OBJECTIVE: Music perception with a cochlear implant (CI) can be unsatisfactory because current-day implants are primarily designed to enable speech discrimination. The present study aimed at evaluating electrophysiological correlates of musical sound perception in CI users to help achieve the long-term goal of improved restoration of hearing in those individuals. METHODS: Auditory discrimination accuracy in adult CI users (n=12) and matched normal-hearing controls (n=12) was measured by behavioral discrimination tasks and mismatch negativity (MMN) recordings. Discrimination profiles were obtained by using a set of clarinet sounds (original/vocoded) varying along different acoustic dimensions (frequency/intensity/duration) and deviation magnitudes (four levels). RESULTS: Behavioral results and MMN recordings revealed reduced auditory discrimination accuracy in CI users. An inverse relationship was found between MMN amplitudes and duration of profound deafness. CONCLUSIONS: CI users have difficulties in discriminating small changes in the acoustic properties of musical sounds. The recently developed multi-feature MMN paradigm (Pakarinen et al., 2007) can be used to objectively evaluate discrimination abilities of CI users for musical sounds. SIGNIFICANCE: Measuring auditory discrimination functions by means of a multi-feature MMN paradigm could be of substantial clinical value by providing a comprehensive profile of the extent of restored hearing in CI users.

Concurrent EEG-fMRI studies have provided increasing details of the dynamics of intrinsic brain activity during the resting state. Here, we investigate a prominent effect in EEG during relaxed resting, i.e. the increase of the alpha power when the eyes are closed compared to when the eyes are open. This phenomenon is related to changes in thalamo-cortical and cortico-cortical synchronization. In order to investigate possible changes to EEG-fMRI coupling and fMRI functional connectivity during the two states we adopted a data-driven approach that fuses the multimodal data on the basis of parallel ICA decompositions of the fMRI data in the spatial domain and of the EEG data in the spectral domain. The power variation of a posterior alpha component was used as a reference function to deconvolve the hemodynamic responses from occipital, frontal, temporal, and subcortical fMRI components. Additionally, we computed the functional connectivity between these components. The results showed widespread alpha hemodynamic responses and high functional connectivity during eyes-closed (EC) rest, while eyes open (EO) resting abolished many of the hemodynamic responses and markedly decreased functional connectivity. These data suggest that generation of local hemodynamic responses is highly sensitive to state changes that do not involve changes of mental effort or awareness. They also indicate the localized power differences in posterior alpha between EO and EC in resting state data are accompanied by spatially widespread amplitude changes in hemodynamic responses and inter-regional functional connectivity, i.e. low frequency hemodynamic signals display an equivalent of alpha reactivity.

Imaging research into age-related changes in episodic memory has mainly focused on changes in cortical areas in the medial temporal lobe and the Hippocampus. However, several lines of evidence indicate that subcortical structures such as the Basal Ganglia and the Thalamus are also involved in episodic memory function. Recent studies have revealed age-related changes in functional connectivity between different brain areas, as measured by resting state fMRI. It remains to be shown whether functional connectivity measures in the Basal Ganglia and the Thalamus can be associated with age-related changes in memory function. Here, we investigate this question by applying high-model-order spatial independent component analysis to resting state fMRI data in a cohort of 100 healthy elderly and relate connectivity features to verbal episodic memory function as assessed by the California Verbal Learning Test (CVLT). We identified five components that were located within different parts of the Thalamus and the Basal Ganglia. Two of these components demonstrated negative correlations between their functional connectivity level and CVLT performance. We also found negative correlations between connectional strength within subcortical structures and CVLT performance. These results indicate a previously undocumented role for the Putamen and the Thalamus in verbal episodic memory function in aging.

Abstract Memories of past episodes frequently come to mind incidentally, without directed search. It has remained unclear how incidental retrieval processes are initiated in the brain. Here we used fMRI and ERP recordings to find brain activity that specifically correlates with incidental retrieval, as compared to intentional retrieval. Intentional retrieval was associated with increased activation in dorsolateral prefrontal cortex. By contrast, incidental retrieval was associated with a reduced fMRI signal in posterior brain regions, including extrastriate and parahippocampal cortex, and a modulation of a posterior ERP component 170 msec after the onset of visual retrieval cues. Successful retrieval under both intentional and incidental conditions was associated with increased activation in the hippocampus, precuneus, and ventrolateral prefrontal cortex, as well as increased amplitude of the P600 ERP component. These results demonstrate how early bottom-up signals from posterior cortex can lead to reactivation of episodic memories in the absence of strategic retrieval attempts.

Functional magnetic resonance imaging (fMRI) data and electroencephalography (EEG) data provide complementary spatio-temporal information about brain function. Methods to couple the relative strengths of these modalities usually involve two stages: first forming a feature set from each dataset based on one criterion followed by exploration of connections among the features using a second criterion. We propose a data fusion method for simultaneously acquired fMRI and EEG data that combines these steps using a single criterion for finding the cross-modality associations and performing source separation. Using multi-set canonical correlation analysis (M-CCA), we obtain a decomposition of the two modalities, into spatial maps for fMRI data and a corresponding temporal evolution for EEG data, based on trial-to-trial covariation across the two modalities. Additionally, the analysis is performed on data from a group of subjects in order to make group inferences about the covariation across modalities. Being multivariate, the proposed method facilitates the study of brain connectivity along with localization of brain function. MCCA can be easily extended to incorporate different data types and additional modalities. We demonstrate the promise of the proposed method in finding covarying trial-to-trial amplitude modulations (AMs) in an auditory task involving implicit pattern learning. The results show approximately linear decreasing trends in AMs for both modalities and the corresponding spatial activations occur mainly in motor, frontal, temporal, inferior parietal, and orbito-frontal areas that are linked both to sensory function as well as learning and expectation all of which match activations related to the presented paradigm.

This study compared performance and performance monitoring in 19 boys with Tourette syndrome (TS)(12.64 years,+/- 2.05) and 19 age-matched controls (13.16 years,+/- 2.29) using a Go/NoGo task. The results indicated similar performance accuracy in the TS group and the control group. TS participants showed slower correct responses than the control group, whereas error response times were not different between the groups. The results are discussed with reference to inhibitory adaptive effects that may be employed by TS participants to maintain high accuracy at the cost of overall slower performance. These effects may be suspended prior to errors.

Functional magnetic resonance imaging (fMRI) has become a major technique for studying cognitive function and its disruption in mental illness, including schizophrenia. The major proportion of imaging studies focused primarily upon identifying regions which hemodynamic response amplitudes covary with particular stimuli and differentiate between patient and control groups. In addition to such amplitude based comparisons, one can estimate temporal correlations and compute maps of functional connectivity between regions which include the variance associated with event-related responses as well as intrinsic fluctuations of hemodynamic activity. Functional connectivity maps can be computed by correlating all voxels with a seed region when a spatial prior is available. An alternative are multivariate decompositions such as independent component analysis (ICA) which extract multiple components, each of which is a spatially distinct map of voxels with a common time course. Recent work has shown that these networks are pervasive in relaxed resting and during task performance and hence provide robust measures of intact and disturbed brain activity. This in turn bears the prospect of yielding biomarkers for schizophrenia, which can be described both in terms of disrupted local processing as well as altered global connectivity between large-scale networks. In this review we will summarize functional connectivity measures with a focus upon work with ICA and discuss the meaning of intrinsic fluctuations. In addition, examples of how brain networks have been used for classification of disease will be shown. We present work with functional network connectivity, an approach that enables the evaluation of the interplay between multiple networks and how they are affected in disease. We conclude by discussing new variants of ICA for extracting maximally group discriminative networks from data. In summary, it is clear that identification of brain networks and their inter-relationships with fMRI has great potential to improve our understanding of schizophrenia.

Glutamate is critically involved in the regulation of cognitive functions in humans. There is, however, sparse evidence regarding how blocking glutamate action at the receptor site during a cognitive task affects brain activation. In the current study, the effects of the glutamate antagonist memantine were examined with functional magnetic resonance imaging (fMRI). Thirty-one healthy adults were scanned twice in a counter-balanced design, either in a no-drug session or after administration of memantine for 21 days. The subjects performed a simple auditory perception task with consonant-vowel stimuli. Group-level spatial independent component analysis (ICA) was used to decompose the data and to extract task-related activations. The focus was on four task-related ICA components with frontotemporal localization. The results showed that glutamate-blockage resulted in a significant enhancement in one component, with no significant effect in the other three components. The enhanced effect of memantine was in the middle temporal gyrus, superior frontal gyrus, and middle frontal gyrus. It is suggested that the results reflect effects of glutamatergic processes primarily through non-N-methyl-D-aspartate (NMDA) receptor pathways. Moreover, the results demonstrate that memantine can be used as a probe which allows for studying the effect of excitatory neurotransmission on neuronal activation. Hum Brain Mapp, 2009.(c) 2009 Wiley-Liss, Inc.

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.

Auditory evoked potentials are tools widely used to assess auditory cortex functions in clinical context. However, in cochlear implant users, electrophysiological measures are challenging due to implant-created artefacts in the EEG. Here, we used independent component analysis to reduce cochlear implant-related artefacts in event-related EEGs of cochlear implant users (n = 12), which allowed detailed spatio-temporal evaluation of auditory evoked potentials by means of dipole source analysis. The present study examined hemispheric asymmetries of auditory evoked potentials to musical sounds in cochlear implant users to evaluate the effect of this type of implantation on neuronal activity. In particular, implant users were presented with two dyadic tonal intervals in an active oddball design and in a passive listening condition. Principally, the results show that independent component analysis is an efficient approach that enables the study of neurophysiological mechanisms of restored auditory function in cochlear implant users. Moreover, our data indicate altered hemispheric asymmetries for dyadic tone processing in implant users compared with listeners with normal hearing (n = 12). We conclude that the evaluation of auditory evoked potentials are of major relevance to understanding auditory cortex function after cochlear implantation and could be of substantial clinical value by indicating the maturation/reorganization of the auditory system after implantation.