Background: Developmental language disorders (DLDs) are the most common developmental disorders in children. For screening DLDs, speech ability (SA) is one of the most important indicators. Methods:In this paper, we propose a solution for the fast screening of children's DLDs based on a comprehensive SA evaluation and a deep framework of machine learning. Fast screening is crucial for promoting the prevalence and practicality of DLD screening which in turn is important for the treatment of DLDs and related social and behavioral abnormalities (e.g., dyslexia and autism). Our solution is focused on addressing the drawbacks existing in the previous DLD screening methods which include test failure due to text-based inducing material design and illiteracy of most young children, incomplete language evaluation indicators, and professional-reliant evaluation procedures. First, to avoid test failure, a novel comprehensive inducing procedure (CIP) with non-text (i.e., audio-visual) stimulus materials was designed that could cover a large range of modalities to adequately explore the comprehensive SA of the subjects. Second, to address incomplete language evaluation, a set of comprehensive evaluation indicators with full consideration of the characteristics of the children's language acquisition is proposed; furthermore, to break the professionalreliant limitation, we specifically designed a deep framework for fast and accurate screening.Results: Experimental results showed that the proposed deep framework is effective and professional with a 92.6% accuracy on DLD screening. Additionally, to provide a benchmark for the novel problem, we provide a CIP dataset with about 2,200 responses from over 200 children, which may also be useful for further DLD studies and insightful for the fast screening design of other behavioral abnormalities.Conclusions: Fast screening of children's DLDs can be achieved at accuracy up to 92.6% by our proposed deep learning framework. For successful fast screening, an elaborated CIP with corresponding comprehensive evaluating indicators is necessary to be designed for children suspected to have DLDs.

Vision is a key source of information input for humans, which involves various cognitive functions and a great range of neural networks inside and beyond the visual cortex. There has been increasing observation that the cognitive outcomes after a brain lesion cannot be well predicted by the attributes of the lesion itself but are influenced by the functional network plasticity. However, the mechanisms of impaired or preserved visual cognition have not been probed from direct function-execution conditions and few works have observed it on whole-brain dynamic networks. We used high-resolution electroencephalogram recordings from 25 patients with brain tumors to track the dynamical patterns of functional reorganization in visual processing tasks with multilevel complexity. By comparing with 24 healthy controls, increased cortical responsiveness as functional compensation was identified in the early phase of processing, which was highly localized to the visual cortex and functional networks and less relevant to the tumor position. Besides, a spreading wide enhancement in whole-brain functional connectivity was elicited by the visual word-recognition task. Enhanced early rapid-onset cortical compensation in the local functional networks may contribute to largely preserved basic vision functions, and higher-cognitive tasks are vulnerable to impairment but with high sensitivity of functional plasticity being elicited.

Catchy utterances, such as proverbs, verses, and nursery rhymes (i.e., “No pain, no gain” in English), contain strong-prosodic (SP) features and are child-friendly in repeating and memorizing; yet the way those prosodic features encoded by neural activity and their influence on speech development in children are still largely unknown. Using functional near-infrared spectroscopy (fNIRS), this study investigated the cortical responses to the perception of natural speech sentences with strong/weak-prosodic (SP/WP) features and evaluated the speech communication ability in 21 pre-lingually deaf children with cochlear implantation (CI) and 25 normal hearing (NH) children. A comprehensive evaluation of speech communication ability was conducted on all the participants to explore the potential correlations between neural activities and children’s speech development. The SP information evoked right-lateralized cortical responses across a broad brain network in NH children and facilitated the early integration of linguistic information, highlighting children’s neural sensitivity to natural SP sentences. In contrast, children with CI showed significantly weaker cortical activation and characteristic deficits in speech perception with SP features, suggesting hearing loss at the early age of life, causing significantly impaired sensitivity to prosodic features of sentences. Importantly, the level of neural sensitivity to SP sentences was significantly related to the speech behaviors of all children participants. These findings demonstrate the significance of speech prosodic features in children’s speech development.

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Purpose To probe the dynamic alternations of neural networks in real-time visual processing after visual deprivation (VD) removal. Methods A prospective cross-sectional study was conducted. Twenty children with a history of early binocular VD caused by congenital cataracts and 20 matched typically developing (TD) children were enrolled. The event-related potential (ERP) data were obtained via high-density electroencephalography. ERP data were analyzed based on three components (P1, N170, and P2), three test conditions (objects, human faces, and Chinese characters), and peak time and region of interest (ROI) chosen on a grand average head map collapsed from the averaged waveform of each group. Source localization and alpha power spectrum density were applied to define the functional pattern of brain areas and evaluate the attention function. Results The VD group showed significantly lower P1 amplitudes than the TD group under all conditions in peak ROIs, which were situated in the left occipito-temporal region. For both VD and TD groups, there were strong N170 effects in the character and human face conditions in the component's peak ROIs. Furthermore, source mapping indicated that the VD group generally showed significantly lower activation in the visual cortex and ventral stream, whereas the beyond network areas (mostly frontal areas) intensively participated in functional compensation in the VD group. The VD group showed significant poststimulus alpha desynchronization in object recognition. Conclusions Our research described the mechanisms of visual networks after early binocular VD removal. Our findings may provide a new basis for the poor visual recovery after early binocular VD removal and offer clues for visual recovery strategies.