An inclusive multivariate approach to neural localization of language components.

To determine how language is implemented in the brain, it is important to know which brain areas are primarily engaged in language processing and which are not. Existing protocols for localizing language are typically univariate, treating each small unit of brain volume as independent. One prominent example that focuses on the overall language network in functional magnetic resonance imaging (fMRI) uses a contrast between neural responses to sentences and sets of pseudowords (pronounceable nonwords). This contrast reliably activates peri-sylvian language areas but is less sensitive to extra-sylvian areas that are also known to support aspects of language such as word meanings (semantics). In this study, we assess areas where a multivariate, pattern-based approach shows high reproducibility across multiple measurements and participants, identifying these areas as multivariate regions of interest (mROI). We then perform a representational similarity analysis (RSA) of an fMRI dataset where participants made familiarity judgments on written words. We also compare those results to univariate regions of interest (uROI) taken from previous sentences > pseudowords contrasts. RSA with word stimuli defined in terms of their semantic distance showed greater correspondence with neural patterns in mROI than uROI. This was confirmed in two independent datasets, one involving single-word recognition, and the other focused on the meaning of noun-noun phrases by contrasting meaningful phrases > pseudowords. In all cases, areas of spatial overlap between mROI and uROI showed the greatest neural association. This suggests that ROIs defined in terms of multivariate reproducibility can help localize components of language such as semantics. The multivariate approach can also be extended to focus on other aspects of language such as phonology, and can be used along with the univariate approach for inclusively mapping language cortex.

Correspondence between cognitive and neural representations for phonology, orthography, and semantics in supramarginal compared to angular gyrus.

The angular and supramarginal gyri (AG and SMG) together constitute the inferior parietal lobule (IPL) and have been associated with cognitive functions that support reading. How those functions are distributed across the AG and SMG is a matter of debate, the resolution of which is hampered by inconsistencies across stereotactic atlases provided by the major brain image analysis software packages. Schematic results from automated meta-analyses suggest primarily semantic (word meaning) processing in the left AG, with more spatial overlap among phonological (auditory word form), orthographic (visual word form), and semantic processing in the left SMG. To systematically test for correspondence between patterns of neural activation and phonological, orthographic, and semantic representations, we re-analyze a functional magnetic resonance imaging data set of participants reading aloud 465 words. Using representational similarity analysis, we test the hypothesis that within cytoarchitecture-defined subregions of the IPL, phonological representations are primarily associated with the SMG, while semantic representations are primarily associated with the AG. To the extent that orthographic representations can be de-correlated from phonological representations, they will be associated with cortex peripheral to the IPL, such as the intraparietal sulcus. Results largely confirmed these hypotheses, with some nuanced exceptions, which we discuss in terms of neurally inspired computational cognitive models of reading that learn mappings among distributed representations for orthography, phonology, and semantics. De-correlating constituent representations making up complex cognitive processes, such as reading, by careful selection of stimuli, representational formats, and analysis techniques, are promising approaches for bringing additional clarity to brain structure-function relationships.

Neural Components of Reading Revealed by Distributed and Symbolic Computational Models.

Determining how the cognitive components of reading - orthographic, phonological, and semantic representations - are instantiated in the brain has been a longstanding goal of psychology and human cognitive neuroscience. The two most prominent computational models of reading instantiate different cognitive processes, implying different neural processes. Artificial neural network (ANN) models of reading posit non-symbolic, distributed representations. The dual-route cascaded (DRC) model instead suggests two routes of processing, one representing symbolic rules of spelling-sound correspondence, the other representing orthographic and phonological lexicons. These models are not adjudicated by behavioral data and have never before been directly compared in terms of neural plausibility. We used representational similarity analysis to compare the predictions of these models to neural data from participants reading aloud. Both the ANN and DRC model representations corresponded with neural activity. However, ANN model representations correlated to more reading-relevant areas of cortex. When contributions from the DRC model were statistically controlled, partial correlations revealed that the ANN model accounted for significant variance in the neural data. The opposite analysis, examining the variance explained by the DRC model with contributions from the ANN model factored out, revealed no correspondence to neural activity. Our results suggest that ANNs trained using distributed representations provide a better correspondence between cognitive and neural coding. Additionally, this framework provides a principled approach for comparing computational models of cognitive function to gain insight into neural representations.

Individual Differences in Reading Skill Are Related to Trial-by-Trial Neural Activation Variability in the Reading Network.

Recent work has suggested that variability in levels of neural activation may be related to behavioral and cognitive performance across a number of domains and may offer information that is not captured by more traditional measures that use the average level of brain activation. We examined the relationship between reading skill in school-aged children and neural activation variability during a functional MRI reading task after taking into account average levels of activity. The reading task involved matching printed and spoken words to pictures of items. Single trial activation estimates were used to calculate the mean and standard deviation of children's responses to print and speech stimuli; multiple regression analyses evaluated the relationship between reading skill and trial-by-trial activation variability. The reliability of observed findings from the discovery sample (n = 44; ages 8-11; 18 female) was then confirmed in an independent sample of children (n = 32; ages 8-11; 14 female). Across the two samples, reading skill was positively related to trial-by-trial variability in the activation response to print in the left inferior frontal gyrus pars triangularis. This relationship held even when accounting for mean levels of activation. This finding suggests that intrasubject variability in trial-by-trial fMRI activation responses to printed words accounts for individual differences in human reading ability that are not fully captured by traditional mean levels of brain activity. Furthermore, this positive relationship between trial-by-trial activation variability and reading skill may provide evidence that neural variability plays a beneficial role during early reading development.SIGNIFICANCE STATEMENT Recent work has suggested that neural activation variability, or moment-to-moment changes in the engagement of brain regions, is related to individual differences in behavioral and cognitive performance across multiple domains. However, differences in neural activation variability have not yet been evaluated in relation to reading skill. In the current study, we analyzed data from two independent groups of children who performed an fMRI task involving reading and listening to words. Across both samples, reading skill was positively related to trial-by-trial variability in activation to print stimuli in the left inferior frontal gyrus pars triangularis, even when accounting for the more conventional measure of mean levels of brain activity. This finding suggests that neural variability could be beneficial in developing readers.