Biomedical subjects

Camillo Porcaro

Publications and source records attributed to Camillo Porcaro.

5 recordsLinked to original sources

PubMed2006

Fetal auditory responses to external sounds and mother's heart beat: detection improved by Independent Component Analysis.

In this paper, we present a magnetoencephalographic study of the fetal auditory response to external stimuli and to the sound of the mother's heartbeat. We describe how an ad hoc functional selection procedure allowed us to isolate the sources in the fetal brain responding to sounds only, after the application to the recorded data of a standard Independent Component Analysis algorithm. In our experiment, acoustic stimuli were delivered to twelve healthy women with uncomplicated pregnancies at a time between 36 and 40 weeks gestational age, with their fetuses in breech presentation. Ultrasound images allowed determination of the region over the women's abdomen nearest to the fetal head, over which both the acoustic stimulator and the MEG sensors were subsequently placed. In 8 out of the 12 cases, our analysis provided consistent evidence of a fetal response both to the mother's heartbeat and to the external auditory stimulation; both were characterized by a clear prominent component at around 200 ms latency, which is widely accepted as the marker of the fetal response to auditory stimuli.

Acoustic Stimulation↗

PubMed2006

Cortical short-term fatigue effects assessed via rhythmic brain-muscle coherence.

This study is aimed at assessing the short-term effects of muscular fatigue on the sensorimotor areas organization in the left and right hemispheres. Magnetoencephalographic (MEG) and electromyographic (EMG) activities were simultaneously recorded during the execution of a non-fatiguing motor task, performed before and after a task known to induce muscle fatigue (Fatigue). Coherence between cerebral and muscular rhythms as well as cerebral and muscular rhythms spectral densities were estimated during this non-fatiguing task and at rest. The MEG-EMG coherence in the beta band (13-32 Hz) was higher after than before Fatigue. The background activity reduction during contraction with respect to rest (i.e. the cerebral reactivity) was less evident after than before Fatigue in the gamma (33-45 Hz) and beta bands. When differentiating subjects on the base of Fatigue endurance times, while a huge inter-subject variability was found, an evident intra-subject similarity was observed for left and right arms, suggesting that resistance to fatigue is more an individual ability than a motor skill differentiated for the dominant and non-dominant side. In conclusion, signs of a more selective neural recruitment, more coupled with muscular activity, appeared as short-term effects of muscular fatigue in primary sensorimotor cortical areas. Evidence suggested that the reduction of cortical recruitment and the increased cortico-muscular coupling are distinct mechanisms.

Adult↗

PubMed2006

Functional source separation from magnetoencephalographic signals.

We propose a novel cerebral source extraction method (functional source separation, FSS) starting from extra-cephalic magnetoencephalographic (MEG) signals in humans. It is obtained by adding a functional constraint to the cost function of a basic independent component analysis (ICA) model, defined according to the specific experiment under study, and removing the orthogonality constraint, (i.e., in a single-unit approach, skipping decorrelation of each new component from the subspace generated by the components already found). Source activity was obtained all along processing of a simple separate sensory stimulation of thumb, little finger, and median nerve. Being the sources obtained one by one in each stage applying different criteria, the a posteriori "interesting sources selection" step is avoided. The obtained solutions were in agreement with the homuncular organization in all subjects, neurophysiologically reacting properly and with negligible residual activity. On this basis, the separated sources were interpreted as satisfactorily describing highly superimposed and interconnected neural networks devoted to cortical finger representation. The proposed procedure significantly improves the quality of the extraction with respect to a standard BSS algorithm. Moreover, it is very flexible in including different functional constraints, providing a promising tool to identify neuronal networks in very general cerebral processing.

Adult↗

PubMed2005

Fetal magnetocardiographic signals extracted by 'signal subspace' blind source separation.

In this paper, we apply independent component analysis to fetal magnetocardiographic data. In particular, we propose an extension of the "cumulant-based iterative inversion" algorithm to include a two-step "signal subspace" subdivision, which allows the user to control the number of components to be estimated by analyzing the eigenvalues distribution in an interactive way. Our results show that this method is a powerful means not only for the extraction of the cardiac signals from the background noise but also for a sharp separation of the baby's heart from the mother's.

Algorithms↗

PubMed2004

Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals.

OBJECTIVE: To propose a noise reduction procedure for magnetoencephalography (MEG) signals introducing an automatic detection system of artifactual components (ICs) separated by an independent component analysis (ICA) algorithm, and a control cycle on reconstructed cleaned data to recovery part of non-artifactual signals possibly lost by the blind mechanism. METHODS: The procedure consisted of three main steps: (1) ICA for blind source separation (BSS); (2) automatic detection method of artifactual components, based on statistical and spectral ICs characteristics; (3) control cycle on 'discrepancy,' i.e. on the difference between original data and those reconstructed using only ICs automatically retained. Simulated data were generated as representative mixtures of some common brain frequencies, a source of internal Gaussian noise, power line interference, and two real artifacts (electrocardiogram=ECG, electrooculogram=EOG), with the adjunction of a matrix of Gaussian external noise. Three real data samples were chosen as representative of spontaneous noisy MEG data. RESULTS: In simulated data the proposed set of markers selected three components corresponding to ECG, EOG and the Gaussian internal noise; in real-data examples, the automatic detection system showed a satisfactory performance in detecting artifactual ICs. 'Discrepancy' control cycle was redundant in simulated data, as expected, but it was a significant amelioration in two of the three real-data cases. CONCLUSIONS: The proposed automatic detection approach represents a suitable strengthening and simplification of pre-processing data analyses. The proposed 'discrepancy' evaluation, after automatic pruning, seems to be a suitable way to render negligible the risk of loose non-artifactual activity when applying BSS methods to real data. SIGNIFICANCE: The present noise reduction procedure, including ICA separation phase, automatic artifactual ICs selection and 'discrepancy' control cycle, showed good performances both on simulated and real MEG data. Moreover, application to real signals suggests the procedure to be able to separate different cerebral activity sources, even if characterized by very similar frequency contents.

Algorithms↗