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Trial-to-trial variability in decision making can be caused by variability in information processing as well as by variability in response caution. In this paper, we study which neural components code for trial-to-trial adjustments in response caution using a new computational approach that quantifies response caution on a single-trial level. We found that the frontostriatal network updates the amount of response caution. In particular, when human participants were required to respond quickly, we found a positive correlation between trial-to-trial fluctuations in response caution and the hemodynamic response in the presupplementary motor area and dorsal anterior cingulate. In contrast, on trials that required a change from a speeded response mode to a more accurate response mode or vice versa, we found a positive correlation between response caution and hemodynamic response in the anterior cingulate proper. These results indicate that for each decision, response caution is set through corticobasal ganglia functioning, but that individual choices differ according to the mechanisms that trigger changes in response caution.

Cognitive neuroscientists study how the brain implements particular cognitive processes such as perception, learning, and decision-making. Traditional approaches in which experiments are designed to target a specific cognitive process have been supplemented by two recent innovations. First, formal models of cognition can decompose observed behavioral data into multiple latent cognitive processes, allowing brain measurements to be associated with a particular cognitive process more precisely and more confidently. Second, cognitive neuroscience can provide additional data to inform the development of cognitive models, providing greater constraint than behavioral data alone. We argue that these fields are mutually dependent: not only can models guide neuroscientific endeavors, but understanding neural mechanisms can provide critical insights into formal models of cognition.

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.

We investigate lateral differences in the intrinsic fluctuations comprising the default mode network (DMN) for healthy controls (HCs) and patients with schizophrenia (SZ), both during rest and during an auditory oddball (AOD) task. Our motivation for this study comes from multiple prior hypotheses of disturbed hemispheric asymmetry in SZ and more recently observed lateral abnormalities in the DMN for SZ during AOD. We hypothesized that significant lateral differences would be found in HCs during both rest and AOD, and SZ would show differences from HCs due to hemispheric dysfunction. Our study examined 28 HCs and 28 outpatients with schizophrenia. The scans were conducted on a Siemens Allegra 3T dedicated head scanner. There were numerous crossgroup lateral fluctuations that were found in both AOD and rest. During the resting state, within-group results showed the largest functional asymmetries in the inferior parietal lobule for HCs, whereas functional asymmetries were seen in posterior cingulate gyrus for SZ. Comparing asymmetries between groups, in resting state and/or performing AOD, areas showing significant differences between group (HC > SZ) included inferior parietal lobule and posterior cingulate. Our results support the hypothesis that schizophrenia is characterized by abnormal hemispheric asymmetry. Secondly, the number of similarities in crossgroup AOD and rest data suggests that neurological disruptions in SZ that may cause evoked symptoms are also detectable in SZ during resting conditions. Furthermore, the results suggest a reduction in activity in language-related areas for SZ compared to HCs during rest.

Calhoun VD, Wu L, Kiehl KA, Eichele T, Pearlson GD. Aberrant processing of deviant stimuli in schizophrenia revealed by fusion of fMRI and EEG data.Background:Aberrant electrophysiological and haemodynamic processing of auditory oddball stimuli is among the most robustly documented findings in patients with schizophrenia. However, no study to date has directly examined linked patterns of electrical and haemodynamic differences in patients and controls.Methods:In a recent paper we demonstrated a data-driven approach, joint independent component analysis (jICA) to fuse together functional magnetic resonance imaging (fMRI) and event-related potential (ERP) data and elucidated the chronometry of auditory oddball target detection in healthy control subjects. In this paper we extend our fusion method to identify specific differences in the neuronal chronometry of target detection for chronic schizophrenia patients compared to healthy controls.Results:We found one linked source, consistent with the N2 response, known to be related to cognitive processing of deviant stimuli, spatially localized to bilateral fronto-temporal regions. This source showed significant between-group differences both in amplitude response and in the fMRI/ERP distribution pattern. These findings are consistent with previous work showing N2 amplitude and latency abnormalities in schizophrenia, and provide new information about the linkage between the two.Conclusions:In summary, we use a novel approach to isolate and identify a linked fMRI/ERP component which shows marked differences in chronic schizophrenia patients. We also show that jointly using both fMRI and ERP measures provides a fully picture of the underlying haemodynamic and electrical changes which are present in patients. Our approach also has broad applicability to other diseases such as autism, Alzheimer's disease, or bipolar disorder.

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.

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.

Background: We examined the blood-oxygen level–dependent (BOLD) activation in brain regions that signal errors and their association with intraindividual behavioural variability and adaptation to errors in children with attention-deficit/hyperactivity disorder (ADHD). Methods: We acquired functional MRI data during a Flanker task in medication-naive children with ADHD and healthy controls aged 8–12 years and analyzed the data using independent component analysis. For components corresponding to performance monitoring networks, we compared activations across groups and conditions and correlated them with reaction times (RT). Additionally, we analyzed post-error adaptations in behaviour and motor component activations. Results: We included 25 children with ADHD and 29 controls in our analysis. Children with ADHD displayed reduced activation to errors in cingulo-opercular regions and higher RT variability, but no differences of interference control. Larger BOLD amplitude to error trials significantly predicted reduced RT variability across all participants. Neither group showed evidence of post-error response slowing; however, post-error adaptation in motor networks was significantly reduced in children with ADHD. This adaptation was inversely related to activation of the right-lateralized ventral attention network (VAN) on error trials and to task-driven connectivity between the cingulo-opercular system and the VAN. Limitations: Our study was limited by the modest sample size and imperfect matching across groups. Conclusion: Our findings show a deficit in cingulo-opercular activation in children with ADHD that could relate to reduced signalling for errors. Moreover, the reduced orienting of the VAN signal may mediate deficient post-error motor adaptions. Pinpointing general performance monitoring problems to specific brain regions and operations in error processing may help to guide the targets of future treatments for ADHD.

The present paper reports results from, to our knowledge, the first study designed to examine the neuronal responses to income inequality in situations in which individuals have made different contributions in terms of work effort. We conducted an experiment that included a prescanning phase in which the participants earned money by working, and a neuronal scanning phase in which we examined how the brain responded when the participants evaluated different distributions of their earnings. We provide causal evidence for the relative contribution of work effort being crucial for understanding the hemodynamic response in the brain to inequality. We found a significant hemodynamic response in the striatum to deviations from the distribution of income that was proportional to work effort, but found no effect of deviations from the equal distribution of income. We also observed a striking correlation between the hemodynamic response in the striatum and the self-reported evaluation of the income distributions. Our results provide, to our knowledge, the first set of neuronal evidence for equity theory and suggest that people distinguish between fair and unfair inequalities.

Alzheimer’s disease (AD) and vascular dementia (VaD) present with similar clinical symptoms of cognitive decline, but the underlying pathophysiological mechanisms differ. To determine whether clinical electroencephalography (EEG) can provide information relevant to discriminate between these diagnoses, we used quantitative EEG analysis to compare the spectra between non-medicated patients with AD (n = 77) and VaD (n = 77) and healthy elderly normal controls (NC) (n = 77). We use curve-fitting with a combination of a power loss and Gaussian function to model the averaged resting-state spectra of each EEG channel extracting six parameters. We assessed the performance of our model and tested the extracted parameters for group differentiation. We performed regression analysis in a multivariate analysis of covariance with group, age, gender, and number of epochs as predictors and further explored the topographical group differences with pair-wise contrasts. Significant topographical differences between the groups were found in several of the extracted features. Both AD and VaD groups showed increased delta power when compared to NC, whereas the AD patients showed a decrease in alpha power for occipital and temporal regions when compared with NC. The VaD patients had higher alpha power than NC and AD. The AD and VaD groups showed slowing of the alpha rhythm. Variability of the alpha frequency was wider for both AD and VaD groups. There was a general decrease in beta power for both AD and VaD. The proposed model is useful to parameterize spectra, which allowed extracting relevant clinical EEG key features that move toward simple and interpretable diagnostic criteria.

Dichotic listening (DL) is the most frequently used method to study language lateralization. The current study investigated the effect of voice-onset-time (VOT) in dichotic listening with consonant-vowel (CV) syllables. Eighty-nine subjects with normal hearing and overall right-ear-advantage (REA) were tested with a PC version of the DL test. Voiced and unvoiced stop-consonants were used in combination with the vowel /a/. This produced three syllables with short VOT /ba, da, ga/ and three syllables with long VOT /pa, ta, ka/. There were, therefore, four possible combinations of VOTs when the syllables were presented as dichotic pairs. These were short-long (SL), i.e. syllable-pairs with a short VOT in the left ear and a syllable with a long VOT in the right ear; and similarly long-short (LS), short-short (SS), and long-long (LL). The results showed that syllable pairs with long VOT presented in the right ear and short VOT simultaneously presented in the left ear, produced the largest REA. This was followed by the LL and SS conditions. The LS condition produced a significant left-ear-advantage (LEA). These results demonstrate that VOT significantly affects ear-advantage as observed in the DL test and suggest that VOT may be a more powerful determinant of DL performance than the classic REA effect. The findings are discussed within the framework of different hypotheses about speech laterality.

Contains fulltext : 56989.pdf (Publisher’s version ) (Closed access) The brain acts as an integrated information processing system, which methods in cognitive neuroscience have so far depicted in a fragmented fashion. Here, we propose a simple and robust way to integrate functional MRI (fMRI) with single trial event-related potentials (ERP) to provide a more complete spatiotemporal characterization of evoked responses in the human brain. The idea behind the approach is to find brain regions whose fMRI responses can be predicted by paradigm-induced amplitude modulations of simultaneously acquired single trial ERPs. The method was used to study a variant of a two-stimulus auditory target detection (odd-ball) paradigm that manipulated predictability through alternations of stimulus sequences with random or regular target-to-target intervals. In addition to electrophysiologic and hemodynamic evoked responses to auditory targets per se, single-trial modulations were expressed during the latencies of the P2 (170-ms), N2 (200-ms), and P3 (320-ms) components and predicted spatially separated fMRI activation patterns. These spatiotemporal matches, i.e., the prediction of hemodynamic activation by time-variant information from single trial ERPs, permit inferences about regional responses using fMRI with the temporal resolution provided by electrophysiology. 6 p.

Epilepsy is common in polymerase gamma (POLG) related disease and is associated with high morbidity and mortality. Epileptiform discharges typically affect the occipital regions initially and focal seizures, commonly evolving to bilateral convulsive seizures which are the most common seizure types in both adults and children. Our work has shown that mtDNA depletion—i.e., the quantitative loss of mtDNA—in neurones is the earliest and most important factor of the subsequent development of cellular dysfunction. Loss of mtDNA leads to loss of mitochondrial respiratory chain (MRC) components that, in turn, progressively disables energy metabolism. This critically balanced neuronal energy metabolism leads to both a chronic and continuous attrition (i.e., neurodegeneration) and it leaves the neurone unable to cope with increased demand that can trigger a potentially catastrophic cycle that results in acute focal necrosis. We believe that it is the onset of epilepsy that triggers the cascade of damage. These events can be identified in the stepwise evolution that characterizes the clinical, Electroencephalography (EEG), neuro-imaging, and neuropathology findings. Early recognition with prompt and aggressive seizure management is vital and may play a role in modifying the epileptogenic process and improving survival.

BackgroundSchizophrenia and bipolar disorder are severe mental disorders with overlapping genetic and clinical characteristics, including cognitive impairments. An important question is whether these disorders also have overlapping neuronal deficits.AimsTo determine whether large-scale brain networks associated with working memory, as measured with functional magnetic resonance imaging (fMRI), are the same in both schizophrenia and bipolar disorder, and how they differ from those in healthy individuals.MethodPatients with schizophrenia (n = 100) and bipolar disorder (n = 100) and a healthy control group (n = 100) performed a 2-back working memory task while fMRI data were acquired. The imaging data were analysed using independent component analysis to extract large-scale networks of task-related activations.ResultsSimilar working memory networks were activated in all groups. However, in three out of nine networks related to the experimental task there was a graded response difference in fMRI signal amplitudes, where patients with schizophrenia showed greater activation than those with bipolar disorder, who in turn showed more activation than healthy controls. Secondary analysis of the patient groups showed that these activation patterns were associated with history of psychosis and current elevated mood in bipolar disorder.ConclusionsThe same brain networks were related to working memory in schizophrenia, bipolar disorder and controls. However, some key networks showed a graded hyperactivation in the two patient groups, in line with a continuum of neuronal abnormalities across psychotic disorders.

Different imaging modalities capture different aspects of brain activity. Functional magnetic resonance imaging (fMRI) reveals intrinsic networks whose BOLD signals have periods from 100 s (0.01 Hz) to about 10s (0.1 Hz). Electroencephalographic (EEG) recordings, in contrast, commonly reflect cortical electrical fluctuations with periods up to 20 ms (50 Hz) or above. We examined the correspondence between intrinsic fMRI and EEG network activity at rest in order to characterize brain networks both spatially (with fMRI) and spectrally (with EEG). Brain networks were separately identified within the concurrently recorded fMRI and EEG at the aggregate group level with group independent component analysis and the association between spatial fMRI and frequency by spatial EEG sources was examined by deconvolving their component time courses. The two modalities are considered linked if the estimated impulse response function (IRF) is significantly non-zero at biologically plausible delays. We found that negative associations were primarily present within two of five alpha components, which highlights the importance of considering multiple alpha sources in EEG-fMRI. Positive associations were primarily present within the lower (e.g. delta and theta) and higher (e.g. upper beta and lower gamma) spectral regions, sometimes within the same fMRI components. Collectively, the results demonstrate a promising approach to characterize brain networks spatially and spectrally, and reveal that positive and negative associations appear within partially distinct regions of the EEG spectrum.

Each method for imaging brain activity has technical or physiological limits. Thus, combinations of neuroimaging modalities that can alleviate these limitations such as simultaneous recordings of neurophysiological and hemodynamic activity have become increasingly popular. Multimodal imaging setups can take advantage of complementary views on neural activity and enhance our understanding about how neural information processing is reflected in each modality. However, dedicated analysis methods are needed to exploit the potential of multimodal methods. Many solutions to this data integration problem have been proposed, which often renders both comparisons of results and the choice of the right method for the data at hand difficult. In this review we will discuss different multimodal neuroimaging setups, the advances achieved in basic research and clinical application and the methods used. We will provide a comprehensive overview of mathematical tools reoccurring in multimodal neuroimaging studies for artifact removal, data-driven and model-driven analyses, enabling the practitioner to try established or new combinations from these algorithmic building blocks.

We present a novel method to extract classification features from functional magnetic resonance imaging (fMRI) data collected at rest or during the performance of a task. By combining a two-level feature identification scheme with kernel principal component analysis (KPCA) and Fisher’s linear discriminant analysis (FLD), we achieve high classification rates in discriminating healthy controls from patients with schizophrenia. Experimental results using leave-one-out cross-validation show that features extracted from the default mode network (DMN) lead to a classification accuracy of over 90% in both data sets. Moreover, using a majority vote method that uses multiple features, we achieve a classification accuracy of 98% in auditory oddball (AOD) task and 93% in rest data. Several components, including DMN, temporal, and medial visual regions, are consistently present in the set of features that yield high classification accuracy. The features we have extracted thus show promise to be used as biomarkers for schizophrenia. Results also suggest that there may be different advantages to using resting fMRI data or task fMRI data.

We introduce SimTB, a MATLAB toolbox designed to simulate functional magnetic resonance imaging (fMRI) datasets under a model of spatiotemporal separability. The toolbox meets the increasing need of the fMRI community to more comprehensively understand the effects of complex processing strategies by providing a ground truth that estimation methods may be compared against. SimTB captures the fundamental structure of real data, but data generation is fully parameterized and fully controlled by the user, allowing for accurate and precise comparisons. The toolbox offers a wealth of options regarding the number and configuration of spatial sources, implementation of experimental paradigms, inclusion of tissue-specific properties, addition of noise and head movement, and much more. A straightforward data generation method and short computation time (3–10 seconds for each dataset) allow a practitioner to simulate and analyze many datasets to potentially understand a problem from many angles. Beginning MATLAB users can use the SimTB graphical user interface (GUI) to design and execute simulations while experienced users can write batch scripts to automate and customize this process. The toolbox is freely available at http://mialab.mrn.org/software together with sample scripts and tutorials.

The complexity of the human brain and the limitation of any one imaging approach motivates the need for multimodal measurements to better understand cerebral processing. A very natural goal is to integrate electrophysiological and hemodynamic activity. Among them, concurrent EEG-fMRI studies have shown great promise for understanding intrinsic brain properties yet analyzing such data presents a significant methodological challenge. Here, we propose a multivariate parallel ICA decomposition incorporating dynamic neurovascular coupling for concurrent EEG-fMRI recordings. The goal of our algorithm is to fuse multimodal EEG-fMRI information and detect/interpret the relationship between electrophysiological and hemodynamic sources via a temporal neurovascular connection enhancement. We analyze the performance of the algorithm on a valid simulation based on real EEG and fMRI components (sources) from our previous works and a neurovascular coupling built from an extended 'balloon model'. The results from our simulations yield an accurate source tracking and linkage for concurrent EEG-fMRI, and provide a novel and efficient way to combine EEG and hemodynamic responses.

Contains fulltext : 90788.pdf (Publisher’s version ) (Open Access) As Seneca the Younger put it, "To err is human, but to persist is diabolical." To prevent repetition of errors, human performance monitoring often triggers adaptations such as general slowing and/or attentional focusing. The posterior medial frontal cortex (pMFC) is assumed to monitor performance problems and to interact with other brain areas that implement the necessary adaptations. Whereas previous research showed interactions between pMFC and lateral-prefrontal regions, here we demonstrate that upon the occurrence of errors the pMFC selectively interacts with perceptual and motor regions and thereby drives attentional focusing toward task-relevant information and induces motor adaptation observed as post-error slowing. Functional magnetic resonance imaging data from an interference task reveal that error-related pMFC activity predicts the following: (1) subsequent activity enhancement in perceptual areas encoding task-relevant stimulus features; (2) activity suppression in perceptual areas encoding distracting stimulus features; and (3) post-error slowing-related activity decrease in the motor system. Additionally, diffusion-weighted imaging revealed a correlation of individual post-error slowing and white matter integrity beneath pMFC regions that are connected to the motor inhibition system, encompassing right inferior frontal gyrus and subthalamic nucleus. Thus, disturbances in task performance are remedied by functional interactions of the pMFC with multiple task-related brain regions beyond prefrontal cortex that result in a broad repertoire of adaptive processes at perceptual as well as motor levels. 10 p.

Resting state fMRI studies have found that cognitive decline in aging is associated with alterations in functional connectivity of distributed neural systems in the brain. While functional connections have been shown to rely on the underlying structural connectivity, direct structural connections have been studied in only a few distributed cortical systems so far. It is well known that subcortical nuclei have structural connections to the entire cortex. We hypothesized that structural subcortico-cortical connections may provide integral routes for communication between cortical resting state networks, and that changes in the integrity of these connections have a role in cognitive aging. We combined anatomical MRI, diffusion tensor MRI, and resting state fMRI in 100 healthy elderly to identify fiber bundles connecting cortical resting state networks to subcortical nuclei. In identified tracts, white matter fiber bundle integrity measures were compared to composite cognitive measures on executive function, processing speed, and memory performance. The integrity (FA values) in selected fiber bundles correlated strongly with cognitive measures on executive function and processing speed. Correlation was most pronounced between executive function and fiber bundles connecting the putamen to the dorsal attention network (r=0.73, p

This chapter introduces and applies the concept of parallel spatial and temporal unmixing with group independent component analysis (ICA) for concurrent electroencephalography-functional magnetic resonance imaging (EEG-fMRI). Hemodynamic response function (HRF) deconvolution and single-trial estimation in the fMRI data were employed, and the single-trial weights were used as predictors for the amplitude modulation in the EEG. For illustration, data from a previously published performance-monitoring experiment were analyzed, in order to identify error-preceding activity in the EEG modality. EEG components that displayed such slow trends, and which were coupled to the corresponding fMRI components, are described. Parallel ICA for analysis of concurrent EEG-fMRI on a trial-by-trial basis is a very useful addition to the toolbelt of researchers interested in multimodal integration.

Independent component analysis (ICA) is increasingly utilized as a tool for evaluating the hidden spatiotemporal structure contained within brain imaging data. This chapter first provides a brief overview of ICA and how ICA is applied to functional magnetic resonance imaging (fMRI) data. It then discusses group ICA and the application of group ICA for data fusion, with an emphasis on the methods developed within our group. It also discusses, within a larger context, the many alternative approaches that are feasible and currently in use.

The simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) provides several advantages over multimodal integration based on separate EEG and fMRI recording protocols. However, the recording and analysis of simultaneous EEG-fMRI is not without pitfalls. The potential benefits of simultaneous recordings come at the expense of a massive, inevitable presence of artifacts, which corrupt the EEG signals recorded in the MR environment. This chapter presents different methods of EEG artifact correction. It also discusses the limitations of currently available approaches as well as possible future directions.

We propose a data fusion method for the fusion of simultaneously acquired functional magnetic resonance imaging (fMRI) and single trial electroencephalography (EEG) data from multiple subjects using multi-set canonical correlation analysis (M-CCA). Our proposed technique utilizes the common time series information in the multimodal datasets to find trial-to-trial covariations across modalities, and based on these covariations, the data is decomposed into spatial maps for the fMRI data and a corresponding temporal evolution for the EEG data. Additionally, the analysis is performed simultaneously on data from a group of subjects, thus providing an efficient tool to make group inferences about cross-modality covariation. The proposed method is multivariate and hence facilitates the study of brain connectivity along with localization of brain function. We demonstrate the promise of the method in finding covarying trial-to-trial amplitude modulations in an auditory task involving implicit pattern learning.

Although functional magnetic resonance imaging (fMRI) data are acquired as complex-valued images, traditionally most fMRI studies only use the magnitude of the data. FMRI analysis in the complex domain promises to provide more statistically significant information; however, the noisy nature of the phase poses a challenge for successful study of fMRI by complex-valued signal processing algorithms. In this paper, we introduce a physiologically motivated de-noising method that uses phase quality maps to successfully identify and eliminate noisy areas in the fMRI data so they can be used in individual and group studies. Additionally, we show how the developed de-noising method improves the results of complex-valued independent component analysis of fMRI data, a very successful tool for blind source separation of biomedical data.

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. © 2009 Wiley‐Liss, Inc.

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.

Processing of novel and target stimuli in the auditory target detection or ‘oddball’ task encompasses the chronometry of perception, attention and working memory and is reflected in scalp recorded event-related potentials (ERPs). A variety of ERP components related to target and novelty processing have been described and extensively studied, and linked to deficits of cognitive processing. However, little is known about associations of genotypes with ERP endophenotypes. Here we sought to elucidate the genetic underpinnings of auditory oddball ERP components using a novel data analysis technique. A parallel independent component analysis of the electrophysiology and single nucleotide polymorphism (SNP) data was used to extract relations between patterns of ERP components and SNP associations purely based on an analysis incorporating higher order statistics. The method allows for broader associations of genotypes with phenotypes than traditional hypothesis-driven univariate correlational analyses. We show that target detection and processing of novel stimuli are both associated with a shared cluster of genes linked to the adrenergic and dopaminergic pathways. These results provide evidence of genetic influences on normal patterns of ERP generation during auditory target detection and novelty processing at the SNP association level.

Alzheimer's disease (AD) is a neurodegenerative disorder that prominently affects cerebral connectivity. Assessing the functional connectivity at rest, recent functional MRI (fMRI) studies reported on the existence of resting-state networks (RSNs). RSNs are characterized by spatially coherent, spontaneous fluctuations in the blood oxygen level-dependent signal and are made up of regional patterns commonly involved in functions such as sensory, attention, or default mode processing. In AD, the default mode network (DMN) is affected by reduced functional connectivity and atrophy. In this work, we analyzed functional and structural MRI data from healthy elderly ( n = 16) and patients with amnestic mild cognitive impairment (aMCI) ( n = 24), a syndrome of high risk for developing AD. Two questions were addressed: ( i ) Are any RSNs altered in aMCI? ( ii ) Do changes in functional connectivity relate to possible structural changes? Independent component analysis of resting-state fMRI data identified eight spatially consistent RSNs. Only selected areas of the DMN and the executive attention network demonstrated reduced network-related activity in the patient group. Voxel-based morphometry revealed atrophy in both medial temporal lobes (MTL) of the patients. The functional connectivity between both hippocampi in the MTLs and the posterior cingulate of the DMN was present in healthy controls but absent in patients. We conclude that in individuals at risk for AD, a specific subset of RSNs is altered, likely representing effects of ongoing early neurodegeneration. We interpret our finding as a proof of principle, demonstrating that functional brain disorders can be characterized by functional-disconnectivity profiles of RSNs.

In order to examine auditory lateralization of prelexical speech processing, a dichotic listening task was performed with concurrent EEG measurement.Subjects were tested with dichotic pairs of six consonant-vowel (CV) syllables that initially started with a voiced (/ba/, /da/, /ga/) or a voiceless stop consonant (/pa/, /ta/, /ka/). Electrophysiological correlates were analyzed by a low resolution electromagnetic tomography (LORETA) approach to estimate the sources of N1 event-related potentials (ERP) in the 3D brain.Behavioral and electrophysiological measures revealed different ear advantages and ERP amplitude measures for voiced and voiceless syllables. Fronto-central N1 amplitudes were larger for syllables with voiced than voiceless initial consonants. LORETA source estimates revealed a lateralization effect, with stronger leftward lateralization for voiced than voiceless CV syllables.The present study demonstrates that auditory lateralization is affected by temporal cues in CV syllables. The lateralization effect suggests that functional hemispheric differences exist at an early prelexical level of speech processing.

An optimized scheme for the fusion of electroencephalography and event related potentials with functional magnetic resonance imaging (BOLD-fMRI) data should simultaneously assess all available electrophysiologic and hemodynamic information in a common data space. In doing so, it should be possible to identify features of latent neural sources whose trial-to-trial dynamics are jointly reflected in both modalities. We present a joint independent component analysis (jICA) model for analysis of simultaneous single trial EEG-fMRI measurements from multiple subjects. We outline the general idea underlying the jICA approach and present results from simulated data under realistic noise conditions. Our results indicate that this approach is a feasible and physiologically plausible data-driven way to achieve spatiotemporal mapping of event related responses in the human brain.

Concurrent event-related EEG-fMRI recordings pick up volume-conducted and hemodynamically convoluted signals from latent neural sources that are spatially and temporally mixed across the brain, i.e. the observed data in both modalities represent multiple, simultaneously active, regionally overlapping neuronal mass responses. This mixing process decreases the sensitivity of voxel-by-voxel prediction of hemodynamic activation by the EEG when multiple sources contribute to either the predictor and/or the response variables. In order to address this problem, we used independent component analysis (ICA) to recover maps from the fMRI and timecourses from the EEG, and matched these components across the modalities by correlating their trial-to-trial modulation. The analysis was implemented as a group-level ICA that extracts a single set of components from the data and directly allows for population inferences about consistently expressed function-relevant spatiotemporal responses. We illustrate the utility of this method by extracting a previously undetected but relevant EEG-fMRI component from a concurrent auditory target detection experiment.

Objective: The main aim was to track the dynamics of pattern-learning using single-trial event-related potentials (ERPs). A new ‘learning-oddball’ paradigm was employed presenting eight random targets (the ‘no-pattern’) followed by eight regular targets (the ‘pattern’). In total, six repetitions of the ‘no-pattern’ followed by the ‘pattern’ were presented. Methods: We traced the dynamics of learning by measuring responses to 16 (eight random–eight regular) targets. Since this alternation of the ‘no-pattern’ followed by the ‘pattern’ was repeated six times, we extracted single-trial responses to all 96 targets to determine if learning occurred more rapidly with each repetition of the ‘pattern.’ Results: Following random targets, ERPs contained a marked P3–N2 component that decreased to regular targets, whereas a contingent negative variation (CNV) appeared. ERP changes could be best described by sigmoid ‘learning’ curves. Single-trial analyses showed that learning occurred more rapidly over repetitions and suggested that the CNV developed prior to the decay of the N2-P3 component. Conclusions: We show a new paradigm-analysis methodology to track learning processes directly from brain signals. Significance: Single-trial ERPs analyses open a wide range of applications. Tracking the dynamic structure of cognitive functions may prove crucial in the understanding of learning and in the study of different pathologies. � 2006 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Contains fulltext : 57002.pdf (Publisher’s version ) (Closed access) An expanded omitted stimulus paradigm was investigated to determine whether expectancy would modulate the amplitude of the omission evoked potentials (OEPs). In addition, we examined the effects of musical expertise on OEPs. Trials started with 3-7 beats randomly and contained 5 omitted beats. Three types of trials (n = 90) were presented with 1, 2, or 3 beats occurring between omissions. A tap response at the end of each trial was used to determine timing accuracy. Clear OEPs were observed over midline sites. We found main omission effects with respect to an N150 and a P400 OEPs component, such that peak amplitudes diminished whenever the occurrence of an omitted stimulus could be expected. In addition, an N600 OEPs component emerged in response to expectedly omitted stimuli toward the end of each trial within the group of musicians. Thus, musical training seems to lead to more efficient and more refined processing of auditory temporal patterns. 11 p.

The nine papers in this special section represent the state-of-art in the area of multimodal biomedical imaging algorithms and applications.

The functional organization of the human auditory cortex is still not well understood with respect to speech perception and language lateralization. Especially, there is comparatively little data available in the brain imaging literature focusing on the timing of phonetic processing. We recorded auditory-evoked potentials (AEP) from 27 scalp and additional EOG channels in 12 healthy volunteers performing a free report dichotic listening task with simple speech sounds (CV syllables: [ba], [da], [ga], [pa], [ta], [ka]). ERP analysis employed independent components analysis (ICA) wavelet denoising for artifact reduction and improvement of the SNR. The main finding was a 15-ms shorter average latency of the N1-AEP recorded from the scalp approximately overlying the left supratemporal cortical plane compared to the N1-AEP over the homologous right side. Corresponding N1 amplitudes did not differ between these sites. The individual AEP latency differences significantly correlated with the ear advantage as an index of speech/language lateralization. The behaviorally relevant difference in N1 latency between the hemispheres indicates that an important key to understanding speech perception is to consider the functional implications of neuronal event timing.

Smith and colleagues recently presented a temporal independent component analysis (tICA) decomposition of resting-state functional MRI data. Compared to the widely used spatial ICA (sICA), tICA better allows for a brain region to engage in multiple, independent interactions with other regions and will potentially offer new insights into brain function.

A relatively large sample of individuals (N = 240) ranging from 7 to 70 years were studied with the dichotic listening (DL) technique as a measure of language lateralization. Two questions were asked: (a) Is there a developmental effect for the right-ear advantage (REA) with age? (b) Is there a developmental effect for attentional modulation of the REA? The participants were all right-handed, healthy individuals tested in Sweden and Norway. The stimuli were dichotic presentations of consonant-vowel (CV) syllable pairs. There were 3 different attentional conditions: nonforced (NF) attention, with no specific instructions about focusing of attention; forced-right (FR) attention, with instructions to focus attention on the right-ear stimulus; and forced-left (FL) attention, with instructions to focus attention on the left-ear stimulus. The results showed a REA in all age groups, except for the 9-year-olds, in the NF condition. In the FR condition, all age groups showed a REA. The youngest age groups did not, however, increase the REA to the same degree as the older age groups, as indicated by the significant age x ear interaction. In the FL condition, all age groups, with the exception of the youngest groups, showed a left-ear advantage (LEA). It is concluded that the REA effect in DL is subject to developmental effects and that attentional effects on laterality also develop with increasing age.

a Department of Biological and Medical Psychology, University of Bergen, Jonas Lies Vei 91, 5011 Bergen, Norway b Haukeland University Hospital, Bergen, Norway c MIND Institute, Albuquerque, New Mexico, United States d Department of Elecrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, United States e Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States