1
|
Markett S, Boeken OJ, Wudarczyk OA. Multimodal imaging investigation of structural rich club alterations in Alzheimer's disease and mild cognitive impairment: Amyloid deposition, structural atrophy, and functional activation differences. Eur J Neurosci 2024. [PMID: 38779858 DOI: 10.1111/ejn.16384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 04/03/2024] [Accepted: 04/22/2024] [Indexed: 05/25/2024]
Abstract
Alzheimer's disease (AD) is characterized by significant cerebral dysfunction, including increased amyloid deposition, gray matter atrophy, and changes in brain function. The involvement of highly connected network hubs, known as the "rich club," in the pathology of the disease remains inconclusive despite previous research efforts. In this study, we aimed to systematically assess the link between the rich club and AD using a multimodal neuroimaging approach. We employed network analyses of diffusion magnetic resonance imaging (MRI), longitudinal assessments of gray matter atrophy, amyloid deposition measurements using positron emission tomography (PET) imaging, and meta-analytic data on functional activation differences. Our study focused on evaluating the role of both the structural brain network's core and extended rich club regions in individuals with mild cognitive impairment (MCI) and those diagnosed with AD. Our findings revealed that structural rich club regions exhibited accelerated gray matter atrophy and increased amyloid deposition in both MCI and AD. Importantly, these regions remained unaffected by altered functional activation patterns observed outside the core rich club regions. These results shed light on the connection between two major AD biomarkers and the rich club, providing valuable insights into AD as a potential disconnection syndrome.
Collapse
Affiliation(s)
| | - Ole J Boeken
- Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin, Berlin, Germany
| | | |
Collapse
|
2
|
Chen H, Xu J, Li W, Hu Z, Ke Z, Qin R, Xu Y. The characteristic patterns of individual brain susceptibility networks underlie Alzheimer's disease and white matter hyperintensity-related cognitive impairment. Transl Psychiatry 2024; 14:177. [PMID: 38575556 PMCID: PMC10994911 DOI: 10.1038/s41398-024-02861-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 03/04/2024] [Accepted: 03/06/2024] [Indexed: 04/06/2024] Open
Abstract
Excessive iron accumulation in the brain cortex increases the risk of cognitive deterioration. However, interregional relationships (defined as susceptibility connectivity) of local brain iron have not been explored, which could provide new insights into the underlying mechanisms of cognitive decline. Seventy-six healthy controls (HC), 58 participants with mild cognitive impairment due to probable Alzheimer's disease (MCI-AD) and 66 participants with white matter hyperintensity (WMH) were included. We proposed a novel approach to construct a brain susceptibility network by using Kullback‒Leibler divergence similarity estimation from quantitative susceptibility mapping and further evaluated its topological organization. Moreover, sparse logistic regression (SLR) was applied to classify MCI-AD from HC and WMH with normal cognition (WMH-NC) from WMH with MCI (WMH-MCI).The altered susceptibility connectivity in the MCI-AD patients indicated that relatively more connectivity was involved in the default mode network (DMN)-related and visual network (VN)-related connectivity, while more altered DMN-related and subcortical network (SN)-related connectivity was found in the WMH-MCI patients. For the HC vs. MCI-AD classification, the features selected by the SLR were primarily distributed throughout the DMN-related and VN-related connectivity (accuracy = 76.12%). For the WMH-NC vs. WMH-MCI classification, the features with high appearance frequency were involved in SN-related and DMN-related connectivity (accuracy = 84.85%). The shared and specific patterns of the susceptibility network identified in both MCI-AD and WMH-MCI may provide a potential diagnostic biomarker for cognitive impairment, which could enhance the understanding of the relationships between brain iron burden and cognitive decline from a network perspective.
Collapse
Affiliation(s)
- Haifeng Chen
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Jingxian Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Weikai Li
- School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, China
| | - Zheqi Hu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Zhihong Ke
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Ruomeng Qin
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China.
| |
Collapse
|
3
|
Chung MK, Azizi T, Hanson JL, Alexander AL, Pollak SD, Davidson RJ. Altered topological structure of the brain white matter in maltreated children through topological data analysis. Netw Neurosci 2024; 8:355-376. [PMID: 38711544 PMCID: PMC11073548 DOI: 10.1162/netn_a_00355] [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: 04/07/2023] [Accepted: 11/30/2023] [Indexed: 05/08/2024] Open
Abstract
Childhood maltreatment may adversely affect brain development and consequently influence behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter in maltreated and typically developing children. We perform topological data analysis (TDA) to assess the alteration in the global topology of the brain white matter structural covariance network among children. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging and diffusion tensor imaging. We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children with a typically developing control group, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly suggest that TDA can be a valuable framework to model altered topological structures of the brain. The MATLAB codes and processed data used in this study can be found at https://github.com/laplcebeltrami/maltreated.
Collapse
Affiliation(s)
- Moo K. Chung
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, WI, USA
| | - Tahmineh Azizi
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, WI, USA
| | - Jamie L. Hanson
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Andrew L. Alexander
- Department of Medical Physics, University of Wisconsin–Madison, Madison, WI, USA
| | - Seth D. Pollak
- Department of Psychology, University of Wisconsin–Madison, Madison, WI, USA
| | | |
Collapse
|
4
|
Zhou Y, Jing J, Zhang Z, Pan Y, Cai X, Zhu W, Li Z, Liu C, Liu H, Meng X, Cheng J, Wang Y, Li H, Wang S, Niu H, Wen W, Sachdev PS, Wei T, Liu T, Wang Y. Disrupted pattern of rich-club organization in structural brain network from prediabetes to diabetes: A population-based study. Hum Brain Mapp 2024; 45:e26598. [PMID: 38339955 PMCID: PMC10839741 DOI: 10.1002/hbm.26598] [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: 04/28/2023] [Revised: 12/22/2023] [Accepted: 01/04/2024] [Indexed: 02/12/2024] Open
Abstract
The network nature of the brain is gradually becoming a consensus in the neuroscience field. A set of highly connected regions in the brain network called "rich-club" are crucial high efficiency communication hubs in the brain. The abnormal rich-club organization can reflect underlying abnormal brain function and metabolism, which receives increasing attention. Diabetes is one of the risk factors for neurological diseases, and most individuals with prediabetes will develop overt diabetes within their lifetime. However, the gradual impact of hyperglycemia on brain structures, including rich-club organization, remains unclear. We hypothesized that the brain follows a special disrupted pattern of rich-club organization in prediabetes and diabetes. We used cross-sectional baseline data from the population-based PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study, which included 2218 participants with a mean age of 61.3 ± 6.6 years and 54.1% females comprising 1205 prediabetes, 504 diabetes, and 509 normal control subjects. The rich-club organization and network properties of the structural networks derived from diffusion tensor imaging data were investigated using a graph theory approach. Linear mixed models were used to assess associations between rich-club organization disruptions and the subjects' glucose status. Based on the graphical analysis methods, we observed the disrupted pattern of rich-club organization was from peripheral regions mainly located in frontal areas to rich-club regions mainly located in subcortical areas from prediabetes to diabetes. The rich-club organization disruptions were associated with elevated glucose levels. These findings provided more details of the process by which hyperglycemia affects the brain, contributing to a better understanding of the potential neurological consequences. Furthermore, the disrupted pattern observed in rich-club organization may serve as a potential neuroimaging marker for early detection and monitoring of neurological disorders in individuals with prediabetes or diabetes.
Collapse
Affiliation(s)
- Yijun Zhou
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Jing Jing
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Zhe Zhang
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Yuesong Pan
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Xueli Cai
- Department of Neurology, Lishui HospitalZhejiang University School of MedicineLishuiZhejiangChina
| | - Wanlin Zhu
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Zixiao Li
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Chang Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Hao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Xia Meng
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang UniversityBeijingChina
| | - Yilong Wang
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Hao Li
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Suying Wang
- Cerebrovascular Research Lab, Lishui Hospital, Zhejiang University School of MedicineLishuiZhejiangChina
| | - Haijun Niu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Wei Wen
- Division of Psychiatry and Mental Health, Faculty of Medicine and Health, Centre for Healthy Brain Ageing (CHeBA)UNSWSydneyNew South WalesAustralia
- Neuropsychiatric Institute, Prince of Wales HospitalSydneyNew South WalesAustralia
| | - Perminder S. Sachdev
- Division of Psychiatry and Mental Health, Faculty of Medicine and Health, Centre for Healthy Brain Ageing (CHeBA)UNSWSydneyNew South WalesAustralia
- Neuropsychiatric Institute, Prince of Wales HospitalSydneyNew South WalesAustralia
| | - Tiemin Wei
- Department of Cardiology, Lishui HospitalZhejiang University School of MedicineZhejiangChina
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Yongjun Wang
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
- Research Unit of Artificial Intelligence in Cerebrovascular DiseaseChinese Academy of Medical Sciences, 2019RU018BeijingChina
| |
Collapse
|
5
|
Guo F, Zhang T, Wang C, Xu Z, Chang Y, Zheng M, Fang P, Zhu Y. White matter structural topologic efficiency predicts individual resistance to sleep deprivation. CNS Neurosci Ther 2024; 30:e14349. [PMID: 37408437 PMCID: PMC10848061 DOI: 10.1111/cns.14349] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 06/05/2023] [Accepted: 06/24/2023] [Indexed: 07/07/2023] Open
Abstract
BACKGROUND Sleep deprivation (SD) is commonplace in modern society and there are large individual differences in the vulnerability to SD. We aim to identify the structural network differences based on diffusion tensor imaging (DTI) that contribute to the individual different vulnerability to SD. METHODS The number of psychomotor vigilance task (PVT) lapses was used to classify 49 healthy subjects on the basis of whether they were vulnerable or resistant to SD. DTI and graph theory approaches were used to investigate the topologic organization differences of the brain structural connectome between SD-vulnerable and -resistant individuals. We measured the level of global efficiency and clustering in rich club and non-rich club organizations. RESULTS We demonstrated that participants vulnerable to SD had less global efficiency, network strength, and local efficiency but longer shortest path length compared with participants resistant to SD. Lower efficiency was mainly distributed in the right insula, bilateral thalamus, bilateral frontal, temporal, and temporal lobes. Furthermore, a disrupted subnetwork was observed that consisted of widespread connections. Moreover, the vulnerable group showed significantly decreased strength of the rich club compared with the resistant group. The strength of rich club connectivity was found to be correlated negatively with PVT performance (r = -0.395, p = 0.005). We further tested the reliability of the results. CONCLUSION The findings revealed that individual differences in resistance to SD are related to disrupted topologic efficiency connectome pattern, and our study may provide potential connectome-based biomarkers for the early detection of the vulnerable degree to SD.
Collapse
Affiliation(s)
- Fan Guo
- Department of Radiology, Xijing HospitalAir Force Medical UniversityXi'anChina
| | - Tian Zhang
- Department of Military Medical PsychologyAir Force Medical UniversityXi'anChina
| | - Chen Wang
- Department of Radiology, Xijing HospitalAir Force Medical UniversityXi'anChina
| | - Ziliang Xu
- Department of Radiology, Xijing HospitalAir Force Medical UniversityXi'anChina
| | - Yingjuan Chang
- Department of Radiology, Xijing HospitalAir Force Medical UniversityXi'anChina
| | - Minwen Zheng
- Department of Radiology, Xijing HospitalAir Force Medical UniversityXi'anChina
| | - Peng Fang
- Department of Military Medical PsychologyAir Force Medical UniversityXi'anChina
| | - Yuanqiang Zhu
- Department of Radiology, Xijing HospitalAir Force Medical UniversityXi'anChina
| |
Collapse
|
6
|
Zhang M, Chen H, Huang W, Guo T, Ma G, Han Y, Shu N. Relationship between topological efficiency of white matter structural connectome and plasma biomarkers across the Alzheimer's disease continuum. Hum Brain Mapp 2024; 45:e26566. [PMID: 38224535 PMCID: PMC10785192 DOI: 10.1002/hbm.26566] [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: 08/23/2023] [Revised: 11/11/2023] [Accepted: 11/30/2023] [Indexed: 01/17/2024] Open
Abstract
Both plasma biomarkers and brain network topology have shown great potential in the early diagnosis of Alzheimer's disease (AD). However, the specific associations between plasma AD biomarkers, structural network topology, and cognition across the AD continuum have yet to be fully elucidated. This retrospective study evaluated participants from the Sino Longitudinal Study of Cognitive Decline cohort between September 2009 and October 2022 with available blood samples or 3.0-T MRI brain scans. Plasma biomarker levels were measured using the Single Molecule Array platform, including β-amyloid (Aβ), phosphorylated tau181 (p-tau181), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). The topological structure of brain white matter was assessed using network efficiency. Trend analyses were carried out to evaluate the alterations of the plasma markers and network efficiency with AD progression. Correlation and mediation analyses were conducted to further explore the relationships among plasma markers, network efficiency, and cognitive performance across the AD continuum. Among the plasma markers, GFAP emerged as the most sensitive marker (linear trend: t = 11.164, p = 3.59 × 10-24 ; quadratic trend: t = 7.708, p = 2.25 × 10-13 ; adjusted R2 = 0.475), followed by NfL (linear trend: t = 6.542, p = 2.9 × 10-10 ; quadratic trend: t = 3.896, p = 1.22 × 10-4 ; adjusted R2 = 0.330), p-tau181 (linear trend: t = 8.452, p = 1.61 × 10-15 ; quadratic trend: t = 6.316, p = 1.05 × 10-9 ; adjusted R2 = 0.346) and Aβ42/Aβ40 (linear trend: t = -3.257, p = 1.27 × 10-3 ; quadratic trend: t = -1.662, p = 9.76 × 10-2 ; adjusted R2 = 0.101). Local efficiency decreased in brain regions across the frontal and temporal cortex and striatum. The principal component of local efficiency within these regions was correlated with GFAP (Pearson's R = -0.61, p = 6.3 × 10-7 ), NfL (R = -0.57, p = 6.4 × 10-6 ), and p-tau181 (R = -0.48, p = 2.0 × 10-4 ). Moreover, network efficiency mediated the relationship between general cognition and GFAP (ab = -0.224, 95% confidence interval [CI] = [-0.417 to -0.029], p = .0196 for MMSE; ab = -0.198, 95% CI = [-0.42 to -0.003], p = .0438 for MOCA) or NfL (ab = -0.224, 95% CI = [-0.417 to -0.029], p = .0196 for MMSE; ab = -0.198, 95% CI = [-0.42 to -0.003], p = .0438 for MOCA). Our findings suggest that network efficiency mediates the association between plasma biomarkers, specifically GFAP and NfL, and cognitive performance in the context of AD progression, thus highlighting the potential utility of network-plasma approaches for early detection, monitoring, and intervention strategies in the management of AD.
Collapse
Affiliation(s)
- Mingkai Zhang
- Department of NeurologyXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Haojie Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
- BABRI CentreBeijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and ConnectomicsBeijing Normal UniversityBeijingChina
| | - Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
- BABRI CentreBeijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and ConnectomicsBeijing Normal UniversityBeijingChina
| | - Tengfei Guo
- Institute of Biomedical EngineeringShenzhen Bay LaboratoryShenzhenChina
| | - Guolin Ma
- Department of RadiologyChina‐Japan Friendship HospitalBeijingChina
| | - Ying Han
- Department of NeurologyXuanwu Hospital, Capital Medical UniversityBeijingChina
- Institute of Biomedical EngineeringShenzhen Bay LaboratoryShenzhenChina
- School of Biomedical EngineeringHainan UniversityHaikouChina
- National Clinical Research Center for Geriatric DiseasesBeijingChina
- Center of Alzheimer's DiseaseBeijing Institute for Brain DisordersBeijingChina
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
- BABRI CentreBeijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and ConnectomicsBeijing Normal UniversityBeijingChina
| |
Collapse
|
7
|
Chung MK, Azizi T, Hanson JL, Alexander AL, Davidson RJ, Pollak SD. Altered Topological Structure of the Brain White Matter in Maltreated Children through Topological Data Analysis. ARXIV 2023:arXiv:2304.05908v3. [PMID: 37090232 PMCID: PMC10120754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Childhood maltreatment may adversely affect brain development and consequently influence behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter in maltreated and typically developing children. We perform topological data analysis (TDA) to assess the alteration in the global topology of the brain white-matter structural covariance network among children. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children to a typically developing control group, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly suggest that TDA can be a valuable framework to model altered topological structures of the brain. The MATLAB codes and processed data used in this study can be found at https://github.com/laplcebeltrami/maltreated.
Collapse
Affiliation(s)
- Moo K. Chung
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, USA
| | - Tahmineh Azizi
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, USA
| | | | | | | | - Seth D. Pollak
- Department of Psychology, University of Wisconsin-Madison, USA
| |
Collapse
|
8
|
Tu JC, Millar PR, Strain JF, Eck A, Adeyemo B, Daniels A, Karch C, Huey ED, McDade E, Day GS, Yakushev I, Hassenstab J, Morris J, Llibre-Guerra JJ, Ibanez L, Jucker M, Mendez PC, Bateman RJ, Perrin RJ, Benzinger T, Jack CR, Betzel R, Ances BM, Eggebrecht AT, Gordon BA, Wheelock MD. Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.29.564633. [PMID: 37961586 PMCID: PMC10634945 DOI: 10.1101/2023.10.29.564633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer Disease (AD). Given their essential role in neural communication, disruptions to these hubs have profound implications for overall brain network integrity and functionality. Hub disruption, or targeted impairment of functional connectivity at the hubs, is recognized in AD patients. Computational models paired with evidence from animal experiments hint at a mechanistic explanation, suggesting that these hubs may be preferentially targeted in neurodegeneration, due to their high neuronal activity levels-a phenomenon termed "activity-dependent degeneration". Yet, two critical issues were unresolved. First, past research hasn't definitively shown whether hub regions face a higher likelihood of impairment (targeted attack) compared to other regions or if impairment likelihood is uniformly distributed (random attack). Second, human studies offering support for activity-dependent explanations remain scarce. We applied a refined hub disruption index to determine the presence of targeted attacks in AD. Furthermore, we explored potential evidence for activity-dependent degeneration by evaluating if hub vulnerability is better explained by global connectivity or connectivity variations across functional systems, as well as comparing its timing relative to amyloid beta deposition in the brain. Our unique cohort of participants with autosomal dominant Alzheimer Disease (ADAD) allowed us to probe into the preclinical stages of AD to determine the hub disruption timeline in relation to expected symptom emergence. Our findings reveal a hub disruption pattern in ADAD aligned with targeted attacks, detectable even in pre-clinical stages. Notably, the disruption's severity amplified alongside symptomatic progression. Moreover, since excessive local neuronal activity has been shown to increase amyloid deposition and high connectivity regions show high level of neuronal activity, our observation that hub disruption was primarily tied to regional differences in global connectivity and sequentially followed changes observed in Aβ PET cortical markers is consistent with the activity-dependent degeneration model. Intriguingly, these disruptions were discernible 8 years before the expected age of symptom onset. Taken together, our findings not only align with the targeted attack on hubs model but also suggest that activity-dependent degeneration might be the cause of hub vulnerability. This deepened understanding could be instrumental in refining diagnostic techniques and developing targeted therapeutic strategies for AD in the future.
Collapse
Affiliation(s)
- Jiaxin Cindy Tu
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Peter R Millar
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Jeremy F Strain
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Andrew Eck
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Babatunde Adeyemo
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Alisha Daniels
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Celeste Karch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Edward D Huey
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, 02912
| | - Eric McDade
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Gregory S Day
- Department of Neurology, Mayo Clinic College of Medicine, Jacksonville, FL, USA, 32224
| | - Igor Yakushev
- Department of Nuclear Medicine, Technical University of Munich, Munich, Germany, 81675
| | - Jason Hassenstab
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - John Morris
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Jorge J Llibre-Guerra
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Laura Ibanez
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA, 63108
- NeuroGenomics and Informatics Center, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Mathias Jucker
- Department of Cellular Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany, 72076
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany, 72076
| | | | - Randell J Bateman
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Richard J Perrin
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Tammie Benzinger
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, USA 55905
| | - Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN USA, 47405
| | - Beau M Ances
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Adam T Eggebrecht
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Brian A Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| | - Muriah D Wheelock
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA, 63108
| |
Collapse
|
9
|
Kim SJ, Bae YJ, Park YH, Jang H, Kim JP, Seo SW, Seong JK, Kim GH. Sex differences in the structural rich-club connectivity in patients with Alzheimer's disease. Front Aging Neurosci 2023; 15:1209027. [PMID: 37771522 PMCID: PMC10525353 DOI: 10.3389/fnagi.2023.1209027] [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: 04/20/2023] [Accepted: 08/24/2023] [Indexed: 09/30/2023] Open
Abstract
Background and objectives Alzheimer's disease (AD) is more prevalent in women than in men; however, there is a discrepancy in research on sex differences in AD. The human brain is a large-scale network with hub regions forming a central core, the rich-club, which is vital to cognitive functions. However, it is unknown whether alterations in the rich-clubs in AD differ between men and women. We aimed to investigate sex differences in the rich-club organization in the brains of patients with AD. Methods In total, 260 cognitively unimpaired individuals with negative amyloid positron emission tomography (PET) scans, 281 with prodromal AD (mild cognitive impairment due to AD) and 285 with AD dementia who confirmed with positive amyloid PET scans participated in the study. We obtained high-resolution T1-weighted and diffusion tensor images and performed network analysis. Results We observed sex differences in the rich-club and feeder connections in patients with AD, suggesting lower structural connectivity strength in women than in men. We observed a significant group-by-sex interaction in the feeder connections, particularly in the thalamus. In addition, the connectivity strength of the thalamus in the feeder connections was significantly correlated with general cognitive function in only men with prodromal AD and women with AD dementia. Conclusion Our findings provide important evidence for sex-specific alterations in the structural brain network related to AD.
Collapse
Affiliation(s)
- Soo-Jong Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Youn Jung Bae
- School of Biomedical Engineering, Korea University, Seoul, Republic of Korea
| | - Yu Hyun Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Joon-Kyung Seong
- School of Biomedical Engineering, Korea University, Seoul, Republic of Korea
- Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea
| | - Geon Ha Kim
- Department of Neurology, Ewha Womans University College of Medicine, Seoul, Republic of Korea
| |
Collapse
|
10
|
Amoroso N, Quarto S, La Rocca M, Tangaro S, Monaco A, Bellotti R. An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease. Front Aging Neurosci 2023; 15:1238065. [PMID: 37719873 PMCID: PMC10501457 DOI: 10.3389/fnagi.2023.1238065] [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: 06/10/2023] [Accepted: 08/08/2023] [Indexed: 09/19/2023] Open
Abstract
The advent of eXplainable Artificial Intelligence (XAI) has revolutionized the way human experts, especially from non-computational domains, approach artificial intelligence; this is particularly true for clinical applications where the transparency of the results is often compromised by the algorithmic complexity. Here, we investigate how Alzheimer's disease (AD) affects brain connectivity within a cohort of 432 subjects whose T1 brain Magnetic Resonance Imaging data (MRI) were acquired within the Alzheimer's Disease Neuroimaging Initiative (ADNI). In particular, the cohort included 92 patients with AD, 126 normal controls (NC) and 214 subjects with mild cognitive impairment (MCI). We show how graph theory-based models can accurately distinguish these clinical conditions and how Shapley values, borrowed from game theory, can be adopted to make these models intelligible and easy to interpret. Explainability analyses outline the role played by regions like putamen, middle and superior temporal gyrus; from a class-related perspective, it is possible to outline specific regions, such as hippocampus and amygdala for AD and posterior cingulate and precuneus for MCI. The approach is general and could be adopted to outline how brain connectivity affects specific brain regions.
Collapse
Affiliation(s)
- Nicola Amoroso
- Dipartimento di Farmacia-Scienze del Farmaco, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Silvano Quarto
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Marianna La Rocca
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| |
Collapse
|
11
|
Abbate C. The Adult Neurogenesis Theory of Alzheimer's Disease. J Alzheimers Dis 2023:JAD221279. [PMID: 37182879 DOI: 10.3233/jad-221279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Alzheimer's disease starts in neural stem cells (NSCs) in the niches of adult neurogenesis. All primary factors responsible for pathological tau hyperphosphorylation are inherent to adult neurogenesis and migration. However, when amyloid pathology is present, it strongly amplifies tau pathogenesis. Indeed, the progressive accumulation of extracellular amyloid-β deposits in the brain triggers a state of chronic inflammation by microglia. Microglial activation has a significant pro-neurogenic effect that fosters the process of adult neurogenesis and supports neuronal migration. Unfortunately, this "reactive" pro-neurogenic activity ultimately perturbs homeostatic equilibrium in the niches of adult neurogenesis by amplifying tau pathogenesis in AD. This scenario involves NSCs in the subgranular zone of the hippocampal dentate gyrus in late-onset AD (LOAD) and NSCs in the ventricular-subventricular zone along the lateral ventricles in early-onset AD (EOAD), including familial AD (FAD). Neuroblasts carrying the initial seed of tau pathology travel throughout the brain via neuronal migration driven by complex signals and convey the disease from the niches of adult neurogenesis to near (LOAD) or distant (EOAD) brain regions. In these locations, or in close proximity, a focus of degeneration begins to develop. Then, tau pathology spreads from the initial foci to large neuronal networks along neural connections through neuron-to-neuron transmission.
Collapse
Affiliation(s)
- Carlo Abbate
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| |
Collapse
|
12
|
Yang Z, Chen Y, Hou X, Xu Y, Bai F. Topologically convergent and divergent large scale complex networks among Alzheimer's disease spectrum patients: A systematic review. Heliyon 2023; 9:e15389. [PMID: 37101638 PMCID: PMC10123263 DOI: 10.1016/j.heliyon.2023.e15389] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 03/16/2023] [Accepted: 04/05/2023] [Indexed: 04/28/2023] Open
Abstract
Alzheimer's disease (AD) is associated with disruption at the level of a large-scale complex network. To explore the underlying mechanisms in the progression of AD, graph theory was used to quantitatively analyze the topological properties of structural and functional connections. Although an increasing number of studies have shown altered global and nodal network properties, little is known about the topologically convergent and divergent patterns between structural and functional networks among AD-spectrum patients. In this review, we summarized the topological patterns of the large-scale complex networks using multimodal neuroimaging graph theory analysis in AD spectrum patients. Convergent deficits in the connectivity characteristics were primarily in the default mode network (DMN) itself both in the structural and functional networks, while a divergent changes in the neighboring regions of the DMN were also observed between the patient groups. Together, the application of graph theory to large-scale complex brain networks provides quantitative insights into topological principles of brain network organization, which may lead to increasing attention in identifying the underlying neuroimaging pathological changes and predicting the progression of AD.
Collapse
Affiliation(s)
- Zhiyuan Yang
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Ya Chen
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing 210008, China
| | - Xinle Hou
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
- Department of Neurology, Nanjing Drum Tower Hospital, State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing 210008, China
| | - Feng Bai
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
- Geriatric Medicine Center, Affiliated Taikang Xianlin Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
- Correspondence to: 321 Zhongshan Road, Nanjing, 210008, China.
| |
Collapse
|
13
|
Wei L, Du X, Yang Z, Ding M, Yang B, Wang J, Long S, Qiao Z, Jiang Y, Wang Y, Wang H. Disrupted Topological Organization of White Matter Network in Angelman Syndrome. J Magn Reson Imaging 2023; 57:1212-1221. [PMID: 35856797 DOI: 10.1002/jmri.28360] [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: 04/13/2022] [Revised: 07/03/2022] [Accepted: 07/05/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Angelman syndrome (AS) is a genetic disorder that affects neurodevelopment. The investigation of changes in the brain white matter network, which would contribute to a better understanding of the pathogenesis of AS brain, was lacking. PURPOSE To investigate both local and global alterations of white matter in patients with AS. STUDY TYPE Prospective. SUBJECTS A total of 29 AS patients (6.6 ± 1.4 years, 15 [52%] females) and 19 age-matched healthy controls (HC) (7.0 ± 1.5 years, 10 [53%] females). FIELD STRENGTH/SEQUENCE A 3-T, three-dimensional (3D) T1-weighted imaging by using gradient-echo-based sequence, single shell diffusion tensor imaging by using spin-echo-based echo-planar imaging. ASSESSMENT Network metrics including global efficiency (Eg ), local efficiency (Eloc ), small world coefficient (Swc), rich-club coefficient (Φ), and nodal degree (ND) were estimated from diffusion MR (dMR) data. Connections among highly connected (hub) regions and less connected (peripheral) regions were also assessed. Correlation between the topological parameters and age for each group was also calculated to assess the development of the brain. STATISTICAL TESTS Linear regression model, permutation test. P values estimated from the regression model for each brain region were adjusted by false discovery rate (FDR) correction. RESULTS AS patients showed significantly lower Eg and higher swc compared to HC. Φn significantly increased at higher k-levels in AS patients. In addition, the connections among hub regions and peripheral regions were significantly interrupted in AS patients. DATA CONCLUSION The AS brain showed diminished connectivity, reflected by reduced network efficiency compared to HC. Compared to densely connected regions, less connected regions were more vulnerable in AS. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 3.
Collapse
Affiliation(s)
- Lei Wei
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Xiaonan Du
- Department of Neurology, Children's Hospital of Fudan University, Shanghai, China
| | - Zidong Yang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Ming Ding
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Baofeng Yang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Ji Wang
- Department of Neurology, Children's Hospital of Fudan University, Shanghai, China
| | - Shasha Long
- Department of Neurology, Children's Hospital of Fudan University, Shanghai, China
| | - Zhongwei Qiao
- Department of Radiology, Children's Hospital of Fudan University, Shanghai, China
| | - Yonghui Jiang
- Department of Genetics, Yale School of Medicine, New Haven, Connecticut, USA
| | - Yi Wang
- Department of Neurology, Children's Hospital of Fudan University, Shanghai, China
| | - He Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China.,Human Phenome Institute, Fudan University, Shanghai, China
| |
Collapse
|
14
|
Chen Y, Wang Y, Song Z, Fan Y, Gao T, Tang X. Abnormal white matter changes in Alzheimer's disease based on diffusion tensor imaging: A systematic review. Ageing Res Rev 2023; 87:101911. [PMID: 36931328 DOI: 10.1016/j.arr.2023.101911] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 03/01/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023]
Abstract
Alzheimer's disease (AD) is a degenerative neurological disease in elderly individuals. Subjective cognitive decline (SCD), mild cognitive impairment (MCI) and further development to dementia (d-AD) are considered to be major stages of the progressive pathological development of AD. Diffusion tensor imaging (DTI), one of the most important modalities of MRI, can describe the microstructure of white matter through its tensor model. It is widely used in understanding the central nervous system mechanism and finding appropriate potential biomarkers for the early stages of AD. Based on the multilevel analysis methods of DTI (voxelwise, fiberwise and networkwise), we summarized that AD patients mainly showed extensive microstructural damage, structural disconnection and topological abnormalities in the corpus callosum, fornix, and medial temporal lobe, including the hippocampus and cingulum. The diffusion features and structural connectomics of specific regions can provide information for the early assisted recognition of AD. The classification accuracy of SCD and normal controls can reach 92.68% at present. And due to the further changes of brain structure and function, the classification accuracy of MCI, d-AD and normal controls can reach more than 97%. Finally, we summarized the limitations of current DTI-based AD research and propose possible future research directions.
Collapse
Affiliation(s)
- Yu Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yifei Wang
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Zeyu Song
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yingwei Fan
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Tianxin Gao
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
| | - Xiaoying Tang
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China; School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
| |
Collapse
|
15
|
Peng L, Chen Z, Gao X. Altered rich-club organization of brain functional network in autism spectrum disorder. Biofactors 2023. [PMID: 36785880 DOI: 10.1002/biof.1933] [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: 12/06/2022] [Accepted: 12/20/2022] [Indexed: 02/15/2023]
Abstract
Despite numerous research showing the association between brain network abnormalities and autism spectrum disorder (ASD), contrasting findings have been reported from broad functional underconnectivity to broad overconnectivity. Thus, the significance of rich-hub organizations in the brain functional connectome of individuals with ASD remains largely unknown. High-quality data subset of ASD (n = 45) and healthy controls (HC; n = 47) children (7-15 years old) were retrieved from the ABIDE data set, and rich-club organization and network-based statistic (NBS) were assessed from resting-state functional magnetic resonance imaging (rs-fMRI). The rich-club organization functional network (normalized rich-club coefficients >1) was observed in all subjects under a range of thresholds. Compared with HC, ASD patients had higher degree of feeder connections and lower degree of local connections (degree of feeder connections: ASD = 259.20 ± 32.97, HC = 244.98 ± 30.09, p = 0.041; degree of local connections: ASD = 664.02 ± 39.19, HC = 679.89 ± 34.05, p = 0.033) but had similar in rich-club connections. Further, nonparametric NBS analysis showed the presence of abnormal connectivity in the functional network of ASD individuals. Our findings indicated that local connection might be more vulnerable, and feeder connection may compensate for its disruption in ASD, enhancing our understanding on the mechanism of functional connectome dysfunction in ASD.
Collapse
Affiliation(s)
- Liling Peng
- Department of PET/MR, Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, People's Republic of China
| | - Zhuang Chen
- Department of Cardiology, The Fifth People's Hospital of Jinan, Jinan, Shandong, People's Republic of China
| | - Xin Gao
- Department of PET/MR, Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, People's Republic of China
| |
Collapse
|
16
|
EEG emotion recognition based on PLV-rich-club dynamic brain function network. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04366-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
|
17
|
Yang D, Chen J, Yan C, Kim M, Laurienti PJ, Styner M, Wu G. Group-Wise Hub Identification by Learning Common Graph Embeddings on Grassmannian Manifold. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022; 44:8249-8260. [PMID: 34010126 PMCID: PMC9596171 DOI: 10.1109/tpami.2021.3081744] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Human brain is a complex yet economically organized system, where a small portion of critical hub regions support the majority of brain functions. The identification of common hub nodes in a population of networks is often simplified as a voting procedure on the set of identified hub nodes across individual brain networks, which ignores the intrinsic data geometry and partially lacks the reproducible findings in neuroscience. Hence, we propose a first-ever group-wise hub identification method to identify hub nodes that are common across a population of individual brain networks. Specifically, the backbone of our method is to learn common graph embedding that can represent the majority of local topological profiles. By requiring orthogonality among the graph embedding vectors, each graph embedding as a data element is residing on the Grassmannian manifold. We present a novel Grassmannian manifold optimization scheme that allows us to find the common graph embeddings, which not only identify the most reliable hub nodes in each network but also yield a population-based common hub node map. Results of the accuracy and replicability on both synthetic and real network data show that the proposed manifold learning approach outperforms all hub identification methods employed in this evaluation.
Collapse
|
18
|
Huisman SI, van der Boog ATJ, Cialdella F, Verhoeff JJC, David S. Quantifying the post-radiation accelerated brain aging rate in glioma patients with deep learning. Radiother Oncol 2022; 175:18-25. [PMID: 35963398 DOI: 10.1016/j.radonc.2022.08.002] [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: 02/21/2022] [Revised: 07/12/2022] [Accepted: 08/01/2022] [Indexed: 10/15/2022]
Abstract
BACKGROUND AND PURPOSE Changes of healthy appearing brain tissue after radiotherapy (RT) have been previously observed. Patients undergoing RT may have a higher risk of cognitive decline, leading to a reduced quality of life. The experienced tissue atrophy is similar to the effects of normal aging in healthy individuals. We propose a new way to quantify tissue changes after cranial RT as accelerated brain aging using the BrainAGE framework. MATERIALS AND METHODS BrainAGE was applied to longitudinal MRI scans of 32 glioma patients. Utilizing a pre-trained deep learning model, brain age is estimated for all patients' pre-radiotherapy planning and follow-up MRI scans to acquire a quantification of the changes occurring in the brain over time. Saliency maps were extracted from the model to spatially identify which areas of the brain the deep learning model weighs highest for predicting age. The predicted ages from the deep learning model were used in a linear mixed effects model to quantify aging of patients after RT. RESULTS The linear mixed effects model resulted in an accelerated aging rate of 2.78 years/year, a significant increase over a normal aging rate of 1 (p < 0.05, confidence interval = 2.54-3.02). Furthermore, the saliency maps showed numerous anatomically well-defined areas, e.g.: Heschl's gyrus among others, determined by the model as important for brain age prediction. CONCLUSION We found that patients undergoing RT are affected by significant post-radiation accelerated aging, with several anatomically well-defined areas contributing to this aging. The estimated brain age could provide a method for quantifying quality of life post-radiotherapy.
Collapse
Affiliation(s)
- Selena I Huisman
- Department of Radiation Oncology, UMC Utrecht, 3584 CX Utrecht, The Netherlands.
| | | | - Fia Cialdella
- Department of Radiation Oncology, UMC Utrecht, 3584 CX Utrecht, The Netherlands; Department of Medical Oncology, UMC Utrecht, 3584 CX Utrecht, The Netherlands.
| | - Joost J C Verhoeff
- Department of Radiation Oncology, UMC Utrecht, 3584 CX Utrecht, The Netherlands.
| | - Szabolcs David
- Department of Radiation Oncology, UMC Utrecht, 3584 CX Utrecht, The Netherlands.
| |
Collapse
|
19
|
Li F, Liu Y, Lu L, Shang S, Chen H, Haidari NA, Wang P, Yin X, Chen YC. Rich-club reorganization of functional brain networks in acute mild traumatic brain injury with cognitive impairment. Quant Imaging Med Surg 2022; 12:3932-3946. [PMID: 35782237 PMCID: PMC9246720 DOI: 10.21037/qims-21-915] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 03/30/2022] [Indexed: 06/12/2024]
Abstract
BACKGROUND Mild traumatic brain injury (mTBI) is typically characterized by temporally limited cognitive impairment and regarded as a brain connectome disorder. Recent findings have suggested that a higher level of organization named the "rich-club" may play a central role in enabling the integration of information and efficient communication across different systems of the brain. However, the alterations in rich-club organization and hub topology in mTBI and its relationship with cognitive impairment after mTBI have been scarcely elucidated. METHODS Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 88 patients with mTBI and 85 matched healthy controls (HCs). Large-scale functional brain networks were established for each participant. Rich-club organizations and network properties were assessed and analyzed between groups. Finally, we analyzed the correlations between the cognitive performance and changes in rich-club organization and network properties. RESULTS Both mTBI and HCs groups showed significant rich-club organization. Meanwhile, the rich-club organization was aberrant, with enhanced functional connectivity (FC) among rich-club nodes and peripheral regions in acute mTBI. In addition, significant differences in partial global and local network topological property measures were found between mTBI patients and HCs (P<0.01). In patients with mTBI, changes in rich-club organization and network properties were found to be related to early cognitive impairment after mTBI (P<0.05). CONCLUSIONS Our findings suggest that such patterns of disruption and reorganization will provide the basic functional architecture for cognitive function, which may subsequently be used as an earlier biomarker for cognitive impairment after mTBI.
Collapse
Affiliation(s)
| | | | - Liyan Lu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Song’an Shang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Huiyou Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Nasir Ahmad Haidari
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Peng Wang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | | |
Collapse
|
20
|
Aili X, Wang W, Zhang A, Jiao Z, Li X, Rao B, Li R, Li H. Rich-Club Analysis of Structural Brain Network Alterations in HIV Positive Patients With Fully Suppressed Plasma Viral Loads. Front Neurol 2022; 13:825177. [PMID: 35812120 PMCID: PMC9263507 DOI: 10.3389/fneur.2022.825177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 05/23/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveEven with successful combination antiretroviral therapy (cART), patients with human immunodeficiency virus positive (HIV+) continue to present structural alterations and neuropsychological impairments. The purpose of this study is to investigate structural brain connectivity alterations and identify the hub regions in HIV+ patients with fully suppressed plasma viral loads.MethodsIn this study, we compared the brain structural connectivity in 48 patients with HIV+ treated with a combination of antiretroviral therapy and 48 healthy controls, using diffusion tensor imaging. Further comparisons were made in 24 patients with asymptomatic neurocognitive impairment (ANI) and 24 individuals with non-HIV-associated neurocognitive disorders forming a subset of HIV+ patients. The graph theory model was used to establish the topological metrics. Rich-club analysis was used to identify hub nodes across groups and abnormal rich-club connections. Correlations of connectivity metrics with cognitive performance and clinical variables were investigated as well.ResultsAt the regional level, HIV+ patients demonstrated lower degree centrality (DC), betweenness centrality (BC), and nodal efficiency (NE) at the occipital lobe and the limbic cortex; and increased BC and nodal cluster coefficient (NCC) in the occipital lobe, the frontal lobe, the insula, and the thalamus. The ANI group demonstrated a significant reduction in the DC, NCC, and NE in widespread brain regions encompassing the occipital lobe, the frontal lobe, the temporal pole, and the limbic system. These results did not survive the Bonferroni correction. HIV+ patients and the ANI group had similar hub nodes that were mainly located in the occipital lobe and subcortical regions. The abnormal connections were mainly located in the occipital lobe in the HIV+ group and in the parietal lobe in the ANI group. The BC in the calcarine fissure was positively correlated with complex motor skills. The disease course was negatively correlated with NE in the middle occipital gyrus.ConclusionThe results suggest that the occipital lobe and the subcortical regions may be important in structural connectivity alterations and cognitive impairment. Rich-club analysis may contribute to our understanding of the neuropathology of HIV-associated neurocognitive disorders.
Collapse
Affiliation(s)
- Xire Aili
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Wei Wang
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Aidong Zhang
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Zengxin Jiao
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Xing Li
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Bo Rao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China
- Bo Rao
| | - Ruili Li
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Ruili Li
| | - Hongjun Li
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
- *Correspondence: Hongjun Li
| |
Collapse
|
21
|
Zhou Y, Si X, Chao YP, Chen Y, Lin CP, Li S, Zhang X, Sun Y, Ming D, Li Q. Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network. Front Aging Neurosci 2022; 14:866230. [PMID: 35774112 PMCID: PMC9237212 DOI: 10.3389/fnagi.2022.866230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/21/2022] [Indexed: 11/13/2022] Open
Abstract
Background Detection of mild cognitive impairment (MCI) is essential to screen high risk of Alzheimer’s disease (AD). However, subtle changes during MCI make it challenging to classify in machine learning. The previous pathological analysis pointed out that the hippocampus is the critical hub for the white matter (WM) network of MCI. Damage to the white matter pathways around the hippocampus is the main cause of memory decline in MCI. Therefore, it is vital to biologically extract features from the WM network driven by hippocampus-related regions to improve classification performance. Methods Our study proposes a method for feature extraction of the whole-brain WM network. First, 42 MCI and 54 normal control (NC) subjects were recruited using diffusion tensor imaging (DTI), resting-state functional magnetic resonance imaging (rs-fMRI), and T1-weighted (T1w) imaging. Second, mean diffusivity (MD) and fractional anisotropy (FA) were calculated from DTI, and the whole-brain WM networks were obtained. Third, regions of interest (ROIs) with significant functional connectivity to the hippocampus were selected for feature extraction, and the hippocampus (HIP)-related WM networks were obtained. Furthermore, the rank sum test with Bonferroni correction was used to retain significantly different connectivity between MCI and NC, and significant HIP-related WM networks were obtained. Finally, the classification performances of these three WM networks were compared to select the optimal feature and classifier. Results (1) For the features, the whole-brain WM network, HIP-related WM network, and significant HIP-related WM network are significantly improved in turn. Also, the accuracy of MD networks as features is better than FA. (2) For the classification algorithm, the support vector machine (SVM) classifier with radial basis function, taking the significant HIP-related WM network in MD as a feature, has the optimal classification performance (accuracy = 89.4%, AUC = 0.954). (3) For the pathologic mechanism, the hippocampus and thalamus are crucial hubs of the WM network for MCI. Conclusion Feature extraction from the WM network driven by hippocampus-related regions provides an effective method for the early diagnosis of AD.
Collapse
Affiliation(s)
- Yu Zhou
- School of Microelectronics, Tianjin University, Tianjin, China
| | - Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
- Institute of Applied Psychology, Tianjin University, Tianjin, China
- *Correspondence: Xiaopeng Si,
| | - Yi-Ping Chao
- Graduate Institute of Biomedical Engineering, Chang Gung University, Taoyuan, Taiwan
- Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan
| | - Yuanyuan Chen
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Sicheng Li
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Xingjian Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Yulin Sun
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
- Dong Ming,
| | - Qiang Li
- School of Microelectronics, Tianjin University, Tianjin, China
- Qiang Li,
| |
Collapse
|
22
|
Zhou DA, Xu K, Zhao X, Chen Q, Sang F, Fan D, Su L, Zhang Z, Ai L, Chen Y. Spatial Distribution and Hierarchical Clustering of β-Amyloid and Glucose Metabolism in Alzheimer’s Disease. Front Aging Neurosci 2022; 14:788567. [PMID: 35734543 PMCID: PMC9207533 DOI: 10.3389/fnagi.2022.788567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 05/09/2022] [Indexed: 11/24/2022] Open
Abstract
Increased amyloid burden and decreased glucose metabolism are important characteristics of Alzheimer’s disease (AD), but their spatial distribution and hierarchical clustering organization are still poorly understood. In this study, we explored the distribution and clustering organization of amyloid and glucose metabolism based on 18F-florbetapir and 18F-fluorodeoxyglucose PET data from 68 AD patients and 20 cognitively normal individuals. We found that: (i) cortical regions with highest florbetapir binding were the regions with high glucose metabolism; (ii) the percentage changes of amyloid deposition were greatest in the frontal and temporal areas, and the hypometabolism was greatest in the parietal and temporal areas; (iii) brain areas can be divided into three hierarchical clusters by amyloid and into five clusters by metabolism using a hierarchical clustering approach, indicating that adjacent regions are more likely to be grouped into one sub-network; and (iv) there was a significant positive correlation in any pair of amyloid-amyloid and metabolism-metabolism sub-networks, and a significant negative correlation in amyloid-metabolism sub-networks. This may suggest that the influence forms and brain regions of AD on different pathological markers may not be synchronous, but they are closely related.
Collapse
Affiliation(s)
- Da-An Zhou
- Department of Rehabilitation, The Third Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Kai Xu
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Xiaobin Zhao
- Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Qian Chen
- Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Feng Sang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Di Fan
- Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Li Su
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Lin Ai
- Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- *Correspondence: Lin Ai,
| | - Yaojing Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Yaojing Chen,
| |
Collapse
|
23
|
Fu Z, Zhao M, He Y, Wang X, Li X, Kang G, Han Y, Li S. Aberrant topological organization and age-related differences in the human connectome in subjective cognitive decline by using regional morphology from magnetic resonance imaging. Brain Struct Funct 2022; 227:2015-2033. [PMID: 35579698 DOI: 10.1007/s00429-022-02488-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 03/24/2022] [Indexed: 11/25/2022]
Abstract
Subjective cognitive decline (SCD) is characterized by self-experienced deficits in cognitive capacity with normal performance in objective cognitive tests. Previous structural covariance studies showed specific insights into understanding the structural alterations of the brain in neurodegenerative diseases. Moreover, in subjects with neurodegenerative diseases, accelerated brain degeneration with aging was shown. However, the age-related variations in coordinated topological patterns of morphological networks in individuals with SCD remain poorly understood. In this study, 77 individual morphological networks were constructed, including 42 normal controls (NCs) and 35 SCD individuals, from structural magnetic resonance imaging (sMRI). A stepwise linear regression model and partial correlation analysis were constructed to evaluate the differences in age-related alterations of the network properties in individuals with SCD compared with NCs. Compared with NC, the properties of integration and segregation in individuals with SCD were lower, and the aberrant metrics were negatively correlated with age in SCD. The rich-club connections persevered, but the paralimbic system connections were disrupted in individuals with SCD compared with NCs. In addition, age-related differences in nodal global efficiency are distributed mainly in prefrontal cortex regions. In conclusion, the age-related disruption of topological organizations in individuals with SCD may indicate that the degeneration of brain efficiency with aging was accelerated in individuals with SCD.
Collapse
Affiliation(s)
- Zhenrong Fu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical 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
| | - Yirong He
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China
| | - Xuetong Wang
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China
| | - Xin Li
- School of Electrical Engineering, Yanshan University, Qinhuangdao, China
- Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, China
| | - Guixia Kang
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, 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
- Biomedical Engineering Institute, Hainan University, Haikou, China
- National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Shuyu Li
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China.
| |
Collapse
|
24
|
Chen Z, Ye N, Teng C, Li X. Alternations and Applications of the Structural and Functional Connectome in Gliomas: A Mini-Review. Front Neurosci 2022; 16:856808. [PMID: 35478847 PMCID: PMC9035851 DOI: 10.3389/fnins.2022.856808] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 02/28/2022] [Indexed: 12/12/2022] Open
Abstract
In the central nervous system, gliomas are the most common, but complex primary tumors. Genome-based molecular and clinical studies have revealed different classifications and subtypes of gliomas. Neuroradiological approaches have non-invasively provided a macroscopic view for surgical resection and therapeutic effects. The connectome is a structural map of a physical object, the brain, which raises issues of spatial scale and definition, and it is calculated through diffusion magnetic resonance imaging (MRI) and functional MRI. In this study, we reviewed the basic principles and attributes of the structural and functional connectome, followed by the alternations of connectomes and their influences on glioma. To extend the applications of connectome, we demonstrated that a series of multi-center projects still need to be conducted to systemically investigate the connectome and the structural–functional coupling of glioma. Additionally, the brain–computer interface based on accurate connectome could provide more precise structural and functional data, which are significant for surgery and postoperative recovery. Besides, integrating the data from different sources, including connectome and other omics information, and their processing with artificial intelligence, together with validated biological and clinical findings will be significant for the development of a personalized surgical strategy.
Collapse
Affiliation(s)
- Ziyan Chen
- Department of Neurosurgery, Xiangya Hospital, Central South University, Hunan, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Ningrong Ye
- Department of Neurosurgery, Xiangya Hospital, Central South University, Hunan, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Chubei Teng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Hunan, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
- Department of Neurosurgery, The First Affiliated Hospital, University of South China, Hengyang, China
| | - Xuejun Li
- Department of Neurosurgery, Xiangya Hospital, Central South University, Hunan, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Xuejun Li,
| |
Collapse
|
25
|
Peng L, Feng J, Ma D, Xu X, Gao X. Rich-Club Organization Disturbances of the Individual Morphological Network in Subjective Cognitive Decline. Front Aging Neurosci 2022; 14:834145. [PMID: 35283748 PMCID: PMC8914315 DOI: 10.3389/fnagi.2022.834145] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 01/28/2022] [Indexed: 12/11/2022] Open
Abstract
BackgroundSubjective cognitive decline (SCD) was considered to be the preclinical stage of Alzheimer’s disease (AD). However, less is known about the altered rich-club organizations of the morphological networks in individuals with SCD.MethodsThis study included 53 individuals with SCD and 54 well-matched healthy controls (HC) from the Alzheimer’s disease Neuroimaging Initiative (ADNI) database. Individual-level brain morphological networks were constructed by estimating the Jensen-Shannon distance-based similarity in the distribution of regional gray matter volume. Rich-club properties were then detected, followed by statistical comparison.ResultsThe characteristic rich-club organization of morphological networks (normalized rich-club coefficients > 1) was observed for both the SCD and HC groups under a range of thresholds. The SCD group showed a reduced normalized rich-club coefficient compared with the HC group. The SCD group exhibited the decreased strength and degree of rich-club connections than the HC group (strength: HC = 79.93, SCD = 74.37, p = 0.028; degree: HC = 85.28, SCD = 79.34, p = 0.027). Interestingly, the SCD group showed an increased strength of local connections than the HC group (strength: HC = 1982.16, SCD = 2003.38, p = 0.036).ConclusionRich-club organization disturbances of morphological networks in individuals with SCD reveal a distinct pattern between the rich-club and peripheral regions. This altered rich-club organization pattern provides novel insights into the underlying mechanism of SCD and could be used to investigate prevention strategies at the preclinical stage of AD.
Collapse
Affiliation(s)
- Liling Peng
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Jing Feng
- The Fifth People’s Hospital of Jinan, Jinan, China
| | - Di Ma
- College of Information Science and Technology, Nanjing Forestry University, Nanjing, China
| | - Xiaowen Xu
- Department of Medical Imaging, School of Medicine, Tongji Hospital, Tongji University, Shanghai, China
- *Correspondence: Xiaowen Xu,
| | - Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
- Xin Gao,
| |
Collapse
|
26
|
Miao G, Rao B, Wang S, Fang P, Chen Z, Chen L, Zhang X, Zheng J, Xu H, Liao W. Decreased Functional Connectivities of Low-Degree Level Rich Club Organization and Caudate in Post-stroke Cognitive Impairment Based on Resting-State fMRI and Radiomics Features. Front Neurosci 2022; 15:796530. [PMID: 35250435 PMCID: PMC8890030 DOI: 10.3389/fnins.2021.796530] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 12/31/2021] [Indexed: 11/13/2022] Open
Abstract
BackgroundStroke is an important cause of cognitive impairment. Rich club organization, a highly interconnected network brain core region, is closely related to cognition. We hypothesized that the disturbance of rich club organization exists in patients with post-stroke cognitive impairment (PSCI).MethodsWe collected data on resting-state functional magnetic resonance imaging (rs-fMRI) with 21 healthy controls (HC), 16 hemorrhagic stroke (hPSCI), and 21 infarct stroke (iPSCI). 3D shape features and first-order statistics of stroke lesions were extracted using 3D slicer software. Additionally, we assessed cognitive function using the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE).ResultsNormalized rich club coefficients were higher in hPSCI and iPSCI than HC at low-degree k-levels (k = 1–8 in iPSCI, k = 2–8 in hPSCI). Feeder and local connections were significantly decreased in PSCI patients versus HC, mainly distributed in salience network (SN), default-mode network (DMN), cerebellum network (CN), and orbitofrontal cortex (ORB), especially involving the right and left caudate with changed nodal efficiency. The feeder and local connections of significantly between-group difference were positively related to MMSE and MoCA scores, primarily distributed in the sensorimotor network (SMN) and visual network (VN) in hPSCI, SN, and DMN in iPSCI. Additionally, decreased local connections and low-degree ϕnorm(k) were correlated to 3D shape features and first-order statistics of stroke lesions.ConclusionThis study reveals the disrupted low-degree level rich club organization and relatively preserved functional core network in PSCI patients. Decreased feeder and local connections in cognition-related networks (DMN, SN, CN, and ORB), particularly involving the caudate nucleus, may offer insight into pathological mechanism of PSCI patients. The shape and signal features of stroke lesions may provide an essential clue for the damage of functional connectivity and the whole brain networks.
Collapse
Affiliation(s)
- Guofu Miao
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Bo Rao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Sirui Wang
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Pinyan Fang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
- Department of Radiology, TEDA International Cardiovascular Hospital, Tianjin, China
| | - Zhuo Chen
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Linglong Chen
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xin Zhang
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jun Zheng
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
- *Correspondence: Haibo Xu,
| | - Weijing Liao
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
- Weijing Liao,
| |
Collapse
|
27
|
Yang J, Sui H, Jiao R, Zhang M, Zhao X, Wang L, Deng W, Liu X. Random-Forest-Algorithm-Based Applications of the Basic Characteristics and Serum and Imaging Biomarkers to Diagnose Mild Cognitive Impairment. Curr Alzheimer Res 2022; 19:76-83. [PMID: 35088670 PMCID: PMC9189735 DOI: 10.2174/1567205019666220128120927] [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: 03/21/2021] [Revised: 12/04/2021] [Accepted: 01/13/2022] [Indexed: 11/24/2022]
Abstract
Background
Mild cognitive impairment (MCI) is considered the early stage of Alzheimer's Disease (AD). The purpose of our study was to analyze the basic characteristics and serum and imaging biomarkers for the diagnosis of MCI patients as a more objective and accurate approach. Methods
The Montreal Cognitive Test was used to test 119 patients aged ≥65. Such serum biomarkers were detected as preprandial blood glucose, triglyceride, total cholesterol, Aβ1-40, Aβ1-42, and P-tau. All the subjects were scanned with 1.5T MRI (GE Healthcare, WI, USA) to obtain DWI, DTI, and ASL images. DTI was used to calculate the anisotropy fraction (FA), DWI was used to calculate the apparent diffusion coefficient (ADC), and ASL was used to calculate the cerebral blood flow (CBF). All the images were then registered to the SPACE of the Montreal Neurological Institute (MNI). In 116 brain regions, the medians of FA, ADC, and CBF were extracted by automatic anatomical labeling. The basic characteristics included gender, education level, and previous disease history of hypertension, diabetes, and coronary heart disease. The data were randomly divided into training sets and test ones. The recursive random forest algorithm was applied to the diagnosis of MCI patients, and the recursive feature elimination (RFE) method was used to screen the significant basic features and serum and imaging biomarkers. The overall accuracy, sensitivity, and specificity were calculated, respectively, and so were the ROC curve and the area under the curve (AUC) of the test set. Results
When the variable of the MCI diagnostic model was an imaging biomarker, the training accuracy of the random forest was 100%, the correct rate of the test was 86.23%, the sensitivity was 78.26%, and the specificity was 100%. When combining the basic characteristics, the serum and imaging biomarkers as variables of the MCI diagnostic model, the training accuracy of the random forest was found to be 100%; the test accuracy was 97.23%, the sensitivity was 94.44%, and the specificity was 100%. RFE analysis showed that age, Aβ1-40, and cerebellum_4_6 were the most important basic feature, serum biomarker, imaging biomarker, respectively. Conclusion
Imaging biomarkers can effectively diagnose MCI. The diagnostic capacity of the basic trait biomarkers or serum biomarkers for MCI is limited, but their combination with imaging biomarkers can improve the diagnostic capacity, as indicated by the sensitivity of 94.44% and the specificity of 100% in our model. As a machine learning method, a random forest can help diagnose MCI effectively while screening important influencing factors.
Collapse
Affiliation(s)
- Juan Yang
- Department of Neurology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200092, China
- Department of Neurology, Shanghai Pudong New Area People's Hospital,Shanghai, 201299, China
| | - Haijing Sui
- Department of Radiology, Shanghai Pudong New Area People's Hospital, Shanghai, People's Republic of China
| | - Ronghong Jiao
- Department of Clinical Laboratory, Shanghai Pudong New Area People's Hospital, Shanghai, People's Republic of China
| | - Min Zhang
- hcit.ai Co., Shanghai, People's Republic of China
| | - Xiaohui Zhao
- Department of Neurology, Shanghai Pudong New Area People's Hospital, Shanghai, People's Republic of China
| | - Lingling Wang
- Department of Neurology, Shanghai Pudong New Area People's Hospital, Shanghai, People's Republic of China
| | - Wenping Deng
- Huawei Technology Co., Ltd Co, Shanghai, People's Republic of China
| | - Xueyuan Liu
- Department of Neurology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200092, China
- Department of Neurology, Shanghai Pudong New Area People's Hospital,Shanghai, 201299, China
| |
Collapse
|
28
|
Prescott JW, Doraiswamy PM, Gamberger D, Benzinger T, Petrella JR. Diffusion Tensor MRI Structural Connectivity and PET Amyloid Burden in Preclinical Autosomal Dominant Alzheimer Disease: The DIAN Cohort. Radiology 2022; 302:143-150. [PMID: 34636637 PMCID: PMC9127824 DOI: 10.1148/radiol.2021210383] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Background Pathologic evidence of Alzheimer disease (AD) is detectable years before onset of clinical symptoms. Imaging-based identification of structural changes of the brain in people at genetic risk for early-onset AD may provide insights into how genes influence the pathologic cascade that leads to dementia. Purpose To assess structural connectivity differences in cortical networks between cognitively normal autosomal dominant Alzheimer disease (ADAD) mutation carriers versus noncarriers and to determine the cross-sectional relationship of structural connectivity and cortical amyloid burden with estimated years to symptom onset (EYO) of dementia in carriers. Materials and Methods In this exploratory analysis of a prospective trial, all participants enrolled in the Dominantly Inherited Alzheimer Network between January 2009 and July 2014 who had normal cognition at baseline, T1-weighted MRI scans, and diffusion tensor imaging (DTI) were analyzed. Amyloid PET imaging using Pittsburgh compound B was also analyzed for mutation carriers. Areas of the cerebral cortex were parcellated into three cortical networks: the default mode network, frontoparietal control network, and ventral attention network. The structural connectivity of the three networks was calculated from DTI. General linear models were used to examine differences in structural connectivity between mutation carriers and noncarriers and the relationship between structural connectivity, amyloid burden, and EYO in mutation carriers. Correlation network analysis was performed to identify clusters of related clinical and imaging markers. Results There were 30 mutation carriers (mean age ± standard deviation, 34 years ± 10; 17 women) and 38 noncarriers (mean age, 37 years ± 10; 20 women). There was lower structural connectivity in the frontoparietal control network in mutation carriers compared with noncarriers (estimated effect of mutation-positive status, -0.0266; P = .04). Among mutation carriers, there was a correlation between EYO and white matter structural connectivity in the frontoparietal control network (estimated effect of EYO, -0.0015, P = .01). There was no significant relationship between cortical global amyloid burden and EYO among mutation carriers (P > .05). Conclusion White matter structural connectivity was lower in autosomal dominant Alzheimer disease mutation carriers compared with noncarriers and correlated with estimated years to symptom onset. Clinical trial registration no. NCT00869817 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by McEvoy in this issue.
Collapse
Affiliation(s)
- Jeffrey W. Prescott
- Department of Radiology, The MetroHealth System, 2500 MetroHealth Dr, Cleveland, OH 44109,Departments of Radiology and Psychiatry, Duke University Medical Center, Durham, NC
| | - P. Murali Doraiswamy
- Departments of Radiology and Psychiatry, Duke University Medical Center, Durham, NC
| | | | - Tammie Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo
| | - Jeffrey R. Petrella
- Departments of Radiology and Psychiatry, Duke University Medical Center, Durham, NC
| | | |
Collapse
|
29
|
Drenthen GS, Backes WH, Freeze WM, Jacobs HI, Verheggen IC, van Boxtel MP, Hoff EI, Verhey FR, Jansen JF. Rich-Club Connectivity of the Structural Covariance Network Relates to Memory Processes in Mild Cognitive Impairment and Alzheimer's Disease. J Alzheimers Dis 2022; 89:209-217. [PMID: 35871335 PMCID: PMC9484119 DOI: 10.3233/jad-220175] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/13/2022] [Indexed: 11/15/2022]
Abstract
BACKGROUND Though mediotemporal lobe volume changes are well-known features of Alzheimer's disease (AD), grey matter volume changes may be distributed throughout the brain. These distributed changes are not independent due to the underlying network structure and can be described in terms of a structural covariance network (SCN). OBJECTIVE To investigate how the cortical brain organization is altered in AD we studied the mutual connectivity of hubs in the SCN, i.e., the rich-club. METHODS To construct the SCNs, cortical thickness was obtained from structural MRI for 97 participants (normal cognition, n = 37; mild cognitive impairment, n = 41; Alzheimer-type dementia, n = 19). Subsequently, rich-club coefficients were calculated from the SCN, and related to memory performance and hippocampal volume using linear regression. RESULTS Lower rich-club connectivity was related to lower memory performance as well as lower hippocampal volume. CONCLUSION Therefore, this study provides novel evidence of reduced connectivity in hub areas in relation to AD-related cognitive impairments and atrophy.
Collapse
Affiliation(s)
- Gerhard S. Drenthen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Walter H. Backes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Whitney M. Freeze
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Psychiatry & Neuropsychology, Maastricht University, Maastricht, the Netherlands
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Heidi I.L. Jacobs
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Gordon Center for Medical Imaging Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Inge C.M. Verheggen
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Psychiatry & Neuropsychology, Maastricht University, Maastricht, the Netherlands
| | - Martin P.J. van Boxtel
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Psychiatry & Neuropsychology, Maastricht University, Maastricht, the Netherlands
| | - Erik I. Hoff
- Department of Neurology, Zuyderland Medical Centre Heerlen, Heerlen, the Netherlands
| | - Frans R. Verhey
- Department of Psychiatry & Neuropsychology, Maastricht University, Maastricht, the Netherlands
| | - Jacobus F.A. Jansen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| |
Collapse
|
30
|
Reorganization of rich clubs in functional brain networks of dementia with Lewy bodies and Alzheimer's disease. Neuroimage Clin 2021; 33:102930. [PMID: 34959050 PMCID: PMC8856913 DOI: 10.1016/j.nicl.2021.102930] [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: 08/17/2021] [Revised: 11/18/2021] [Accepted: 12/23/2021] [Indexed: 12/12/2022]
Abstract
DLB and AD had the different functional reorganization patterns. Rich club nodes increased in frontal-parietal network in patients with DLB. The rich club nodes in temporal lobe decreased and those in cerebellum increased for AD. Compared with HC, rich club connectivity was enhanced in the DLB and AD groups.
The purpose of this study was to reveal the patterns of reorganization of rich club organization in brain functional networks in dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD). The study found that the rich club node shifts from sensory/somatomotor network to fronto-parietal network in DLB. For AD, the rich club nodes switch between the temporal lobe with obvious structural atrophy and the frontal lobe, parietal lobe and cerebellum with relatively preserved structure and function. In addition, compared with healthy controls, rich club connectivity was enhanced in the DLB and AD groups. The connection strength of DLB patients was related to cognitive assessment. In conclusion, we revealed the different functional reorganization patterns of DLB and AD. The conversion and redistribution of rich club members may play a causal role in disease-specific outcomes. It may be used as a potential biomarker to provide more accurate prevention and treatment strategies.
Collapse
|
31
|
Zhou Y, Si X, Chen Y, Chao Y, Lin CP, Li S, Zhang X, Ming D, Li Q. Hippocampus- and Thalamus-Related Fiber-Specific White Matter Reductions in Mild Cognitive Impairment. Cereb Cortex 2021; 32:3159-3174. [PMID: 34891164 DOI: 10.1093/cercor/bhab407] [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: 06/08/2021] [Revised: 09/04/2021] [Accepted: 10/20/2021] [Indexed: 11/13/2022] Open
Abstract
Early diagnosis of mild cognitive impairment (MCI) fascinates screening high-risk Alzheimer's disease (AD). White matter is found to degenerate earlier than gray matter and functional connectivity during MCI. Although studies reveal white matter degenerates in the limbic system for MCI, how other white matter degenerates during MCI remains unclear. In our method, regions of interest with a high level of resting-state functional connectivity with hippocampus were selected as seeds to track fibers based on diffusion tensor imaging (DTI). In this way, hippocampus-temporal and thalamus-related fibers were selected, and each fiber's DTI parameters were extracted. Then, statistical analysis, machine learning classification, and Pearson's correlations with behavior scores were performed between MCI and normal control (NC) groups. Results show that: 1) the mean diffusivity of hippocampus-temporal and thalamus-related fibers are significantly higher in MCI and could be used to classify 2 groups effectively. 2) Compared with normal fibers, the degenerated fibers detected by the DTI indexes, especially for hippocampus-temporal fibers, have shown significantly higher correlations with cognitive scores. 3) Compared with the hippocampus-temporal fibers, thalamus-related fibers have shown significantly higher correlations with depression scores within MCI. Our results provide novel biomarkers for the early diagnoses of AD.
Collapse
Affiliation(s)
- Yu Zhou
- School of Microelectronics, Tianjin University, Tianjin 300072, China
| | - Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China.,Institute of Applied Psychology, Tianjin University, Tianjin 300350, China
| | - Yuanyuan Chen
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China
| | - Yiping Chao
- Graduate Institute of Biomedical Engineering, Chang Gung University, Taoyuan 33302, Taiwan.,Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan 33302, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience Hsinchu City, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
| | - Sicheng Li
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China
| | - Xingjian Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China
| | - Qiang Li
- School of Microelectronics, Tianjin University, Tianjin 300072, China
| |
Collapse
|
32
|
Lu Y, Li Y, Feng Q, Shen R, Zhu H, Zhou H, Zhao Z. Rich-Club Analysis of the Structural Brain Network in Cases with Cerebral Small Vessel Disease and Depression Symptoms. Cerebrovasc Dis 2021; 51:92-101. [PMID: 34537766 DOI: 10.1159/000517243] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 05/13/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Altered white matter brain networks have been extensively studied in cerebral small vessel disease (SVD). However, there exists currently a deficiency of comprehending the performance of changes within the structural networks of the brain in cases with cerebral SVD and depression symptoms. The main aim of the present research is to study the network topology behaviors and features of rich-club organization in SVD patients using graph theory and diffusion tensor imaging (DTI) to characterize changes in the microstructure of the brain. METHODS DTI datasets were acquired from 26 SVD patients with symptoms of depression (SVD + D) and 26 SVD patients without symptoms of depression (SVD - D), and a series of neuropsychological assessments were completed. A structural network was created using a deterministic fiber tracking method. The analysis of rich-club was performed in company with analysis of the global network features of the network to characterize the topological properties of all subjects. RESULTS DTI data were obtained from SVD patients who manifested symptoms of depression (SVD + D) and from control SVD patients (SVD - D). In comparison with SVD - D patients, SVD + D cases demonstrated a diminished coefficient of clustering along with lower global efficiencies and longer path length characteristics. Rich-club analysis showed SVD + D patients had decreased feeder connectivity and local connectivity strengths compared to SVD - D patients. Our data also showed that the feeder connections in the brain correlated significantly with the severity of depression in SVD + D patients. CONCLUSIONS Our study revealed that SVD patients with depressive symptoms have disrupted white matter networks that characteristically have reduced network efficiency compared to the networks in other SVD patients. Disrupted information interactions among the regions of nonrich-club and rich-club in SVD cases are related to the severity of depression. Our data suggest that DTI may be utilized as an appropriate biomarker for the diagnosis of depression in comorbid SVD patients.
Collapse
Affiliation(s)
- Yanjing Lu
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Yifan Li
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Qian Feng
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Rong Shen
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Hao Zhu
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Hua Zhou
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Zhong Zhao
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| |
Collapse
|
33
|
Wu Z, Gao Y, Potter T, Benoit J, Shen J, Schulz PE, Zhang Y. Interactions Between Aging and Alzheimer's Disease on Structural Brain Networks. Front Aging Neurosci 2021; 13:639795. [PMID: 34177548 PMCID: PMC8222527 DOI: 10.3389/fnagi.2021.639795] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 05/11/2021] [Indexed: 11/13/2022] Open
Abstract
Normative aging and Alzheimer's disease (AD) propagation alter anatomical connections among brain parcels. However, the interaction between the trajectories of age- and AD-linked alterations in the topology of the structural brain network is not well understood. In this study, diffusion-weighted magnetic resonance imaging (MRI) datasets of 139 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used to document their structural brain networks. The 139 participants consist of 45 normal controls (NCs), 37 with early mild cognitive impairment (EMCI), 27 with late mild cognitive impairment (LMCI), and 30 AD patients. All subjects were further divided into three subgroups based on their age (56-65, 66-75, and 71-85 years). After the structural connectivity networks were built using anatomically-constrained deterministic tractography, their global and nodal topological properties were estimated, including network efficiency, characteristic path length, transitivity, modularity coefficient, clustering coefficient, and betweenness. Statistical analyses were then performed on these metrics using linear regression, and one- and two-way ANOVA testing to examine group differences and interactions between aging and AD propagation. No significant interactions were found between aging and AD propagation in the global topological metrics (network efficiency, characteristic path length, transitivity, and modularity coefficient). However, nodal metrics (clustering coefficient and betweenness centrality) of some cortical parcels exhibited significant interactions between aging and AD propagation, with affected parcels including left superior temporal, right pars triangularis, and right precentral. The results collectively confirm the age-related deterioration of structural networks in MCI and AD patients, providing novel insight into the cross effects of aging and AD disorder on brain structural networks. Some early symptoms of AD may also be due to age-associated anatomic vulnerability interacting with early anatomic changes associated with AD.
Collapse
Affiliation(s)
- Zhanxiong Wu
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, China
| | - Yunyuan Gao
- Department of Intelligent Control and Robotics Institute, College of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Thomas Potter
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
| | - Julia Benoit
- Texas Institute for Measurement Evaluation and Statistics, Department of Basic Vision Sciences, University of Houston, Houston, TX, United States
| | - Jian Shen
- Neurosurgery Department, The First Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Paul E. Schulz
- Department of Neurology, The McGovern Medical School of UTHealth-Houston, Houston, TX, United States
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
| | | |
Collapse
|
34
|
Durusoy G, Yldrm Z, Dal DY, Ulasoglu-Yildiz C, Kurt E, Bayr G, Ozacar E, Ozarslan E, Demirtas-Tatldede A, Bilgic B, Demiralp T, Gurvit H, Kabakcoglu A, Acar B. B-Tensor: Brain Connectome Tensor Factorization for Alzheimer's Disease. IEEE J Biomed Health Inform 2021; 25:1591-1600. [PMID: 32915753 DOI: 10.1109/jbhi.2020.3023610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
AD is the highly severe part of the dementia spectrum and impairs cognitive abilities of individuals, bringing economic, societal and psychological burdens beyond the diseased. A promising approach in AD research is the analysis of structural and functional brain connectomes, i.e., sNETs and fNETs, respectively. We propose to use tensor representation (B-tensor) of uni-modal and multi-modal brain connectomes to define a low-dimensional space via tensor factorization. We show on a cohort of 47 subjects, spanning the spectrum of dementia, that diagnosis with an accuracy of 77% to 100% is achievable in a 5D connectome space using different structural and functional connectome constructions in a uni-modal and multi-modal fashion. We further show that multi-modal tensor factorization improves the results suggesting complementary information in structure and function. A neurological assessment of the connectivity patterns identified largely agrees with prior knowledge, yet also suggests new associations that may play a role in the disease progress.
Collapse
|
35
|
Stefanovski L, Meier JM, Pai RK, Triebkorn P, Lett T, Martin L, Bülau K, Hofmann-Apitius M, Solodkin A, McIntosh AR, Ritter P. Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain. Front Neuroinform 2021; 15:630172. [PMID: 33867964 PMCID: PMC8047422 DOI: 10.3389/fninf.2021.630172] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 03/08/2021] [Indexed: 12/18/2022] Open
Abstract
Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.
Collapse
Affiliation(s)
- Leon Stefanovski
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Jil Mona Meier
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Roopa Kalsank Pai
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Paul Triebkorn
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France
| | - Tristram Lett
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Leon Martin
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Konstantin Bülau
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Martin Hofmann-Apitius
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Sankt Augustin, Germany
| | - Ana Solodkin
- Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, United States
| | | | - Petra Ritter
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
- Einstein Center for Neuroscience Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, Germany
| |
Collapse
|
36
|
Xu CX, Jiang H, Zhao ZJ, Sun YH, Chen X, Sun BM, Sun QF, Bian LG. Disruption of Rich-Club Connectivity in Cushing Disease. World Neurosurg 2021; 148:e275-e281. [PMID: 33412326 DOI: 10.1016/j.wneu.2020.12.146] [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: 10/14/2020] [Revised: 12/24/2020] [Accepted: 12/26/2020] [Indexed: 10/22/2022]
Abstract
OBJECTIVE Cushing disease (CD) is a rare clinical disease in which brain structural and function are impaired as the result of excessive cortisol. However, little is known whether rich-club organization changes in patients with CD, as visualized on resting-state magnetic resonance imaging (fMRI), can reverse to normal conditions after transsphenoidal surgery (TSS). In this study, we aimed to investigate whether the functional connectivity of rich-club organization is affected and whether any abnormal changes may reverse after TSS. METHODS In this study, 38 patients with active CD, 33 with patients with CD in remission, and 41 age-, sex-, and education-matched healthy control participants underwent resting-state fMRI. Brain functional connectivity was constructed based on fMRI and rich club was calculated with graph theory approach. We constructed the functional brain networks for all participants and calculated rich-club connectivity based on fMRI. RESULTS We identified left precuneus, right precuneus, left middle cingulum, right middle cingulum, right inferior temporal, right middle temporal, right lingual, right postcentral, right middle occipital, and right precentral regions as rich club nodes. Compared with healthy control participants, rich-club connectivity was significantly lower in patients with active CD (P < 0.001). Moreover, abnormal rich-club connectivity improved to normal after TSS. CONCLUSIONS Our results show rich-club organization was disrupted in patients with active CD with excessive cortisol production. TSS can reverse abnormal rich-club connectivity. Rich club may be a new indicator to investigate the outcomes of TSS and to increase our understanding of the effect of excessive cortisol on brain functional connectivity in patients with CD.
Collapse
Affiliation(s)
- Can-Xin Xu
- Department of Neurosurgery, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hong Jiang
- Department of Neurosurgery, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhi-Jie Zhao
- Department of Neurosurgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yu-Hao Sun
- Department of Neurosurgery, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiao Chen
- Department of Neurosurgery, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bo-Min Sun
- Department of Functional Neurosurgery, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qing-Fang Sun
- Department of Neurosurgery, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Neurosurgery, Rui-Jin Lu-Wan Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Liu-Guan Bian
- Department of Neurosurgery, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| |
Collapse
|
37
|
Chen W, Lin H, Lyu M, Wang VJ, Li X, Bao S, Sun G, Xia J, Wang P. The potential role of leukoaraiosis in remodeling the brain network to buffer cognitive decline: a Leukoaraiosis And Disability study from Alzheimer's Disease Neuroimaging Initiative. Quant Imaging Med Surg 2021; 11:183-203. [PMID: 33392021 DOI: 10.21037/qims-20-580] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Background Leukoaraiosis (LA) is a phenomenon of the brain that is often observed in elderly people. However, little is known about the role of LA in cognitive impairment in neurodegeneration and disease. This cross-sectional, retrospective Leukoaraiosis And Disability (LADIS) study aimed to characterize the relationship between brain white matter connectivity properties with LA ratings in patients with Alzheimer's disease (AD) as compared with age-matched cognitively normal controls. Methods Patients with AD (n=76) and elderly individuals with normal cognitive (NC) function (n=82) were classified into 3 groups, LA1, LA2, and LA3, according to the rating of their white matter changes (WMCs). Diffusion tensor imaging (DTI) data were analyzed by quantifying and comparing the white matter connectivity properties and gray matter (GM) volume of brain regions of interest (ROIs). Results The rich-club network properties in the AD LA1 and LA2 groups showed significant patterns of disrupted peripheral regions and reduced connectivity compared to those in the NC LA1 and LA2 groups, respectively. However, the rich-club network properties in the AD LA3 group showed similar patterns of disrupted peripheral regions and reduced connectivity compared to those in the NC LA3 group, despite there being significant hippocampal and amygdala atrophic differences between AD patients and NC elders. Compared to the NC LA1 group, the characteristic path length of white matter fiber connectivity in the NC LA3 group was significantly increased, and the brain's global efficiency, clustering coefficient, and network connectivity strength were significantly reduced (P<0.05, respectively). However, no significant differences (P>0.05) were observed in characteristic path length, reduced global efficiency, or the clustering coefficient between the NC LA3 and AD LA1 groups, or between the NC LA3 and AD LA2 groups. Conclusions Our findings offer some insights into a potential role of LA in cognitive impairment that may predict the development of disability in older adults. The occurrence of LA, an intermediate degenerative change, during neurodegeneration and disease may potentially lead to the remodeling of the brain network through brain plasticity. LA, therefore, representing a possible compensatory mechanism to buffer cognitive decline.
Collapse
Affiliation(s)
- Wei Chen
- Department of Radiology, Tongji Hospital, Tongji University School of Medicine, Shanghai, China.,Department of Radiology, Pingshan District People's Hospital, Pingshan General Hospital of Southern Medical University, Shenzhen, China
| | - Hai Lin
- Department of Neurosurgery, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Minrui Lyu
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Victoria J Wang
- Department of Nephrology, Tufts Medical Center, Boston, MA, USA
| | - Xiang Li
- Guangdong Provincial Key Laboratory of Brain Connectome and Behaviour, the Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Shixing Bao
- Department of Radiology, Osaka University, Osaka, Japan
| | - Guoping Sun
- Department of Radiology, Pingshan District People's Hospital, Pingshan General Hospital of Southern Medical University, Shenzhen, China
| | - Jun Xia
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Peijun Wang
- Department of Radiology, Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | | |
Collapse
|
38
|
Shao W, He X, Li X, Tao W, Zhang J, Zhang S, Wang L, Qiao Y, Wang Y, Zhang Z, Peng D. Disrupted White Matter Networks from Subjective Memory Impairment to Amnestic Mild Cognitive Impairment. Curr Alzheimer Res 2021; 18:35-44. [PMID: 33761859 DOI: 10.2174/1567205018666210324115817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Revised: 12/13/2020] [Accepted: 03/08/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND AND OBJECTIVE Subjective memory impairment (SMI) is a preclinical stage prior to amnestic mild cognitive impairment (aMCI) along with the Alzheimer's disease (AD) continuum. We hypothesized that SMI patients had white matter (WM) network disruptions similar to those in aMCI patients. METHODS We used diffusion-tensor magnetic resonance imaging and graph theory to construct, analyze, and compare the WM networks among 20 normal controls (NC), 20 SMI patients, and 20 aMCI patients. RESULTS Compared with the NC group, the SMI group had significantly decreased global and local efficiency and an increased shortest path length. Moreover, similar to the aMCI group, the SMI group had lower nodal efficiency in regions located in the frontal and parietal lobes, limbic systems, and caudate nucleus compared to that of the NC group. CONCLUSION Similar to aMCI patient, SMI patients exhibited WM network disruptions, and detection of these disruptions could facilitate the early detection of SMI.
Collapse
Affiliation(s)
- Wen Shao
- Department of Neurology, China-Japan Friendship Hospital, Beijing 100029,China
| | - Xuwen He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875,China
| | - Xin Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875,China
| | - Wuhai Tao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875,China
| | - Junying Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875,China
| | - Shujuan Zhang
- Department of Neurology, China-Japan Friendship Hospital, Beijing 100029,China
| | - Lei Wang
- Department of Neurology, China-Japan Friendship Hospital, Beijing 100029,China
| | - Yanan Qiao
- Department of Neurology, China-Japan Friendship Hospital, Beijing 100029,China
| | - Yu Wang
- Department of Neurology, China-Japan Friendship Hospital, Beijing 100029,China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875,China
| | - Dantao Peng
- Department of Neurology, China-Japan Friendship Hospital, Beijing 100029,China
| |
Collapse
|
39
|
Au CKF, Abrigo J, Liu C, Liu W, Lee J, Au LWC, Chan Q, Chen S, Leung EYL, Ho CL, Ko H, Mok VCT, Chen W. Quantitative Susceptibility Mapping of the Hippocampal Fimbria in Alzheimer's Disease. J Magn Reson Imaging 2020; 53:1823-1832. [PMID: 33295658 DOI: 10.1002/jmri.27464] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 11/22/2020] [Accepted: 11/24/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The fimbria is a small white matter bundle that connects the hippocampus to the rest of the brain. Damage to the hippocampal gray matter is established in Alzheimer's disease (AD), but the hippocampal fimbrial status in the pathogenesis of AD is unclear. AD-related demyelination and iron deposition alter the diamagnetic and paramagnetic composition of tissues, which can be measured by quantitative susceptibility mapping (QSM). HYPOTHESIS AD is associated with microstructural changes in the fimbria that might be detected by QSM. STUDY TYPE Retrospective cross-sectional study. SUBJECTS In all, 53 adults comprised of controls (n = 30), subjects with early stage AD (n = 13), and late stage AD (n = 10) who were classified according to their amyloid and tau status and presence of hippocampal atrophy. FIELD STRENGTH / SEQUENCE 3T; 3D fast-field echo sequence for QSM analysis and 3D T1 -weighted MP-RAGE sequence for anatomical analysis. ASSESSMENT Segmentation of the left hippocampal fimbria subfield was performed on T1 -weighted images and was applied to the coregistered QSM map for extraction of the mean, median, minimum, and maximum values of QSM. STATISTICAL TESTS Group comparison of QSM values using analysis of variance (ANOVA) with post-hoc Tukey's test, accuracy of binary differentiation using receiver operating characteristic (ROC), and individual classification using discriminant analysis. RESULTS QSMmean and QSMmedian values were significantly different among the three groups (P < 0.05) and showed a shifting from negative in the control group to positive in the AD group. The control and early AD subjects, who have normal hippocampal volumes, were differentiated by the QSMmean value (area under the curve [AUC] 0.744, P < 0.05) and the QSMmedian value (AUC 0.782, P < 0.05). Up to 76% of subjects (inclusive of 26 controls and six with early AD) were correctly classified using a model incorporating clinical and radiologic data. DATA CONCLUSION The fimbria showed higher magnetic susceptibility in AD compared with controls. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY STAGE: 3.
Collapse
Affiliation(s)
- Chun Ki Franklin Au
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Jill Abrigo
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, 94720, USA.,Helen Wills Neuroscience Institute, University of California, Berkeley, California, 94720, USA
| | - Wanting Liu
- Gerald Choa Neuroscience Centre, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Division of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Jack Lee
- Clinical Trials and Biostatistics Lab, CUHK Shenzhen Research Institute, Shenzhen, 518063, China.,Division of Biostatistics, Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Lisa Wing Chi Au
- Gerald Choa Neuroscience Centre, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Division of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | | | - Sirong Chen
- Department of Nuclear Medicine & PET, Hong Kong Sanatorium & Hospital, Hong Kong, China
| | - Eric Yim Lung Leung
- Department of Nuclear Medicine & PET, Hong Kong Sanatorium & Hospital, Hong Kong, China
| | - Chi Lai Ho
- Department of Nuclear Medicine & PET, Hong Kong Sanatorium & Hospital, Hong Kong, China
| | - Ho Ko
- Gerald Choa Neuroscience Centre, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Division of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China.,Li Ka Shing Institute of Health Sciences; School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Vincent Chung Tong Mok
- Gerald Choa Neuroscience Centre, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Division of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Weitian Chen
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| |
Collapse
|
40
|
Du J, Zhu H, Zhou J, Lu P, Qiu Y, Yu L, Cao W, Zhi N, Yang J, Xu Q, Sun J, Zhou Y. Structural Brain Network Disruption at Preclinical Stage of Cognitive Impairment Due to Cerebral Small Vessel Disease. Neuroscience 2020; 449:99-115. [PMID: 32896599 DOI: 10.1016/j.neuroscience.2020.08.037] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 08/26/2020] [Accepted: 08/27/2020] [Indexed: 11/25/2022]
Abstract
Cerebral small vessel disease (CSVD) is a common disease among elderly individuals and recognized as a major cause of vascular cognitive impairment. Recent studies demonstrated that CSVD is a disconnection syndrome. However, due to the covert neurological symptoms and subtle changes in clinical performance, the connection alterations during the stage of preclinical cognitive impairment (PCI) and mild cognitive impairment (MCI) are usually neglected and still largely unknown. Using diffusion tensor imaging (DTI), we investigated the early structural network changes in PCI and MCI patients. The PCI group demonstrated well preserved rich-club organization, less nodal strength loss, and disruption of connections centered in the feeder and local connections. Nevertheless, the MCI group manifested a disruption in the rich-club organization, a worse nodal strength loss especially in hub nodes, and an overall disturbance in rich-club, feeder and local connections. Moreover, in all CSVD patients, the strength of the rich-club, feeder and local connections was significantly correlated with multiple cognitive scores, especially in attention, executive, and memory domains; while in MCI patients, only the strength of the rich-club connections was significantly correlated with cognition. Furthermore, based on the network-based statistic analysis, we also identified distinct network component disruption pattern between the PCI group and the MCI group, validating the results described above. These results suggest a disruption pattern from peripheral to central connections with the change of cognitive status, shedding light on the early identification and the underlying disruption of CSVD.
Collapse
Affiliation(s)
- Jing Du
- Renji-UNSW CHeBA Neurocognitive Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Neurology, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Health Management Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China
| | - Hong Zhu
- Shanghai Med-X Engineering Research Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Jie Zhou
- Shanghai Med-X Engineering Research Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Peiwen Lu
- Renji-UNSW CHeBA Neurocognitive Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Neurology, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Health Management Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China
| | - Yage Qiu
- Department of Radiology, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China
| | - Ling Yu
- Renji-UNSW CHeBA Neurocognitive Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Neurology, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China
| | - Wenwei Cao
- Renji-UNSW CHeBA Neurocognitive Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Neurology, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China
| | - Nan Zhi
- Renji-UNSW CHeBA Neurocognitive Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Neurology, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China
| | - Jie Yang
- Renji-UNSW CHeBA Neurocognitive Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Neurology, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Health Management Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China
| | - Qun Xu
- Renji-UNSW CHeBA Neurocognitive Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Neurology, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Health Management Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China.
| | - Junfeng Sun
- Shanghai Med-X Engineering Research Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
| | - Yan Zhou
- Department of Radiology, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China.
| |
Collapse
|
41
|
Lombardi A, Amoroso N, Diacono D, Monaco A, Logroscino G, De Blasi R, Bellotti R, Tangaro S. Association between Structural Connectivity and Generalized Cognitive Spectrum in Alzheimer's Disease. Brain Sci 2020; 10:E879. [PMID: 33233622 PMCID: PMC7699729 DOI: 10.3390/brainsci10110879] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 11/10/2020] [Accepted: 11/17/2020] [Indexed: 01/10/2023] Open
Abstract
Modeling disease progression through the cognitive scores has become an attractive challenge in the field of computational neuroscience due to its importance for early diagnosis of Alzheimer's disease (AD). Several scores such as Alzheimer's Disease Assessment Scale cognitive total score, Mini Mental State Exam score and Rey Auditory Verbal Learning Test provide a quantitative assessment of the cognitive conditions of the patients and are commonly used as objective criteria for clinical diagnosis of dementia and mild cognitive impairment (MCI). On the other hand, connectivity patterns extracted from diffusion tensor imaging (DTI) have been successfully used to classify AD and MCI subjects with machine learning algorithms proving their potential application in the clinical setting. In this work, we carried out a pilot study to investigate the strength of association between DTI structural connectivity of a mixed ADNI cohort and cognitive spectrum in AD. We developed a machine learning framework to find a generalized cognitive score that summarizes the different functional domains reflected by each cognitive clinical index and to identify the connectivity biomarkers more significantly associated with the score. The results indicate that the efficiency and the centrality of some regions can effectively track cognitive impairment in AD showing a significant correlation with the generalized cognitive score (R = 0.7).
Collapse
Affiliation(s)
- Angela Lombardi
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy; (A.L.); (N.A.); (D.D.); (R.B.)
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy; (A.L.); (N.A.); (D.D.); (R.B.)
- Dipartimento di Farmacia–Scienze del Farmaco, Università degli Studi di Bari, 70125 Bari, Italy
| | - Domenico Diacono
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy; (A.L.); (N.A.); (D.D.); (R.B.)
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy; (A.L.); (N.A.); (D.D.); (R.B.)
| | - Giancarlo Logroscino
- Center for Neurodegenerative Diseases and the Aging Brain, Università degli Studi di Bari at Pia Fondazione “Card. G. Panico”, 73039 Tricase, Italy;
- Department of Basic Medicine Neuroscience and Sense Organs, Università degli Studi di Bari, 70124 Bari, Italy
| | | | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy; (A.L.); (N.A.); (D.D.); (R.B.)
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari, 70126 Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy; (A.L.); (N.A.); (D.D.); (R.B.)
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari, 70126 Bari, Italy
| |
Collapse
|
42
|
Xue C, Sun H, Hu G, Qi W, Yue Y, Rao J, Yang W, Xiao C, Chen J. Disrupted Patterns of Rich-Club and Diverse-Club Organizations in Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment. Front Neurosci 2020; 14:575652. [PMID: 33177982 PMCID: PMC7593791 DOI: 10.3389/fnins.2020.575652] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 08/25/2020] [Indexed: 01/06/2023] Open
Abstract
Background Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) were considered to be a continuum of Alzheimer’s disease (AD) spectrum. The abnormal topological architecture and rich-club organization in the brain functional network can reveal the pathology of the AD spectrum. However, few studies have explored the disrupted patterns of diverse club organizations and the combination of rich- and diverse-club organizations in SCD and aMCI. Methods We collected resting-state functional magnetic resonance imaging data of 19 SCDs, 29 aMCIs, and 28 healthy controls (HCs) from the Alzheimer’s Disease Neuroimaging Initiative. Graph theory analysis was used to analyze the network metrics and rich- and diverse-club organizations simultaneously. Results Compared with HC, the aMCI group showed altered small-world and network efficiency, whereas the SCD group remained relatively stable. The aMCI group showed reduced rich-club connectivity compared with the HC. In addition, the aMCI group showed significantly increased feeder connectivity and decreased local connectivity of the diverse club compared with the SCD group. The overlapping nodes of the rich club and diverse club showed a significant difference in nodal efficiency and shortest path length (Lp) between groups. Notably, the Lp values of overlapping nodes in the SCD and aMCI groups were significantly associated with episodic memory. Conclusion The present study demonstrates that the network properties of SCD and aMCI have varying degrees of damage. The combination of the rich club and the diverse club can provide a novel insight into the pathological mechanism of the AD spectrum. The altered patterns in overlapping nodes might be potential biomarkers in the diagnosis of the AD spectrum.
Collapse
Affiliation(s)
- Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Haiting Sun
- Department of Pediatrics, Xijing Hospital, The Fourth Military Medical University (Air Force Medical University), Xi'an, China
| | - Guanjie Hu
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Wenzhang Qi
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yingying Yue
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Jiang Rao
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenjie Yang
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chaoyong Xiao
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China.,Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
| |
Collapse
|
43
|
Common Brain Structural Alterations Associated with Cardiovascular Disease Risk Factors and Alzheimer's Dementia: Future Directions and Implications. Neuropsychol Rev 2020; 30:546-557. [PMID: 33011894 DOI: 10.1007/s11065-020-09460-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 09/24/2020] [Indexed: 01/18/2023]
Abstract
Recent reports suggest declines in the age-specific risk of Alzheimer's dementia in higher income Western countries. At the same time, investigators believe that worldwide trends of increasing mid-life modifiable risk factors [e.g., cardiovascular disease (CVD) risk factors] coupled with the growth of the world's oldest age groups may nonetheless lead to an increase in Alzheimer's dementia. Thus, understanding the overlap in neuroanatomical profiles associated with CVD risk factors and AD may offer more relevant targets for investigating ways to reduce the growing dementia epidemic than current targets specific to isolated AD-related neuropathology. We hypothesized that a core group of common brain structural alterations exist between CVD risk factors and Alzheimer's dementia. Two co-authors conducted independent literature reviews in PubMed using search terms for CVD risk factor burden (separate searches for 'cardiovascular disease risk factors', 'hypertension', and 'Type 2 diabetes') and 'aging' or 'Alzheimer's dementia' with either 'grey matter volumes' or 'white matter'. Of studies that reported regionally localized results, we found support for our hypothesis, determining 23 regions commonly associated with both CVD risk factors and Alzheimer's dementia. Within this context, we outline future directions for research as well as larger cerebrovascular implications for these commonalities. Overall, this review supports previous as well as more recent calls for the consideration that both vascular and neurodegenerative factors contribute to the pathogenesis of dementia.
Collapse
|
44
|
Wang X, Huang W, Su L, Xing Y, Jessen F, Sun Y, Shu N, Han Y. Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease. Mol Neurodegener 2020; 15:55. [PMID: 32962744 PMCID: PMC7507636 DOI: 10.1186/s13024-020-00395-3] [Citation(s) in RCA: 97] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 08/07/2020] [Indexed: 12/15/2022] Open
Abstract
Subjective cognitive decline (SCD) is regarded as the first clinical manifestation in the Alzheimer’s disease (AD) continuum. Investigating populations with SCD is important for understanding the early pathological mechanisms of AD and identifying SCD-related biomarkers, which are critical for the early detection of AD. With the advent of advanced neuroimaging techniques, such as positron emission tomography (PET) and magnetic resonance imaging (MRI), accumulating evidence has revealed structural and functional brain alterations related to the symptoms of SCD. In this review, we summarize the main imaging features and key findings regarding SCD related to AD, from local and regional data to connectivity-based imaging measures, with the aim of delineating a multimodal imaging signature of SCD due to AD. Additionally, the interaction of SCD with other risk factors for dementia due to AD, such as age and the Apolipoprotein E (ApoE) ɛ4 status, has also been described. Finally, the possible explanations for the inconsistent and heterogeneous neuroimaging findings observed in individuals with SCD are discussed, along with future directions. Overall, the literature reveals a preferential vulnerability of AD signature regions in SCD in the context of AD, supporting the notion that individuals with SCD share a similar pattern of brain alterations with patients with mild cognitive impairment (MCI) and dementia due to AD. We conclude that these neuroimaging techniques, particularly multimodal neuroimaging techniques, have great potential for identifying the underlying pathological alterations associated with SCD. More longitudinal studies with larger sample sizes combined with more advanced imaging modeling approaches such as artificial intelligence are still warranted to establish their clinical utility.
Collapse
Affiliation(s)
- Xiaoqi Wang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China
| | - Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Li Su
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,Sino-Britain Centre for Cognition and Ageing Research, Southwest University, Chongqing, China
| | - Yue Xing
- Radiological Sciences, Division of Clinical Neuroscience, University of Nottingham, Nottingham, UK
| | - Frank Jessen
- Department of Psychiatry and Psychotherapy, Medical Faculty, University of Cologne, 50937, Cologne, Germany.,German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Yu Sun
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China. .,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China. .,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China. .,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China. .,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China. .,National Clinical Research Center for Geriatric Disorders, Beijing, China.
| |
Collapse
|
45
|
Determining the effects of LLD and MCI on brain decline according to machine learning and a structural covariance network analysis. J Psychiatr Res 2020; 126:43-54. [PMID: 32416386 DOI: 10.1016/j.jpsychires.2020.04.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 04/21/2020] [Accepted: 04/27/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Late-life depression (LLD) and mild cognitive impairment (MCI) are risk factors for Alzheimer disease (AD). However, the interactive effect between LLD and MCI in the progression to AD remains unknown. The purpose of this research is to clarify whether this interaction exists and determined the characteristics of the structural change patterns in LLD and MCI. METHOD To address this question, a total 225 participants (91 with intact cognitive function (IC), 34 with MCI, 35 with LLD-IC, 47 with LLD-MCI and 18 with AD) were recruited for the current study and their T1 scanning were acquired. Machine learning was applied to estimate the brain's age gap according to grey matter information (thickness and volume was calculated based on the Human Connectome Project Multi-Modal Parcellation version 1.0 and the Desikan atlas). A structural covariance network (SCN) was constructed based on grey matter volume. Rich-club analysis, global network properties and the Jaccard distance were utilized to describe the topological features in each cohort. Their cognitive functions (executive function, processing speed and memory) were evaluated by a full-scale battery of neuropsychological tests. RESULT The interactive effect between LLD and MCI was detected through the brain age gap. The estimated age was positively correlated with processing speed and memory in LLD and non-LLD subjects. In the SCN analysis, the rich-club coefficient and global network properties were disrupted in the MCI group, but remained normal in the LLD-IC, LLD-MCI and AD groups. There was a significant discrepancy in brain structural change patterns between the AD and other cohorts by the Jaccard distance. CONCLUSION The application of machine learning reflects that synergies between LLD and MCI could increase the risk of developing AD. According to the SCN, the structural coordination was disrupted in MCI and was kept normal in the other cohorts, while the discrepancies in brain structural change patterns appeared in AD. Overall, the brain age gap could be a potential predictor of AD, and the Jaccard distance has the potential to be a new type of SCN analysis indicator.
Collapse
|
46
|
Kim DJ, Min BK. Rich-club in the brain's macrostructure: Insights from graph theoretical analysis. Comput Struct Biotechnol J 2020; 18:1761-1773. [PMID: 32695269 PMCID: PMC7355726 DOI: 10.1016/j.csbj.2020.06.039] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 06/19/2020] [Accepted: 06/22/2020] [Indexed: 02/07/2023] Open
Abstract
The brain is a complex network. Growing evidence supports the critical roles of a set of brain regions within the brain network, known as the brain’s cores or hubs. These regions require high energy cost but possess highly efficient neural information transfer in the brain’s network and are termed the rich-club. The rich-club of the brain network is essential as it directly regulates functional integration across multiple segregated regions and helps to optimize cognitive processes. Here, we review the recent advances in rich-club organization to address the fundamental roles of the rich-club in the brain and discuss how these core brain regions affect brain development and disorders. We describe the concepts of the rich-club behind network construction in the brain using graph theoretical analysis. We also highlight novel insights based on animal studies related to the rich-club and illustrate how human studies using neuroimaging techniques for brain development and psychiatric/neurological disorders may be relevant to the rich-club phenomenon in the brain network.
Collapse
Key Words
- AD, Alzheimer’s disease
- ADHD, attention deficit hyperactivity disorder
- ASD, autism spectrum disorder
- BD, bipolar disorder
- Brain connectivity
- Brain network
- DTI, diffusion tensor imaging
- EEG, electroencephalography
- Graph theory
- MDD, major depressive disorder
- MEG, magnetoencephalography
- MRI, magnetic resonance imaging
- Neuroimaging
- Rich-club
- TBI, traumatic brain injury
Collapse
Affiliation(s)
- Dae-Jin Kim
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Byoung-Kyong Min
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| |
Collapse
|
47
|
Kuang L, Gao Y, Chen Z, Xing J, Xiong F, Han X. White Matter Brain Network Research in Alzheimer's Disease Using Persistent Features. Molecules 2020; 25:molecules25112472. [PMID: 32471036 PMCID: PMC7321261 DOI: 10.3390/molecules25112472] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 05/20/2020] [Accepted: 05/25/2020] [Indexed: 12/11/2022] Open
Abstract
Despite the severe social burden caused by Alzheimer’s disease (AD), no drug than can change the disease progression has been identified yet. The structural brain network research provides an opportunity to understand physiological deterioration caused by AD and its precursor, mild cognitive impairment (MCI). Recently, persistent homology has been used to study brain network dynamics and characterize the global network organization. However, it is unclear how these parameters reflect changes in structural brain networks of patients with AD or MCI. In this study, our previously proposed persistent features and various traditional graph-theoretical measures are used to quantify the topological property of white matter (WM) network in 150 subjects with diffusion tensor imaging (DTI). We found significant differences in these measures among AD, MCI, and normal controls (NC) under different brain parcellation schemes. The decreased network integration and increased network segregation are presented in AD and MCI. Moreover, the persistent homology-based measures demonstrated stronger statistical capability and robustness than traditional graph-theoretic measures, suggesting that they represent a more sensitive approach to detect altered brain structures and to better understand AD symptomology at the network level. These findings contribute to an increased understanding of structural connectome in AD and provide a novel approach to potentially track the progression of AD.
Collapse
Affiliation(s)
- Liqun Kuang
- School of Data Science and Technology, North University of China, Taiyuan 030051, China; (Y.G.); (Z.C.); (F.X.)
- Correspondence: (L.K.); (X.H.)
| | - Yan Gao
- School of Data Science and Technology, North University of China, Taiyuan 030051, China; (Y.G.); (Z.C.); (F.X.)
| | - Zhongyu Chen
- School of Data Science and Technology, North University of China, Taiyuan 030051, China; (Y.G.); (Z.C.); (F.X.)
| | - Jiacheng Xing
- School of Software, Nanchang University, Nanchang 330047, China;
| | - Fengguang Xiong
- School of Data Science and Technology, North University of China, Taiyuan 030051, China; (Y.G.); (Z.C.); (F.X.)
| | - Xie Han
- School of Data Science and Technology, North University of China, Taiyuan 030051, China; (Y.G.); (Z.C.); (F.X.)
- Correspondence: (L.K.); (X.H.)
| |
Collapse
|
48
|
Chen H, Sheng X, Luo C, Qin R, Ye Q, Zhao H, Xu Y, Bai F. The compensatory phenomenon of the functional connectome related to pathological biomarkers in individuals with subjective cognitive decline. Transl Neurodegener 2020; 9:21. [PMID: 32460888 PMCID: PMC7254770 DOI: 10.1186/s40035-020-00201-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 05/20/2020] [Indexed: 01/01/2023] Open
Abstract
Background Subjective cognitive decline (SCD) is a preclinical stage along the Alzheimer’s disease (AD) continuum. However, little is known about the aberrant patterns of connectivity and topological alterations of the brain functional connectome and their diagnostic value in SCD. Methods Resting-state functional magnetic resonance imaging and graph theory analyses were used to investigate the alterations of the functional connectome in 66 SCD individuals and 64 healthy controls (HC). Pearson correlation analysis was computed to assess the relationships among network metrics, neuropsychological performance and pathological biomarkers. Finally, we used the multiple kernel learning-support vector machine (MKL-SVM) to differentiate the SCD and HC individuals. Results SCD individuals showed higher nodal topological properties (including nodal strength, nodal global efficiency and nodal local efficiency) associated with amyloid-β levels and memory function than the HC, and these regions were mainly located in the default mode network (DMN). Moreover, increased local and medium-range connectivity mainly between the bilateral parahippocampal gyrus (PHG) and other DMN-related regions was found in SCD individuals compared with HC individuals. These aberrant functional network measures exhibited good classification performance in the differentiation of SCD individuals from HC individuals at an accuracy up to 79.23%. Conclusion The findings of this study provide insight into the compensatory mechanism of the functional connectome underlying SCD. The proposed classification method highlights the potential of connectome-based metrics for the identification of the preclinical stage of AD.
Collapse
Affiliation(s)
- Haifeng Chen
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Xiaoning Sheng
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Caimei Luo
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Ruomeng Qin
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Qing Ye
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Hui Zhao
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Yun Xu
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Feng Bai
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China. .,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China. .,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China. .,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China.
| | | |
Collapse
|
49
|
de Brito Robalo BM, Vlegels N, Meier J, Leemans A, Biessels GJ, Reijmer YD. Effect of Fixed-Density Thresholding on Structural Brain Networks: A Demonstration in Cerebral Small Vessel Disease. Brain Connect 2020; 10:121-133. [PMID: 32103679 DOI: 10.1089/brain.2019.0686] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
A popular solution to control for edge density variability in structural brain network analysis is to threshold the networks to a fixed density across all subjects. However, it remains unclear how this type of thresholding affects the basic network architecture in terms of edge weights, hub location, and hub connectivity and, especially, how it affects the sensitivity to detect disease-related abnormalities. We investigated these two questions in a cohort of patients with cerebral small vessel disease and age-matched controls. Brain networks were reconstructed from diffusion magnetic resonance imaging data using deterministic fiber tractography. Networks were thresholded to a fixed density by removing edges with the lowest number of streamlines. We compared edge length (mm), fractional anisotropy (FA), proportion of hub connections, and hub location between the unthresholded and the thresholded networks of each subject. Moreover, we compared weighted graph measures of global and local connectivity obtained from the (un)thresholded networks between patients and controls. We performed these analyses over a range of densities (2-20%). Results indicate that fixed-density thresholding disproportionally removes edges composed of long streamlines, but is independent of FA. The edges removed were not preferentially connected to hub or nonhub nodes. Over half of the original hubs were reproducible when networks were thresholded to a density ≥10%. Furthermore, the between-group differences in graph measures observed in the unthresholded network remained present after thresholding, irrespective of the chosen density. We therefore conclude that moderate fixed-density thresholds can successfully be applied to control for the effects of density in structural brain network analysis.
Collapse
Affiliation(s)
- Bruno M de Brito Robalo
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Naomi Vlegels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jil Meier
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Alexander Leemans
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yael D Reijmer
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | | |
Collapse
|
50
|
Cao R, Wang X, Gao Y, Li T, Zhang H, Hussain W, Xie Y, Wang J, Wang B, Xiang J. Abnormal Anatomical Rich-Club Organization and Structural-Functional Coupling in Mild Cognitive Impairment and Alzheimer's Disease. Front Neurol 2020; 11:53. [PMID: 32117016 PMCID: PMC7013042 DOI: 10.3389/fneur.2020.00053] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 01/14/2020] [Indexed: 12/17/2022] Open
Abstract
Emerging research indicates interruptions in the wiring organization of the brain network in Mild cognitive impairment (MCI) and Alzheimer's disease (AD). Due to the important role of rich-club organization in distinguishing abnormalities of AD patients and the close relationship between structural connectivity (SC) and functional connectivity (FC), our study examined whether changes in SC-FC coupling and the relationship with abnormal rich-club organizations during the development of diseases may contribute to the pathophysiology of AD. Structural diffusion-tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) were performed in 38 normal controls (NCs), 40 MCI patients and 19 AD patients. Measures of the rich-club structure and its role in global structural-functional coupling were administered. Our study found decreased levels of feeder and local connectivity in MCI and AD patients, which were the main contributing factors to the lower efficiency of the brain structural network. Another important finding was that we have more accurately characterized the changing pattern of functional brain dynamics. The enhanced coupling between SC and FC in MCI and AD patients might be due to disruptions in optimal structural organization. More interestingly, we also found increases in the SC-FC coupling for feeder and local connections in MCI and AD patients. SC-FC coupling also showed significant differences between MCI and AD patients, mainly between the abnormal feeder connections. The connection density and coupling strength were significantly correlated with clinical metrics in patients. The present findings enhanced our understanding of the neurophysiologic mechanisms associated with MCI and AD.
Collapse
Affiliation(s)
- Rui Cao
- College of Software, Taiyuan University of Technology, Taiyuan, China
| | - Xin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yuan Gao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Ting Li
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Hui Zhang
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Waqar Hussain
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yunyan Xie
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jing Wang
- Department of Health management, Aerospace Center Hospital, Peking University Aerospace School of Clinical Medicine, Beijing, China
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| |
Collapse
|