Although an effective treatment for pediatric brain tumors, cranial radiation therapy (CRT) damages surrounding healthy tissue, thereby disrupting brain development. Animal models of pediatric CRT have primarily relied on visual tasks to assess cognitive impairment. Moreover, there has been a lack of sex comparisons as most research on the cognitive effects of pediatric CRT does not include females. Therefore, we utilized olfaction, an ethologically relevant sensory modality, to assess cognitive impairment in an animal model of CRT that included both male and female mice. Specifically, we used the novel odor recognition (NOdorR) task with social odors to test recognition memory, a cognitive parameter that has been associated with olfactory neurogenesis, a form of cellular plasticity damaged by CRT. In addition to odor recognition memory, olfactory ability or discrimination of non-social and social odors were assessed both acutely and 3 months after CRT. Magnetic resonance imaging (MRI) and histology were performed after behavioral testing to assess long-term damage by CRT. Long-term but not acute radiation-induced impairment in odor recognition memory was observed, consistent with delayed onset of cognitive impairment in human patients. Males showed greater exploration of social odors than females, but general exploration was not affected by irradiation. However, irradiated males had impaired odor recognition memory in adulthood, compared to non-irradiated males (or simply male controls). Female olfactory recognition memory, in contrast, was dependent on estrus stage. CRT damage was demonstrated by (1) histological evaluation of olfactory neurogenesis, which suggested a reduction in CRT versus control, and (2) imaging analyses which showed that the majority of brain regions were reduced in volume by CRT. Specifically, two regions involved in social odor processing (amygdala and piriform cortex) were damaged by cranial irradiation in males but not females, paralleling olfactory recognition findings.
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http://dx.doi.org/10.3389/fnbeh.2018.00158 | DOI Listing |
Behav Res Methods
December 2024
Department of Education Studies, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong.
The absence of explicit word boundaries is a distinctive characteristic of Chinese script, setting it apart from most alphabetic scripts, leading to word boundary disagreement among readers. Previous studies have examined how this feature may influence reading performance. However, further investigations are required to generate more ecologically valid and generalizable findings.
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December 2024
The Department of Mechanical Engineering and Mechatronics, Ariel University, Ariel, Israel.
Autism spectrum disorder (ASD) involves challenges in communication and social interaction, including challenges in recognizing emotions. Existing technological solutions aim to improve social behaviors in individuals with ASD by providing learning aids. This paper presents a real-time environmental translator designed to enhance social behaviors in individuals with ASD using sensory substitution.
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December 2024
ETSI de Telecomunicación, Universidad Politécnica de Madrid, Avenida Complutense, 30, 28040, Madrid, Spain.
This study investigates the potential of large language models (LLMs) to estimate the familiarity of words and multi-word expressions (MWEs). We validated LLM estimates for isolated words using existing human familiarity ratings and found strong correlations. LLM familiarity estimates performed even better in predicting lexical decision and naming performance in megastudies than the best available word frequency measures.
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December 2024
Department of Psychology, University of Milano-Bicocca, P.zza dell'Ateneo Nuovo, 1, 20126, Milano, Italy.
Despite being largely spoken and studied by language and cognitive scientists, Italian lacks large resources of language processing data. The Italian Crowdsourcing Project (ICP) is a dataset of word recognition times and accuracy including responses to 130,465 words, which makes it the largest dataset of its kind item-wise. The data were collected in an online word knowledge task in which over 156,000 native speakers of Italian took part.
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December 2024
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran.
Surface electromyography (sEMG) data has been extensively utilized in deep learning algorithms for hand movement classification. This paper aims to introduce a novel method for hand gesture classification using sEMG data, addressing accuracy challenges seen in previous studies. We propose a U-Net architecture incorporating a MobileNetV2 encoder, enhanced by a novel Bidirectional Long Short-Term Memory (BiLSTM) and metaheuristic optimization for spatial feature extraction in hand gesture and motion recognition.
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