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.

Synchrotron X-ray interlaced microbeams suppress paroxysmal oscillations in neuronal networks initiating generalized epilepsy

Radiotherapy has shown some efficacy for epilepsies but the insufficient confinement of the radiation dose to the pathological target reduces its indications. Synchrotron-generated X-rays overcome this limitation and allow the delivery of focalized radiation doses to discrete brain volumes via interlaced arrays of microbeams (IntMRT). Here, we used IntMRT to target brain structures involved in seizure generation in a rat model of absence epilepsy (GAERS). We addressed the issue of whether and how synchrotron radiotherapeutic treatment suppresses epileptic activities in neuronal networks. IntMRT was used to target the somatosensory cortex (S1Cx), a region involved in seizure generation in the GAERS. The antiepileptic mechanisms were investigated by recording multisite local-field potentials and the intracellular activity of irradiated S1Cx pyramidal neurons in vivo. MRI and histopathological images displayed precise and sharp dose deposition and revealed no impairment of surrounding tissues. Local-field potentials from behaving animals demonstrated a quasi-total abolition of epileptiform activities within the target. The irradiated S1Cx was unable to initiate seizures, whereas neighboring non-irradiated cortical and thalamic regions could still produce pathological oscillations. In vivo intracellular recordings showed that irradiated pyramidal neurons were strongly hyperpolarized and displayed a decreased excitability and a reduction of spontaneous synaptic activities. These functional alterations explain the suppression of large-scale synchronization within irradiated cortical networks. Our work provides the first post-irradiation electrophysiological recordings of individual neurons. Altogether, our data are a critical step towards understanding how X-ray radiation impacts neuronal physiology and epileptogenic processes.

Quantification of network perfusion in ASL cerebral blood flow data with seed based and ICA approaches

Independent component analysis (ICA) or seed based approaches (SBA) in functional magnetic resonance imaging blood oxygenation level dependent (BOLD) data became widely applied tools to identify functionally connected, large scale brain networks. Differences between task conditions as well as specific alterations of the networks in patients as compared to healthy controls were reported. However, BOLD lacks the possibility of quantifying absolute network metabolic activity, which is of particular interest in the case of pathological alterations. In contrast, arterial spin labeling (ASL) techniques allow quantifying absolute cerebral blood flow (CBF) in rest and in task-related conditions. In this study, we explored the ability of identifying networks in ASL data using ICA and to quantify network activity in terms of absolute CBF values. Moreover, we compared the results to SBA and performed a test-retest analysis. Twelve healthy young subjects performed a fingertapping block-design experiment. During the task pseudo-continuous ASL was measured. After CBF quantification the individual datasets were concatenated and subjected to the ICA algorithm. ICA proved capable to identify the somato-motor and the default mode network. Moreover, absolute network CBF within the separate networks during either condition could be quantified. We could demonstrate that using ICA and SBA functional connectivity analysis is feasible and robust in ASL-CBF data. CBF functional connectivity is a novel approach that opens a new strategy to evaluate differences of network activity in terms of absolute network CBF and thus allows quantifying inter-individual differences in the resting state and task-related activations and deactivations.

Structural and resting state functional connectivity of the subthalamic nucleus: identification of motor STN parts and the hyperdirect pathway

Deep brain stimulation (DBS) for Parkinson's disease often alleviates the motor symptoms, but causes cognitive and emotional side effects in a substantial number of cases. Identification of the motor part of the subthalamic nucleus (STN) as part of the presurgical workup could minimize these adverse effects. In this study, we assessed the STN's connectivity to motor, associative, and limbic brain areas, based on structural and functional connectivity analysis of volunteer data. For the structural connectivity, we used streamline counts derived from HARDI fiber tracking. The resulting tracks supported the existence of the so-called "hyperdirect" pathway in humans. Furthermore, we determined the connectivity of each STN voxel with the motor cortical areas. Functional connectivity was calculated based on functional MRI, as the correlation of the signal within a given brain voxel with the signal in the STN. Also, the signal per STN voxel was explained in terms of the correlation with motor or limbic brain seed ROI areas. Both right and left STN ROIs appeared to be structurally and functionally connected to brain areas that are part of the motor, associative, and limbic circuit. Furthermore, this study enabled us to assess the level of segregation of the STN motor part, which is relevant for the planning of STN DBS procedures.

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.

Extending lifetime of plastic changes in the human brain

The ability of the brain to adjust to changing environments and to recover from damage rests on its remarkable capacity to adapt through plastic changes of underlying neural networks. We show here with an eye movement paradigm that a lifetime of plastic changes can be extended to several hours by repeated applications of theta burst transcranial magnetic stimulation to the frontal eye field of the human cortex. The results suggest that repeated application of the same stimulation protocol consolidates short-lived plasticity into long-lasting changes.

Imaging emotional brain functions: conceptual and methodological issues

This article reviews the psychophysiological and brain imaging literature on emotional brain function from a methodological point of view. The difficulties in defining, operationalising and measuring emotional activation and, in particular, aversive learning will be considered. Emotion is a response of the organism during an episode of major significance and involves physiological activation, motivational, perceptual, evaluative and learning processes, motor expression, action tendencies and monitoring/subjective feelings. Despite the advances in assessing the physiological correlates of emotional perception and learning processes, a critical appraisal shows that functional neuroimaging approaches encounter methodological difficulties regarding measurement precision (e.g., response scaling and reproducibility) and validity (e.g., response specificity, generalisation to other paradigms, subjects or settings). Since emotional processes are not only the result of localised but also of widely distributed activation, a more representative model of assessment is needed that systematically relates the hierarchy of high- and low-level emotion constructs with the corresponding patterns of activity and functional connectivity of the brain.

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.

Sex Differences in Semantic Processing: Event-Related Brain Potentials Distinguish between Lower and Higher Order Semantic Analysis during Word Reading

Behavioral studies suggest that women and men differ in the strategic elaboration of verbally encoded information especially in the absence of external task demand. However, measuring such covert processing requires other than behavioral data. The present study used event-related potentials to compare sexes in lower and higher order semantic processing during the passive reading of semantically related and unrelated word pairs. Women and men showed the same early context effect in the P1-N1 transition period. This finding indicates that the initial lexical-semantic access is similar in men and women. In contrast, sexes differed in higher order semantic processing. Women showed an earlier and longer lasting context effect in the N400 accompanied by larger signal strength in temporal networks similarly recruited by men and women. The results suggest that women spontaneously conduct a deeper semantic analysis. This leads to faster processing of related words in the active neural networks as reflected in a shorter stability of the N400 map in women. Taken together, the findings demonstrate that there is a selective sex difference in the controlled semantic analysis during passive word reading that is not reflected in different functional organization but in the depth of processing.

Cerebral connectivity and psychotic personality traits : A diffusion tensor imaging study

This study aims to investigate the relationship between regional connectivity in the brain white matter and the presence of psychotic personality traits, in healthy subjects with psychotic traits. Thirteen healthy controls were administered the MMPI-2, to assess psychotic traits and, according to MMPI results, a dichotomization into a group of "high-psychotic" and "low-psychotic" was performed. Diffusion tensor imaging (DTI) was used as a non-invasive measure, in order to obtain information about the fractional anisotropy (FA), an intravoxel index of local connectivity and, by means of a voxelwise approach, the between-group differences of the FA values were calculated. The "high-psychotic" group showed higher FA in the left arcuate fasciculus. Subjects with low scores for psychotic traits had significantly higher FA in the corpus callosum, right arcuate fasciculus, and fronto-parietal fibers. In line with previous brain imaging studies of schizophrenia spectrum disorders, our results suggest that psychotic personality traits are related to altered connectivity and brain asymmetry.

An EEG approach to the neurodevelopmental hypothesis of schizophrenia studying schizophrenics, normal controls and adolescents

Based on an integrative brain model which focuses on memory-driven and EEG state-dependent information processing for the organisation of behaviour, we used the developmental changes of the awake EEG to further investigate the hypothesis that neurodevelopmental abnormalities (deviations in organisation and reorganisation of cortico-cortical connectivity during development) are involved in the pathogenesis of schizophrenia. First-episode, neuroleptic-naive schizophrenics and their matched controls and three age groups of normal adolescents were studied (total: 70 subjects). 19-channel EEG delta-theta, alpha and beta spectral band centroid frequencies during resting (baseline) and after verbal stimuli were used as measure of the level of attained complexity and momentary excitability of the neuronal network (working memory). Schizophrenics compared with all control groups showed lower delta-theta activity centroids and higher alpha and beta activity centroids. Reactivity centroids (centroid after stimulus minus centroid during resting) were used as measure of update of working memory. Schizophrenics showed partial similarities in delta-theta and beta reactivity centroids with the 11-year olds and in alpha reactivity centroids with the 13-year olds. Within the framework of our model, the results suggest multifactorially elicited imbalances in the level of excitability of neuronal networks in schizophrenia, resulting in network activation at dissociated complexity levels, partially regressed and partially prematurely developed. It is hypothesised that activation of age- and/or state-inadequate representations for coping with realities becomes manifest as productive schizophrenic symptoms. Thus, the results support some aspects of the neurodevelopmental hypothesis.

A Smart TCP Acknowledgment Approach for Multihop Wireless Networks

Reliable data transfer is one of the most difficult tasks to be accomplished in multihop wireless networks. Traditional transport protocols like TCP face severe performance degradation over multihop networks given the noisy nature of wireless media as well as unstable connectivity conditions in place. The success of TCP in wired networks motivates its extension to wireless networks. A crucial challenge faced by TCP over these networks is how to operate smoothly with the 802.11 wireless MAC protocol which also implements a retransmission mechanism at link level in addition to short RTS/CTS control frames for avoiding collisions. These features render TCP acknowledgments (ACK) transmission quite costly. Data and ACK packets cause similar medium access overheads despite the much smaller size of the ACKs. In this paper, we further evaluate our dynamic adaptive strategy for reducing ACK-induced overhead and consequent collisions. Our approach resembles the sender side's congestion control. The receiver is self-adaptive by delaying more ACKs under nonconstrained channels and less otherwise. This improves not only throughput but also power consumption. Simulation evaluations exhibit significant improvement in several scenarios

IL-6 transsignalling modulates the early effector phase of EAE and targets the blood-brain barrier

Interleukin-6 (IL-6) plays a crucial role in the pathogenesis of experimental autoimmune encephalomyelitis (EAE). It exerts its cellular effects by a membrane-bound IL-6 receptor (IL-6R), or, alternatively, by forming a complex with the soluble IL-6R (sIL-6R), a process named IL-6 transsignalling. Here we investigate the role of IL-6 transsignalling in myelin basic protein (MBP)-induced EAE in the Lewis rat. In vivo blockade of IL-6 transsignalling by the injection of a specifically designed gp130-Fc fusion protein significantly delayed the onset of adoptively transferred EAE in comparison to control rats injected with PBS or isotype IgG. Histological evaluation on day 3 after immunization revealed reduced numbers of T cells and macrophages in the lumbar spinal cord of gp130-Fc treated rats. At the same time, blockade of IL-6 transsignalling resulted in a reduced expression of vascular cell adhesion molecule-1 on spinal cord microvessels while experiments in cell culture failed to show a direct effect on the regulation of endothelial adhesion molecules. In experiments including active EAE and T cell culture, inhibition of IL-6 transsignalling mildly increased T cell proliferation, but did not change severity of active MBP-EAE or regulate Th1/Th17 responses. We conclude that IL-6 transsignalling may play a role in autoimmune inflammation of the CNS mainly by regulating early expression of adhesion molecules, possibly via cellular networks at the blood-brain barrier.

Parameter-free extraction of the functional network topology from intracranial EEG recordings: a time-resolved study of graph properties in focal onset seizures

Rationale: Focal onset epileptic seizures are due to abnormal interactions between distributed brain areas. By estimating the cross-correlation matrix of multi-site intra-cerebral EEG recordings (iEEG), one can quantify these interactions. To assess the topology of the underlying functional network, the binary connectivity matrix has to be derived from the cross-correlation matrix by use of a threshold. Classically, a unique threshold is used that constrains the topology [1]. Our method aims to set the threshold in a data-driven way by separating genuine from random cross-correlation. We compare our approach to the fixed threshold method and study the dynamics of the functional topology. Methods: We investigate the iEEG of patients suffering from focal onset seizures who underwent evaluation for the possibility of surgery. The equal-time cross-correlation matrices are evaluated using a sliding time window. We then compare 3 approaches assessing the corresponding binary networks. For each time window: * Our parameter-free method derives from the cross-correlation strength matrix (CCS)[2]. It aims at disentangling genuine from random correlations (due to finite length and varying frequency content of the signals). In practice, a threshold is evaluated for each pair of channels independently, in a data-driven way. * The fixed mean degree (FMD) uses a unique threshold on the whole connectivity matrix so as to ensure a user defined mean degree. * The varying mean degree (VMD) uses the mean degree of the CCS network to set a unique threshold for the entire connectivity matrix. * Finally, the connectivity (c), connectedness (given by k, the number of disconnected sub-networks), mean global and local efficiencies (Eg, El, resp.) are computed from FMD, CCS, VMD, and their corresponding random and lattice networks. Results: Compared to FMD and VMD, CCS networks present: *topologies that are different in terms of c, k, Eg and El. *from the pre-ictal to the ictal and then post-ictal period, topological features time courses that are more stable within a period, and more contrasted from one period to the next. For CCS, pre-ictal connectivity is low, increases to a high level during the seizure, then decreases at offset. k shows a ‘‘U-curve’’ underlining the synchronization of all electrodes during the seizure. Eg and El time courses fluctuate between the corresponding random and lattice networks values in a reproducible manner. Conclusions: The definition of a data-driven threshold provides new insights into the topology of the epileptic functional networks.

Brain electrical microstates in subjects with panic disorder

Brain electrical microstates represent spatial configurations of scalp recorded brain electrical activity and are considered to be the basic elements of stepwise processing of information in the brain. In the present study, the hypothesis of a temporo-limbic dysfunction in panic disorder (PD) was tested by investigating the topographic descriptors of brain microstates, in particular the one corresponding to the Late Positive Complex (LPC), an event-related potential (ERP) component with generators in these regions. ERPs were recorded in PD patients and matched healthy subjects during a target detection task, in a central (CC) and a lateral condition (LC). In the CC, a leftward shift of the LPC microstate positive centroid was observed in the patients with PD versus the healthy control subjects. In the LC, the topographic descriptor of the first microstate showed a rightward shift, while those of both the second and the fourth microstate, corresponding to the LPC, revealed a leftward shift in the PD patients versus the healthy control subjects. These findings indicate an overactivation of the right hemisphere networks involved in early visual processing and a hypoactivation of the right hemisphere circuits involved in LPC generators in PD. In line with this interpretation, the abnormal topography of the LPC microstate, observed in the CC, was associated with a worse performance on a test exploring right temporo-hippocampal functioning. Topographical abnormalities found for the LPC microstate in the LC were associated with a higher number of panic attacks, suggesting a pathogenetic role of the right temporo-hippocampal dysfunction in PD.