Functional dissection of neural connectivity in generalized anxiety disorder

Differences in the correlated activity of networked brain regions have been reported in individuals with generalized anxiety disorder (GAD) but an overreliance on the null-hypothesis significance testing (NHST) framework limits the identification and characterization of disorder-relevant relationships. In this preregistered study, we applied a Bayesian statistical framework as well as NHST to the analysis of resting-state fMRI scans from females with GAD and demographically matched healthy comparison females. Eleven a-priori hypotheses about functional correlativity (FC) were evaluated using Bayesian (multilevel model) and frequentist (t-test) inference. Reduced FC between the ventromedial prefrontal cortex (vmPFC) and the posterior-mid insula (PMI) was confirmed by both statistical approaches. FC between the vmPFC-anterior insula, the amygdala-PMI, and the amygdala-dorsolateral prefrontal cortex (dlPFC) region pairs did not survive multiple comparison correction using the frequentist approach. However, the Bayesian model provided evidence for these region pairs having decreased FC in the GAD group. Leveraging Bayesian modeling, we demonstrate decreased FC of the vmPFC, insula, amygdala, and dlPFC in females with GAD. Exploiting the Bayesian framework revealed FC abnormalities between region pairs excluded by the frequentist analysis, as well as other previously undescribed regions, demonstrating the benefits of applying this statistical approach to resting state FC data.

Accuracy of mutational signature software on correlated signatures

Mutational signatures are characteristic patterns of mutations generated by exogenous mutagens or by endogenous mutational processes. Mutational signatures are important for research into DNA damage and repair, aging, cancer biology, genetic toxicology, and epidemiology. Unsupervised learning can infer mutational signatures from the somatic mutations in large numbers of tumors, and separating correlated signatures is a notable challenge for this task. To investigate which methods can best meet this challenge, we assessed 18 computational methods for inferring mutational signatures on 20 synthetic data sets that incorporated varying degrees of correlated activity of two common mutational signatures. Performance varied widely, and four methods noticeably outperformed the others: hdp (based on hierarchical Dirichlet processes), SigProExtractor (based on multiple non-negative matrix factorizations over resampled data), TCSM (based on an approach used in document topic analysis), and mutSpec.NMF (also based on non-negative matrix factorization). The results underscored the complexities of mutational signature extraction, including the importance and difficulty of determining the correct number of signatures and the importance of hyperparameters. Our findings indicate directions for improvement of the software and show a need for care when interpreting results from any of these methods, including the need for assessing sensitivity of the results to input parameters.

Cortical state dynamics and selective attention define the spatial pattern of correlated variability in neocortex

Correlated activity fluctuations in the neocortex influence sensory responses and behavior. Neural correlations reflect anatomical connectivity but also change dynamically with cognitive states such as attention. Yet, the network mechanisms defining the population structure of correlations remain unknown. We measured correlations within columns in the visual cortex. We show that the magnitude of correlations, their attentional modulation, and dependence on lateral distance are explained by columnar On-Off dynamics, which are synchronous activity fluctuations reflecting cortical state. We developed a network model in which the On-Off dynamics propagate across nearby columns generating spatial correlations with the extent controlled by attentional inputs. This mechanism, unlike previous proposals, predicts spatially non-uniform changes in correlations during attention. We confirm this prediction in our columnar recordings by showing that in superficial layers the largest changes in correlations occur at intermediate lateral distances. Our results reveal how spatially structured patterns of correlated variability emerge through interactions of cortical state dynamics, anatomical connectivity, and attention.

Effective elasticity and persistence of strain in active filament-motor assemblies

Cross-linked, elastic, filamentous networks that are deformed by active molecular motors feature in several natural and synthetic settings. The effective active elasticity of these composite systems determines the length scale over which active deformations persist in fluctuating environments. This fundamental quantity has been studied in passive systems; however mechanisms determining and modulating this length-scale in active systems has not been clarified. Here, focusing on active arrayed filament-motor assemblies, we propose and analyze a minimal model in order to estimate the length scale over which imposed or emergent elastic deformations or stresses persist. We combine a mean-field continuum theory valid for weakly elastic assemblies with high dimensional Multi-Particle Collision (MPC) based Brownian simulations valid for moderate to strongly elastic and noisy systems. Integrating analytical and numerical results, we show that localized strains - steady or oscillatory - persist over well-defined length scales that dependent on motor activity, effective shear elasticity and filament extensibility. Extensibility is key even in very stiff filaments, and cannot be ignored when global deformations are considered. We clarify mechanisms by which motor derived active elasticity and passive shear elasticity of the filamentous backbone combine to effectively soften filaments. Surprisingly, the predictions of the mean-field theory agree qualitatively with results from stochastic discrete filament-motor model, even for moderately strong noise. We also find that athermal motor noise impacts the overall duty ratio of the motors and thereby the persistence length in these driven assemblies. Our study demonstrates how correlated activity in natural ordered active matter possesses a finite range of influence with clear testable experimental implications.

Blind cavefish retain functional connectivity in the tectum despite loss of retinal input

Sensory systems display remarkable plasticity and are under strong evolutionary selection. The Mexican cavefish, Astyanax mexicanus, consists of eyed river-dwelling surface populations, and multiple independent cave populations which have converged on eye loss, providing the opportunity to examine the evolution of sensory circuits in response to environmental perturbation. Functional analysis across multiple transgenic populations expressing GCaMP6s showed that functional connectivity of the optic tectum largely did not differ between populations, except for the selective loss of negatively correlated activity within the cavefish tectum, suggesting positively correlated neural activity is resistant to an evolved loss of input from the retina. Further, analysis of surface-cave hybrid fish reveals that changes in the tectum are genetically distinct from those encoding eye-loss. Together, these findings uncover the independent evolution of multiple components of the visual system and establish the use of functional imaging in A. mexicanus to study neural circuit evolution.

Movement-Dependent Electrical Stimulation for Volitional Strengthening of Cortical Connections in Behaving Monkeys

Correlated activity of neurons can lead to long-term strengthening or weakening of the connections between them. In addition, the behavioral context, imparted by execution of physical movements or the presence of a reward, can modulate the plasticity induced by Hebbian mechanisms. In the present study, we have combined behavior and induced neuronal correlations to strengthen connections in the motor cortex of adult behaving monkeys. Correlated activity was induced using an electrical-conditioning protocol in which stimuli gated by voluntary movements were used to produce co-activation of neurons at motor-cortical sites involved in those movements. Delivery of movement-dependent stimulation resulted in small increases in the strength of associated cortical connections immediately after conditioning. Remarkably, when paired with further repetition of the movements that gated the conditioning stimuli, there were substantially larger gains in the strength of cortical connections, that occurred in a use-dependent manner, without delivery of additional conditioning stimulation. In the absence of such movements, little change was observed in the strength of motor-cortical connections. Performance of the motor behavior in the absence of conditioning also did not produce any changes in connectivity. Our results show that combining movement-gated stimulation with further natural use of the “conditioned” pathways after stimulation ends can produce use-dependent strengthening of connections in adult primates, highlighting an important role for behavior in cortical plasticity. Our data also provide strong support for combining movement-gated stimulation with use-dependent physical rehabilitation for strengthening connections weakened by a stroke or spinal-cord injury.Significance StatementWe describe an electrical-conditioning protocol in adult behaving monkeys in which stimuli gated by voluntary movements were used to strengthen connections between motor-cortical neurons involved in those movements. Movement-gated stimulation created a plastic landscape in which repetition of the movements that gated conditioning stimuli produced strengthening of cortical connections, in a use-dependent manner, long after stimulation had ended, a finding that is both novel and unique. In the absence of such behavior, little change was observed in the strength of connections. Similarly, movements alone did not produce any changes in connectivity. Our data highlight a critical role for behavior in plasticity and provide strong support for combining movement-gated stimulation with use-dependent rehabilitation for strengthening connections weakened by injury or disease.

Thermal remote sensing reveals communication between volcanoes of the Klyuchevskoy Volcanic Group

Volcanoes are traditionally considered isolated with an activity that is mostly independent of the surrounding, with few eruptions only (< 2%) associated with a tectonic earthquake trigger. Evidence is now increasing that volcanoes forming clusters of eruptive centers may simultaneously erupt, show unrest, or even shut-down activity. Using infrared satellite data, we detail 20 years of eruptive activity (2000–2020) at Klyuchevskoy, Bezymianny, and Tolbachik, the three active volcanoes of the Klyuchevskoy Volcanic Group (KVG), Kamchatka. We show that the neighboring volcanoes exhibit multiple and reciprocal interactions on different timescales that unravel the magmatic system’s complexity below the KVG. Klyuchevskoy and Bezymianny volcanoes show correlated activity with time-predictable and quasiperiodic behaviors, respectively. This is consistent with magma accumulation and discharge dynamics at both volcanoes, typical of steady-state volcanism. However, Tolbachik volcano can interrupt this steady-state regime and modify the magma output rate of its neighbors for several years. We suggest that below the KVG the transfer of magma at crustal level is modulated by the presence of three distinct but hydraulically connected plumbing systems. Similar complex interactions may occur at other volcanic groups and must be considered to evaluate the hazard of grouped volcanoes.

Dissociable Contribution of Extrastriate Responses to Representational Enhancement of Gaze Targets

Before saccadic eye movements, our perception of the saccade targets is enhanced. Changes in the visual representation of saccade targets, which presumably underlie this perceptual benefit, emerge even before the eye begins to move. This perisaccadic enhancement has been shown to involve changes in the response magnitude, selectivity, and reliability of visual neurons. In this study, we quantified multiple aspects of perisaccadic changes in the neural response, including gain, feature tuning, contrast response function, reliability, and correlated activity between neurons. We then assessed the contributions of these various perisaccadic modulations to the population's enhanced perisaccadic representation of saccade targets. We found a partial dissociation between the motor information, carried entirely by gain changes, and visual information, which depended on all three types of modulation. These findings expand our understanding of the perisaccadic enhancement of visual representations and further support the existence of multiple sources of motor modulation and visual enhancement within extrastriate visual cortex.