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Wang G, Jiang N, Ma Y, Chen D, Wu J, Li G, Liang D, Yan T. Connectional-style-guided contextual representation learning for brain disease diagnosis. Neural Netw 2024; 175:106296. [PMID: 38653077 DOI: 10.1016/j.neunet.2024.106296] [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: 08/09/2023] [Revised: 01/26/2024] [Accepted: 04/06/2024] [Indexed: 04/25/2024]
Abstract
Structural magnetic resonance imaging (sMRI) has shown great clinical value and has been widely used in deep learning (DL) based computer-aided brain disease diagnosis. Previous DL-based approaches focused on local shapes and textures in brain sMRI that may be significant only within a particular domain. The learned representations are likely to contain spurious information and have poor generalization ability in other diseases and datasets. To facilitate capturing meaningful and robust features, it is necessary to first comprehensively understand the intrinsic pattern of the brain that is not restricted within a single data/task domain. Considering that the brain is a complex connectome of interlinked neurons, the connectional properties in the brain have strong biological significance, which is shared across multiple domains and covers most pathological information. In this work, we propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain, used for multiple brain disease diagnosis. Specifically, it has a vision transformer (ViT) encoder and leverages mask reconstruction as the proxy task and Gram matrices to guide the representation of connectional information. It facilitates the capture of global context and the aggregation of features with biological plausibility. The results indicate that CS-CRL achieves superior accuracy in multiple brain disease diagnosis tasks across six datasets and three diseases and outperforms state-of-the-art models. Furthermore, we demonstrate that CS-CRL captures more brain-network-like properties, and better aggregates features, is easier to optimize, and is more robust to noise, which explains its superiority in theory.
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Affiliation(s)
- Gongshu Wang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Ning Jiang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Yunxiao Ma
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Duanduan Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Jinglong Wu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Guoqi Li
- Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Dong Liang
- Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
| | - Tianyi Yan
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
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Zhao L, Tang Y, Tu Y, Cao J. Genetic evidence for the causal relationships between migraine, dementia, and longitudinal brain atrophy. J Headache Pain 2024; 25:93. [PMID: 38840235 DOI: 10.1186/s10194-024-01801-7] [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: 05/06/2024] [Accepted: 05/30/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND Migraine is a neurological disease with a significant genetic component and is characterized by recurrent and prolonged episodes of headache. Previous epidemiological studies have reported a higher risk of dementia in migraine patients. Neuroimaging studies have also shown structural brain atrophy in regions that are common to migraine and dementia. However, these studies are observational and cannot establish causality. The present study aims to explore the genetic causal relationship between migraine and dementia, as well as the mediation roles of brain structural changes in this association using Mendelian randomization (MR). METHODS We collected the genome-wide association study (GWAS) summary statistics of migraine and its two subtypes, as well as four common types of dementia, including Alzheimer's disease (AD), vascular dementia, frontotemporal dementia, and Lewy body dementia. In addition, we collected the GWAS summary statistics of seven longitudinal brain measures that characterize brain structural alterations with age. Using these GWAS, we performed Two-sample MR analyses to investigate the causal effects of migraine and its two subtypes on dementia and brain structural changes. To explore the possible mediation of brain structural changes between migraine and dementia, we conducted a two-step MR mediation analysis. RESULTS The MR analysis demonstrated a significant association between genetically predicted migraine and an increased risk of AD (OR = 1.097, 95% CI = [1.040, 1.158], p = 7.03 × 10- 4). Moreover, migraine significantly accelerated annual atrophy of the total cortical surface area (-65.588 cm2 per year, 95% CI = [-103.112, -28.064], p = 6.13 × 10- 4) and thalamic volume (-9.507 cm3 per year, 95% CI = [-15.512, -3.502], p = 1.91 × 10- 3). The migraine without aura (MO) subtype increased the risk of AD (OR = 1.091, 95% CI = [1.059, 1.123], p = 6.95 × 10- 9) and accelerated annual atrophy of the total cortical surface area (-31.401 cm2 per year, 95% CI = [-43.990, -18.811], p = 1.02 × 10- 6). The two-step MR mediation analysis revealed that thalamic atrophy partly mediated the causal effect of migraine on AD, accounting for 28.2% of the total effect. DISCUSSION This comprehensive MR study provided genetic evidence for the causal effect of migraine on AD and identified longitudinal thalamic atrophy as a potential mediator in this association. These findings may inform brain intervention targets to prevent AD risk in migraine patients.
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Affiliation(s)
- Lei Zhao
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, 16 Lincui Road, Beijing, China
| | - Yilan Tang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, 16 Lincui Road, Beijing, China
| | - Yiheng Tu
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, 16 Lincui Road, Beijing, China
| | - Jin Cao
- School of Life Sciences, Beijing University of Chinese Medicine, 11 North third Ring Road East, Beijing, China.
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De Roeck L, Blommaert J, Dupont P, Sunaert S, Sleurs C, Lambrecht M. Brain network topology and its cognitive impact in adult glioma survivors. Sci Rep 2024; 14:12782. [PMID: 38834633 DOI: 10.1038/s41598-024-63716-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 05/31/2024] [Indexed: 06/06/2024] Open
Abstract
Structural brain network topology can be altered in case of a brain tumor, due to both the tumor itself and its treatment. In this study, we explored the role of structural whole-brain and nodal network metrics and their association with cognitive functioning. Fifty WHO grade 2-3 adult glioma survivors (> 1-year post-therapy) and 50 matched healthy controls underwent a cognitive assessment, covering six cognitive domains. Raw cognitive assessment scores were transformed into w-scores, corrected for age and education. Furthermore, based on multi-shell diffusion-weighted MRI, whole-brain tractography was performed to create weighted graphs and to estimate whole-brain and nodal graph metrics. Hubs were defined based on nodal strength, betweenness centrality, clustering coefficient and shortest path length in healthy controls. Significant differences in these metrics between patients and controls were tested for the hub nodes (i.e. n = 12) and non-hub nodes (i.e. n = 30) in two mixed-design ANOVAs. Group differences in whole-brain graph measures were explored using Mann-Whitney U tests. Graph metrics that significantly differed were ultimately correlated with the cognitive domain-specific w-scores. Bonferroni correction was applied to correct for multiple testing. In survivors, the bilateral putamen were significantly less frequently observed as a hub (pbonf < 0.001). These nodes' assortativity values were positively correlated with attention (r(90) > 0.573, pbonf < 0.001), and proxy IQ (r(90) > 0.794, pbonf < 0.001). Attention and proxy IQ were significantly more often correlated with assortativity of hubs compared to non-hubs (pbonf < 0.001). Finally, the whole-brain graph measures of clustering coefficient (r = 0.685), global (r = 0.570) and local efficiency (r = 0.500) only correlated with proxy IQ (pbonf < 0.001). This study demonstrated potential reorganization of hubs in glioma survivors. Assortativity of these hubs was specifically associated with cognitive functioning, which could be important to consider in future modeling of cognitive outcomes and risk classification in glioma survivors.
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Affiliation(s)
- Laurien De Roeck
- Department of Radiotherapy and Oncology, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium.
- Department of Oncology, KU Leuven, Leuven, Belgium.
| | - Jeroen Blommaert
- Department of Oncology, KU Leuven, Leuven, Belgium
- Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Patrick Dupont
- Leuven Brain Institute, KU Leuven, Leuven, Belgium
- Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Stefan Sunaert
- Leuven Brain Institute, KU Leuven, Leuven, Belgium
- Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Charlotte Sleurs
- Department of Oncology, KU Leuven, Leuven, Belgium
- Department of Cognitive Neuropsychology, Tilburg University, Tilburg, the Netherlands
| | - Maarten Lambrecht
- Department of Radiotherapy and Oncology, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
- Leuven Brain Institute, KU Leuven, Leuven, Belgium
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Dong C, Thalamuthu A, Jiang J, Mather KA, Sachdev PS, Wen W. Brain structural covariances in the ageing brain in the UK Biobank. Brain Struct Funct 2024; 229:1165-1177. [PMID: 38625555 PMCID: PMC11147885 DOI: 10.1007/s00429-024-02794-4] [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: 07/18/2023] [Accepted: 03/21/2024] [Indexed: 04/17/2024]
Abstract
The morphologic properties of brain regions co-vary or correlate with each other. Here we investigated the structural covariances of cortical thickness and subcortical volumes in the ageing brain, along with their associations with age and cognition, using cross-sectional data from the UK Biobank (N = 42,075, aged 45-83 years, 53% female). As the structural covariance should be estimated in a group of participants, all participants were divided into 84 non-overlapping, equal-sized age groups ranging from the youngest to the oldest. We examined 84 cortical thickness covariances and subcortical covariances. Our findings include: (1) there were significant differences in the variability of structural covariance in the ageing process, including an increased variance, and a decreased entropy. (2) significant enrichment in pairwise correlations between brain regions within the occipital lobe was observed in all age groups; (3) structural covariance in older age, especially after the age of around 64, was significantly different from that in the youngest group (median age 48 years); (4) sixty-two of the total 528 pairs of cortical thickness correlations and 10 of the total 21 pairs of subcortical volume correlations showed significant associations with age. These trends varied, with some correlations strengthening, some weakening, and some reversing in direction with advancing age. Additionally, as ageing was associated with cognitive decline, most of the correlations with cognition displayed an opposite trend compared to age associated patterns of correlations.
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Affiliation(s)
- Chao Dong
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW, Sydney, Australia.
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW, Sydney, Australia
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW, Sydney, Australia
| | - Karen A Mather
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW, Sydney, Australia
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW, Sydney, Australia
- Neuropsychiatric Institute (NPI), Prince of Wales Hospital, Randwick, NSW, 2031, Australia
| | - Wei Wen
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW, Sydney, Australia
- Neuropsychiatric Institute (NPI), Prince of Wales Hospital, Randwick, NSW, 2031, Australia
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5
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Mandlik DS, Mandlik SK, S A. Therapeutic implications of glycogen synthase kinase-3β in Alzheimer's disease: a novel therapeutic target. Int J Neurosci 2024; 134:603-619. [PMID: 36178363 DOI: 10.1080/00207454.2022.2130297] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 09/03/2022] [Accepted: 09/10/2022] [Indexed: 10/17/2022]
Abstract
Alzheimer's disease (AD) is an extremely popular neurodegenerative condition associated with dementia, responsible for around 70% of the cases. There are presently 50 million people living with dementia in the world, but this number is anticipated to increase to 152 million by 2050, posing a substantial socioeconomic encumbrance. Despite extensive research, the precise mechanisms that cause AD remain unidentified, and currently, no therapy is available. Numerous signalling paths related to AD neuropathology, including glycogen synthase kinase 3-β (GSK-3β), have been investigated as potential targets for the treatment of AD in current years.GSK-3β is a proline-directed serine/threonine kinase that is linked to a variety of biological activities, comprising glycogen metabolism to gene transcription. GSK-3β is also involved in the pathophysiology of sporadic as well as familial types of AD, which has led to the development of the GSK3 theory of AD. GSK-3β is a critical performer in the pathology of AD because dysregulation of this kinase affects all the main symbols of the disease such as amyloid formation, tau phosphorylation, neurogenesis and synaptic and memory function. The current review highlights present-day knowledge of GSK-3β-related neurobiology, focusing on its role in AD pathogenesis signalling pathways. It also explores the possibility of targeting GSK-3β for the management of AD and offers an overview of the present research work in preclinical and clinical studies to produce GSK-3β inhibitors.
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Affiliation(s)
- Deepa S Mandlik
- Department of Pharmacology, Poona College of Pharmacy, Bharati Vidyapeeth (Deemed to be University), Erandawane, Pune, India
| | - Satish K Mandlik
- Department of Pharmaceutics, Poona College of Pharmacy, Bharati Vidyapeeth (Deemed to be University), Erandawane, Pune, India
| | - Arulmozhi S
- Department of Pharmacology, Poona College of Pharmacy, Bharati Vidyapeeth (Deemed to be University), Erandawane, Pune, India
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Zhang F, Li L, Liu B, Shao Y, Tan Y, Niu Q, Zhang H. Decoupling of gray and white matter functional networks in cognitive impairment induced by occupational aluminum exposure. Neurotoxicology 2024; 103:1-8. [PMID: 38777096 DOI: 10.1016/j.neuro.2024.05.001] [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: 02/16/2024] [Revised: 04/21/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024]
Abstract
Aluminum (Al) is a low-toxic, accumulative substance with neurotoxicity properties that adversely affect human cognitive function. This study aimed to investigate the neurobiological mechanisms underlying cognitive impairment resulting from occupational Al exposure. Resting-state functional magnetic resonance imaging was conducted on 54 individuals with over 10 years of Al exposure. Al levels were measured, and cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). Subsequently, the K-means clustering algorithm was employed to identify functional gray matter (GM) and white matter (WM) networks. Two-sample t-tests were conducted between the cognition impairment group and the control group. Al exhibited a negative correlation with MoCA scores. Participants with cognitive impairment demonstrated reduced functional connectivity (FC) between the middle cingulum network (WM1) and anterior cingulum network (WM2), as well as between the executive control network (WM6) and limbic network (WM10). Notably, decreased FCs were observed between the executive control network (GM5) and WM1, WM4, WM6, and WM10. Additionally, the FC of GM5-GM4 and WM1-WM2 negatively correlated with Trail Making Test Part A (TMT-A) scores. Prolonged Al accumulation detrimentally affects cognition, primarily attributable to executive control and limbic network disruptions.
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Affiliation(s)
- Feifei Zhang
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China; Shanxi Key Laboratory of Intelligent Imaging and Nanomedicine, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China
| | - Lina Li
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China; Shanxi Key Laboratory of Intelligent Imaging and Nanomedicine, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China
| | - Bo Liu
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China; Shanxi Key Laboratory of Intelligent Imaging and Nanomedicine, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China
| | - Yingbo Shao
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China; Shanxi Key Laboratory of Intelligent Imaging and Nanomedicine, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China
| | - Yan Tan
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China; Shanxi Key Laboratory of Intelligent Imaging and Nanomedicine, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China
| | - Qiao Niu
- Department of Occupational Health, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China.
| | - Hui Zhang
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China; Shanxi Key Laboratory of Intelligent Imaging and Nanomedicine, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China.
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7
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Heo S, Yoon CW, Kim SY, Kim WR, Na DL, Noh Y. Alterations of Structural Network Efficiency in Early-Onset and Late-Onset Alzheimer's Disease. J Clin Neurol 2024; 20:265-275. [PMID: 38330417 PMCID: PMC11076196 DOI: 10.3988/jcn.2023.0092] [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: 03/08/2023] [Revised: 08/17/2023] [Accepted: 10/05/2023] [Indexed: 02/10/2024] Open
Abstract
BACKGROUND AND PURPOSE Early- and late-onset Alzheimer's disease (EOAD and LOAD, respectively) share the same neuropathological hallmarks of amyloid and neurofibrillary tangles but have distinct cognitive features. We compared structural brain connectivity between the EOAD and LOAD groups using structural network efficiency and evaluated the association of structural network efficiency with the cognitive profile and pathological markers of Alzheimer's disease (AD). METHODS The structural brain connectivity networks of 80 AD patients (47 with EOAD and 33 with LOAD) and 57 healthy controls were reconstructed using diffusion-tensor imaging. Graph-theoretic indices were calculated and intergroup differences were evaluated. Correlations between network parameters and neuropsychological test results were analyzed. The correlations of the amyloid and tau burdens with network parameters were evaluated for the patients and controls. RESULTS Compared with the age-matched control group, the EOAD patients had increased global path length and decreased global efficiency, averaged local efficiency, and averaged clustering coefficient. In contrast, no significant differences were found in the LOAD patients. Locally, the EOAD patients showed decreases in local efficiency and the clustering coefficient over a wide area compared with the control group, whereas LOAD patients showed such decreases only within a limited area. Changes in network parameters were significantly correlated with multiple cognitive domains in EOAD patients, but only with Clinical Dementia Rating Sum-of-Boxes scores in LOAD patients. Finally, the tau burden was correlated with changes in network parameters in AD signature areas in both patient groups, while there was no correlation with the amyloid burden. CONCLUSIONS The impairment of structural network efficiency and its effects on cognition may differ between EOAD and LOAD.
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Affiliation(s)
- Suyeon Heo
- Gachon University, College of Medicine, Incheon, Korea
| | - Cindy W Yoon
- Department of Neurology, Inha University School of Medicine, Incheon, Korea
| | - Sang-Young Kim
- Neuroscience Research Institute, Gachon University, Incheon, Korea
- MR Clinical Science, Health Systems, Philips Healthcare, Seoul, Korea
| | - Woo-Ram Kim
- Neuroscience Research Institute, Gachon University, Incheon, Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Happymind Clinic, Seoul, Korea
| | - Young Noh
- Neuroscience Research Institute, Gachon University, Incheon, Korea
- Department of Neurology, Gil Medical Center, Gachon University College of Medicine, Incheon, Korea.
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Lim YA, Tan LS, Lee WT, Sim WL, Lv Y, Takakuni M, Saito S, Ihara M, Arumugam TV, Chen C, Wong FWS, Dawe GS. Hope for vascular cognitive impairment: Ac-YVAD-cmk as a novel treatment against white matter rarefaction. PLoS One 2024; 19:e0299703. [PMID: 38630707 PMCID: PMC11023579 DOI: 10.1371/journal.pone.0299703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 02/14/2024] [Indexed: 04/19/2024] Open
Abstract
Vascular cognitive impairment (VCI) is the second leading cause of dementia with limited treatment options, characterised by cerebral hypoperfusion-induced white matter rarefaction (WMR). Subcortical VCI is the most common form of VCI, but the underlying reasons for region susceptibility remain elusive. Recent studies employing the bilateral cortical artery stenosis (BCAS) method demonstrate that various inflammasomes regulate white matter injury and blood-brain barrier dysfunction but whether caspase-1 inhibition will be beneficial remains unclear. To address this, we performed BCAS on C57/BL6 mice to study the effects of Ac-YVAD-cmk, a caspase-1 inhibitor, on the subcortical and cortical regions. Cerebral blood flow (CBF), WMR, neuroinflammation and the expression of tight junction-related proteins associated with blood-brain barrier integrity were assessed 15 days post BCAS. We observed that Ac-YVAD-cmk restored CBF, attenuated BCAS-induced WMR and restored subcortical myelin expression. Within the subcortical region, BCAS activated the NLRP3/caspase-1/interleukin-1beta axis only within the subcortical region, which was attenuated by Ac-YVAD-cmk. Although we observed that BCAS induced significant increases in VCAM-1 expression in both brain regions that were attenuated with Ac-YVAD-cmk, only ZO-1 and occludin were observed to be significantly altered in the subcortical region. Here we show that caspase-1 may contribute to subcortical regional susceptibility in a mouse model of VCI. In addition, our results support further investigations into the potential of Ac-YVAD-cmk as a novel treatment strategy against subcortical VCI and other conditions exhibiting cerebral hypoperfusion-induced WMR.
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Affiliation(s)
- Yun-An Lim
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Li Si Tan
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Wei Thye Lee
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Wei Liang Sim
- Department of Physiology, National University of Singapore, Singapore, Singapore
| | - Yang Lv
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Maki Takakuni
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Satoshi Saito
- Department of Neurology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
- Japan Society for the Promotion of Science, Chiyoda-ku, Tokyo, Japan
| | - Masafumi Ihara
- Department of Neurology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | | | - Christopher Chen
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Fred Wai-Shiu Wong
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Gavin Stewart Dawe
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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Yu X, Sun X, Wei M, Deng S, Zhang Q, Guo T, Shao K, Zhang M, Jiang J, Han Y. Innovative Multivariable Model Combining MRI Radiomics and Plasma Indexes Predicts Alzheimer's Disease Conversion: Evidence from a 2-Cohort Longitudinal Study. RESEARCH (WASHINGTON, D.C.) 2024; 7:0354. [PMID: 38711474 PMCID: PMC11070845 DOI: 10.34133/research.0354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 03/21/2024] [Indexed: 05/08/2024]
Abstract
To explore the complementary relationship between magnetic resonance imaging (MRI) radiomic and plasma biomarkers in the early diagnosis and conversion prediction of Alzheimer's disease (AD), our study aims to develop an innovative multivariable prediction model that integrates those two for predicting conversion results in AD. This longitudinal multicentric cohort study included 2 independent cohorts: the Sino Longitudinal Study on Cognitive Decline (SILCODE) project and the Alzheimer Disease Neuroimaging Initiative (ADNI). We collected comprehensive assessments, MRI, plasma samples, and amyloid positron emission tomography data. A multivariable logistic regression analysis was applied to combine plasma and MRI radiomics biomarkers and generate a new composite indicator. The optimal model's performance and generalizability were assessed across populations in 2 cross-racial cohorts. A total of 897 subjects were included, including 635 from the SILCODE cohort (mean [SD] age, 64.93 [6.78] years; 343 [63%] female) and 262 from the ADNI cohort (mean [SD] age, 73.96 [7.06] years; 140 [53%] female). The area under the receiver operating characteristic curve of the optimal model was 0.9414 and 0.8979 in the training and validation dataset, respectively. A calibration analysis displayed excellent consistency between the prognosis and actual observation. The findings of the present study provide a valuable diagnostic tool for identifying at-risk individuals for AD and highlight the pivotal role of the radiomic biomarker. Importantly, built upon data-driven analyses commonly seen in previous radiomics studies, our research delves into AD pathology to further elucidate the underlying reasons behind the robust predictive performance of the MRI radiomic predictor.
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Affiliation(s)
- Xianfeng Yu
- Department of Neurology,
Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Xiaoming Sun
- Institute of Biomedical Engineering, School of Life Science,
Shanghai University, Shanghai 200444, China
| | - Min Wei
- Department of Neurology,
Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Shuqing Deng
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Qi Zhang
- Institute of Biomedical Engineering, School of Life Science,
Shanghai University, Shanghai 200444, China
| | - Tengfei Guo
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Kai Shao
- Department of Neurology,
Xuanwu Hospital of Capital Medical University, Beijing 100053, China
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
| | - Mingkai Zhang
- Department of Neurology,
Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Science,
Shanghai University, Shanghai 200444, China
| | - Ying Han
- Department of Neurology,
Xuanwu Hospital of Capital Medical University, Beijing 100053, China
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen 518132, China
- Center of Alzheimer’s Disease,
Beijing Institute for Brain Disorders, Beijing 100069, China
- National Clinical Research Center for Geriatric Disorders, Beijing 100053, China
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10
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Mohammadi S, Ghaderi S. Parkinson's disease and Parkinsonism syndromes: Evaluating iron deposition in the putamen using magnetic susceptibility MRI techniques - A systematic review and literature analysis. Heliyon 2024; 10:e27950. [PMID: 38689949 PMCID: PMC11059419 DOI: 10.1016/j.heliyon.2024.e27950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 02/29/2024] [Accepted: 03/08/2024] [Indexed: 05/02/2024] Open
Abstract
Magnetic resonance imaging (MRI) techniques, such as quantitative susceptibility mapping (QSM) and susceptibility-weighted imaging (SWI), can detect iron deposition in the brain. Iron accumulation in the putamen (PUT) can contribute to the pathogenesis of Parkinson's disease (PD) and atypical Parkinsonian disorders. This systematic review aimed to synthesize evidence on iron deposition in the PUT assessed by MRI susceptibility techniques in PD and Parkinsonism syndromes. The PubMed and Scopus databases were searched for relevant studies. Thirty-four studies from January 2007 to October 2023 that used QSM, SWI, or other MRI susceptibility methods to measure putaminal iron in PD, progressive supranuclear palsy (PSP), multiple system atrophy (MSA), and healthy controls (HCs) were included. Most studies have found increased putaminal iron levels in PD patients versus HCs based on higher quantitative susceptibility. Putaminal iron accumulation correlates with worse motor scores and cognitive decline in patients with PD. Evidence regarding differences in susceptibility between PD and atypical Parkinsonism is emerging, with several studies showing greater putaminal iron deposition in PSP and MSA than in PD patients. Alterations in putaminal iron levels help to distinguish these disorders from PD. Increased putaminal iron levels appear to be associated with increased disease severity and progression. Thus, magnetic susceptibility MRI techniques can detect abnormal iron accumulation in the PUT of patients with Parkinsonism. Moreover, quantifying putaminal susceptibility may serve as an MRI biomarker to monitor motor and cognitive changes in PD and aid in the differential diagnosis of Parkinsonian disorders.
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Affiliation(s)
- Sana Mohammadi
- Department of Medical Sciences, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Sadegh Ghaderi
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
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11
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Hwang H, Kim SE, Lee HJ, Lee DA, Park KM. Identification of amnestic mild cognitive impairment by structural and functional MRI using a machine-learning approach. Clin Neurol Neurosurg 2024; 238:108177. [PMID: 38402707 DOI: 10.1016/j.clineuro.2024.108177] [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: 06/15/2022] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 02/27/2024]
Abstract
OBJECTIVE The importance of early treatment for mild cognitive impairment (MCI) has been extensively shown. However, classifying patients presenting with memory complaints in clinical practice as having MCI vs normal results is difficult. Herein, we assessed the feasibility of applying a machine learning approach based on structural volumes and functional connectomic profiles to classify the cognitive levels of cognitively unimpaired (CU) and amnestic MCI (aMCI) groups. We further applied the same method to distinguish aMCI patients with a single memory impairment from those with multiple memory impairments. METHODS Fifty patients with aMCI were enrolled and classified as having either verbal or visual-aMCI (verbal or visual memory impairment), or both aMCI (verbal and visual memory impairments) based on memory test results. In addition, 26 CU patients were enrolled in the control group. All patients underwent structural T1-weighted magnetic resonance imaging (MRI) and resting-state functional MRI. We obtained structural volumes and functional connectomic profiles from structural and functional MRI, respectively, using graph theory. A support vector machine (SVM) algorithm was employed, and k-fold cross-validation was performed to discriminate between groups. RESULTS The SVM classifier based on structural volumes revealed an accuracy of 88.9% at classifying the cognitive levels of patients with CU and aMCI. However, when the structural volumes and functional connectomic profiles were combined, the accuracy increased to 92.9%. In the classification of verbal or visual-aMCI (n = 22) versus both aMCI (n = 28), the SVM classifier based on structural volumes revealed a low accuracy of 36.7%. However, when the structural volumes and functional connectomic profiles were combined, the accuracy increased to 53.1%. CONCLUSION Structural volumes and functional connectomic profiles obtained using a machine learning approach can be used to classify cognitive levels to distinguish between aMCI and CU patients. In addition, combining the functional connectomic profiles with structural volumes results in a better classification performance than the use of structural volumes alone for identifying both "aMCI versus CU" and "verbal- or visual-aMCI versus both aMCI" patients.
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Affiliation(s)
- Hyunyoung Hwang
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Si Eun Kim
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Ho-Joon Lee
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Dong Ah Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
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12
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Bloch L, Friedrich CM. Systematic comparison of 3D Deep learning and classical machine learning explanations for Alzheimer's Disease detection. Comput Biol Med 2024; 170:108029. [PMID: 38308870 DOI: 10.1016/j.compbiomed.2024.108029] [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: 07/09/2023] [Revised: 01/25/2024] [Accepted: 01/25/2024] [Indexed: 02/05/2024]
Abstract
Black-box deep learning (DL) models trained for the early detection of Alzheimer's Disease (AD) often lack systematic model interpretation. This work computes the activated brain regions during DL and compares those with classical Machine Learning (ML) explanations. The architectures used for DL were 3D DenseNets, EfficientNets, and Squeeze-and-Excitation (SE) networks. The classical models include Random Forests (RFs), Support Vector Machines (SVMs), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting (LightGBM), Decision Trees (DTs), and Logistic Regression (LR). For explanations, SHapley Additive exPlanations (SHAP) values, Local Interpretable Model-agnostic Explanations (LIME), Gradient-weighted Class Activation Mapping (GradCAM), GradCAM++ and permutation-based feature importance were implemented. During interpretation, correlated features were consolidated into aspects. All models were trained on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The validation includes internal and external validation on the Australian Imaging and Lifestyle flagship study of Ageing (AIBL) and the Open Access Series of Imaging Studies (OASIS). DL and ML models reached similar classification performances. Regarding the brain regions, both types focus on different regions. The ML models focus on the inferior and middle temporal gyri, and the hippocampus, and amygdala regions previously associated with AD. The DL models focus on a wider range of regions including the optical chiasm, the entorhinal cortices, the left and right vessels, and the 4th ventricle which were partially associated with AD. One explanation for the differences is the input features (textures vs. volumes). Both types show reasonable similarity to a ground truth Voxel-Based Morphometry (VBM) analysis. Slightly higher similarities were measured for ML models.
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Affiliation(s)
- Louise Bloch
- Department of Computer Science, University of Applied Sciences and Arts Dortmund (FH Dortmund), Emil-Figge-Straße 42, Dortmund, 44227, North Rhine-Westphalia, Germany; Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University Hospital Essen, Hufelandstraße 55, Essen, 45122, North Rhine-Westphalia, Germany; Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Hufelandstraße 55, Essen, 45122, North Rhine-Westphalia, Germany.
| | - Christoph M Friedrich
- Department of Computer Science, University of Applied Sciences and Arts Dortmund (FH Dortmund), Emil-Figge-Straße 42, Dortmund, 44227, North Rhine-Westphalia, Germany; Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University Hospital Essen, Hufelandstraße 55, Essen, 45122, North Rhine-Westphalia, Germany.
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13
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Wu Z, Chen J, Liu Y, Yang Y, Feng M, Dai H. The Effects of PICALM rs3851179 and Age on Brain Atrophy and Cognition Along the Alzheimer's Disease Continuum. Mol Neurobiol 2024:10.1007/s12035-024-03953-8. [PMID: 38363532 DOI: 10.1007/s12035-024-03953-8] [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: 11/06/2023] [Accepted: 01/12/2024] [Indexed: 02/17/2024]
Abstract
Rs3851179, a variant of PICALM gene, and age are the risk factors of Alzheimer's disease (AD). AD is divided into early-onset AD (EOAD, < 65 years) and late-onset AD (LOAD, ≥ 65 years) by age. The purpose was to investigate the impact of different genotypes of PICALM rs3851179 on brain atrophy and cognitive decline across the AD continuum in different age groups. Four hundred seven cognitive normal (CN) controls, 362 mild cognitive impairment (MCI) patients, and 94 AD patients were enrolled to assess the interaction between AD continuum, age status, and PICALM on gray matter volume (GMV), global cognition, memory function, and executive function using full factorial ANCOVA (3 × 2 × 2). The interaction between AD continuum and PICALM significantly affected the GMV of the left putamen (PUT.L). rs3851179 A-allele carriers did not show a significant decrease in PUT.L GMV from CN to MCI to AD, while GG-allele carriers did. The interaction between AD continuum and age status was significant on GMV of the left angular gyrus (ANG.L) and right superior occipital gyrus (SOG.R). LOAD had higher GMV of ANG.L and SOG.R than EOAD. The interactive effects among AD continuum, age status, and PICALM were not significant on GMV but were significant on global cognition and executive function. The A-allele was found to have a protective effect on global cognition and executive function in EOAD, but not significantly so in LOAD. PICALM rs3851179 A-allele might alleviate the atrophy of PUT.L across the AD continuum than GG-allele. Age status did not affect the interaction between AD continuum and PICALM on brain atrophy. The ANG.L and SOG.R atrophied more severely in EOAD than in LOAD. Rs3851179 A-allele was protective for global cognition and executive function in EOAD.
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Affiliation(s)
- Zhiwei Wu
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui Province, 230001, People's Republic of China
| | - Jinhong Chen
- Department of Ultrasound, Hefei Hospital affiliated to Anhui Medical University: The Second People's Hospital of Hefei, Hefei, Anhui Province, 230011, People's Republic of China
- The Fifth Clinical Medical College of Anhui Medical University, Hefei, Anhui Province, 230032, People's Republic of China
| | - Yuanqing Liu
- Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215006, People's Republic of China
| | - Yiwen Yang
- Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215006, People's Republic of China
| | - Mengmeng Feng
- Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215006, People's Republic of China
| | - Hui Dai
- Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215006, People's Republic of China.
- Institute of Medical Imaging, Soochow University, Suzhou, Jiangsu Province, 215006, People's Republic of China.
- Suzhou Key Laboratory of Intelligent Medicine and Equipment, Suzhou, Jiangsu Province, 215123, People's Republic of China.
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14
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Shah SN, Dounavi ME, Malhotra PA, Lawlor B, Naci L, Koychev I, Ritchie CW, Ritchie K, O’Brien JT. Dementia risk and thalamic nuclei volumetry in healthy midlife adults: the PREVENT Dementia study. Brain Commun 2024; 6:fcae046. [PMID: 38444908 PMCID: PMC10914447 DOI: 10.1093/braincomms/fcae046] [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: 07/26/2023] [Revised: 12/31/2023] [Accepted: 02/13/2024] [Indexed: 03/07/2024] Open
Abstract
A reduction in the volume of the thalamus and its nuclei has been reported in Alzheimer's disease, mild cognitive impairment and asymptomatic individuals with risk factors for early-onset Alzheimer's disease. Some studies have reported thalamic atrophy to occur prior to hippocampal atrophy, suggesting thalamic pathology may be an early sign of cognitive decline. We aimed to investigate volumetric differences in thalamic nuclei in middle-aged, cognitively unimpaired people with respect to dementia family history and apolipoprotein ε4 allele carriership and the relationship with cognition. Seven hundred participants aged 40-59 years were recruited into the PREVENT Dementia study. Individuals were stratified according to dementia risk (approximately half with and without parental dementia history). The subnuclei of the thalamus of 645 participants were segmented on T1-weighted 3 T MRI scans using FreeSurfer 7.1.0. Thalamic nuclei were grouped into six regions: (i) anterior, (ii) lateral, (iii) ventral, (iv) intralaminar, (v) medial and (vi) posterior. Cognitive performance was evaluated using the computerized assessment of the information-processing battery. Robust linear regression was used to analyse differences in thalamic nuclei volumes and their association with cognitive performance, with age, sex, total intracranial volume and years of education as covariates and false discovery rate correction for multiple comparisons. We did not find significant volumetric differences in the thalamus or its subregions, which survived false discovery rate correction, with respect to first-degree family history of dementia or apolipoprotein ε4 allele status. Greater age was associated with smaller volumes of thalamic subregions, except for the medial thalamus, but only in those without a dementia family history. A larger volume of the mediodorsal medial nucleus (Pfalse discovery rate = 0.019) was associated with a faster processing speed in those without a dementia family history. Larger volumes of the thalamus (P = 0.016) and posterior thalamus (Pfalse discovery rate = 0.022) were associated with significantly worse performance in the immediate recall test in apolipoprotein ε4 allele carriers. We did not find significant volumetric differences in thalamic subregions in relation to dementia risk but did identify an interaction between dementia family history and age. Larger medial thalamic nuclei may exert a protective effect on cognitive performance in individuals without a dementia family history but have little effect on those with a dementia family history. Larger volumes of posterior thalamic nuclei were associated with worse recall in apolipoprotein ε4 carriers. Our results could represent initial dysregulation in the disease process; further study is needed with functional imaging and longitudinal analysis.
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Affiliation(s)
- Sita N Shah
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Maria-Eleni Dounavi
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Paresh A Malhotra
- Department of Brain Sciences, Imperial College London, London W12 0NN, UK
- UK Dementia Research Institute Care Research and Technology Centre, Imperial College London and the University of Surrey, London SW7 2AZ, UK
| | - Brian Lawlor
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin D02 PX31, Ireland
- Global Brain Health Institute, Trinity College Dublin, Dublin D02 X9W9, Ireland
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin D02 PX31, Ireland
- Global Brain Health Institute, Trinity College Dublin, Dublin D02 X9W9, Ireland
| | - Ivan Koychev
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK
| | - Craig W Ritchie
- Centre for Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Karen Ritchie
- Institute de Neurosciences de Montpellier, INSERM, Montpellier 34093, France
| | - John T O’Brien
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
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15
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Herrejon IA, Jackson TB, Hicks TH, Bernard JA. Functional Connectivity Differences in Distinct Dentato-Cortical Networks in Alzheimer's Disease and Mild Cognitive Impairment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.02.578249. [PMID: 38352603 PMCID: PMC10862898 DOI: 10.1101/2024.02.02.578249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Recent research has implicated the cerebellum in Alzheimer's disease (AD), and cerebrocerebellar network connectivity is emerging as a possible contributor to symptom severity. The cerebellar dentate nucleus (DN) has parallel motor and non-motor sub-regions that project to motor and frontal regions of the cerebral cortex, respectively. These distinct dentato-cortical networks have been delineated in the non-human primate and human brain. Importantly, cerebellar regions prone to atrophy in AD are functionally connected to atrophied regions of the cerebral cortex, suggesting that dysfunction perhaps occurs at a network level. Investigating functional connectivity (FC) alterations of the DN is a crucial step in understanding the cerebellum in AD and in mild cognitive impairment (MCI). Inclusion of this latter group stands to provide insights into cerebellar contributions prior to diagnosis of AD. The present study investigated FC differences in dorsal (dDN) and ventral (vDN) DN networks in MCI and AD relative to cognitively normal participants (CN) and relationships between FC and behavior. Our results showed patterns indicating both higher and lower functional connectivity in both dDN and vDN in AD compared to CN. However, connectivity in the AD group was lower when compared to MCI. We argue that these findings suggest that the patterns of higher FC in AD may act as a compensatory mechanism. Additionally, we found associations between the individual networks and behavior. There were significant interactions between dDN connectivity and motor symptoms. However, both DN seeds were associated with cognitive task performance. Together, these results indicate that cerebellar DN networks are impacted in AD, and this may impact behavior. In concert with the growing body of literature implicating the cerebellum in AD, our work further underscores the importance of investigations of this region. We speculate that much like in psychiatric diseases such as schizophrenia, cerebellar dysfunction results in negative impacts on thought and the organization therein. Further, this is consistent with recent arguments that the cerebellum provides crucial scaffolding for cognitive function in aging. Together, our findings stand to inform future clinical work in the diagnosis and understanding of this disease.
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Affiliation(s)
- Ivan A. Herrejon
- Department of Psychological and Brain Sciences Texas A&M University
| | - T. Bryan Jackson
- Department of Psychological and Brain Sciences Texas A&M University
- Vanderbilt Memory and Alzheimer’s Center Vanderbilt University Medical Center
| | - Tracey H. Hicks
- Department of Psychological and Brain Sciences Texas A&M University
| | - Jessica A. Bernard
- Department of Psychological and Brain Sciences Texas A&M University
- Texas A&M Institute for Neuroscience Texas A&M University
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16
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Pang X, Liang X, Chang W, Lv Z, Zhao J, Wu P, Li X, Wei W, Zheng J. The role of the thalamus in modular functional networks in temporal lobe epilepsy with cognitive impairment. CNS Neurosci Ther 2024; 30:e14345. [PMID: 37424152 PMCID: PMC10848054 DOI: 10.1111/cns.14345] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 06/04/2023] [Accepted: 06/27/2023] [Indexed: 07/11/2023] Open
Abstract
OBJECTIVE Cognitive deficit is common in patients with temporal lobe epilepsy (TLE). Here, we aimed to investigate the modular architecture of functional networks associated with distinct cognitive states in TLE patients together with the role of the thalamus in modular networks. METHODS Resting-state functional magnetic resonance imaging scans were acquired from 53 TLE patients and 37 matched healthy controls. All patients received the Montreal Cognitive Assessment test and accordingly were divided into TLE patients with normal cognition (TLE-CN, n = 35) and TLE patients with cognitive impairment (TLE-CI, n = 18) groups. The modular properties of functional networks were calculated and compared including global modularity Q, modular segregation index, intramodular connections, and intermodular connections. Thalamic subdivisions corresponding to the modular networks were generated by applying a 'winner-take-all' strategy before analyzing the modular properties (participation coefficient and within-module degree z-score) of each thalamic subdivision to assess the contribution of the thalamus to modular functional networks. Relationships between network properties and cognitive performance were then further explored. RESULTS Both TLE-CN and TLE-CI patients showed lower global modularity, as well as lower modular segregation index values for the ventral attention network and the default mode network. However, different patterns of intramodular and intermodular connections existed for different cognitive states. In addition, both TLE-CN and TLE-CI patients exhibited anomalous modular properties of functional thalamic subdivisions, with TLE-CI patients presenting a broader range of abnormalities. Cognitive performance in TLE-CI patients was not related to the modular properties of functional network but rather to the modular properties of functional thalamic subdivisions. CONCLUSIONS The thalamus plays a prominent role in modular networks and potentially represents a key neural mechanism underlying cognitive impairment in TLE.
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Affiliation(s)
- Xiaomin Pang
- Department of NeurologyGuangxi Medical University First Affiliated HospitalNanningChina
| | - Xiulin Liang
- Department of NeurologyGuangxi Medical University First Affiliated HospitalNanningChina
| | - Weiwei Chang
- Department of NeurologyGuangxi Medical University First Affiliated HospitalNanningChina
| | - Zongxia Lv
- Department of NeurologyGuangxi Medical University First Affiliated HospitalNanningChina
| | - Jingyuan Zhao
- Department of NeurologyGuangxi Medical University First Affiliated HospitalNanningChina
| | - Peirong Wu
- Department of NeurologyGuangxi Medical University First Affiliated HospitalNanningChina
| | - Xinrong Li
- Department of NeurologyGuangxi Medical University First Affiliated HospitalNanningChina
| | - Wutong Wei
- Department of NeurologyGuangxi Medical University First Affiliated HospitalNanningChina
| | - Jinou Zheng
- Department of NeurologyGuangxi Medical University First Affiliated HospitalNanningChina
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17
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Saha C, Figley CR, Dastgheib Z, Lithgow BJ, Moussavi Z. Gray and White Matter Voxel-Based Morphometry of Alzheimer's Disease With and Without Significant Cerebrovascular Pathologies. Neurosci Insights 2024; 19:26331055231225657. [PMID: 38304550 PMCID: PMC10832430 DOI: 10.1177/26331055231225657] [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: 06/19/2023] [Accepted: 12/22/2023] [Indexed: 02/03/2024] Open
Abstract
Alzheimer's disease (AD) is the most common type of dementia, and AD individuals often present significant cerebrovascular disease (CVD) symptomology. AD with significant levels of CVD is frequently labeled mixed dementia (or sometimes AD-CVD), and the differentiation of these two neuropathologies (AD, AD-CVD) from each other is challenging, especially at early stages. In this study, we compared the gray matter (GM) and white matter (WM) volumes in AD (n = 83) and AD-CVD (n = 37) individuals compared with those of cognitively healthy controls (n = 85) using voxel-based morphometry (VBM) of their MRI scans. The control individuals, matched for age and sex with our two dementia groups, were taken from the ADNI. The VBM analysis showed widespread patterns of significantly lower GM and WM volume in both dementia groups compared to the control group (P < .05, family-wise error corrected). While comparing with AD-CVD, the AD group mainly demonstrated a trend of lower volumes in the GM of the left putamen and right hippocampus and WM of the right thalamus (uncorrected P < .005 with cluster threshold, K = 10). The AD-CVD group relative to AD tended to present lower GM and WM volumes, mainly in the cerebellar lobules and right brainstem regions, respectively (uncorrected P < .005 with cluster threshold, K = 10). Although finding a discriminatory feature in structural MRI data between AD and AD-CVD neuropathologies is challenging, these results provide preliminary evidence that demands further investigation in a larger sample size.
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Affiliation(s)
- Chandan Saha
- Biomedical Engineering Program, University of Manitoba, Winnipeg, MB, Canada
| | - Chase R Figley
- Biomedical Engineering Program, University of Manitoba, Winnipeg, MB, Canada
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
| | - Zeinab Dastgheib
- Biomedical Engineering Program, University of Manitoba, Winnipeg, MB, Canada
| | - Brian J Lithgow
- Biomedical Engineering Program, University of Manitoba, Winnipeg, MB, Canada
| | - Zahra Moussavi
- Biomedical Engineering Program, University of Manitoba, Winnipeg, MB, Canada
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18
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De Simone MS, Spalletta G, Vecchio D, Bassi A, Carlesimo GA, Piras F. The Role of the Anterior Thalamic Nuclei in the Genesis of Memory Disorders in Alzheimer's Disease: An Exploratory Study. J Alzheimers Dis 2024; 97:507-519. [PMID: 38189755 DOI: 10.3233/jad-230606] [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: 01/09/2024]
Abstract
BACKGROUND Increasing evidence is demonstrating that degeneration of specific thalamic nuclei, in addition to the hippocampus, may occur in Alzheimer's disease (AD) from the prodromal stage (mild cognitive impairment - MCI) and contribute to memory impairment. OBJECTIVE Here, we evaluated the presence of macro and micro structural alterations at the level of the anterior thalamic nuclei (ATN) and medio-dorsal thalamic nuclei (MDTN) in AD and amnestic MCI (aMCI) and the possible relationship between such changes and the severity of memory impairment. METHODS For this purpose, a sample of 50 patients with aMCI, 50 with AD, and 50 age- and education-matched healthy controls (HC) were submitted to a 3-T MRI protocol with whole-brain T1-weighted and diffusion tensor imaging and a comprehensive neuropsychological assessment. RESULTS At macro-structural level, both the ATN and MDTN were found significantly smaller in patients with aMCI and AD when compared to HC subjects. At micro-structural level, instead, diffusion alterations that significantly differentiated aMCI and AD patients from HC subjects were found only in the ATN, but not in the MDTN. Moreover, diffusion values of the ATN were significantly associated with poor episodic memory in the overall patients' group. CONCLUSIONS These findings represent the first in vivo evidence of a relevant involvement of ATN in the AD-related neurodegeneration and memory profile and strengthen the importance to look beyond the hippocampus when considering neurological conditions characterized by memory decline.
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Affiliation(s)
- Maria Stefania De Simone
- Department of Clinical Neuroscience and Neurorehabilitation, Laboratory of Neuropsychology of Memory, IRCCS Santa Lucia Foundation, Rome, Italy
- Niccolò Cusano University, Rome, Italy
| | - Gianfranco Spalletta
- Department of Clinical Neuroscience and Neurorehabilitation, Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
- Department of Systems Medicine, Tor Vergata University, Rome, Italy
| | - Daniela Vecchio
- Department of Clinical Neuroscience and Neurorehabilitation, Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Andrea Bassi
- Department of Clinical Neuroscience and Neurorehabilitation, Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Giovanni Augusto Carlesimo
- Department of Clinical Neuroscience and Neurorehabilitation, Laboratory of Neuropsychology of Memory, IRCCS Santa Lucia Foundation, Rome, Italy
- Department of Systems Medicine, Tor Vergata University, Rome, Italy
- Senior Authors
| | - Fabrizio Piras
- Department of Clinical Neuroscience and Neurorehabilitation, Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
- Senior Authors
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19
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Ruan Y, Zheng D, Guo W, Cao X, Qi W, Yuan Q, Zhang X, Liang X, Zhang D, Xue C, Xiao C. Shared and Specific Changes of Cortico-Striatal Functional Connectivity in Stable Mild Cognitive Impairment and Progressive Mild Cognitive Impairment. J Alzheimers Dis 2024; 98:1301-1317. [PMID: 38517789 DOI: 10.3233/jad-231174] [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: 03/24/2024]
Abstract
Background Mild cognitive impairment (MCI), the prodromal stage of Alzheimer's disease, has two distinct subtypes: stable MCI (sMCI) and progressive MCI (pMCI). Early identification of the two subtypes has important clinical significance. Objective We aimed to compare the cortico-striatal functional connectivity (FC) differences between the two subtypes of MCI and enhance the accuracy of differential diagnosis between sMCI and pMCI. Methods We collected resting-state fMRI data from 31 pMCI patients, 41 sMCI patients, and 81 healthy controls. We chose six pairs of seed regions, including the ventral striatum inferior, ventral striatum superior, dorsal-caudal putamen, dorsal-rostral putamen, dorsal caudate, and ventral-rostral putamen and analyzed the differences in cortico-striatal FC among the three groups, additionally, the relationship between the altered FC within the MCI subtypes and cognitive function was examined. Results Compared to sMCI, the pMCI patients exhibited decreased FC between the left dorsal-rostral putamen and right middle temporal gyrus, the right dorsal caudate and right inferior temporal gyrus, and the left dorsal-rostral putamen and left superior frontal gyrus. Additionally, the altered FC between the right inferior temporal gyrus and right putamen was significantly associated with episodic memory and executive function. Conclusions Our study revealed common and distinct cortico-striatal FC changes in sMCIs and pMCI across different seeds; these changes were associated with cognitive function. These findings can help us understand the underlying pathophysiological mechanisms of MCI and distinguish pMCI and sMCI in the early stage potentially.
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Affiliation(s)
- Yiming Ruan
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Darui Zheng
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wenxuan Guo
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xuan Cao
- Department of Mathematical Sciences, Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH, USA
| | - Wenzhang Qi
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Qianqian Yuan
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xulian Zhang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xuhong Liang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Da Zhang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Chen Xue
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Chaoyong Xiao
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
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Biswas R, Sripada S. Causal functional connectivity in Alzheimer's disease computed from time series fMRI data. Front Comput Neurosci 2023; 17:1251301. [PMID: 38169714 PMCID: PMC10758424 DOI: 10.3389/fncom.2023.1251301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 11/28/2023] [Indexed: 01/05/2024] Open
Abstract
Functional connectivity between brain regions is known to be altered in Alzheimer's disease and promises to be a biomarker for early diagnosis. Several approaches for functional connectivity obtain an un-directed network representing stochastic associations (correlations) between brain regions. However, association does not necessarily imply causation. In contrast, Causal Functional Connectivity (CFC) is more informative, providing a directed network representing causal relationships between brain regions. In this paper, we obtained the causal functional connectome for the whole brain from resting-state functional magnetic resonance imaging (rs-fMRI) recordings of subjects from three clinical groups: cognitively normal, mild cognitive impairment, and Alzheimer's disease. We applied the recently developed Time-aware PC (TPC) algorithm to infer the causal functional connectome for the whole brain. TPC supports model-free estimation of whole brain CFC based on directed graphical modeling in a time series setting. We compared the CFC outcome of TPC with that of other related approaches in the literature. Then, we used the CFC outcomes of TPC and performed an exploratory analysis of the difference in strengths of CFC edges between Alzheimer's and cognitively normal groups, based on edge-wise p-values obtained by Welch's t-test. The brain regions thus identified are found to be in agreement with literature on brain regions impacted by Alzheimer's disease, published by researchers from clinical/medical institutions.
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Affiliation(s)
- Rahul Biswas
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
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21
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Itkyal VS, Abrol A, LaGrow TJ, Fedorov A, Calhoun VD. Voxel-wise Fusion of Resting fMRI Networks and Gray Matter Volume for Alzheimer's Disease Classification using Deep Multimodal Learning. RESEARCH SQUARE 2023:rs.3.rs-3740218. [PMID: 38168287 PMCID: PMC10760243 DOI: 10.21203/rs.3.rs-3740218/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Alzheimer's disease (AD) is a prevalent neurodegenerative disorder requiring accurate and early diagnosis for effective treatment. Resting-state functional magnetic resonance imaging (rs-fMRI) and gray matter volume analysis from structural MRI have emerged as valuable tools for investigating AD-related brain alterations. However, the potential benefits of integrating these modalities using deep learning techniques remain unexplored. In this study, we propose a novel framework that fuses composite images of multiple rs-fMRI networks (called voxelwise intensity projection) and gray matter segmentation images through a deep learning approach for improved AD classification. We demonstrate the superiority of fMRI networks over commonly used metrics such as amplitude of low-frequency fluctuations (ALFF) and fractional ALFF in capturing spatial maps critical for AD classification. We use a multi-channel convolutional neural network incorporating the AlexNet dropout architecture to effectively model spatial and temporal dependencies in the integrated data. Extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset of AD patients and cognitively normal (CN) validate the efficacy of our approach, showcasing improved classification performance of 94.12% test accuracy and an area under the curve (AUC) score of 97.79 compared to existing methods. Our results show that the fusion results generally outperformed the unimodal results. The saliency visualizations also show significant differences in the hippocampus, amygdala, putamen, caudate nucleus, and regions of basal ganglia which are in line with the previous neurobiological literature. Our research offers a novel method to enhance our grasp of AD pathology. By integrating data from various functional networks with structural MRI insights, we significantly improve diagnostic accuracy. This accuracy is further boosted by the effective visualization of this combined information. This lays the groundwork for further studies focused on providing a more accurate and personalized approach to AD diagnosis. The proposed framework and insights gained from fMRI networks provide a promising avenue for future research in deep multimodal fusion and neuroimaging analysis.
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22
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Khalilullah KMI, Agcaoglu O, Sui J, Adali T, Duda M, Calhoun VD. Multimodal fusion of multiple rest fMRI networks and MRI gray matter via parallel multilink joint ICA reveals highly significant function/structure coupling in Alzheimer's disease. Hum Brain Mapp 2023; 44:5167-5179. [PMID: 37605825 PMCID: PMC10502647 DOI: 10.1002/hbm.26456] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 07/11/2023] [Accepted: 08/01/2023] [Indexed: 08/23/2023] Open
Abstract
In this article, we focus on estimating the joint relationship between structural magnetic resonance imaging (sMRI) gray matter (GM), and multiple functional MRI (fMRI) intrinsic connectivity networks (ICNs). To achieve this, we propose a multilink joint independent component analysis (ml-jICA) method using the same core algorithm as jICA. To relax the jICA assumption, we propose another extension called parallel multilink jICA (pml-jICA) that allows for a more balanced weight distribution over ml-jICA/jICA. We assume a shared mixing matrix for both the sMRI and fMRI modalities, while allowing for different mixing matrices linking the sMRI data to the different ICNs. We introduce the model and then apply this approach to study the differences in resting fMRI and sMRI data from patients with Alzheimer's disease (AD) versus controls. The results of the pml-jICA yield significant differences with large effect sizes that include regions in overlapping portions of default mode network, and also hippocampus and thalamus. Importantly, we identify two joint components with partially overlapping regions which show opposite effects for AD versus controls, but were able to be separated due to being linked to distinct functional and structural patterns. This highlights the unique strength of our approach and multimodal fusion approaches generally in revealing potentially biomarkers of brain disorders that would likely be missed by a unimodal approach. These results represent the first work linking multiple fMRI ICNs to GM components within a multimodal data fusion model and challenges the typical view that brain structure is more sensitive to AD than fMRI.
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Affiliation(s)
- K. M. Ibrahim Khalilullah
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Oktay Agcaoglu
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Jing Sui
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Tülay Adali
- Department of Electrical and Computer EngineeringUniversity of MarylandBaltimoreMarylandUSA
| | - Marlena Duda
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
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23
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Cao E, Ma D, Nayak S, Duong TQ. Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis. Neurobiol Dis 2023; 187:106310. [PMID: 37769746 DOI: 10.1016/j.nbd.2023.106310] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 09/20/2023] [Accepted: 09/25/2023] [Indexed: 10/03/2023] Open
Abstract
INTRODUCTION This study reports a novel deep learning approach to predict mild cognitive impairment (MCI) conversion to Alzheimer's dementia (AD) within three years using whole-brain fluorodeoxyglucose (FDG) positron emission tomography (PET) and cognitive scores (CS). METHODS This analysis consisted of 150 normal controls (CN), 257 MCI, and 205 AD subjects from ADNI. FDG-PET and CS were obtained at MCI diagnosis to predict AD conversion within three years of MCI diagnosis using convolutional neural networks. RESULTS Neurocognitive scores predicted better than FDG-PET per se, but the best model was a combination of FDG-PET, age, and neurocognitive data, yielding an AUC of 0.785 ± 0.096 and a balanced accuracy of 0.733 ± 0.098. Saliency maps highlighted putamen, thalamus, inferior frontal gyrus, parietal operculum, precuneus cortices, calcarine cortices, temporal gyrus, and planum temporale to be important for prediction. DISCUSSION Deep learning accurately predicts MCI conversion to AD and provides neural correlates of brain regions associated with AD conversion.
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Affiliation(s)
- Eric Cao
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY 10467, United States
| | - Da Ma
- Department of Internal Medicine Section of Gerontology and Geriatric Medicine, Wake Forest, University School of Medicine, Winston-Salam, NC 27109, United States
| | - Siddharth Nayak
- Department of Radiology, Weill Cornell Medicine, New York, 10065, United States
| | - Tim Q Duong
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY 10467, United States.
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24
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Wang M, Shao W, Huang S, Zhang D. Hypergraph-regularized multimodal learning by graph diffusion for imaging genetics based Alzheimer's Disease diagnosis. Med Image Anal 2023; 89:102883. [PMID: 37467641 DOI: 10.1016/j.media.2023.102883] [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: 04/04/2022] [Revised: 04/06/2023] [Accepted: 06/28/2023] [Indexed: 07/21/2023]
Abstract
Recent studies show that multi-modal data fusion techniques combining information from diverse sources are helpful to diagnose and predict complex brain disorders. However, most existing diagnosis methods have only simply employed a feature combination strategy for multiple imaging and genetic data, ignoring the imaging phenotypes associated with the risk gene information. To this end, we present a hypergraph-regularized multimodal learning by graph diffusion (HMGD) for joint association learning and outcome prediction. Specifically, we first present a graph diffusion method for enhancing similarity measures among subjects given from multi-modality phenotypes, which fully uses multiple input similarity graphs and integrates them into a unified graph with valuable geometric structures among different imaging phenotypes. Then, we employ the unified graph to represent the high-order similarity relationships among subjects, and enforce a hypergraph-regularized term to incorporate both inter- and cross-modality information for selecting the imaging phenotypes associated with the risk single nucleotide polymorphism (SNP). Finally, a multi-kernel support vector machine (MK-SVM) is adopted to fuse such phenotypic features selected from different modalities for the final diagnosis and prediction. The proposed approach is experimentally explored on brain imaging genetic data of the Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets. Relevant results present that the proposed approach is superior to several competing algorithms, and realizes strong associations and discovers significant consistent and robust ROIs across different imaging phenotypes associated with the genetic risk biomarkers to guide disease interpretation and prediction.
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Affiliation(s)
- Meiling Wang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing 211106, China
| | - Wei Shao
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing 211106, China
| | - Shuo Huang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing 211106, China
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing 211106, China.
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25
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Rodriguez-Lopez A, Torres-Paniagua AM, Acero G, Díaz G, Gevorkian G. Increased TSPO expression, pyroglutamate-modified amyloid beta (AβN3(pE)) accumulation and transient clustering of microglia in the thalamus of Tg-SwDI mice. J Neuroimmunol 2023; 382:578150. [PMID: 37467699 DOI: 10.1016/j.jneuroim.2023.578150] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/07/2023] [Accepted: 07/09/2023] [Indexed: 07/21/2023]
Abstract
Epidemiological studies showed that Alzheimer's disease (AD) and cerebral amyloid angiopathy (CAA) frequently co-occur; however, the precise mechanism is not well understood. A unique animal model (Tg-SwDI mice) was developed to investigate the early-onset and robust accumulation of both parenchymal and vascular Aβ in the brain. Tg-SwDI mice have been extensively used to study the mechanisms of cerebrovascular dysfunction, neuroinflammation, neurodegeneration, and cognitive decline observed in AD/CAA patients and to design biomarkers and therapeutic strategies. In the present study, we documented interesting new features in the thalamus of Tg-SwDI mice: 1) a sharp increase in the expression of ionized calcium-binding adapter molecule 1 (Iba-1) in microglia in 6-month-old animals; 2) microglia clustering at six months that disappeared in old animals; 3) N-truncated/modified AβN3(pE) peptide in 9-month-old female and 12-month-old male mice; 4) an age-dependent increase in translocator protein (TSPO) expression. These findings reinforce the versatility of this model for studying multiple pathological issues involved in AD and CAA.
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Affiliation(s)
- Adrian Rodriguez-Lopez
- Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México (UNAM), Apartado Postal 70228, Cuidad Universitaria, CDMX, CP 04510, Mexico
| | - Alicia M Torres-Paniagua
- Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México (UNAM), Apartado Postal 70228, Cuidad Universitaria, CDMX, CP 04510, Mexico
| | - Gonzalo Acero
- Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México (UNAM), Apartado Postal 70228, Cuidad Universitaria, CDMX, CP 04510, Mexico
| | - Georgina Díaz
- Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México (UNAM), Apartado Postal 70228, Cuidad Universitaria, CDMX, CP 04510, Mexico
| | - Goar Gevorkian
- Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México (UNAM), Apartado Postal 70228, Cuidad Universitaria, CDMX, CP 04510, Mexico.
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Cui F, Ouyang ZQ, Zeng YZ, Ling BB, Shi L, Zhu Y, Gu HY, Jiang WL, Zhou T, Sun XJ, Han D, Lu Y. Effects of hypertension on subcortical nucleus morphological alternations in patients with type 2 diabetes. Front Endocrinol (Lausanne) 2023; 14:1201281. [PMID: 37780620 PMCID: PMC10534025 DOI: 10.3389/fendo.2023.1201281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 08/28/2023] [Indexed: 10/03/2023] Open
Abstract
Objectives Type 2 diabetes mellitus(T2DM) and hypertension(HTN) are common comorbidities, and known to affect the brain. However, little is known about the effects of the coexisting HTN on brain in T2DM patients. So we aim to investigate the impact of HTN on the subcortical nucleus morphological alternations in T2DM patients. Materials & methods This work was registered by the clinicaltrials.gov (grant number NCT03564431). We recruited a total of 92 participants, comprising 36 only T2DM patients, 28 T2DM patients with HTN(T2DMH) and 28 healthy controls(HCs) in our study. All clinical indicators were assessed and brain image data was collected for each participant. Voxel-based morphometry(VBM), automatic volume and vertex-based shape analyses were used to determine the subcortical nucleus alternations from each participant's 3D-T1 brain images and evaluate the relationship between the alternations and clinical indicators. Results T2DMH patients exhibited volumetric reduction and morphological alterations in thalamus compared to T2DM patients, whereas T2DM patients did not demonstrate any significant subcortical alterations compared to HCs. Furthermore, negative correlations have been found between thalamic alternations and the duration of HTN in T2DMH patients. Conclusion Our results revealed that HTN may exacerbate subcortical nucleus alternations in T2DM patients, which highlighted the importance of HTN management in T2DM patients to prevent further damage to the brain health.
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Affiliation(s)
- Feng Cui
- Department of Medical Imaging, Laboratory of Brain Function, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Zhi-Qiang Ouyang
- Department of Medical Imaging, Laboratory of Brain Function, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Yi-Zhen Zeng
- Department of Medical Imaging, Laboratory of Brain Function, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Bing-Bing Ling
- Department of Medical Imaging, Laboratory of Brain Function, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Li Shi
- Department of Endocrinology, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Yun Zhu
- Department of Medical Imaging, Laboratory of Brain Function, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - He-Yi Gu
- Department of Medical Imaging, Laboratory of Brain Function, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Wan-Lin Jiang
- Department of Medical Imaging, Laboratory of Brain Function, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Ting Zhou
- Department of Medical Imaging, Laboratory of Brain Function, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Xue-Jin Sun
- Department of Medical Imaging, Laboratory of Brain Function, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Dan Han
- Department of Medical Imaging, Laboratory of Brain Function, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Yi Lu
- Department of Medical Imaging, Laboratory of Brain Function, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
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Li T, Zhu H, Li T, Zhu H. Asynchronous functional linear regression models for longitudinal data in reproducing kernel Hilbert space. Biometrics 2023; 79:1880-1895. [PMID: 36205584 DOI: 10.1111/biom.13767] [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: 06/30/2021] [Accepted: 09/21/2022] [Indexed: 11/02/2022]
Abstract
Motivated by the analysis of longitudinal neuroimaging studies, we study the longitudinal functional linear regression model under asynchronous data setting for modeling the association between clinical outcomes and functional (or imaging) covariates. In the asynchronous data setting, both covariates and responses may be measured at irregular and mismatched time points, posing methodological challenges to existing statistical methods. We develop a kernel weighted loss function with roughness penalty to obtain the functional estimator and derive its representer theorem. The rate of convergence, a Bahadur representation, and the asymptotic pointwise distribution of the functional estimator are obtained under the reproducing kernel Hilbert space framework. We propose a penalized likelihood ratio test to test the nullity of the functional coefficient, derive its asymptotic distribution under the null hypothesis, and investigate the separation rate under the alternative hypotheses. Simulation studies are conducted to examine the finite-sample performance of the proposed procedure. We apply the proposed methods to the analysis of multitype data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, which reveals significant association between 21 regional brain volume density curves and the cognitive function. Data used in preparation of this paper were obtained from the ADNI database (adni.loni.usc.edu).
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Affiliation(s)
- Ting Li
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China
- Shanghai Institute of International Finance and Economics, Shanghai University of Finance and Economics, Shanghai, China
| | - Huichen Zhu
- Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Tengfei Li
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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28
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Zhao R, Wang P, Liu L, Zhang F, Hu P, Wen J, Li H, Biswal BB. Whole-brain structure-function coupling abnormalities in mild cognitive impairment: a study combining amplitude of low-frequency fluctuations and voxel-based morphometry. Front Neurosci 2023; 17:1236221. [PMID: 37583417 PMCID: PMC10424122 DOI: 10.3389/fnins.2023.1236221] [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: 06/07/2023] [Accepted: 07/12/2023] [Indexed: 08/17/2023] Open
Abstract
Alzheimer's disease (AD), one of the leading diseases of the nervous system, is accompanied by symptoms such as loss of memory, thinking and language skills. Both mild cognitive impairment (MCI) and very mild cognitive impairment (VMCI) are the transitional pathological stages between normal aging and AD. While the changes in whole-brain structural and functional information have been extensively investigated in AD, The impaired structure-function coupling remains unknown. The current study employed the OASIS-3 dataset, which includes 53 MCI, 90 VMCI, and 100 Age-, gender-, and education-matched normal controls (NC). Several structural and functional parameters, such as the amplitude of low-frequency fluctuations (ALFF), voxel-based morphometry (VBM), and The ALFF/VBM ratio, were used To estimate The whole-brain neuroimaging changes In MCI, VMCI, and NC. As disease symptoms became more severe, these regions, distributed in the frontal-inf-orb, putamen, and paracentral lobule in the white matter (WM), exhibited progressively increasing ALFF (ALFFNC < ALFFVMCI < ALFFMCI), which was similar to the tendency for The cerebellum and putamen in the gray matter (GM). Additionally, as symptoms worsened in AD, the cuneus/frontal lobe in the WM and the parahippocampal gyrus/hippocampus in the GM showed progressively decreasing structure-function coupling. As the typical focal areas in AD, The parahippocampal gyrus and hippocampus showed significant positive correlations with the severity of cognitive impairment, suggesting the important applications of the ALFF/VBM ratio in brain disorders. On the other hand, these findings from WM functional signals provided a novel perspective for understanding the pathophysiological mechanisms involved In cognitive decline in AD.
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Affiliation(s)
- Rong Zhao
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Pan Wang
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Lin Liu
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Fanyu Zhang
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Hu
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiaping Wen
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongyi Li
- The Fourth People’s Hospital of Chengdu, Chengdu, China
| | - Bharat B. Biswal
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
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Fislage M, Feinkohl I, Borchers F, Pischon T, Spies CD, Winterer G, Zacharias N. Preoperative thalamus volume is not associated with preoperative cognitive impairment (preCI) or postoperative cognitive dysfunction (POCD). Sci Rep 2023; 13:11732. [PMID: 37474784 PMCID: PMC10359451 DOI: 10.1038/s41598-023-38673-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 07/12/2023] [Indexed: 07/22/2023] Open
Abstract
A growing body of literature suggests the important role of the thalamus in cognition and neurodegenerative diseases. This study aims to elucidate whether the preoperative thalamic volume is associated with preoperative cognitive impairment (preCI) and whether it is predictive for postoperative cognitive dysfunction at 3 months (POCD). We enrolled 301 patients aged 65 or older and without signs of dementia who were undergoing elective surgery. Magnetic resonance imaging was conducted prior to surgery. Freesurfer (version 5.3.) was used to automatically segment the thalamus volume. A neuropsychological test battery was administered before surgery and at a 3 month follow-up. It included the computerized tests Paired Associate Learning (PAL), Verbal Recognition Memory (VRM), Spatial Span Length (SSP), Simple Reaction Time (SRT), the pen-and-paper Trail-Making-Test (TMT) and the manual Grooved Pegboard Test (GPT). Using a reliable change index, preCI and POCD were defined as total Z-score > 1.96 (sum score over all tests) and/or Z-scores > 1.96 in ≥ 2 individual cognitive test parameters. For statistical analyses, multivariable logistic regression models were applied. Age, sex and intracranial volume were covariates in the models. Of 301 patients who received a presurgical neuropsychological testing and MRI, 34 (11.3%) had preCI. 89 patients (29.5%) were lost to follow-up. The remaining 212 patients received a follow-up cognitive test after 3 months, of whom 25 (8.3%) presented with POCD. Independently of age, sex and intracranial volume, neither preCI (OR per cm3 increment 0.81 [95% CI 0.60-1.07] p = 0.14) nor POCD (OR 1.02 per cm3 increment [95% CI 0.75-1.40] p = 0.87) were statistically significantly associated with patients' preoperative thalamus volume. In this cohort we could not show an association of presurgical thalamus volume with preCI or POCD.Clinical Trial Number: NCT02265263 ( https://clinicaltrials.gov/ct2/show/results/NCT02265263 ).
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Affiliation(s)
- Marinus Fislage
- Department of Anesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
| | - Insa Feinkohl
- Faculty of Health/School of Medicine, Witten/Herdecke University, Witten, Germany
- Molecular Epidemiology Research Group, Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Friedrich Borchers
- Department of Anesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Tobias Pischon
- Molecular Epidemiology Research Group, Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Biobank Technology Platform, Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Core Facility Biobank, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Claudia D Spies
- Department of Anesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Georg Winterer
- Department of Anesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Pharmaimage Biomarker Solutions GmbH, Berlin, Germany
| | - Norman Zacharias
- Department of Anesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Pharmaimage Biomarker Solutions GmbH, Berlin, Germany
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30
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Tregidgo HFJ, Soskic S, Althonayan J, Maffei C, Van Leemput K, Golland P, Insausti R, Lerma-Usabiaga G, Caballero-Gaudes C, Paz-Alonso PM, Yendiki A, Alexander DC, Bocchetta M, Rohrer JD, Iglesias JE. Accurate Bayesian segmentation of thalamic nuclei using diffusion MRI and an improved histological atlas. Neuroimage 2023; 274:120129. [PMID: 37088323 PMCID: PMC10636587 DOI: 10.1016/j.neuroimage.2023.120129] [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: 09/20/2022] [Revised: 03/30/2023] [Accepted: 04/20/2023] [Indexed: 04/25/2023] Open
Abstract
The human thalamus is a highly connected brain structure, which is key for the control of numerous functions and is involved in several neurological disorders. Recently, neuroimaging studies have increasingly focused on the volume and connectivity of the specific nuclei comprising this structure, rather than looking at the thalamus as a whole. However, accurate identification of cytoarchitectonically designed histological nuclei on standard in vivo structural MRI is hampered by the lack of image contrast that can be used to distinguish nuclei from each other and from surrounding white matter tracts. While diffusion MRI may offer such contrast, it has lower resolution and lacks some boundaries visible in structural imaging. In this work, we present a Bayesian segmentation algorithm for the thalamus. This algorithm combines prior information from a probabilistic atlas with likelihood models for both structural and diffusion MRI, allowing segmentation of 25 thalamic labels per hemisphere informed by both modalities. We present an improved probabilistic atlas, incorporating thalamic nuclei identified from histology and 45 white matter tracts surrounding the thalamus identified in ultra-high gradient strength diffusion imaging. We present a family of likelihood models for diffusion tensor imaging, ensuring compatibility with the vast majority of neuroimaging datasets that include diffusion MRI data. The use of these diffusion likelihood models greatly improves identification of nuclear groups versus segmentation based solely on structural MRI. Dice comparison of 5 manually identifiable groups of nuclei to ground truth segmentations show improvements of up to 10 percentage points. Additionally, our chosen model shows a high degree of reliability, with median test-retest Dice scores above 0.85 for four out of five nuclei groups, whilst also offering improved detection of differential thalamic involvement in Alzheimer's disease (AUROC 81.98%). The probabilistic atlas and segmentation tool will be made publicly available as part of the neuroimaging package FreeSurfer (https://freesurfer.net/fswiki/ThalamicNucleiDTI).
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Affiliation(s)
- Henry F J Tregidgo
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK.
| | - Sonja Soskic
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Juri Althonayan
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Chiara Maffei
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA
| | - Koen Van Leemput
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA; Department of Health Technology, Technical University of Denmark, Denmark
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
| | - Ricardo Insausti
- Human Neuroanatomy Laboratory, University of Castilla-La Mancha, Spain
| | - Garikoitz Lerma-Usabiaga
- BCBL. Basque Center on Cognition, Brain and Language, Spain; Ikerbasque, Basque Foundation for Science, Bilbao, Spain
| | | | - Pedro M Paz-Alonso
- BCBL. Basque Center on Cognition, Brain and Language, Spain; Ikerbasque, Basque Foundation for Science, Bilbao, Spain
| | - Anastasia Yendiki
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
| | - Martina Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, UK; Centre for Cognitive and Clinical Neuroscience, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, UK
| | - Jonathan D Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, UK
| | - Juan Eugenio Iglesias
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK; Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
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31
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He XY, Wu BS, Kuo K, Zhang W, Ma Q, Xiang ST, Li YZ, Wang ZY, Dong Q, Feng JF, Cheng W, Yu JT. Association between polygenic risk for Alzheimer's disease and brain structure in children and adults. Alzheimers Res Ther 2023; 15:109. [PMID: 37312172 DOI: 10.1186/s13195-023-01256-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 06/01/2023] [Indexed: 06/15/2023]
Abstract
BACKGROUND The correlations between genetic risk for Alzheimer's disease (AD) with comprehensive brain regions at a regional scale are still not well understood. We aim to explore whether these associations vary across different age stages. METHODS This study used large existing genome-wide association datasets to calculate polygenic risk score (PRS) for AD in two populations from the UK Biobank (N ~ 23 000) and Adolescent Brain Cognitive Development Study (N ~ 4660) who had multimodal macrostructural and microstructural magnetic resonance imaging (MRI) metrics. We used linear mixed-effect models to assess the strength of the association between AD PRS and multiple MRI metrics of regional brain structures at different stages of life. RESULTS Compared to those with lower PRSs, adolescents with higher PRSs had thinner cortex in the caudal anterior cingulate and supramarginal. In the middle-aged and elderly population, AD PRS had correlations with regional structure shrink primarily located in the cingulate, prefrontal cortex, hippocampus, thalamus, amygdala, and striatum, whereas the brain expansion was concentrated near the occipital lobe. Furthermore, both adults and adolescents with higher PRSs exhibited widespread white matter microstructural changes, indicated by decreased fractional anisotropy (FA) or increased mean diffusivity (MD). CONCLUSIONS In conclusion, our results suggest genetic loading for AD may influence brain structures in a highly dynamic manner, with dramatically different patterns at different ages. This age-specific change is consistent with the classical pattern of brain impairment observed in AD patients.
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Affiliation(s)
- Xiao-Yu He
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Kevin Kuo
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Qing Ma
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Shi-Tong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Yu-Zhu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Zi-Yi Wang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Qiang Dong
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, China
- ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China.
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, China.
- ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China.
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32
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Segobin S, Ambler M, Laniepce A, Platel H, Chételat G, Groussard M, Pitel AL. Korsakoff's Syndrome and Alzheimer's Disease-Commonalities and Specificities of Volumetric Brain Alterations within Papez Circuit. J Clin Med 2023; 12:jcm12093147. [PMID: 37176588 PMCID: PMC10179200 DOI: 10.3390/jcm12093147] [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: 03/10/2023] [Revised: 04/11/2023] [Accepted: 04/18/2023] [Indexed: 05/15/2023] Open
Abstract
Background: Alzheimer's disease (AD) and Korsakoff's syndrome (KS) are two major neurocognitive disorders characterized by amnesia but AD is degenerative while KS is not. The objective is to compare regional volume deficits within the Papez circuit in AD and KS, considering AD progression. Methods: 18 KS patients, 40 AD patients (20 with Moderate AD (MAD) matched on global cognitive deficits with KS patients and 20 with Severe AD (SAD)), and 70 healthy controls underwent structural MRI. Volumes of the hippocampi, thalami, cingulate gyri, mammillary bodies (MB) and mammillothalamic tracts (MTT) were extracted. Results: For the cingulate gyri, and anterior thalamic nuclei, all patient groups were affected compared to controls but did not differ between each other. Smaller volumes were observed in all patient groups compared to controls in the mediodorsal thalamic nuclei and MB, but these regions were more severely damaged in KS than AD. MTT volumes were damaged in KS only. Hippocampi were affected in all patient groups but more severely in the SAD than in the KS and MAD. Conclusions: There are commonalities in the pattern of volume deficits in KS and AD within the Papez circuit with the anterior thalamic nuclei, cingulate cortex and hippocampus (in MAD only) being damaged to the same extent. The specificity of KS relies on the alteration of the MTT and the severity of the MB shrinkage. Further comparative studies including other imaging modalities and a neuropsychological assessment are required.
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Affiliation(s)
- Shailendra Segobin
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine (NIMH), 14000 Caen, France
| | - Melanie Ambler
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine (NIMH), 14000 Caen, France
| | - Alice Laniepce
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine (NIMH), 14000 Caen, France
- Normandie Univ, UNIROUEN, CRFDP (EA 7475), 76821 Rouen, France
- Normandie Univ, UNICAEN, INSERM, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, 14000 Caen, France
| | - Hervé Platel
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine (NIMH), 14000 Caen, France
| | - Gael Chételat
- Normandie Univ, UNICAEN, INSERM, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, 14000 Caen, France
| | - Mathilde Groussard
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine (NIMH), 14000 Caen, France
| | - Anne-Lise Pitel
- Normandie Univ, UNICAEN, INSERM, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, 14000 Caen, France
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Zhang Y, Wang Y, Li Z, Wang Z, Cheng J, Bai X, Hsu YC, Sun Y, Li S, Shi J, Sui B, Bai R. Vascular-water-exchange MRI (VEXI) enables the detection of subtle AXR alterations in Alzheimer's disease without MRI contrast agent, which may relate to BBB integrity. Neuroimage 2023; 270:119951. [PMID: 36805091 DOI: 10.1016/j.neuroimage.2023.119951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 02/14/2023] [Accepted: 02/16/2023] [Indexed: 02/21/2023] Open
Abstract
Blood-brain barrier (BBB) impairment is an important pathophysiological process in Alzheimer's disease (AD) and a potential biomarker for early diagnosis of AD. However, most current neuroimaging methods assessing BBB function need the injection of exogenous contrast agents (or tracers), which limits the application of these methods in a large population. In this study, we aim to explore the feasibility of vascular water exchange MRI (VEXI), a diffusion-MRI-based method proposed to assess the BBB permeability to water molecules without using a contrast agent, in the detection of the BBB breakdown in AD. We tested VEXI on a 3T MRI scanner on three groups: AD patients (AD group), mild cognitive impairment (MCI) patients due to AD (MCI group), and the age-matched normal cognition subjects (NC group). Interestingly, we find that the apparent water exchange across the BBB (AXRBBB) measured by VEXI shows higher values in MCI compared with NC, and this higher AXRBBB happens specifically in the hippocampus. This increase in AXRBBB value gets larger and extends to more brain regions (medial orbital frontal cortex and thalamus) from MCI group to the AD group. Furthermore, we find that the AXRBBB values of these three regions is correlated significantly with the impairment of respective cognitive domains independent of age, sex and education. These results suggest VEXI is a promising method to assess the BBB breakdown in AD.
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Affiliation(s)
- Yifan Zhang
- Department of Physical Medicine and Rehabilitation of the Affiliated Sir Run Shaw Hospital AND Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China; Key Laboratory of Biomedical Engineering of Education Ministry, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Yue Wang
- National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhaoqing Li
- Department of Physical Medicine and Rehabilitation of the Affiliated Sir Run Shaw Hospital AND Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China; Key Laboratory of Biomedical Engineering of Education Ministry, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Zejun Wang
- Department of Physical Medicine and Rehabilitation of the Affiliated Sir Run Shaw Hospital AND Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China; Key Laboratory of Biomedical Engineering of Education Ministry, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Juange Cheng
- Department of Physical Medicine and Rehabilitation of the Affiliated Sir Run Shaw Hospital AND Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xiaoyan Bai
- Tiantan Neuroimaging Center of Excellence, China National Clinical Research Center for Neurological Diseases, Beijing, China; Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing Neurosurgical Institute, Beijing, China
| | - Yi-Cheng Hsu
- MR Collaboration, Siemens Healthcare, Shanghai, China
| | - Yi Sun
- MR Collaboration, Siemens Healthcare, Shanghai, China
| | - Shiping Li
- National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Jiong Shi
- National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Binbin Sui
- National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| | - Ruiliang Bai
- Department of Physical Medicine and Rehabilitation of the Affiliated Sir Run Shaw Hospital AND Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China; Key Laboratory of Biomedical Engineering of Education Ministry, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China; MOE Frontier Science Center for Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, Zhejiang University.
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Faisal M, Aid J, Nodirov B, Lee B, Hickey MA. Preclinical trials in Alzheimer's disease: Sample size and effect size for behavioural and neuropathological outcomes in 5xFAD mice. PLoS One 2023; 18:e0281003. [PMID: 37036878 PMCID: PMC10085059 DOI: 10.1371/journal.pone.0281003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 01/13/2023] [Indexed: 04/11/2023] Open
Abstract
5xFAD transgenic (TG) mice are used widely in AD preclinical trials; however, data on sample sizes are largely unaddressed. We therefore performed estimates of sample sizes and effect sizes for typical behavioural and neuropathological outcome measures in TG 5xFAD mice, based upon data from single-sex (female) groups. Group-size estimates to detect normalisation of TG body weight to WT littermate levels at 5.5m of age were N = 9-15 depending upon algorithm. However, by 1 year of age, group sizes were small (N = 1 -<6), likely reflecting the large difference between genotypes at this age. To detect normalisation of TG open-field hyperactivity to WT levels at 13-14m, group sizes were also small (N = 6-8). Cued learning in the Morris water maze (MWM) was normal in Young TG mice (5m of age). Mild deficits were noted during MWM spatial learning and memory. MWM reversal learning and memory revealed greater impairment, and groups of up to 22 TG mice were estimated to detect normalisation to WT performance. In contrast, Aged TG mice (tested between 13 and 14m) failed to complete the visual learning (non-spatial) phase of MWM learning, likely due to a failure to recognise the platform as an escape. Estimates of group size to detect normalisation of this severe impairment were small (N = 6-9, depending upon algorithm). Other cognitive tests including spontaneous and forced alternation and novel-object recognition either failed to reveal deficits in TG mice or deficits were negligible. For neuropathological outcomes, plaque load, astrocytosis and microgliosis in frontal cortex and hippocampus were quantified in TG mice aged 2m, 4m and 6m. Sample-size estimates were ≤9 to detect the equivalent of a reduction in plaque load to the level of 2m-old TG mice or the equivalent of normalisation of neuroinflammation outcomes. However, for a smaller effect size of 30%, larger groups of up to 21 mice were estimated. In light of published guidelines on preclinical trial design, these data may be used to provide provisional sample sizes and optimise preclinical trials in 5xFAD TG mice.
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Affiliation(s)
- Mahvish Faisal
- Department of Pharmacology, Institute of Biomedicine and
Translational Medicine, University of Tartu, Tartu, Estonia
| | - Jana Aid
- Department of Pharmacology, Institute of Biomedicine and
Translational Medicine, University of Tartu, Tartu, Estonia
| | - Bekzod Nodirov
- Department of Pharmacology, Institute of Biomedicine and
Translational Medicine, University of Tartu, Tartu, Estonia
| | - Benjamin Lee
- Department of Pharmacology, Institute of Biomedicine and
Translational Medicine, University of Tartu, Tartu, Estonia
| | - Miriam A. Hickey
- Department of Pharmacology, Institute of Biomedicine and
Translational Medicine, University of Tartu, Tartu, Estonia
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35
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Zhao Z, Wang J, Wang Y, Liu X, He K, Guo Q, Xie F, Huang Q, Li Z. 18F-AV45 PET and MRI Reveal the Influencing Factors of Alzheimer's Disease Biomarkers in Subjective Cognitive Decline Population. J Alzheimers Dis 2023; 93:585-594. [PMID: 37066915 DOI: 10.3233/jad-221251] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
BACKGROUND Subjective cognitive decline (SCD) is a self-perceived decline in cognitive ability, which exhibits no objective impairment but increased risk of conversion to mild cognitive impairment and Alzheimer's disease (AD). OBJECTIVE To investigate how influencing factors (risk gene, age, sex, and education) affect amyloid-β (Aβ) deposition and gray matter (GM) atrophy in SCD population. METHODS 281 SCD subjects were included in this study, who underwent clinical evaluation, cognitive ability assessment, apolipoprotein E (APOE) genotyping, 18F-Florbetapir positron emission computed tomography, and magnetic resonance imaging screening. Two-sample t tests and analysis of variance were performed based on voxel-wise outcome. RESULTS In 281 SCD subjects with an average age of 63.86, 194 subjects (69.04%) were female, and 56 subjects carried APOE ɛ4 genes. Statistical results revealed APOE ɛ4 gene, age, and sex influenced Aβ deposition in different brain regions; moreover, only the interaction exhibited between age and APOE ɛ4 genes. The GM atrophy of hippocampal, amygdala, precentral, and occipital lobes occurred in the group age over 60. The GM volume of the hippocampal, frontal, and occipital lobe in females was less than males. Education has an effect only on cognitive function. CONCLUSION In SCD, APOE ɛ4 gene, age, and sex significantly influenced Aβ deposition and APOE ɛ4 gene can interact with age in impacting Aβ deposition. Both age and sex can affect GM atrophy. The results suggested that female SCD with APOE ɛ4 genes and aged more than 60 years old might exhibit advanced AD biomarkers.
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Affiliation(s)
- Zixiao Zhao
- Center for Molecular Imaging and Translational Medicine, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Department of Laboratory Medicine, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Jie Wang
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Ying Wang
- Department of Gerontology, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Xia Liu
- Center for Molecular Imaging and Translational Medicine, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Department of Laboratory Medicine, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Kun He
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Qihao Guo
- Department of Gerontology, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Fang Xie
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Qi Huang
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Zijing Li
- Center for Molecular Imaging and Translational Medicine, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Department of Laboratory Medicine, School of Public Health, Xiamen University, Xiamen, Fujian, China
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36
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Bergamino M, Nelson MR, Numani A, Scarpelli M, Healey D, Fuentes A, Turner G, Stokes AM. Assessment of complementary white matter microstructural changes and grey matter atrophy in a preclinical model of Alzheimer's disease. Magn Reson Imaging 2023; 101:57-66. [PMID: 37028608 DOI: 10.1016/j.mri.2023.03.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/30/2023] [Accepted: 03/31/2023] [Indexed: 04/08/2023]
Abstract
Alzheimer's disease (AD) has been associated with amyloid and tau pathology, as well as neurodegeneration. Beyond these hallmark features, white matter microstructural abnormalities have been observed using MRI. The objective of this study was to assess grey matter atrophy and white matter microstructural changes in a preclinical mouse model of AD (3xTg-AD) using voxel-based morphometry (VBM) and free-water (FW) diffusion tensor imaging (FW-DTI). Compared to controls, lower grey matter density was observed in the 3xTg-AD model, corresponding to the small clusters in the caudate-putamen, hypothalamus, and cortex. DTI-based fractional anisotropy (FA) was decreased in the 3xTg model, while the FW index was increased. Notably, the largest clusters for both FW-FA and FW index were in the fimbria, with other regions including the anterior commissure, corpus callosum, forebrain septum, and internal capsule. Additionally, the presence of amyloid and tau in the 3xTg model was confirmed with histopathology, with significantly higher levels observed across many regions of the brain. Taken together, these results are consistent with subtle neurodegenerative and white matter microstructural changes in the 3xTg-AD model that manifest as increased FW, decreased FW-FA, and decreased grey matter density.
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Affiliation(s)
- Maurizio Bergamino
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Megan R Nelson
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Asfia Numani
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Matthew Scarpelli
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Deborah Healey
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Alberto Fuentes
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Gregory Turner
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Ashley M Stokes
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA.
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Noroozian M, Kormi-Nouri R, Nyberg L, Persson J. Hippocampal and motor regions contribute to memory benefits after enacted encoding: cross-sectional and longitudinal evidence. Cereb Cortex 2023; 33:3080-3097. [PMID: 35802485 DOI: 10.1093/cercor/bhac262] [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: 10/19/2021] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 11/13/2022] Open
Abstract
The neurobiological underpinnings of action-related episodic memory and how enactment contributes to efficient memory encoding are not well understood. We examine whether individual differences in level (n = 338) and 5-year change (n = 248) in the ability to benefit from motor involvement during memory encoding are related to gray matter (GM) volume, white matter (WM) integrity, and dopamine-regulating genes in a population-based cohort (age range = 25-80 years). A latent profile analysis identified 2 groups with similar performance on verbal encoding but with marked differences in the ability to benefit from motor involvement during memory encoding. Impaired ability to benefit from enactment was paired with smaller HC, parahippocampal, and putamen volume along with lower WM microstructure in the fornix. Individuals with reduced ability to benefit from encoding enactment over 5 years were characterized by reduced HC and motor cortex GM volume along with reduced WM microstructure in several WM tracts. Moreover, the proportion of catechol-O-methyltransferase-Val-carriers differed significantly between classes identified from the latent-profile analysis. These results provide converging evidence that individuals with low or declining ability to benefit from motor involvement during memory encoding are characterized by low and reduced GM volume in regions critical for memory and motor functions along with altered WM microstructure.
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Affiliation(s)
- Maryam Noroozian
- Department of Psychiatry, School of Medicine, South Kargar Str., Tehran 13185/1741, Iran
| | - Reza Kormi-Nouri
- School of Law, Psychology and Social Work, Örebro University, Fakultetsgatan 1, Örebro 702 81, Sweden
| | - Lars Nyberg
- Department of Radiation Sciences, Radiology, Umeå University, Universitetstorget 4, Umeå 901 87, Sweden
- Department of Integrative Medical Biology, Umeå University, Universitetstorget 4, Umeå 901 87, Sweden
- Umeå Center for Functional Brain Imaging, Umeå University, Universitetstorget 4, Umeå 901 87, Sweden
| | - Jonas Persson
- School of Law, Psychology and Social Work, Center for Lifespan Developmental Research (LEADER), Örebro University, Fakultetsgatan 1, Örebro 702 81, Sweden
- Aging Research Center (ARC), Stockholm University and Karolinska Institute, Tomtebodavägen 18A, Solna 171 65, Sweden
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Nguyen HD, Clément M, Mansencal B, Coupé P. Towards better interpretable and generalizable AD detection using collective artificial intelligence. Comput Med Imaging Graph 2023; 104:102171. [PMID: 36640484 DOI: 10.1016/j.compmedimag.2022.102171] [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: 05/23/2022] [Revised: 12/24/2022] [Accepted: 12/24/2022] [Indexed: 01/03/2023]
Abstract
Alzheimer's Disease is the most common cause of dementia. Accurate diagnosis and prognosis of this disease are essential to design an appropriate treatment plan, increasing the life expectancy of the patient. Intense research has been conducted on the use of machine learning to identify Alzheimer's Disease from neuroimaging data, such as structural magnetic resonance imaging. In recent years, advances of deep learning in computer vision suggest a new research direction for this problem. Current deep learning-based approaches in this field, however, have a number of drawbacks, including the interpretability of model decisions, a lack of generalizability information and a lower performance compared to traditional machine learning techniques. In this paper, we design a two-stage framework to overcome these limitations. In the first stage, an ensemble of 125 U-Nets is used to grade the input image, producing a 3D map that reflects the disease severity at voxel-level. This map can help to localize abnormal brain areas caused by the disease. In the second stage, we model a graph per individual using the generated grading map and other information about the subject. We propose to use a graph convolutional neural network classifier for the final classification. As a result, our framework demonstrates comparative performance to the state-of-the-art methods in different datasets for both diagnosis and prognosis. We also demonstrate that the use of a large ensemble of U-Nets offers a better generalization capacity for our framework.
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Affiliation(s)
- Huy-Dung Nguyen
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, 33400 Talence, France.
| | - Michaël Clément
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, 33400 Talence, France
| | - Boris Mansencal
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, 33400 Talence, France
| | - Pierrick Coupé
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, 33400 Talence, France
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Khalilullah KMI, Agcaoglu O, Sui J, Adali T, Duda M, Calhoun VD. Multimodal fusion of multiple rest fMRI networks and MRI gray matter via multilink joint ICA reveals highly significant function/structure coupling in Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.28.530458. [PMID: 36909478 PMCID: PMC10002680 DOI: 10.1101/2023.02.28.530458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
Abstract
In this paper we focus on estimating the joint relationship between structural MRI (sMRI) gray matter (GM) and multiple functional MRI (fMRI) intrinsic connectivity networks (ICN) using a novel approach called multi-link joint independent component analysis (ml-jICA). The proposed model offers several improvements over the existing joint independent component analysis (jICA) model. We assume a shared mixing matrix for both the sMRI and fMRI modalities, while allowing for different mixing matrices linking the sMRI data to the different ICNs. We introduce the model and then apply this approach to study the differences in resting fMRI and sMRI data from patients with Alzheimer's disease (AD) versus controls. The results yield significant differences with large effect sizes that include regions in overlapping portions of default mode network, and also hippocampus and thalamus. Importantly, we identify two joint components with partially overlapping regions which show opposite effects for Alzheimer's disease versus controls, but were able to be separated due to being linked to distinct functional and structural patterns. This highlights the unique strength of our approach and multimodal fusion approaches generally in revealing potentially biomarkers of brain disorders that would likely be missed by a unimodal approach. These results represent the first work linking multiple fMRI ICNs to gray matter components within a multimodal data fusion model and challenges the typical view that brain structure is more sensitive to AD than fMRI.
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40
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Chen Z, Liu Y, Zhang Y, Li Q. Orthogonal latent space learning with feature weighting and graph learning for multimodal Alzheimer's disease diagnosis. Med Image Anal 2023; 84:102698. [PMID: 36462372 DOI: 10.1016/j.media.2022.102698] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 10/18/2022] [Accepted: 11/17/2022] [Indexed: 11/23/2022]
Abstract
Recent studies have shown that multimodal neuroimaging data provide complementary information of the brain and latent space-based methods have achieved promising results in fusing multimodal data for Alzheimer's disease (AD) diagnosis. However, most existing methods treat all features equally and adopt nonorthogonal projections to learn the latent space, which cannot retain enough discriminative information in the latent space. Besides, they usually preserve the relationships among subjects in the latent space based on the similarity graph constructed on original features for performance boosting. However, the noises and redundant features significantly corrupt the graph. To address these limitations, we propose an Orthogonal Latent space learning with Feature weighting and Graph learning (OLFG) model for multimodal AD diagnosis. Specifically, we map multiple modalities into a common latent space by orthogonal constrained projection to capture the discriminative information for AD diagnosis. Then, a feature weighting matrix is utilized to sort the importance of features in AD diagnosis adaptively. Besides, we devise a regularization term with learned graph to preserve the local structure of the data in the latent space and integrate the graph construction into the learning processing for accurately encoding the relationships among samples. Instead of constructing a similarity graph for each modality, we learn a joint graph for multiple modalities to capture the correlations among modalities. Finally, the representations in the latent space are projected into the target space to perform AD diagnosis. An alternating optimization algorithm with proved convergence is developed to solve the optimization objective. Extensive experimental results show the effectiveness of the proposed method.
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Affiliation(s)
- Zhi Chen
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yongguo Liu
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Yun Zhang
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Qiaoqin Li
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
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Geng J, Gao F, Ramirez J, Honjo K, Holmes MF, Adamo S, Ozzoude M, Szilagyi GM, Scott CJM, Stebbins GT, Nyenhuis DL, Goubran M, Black SE. Secondary thalamic atrophy related to brain infarction may contribute to post-stroke cognitive impairment. J Stroke Cerebrovasc Dis 2023; 32:106895. [PMID: 36495644 DOI: 10.1016/j.jstrokecerebrovasdis.2022.106895] [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: 07/27/2022] [Revised: 10/24/2022] [Accepted: 11/10/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND AND PURPOSE The thalamus is a key brain hub that is globally connected to many cortical regions. Previous work highlights thalamic contributions to multiple cognitive functions, but few studies have measured thalamic volume changes or cognitive correlates. This study investigates associations between thalamic volumes and post-stroke cognitive function. METHODS Participants with non-thalamic brain infarcts (3-42 months) underwent MRI and cognitive testing. Focal infarcts and thalami were traced manually. In cases with bilateral infarcts, the side of the primary infarct volume defined the hemisphere involved. Brain parcellation and volumetrics were extracted using a standardized and previously validated neuroimaging pipeline. Age and gender-matched healthy controls provided normal comparative thalamic volumes. Thalamic atrophy was considered when the volume exceeded 2 standard deviations greater than the controls. RESULTS Thalamic volumes ipsilateral to the infarct in stroke patients (n=55) were smaller than left (4.4 ± 1.4 vs. 5.4 ± 0.5 cc, p < 0.001) and right (4.4 ± 1.4 vs. 5.5 ± 0.6 cc, p < 0.001) thalamic volumes in the controls. After controlling for head-size and global brain atrophy, infarct volume independently correlated with ipsilateral thalamic volume (β= -0.069, p=0.024). Left thalamic atrophy correlated significantly with poorer cognitive performance (β = 4.177, p = 0.008), after controlling for demographics and infarct volumes. CONCLUSIONS Our results suggest that the remote effect of infarction on ipsilateral thalamic volume is associated with global post-stroke cognitive impairment.
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Affiliation(s)
- Jieli Geng
- Department of Neurology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Fuqiang Gao
- LC Campbell Cognitive Neurology, Dr. Sandra Black Centre for Brain Resilience & Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Ontario, Canada
| | - Joel Ramirez
- LC Campbell Cognitive Neurology, Dr. Sandra Black Centre for Brain Resilience & Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Ontario, Canada; Heart and Stroke Foundation Canadian Partnership for Stroke Recovery (Sunnybrook site), Toronto, Ontario, Canada
| | - Kie Honjo
- LC Campbell Cognitive Neurology, Dr. Sandra Black Centre for Brain Resilience & Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Ontario, Canada; Heart and Stroke Foundation Canadian Partnership for Stroke Recovery (Sunnybrook site), Toronto, Ontario, Canada
| | - Melissa F Holmes
- LC Campbell Cognitive Neurology, Dr. Sandra Black Centre for Brain Resilience & Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Ontario, Canada
| | - Sabrina Adamo
- LC Campbell Cognitive Neurology, Dr. Sandra Black Centre for Brain Resilience & Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Ontario, Canada
| | - Miracle Ozzoude
- LC Campbell Cognitive Neurology, Dr. Sandra Black Centre for Brain Resilience & Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Ontario, Canada
| | - Gregory M Szilagyi
- LC Campbell Cognitive Neurology, Dr. Sandra Black Centre for Brain Resilience & Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Ontario, Canada
| | - Christopher J M Scott
- LC Campbell Cognitive Neurology, Dr. Sandra Black Centre for Brain Resilience & Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Ontario, Canada
| | - Glen T Stebbins
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - David L Nyenhuis
- Hauenstein Neuroscience Center, Saint Mary's Health Care, Grand Rapids, MI, USA; LCC International University
| | - Maged Goubran
- LC Campbell Cognitive Neurology, Dr. Sandra Black Centre for Brain Resilience & Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Ontario, Canada; Heart and Stroke Foundation Canadian Partnership for Stroke Recovery (Sunnybrook site), Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Ontario, Canada
| | - Sandra E Black
- LC Campbell Cognitive Neurology, Dr. Sandra Black Centre for Brain Resilience & Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Ontario, Canada; Heart and Stroke Foundation Canadian Partnership for Stroke Recovery (Sunnybrook site), Toronto, Ontario, Canada; Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Ontario, Canada.
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Cui Y, Huang H, Gao J, Jiang T, Zhang C, Yu S. Mapping blood traits to structural organization of the brain in rhesus monkeys. Cereb Cortex 2022; 33:247-257. [PMID: 35253862 DOI: 10.1093/cercor/bhac065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 01/29/2022] [Accepted: 01/31/2022] [Indexed: 01/17/2023] Open
Abstract
Hematological and biochemical blood traits have been linked to brain structural characteristics in humans. However, the relationship between these two domains has not been systematically explored in nonhuman primates, which are crucial animal models for understanding the mechanisms of brain function and developing therapeutics for various disorders. Here we investigated the associations between hematological/biochemical parameters and the brain's gray matter volume and white matter integrity derived from T1-weighted and diffusion magnetic resonance imaging in 36 healthy macaques. We found that intersubject variations in basophil count and hemoglobin levels correlated with gray matter volumes in the anterior cingulum, prefrontal cortex, and putamen. Through interactions between these key elements, the blood parameters' covariation network was linked with that of the brain structures, forming overarching networks connecting blood traits with structural brain features. These networks exhibited hierarchical small-world architecture, indicating highly effective interactions between their constituent elements. In addition, different subnetworks of the brain areas or fiber tracts tended to correlate with unique groups of blood indices, revealing previously unknown brain structural organization. These results provide a quantitative characterization of the interactions between blood parameters and brain structures in macaques and may increase the understanding of the body-brain relationship and the pathogenesis of relevant disorders.
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Affiliation(s)
- Yue Cui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haibin Huang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jinquan Gao
- Model R&D Center, Life Biosciences Company Limited, Beijing 100176, China.,Technology Management Center, SAFE Pharmaceutical Technology Company Limited, Beijing 100176, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.,Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Chen Zhang
- Department of Neurobiology, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing 100069, China
| | - Shan Yu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
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Guan X, Guo T, Zhou C, Wu J, Zeng Q, Li K, Luo X, Bai X, Wu H, Gao T, Gu L, Liu X, Cao Z, Wen J, Chen J, Wei H, Zhang Y, Liu C, Song Z, Yan Y, Pu J, Zhang B, Xu X, Zhang M. Altered brain iron depositions from aging to Parkinson's disease and Alzheimer's disease: A quantitative susceptibility mapping study. Neuroimage 2022; 264:119683. [PMID: 36243270 DOI: 10.1016/j.neuroimage.2022.119683] [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: 05/28/2022] [Revised: 10/09/2022] [Accepted: 10/11/2022] [Indexed: 11/07/2022] Open
Abstract
Brain iron deposition is a promising marker for human brain health, providing insightful information for understanding aging as well as neurodegenerations, e.g., Parkinson's disease (PD) and Alzheimer's disease (AD). To comprehensively evaluate brain iron deposition along with aging, PD-related neurodegeneration, from prodromal PD (pPD) to clinical PD (cPD), and AD-related neurodegeneration, from mild cognitive impairment (MCI) to AD, a total of 726 participants from July 2013 to December 2020, including 100 young adults, 189 old adults, 184 pPD, 171 cPD, 31 MCI and 51 AD patients, were included. Quantitative susceptibility mapping data were acquired and used to quantify regional magnetic susceptibility, and the resulting spatial standard deviations were recorded. A general linear model was applied to perform the inter-group comparison. As a result, relative to young adults, old adults showed significantly higher iron deposition with higher spatial variation in all of the subcortical nuclei (p < 0.01). pPD showed a high spatial variation of iron distribution in the subcortical nuclei except for substantia nigra (SN); and iron deposition in SN and red nucleus (RN) were progressively increased from pPD to cPD (p < 0.01). AD showed significantly higher iron deposition in caudate and putamen with higher spatial variation compared with old adults, pPD and cPD (p < 0.01), and significant iron deposition in SN compared with old adults (p < 0.01). Also, linear regression models had significances in predicting motor score in pPD and cPD (Rmean = 0.443, Ppermutation = 0.001) and cognition score in MCI and AD (Rmean = 0.243, Ppermutation = 0.037). In conclusion, progressive iron deposition in the SN and RN may characterize PD-related neurodegeneration, namely aging to cPD through pPD. On the other hand, extreme iron deposition in the caudate and putamen may characterize AD-related neurodegeneration.
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Affiliation(s)
- Xiaojun Guan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Tao Guo
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Cheng Zhou
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Jingjing Wu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Qingze Zeng
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Kaicheng Li
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Xiao Luo
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Xueqin Bai
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Haoting Wu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Ting Gao
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Luyan Gu
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Zhengye Cao
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Jiaqi Wen
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Jingwen Chen
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yuyao Zhang
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, United States; Helen Wills Neuroscience Institute, University of California, Berkeley, CA, United States
| | - Zhe Song
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yaping Yan
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiali Pu
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China.
| | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China.
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Imai A, Matsuoka T, Narumoto J. Older people with severe loneliness have an atrophied thalamus, hippocampus, and entorhinal cortex. Int J Geriatr Psychiatry 2022; 37. [PMID: 36394171 DOI: 10.1002/gps.5845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 11/04/2022] [Indexed: 11/15/2022]
Abstract
OBJECTIVES Loneliness has been shown to increase the risk of dementia. However, it is unclear why greater loneliness is associated with greater susceptibility to dementia. Herein, we aimed to examine the morphological characteristics of the brain associated with loneliness in older people concerned about cognitive dysfunction. METHODS In this retrospective study, 110 participants (80 with amnestic mild cognitive impairment, and 30 cognitively healthy individuals) were included. Participants were assessed using the revised University of California at Los Angeles (UCLA) loneliness scale and had undergone magnetic resonance imaging. Spearman correlation analysis and Mann-Whitney U tests were used to examine the clinical factors associated with loneliness. Multiple regression was performed to examine the relationship between the revised UCLA loneliness scale score and regional gray matter (GM) volume on voxel-based morphometry. RESULTS The revised UCLA loneliness scale scores were not significantly correlated with age, sex, or mini-mental state examination (MMSE) scores. Multiple regression using age, sex, MMSE score, and total brain volume as covariates showed negative correlations of the revised UCLA loneliness scale scores with the grey matter volume in regions centered on the bilateral thalamus, left hippocampus and left parahippocampal gyrus, and left entorhinal area. CONCLUSIONS Subjective loneliness was associated with decreased GM volume in the bilateral thalamus, left hippocampus, and left entorhinal cortex of the brain in the older people, thereby providing a morphological basis for the increased risk of dementia associated with greater loneliness.
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Affiliation(s)
- Ayu Imai
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Teruyuki Matsuoka
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Jin Narumoto
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
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Banerjee N, Kaur S, Saporta A, Lee SH, Alperin N, Levin BE. Structural Basal Ganglia Correlates of Subjective Fatigue in Middle-Aged and Older Adults. J Geriatr Psychiatry Neurol 2022; 35:800-809. [PMID: 35202547 DOI: 10.1177/08919887211070264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
OBJECTIVE Fatigue is among the most common complaints in community-dwelling older adults, yet its etiology is poorly understood. Based on models implicating frontostriatal pathways in fatigue pathogenesis, we hypothesized that smaller basal ganglia volume would be associated with higher levels of subjective fatigue and reduced set-shifting in middle-aged and older adults without dementia or other neurologic conditions. METHODS Forty-eight non-demented middle-aged and older adults (Mage = 68.1, SD = 9.4; MMMSE = 27.3, SD = 1.9) completed the Fatigue Symptom Inventory, set-shifting measures, and structural MRI as part of a clinical evaluation for subjective cognitive complaints. Associations were examined cross-sectionally. RESULTS Linear regression analyses showed that smaller normalized basal ganglia volumes were associated with more severe fatigue (β = -.29, P = .041) and poorer Trail Making Test B-A (TMT B-A) performance (β = .30, P = .033) controlling for depression, sleep quality, vascular risk factors, and global cognitive status. Putamen emerged as a key structure linked with both fatigue (r = -.43, P = .003) and TMT B-A (β = .35, P = .021). The link between total basal ganglia volume and reduced TMT B-A was particularly strong in clinically fatigued patients. CONCLUSION This study is among the first to show that reduced basal ganglia volume is an important neurostructural correlate of subjective fatigue in physically able middle-aged and older adults without neurological conditions. Findings suggest that fatigue and rapid set-shifting deficits may share common neural underpinnings involving the basal ganglia, and provide a framework for studying the neuropathogenesis and treatment of subjective fatigue.
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Affiliation(s)
- Nikhil Banerjee
- Department of Neurology, 12235University of Miami Miller School of Medicine, Miami, FL, USA
| | - Sonya Kaur
- Department of Neurology, 12235University of Miami Miller School of Medicine, Miami, FL, USA
| | - Anita Saporta
- Department of Neurology, 12235University of Miami Miller School of Medicine, Miami, FL, USA
| | - Sang H Lee
- Department of Radiology, 12235University of Miami Miller School of Medicine, Miami, FL, USA
| | - Noam Alperin
- Department of Radiology, 12235University of Miami Miller School of Medicine, Miami, FL, USA
| | - Bonnie E Levin
- Department of Neurology, 12235University of Miami Miller School of Medicine, Miami, FL, USA
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Liu S, Masurkar AV, Rusinek H, Chen J, Zhang B, Zhu W, Fernandez-Granda C, Razavian N. Generalizable deep learning model for early Alzheimer's disease detection from structural MRIs. Sci Rep 2022; 12:17106. [PMID: 36253382 PMCID: PMC9576679 DOI: 10.1038/s41598-022-20674-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 09/16/2022] [Indexed: 01/25/2023] Open
Abstract
Early diagnosis of Alzheimer's disease plays a pivotal role in patient care and clinical trials. In this study, we have developed a new approach based on 3D deep convolutional neural networks to accurately differentiate mild Alzheimer's disease dementia from mild cognitive impairment and cognitively normal individuals using structural MRIs. For comparison, we have built a reference model based on the volumes and thickness of previously reported brain regions that are known to be implicated in disease progression. We validate both models on an internal held-out cohort from The Alzheimer's Disease Neuroimaging Initiative (ADNI) and on an external independent cohort from The National Alzheimer's Coordinating Center (NACC). The deep-learning model is accurate, achieved an area-under-the-curve (AUC) of 85.12 when distinguishing between cognitive normal subjects and subjects with either MCI or mild Alzheimer's dementia. In the more challenging task of detecting MCI, it achieves an AUC of 62.45. It is also significantly faster than the volume/thickness model in which the volumes and thickness need to be extracted beforehand. The model can also be used to forecast progression: subjects with mild cognitive impairment misclassified as having mild Alzheimer's disease dementia by the model were faster to progress to dementia over time. An analysis of the features learned by the proposed model shows that it relies on a wide range of regions associated with Alzheimer's disease. These findings suggest that deep neural networks can automatically learn to identify imaging biomarkers that are predictive of Alzheimer's disease, and leverage them to achieve accurate early detection of the disease.
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Affiliation(s)
- Sheng Liu
- Center for Data Science, NYU, 60 Fifth Avenue, 5th Floor, New York, NY, 10011, USA
| | - Arjun V Masurkar
- Center for Cognitive Neurology, Department of Neurology, NYU Grossman School of Medicine, 60 Fifth Avenue, 5th Floor, New York, NY, 10011, USA
- Neuroscience Institute, NYU Grossman School of Medicine, 145 E 32nd St #2, New York, NY, 10016, USA
| | - Henry Rusinek
- Department of Radiology, NYU Grossman School of Medicine, 660 First Avenue, New York, NY, 10016, USA
- Department of Psychiatry, NYU Grossman School of Medicine, 227 East 30th St, 6th Floor, New York, NY, 10016, USA
| | - Jingyun Chen
- Center for Cognitive Neurology, Department of Neurology, NYU Grossman School of Medicine, 60 Fifth Avenue, 5th Floor, New York, NY, 10011, USA
- Department of Radiology, NYU Grossman School of Medicine, 660 First Avenue, New York, NY, 10016, USA
| | - Ben Zhang
- Department of Radiology, NYU Grossman School of Medicine, 660 First Avenue, New York, NY, 10016, USA
| | - Weicheng Zhu
- Center for Data Science, NYU, 60 Fifth Avenue, 5th Floor, New York, NY, 10011, USA
| | - Carlos Fernandez-Granda
- Center for Data Science, NYU, 60 Fifth Avenue, 5th Floor, New York, NY, 10011, USA.
- Courant Institute of Mathematical Sciences, NYU, 251 Mercer St # 801, New York, NY, 10012, USA.
| | - Narges Razavian
- Center for Data Science, NYU, 60 Fifth Avenue, 5th Floor, New York, NY, 10011, USA.
- Center for Cognitive Neurology, Department of Neurology, NYU Grossman School of Medicine, 60 Fifth Avenue, 5th Floor, New York, NY, 10011, USA.
- Department of Radiology, NYU Grossman School of Medicine, 660 First Avenue, New York, NY, 10016, USA.
- Department of Population Health, NYU Grossman School of Medicine, 227 East 30th street 639, New York, NY, 10016, USA.
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Besson P, Rogalski E, Gill NP, Zhang H, Martersteck A, Bandt SK. Geometric deep learning reveals a structuro-temporal understanding of healthy and pathologic brain aging. Front Aging Neurosci 2022; 14:895535. [PMID: 36081894 PMCID: PMC9445244 DOI: 10.3389/fnagi.2022.895535] [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: 03/14/2022] [Accepted: 07/27/2022] [Indexed: 11/13/2022] Open
Abstract
Background Brain age has historically been investigated primarily at the whole brain level. The ability to deconstruct the brain into its composite parts and explore brain age at the sub-structure level offers unique advantages. These include the exploration of dynamic and interconnected relationships between different brain structures in healthy and pathologic aging. To achieve this, individual brain structures can be rendered as surface representations on which morphologic analysis is carried out. Combining the advantages of deep learning with the strengths of surface analysis, we investigate the aging process at the individual structure level with the hypothesis being that pathologic aging does not uniformly affect the aging process of individual structures. Methods MRI data, age at scan time and diagnosis of dementia were collected from seven publicly available data repositories. The data from 17,440 unique subjects were collected, representing a total of 26,276 T1-weighted MRI accounting for longitudinal acquisitions. Surfaces were extracted for the cortex and seven subcortical structures. Deep learning networks were trained to estimate a subject's age either using several structures together or a single structure. We conducted a cross-sectional analysis to assess the difference between the predicted and actual ages for all structures between healthy subjects, individuals with mild cognitive impairment (MCI) or Alzheimer's disease dementia (ADD). We then performed a longitudinal analysis to assess the difference in the aging pace for each structure between stable healthy controls and healthy controls converting to either MCI or ADD. Findings Using an independent cohort of healthy subjects, age was well estimated for all structures. Cross-sectional analysis identified significantly larger predicted age for all structures in patients with either MCI and ADD compared to healthy subjects. Longitudinal analysis revealed varying degrees of involvement of individual subcortical structures for both age difference across groups and aging pace across time. These findings were most notable in the whole brain, cortex, hippocampus and amygdala. Conclusion Although similar patterns of abnormal aging were found related to MCI and ADD, the involvement of individual subcortical structures varied greatly and was consistently more pronounced in ADD patients compared to MCI patients.
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Affiliation(s)
- Pierre Besson
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States,Advanced Neuroimaging and Surgical Epilepsy (ANISE) Lab, Northwestern University, Chicago, IL, United States
| | - Emily Rogalski
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States,Department of Psychiatry and Behavioral Science, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Nathan P. Gill
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Hui Zhang
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Adam Martersteck
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States,Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States,Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
| | - S. Kathleen Bandt
- Advanced Neuroimaging and Surgical Epilepsy (ANISE) Lab, Northwestern University, Chicago, IL, United States,Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States,*Correspondence: S. Kathleen Bandt,
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Methamphetamine induced neurotoxic diseases, molecular mechanism, and current treatment strategies. Biomed Pharmacother 2022; 154:113591. [PMID: 36007276 DOI: 10.1016/j.biopha.2022.113591] [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: 05/26/2022] [Revised: 08/18/2022] [Accepted: 08/18/2022] [Indexed: 11/21/2022] Open
Abstract
Methamphetamine (MA) is a extremely addictive psychostimulant drug with a significant abuse potential. Long-term MA exposure can induce neurotoxic effects through oxidative stress, mitochondrial functional impairment, endoplasmic reticulum stress, the activation of astrocytes and microglial cells, axonal transport barriers, autophagy, and apoptosis. However, the molecular and cellular mechanisms underlying MA-induced neurotoxicity remain unclear. MA abuse increases the chances of developing neurotoxic conditions such as Parkinson's disease (PD), Alzheimer's disease (AD) and other neurotoxic diseases. MA increases the risk of PD by increasing the expression of alpha-synuclein (ASYN). Furthermore, MA abuse is linked to high chances of developing AD and subsequent neurodegeneration due to biological variations in the brain region or genetic and epigenetic variations. To date, there is no Food and Drug Administration (FDA)-approved therapy for MA-induced neurotoxicity, although many studies are being conducted to develop effective therapeutic strategies. Most current studies are now focused on developing therapies to diminish the neurotoxic effects of MA, based on the underlying mechanism of neurotoxicity. This review article highlights current research on several therapeutic techniques targeting multiple pathways to reduce the neurotoxic effects of MA in the brain, as well as the putative mechanism of MA-induced neurotoxicity.
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49
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Gentreau M, Reynes C, Sabatier R, Maller JJ, Meslin C, Deverdun J, Le Bars E, Raymond M, Berticat C, Artero S. Glucometabolic Changes Are Associated with Structural Gray Matter Alterations in Prodromal Dementia. J Alzheimers Dis 2022; 89:1293-1302. [PMID: 36031896 DOI: 10.3233/jad-220490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Glucometabolic changes, such as high glycemic load (GL) diet and insulin resistance (IR), are potential risk factor of Alzheimer's disease (AD). Yet, the effect of these factors on brain alterations that contribute to AD pathology has not been clearly demonstrated. OBJECTIVE We aimed to assess the relationship of GL and IR with gray matter volumes involved in prodromal dementia. METHODS GL and Triglyceride-Glucose (TyG) index, an IR surrogate marker, were calculated in 497 participants who underwent magnetic resonance imaging (MRI). The gray matter volumes most related to prodromal dementia/mild cognitive impairment (diagnosed in 18/158 participants during the 7-year follow-up) were identified using a data-driven machine learning algorithm. RESULTS Higher GL diet was associated with reduced amygdala volume. The TyG index was negatively associated with the hippocampus, amygdala, and putamen volumes. CONCLUSION These results suggest that GL and IR are associated with lower gray matter volumes in brain regions involved in AD pathology.
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Affiliation(s)
- Mélissa Gentreau
- IGF, University of Montpellier, CNRS, INSERM, Montpellier, France
| | | | - Robert Sabatier
- IGF, University of Montpellier, CNRS, INSERM, Montpellier, France
| | - Jerome J Maller
- Monash Alfred Psychiatry Research Centre, Melbourne, Victoria, Australia.,General Electric Healthcare, Richmond, Melbourne, Australia
| | - Chantal Meslin
- Centre for Mental Health Research, Australian National University, Canberra, Australia
| | - Jeremy Deverdun
- I2FH, Department of Neuroradiology, Montpellier University Hospital Center, Gui de Chauliac Hospital, University of Montpellier, Montpellier, France
| | - Emmanuelle Le Bars
- I2FH, Department of Neuroradiology, Montpellier University Hospital Center, Gui de Chauliac Hospital, University of Montpellier, Montpellier, France
| | - Michel Raymond
- ISEM, University of Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Claire Berticat
- ISEM, University of Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Sylvaine Artero
- IGF, University of Montpellier, CNRS, INSERM, Montpellier, France
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50
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Chen CY, Lin YS, Lee WJ, Liao YC, Kuo YS, Yang AC, Fuh JL. Endophenotypic effects of the SORL1 variant rs2298813 on regional brain volume in patients with late-onset Alzheimer’s disease. Front Aging Neurosci 2022; 14:885090. [PMID: 35992588 PMCID: PMC9389408 DOI: 10.3389/fnagi.2022.885090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 07/12/2022] [Indexed: 12/02/2022] Open
Abstract
Introduction: Two common variants of sortilin-related receptor 1 gene (SORL1), rs2298813 and rs1784933, have been associated with late-onset Alzheimer’s disease (AD) in the Han Chinese population in Taiwan. However, neuroimaging correlates of these two SORL1 variants remain unknown. We aimed to determine whether the two SORL1 polymorphisms were associated with any volumetric differences in brain regions in late-onset AD patients. Methods: We recruited 200 patients with late-onset AD from Taipei Veterans General Hospital. All patients received a structural magnetic resonance (MR) imaging brain scan and completed a battery of neurocognitive tests at enrollment. We followed up to assess changes in Mini-Mental State Examination (MMSE) scores in 155 patients (77.5%) at an interval of 2 years. Volumetric measures and cortical thickness of various brain regions were performed using FreeSurfer. Regression analysis controlled for apolipoprotein E status. Multiple comparisons were corrected for using the false discovery rate. Results: The homozygous major allele of rs2298813 was associated with larger volumes in the right putamen (p = 0.0442) and right pallidum (p = 0.0346). There was no link between the rs1784933 genotypes with any regional volume or thickness of the brain. In the rs2298813 homozygous major allele carriers, the right putaminal volume was associated with verbal fluency (p = 0.008), and both the right putaminal and pallidal volumes were predictive of clinical progression at follow-up (p = 0.020). In the minor allele carriers, neither of the nuclei was related to cognitive test performance or clinical progression. Conclusion: The major and minor alleles of rs2298813 had differential effects on the right lentiform nucleus volume and distinctively modulated the association between the regional volume and cognitive function in patients with AD.
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Affiliation(s)
- Chun-Yu Chen
- Department of Medicine, Taipei Veterans General Hospital Yuli Branch, Hualien, Taiwan
- Division of General Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yung-Shuan Lin
- Division of General Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Brain Research Center, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Wei-Ju Lee
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Brain Research Center, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- Neurological Institute, Taichung Veterans General Hospital, Taichung, Taiwan
- Dementia Center and Center for Geriatrics and Gerontology, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan
| | - Yi-Chu Liao
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Brain Research Center, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- Division of Peripheral Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yu-Shan Kuo
- Division of General Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Albert C. Yang
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jong-Ling Fuh
- Division of General Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Brain Research Center, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- *Correspondence: Jong-Ling Fuh
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