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Understanding the biological basis of dyslexia at a neural systems level.

ABSTRACT: We examined the naming speed performance of 18 typically achieving and 16 dyslexic adults while simultaneously recording eye movements, articulations and fMRI data. Naming speed tasks, which require participants to name a list of letters or objects, have been proposed as a proxy for reading and are thought to recruit similar reading networks in the left hemisphere of the brain as more complex reading tasks. We employed letter and object naming speed tasks, with task manipulations to make the stimuli more or less phonologically and/or visually similar. Compared to typically achieving readers, readers with dyslexia had a poorer behavioural naming speed task performance, longer fixation durations, more regressions and increased activation in areas of the reading network in the left-hemisphere. Whereas increased network activation was positively associated with performance in dyslexics, it was negatively related to performance in typically achieving readers. Readers with dyslexia had greater bilateral activation and recruited additional regions involved with memory, namely the amygdala and hippocampus; in contrast, the typically achieving readers additionally activated the dorsolateral prefrontal cortex. Areas within the reading network were differentially activated by stimulus manipulations to the naming speed tasks. There was less efficient naming speed behavioural performance, longer fixation durations, more regressions and increased neural activity when letter stimuli were both phonologically and visually similar. Discussion focuses on the differences in activation within the reading network, how they are related to behavioural task differences, and how progress in furthering the understanding of the relationship between behavioural performance and brain activity can change the overall trajectories of children with reading difficulties by contributing to both early identification and remediation processes.

SUBMITTER: Al Dahhan NZ

PROVIDER: S-EPMC7713994 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundProteins, individual cells, and cell populations denote different levels of an organizational hierarchy, each of which with its own dynamics. Multi-level modeling is concerned with describing a system at these different levels and relating their dynamics. Rule-based modeling has increasingly attracted attention due to enabling a concise and compact description of biochemical systems. In addition, it allows different methods for model analysis, since more than one semantics can be defined for the same syntax.ResultsMulti-level modeling implies the hierarchical nesting of model entities and explicit support for downward and upward causation between different levels. Concepts to support multi-level modeling in a rule-based language are identified. To those belong rule schemata, hierarchical nesting of species, assigning attributes and solutions to species at each level and preserving content of nested species while applying rules. Further necessities are the ability to apply rules and flexibly define reaction rate kinetics and constraints on nested species as well as species that are nested within others. An example model is presented that analyses the interplay of an intracellular control circuit with states at cell level, its relation to cell division, and connections to intercellular communication within a population of cells. The example is described in ML-Rules - a rule-based multi-level approach that has been realized within the plug-in-based modeling and simulation framework JAMES II.ConclusionsRule-based languages are a suitable starting point for developing a concise and compact language for multi-level modeling of cell biological systems. The combination of nesting species, assigning attributes, and constraining reactions according to these attributes is crucial in achieving the desired expressiveness. Rule schemata allow a concise and compact description of complex models. As a result, the presented approach facilitates developing and maintaining multi-level models that, for instance, interrelate intracellular and intercellular dynamics.

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Understanding the biological basis of dyslexia at a neural systems level.

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