Tactile spatial attention enhances gamma-band activity in somatosensory cortex and reduces low-frequency activity in parieto-occipital areas.

We investigated the effects of spatial-selective attention on oscillatory neuronal dynamics in a tactile delayed-match-to-sample task. Whole-head magnetoencephalography was recorded in healthy subjects while dot patterns were presented to their index fingers using Braille stimulators. The subjects’ task was to report the reoccurrence of an initially presented sample pattern in a series of up to eight test stimuli that were presented unpredictably to their right or left index finger. Attention was cued to one side (finger) at the beginning of each trial, and subjects performed the task at the attended side, ignoring the unattended side. After stimulation, high-frequency gamma-band activity (60 –95 Hz) in presumed primary somatosensory cortex (S1) was enhanced, whereas alpha- and beta-band activity were suppressed in somatosensory and occipital areas and then rebounded. Interestingly, despite the absence of any visual stimulation, we also found time-locked activation of medial occipital, presumably visual, cortex. Most relevant, spatial tactile attention enhanced stimulus-induced gamma-band activity in brain regions consistent with contralateral S1 and deepened and prolonged the stimulus induced suppression of beta- and alpha-band activity, maximal in parieto-occipital cortex. Additionally, the beta rebound over contralateral sensorimotor areas was suppressed. Wehypothesize that spatial-selective attention enhances the saliency of sensory representations by synchronizing neuronal responses in early somatosensory cortex and thereby enhancing their impact on downstream areas and facilitating interareal processing. Furthermore, processing of tactile patterns also seems to recruit visual cortex and this even more so for attended compared with unattended stimuli

Are alpha and beta oscillations spatially dissociated over the cortex in context‐driven spoken‐word production?

Decreases in oscillatory alpha- and beta-band power have been consistently found in spoken-word production. These have been linked to both motor preparation and conceptual-lexical retrieval processes. However, the observed power decreases have a broad frequency range that spans two “classic” (sensorimotor) bands: alpha and beta. It remains unclear whether alpha- and beta-band power decreases contribute independently when a spoken word is planned. Using a re-analysis of existing magnetoencephalography data, we probed whether the effects in alpha and beta bands are spatially distinct. Participants read a sentence that was either constraining or non-constraining toward the final word, which was presented as a picture. In separate blocks participants had to name the picture or score its predictability via button press. Irregular-resampling auto-spectral analysis (IRASA) was used to isolate the oscillatory activity in the alpha and beta bands from the background 1-over-f spectrum. The sources of alpha- and beta-band oscillations were localized based on the participants’ individualized peak frequencies. For both tasks, alpha- and beta-power decreases overlapped in left posterior temporal and inferior parietal cortex, regions that have previously been associated with conceptual and lexical processes. The spatial distributions of the alpha and beta power effects were spatially similar in these regions to the extent we could assess it. By contrast, for left frontal regions, the spatial distributions differed between alpha and beta effects. Our results suggest that for conceptual-lexical retrieval, alpha and beta oscillations do not dissociate spatially and, thus, are distinct from the classical sensorimotor alpha and beta oscillations

The time course of language production as revealed by pattern classification of MEG sensor data

Language production involves a complex set of computations, from conceptualization to articulation, which are thought to engage cascading neural events in the language network. However, recent neuromagnetic evidence suggests simultaneous meaning-to-speech mapping in picture naming tasks, as indexed by early parallel activation of frontotemporal regions to lexical semantic, phonological, and articulatory information. Here we investigate the time course of word production, asking to what extent such “earliness” is a distinctive property of the associated spatiotemporal dynamics. Using MEG, we recorded the neural signals of 34 human subjects (26 males) overtly naming 134 images from four semantic object categories (animals, foods, tools, clothes). Within each category, we covaried word length, as quantified by the number of syllables contained in a word, and phonological neighborhood density to target lexical and post-lexical phonological/phonetic processes. Multivariate pattern analyses searchlights in sensor space distinguished the stimulus-locked spatiotemporal responses to object categories early on, from 150 to 250 ms after picture onset, whereas word length was decoded in left frontotemporal sensors at 250-350 ms, followed by the latency of phonological neighborhood density (350-450 ms). Our results suggest a progression of neural activity from posterior to anterior language regions for the semantic and phonological/phonetic computations preparing overt speech, thus supporting serial cascading models of word productio

Motor-cortical beta oscillations are modulated by correctness of observed action

Contains fulltext : 73550.pdf (Publisher’s version ) (Closed access)How humans understand the intention of others’ actions remains controversial. Some authors have suggested that intentions are recognized by means of a motor simulation of the observed action with the mirror-neuron system [1–3]. Others emphasize that intention recognition is an inferential process, often called ‘‘mentalizing’’ or employing a ‘‘theory of mind,’’ which activates areas well outside the motor system [4–6]. Here, we assessed the contribution of brain regions involved in motor simulation and mentalizing for understanding action intentions via functional brain imaging. Results show that the inferior frontal gyrus (part of the mirror-neuron system) processes the intentionality of an observed action on the basis of the visual properties of the action, irrespective of whether the subject paid attention to the intention or not. Conversely, brain areas that are part of a ‘‘mentalizing’’ network become active when subjects reflect about the intentionality of an observed action, but they are largely insensitive to the visual properties of the observed action. This supports the hypothesis that motor simulation and mentalizing have distinct but complementary functions for the recognition of others’ intentions

A unified view on beamformers for M/EEG source reconstruction

Beamforming is a popular method for functional source reconstruction using magnetoencephalography (MEG) and electroencephalography (EEG) data. Beamformers, which were first proposed for MEG more than two decades ago, have since been applied in hundreds of studies, demonstrating that they are a versatile and robust tool for neuroscience. However, certain characteristics of beamformers remain somewhat elusive and there currently does not exist a unified documentation of the mathematical underpinnings and computational subtleties of beamformers as implemented in the most widely used academic open source software packages for MEG analysis (Brainstorm, FieldTrip, MNE, and SPM). Here, we provide such documentation that aims at providing the mathematical background of beamforming and unifying the terminology. Beamformer implementations are compared across toolboxes and pitfalls of beamforming analyses are discussed. Specifically, we provide details on handling rank deficient covariance matrices, prewhitening, the rank reduction of forward fields, and on the combination of heterogeneous sensor types, such as magnetometers and gradiometers. The overall aim of this paper is to contribute to contemporary efforts towards higher levels of computational transparency in functional neuroimaging

Incarnations: exploring the human condition through Patrick White's Voss and Nikos Kazantzakis' Captain Michales.

Nikos Kazantzakis' Captain Michales is a freedom fighter in nineteenth century Crete. Patrick White's Voss is a German explorer in nineteenth century Australia. Two men struggling for achievement, their disparate social contexts united in the same fundamental search for meaning. This thesis makes comparison of these different struggles through thematic analysis of the texts, examining within the narratives the role of food, perceptions of body and soul, landscapes, gender relations, home-coming and religious experience. Themes from the novels are extracted and intertwined, within a range of theoretical frameworks: history, anthropology, science, literary and social theories, religion and politics; allowing close investigation of each novel's social, political and historical particularities, as well as their underlying discussion of perennial human issues. These novels are each essentially explorations of the human experience. Read together, they highlight the commonest of human elements, most poignantly the need for communion; facilitating analysis of the individual and all our communities. Comparing the two novels also continues the process of each: examining the self both within and outside of the narratives, producing a new textual self, arising from both primary sources and the contextual breadth of such rewriting

Electrocortical evidence for long-term incidental spatial learning through modified navigation instructions

© Springer Nature Switzerland AG 2018. The use of Navigation Assistance Systems for spatial orienting has become increasingly popular. Such automated navigation support, however, comes with a reduced processing of the surrounding environment and often with a decline of spatial orienting ability. To prevent such deskilling and to support spatial learning, the present study investigated incidental spatial learning by comparing standard navigation instructions with two modified navigation instruction conditions. The first modified instruction condition highlighted landmarks and provided additional redundant information regarding the landmark (contrast condition), while the second highlighted landmarks and included information of personal interest to the participant (personal-reference condition). Participants’ spatial knowledge of the previously unknown virtual city was tested three weeks later. Behavioral and electroencephalographic (EEG) data demonstrated enhanced spatial memory performance for participants in the modified navigation instruction conditions without further differentiating between modified instructions. Recognition performance of landmarks was better and the late positive complex of the event-related potential (ERP) revealed amplitude differences reflecting an increased amount of recollected information for modified navigation instructions. The results indicate a significant long-term spatial learning effect when landmarks are highlighted during navigation instructions

International Federation of Clinical Neurophysiology (IFCN) – EEG research workgroup: Recommendations on frequency and topographic analysis of resting state EEG rhythms. Part 1: Applications in clinical research studies

In 1999, the International Federation of Clinical Neurophysiology (IFCN) published “IFCN Guidelines for topographic and frequency analysis of EEGs and EPs” (Nuwer et al., 1999). Here a Workgroup of IFCN experts presents unanimous recommendations on the following procedures relevant for the topographic and frequency analysis of resting state EEGs (rsEEGs) in clinical research defined as neurophysiological experimental studies carried out in neurological and psychiatric patients: (1) recording of rsEEGs (environmental conditions and instructions to participants; montage of the EEG electrodes; recording settings); (2) digital storage of rsEEG and control data; (3) computerized visualization of rsEEGs and control data (identification of artifacts and neuropathological rsEEG waveforms); (4) extraction of “synchronization” features based on frequency analysis (band-pass filtering and computation of rsEEG amplitude/power density spectrum); (5) extraction of “connectivity” features based on frequency analysis (linear and nonlinear measures); (6) extraction of “topographic” features (topographic mapping; cortical source mapping; estimation of scalp current density and dura surface potential; cortical connectivity mapping), and (7) statistical analysis and neurophysiological interpretation of those rsEEG features. As core outcomes, the IFCN Workgroup endorsed the use of the most promising “synchronization” and “connectivity” features for clinical research, carefully considering the limitations discussed in this paper. The Workgroup also encourages more experimental (i.e. simulation studies) and clinical research within international initiatives (i.e., shared software platforms and databases) facing the open controversies about electrode montages and linear vs. nonlinear and electrode vs. source levels of those analyses

EEG-BIDS, an extension to the brain imaging data structure for electroencephalography

The Brain Imaging Data Structure (BIDS) project is a rapidly evolving effort in the human brain imaging research community to create standards allowing researchers to readily organize and share study data within and between laboratories. Here we present an extension to BIDS for electroencephalography (EEG) data, EEG-BIDS, along with tools and references to a series of public EEG datasets organized using this new standard

Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50–100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing