The Genetic Origin of Uneven Cognitive Profiles in Heritable Neurodevelopmental Conditions and Individual Differences: Computational Investigations
Saved in:
| Title: | The Genetic Origin of Uneven Cognitive Profiles in Heritable Neurodevelopmental Conditions and Individual Differences: Computational Investigations |
|---|---|
| Language: | English |
| Authors: | Maitrei Kohli, George Magoulas (ORCID |
| Source: | Developmental Science. 2026 29(3). |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 16 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Neurodevelopmental Disorders, Genetics, Brain, Cognitive Development, Profiles, Individual Differences |
| DOI: | 10.1111/desc.70186 |
| ISSN: | 1363-755X 1467-7687 |
| Abstract: | While the heterogeneity and co-occurrence of heritable neurodevelopmental conditions such as autism, attention deficit hyperactivity disorder (ADHD), and dyslexia remain issues of debate, these conditions are nevertheless all characterised by uneven cognitive profiles exhibiting strengths and weaknesses. There have been advances in understanding neural markers and genetic predictors of these conditions, but little insight into how DNA variation can influence functional brain development in such a way as to produce uneven cognitive profiles as developmental outcomes. Uneven cognitive profiles (e.g., across verbal and non-verbal intelligence) also characterise individual differences, and similarly, their genetic basis is little understood. Two main sources of uneven profiles appear possible: that there are regional genetic effects on brain development that act on mechanisms which play an influential role in the development of a particular cognitive or socioemotional ability (domain-specificity); or that genetic effects on brain development have a more widespread influence on neurocomputational properties, but the development of particular abilities is differentially sensitive to variation in those properties (domain-relevance). In this article, we present computational simulations that combine genetic algorithms and artificial neural networks to explore the second of these possibilities, domain relevance. Selection is used to alter the population frequency of alleles that influence the neurocomputational properties of a common substrate, under a polygenic model. Different regions of the substrate become specialised for developing different functions, modelled by five tasks. Across 20 generations, we assess how selection for a given task, which serves to tune substrate-wide neurocomputational properties in favour of this task, serves to alter the development of the other four tasks, which must employ the same range of neurocomputational properties. We demonstrate that such selection can enhance or impair acquisition in non-selected domains, depending on the computational demands of each task domain. We also show that behavioural deficits associate with an increase in the heritability of individual differences. We discuss the results in the context of contemporary theories of the influence of genetic variation on functional and structural brain development, and assess the merits of the domain-specific and domain-relevant accounts of uneven cognitive profiles in neurodevelopmental conditions and individual differences. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1504574 |
| Database: | ERIC |
| Abstract: | While the heterogeneity and co-occurrence of heritable neurodevelopmental conditions such as autism, attention deficit hyperactivity disorder (ADHD), and dyslexia remain issues of debate, these conditions are nevertheless all characterised by uneven cognitive profiles exhibiting strengths and weaknesses. There have been advances in understanding neural markers and genetic predictors of these conditions, but little insight into how DNA variation can influence functional brain development in such a way as to produce uneven cognitive profiles as developmental outcomes. Uneven cognitive profiles (e.g., across verbal and non-verbal intelligence) also characterise individual differences, and similarly, their genetic basis is little understood. Two main sources of uneven profiles appear possible: that there are regional genetic effects on brain development that act on mechanisms which play an influential role in the development of a particular cognitive or socioemotional ability (domain-specificity); or that genetic effects on brain development have a more widespread influence on neurocomputational properties, but the development of particular abilities is differentially sensitive to variation in those properties (domain-relevance). In this article, we present computational simulations that combine genetic algorithms and artificial neural networks to explore the second of these possibilities, domain relevance. Selection is used to alter the population frequency of alleles that influence the neurocomputational properties of a common substrate, under a polygenic model. Different regions of the substrate become specialised for developing different functions, modelled by five tasks. Across 20 generations, we assess how selection for a given task, which serves to tune substrate-wide neurocomputational properties in favour of this task, serves to alter the development of the other four tasks, which must employ the same range of neurocomputational properties. We demonstrate that such selection can enhance or impair acquisition in non-selected domains, depending on the computational demands of each task domain. We also show that behavioural deficits associate with an increase in the heritability of individual differences. We discuss the results in the context of contemporary theories of the influence of genetic variation on functional and structural brain development, and assess the merits of the domain-specific and domain-relevant accounts of uneven cognitive profiles in neurodevelopmental conditions and individual differences. |
|---|---|
| ISSN: | 1363-755X 1467-7687 |
| DOI: | 10.1111/desc.70186 |
