Dataset Information

Neural Effects of Gender and Age Interact in Reading.

ABSTRACT: There has been an enduring fascination with the possibility of gender differences in the brain basis of language, yet the evidence has been largely equivocal. Evidence does exist, however, for women being at greater risk than men for developing psychomotor slowing and even Alzheimer disease with advancing age, although this may in part at least be due to women living longer. We examined whether gender, age, or their interaction influenced language-related or more general processes in reading. Reading consists of elements related to language, such as the processing of word sound patterns (phonology) and meanings (semantics), along with the lead-in processes of visual perception and orthographic (visual word form) processing that are specific to reading. To test for any influence of gender and age on either semantic processing or orthography-phonology mapping, we tested for an interaction of these factors on differences between meaningful words and meaningless but pronounceable non-words. We also tested for effects of gender and age on how the number of letters in a word modulates neural activity for reading. This lead-in process presumably relates most to orthography. Behaviorally, reading accuracy declined with age for both men and women, but the decline was steeper for men. Neurally, interactions between gender and age were found exclusively in medial orbitofrontal cortex (mOFC). These factors influenced the word-non-word contrast, but not the parametric effect of number of letters. Men showed increasing activation with age for non-words compared to words. Women showed only slightly decreasing activation with age for novel letter strings. Overall, we found interactive effects of gender and age in the mOFC on the left primarily for novel letter strings, but no such interaction for a contrast that emphasized visual form processing. Thus the interaction of gender with age in the mOFC may relate most to orthography-phonology conversion for unfamiliar letter strings. More generally, this suggests that efforts to investigate effects of gender on language-related tasks may benefit from taking into account age and the type of cognitive process being highlighted.

SUBMITTER: Graves WW

PROVIDER: S-EPMC6812500 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Neural Effects of Gender and Age Interact in Reading.

Graves William W WW Coulanges Linsah L Levinson Hillary H Boukrina Olga O Conant Lisa L LL

Frontiers in neuroscience 20191017

There has been an enduring fascination with the possibility of gender differences in the brain basis of language, yet the evidence has been largely equivocal. Evidence does exist, however, for women being at greater risk than men for developing psychomotor slowing and even Alzheimer disease with advancing age, although this may in part at least be due to women living longer. We examined whether gender, age, or their interaction influenced language-related or more general processes in reading. Re ...[more]

PMID: 31680843

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Project description:Word learning depends not only on efficient online binding of phonological, orthographic and lexical information, but also on consolidation of new word representations into permanent lexical memory. Work on word learning under a variety of contexts indicates that reading and language skill impact facility of word learning in both print and speech. In addition, recent research finds that individuals with language impairments show deficits in both initial word form learning and in maintaining newly learned representations over time, implicating mechanisms associated with maintenance that may be driven by deficits in overnight consolidation. Although several recent studies have explored the neural bases of overnight consolidation of newly learned words, no extant work has examined individual differences in overnight consolidation at the neural level. The current study addresses this gap in the literature by investigating how individual differences in reading and language skills modulate patterns of neural activation associated with newly learned words following a period of overnight consolidation. Specifically, a community sample of adolescents and young adults with significant variability in reading and oral language (vocabulary) ability were trained on two spoken artificial lexicons, one in the evening on the day before fMRI scanning and one in the morning just prior to scanning. Comparisons of activation between words that were trained and consolidated vs. those that were trained but not consolidated revealed increased cortical activation in a number of language associated and memory associated regions. In addition, individual differences in age, reading skill and vocabulary modulated learning rate in our artificial lexicon learning task and the size of the cortical consolidation effect in the precuneus/posterior cingulate, such that older readers and more skilled readers had larger cortical consolidation effects in this learning-critical region. These findings suggest that age (even into late adolescence) and reading and language skills are important individual differences that affect overnight consolidation of newly learned words. These findings have significant implications for understanding reading and language disorders and should inform pedagogical models.

Project description:Conventionally, convolutional neural networks (CNNs) have been used to identify and detect thorax diseases on chest x-ray images. To identify thorax diseases, CNNs typically learn two types of information: disease-specific features and generic anatomical features. CNNs focus on disease-specific features while ignoring the rest of the anatomical features during their operation. There is no evidence that generic anatomical features improve or worsen the performance of convolutional neural networks for thorax disease classification in the current research. As a consequence, the relevance of general anatomical features in boosting the performance of CNNs for thorax disease classification is investigated in this study. We employ a dual-stream CNN model to learn anatomical features before training the model for thorax disease classification. The dual-stream technique is used to compel the model to learn structural information because initial layers of CNNs often learn features of edges and boundaries. As a result, a dual-stream model with minimal layers learns structural and anatomical features as a priority. To make the technique more comprehensive, we first train the model to identify gender and age and then classify thorax diseases using the information acquired. Only when the model learns the anatomical features can it detect gender and age. We also use Non-negative Matrix Factorization (NMF) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to pre-process the training data, which suppresses disease-related information while amplifying general anatomical features, allowing the model to acquire anatomical features considerably faster. Finally, the model that was earlier trained for gender and age detection is retrained for thorax disease classification using original data. The proposed technique increases the performance of convolutional neural networks for thorax disease classification, as per experiments on the Chest X-ray14 dataset. We can also see the significant parts of the image that contribute more for gender, age, and a certain thorax disease by visualizing the features. The proposed study achieves two goals: first, it produces novel gender and age identification results on chest X-ray images that may be used in biometrics, forensics, and anthropology, and second, it highlights the importance of general anatomical features in thorax disease classification. In comparison to state-of-the-art results, the proposed work also produces competitive results.

Dataset Information

Neural Effects of Gender and Age Interact in Reading.

Publications

Neural Effects of Gender and Age Interact in Reading.

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