Complex Patterns Of Activity Research Articles

An Unsupervised Framework for Online Spatiotemporal Detection of Activities of Daily Living by Hierarchical Activity Models.

Automatic detection and analysis of human activities captured by various sensors (e.g., sequences of images captured by RGB camera) play an essential role in various research fields in order to understand the semantic content of a captured scene. The main focus of the earlier studies has been widely on supervised classification problem, where a label is assigned to a given short clip. Nevertheless, in real-world scenarios, such as in Activities of Daily Living (ADL), the challenge is to automatically browse long-term (days and weeks) stream of videos to identify segments with semantics corresponding to the model activities and their temporal boundaries. This paper proposes an unsupervised solution to address this problem by generating hierarchical models that combine global trajectory information with local dynamics of the human body. Global information helps in modeling the spatiotemporal evolution of long-term activities, hence, their spatial and temporal localization. Moreover, the local dynamic information incorporates complex local motion patterns of daily activities into the models. Our proposed method is evaluated using realistic datasets captured from observation rooms in hospitals and nursing homes. The experimental data on a variety of monitoring scenarios in hospital settings reveals how this framework can be exploited to provide timely diagnose and medical interventions for cognitive disorders, such as Alzheimer’s disease. The obtained results show that our framework is a promising attempt capable of generating activity models without any supervision.

Transient Activations of Rac1 at the Lamellipodium Tip Trigger Membrane Protrusion.

The spatiotemporal coordination of actin regulators in the lamellipodium determines the dynamics and architecture of branched F-actin networks during cell migration. The WAVE regulatory complex (WRC), an effector of Rac1 during cell protrusion, is concentrated at the lamellipodium tip. Thus, activated Rac1 should operate at this location to activate WRC and trigger membrane protrusion. Yet correlation of Rho GTPase activation with cycles of membrane protrusion previously revealed complex spatiotemporal patterns of Rac1 and RhoA activation in the lamellipodium. Combining single protein tracking (SPT) and super-resolution imaging with loss- or gain-of-function mutants of Rho GTPases, we show that Rac1 immobilizations at the lamellipodium tip correlate with its activation, in contrast to RhoA. Using Rac1 effector loop mutants and wild-type versus mutant variants of WRC, we show that selective immobilizations of activated Rac1 at the lamellipodium tip depend on effector binding, including WRC. In contrast, wild-type Rac1 only displays slower diffusion at the lamellipodium tip, suggesting transient activations. Local optogenetic activation of Rac1, triggered by membrane recruitment of Tiam1, shows that Rac1 activation must occur close to the lamellipodium tip and not behind the lamellipodium to trigger efficient membrane protrusion. However, coupling tracking with optogenetic activation of Rac1 demonstrates that diffusive properties of wild-type Rac1 are unchanged despite enhanced lamellipodium protrusion. Taken together, our results support a model whereby transient activations of Rac1 occurring close to the lamellipodium tip trigger WRC binding. This short-lived activation ensures a local and rapid control of Rac1 actions on its effectors to trigger actin-based protrusion.

Biologically Active Ultra-Simple Proteins Reveal Principles of Transmembrane Domain Interactions

Specific interactions between the helical membrane-spanning domains of transmembrane proteins play central roles in the proper folding and oligomerization of these proteins. However, the relationship between the hydrophobic amino acid sequences of transmembrane domains and their functional interactions is in most cases unknown. Here, we use ultra-simple artificial proteins to systematically study the sequence basis for transmembrane domain interactions. We show that most short homopolymeric polyleucine transmembrane proteins containing single amino acid substitutions can activate the platelet-derived growth factor β receptor or the erythropoietin receptor in cultured mouse cells, resulting in cell transformation or proliferation. These proteins displayed complex patterns of activity that were markedly affected by seemingly minor sequence differences in the ultra-simple protein itself or in the transmembrane domain of the target receptor, and the effects of these sequence differences are not additive. In addition, specific leucine residues along the length of these proteins are required for activity, and the positions of these required leucines differ based on the identity and position of the central substituted amino acid. Our results suggest that these ultra-simple proteins use a variety of molecular mechanisms to activate the same target and that diversification of transmembrane domain sequences over the course of evolution minimized off-target interactions.

Theoretical Assessment of the Effect of Vertical Dispersivity on Coastal Seawater Radium Distribution

Trends in radium activity in coastal seawater are frequently used to infer submarine groundwater discharge. In general, unlike in the deep oceans, radium samples are only collected from the surface of the mixed layer in coastal areas. The assumption is that the water column is well mixed, as often evidenced by uniform temperature and salinity profiles. However, if the timescale for vertical mixing is similar to or less than the timescale for radioactive decay, the vertical profiles in Ra activity may not be uniform. In the present work, a two-dimensional dispersion model was developed to evaluate the potential effects of slow vertical mixing on Ra distribution in the mixed layer of an inner shelf. The variables considered were the vertical coefficient of solute dispersivity (Kz), the offshore coefficient of solute dispersivity (Kx), the coastal Ra flux (Fo), the benthic Ra flux (FB) and the slope of the seabed. The shorter-lived Ra isotopes (223Ra and 224Ra; t1/2 = 3.66 and 11.4 days, respectively) were sensitive to Kz when its value was low (<10–4 m2 s–1), resulting in complex activity patterns in the water column as a function of the other variables. Radium-228 (t1/2 = 5.75 years) was only moderately impacted by low Kz but the long-lived 226Ra (t1/2 = 1600 years) was insensitive to Kz. Surface water samples may not always be representative of water column Ra activity when Kz is low, which will need to be taken into account in future field programs for seawater Ra distribution in shelf environments.

A Wearable Wireless Armband Sensor for High-Density Surface Electromyography Recording.

This paper presents a portable and modular wireless multichannel sensor system for high-density surface electromyography (HD-sEMG) signals acquisition. Featuring low-power and high-quality off-the-shelf components such as the Intan Technologies RHD2132 digital electrophysiology interface chip, the current iteration of the proposed sensor system allows the recording of 32 surface electromyography (sEMG) channels, each at a sampling rate of 1 kHz, and a sample resolution of 16 bits. It features the RHD2132's typical input-referred noise of 2.4 μVrms, with <; 15% variation with amplifier bandwidth as specified by the manufacturer, and a total power consumption of 49.5 mW. Data is sent in real-time to a base station using a 2.4-GHz industrial, scientific and medical (ISM) wireless link. Along with the recording platform, the integrated sensor system includes a dry surface electrodes array prototype directly built on a printed circuit board. Intended for complex muscles activity patterns detection on the forearm, the flexible 32 surface electrodes array is designed to be placed flat or to fit a curved area like the forearm in a hand gestures recognition prosthetic system. In such applications, this device will offer improved prosthesis control scheme intuitiveness and ease-of-use. Among other core features of the system are its compact, light-weight and easy to install physical design. The complete system fits on a 2 by 6.5 cm2 printed circuit board mounted on a 7.6 by 11.8 cm2 electrodes array. HD-sEMG user forearm output data collected with the system is presented with a proposed frequency-time-space cross-domain preprocessing method for visualization of HD-EMG data and building training datasets.

Schlafen-11 expression is associated with immune signatures and basal-like phenotype in breast cancer.

Breast cancer (BC) is a heterogeneous disorder, with variable response to systemic chemotherapy. Likewise, BC shows highly complex immune activation patterns, only in part reflecting classical histopathological subtyping. Schlafen-11 (SLFN11) is a nuclear protein we independently described as causal factor of sensitivity to DNA damaging agents (DDA) in cancer cell line models. SLFN11 has been reported as a predictive biomarker for DDA and PARP inhibitors in human neoplasms. SLFN11 has been implicated in several immune processes such as thymocyte maturation and antiviral response through the activation of interferon signaling pathway, suggesting its potential relevance as a link between immunity and cancer. In the present work, we investigated the transcriptional landscape of SLFN11, its potential prognostic value, and the clinico-pathological associations with its variability in BC. We assessed SLFN11 determinants in a gene expression meta-set of 5061 breast cancer patients annotated with clinical data and multigene signatures. We found that 537 transcripts are highly correlated with SLFN11, identifying "immune response", "lymphocyte activation", and "T cell activation" as top Gene Ontology processes. We established a strong association of SLFN11 with stromal signatures of basal-like phenotype and response to chemotherapy in estrogen receptor negative (ER-) BC. We identified a distinct subgroup of patients, characterized by high SLFN11 levels, ER- status, basal-like phenotype, immune activation, and younger age. Finally, we observed an independent positive predictive role for SLFN11 in BC. Our findings are suggestive of a relevant role for SLFN11 in BC and its immune and molecular variability.

Discrimination of bursts and tonic activity in multifunctional sensorimotor neural network using the extended hill-valley method.

Individual neurons can exhibit a wide range of activity, including spontaneous spiking, tonic spiking, bursting, or spike-frequency adaptation, and can also transition between these activity types. Manual identification of these activity patterns can be subjective and inconsistent. The extended hill-valley (EHV) analysis discriminates tonic spiking and bursts in a spike train by detecting fluctuations in a local, history-dependent analysis signal derived from the spike train. Consequently, the EHV method is not susceptible to changes in baseline firing rate and can identify different types of activity patterns. In addition, output from the EHV method can be used to identify more complex activity patterns such as phasotonic bursting, in which a burst is immediately followed by a period of tonic spiking.NEW & NOTEWORTHY Neurons exhibit diverse spiking patterns, but automated activity classification has focused mainly on detecting bursts. The novel extended hill-valley algorithm uses a smoothed, history-dependent signal to discriminate different types of activity, such as bursts and tonic spiking.

Representation of abstract semantic knowledge in populations of human single neurons in the medial temporal lobe.

Sensory experience elicits complex activity patterns throughout the neocortex. Projections from the neocortex converge onto the medial temporal lobe (MTL), in which distributed neocortical firing patterns are distilled into sparse representations. The precise nature of these neuronal representations is still unknown. Here, we show that population activity patterns in the MTL are governed by high levels of semantic abstraction. We recorded human single-unit activity in the MTL (4,917 units, 25 patients) while subjects viewed 100 images grouped into 10 semantic categories of 10 exemplars each. High levels of semantic abstraction were indicated by representational similarity analyses (RSAs) of patterns elicited by individual stimuli. Moreover, pattern classifiers trained to decode semantic categories generalised successfully to unseen exemplars, and classifiers trained to decode exemplar identity more often confused exemplars of the same versus different categories. Semantic abstraction and generalisation may thus be key to efficiently distill the essence of an experience into sparse representations in the human MTL. Although semantic abstraction is efficient and may facilitate generalisation of knowledge to novel situations, it comes at the cost of a loss of detail and may be central to the generation of false memories.

Hierarchical Structured Sparse Learning for Schizophrenia Identification

Fractional amplitude of low-frequency fluctuation (fALFF) has been widely used for resting-state functional magnetic resonance imaging (rs-fMRI) based schizophrenia (SZ) diagnosis. However, previous studies usually measure the fALFF within low-frequency fluctuation (from 0.01 to 0.08Hz), which cannot fully cover the complex neural activity pattern in the resting-state brain. In addition, existing studies usually ignore the fact that each specific frequency band can delineate the unique spontaneous fluctuations of neural activities in the brain. Accordingly, in this paper, we propose a novel hierarchical structured sparse learning method to sufficiently utilize the specificity and complementary structure information across four different frequency bands (from 0.01Hz to 0.25Hz) for SZ diagnosis. The proposed method can help preserve the partial group structures among multiple frequency bands and the specific characters in each frequency band. We further develop an efficient optimization algorithm to solve the proposed objective function. We validate the efficacy of our proposed method on a real SZ dataset. Also, to demonstrate the generality of the method, we apply our proposed method on a subset of Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results on both datasets demonstrate that our proposed method achieves promising performance in brain disease classification, compared with several state-of-the-art methods.

Calcium Activity Dynamics Correlate with Neuronal Phenotype at a Single Cell Level and in a Threshold-Dependent Manner.

Calcium is a ubiquitous signaling molecule that plays a vital role in many physiological processes. Recent work has shown that calcium activity is especially critical in vertebrate neural development. Here, we investigated if calcium activity and neuronal phenotype are correlated only on a population level or on the level of single cells. Using Xenopus primary cell culture in which individual cells can be unambiguously identified and associated with a molecular phenotype, we correlated calcium activity with neuronal phenotype on the single-cell level. This analysis revealed that, at the neural plate stage, a high frequency of low-amplitude spiking activity correlates with an excitatory, glutamatergic phenotype, while high-amplitude spiking activity correlates with an inhibitory, GABAergic phenotype. Surprisingly, we also found that high-frequency, low-amplitude spiking activity correlates with neural progenitor cells and that differentiating cells exhibit higher spike amplitude. Additional methods of analysis suggested that differentiating marker tubb2b-expressing cells exhibit relatively persistent and predictable calcium activity compared to the irregular activity of neural progenitor cells. Our study highlights the value of using a range of thresholds for analyzing calcium activity data and underscores the importance of employing multiple methods to characterize the often irregular, complex patterns of calcium activity during early neural development.

Eeg-Based Functional Connectivity – A Possible Biomarker for Neuropathic Pain in DPN

ABSTRACTBackground: The reason some diabetic polyneuropathy (DPN) patients develop neuropathic pain while others present with painless symptoms is unknown. Altered central pain processing has been associated with neuropathic pain. Previous EEG studies found unique cortical patterns in patients with neuropathic pain however these studies were limited to univariate analysis of locally activated areas of cortex. It has been proposed that pain is encoded by complex activity and connectivity patterns which can be explored by application of Machine Learning techniques on the EEG signal. We aimed to investigate whether EEG based functional connectivity can be a potential biomarker for distinction between painful and non-painful DPN.Methods: We recorded contact-heat pain evoked potentials (CHEPs) of 120 painful DPN patients (33F, 63.2 ± 9.9 yrs) and 38 non-painful DPN patients (7F, 64.3 ± 9.5 yrs). Connectivity analysis was conducted based on a measure of synchrony (Phase Locking Value) between the recording sites. We then applied a data-driven analysis scheme to identify the most differentiating functional connections in each frequency band. These connections were validated and used to predict neuropathic pain.Results: We found overall higher cortical functional connectivity between areas of the pain matrix among painful DPN patients. In alpha and theta bands, the connectivity values were significantly different, and differentiated between the two groups with fair-to-excellent specificity (0.797) and sensitivity (0.9).Conclusion: Patients with DPN can be successfully classified into painful and non-painful based solely on EEG functional connectivity data. Increased connectivity between areas of the pain matrix in alpha and tetha band might be a biomarker for neuropathic pain.

Oscillatory brain activity during acute exercise: Tonic and transient neural response to an oddball task.

Intense physical exercise exerts measurable changes at various physiological levels that are well documented in the literature. However, despite the key role of the brain in processing inputs from internal organ systems and the external environment to coordinate and optimize behavior, little is known about brain dynamics during exercise. The present study investigates tonic and transient oscillatory brain activity in a group of participants performing an oddball task during a single bout of aerobic exercise. Twenty young males (19-32years) were recruited for two experimental sessions on separate days. EEG activity was recorded during a session of cycling at 80% (moderate-to-high intensity) of VO2max (maximum rate of oxygen consumption) while participants responded to infrequent targets (red square and big blue circle) presented among frequent nontargets (small blue circle). This was compared to a (baseline) light intensity session (30% VO2max ) to control any potential effect of dual tasking (i.e., pedaling and performing the oddball task). A cluster-based nonparametric permutations test revealed an increase in power across the entire frequency spectrum during the moderate-to-high intensity exercise compared to light intensity. Furthermore, the more salient target (red square) elicited a lower increase in (stimulus-evoked) theta power in the 80% VO2max than in the light intensity condition. Alpha and lower beta power decreased less in the standard trials (small blue circle) during the moderate-to-high exercise condition than in the light exercise condition. The present study unveils, for the first time, a complex brain activity pattern during vigorous exercise while attending to task-relevant stimuli.

Locomotor-related V3 interneurons initiate and coordinate muscles spasms after spinal cord injury.

Spinal cord injury leads to a devastating loss of motor function and yet is accompanied by a paradoxical emergence of muscle spasms, which often involve complex muscle activation patterns across multiple joints, reciprocal muscle timing, and rhythmic clonus. We investigated the hypothesis that spasms are a manifestation of partially recovered function in spinal central pattern-generating (CPG) circuits that normally coordinate complex postural and locomotor functions. We focused on the commissural propriospinal V3 neurons that coordinate interlimb movements during locomotion and examined mice with a chronic spinal transection. When the V3 neurons were optogenetically activated with a light pulse, a complex coordinated pattern of motoneuron activity was evoked with reciprocal, crossed, and intersegmental activity. In these same mice, brief sensory stimulation evoked spasms with a complex pattern of activity very similar to that evoked by light, and the timing of these spasms was readily reset by activation of V3 neurons. Given that V3 neurons receive abundant sensory input, these results suggest that sensory activation of V3 neurons is alone sufficient to generate spasms. Indeed, when we silenced V3 neurons optogenetically, sensory evoked spasms were inhibited. Also, inhibiting general CPG activity by blocking N-methyl-d-aspartate (NMDA) receptors inhibited V3 evoked activity and associated spasms, whereas NMDA application did the opposite. Furthermore, overwhelming the V3 neurons with repeated optogenetic stimulation inhibited subsequent sensory evoked spasms, both in vivo and in vitro. Taken together, these results demonstrate that spasms are generated in part by sensory activation of V3 neurons and associated CPG circuits. NEW & NOTEWORTHY We investigated whether locomotor-related excitatory interneurons (V3) play a role in coordinating muscle spasm activity after spinal cord injury (SCI). Unexpectedly, we found that these neurons not only coordinate reciprocal motor activity but are critical for initiating spasms, as well. More generally, these results suggest that V3 neurons are important in initiating and coordinating motor output after SCI and thus provide a promising target for restoring residual motor function.

Multiple Modal Features and Multiple Kernel Learning for Human Daily Activity Recognition

Introduction: Recognizing human activity in a daily environment has attracted much research in computer vision and recognition in recent years. It is a difficult and challenging topic not only inasmuch as the variations of background clutter, occlusion or intra-class variation in image sequences but also inasmuch as complex patterns of activity are created by interactions among people-people or people-objects. In addition, it also is very valuable for many practical applications, such as smart home, gaming, health care, human-computer interaction and robotics. Now, we are living in the beginning age of the industrial revolution 4.0 where intelligent systems have become the most important subject, as reflected in the research and industrial communities. There has been emerging advances in 3D cameras, such as Microsoft's Kinect and Intel's RealSense, which can capture RGB, depth and skeleton in real time. This creates a new opportunity to increase the capabilities of recognizing the human activity in the daily environment. In this research, we propose a novel approach of daily activity recognition and hypothesize that the performance of the system can be promoted by combining multimodal features.&#x0D; Methods: We extract spatial-temporal feature for the human body with representation of parts based on skeleton data from RGB-D data. Then, we combine multiple features from the two sources to yield the robust features for activity representation. Finally, we use the Multiple Kernel Learning algorithm to fuse multiple features to identify the activity label for each video. To show generalizability, the proposed framework has been tested on two challenging datasets by cross-validation scheme.&#x0D; Results: The experimental results show a good outcome on both CAD120 and MSR-Daily Activity 3D datasets with 94.16% and 95.31% in accuracy, respectively.&#x0D; Conclusion: These results prove our proposed methods are effective and feasible for activity recognition system in the daily environment.&#x0D;

Complex spatiotemporal behavior and coherent excitations in critically-coupled chains of neural circuits.

We investigate a critically-coupled chain of nonlinear oscillators, whose dynamics displays complex spatiotemporal patterns of activity, including regimes in which glider-like coherent excitations move about and interact. The units in the network are identical simple neural circuits whose dynamics is given by the Wilson-Cowan model and are arranged in space along a one-dimensional lattice with nearest neighbor interactions. The interactions follow an alternating sign rule, and hence the "synaptic matrix" embodying them is tridiagonal antisymmetric and has purely imaginary (critical) eigenvalues. The model illustrates the interplay of two properties: circuits with a complex internal dynamics, such as multiple stable periodic solutions and period doubling bifurcations, and coupling with a "critical" synaptic matrix, i.e., having purely imaginary eigenvalues. In order to identify the dynamical underpinnings of these behaviors, we explored a discrete-time coupled-map lattice inspired by our system: the dynamics of the units is dictated by a chaotic map of the interval, and the interactions are given by allowing the critical coupling to act for a finite period , thus given by a unitary matrix . It is now explicit that such critical couplings are volume-preserving in the sense of Liouville's theorem. We show that this map is also capable of producing a variety of complex spatiotemporal patterns including gliders, like our original chain of neural circuits. Our results suggest that if the units in isolation are capable of featuring multiple dynamical states, then local critical couplings lead to a wide variety of emergent spatiotemporal phenomena.

Age-related decline in leg-extensor power development in single- versus multi-joint movements.

Abstract Rapid muscle characteristics, such as the rate of power development (RPD), are shown to decline more than maximal muscle characteristics during ageing in single-joint actions. However, functional disability is mainly the result of multi-joint lower limb failure. The complex activation patterns inherent to multi-joint actions and the deteriorating effect of age on that neural drive suggest a larger effect of age on RPD multi-joint. Yet, this is the first study that compared multi- with single-joint leg extension tests in terms of RPD across the adult life span and assessed its transferability to functional performance. 96 healthy adults (♂ 49, ♀ 47, age = 20–69 years) performed dynamic single-joint knee-extension tests on a Biodex System 3 dynamometer and multi-joint leg-extension tests on a custom-made device at low, moderate and high speed. Peak power (Ppeak) was calculated as the highest value of the power-time curve and RPD as the linear slope till isokinetic speed was reached. Functional performance was tested using squat jump height. RPD showed greater age-related declines in multi-joint (−1.92%/year) versus single-joint (−1.42%/year) actions, which is in contrast with the finding of Ppeak (−0.77% vs. –1.04%/year). Squat jump height was more strongly associated with RPD multi-joint than single-joint (r = 0.77–0.82 vs. 0.44–0.61). These results show greater age-related declines of RPD multi-joint versus single-joint and demonstrate its functional relevance. We believe that this finding may be of high importance for the detection and prevention of functional disability during ageing.

Precise multimodal optical control of neural ensemble activity.

Understanding brain function requires technologies that can control the activity of large populations of neurons with high fidelity in space and time. We developed a new multiphoton holographic approach to activate or suppress the activity of ensembles of cortical neurons with cellular resolution and sub-millisecond precision. Since existing opsins were inadequate, we engineered new soma-targeted (ST) optogenetic tools, ST-ChroME and IRES-ST-eGtACR1, optimized for multiphoton activation and suppression. Employing a three-dimensional all-optical read/write interface, we demonstrate the ability to photo-stimulate up to 50 neurons simultaneously distributed in three dimensions in a 550 × 550 × 100 μm volume of brain tissue. This new approach allows the synthesis and editing of complex neural activity patterns needed to gain insight into the principles of neural codes.

Bronchoalveolar lavage (BAL) cells in idiopathic pulmonary fibrosis express a complex pro-inflammatory, pro-repair, angiogenic activation pattern, likely associated with macrophage iron accumulation.

Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease of unknown cause characterized by alveolar epithelial damage, patchy interstitial fibrosis and diffuse microvascular abnormalities. In IPF, alveolar clustering of iron-laden alveolar macrophages—a common sign of microhemorrhage, has been associated with vascular abnormalities and worsening of pulmonary hypertension. As iron-dependent ROS generation has been shown to induce unrestrained macrophage activation in disease models of vascular damage, we explored alveolar macrophage activation phenotype in IPF patients (n = 16) and healthy controls (CTR, n = 7) by RNA sequencing of bronchoalveolar lavage (BAL) cells. The frequencies of macrophages in BAL cells were 86+4% and 83.4+8% in IPF and CTR groups, respectively (p-value = 0.41). In IPF patients, BAL cells showed increased iron-dependent ROS generation (p-value<0.05 vs CTR). Gene expression analysis showed overrepresentation of Gene Ontology processes/functions and KEGG pathways enriched in upregulated M1-type inflammatory (p-value<0.01), M2-type anti-inflammatory/tissue remodeling (p-value<0.0001), and MTPP-type chronic inflammatory/angiogenic (p-value<0.0001) chemokine and cytokine genes. The ex vivo finding was confirmed by the induction of iron-dependent ROS generation and chemokine/cytokine overexpression of Ccl4, Cxcl10 (M1), Il1rn (M2), Cxcl2, and Cxcl7 (MTPP) in MH-S murine immortalized alveolar macrophages exposed to ferric ammonium citrate in culture (p-value<0.05 vs CTR). The data show alveolar macrophage expression of a pro-inflammatory, tissue remodeling and angiogenic complex activation pattern, suggesting that iron accumulation may play a role in macrophage activation.

A continuous time-resolved measure decoded from EEG oscillatory activity predicts working memory task performance

Objective. Working memory (WM), crucial for successful behavioral performance in most of our everyday activities, holds a central role in goal-directed behavior. As task demands increase, inducing higher WM load, maintaining successful behavioral performance requires the brain to work at the higher end of its capacity. Because it is depending on both external and internal factors, individual WM load likely varies in a continuous fashion. The feasibility to extract such a continuous measure in time that correlates to behavioral performance during a working memory task remains unsolved. Approach. Multivariate pattern decoding was used to test whether a decoder constructed from two discrete levels of WM load can generalize to produce a continuous measure that predicts task performance. Specifically, a linear regression with L2-regularization was chosen with input features from EEG oscillatory activity recorded from healthy participants while performing the n-back task, . Main results. The feasibility to extract a continuous time-resolved measure that correlates positively to trial-by-trial working memory task performance is demonstrated (r = 0.47, p < 0.05). It is furthermore shown that this measure allows to predict task performance before action (r = 0.49, p < 0.05). We show that the extracted continuous measure enables to study the temporal dynamics of the complex activation pattern of WM encoding during the n-back task. Specifically, temporally precise contributions of different spectral features are observed which extends previous findings of traditional univariate approaches. Significance. These results constitute an important contribution towards a wide range of applications in the field of cognitive brain–machine interfaces. Monitoring mental processes related to attention and WM load to reduce the risk of committing errors in high-risk environments could potentially prevent many devastating consequences or using the continuous measure as neurofeedback opens up new possibilities to develop novel rehabilitation techniques for individuals with degraded WM capacity.

The RoPES project with HARPS and HARPS-N

We report the discovery of a system of two super-Earths orbiting the moderately active K-dwarf HD 176986. This work is part of the RoPES RV program of G- and K-type stars, which combines radial velocities (RVs) from the HARPS and HARPS-N spectrographs to search for short-period terrestrial planets. HD 176986 b and c are super-Earth planets with masses of 5.74 and 9.18 M⊕, orbital periods of 6.49 and 16.82 days, and distances of 0.063 and 0.119 AU in orbits that are consistent with circular. The host star is a K2.5 dwarf, and despite its modest level of chromospheric activity (log10 (RHK' = –4.90 ± 0.04), it shows a complex activity pattern. Along with the discovery of the planets, we study the magnetic cycle and rotation of the star. HD 176986 proves to be suitable for testing the available RV analysis technique and further our understanding of stellar activity.