ERP evidence for the recognition of emotional prosody through simulated cochlear implant strategies.

BACKGROUND: Emotionally salient information in spoken language can be provided by variations in speech melody (prosody) or by emotional semantics. Emotional prosody is essential to convey feelings through speech. In sensori-neural hearing loss, impaired speech perception can be improved by cochlear implants (CIs). Aim of this study was to investigate the performance of normal-hearing (NH) participants on the perception of emotional prosody with vocoded stimuli. Semantically neutral sentences with emotional (happy, angry and neutral) prosody were used. Sentences were manipulated to simulate two CI speech-coding strategies: the Advance Combination Encoder (ACE) and the newly developed Psychoacoustic Advanced Combination Encoder (PACE). Twenty NH adults were asked to recognize emotional prosody from ACE and PACE simulations. Performance was assessed using behavioral tests and event-related potentials (ERPs). RESULTS: Behavioral data revealed superior performance with original stimuli compared to the simulations. For simulations, better recognition for happy and angry prosody was observed compared to the neutral. Irrespective of simulated or unsimulated stimulus type, a significantly larger P200 event-related potential was observed for happy prosody after sentence onset than the other two emotions. Further, the amplitude of P200 was significantly more positive for PACE strategy use compared to the ACE strategy. CONCLUSIONS: Results suggested P200 peak as an indicator of active differentiation and recognition of emotional prosody. Larger P200 peak amplitude for happy prosody indicated importance of fundamental frequency (F0) cues in prosody processing. Advantage of PACE over ACE highlighted a privileged role of the psychoacoustic masking model in improving prosody perception. Taken together, the study emphasizes on the importance of vocoded simulation to better understand the prosodic cues which CI users may be utilizing.

Heartbeat evoked potentials mirror altered body perception in depressed patients.

OBJECTIVE: Awareness of stimuli originating inside of the body (interoceptive awareness) is thought to have an impact on psychopathology. The aim of the present study was to analyze whether heartbeat perception accuracy is reduced in depressed patients. Furthermore, we investigated whether putative differences are reflected in heartbeat-evoked potentials. METHOD: We assessed the heartbeat perception score in 16 depressed patients and in matched healthy controls. A 63-channel EEG was recorded while participants counted pseudo-randomly presented target tones or heartbeats during a fixed number of cardiac cycles. ECG R-waves served as the trigger for EEG averaging. The cardiac-field artifact was minimized using independent component analysis and current-source density. RESULTS: Behaviorally, the depressed sample showed less accurate heartbeat perception in comparison to the control group (p=.011). The two groups also demonstrated psychophysiological differences, showing that heartbeat-evoked potentials were significantly reduced in depressed patients. CONCLUSIONS: Our results suggest that heartbeat evoked potentials are objective markers of altered bodily awareness. Reduced interoception during depression may be linked to alexithymia, as well as to both decreased capacity for decision-making and for cognitive processing. SIGNIFICANCE: It may be helpful to practice interoceptive awareness to improve depressive symptoms, for example by practicing meditation.

Semi-automatic attenuation of cochlear implant artifacts for the evaluation of late auditory evoked potentials.

Electrical artifacts caused by the cochlear implant (CI) contaminate electroencephalographic (EEG) recordings from implanted individuals and corrupt auditory evoked potentials (AEPs). Independent component analysis (ICA) is efficient in attenuating the electrical CI artifact and AEPs can be successfully reconstructed. However the manual selection of CI artifact related independent components (ICs) obtained with ICA is unsatisfactory, since it contains expert-choices and is time consuming. We developed a new procedure to evaluate temporal and topographical properties of ICs and semi-automatically select those components representing electrical CI artifact. The CI Artifact Correction (CIAC) algorithm was tested on EEG data from two different studies. The first consists of published datasets from 18 CI users listening to environmental sounds. Compared to the manual IC selection performed by an expert the sensitivity of CIAC was 91.7% and the specificity 92.3%. After CIAC-based attenuation of CI artifacts, a high correlation between age and N1-P2 peak-to-peak amplitude was observed in the AEPs, replicating previously reported findings and further confirming the algorithm's validity. In the second study AEPs in response to pure tone and white noise stimuli from 12 CI users that had also participated in the other study were evaluated. CI artifacts were attenuated based on the IC selection performed semi-automatically by CIAC and manually by one expert. Again, a correlation between N1 amplitude and age was found. Moreover, a high test-retest reliability for AEP N1 amplitudes and latencies suggested that CIAC-based attenuation reliably preserves plausible individual response characteristics. We conclude that CIAC enables the objective and efficient attenuation of the CI artifact in EEG recordings, as it provided a reasonable reconstruction of individual AEPs. The systematic pattern of individual differences in N1 amplitudes and latencies observed with different stimuli at different sessions, strongly suggests that CIAC can overcome the electrical artifact problem. Thus CIAC facilitates the use of cortical AEPs as an objective measurement of auditory rehabilitation.

Differential processing of laser stimuli by Aδ and C fibres in major depression.

Clinical studies have revealed that up to 92% of major depressed patients report pain complaints such as back or abdominal pain. Furthermore, patients suffering from depression exhibit increased superficial pain thresholds and decreased ischemic (deep) pain thresholds during experimental pain testing in comparison to healthy controls. Here, we aimed to investigate a putative role of Aδ- and C-fibre activation in altered pain perception in the disease. Laser-evoked potentials (LEPs) of 27 unmedicated depressed patients and 27 matched controls were recorded. Aδ and C fibres were activated separately. Amplitudes and latencies of N2 and P2 peaks of Aδ- (Aδ-LEP) and C-fibre- (C-LEP) related LEPs were evaluated. Depressed patients showed significantly decreased Aδ-LEP amplitudes (N2 peak: P=0.019; P2 peak: P=0.024) and delayed C-LEP latencies (P2 peak: P=0.0495; N2 peak: P=0.0556). In contrast, C-LEP amplitudes and Aδ-LEP latencies were unaffected. Our results might be suggestive of the differential impact of physiological changes on pain processing in depression. Thus, Aδ-LEP might reflect the physiological correlate of the augmented superficial pain thresholds during depression. On the contrary, the C-fibre component mediates the facets of pain processing, outlasting the stimulation period, and has been shown to be exaggerated in chronic pain states. Therefore, the functional over-representation of the C-fibre component found in our study might be a possible link between depression and associated pain complaints.

Cross-modal phase reset predicts auditory task performance in humans.

In the multisensory environment, inputs to each sensory modality are rarely independent. Sounds often follow a visible action or event. Here we present behaviorally relevant evidence from the human EEG that visual input prepares the auditory system for subsequent auditory processing by resetting the phase of neuronal oscillatory activity in auditory cortex. Subjects performed a simple auditory frequency discrimination task using paired but asynchronous auditory and visual stimuli. Auditory cortex activity was modeled from the scalp-recorded EEG using spatiotemporal dipole source analysis. Phase resetting activity was assessed using time-frequency analysis of the source waveforms. Significant cross-modal phase resetting was observed in auditory cortex at low alpha frequencies (8-10 Hz) peaking 80 ms after auditory onset, at high alpha frequencies (10-12 Hz) peaking at 88 ms, and at high theta frequencies (∼ 7 Hz) peaking at 156 ms. Importantly, significant effects were only evident when visual input preceded auditory by between 30 and 75 ms. Behaviorally, cross-modal phase resetting accounted for 18% of the variability in response speed in the auditory task, with stronger resetting overall leading to significantly faster responses. A direct link was thus shown between visual-induced modulations of auditory cortex activity and performance in an auditory task. The results are consistent with a model in which the efficiency of auditory processing is improved when natural associations between visual and auditory inputs allow one input to reliably predict the next.

Semi-automatic identification of independent components representing EEG artifact.

OBJECTIVE: Independent component analysis (ICA) can disentangle multi-channel electroencephalogram (EEG) signals into a number of artifacts and brain-related signals. However, the identification and interpretation of independent components is time-consuming and involves subjective decision making. We developed and evaluated a semi-automatic tool designed for clustering independent components from different subjects and/or EEG recordings. METHODS: CORRMAP is an open-source EEGLAB plug-in, based on the correlation of ICA inverse weights, and finds independent components that are similar to a user-defined template. Component similarity is measured using a correlation procedure that selects components that pass a threshold. The threshold can be either user-defined or determined automatically. CORRMAP clustering performance was evaluated by comparing it with the performance of 11 users from different laboratories familiar with ICA. RESULTS: For eye-related artifacts, a very high degree of overlap between users (phi>0.80), and between users and CORRMAP (phi>0.80) was observed. Lower degrees of association were found for heartbeat artifact components, between users (phi<0.70), and between users and CORRMAP (phi<0.65). CONCLUSIONS: These results demonstrate that CORRMAP provides an efficient, convenient and objective way of clustering independent components. SIGNIFICANCE: CORRMAP helps to efficiently use ICA for the removal EEG artifacts.