Navigated transcranial magnetic stimulation (nTMS) can be applied to locate and outline cortical motor representations. This may be important, e.g., when planning neurosurgery or focused nTMS therapy, or when assessing plastic changes during neurorehabilitation. Conventionally, a cortical location is considered to belong to the motor cortex if the maximum electric field (E-field) targeted there evokes a motor-evoked potential in a muscle. However, the cortex is affected by a broad E-field distribution, which tends to broaden estimates of representation areas by stimulating also the neighboring areas in addition to the maximum E-field location. Our aim was to improve the estimation of nTMS-based motor maps by taking into account the E-field distribution of the stimulation pulse. The effect of the E-field distribution was considered by calculating the minimum-norm estimate (MNE) of the motor representation area. We tested the method on simulated data and then applied it to recordings from six healthy volunteers and one stroke patient. We compared the motor representation areas obtained with the MNE method and a previously introduced interpolation method. The MNE hotspots and centers of gravity were close to those obtained with the interpolation method. The areas of the maps, however, depend on the thresholds used for outlining the areas. The MNE method may improve the definition of cortical motor areas, but its accuracy should be validated by comparing the results with maps obtained with direct cortical stimulation of the cortex where the E-field distribution can be better focused.
This paper addresses the important problem of efficiently mining numerical data with formal concept analysis (FCA). Classically, the only way to apply FCA is to binarize the data, thanks to a so-called scaling procedure. This may either involve loss of information, or produce large and dense binary data known as hard to process. In the context of gene expression data analysis, we propose and compare two FCA-based methods for mining numerical data and we show that they are equivalent. The first one relies on a particular scaling, encoding all possible intervals of attribute values, and uses standard FCA techniques. The second one relies on pattern structures without a priori transformation, and is shown to be more computationally efficient and to provide more readable results. Experiments with real-world gene expression data are discussed and give a practical basis for the comparison and evaluation of the methods.
The short-time dynamics of bacterial chromosomal loci is a mixture of subdiffusive and active motion, in the form of rapid relocations with near-ballistic dynamics. While previous work has shown that such rapid motions are ubiquitous, we still have little grasp on their physical nature, and no positive model is available that describes them. Here, we propose a minimal theoretical model for loci movements as a fractional Brownian motion subject to a constant but intermittent driving force, and compare simulations and analytical calculations to data from high-resolution dynamic tracking in E. coli. This analysis yields the characteristic time scales for intermittency. Finally, we discuss the possible shortcomings of this model, and show that an increase in the effective local noise felt by the chromosome associates to the active relocations.
The problem of functional localization in the brain is one of the most fundamental in neuroscience. For this problem two opposite ideologies: "modular" versus "holistic" nature of the brain also known as "localism" and "holism" have been discussed for a long time (Flourens 1825; Luria 1967). The debate in favor of one or another ideology still can be traced at all methodological levels - from a cell to a system. In this opinion paper we want to raise a question - what is nowadays meant by mapping of the brain? In addition we want to highlight the necessity of being aware of occasionally occurring discontinuity in the research at different methodological scales.
Although thalamic deep brain stimulation is an effective treatment for patients with essential tremor, little is known about its effect on cortical neural dynamics. Therefore, we investigated long-range temporal correlations and spectral power in electroencephalographic recordings of patients during OFF versus ON bilateral thalamic deep brain stimulation in comparison with healthy controls. Cortical dynamics were analyzed in the range of 6-30 Hz. We found the presence of long-range temporal correlations up to 20 s in patients and controls. Thalamic deep brain stimulation was associated with increased long-range temporal correlations in the high beta band (21-30 Hz) and decreased power in the low beta band (13-20 Hz) compared with OFF stimulation and healthy controls. Long-range temporal correlations in the 6-10 Hz range were increased with OFF stimulation compared with the controls. Importantly, deep brain stimulation-induced changes in long-range temporal correlations within 6-10 Hz and in the beta ranges (13-20, 21-30 Hz) were correlated with OFF-ON changes in the tremor severity and with the disease duration, respectively. The differential reactivity of long-range temporal correlations and spectral power to deep brain stimulation might suggest that both measures reflect distinct aspects of cortical dynamics and might represent biomarkers for stimulation-induced modulations of neural dynamics in electroencephalography. The fact that long-range temporal correlations, but not spectral power, were correlated with clinical information might suggest long-range temporal correlations as a potential marker for disease severity in essential tremor.
Due to their high durability and immobilization properties, cementitious materials have found a considerable application in the design and construction of radioactive waste repositories in the last decades. During cement paste production, organic additives are introduced to modify various properties of cement. The presence of such organic complexants may negatively affect the immobilizing properties of cement with respect to radionuclides. For better understanding and prediction of the effects of interactions between organic molecules and cementitious materials with radionuclides, we have developed several representative models consisting of three principal components: (i) calcium silicate hydrate (C-S-H) phase - the main binding phase of cement; (ii) gluconate, a simple well-described molecule, as a representative of organic additives; (iii) U(VI), as one of the most studied radionuclides of the actinide series. The C-S-H phase with low Ca/Si ratio (~0.83) typical for â€œlow-pHâ€ and degraded cement pastes has been selected for this modelling study. Structural, and energetic aspects of the sorption processes of uranyl, gluconate, and their mutual correlations on the surface of cement were quantitatively modeled by classical molecular dynamics (MD) and potential of mean force (PMF) calculations. The ternary surface complex formation between uranyl hydroxides and Ca2+ cations at the C-S-H aqueous interfaces is shown to have an important role in the overall sorption process. In the presence of gluconate, U(VI) sorption on C-S-H is facilitated by weakening the Ca2+ binding with the surface. Additionally, Na+ is proven to be an important competitor for certain surface sorption sites and can potentially affect the equilibrium properties of the interface.
Previous studies demonstrated the presence of Monochromatic Ultra-Slow Oscillations (MUSO) in human EEG. In the present study we explored the biological origin of MUSO by simultaneous recordings of EEG, Near-Infrared Spectroscopy (NIRS), arterial blood pressure, respiration and Laser Doppler flowmetry. We used a head-up tilt test in order to check whether MUSO might relate to Mayer waves in arterial blood pressure, known to be enhanced by the tilting procedure. MUSO were detected in 8 out of 10 subjects during rest and showed a striking monochromatic spectrum (0.07–0.14 Hz). The spatial topography of MUSO was complex, showing multiple foci variable across subjects. While the head-up tilt test increased the relative power of Mayer waves, it had no effect on MUSO. On the other hand, the relative spectral power of 0.1 Hz oscillations in EEG, NIRS and blood pressure signals were positively correlated across subjects in the tilted condition. Eight subjects showed a coherence between MUSO and NIRS/arterial blood pressure. Moreover, MUSO at different electrode sites demonstrated coherence not reducible to volume conduction, thus indicating that MUSO are unlikely to be generated by one source. We related our experimental findings to known biological phenomena being generated at about 0.1 Hz, i.e.: arterial blood pressure, cerebral and skin vasomotion, respiration and neuronal activity. While no definite conclusion can yet be drawn as to an exact physiological mechanism of MUSO, we suggest that these oscillations might be of a rather extraneuronal origin reflecting cerebral vasomotion.
We study the propagation and stability of kink waves in a twisted magnetic tube with the flow. The flow velocity is assumed to be parallel to the magnetic field, and the magnetic field lines are straight outside the tube. The density is constant inside and outside of the tube, and it monotonically decreases from its value inside the tube to that outside in the transitional or boundary layer. The flow speed and magnetic twist monotonically decrease in the transitional layer from their values inside the tube to zero outside. Using the thin tube and thin boundary layer (TTTB) approximation, we derived the dispersion equation determining the dependence of the wave frequency and decrement/increment on the wavenumber. When the kink wave frequency coincides with the local Alfvén frequency at a resonant surface inside the transitional layer, the kink wave is subjected to either resonant damping or resonant instability. We study the properties of kink waves in a particular unperturbed state where there is no flow and magnetic twist in the transitional layer. It is shown that in a tube with flow, the kink waves can propagate without damping for particular values of the flow speed. Kink waves propagating in the flow direction either damp or propagate without damping. Waves propagating in the opposite direction can either propagate without damping, or damp, or become unstable. The theoretical results are applied to the problem of excitation of kink waves in spicules and filaments in the solar atmosphere.
Cheliped construction, in particular the teeth pattern on chelae fingers is considered as most important character suit (along with burrowing/swimming apparatus) for the diagnosis of Portunoidea. Heterochelic and heterodontic chelipeds with the molariform tooth in the larger chela and multi-lobed serial teeth are presumably ancestral and most common pattern for the group. New material (mostly species of Thalamitinae Paulson, 1875, Lupocyclus Adamd and White, 1848 and Portunus Weber, 1795 sensu lato) have been combined with the existing sequences from the GenBank to produce molecular phylogenetic reconstructions based on the histone H3 gene fragment and a multi-gene tree (for smaller set of species) based on partial sequences of H3, D1 region of 28S gene and mitochondrial COI gene. These reconstructions have not provided necessary support to the monophyly of Portunoidea sensu lato but indicated the presence of several monophyletic lineages, i.e. Portunidae sensu stricto, Polybiidae + Thiidae + Carcinidae + Pirimelidae, Benthochascon + Geryonidae (to lesser extent), and Ovalipes. Monophyly of the Portunidae sensu stricto is supported by both the H3 and multigene trees and morphological evidence. Swimming capacity probably evolves as a result of parallel evolution in at least three different lineages of portunoids. A new version of the family level classification of Portunoidea and a key to their families are provided with the following taxa: Geryonidae (Geryoninae + Benthochasconinaeˇ ́ 1991, Thiidae, Pirimelidae, Carcinidae McLeay, subfam. nov.), Ovalipidae fam. nov., Brusiniidae Stevˇci c,1838 (Carcininae + Portumninae Ortmann, 1893), Polybiidae Ortmann, 1893, and Portunidae Rafinesque, 1815 sensu stricto. The most radical change in the systematics of Portunidae sensu stricto is the final recognition of the polyphyly of Portunus sensu lato and the need for revalidization and re-diagnozing of several taxa that were synonymized by Stephenson and Campbell (1959) and Stephenson (1972) under Portunus. While some subfamilies of the Portunidae (Podophthalminae Dana, 1851, Thalamitinae, and Lupocyclinae Alcock, 1895) are well supported by molecular phylogenies and the presence of morphological synapomorphies, the others need re-assessment.
Are compound words represented as unitary lexical units, or as individual constituents that are processed combinatorially? We investigated the neuro-cognitive processing of compounds using EEG and a passive-listening oddball design in which lexical access and combinatorial processing elicit dissociating Mismatch Negativity (MMN) brain-response patterns. MMN amplitude varied with compound frequency and semantic transparency (the clarity of the relationship between compound and constituent meanings). Opaque compounds elicited an enhanced 'lexical' MMN, reflecting stronger lexical representations, to high- vs. low-frequency compounds. Transparent compounds showed no frequency effect, nor differed to pseudo-compounds, reflecting the combination of a reduced 'syntactic' MMN indexing combinatorial links, and an enhanced 'lexical' MMN for real-word compounds compared to pseudo-compounds. We argue that transparent compounds are processed combinatorially alongside parallel lexical access of the whole-form representation, but whole-form access is the dominant mechanism for opaque compounds, particularly those of high-frequency. Results support a flexible dual-route account of compound processing.
Spatial component analysis is often used to explore multidimensional time series data whose sources cannot be measured directly. Several methods may be used to decompose the data into a set of spatial components with temporal loadings. Component selection is of crucial importance, and should be supported by objective criteria. In some applications, the use of a well defined component selection criterion may provide for automation of the analysis. In this paper we describe a novel approach for ranking of spatial components calculated from the EEG or MEG data recorded within evoked response paradigm. Our method is called Mutual Information (MI) Spectrum and is based on gauging the amount of MI of spatial component temporal loadings with a synthetically created reference signal. We also describe the appropriate randomization based statistical assessment scheme that can be used for selection of components with statistically significant amount of MI. Using simulated data with realistic trial to trial variations and SNR corresponding to the real recordings we demonstrate the superior performance characteristics of the described MI based measure as compared to a more conventionally used power driven gauge. We also demonstrate the application of the MI Spectrum for the selection of task-related independent components from real MEG data. We show that the MI spectrum allows to identify task-related components reliably in a consistent fashion, yielding stable results even from a small number of trials. We conclude that the proposed method fits naturally the information driven nature of ICA and can be used for routine and automatic ranking of independent components calculated from the functional neuroimaging data collected within event-related paradigms.
Critical conditions for natural selection in multidimensional evolutionary spaces and general requirements following from these conditions and corresponding to the prebiotic evolutionary stage are discussed.
A phenomenon termed negative priming is defined as an increase in reaction time and/or decrease in performance during instances in which current target stimuli are employed as distractor stimuli in the previous trial. A recent qualitative review on negative priming reported neural regions of interest underlined by activity within the right middle frontal gyrus and left middle temporal gyrus; however, these areas of interest have not been tested and supported by using coordinate-based, quantitative meta-analysis. We compiled functional magnetic resonance imaging studies that examined neural correlates of priming tasks using perceptual, conceptual and lexical primes. Effect-size signed differential mapping was used to perform a neuroimaging meta-analysis on the negative priming effect. Results from fourteen studies (245 participants; 85 foci) show concordance across studies in the right middle frontal gyrus and the left superior temporal gyrus, as suggested by the previous review; however, results also yielded concordance within the anterior cingulate cortex. Our data support the extant hypothesis and offer new insights into the neural mechanisms of the negative priming effect.
Humans are unique in developing large lexicons as their communication tool; to achieve this, they are able to learn new words rapidly. However, neural bases of this rapid learning, which may be an expression of a more general cognitive mechanism likely rooted in plasticity at cellular and synaptic levels, are not yet understood. In this update, the author highlights a selection of recent studies that attempted to trace word learning in the human brain noninvasively. A number of brain areas, most notably in hippocampus and neocortex, appear to take part in word acquisition. Critically, the currently available data not only demonstrate the hippocampal role in rapid encoding followed by slow-rate consolidation of cortical word memory traces but also suggest immediate neocortical involvement in the word memory trace formation. Echoing early behavioral studies in ultra-rapid word learning, the reviewed neuroimaging experiments can be taken to suggest that our brain may effectively form new cortical circuits online, as it gets exposed to novel linguistic patterns in the sensory input.
We investigated neural distinctions between inflectional and derivational morphology and their interaction with lexical frequency using the mismatch negativity (MMN), an established neurophysiological index of experience-dependent linguistic memory traces and automatic syntactic processing. We presented our electroencephalography (EEG) study participants with derived and inflected words of variable lexical frequencies against their monomorphemic base forms in a passive oddball paradigm, along with acoustically matched pseudowords. Sensor space and distributed source modelling results showed that at 100-150 msec after the suffix onset, derived words elicited larger responses than inflected words. Furthermore, real derived words showed advantage over pseudo-derivations and frequent derivations elicited larger activation than less frequent ones. This pattern of results is fully in line with previous research that explained lexical MMN enhancement by an activation of strongly connected word-specific long-term memory circuits, and thus suggests stronger lexicalisation for frequently used complex words. At the same time, a strikingly different pattern was found for inflectional forms: higher response amplitude for pseudo-inflections than for real inflected words, with no clear frequency effects. This is fully in line with previous MMN results on combinatorial processing of (morpho)syntactic stimuli: higher response to ungrammatical morpheme strings than grammatical ones, which does not depend on the string's surface frequency. This pattern suggests that, for inflectional forms, combinatorial processing route dominates over whole-form storage and access. In sum, our results suggest that derivations are more likely to form unitary representations than inflections which are likely to be processed combinatorially, and imply at least partially distinct brain mechanisms for the processing and representation of these two types of morphology. These dynamic mechanisms, underpinned by perisylvian networks, are activated rapidly, at 100-150 msec after the information arrives at the input, and in a largely automatic fashion, possibly providing neural basis for the first-pass morphological processing of spoken words.
Rapid and automatic processing of grammatical complexity is argued to take place during speech comprehension, engaging a left-lateralized fronto-temporal language network. Here we address how neural activity in these regions is modulated by the grammatical properties of spoken words. We used combined magneto- and electroencephalography to delineate the spatiotemporal patterns of activity that support the recognition of morphologically complex words in English with inflectional (-s) and derivational (-er) affixes (e.g., bakes, baker). The mismatch negativity, an index of linguistic memory traces elicited in a passive listening paradigm, was used to examine the neural dynamics elicited by morphologically complex words. Results revealed an initial peak 130-180 ms after the deviation point with a major source in left superior temporal cortex. The localization of this early activation showed a sensitivity to two grammatical properties of the stimuli: (1) the presence of morphological complexity, with affixed words showing increased left-laterality compared to non-affixed words; and (2) the grammatical category, with affixed verbs showing greater left-lateralization in inferior frontal gyrus compared to affixed nouns (bakes vs. beaks). This automatic brain response was additionally sensitive to semantic coherence (the meaning of the stem vs. the meaning of the whole form) in left middle temporal cortex. These results demonstrate that the spatiotemporal pattern of neural activity in spoken word processing is modulated by the presence of morphological structure, predominantly engaging the left-hemisphere's fronto-temporal language network, and does not require focused attention on the linguistic input.
The neurobiological basis and temporal dynamics of communicative language processing pose important yet unresolved questions. It has previously been suggested that comprehension of the communicative function of an utterance, i.e. the so-called speech act, is supported by an ensemble of neural networks, comprising lexico-semantic, action and mirror neuron as well as theory of mind circuits, all activated in concert. It has also been demonstrated that recognition of the speech act type occurs extremely rapidly. These findings however, were obtained in experiments with insufficient spatio-temporal resolution, thus possibly concealing important facets of the neural dynamics of the speech act comprehension process. Here, we used magnetoencephalography to investigate the comprehension of Naming and Request actions performed with utterances controlled for physical features, psycholinguistic properties and the probability of occurrence in variable contexts. The results show that different communicative actions are underpinned by a dynamic neural network, which differentiates between speech act types very early after the speech act onset. Within 50-90 ms, Requests engaged mirror-neuron action-comprehension systems in sensorimotor cortex, possibly for processing action knowledge and intentions. Still, within the first 200 ms of stimulus onset (100-150 ms), Naming activated brain areas involved in referential semantic retrieval. Subsequently (200-300 ms), theory of mind and mentalising circuits were activated in medial prefrontal and temporo-parietal areas, possibly indexing processing of intentions and assumptions of both communication partners. This cascade of stages of processing information about actions and intentions, referential semantics, and theory of mind may underlie dynamic and interactive speech act comprehension.
Cognitive dissonance theory suggests that our preferences are modulated by the mere act of choosing. A choice between two similarly valued alternatives creates psychological tension (cognitive dissonance) that is reduced by a post-decisional reevaluation of the alternatives. Our study demonstrates that choices associated with stronger cognitive dissonance trigger a larger negative fronto-central evoked response similar to error-related negativity (ERN), which has in turn been implicated in general performance monitoring. Furthermore, the amplitude of the evoked response is correlated with the reevaluation of the alternatives. We also found a link between individual neural dynamics (long-range temporal correlations-LRTC) of the fronto-central cortices during rest and follow-up neural and behavioral effects of cognitive dissonance. Individuals with stronger resting-state LRTC demonstrated a greater post-decisional reevaluation of the alternatives and larger evoked brain responses associated with stronger cognitive dissonance. Thus, our results suggest that cognitive dissonance is reflected in both resting-state and choice-related activity of the prefrontal cortex as part of the general performance-monitoring circuitry.
We study the fraction f of nucleotides involved in the formation of a cactuslike secondary structure of random heteropolymer RNA-like molecules. In the low-temperature limit, we study this fraction as a function of the number c of different nucleotide species. We show, that with changing c, the secondary structures of random RNAs undergo a morphological transition:f(c)→1 for c≤ccr as the chain length n goes to infinity, signaling the formation of a virtually perfect gapless secondary structure; while f(c)<1 for c>ccr, which means that a nonperfect structure with gaps is formed. The strict upper and lower bounds 2≤ccr≤4 are proven, and the numerical evidence for ccr is presented. The relevance of the transition from the evolutional point of view is discussed.
Neuraminidase 1 (NEU1) is a lysosomal sialidase catalyzing the removal of terminal sialic acids from sialyloconjugates. A plasma membrane-bound NEU1 modulating a plethora of receptors by desialylation, has been consistently documented from the last ten years. Despite a growing interest of the scientific community to NEU1, its membrane organization is not understood and current structural and biochemical data cannot account for such membrane localization. By combining molecular biology and biochemical analyses with structural biophysics and computational approaches, we identified here two regions in human NEU1 - segments 139–159 (TM1) and 316–333 (TM2) - as potential transmembrane (TM) domains. In membrane mimicking environments, the corresponding peptides form stable α-helices and TM2 is suited for self-association. This was confirmed with full-size NEU1 by co-immunoprecipitations from membrane preparations and split-ubiquitin yeast two hybrids. The TM2 region was shown to be critical for dimerization since introduction of point mutations within TM2 leads to disruption of NEU1 dimerization and decrease of sialidase activity in membrane. In conclusion, these results bring new insights in the molecular organization of membrane-bound NEU1 and demonstrate, for the first time, the presence of two potential TM domains that may anchor NEU1 in the membrane, control its dimerization and sialidase activity.