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

Occipital alpha activity during stimulus processing gates the information flow to object-selective cortex.

Given the limited processing capabilities of the sensory system, it is essential that attended information is gated to downstream areas, whereas unattended information is blocked. While it has been proposed that alpha band (8–13 Hz) activity serves to route information to downstream regions by inhibiting neuronal processing in task-irrelevant regions, this hypothesis remains untested. Here we investigate how neuronal oscillations detected by electroencephalography in visual areas during working memory encoding serve to gate information reflected in the simultaneously recorded blood-oxygenation-level-dependent (BOLD) signals recorded by functional magnetic resonance imaging in downstream ventral regions. We used a paradigm in which 16 participants were presented with faces and landscapes in the right and left hemifields; one hemifield was attended and the other unattended. We observed that decreased alpha power contralateral to the attended object predicted the BOLD signal representing the attended object in ventral object-selective regions. Furthermore, increased alpha power ipsilateral to the attended object predicted a decrease in the BOLD signal representing the unattended object. We also found that the BOLD signal in the dorsal attention network inversely correlated with visual alpha power. This is the first demonstration, to our knowledge, that oscillations in the alpha band are implicated in the gating of information from the visual cortex to the ventral stream, as reflected in the representationally specific BOLD signal. This link of sensory alpha to downstream activity provides a neurophysiological substrate for the mechanism of selective attention during stimulus processing, which not only boosts the attended information but also suppresses distraction. Although previous studies have shown a relation between the BOLD signal from the dorsal attention network and the alpha band at rest, we demonstrate such a relation during a visuospatial task, indicating that the dorsal attention network exercises top-down control of visual alpha activity

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

Total daily energy expenditure in black and white, lean and obese South African women.

Background/Objectives:In South Africa (SA), the prevalence of obesity in women is 56%, with black women being most at risk (62%). Studies in the United States have demonstrated ethnic differences in resting (REE) and total daily energy expenditure (TDEE) between African American (AA) and their white counterparts. We investigated whether differences in EE exist in black and white SA women, explaining, in part, the ethnic obesity prevalence differences.Subjects/Methods:We measured REE, TDEE and physical activity EE (PAEE) in lean (BMI 30 kg m(-2)) SA women (N=44, 30+/-6 year). REE, TDEE, PAEE and total awake EE were measured during a 21 h stay in a respiration chamber.Results:Black and white subjects within obese and lean groups were not significantly different for age, mass, BMI and % body fat. However, fat-free mass (kg FFM) was consistently lower in the black women (P<0.01) in both weight groups. After adjusting EE measurements for differences in FFM, REE was not significantly different for either body weight or ethnicity, although 24 h TDEE (kJ) was significantly greater in the obese women (P<0.01) and white women (P<0.05). Total awake non-PAEE was not significantly different for either groups, while total awake time was only significantly lower for the lean groups (P<0.01). Total PAEE (kJ min(-1)) was significantly lower in the lean (P<0.001) and black groups (P<0.01).Conclusions:In this sample of matched, lean and obese, black and white SA women, differences in TDEE were largely explained by ethnic differences in PAEE, and were not as a result of ethnic differences in REE.European Journal of Clinical Nutrition advance online publication, 13 February 2008; doi:10.1038/ejcn.2008.8

Addendum bij Effecten van het gebruiksverbod gewasbeschermingsmiddelen buiten de landbouw op oppervlaktewater: een aanvullende analyse van meetgegevens t/m 2019

Environmental Biolog

Hippocampal-Prefrontal theta oscillations support memory integration

Integration of separate memories forms the basis of inferential reasoning—an essential cognitive process that enables complex behavior. Considerable evidence suggests that both hippocampus and medial prefrontal cortex (mPFC) play a crucial role in memory integration. Although previous studies indicate that theta oscillations facilitate memory processes, the electrophysiological mechanisms underlying memory integration remain elusive. To bridge this gap, we recorded magnetoencephalography data while participants performed an inference task and employed novel source reconstruction techniques to estimate oscillatory signals from the hippocampus. We found that hippocampal theta power during encoding predicts subsequent memory integration. Moreover, we observed increased theta coherence between hippocampus and mPFC. Our results suggest that integrated memory representations arise through hippocampal theta oscillations, possibly reflecting dynamic switching between encoding and retrieval states, and facilitating communication with mPFC. These findings have important implications for our understanding of memory-based decision making and knowledge acquisition

Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research

The Organization for Human Brain Mapping (OHBM) has been active in advocating for the instantiation of best practices in neuroimaging data acquisition, analysis, reporting and sharing of both data and analysis code to deal with issues in science related to reproducibility and replicability. Here we summarize recommendations for such practices in magnetoencephalographic (MEG) and electroencephalographic (EEG) research, recently developed by the OHBM neuroimaging community known by the abbreviated name of COBIDAS MEEG. We discuss the rationale for the guidelines and their general content, which encompass many topics under active discussion in the field. We highlight future opportunities and challenges to maximizing the sharing and exploitation of MEG and EEG data, and we also discuss how this ‘living’ set of guidelines will evolve to continually address new developments in neurophysiological assessment methods and multimodal integration of neurophysiological data with other data types.Peer reviewe

Periodogram Connectivity of EEG Signals for the Detection of Dyslexia

Electroencephalography (EEG) signals provide an important source of information of brain activity at different areas. This information can be used to diagnose brain disorders according to different activation patterns found in controls and patients. This acquisition technology can be also used to explore the neural basis of less evident learning disabilities such as Developmental Dyslexia (DD). DD is a specific difficulty in the acquisition of reading skills not related to mental age or inadequate schooling, whose prevalent is estimated between 5% and 12% of the population. In this paper we propose a method to extract discriminative features from EEG signals based on the relationship among the spectral density at each channel. This relationship is computed by means of different correlation measures, inferring connectivity-like markers that are eventually selected and classified by a linear support vector machine. The experiments performed shown AUC values up to 0.7, demonstrating the applicability of the proposed approach for objective DD diagnosis