Perception-action circuits for word learning and semantic grounding: a neurocomputational model and neuroimaging study

A neurocomputational architecture of the left-hemispheric areas of the brain is presented which was used to simulate and explain neural correlates of word learning and semantic grounding. The model’s main distinguishing features are that (i) it replicates connectivity and anatomical structure of the relevant brain areas, and (ii) it implements only functional mechanisms reflecting known cellular- and synaptic-level properties of the cerebral cortex. Stimulation of the “sensorimotor” model areas (mimicking early stages of word acquisition) leads to the spontaneous formation of cell assemblies (CAs), network correlates of memory traces for meaningful words. Preliminary results of a recent functional Magnetic Resonance Imaging study confirm the model's predictions, and, for the first time, localise the neural correlates of semantic grounding of novel spoken items in primary visual cortex. Taken together, these results provide strong support for perceptual accounts of word meaning acquisition in the brain, and point to a unifying theory of cognition based on action-perception circuits whose emergence, dynamics and interactions are grounded in known neuroanatomy and neurobiological learning mechanisms

Semantic grounding of novel spoken words in the primary visual cortex

Embodied theories of grounded semantics postulate that, when word meaning is first acquired, a link is established between symbol (word form) and corresponding semantic information present in modality-specific – including primary – sensorimotor cortices of the brain. Direct experimental evidence documenting the emergence of such a link (i.e., showing that presentation of a previously unknown, meaningless word sound induces, after learning, category specific reactivation of relevant primary sensory or motor brain areas), however, is still missing. Here, we present new neuroimaging results that provide such evidence. We taught participants aspects of the referential meaning of previously unknown, senseless novel spoken words (such as “Shruba” or “Flipe”) by associating them with either a familiar action or a familiar object. After training, we used functional magnetic resonance imaging to analyse the participants’ brain responses to the new speech items. We found that hearing the newly learnt object-related word sounds selectively triggered activity in primary visual cortex, as well as secondary and higher visual areas. These results for the first time directly document the formation of a link between novel, previously meaningless spoken items and corresponding semantic information in primary sensory areas in a category specific manner, providing experimental support for perceptual accounts of word meaning acquisition in the brain

Microstructural imaging of human neocortex in vivo

The neocortex of the human brain is the seat of higher brain function. Modern imaging techniques, chief among them magnetic resonance imaging (MRI), allow non-invasive imaging of this important structure. Knowledge of the microstructure of the neocortex has classically come from post-mortem histological studies of human tissue, and extrapolations from invasive animal studies. From these studies, we know that the scale of important neocortical structure spans six orders of magnitude, ranging from the size of axonal diameters (microns), to the size of cortical areas responsible for integrating sensory information (centimetres). MRI presents an opportunity to move beyond classical methods, because MRI is non-invasive and MRI contrast is sensitive to neocortical microstructure over all these length scales. MRI thus allows inferences to be made about neocortical microstructure in vivo, i.e. MRI-based in vivo histology. We review recent literature that has applied and developed MRI-based in vivo histology to probe the microstructure of the human neocortex, focusing specifically on myelin, iron, and neuronal fibre mapping. We find that applications such as cortical parcellation (using maps as proxies for myelin content) and investigation of cortical iron deposition with age (using maps) are already contributing to the frontiers of knowledge in neuroscience. Neuronal fibre mapping in the cortex remains challenging in vivo, but recent improvements in diffusion MRI hold promise for exciting applications in the near future. The literature also suggests that utilising multiple complementary quantitative MRI maps could increase the specificity of inferences about neocortical microstructure relative to contemporary techniques, but that further investment in modelling is required to appropriately combine the maps. In vivo histology of human neocortical microstructure is undergoing rapid development. Future developments will improve its specificity, sensitivity, and clinical applicability, granting an ever greater ability to investigate neuroscientific and clinical questions about the human neocortex

The quest for the best: The impact of different EPI sequences on the sensitivity of random effect fMRI group analyses.

We compared the sensitivity of standard single-shot 2D echo planar imaging (EPI) to three advanced EPI sequences, i.e., 2D multi-echo EPI, 3D high resolution EPI and 3D dual-echo fast EPI in fixed effect and random effects group level fMRI analyses at 3T. The study focused on how well the variance reduction in fixed effect analyses achieved by advanced EPI sequences translates into increased sensitivity in the random effects group level analysis. The sensitivity was estimated in a functional MRI experiment of an emotional learning and a reward based learning tasks in a group of 24 volunteers. Each experiment was acquired with the four different sequences. The task-related response amplitude, contrast level and respective t-value were proxies for the functional sensitivity across the brain. All three advanced EPI methods increased the sensitivity in the fixed effects analyses, but standard single-shot 2D EPI provided a comparable performance in random effects group analysis when whole brain coverage and moderate resolution are required. In this experiment inter-subject variability determined the sensitivity of the random effects analysis for most brain regions, making the impact of EPI pulse sequence improvements less relevant or even negligible for random effects analyses. An exception concerns the optimization of EPI reducing susceptibility-related signal loss that translates into an enhanced sensitivity e.g. in the orbitofrontal cortex for multi-echo EPI. Thus, future optimization strategies may best aim at reducing inter-subject variability for higher sensitivity in standard fMRI group studies at moderate spatial resolution

Supply Chain Management Application in Functional Food Industry

Abstract— As follows from the analysis, the producers of functional food production do not increase their production and its supply in the domestic market intensively enough and have a quite low export capacity. During production of functional foods, producers do not pay sufficient attention to patenting of the developed intellectual property objects. It was revealed that Russia, that shows a considerable potential of agricultural land and positive experience of agricultural sector development, needs to ensure the development of domestic supply chain management in businesses and industries to provide promising new-generation functional foods competitive in the internal and external markets. Competitiveness of such products can be improved by development of supply chain management – specialized functional foods. This paper explores ways of improving supply chain management within the food processing industry looking into characteristics on justification and development of advanced functional food, its formulations and production technologies. This work presents a supply chai nstrategy in terms of food industry subsectors and producers, food effects obtained from functional foods, and in terms of consumers of functional food products depending on product purpose

Biophysically motivated efficient estimation of the spatially isotropic R*2 component from a single gradient‐recalled echo measurement

Purpose To propose and validate an efficient method, based on a biophysically motivated signal model, for removing the orientation‐dependent part of R*2 using a single gradient‐recalled echo (GRE) measurement. Methods The proposed method utilized a temporal second‐order approximation of the hollow‐cylinder‐fiber model, in which the parameter describing the linear signal decay corresponded to the orientation‐independent part of R*2. The estimated parameters were compared to the classical, mono‐exponential decay model for R*2 in a sample of an ex vivo human optic chiasm (OC). The OC was measured at 16 distinct orientations relative to the external magnetic field using GRE at 7T. To show that the proposed signal model can remove the orientation dependence of R*2, it was compared to the established phenomenological method for separating R*2 into orientation‐dependent and ‐independent parts. Results Using the phenomenological method on the classical signal model, the well‐known separation of R*2 into orientation‐dependent and ‐independent parts was verified. For the proposed model, no significant orientation dependence in the linear signal decay parameter was observed. Conclusions Since the proposed second‐order model features orientation‐dependent and ‐independent components at distinct temporal orders, it can be used to remove the orientation dependence of R*2 using only a single GRE measurement

More then simply iron: Macro- to microscopic cellular iron distribution in the brain determines MR contrast

Myelin and iron are the major source of MR contrast in the brain. Iron dominates R2*, R2 and QSM in the cortex as well as in subcortical areas and contributes to white matter contrast. To exploit this contrast for cortical parcellation, myeloarchitecture mapping, or iron quantification, significant theoretical and experimental efforts were devoted to the understanding of iron-induced contrast. However, the impact of the cellular and subcellular iron distribution is not well understood. Frequently, it is described by a simple linear dependence of the MRI contrast parameters on iron concentration, largely disregarding the inhomogeneous distribution of iron in the brain. A major reason for this simplification is a lack of quantitative knowledge on the cellular iron distribution. Moreover, the interplay between the microscopic iron distribution and diffusion in creating MR contrast in static de-phasing, motional narrowing or intermediate regime is not fully understood. We set out to address this lack in knowledge and modelling by combining state of the art quantitative 7T MRI with cutting-edge quantitative iron and myelin mapping on post mortem brain samples. Quantitative R2*, R2, R1 and QSM maps were obtained for the human cortex, the subcortical and the deep white matter as well as for brain nuclei before and after de-ironing. Laser Ablation Inductively Coupled Plasma Mass Spectroscopic Imaging (LA ICP MSI) yielded quantitative iron maps with a mesoscopic resolution of 60x120μm. Proton Induced X-ray Emission (PIXE) provided quantitative iron maps with a cellular resolution down to 1μm. MSI and PIXE demonstrated the inhomogenous distribution of iron in both grey and white matter at different spatial scales. In grey matter iron rich fibers, and small (1-3μm) micro-, astro- and oligodendroglia contained most of the iron and were sparsely distributed. In superficial and deep white matter, however, oligodendrocytes somas with the sizes of 5±1.5μm (distance between cells of 20±5μm) and iron rich fibers contained most of the iron. In addition, patches of enhanced iron concentration around small vessels with a typical size of 100-200μm contribute to up to 20% of R2* and QSM and their orientation dependence in white matter. A different contrast mechanism prevailed in brain nuclei where densely packed 20μm large iron loaded neurons dominated the MR contrast. These results provide an important basis for understanding the iron induced MR-contrast and its microstructural underpinnings. Based on these measured microscopic iron distributions and a Gaussian diffusion model we are now in the process of simulating the MR contrast mechanisms in different tissue types

Simultaneous measurement of time-domain fNIRS and physiological signals during a cognitive task

Functional near-infrared spectroscopy (fNIRS) is a commonly used technique to measure the cerebral vascular response related to brain activation. It is known that systemic physiological processes, either independent or correlated with the stimulation task, can influence the optical signal making its interpretation challenging. The aim of the present work is to investigate the impact of task-evoked changes in the systemic physiology on fNIRS measurements for a cognitive paradigm. For this purpose we carried out simultaneous measurements of time-domain fNIRS on the forehead and systemic physiological signals, i.e. mean blood pressure, heart rate, respiration, galvanic skin response, scalp blood flow (flux) and red blood cell (RBC) concentration changes. We performed measurements on 15 healthy volunteers during a semantic continuous performance task (CPT). The optical data was analyzed in terms of depth-selective moments of distributions of times of flight of photons through the tissue. In addition, cerebral activation was localized by a subsequent fMRI experiment on the same subject population using the same task. We observed strong non-cerebral task-evoked changes in concentration changes of oxygenated hemoglobin in the forehead. We investigated the temporal behavior and mutual correlations between hemoglobin changes and the systemic processes. Mean blood pressure (BP), galvanic skin response (GSR) and heart rate exhibited significant changes during the activation period, whereby BP and GSR showed the highest correlation with optical measurements