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Tang H, Ma G, Guo L, Fu X, Huang H, Zhan L. Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:7363-7375. [PMID: 36374890 PMCID: PMC10183052 DOI: 10.1109/tnnls.2022.3220220] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Recently, brain networks have been widely adopted to study brain dynamics, brain development, and brain diseases. Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. However, current graph learning techniques have several issues on brain network mining. First, most current graph learning models are designed for unsigned graph, which hinders the analysis of many signed network data (e.g., brain functional networks). Meanwhile, the insufficiency of brain network data limits the model performance on clinical phenotypes' predictions. Moreover, few of the current graph learning models are interpretable, which may not be capable of providing biological insights for model outcomes. Here, we propose an interpretable hierarchical signed graph representation learning (HSGPL) model to extract graph-level representations from brain functional networks, which can be used for different prediction tasks. To further improve the model performance, we also propose a new strategy to augment functional brain network data for contrastive learning. We evaluate this framework on different classification and regression tasks using data from human connectome project (HCP) and open access series of imaging studies (OASIS). Our results from extensive experiments demonstrate the superiority of the proposed model compared with several state-of-the-art techniques. In addition, we use graph saliency maps, derived from these prediction tasks, to demonstrate detection and interpretation of phenotypic biomarkers.
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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.
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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
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Manos T, Diaz-Pier S, Fortel I, Driscoll I, Zhan L, Leow A. Enhanced simulations of whole-brain dynamics using hybrid resting-state structural connectomes. Front Comput Neurosci 2023; 17:1295395. [PMID: 38188355 PMCID: PMC10770256 DOI: 10.3389/fncom.2023.1295395] [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: 09/16/2023] [Accepted: 12/05/2023] [Indexed: 01/09/2024] Open
Abstract
The human brain, composed of billions of neurons and synaptic connections, is an intricate network coordinating a sophisticated balance of excitatory and inhibitory activities between brain regions. The dynamical balance between excitation and inhibition is vital for adjusting neural input/output relationships in cortical networks and regulating the dynamic range of their responses to stimuli. To infer this balance using connectomics, we recently introduced a computational framework based on the Ising model, which was first developed to explain phase transitions in ferromagnets, and proposed a novel hybrid resting-state structural connectome (rsSC). Here, we show that a generative model based on the Kuramoto phase oscillator can be used to simulate static and dynamic functional connectomes (FC) with rsSC as the coupling weight coefficients, such that the simulated FC aligns well with the observed FC when compared with that simulated traditional structural connectome.
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Affiliation(s)
- Thanos Manos
- ETIS, ENSEA, CNRS, UMR8051, CY Cergy-Paris University, Cergy, France
- Laboratoire de Physique Théorique et Modélisation, UMR 8089, CNRS, Cergy-Pontoise, CY Cergy Paris Université, Cergy, France
| | - Sandra Diaz-Pier
- Simulation and Data Lab Neuroscience, Institute for Advanced Simulation, Jülich Supercomputing Centre (JSC), JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Igor Fortel
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States
| | - Ira Driscoll
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Alex Leow
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
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Fortel I, Zhan L, Ajilore O, Wu Y, Mackin S, Leow A. Disrupted excitation-inhibition balance in cognitively normal individuals at risk of Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.21.554061. [PMID: 37662359 PMCID: PMC10473582 DOI: 10.1101/2023.08.21.554061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Background Sex differences impact Alzheimer's disease (AD) neuropathology, but cell-to-network level dysfunctions in the prodromal phase are unclear. Alterations in hippocampal excitation-inhibition balance (EIB) have recently been linked to early AD pathology. Objective Examine how AD risk factors (age, APOE-ɛ4, amyloid-β) relate to hippocampal EIB in cognitively normal males and females using connectome-level measures. Methods Individuals from the OASIS-3 cohort (age 42-95) were studied (N = 437), with a subset aged 65+ undergoing neuropsychological testing (N = 231). Results In absence of AD risk factors (APOE-ɛ4/Aβ+), whole-brain EIB decreases with age more significantly in males than females (p = 0.021, β = -0.007). Regression modeling including APOE-ɛ4 allele carriers (Aβ-) yielded a significant positive AGE-by-APOE interaction in the right hippocampus for females only (p = 0.013, β = 0.014), persisting with inclusion of Aβ+ individuals (p = 0.012, β = 0.014). Partial correlation analyses of neuropsychological testing showed significant associations with EIB in females: positive correlations between right hippocampal EIB with categorical fluency and whole-brain EIB with the trail-making test (p < 0.05). Conclusion Sex differences in EIB emerge during normal aging and progresses differently with AD risk. Results suggest APOE-ɛ4 disrupts hippocampal balance more than amyloid in females. Increased excitation correlates positively with neuropsychological performance in the female group, suggesting a duality in terms of potential beneficial effects prior to cognitive impairment. This underscores the translational relevance of APOE-ɛ4 related hyperexcitation in females, potentially informing therapeutic targets or early interventions to mitigate AD progression in this vulnerable population.
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Affiliation(s)
- Igor Fortel
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL
| | - Yichao Wu
- Department of Math, Statistics and Computer Science, University of Illinois at Chicago, Chicago, IL
| | - Scott Mackin
- Department of Psychiatry, University of California - San Francisco, San Francisco, CA
| | - Alex Leow
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL
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Manos T, Diaz-Pier S, Fortel I, Driscoll I, Zhan L, Leow A. Enhanced simulations of whole-brain dynamics using hybrid resting-state structural connectomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.16.528836. [PMID: 36824821 PMCID: PMC9948985 DOI: 10.1101/2023.02.16.528836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
The human brain, composed of billions of neurons and synaptic connections, is an intricate network coordinating a sophisticated balance of excitatory and inhibitory activity between brain regions. The dynamical balance between excitation and inhibition is vital for adjusting neural input/output relationships in cortical networks and regulating the dynamic range of their responses to stimuli. To infer this balance using connectomics, we recently introduced a computational framework based on the Ising model, first developed to explain phase transitions in ferromagnets, and proposed a novel hybrid resting-state structural connectome (rsSC). Here, we show that a generative model based on the Kuramoto phase oscillator can be used to simulate static and dynamic functional connectomes (FC) with rsSC as the coupling weight coefficients, such that the simulated FC well aligns with the observed FC when compared to that simulated with traditional structural connectome. Simulations were performed using the open source framework The Virtual Brain on High Performance Computing infrastructure.
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Fortel I, Zhan L, Ajilore O, Wu Y, Mackin S, Leow A. Disrupted Excitation-Inhibition Balance in Cognitively Normal Individuals at Risk of Alzheimer's Disease. J Alzheimers Dis 2023; 95:1449-1467. [PMID: 37718795 PMCID: PMC11260287 DOI: 10.3233/jad-230035] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
BACKGROUND Sex differences impact Alzheimer's disease (AD) neuropathology, but cell-to-network level dysfunctions in the prodromal phase are unclear. Alterations in hippocampal excitation-inhibition balance (EIB) have recently been linked to early AD pathology. OBJECTIVE Examine how AD risk factors (age, APOEɛ4, amyloid-β) relate to hippocampal EIB in cognitively normal males and females using connectome-level measures. METHODS Individuals from the OASIS-3 cohort (age 42-95) were studied (N = 437), with a subset aged 65+ undergoing neuropsychological testing (N = 231). RESULTS In absence of AD risk factors (APOEɛ4/Aβ+), whole-brain EIB decreases with age more significantly in males than females (p = 0.021, β= -0.007). Regression modeling including APOEɛ4 allele carriers (Aβ-) yielded a significant positive AGE-by-APOE interaction in the right hippocampus for females only (p = 0.013, β= 0.014), persisting with inclusion of Aβ+ individuals (p = 0.012, β= 0.014). Partial correlation analyses of neuropsychological testing showed significant associations with EIB in females: positive correlations between right hippocampal EIB with categorical fluency and whole-brain EIB with the Trail Making Test (p < 0.05). CONCLUSIONS Sex differences in EIB emerge during normal aging and progresses differently with AD risk. Results suggest APOEɛ4 disrupts hippocampal balance more than amyloid in females. Increased excitation correlates positively with neuropsychological performance in the female group, suggesting a duality in terms of potential beneficial effects prior to cognitive impairment. This underscores the translational relevance of APOEɛ4 related hyperexcitation in females, potentially informing therapeutic targets or early interventions to mitigate AD progression in this vulnerable population.
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Affiliation(s)
- Igor Fortel
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | - Yichao Wu
- Department of Math, Statistics and Computer Science, University of Illinois at Chicago, Chicago, IL, USA
| | - Scott Mackin
- Department of Psychiatry, University of California – San Francisco, San Francisco, CA, USA
| | - Alex Leow
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, USA
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
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Tang H, Guo L, Fu X, Wang Y, Mackin S, Ajilore O, Leow AD, Thompson PM, Huang H, Zhan L. Signed graph representation learning for functional-to-structural brain network mapping. Med Image Anal 2023; 83:102674. [PMID: 36442294 PMCID: PMC9904311 DOI: 10.1016/j.media.2022.102674] [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: 06/20/2022] [Revised: 10/04/2022] [Accepted: 10/27/2022] [Indexed: 11/18/2022]
Abstract
MRI-derived brain networks have been widely used to understand functional and structural interactions among brain regions, and factors that affect them, such as brain development and diseases. Graph mining on brain networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. Since brain functional and structural networks describe the brain topology from different perspectives, exploring a representation that combines these cross-modality brain networks has significant clinical implications. Most current studies aim to extract a fused representation by projecting the structural network to the functional counterpart. Since the functional network is dynamic and the structural network is static, mapping a static object to a dynamic object may not be optimal. However, mapping in the opposite direction (i.e., from functional to structural networks) are suffered from the challenges introduced by negative links within signed graphs. Here, we propose a novel graph learning framework, named as Deep Signed Brain Graph Mining or DSBGM, with a signed graph encoder that, from an opposite perspective, learns the cross-modality representations by projecting the functional network to the structural counterpart. We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets (HCP and OASIS). Our experimental results clearly demonstrate the advantages of our model compared to several state-of-the-art methods.
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Affiliation(s)
- Haoteng Tang
- University of Pittsburgh, 3700 O'Hara St., Pittsburgh, 15261, PA, USA.
| | - Lei Guo
- University of Pittsburgh, 3700 O'Hara St., Pittsburgh, 15261, PA, USA
| | - Xiyao Fu
- University of Pittsburgh, 3700 O'Hara St., Pittsburgh, 15261, PA, USA
| | - Yalin Wang
- Arizona State University, 699 S Mill Ave., Tempe, 85281, AZ, USA
| | - Scott Mackin
- University of California San Francisco, 505 Parnassus Ave., San Francisco, 94143, CA, USA
| | - Olusola Ajilore
- University of Illinois Chicago, 1601 W. Taylor St., Chicago, 60612, IL, USA
| | - Alex D Leow
- University of Illinois Chicago, 1601 W. Taylor St., Chicago, 60612, IL, USA
| | - Paul M Thompson
- University of Southern California, 2001 N. Soto St., Los Angeles, 90032, CA, USA
| | - Heng Huang
- University of Pittsburgh, 3700 O'Hara St., Pittsburgh, 15261, PA, USA
| | - Liang Zhan
- University of Pittsburgh, 3700 O'Hara St., Pittsburgh, 15261, PA, USA.
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Zhang X, Li R, Xia Y, Zhao H, Cai L, Sha J, Xiao Q, Xiang J, Zhang C, Xu K. Topological patterns of motor networks in Parkinson’s disease with different sides of onset: A resting-state-informed structural connectome study. Front Aging Neurosci 2022; 14:1041744. [DOI: 10.3389/fnagi.2022.1041744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 10/12/2022] [Indexed: 11/13/2022] Open
Abstract
Parkinson’s disease (PD) has a characteristically unilateral pattern of symptoms at onset and in the early stages; this lateralization is considered a diagnostically important diagnosis feature. We aimed to compare the graph-theoretical properties of whole-brain networks generated by using resting-state functional MRI (rs-fMRI), diffusion tensor imaging (DTI), and the resting-state-informed structural connectome (rsSC) in patients with left-onset PD (LPD), right-onset PD (RPD), and healthy controls (HCs). We recruited 26 patients with PD (13 with LPD and 13 with RPD) as well as 13 age- and sex-matched HCs. Rs-fMRI and DTI were performed in all subjects. Graph-theoretical analysis was used to calculate the local and global efficiency of a whole-brain network generated by rs-fMRI, DTI, and rsSC. Two-sample t-tests and Pearson correlation analysis were conducted. Significantly decreased global and local efficiency were revealed specifically in LPD patients compared with HCs when the rsSC network was used; no significant intergroup difference was found by using rs-fMRI or DTI alone. For rsSC network analysis, multiple network metrics were found to be abnormal in LPD. The degree centrality of the left precuneus was significantly correlated with the Unified Parkinson’s Disease Rating Scale (UPDRS) score and disease duration (p = 0.030, r = 0.599; p = 0.037, r = 0.582). The topological properties of motor-related brain networks can differentiate LPD and RPD. Nodal metrics may serve as important structural features for PD diagnosis and monitoring of disease progression. Collectively, these findings may provide neurobiological insights into the lateralization of PD onset.
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Li L, Yu X, Sheng C, Jiang X, Zhang Q, Han Y, Jiang J. A review of brain imaging biomarker genomics in Alzheimer’s disease: implementation and perspectives. Transl Neurodegener 2022; 11:42. [PMID: 36109823 PMCID: PMC9476275 DOI: 10.1186/s40035-022-00315-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/24/2022] [Indexed: 11/25/2022] Open
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disease with phenotypic changes closely associated with both genetic variants and imaging pathology. Brain imaging biomarker genomics has been developed in recent years to reveal potential AD pathological mechanisms and provide early diagnoses. This technique integrates multimodal imaging phenotypes with genetic data in a noninvasive and high-throughput manner. In this review, we summarize the basic analytical framework of brain imaging biomarker genomics and elucidate two main implementation scenarios of this technique in AD studies: (1) exploring novel biomarkers and seeking mutual interpretability and (2) providing a diagnosis and prognosis for AD with combined use of machine learning methods and brain imaging biomarker genomics. Importantly, we highlight the necessity of brain imaging biomarker genomics, discuss the strengths and limitations of current methods, and propose directions for development of this research field.
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Fortel I, Butler M, Korthauer LE, Zhan L, Ajilore O, Sidiropoulos A, Wu Y, Driscoll I, Schonfeld D, Leow A. Inferring excitation-inhibition dynamics using a maximum entropy model unifying brain structure and function. Netw Neurosci 2022; 6:420-444. [PMID: 35733430 PMCID: PMC9205431 DOI: 10.1162/netn_a_00220] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 12/07/2021] [Indexed: 11/04/2022] Open
Abstract
Neural activity coordinated across different scales from neuronal circuits to large-scale brain networks gives rise to complex cognitive functions. Bridging the gap between micro- and macroscale processes, we present a novel framework based on the maximum entropy model to infer a hybrid resting-state structural connectome, representing functional interactions constrained by structural connectivity. We demonstrate that the structurally informed network outperforms the unconstrained model in simulating brain dynamics, wherein by constraining the inference model with the network structure we may improve the estimation of pairwise BOLD signal interactions. Further, we simulate brain network dynamics using Monte Carlo simulations with the new hybrid connectome to probe connectome-level differences in excitation-inhibition balance between apolipoprotein E (APOE)-ε4 carriers and noncarriers. Our results reveal sex differences among APOE-ε4 carriers in functional dynamics at criticality; specifically, female carriers appear to exhibit a lower tolerance to network disruptions resulting from increased excitatory interactions. In sum, the new multimodal network explored here enables analysis of brain dynamics through the integration of structure and function, providing insight into the complex interactions underlying neural activity such as the balance of excitation and inhibition.
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Affiliation(s)
- Igor Fortel
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Mitchell Butler
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Laura E. Korthauer
- Department of Psychology, University of Wisconsin–Milwaukee, Milwaukee, WI, USA
- Warren Alpert Medical School, Brown University, Providence, RI, USA
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | | | - Yichao Wu
- Department of Math, Statistics, and Computer Science, University of Illinois at Chicago, Chicago, IL, USA
| | - Ira Driscoll
- Department of Psychology, University of Wisconsin–Milwaukee, Milwaukee, WI, USA
| | - Dan Schonfeld
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
- Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Alex Leow
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
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11
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Blujus JK, Korthauer LE, Awe E, Frahmand M, Driscoll I. BDNF and KIBRA Polymorphisms Are Related to Altered Resting State Network Connectivity in Middle Age. J Alzheimers Dis 2022; 88:323-334. [PMID: 35599479 DOI: 10.3233/jad-215477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Disease-modifying treatments for Alzheimer's disease (AD) may be more successful if interventions occur early, prior to significant neurodegeneration and subsequent to the onset of clinical symptoms, potentially during middle age. Polymorphisms within BDNF, COMT, and KIBRA have been implicated in AD and relate to episodic memory and executive functioning, two domains that decline early in AD. OBJECTIVE The purpose of the current study was to use an endophenotype approach to examine in healthy, non-demented middle-aged adults the association between polymorphisms in BDNF, COMT, and KIBRA and functional connectivity within networks related to episodic memory and executive function (i.e., default mode network (DMN), executive control network (ECN), and frontoparietal network (FPN)). METHODS Resting state networks were identified using independent component analysis and spatial maps with associated time courses were extracted using a dual regression approach. RESULTS Functional connectivity within the DMN was associated with polymorphisms in BDNF (rs11030096, rs1491850) and KIBRA (rs1030182, rs6555791, rs6555802) (ps < 0.05), ECN connectivity was associated with polymorphisms in KIBRA (rs10475878, rs6555791) (ps < 0.05), and FPN connectivity was associated with KIBRA rs6555791 (p < 0.05). There were no COMT-related differences in functional connectivity of any of the three networks investigated (ps > 0.05). CONCLUSION Our study demonstrates that in middle age, polymorphisms in BDNF and KIBRA are associated with altered functional connectivity in networks that are affected early in AD. Future preclinical work should consider these polymorphisms to further elucidate their role in pathological aging and to aid in the identification of biomarkers.
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Affiliation(s)
| | - Laura Elizabeth Korthauer
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.,Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA.,Department of Psychiatry, Rhode Island Hospital, Providence, RI, USA
| | - Elizabeth Awe
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.,Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Marijam Frahmand
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
| | - Ira Driscoll
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
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Tang H, Guo L, Fu X, Qu B, Thompson PM, Huang H, Zhan L. HIERARCHICAL BRAIN EMBEDDING USING EXPLAINABLE GRAPH LEARNING. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2022; 2022:10.1109/isbi52829.2022.9761543. [PMID: 36687764 PMCID: PMC9851647 DOI: 10.1109/isbi52829.2022.9761543] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Brain networks have been extensively studied in neuroscience, to better understand human behavior, and to identify and characterize distributed brain abnormalities in neurological and psychiatric conditions. Several deep graph learning models have been proposed for brain network analysis, yet most current models lack interpretability, which makes it hard to gain any heuristic biological insights into the results. In this paper, we propose a new explainable graph learning model, named hierarchical brain embedding (HBE), to extract brain network representations based on the network community structure, yielding interpretable hierarchical patterns. We apply our new method to predict aggressivity, rule-breaking, and other standardized behavioral scores from functional brain networks derived using ICA from 1,000 young healthy subjects scanned by the Human Connectome Project. Our results show that the proposed HBE outperforms several state-of-the-art graph learning methods in predicting behavioral measures, and demonstrates similar hierarchical brain network patterns associated with clinical symptoms.
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Affiliation(s)
- Haoteng Tang
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, USA
| | - Lei Guo
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, USA
| | - Xiyao Fu
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, USA
| | | | - Paul M. Thompson
- Institute for Neuroimaging and Informatics, University of Southern California, Marina del Rey, USA
| | - Heng Huang
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, USA
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, USA,Corresponding Author
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13
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Xue X, Wu JJ, Huo BB, Xing XX, Ma J, Li YL, Zheng MX, Hua XY, Xu JG. Age-related alterations of brain metabolic network based on [18F]FDG-PET of rats. Aging (Albany NY) 2022; 14:923-942. [PMID: 35077393 PMCID: PMC8833125 DOI: 10.18632/aging.203851] [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: 08/28/2021] [Accepted: 01/14/2022] [Indexed: 11/25/2022]
Abstract
Using animal models to study the underlying mechanisms of aging will create a critical foundation from which to develop new interventions for aging-related brain disorders. Aging-related reorganization of the brain network has been described for the human brain based on functional, metabolic and structural connectivity. However, alterations in the brain metabolic network of aging rats remain unknown. Here, we submitted young and aged rats to [18F]fluorodeoxyglucose with positron emission tomography (18F-FDG PET) and constructed brain metabolic networks. The topological properties were detected, and the network robustness against random failures and targeted attacks was analyzed for age-group comparison. Compared with young rats, aged rats showed reduced betweenness centrality (BC) in the superior colliculus and a decreased degree (D) in the parietal association cortex. With regard to network robustness, the brain metabolic networks of aged rats were more vulnerable to simulated damage, which showed significantly lower local efficiency and clustering coefficients than those of the young rats against targeted attacks and random failures. The findings support the idea that aged rats have similar aging-related changes in the brain metabolic network to the human brain and can therefore be used as a model for aging studies to provide targets for potential therapies that promote healthy aging.
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Affiliation(s)
- Xin Xue
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Jia-Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Bei-Bei Huo
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Xiang-Xin Xing
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.,Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Jie Ma
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Yu-Lin Li
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Mou-Xiong Zheng
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China.,Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Xu-Yun Hua
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China.,Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Jian-Guang Xu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.,Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China.,Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai 201203, China
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14
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Yu M, Sporns O, Saykin AJ. The human connectome in Alzheimer disease - relationship to biomarkers and genetics. Nat Rev Neurol 2021; 17:545-563. [PMID: 34285392 PMCID: PMC8403643 DOI: 10.1038/s41582-021-00529-1] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/10/2021] [Indexed: 02/06/2023]
Abstract
The pathology of Alzheimer disease (AD) damages structural and functional brain networks, resulting in cognitive impairment. The results of recent connectomics studies have now linked changes in structural and functional network organization in AD to the patterns of amyloid-β and tau accumulation and spread, providing insights into the neurobiological mechanisms of the disease. In addition, the detection of gene-related connectome changes might aid in the early diagnosis of AD and facilitate the development of personalized therapeutic strategies that are effective at earlier stages of the disease spectrum. In this article, we review studies of the associations between connectome changes and amyloid-β and tau pathologies as well as molecular genetics in different subtypes and stages of AD. We also highlight the utility of connectome-derived computational models for replicating empirical findings and for tracking and predicting the progression of biomarker-indicated AD pathophysiology.
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Affiliation(s)
- Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana University Network Science Institute, Bloomington, IN, USA
| | - Olaf Sporns
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana University Network Science Institute, Bloomington, IN, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Andrew J Saykin
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA.
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
- Indiana University Network Science Institute, Bloomington, IN, USA.
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15
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Kucikova L, Goerdten J, Dounavi ME, Mak E, Su L, Waldman AD, Danso S, Muniz-Terrera G, Ritchie CW. Resting-state brain connectivity in healthy young and middle-aged adults at risk of progressive Alzheimer's disease. Neurosci Biobehav Rev 2021; 129:142-153. [PMID: 34310975 DOI: 10.1016/j.neubiorev.2021.07.024] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 05/18/2021] [Accepted: 07/21/2021] [Indexed: 11/15/2022]
Abstract
Functional brain connectivity of the resting-state networks has gained recent attention as a possible biomarker of Alzheimer's Disease (AD). In this paper, we review the literature of functional connectivity differences in young adults and middle-aged cognitively intact individuals with non-modifiable risk factors of AD (n = 17). We focus on three main intrinsic resting-state networks: The Default Mode network, Executive network, and the Salience network. Overall, the evidence from the literature indicated early vulnerability of functional connectivity across different at-risk groups, particularly in the Default Mode Network. While there was little consensus on the interpretation on directionality, the topography of the findings showed frequent overlap across studies, especially in regions that are characteristic of AD (i.e., precuneus, posterior cingulate cortex, and medial prefrontal cortex areas). We conclude that while resting-state functional connectivity markers have great potential to identify at-risk individuals, implementing more data-driven approaches, further longitudinal and cross-validation studies, and the analysis of greater sample sizes are likely to be necessary to fully establish the effectivity and utility of resting-state network-based analyses.
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Affiliation(s)
- Ludmila Kucikova
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom.
| | - Jantje Goerdten
- Department of Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Maria-Eleni Dounavi
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Elijah Mak
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Li Su
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Adam D Waldman
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Samuel Danso
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Graciela Muniz-Terrera
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Craig W Ritchie
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
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16
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Li J, Bian C, Chen D, Meng X, Luo H, Liang H, Shen L. Effect of APOE ε4 on multimodal brain connectomic traits: a persistent homology study. BMC Bioinformatics 2020; 21:535. [PMID: 33371873 PMCID: PMC7768655 DOI: 10.1186/s12859-020-03877-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 11/13/2020] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Although genetic risk factors and network-level neuroimaging abnormalities have shown effects on cognitive performance and brain atrophy in Alzheimer's disease (AD), little is understood about how apolipoprotein E (APOE) ε4 allele, the best-known genetic risk for AD, affect brain connectivity before the onset of symptomatic AD. This study aims to investigate APOE ε4 effects on brain connectivity from the perspective of multimodal connectome. RESULTS Here, we propose a novel multimodal brain network modeling framework and a network quantification method based on persistent homology for identifying APOE ε4-related network differences. Specifically, we employ sparse representation to integrate multimodal brain network information derived from both the resting state functional magnetic resonance imaging (rs-fMRI) data and the diffusion-weighted magnetic resonance imaging (dw-MRI) data. Moreover, persistent homology is proposed to avoid the ad hoc selection of a specific regularization parameter and to capture valuable brain connectivity patterns from the topological perspective. The experimental results demonstrate that our method outperforms the competing methods, and reasonably yields connectomic patterns specific to APOE ε4 carriers and non-carriers. CONCLUSIONS We have proposed a multimodal framework that integrates structural and functional connectivity information for constructing a fused brain network with greater discriminative power. Using persistent homology to extract topological features from the fused brain network, our method can effectively identify APOE ε4-related brain connectomic biomarkers.
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Affiliation(s)
- Jin Li
- College of Automation, Harbin Engineering University, 145 Nantong Street, Harbin, 150001, Heilongjiang, China
| | - Chenyuan Bian
- College of Automation, Harbin Engineering University, 145 Nantong Street, Harbin, 150001, Heilongjiang, China
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, B306 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - Dandan Chen
- College of Automation, Harbin Engineering University, 145 Nantong Street, Harbin, 150001, Heilongjiang, China
| | - Xianglian Meng
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, 213032, China
| | - Haoran Luo
- College of Automation, Harbin Engineering University, 145 Nantong Street, Harbin, 150001, Heilongjiang, China
| | - Hong Liang
- College of Automation, Harbin Engineering University, 145 Nantong Street, Harbin, 150001, Heilongjiang, China.
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, B306 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
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17
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Fortel I, Korthauer LE, Morrissey Z, Zhan L, Ajilore O, Wolfson O, Driscoll I, Schonfeld D, Leow A. Connectome Signatures of Hyperexcitation in Cognitively Intact Middle-Aged Female APOE-ε4 Carriers. Cereb Cortex 2020; 30:6350-6362. [PMID: 32662517 PMCID: PMC7609923 DOI: 10.1093/cercor/bhaa190] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 05/08/2020] [Accepted: 06/07/2020] [Indexed: 12/20/2022] Open
Abstract
Synaptic dysfunction is hypothesized to be one of the earliest brain changes in Alzheimer's disease, leading to "hyperexcitability" in neuronal circuits. In this study, we evaluated a novel hyperexcitation indicator (HI) for each brain region using a hybrid resting-state structural connectome to probe connectome-level excitation-inhibition balance in cognitively intact middle-aged apolipoprotein E (APOE) ε4 carriers with noncarriers (16 male/22 female in each group). Regression with three-way interactions (sex, age, and APOE-ε4 carrier status) to assess the effect of APOE-ε4 on excitation-inhibition balance within each sex and across an age range of 40-60 years yielded a significant shift toward higher HI in female carriers compared with noncarriers (beginning at 50 years). Hyperexcitation was insignificant in the male group. Further, in female carriers the degree of hyperexcitation exhibited significant positive correlation with working memory performance (evaluated via a virtual Morris Water task) in three regions: the left pars triangularis, left hippocampus, and left isthmus of cingulate gyrus. Increased excitation of memory-related circuits may be evidence of compensatory recruitment of neuronal resources for memory-focused activities. In sum, our results are consistent with known Alzheimer's disease sex differences; in that female APOE-ε4 carriers have globally disrupted excitation-inhibition balance that may confer greater vulnerability to disease neuropathology.
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Affiliation(s)
- Igor Fortel
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Laura E Korthauer
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA
- Warren Alpert Medical School, Brown University, Providence, RI 02903, USA
| | - Zachery Morrissey
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Ouri Wolfson
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Ira Driscoll
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA
| | - Dan Schonfeld
- Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL 60607 USA
| | - Alex Leow
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
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18
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Zhu Y, Gong L, He C, Wang Q, Ren Q, Xie C. Default Mode Network Connectivity Moderates the Relationship Between the APOE Genotype and Cognition and Individualizes Identification Across the Alzheimer's Disease Spectrum. J Alzheimers Dis 2020; 70:843-860. [PMID: 31282419 DOI: 10.3233/jad-190254] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Although previous studies have investigated the effects of the apolipoprotein E (APOE) ɛ4 genotype on the default mode network (DMN) in the Alzheimer's disease (AD) spectrum, it is still unclear how the APOE genotype regulates the DMN and subsequently affects cognitive decline in the AD spectrum. One hundred sixty-nine subjects with resting-state functional magnetic resonance imaging data and neuropsychological test scores were selected from the Alzheimer's Disease Neuroimaging Initiative. The main effects and interaction of the APOE genotype and disease status on the DMN were explored. A moderation analysis was performed to investigate the relationship among the APOE genotype, DMN connectivity, and cognition. Additionally, the pair-wised DMN connectivity was used to classify AD spectrum, and the classification accuracy was validated. Compared to APOEɛ4 non-carriers, APOEɛ4 carriers showed the opposite trajectory of DMN connectivity across the AD spectrum. Specifically, the strengths in the posterior cingulate cortex (PCC) connecting with the right precuneus, insular, and fusiform area (FFA) were positively correlated with Mini-Mental State Examination (MMSE) scores in APOEɛ4 non-carriers but not in APOEɛ4 carriers. Furthermore, PCC-right FFA connectivity could moderate the effects of the APOE genotype on MMSE scores across the disease groups. More importantly, using a receiver operating characteristic analysis, these altered connectivities yielded strong classification powers in a pathological stage-dependent manner in the AD spectrum. These findings first identified the intrinsic DMN connectivity moderating the effect of the APOE genotype on cognition and provided a pathological stage-dependent neuroimaging biomarker for early differentiation of the AD spectrum.
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Affiliation(s)
- Yao Zhu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - Liang Gong
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - Cancan He
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - Qing Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - Qingguo Ren
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
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19
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Blujus JK, Korthauer LE, Awe E, Frahmand M, Driscoll I. Single Nucleotide Polymorphisms in Alzheimer's Disease Risk Genes Are Associated with Intrinsic Connectivity in Middle Age. J Alzheimers Dis 2020; 78:309-320. [PMID: 32986668 DOI: 10.3233/jad-200444] [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] [Indexed: 11/15/2022]
Abstract
BACKGROUND It is critical to identify individuals at risk for Alzheimer's disease (AD) earlier in the disease time course, such as middle age and preferably well prior to the onset of clinical symptoms, when intervention efforts may be more successful. Genome-wide association and candidate gene studies have identified single nucleotide polymorphisms (SNPs) in APOE, CLU, CR1, PICALM, and SORL1 that confer increased risk of AD. OBJECTIVE In the current study, we investigated the associations between SNPs in these genes and resting-state functional connectivity within the default mode network (DMN), frontoparietal network (FPN), and executive control network (ECN) in healthy, non-demented middle-aged adults (age 40 -60; N = 123; 74 females). METHODS Resting state networks of interest were identified through independent components analysis using a template-matching procedure and individual spatial maps and time courses were extracted using dual regression. RESULTS Within the posterior DMN, functional connectivity was associated with CR1 rs1408077 and CLU rs9331888 polymorphisms (p's < 0.05). FPN connectivity was associated with CR1 rs1408077, CLU rs1136000, SORL1 rs641120, and SORL1 rs689021 (p's < 0.05). Functional connectivity within the ECN was associated with the CLU rs11136000 (p < 0.05). There were no APOE- or PICALM-related differences in any of the networks investigated (p's > 0.05). CONCLUSION This is the first demonstration of the relationship between intrinsic network connectivity and AD risk alleles in CLU, CR1, and SORL1 in healthy, middle-aged adults. These SNPs should be considered in future investigations aimed at identifying potential preclinical biomarkers for AD.
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Affiliation(s)
| | - Laura Elizabeth Korthauer
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.,Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
| | - Elizabeth Awe
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.,Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Marijam Frahmand
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
| | - Ira Driscoll
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
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20
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Kuang L, Jia J, Zhao D, Xiong F, Han X, Wang Y. Default Mode Network Analysis of APOE Genotype in Cognitively Unimpaired Subjects Based on Persistent Homology. Front Aging Neurosci 2020; 12:188. [PMID: 32733231 PMCID: PMC7358981 DOI: 10.3389/fnagi.2020.00188] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 06/02/2020] [Indexed: 12/22/2022] Open
Abstract
Current researches on default mode network (DMN) in normal elderly have mainly focused on finding some dysfunctional areas with decreased or increased connectivity. The global network dynamics of apolipoprotein E (APOE) e4 allele group is rarely studied. In our previous brain network study, we have demonstrated the advantage of persistent homology. It can distinguish robust and noisy topological features over multiscale nested networks, and the derived properties are more stable. In this study, for the first time we applied persistent homology to analyze APOE-related effects on whole-brain functional network. In our experiments, the risk allele group exhibited lower network radius and modularity in whole brain DMN based on graph theory, suggesting the abnormal organization structure. Moreover, two suggested measures from persistent homology detected significant differences between groups within the left hemisphere and in the whole brain in two datasets. They were more statistically sensitive to APOE genotypic differences than standard graph-based measures. In summary, we provide evidence that the e4 genotype leads to distinct DMN functional alterations in the early phases of Alzheimer's disease using persistent homology approach. Our study offers a novel insight to explore potential biomarkers in healthy elderly populations carrying APOE e4 allele.
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Affiliation(s)
- Liqun Kuang
- School of Data Science and Technology, North University of China, Taiyuan, China
| | - Jiaying Jia
- School of Data Science and Technology, North University of China, Taiyuan, China
| | - Deyu Zhao
- School of Data Science and Technology, North University of China, Taiyuan, China
| | - Fengguang Xiong
- School of Data Science and Technology, North University of China, Taiyuan, China
| | - Xie Han
- School of Data Science and Technology, North University of China, Taiyuan, China
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
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21
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Iacono D, Feltis GC. Impact of Apolipoprotein E gene polymorphism during normal and pathological conditions of the brain across the lifespan. Aging (Albany NY) 2020; 11:787-816. [PMID: 30677746 PMCID: PMC6366964 DOI: 10.18632/aging.101757] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 01/05/2019] [Indexed: 12/12/2022]
Abstract
The central nervous system (CNS) is the cellular substrate for the integration of complex, dynamic, constant, and simultaneous interactions among endogenous and exogenous stimuli across the entire human lifespan. Numerous studies on aging-related brain diseases show that some genes identified as risk factors for some of the most common neurodegenerative diseases - such as the allele 4 of APOE gene (APOE4) for Alzheimer's disease (AD) - have a much earlier neuro-anatomical and neuro-physiological impact. The impact of APOE polymorphism appears in fact to start as early as youth and early-adult life. Intriguingly, though, those same genes associated with aging-related brain diseases seem to influence different aspects of the brain functioning much earlier actually, that is, even from the neonatal periods and earlier. The APOE4, an allele classically associated with later-life neurodegenerative disorders as AD, seems in fact to exert a series of very early effects on phenomena of neuroplasticity and synaptogenesis that begin from the earliest periods of life such as the fetal ones.We reviewed some of the findings supporting the hypothesis that APOE polymorphism is an early modifier of various neurobiological aspects across the entire human lifespan - from the in-utero to the centenarian life - during both normal and pathological conditions of the brain.
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Affiliation(s)
- Diego Iacono
- Neuropathology Research, Biomedical Research Institute of New Jersey (BRInj), Cedar Knolls, NJ 07927, USA.,MidAtlantic Neonatology Associates (MANA), Morristown, NJ 07960, USA.,Atlantic Neuroscience Institute, Atlantic Health System (AHS), Overlook Medical Center, Summit, NJ 07901, USA
| | - Gloria C Feltis
- Neuropathology Research, Biomedical Research Institute of New Jersey (BRInj), Cedar Knolls, NJ 07927, USA
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22
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Genetic influence on ageing-related changes in resting-state brain functional networks in healthy adults: A systematic review. Neurosci Biobehav Rev 2020; 113:98-110. [PMID: 32169413 DOI: 10.1016/j.neubiorev.2020.03.011] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 02/08/2020] [Accepted: 03/09/2020] [Indexed: 11/21/2022]
Abstract
This systematic review examines the genetic and epigenetic factors associated with resting-state functional connectivity (RSFC) in healthy human adult brains across the lifespan, with a focus on genes associated with Alzheimer's disease (AD). There were 58 studies included. The key findings are: (i) genetic factors have a low to moderate contribution; (ii) the apolipoprotein E ε2/3/4 polymorphism was the most studied genetic variant, with the APOE-ε4 allele most consistently associated with deficits of the default mode network, but there were insufficient studies to determine the relationships with other AD candidate risk genes; (iii) a single genome-wide association study identified several variants related to RSFC; (iv) two epigenetic independent studies showed a positive relationship between blood DNA methylation of the SLC6A4 promoter and RSFC measures. Thus, there is emerging evidence that genetic and epigenetic variation influence the brain's functional organisation and connectivity over the adult lifespan. However, more studies are required to elucidate the roles genetic and epigenetic factors play in RSFC measures across the adult lifespan.
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23
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Quevenco FC, van Bergen JM, Treyer V, Studer ST, Kagerer SM, Meyer R, Gietl AF, Kaufmann PA, Nitsch RM, Hock C, Unschuld PG. Functional Brain Network Connectivity Patterns Associated With Normal Cognition at Old-Age, Local β-amyloid, Tau, and APOE4. Front Aging Neurosci 2020; 12:46. [PMID: 32210782 PMCID: PMC7075450 DOI: 10.3389/fnagi.2020.00046] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Accepted: 02/10/2020] [Indexed: 12/30/2022] Open
Abstract
Background: Integrity of functional brain networks is closely associated with maintained cognitive performance at old age. Consistently, both carrier status of Apolipoprotein E ε4 allele (APOE4), and age-related aggregation of Alzheimer’s disease (AD) pathology result in altered brain network connectivity. The posterior cingulate and precuneus (PCP) is a node of particular interest due to its role in crucial memory processes. Moreover, the PCP is subject to the early aggregation of AD pathology. The current study aimed at characterizing brain network properties associated with unimpaired cognition in old aged adults. To determine the effects of age-related brain change and genetic risk for AD, pathological proteins β-amyloid and tau were measured by Positron-emission tomography (PET), PCP connectivity as a proxy of cognitive network integrity, and genetic risk by APOE4 carrier status. Methods: Fifty-seven cognitively unimpaired old-aged adults (MMSE = 29.20 ± 1.11; 73 ± 8.32 years) were administered 11C Pittsburgh Compound B and 18F Flutemetamol PET for assessing β-amyloid, and 18F AV-1451 PET for tau. Individual functional connectivity seed maps of the PCP were obtained by resting-state multiband BOLD functional MRI at 3-Tesla for increased temporal resolution. Voxelwise correlations between functional connectivity, β-amyloid- and tau-PET were explored by Biological Parametric Mapping (BPM). Results: Local β-amyloid was associated with increased connectivity in frontal and parietal regions of the brain. Tau was linked to increased connectivity in more spatially distributed clusters in frontal, parietal, occipital, temporal, and cerebellar regions. A positive interaction was observable for APOE4 carrier status and functional connectivity with brain regions characterized by increased local β-amyloid and tau tracer retention. Conclusions: Our data suggest an association between spatially differing connectivity systems and local β-amyloid, and tau aggregates in cognitively normal, old-aged adults, which is moderated by APOE4. Additional longitudinal studies may determine protective connectivity patterns associated with healthy aging trajectories of AD-pathology aggregation.
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Affiliation(s)
- Frances C Quevenco
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
| | - Jiri M van Bergen
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
| | - Valerie Treyer
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland.,Department of Nuclear Medicine, University of Zurich, Zurich, Switzerland
| | - Sandro T Studer
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
| | - Sonja M Kagerer
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland.,Neurimmune, Schlieren, Switzerland
| | - Rafael Meyer
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
| | - Anton F Gietl
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
| | - Philipp A Kaufmann
- Department of Nuclear Medicine, University of Zurich, Zurich, Switzerland
| | - Roger M Nitsch
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland.,Neurimmune, Schlieren, Switzerland
| | - Christoph Hock
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland.,Neurimmune, Schlieren, Switzerland
| | - Paul G Unschuld
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland.,Department of Psychogeriatric Medicine, Psychiatric University Hospital Zurich (PUK), Zurich, Switzerland.,Zurich Neuroscience Center (ZNZ), Zurich, Switzerland
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24
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Badea A, Wu W, Shuff J, Wang M, Anderson RJ, Qi Y, Johnson GA, Wilson JG, Koudoro S, Garyfallidis E, Colton CA, Dunson DB. Identifying Vulnerable Brain Networks in Mouse Models of Genetic Risk Factors for Late Onset Alzheimer's Disease. Front Neuroinform 2019; 13:72. [PMID: 31920610 PMCID: PMC6914731 DOI: 10.3389/fninf.2019.00072] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 11/13/2019] [Indexed: 12/18/2022] Open
Abstract
The major genetic risk for late onset Alzheimer’s disease has been associated with the presence of APOE4 alleles. However, the impact of different APOE alleles on the brain aging trajectory, and how they interact with the brain local environment in a sex specific manner is not entirely clear. We sought to identify vulnerable brain circuits in novel mouse models with homozygous targeted replacement of the mouse ApoE gene with either human APOE3 or APOE4 gene alleles. These genes are expressed in mice that also model the human immune response to age and disease-associated challenges by expressing the human NOS2 gene in place of the mouse mNos2 gene. These mice had impaired learning and memory when assessed with the Morris water maze (MWM) and novel object recognition (NOR) tests. Ex vivo MRI-DTI analyses revealed global and local atrophy, and areas of reduced fractional anisotropy (FA). Using tensor network principal component analyses for structural connectomes, we inferred the pairwise connections which best separate APOE4 from APOE3 carriers. These involved primarily interhemispheric connections among regions of olfactory areas, the hippocampus, and the cerebellum. Our results also suggest that pairwise connections may be subdivided and clustered spatially to reveal local changes on a finer scale. These analyses revealed not just genotype, but also sex specific differences. Identifying vulnerable networks may provide targets for interventions, and a means to stratify patients.
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Affiliation(s)
- Alexandra Badea
- Department of Radiology, Duke University, Durham, NC, United States.,Department of Neurology, Duke University School of Medicine, Durham, NC, United States.,Brain Imaging and Analysis Center, Duke University, Durham, NC, United States
| | - Wenlin Wu
- Pratt School of Engineering, Duke University, Durham, NC, United States
| | - Jordan Shuff
- Department of Biomedical Engineering, University of Delaware, Newark, NJ, United States
| | - Michele Wang
- Department of Psychology and Neuroscience, Trinity College of Arts & Sciences, Duke University, Durham, NC, United States
| | | | - Yi Qi
- Department of Radiology, Duke University, Durham, NC, United States
| | - G Allan Johnson
- Department of Radiology, Duke University, Durham, NC, United States
| | - Joan G Wilson
- Department of Neurology, Duke University School of Medicine, Durham, NC, United States
| | - Serge Koudoro
- School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, United States
| | - Eleftherios Garyfallidis
- School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, United States
| | - Carol A Colton
- Department of Neurology, Duke University School of Medicine, Durham, NC, United States
| | - David B Dunson
- Department of Statistical Science, Trinity College of Arts & Sciences, Duke University, Durham, NC, United States
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