Ricerc@Sapienza

In the last 20 years, motor imagery (MI) has been extensively used to train motor abilities in sport and in rehabilitation. However, MI procedures are not all alike as much as their potential beneficiaries. Here we assessed whether the addition of visual cues could make MI performance more comparable with explicit motor performance in gait tasks. With fMRI we also explored the neural correlates of these experimental manipulations. We did this in elderly subjects who are known to rely less on kinesthetic information while favoring visual strategies during motor performance.

Dynamic motor imagery (dMI) is a motor imagery task associated with movements partially mimicking those mentally represented. As well as conventional motor imagery, dMI has been typically assessed by mental chronometry tasks. In this paper, an instrumented approach was proposed for quantifying the correspondence between upper and lower limb oscillatory movements performed on the spot during the dMI of walking vs. during actual walking. Magneto-inertial measurement units were used to measure limb swinging in three different groups: young adults, older adults and stroke patients.

A new form of Motor Imagery (MI), called dynamic Motor Imagery (dMI) has recently been proposed. The dMI adds to conventional static Motor Imagery (sMI) the presence of simultaneous actual movements partially replicating those mentally represented. In a previous research conducted on young participants, dMI showed to be temporally closer than sMI in replicating the real performance for some specific locomotor conditions. In this study, we evaluated if there is any influence of the ageing on dMI.

Discriminating mental states from brain signals is crucial for many applications in cognitive and clinical neuroscience. Most of the studies relied on the feature extraction from the activity of single brain areas, thus neglecting the potential contribution of their functional coupling, or connectivity. Here, we consider spectral coherence and imaginary coherence to infer brain connectivity networks from electroencephalographic (EEG) signals recorded during motor imagery and resting states in a group of healthy subjects.