Restoration of touch after hand amputation is a desirable feature of ideal prostheses. Here, we show that texture discrimination can be artificially provided in human subjects by implementing a neuromorphic real-time mechano-neuro-transduction (MNT), which emulates to some extent the firing dynamics of SA1 cutaneous afferents. The MNT process was used to modulate the temporal pattern of electrical spikes delivered to the human median nerve via percutaneous microstimulation in four intact subjects and via implanted intrafascicular stimulation in one transradial amputee. Both approaches allowed the subjects to reliably discriminate spatial coarseness of surfaces as confirmed also by a hybrid neural model of the median nerve. Moreover, MNT-evoked EEG activity showed physiologically plausible responses that were superimposable in time and topography to the ones elicited by a natural mechanical tactile stimulation. These findings can open up novel opportunities for sensory restoration in the next generation of neuro-prosthetic hands.

The aim of this study was to investigate the neuronal network characteristics in physiological and pathological brain aging. A database of 378 participants divided in three groups was analyzed: Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal elderly (Nold) subjects. Through EEG recordings, cortical sources were evaluated by sLORETA software, while graph theory parameters (Characteristic Path Length λ, Clustering coefficient γ, and small-world network σ) were computed to the undirected and weighted networks, obtained by the lagged linear coherence evaluated by eLORETA software. EEG cortical sources from spectral analysis showed significant differences in delta, theta, and alpha 1 bands. Furthermore, the analysis of eLORETA cortical connectivity suggested that for the normalized Characteristic Path Length (λ) the pattern differences between normal cognition and dementia were observed in the theta band (MCI subjects are find similar to healthy subjects), while for the normalized Clustering coefficient (γ) a significant increment was found for AD group in delta, theta, and alpha 1 bands; finally, the small world (σ) parameter presented a significant interaction between AD and MCI groups showing a theta increase in MCI. The fact that AD patients respect the MCI subjects were significantly impaired in theta but not in alpha bands connectivity are in line with the hypothesis of an intermediate status of MCI between normal condition and overt dementia.

Modern analysis of electroencephalographic (EEG) rhythms provides information on dynamic brain connectivity. To test the hypothesis that aging processes modulate the brain connectivity network, EEG recording was conducted on 113 healthy volunteers. They were divided into three groups in accordance with their ages: 36 Young (15–45 years), 46 Adult (50–70 years), and 31 Elderly (>70 years). To evaluate the stability of the investigated parameters, a subgroup of 10 subjects underwent a second EEG recording two weeks later. Graph theory functions were applied to the undirected and weighted networks obtained by the lagged linear coherence evaluated by eLORETA on cortical sources. EEG frequency bands of interest were: delta (2–4 Hz), theta (4–8 Hz), alpha1 (8–10.5 Hz), alpha2 (10.5–13 Hz), beta1 (13–20 Hz), beta2 (20–30 Hz), and gamma (30–40 Hz). The spectral connectivity analysis of cortical sources showed that the normalized Characteristic Path Length (λ) presented the pattern Young > Adult>Elderly in the higher alpha band. Elderly also showed a greater increase in delta and theta bands than Young. The correlation between age and λ showed that higher ages corresponded to higher λ in delta and theta and lower in the alpha2 band; this pattern reflects the age-related modulation of higher (alpha) and decreased (delta) connectivity. The Normalized Clustering coefficient (γ) and small-world network modeling (σ) showed non-significant age-modulation. Evidence from the present study suggests that graph theory can aid in the analysis of connectivity patterns estimated from EEG and can facilitate the study of the physiological and pathological brain aging features of functional connectivity networks.