Neural Effects of Gender and Age Interact in Reading

Digital neuropsychological measures by defense automated neurocognitive assessment: reference values and clinical correlates

Introduction

Although the growth of digital tools for cognitive health assessment, there's a lack of known reference values and clinical implications for these digital methods. This study aims to establish reference values for digital neuropsychological measures obtained through the smartphone-based cognitive assessment application, Defense Automated Neurocognitive Assessment (DANA), and to identify clinical risk factors associated with these measures.

Methods

The sample included 932 cognitively intact participants from the Framingham Heart Study, who completed at least one DANA task. Participants were stratified into subgroups based on sex and three age groups. Reference values were established for digital cognitive assessments within each age group, divided by sex, at the 2.5th, 25th, 50th, 75th, and 97.5th percentile thresholds. To validate these values, 57 cognitively intact participants from Boston University Alzheimer's Disease Research Center were included. Associations between 19 clinical risk factors and these digital neuropsychological measures were examined by a backward elimination strategy.

Results

Age- and sex-specific reference values were generated for three DANA tasks. Participants below 60 had median response times for the Go-No-Go task of 796 ms (men) and 823 ms (women), with age-related increases in both sexes. Validation cohort results mostly aligned with these references. Different tasks showed unique clinical correlations. For instance, response time in the Code Substitution task correlated positively with total cholesterol and diabetes, but negatively with high-density lipoprotein and low-density lipoprotein cholesterol levels, and triglycerides.

Discussion

This study established and validated reference values for digital neuropsychological measures of DANA in cognitively intact white participants, potentially improving their use in future clinical studies and practice.

Age- and gender-related dispersion of brain networks across the lifespan

The effects of age and gender on large-scale resting-state networks (RSNs) reflecting within- and between-network connectivity in the healthy brain remain unclear. This study investigated how age and gender influence the brain network roles and topological properties underlying the ageing process. Ten RSNs were constructed based on 998 participants from the REST-meta-MDD cohort. Multivariate linear regression analysis was used to examine the independent and interactive influences of age and gender on large-scale RSNs and their topological properties. A support vector regression model integrating whole-brain network features was used to predict brain age across the lifespan and cognitive decline in an Alzheimer's disease spectrum (ADS) sample. Differential effects of age and gender on brain network roles were demonstrated across the lifespan. Specifically, cingulo-opercular, auditory, and visual (VIS) networks showed more incohesive features reflected by decreased intra-network connectivity with ageing. Further, females displayed distinctive brain network trajectory patterns in middle-early age, showing enhanced network connectivity within the fronto-parietal network (FPN) and salience network (SAN) and weakened network connectivity between the FPN-somatomotor, FPN-VIS, and SAN-VIS networks. Age - but not gender - induced widespread decrease in topological properties of brain networks. Importantly, these differential network features predicted brain age and cognitive impairment in the ADS sample. By showing that age and gender exert specific dispersion of dynamic network roles and trajectories across the lifespan, this study has expanded our understanding of age- and gender-related brain changes with ageing. Moreover, the findings may be useful for detecting early-stage dementia.

Sex differences in the intrinsic reading neural networks of Chinese children

Sex differences in reading performance have been considered a relatively stable phenomenon. However, there is no general agreement about their neural basis, which might be due to that sex differences are largely influenced by age. This paper focuses on the sex differences in the reading-related neural network of Chinese children and its interaction with age. We also attempt to predict reading abilities based on neural network. Fifty-three boys and 56 girls (8.2–14.6 years of age) were recruited. We collected their resting-state fMRI and behavioural data. Restricted sex differences were found in the resting-state reading neural network compared to extensive age by sex interaction effect. Specifically, the interactions between sex and age indicated that with increasing age, girls showed greater connectivity strength between visual orthographic areas and other brain areas within the reading network, while boys showed an opposite trend. After controlling age, the prediction models of reading performance for the girls mainly included interhemispheric connections, while the intrahemispheric connections (particularly the phonological route) mainly contributed to predicting the reading ability for boys. Taken together, these findings suggest that sex differences in reading neural networks are modulated by age. Partialling out age, boys and girls also show the stable sex differences in relationship between reading neural circuit and reading behaviour.

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Sex differences in reading neural networks are modulated by age.

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Girls’ RSFCs within reading neural networks increase with age, contrary to boys.

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Intra- and interhemispheric RSFCs predict the reading ability of boys and girls, respectively.

Patterns and networks of language control in bilingual language production

Many studies have examined the cognitive and neural mechanisms of bilingual language control, but few of them have captured the pattern information of brain activation. However, language control is a functional combination of both cognitive control and language production which demonstrates distinct patterns of neural representations under different language contexts. The first aim of the present study was to explore the brain activation patterns of language control using multivoxel pattern analysis (MVPA). During the experiment, Chinese-English bilinguals were instructed to name pictures in either Chinese or English according to a visually presented cue while being scanned with functional magnetic resonance imaging (fMRI). We found that patterns of neural activity in frontal brain regions including the left dorsolateral prefrontal cortex, left inferior frontal gyrus, left supplementary motor area, anterior cingulate cortex, bilateral precentral gyri, and the left cerebellum reliably discriminated between switch and non-switch conditions. We then modeled causal interactions between these regions by applying effective connectivity analyses based on an extended unified structure equation model (euSEM). The results showed that frontal and fronto-cerebellar connectivity were key components of the language control network. These findings further reveal the engagement of the cognitive control network in bilingual language production.