Search

The long-term reliability of neural interfaces and stability of high-quality recordings are still unsolved issues in neuroscience research. High surface area PEDOT-PSS-CNT composites are able to greatly improve the performance of recording and stimulation for traditional intracortical metal microelectrodes by decreasing their impedance and increasing their charge transfer capability. This enhancement significantly reduces the size of the implantable device though preserving excellent electrical performances. On the other hand, the presence of nanomaterials often rises concerns regarding possible health hazards, especially when considering a clinical application of the devices. For this reason, we decided to explore the problem from a new perspective by designing and testing an innovative device based on nanostructured microspheres grown on a thin tether, integrating PEDOT-PSS-CNT nanocomposites with a soft synthetic permanent biocompatible hydrogel. The pHEMA hydrogel preserves the electrochemical performance and high quality recording ability of PEDOT-PSS-CNT coated devices, reduces the mechanical mismatch between soft brain tissue and stiff devices and also avoids direct contact between the neural tissue and the nanocomposite, by acting as a biocompatible protective barrier against potential nanomaterial detachment. Moreover, the spherical shape of the electrode together with the surface area increase provided by the nanocomposite deposited on it, maximize the electrical contact and may improve recording stability over time. These results have a good potential to contribute to fulfill the grand challenge of obtaining stable neural interfaces for long-term applications.

In Motor Neglect (MN) syndrome, a specific impairment in non-congruent bimanual movements has been described. In the present case-control study, we investigated the neuro-functional correlates of this behavioral deficit. Two right-brain-damaged patients, one with (MN+) and one without (MN-) MN, were evaluated by means of functional Magnetic Resonance Imaging (fMRI) in a bimanual Circles-Lines paradigm. Patients were requested to perform right-hand movements (lines-drawing) and, simultaneously, congruent (lines-drawing) or non-congruent (circles-drawing) left-hand movements. In the behavioral task, MN- patient showed a bimanual-coupling-effect, while MN+ patient did not. The fMRI study showed that in MN-, a fronto-parietal network, mainly involving the pre-supplementary motor area (pre-SMA) and the posterior parietal cortex (PPC), was significantly more active in non-congruent than in congruent conditions, as previously shown in healthy subjects. On the contrary, MN+ patient showed an opposite pattern of activation both in pre-SMA and in PPC. Within this fronto-parietal network, the pre-SMA is supposed to exert an inhibitory influence on the default coupling of homologous muscles, thus allowing the execution of non-congruent movements. In MN syndrome, the described abnormal pre-SMA activity supports the hypothesis that a failure to inhibit ipsilesional motor programs might determine a specific impairment of non-congruent movements.

Classical models of speech consider an antero-posterior distinction between perceptive and productive functions. However, the selective alteration of neural activity in speech motor centers, via transcranial magnetic stimulation, was shown to affect speech discrimination. On the automatic speech recognition (ASR) side, the recognition systems have classically relied solely on acoustic data, achieving rather good performance in optimal listening conditions. The main limitations of current ASR are mainly evident in the realistic use of such systems. These limitations can be partly reduced by using normalization strategies that minimize inter-speaker variability by either explicitly removing speakers’ peculiarities or adapting different speakers to a reference model. In this paper we aim at modeling a motor-based imitation learning mechanism in ASR. We tested the utility of a speaker normalization strategy that uses motor representations of speech and compare it with strategies that ignore the motor domain. Specifically, we first trained a regressor through state-of-the-art machine learning techniques to build an auditory-motor mapping, in a sense mimicking a human learner that tries to reproduce utterances produced by other speakers. This auditory-motor mapping maps the speech acoustics of a speaker into the motor plans of a reference speaker. Since, during recognition, only speech acoustics are available, the mapping is necessary to recover motor information. Subsequently, in a phone classification task, we tested the system on either one of the speakers that was used during training or a new one. Results show that in both cases the motor-based speaker normalization strategy almost always outperforms all other strategies where only acoustics is taken into account.