Investigating human audio-visual object perception with a combination of hypothesis-generating and hypothesis-testing fMRI analysis tools

Primate multisensory object perception involves distributed brain regions. To investigate the network character of these regions of the human brain, we applied data-driven group spatial independent component analysis (ICA) to a functional magnetic resonance imaging (fMRI) data set acquired during a passive audio-visual (AV) experiment with common object stimuli. We labeled three group-level independent component (IC) maps as auditory (A), visual (V), and AV, based on their spatial layouts and activation time courses. The overlap between these IC maps served as definition of a distributed network of multisensory candidate regions including superior temporal, ventral occipito-temporal, posterior parietal and prefrontal regions. During an independent second fMRI experiment, we explicitly tested their involvement in AV integration. Activations in nine out of these twelve regions met the max-criterion (A < AV > V) for multisensory integration. Comparison of this approach with a general linear model-based region-of-interest definition revealed its complementary value for multisensory neuroimaging. In conclusion, we estimated functional networks of uni- and multisensory functional connectivity from one dataset and validated their functional roles in an independent dataset. These findings demonstrate the particular value of ICA for multisensory neuroimaging research and using independent datasets to test hypotheses generated from a data-driven analysis.

On seeing the trees and the forest: Single-signal and multisignal analysis of periictal intracranial EEG

Epileptic seizures are associated with a dysregulation of electrical brain activity on many different spatial scales. To better understand the dynamics of epileptic seizures, that is, how the seizures initiate, propagate, and terminate, it is important to consider changes of electrical brain activity on different spatial scales. Herein we set out to analyze periictal electrical brain activity on comparatively small and large spatial scales by assessing changes in single intracranial electroencephalography (EEG) signals and of averaged interdependences of pairs of EEG signals.

Spike-triggered reaction-time EEG as a possible assessment tool for driving ability

The impact of interictal epileptic activity (IEA) on driving is a rarely investigated issue. We analyzed the impact of IEA on reaction time in a pilot study. Reactions to simple visual stimuli (light flash) in the Flash test or complex visual stimuli (obstacle on a road) in a modified car driving computer game, the Steer Clear, were measured during IEA bursts and unremarkable electroencephalography (EEG) periods. Individual epilepsy patients showed slower reaction times (RTs) during generalized IEA compared to RTs during unremarkable EEG periods. RT differences were approximately 300 ms (p < 0.001) in the Flash test and approximately 200 ms (p < 0.001) in the Steer Clear. Prior work suggested that RT differences >100 ms may become clinically relevant. This occurred in 40% of patients in the Flash test and in up to 50% in the Steer Clear. When RT were pooled, mean RT differences were 157 ms in the Flash test (p < 0.0001) and 116 ms in the Steer Clear (p < 0.0001). Generalized IEA of short duration seems to impair brain function, that is, the ability to react. The reaction-time EEG could be used routinely to assess driving ability.

Activation of the parieto-premotor network is associated with vivid motor imagery - a parametric FMRI study

The present study examined the neural basis of vivid motor imagery with parametrical functional magnetic resonance imaging. 22 participants performed motor imagery (MI) of six different right-hand movements that differed in terms of pointing accuracy needs and object involvement, i.e., either none, two big or two small squares had to be pointed at in alternation either with or without an object grasped with the fingers. After each imagery trial, they rated the perceived vividness of motor imagery on a 7-point scale. Results showed that increased perceived imagery vividness was parametrically associated with increasing neural activation within the left putamen, the left premotor cortex (PMC), the posterior parietal cortex of the left hemisphere, the left primary motor cortex, the left somatosensory cortex, and the left cerebellum. Within the right hemisphere, activation was found within the right cerebellum, the right putamen, and the right PMC. It is concluded that the perceived vividness of MI is parametrically associated with neural activity within sensorimotor areas. The results corroborate the hypothesis that MI is an outcome of neural computations based on movement representations located within motor areas.

Is gamma band EEG synchronization reduced during auditory driving in schizophrenia patients with auditory verbal hallucinations?

Auditory verbal hallucinations (AVH) in schizophrenia patients assumingly result from a state inadequate activation of the primary auditory system. We tested brain responsiveness to auditory stimulation in healthy controls (n=26), and in schizophrenia patients that frequently (n=18) or never (n=11) experienced AVH. Responsiveness was assessed by driving the EEG with click-tones at 20, 30 and 40Hz. We compared stimulus induced EEG changes between groups using spectral amplitude maps and a global measure of phase-locking (GFS). As expected, the 40Hz stimulation elicited the strongest changes. However, while controls and non-hallucinators increased 40Hz EEG activity during stimulation, a left-lateralized decrease was observed in the hallucinators. These differences were significant (p=.02). As expected, GFS increased during stimulation in controls (p=.08) and non-hallucinating patients (p=.06), which was significant when combining the two groups (p=.01). In contrast, GFS decreased with stimulation in hallucinating patients (p=0.13), resulting in a significantly different GFS response when comparing subjects with and without AVH (p<.01). Our data suggests that normally, 40Hz stimulation leads to the activation of a synchronized network representing the sensory input, but in hallucinating patients, the same stimulation partly disrupts ongoing activity in this network.

Social Cognition And Functional Brain Imaging: Extending The Broader Autism Phenotype

Evidence suggests that the social cognition deficits prevalent in autism spectrum disorders (ASDs) are widely distributed in first degree and extended relatives. This ¿broader autism phenotype¿ (BAP) can be extended into non-clinical populations and show wide distributions of social behaviors such as empathy and social responsiveness ¿ with ASDs exhibiting these behaviors on the lower ends of the distributions. Little evidence has previously shown relationships between self-report measures of social cognition and more objective tasks such as face perception in functional magnetic resonance imaging (fMRI) and event-related potentials (ERPs). In this study, three specific hypotheses were addressed: a) increased social ability, as measured by an increased Empathy Quotient, decreased Social Responsiveness Scale (SRS-A) score, and increased Social Attribution Task score, will predict increased activation of the fusiform gyrus in response to faces as compared to houses; b) these same measures will predict N170 amplitude and latency showing decreased latency and increased amplitude for faces as compared to houses with increased social ability; c) increased amygdala volume will predict increased fusiform gyrus activation when viewing faces as compared to houses. Findings supported all of the hypotheses. Empathy scores significantly predicted both right FFG activation [F(1,20) = 4.811, p = .041, ß = .450, R2 = 0.20] and left FFG activation [F(1,20) = 7.70, p = .012, ß = .537, R2 = 0.29]. Based on ERP results increased right lateralization face-related N170 was significantly predicted by the EQ [F(1,54) = 6.94, p = .011, ß = .338, R2 = 0.11]. Finally, total amygdala volume significantly predicted right [F(1,20) = 7.217, p = .014, ß = .515, R2 = 0.27] and left [F(1,20) = 36.77, p < .001, ß = .805, R2 = 0.65] FFG activation. Consistent with the a priori hypotheses, traits attributed to the BAP can significantly predict neural responses to faces in a non-clinical population. This is consistent with the face processing deficits seen in ASDs. The findings presented here contribute to the extension of the BAP from unaffected relatives of individuals with ASDs to the general population. These findings also give continued evidence in support of a continuous distribution of traits found in psychiatric illnesses in place of a traditional, dichotomous ¿all-or-nothing¿ diagnostic framework of neurodevelopmental and neuropsychiatric disorders.

Social Cognition and Neural Substrates of Face Perception: Implications for Neurodevelopmental and Neuropsychiatric Disorders

BACKGROUND: Social cognition is an important aspect of social behavior in humans. Social cognitive deficits are associated with neurodevelopmental and neuropsychiatric disorders. In this study we examine the neural substrates of social cognition and face processing in a group of healthy young adults to examine the neural substrates of social cognition. METHODS: Fifty-seven undergraduates completed a battery of social cognition tasks and were assessed with electroencephalography (EEG) during a face-perception task. A subset (N=22) were administered a face-perception task during functional magnetic resonance imaging. RESULTS: Variance in the N170 EEG was predicted by social attribution performance and by a quantitative measure of empathy. Neurally, face processing was more bilateral in females than in males. Variance in fMRI voxel count in the face-sensitive fusiform gyrus was predicted by quantitative measures of social behavior, including the Social Responsiveness Scale (SRS) and the Empathizing Quotient. CONCLUSIONS: When measured as a quantitative trait, social behaviors in typical and pathological populations share common neural pathways. The results highlight the importance of viewing neurodevelopmental and neuropsychiatric disorders as spectrum phenomena that may be informed by studies of the normal distribution of relevant traits in the general population. Copyright 2014 Elsevier B.V. All rights reserved.

Time course based artifact identification for independent components of resting-state FMRI

In functional magnetic resonance imaging (fMRI) coherent oscillations of the blood oxygen level-dependent (BOLD) signal can be detected. These arise when brain regions respond to external stimuli or are activated by tasks. The same networks have been characterized during wakeful rest when functional connectivity of the human brain is organized in generic resting-state networks (RSN). Alterations of RSN emerge as neurobiological markers of pathological conditions such as altered mental state. In single-subject fMRI data the coherent components can be identified by blind source separation of the pre-processed BOLD data using spatial independent component analysis (ICA) and related approaches. The resulting maps may represent physiological RSNs or may be due to various artifacts. In this methodological study, we propose a conceptually simple and fully automatic time course based filtering procedure to detect obvious artifacts in the ICA output for resting-state fMRI. The filter is trained on six and tested on 29 healthy subjects, yielding mean filter accuracy, sensitivity and specificity of 0.80, 0.82, and 0.75 in out-of-sample tests. To estimate the impact of clearly artifactual single-subject components on group resting-state studies we analyze unfiltered and filtered output with a second level ICA procedure. Although the automated filter does not reach performance values of visual analysis by human raters, we propose that resting-state compatible analysis of ICA time courses could be very useful to complement the existing map or task/event oriented artifact classification algorithms.

A neurophysiological signature of motivational incongruence: EEG changes related to insufficient goal satisfaction

Human behavior and psychological functioning is motivated and guided by individual goals. Motivational incongruence refers to states of insufficient goal satisfaction and is tightly related to psychological problems and even psychopathology. In the present study, individual levels of motivational incongruence were assessed with the incongruence-questionnaire (INC) in a healthy sample. In addition, multi-channel resting-state EEG was measured. Individual variations of EEG synchronization and spectral power were related to individual levels of motivational incongruence. For significant correlations, the relation to intracerebral sources of electrical brain activity was investigated with sLORETA. The results indicate that, even in a healthy sample with rather low degrees of motivational incongruence, this insufficient goal satisfaction is related to consistent changes in resting state brain activity. Upper Alpha band attenuation seems to be most indicative of increased levels of motivational incongruence. This is reflected not only in significantly reduced functional connectivity, but also in changes regarding the level of brain activation, as indicated by significant effects in the spectral power and LORETA analyses. Results are related to research investigating the upper Alpha band and are discussed in the framework of Grawe's consistency theory.

Comparison of spatial and temporal pattern for fMRI obtained with BOLD and arterial spin labeling

Functional magnetic resonance imaging (fMRI) is presently either performed using blood oxygenation level-dependent (BOLD) contrast or using cerebral blood flow (CBF), measured with arterial spin labeling (ASL) technique. The present fMRI study aimed to provide practical hints to favour one method over the other. It involved three different acquisition methods during visual checkerboard stimulation on nine healthy subjects: 1) CBF contrast obtained from ASL, 2) BOLD contrast extracted from ASL and 3) BOLD contrast from Echo planar imaging. Previous findings were replicated; i) no differences between the three measurements were found in the location of the activated region; ii) differences were found in the temporal characteristics of the signals and iii) BOLD has significantly higher sensitivity than ASL perfusion. ASL fMRI was favoured when the investigation demands for perfusion and task related signal changes. BOLD fMRI is more suitable in conjunction with fast event related design.

Enhancing BOLD response in the auditory system by neurophysiologically tuned fMRI sequence

Auditory neuroscience has not tapped fMRI's full potential because of acoustic scanner noise emitted by the gradient switches of conventional echoplanar fMRI sequences. The scanner noise is pulsed, and auditory cortex is particularly sensitive to pulsed sounds. Current fMRI approaches to avoid stimulus-noise interactions are temporally inefficient. Since the sustained BOLD response to pulsed sounds decreases with repetition rate and becomes minimal with unpulsed sounds, we developed an fMRI sequence emitting continuous rather than pulsed gradient sound by implementing a novel quasi-continuous gradient switch pattern. Compared to conventional fMRI, continuous-sound fMRI reduced auditory cortex BOLD baseline and increased BOLD amplitude with graded sound stimuli, short sound events, and sounds as complex as orchestra music with preserved temporal resolution. Response in subcortical auditory nuclei was enhanced, but not the response to light in visual cortex. Finally, tonotopic mapping using continuous-sound fMRI demonstrates that enhanced functional signal-to-noise in BOLD response translates into improved spatial separability of specific sound representations.

Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)

The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.

Increased sensitivity in mapping task demand in visuospatial processing using reaction-time-dependent hemodynamic response predictors in rapid event-related fMRI

Searching for the neural correlates of visuospatial processing using functional magnetic resonance imaging (fMRI) is usually done in an event-related framework of cognitive subtraction, applying a paradigm comprising visuospatial cognitive components and a corresponding control task. Besides methodological caveats of the cognitive subtraction approach, the standard general linear model with fixed hemodynamic response predictors bears the risk of being underspecified. It does not take into account the variability of the blood oxygen level-dependent signal response due to variable task demand and performance on the level of each single trial. This underspecification may result in reduced sensitivity regarding the identification of task-related brain regions. In a rapid event-related fMRI study, we used an extended general linear model including single-trial reaction-time-dependent hemodynamic response predictors for the analysis of an angle discrimination task. In addition to the already known regions in superior and inferior parietal lobule, mapping the reaction-time-dependent hemodynamic response predictor revealed a more specific network including task demand-dependent regions not being detectable using the cognitive subtraction method, such as bilateral caudate nucleus and insula, right inferior frontal gyrus and left precentral gyrus.