Spatial Activity Patterns Research Articles

Preliminary Insights on the Spatial Ecology, Population Demography, and Sexual Dimorphism of the Critically Endangered Sulawesi Forest Turtle (Leucocephalon yuwonoi)

Sulawesi Forest Turtles (Leucocephalon yuwonoi) are critically endangered and endemic to the island of Sulawesi. We conducted radiotelemetry and capture–mark–recapture to study their spatial ecology, habitat selection, activity patterns, and demography in February–April and June–July, 2019. The average area occupied by 14 turtles using the minimum convex polygon (MCP) method was 0.49 ± 0.42 ha (standard deviation [SD]), whereas using the fixed 50% kernel density method averaged 0.076 ± 0.061 ha (SD). Males (mean = 0.53 ± 0.40 ha, N = 7) occupied a larger area (MCP method) than females (mean = 0.44 ± 0.46 ha, N = 7) but the difference was not statistically significant. Turtles took refuge in habitat containing significantly more canopy cover, broadleaf plant cover, and more and deeper ground cover than that in random plots. Males were predominantly active at night and females were exclusively so. We caught 25 adult females, 16 adult males, and 38 unsexed juveniles in two stream sections. Estimated population size (95% confidence intervals) in the lower stream was 17 turtles (15–28) in the wet season and 10 turtles (8–21) in the dry season. The population estimate in the upper stream was 25 (24–33) and 13 (9–28) turtles in the wet and dry seasons, respectively. Our study provides important baseline ecological data that can be used to inform future conservation and recovery programs for the species.

Reconstructing attended and unattended colors from human scalp electroencephalography

Human electroencephalography (EEG) studies have shown that attention enhances relevant over irrelevant visual features (e.g., Andersen et al., 2013). Such amplitude modulations have been a major focus of feature-based attention research, alongside recent studies showing that the spatial pattern of scalp-recorded EEG activity conveys critical information about different stimuli. To date, the majority of these studies have focused on decoding visual-spatial features (location, orientation). Here, we test whether color—a non-spatial feature—can be reconstructed from the scalp activity pattern using steady-state visual evoked potentials (SSVEPs) and inverted encoding modeling (IEM). Furthermore, we test how these color reconstructions vary across different types of feature-based attention tasks. Participants (N=17) performed two color-based attention tasks: 1) a non-competitive task, in which they monitored a single array of colored dots to detect a brief interval of coherent motion; 2) a selective attention task in which participants focused on target-colored dots among distractors to detect a brief luminance decrease. In the selective task, the target and distractor colors were either distinct (180° away on a CIELab colorwheel) or similar (63° away). Stimuli flickered at different frequencies to elicit separable SSVEPs that we used to generate color-selective response profiles based on their spatially distributed patterns. Across both tasks, we found that information about the target and distractor colors was reliably recovered from single trials with IEMs, confirming the usefulness of this technique for investigating feature representations. Model-based reconstructions in the selective attention task were stronger for more distinct colors, suggesting that perceptually similar features might elicit less informative SSVEP signals due to greater overlap in the neural populations that encode them. Broadly, our results demonstrate that SSVEPs along with IEMs can be used to investigate non-spatial visual features that may not be as well represented as spatial information in event-related potentials or alpha-band signals.

The Cup Whisperer

The Cup Whisperer

Cortex-wide spontaneous activity non-linearly steers propagating sensory-evoked activity in awake mice.

The brain responds highly variably to identical sensory inputs, but there is no consensus on the nature of this variability. We explore this question using cortex-wide optical voltage imaging and whisker stimulation in awake mice. Clustering analysis reveals that the sensory-evoked activity propagates over the cortex via distinct pathways associated with distinct behavioral states. The pathway taken by each trial is independent of the level of primary sensory-evoked activation but is partially predictable by the spatiotemporal features of the preceding cortical spontaneous activity patterns. The sensory inputs reduce trial-to-trial variability in brain activity and alter temporal autocorrelation in spatial activity pattern evolutions, suggesting non-linear interactions between evoked activities and spontaneous activities. Further, evoked activities and spontaneous activities occupy different positions in the state space, suggesting that sensory inputs can intricately interact with the internal state to generate large-scale evoked activity patterns not frequented by spontaneous brain states.

Recording from Fly Olfactory Sensilla.

Olfactory systems detect and discriminate an enormous diversity of volatile environmental stimuli and provide important paradigms to investigate how sensory cues are represented in the brain. Key stimulus-coding events occur in peripheral olfactory sensory neurons, which typically express a single olfactory receptor-from a large repertoire encoded in the genome-with a defined ligand-response profile. These receptors convert odor ligand recognition into spatial and temporal patterns of neural activity that are transmitted to, and interpreted in, central brain regions. Drosophila provides an attractive model to study olfactory coding because it possesses a relatively simple peripheral olfactory system that displays many organizational parallels to those of vertebrates. Moreover, nearly all olfactory sensory neurons have been molecularly characterized and are accessible for physiological analysis, as they are exposed on the surface of sensory organs (antennae and maxillary palps) housed in specialized hairs called sensilla. This protocol describes how to perform recordings of odor-evoked activity from Drosophila olfactory sensilla, covering the basics of sample preparation, setting up the electrophysiology rig, assembling an odor stimulus-delivery device, and data analysis. The methodology can be used to characterize the ligand-recognition properties of most olfactory sensory neurons and the role of olfactory receptors (and other molecular components) in signal transduction.

Chemosensory Coding in Drosophila Single Sensilla.

The chemical senses-smell and taste-detect and discriminate an enormous diversity of environmental stimuli and provide fascinating but challenging models to investigate how sensory cues are represented in the brain. Important stimulus-coding events occur in peripheral sensory neurons, which express specific combinations of chemosensory receptors with defined ligand-response profiles. These receptors convert ligand recognition into spatial and temporal patterns of neural activity that are transmitted to, and interpreted in, central brain regions. Drosophila melanogaster provides an attractive model to study chemosensory coding because it possesses relatively simple peripheral olfactory and gustatory systems that display many organizational parallels to those of vertebrates. Moreover, nearly all peripheral chemosensory neurons have been molecularly characterized and are accessible for physiological analysis, as they are exposed on the surface of sensory organs housed in specialized hairs called sensilla. Here, we briefly review anatomical, molecular, and physiological properties of adult Drosophila olfactory and gustatory systems and provide background to methods for electrophysiological recordings of ligand-evoked activity from different types of chemosensory sensilla.

Influence of forward head posture on muscle activation pattern of the trapezius pars descendens muscle in young adults

Forward head posture (FHP) is a serious problem causing head and neck disability, but the characteristics of muscle activity during long-term postural maintenance are unclear. This study aimed to investigate a comparison of electromyography (EMG) activation properties and subjective fatigue between young adults with and without habitual FHP. In this study, we examined the changes in the spatial and temporal distribution patterns of muscle activity using high-density surface EMG (HD-SEMG) in addition to mean frequency, a conventional measure of muscle fatigue. Nineteen male participants were included in the study (FHP group (n = 9; age = 22.3 ± 1.5 years) and normal group (n = 10; age = 22.5 ± 1.4 years)). Participants held three head positions (e.g., forward, backward, and neutral positions) for a total of 30 min each, and the EMG activity of the trapezius pars descendens muscle during posture maintenance was measured by HD-SEMG. The root mean square (RMS), the modified entropy, and the correlation coefficient were calculated. Additionally, the visual analogue scale (VAS) was evaluated to assess subjective fatigue. The RMS, VAS, modified entropy, and correlation coefficients were significantly higher in the FHP group than in the normal group (p < 0.001). With increasing postural maintenance time, the modified entropy and correlation coefficient values significantly decreased, and the mean frequency and VAS values significantly increased (p < 0.001). Furthermore, the forward position had significantly higher RMS, correlation coefficient, modified entropy, and VAS values than in the neutral position (p < 0.001). The HD-SEMG potential distribution patterns in the FHP group showed less heterogeneity and greater muscle activity in the entire muscle and subjective fatigue than those in the normal group. Excess muscle activity even in the neutral/comfortable position in the FHP group could potentially be a mechanism of neuromuscular conditions in this population.

Spatial patterns of fishing activity inside the Gorringe bank MPA based on VMS, AIS and e-logbooks data

In this study, the fishing distribution and intensity over the Gorringe bank, a large oceanic underwater mountain located in the NE Atlantic, Natura 2000 Network MPA inside Portugal‘s EEZ is evaluated in the period from 2012 to 2016. Based on the combined analysis of fisheries-dependent data comprising Vessel Monitoring System (VMS), Automatic Identification System (AIS) and electronic logbooks available for the Portuguese fleet operating within the area, it was possible to identify fishing trips and fishing events and define fishing trajectories, allowing the estimation of the fishing effort and the CPUE at a high spatial resolution. A total of 32 Portuguese vessels were identified to have undertaken fishing activity within the limits of this MPA, mainly longliners based on mainland ports, operating drifting and bottom longlines on a seasonal basis. Fleet-related differences in the distribution and intensity of fishing effort and CPUE for the main target species were addressed. The bottom longline activity concentrated, as expected, around the two seamount summits, where the presence of VMEs and sensitive habitat-forming species have been registered. The analysis of AIS data, the only dataset made available in this study for non-Portuguese vessels, strongly suggests that the foreign fleet operating within the Gorringe MPA represents a significant fraction of the fishing activity in the area. If assessing the fishing impacts is deemed as an important objective in the management of this MPA, then the results obtained in this study can be a valuable contribution towards the environmental protection of this oceanic seamount.

The temporal dynamics of Large-Scale brain network changes in disorders of consciousness: A Microstate-Based study.

The resting-state brain is composed of several discrete networks, which remain stable for 10-100 ms. These functional microstates are considered the building blocks of spontaneous consciousness. Electroencephalography (EEG) microstate analysis may provide insight into the altered brain dynamics underlying consciousness recovery in patients with disorders of consciousness (DOC). We aimed to analyze microstates in the resting-state EEG source space in patients with DOC, the relationship between state-specific features and consciousness levels, and the corresponding patterns of microstates and functional networks. We obtained resting-state EEG data from 84 patients with DOC (27 in a minimally conscious state [MCS] and 57 in a vegetative state [VS] or with unresponsive wakefulness syndrome). We conducted a microstate analysis of the resting-state (EEG) source space and developed a state-transition analysis protocol for patients with DOC. We identified seven microstates with distinct spatial distributions of cortical activation. Multivariate pattern analyses revealed that different functional connectivity patterns were associated with source-level microstates. There were significant differences in the microstate properties, including spatial activation patterns, temporal dynamics, state shifts, and connectivity construction, between the MCS and VS groups. Our findings suggest that consciousness depends on complex dynamics within the brain and may originate from the anterior cortex.

Evidence for independent representational contents in inhibitory control subprocesses associated with frontoparietal cortices.

Inhibitory control processes have intensively been studied in cognitive science for the past decades. Even though the neural dynamics underlying these processes are increasingly better understood, a critical open question is how the representational dynamics of the inhibitory control processes are modulated when engaging in response inhibition in a relatively automatic or a controlled mode. Against the background of an overarching theory of perception-action integration, we combine temporal and spatial EEG signal decomposition methods with multivariate pattern analysis and source localization to obtain fine-grained insights into the neural dynamics of the representational content of response inhibition. For this purpose, we used a sample of N= 40 healthy adult participants. The behavioural data suggest that response inhibition was better in a more controlled than a more automated response execution mode. Regarding neural dynamics, effects of response inhibition modes relied on a concomitant coding of stimulus-related information and rules of how stimulus information is related to the appropriate motor programme. Crucially, these fractions of information, which are encoded at the same time in the neurophysiological signal, are based on two independent spatial neurophysiological activity patterns, also showing differences in the temporal stability of the representational content. Source localizations revealed that the precuneus and inferior parietal cortex regions are more relevant than prefrontal areas for the representation of stimulus-response selection codes. We provide a blueprint how a concatenation of EEG signal analysis methods, capturing distinct aspects of neural dynamics, can be connected to cognitive science theory on the importance of representations in action control.

An analysis of Amud Anan - A unique volunteered geographic information platform for nature-based recreation in Israel

Nature-based recreation activities are popular worldwide, yet traditional monitoring methods and their derived databases often limit spatial studies of these activities, partially due to the large area and remoteness of many open spaces. Volunteered Geographic Information (VGI) from crowdsourced data could be used to study nature-based recreational activities. Most studies using VGI for recreation analysis are based on international English-speaking apps, and thus may miss information which is only available in local languages. Here we used a database from the Israeli unique VGI app and Hebrew website ‘Amud Anan’ (https://amudanan.co.il/) to study spatial patterns of recreation activity, using the points of interest's (POIs) density as the response variable, and several explanatory variables such as accessibility and attraction of the POIs. We also conducted in-depth interviews to study the motivations of users to share POIs. We found strong correlations between POIs' density and accessibility variables such as distance from roads and hiking trails, as well as with attraction variables such as distance from camping and picnic sites. We conclude that the Amud Anan database and VGIs in general can serve as effective tools for mapping nature-based recreational activity Israel and world-wide.

Seasonal variation modifies the spatial patterns of soil microbial community structure and enzyme activity in a meadow steppe

Understanding how spatial heterogeneity in soil microbial community structure and enzyme activities vary seasonally, and identifying the underlying mechanisms, are crucial for predicting how soil organic matter dynamics and nutrient cycling may respond to environmental change. We examined spatial variability in microbial community structure and enzyme activity and measured associated changes in plant biomass, soil microclimate, soil nutrients and other soil characteristics for 75 sampling points across a 15 × 15 m area of a Leymus chinensis meadow steppe in northeastern China over three seasons (summer, fall and spring). We observed seasonal variation in the spatial patterns of soil microbial community structure and enzyme activity, and the plant and soil variables associated with this variation also changed over time. Specifically, the microbial community structure exhibited strong spatial dependence, with high a relative spatial variance [C / (C + C0) ≥ 0.9] in summer, whereas it was more homogeneous in spring and fall. In summer, the spatial patterns of plants, soil dissolved inorganic N and dissolved organic C were the most significant predictors of spatial variation in microbial community structure. In fall, a mix of plant and soil physical explanatory variables explained 22–46 % of the spatial variation in microbial community structure. Meanwhile, only soil physical variables (soil bulk density and electrical conductivity) were strongly associated with spatial variation in microbial community structure in spring. The spatial links between microbial community structure and enzyme activity were reshaped throughout the growing season. We observed strong spatial links between soil microbial community structure and enzyme activity in spring and fall. In summer, the association between soil enzyme activities and microbial biomass was decoupled, indicating that microbial biomass showed higher turnover than soil enzymes at this time. Overall, our results reveal the drivers of spatial variation in soil microbial community structure and enzyme activity shift seasonally, highlighting the value of seasonal sampling to accurately estimate the heterogeneity and complexity of ecosystem-level processes in grasslands.

Spontaneous activity patterns in human motor cortex replay evoked activity patterns for hand movements

Spontaneous brain activity, measured with resting state fMRI (R-fMRI), is correlated among regions that are co-activated by behavioral tasks. It is unclear, however, whether spatial patterns of spontaneous activity within a cortical region correspond to spatial patterns of activity evoked by specific stimuli, actions, or mental states. The current study investigated the hypothesis that spontaneous activity in motor cortex represents motor patterns commonly occurring in daily life. To test this hypothesis 15 healthy participants were scanned while performing four different hand movements. Three movements (Grip, Extend, Pinch) were ecological involving grip and grasp hand movements; one control movement involving the rotation of the wrist was not ecological and infrequent (Shake). They were also scanned at rest before and after the execution of the motor tasks (resting-state scans). Using the task data, we identified movement-specific patterns in the primary motor cortex. These task-defined patterns were compared to resting-state patterns in the same motor region. We also performed a control analysis within the primary visual cortex. We found that spontaneous activity patterns in the primary motor cortex were more like task patterns for ecological than control movements. In contrast, there was no difference between ecological and control hand movements in the primary visual area. These findings provide evidence that spontaneous activity in human motor cortex forms fine-scale, patterned representations associated with behaviors that frequently occur in daily life.

Closing the mechanistic gap: the value of microarchitecture in understanding cognitive networks.

Cognitive neuroscience aims to provide biologically relevant accounts of cognition. Contemporary research linking spatial patterns of neural activity to psychological constructs describes 'where' hypothesised functions occur, but not 'how' these regions contribute to cognition. Technological, empirical, and conceptual advances allow this mechanistic gap to be closed by embedding patterns of functional activity in macro- and microscale descriptions of brain organisation. Recent work on the default mode network (DMN) and the multiple demand network (MDN), for example, highlights a microarchitectural landscape that may explain how activity in these networks integrates varied information, thus providing an anatomical foundation that will help to explain how these networks contribute to many different cognitive states. This perspective highlights emerging insights into how microarchitecture can constrain network accounts of human cognition.

Computational Study of Mechanochemical Activation in Nanostructured Triblock Copolymers.

Force-driven chemical reactions have emerged as an attractive platform for diverse applications in polymeric materials. However, the microscopic chain conformations and topologies necessary for efficiently transducing macroscopic forces to the molecular scale are not well-understood. In this work, we use a coarse-grained model to investigate the impact of network-like topologies on mechanochemical activation in self-assembled triblock copolymers. We find that mechanochemical activation during tensile deformation depends strongly on both the polymer composition and chain conformation in these materials. Activation primarily occurs in the tie chains connecting different glassy domains and in loop chains that are hooked onto each other by physical entanglements. Activation also requires a higher stress in materials having a higher glassy block content. Overall, the lamellar samples show the highest percent activation at high stress. In contrast, at low stress, the spherical morphology, which has the lowest glassy fraction, shows the highest activation. Additionally, we observe a spatial pattern of activation, which appears to be tied to distortion of the self-assembled morphology. Higher activation is observed in the tips of the chevrons formed during deformation of lamellar samples as well as in the centers between the cylinders in the cylindrical morphology. Our work shows that changes in the network-like topology in different morphologies significantly impact mechanochemical activation efficiencies in these materials, suggesting that this area will be a fruitful avenue for further experimental research.

Spatial uniformity of action potentials indicates base-to-apex depolarization and repolarization of rainbow trout (Oncorhynchus mykiss) ventricle.

The spatial pattern of electrical activation is crucial for a full understanding of fish heart function. However, it remains unclear whether there is regional variation in action potential (AP) morphologies and underlying ion currents. Because the direction of depolarization and spatial differences in the durations of ventricular APs set limits to potential patterns of ventricular repolarization, we determined AP morphologies, underlying ion currents and ion channel expression in four different ventricular regions (spongy myocardium; and apex, base and middle of the compact myocardium), and correlated them with in vivo electrocardiograms (ECGs) in rainbow trout (Oncorhynchus mykiss). ECGs recorded from three leads indicated that the depolarization and repolarization of APs propagate from base to apex, and the main depolarization axis of the ventricle is between +90 and +120deg. AP shape was uniform across the whole ventricle, and little regional differences were found in the density of repolarizing K+ currents or depolarizing Ca2+ and Na+ currents and the underlying transcripts of ion channels, providing compelling evidence for the suggested excitation pattern. The spatial uniformity of AP durations and base-to-apex propagation of activation with a relatively slow velocity of propagation indicates no special ventricular conduction pathway in the trout ventricle such as the His-Purkinje system of mammalian hearts. The sequence of repolarization is solely determined by activation time without being affected by regional differences in AP duration.

Sex is predicted by spatial memory multivariate activation patterns.

Whether sex differences exist in the brain at the macroscopic level, as measured with magnetic resonance imaging (MRI), is a topic of debate. The present spatial long-term memory functional MRI (fMRI) study predicted sex based on event-related patterns of brain activity. Within spatial memory regions of interest, patterns of activity associated with females and males were used to predict the sex of each member of left-out female-male pairs at above-chance accuracy. The current results provide evidence for sex differences in the brain processes underlying spatial long-term memory. This is the first time that sex has been predicted using event-related fMRI activation patterns. The present findings contribute to a growing body of evidence that there are functional and anatomic sex differences in the brain and, more broadly, question the widespread practice of collapsing across sex in the field of cognitive neuroscience.

Fire Dynamics of the Bolivian Amazon

This study identifies the spatial and temporal trends, as well as the drivers, of fire dynamics in the Bolivian Amazon basin. Bolivia ranks in the top ten countries in terms of total annual burnt, with fires affecting an estimated 2.3 million hectares of forest in 2020. However, in comparison to the Brazilian Amazon, there has been little research into the fire regime in Bolivia. The sparse research and the limited literature on the subject indicate that fire activity is higher in the Bolivian Amazon basin’s dry forests and flooded savanna zones, and that agriculture and drought are the primary causes of fire activity. In this study, trend analysis and emerging hotspot analysis are deployed to identify the spatial and temporal patterns of fire activity and boosted regression tree models to identify the drivers of forest fire within each ecoregion of the Bolivian Amazon basin. Comparable to most of the Brazilian literature, this study finds that fire activity and fire season length is higher in the flooded Beni Savanna, and Chiquitano seasonally dry tropical forests than in the Bolivian Amazon ecoregion. This study also identifies moisture stress and human activity as the main drivers of fire dynamics within the region. It is intended that this research will offer a foundation for future research and conservation activities aimed at better understanding the fire regime of the Bolivian Amazon basin.

Decoding of Motor Imagery Involving Whole-body Coordination

The present study investigated whether different types of motor imageries can be classified based on the location of the activation peaks or the multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) and compared the difference between visual motor imagery (VI) and kinesthetic motor imagery (KI). During fMRI scanning sessions, 25 participants imagined four movements included in the Motor Imagery Questionnaire-Revised (MIQ-R): knee lift, jump, arm movement, and waist bend. These four imagined movements were then classified based on the peak location or the patterns of fMRI signal values. We divided the participants into two groups based on whether they found it easier to generate VI (VI group, n = 10) or KI (KI group, n = 15). Our results show that the imagined movements can be classified using both the location of the activation peak and the spatial activation patterns within the sensorimotor cortex, and MVPA performs better than the activation peak classification. Furthermore, our result reveals that the KI group achieved a higher MVPA decoding accuracy within the left primary somatosensory cortex than the VI group, suggesting that the modality of motor imagery differently affects the classification performance in distinct brain regions.

Multimodal explainable AI predicts upcoming speech behavior in adults who stutter.

A key goal of cognitive neuroscience is to better understand how dynamic brain activity relates to behavior. Such dynamics, in terms of spatial and temporal patterns of brain activity, are directly measured with neurophysiological methods such as EEG, but can also be indirectly expressed by the body. Autonomic nervous system activity is the best-known example, but, muscles in the eyes and face can also index brain activity. Mostly parallel lines of artificial intelligence research show that EEG and facial muscles both encode information about emotion, pain, attention, and social interactions, among other topics. In this study, we examined adults who stutter (AWS) to understand the relations between dynamic brain and facial muscle activity and predictions about future behavior (fluent or stuttered speech). AWS can provide insight into brain-behavior dynamics because they naturally fluctuate between episodes of fluent and stuttered speech behavior. We focused on the period when speech preparation occurs, and used EEG and facial muscle activity measured from video to predict whether the upcoming speech would be fluent or stuttered. An explainable self-supervised multimodal architecture learned the temporal dynamics of both EEG and facial muscle movements during speech preparation in AWS, and predicted fluent or stuttered speech at 80.8% accuracy (chance=50%). Specific EEG and facial muscle signals distinguished fluent and stuttered trials, and systematically varied from early to late speech preparation time periods. The self-supervised architecture successfully identified multimodal activity that predicted upcoming behavior on a trial-by-trial basis. This approach could be applied to understanding the neural mechanisms driving variable behavior and symptoms in a wide range of neurological and psychiatric disorders. The combination of direct measures of neural activity and simple video data may be applied to developing technologies that estimate brain state from subtle bodily signals.