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Besides being responsible for olfaction and air intake, the nose contains abundant vasculature and autonomic nervous system innervations, and it is a cerebrospinal fluid clearance site. Therefore, the nose is an attractive target for functional MRI (fMRI). Yet, nose fMRI has not been possible so far due to signal losses originating from nasal air-tissue interfaces. Here, we demonstrated feasibility of nose fMRI by using novel ultrashort/zero echo time (TE) MRI. Results obtained in the resting-state from 13 healthy participants at 7T and in 5 awake mice at 9.4T revealed a highly reproducible resting-state nose functional network that likely reflects autonomic nervous system activity. Another network observed in humans involves the nose, major brain vessels and CSF spaces, presenting a temporal dynamic that correlates with heart rate and breathing rate. These resting-state nose functional signals should help elucidate peripheral and central nervous system integrations., (© 2024. The Author(s).)

Deep learning (DL) approaches may also inform the analysis of human brain activity. Here, a state-of-art DL tool for natural language processing, the Generative Pre-trained Transformer version 2 (GPT-2), is shown to generate meaningful neural encodings in functional MRI during narrative listening. Linguistic features of word unpredictability (surprisal) and contextual importance (saliency) were derived from the GPT-2 applied to the text of a 12-min narrative. Segments of variable duration (from 15 to 90 s) defined the context for the next word, resulting in different sets of neural predictors for functional MRI signals recorded in 27 healthy listeners of the narrative. GPT-2 surprisal, estimating word prediction errors from the artificial network, significantly explained the neural data in superior and middle temporal gyri (bilaterally), in anterior and posterior cingulate cortices, and in the left prefrontal cortex. GPT-2 saliency, weighing the importance of context words, significantly explained the neural data for longer segments in left superior and middle temporal gyri. These results add novel support to the use of DL tools in the search for neural encodings in functional MRI. A DL language model like the GPT-2 may feature useful data about neural processes subserving language comprehension in humans, including next-word context-related prediction., (© 2022. The Author(s).)

Few studies have investigated the neuropsychological profile of Hearing Loss (HL) subjects and the effects of hearing-aid on cognitive decline. We investigated the neuropsychological profile of HL patients at baseline and compared the neuropsychological profiles of patients with and without hearing-aid at 6 month. Fifty-six HL patients and 40 healthy subjects (HC) underwent neuropsychological and behavioral examination and were compared at baseline. Changes at follow-up were compared between HL patients with (N = 25) and without (N = 31) hearing-aids. At baseline, significant differences between HL and HC were found in MOCA test, Raven's Coloured Progressive Matrices (CPM) and SF-36. Among mild-HL patients, patients with hearing-aid significantly improved on the Clock Drawing Test (CDT) as compared to patients without hearing-aid. Our findings indicate that hearing loss is associated with both a reduced efficiency of the global cognitive state and a worse quality of life as compared to HC, supporting the association between HL and cognitive impairment. Moreover, only patients with mild-HL shows some cognitive improvement after using hearing-aid, suggesting that rehabilitative strategies may be more effective to delay cognitive decline in such patients. However, we cannot exclude that hearing-aids may affect cognitive decline in more severe-HL, but a longer follow-up is needed.