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Duong-Tran D, Nguyen N, Mu S, Chen J, Bao J, Xu F, Garai S, Cadena-Pico J, Kaplan AD, Chen T, Zhao Y, Shen L, Goñi J. A principled framework to assess the information-theoretic fitness of brain functional sub-circuits. ARXIV 2024:arXiv:2406.18531v2. [PMID: 38979488 PMCID: PMC11230349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub-circuits, like functional networks (FNs), onto subjects' functional connectomes (FCs) without adequately assessing the information-theoretic appropriateness of the partition. Another practice that goes unchallenged is thresholding weighted FCs to remove spurious connections without justifying the chosen threshold. This paper leverages recent theoretical advances in Stochastic Block Models (SBMs) to formally define and quantify the information-theoretic fitness (e.g., prominence) of a predetermined set of FNs when mapped to individual FCs under different fMRI task conditions. Our framework allows for evaluating any combination of FC granularity, FN partition, and thresholding strategy, thereby optimizing these choices to preserve important topological features of the human brain connectomes. By applying to the Human Connectome Project with Schaefer parcellations at multiple levels of granularity, the framework showed that the common thresholding value of 0.25 was indeed information-theoretically valid for group-average FCs despite its previous lack of justification. Our results pave the way for the proper use of FNs and thresholding methods and provide insights for future research in individualized parcellations.
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Affiliation(s)
- Duy Duong-Tran
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Mathematics, United States Naval Academy, Annapolis, MD, USA
| | - Nghi Nguyen
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
| | - Shizhuo Mu
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jiong Chen
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Frederick Xu
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sumita Garai
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jose Cadena-Pico
- Machine Learning Group, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Alan David Kaplan
- Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Tianlong Chen
- Department of Computer Science, The University of North Carolina at Chapel Hill
| | - Yize Zhao
- School of Public Health, Yale University, New Heaven, CT, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joaquín Goñi
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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Nordin K, Gorbach T, Pedersen R, Panes Lundmark V, Johansson J, Andersson M, McNulty C, Riklund K, Wåhlin A, Papenberg G, Kalpouzos G, Bäckman L, Salami A. DyNAMiC: A prospective longitudinal study of dopamine and brain connectomes: A new window into cognitive aging. J Neurosci Res 2022; 100:1296-1320. [PMID: 35293013 PMCID: PMC9313590 DOI: 10.1002/jnr.25039] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 01/18/2022] [Accepted: 02/16/2022] [Indexed: 11/07/2022]
Abstract
Concomitant exploration of structural, functional, and neurochemical brain mechanisms underlying age‐related cognitive decline is crucial in promoting healthy aging. Here, we present the DopamiNe, Age, connectoMe, and Cognition (DyNAMiC) project, a multimodal, prospective 5‐year longitudinal study spanning the adult human lifespan. DyNAMiC examines age‐related changes in the brain’s structural and functional connectome in relation to changes in dopamine D1 receptor availability (D1DR), and their associations to cognitive decline. Critically, due to the complete lack of longitudinal D1DR data, the true trajectory of one of the most age‐sensitive dopamine systems remains unknown. The first DyNAMiC wave included 180 healthy participants (20–80 years). Brain imaging included magnetic resonance imaging assessing brain structure (white matter, gray matter, iron), perfusion, and function (during rest and task), and positron emission tomography (PET) with the [11C]SCH23390 radioligand. A subsample (n = 20, >65 years) was additionally scanned with [11C]raclopride PET measuring D2DR. Age‐related variation was evident for multiple modalities, such as D1DR; D2DR, and performance across the domains of episodic memory, working memory, and perceptual speed. Initial analyses demonstrated an inverted u‐shaped association between D1DR and resting‐state functional connectivity across cortical network nodes, such that regions with intermediate D1DR levels showed the highest levels of nodal strength. Evident within each age group, this is the first observation of such an association across the adult lifespan, suggesting that emergent functional architecture depends on underlying D1DR systems. Taken together, DyNAMiC is the largest D1DR study worldwide, and will enable a comprehensive examination of brain mechanisms underlying age‐related cognitive decline.
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Affiliation(s)
- Kristin Nordin
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden.,Department of Integrative Medical Biology, Umeå University, Umeå, Sweden.,Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Tetiana Gorbach
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden.,Department of Integrative Medical Biology, Umeå University, Umeå, Sweden.,Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden.,Umeå School of Business, Economics and Statistics, Umeå University, Umeå, Sweden
| | - Robin Pedersen
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden.,Department of Integrative Medical Biology, Umeå University, Umeå, Sweden.,Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Vania Panes Lundmark
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden.,Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | - Jarkko Johansson
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden.,Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden.,Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Micael Andersson
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden.,Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | - Charlotte McNulty
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden.,Department of Integrative Medical Biology, Umeå University, Umeå, Sweden.,Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Katrine Riklund
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden.,Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Anders Wåhlin
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden.,Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Goran Papenberg
- Aging Research Center, Karolinska Institutet & Stockholm University, Stockholm, Sweden
| | - Grégoria Kalpouzos
- Aging Research Center, Karolinska Institutet & Stockholm University, Stockholm, Sweden
| | - Lars Bäckman
- Aging Research Center, Karolinska Institutet & Stockholm University, Stockholm, Sweden
| | - Alireza Salami
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden.,Department of Integrative Medical Biology, Umeå University, Umeå, Sweden.,Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden.,Aging Research Center, Karolinska Institutet & Stockholm University, Stockholm, Sweden
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Pedersen R, Geerligs L, Andersson M, Gorbach T, Avelar-Pereira B, Wåhlin A, Rieckmann A, Nyberg L, Salami A. When functional blurring becomes deleterious: Reduced system segregation is associated with less white matter integrity and cognitive decline in aging. Neuroimage 2021; 242:118449. [PMID: 34358662 DOI: 10.1016/j.neuroimage.2021.118449] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 06/24/2021] [Accepted: 08/02/2021] [Indexed: 11/18/2022] Open
Abstract
Healthy aging is accompanied by progressive decline in cognitive performance and concomitant changes in brain structure and functional architecture. Age-accompanied alterations in brain function have been characterized on a network level as weaker functional connections within brain networks along with stronger interactions between networks. This phenomenon has been described as age-related differences in functional network segregation. It has been suggested that functional networks related to associative processes are particularly sensitive to age-related deterioration in segregation, possibly related to cognitive decline in aging. However, there have been only a few longitudinal studies with inconclusive results. Here, we used a large longitudinal sample of 284 participants between 25 to 80 years of age at baseline, with cognitive and neuroimaging data collected at up to three time points over a 10-year period. We investigated age-related changes in functional segregation among two large-scale systems comprising associative and sensorimotor-related resting-state networks. We found that functional segregation of associative systems declines in aging with exacerbated deterioration from the late fifties. Changes in associative segregation were positively associated with changes in global cognitive ability, suggesting that decreased segregation has negative consequences for domain-general cognitive functions. Age-related changes in system segregation were partly accounted for by changes in white matter integrity, but white matter integrity only weakly influenced the association between segregation and cognition. Together, these novel findings suggest a cascade where reduced white-matter integrity leads to less distinctive functional systems which in turn contributes to cognitive decline in aging.
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Affiliation(s)
- Robin Pedersen
- Department of Integrative Medical Biology, Umeå University, Umeå, Sweden; Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden; Wallenberg Centre for Molecular Medicine (WCMM), Umeå University, Umeå, Sweden.
| | - Linda Geerligs
- Donders Institute for Brain, Cognition and Behaviour, Radbound University, Nijmegen, the Netherlands
| | - Micael Andersson
- Department of Integrative Medical Biology, Umeå University, Umeå, Sweden; Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden; Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Tetiana Gorbach
- Department of Integrative Medical Biology, Umeå University, Umeå, Sweden; Wallenberg Centre for Molecular Medicine (WCMM), Umeå University, Umeå, Sweden; Department of Statistics, Umeå School of Business, Economics and Statistics, Umeå University, Umeå, Sweden
| | - Bárbara Avelar-Pereira
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, California, USA; Aging Research Center (ARC), Karolinska Institutet and Stockholm University, Stockholm, Sweden
| | - Anders Wåhlin
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden; Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Anna Rieckmann
- Department of Integrative Medical Biology, Umeå University, Umeå, Sweden; Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden; Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Lars Nyberg
- Department of Integrative Medical Biology, Umeå University, Umeå, Sweden; Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden; Wallenberg Centre for Molecular Medicine (WCMM), Umeå University, Umeå, Sweden; Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Alireza Salami
- Department of Integrative Medical Biology, Umeå University, Umeå, Sweden; Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden; Wallenberg Centre for Molecular Medicine (WCMM), Umeå University, Umeå, Sweden; Aging Research Center (ARC), Karolinska Institutet and Stockholm University, Stockholm, Sweden
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