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ABSTRACT: The human brain is continually, dynamically active and spontaneous fluctuations in this activity play a functional role in affecting both behavioural and neuronal responses. However, the mechanisms through which this occurs remain poorly understood. Simultaneous EEG-fMRI is a promising technique to study how spontaneous activity modulates the brain's response to stimulation, as temporal indices of ongoing cortical excitability can be integrated with spatially localised evoked responses. Here we demonstrate an interaction between the ongoing power of the electrophysiological alpha oscillation and the magnitude of both positive (PBR) and negative (NBR) fMRI responses to two contrasts of visual checkerboard reversal. Furthermore, the amplitude of pre-stimulus EEG alpha-power significantly modulated the amplitude and shape of subsequent PBR and NBR to the visual stimulus. A nonlinear reduction of visual PBR and an enhancement of auditory NBR and default-mode network NBR were observed in trials preceded by high alpha-power. These modulated areas formed a functionally connected network during a separate resting-state recording. Our findings suggest that the "baseline" state of the brain exhibits considerable trial-to-trial variability which arises from fluctuations in the balance of cortical inhibition/excitation that are represented by respective increases/decreases in the power of the EEG alpha oscillation. The consequence of this spontaneous electrophysiological variability is modulated amplitudes of both PBR and NBR to stimulation. Fluctuations in alpha-power may subserve a functional relationship in the visual-auditory network, acting as mediator for both short and long-range cortical inhibition, the strength of which is represented in part by NBR.
NeuroImage 03/2013; · 5.89 Impact Factor
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ABSTRACT: Accumulating empirical evidence suggests a role of Bayesian inference and learning for shaping neural responses in auditory and visual perception. However, its relevance for somatosensory processing is unclear. In the present study we test the hypothesis that cortical somatosensory processing exhibits dynamics that are consistent with Bayesian accounts of brain function. Specifically, we investigate the cortical encoding of Bayesian surprise, a recently proposed marker of Bayesian perceptual learning, using EEG data recorded from 15 subjects. Capitalizing on a somatosensory mismatch roving paradigm, we performed computational single-trial modeling of evoked somatosensory potentials for the entire peri-stimulus time period in source space. By means of Bayesian model selection, we find that, at 140 ms post-stimulus onset, secondary somatosensory cortex represents Bayesian surprise rather than stimulus change, which is the conventional marker of EEG mismatch responses. In contrast, at 250 ms, right inferior frontal cortex indexes stimulus change. Finally, at 360 ms, our analyses indicate additional perceptual learning attributable to medial cingulate cortex. In summary, the present study provides novel evidence for anatomical-temporal/functional segregation in human somatosensory processing that is consistent with the Bayesian brain hypothesis.
NeuroImage 05/2012; 62(1):177-88. · 5.89 Impact Factor
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ABSTRACT: The modern metaphor of the brain is that of a dynamic information processing device. In the current study we investigate how a core cognitive network of the human brain, the perceptual decision system, can be characterized regarding its spatiotemporal representation of task-relevant information. We capitalize on a recently developed information theoretic framework for the analysis of simultaneously acquired electroencephalography (EEG) and functional magnetic resonance imaging data (fMRI) (Ostwald et al. (2010), NeuroImage 49: 498-516). We show how this framework naturally extends from previous validations in the sensory to the cognitive domain and how it enables the economic description of neural spatiotemporal information encoding. Specifically, based on simultaneous EEG-fMRI data features from n = 13 observers performing a visual perceptual decision task, we demonstrate how the information theoretic framework is able to reproduce earlier findings on the neurobiological underpinnings of perceptual decisions from the response signal features' marginal distributions. Furthermore, using the joint EEG-fMRI feature distribution, we provide novel evidence for a highly distributed and dynamic encoding of task-relevant information in the human brain.
PLoS ONE 01/2012; 7(4):e33896. · 4.09 Impact Factor
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ABSTRACT: Information theory is a probabilistic framework that allows the quantification of statistical non-independence between signals of interest. In contrast to other methods used for this purpose, it is model free, i.e., it makes no assumption about the functional form of the statistical dependence or the underlying probability distributions. It thus has the potential to unveil important signal characteristics overlooked by classical data analysis techniques. In this review, we discuss how information theoretic concepts have been applied to the analysis of functional brain imaging data such as functional magnetic resonance imaging and magneto/electroencephalography. We review studies from a number of imaging domains, including the investigation of the brain's functional specialization and integration, neurovascular coupling and multimodal imaging. We demonstrate how information theoretical concepts can be used to answer neurobiological questions and discuss their limitations as well as possible future developments of the framework to advance our understanding of brain function.
Magnetic Resonance Imaging 09/2011; 29(10):1417-28. · 1.99 Impact Factor
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ABSTRACT: Gamma Band Activity (GBA) is increasingly studied for its relation with attention, change detection, maintenance of working memory and the processing of sensory stimuli. Activity around the gamma range has also been linked with early visual processing, although the relationship between this activity and the low frequency visual evoked potential (VEP) remains unclear. This study examined the ability of blind and semi-blind source separation techniques to extract sources specifically related to the VEP and GBA in order to shed light on the relationship between them. Blind (Independent Component Analysis-ICA) and semi-Blind (Functional Source Separation-FSS) methods were applied to dense array EEG data recorded during checkerboard stimulation. FSS was performed with both temporal and spectral constraints to identify specifically the generators of the main peak of the VEP (P100) and of the GBA. Source localisation and time-frequency analyses were then used to investigate the properties and co-dependencies between VEP/P100 and GBA. Analysis of the VEP extracted using the different methods demonstrated very similar morphology and localisation of the generators. Single trial time frequency analysis showed higher GBA when a larger amplitude VEP/P100 occurred. Further examination indicated that the evoked (phase-locked) component of the GBA was more related to the P100, whilst the induced component correlated with the VEP as a whole. The results suggest that the VEP and GBA may be generated by the same neuronal populations, and implicate this relationship as a potential mediator of the correlation between the VEP and the Blood Oxygenation Level Dependent (BOLD) effect measured with fMRI.
NeuroImage 03/2011; 56(3):1059-71. · 5.89 Impact Factor
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ABSTRACT: EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA). Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI). Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state.
PLoS ONE 01/2011; 6(9):e24642. · 4.09 Impact Factor
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ABSTRACT: We have recently proposed the evaluation of a set of information theoretic quantities (ITQs) for the integration of simultaneously acquired EEG-fMRI data (Ostwald, D., Porcaro, C., Bagshaw, A.P., 2010. An information theoretic approach to EEG-fMRI integration of visually evoked responses. Neuroimage. 49, 498-516). In our previous experimental evaluation of the information theoretic framework, we defined the data subsets from which to calculate the ITQs using a priori constraints. In the case of EEG, this meant that data were extracted from a single electrode, while for fMRI the analysed data came from voxels contained within a sphere surrounding the most responsive voxel of visual cortex. While this approach was a natural starting point for the evaluation of the framework in the application to combined EEG-fMRI data sets, a more principled approach to data selection is desirable. Here, we propose to combine standard fMRI data pre-processing and low-resolution electromagnetic tomography (LORETA) for the evaluation of ITQs across the entire three-dimensional brain space. We apply the proposed method to a simultaneous EEG-fMRI data set acquired during checkerboard stimulation and assess the topographical informativeness of EEG (time and frequency domain) and fMRI features with respect to the stimulus and each other. The resulting information theoretic effect size maps are supplemented with a statistical evaluation based on Gaussian null model simulations using a false-discovery rate procedure. Given the contamination of EEG recordings by artefacts induced by the MR scanning environment we further assessed the influence of different advanced EEG pre-processing methods (independent component analysis and functional source separation) on the information topography. The results of this analysis provide evidence for the topographically focussed informativeness of both EEG and fMRI features with respect to the stimulus, but for the current feature selection do not detect EEG-fMRI activity dependence. More advanced EEG data pre-processing rendered the feature distributions more stimulus-informative, but did not alter the EEG-fMRI activity and conditional dependencies.
NeuroImage 12/2010; 55(3):1270-86. · 5.89 Impact Factor
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ABSTRACT: To form perceptual decisions in our multisensory environment, the brain needs to integrate sensory information derived from a common source and segregate information emanating from different sources. Combining fMRI and psychophysics in humans, we investigated how the brain accumulates sensory evidence about a visual source in the context of congruent or conflicting auditory information. In a visual selective attention paradigm, subjects (12 females, 7 males) categorized video clips while ignoring concurrent congruent or incongruent soundtracks. Visual and auditory information were reliable or unreliable. Our behavioral data accorded with accumulator models of perceptual decision making, where sensory information is integrated over time until a criterion amount of information is obtained. Behaviorally, subjects exhibited audiovisual incongruency effects that increased with the variance of the visual and the reliability of the interfering auditory input. At the neural level, only the left inferior frontal sulcus (IFS) showed an "audiovisual-accumulator" profile consistent with the observed reaction time pattern. By contrast, responses in the right fusiform were amplified by incongruent auditory input regardless of sensory reliability. Dynamic causal modeling showed that these incongruency effects were mediated via connections from auditory cortex. Further, while the fusiform interacted with IFS in an excitatory recurrent loop that was strengthened for unreliable task-relevant visual input, the IFS did not amplify and even inhibited superior temporal activations for unreliable auditory input. To form decisions that guide behavioral responses, the IFS may accumulate audiovisual evidence by dynamically weighting its connectivity to auditory and visual regions according to sensory reliability and decisional relevance.
Journal of Neuroscience 05/2010; 30(21):7434-46. · 7.11 Impact Factor
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ABSTRACT: In the present study we tested the applicability of a paired-stimulus paradigm for the investigation of near-threshold (NT) stimulus processing in the somatosensory system using magnetoencephalography. Cortical processing of the NT stimuli was studied indirectly by investigating the impact of NT stimuli on the source activity of succeeding suprathreshold test stimuli. We hypothesized that cortical responses evoked by test stimuli are reduced due to the preactivation of the same finger representation by the preceding NT stimulus. We observed attenuation of the magnetic responses in the secondary somatosensory (SII) cortex, with stronger decreases for perceived than for missed NT stimuli. Our data suggest that processing in the primary somatosensory cortex including recovery lasts for <200 ms. Conversely, the occupancy of SII lasts >/=500 ms, which points to its role in temporal integration and conscious perception of sensory input.
Psychophysiology 02/2010; 47(3):523-34. · 3.29 Impact Factor
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ABSTRACT: EEG quality is a crucial issue when acquiring combined EEG-fMRI data, particularly when the focus is on using single trial (ST) variability to integrate the data sets. The most common method for improving EEG data quality following removal of gross MRI artefacts is independent component analysis (ICA), a completely blind source separation technique. In the current study, a different approach is proposed based on the functional source separation (FSS) algorithm. FSS is an extension of ICA that incorporates prior knowledge about the signal of interest into the data decomposition. Since in general the part of the EEG signal that will contain the most relevant information is known beforehand (i.e. evoked potential peaks, spectral bands), FSS separates the signal of interest by exploiting this prior knowledge without renouncing the advantages of using only information contained in the original signal waveforms. A reversing checkerboard stimulus was used to generate visual evoked potentials (VEPs) in healthy control subjects. Gradient and ballistocardiogram artefacts were removed with template subtraction techniques to form the raw data, which were then subjected to ICA denoising and FSS. The resulting EEG data sets were compared using several metrics derived from average and ST data and correlated with fMRI data. In all cases, ICA was an improvement on the raw data, but the most obvious improvement was provided by FSS, which consistently outperformed ICA. The results show the benefit of FSS for the recovery of good quality single trial evoked potentials during concurrent EEG-fMRI recordings.
NeuroImage 12/2009; 50(1):112-23. · 5.89 Impact Factor
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ABSTRACT: The integration of signals from electro-encephalography (EEG) and functional magnetic resonance imaging (fMRI), acquired simultaneously from the same observer, holds great potential for the elucidation of the neurobiological underpinnings of human brain function. However, the most appropriate way in which to combine the data in order to achieve this goal is not clear. Here, we apply a novel route to the integration of simultaneously acquired multimodal brain imaging data. We adopt a theoretical framework developed in the study of neuronal population codes which explicitly takes into account the experimentally observed stimulus-response signal probability distributions using the concept of mutual information. We study the implications of this framework using simulated data sets generated from a set of linear Gaussian models, and apply the framework to EEG-fMRI data acquired during checkerboard stimulation of low and high contrast. We focus our evaluation on single-trial time-domain signal features from both modalities and provide evidence for the informativeness of a subset of these features with respect to the stimulus and each other. Specifically, the framework was able to identify the contrast dependency of the haemodynamic response and the P100 peak of the visual evoked potential, and showed that combining EEG and fMRI time-domain features by quantifying the information in their joint distribution was more informative than treating each one in isolation. In addition, the effect of different pre-processing strategies for EEG-fMRI data can be assessed quantitatively, indicating the improvements to be gained by more advanced methods. We conclude that the information theoretic framework is a promising methodology to quantify the relative importance of different response features in neural coding and neurovascular coupling, as well as the success of data pre-processing strategies.
NeuroImage 08/2009; 49(1):498-516. · 5.89 Impact Factor
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ABSTRACT: Extensive psychophysical and computational work proposes that the perception of coherent and meaningful structures in natural images relies on neural processes that convert information about local edges in primary visual cortex to complex object features represented in the temporal cortex. However, the neural basis of these mid-level vision mechanisms in the human brain remains largely unknown. Here, we examine functional MRI (fMRI) selectivity for global forms in the human visual pathways using sensitive multivariate analysis methods that take advantage of information across brain activation patterns. We use Glass patterns, parametrically varying the perceived global form (concentric, radial, translational) while ensuring that the local statistics remain similar. Our findings show a continuum of integration processes that convert selectivity for local signals (orientation, position) in early visual areas to selectivity for global form structure in higher occipitotemporal areas. Interestingly, higher occipitotemporal areas discern differences in global form structure rather than low-level stimulus properties with higher accuracy than early visual areas while relying on information from smaller but more selective neural populations (smaller voxel pattern size), consistent with global pooling mechanisms of local orientation signals. These findings suggest that the human visual system uses a code of increasing efficiency across stages of analysis that is critical for the successful detection and recognition of objects in complex environments.
Journal of Neurophysiology 06/2008; 99(5):2456-69. · 3.32 Impact Factor
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ABSTRACT: Despite the importance of visual categorization for interpreting sensory experiences, little is known about the neural representations that mediate categorical decisions in the human brain. Here, we used psychophysics and pattern classification for the analysis of functional magnetic resonance imaging data to predict the features critical for categorical decisions from brain activity when observers categorized the same stimuli using different rules. Although a large network of cortical and subcortical areas contain information about visual categories, we show that only a subset of these areas shape their selectivity to reflect the behaviorally relevant features rather than simply physical similarity between stimuli. Specifically, temporal and parietal areas show selectivity for the perceived form and motion similarity, respectively. In contrast, frontal areas and the striatum represent the conjunction of spatiotemporal features critical for complex and adaptive categorization tasks and potentially modulate selectivity in temporal and parietal areas. These findings provide novel evidence for flexible neural coding in the human brain that translates sensory experiences to categorical decisions by shaping neural representations across a network of areas with dissociable functional roles in visual categorization.
Journal of Neuroscience 12/2007; 27(45):12321-30. · 7.11 Impact Factor
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ABSTRACT: In humans changes in serum cortisol levels have been observed with aging, stress, and with affective disorders such as major depression and post-traumatic stress disorder. Corticosteroids are known to influence hippocampal structure and function; specifically, plasma corticosteroid levels have been inversely correlated with hippocampal cell proliferation, cell death, and impaired memory function. The relationship between corticosteroids and structure and function of the hippocampus has been studied in experimental systems in adult animals by increasing or decreasing corticosterone levels through pharmacological supplementation and through surgical removal of the adrenal gland. Here, we utilized the genetically engineered pro-opiomelanocortin (POMC) null mutant mouse, which because of the lack of all POMC peptides has no corticosterone from birth throughout life. The effect of this lifelong absence of corticosterone on the dentate gyrus of the hippocampus is a decrease in granule cell density, which correlated with a decrease in cell proliferation but not an increase in cell degeneration. Fine morphology of granule cells was unaltered. Analyses of gene expression revealed no changes in POMC null mutant vs wild-type hippocampus with respect to levels of expression of corticoid receptor genes or genes known to be regulated by corticosterone. Spatial learning as tested by the Morris water maze was not altered in the POMC null mutant mouse. Taken together with findings from other studies of the effects of altered levels of corticosteroids on the hippocampus, our results argue for a complex homeostasis in which disturbances of any one factor can offset the system in varying ways.
Journal of Molecular Neuroscience 02/2006; 28(3):291-302. · 2.50 Impact Factor
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ABSTRACT: Adult mouse mutants homozygous for an engineered proopiomelanocortin (POMC)-null allele lack macroscopically distinct adrenal glands and circulating adrenal hormones. To understand the basis for this adrenal defect, we compared the development of adrenal primordia in POMC-null mice and littermate controls. POMC-null mutant mice are born with adrenal glands that are morphologically indistinguishable from those of their wild-type littermates. However, in mutants adrenal cells fail to proliferate postnatally and adrenals atrophy until they have disappeared macroscopically in the adult. While present, mutant adrenals are differentiated as evidenced by the presence of enzymes for the final steps in the synthesis of corticosterone, aldosterone, and catecholamines. However, in contrast to adrenals of wild-type littermates, adrenals of POMC-null mutants do not produce corticosterone, not even in response to acute stimulation with exogenous ACTH. They do produce aldosterone; however, it is produced at reduced levels correlating with adrenal size. Transplantation of POMC-null mutant adrenals to adrenalectomized wild-type littermates results in adrenals with normal morphology and production of both corticosterone and aldosterone. These findings demonstrate that POMC peptides are not required for prenatal adrenal development and that POMC peptides in addition to ACTH are required for postnatal proliferation and maintenance of adrenal structures capable of producing both glucocorticoids and mineralocorticoids.
Endocrinology 07/2005; 146(6):2555-62. · 4.46 Impact Factor
