The constructing of cognitive functions ontology

Murtazina, Marina; Avdeenko, Tatiana

doi:10.1016/j.procs.2021.04.181

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…A summary of these tasks is provided in Table 1. There are several methods to derive a cognitive ontological factor from scores on a set of cognitive tasks, and a consensus comprehensive framework is yet to be established to define cognitive ontology entity [7,8,16]. However, previous research indicated that cognitive domains, such as executive functions, fluid intelligence, and language abilities, are not independent but rather exhibit a hierarchical cognitive organization [3].…”

Section: (C) Cognitive Factor Computationmentioning

confidence: 99%

“…Unraveling the complexity of the human mind necessitates an in-depth examination of its cognitive functions such as attention, memory, executive functions, language, social cognition, all intertwined and foundational to our understanding of neuroscience [1][2][3]. The advent of cognitive ontology has paved the way for a structured exploration of these mental capacities, mapping their intricate interrelations with a data-driven approach [4][5][6][7][8]. Despite advancements in the construction of cognitive ontology, revealing its neural underpinnings remains a significant challenge.…”

Section: Introductionmentioning

confidence: 99%

“…Given the cognitive factor’s stronger link to cognitive functions over sensory, motor, or emotional functions [7,16] we posited that distinct FC networks would preferentially represent cognitive abilities like executive function and language, but not those less cognitively related, such as motor function and emotion. Furthermore, we hypothesize that the FC networks crucial for representing the cognitive factor may primarily consist of connections within the association cortex.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Unveiling the Core Functional Networks of Cognition: An Ontology-Guided Machine Learning Approach

Wu,

Cui,

Wang

et al. 2024

Preprint

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Deciphering the functional architecture that underpins diverse cognitive functions is fundamental quest in neuroscience. In this study, we employed an innovative machine learning framework that integrated cognitive ontology with functional connectivity analysis to identify brain networks essential for cognition. We identified a core assembly of functional connectomes, primarily located within the association cortex, which showed superior predictive performance compared to two conventional methods widely employed in previous research across various cognitive domains. Our approach achieved a mean prediction accuracy of 0.13 across 16 cognitive tasks, including working memory, reading comprehension, and sustained attention, outperforming the traditional methods' accuracy of 0.08. In contrast, our method showed limited predictive power for sensory, motor, and emotional functions, with a mean prediction accuracy of 0.03 across 9 relevant tasks, slightly lower than the traditional methods' accuracy of 0.04. These cognitive connectomes were further characterized by distinctive patterns of resting-state functional connectivity, structural connectivity via white matter tracts, and gene expression, highlighting their neurogenetic underpinnings. Our findings reveal a domain-general functional network fingerprint that pivotal to cognition, offering a novel computational approach to explore the neural foundations of cognitive abilities.

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