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Huang X, Yuan S, Ling Y, Tan S, Cheng H, Xu A, Lyu J. Association of birthweight and risk of incident dementia: a prospective cohort study. GeroScience 2024:10.1007/s11357-024-01105-3. [PMID: 38436791 DOI: 10.1007/s11357-024-01105-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 02/19/2024] [Indexed: 03/05/2024] Open
Abstract
Given the epidemiological studies investigating the relationship between birthweight and dementia are limited. Our study aimed to explore the association between birthweight and the risk of dementia, cognitive function, and brain structure. We included 275,648 participants from the UK Biobank, categorizing birthweight into quartiles (Q1 ≤ 2.95 kg; Q2 > 2.95 kg, ≤ 3.32 kg; Q3 > 3.32 kg, ≤ 3.66 kg; Q4 > 3.66 kg), with Q3 as the reference. Cox regression models and restricted cubic splines estimated the relationship between birthweight and the risk of all causes of dementia (ACD), Alzheimer's disease (AD), and vascular dementia (VD). Multivariable linear regression models assessed the relationship between birthweight, cognitive function, and MRI biomarkers. Over a median follow-up of 13.0 years, 3103 incident dementia cases were recorded. In the fully adjusted model, compared to Q3 (> 3.32 kg, ≤ 3.66 kg), lower birthweight in Q1 (≤ 2.95 kg) was significantly associated with increased risk of ACD (HR = 1.18, 95%CI 1.06-1.30, P = 0.001) and VD (HR = 1.32, 95%CI 1.07-1.62, P = 0.010), but no significant association with AD was found. Continuous birthweight showed a U-shaped nonlinear association with dementia. Lower birthweight was associated with worse performance in cognitive tasks, including reaction time, fluid intelligence, numeric, and prospective memory. Additionally, certain brain structure indices were identified, including brain atrophy and reductions in area, thickness, and volume of regional subcortical areas. Our study emphasizes the association between lower birthweight and increased dementia risk, correlating cognitive function and MRI biomarkers of brain structure, suggesting that in utero or early-life exposures might impact cognitive health in adulthood.
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Affiliation(s)
- Xiaxuan Huang
- Department of Neurology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Shiqi Yuan
- Department of Neurology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Yitong Ling
- Department of Neurology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Shanyuan Tan
- Department of Neurology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Hongtao Cheng
- School of Nursing, Jinan University, Guangzhou, 510630, China
| | - Anding Xu
- Department of Neurology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China.
| | - Jun Lyu
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China.
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangzhou, 510630, China.
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Chen YC, Tiego J, Segal A, Chopra S, Holmes A, Suo C, Pang JC, Fornito A, Aquino KM. A multiscale characterization of cortical shape asymmetries in early psychosis. Brain Commun 2024; 6:fcae015. [PMID: 38347944 PMCID: PMC10859637 DOI: 10.1093/braincomms/fcae015] [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: 10/12/2023] [Revised: 12/29/2023] [Accepted: 01/19/2024] [Indexed: 02/15/2024] Open
Abstract
Psychosis has often been linked to abnormal cortical asymmetry, but prior results have been inconsistent. Here, we applied a novel spectral shape analysis to characterize cortical shape asymmetries in patients with early psychosis across different spatial scales. We used the Human Connectome Project for Early Psychosis dataset (aged 16-35), comprising 56 healthy controls (37 males, 19 females) and 112 patients with early psychosis (68 males, 44 females). We quantified shape variations of each hemisphere over different spatial frequencies and applied a general linear model to compare differences between healthy controls and patients with early psychosis. We further used canonical correlation analysis to examine associations between shape asymmetries and clinical symptoms. Cortical shape asymmetries, spanning wavelengths from about 22 to 75 mm, were significantly different between healthy controls and patients with early psychosis (Cohen's d = 0.28-0.51), with patients showing greater asymmetry in cortical shape than controls. A single canonical mode linked the asymmetry measures to symptoms (canonical correlation analysis r = 0.45), such that higher cortical asymmetry was correlated with more severe excitement symptoms and less severe emotional distress. Significant group differences in the asymmetries of traditional morphological measures of cortical thickness, surface area, and gyrification, at either global or regional levels, were not identified. Cortical shape asymmetries are more sensitive than other morphological asymmetries in capturing abnormalities in patients with early psychosis. These abnormalities are expressed at coarse spatial scales and are correlated with specific symptom domains.
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Affiliation(s)
- Yu-Chi Chen
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne 3800, Australia
- Brain and Mind Centre, University of Sydney, Sydney 2050, Australia
- Brain Dynamic Centre, Westmead Institute for Medical Research, University of Sydney, Sydney 2145, Australia
| | - Jeggan Tiego
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
| | - Ashlea Segal
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- Department of Psychology, Yale University, New Haven, CT 06511, USA
| | - Sidhant Chopra
- Department of Psychology, Yale University, New Haven, CT 06511, USA
| | - Alexander Holmes
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
| | - Chao Suo
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- BrainPark, School of Psychological Sciences, Monash University, Melbourne 3800, Australia
| | - James C Pang
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
| | - Alex Fornito
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
| | - Kevin M Aquino
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- School of Physics, University of Sydney, Sydney 2050, Australia
- Center of Excellence for Integrative Brain Function, University of Sydney, Sydney 2050, Australia
- BrainKey Inc, San Francisco, CA 94103, USA
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Kameyama H, Tagai K, Takasaki E, Kashibayashi T, Takahashi R, Kanemoto H, Ishii K, Ikeda M, Shigeta M, Shinagawa S, Kazui H. Examining Frontal Lobe Asymmetry and Its Potential Role in Aggressive Behaviors in Early Alzheimer's Disease. J Alzheimers Dis 2024; 98:539-547. [PMID: 38393911 DOI: 10.3233/jad-231306] [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: 02/25/2024]
Abstract
Background Neuropsychiatric symptoms (NPS) in patients with dementia lead to caregiver burdens and worsen the patient's prognosis. Although many neuroimaging studies have been conducted, the etiology of NPS remains complex. We hypothesize that brain structural asymmetry could play a role in the appearance of NPS. Objective This study explores the relationship between NPS and brain asymmetry in patients with Alzheimer's disease (AD). Methods Demographic and MRI data for 121 mild AD cases were extracted from a multicenter Japanese database. Brain asymmetry was assessed by comparing the volumes of gray matter in the left and right brain regions. NPS was evaluated using the Neuropsychiatric Inventory (NPI). Subsequently, a comprehensive assessment of the correlation between brain asymmetry and NPS was conducted. Results Among each NPS, aggressive NPS showed a significant correlation with asymmetry in the frontal lobe, indicative of right-side atrophy (r = 0.235, p = 0.009). This correlation remained statistically significant even after adjustments for multiple comparisons (p < 0.01). Post-hoc analysis further confirmed this association (p < 0.05). In contrast, no significant correlations were found for other NPS subtypes, including affective and apathetic symptoms. Conclusions The study suggests frontal lobe asymmetry, particularly relative atrophy in the right hemisphere, may be linked to aggressive behaviors in early AD. These findings shed light on the neurobiological underpinnings of NPS, contributing to the development of potential interventions.
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Affiliation(s)
- Hiroshi Kameyama
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan
| | - Kenji Tagai
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan
| | - Emi Takasaki
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan
| | - Tetsuo Kashibayashi
- Dementia-Related Disease Medical Center, Hyogo Prefectural Rehabilitation Hospital at Nishi-Harima, Hyogo, Japan
| | - Ryuichi Takahashi
- Dementia-Related Disease Medical Center, Hyogo Prefectural Rehabilitation Hospital at Nishi-Harima, Hyogo, Japan
| | - Hideki Kanemoto
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Kazunari Ishii
- Department of Radiology, Faculty of Medicine, Kindai University, Osaka, Japan
| | - Manabu Ikeda
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Masatoshi Shigeta
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan
| | | | - Hiroaki Kazui
- Department of Neuropsychiatry, Kochi Medical School, Kochi University, Kochi, Japan
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Wang H, Lei C, Zhao D, Gao L, Gao J. DeepHipp: accurate segmentation of hippocampus using 3D dense-block based on attention mechanism. BMC Med Imaging 2023; 23:158. [PMID: 37833644 PMCID: PMC10576314 DOI: 10.1186/s12880-023-01103-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 09/14/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND The hippocampus is a key area of the brain responsible for learning, memory, and other abilities. Accurately segmenting the hippocampus and precisely calculating the volume of the hippocampus is of great significance for predicting Alzheimer's disease and amnesia. Most of the segmentation algorithms currently involved are based on templates, such as the more popular FreeSufer. METHODS This study proposes Deephipp, a deep learning network based on a 3D dense block using an attention mechanism for accurate segmentation of the hippocampus. DeepHipp is based on the following novelties: (i) DeepHipp adopts powerful data augmentation schemes to enhance the segmentation ability. (ii) DeepHipp is designed to incorporate 3D dense-block to capture multiple-scale features of the hippocampus. (iii) DeepHipp creatively uses the attention mechanism in the field of hippocampal image segmentation, extracting useful hippocampus information in a massive feature map, and improving the accuracy and sensitivity of the model. CONCLUSIONS We describe the illustrative results and show extensive qualitative and quantitative comparisons with other methods. Our achievement demonstrates that the accuracy of DeepHipp can reach 83.63%, which is superior to most existing methods in terms of accuracy and efficiency of hippocampus segmentation. It is noticeable that deep learning can potentially lead to an effective segmentation of medical images.
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Affiliation(s)
- Han Wang
- Department of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China
| | - Cai Lei
- Department of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China
| | - Di Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Liwei Gao
- Department of Radiation Oncology China, Japan Friendship Hospital, Beijing, China
| | - Jingyang Gao
- Department of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China
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Padulo C, Sestieri C, Punzi M, Picerni E, Chiacchiaretta P, Tullo MG, Granzotto A, Baldassarre A, Onofrj M, Ferretti A, Delli Pizzi S, Sensi SL. Atrophy of specific amygdala subfields in subjects converting to mild cognitive impairment. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2023; 9:e12436. [PMID: 38053753 PMCID: PMC10694338 DOI: 10.1002/trc2.12436] [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] [Received: 07/16/2023] [Revised: 10/09/2023] [Accepted: 10/23/2023] [Indexed: 12/07/2023]
Abstract
Introduction Accumulating evidence indicates that the amygdala exhibits early signs of Alzheimer's disease (AD) pathology. However, it is still unknown whether the atrophy of distinct subfields of the amygdala also participates in the transition from healthy cognition to mild cognitive impairment (MCI). Methods Our sample was derived from the AD Neuroimaging Initiative 3 and consisted of 97 cognitively healthy (HC) individuals, sorted into two groups based on their clinical follow-up: 75 who remained stable (s-HC) and 22 who converted to MCI within 48 months (c-HC). Anatomical magnetic resonance (MR) images were analyzed using a semi-automatic approach that combines probabilistic methods and a priori information from ex vivo MR images and histology to segment and obtain quantitative structural metrics for different amygdala subfields in each participant. Spearman's correlations were performed between MR measures and baseline and longitudinal neuropsychological measures. We also included anatomical measurements of the whole amygdala, the hippocampus, a key target of AD-related pathology, and the whole cortical thickness as a test of spatial specificity. Results Compared with s-HC individuals, c-HC subjects showed a reduced right amygdala volume, whereas no significant difference was observed for hippocampal volumes or changes in cortical thickness. In the amygdala subfields, we observed selected atrophy patterns in the basolateral nuclear complex, anterior amygdala area, and transitional area. Macro-structural alterations in these subfields correlated with variations of global indices of cognitive performance (measured at baseline and the 48-month follow-up), suggesting that amygdala changes shape the cognitive progression to MCI. Discussion Our results provide anatomical evidence for the early involvement of the amygdala in the preclinical stages of AD. Highlights Amygdala's atrophy marks elderly progression to mild cognitive impairment (MCI).Amygdala's was observed within the basolateral and amygdaloid complexes.Macro-structural alterations were associated with cognitive decline.No atrophy was found in the hippocampus and cortex.
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Affiliation(s)
- Caterina Padulo
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Department of HumanitiesUniversity of Naples Federico IINaplesItaly
| | - Carlo Sestieri
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)“G. d'Annunzio” University, Chieti‐PescaraChietiItaly
| | - Miriam Punzi
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Molecular Neurology UnitCenter for Advanced Studies and Technology (CAST)University “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Eleonora Picerni
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Molecular Neurology UnitCenter for Advanced Studies and Technology (CAST)University “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Piero Chiacchiaretta
- Department of Innovative Technologies in Medicine and Dentistry“G. d'Annunzio” University of Chieti‐Pescara, ChietiChietiItaly
- Advanced Computing CoreCenter for Advanced Studies and Technology (CAST)University “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Maria Giulia Tullo
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Alberto Granzotto
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Molecular Neurology UnitCenter for Advanced Studies and Technology (CAST)University “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Antonello Baldassarre
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Marco Onofrj
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Antonio Ferretti
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Molecular Neurology UnitCenter for Advanced Studies and Technology (CAST)University “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Stefano Delli Pizzi
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Molecular Neurology UnitCenter for Advanced Studies and Technology (CAST)University “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Stefano L. Sensi
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)“G. d'Annunzio” University, Chieti‐PescaraChietiItaly
- Molecular Neurology UnitCenter for Advanced Studies and Technology (CAST)University “G. d'Annunzio” of Chieti‐PescaraChietiItaly
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Deangeli D, Iarussi F, Külsgaard H, Braggio D, Princich JP, Bendersky M, Iarussi E, Larrabide I, Orlando JI. NORHA: A NORmal Hippocampal Asymmetry Deviation Index Based on One-Class Novelty Detection and 3D Shape Features. Brain Topogr 2023; 36:644-660. [PMID: 37382838 DOI: 10.1007/s10548-023-00985-6] [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: 09/12/2022] [Accepted: 06/21/2023] [Indexed: 06/30/2023]
Abstract
Radiologists routinely analyze hippocampal asymmetries in magnetic resonance (MR) images as a biomarker for neurodegenerative conditions like epilepsy and Alzheimer's Disease. However, current clinical tools rely on either subjective evaluations, basic volume measurements, or disease-specific models that fail to capture more complex differences in normal shape. In this paper, we overcome these limitations by introducing NORHA, a novel NORmal Hippocampal Asymmetry deviation index that uses machine learning novelty detection to objectively quantify it from MR scans. NORHA is based on a One-Class Support Vector Machine model learned from a set of morphological features extracted from automatically segmented hippocampi of healthy subjects. Hence, in test time, the model automatically measures how far a new unseen sample falls with respect to the feature space of normal individuals. This avoids biases produced by standard classification models, which require being trained using diseased cases and therefore learning to characterize changes produced only by the ones. We evaluated our new index in multiple clinical use cases using public and private MRI datasets comprising control individuals and subjects with different levels of dementia or epilepsy. The index reported high values for subjects with unilateral atrophies and remained low for controls or individuals with mild or severe symmetric bilateral changes. It also showed high AUC values for discriminating individuals with hippocampal sclerosis, further emphasizing its ability to characterize unilateral abnormalities. Finally, a positive correlation between NORHA and the functional cognitive test CDR-SB was observed, highlighting its promising application as a biomarker for dementia.
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Affiliation(s)
- Duilio Deangeli
- Yatiris, PLADEMA, UNICEN, Tandil, Buenos Aires, Argentina.
- CONICET, CABA, Argentina.
| | | | - Hernán Külsgaard
- Yatiris, PLADEMA, UNICEN, Tandil, Buenos Aires, Argentina
- CONICET, CABA, Argentina
| | - Delfina Braggio
- Yatiris, PLADEMA, UNICEN, Tandil, Buenos Aires, Argentina
- CONICET, CABA, Argentina
| | | | - Mariana Bendersky
- ENyS, CONICET-HEC-UNAJ, Florencio Varela, Buenos Aires, Argentina
- Normal Anatomy Department, UBA, CABA, Argentina
| | - Emmanuel Iarussi
- CONICET, CABA, Argentina
- Laboratorio de Inteligencia Artificial, Universidad Torcuato Di Tella, CABA, Argentina
| | - Ignacio Larrabide
- Yatiris, PLADEMA, UNICEN, Tandil, Buenos Aires, Argentina
- CONICET, CABA, Argentina
| | - José Ignacio Orlando
- Yatiris, PLADEMA, UNICEN, Tandil, Buenos Aires, Argentina
- CONICET, CABA, Argentina
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7
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Harms A, Bauer T, Witt JA, Baumgartner T, von Wrede R, Racz A, Ernst L, Becker AJ, Helmstaedter C, Surges R, Rüber T. Mesiotemporal Volumetry, Cortical Thickness, and Neuropsychological Deficits in the Long-term Course of Limbic Encephalitis. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2023; 10:10/4/e200125. [PMID: 37230543 DOI: 10.1212/nxi.0000000000200125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 03/30/2023] [Indexed: 05/27/2023]
Abstract
BACKGROUND AND OBJECTIVES Limbic encephalitis (LE) is an autoimmune disease often associated with temporal lobe epilepsy and subacute memory deficits. It is categorized into serologic subgroups, which differ in clinical progress, therapy response, and prognosis. Using longitudinal MRI analysis, we hypothesized that mesiotemporal and cortical atrophy rates would reveal serotype-specific patterns and reflect disease severity. METHODS In this longitudinal case-control study, all individuals with antibody-positive (glutamic acid decarboxylase 65 [GAD], leucine-rich glioma-inactivated protein 1 [LGI1], contactin-associated protein 2 [CASPR2], and N-methyl-d-aspartate receptor [NMDAR]) nonparaneoplastic LE according to Graus' diagnostic criteria treated between 2005 and 2019 at the University Hospital Bonn were enrolled. A longitudinal healthy cohort was included as the control group. Subcortical segmentation and cortical reconstruction of T1-weighted MRI were performed using the longitudinal framework in FreeSurfer. We applied linear mixed models to examine mesiotemporal volumes and cortical thickness longitudinally. RESULTS Two hundred fifty-seven MRI scans from 59 individuals with LE (34 female, age at disease onset [mean ± SD] 42.5 ± 20.4 years; GAD: n = 30, 135 scans; LGI1: n = 15, 55 scans; CASPR2: n = 9, 37 scans; and NMDAR: n = 5, 30 scans) were included. The healthy control group consisted of 128 scans from 41 individuals (22 female, age at first scan [mean ± SD] 37.7 ± 14.6 years). The amygdalar volume at disease onset was significantly higher in individuals with LE (p ≤ 0.048 for all antibody subgroups) compared with that in healthy controls and decreased over time in all antibody subgroups, except in the GAD subgroup. We observed a significantly higher hippocampal atrophy rate in all antibody subgroups compared with that in healthy controls (all p ≤ 0.002), except in the GAD subgroup. Cortical atrophy rates exceeded normal aging in individuals with impaired verbal memory, while those who were not impaired did not differ significantly from healthy controls. DISCUSSION Our data depict higher mesiotemporal volumes in the early disease stage, most likely due to edematous swelling, followed by volume regression and atrophy/hippocampal sclerosis in the late disease stage. Our study reveals a continuous and pathophysiologically meaningful trajectory of mesiotemporal volumetry across all serogroups and provides evidence that LE should be considered a network disorder in which extratemporal involvement is an important determinant of disease severity.
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Affiliation(s)
- Antonia Harms
- From the Department of Epileptology (A.H., T. Bauer, J.-A.W., T. Baumgartner, R.v.W., A.R., L.E., C.H., R.S., T.R.), and Department of Neuropathology (A.J.B.), Section for Translational Epilepsy Research, University Hospital Bonn, Germany
| | - Tobias Bauer
- From the Department of Epileptology (A.H., T. Bauer, J.-A.W., T. Baumgartner, R.v.W., A.R., L.E., C.H., R.S., T.R.), and Department of Neuropathology (A.J.B.), Section for Translational Epilepsy Research, University Hospital Bonn, Germany
| | - Juri-Alexander Witt
- From the Department of Epileptology (A.H., T. Bauer, J.-A.W., T. Baumgartner, R.v.W., A.R., L.E., C.H., R.S., T.R.), and Department of Neuropathology (A.J.B.), Section for Translational Epilepsy Research, University Hospital Bonn, Germany
| | - Tobias Baumgartner
- From the Department of Epileptology (A.H., T. Bauer, J.-A.W., T. Baumgartner, R.v.W., A.R., L.E., C.H., R.S., T.R.), and Department of Neuropathology (A.J.B.), Section for Translational Epilepsy Research, University Hospital Bonn, Germany
| | - Randi von Wrede
- From the Department of Epileptology (A.H., T. Bauer, J.-A.W., T. Baumgartner, R.v.W., A.R., L.E., C.H., R.S., T.R.), and Department of Neuropathology (A.J.B.), Section for Translational Epilepsy Research, University Hospital Bonn, Germany
| | - Attila Racz
- From the Department of Epileptology (A.H., T. Bauer, J.-A.W., T. Baumgartner, R.v.W., A.R., L.E., C.H., R.S., T.R.), and Department of Neuropathology (A.J.B.), Section for Translational Epilepsy Research, University Hospital Bonn, Germany
| | - Leon Ernst
- From the Department of Epileptology (A.H., T. Bauer, J.-A.W., T. Baumgartner, R.v.W., A.R., L.E., C.H., R.S., T.R.), and Department of Neuropathology (A.J.B.), Section for Translational Epilepsy Research, University Hospital Bonn, Germany
| | - Albert J Becker
- From the Department of Epileptology (A.H., T. Bauer, J.-A.W., T. Baumgartner, R.v.W., A.R., L.E., C.H., R.S., T.R.), and Department of Neuropathology (A.J.B.), Section for Translational Epilepsy Research, University Hospital Bonn, Germany
| | - Christoph Helmstaedter
- From the Department of Epileptology (A.H., T. Bauer, J.-A.W., T. Baumgartner, R.v.W., A.R., L.E., C.H., R.S., T.R.), and Department of Neuropathology (A.J.B.), Section for Translational Epilepsy Research, University Hospital Bonn, Germany
| | - Rainer Surges
- From the Department of Epileptology (A.H., T. Bauer, J.-A.W., T. Baumgartner, R.v.W., A.R., L.E., C.H., R.S., T.R.), and Department of Neuropathology (A.J.B.), Section for Translational Epilepsy Research, University Hospital Bonn, Germany
| | - Theodor Rüber
- From the Department of Epileptology (A.H., T. Bauer, J.-A.W., T. Baumgartner, R.v.W., A.R., L.E., C.H., R.S., T.R.), and Department of Neuropathology (A.J.B.), Section for Translational Epilepsy Research, University Hospital Bonn, Germany.
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8
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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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9
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Functional re-organization of hippocampal-cortical gradients during naturalistic memory processes. Neuroimage 2023; 271:119996. [PMID: 36863548 DOI: 10.1016/j.neuroimage.2023.119996] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 02/12/2023] [Accepted: 02/27/2023] [Indexed: 03/04/2023] Open
Abstract
The functional organization of the hippocampus mirrors that of the cortex, changing smoothly along connectivity gradients and abruptly at inter-areal boundaries. Hippocampal-dependent cognitive processes require flexible integration of these hippocampal gradients into functionally related cortical networks. To understand the cognitive relevance of this functional embedding, we acquired fMRI data while participants viewed brief news clips, either containing or lacking recently familiarized cues. Participants were 188 healthy mid-life adults and 31 adults with mild cognitive impairment (MCI) or Alzheimer's disease (AD). We employed a recently developed technique - connectivity gradientography - to study gradually changing patterns of voxel to whole brain functional connectivity and their sudden transitions. We observed that functional connectivity gradients of the anterior hippocampus map onto connectivity gradients across the default mode network during these naturalistic stimuli. The presence of familiar cues in the news clips accentuates a stepwise transition across the boundary from the anterior to the posterior hippocampus. This functional transition is shifted in the posterior direction in the left hippocampus of individuals with MCI or AD. These findings shed new light on the functional integration of hippocampal connectivity gradients into large-scale cortical networks, how these adapt with memory context and how these change in the presence of neurodegenerative disease.
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10
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Zhu DC, Gwo C, Deng A, Scheel N, Dowling MA, Zhang R. Hippocampus shape characterization with 3D Zernike transformation in clinical Alzheimer's disease progression. Hum Brain Mapp 2023; 44:1432-1444. [PMID: 36346203 PMCID: PMC9921247 DOI: 10.1002/hbm.26130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 07/30/2022] [Accepted: 10/05/2022] [Indexed: 11/11/2022] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease and the most common cause of dementia among older adults. Mild cognitive impairment (MCI) is considered a transitional phase between healthy cognitive aging and dementia. Progressive brain volume reduction/atrophy, particularly of the hippocampus, is associated with the transition from normal to MCI, and then to AD. We aimed to develop methods to characterize the shape of hippocampus and explore its potential as an imaging marker to monitor clinical AD progression. We implemented a 3D Zernike transformation to characterize the shape changes of hippocampus in 428 older subjects with high-quality T1 -weighted volumetric brain scans from the Alzheimer's Disease Neuroimaging Initiative data set (151 normal, 258 MCI, and 19 AD). Over 2 years, 15 cognitively normal subjects converted to MCI, and 42 subjects with MCI converted to AD. We found a significant correlation between hippocampal volume changes and Zernike shape metrics. Before a clinical diagnosis of AD, the shapes of the left and right hippocampi changed slowly. After AD diagnosis, both volume and shape changed rapidly but were uncorrelated to each other. During the transition from a clinical diagnosis of MCI to AD, the shape of the left and right hippocampi changed in a correlated manner but became uncorrelated after AD diagnosis. Finally, the pace of hippocampus shape change was associated with its shape and the subject's age and disease condition. In conclusion, the hippocampus shape features characterized with 3D Zernike transformation, in complement to volume measures, may serve as a novel imaging marker to monitor clinical AD progression.
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Affiliation(s)
- David C. Zhu
- Department of Radiology and Cognitive Imaging Research CenterMichigan State UniversityEast LansingMichiganUSA
| | - Chih‐Ying Gwo
- Department of Information ManagementChien Hsin University of Science and TechnologyTaoyuan CityTaiwan
| | - An‐Wen Deng
- Department of Information ManagementChien Hsin University of Science and TechnologyTaoyuan CityTaiwan
| | - Norman Scheel
- Department of Radiology and Cognitive Imaging Research CenterMichigan State UniversityEast LansingMichiganUSA
| | - Mari A. Dowling
- Department of Radiology and Cognitive Imaging Research CenterMichigan State UniversityEast LansingMichiganUSA
| | - Rong Zhang
- Departments of Neurology and Internal MedicineUniversity of Texas Southwestern Medical CenterDallasTexasUSA
- Institute for Exercise and Environmental MedicineTexas Health Presbyterian Hospital DallasDallasTexasUSA
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11
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van der Velpen IF, Vlasov V, Evans TE, Ikram MK, Gutman BA, Roshchupkin GV, Adams HH, Vernooij MW, Ikram MA. Subcortical brain structures and the risk of dementia in the Rotterdam Study. Alzheimers Dement 2023; 19:646-657. [PMID: 35633518 DOI: 10.1002/alz.12690] [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: 10/26/2021] [Revised: 04/05/2022] [Accepted: 04/10/2022] [Indexed: 11/07/2022]
Abstract
INTRODUCTION Volumetric and morphological changes in subcortical brain structures are present in persons with dementia, but it is unknown if these changes occur prior to diagnosis. METHODS Between 2005 and 2016, 5522 Rotterdam Study participants (mean age: 64.4) underwent cerebral magnetic resonance imaging (MRI) and were followed for development of dementia until 2018. Volume and shape measures were obtained for seven subcortical structures. RESULTS During 12 years of follow-up, 272 dementia cases occurred. Mean volumes of thalamus (hazard ratio [HR] per standard deviation [SD] decrease 1.94, 95% confidence interval [CI]: 1.55-2.43), amygdala (HR 1.66, 95% CI: 1.44-1.92), and hippocampus (HR 1.64, 95% CI: 1.43-1.88) were strongly associated with dementia risk. Associations for accumbens, pallidum, and caudate volumes were less pronounced. Shape analyses identified regional surface changes in the amygdala, limbic thalamus, and caudate. DISCUSSION Structure of the amygdala, thalamus, hippocampus, and caudate is associated with risk of dementia in a large population-based cohort of older adults.
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Affiliation(s)
- Isabelle F van der Velpen
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Vanja Vlasov
- Interventional Neuroscience Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg
| | - Tavia E Evans
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Mohammad Kamran Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Neurology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Boris A Gutman
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Gennady V Roshchupkin
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Hieab H Adams
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Mohammad Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
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Mabrouk B, BenHamida A, Drissi N, Bouzidi N, Mhiri C. Contribution of Brain Regions Asymmetry Scores Combined with Random Forest Classifier in the Diagnosis of Alzheimer’s Disease in His Earlier Stage. J Med Biol Eng 2023. [DOI: 10.1007/s40846-023-00775-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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13
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Garg N, Choudhry MS, Bodade RM. A review on Alzheimer's disease classification from normal controls and mild cognitive impairment using structural MR images. J Neurosci Methods 2023; 384:109745. [PMID: 36395961 DOI: 10.1016/j.jneumeth.2022.109745] [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: 02/04/2022] [Revised: 10/04/2022] [Accepted: 11/11/2022] [Indexed: 11/16/2022]
Abstract
Alzheimer's disease (AD) is an irreversible neurodegenerative brain disorder that degrades the memory and cognitive ability in elderly people. The main reason for memory loss and reduction in cognitive ability is the structural changes in the brain that occur due to neuronal loss. These structural changes are most conspicuous in the hippocampus, cortex, and grey matter and can be assessed by using neuroimaging techniques viz. Positron Emission Tomography (PET), structural Magnetic Resonance Imaging (MRI) and functional MRI (fMRI), etc. Out of these neuroimaging techniques, structural MRI has evolved as the best technique as it indicates the best soft tissue contrast and high spatial resolution which is important for AD detection. Currently, the focus of researchers is on predicting the conversion of Mild Cognitive Impairment (MCI) into AD. MCI represents the transition state between expected cognitive changes with normal aging and Alzheimer's disease. Not every MCI patient progresses into Alzheimer's disease. MCI can develop into stable MCI (sMCI, patients are called non-converters) or into progressive MCI (pMCI, patients are diagnosed as MCI converters). This paper discusses the prognosis of MCI to AD conversion and presents a review of structural MRI-based studies for AD detection. AD detection framework includes feature extraction, feature selection, and classification process. This paper reviews the studies for AD detection based on different feature extraction methods and machine learning algorithms for classification. The performance of various feature extraction methods has been compared and it has been observed that the wavelet transform-based feature extraction method would give promising results for AD classification. The present study indicates that researchers are successful in classifying AD from Normal Controls (NrmC) but, it still requires a lot of work to be done for MCI/ NrmC and MCI/AD, which would help in detecting AD at its early stage.
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Affiliation(s)
- Neha Garg
- Delhi Technological University, Department of Electronics and Communication, Delhi 110042, India.
| | - Mahipal Singh Choudhry
- Delhi Technological University, Department of Electronics and Communication, Delhi 110042, India.
| | - Rajesh M Bodade
- Military College of Telecommunication Engineering (MCTE), Mhow, Indore 453441, Madhya Pradesh, India.
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14
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Xiao Y, Liao L, Huang K, Yao S, Gao L. Coupling Between Hippocampal Parenchymal Fraction and Cortical Grey Matter Atrophy at Different Stages of Cognitive Decline. J Alzheimers Dis 2023; 93:791-801. [PMID: 37092228 PMCID: PMC10200204 DOI: 10.3233/jad-230124] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/14/2023] [Indexed: 04/25/2023]
Abstract
BACKGROUND Hippocampal atrophy is a significant brain marker of pathology in Alzheimer's disease (AD). The hippocampal parenchymal fraction (HPF) was recently developed to better assess the hippocampal volumetric integrity, and it has been shown to be a sensitive measure of hippocampal atrophy in AD. OBJECTIVE To investigate the clinical relevance of hippocampal volumetric integrity as measured by the HPF and the coupling between the HPF and brain atrophy during AD progression. METHODS We included data from 143 cognitively normal (CN), 101 mild cognitive impairment (MCI), and 125 AD participants. We examined group differences in the HPF, associations between HPF and cognitive ability, and coupling between the HPF and cortical grey matter volume in the CN, MCI, and AD groups. RESULTS We observed progressive decreases in HPF from CN to MCI and from MCI to AD, and increases in the asymmetry of HPF, with the lowest asymmetry index (AI) in the CN group and the highest AI in the AD group. There was a significant association between HPF and cognitive ability across participants. The coupling between HPF and cortical regions was observed in bilateral hippocampus, parahippocampal gyrus, temporal, frontal, and occipital regions, thalamus, and amygdala in CN, MCI, and AD groups, with a greater involvement of temporal, occipital, frontal, and subcortical regions in MCI and AD patients, especially in AD patients. CONCLUSION This study provides novel evidence for the neuroanatomical basis of cognitive decline and brain atrophy during AD progression, which may have important clinical implications for the prognosis of AD.
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Affiliation(s)
- Yaqiong Xiao
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Liangjun Liao
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Kaiyu Huang
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Shun Yao
- Department of Neurosurgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Lei Gao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
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15
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Bergamino M, Burke A, Baxter LC, Caselli RJ, Sabbagh MN, Talboom JS, Huentelman MJ, Stokes AM. Longitudinal Assessment of Intravoxel Incoherent Motion Diffusion-Weighted MRI Metrics in Cognitive Decline. J Magn Reson Imaging 2022; 56:1845-1862. [PMID: 35319142 DOI: 10.1002/jmri.28172] [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: 12/17/2021] [Revised: 03/09/2022] [Accepted: 03/11/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Advanced diffusion-based MRI biomarkers may provide insight into microstructural and perfusion changes associated with neurodegeneration and cognitive decline. PURPOSE To assess longitudinal microstructural and perfusion changes using apparent diffusion coefficient (ADC) and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) parameters in cognitively impaired (CI) and healthy control (HC) groups. STUDY TYPE Prospective/longitudinal. POPULATION Twelve CI patients (75% female) and 13 HC subjects (69% female). FIELD STRENGTH/SEQUENCE 3 T; Spin-Echo-IVIM-DWI. ASSESSMENT Two MRI scans were performed with a 12-month interval. ADC and IVIM-DWI metrics (diffusion coefficient [D] and perfusion fraction [f]) were generated from monoexponential and biexponential fits, respectively. Additionally, voxel-based correlations were evaluated between change in Montreal Cognitive Assessment (ΔMoCA) and baseline imaging parameters. STATISTICAL TESTS Analysis of covariance with sex and age as covariates was performed for main effects of group and time (false discovery rate [FDR] corrected) with post hoc comparisons using Bonferroni correction. Partial-η2 and Hedges' g were used for effect-size analysis. Spearman's correlations (FDR corrected) were used for the relationship between ΔMoCA score and imaging. P < 0.05 was considered statistically significant. RESULTS Significant differences were found for the main effects of group (HC vs. CI) and time. For group effects, higher ADC, IVIM-D, and IVIM-f were observed in the CI group compared to HC (ADC: 1.23 ± 0.08. 10-3 vs. 1.09 ± 0.07. 10-3 mm2 /sec; IVIM-D: 0.82 ± 0.01. 10-3 vs. 0.73 ± 0.01. 10-3 mm2 /sec; and IVIM-f: 0.317 ± 0.008 vs. 0.253 ± 0.009). Significantly higher ADC, IVIM-D, and IVIM-f values were observed in the CI group after 12 months (ADC: 1.45 ± 0.05. 10-3 vs. 1.50 ± 0.07. 10-3 mm2 /sec; IVIM-D: 0.87 ± 0.01. 10-3 vs. 0.94 ± 0.02. 10-3 mm2 /sec; and IVIM-f: 0.303 ± 0.007 vs. 0.332 ± 0.008), but not in the HC group at large effect size. ADC, IVIM-D, and IVIM-f negatively correlated with ΔMoCA score (ρ = -0.49, -0.51, and -0.50, respectively). DATA CONCLUSION These findings demonstrate that longitudinal differences between CI and HC cohorts can be measured using IVIM-based metrics. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Maurizio Bergamino
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Anna Burke
- Department of Neurology, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Leslie C Baxter
- Department of Neurology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Richard J Caselli
- Department of Psychiatry and Psychology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Marwan N Sabbagh
- Department of Neurology, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Joshua S Talboom
- Neurogenomics Division, Translational Genomics Research Institute, Phoenix, Arizona, USA
| | - Matthew J Huentelman
- Neurogenomics Division, Translational Genomics Research Institute, Phoenix, Arizona, USA
| | - Ashley M Stokes
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, Arizona, USA
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16
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Gwo CY, Zhu DC, Zhang R. Brain white matter hyperintensity lesion characterization in 3D T 2 fluid-attenuated inversion recovery magnetic resonance images: Shape, texture, and their correlations with potential growth. Front Neurosci 2022; 16:1028929. [PMID: 36507337 PMCID: PMC9731131 DOI: 10.3389/fnins.2022.1028929] [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: 08/26/2022] [Accepted: 11/07/2022] [Indexed: 11/25/2022] Open
Abstract
Analyses of age-related white matter hyperintensity (WMH) lesions manifested in T2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance images (MRI) have been mostly on understanding the size and location of the WMH lesions and rarely on the morphological characterization of the lesions. This work extends our prior analyses of the morphological characteristics and texture of WMH from 2D to 3D based on 3D T2 FLAIR images. 3D Zernike transformation was used to characterize WMH shape; a fuzzy logic method was used to characterize the lesion texture. We then clustered 3D WMH lesions into groups based on their 3D shape and texture features. A potential growth index (PGI) to assess dynamic changes in WMH lesions was developed based on the image texture features of the WMH lesion penumbra. WMH lesions with various sizes were segmented from brain images of 32 cognitively normal older adults. The WMH lesions were divided into two groups based on their size. Analyses of Variance (ANOVAs) showed significant differences in PGI among WMH shape clusters (P = 1.57 × 10-3 for small lesions; P = 3.14 × 10-2 for large lesions). Significant differences in PGI were also found among WMH texture group clusters (P = 1.79 × 10-6). In conclusion, we presented a novel approach to characterize the morphology of 3D WMH lesions and explored the potential to assess the dynamic morphological changes of WMH lesions using PGI.
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Affiliation(s)
- Chih-Ying Gwo
- Department of Information Management, Chien Hsin University of Science and Technology, Taoyuan City, Taiwan
| | - David C. Zhu
- Department of Radiology, Cognitive Imaging Research Center, Michigan State University, East Lansing, MI, United States
- Department of Psychology, Cognitive Imaging Research Center, Michigan State University, East Lansing, MI, United States
| | - Rong Zhang
- Department of Neurology and Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, United States
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital Dallas, Dallas, TX, United States
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17
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Vieira BH, Liem F, Dadi K, Engemann DA, Gramfort A, Bellec P, Craddock RC, Damoiseaux JS, Steele CJ, Yarkoni T, Langer N, Margulies DS, Varoquaux G. Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging. Neurobiol Aging 2022; 118:55-65. [PMID: 35878565 PMCID: PMC9853405 DOI: 10.1016/j.neurobiolaging.2022.06.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 06/21/2022] [Accepted: 06/23/2022] [Indexed: 01/24/2023]
Abstract
Previous literature has focused on predicting a diagnostic label from structural brain imaging. Since subtle changes in the brain precede a cognitive decline in healthy and pathological aging, our study predicts future decline as a continuous trajectory instead. Here, we tested whether baseline multimodal neuroimaging data improve the prediction of future cognitive decline in healthy and pathological aging. Nonbrain data (demographics, clinical, and neuropsychological scores), structural MRI, and functional connectivity data from OASIS-3 (N = 662; age = 46-96 years) were entered into cross-validated multitarget random forest models to predict future cognitive decline (measured by CDR and MMSE), on average 5.8 years into the future. The analysis was preregistered, and all analysis code is publicly available. Combining non-brain with structural data improved the continuous prediction of future cognitive decline (best test-set performance: R2 = 0.42). Cognitive performance, daily functioning, and subcortical volume drove the performance of our model. Including functional connectivity did not improve predictive accuracy. In the future, the prognosis of age-related cognitive decline may enable earlier and more effective individualized cognitive, pharmacological, and behavioral interventions.
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Affiliation(s)
- Bruno Hebling Vieira
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland,Neuroscience Center Zurich (ZNZ), University of Zurich & ETH Zurich, Zurich, Switzerland,Corresponding author. (B. Hebling Vieira)
| | - Franziskus Liem
- University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Zurich, Switzerland
| | | | - Denis A. Engemann
- UniversitéParis-Saclay, Inria, CEA, Palaiseau, France,Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | | | - Pierre Bellec
- Functional Neuroimaging Unit, Geriatric Institute, University of Montreal, Montreal, Quebec, Canada
| | | | - Jessica S. Damoiseaux
- Institute of Gerontology and the Department of Psychology, Wayne State University, Detroit, MI, USA
| | | | - Tal Yarkoni
- Department of Psychology, The University of Texas, Austin, TX, USA
| | - Nicolas Langer
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland,Neuroscience Center Zurich (ZNZ), University of Zurich & ETH Zurich, Zurich, Switzerland,University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Zurich, Switzerland
| | - Daniel S. Margulies
- Cognitive Neuroanatomy Lab, Institut du Cerveau et de la Moelle épinière, Paris, France
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18
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Pan Y, Liu M, Xia Y, Shen D. Disease-Image-Specific Learning for Diagnosis-Oriented Neuroimage Synthesis With Incomplete Multi-Modality Data. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022; 44:6839-6853. [PMID: 34156939 PMCID: PMC9297233 DOI: 10.1109/tpami.2021.3091214] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Incomplete data problem is commonly existing in classification tasks with multi-source data, particularly the disease diagnosis with multi-modality neuroimages, to track which, some methods have been proposed to utilize all available subjects by imputing missing neuroimages. However, these methods usually treat image synthesis and disease diagnosis as two standalone tasks, thus ignoring the specificity conveyed in different modalities, i.e., different modalities may highlight different disease-relevant regions in the brain. To this end, we propose a disease-image-specific deep learning (DSDL) framework for joint neuroimage synthesis and disease diagnosis using incomplete multi-modality neuroimages. Specifically, with each whole-brain scan as input, we first design a Disease-image-Specific Network (DSNet) with a spatial cosine module to implicitly model the disease-image specificity. We then develop a Feature-consistency Generative Adversarial Network (FGAN) to impute missing neuroimages, where feature maps (generated by DSNet) of a synthetic image and its respective real image are encouraged to be consistent while preserving the disease-image-specific information. Since our FGAN is correlated with DSNet, missing neuroimages can be synthesized in a diagnosis-oriented manner. Experimental results on three datasets suggest that our method can not only generate reasonable neuroimages, but also achieve state-of-the-art performance in both tasks of Alzheimer's disease identification and mild cognitive impairment conversion prediction.
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19
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Ran C, Yang Y, Ye C, Lv H, Ma T. Brain age vector: A measure of brain aging with enhanced neurodegenerative disorder specificity. Hum Brain Mapp 2022; 43:5017-5031. [PMID: 36094058 PMCID: PMC9582375 DOI: 10.1002/hbm.26066] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 07/31/2022] [Accepted: 08/23/2022] [Indexed: 11/14/2022] Open
Abstract
Neuroimaging‐driven brain age estimation has become popular in measuring brain aging and identifying neurodegenerations. However, the single estimated brain age (gap) compromises regional variations of brain aging, losing spatial specificity across diseases which is valuable for early screening. In this study, we combined brain age modeling with Shapley Additive Explanations to measure brain aging as a feature contribution vector underlying spatial pathological aging mechanism. Specifically, we regressed age with volumetric brain features using machine learning to construct the brain age model, and model‐agnostic Shapley values were calculated to attribute regional brain aging for each subject's age estimation, forming the brain age vector. Spatial specificity of the brain age vector was evaluated among groups of normal aging, prodromal Parkinson disease (PD), stable mild cognitive impairment (sMCI), and progressive mild cognitive impairment (pMCI). Machine learning methods were adopted to examine the discriminability of the brain age vector in early disease screening, compared with the other two brain aging metrics (single brain age gap, regional brain age gaps) and brain volumes. Results showed that the proposed brain age vector accurately reflected disorder‐specific abnormal aging patterns related to the medial temporal and the striatum for prodromal AD (sMCI vs. pMCI) and PD (healthy controls [HC] vs. prodromal PD), respectively, and demonstrated outstanding performance in early disease screening, with area under the curves of 83.39% and 72.28% in detecting pMCI and prodromal PD, respectively. In conclusion, the proposed brain age vector effectively improves spatial specificity of brain aging measurement and enables individual screening of neurodegenerative diseases.
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Affiliation(s)
- Chen Ran
- Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | - Yanwu Yang
- Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China.,Peng Cheng Laboratory, Shenzhen, China
| | - Chenfei Ye
- International Research Institute for Artificial Intelligence, Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | - Haiyan Lv
- MindsGo Shenzhen Life Science Co. Ltd, Shenzhen, China
| | - Ting Ma
- Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China.,Peng Cheng Laboratory, Shenzhen, China.,International Research Institute for Artificial Intelligence, Harbin Institute of Technology at Shenzhen, Shenzhen, China
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20
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Arreola F, Salazar B, Martinez A. Fitting Contralateral Neuroanatomical Asymmetry into the Amyloid Cascade Hypothesis. Healthcare (Basel) 2022; 10:1643. [PMID: 36141255 PMCID: PMC9498691 DOI: 10.3390/healthcare10091643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/24/2022] [Accepted: 08/25/2022] [Indexed: 12/04/2022] Open
Abstract
Alzheimer's Disease (AD) is the most common cause of dementia. Due to the progressive nature of the neurodegeneration associated with the disease, it is of clinical interest to achieve an early diagnosis of AD. In this study, we analyzed the viability of asymmetry-related measures as potential biomarkers to facilitate the early diagnosis of AD. These measures were obtained from MAPER-segmented MP-RAGE MRI studies available at the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and by analyzing these studies at the level of individual segmented regions. The temporal evolution of these measures was obtained and then analyzed by generating spline regression models. Data imputation was performed where missing information prevented the temporal analysis of each measure from being realized, using additional information provided by ADNI for each patient. The temporal evolution of these measures was compared to the evolution of other commonly used markers for the diagnosis of AD, such as cognitive function, concentrations of Phosphorylated-Tau, Amyloid-β, and structural MRI volumetry. The results of the regression models showed that asymmetry measures, in particular regions such as the parahippocampal gyrus, differentiated themselves temporally before most of the other evaluated biomarkers. Further studies are suggested to corroborate these results.
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Affiliation(s)
- Fernando Arreola
- Programa de Ingeniería Biomédica, Universidad de Monterrey, San Pedro Garza García 66238, Mexico
| | - Benjamín Salazar
- Programa de Ingeniería Biomédica, Universidad de Monterrey, San Pedro Garza García 66238, Mexico
| | - Antonio Martinez
- Departamento de Ingeniería, Universidad de Monterrey, San Pedro Garza García 66238, Mexico
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21
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Kannappan B, Gunasekaran TI, te Nijenhuis J, Gopal M, Velusami D, Kothandan G, Lee KH. Polygenic score for Alzheimer’s disease identifies differential atrophy in hippocampal subfield volumes. PLoS One 2022; 17:e0270795. [PMID: 35830443 PMCID: PMC9278752 DOI: 10.1371/journal.pone.0270795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 06/20/2022] [Indexed: 01/18/2023] Open
Abstract
Hippocampal subfield atrophy is a prime structural change in the brain, associated with cognitive aging and neurodegenerative diseases such as Alzheimer’s disease. Recent developments in genome-wide association studies (GWAS) have identified genetic loci that characterize the risk of hippocampal volume loss based on the processes of normal and abnormal aging. Polygenic risk scores are the genetic proxies mimicking the genetic role of the pre-existing vulnerabilities of the underlying mechanisms influencing these changes. Discriminating the genetic predispositions of hippocampal subfield atrophy between cognitive aging and neurodegenerative diseases will be helpful in understanding the disease etiology. In this study, we evaluated the polygenic risk of Alzheimer’s disease (AD PGRS) for hippocampal subfield atrophy in 1,086 individuals (319 cognitively normal (CN), 591 mild cognitively impaired (MCI), and 176 Alzheimer’s disease dementia (ADD)). Our results showed a stronger association of AD PGRS effect on the left hemisphere than on the right hemisphere for all the hippocampal subfield volumes in a mixed clinical population (CN+MCI+ADD). The subfields CA1, CA4, hippocampal tail, subiculum, presubiculum, molecular layer, GC-ML-DG, and HATA showed stronger AD PGRS associations with the MCI+ADD group than with the CN group. The subfields CA3, parasubiculum, and fimbria showed moderately higher AD PGRS associations with the MCI+ADD group than with the CN group. Our findings suggest that the eight subfield regions, which were strongly associated with AD PGRS are likely involved in the early stage ADD and a specific focus on the left hemisphere could enhance the early prediction of ADD.
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Affiliation(s)
- Balaji Kannappan
- Gwangju Alzheimer’s & Related Dementia Cohort Research Center, Chosun University, Gwangju, Republic of Korea
- Department of Biomedical Science, Chosun University, Gwangju, Republic of Korea
| | - Tamil Iniyan Gunasekaran
- Gwangju Alzheimer’s & Related Dementia Cohort Research Center, Chosun University, Gwangju, Republic of Korea
- Department of Biomedical Science, Chosun University, Gwangju, Republic of Korea
| | - Jan te Nijenhuis
- Gwangju Alzheimer’s & Related Dementia Cohort Research Center, Chosun University, Gwangju, Republic of Korea
- Department of Biomedical Science, Chosun University, Gwangju, Republic of Korea
- * E-mail: (JN); (KHL)
| | - Muthu Gopal
- Health Systems Research & MRHRU, ICMR-National Institute of Epidemiology, Tirunelveli, Tamil Nadu, India
| | - Deepika Velusami
- Department of Physiology, Sri Manakula Vinayagar Medical College and Hospital, Puducherry, Tamil Nadu, India
| | - Gugan Kothandan
- Biopolymer Modeling and Protein Chemistry Laboratory, Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Chennai, Tamil Nadu, India
| | - Kun Ho Lee
- Gwangju Alzheimer’s & Related Dementia Cohort Research Center, Chosun University, Gwangju, Republic of Korea
- Department of Biomedical Science, Chosun University, Gwangju, Republic of Korea
- Korea Brain Research Institute, Daegu, Republic of Korea
- * E-mail: (JN); (KHL)
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22
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Kobayashi R, Hayashi H, Kawakatsu S, Shibuya Y, Morioka D, Ohba M, Yoshioka M, Sakamoto K, Kanoto M, Otani K. Comparing Medial Temporal Atrophy Between Early-Onset Semantic Dementia and Early-Onset Alzheimer's Disease Using Voxel-Based Morphometry: A Multicenter MRI Study. Curr Alzheimer Res 2022; 19:503-510. [PMID: 35996258 DOI: 10.2174/1567205019666220820145429] [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: 04/11/2022] [Revised: 06/06/2022] [Accepted: 06/10/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND Early-onset Semantic dementia (EOSD) and early-onset Alzheimer's disease (EOAD) are often difficult to clinically differentiate in the early stages of the diseases because of the overlaps of clinical symptoms such as language symptoms. We compared the degree of atrophy in medial temporal structures between the two types of dementia using the voxel-based specific regional analysis system for Alzheimer's disease (VSRAD). METHODS The participants included 29 (age: 61.7±4.5 years) and 39 (age: 60.2±4.9 years) patients with EOSD and EOAD, respectively. The degree of atrophy in medial temporal structures was quantified using the VSRAD for magnetic resonance imaging data. Receiver operating characteristic (ROC) analysis was performed to distinguish patients with EOSD and EOAD using the mean Z score (Z-score) in bilateral medial temporal structures and the absolute value (laterality score) of the laterality of Z-score (| right-left |) for indicating the degree of asymmetrical atrophy in medial temporal structures. RESULTS The EOSD group had significantly higher Z and laterality scores than the EOAD group (Zscores: mean ± standard deviation: 3.74±1.05 vs. 1.56±0.81, respectively; P<0.001; laterality score: mean ± standard deviation: 2.35±1.23 vs. 0.68±0.51, respectively; P<0.001). In ROC analysis, the sensitivity and specificity to differentiate EOSD from EOAD by a Z-score of 2.29 were 97% and 85%, respectively and by the laterality score of 1.05 were 93% and 85%, respectively. CONCLUSION EOSD leads to more severe and asymmetrical atrophy in medial temporal structures than EOAD. The VSRAD may be useful to distinguish between these dementias that have several clinically similar symptoms.
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Affiliation(s)
- Ryota Kobayashi
- Department of Psychiatry, Yamagata University School of Medicine, Iidanishi 2-2-2, Yamagata 990-9585, Japan
| | - Hiroshi Hayashi
- Department of Occupational Therapy, Fukushima Medical University School of Health Sciences, Sakaemachi 10-6, Fukushima 960-8516, Japan
| | - Shinobu Kawakatsu
- Department of Neuropsychiatry, Aizu Medical Center, Fukushima Medical University, Kawahigashi 21-2, Aizuwakamatsu 969-3492, Japan
| | - Yuzuru Shibuya
- Department of Psychiatry, Nihonkai General Hospital, Akihocho 30, Sakata 998-8501, Japan
| | - Daichi Morioka
- Department of Psychiatry, Yamagata University School of Medicine, Iidanishi 2-2-2, Yamagata 990-9585, Japan
| | - Makoto Ohba
- Department of Radiology, Yamagata University Hospital, Iidanishi 2-2-2, Yamagata 990- 9585, Japan
| | - Masanori Yoshioka
- Department of Radiology, Yamagata University Hospital, Iidanishi 2-2-2, Yamagata 990- 9585, Japan
| | - Kazutaka Sakamoto
- Department of Psychiatry, Yamagata University School of Medicine, Iidanishi 2-2-2, Yamagata 990-9585, Japan.,Department of Neuropsychiatry, Aizu Medical Center, Fukushima Medical University, Kawahigashi 21-2, Aizuwakamatsu 969-3492, Japan
| | - Masafumi Kanoto
- Department of Radiology, Division of Diagnostic Radiology, Yamagata University School of Medicine, Iidanishi 2-2-2, Yamagata 990-9585, Japan
| | - Koichi Otani
- Department of Psychiatry, Yamagata University School of Medicine, Iidanishi 2-2-2, Yamagata 990-9585, Japan
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23
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Sarasua I, Pölsterl S, Wachinger C. Hippocampal representations for deep learning on Alzheimer's disease. Sci Rep 2022; 12:8619. [PMID: 35597814 PMCID: PMC9124220 DOI: 10.1038/s41598-022-12533-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 05/12/2022] [Indexed: 01/18/2023] Open
Abstract
Deep learning offers a powerful approach for analyzing hippocampal changes in Alzheimer's disease (AD) without relying on handcrafted features. Nevertheless, an input format needs to be selected to pass the image information to the neural network, which has wide ramifications for the analysis, but has not been evaluated yet. We compare five hippocampal representations (and their respective tailored network architectures) that span from raw images to geometric representations like meshes and point clouds. We performed a thorough evaluation for the prediction of AD diagnosis and time-to-dementia prediction with experiments on an independent test dataset. In addition, we evaluated the ease of interpretability for each representation-network pair. Our results show that choosing an appropriate representation of the hippocampus for predicting Alzheimer's disease with deep learning is crucial, since it impacts performance and ease of interpretation.
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Affiliation(s)
- Ignacio Sarasua
- Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Waltherstr. 23, 80337, Munich, Germany. .,Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.
| | - Sebastian Pölsterl
- Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Waltherstr. 23, 80337, Munich, Germany
| | - Christian Wachinger
- Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Waltherstr. 23, 80337, Munich, Germany.,Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
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24
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Lai Z, Zhang Q, Liang L, Wei Y, Duan G, Mai W, Zhao L, Liu P, Deng D. Efficacy and Mechanism of Moxibustion Treatment on Mild Cognitive Impairment Patients: An fMRI Study Using ALFF. Front Mol Neurosci 2022; 15:852882. [PMID: 35620445 PMCID: PMC9127659 DOI: 10.3389/fnmol.2022.852882] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Background Mild Cognitive Impairment (MCI), as a high risk of Alzheimer’s disease (AD), represents a state of cognitive function between normal aging and dementia. Moxibustion may effectively delay the progression of AD, while there is a lack of studies on the treatments in MCI. This study aimed to evaluate the effect of moxibustion treatment revealed by the amplitude of low-frequency fluctuation (ALFF) in MCI. Method We enrolled 30 MCI patients and 30 matched healthy controls (HCs) in this study. We used ALFF to compare the difference between MCI and HCs at baseline and the regulation of spontaneous neural activity in MCI patients by moxibustion. The Mini-Mental State Examination and Montreal Cognitive Assessment scores were used to evaluate cognitive function. Results Compared with HCs, the ALFF values significantly decreased in the right temporal poles: middle temporal gyrus (TPOmid), right inferior temporal gyrus, left middle cingulate gyrus, and increased in the left hippocampus, left middle temporal gyrus, right lingual gyrus, and right middle occipital gyrus in MCI patients. After moxibustion treatment, the ALFF values notably increased in the left precuneus, left thalamus, right temporal poles: middle temporal gyrus, right middle frontal gyrus, right inferior temporal gyrus, right putamen, right hippocampus, and right fusiform gyrus, while decreased in the bilateral lingual gyrus in MCI patients. The Mini-Mental State Examination and Montreal Cognitive Assessment scores increased after moxibustion treatment, and the increase in Mini-Mental State Examination score was positively correlated with the increase of ALFF value in the right TPOmid, the right insula, and the left superior temporal gyrus. Conclusion Moxibustion treatment might improve the cognitive function of MCI patients by modulating the brain activities within the default mode network, visual network, and subcortical network with a trend of increased ALFF values and functional asymmetry of the hippocampus. These results indicate that moxibustion holds great potential in the treatment of MCI.
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Affiliation(s)
- Ziyan Lai
- Department of Radiology, The People’s Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, China
| | - Qingping Zhang
- Department of Radiology, The People’s Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, China
| | - Lingyan Liang
- Department of Radiology, The People’s Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, China
| | - Yichen Wei
- Department of Radiology, The People’s Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, China
| | - Gaoxiong Duan
- Department of Radiology, The People’s Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, China
| | - Wei Mai
- Department of Acupuncture, The First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning, China
| | - Lihua Zhao
- Department of Acupuncture, The First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning, China
| | - Peng Liu
- Life Science Research Center, School of Life Science and Technology, Xidian University, Xi’an, China
| | - Demao Deng
- Department of Radiology, The People’s Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, China
- *Correspondence: Demao Deng
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25
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Harms A, Bauer T, Fischbach L, David B, Ernst L, Witt JA, Diers K, Baumgartner T, Weber B, Radbruch A, Becker AJ, Helmstaedter C, Reuter M, Elger CE, Surges R, Rüber T. Shape description and volumetry of hippocampus and amygdala in temporal lobe epilepsy - A beneficial combination with a clinical perspective. Epilepsy Behav 2022; 128:108560. [PMID: 35066389 DOI: 10.1016/j.yebeh.2022.108560] [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] [Received: 09/20/2021] [Revised: 01/02/2022] [Accepted: 01/04/2022] [Indexed: 11/25/2022]
Abstract
Shape-based markers have entered the field of morphometric neuroimaging analysis as a second mainstay alongside conventional volumetric approaches. We aimed to assess the added value of shape description for the analysis of lesional and autoimmune temporal lobe epilepsy (TLE) focusing on hippocampus and amygdala. We retrospectively investigated MRI and clinical data from 65 patients with lesional TLE (hippocampal sclerosis (HS) and astrogliosis) and from 62 patients with limbic encephalitis (LE) with serologically proven autoantibodies. Surface reconstruction and volumetric segmentation were performed with FreeSurfer. For the shape analysis, we used BrainPrint, a tool that utilizes eigenvalues of the Laplace-Beltrami operator on triangular meshes to calculate intra-subject asymmetry. Psychometric tests of memory performance were ascertained, to evaluate clinical relevance of the shape descriptor. The potential benefit of shape in addition to volumetric information for classification was assessed by five-fold repeated cross validation and logistic regression. For the LE group, the best performing classification model consisted of a combination of volume and shape asymmetry (mean AUC = 0.728), the logistic regression model was significantly improved considering both modalities instead of just volume asymmetry. For lesional TLE, the best model only considered volumetric information (mean AUC = 0.867). Shape asymmetry of the hippocampus was largely associated with verbal memory performance only in LE patients (OR = 1.07, p = 0.02). For lesional TLE, shape description is robust, but redundant when compared to volumetric approaches. For LE, in contrast, shape asymmetry as a complementary modality significantly improves the detection of subtle morphometric changes and is further associated with memory performance, which underscores the clinical relevance of shape asymmetry as a novel imaging biomarker.
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Affiliation(s)
- Antonia Harms
- Department of Epileptology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
| | - Tobias Bauer
- Department of Epileptology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
| | - Laura Fischbach
- Department of Epileptology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
| | - Bastian David
- Department of Epileptology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
| | - Leon Ernst
- Department of Epileptology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
| | - Juri-Alexander Witt
- Department of Epileptology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
| | - Kersten Diers
- Deutsches Zentrum für neurodegenerative Erkrankungen (DZNE), Venusberg-Campus 1, 53127 Bonn, Germany.
| | - Tobias Baumgartner
- Department of Epileptology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
| | - Bernd Weber
- Institute of Experimental Epileptology and Cognition Research, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
| | - Alexander Radbruch
- Department of Neuroradiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
| | - Albert J Becker
- Department of Neuropathology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
| | - Christoph Helmstaedter
- Department of Epileptology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
| | - Martin Reuter
- Deutsches Zentrum für neurodegenerative Erkrankungen (DZNE), Venusberg-Campus 1, 53127 Bonn, Germany; Martinos Center for Biomedical Imaging, MGH/Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA.
| | - Christian E Elger
- Department of Epileptology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
| | - Rainer Surges
- Department of Epileptology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
| | - Theodor Rüber
- Department of Epileptology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
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26
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Chen YC, Arnatkevičiūtė A, McTavish E, Pang JC, Chopra S, Suo C, Fornito A, Aquino KM. The individuality of shape asymmetries of the human cerebral cortex. eLife 2022; 11:75056. [PMID: 36197720 PMCID: PMC9668337 DOI: 10.7554/elife.75056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 10/04/2022] [Indexed: 01/05/2023] Open
Abstract
Asymmetries of the cerebral cortex are found across diverse phyla and are particularly pronounced in humans, with important implications for brain function and disease. However, many prior studies have confounded asymmetries due to size with those due to shape. Here, we introduce a novel approach to characterize asymmetries of the whole cortical shape, independent of size, across different spatial frequencies using magnetic resonance imaging data in three independent datasets. We find that cortical shape asymmetry is highly individualized and robust, akin to a cortical fingerprint, and identifies individuals more accurately than size-based descriptors, such as cortical thickness and surface area, or measures of inter-regional functional coupling of brain activity. Individual identifiability is optimal at coarse spatial scales (~37 mm wavelength), and shape asymmetries show scale-specific associations with sex and cognition, but not handedness. While unihemispheric cortical shape shows significant heritability at coarse scales (~65 mm wavelength), shape asymmetries are determined primarily by subject-specific environmental effects. Thus, coarse-scale shape asymmetries are highly personalized, sexually dimorphic, linked to individual differences in cognition, and are primarily driven by stochastic environmental influences.
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Affiliation(s)
- Yu-Chi Chen
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia,Monash Data Futures Institute, Monash UniversityMelbourneAustralia
| | - Aurina Arnatkevičiūtė
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia
| | - Eugene McTavish
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia,Healthy Brain and Mind Research Centre, Faculty of Health Sciences, Australian Catholic UniversityFitzroyAustralia
| | - James C Pang
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia
| | - Sidhant Chopra
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia,Department of Psychology, Yale UniversityNew HavenUnited States
| | - Chao Suo
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia,BrainPark, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia
| | - Kevin M Aquino
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia,School of Physics, University of SydneySydneyAustralia,Center of Excellence for Integrative Brain Function, University of SydneySydneyAustralia,BrainKey IncSan FranciscoUnited States
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27
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Structural Asymmetries in Normal Brain Anatomy: A Brief Overview. Ann Anat 2022; 241:151894. [DOI: 10.1016/j.aanat.2022.151894] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/17/2022] [Accepted: 01/18/2022] [Indexed: 12/19/2022]
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28
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Drouin SM, McFall GP, Potvin O, Bellec P, Masellis M, Duchesne S, Dixon RA. Data-Driven Analyses of Longitudinal Hippocampal Imaging Trajectories: Discrimination and Biomarker Prediction of Change Classes. J Alzheimers Dis 2022; 88:97-115. [PMID: 35570482 PMCID: PMC9277685 DOI: 10.3233/jad-215289] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/11/2022] [Indexed: 11/15/2022]
Abstract
BACKGROUND Hippocampal atrophy is a well-known biomarker of neurodegeneration, such as that observed in Alzheimer's disease (AD). Although distributions of hippocampal volume trajectories for asymptomatic individuals often reveal substantial heterogeneity, it is unclear whether interpretable trajectory classes can be objectively detected and used for prediction analyses. OBJECTIVE To detect and predict hippocampal trajectory classes in a computationally competitive context using established AD-related risk factors/biomarkers. METHODS We used biomarker/risk factor and longitudinal MRI data in asymptomatic adults from the AD Neuroimaging Initiative (n = 351; Mean = 75 years; 48.7% female). First, we applied latent class growth analyses to left (LHC) and right (RHC) hippocampal trajectory distributions to identify distinct classes. Second, using random forest analyses, we tested 38 multi-modal biomarkers/risk factors for their relative importance in discriminating the lower (potentially elevated atrophy risk) from the higher (potentially reduced risk) class. RESULTS For both LHC and RHC trajectory distribution analyses, we observed three distinct trajectory classes. Three biomarkers/risk factors predicted membership in LHC and RHC lower classes: male sex, higher education, and lower plasma Aβ1-42. Four additional factors selectively predicted membership in the lower LHC class: lower plasma tau and Aβ1-40, higher depressive symptomology, and lower body mass index. CONCLUSION Data-driven analyses of LHC and RHC trajectories detected three classes underlying the heterogeneous distributions. Machine learning analyses determined three common and four unique biomarkers/risk factors discriminating the higher and lower LHC/RHC classes. Our sequential analytic approach produced evidence that the dynamics of preclinical hippocampal trajectories can be predicted by AD-related biomarkers/risk factors from multiple modalities.
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Affiliation(s)
- Shannon M. Drouin
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
| | - G. Peggy McFall
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
| | | | - Pierre Bellec
- Département de Psychologie, Université de Montréal, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada
| | - Mario Masellis
- Hurvitz Brain Sciences Research Program, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Medicine (Neurology), University of Toronto, Toronto, ON, Canada
| | - Simon Duchesne
- CERVO Brain Research Centre, Quebec, QC, Canada
- Radiology and Nuclear Medicine Department, Université Laval, Quebec, QC, Canada
| | - Roger A. Dixon
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
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29
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Ren B, Wu Y, Huang L, Zhang Z, Huang B, Zhang H, Ma J, Li B, Liu X, Wu G, Zhang J, Shen L, Liu Q, Ni J. Deep transfer learning of structural magnetic resonance imaging fused with blood parameters improves brain age prediction. Hum Brain Mapp 2021; 43:1640-1656. [PMID: 34913545 PMCID: PMC8886664 DOI: 10.1002/hbm.25748] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 11/14/2021] [Accepted: 12/01/2021] [Indexed: 12/27/2022] Open
Abstract
Machine learning has been applied to neuroimaging data for estimating brain age and capturing early cognitive impairment in neurodegenerative diseases. Blood parameters like neurofilament light chain are associated with aging. In order to improve brain age predictive accuracy, we constructed a model based on both brain structural magnetic resonance imaging (sMRI) and blood parameters. Healthy subjects (n = 93; 37 males; aged 50–85 years) were recruited. A deep learning network was firstly pretrained on a large set of MRI scans (n = 1,481; 659 males; aged 50–85 years) downloaded from multiple open‐source datasets, to provide weights on our recruited dataset. Evaluating the network on the recruited dataset resulted in mean absolute error (MAE) of 4.91 years and a high correlation (r = .67, p <.001) against chronological age. The sMRI data were then combined with five blood biochemical indicators including GLU, TG, TC, ApoA1 and ApoB, and 9 dementia‐associated biomarkers including ApoE genotype, HCY, NFL, TREM2, Aβ40, Aβ42, T‐tau, TIMP1, and VLDLR to construct a bilinear fusion model, which achieved a more accurate prediction of brain age (MAE, 3.96 years; r = .76, p <.001). Notably, the fusion model achieved better improvement in the group of older subjects (70–85 years). Extracted attention maps of the network showed that amygdala, pallidum, and olfactory were effective for age estimation. Mediation analysis further showed that brain structural features and blood parameters provided independent and significant impact. The constructed age prediction model may have promising potential in evaluation of brain health based on MRI and blood parameters.
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Affiliation(s)
- Bingyu Ren
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Yingtong Wu
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Liumei Huang
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Zhiguo Zhang
- MIND Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Huajie Zhang
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Jinting Ma
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Bing Li
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Xukun Liu
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Guangyao Wu
- Radiology Department, Shenzhen University General Hospital and Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, China
| | - Jian Zhang
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China.,Health Science Center, Shenzhen University, Shenzhen, China
| | - Liming Shen
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Qiong Liu
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China.,Shenzhen Bay Laboratory, Shenzhen, China
| | - Jiazuan Ni
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
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Li B, Jang I, Riphagen J, Almaktoum R, Yochim KM, Ances BM, Bookheimer SY, Salat DH. Identifying individuals with Alzheimer's disease-like brains based on structural imaging in the Human Connectome Project Aging cohort. Hum Brain Mapp 2021; 42:5535-5546. [PMID: 34582057 PMCID: PMC8559490 DOI: 10.1002/hbm.25626] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 08/03/2021] [Accepted: 08/04/2021] [Indexed: 11/28/2022] Open
Abstract
Given the difficulty in factoring out typical age effects from subtle Alzheimer's disease (AD) effects on brain structure, identification of very early, as well as younger preclinical “at‐risk” individuals has unique challenges. We examined whether age‐correction procedures could be used to better identify individuals at very early potential risk from adults who did not have any existing cognitive diagnosis. First, we obtained cross‐sectional age effects for each structural feature using data from a selected portion of the Human Connectome Project Aging (HCP‐A) cohort. After age detrending, we weighted AD structural deterioration with patterns quantified from data of the Alzheimer's Disease Neuroimaging Initiative. Support vector machine was then used to classify individuals with brains that most resembled atrophy in AD across the entire HCP‐A sample. Additionally, we iteratively adjusted the pipeline by removing individuals classified as AD‐like from the HCP‐A cohort to minimize atypical brain structural contributions to the age detrending. The classifier had a mean cross‐validation accuracy of 94.0% for AD recognition. It also could identify mild cognitive impairment with more severe AD‐specific biomarkers and worse cognition. In an independent HCP‐A cohort, 8.8% were identified as AD‐like, and they trended toward worse cognition. An “AD risk” score derived from the machine learning models also significantly correlated with cognition. This work provides a proof of concept for the potential to use structural brain imaging to identify asymptomatic individuals at young ages who show structural brain patterns similar to AD and are potentially at risk for a future clinical disorder.
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Affiliation(s)
- Binyin Li
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA.,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ikbeom Jang
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Joost Riphagen
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA.,Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, The Netherlands
| | - Randa Almaktoum
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Kathryn Morrison Yochim
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Beau M Ances
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Susan Y Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, University of California, Los Angeles, California, USA
| | - David H Salat
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA.,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, Massachusetts, USA
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31
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Tudor A, Vasile AI, Trifu SC, Cristea MB. Morphological classification and changes in dementia (Review). Exp Ther Med 2021; 23:33. [PMID: 34824641 DOI: 10.3892/etm.2021.10955] [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: 06/25/2021] [Accepted: 07/27/2021] [Indexed: 11/06/2022] Open
Abstract
The progressive functional decline that involves both cognitive and neuropsychiatric symptoms characteristic to dementia is one of the leading research topics. The risk for dementia is an intertwined mix between aging, genetic risk factors, and environmental influences. APOEε4, which is one of the apolipoprotein E (APOE) alleles, is the major genetic risk factor for late-onset of the most common form of dementia, Alzheimer's. Advances in machine learning have led to the development of artificial intelligence (AI) algorithms to help diagnose dementia by magnetic resonance imaging (MRI) in order to detect it in the preclinical stage. The basis of the determinations starts from the morphometry of cerebral atrophies. The present review focused on MRI techniques which are a leading tool in identifying cortical atrophy, white matter dysfunctionalities, cerebral vessel quality (as a factor for cognitive impairment) and metabolic asymmetries. In addition, a brief overview of Alzheimer's disease was presented and recent neuroimaging in the field of dementia with an emphasis on structural MR imaging and more powerful methods such as diffusion tensor imaging, quantitative susceptibility mapping, and magnetic transfer imaging were explored in order to propose a simple systematic approach for the diagnosis and treatment of dementia.
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Affiliation(s)
- Alexandra Tudor
- Department of Psychiatry, 'Prof. Dr. Alex. Obregia' Clinical Hospital of Psychiatry, 041914 Bucharest, Romania
| | - Antonia Ioana Vasile
- Department of General Medicine, Medical Military Institute, 010919 Bucharest, Romania
| | - Simona Corina Trifu
- Department of Clinical Neurosciences, 'Carol Davila' University of Medicine and Pharmacy, 020021 Bucharest, Romania
| | - Mihai Bogdan Cristea
- Department of Morphological Sciences, 'Carol Davila' University of Medicine and Pharmacy, 020021 Bucharest, Romania
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Perry BL, Roth AR, Peng S, Risacher SL, Saykin AJ, Apostolova LG. Social Networks and Cognitive Reserve: Network Structure Moderates the Association between Amygdalar Volume and Cognitive Outcomes. J Gerontol B Psychol Sci Soc Sci 2021; 77:1490-1500. [PMID: 34655218 DOI: 10.1093/geronb/gbab192] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE The cognitive reserve hypothesis has been proposed as a key mechanism explaining the link between social networks and cognitive function but has rarely been empirically tested using neuroimaging data. This study examines whether social network attributes moderate the association between amygdalar volume and cognitive function. METHODS Data were from the Social Networks in Alzheimer Disease study (N=154) and Indiana Alzheimer's Disease Research Center. Social networks were measured using the PhenX Social Network Battery. Regional data from MRI (amygdalar volume; AV) were analyzed using FreeSurfer software. Cognitive function was measured using the Montreal Cognitive Assessment (MoCA) and consensus diagnosis. Linear regression analyses were conducted to test the moderating role of social networks on the association between AV and cognitive function. RESULTS Participants with greater ability to span multiple social roles and subgroups within their networks scored higher on the MoCA after adjusting for sociodemographic variables, depression, frequency of contact, and AV. Social networks moderated the association between AV and cognitive function. CONCLUSIONS Among participants who engaged in diverse and loosely connected social networks, the expected adverse cognitive effects of brain volume in regions implicated in socioemotional processing were attenuated. These findings suggest that cognitive stimulation achieved through social interaction with a diverse array of social relationships across multiple contexts may help promote cognitive reserve.
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Affiliation(s)
- Brea L Perry
- Department of Sociology, Indiana University, Bloomington, IN, USA.,Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA
| | - Adam R Roth
- Department of Sociology, Indiana University, Bloomington, IN, USA.,Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA
| | - Siyun Peng
- Department of Sociology, Indiana University, Bloomington, IN, USA
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.,Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.,Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Liana G Apostolova
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.,Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA.,Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
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Doborjeh M, Doborjeh Z, Merkin A, Bahrami H, Sumich A, Krishnamurthi R, Medvedev ON, Crook-Rumsey M, Morgan C, Kirk I, Sachdev PS, Brodaty H, Kang K, Wen W, Feigin V, Kasabov N. Personalised predictive modelling with brain-inspired spiking neural networks of longitudinal MRI neuroimaging data and the case study of dementia. Neural Netw 2021; 144:522-539. [PMID: 34619582 DOI: 10.1016/j.neunet.2021.09.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 08/11/2021] [Accepted: 09/12/2021] [Indexed: 11/27/2022]
Abstract
BACKGROUND Longitudinal neuroimaging provides spatiotemporal brain data (STBD) measurement that can be utilised to understand dynamic changes in brain structure and/or function underpinning cognitive activities. Making sense of such highly interactive information is challenging, given that the features manifest intricate temporal, causal relations between the spatially distributed neural sources in the brain. METHODS The current paper argues for the advancement of deep learning algorithms in brain-inspired spiking neural networks (SNN), capable of modelling structural data across time (longitudinal measurement) and space (anatomical components). The paper proposes a methodology and a computational architecture based on SNN for building personalised predictive models from longitudinal brain data to accurately detect, understand, and predict the dynamics of an individual's functional brain state. The methodology includes finding clusters of similar data to each individual, data interpolation, deep learning in a 3-dimensional brain-template structured SNN model, classification and prediction of individual outcome, visualisation of structural brain changes related to the predicted outcomes, interpretation of results, and individual and group predictive marker discovery. RESULTS To demonstrate the functionality of the proposed methodology, the paper presents experimental results on a longitudinal magnetic resonance imaging (MRI) dataset derived from 175 older adults of the internationally recognised community-based cohort Sydney Memory and Ageing Study (MAS) spanning 6 years of follow-up. SIGNIFICANCE The models were able to accurately classify and predict 2 years ahead of cognitive decline, such as mild cognitive impairment (MCI) and dementia with 95% and 91% accuracy, respectively. The proposed methodology also offers a 3-dimensional visualisation of the MRI models reflecting the dynamic patterns of regional changes in white matter hyperintensity (WMH) and brain volume over 6 years. CONCLUSION The method is efficient for personalised predictive modelling on a wide range of neuroimaging longitudinal data, including also demographic, genetic, and clinical data. As a case study, it resulted in finding predictive markers for MCI and dementia as dynamic brain patterns using MRI data.
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Affiliation(s)
- Maryam Doborjeh
- Computer Science and Software Engineering Department, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, New Zealand.
| | - Zohreh Doborjeh
- Department of Audiology, School of Population Health, Faculty of Medical and Health Sciences, The University of Auckland, New Zealand
| | - Alexander Merkin
- The National Institute for Stroke and Applied Neurosciences, School of Clinical Sciences, Auckland University of Technology, New Zealand
| | - Helena Bahrami
- School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, New Zealand
| | - Alexander Sumich
- NTU Psychology, Nottingham Trent University, Nottingham, United Kingdom
| | - Rita Krishnamurthi
- The National Institute for Stroke and Applied Neurosciences, School of Clinical Sciences, Auckland University of Technology, New Zealand
| | - Oleg N Medvedev
- University of Waikato, School of Psychology, Hamilton, New Zealand
| | - Mark Crook-Rumsey
- NTU Psychology, Nottingham Trent University, Nottingham, United Kingdom; School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, New Zealand
| | - Catherine Morgan
- School of Psychology and Centre for Brain Research, University of Auckland, New Zealand; Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand
| | - Ian Kirk
- School of Psychology and Centre for Brain Research, University of Auckland, New Zealand; Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia; Neuropsychiatric Institute, the Prince of Wales Hospital, Sydney, Australia
| | - Henry Brodaty
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Kristan Kang
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Wei Wen
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia; Neuropsychiatric Institute, the Prince of Wales Hospital, Sydney, Australia
| | - Valery Feigin
- The National Institute for Stroke and Applied Neurosciences, School of Clinical Sciences, Auckland University of Technology, New Zealand; Research Center of Neurology, Moscow, Russia
| | - Nikola Kasabov
- School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, New Zealand; George Moore Chair, Ulster University, Londonderry, United Kingdom
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An N, Fu Y, Shi J, Guo HN, Yang ZW, Li YC, Li S, Wang Y, Yao ZJ, Hu B. Synergistic Effects of APOE and CLU May Increase the Risk of Alzheimer's Disease: Acceleration of Atrophy in the Volumes and Shapes of the Hippocampus and Amygdala. J Alzheimers Dis 2021; 80:1311-1327. [PMID: 33682707 DOI: 10.3233/jad-201162] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND The volume loss of the hippocampus and amygdala in non-demented individuals has been reported to increase the risk of developing Alzheimer's disease (AD). Many neuroimaging genetics studies mainly focused on the individual effects of APOE and CLU on neuroimaging to understand their neural mechanisms, whereas their synergistic effects have been rarely studied. OBJECTIVE To assess whether APOE and CLU have synergetic effects, we investigated the epistatic interaction and combined effects of the two genetic variants on morphological degeneration of hippocampus and amygdala in the non-demented elderly at baseline and 2-year follow-up. METHODS Besides the widely-used volume indicator, the surface-based morphometry method was also adopted in this study to evaluate shape alterations. RESULTS Our results showed a synergistic effect of homozygosity for the CLU risk allele C in rs11136000 and APOEɛ4 on the hippocampal and amygdalar volumes during a 2-year follow-up. Moreover, the combined effects of APOEɛ4 and CLU C were stronger than either of the individual effects in the atrophy progress of the amygdala. CONCLUSION These findings indicate that brain morphological changes are caused by more than one gene variant, which may help us to better understand the complex endogenous mechanism of AD.
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Affiliation(s)
- Na An
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Yu Fu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Jie Shi
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Han-Ning Guo
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Zheng-Wu Yang
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Yong-Chao Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Shan Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Yin Wang
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Zhi-Jun Yao
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China.,Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.,Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
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35
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Abramian D, Larsson M, Eklund A, Aganj I, Westin CF, Behjat H. Diffusion-informed spatial smoothing of fMRI data in white matter using spectral graph filters. Neuroimage 2021; 237:118095. [PMID: 34000402 PMCID: PMC8356807 DOI: 10.1016/j.neuroimage.2021.118095] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 03/07/2021] [Accepted: 04/13/2021] [Indexed: 12/15/2022] Open
Abstract
Brain activation mapping using functional magnetic resonance imaging (fMRI) has been extensively studied in brain gray matter (GM), whereas in large disregarded for probing white matter (WM). This unbalanced treatment has been in part due to controversies in relation to the nature of the blood oxygenation level-dependent (BOLD) contrast in WM and its detectability. However, an accumulating body of studies has provided solid evidence of the functional significance of the BOLD signal in WM and has revealed that it exhibits anisotropic spatio-temporal correlations and structure-specific fluctuations concomitant with those of the cortical BOLD signal. In this work, we present an anisotropic spatial filtering scheme for smoothing fMRI data in WM that accounts for known spatial constraints on the BOLD signal in WM. In particular, the spatial correlation structure of the BOLD signal in WM is highly anisotropic and closely linked to local axonal structure in terms of shape and orientation, suggesting that isotropic Gaussian filters conventionally used for smoothing fMRI data are inadequate for denoising the BOLD signal in WM. The fundamental element in the proposed method is a graph-based description of WM that encodes the underlying anisotropy observed across WM, derived from diffusion-weighted MRI data. Based on this representation, and leveraging graph signal processing principles, we design subject-specific spatial filters that adapt to a subject's unique WM structure at each position in the WM that they are applied at. We use the proposed filters to spatially smooth fMRI data in WM, as an alternative to the conventional practice of using isotropic Gaussian filters. We test the proposed filtering approach on two sets of simulated phantoms, showcasing its greater sensitivity and specificity for the detection of slender anisotropic activations, compared to that achieved with isotropic Gaussian filters. We also present WM activation mapping results on the Human Connectome Project's 100-unrelated subject dataset, across seven functional tasks, showing that the proposed method enables the detection of streamline-like activations within axonal bundles.
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Affiliation(s)
- David Abramian
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.
| | - Martin Larsson
- Centre of Mathematical Sciences, Lund University, Lund, Sweden
| | - Anders Eklund
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden; Department of Computer and Information Science, Linköping University, Linköping, Sweden
| | - Iman Aganj
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA; Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Hamid Behjat
- Department of Biomedical Engineering, Lund University, Lund, Sweden; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA.
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Trejo-Castro AI, Caballero-Luna RA, Garnica-López JA, Vega-Lara F, Martinez-Torteya A. Signal and Texture Features from T2 Maps for the Prediction of Mild Cognitive Impairment to Alzheimer's Disease Progression. Healthcare (Basel) 2021; 9:941. [PMID: 34442078 PMCID: PMC8394497 DOI: 10.3390/healthcare9080941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/19/2021] [Accepted: 07/19/2021] [Indexed: 11/16/2022] Open
Abstract
Early detection of Alzheimer's disease (AD) is crucial to preserve cognitive functions and provide the opportunity for patients to enter clinical trials. In recent years, some studies have reported that features related to the signal and texture of MRI images can be an effective biomarker of AD. To test these claims, a study was conducted using T2 maps, a sequence not previously studied, of 40 patients with mild cognitive impairment (MCI) from the Alzheimer's Disease Neuroimaging Initiative database, who either progressed to AD (18) or remained stable (22). From these maps, the mean value and absolute difference of 37 signal and texture imaging features for 40 contralateral pairs of regions were measured. We used seven machine learning methods to analyze whether, by adding these imaging features to the neuropsychological studies currently used for diagnosis, we could more accurately identify patients who will progress to AD. The predictive models improved with the addition of signal and texture features. Additionally, features related to the signal and texture of the images were much more relevant than volumetric ones. Our results suggest that contralateral signal and texture features should be further investigated as potential biomarkers for the prediction of AD.
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Affiliation(s)
| | - Ricardo A. Caballero-Luna
- Programa de Ingeniería Biomédica, Universidad de Monterrey, San Pedro Garza García 66238, Mexico; (R.A.C.-L.); (J.A.G.-L.); (F.V.-L.)
| | - José A. Garnica-López
- Programa de Ingeniería Biomédica, Universidad de Monterrey, San Pedro Garza García 66238, Mexico; (R.A.C.-L.); (J.A.G.-L.); (F.V.-L.)
| | - Fernando Vega-Lara
- Programa de Ingeniería Biomédica, Universidad de Monterrey, San Pedro Garza García 66238, Mexico; (R.A.C.-L.); (J.A.G.-L.); (F.V.-L.)
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Hertel A, Wenz H, Al-Zghloul M, Hausner L, FrÖlich L, Groden C, FÖrster A. Crossed Cerebellar Diaschisis in Alzheimer's Disease Detected by Arterial Spin-labelling Perfusion MRI. In Vivo 2021; 35:1177-1183. [PMID: 33622918 DOI: 10.21873/invivo.12366] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 12/27/2020] [Accepted: 01/05/2021] [Indexed: 01/22/2023]
Abstract
BACKGROUND Crossed cerebellar diaschisis (CCD) is a phenomenon with depressed metabolism and hypoperfusion in the cerebellum. Using arterial spin-labelling perfusion weighted magnetic resonance imaging (ASL PWI), we investigated the frequency of CCD in patients with Alzheimer's disease (AD) and differences between patients with and without CCD. PATIENTS AND METHODS In patients with AD who underwent a standardized magnetic resonance imaging including ASL PWI cerebral blood flow was evaluated in the cerebellum, and brain segmentation/volumetry was performed using mdbrain (mediaire GmbH, Berlin, Germany) and FSL FIRST (Functional Magnetic Resonance Imaging of the Brain Software Library). RESULTS In total, 65 patients were included, and 22 (33.8%) patients were assessed as being CCD-positive. Patients with CCD had a significantly smaller whole brain volume (862.8±49.9 vs. 893.7±62.7 ml, p=0.049) as well as white matter volume (352.9±28.0 vs. 374.3±30.7, p=0.008) in comparison to patients without CCD. CONCLUSION It was possible to detect CCD by ASL PWI in approximately one-third of patients with AD and was associated with smaller whole brain and white matter volume.
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Affiliation(s)
- Alexander Hertel
- Department of Neuroradiology, Universitätsmedizin Mannheim, University of Heidelberg, Mannheim, Germany
| | - Holger Wenz
- Department of Neuroradiology, Universitätsmedizin Mannheim, University of Heidelberg, Mannheim, Germany
| | - Mansour Al-Zghloul
- Department of Neuroradiology, Universitätsmedizin Mannheim, University of Heidelberg, Mannheim, Germany
| | - Lucrezia Hausner
- Department of Geriatric Psychiatry, Zentralinstitut für Seelische Gesundheit, University of Heidelberg, Mannheim, Germany
| | - Lutz FrÖlich
- Department of Geriatric Psychiatry, Zentralinstitut für Seelische Gesundheit, University of Heidelberg, Mannheim, Germany
| | - Christoph Groden
- Department of Neuroradiology, Universitätsmedizin Mannheim, University of Heidelberg, Mannheim, Germany
| | - Alex FÖrster
- Department of Neuroradiology, Universitätsmedizin Mannheim, University of Heidelberg, Mannheim, Germany;
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Bernstein AS, Rapcsak SZ, Hornberger M, Saranathan M. Structural Changes in Thalamic Nuclei Across Prodromal and Clinical Alzheimer's Disease. J Alzheimers Dis 2021; 82:361-371. [PMID: 34024824 DOI: 10.3233/jad-201583] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Increasing evidence suggests that thalamic nuclei may atrophy in Alzheimer's disease (AD). We hypothesized that there will be significant atrophy of limbic thalamic nuclei associated with declining memory and cognition across the AD continuum. OBJECTIVE The objective of this work was to characterize volume differences in thalamic nuclei in subjects with early and late mild cognitive impairment (MCI) as well as AD when compared to healthy control (HC) subjects using a novel MRI-based thalamic segmentation technique (THOMAS). METHODS MPRAGE data from the ADNI database were used in this study (n = 540). Healthy control (n = 125), early MCI (n = 212), late MCI (n = 114), and AD subjects (n = 89) were selected, and their MRI data were parcellated to determine the volumes of 11 thalamic nuclei for each subject. Volumes across the different clinical subgroups were compared using ANCOVA. RESULTS There were significant differences in thalamic nuclei volumes between HC, late MCI, and AD subjects. The anteroventral, mediodorsal, pulvinar, medial geniculate, and centromedian nuclei were significantly smaller in subjects with late MCI and AD when compared to HC subjects. Furthermore, the mediodorsal, pulvinar, and medial geniculate nuclei were significantly smaller in early MCI when compared to HC subjects. CONCLUSION This work highlights nucleus specific atrophy within the thalamus in subjects with early and late MCI and AD. This is consistent with the hypothesis that memory and cognitive changes in AD are mediated by damage to a large-scale integrated neural network that extends beyond the medial temporal lobes.
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Affiliation(s)
- Adam S Bernstein
- Department of Medical Imaging, University of Arizona, Tuscon, AZ, USA
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Kim T, Kim SY, Agarwal V, Cohen A, Roush R, Chang YF, Cheng Y, Snitz B, Huppert TJ, Bagic A, Kamboh MI, Doman J, Becker JT. Cardiac-induced cerebral pulsatility, brain structure, and cognition in middle and older-aged adults. Neuroimage 2021; 233:117956. [PMID: 33716158 PMCID: PMC8145789 DOI: 10.1016/j.neuroimage.2021.117956] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 03/03/2021] [Accepted: 03/05/2021] [Indexed: 12/11/2022] Open
Abstract
Changes of cardiac-induced regional pulsatility can be associated with specific regions of brain volumetric changes, and these are related with cognitive alterations. Thus, mapping of cardiac pulsatility over the entire brain can be helpful to assess these relationships. A total of 108 subjects (age: 66.5 ± 8.4 years, 68 females, 52 healthy controls, 11 subjective cognitive decline, 17 impaired without complaints, 19 MCI and 9 AD) participated. The pulsatility map was obtained directly from resting-state functional MRI time-series data at 3T. Regional brain volumes were segmented from anatomical MRI. Multidomain neuropsychological battery was performed to test memory, language, attention and visuospatial construction. The Montreal Cognitive Assessment (MoCA) was also administered. The sparse partial least square (SPLS) method, which is desirable for better interpreting high-dimensional variables, was applied for the relationship between the entire brain voxels of pulsatility and 45 segmented brain volumes. A multiple holdout SPLS framework was used to optimize sparsity for assessing the pulsatility-volume relationship model and to test the reliability by fitting the models to 9 different splits of the data. We found statistically significant associations between subsets of pulsatility voxels and subsets of segmented brain volumes by rejecting the omnibus null hypothesis (any of 9 splits has p < 0.0056 (=0.05/9) with the Bonferroni correction). The pulsatility was positively associated with the lateral ventricle, choroid plexus, inferior lateral ventricle, and 3rd ventricle and negatively associated with hippocampus, ventral DC, and thalamus volumes for the first pulsatility-volume relationship. The pulsatility had an additional negative relationship with the amygdala and brain stem volumes for the second pulsatility-volume relationship. The spatial distribution of correlated pulsatility was observed in major feeding arteries to the brain regions, ventricles, and sagittal sinus. The indirect mediating pathways through the volumetric changes were statistically significant between the pulsatility and multiple cognitive measures (p < 0.01). Thus, the cerebral pulsatility, along with volumetric measurements, could be a potential marker for better understanding of pathophysiology and monitoring disease progression in age-related neurodegenerative disorders.
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Affiliation(s)
- Tae Kim
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, USA.
| | - Sang-Young Kim
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Vikas Agarwal
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Annie Cohen
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, USA
| | - Rebecca Roush
- Department of Neurology, University of Pittsburgh, Pittsburgh, USA
| | - Yue-Fang Chang
- Department of Neurosurgery, University of Pittsburgh, Pittsburgh, USA
| | - Yu Cheng
- Departments of Statistics and Biostatistics, University of Pittsburgh, Pittsburgh, USA
| | - Beth Snitz
- Department of Neurology, University of Pittsburgh, Pittsburgh, USA
| | - Theodore J Huppert
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, USA; Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, USA
| | - Anto Bagic
- Department of Neurology, University of Pittsburgh, Pittsburgh, USA
| | - M Ilyas Kamboh
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, USA
| | - Jack Doman
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, USA
| | - James T Becker
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, USA
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40
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Kreuzer A, Sauerbeck J, Scheifele M, Stockbauer A, Schönecker S, Prix C, Wlasich E, Loosli SV, M Kazmierczak P, Unterrainer M, Catak C, Janowitz D, Pogarell O, Palleis C, Perneczky R, Albert NL, Bartenstein P, Danek A, Buerger K, Levin J, Zwergal A, Rominger A, Brendel M, Beyer L. Detection Gap of Right-Asymmetric Neuronal Degeneration by CERAD Test Battery in Alzheimer's Disease. Front Aging Neurosci 2021; 13:611595. [PMID: 33603657 PMCID: PMC7884314 DOI: 10.3389/fnagi.2021.611595] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 01/04/2021] [Indexed: 01/18/2023] Open
Abstract
Objectives: Asymmetric disease characteristics on neuroimaging are common in structural and functional imaging of neurodegenerative diseases, particularly in Alzheimer‘s disease (AD). However, a standardized clinical evaluation of asymmetric neuronal degeneration and its impact on clinical findings has only sporadically been investigated for F-18-fluorodeoxyglucose positron emission tomography (F-18-FDG-PET). This study aimed to evaluate the impact of lateralized neuronal degeneration on the detection of AD by detailed clinical testing. Furthermore, we compared associations between clinical evaluation and lateralized neuronal degeneration between FDG-PET hypometabolism and hippocampal atrophy. Finally, we investigated if specific subtests show associations with lateralized neuronal degeneration. Methods: One-hundred and forty-six patients with a clinical diagnosis of AD (age 71 ± 8) were investigated by FDG-PET and the “Consortium to Establish a Registry for Alzheimer’s disease” (CERAD) test battery. For assessment of neuronal degeneration, FDG-PET hypometabolism in brain regions typically affected in AD were graded by visual (3D-surface projections) and semiquantitative analysis. Asymmetry of the hippocampus (left-right) in magnetic resonance tomography (MRI) was rated visually by the Scheltens scale. Measures of asymmetry were calculated to quantify lateralized neuronal degeneration and asymmetry scores were subsequently correlated with CERAD. Results: Asymmetry with left-dominant neuronal degeneration to FDG-PET was an independent predictor of cognitive impairment (visual: β = −0.288, p < 0.001; semiquantitative: β = −0.451, p < 0.001) when controlled for age, gender, years of education and total burden of neuronal degeneration, whereas hippocampal asymmetry to MRI was not (β = −0.034; p = 0.731). Direct comparison of CERAD-PET associations in cases with right- and left-lateralized neuronal degeneration estimated a detection gap of 2.7 years for right-lateralized cases. Left-hemispheric neuronal degeneration was significantly associated with the total CERAD score and multiple subscores, whereas only MMSE (semiquantitative: β = 0.429, p < 0.001) and constructional praxis (semiquantitative: β = 0.292, p = 0.008) showed significant associations with right-hemispheric neuronal degeneration. Conclusions: Asymmetry of deteriorated cerebral glucose metabolism has a significant impact on the coupling between neuronal degeneration and cognitive function. Right dominant neuronal degeneration shows a delayed detection by global CERAD testing and requires evaluation of specific subdomains of cognitive testing.
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Affiliation(s)
- Annika Kreuzer
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Julia Sauerbeck
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Maximilian Scheifele
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Anna Stockbauer
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Sonja Schönecker
- Department of Neurology, University Hospital, Ludwig-Maximilians-University, Munich, Germany
| | - Catharina Prix
- Department of Neurology, University Hospital, Ludwig-Maximilians-University, Munich, Germany
| | - Elisabeth Wlasich
- Department of Neurology, University Hospital, Ludwig-Maximilians-University, Munich, Germany
| | - Sandra V Loosli
- Department of Neurology, University Hospital, Ludwig-Maximilians-University, Munich, Germany
| | - Philipp M Kazmierczak
- Department of Radiology, University Hospital, Ludwig-Maximilians-University, Munich, Germany
| | - Marcus Unterrainer
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany.,Department of Radiology, University Hospital, Ludwig-Maximilians-University, Munich, Germany
| | - Cihan Catak
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-University, Munich, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-University, Munich, Germany
| | - Oliver Pogarell
- Department of Psychiatry, University Hospital, Ludwig-Maximilians-University, Munich, Germany
| | - Carla Palleis
- Department of Neurology, University Hospital, Ludwig-Maximilians-University, Munich, Germany.,DZNE-German Center for Neurodegenerative Diseases, Munich, Germany
| | - Robert Perneczky
- Department of Psychiatry, University Hospital, Ludwig-Maximilians-University, Munich, Germany.,DZNE-German Center for Neurodegenerative Diseases, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.,Ageing Epidemiology (AGE) Research Unit, School of Public Health, Imperial College, London, United Kingdom
| | - Nathalie L Albert
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Peter Bartenstein
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Adrian Danek
- Department of Neurology, University Hospital, Ludwig-Maximilians-University, Munich, Germany
| | - Katharina Buerger
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-University, Munich, Germany.,DZNE-German Center for Neurodegenerative Diseases, Munich, Germany
| | - Johannes Levin
- Department of Neurology, University Hospital, Ludwig-Maximilians-University, Munich, Germany.,DZNE-German Center for Neurodegenerative Diseases, Munich, Germany
| | - Andreas Zwergal
- Department of Neurology, University Hospital, Ludwig-Maximilians-University, Munich, Germany
| | - Axel Rominger
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.,Department of Nuclear Medicine Inselspital, University of Bern, Bern, Switzerland
| | - Matthias Brendel
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Leonie Beyer
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
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41
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Roe JM, Vidal-Piñeiro D, Sørensen Ø, Brandmaier AM, Düzel S, Gonzalez HA, Kievit RA, Knights E, Kühn S, Lindenberger U, Mowinckel AM, Nyberg L, Park DC, Pudas S, Rundle MM, Walhovd KB, Fjell AM, Westerhausen R. Asymmetric thinning of the cerebral cortex across the adult lifespan is accelerated in Alzheimer's disease. Nat Commun 2021; 12:721. [PMID: 33526780 PMCID: PMC7851164 DOI: 10.1038/s41467-021-21057-y] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 01/06/2021] [Indexed: 01/30/2023] Open
Abstract
Aging and Alzheimer's disease (AD) are associated with progressive brain disorganization. Although structural asymmetry is an organizing feature of the cerebral cortex it is unknown whether continuous age- and AD-related cortical degradation alters cortical asymmetry. Here, in multiple longitudinal adult lifespan cohorts we show that higher-order cortical regions exhibiting pronounced asymmetry at age ~20 also show progressive asymmetry-loss across the adult lifespan. Hence, accelerated thinning of the (previously) thicker homotopic hemisphere is a feature of aging. This organizational principle showed high consistency across cohorts in the Lifebrain consortium, and both the topological patterns and temporal dynamics of asymmetry-loss were markedly similar across replicating samples. Asymmetry-change was further accelerated in AD. Results suggest a system-wide dedifferentiation of the adaptive asymmetric organization of heteromodal cortex in aging and AD.
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Affiliation(s)
- James M. Roe
- grid.5510.10000 0004 1936 8921Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Didac Vidal-Piñeiro
- grid.5510.10000 0004 1936 8921Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Øystein Sørensen
- grid.5510.10000 0004 1936 8921Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Andreas M. Brandmaier
- grid.419526.d0000 0000 9859 7917Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Sandra Düzel
- grid.419526.d0000 0000 9859 7917Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | | | - Rogier A. Kievit
- grid.5335.00000000121885934MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Ethan Knights
- grid.5335.00000000121885934MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Simone Kühn
- grid.419526.d0000 0000 9859 7917Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany ,grid.13648.380000 0001 2180 3484Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ulman Lindenberger
- grid.419526.d0000 0000 9859 7917Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany ,grid.4372.20000 0001 2105 1091Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
| | - Athanasia M. Mowinckel
- grid.5510.10000 0004 1936 8921Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Lars Nyberg
- grid.12650.300000 0001 1034 3451Umeå Center for Functional Brain Imaging and Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | - Denise C. Park
- Center for Vital Longevity, University of Texas, Dallas, TX USA
| | - Sara Pudas
- grid.12650.300000 0001 1034 3451Umeå Center for Functional Brain Imaging and Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | | | - Kristine B. Walhovd
- grid.5510.10000 0004 1936 8921Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway ,grid.55325.340000 0004 0389 8485Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Anders M. Fjell
- grid.5510.10000 0004 1936 8921Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway ,grid.55325.340000 0004 0389 8485Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - René Westerhausen
- grid.5510.10000 0004 1936 8921Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
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42
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Sex-Dependent End-of-Life Mental and Vascular Scenarios for Compensatory Mechanisms in Mice with Normal and AD-Neurodegenerative Aging. Biomedicines 2021; 9:biomedicines9020111. [PMID: 33498895 PMCID: PMC7911097 DOI: 10.3390/biomedicines9020111] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 01/17/2021] [Accepted: 01/20/2021] [Indexed: 02/07/2023] Open
Abstract
Life expectancy decreases with aging, with cardiovascular, mental health, and neurodegenerative disorders strongly contributing to the total disability-adjusted life years. Interestingly, the morbidity/mortality paradox points to females having a worse healthy life expectancy. Since bidirectional interactions between cardiovascular and Alzheimer’s diseases (AD) have been reported, the study of this emerging field is promising. In the present work, we further explored the cardiovascular–brain interactions in mice survivors of two cohorts of non-transgenic and 3xTg-AD mice, including both sexes, to investigate the frailty/survival through their life span. Survival, monitored from birth, showed exceptionally worse mortality rates in females than males, independently of the genotype. This mortality selection provided a “survivors” cohort that could unveil brain–cardiovascular interaction mechanisms relevant for normal and neurodegenerative aging processes restricted to long-lived animals. The results show sex-dependent distinct physical (worse in 3xTg-AD males), neuropsychiatric-like and cognitive phenotypes (worse in 3xTg-AD females), and hypothalamic–pituitary–adrenal (HPA) axis activation (higher in females), with higher cerebral blood flow and improved cardiovascular phenotype in 3xTg-AD female mice survivors. The present study provides an experimental scenario to study the suggested potential compensatory hemodynamic mechanisms in end-of-life dementia, which is sex-dependent and can be a target for pharmacological and non-pharmacological interventions.
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43
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Herzog NJ, Magoulas GD. Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia. SENSORS 2021; 21:s21030778. [PMID: 33498908 PMCID: PMC7865614 DOI: 10.3390/s21030778] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 01/20/2021] [Accepted: 01/21/2021] [Indexed: 11/30/2022]
Abstract
Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry between the left and right hemispheres. Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer’s Disease (AD)), and can help to monitor the progress of the disease. In this vein, the paper proposes a data processing pipeline that can be implemented on commodity hardware. It uses features of brain asymmetries, extracted from MRI of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, for the analysis of structural changes, and machine learning classification of the pathology. The experiments provide promising results, distinguishing between subjects with normal cognition (NC) and patients with early or progressive dementia. Supervised machine learning algorithms and convolutional neural networks tested are reaching an accuracy of 92.5% and 75.0% for NC vs. EMCI, and 93.0% and 90.5% for NC vs. AD, respectively. The proposed pipeline offers a promising low-cost alternative for the classification of dementia and can be potentially useful to other brain degenerative disorders that are accompanied by changes in the brain asymmetries.
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Affiliation(s)
- Nitsa J. Herzog
- Department of Computer Science, Birkbeck College, University of London, London WC1E 7HZ, UK;
| | - George D. Magoulas
- Department of Computer Science, Birkbeck College, University of London, London WC1E 7HZ, UK;
- Birkbeck Knowledge Lab, University of London, London WC1E 7HZ, UK
- Correspondence:
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44
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Gimenez-Llort L, Alveal-Mellado D. Digging Signatures in 13-Month-Old 3xTg-AD Mice for Alzheimer's Disease and Its Disruption by Isolation Despite Social Life Since They Were Born. Front Behav Neurosci 2021; 14:611384. [PMID: 33536883 PMCID: PMC7847935 DOI: 10.3389/fnbeh.2020.611384] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 12/22/2020] [Indexed: 01/10/2023] Open
Abstract
The severity of this pandemic's scenarios will leave significant psychological traces in low resistant and resilient individuals. Increased incidence of depression, anxiety, obsessive-compulsive disorder (OCD), and post-traumatic stress disorder has already been reported. The loss of human lives and the implementation of physical distance measures in the pandemic and post-COVID scenarios may have a greater impact on the elderly, mostly in those with dementia, as OCD and other neuropsychiatric symptoms (NPS) are quite prevalent in this population. Modeling NPS in animals relies in neuroethological perspectives since the response to new situations and traumatic events, critical for survival and adaptation to the environment, is strongly preserved in the phylogeny. In the laboratory, mice dig vigorously in deep bedding to bury food pellets or small objects they may find. This behavior, initially used to screen anxiolytic activity, was later proposed to model better meaningless repetitive and perseverative behaviors characteristic of OCD or autism spectrum disorders. Other authors found that digging can also be understood as part of the expression of the animals' general activity. In the present brief report, we studied the digging ethograms in 13-month-old non-transgenic and 3xTg-AD mice modeling normal aging and advanced Alzheimer's disease (AD), respectively. This genetic model presents AD-like cognitive dysfunction and NPS-like phenotype, with high mortality rates at this age, mostly in males. This allowed us to observe the digging pattern's disruption in a subgroup of 3xTg-AD mice that survived to their cage mates. Two digging paradigms involving different anxiogenic and contextual situations were used to investigate their behavior. The temporal course and intensity of digging were found to increase in those 3xTg-AD mice that had lost their "room partners" despite having lived in social structures since they were born. However, when tested under neophobia conditions, this behavior's incidence was low (delayed), and the temporal pattern was disrupted, suggesting worsening of this NPS-like profile. The outcomes showed that this combined behavioral paradigm unveiled distinct features of digging signatures that can be useful to study these perseverative behaviors and their interplay with anxiety states already present in the AD scenario and their worsening by naturalistic/forced isolation.
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Affiliation(s)
- Lydia Gimenez-Llort
- Institut de Neurociències, Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Psychiatry and Forensic Medicine, School of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Daniel Alveal-Mellado
- Institut de Neurociències, Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Psychiatry and Forensic Medicine, School of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
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45
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Fu Z, Zhao M, Wang X, He Y, Tian Y, Yang Y, Han Y, Li S. Altered Neuroanatomical Asymmetries of Subcortical Structures in Subjective Cognitive Decline, Amnestic Mild Cognitive Impairment, and Alzheimer's Disease. J Alzheimers Dis 2021; 79:1121-1132. [PMID: 33386805 DOI: 10.3233/jad-201116] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROUND Individuals with subjective cognitive decline (SCD), defined by self-reported memory complaints but normal performance in objective neuropsychological tests, may be at higher risk of worsening or more frequent memory loss until conversion to Alzheimer's disease (AD) or related dementia. Asymmetry in two hemispheres is a cardinal character of human brain's structure and function, and altered brain asymmetry has also been connected with AD. OBJECTIVE This study aimed to determine whether the asymmetry of subcortical structures in individuals with SCD and amnestic mild cognitive impairment (aMCI) and AD patients are altered compared with normal controls (NC). METHODS We investigated neuroanatomical alterations in 35 SCD, 43 aMCI, and 41 AD subjects compared with 42 NC, focusing on asymmetrical changes in subcortical structures based on structural magnetic resonance images (sMRI). General linear model was conducted to test group differences, and partial correlation was used to model the interaction between asymmetry measurements and cognitive tests. RESULTS Individuals with SCD (lateral ventricle and cerebellum-WM), aMCI patients (lateral ventricle, pallidum, hippocampus, amygdala, accumbens, and ventral DC), and AD patients (lateral-ventricle, cerebellum-cortical pallidum, thalamus, hippocampus, amygdala, accumbens, and ventral DC) exhibited significant altered neuroanatomical asymmetries of volume, surface area, and shape compared with NC. Significant associations between shape asymmetry and neuropsychological examinations were found in the hippocampus and accumbens. CONCLUSION Altered neuroanatomical asymmetries of subcortical structures were significantly detected in SCD individuals and aMCI patients as well AD patients, and these specific asymmetry alterations are potential to be used as neuroimaging markers and for monitoring disease progression.
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Affiliation(s)
- Zhenrong Fu
- School of Biological Science & Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Mingyan Zhao
- Department of Neurology, Tangshan Gongren Hospital, Tangshan, Hebei, China.,Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Xuetong Wang
- School of Biological Science & Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Yirong He
- School of Biological Science & Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Yuan Tian
- School of Biological Science & Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Yujing Yang
- School of Biological Science & Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Ying Han
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Institute of Geriatrics, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Shuyu Li
- School of Biological Science & Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
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46
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Doustar J, Rentsendorj A, Torbati T, Regis GC, Fuchs D, Sheyn J, Mirzaei N, Graham SL, Shah PK, Mastali M, Van Eyk JE, Black KL, Gupta VK, Mirzaei M, Koronyo Y, Koronyo‐Hamaoui M. Parallels between retinal and brain pathology and response to immunotherapy in old, late-stage Alzheimer's disease mouse models. Aging Cell 2020; 19:e13246. [PMID: 33090673 PMCID: PMC7681044 DOI: 10.1111/acel.13246] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 08/14/2020] [Accepted: 09/09/2020] [Indexed: 12/20/2022] Open
Abstract
Despite growing evidence for the characteristic signs of Alzheimer's disease (AD) in the neurosensory retina, our understanding of retina-brain relationships, especially at advanced disease stages and in response to therapy, is lacking. In transgenic models of AD (APPSWE/PS1∆E9; ADtg mice), glatiramer acetate (GA) immunomodulation alleviates disease progression in pre- and early-symptomatic disease stages. Here, we explored the link between retinal and cerebral AD-related biomarkers, including response to GA immunization, in cohorts of old, late-stage ADtg mice. This aged model is considered more clinically relevant to the age-dependent disease. Levels of synaptotoxic amyloid β-protein (Aβ)1-42, angiopathic Aβ1-40, non-amyloidogenic Aβ1-38, and Aβ42/Aβ40 ratios tightly correlated between paired retinas derived from oculus sinister (OS) and oculus dexter (OD) eyes, and between left and right posterior brain hemispheres. We identified lateralization of Aβ burden, with one-side dominance within paired retinal and brain tissues. Importantly, OS and OD retinal Aβ levels correlated with their cerebral counterparts, with stronger contralateral correlations and following GA immunization. Moreover, immunomodulation in old ADtg mice brought about reductions in cerebral vascular and parenchymal Aβ deposits, especially of large, dense-core plaques, and alleviation of microgliosis and astrocytosis. Immunization further enhanced cerebral recruitment of peripheral myeloid cells and synaptic preservation. Mass spectrometry analysis identified new parallels in retino-cerebral AD-related pathology and response to GA immunization, including restoration of homeostatic glutamine synthetase expression. Overall, our results illustrate the viability of immunomodulation-guided CNS repair in old AD model mice, while shedding light onto similar retino-cerebral responses to intervention, providing incentives to explore retinal AD biomarkers.
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Affiliation(s)
- Jonah Doustar
- Department of NeurosurgeryCedars‐Sinai Medical CenterMaxine Dunitz Neurosurgical Research InstituteLos AngelesCAUSA
| | - Altan Rentsendorj
- Department of NeurosurgeryCedars‐Sinai Medical CenterMaxine Dunitz Neurosurgical Research InstituteLos AngelesCAUSA
| | - Tania Torbati
- Department of NeurosurgeryCedars‐Sinai Medical CenterMaxine Dunitz Neurosurgical Research InstituteLos AngelesCAUSA
- College of Osteopathic Medicine of the PacificWestern University of Health SciencesPomonaCAUSA
| | - Giovanna C. Regis
- Department of NeurosurgeryCedars‐Sinai Medical CenterMaxine Dunitz Neurosurgical Research InstituteLos AngelesCAUSA
| | - Dieu‐Trang Fuchs
- Department of NeurosurgeryCedars‐Sinai Medical CenterMaxine Dunitz Neurosurgical Research InstituteLos AngelesCAUSA
| | - Julia Sheyn
- Department of NeurosurgeryCedars‐Sinai Medical CenterMaxine Dunitz Neurosurgical Research InstituteLos AngelesCAUSA
| | - Nazanin Mirzaei
- Department of NeurosurgeryCedars‐Sinai Medical CenterMaxine Dunitz Neurosurgical Research InstituteLos AngelesCAUSA
| | - Stuart L. Graham
- Department of Clinical MedicineMacquarie UniversitySydneyNSWAustralia
- Save Sight InstituteSydney UniversitySydneyNSWAustralia
| | - Prediman K. Shah
- Oppenheimer Atherosclerosis Research CenterCedars‐Sinai Heart InstituteLos AngelesCAUSA
| | - Mitra Mastali
- Department of Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCAUSA
- Cedars‐Sinai Medical CenterSmidt Heart InstituteLos AngelesCAUSA
| | - Jennifer E. Van Eyk
- Department of Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCAUSA
- Barbara Streisand Women’s Heart CenterCedars‐Sinai Medical CenterLos AngelesCAUSA
- Department of MedicineCedars‐Sinai Medical CenterLos AngelesCAUSA
| | - Keith L. Black
- Department of NeurosurgeryCedars‐Sinai Medical CenterMaxine Dunitz Neurosurgical Research InstituteLos AngelesCAUSA
| | - Vivek K. Gupta
- Department of Molecular SciencesMacquarie UniversitySydneyNSWAustralia
| | - Mehdi Mirzaei
- Department of Clinical MedicineMacquarie UniversitySydneyNSWAustralia
- Department of Molecular SciencesMacquarie UniversitySydneyNSWAustralia
- Australian Proteome Analysis FacilityMacquarie UniversitySydneyNSWAustralia
| | - Yosef Koronyo
- Department of NeurosurgeryCedars‐Sinai Medical CenterMaxine Dunitz Neurosurgical Research InstituteLos AngelesCAUSA
| | - Maya Koronyo‐Hamaoui
- Department of NeurosurgeryCedars‐Sinai Medical CenterMaxine Dunitz Neurosurgical Research InstituteLos AngelesCAUSA
- Department of Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCAUSA
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47
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Popuri K, Ma D, Wang L, Beg MF. Using machine learning to quantify structural MRI neurodegeneration patterns of Alzheimer's disease into dementia score: Independent validation on 8,834 images from ADNI, AIBL, OASIS, and MIRIAD databases. Hum Brain Mapp 2020; 41:4127-4147. [PMID: 32614505 PMCID: PMC7469784 DOI: 10.1002/hbm.25115] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 04/15/2020] [Accepted: 06/08/2020] [Indexed: 12/29/2022] Open
Abstract
Biomarkers for dementia of Alzheimer's type (DAT) are sought to facilitate accurate prediction of the disease onset, ideally predating the onset of cognitive deterioration. T1-weighted magnetic resonance imaging (MRI) is a commonly used neuroimaging modality for measuring brain structure in vivo, potentially providing information enabling the design of biomarkers for DAT. We propose a novel biomarker using structural MRI volume-based features to compute a similarity score for the individual's structural patterns relative to those observed in the DAT group. We employed ensemble-learning framework that combines structural features in most discriminative ROIs to create an aggregate measure of neurodegeneration in the brain. This classifier is trained on 423 stable normal control (NC) and 330 DAT subjects, where clinical diagnosis is likely to have the highest certainty. Independent validation on 8,834 unseen images from ADNI, AIBL, OASIS, and MIRIAD Alzheimer's disease (AD) databases showed promising potential to predict the development of DAT depending on the time-to-conversion (TTC). Classification performance on stable versus progressive mild cognitive impairment (MCI) groups achieved an AUC of 0.81 for TTC of 6 months and 0.73 for TTC of up to 7 years, achieving state-of-the-art results. The output score, indicating similarity to patterns seen in DAT, provides an intuitive measure of how closely the individual's brain features resemble the DAT group. This score can be used for assessing the presence of AD structural atrophy patterns in normal aging and MCI stages, as well as monitoring the progression of the individual's brain along with the disease course.
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Affiliation(s)
- Karteek Popuri
- School of Engineering ScienceSimon Fraser UniversityBarnabyBritish ColumbiaCanada
| | - Da Ma
- School of Engineering ScienceSimon Fraser UniversityBarnabyBritish ColumbiaCanada
| | - Lei Wang
- Feinberg School of MedicineNorthwestern UniversityEvanstonIllinoisUSA
| | - Mirza Faisal Beg
- School of Engineering ScienceSimon Fraser UniversityBarnabyBritish ColumbiaCanada
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48
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Senatorov VV, Friedman AR, Milikovsky DZ, Ofer J, Saar-Ashkenazy R, Charbash A, Jahan N, Chin G, Mihaly E, Lin JM, Ramsay HJ, Moghbel A, Preininger MK, Eddings CR, Harrison HV, Patel R, Shen Y, Ghanim H, Sheng H, Veksler R, Sudmant PH, Becker A, Hart B, Rogawski MA, Dillin A, Friedman A, Kaufer D. Blood-brain barrier dysfunction in aging induces hyperactivation of TGFβ signaling and chronic yet reversible neural dysfunction. Sci Transl Med 2020; 11:11/521/eaaw8283. [PMID: 31801886 DOI: 10.1126/scitranslmed.aaw8283] [Citation(s) in RCA: 146] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 07/15/2019] [Accepted: 11/07/2019] [Indexed: 12/16/2022]
Abstract
Aging involves a decline in neural function that contributes to cognitive impairment and disease. However, the mechanisms underlying the transition from a young-and-healthy to aged-and-dysfunctional brain are not well understood. Here, we report breakdown of the vascular blood-brain barrier (BBB) in aging humans and rodents, which begins as early as middle age and progresses to the end of the life span. Gain-of-function and loss-of-function manipulations show that this BBB dysfunction triggers hyperactivation of transforming growth factor-β (TGFβ) signaling in astrocytes, which is necessary and sufficient to cause neural dysfunction and age-related pathology in rodents. Specifically, infusion of the serum protein albumin into the young rodent brain (mimicking BBB leakiness) induced astrocytic TGFβ signaling and an aged brain phenotype including aberrant electrocorticographic activity, vulnerability to seizures, and cognitive impairment. Furthermore, conditional genetic knockdown of astrocytic TGFβ receptors or pharmacological inhibition of TGFβ signaling reversed these symptomatic outcomes in aged mice. Last, we found that this same signaling pathway is activated in aging human subjects with BBB dysfunction. Our study identifies dysfunction in the neurovascular unit as one of the earliest triggers of neurological aging and demonstrates that the aging brain may retain considerable latent capacity, which can be revitalized by therapeutic inhibition of TGFβ signaling.
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Affiliation(s)
- Vladimir V Senatorov
- Helen Wills Neuroscience Institute and Berkeley Stem Cell Center, University of California, Berkeley, Berkeley, CA 94720, USA.,Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Aaron R Friedman
- Helen Wills Neuroscience Institute and Berkeley Stem Cell Center, University of California, Berkeley, Berkeley, CA 94720, USA.,Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Dan Z Milikovsky
- Departments of Physiology and Cell Biology, Cognitive and Brain Sciences, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Jonathan Ofer
- Departments of Physiology and Cell Biology, Cognitive and Brain Sciences, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Rotem Saar-Ashkenazy
- Departments of Physiology and Cell Biology, Cognitive and Brain Sciences, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Adiel Charbash
- Departments of Physiology and Cell Biology, Cognitive and Brain Sciences, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Naznin Jahan
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720, USA.,Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Gregory Chin
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Eszter Mihaly
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Jessica M Lin
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Harrison J Ramsay
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Ariana Moghbel
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Marcela K Preininger
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Chelsy R Eddings
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Helen V Harrison
- School of Public Health, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Rishi Patel
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Yishuo Shen
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Hana Ghanim
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Huanjie Sheng
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Ronel Veksler
- Departments of Physiology and Cell Biology, Cognitive and Brain Sciences, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Peter H Sudmant
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Albert Becker
- Section for Translational Epilepsy Research, Department of Neuropathology, University of Bonn Medical Center, Bonn, Germany
| | - Barry Hart
- Innovation Pathways, Palo Alto, CA 94301, USA
| | - Michael A Rogawski
- Department of Neurology, School of Medicine, University of California, Davis, Sacramento, CA 95817, USA
| | - Andrew Dillin
- Glenn Center for Aging Research, Howard Hughes Medical Institute, Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Alon Friedman
- Departments of Physiology and Cell Biology, Cognitive and Brain Sciences, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.,Department of Medical Neuroscience and Brain Repair Center, Dalhousie University, Halifax, NS B3H4R2, Canada
| | - Daniela Kaufer
- Helen Wills Neuroscience Institute and Berkeley Stem Cell Center, University of California, Berkeley, Berkeley, CA 94720, USA. .,Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720, USA.,Canadian Institute for Advanced Research, Toronto, ON M5G1M1, Canada
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49
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Pan Y, Liu M, Lian C, Xia Y, Shen D. Spatially-Constrained Fisher Representation for Brain Disease Identification With Incomplete Multi-Modal Neuroimages. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2965-2975. [PMID: 32217472 PMCID: PMC7485604 DOI: 10.1109/tmi.2020.2983085] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Multi-modal neuroimages, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), can provide complementary structural and functional information of the brain, thus facilitating automated brain disease identification. Incomplete data problem is unavoidable in multi-modal neuroimage studies due to patient dropouts and/or poor data quality. Conventional methods usually discard data-missing subjects, thus significantly reducing the number of training samples. Even though several deep learning methods have been proposed, they usually rely on pre-defined regions-of-interest in neuroimages, requiring disease-specific expert knowledge. To this end, we propose a spatially-constrained Fisher representation framework for brain disease diagnosis with incomplete multi-modal neuroimages. We first impute missing PET images based on their corresponding MRI scans using a hybrid generative adversarial network. With the complete (after imputation) MRI and PET data, we then develop a spatially-constrained Fisher representation network to extract statistical descriptors of neuroimages for disease diagnosis, assuming that these descriptors follow a Gaussian mixture model with a strong spatial constraint (i.e., images from different subjects have similar anatomical structures). Experimental results on three databases suggest that our method can synthesize reasonable neuroimages and achieve promising results in brain disease identification, compared with several state-of-the-art methods.
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Affiliation(s)
- Yongsheng Pan
- Y. Pan and Y. Xia are with the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China. M. Liu, C. Lian, and D. Shen are with the Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea
| | - Mingxia Liu
- Y. Pan and Y. Xia are with the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China. M. Liu, C. Lian, and D. Shen are with the Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea
| | - Chunfeng Lian
- Y. Pan and Y. Xia are with the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China. M. Liu, C. Lian, and D. Shen are with the Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea
| | - Yong Xia
- Y. Pan and Y. Xia are with the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China. M. Liu, C. Lian, and D. Shen are with the Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea
| | - Dinggang Shen
- Y. Pan and Y. Xia are with the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China. M. Liu, C. Lian, and D. Shen are with the Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea
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50
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Li B, Zhang M, Riphagen J, Morrison Yochim K, Li B, Liu J, Salat DH. Prediction of clinical and biomarker conformed Alzheimer's disease and mild cognitive impairment from multi-feature brain structural MRI using age-correction from a large independent lifespan sample. Neuroimage Clin 2020; 28:102387. [PMID: 32871388 PMCID: PMC7476071 DOI: 10.1016/j.nicl.2020.102387] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 08/13/2020] [Accepted: 08/14/2020] [Indexed: 02/06/2023]
Abstract
Structural neuroimaging has been applied to the identification of individuals with Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, these methods are greatly impacted by age limiting their utility for detection of preclinical pathology. We built linear models for age based on multiple combined structural features using a large independent lifespan sample of 272 healthy adults across a wide age range from the Human Connectome Project Aging study. These models were then used to create a new support vector machine (SVM) training model in 136 AD and 268 control participants based on residues of fit from the expected age-effects relationship. Subsequent validation assessed the accuracy of the SVM model in new datasets. Finally, we applied the classifier to 276 individuals with MCI to evaluate prediction for early impairment and longitudinal cognitive change. The optimal 10-fold cross-validation accuracy was 93.07%, compared to 91.83% without age detrending. In the validation dataset, the classifier for AD obtained an accuracy of 84.85% (56/66), sensitivity of 85.36% (35/41) and specificity of 84% (21/25). Classification accuracy was improved when using the lifespan sample as opposed to the classification sample. Importantly, we observed cross-sectional greater AD specific biomarkers, as well as faster cognitive decline in MCI who were classified as more 'AD-like' (MCI-AD), and these effects were pronounced in individuals who were late MCI. The top five contributive features were volumes of left hippocampus, right hippocampus, left amygdala, the thickness of left and right middle temporal & parahippocampus gyrus. Linear detrending for age in SVM for combined structural features resulted in good performance for recognition of AD and AD-specific biomarkers, as well as prediction of MCI progression. Such procedures may be used in future work to enhance prediction in samples with atypical age distributions.
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Affiliation(s)
- Binyin Li
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Department of Neurology, Ruijin Hospital Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Miao Zhang
- Department of Nuclear Medicine, Ruijin Hospital Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Joost Riphagen
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Kathryn Morrison Yochim
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jun Liu
- Department of Neurology, Ruijin Hospital Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - David H Salat
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, MA, USA
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