Integration of Consonant and Pitch Processing as Revealed by the Absence of Additivity in Mismatch Negativity

Consonants, unlike vowels, are thought to be speech specific and therefore no interactions would be expected between consonants and pitch, a basic element for musical tones. The present study used an electrophysiological approach to investigate whether, contrary to this view, there is integrative processing of consonants and pitch by measuring additivity of changes in the mismatch negativity (MMN) of evoked potentials. The MMN is elicited by discriminable variations occurring in a sequence of repetitive, homogeneous sounds. In the experiment, event-related potentials (ERPs) were recorded while participants heard frequently sung consonant-vowel syllables and rare stimuli deviating in either consonant identity only, pitch only, or in both dimensions. Every type of deviation elicited a reliable MMN. As expected, the two single-deviant MMNs had similar amplitudes, but that of the double-deviant MMN was also not significantly different from them. This absence of additivity in the double-deviant MMN suggests that consonant and pitch variations are processed, at least at a pre-attentive level, in an integrated rather than independent way. Domain-specificity of consonants may depend on higher-level processes in the hierarchy of speech perception

The effects of stimulus complexity on the preattentive processing of self-generated and nonself voices: an ERP study

The ability to differentiate one's own voice from the voice of somebody else plays a critical role in successful verbal self-monitoring processes and in communication. However, most of the existing studies have only focused on the sensory correlates of self-generated voice processing, whereas the effects of attentional demands and stimulus complexity on self-generated voice processing remain largely unknown. In this study, we investigated the effects of stimulus complexity on the preattentive processing of self and nonself voice stimuli. Event-related potentials (ERPs) were recorded from 17 healthy males who watched a silent movie while ignoring prerecorded self-generated (SGV) and nonself (NSV) voice stimuli, consisting of a vocalization (vocalization category condition: VCC) or of a disyllabic word (word category condition: WCC). All voice stimuli were presented as standard and deviant events in four distinct oddball sequences. The mismatch negativity (MMN) ERP component peaked earlier for NSV than for SGV stimuli. Moreover, when compared with SGV stimuli, the P3a amplitude was increased for NSV stimuli in the VCC only, whereas in the WCC no significant differences were found between the two voice types. These findings suggest differences in the time course of automatic detection of a change in voice identity. In addition, they suggest that stimulus complexity modulates the magnitude of the orienting response to SGV and NSV stimuli, extending previous findings on self-voice processing.This work was supported by Grant Numbers IF/00334/2012, PTDC/PSI-PCL/116626/2010, and PTDC/MHN-PCN/3606/2012, funded by the Fundacao para a Ciencia e a Tecnologia (FCT, Portugal) and the Fundo Europeu de Desenvolvimento Regional through the European programs Quadro de Referencia Estrategico Nacional and Programa Operacional Factores de Competitividade, awarded to A.P.P., and by FCT Doctoral Grant Number SFRH/BD/77681/2011, awarded to T.C.info:eu-repo/semantics/publishedVersio

Tangential derivative mapping of axial MEG applied to event-related desynchronization research.

Objectives: A problem with the topographic mapping of MEG data recorded with axial gradiometers is that field extrema are measured at sensors located at either side of a neuronal generator instead of at sensors directly above the source. This is problematic for the computation of event-related desynchronization (ERD) on MEG data, since ERD relies on a correspondence between the signal maximum and the location of the neuronal generator. Methods: We present a new method based on computing spatial derivatives of the MEG data. The limitations of this method were investigated by means of forward simulations, and the method was applied to a 150-channel MEG dataset. Results: The simulations showed that the method has some limitations. (1) Fewer channels reduce accuracy and amplitude. (2) It is less suitable for deep or very extended sources. (3) Multiple sources can only be distinguished if they are not too close to each other. Applying the method in the calculation of ERD on experimental data led to a considerable improvement of the ERD maps. Conclusions: The proposed method offers a significant advantage over raw MEG signals, both for the topographic mapping of MEG and for the analysis of rhythmic MEG activity by means of ERD

On the time resolution of event-related desynchronization: a simulation study.

Objectives: To investigate the time resolution of different methods for the computation of event-related desynchronization/synchronization (ERD/ERS), including one based on Hilbert transform. Methods: In order to better understand the time resolution of ERD/ERS, which is a function of factors such as the exact computation method, the frequency under study, the number of trials, and the sampling frequency, we simulated sudden changes in oscillation amplitude as well as very short and closely spaced events. Results: Hilbert-based ERD yields very similar results to ERD integrated over predefined time intervals (block ERD), if the block length is half the period length of the studied frequency. ERD predicts the onset of a change in oscillation amplitude with an error margin of only 10–30 ms. On the other hand, the time the ERD response needs to climb to its full height after a sudden change in oscillation amplitude is quite long, i.e. between 200 and 500 ms. With respect to sensitivity to short oscillatory events, the ratio between sampling frequency and electroencephalographic frequency band plays a major role. Conclusions: (1) The optimal time interval for the computation of block ERD is half a period of the frequency under investigation. (2) Due to the slow impulse response, amplitude effects in the ERD may in reality be caused by duration differences. (3) Although ERD based on the Hilbert transform does not yield any significant advantages over classical ERD in terms of time resolution, it has some important practical advantages

Supervised classification of white matter fibers based on neighborhood fiber orientation distributions using an ensemble of neural networks

White matter fibers constitute the main information transfer network of the brain and their accurate digital representation and classification is an important goal of neuroscience image computing. In current clinical practice, the reconstruction of desired fibers generally involves manual selection of regions of interest by an expert, which is time-consuming and subject to user bias, expertise and fatigue. Hence, automation of the process is desired. To that end, we propose a supervised classification approach that utilizes an ensemble of neural networks. Each streamline is represented by the fiber orientation distributions in its neighborhood, while the resolved fiber orientations are obtained by generalized q-sampling imaging (GQI) and a subsequent diffusion decomposition method. In order to make the supervised fiber classification succeed in a real scenario where a substantial portion of reconstructed fiber tracts contain spurious fibers, we present a way to create an “invalid” class label through a dedicated training set creation scheme with an ensemble of networks. The performance of the proposed classification method is demonstrated on major fiber pathways in the brainstem. 30 subjects from Human Connectome Project (HCP)’s publicly available “WU-Minn 500 Subjects + MEG2 dataset” are used as the dataset

Spectral mapping of brain functional connectivity from diffusion imaging

Abstract Understanding the relationship between the dynamics of neural processes and the anatomical substrate of the brain is a central question in neuroscience. On the one hand, modern neuroimaging technologies, such as diffusion tensor imaging, can be used to construct structural graphs representing the architecture of white matter streamlines linking cortical and subcortical structures. On the other hand, temporal patterns of neural activity can be used to construct functional graphs representing temporal correlations between brain regions. Although some studies provide evidence that whole-brain functional connectivity is shaped by the underlying anatomy, the observed relationship between function and structure is weak, and the rules by which anatomy constrains brain dynamics remain elusive. In this article, we introduce a methodology to map the functional connectivity of a subject at rest from his or her structural graph. Using our methodology, we are able to systematically account for the role of structural walks in the formation of functional correlations. Furthermore, in our empirical evaluations, we observe that the eigenmodes of the mapped functional connectivity are associated with activity patterns associated with different cognitive systems