Prefrontal hemodynamic changes produced by anodal direct current stimulation.

Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that has been investigated for the treatment of many neurological or neuropsychiatric disorders. Its main effect is to modulate the cortical excitability depending on the polarity of the current applied. However, understanding the mechanisms by which these modulations are induced and persist is still an open question. A possible marker indicating a change in cortical activity is the subsequent variation in regional blood flow and metabolism. These variations can be effectively monitored using functional near-infrared spectroscopy (fNIRS), which offers a noninvasive and portable measure of regional blood oxygenation state in cortical tissue. We studied healthy volunteers at rest and evaluated the changes in cortical oxygenation related to tDCS using fNIRS. Subjects were tested after active stimulation (12 subjects) and sham stimulation (10 subjects). Electrodes were applied at two prefrontal locations; stimulation lasted 10 min and fNIRS data were then collected for 20 min. The anodal stimulation induced a significant increase in oxyhemoglobin (HbO(2)) concentration compared to sham stimulation. Additionally, the effect of active 10-min tDCS was localized in time and lasted up to 8-10 min after the end of the stimulation. The cathodal stimulation manifested instead a negligible effect. The changes induced by tDCS on HbO(2), as captured by fNIRS, agreed with the results of previous studies. Taken together, these results help clarify the mechanisms underlying the regional alterations induced by tDCS and validate the use of fNIRS as a possible noninvasive method to monitor the neuromodulation effect of tDCS.

Model comparison for automatic characterization and classification of average ERPs using visual oddball paradigm.

OBJECTIVE: To determine whether automated classifiers can be used for correctly identifying target categorization responses from averaged event-related potentials (ERPs) along with identifying appropriate features and classification models for computer-assisted investigation of attentional processes. METHODS: ERPs were recorded during a target categorization task. Automated classification of average target ERPs versus average non-target ERPs was performed by extracting different combinations of features from the P300 and N200 components, which were used to train six classifiers: Euclidean classifier (EC), Mahalanobis discriminant (MD), quadratic classifier (QC), Fisher linear discriminant (FLD), multi-layer perceptron neural network (MLP) and support vector machine (SVM). RESULTS: The best classification performance (accuracy: 91-92%; sensitivity: 85-86%; specificity: 95-99%) was provided by QC, MLP, SVM on feature vectors extracted from P300 recorded at multiple sites. In general, non-linear and non-parametric classifiers (QC, MLP, SVM) performed better than linear classifiers (EC, MD, FLD). The N200 did not explain variance beyond that of P300 recorded at multiple sites. CONCLUSIONS: The results suggest that automatic characterization and classification of average target and non-target ERPs is feasible. Features of P300 recorded at multiple sites used to train non-linear classifiers are recommended for optimal classification performance. SIGNIFICANCE: Automatic characterization of target ERPs can provide an objective approach for detecting and diagnosing abnormalities and evaluating interventions for clinical populations, paving the way for future real-time monitoring of attentional processes.