Dynamic Patterns of Brain Structure–Behavior Correlation Across the Lifespan

Razlighi, Qolamreza R.; Oh, Hwamee; Habeck, Christian; O’Shea, Deirdre; Gazes, Elaine; Eich, Teal S.; Parker, David B.; Lee, Seonjoo; Stern, Yaakov

doi:10.1093/cercor/bhw179

Cited by 14 publications

(15 citation statements)

References 51 publications

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“…As can be seen in Table 2 (top), the fit statistics were better for the 4-factor models compared to the 3-factor model. While the 5-factor model had marginally better RMSEA and SRMSR values compared to the 4-factor model [30,31], the four-factor model both converges with previous findings [12,16,20,[32][33][34] and is a more parsimonious model.…”

Section: Behavioral Factor Structuresupporting

confidence: 82%

Towards an ontology of cognitive processes and their neural substrates: A structural equation modeling approach

et al. 2020

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A key challenge in the field of cognitive neuroscience is to identify discriminable cognitive functions, and then map these functions to brain activity. In the current study, we set out to explore the relationships between performance arising from different cognitive tasks thought to tap different domains of cognition, and then to test whether these distinct latent cognitive abilities also are subserved by corresponding "latent" brain substrates. To this end, we tested a large sample of adults under the age of 40 on twelve cognitive tasks as they underwent fMRI scanning. Exploratory factor analysis revealed 4-factor model, dissociating tasks into processes corresponding to episodic memory retrieval, reasoning, speed of processing and vocabulary. An analysis of the topographic covariance patterns of the BOLD-response acquired during each task similarity also converged on four neural networks that corresponded to the 4 latent factors. These results suggest that distinct ontologies of cognition are subserved by corresponding distinct neural networks. OPEN ACCESS Citation: Eich T, Parker D, Gazes Y, Razlighi Q, Habeck C, Stern Y (2020) Towards an ontology of cognitive processes and their neural substrates: A structural equation modeling approach. PLoS ONE 15(2): e0228167. https://doi.org/10.interest really do tap that process, and therefore that brain areas that are commonly activated across these different tasks inferentially index that process.Recently, several groups have used different data-driven approaches to uncover the neural networks underlying different cognitive abilities. Yeo and colleagues [7], for example, leveraged data from the BrainMap database [8], which includes over 10,000 imaging experiments, to create a "latent cognitive structure and its topography" ([7], p. 3665-6). They explored functional specialization and flexibility across 83 different cognitive tasks and reported meaningful functional specialization across tasks (e.g., tasks which in the extant literature are commonly thought to tap inhibitory processes, including the Simon, Posner, Stroop, Flanker, N-Back and Task Switching, all recruited a common neural network), but also reported that individual networks can demonstrate remarkable flexibility (e.g., specific cortical regions participated in multiple cognitive components and divergent cognitive tasks). Bertolero and colleagues [9][10][11] extended this work in several important ways using graph theory network based approaches, first showing with resting state functional MRI data that distinct modules perform distinct cognitive functions, but that, as the number of cognitive functions within a given experimental task increases, so too does activity in connector nodes that link modules [9]. Bertolero and colleagues further showed that hub connectivity accurately predicts performance across 4 different cognitive tasks, and found that those individual who had hubs with higher participation coefficients (e.g., hubs with greater diversity in modular connections) as opposed to higher hub stre...

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Section: Behavioral Factor Structuresupporting

confidence: 82%

Towards an ontology of cognitive processes and their neural substrates: A structural equation modeling approach

et al. 2020

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“…A battery of 12 neuropsychological tests was selected to assess cognitive functioning in four cognitive domains: episodic memory, reasoning ability, perceptual speed, and vocabulary. Previous analyses demonstrated that the included tasks described latent unique latent variables for the four cognitive domains [37] , [38] . There were some missing data for the neuropsychological measures, but we decided to be as inclusive as possible.…”

Section: Methodsmentioning

confidence: 99%

A dopamine receptor genetic variant enhances perceptual speed in cognitive healthy subjects

Barral

Habeck

Gazes

et al. 2017

A&D Transl Res & Clin Interv

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IntroductionCognition is under strong genetic control, yet the specific genes are unknown.MethodsOne hundred and fifty-three cognitive healthy European subjects from the Reference Abilities Study (RANN) were genotyped for 1,160 variants within 446 neuropsychiatric genes. Adjusted linear regression models evaluated the association between the genetic variants and four reference abilities (Vocabulary, Episodic Memory, Perceptual Speed, and Reasoning).ResultsOne hundred and fifty-nine variants nominally were found significant in the RANN cohort and re-evaluated in an independent cohort of 868 cognitive healthy subjects from the Religious Orders Study and Rush Memory Aging Project. Meta-analysis yielded a Bonferroni adjusted statistically significant association between perceptual speed and a variant located in the promoter of the dopamine receptor D4 gene, rs3756450 (β = 0.23, standard error = 0.05, Pmeta = 2.3 × 10−5).DiscussionOur data suggest that genetic variation in a dopamine pathway gene influences perceptual speed performance in cognitively healthy individuals.

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“…Moreover, the association between cognitive performance and brain structural integrity is not uniform from adulthood to elderly years (Burzynska, et al, 2012;Razlighi, et al, 2016;Turner and Spreng, 2012). The later decades of life are also characterised by progressive changes in the performance of everyday functions (Ball, et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

The independent influences of age and education on functional brain networks and cognition in healthy older adults

Perry

Wang²,

Kochan³

et al. 2017

Preprint

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Healthy ageing is accompanied by a constellation of changes in cognitive processes and alterations in functional brain networks. The relationships between brain networks and cognition during ageing in later life are moderated by demographic and environmental factors, such as prior education, in a poorly understood manner. Using multivariate analyses, we identify three latent patterns (or modes) linking resting-state functional connectivity to demographic and cognitive measures in 101 cognitively-normal elders. The first mode (p=0.00043) captures an opposing association between age and core cognitive processes such as attention and processing speed on functional connectivity patterns. The functional subnetwork expressed by this mode links bilateral sensorimotor and visual regions through key areas such as the parietal operculum. A strong, independent association between years of education and functional connectivity loads onto a second mode (p=0.012), characterised by the involvement of key hub-regions. A third mode (p=0.041) captures weak, residual brainbehaviour relations. Our findings suggest that circuits supporting lower-level cognitive processes are most sensitive to the influence of age in healthy older adults. Education, and to a lesser extent, executive functions, load independently onto functional networks -suggesting that the moderating effect of education acts upon networks distinct from those vulnerable with ageing. This has important implications in understanding the contribution of education to cognitive reserve during healthy ageing.

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Dynamic Patterns of Brain Structure–Behavior Correlation Across the Lifespan

Cited by 14 publications

References 51 publications

Towards an ontology of cognitive processes and their neural substrates: A structural equation modeling approach

Towards an ontology of cognitive processes and their neural substrates: A structural equation modeling approach

A dopamine receptor genetic variant enhances perceptual speed in cognitive healthy subjects

The independent influences of age and education on functional brain networks and cognition in healthy older adults

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