Aging-Sensitive Networks Within the Human Structural Connectome Are Implicated in Late-Life Cognitive Declines

Madole, James W.; Ritchie, Stuart J.; Cox, Simon R.; Buchanan, Colin R; Hernández, Maria Valdés; Maniega, Susana Muñoz; Wardlaw, Joanna M.; Harris, Mathew A.; Bastin, Mark E.; Deary, Ian J.; Tucker‐Drob, Elliot M.

doi:10.1016/j.biopsych.2020.06.010

Cited by 34 publications

(81 citation statements)

References 82 publications

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“…The human brain is organised into complex networks of interconnected regions containing a variety of well-studied macroscopic subnetworks [3][4][5]. The specific subnetworks considered in this study have been characterised on the basis of synchronised (i.e., correlated) regional activity in functional MRI data [6], in addition to converging evidence from other modalities (i.e., structural MRI and lesion-based mapping [7][8][9]). These characterisations allow for a whole-brain approach including cortical and subcortical brain volumes (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

“…In this pre-registered study (https://osf.io/7n4qj), we used genome-wide association data to identify broad genetic dimensions underlying morphometric measures across human brain regions. We do so within canonical brain networks that have been implicated in cognitive functioning and cognitive ageing [6]. We compared these genetic dimensions to phenotypic dimensions within the same networks identified in a large recent study (N = 8,185) which examined phenotypic correlations between structural brain networks and cognitive abilities [6].…”

Section: Introductionmentioning

confidence: 99%

“…We present a novel statistical procedure to extract genetic PCs in univariate per-SNP (single nucleotide polymorphism) summary statistics, to represent genetic network structure. They can be used in a genomic structural equation framework [27] to understand whether there is genetic covariation between dimensions of shared morphometry within networks and cognitive abilities, brain age, and cross-sectional patterning of age-related neurodegeneration [6].…”

Section: Introductionmentioning

confidence: 99%

“…Understanding the biological processes underlying these links is paramount for identifying mechanisms of cognitive ageing that can ultimately be targeted by intervention. The human brain is a complex network of interconnected regions (the 'connectome') [4,5], components of which are interrelated with one another [6,7], age unevenly over time [8], and may be differentially relevant to adult cognitive ageing [1][2][3]. Whereas, considerable attention has been devoted separately to the genetic architecture of human brain morphometry [9,10] and the genetic architecture of adult cognitive ability [11], relatively less work has been devoted to scaffolding investigations of the genetic architecture of human brain morphometry onto the well-established network organization of the brain (although see [12] for a recent exception), or to investigating how genetic links between components of human brain networks relate to ageing and cognition.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we consider well-studied macroscopic subnetworks [5,15,16] that are prominent in the literature, using common, but not indisputable, definitions of the exact regions comprising them [16]. We focus on specific network configurations that have been characterised on the basis of synchronised (i.e., correlated) regional activity in functional MRI data [3], in addition to converging evidence from other modalities (i.e., structural MRI and lesion-based mapping [7,17,18]). The characterisations allow for a whole-brain approach including cortical and subcortical brain volumes (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

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General dimensions of human brain morphometry inferred from genome-wide association data

Fürtjes

Arathimos

Coleman

et al. 2021

Preprint

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The human brain is organised into networks of interconnected regions that have highly correlated volumes. In this study, we aim to triangulate insights into brain organisation and its relationship with cognitive ability and ageing, by analysing genetic data. We estimated general genetic dimensions of human brain morphometry within the whole brain, and nine predefined canonical brain networks of interest. We did so based on principal components analysis (PCA) of genetic correlations among grey-matter volumes for 83 cortical and subcortical regions (Nparticipants = 36,778). We found that the corresponding general dimension of brain morphometry accounts for 40% of the genetic variance in the individual brain regions across the whole brain, and 47-65% within each network of interest. This genetic correlation structure of regional brain morphometry closely resembled the phenotypic correlation structure of the same regions. Applying a novel multivariate methodology for calculating SNP effects for each of the general dimensions identified, we find that general genetic dimensions of morphometry within networks are negatively associated with brain age (rg = -0.34) and profiles characteristic of age-related neurodegeneration, as indexed by cross-sectional age-volume correlations (r = -0.27). The same genetic dimensions were positively associated with a genetic general factor of cognitive ability (rg = 0.17-0.21 for different networks). We have provided a statistical framework to index general dimensions of shared genetic morphometry that vary between brain networks, and report evidence for a shared biological basis underlying brain morphometry, cognitive ability, and brain ageing, that are underpinned by general genetic factors.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

General dimensions of human brain morphometry inferred from genome-wide association data

Fürtjes

Arathimos

Coleman

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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Methodological evaluation of individual cognitive prediction based on the brain white matter structural connectome

Feng

Wang

Huang

et al. 2022

Human Brain Mapping

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An emerging trend is to use regression‐based machine learning approaches to predict cognitive functions at the individual level from neuroimaging data. However, individual prediction models are inherently influenced by the vast options for network construction and model selection in machine learning pipelines. In particular, the brain white matter (WM) structural connectome lacks a systematic evaluation of the effects of different options in the pipeline on predictive performance. Here, we focused on the methodological evaluation of brain structural connectome‐based predictions. For network construction, we considered two parcellation schemes for defining nodes and seven strategies for defining edges. For the regression algorithms, we used eight regression models. Four cognitive domains and brain age were targeted as predictive tasks based on two independent datasets (Beijing Aging Brain Rejuvenation Initiative [BABRI]: 633 healthy older adults; Human Connectome Projects in Aging [HCP‐A]: 560 healthy older adults). Based on the results, the WM structural connectome provided a satisfying predictive ability for individual age and cognitive functions, especially for executive function and attention. Second, different parcellation schemes induce a significant difference in predictive performance. Third, prediction results from different data sets showed that dMRI with distinct acquisition parameters may plausibly result in a preference for proper fiber reconstruction algorithms and different weighting options. Finally, deep learning and Elastic‐Net models are more accurate and robust in connectome‐based predictions. Together, significant effects of different options in WM network construction and regression algorithms on the predictive performances are identified in this study, which may provide important references and guidelines to select suitable options for future studies in this field.

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Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

Yeung

Stolicyn

Buchanan

et al. 2022

Human Brain Mapping

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There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population‐wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.

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Aging-Sensitive Networks Within the Human Structural Connectome Are Implicated in Late-Life Cognitive Declines

Cited by 34 publications

References 82 publications

General dimensions of human brain morphometry inferred from genome-wide association data

General dimensions of human brain morphometry inferred from genome-wide association data

Methodological evaluation of individual cognitive prediction based on the brain white matter structural connectome

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

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