jiangsu Key Laboratory for Molecular Medicine, Nanjing University, Nanjing, China
- *Correspondence: Yun Xu
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Mai Y, Cao Z, Xu J, Yu Q, Yang S, Tang J, Zhao L, Fang W, Luo Y, Lei M, Mok VCT, Shi L, Liao W, Liu J. AD Resemblance Atrophy Index of Brain Magnetic Resonance Imaging in Predicting the Progression of Mild Cognitive Impairment Carrying Apolipoprotein E-ε4 Allele. Front Aging Neurosci 2022; 14:859492. [PMID: 35572149 PMCID: PMC9097868 DOI: 10.3389/fnagi.2022.859492] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 03/23/2022] [Indexed: 01/03/2023] Open
Abstract
Background and Objective Early identification is important for timely Alzheimer’s disease (AD) treatment. Apolipoprotein E ε4 allele (APOE-ε4) is an important genetic risk factor for sporadic AD. The AD-Resemblance Atrophy Index (RAI)—a structural magnetic resonance imaging-derived composite index—was found to predict the risk of progression from mild cognitive impairment (MCI) to AD. Therefore, we investigated whether the AD-RAI can predict cognitive decline and progression to AD in patients with MCI carrying APOE ε4. Methods We included 733 participants with MCI from the Alzheimer’s Disease Neuroimaging Initiative Database (ADNI). Their APOE genotypes, cognitive performance, and levels of AD-RAI were assessed at baseline and follow-up. Linear regression models were used to test the correlations between the AD-RAI and baseline cognitive measures, and linear mixed models with random intercepts and slopes were applied to investigate whether AD-RAI and APOE-ε4 can predict the level of cognitive decline. Cox proportional risk regression models were used to test the association of AD-RAI and APOE status with the progression from MCI to AD. Results The baseline AD-RAI was higher in the MCI converted to AD group than in the MCI stable group (P < 0.001). The AD-RAI was significantly correlated with cognition, and had a synergistic effect with APOE-ε4 to predict the rate of cognitive decline. The AD-RAI predicted the risk and timing of MCI progression to AD. Based on the MCI population carrying APOE-ε4, the median time to progression from MCI to AD was 24 months if the AD-RAI > 0.5, while the median time to progression from MCI to AD was 96 months for patients with an AD-RAI ≤ 0.5. Conclusion The AD-RAI can predict the risk of progression to AD in people with MCI carrying APOE ε4, is strongly correlated with cognition, and can predict cognitive decline.
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Affiliation(s)
- Yingren Mai
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhiyu Cao
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jiaxin Xu
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qun Yu
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Shaoqing Yang
- Department of Neurology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jingyi Tang
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Lei Zhao
- BrainNow Research Institute, Shenzhen, China
- BrainNow Medical Technology Limited, Shenzhen, China
| | - Wenli Fang
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yishan Luo
- BrainNow Research Institute, Shenzhen, China
- BrainNow Medical Technology Limited, Shenzhen, China
| | - Ming Lei
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Vincent C. T. Mok
- BrainNow Research Institute, Shenzhen, China
- BrainNow Medical Technology Limited, Shenzhen, China
- Division of Neurology, Department of Medicine and Therapeutics, Gerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Lin Shi
- BrainNow Research Institute, Shenzhen, China
- BrainNow Medical Technology Limited, Shenzhen, China
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Wang Liao
- Department of Neurology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- *Correspondence: Wang Liao,
| | - Jun Liu
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Neurology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- *Correspondence: Wang Liao,
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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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26
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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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Li R, Qi Y, Shi L, Wang W, Zhang A, Luo Y, Kung WK, Jiao Z, Liu G, Li H, Zhang L. Brain Volumetric Alterations in Preclinical HIV-Associated Neurocognitive Disorder Using Automatic Brain Quantification and Segmentation Tool. Front Neurosci 2021; 15:713760. [PMID: 34456678 PMCID: PMC8385127 DOI: 10.3389/fnins.2021.713760] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 07/19/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose This study aimed to determine if people living with HIV (PLWH) in preclinical human immunodeficiency virus (HIV)-associated neurocognitive disorder (HAND), with no clinical symptoms and without decreased daily functioning, suffer from brain volumetric alterations and its patterns. Method Fifty-nine male PLWH at the HAND preclinical stage were evaluated, including 19 subjects with asymptomatic neurocognitive impairment (ANI), 17 subjects with cognitive abnormality that does not reach ANI (Not reach ANI), and 23 subjects with cognitive integrity. Moreover, 23 healthy volunteers were set as the seronegative normal controls (NCs). These individuals underwent sagittal three-dimensional T1-weighted imaging (3D T1WI). Quantified data and volumetric measures of brain structures were automatically segmented and extracted using AccuBrain®. In addition, the multiple linear regression analysis was performed to analyze the relationship of volumes of brain structures and clinical variables in preclinical HAND, and the correlations of the brain volume parameters with different cognitive function states were assessed by Pearson's correlation analysis. Results The significant difference was shown in the relative volumes of the ventricular system, bilateral lateral ventricle, thalamus, caudate, and left parietal lobe gray matter between the preclinical HAND and NCs. Furthermore, the relative volumes of the bilateral thalamus in preclinical HAND were negatively correlated with attention/working memory (left: r = -0.271, p = 0.042; right: r = -0.273, p = 0.040). Higher age was associated with increased relative volumes of the bilateral lateral ventricle and ventricular system and reduced relative volumes of the left thalamus and parietal lobe gray matter. The lower CD4+/CD8+ ratio was associated with increased relative volumes of the left lateral ventricle and ventricular system. Longer disease course was associated with increased relative volumes of the bilateral thalamus. No significant difference was found among preclinical HAND subgroups in all indices, and the difference between the individual groups (Not reach ANI and Cognitive integrity groups) and NCs was also insignificant. However, there was a significant difference between ANI and NCs in the relative volumes of the bilateral caudate and lateral ventricle. Conclusion Male PLWH at the HAND preclinical stage suffer from brain volumetric alterations. AccuBrain® provides potential value in evaluating HIV-related neurocognitive dysfunction.
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Affiliation(s)
- Ruili Li
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.,Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Yu Qi
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Lin Shi
- BrainNow Research Institute, Shenzhen, China.,Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Wei Wang
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Aidong Zhang
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Yishan Luo
- BrainNow Research Institute, Shenzhen, China
| | | | - Zengxin Jiao
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Guangxue Liu
- Department of Natural Medicines, School of Pharmaceutical Sciences, Peking University Health Science Center, Beijing, China
| | - Hongjun Li
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Longjiang Zhang
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
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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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29
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Cui L, Chen K, Huang L, Sun J, Lv Y, Jia X, Guo Q. Changes in local brain function in mild cognitive impairment due to semantic dementia. CNS Neurosci Ther 2021; 27:587-602. [PMID: 33650764 PMCID: PMC8025655 DOI: 10.1111/cns.13621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 01/24/2021] [Accepted: 01/28/2021] [Indexed: 11/30/2022] Open
Abstract
AIMS Mild cognitive impairment due to semantic dementia represents the preclinical stage, involving cognitive decline dominated by semantic impairment below the semantic dementia standard. Therefore, studying mild cognitive impairment due to semantic dementia may identify changes in patients before progression to dementia. However, whether changes in local functional activity occur in preclinical stages of semantic dementia remains unknown. Here, we explored local functional changes in patients with mild cognitive impairment due to semantic dementia using resting-state functional MRI. METHODS We administered a battery of neuropsychological tests to twenty-two patients with mild cognitive impairment due to semantic dementia (MCI-SD group) and nineteen healthy controls (HC group). We performed structural MRI to compare gray matter volumes, and resting-state functional MRI with multiple sub-bands and indicators to evaluate functional activity. RESULTS Neuropsychological tests revealed a significant decline in semantic performance in the MCI-SD group, but no decline in other cognitive domains. Resting-state functional MRI revealed local functional changes in multiple brain regions in the MCI-SD group, distributed in different sub-bands and indicators. In the normal band, local functional changes were only in the gray matter atrophic area. In the other sub-bands, more regions with local functional changes outside atrophic areas were found across various indicators. Among these, the degree centrality of the left precuneus in the MCI-SD group was positively correlated with general semantic tasks (oral sound naming, word-picture verification). CONCLUSION Our study revealed local functional changes in mild cognitive impairment due to semantic dementia, some of which were located outside the atrophic gray matter. Driven by functional connectivity changes, the left precuneus might play a role in preclinical semantic dementia. The study proved the value of frequency-dependent sub-bands, especially the slow-2 and slow-3 sub-bands.
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Affiliation(s)
- Liang Cui
- Department of GerontologyShanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiChina
| | - Keliang Chen
- Department of NeurologyHuashan HospitalFudan UniversityShanghaiChina
| | - Lin Huang
- Department of GerontologyShanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiChina
| | - Jiawei Sun
- School of Information and Electronics TechnologyJiamusi UniversityJiamusiChina
| | - Yating Lv
- Institute of Psychological SciencesHangzhou Normal UniversityHangzhouChina
- Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouChina
| | - Xize Jia
- Institute of Psychological SciencesHangzhou Normal UniversityHangzhouChina
- Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouChina
| | - Qihao Guo
- Department of GerontologyShanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiChina
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