Several wearable devices for physiological and activity monitoring are found on the market, but most of them only allow spot measurements. However, the continuous detection of physiological parameters without any constriction in time or space would be useful in several fields such as healthcare, fitness, and work. This can be achieved with the application of textile technologies for sensorized garments, where the sensors are completely embedded in the fabric. The complete integration of sensors in the fabric leads to several manufacturing techniques that allow dealing with both the technological challenges entailed by the physiological parameters under investigation, and the basic requirements of a garment such as perspiration, washability, and comfort. This review is intended to provide a detailed description of the textile technologies in terms of materials and manufacturing processes employed in the production of sensorized fabrics. The focus is pointed at the technical challenges and the advanced solutions introduced with respect to conventional sensors for recording different physiological parameters, and some interesting textile implementations for the acquisition of biopotentials, respiratory parameters, temperature and sweat are proposed. In the last section, an overview of the main garments on the market is depicted, also exploring some relevant projects under development.

Working memory (WM) plays a central role in cognition, prompting neuroscientists to investigate its functional and structural substrates. The WM dynamic recruits large-scale frequency-specific brain networks that unfold over a few milliseconds – this complexity challenges traditional neuroimaging analyses. In this study, we unravel the WM network dynamics in an unsupervised, data-driven way, applying the time delay embedded-hidden Markov model (TDE-HMM). We acquired MEG data from 38 healthy subjects performing an n-back working memory task. The TDE-HMM model inferred four task-specific states with each unique temporal (activation), spectral (phase-coherence connections), and spatial (power spectral density distribution) profiles. A theta frontoparietal state performs executive functions, an alpha temporo-occipital state maintains the information, and a broad-band and spatially complex state with an M300 temporal profile leads the retrieval process and motor response. The HMM states can be straightforwardly interpreted within the neuropsychological multi-component model of WM, significantly improving the comprehensive description of WM.HighlightsWorking memory recruits different frequency-specific brain networks that wax and wane at a millisecond scale.Through the time-delay embedded hidden (TDE-HMM) we are able to extract data-driven functional networks with unique spatial, spectral, and temporal profiles.We demonstrate the existence of four task-specific brain networks that can be interpreted within the well-known Baddeley’s multicomponent model of working memory.This novel WM description unveils new features that will lead to a more in-depth characterization of cognitive processes in MEG data.

Background: Multiple sclerosis (MS) is a neurodegenerative disease characterized by neuronal and synaptic loss, resulting in an imbalance of excitatory and inhibitory synaptic transmission and potentially cognitive impairment. Current methods for measuring the excitation/inhibition (E/I) ratio are mostly invasive, but recent research combining neurocomputational modeling with measurements of local field potentials has indicated that the slope with which the power spectrum of neuronal activity captured by electro- and/or magnetoencephalography rolls off, is a non-invasive biomarker of the excitation/inhibition (E/I) ratio. A steeper roll-off is associated with a stronger inhibition. This novel method can be applied to assess the E/I ratio in people with multiple sclerosis (pwMS), detect the effect of medication such as benzodiazepines, and explore its utility as a biomarker. Methods: We recruited 44 healthy control subjects and 95 people with multiple sclerosis (pwMS) who underwent resting-state magnetoencephalographic recordings. The 1/f spectral slope of the neural power spectra was calculated for each subject and for each brain region. Results: As expected, the spectral slope was significantly steeper in pwMS treated with benzodiazepines compared to pwMS not receiving this medication (p = 0.01). In the sub-cohort of pwMS not treated with benzodiazepines, we observed a steeper slope in cognitively impaired pwMS compared to cognitively preserved pwMS (p = 0.01) and healthy subjects (p = 0.02). Furthermore, we observed a significant correlation between 1/f slope and verbal and spatial working memory functioning in the brain regions located in the prefrontal and parietal cortex. Conclusions: In this study, we highlighted the value of the spectral slope, a novel non-invasive biomarker of the E/I ratio, in MS by quantifying the inhibitory effect of benzodiazepines and by putting it forward as a potential biomarker of cognitive deficits in pwMS.