1
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Dayan E. Neuropathologic Processes Linked to Brain Network Resilience in Parkinson Disease: Local or Distributed Effects? Neurology 2024; 103:e209731. [PMID: 39042845 DOI: 10.1212/wnl.0000000000209731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/25/2024] Open
Affiliation(s)
- Eran Dayan
- From the Department of Radiology, University of North Carolina at Chapel Hill
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2
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Stanford W, Mucha PJ, Dayan E. Age-related differences in network controllability are mitigated by redundancy in large-scale brain networks. Commun Biol 2024; 7:701. [PMID: 38849512 PMCID: PMC11161655 DOI: 10.1038/s42003-024-06392-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 05/28/2024] [Indexed: 06/09/2024] Open
Abstract
The aging brain undergoes major changes in its topology. The mechanisms by which the brain mitigates age-associated changes in topology to maintain robust control of brain networks are unknown. Here we use diffusion MRI data from cognitively intact participants (n = 480, ages 40-90) to study age-associated differences in the average controllability of structural brain networks, topological features that could mitigate these differences, and the overall effect on cognitive function. We find age-associated declines in average controllability in control hubs and large-scale networks, particularly within the frontoparietal control and default mode networks. Further, we find that redundancy, a hypothesized mechanism of reserve, quantified via the assessment of multi-step paths within networks, mitigates the effects of topological differences on average network controllability. Lastly, we discover that average network controllability, redundancy, and grey matter volume, each uniquely contribute to predictive models of cognitive function. In sum, our results highlight the importance of redundancy for robust control of brain networks and in cognitive function in healthy-aging.
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Affiliation(s)
- William Stanford
- Biological and Biomedical Sciences Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Peter J Mucha
- Department of Mathematics, Dartmouth College, Hanover, NH, USA
| | - Eran Dayan
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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3
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Zheng D, Ruan Y, Cao X, Guo W, Zhang X, Qi W, Yuan Q, Liang X, Zhang D, Huang Q, Xue C. Directed Functional Connectivity Changes of Triple Networks for Stable and Progressive Mild Cognitive Impairment. Neuroscience 2024; 545:47-58. [PMID: 38490330 DOI: 10.1016/j.neuroscience.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/22/2024] [Accepted: 03/05/2024] [Indexed: 03/17/2024]
Abstract
Mild cognitive impairment includes two distinct subtypes, namely progressive mild cognitive impairment and stable mild cognitive impairment. While alterations in extensive functional connectivity have been observed in both subtypes, limited attention has been given to directed functional connectivity. A triple network, composed of the central executive network, default mode network, and salience network, is considered to be the core cognitive network. We evaluated the alterations in directed functional connectivity within and between the triple network in progressive and stable mild cognitive impairment groups and investigated its role in predicting disease conversion. Resting-state functional magnetic resonance imaging was used to analyze directed functional connectivity within the triple networks. A correlation analysis was performed to investigate potential associations between altered directed functional connectivity within the triple networks and the neurocognitive performance of the participants. Our study revealed significant differences in directed functional connectivity within and between the triple network in the progressive and stable mild cognitive impairment groups. Altered directed functional connectivity within the triple network was involved in episodic memory and executive function. Thus, the directed functional connectivity of the triple network may be used as an imaging marker of mild cognitive impairment.
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Affiliation(s)
- Darui Zheng
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Yiming Ruan
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Xuan Cao
- Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati, Cincinnati, USA
| | - Wenxuan Guo
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Xulian Zhang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Wenzhang Qi
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Qianqian Yuan
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Xuhong Liang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Da Zhang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Qingling Huang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.
| | - Chen Xue
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.
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4
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Robinson B, Bhamidi S, Dayan E. The spatial distribution of coupling between tau and neurodegeneration in amyloid-β positive mild cognitive impairment. Neurobiol Aging 2024; 136:70-77. [PMID: 38330641 PMCID: PMC10940182 DOI: 10.1016/j.neurobiolaging.2024.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 01/24/2024] [Accepted: 01/26/2024] [Indexed: 02/10/2024]
Abstract
Synergies between amyloid-β (Aβ), tau, and neurodegeneration persist along the Alzheimer's disease (AD) continuum. This study aimed to evaluate the extent of spatial coupling between tau and neurodegeneration (atrophy) and its relation to Aβ positivity in mild cognitive impairment (MCI). Data from 409 participants were included (95 cognitively normal controls, 158 Aβ positive (Aβ+) MCI, and 156 Aβ negative (Aβ-) MCI). Florbetapir PET, Flortaucipir PET, and structural MRI were used as biomarkers for Aβ, tau and atrophy, respectively. Individual correlation matrices for tau load and atrophy were used to layer a multilayer network, with separate layers for tau and atrophy. A measure of coupling between corresponding regions of interest (ROIs) in the tau and atrophy layers was computed, as a function of Aβ positivity. Fewer than 25% of the ROIs across the brain showed heightened coupling between tau and atrophy in Aβ+ , relative to Aβ- MCI. Coupling strengths in the right rostral middle frontal and right paracentral gyri, in particular, mediated the association between Aβ burden and cognition in this sample.
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Affiliation(s)
- Belfin Robinson
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Shankar Bhamidi
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Eran Dayan
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA; Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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5
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Luppi AI, Rosas FE, Mediano PAM, Menon DK, Stamatakis EA. Information decomposition and the informational architecture of the brain. Trends Cogn Sci 2024; 28:352-368. [PMID: 38199949 DOI: 10.1016/j.tics.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/09/2023] [Accepted: 11/17/2023] [Indexed: 01/12/2024]
Abstract
To explain how the brain orchestrates information-processing for cognition, we must understand information itself. Importantly, information is not a monolithic entity. Information decomposition techniques provide a way to split information into its constituent elements: unique, redundant, and synergistic information. We review how disentangling synergistic and redundant interactions is redefining our understanding of integrative brain function and its neural organisation. To explain how the brain navigates the trade-offs between redundancy and synergy, we review converging evidence integrating the structural, molecular, and functional underpinnings of synergy and redundancy; their roles in cognition and computation; and how they might arise over evolution and development. Overall, disentangling synergistic and redundant information provides a guiding principle for understanding the informational architecture of the brain and cognition.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Fernando E Rosas
- Department of Informatics, University of Sussex, Brighton, UK; Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK; Centre for Complexity Science, Imperial College London, London, UK; Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
| | - Pedro A M Mediano
- Department of Computing, Imperial College London, London, UK; Department of Psychology, University of Cambridge, Cambridge, UK
| | - David K Menon
- Department of Medicine, University of Cambridge, Cambridge, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
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6
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Cui L, Hong H, Wang S, Zeng Q, Jiaerken Y, Yu X, Zhang R, Zhang Y, Xie L, Lin M, Liu L, Luo X, Li K, Liu X, Li J, Huang P, Zhang M. Small vessel disease and cognitive reserve oppositely modulate global network redundancy and cognitive function: A study in middle-to-old aged community participants. Hum Brain Mapp 2024; 45:e26634. [PMID: 38553856 PMCID: PMC10980841 DOI: 10.1002/hbm.26634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 01/05/2024] [Accepted: 02/08/2024] [Indexed: 04/02/2024] Open
Abstract
Cerebral small vessel disease (SVD) can disrupt the global brain network and lead to cognitive impairment. Conversely, cognitive reserve (CR) can improve one's cognitive ability to handle damaging effects like SVD, partly by optimizing the brain network's organization. Understanding how SVD and CR collectively influence brain networks could be instrumental in preventing cognitive impairment. Recently, brain redundancy has emerged as a critical network protective metric, providing a nuanced perspective of changes in network organization. However, it remains unclear how SVD and CR affect global redundancy and subsequently cognitive function. Here, we included 121 community-dwelling participants who underwent neuropsychological assessments and a multimodal MRI examination. We visually examined common SVD imaging markers and assessed lifespan CR using the Cognitive Reserve Index Questionnaire. We quantified the global redundancy index (RI) based on the dynamic functional connectome. We then conducted multiple linear regressions to explore the specific cognitive domains related to RI and the associations of RI with SVD and CR. We also conducted mediation analyses to explore whether RI mediated the relationships between SVD, CR, and cognition. We found negative correlations of RI with the presence of microbleeds (MBs) and the SVD total score, and a positive correlation of RI with leisure activity-related CR (CRI-leisure). RI was positively correlated with memory and fully mediated the relationships between the MBs, CRI-leisure, and memory. Our study highlights the potential benefits of promoting leisure activities and keeping brain redundancy for memory preservation in older adults, especially those with SVD.
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Affiliation(s)
- Lei Cui
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University, School of MedicineHangzhouChina
| | - Hui Hong
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University, School of MedicineHangzhouChina
| | - Shuyue Wang
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University, School of MedicineHangzhouChina
| | - Qingze Zeng
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University, School of MedicineHangzhouChina
| | - Yeerfan Jiaerken
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University, School of MedicineHangzhouChina
| | - Xinfeng Yu
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University, School of MedicineHangzhouChina
| | - Ruiting Zhang
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University, School of MedicineHangzhouChina
| | - Yao Zhang
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University, School of MedicineHangzhouChina
| | - Linyun Xie
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University, School of MedicineHangzhouChina
| | - Miao Lin
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University, School of MedicineHangzhouChina
| | - Lingyun Liu
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University, School of MedicineHangzhouChina
| | - Xiao Luo
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University, School of MedicineHangzhouChina
| | - Kaicheng Li
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University, School of MedicineHangzhouChina
| | - Xiaocao Liu
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University, School of MedicineHangzhouChina
| | - Jixuan Li
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University, School of MedicineHangzhouChina
| | - Peiyu Huang
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University, School of MedicineHangzhouChina
| | - Minming Zhang
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University, School of MedicineHangzhouChina
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7
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Robinson B, Bhamidi S, Dayan E. The spatial distribution of coupling between tau and neurodegeneration in amyloid-β positive mild cognitive impairment. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.13.23288533. [PMID: 37131677 PMCID: PMC10153340 DOI: 10.1101/2023.04.13.23288533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Synergies between amyloid-β (Aβ), tau, and neurodegeneration persist along the Alzheimer's disease (AD) continuum. This study aimed to evaluate the extent of spatial coupling between tau and neurodegeneration (atrophy) and its relation to Aβ positivity in mild cognitive impairment (MCI). Data from 409 subjects were included (95 cognitively normal controls, 158 Aβ positive (Aβ+) MCI, and 156 Aβ negative (Aβ-) MCI) Florbetapir PET, Flortaucipir PET, and structural MRI were used as biomarkers for Aβ, tau and atrophy, respectively. Individual correlation matrices for tau load and atrophy were used to layer a multilayer network, with separate layers for tau and atrophy. A measure of coupling between corresponding regions of interest/nodes in the tau and atrophy layers was computed, as a function of Aβ positivity. The extent to which tau-atrophy coupling mediated associations between Aβ burden and cognitive decline was also evaluated. Heightened coupling between tau and atrophy in Aβ+ MCI was found primarily in the entorhinal and hippocampal regions (i.e., in regions corresponding to Braak stages I/II), and to a lesser extent in limbic and neocortical regions (i.e., corresponding to later Braak stages). Coupling strengths in the right middle temporal and inferior temporal gyri mediated the association between Aβ burden and cognition in this sample. Higher coupling between tau and atrophy in Aβ+ MCI is primarily evident in regions corresponding to early Braak stages and relates to overall cognitive decline. Coupling in neocortical regions is more restricted in MCI.
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8
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Ghanbari M, Li G, Hsu L, Yap P. Accumulation of network redundancy marks the early stage of Alzheimer's disease. Hum Brain Mapp 2023; 44:2993-3006. [PMID: 36896755 PMCID: PMC10171535 DOI: 10.1002/hbm.26257] [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: 08/25/2022] [Revised: 02/15/2023] [Accepted: 02/18/2023] [Indexed: 03/11/2023] Open
Abstract
Brain wiring redundancy counteracts aging-related cognitive decline by reserving additional communication channels as a neuroprotective mechanism. Such a mechanism plays a potentially important role in maintaining cognitive function during the early stages of neurodegenerative disorders such as Alzheimer's disease (AD). AD is characterized by severe cognitive decline and involves a long prodromal stage of mild cognitive impairment (MCI). Since MCI subjects are at high risk of converting to AD, identifying MCI individuals is essential for early intervention. To delineate the redundancy profile during AD progression and enable better MCI diagnosis, we define a metric that reflects redundant disjoint connections between brain regions and extract redundancy features in three high-order brain networks-medial frontal, frontoparietal, and default mode networks-based on dynamic functional connectivity (dFC) captured by resting-state functional magnetic resonance imaging (rs-fMRI). We show that redundancy increases significantly from normal control (NC) to MCI individuals and decreases slightly from MCI to AD individuals. We further demonstrate that statistical features of redundancy are highly discriminative and yield state-of-the-art accuracy of up to 96.8 ± 1.0% in support vector machine (SVM) classification between NC and MCI individuals. This study provides evidence supporting the notion that redundancy serves as a crucial neuroprotective mechanism in MCI.
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Affiliation(s)
- Maryam Ghanbari
- Department of RadiologyUniversity of North CarolinaChapel HillNorth CarolinaUSA
- Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Guoshi Li
- Department of RadiologyUniversity of North CarolinaChapel HillNorth CarolinaUSA
- Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Li‐Ming Hsu
- Department of RadiologyUniversity of North CarolinaChapel HillNorth CarolinaUSA
- Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Pew‐Thian Yap
- Department of RadiologyUniversity of North CarolinaChapel HillNorth CarolinaUSA
- Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillNorth CarolinaUSA
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9
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Stanford W, Mucha PJ, Dayan E. Age-related changes in network controllability are mitigated by redundancy in large-scale brain networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.17.528999. [PMID: 36824776 PMCID: PMC9949152 DOI: 10.1101/2023.02.17.528999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2023]
Abstract
The aging brain undergoes major changes in its topology. The mechanisms by which the brain mitigates age-associated changes in topology to maintain robust control of brain networks are unknown. Here we used diffusion MRI data from cognitively intact participants (n=480, ages 40-90) to study age-associated changes in the controllability of structural brain networks, features that could mitigate these changes, and the overall effect on cognitive function. We found age-associated declines in controllability in control hubs and large-scale networks, particularly within the and frontoparietal control and default mode networks. Redundancy, quantified via the assessment of multi-step paths within networks, mitigated the effects of changes in topology on network controllability. Lastly, network controllability, redundancy, and grey matter volume each played important complementary roles in cognitive function. In sum, our results highlight the importance of redundancy for robust control of brain networks and in cognitive function in healthy-aging.
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10
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Kwak K, Stanford W, Dayan E. Identifying the regional substrates predictive of Alzheimer's disease progression through a convolutional neural network model and occlusion. Hum Brain Mapp 2022; 43:5509-5519. [PMID: 35904092 PMCID: PMC9704798 DOI: 10.1002/hbm.26026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 06/02/2022] [Accepted: 07/08/2022] [Indexed: 01/15/2023] Open
Abstract
Progressive brain atrophy is a key neuropathological hallmark of Alzheimer's disease (AD) dementia. However, atrophy patterns along the progression of AD dementia are diffuse and variable and are often missed by univariate methods. Consequently, identifying the major regional atrophy patterns underlying AD dementia progression is challenging. In the current study, we propose a method that evaluates the degree to which specific regional atrophy patterns are predictive of AD dementia progression, while holding all other atrophy changes constant using a total sample of 334 subjects. We first trained a dense convolutional neural network model to differentiate individuals with mild cognitive impairment (MCI) who progress to AD dementia versus those with a stable MCI diagnosis. Then, we retested the model multiple times, each time occluding different regions of interest (ROIs) from the model's testing set's input. We also validated this approach by occluding ROIs based on Braak's staging scheme. We found that the hippocampus, fusiform, and inferior temporal gyri were the strongest predictors of AD dementia progression, in agreement with established staging models. We also found that occlusion of limbic ROIs defined according to Braak stage III had the largest impact on the performance of the model. Our predictive model reveals the major regional patterns of atrophy predictive of AD dementia progression. These results highlight the potential for early diagnosis and stratification of individuals with prodromal AD dementia based on patterns of cortical atrophy, prior to interventional clinical trials.
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Affiliation(s)
- Kichang Kwak
- Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - William Stanford
- Neuroscience Curriculum, Biological and Biomedical Sciences ProgramUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Eran Dayan
- Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
- Neuroscience Curriculum, Biological and Biomedical Sciences ProgramUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
- Department of RadiologyUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
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11
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Stanford WC, Mucha PJ, Dayan E. A robust core architecture of functional brain networks supports topological resilience and cognitive performance in middle- and old-aged adults. Proc Natl Acad Sci U S A 2022; 119:e2203682119. [PMID: 36282912 PMCID: PMC9636938 DOI: 10.1073/pnas.2203682119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 09/21/2022] [Indexed: 11/18/2022] Open
Abstract
Aging is associated with gradual changes in cognition, yet some individuals exhibit protection against age-related cognitive decline. The topological characteristics of brain networks that promote protection against cognitive decline in aging are unknown. Here, we investigated whether the robustness and resilience of brain networks, queried via the delineation of the brain's core network structure, relate to age and cognitive performance in a cross-sectional dataset of healthy middle- and old-aged adults (n = 478, ages 40 to 90 y). First, we decomposed each subject's functional brain network using k-shell decomposition and found that age was negatively associated with robust core network structures. Next, we perturbed these networks, via attack simulations, and found that resilience of core brain network nodes also declined in relationship to age. We then partitioned our dataset into middle- (ages 40 to 65 y, n = 300) and old- (ages 65 to 90 y, n = 178) aged subjects and observed that older individuals had less robust core connectivity and resilience. Following these analyses, we found that episodic memory was positively related to robust connectivity and core resilience, particularly within the default node, limbic, and frontoparietal control networks. Importantly, we found that age-related differences in episodic memory were positively related to core resilience, which indicates a potential role for core resilience in protection against cognitive decline. Together, these findings suggest that robust core connectivity and resilience of brain networks could facilitate high cognitive performance in aging.
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Affiliation(s)
- William C. Stanford
- Biological and Biomedical Sciences Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
| | - Peter J. Mucha
- Department of Mathematics, Dartmouth College, Hanover, NH 03755
| | - Eran Dayan
- Biological and Biomedical Sciences Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
- Department of Radiology, Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
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12
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Kwak K, Giovanello KS, Bozoki A, Styner M, Dayan E. Subtyping of mild cognitive impairment using a deep learning model based on brain atrophy patterns. Cell Rep Med 2021; 2:100467. [PMID: 35028609 PMCID: PMC8714856 DOI: 10.1016/j.xcrm.2021.100467] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 09/08/2021] [Accepted: 11/13/2021] [Indexed: 12/28/2022]
Abstract
Trajectories of cognitive decline vary considerably among individuals with mild cognitive impairment (MCI). To address this heterogeneity, subtyping approaches have been developed, with the objective of identifying more homogeneous subgroups. To date, subtyping of MCI has been based primarily on cognitive measures, often resulting in indistinct boundaries between subgroups and limited validity. Here, we introduce a subtyping method for MCI based solely upon brain atrophy. We train a deep learning model to differentiate between Alzheimer's disease (AD) and cognitively normal (CN) subjects based on whole-brain MRI features. We then deploy the trained model to classify MCI subjects based on whole-brain gray matter resemblance to AD-like or CN-like patterns. We subsequently validate the subtyping approach using cognitive, clinical, fluid biomarker, and molecular imaging data. Overall, the results suggest that atrophy patterns in MCI are sufficiently heterogeneous and can thus be used to subtype individuals into biologically and clinically meaningful subgroups.
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Affiliation(s)
- Kichang Kwak
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Kelly S. Giovanello
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Andrea Bozoki
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Martin Styner
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Eran Dayan
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - for the Alzheimer’s Disease Neuroimaging Initiative
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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13
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Ghanbari M, Zhou Z, Hsu LM, Han Y, Sun Y, Yap PT, Zhang H, Shen D. Altered Connectedness of the Brain Chronnectome During the Progression to Alzheimer's Disease. Neuroinformatics 2021; 20:391-403. [PMID: 34837154 DOI: 10.1007/s12021-021-09554-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/03/2021] [Indexed: 11/24/2022]
Abstract
Graph theory has been extensively used to investigate brain network topology and its changes in disease cohorts. However, many graph theoretic analysis-based brain network studies focused on the shortest paths or, more generally, cost-efficiency. In this work, we use two new concepts, connectedness and 2-connectedness, to measure different global properties compared to the previously widely adopted ones. We apply them to unravel interesting characteristics in the brain, such as redundancy design and further conduct a time-varying brain functional network analysis for characterizing the progression of Alzheimer's disease (AD). Specifically, we define different connectedness and 2-connectedness states and evaluate their dynamics in AD and its preclinical stage, mild cognitive impairment (MCI), compared to the normal controls (NC). Results indicate that, compared to MCI and NC, brain networks of AD tend to be more frequently connected at a sparse level. For MCI, we found that their brains are more likely to be 2-connected in the minimal connected state as well indicating increasing redundancy in brain connectivity. Such a redundant design could ensure maintained connectedness of the MCI's brain network in the case that pathological damages break down any link or silenced any node, making it possible to preserve cognitive abilities. Our study suggests that the redundancy in the brain functional chronnectome could be altered in the preclinical stage of AD. The findings can be successfully replicated in a retest study and with an independent MCI dataset. Characterizing redundancy design in the brain chronnectome using connectedness and 2-connectedness analysis provides a unique viewpoint for understanding disease affected brain networks.
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Affiliation(s)
- Maryam Ghanbari
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Zhen Zhou
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Li-Ming Hsu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ying Han
- Department of National Clinical Research Center for Geriatric Disorders, Beijing, 100053, China
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China
- Beijing Institute of Geriatrics, Beijing, 100053, China
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Yu Sun
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Han Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
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14
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Langella S, Mucha PJ, Giovanello KS, Dayan E. The association between hippocampal volume and memory in pathological aging is mediated by functional redundancy. Neurobiol Aging 2021; 108:179-188. [PMID: 34614422 DOI: 10.1016/j.neurobiolaging.2021.09.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 08/16/2021] [Accepted: 09/01/2021] [Indexed: 10/20/2022]
Abstract
Hippocampal neurodegeneration, a primary component of Alzheimer's disease pathology, relates to poor cognition; however, the mechanisms underlying this relationship are not well understood. Using a sample of cognitively normal older adults and individuals with mild cognitive impairment, this study aims to determine the topological properties of functional networks accompanying hippocampal atrophy in aging, along with their association to cognition and clinical progression. We considered two conceptually differing topological properties: redundancy (the existence of alternative channels of functional commutation) and local efficiency (the efficiency of local information exchange). Hippocampal redundancy, but not local efficiency, mediated the association between low hippocampal volume and low memory in both the whole sample and in ß-amyloid positive participants. Additionally, participants with high hippocampal volume, redundancy, and memory clustered separately from those with low values on all three measures, with the latter group showing higher conversion rates to dementia within three years. Together, these results demonstrate that reduced hippocampal redundancy is one mechanism through which hippocampal atrophy associates with memory impairment in healthy and pathological aging.
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Affiliation(s)
- Stephanie Langella
- Department of Psychology & Neuroscience, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Peter J Mucha
- Department of Mathematics, Dartmouth College, NH 03755, USA
| | - Kelly S Giovanello
- Department of Psychology & Neuroscience, University of North Carolina at Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC 27514, USA
| | - Eran Dayan
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC 27514, USA; Department of Radiology, University of North Carolina at Chapel Hill, NC 27599, USA.
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15
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Cascone AD, Langella S, Sklerov M, Dayan E. Frontoparietal network resilience is associated with protection against cognitive decline in Parkinson's disease. Commun Biol 2021; 4:1021. [PMID: 34471211 PMCID: PMC8410800 DOI: 10.1038/s42003-021-02478-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 07/22/2021] [Indexed: 02/07/2023] Open
Abstract
Though Parkinson's disease is primarily defined as a movement disorder, it is also characterized by a range of non-motor symptoms, including cognitive decline. The onset and progression of cognitive decline in individuals with Parkinson's disease is variable, and the neurobiological mechanisms that contribute to, or protect against, cognitive decline in Parkinson's disease are poorly understood. Using resting-state functional magnetic resonance imaging data collected from individuals with Parkinson's disease with and without cognitive decline, we examined the relationship between topological brain-network resilience and cognition in Parkinson's disease. By leveraging network attack analyses, we demonstrate that relative to individuals with Parkinson's disease experiencing cognitive decline, the frontoparietal network in cognitively stable individuals with Parkinson's disease is significantly more resilient to network perturbation. Our findings suggest that the topological robustness of the frontoparietal network is associated with the absence of cognitive decline in individuals with Parkinson's disease.
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Affiliation(s)
- Arianna D Cascone
- Neuroscience Curriculum, University of North at Chapel Hill, Chapel Hill, NC, United States
| | - Stephanie Langella
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Miriam Sklerov
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Eran Dayan
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
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16
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Kwak K, Niethammer M, Giovanello KS, Styner M, Dayan E. Differential Role for Hippocampal Subfields in Alzheimer's Disease Progression Revealed with Deep Learning. Cereb Cortex 2021; 32:467-478. [PMID: 34322704 DOI: 10.1093/cercor/bhab223] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Mild cognitive impairment (MCI) is often considered the precursor of Alzheimer's disease. However, MCI is associated with substantially variable progression rates, which are not well understood. Attempts to identify the mechanisms that underlie MCI progression have often focused on the hippocampus but have mostly overlooked its intricate structure and subdivisions. Here, we utilized deep learning to delineate the contribution of hippocampal subfields to MCI progression. We propose a dense convolutional neural network architecture that differentiates stable and progressive MCI based on hippocampal morphometry with an accuracy of 75.85%. A novel implementation of occlusion analysis revealed marked differences in the contribution of hippocampal subfields to the performance of the model, with presubiculum, CA1, subiculum, and molecular layer showing the most central role. Moreover, the analysis reveals that 10.5% of the volume of the hippocampus was redundant in the differentiation between stable and progressive MCI.
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Affiliation(s)
- Kichang Kwak
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Marc Niethammer
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kelly S Giovanello
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Martin Styner
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Eran Dayan
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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