Correlates Of Neural Activity Research Articles

Differences in the expression of cortex-wide neural dynamics are related to behavioral phenotype

Behavior differs across individuals, ranging from typical to atypical phenotypes.1 Understanding how differences in behavior relate to differences in neural activity is critical for developing treatments of neuropsychiatric and neurodevelopmental disorders. One hypothesis is that differences in behavior reflect individual differences in the dynamics of how information flows through the brain. In support of this, the correlation of neural activity between brain areas, termed "functional connectivity," varies across individuals2 and is disrupted in autism,3 schizophrenia,4 and depression.5 However, the changes in neural activity that underlie altered behavior and functional connectivity remain unclear. Here, we show that individual differences in the expression of different patterns of cortical neural dynamics explain variability in both functional connectivity and behavior. Using mesoscale imaging, we recorded neural activity across the dorsal cortex of behaviorally "typical" and "atypical" mice. All mice shared the same recurring cortex-wide spatiotemporal motifs of neural activity, and these motifs explained the large majority of variance in cortical activity (>75%). However, individuals differed in how frequently different motifs were expressed. These differences in motif expression explained differences in functional connectivity and behavior across both typical and atypical mice. Our results suggest that differences in behavior and functional connectivity are due to changes in the processes that select which pattern of neural activity is expressed at each moment in time.

Efficient Decoding of Large-Scale Neural Population Responses With Gaussian-Process Multiclass Regression.

Neural decoding methods provide a powerful tool for quantifying the information content of neural population codes and the limits imposed by correlations in neural activity. However, standard decoding methods are prone to overfitting and scale poorly to high-dimensional settings. Here, we introduce a novel decoding method to overcome these limitations. Our approach, the gaussian process multiclass decoder (GPMD), is well suited to decoding a continuous low-dimensional variable from high-dimensional population activity and provides a platform for assessing the importance of correlations in neural population codes. The GPMD is a multinomial logistic regression model with a gaussian process prior over the decoding weights. The prior includes hyperparameters that govern the smoothness of each neuron's decoding weights, allowing automatic pruning of uninformative neurons during inference. We provide a variational inference method for fitting the GPMD to data, which scales to hundreds or thousands of neurons and performs well even in data sets with more neurons than trials. We apply the GPMD to recordings from primary visual cortex in three species: monkey, ferret, and mouse. Our decoder achieves state-of-the-art accuracy on all three data sets and substantially outperforms independent Bayesian decoding, showing that knowledge of the correlation structure is essential for optimal decoding in all three species.

WormTensor: a clustering method for time-series whole-brain activity data from C. elegans

BackgroundIn the field of neuroscience, neural modules and circuits that control biological functions have been found throughout entire neural networks. Correlations in neural activity can be used to identify such neural modules. Recent technological advances enable us to measure whole-brain neural activity with single-cell resolution in several species including Caenorhabditis elegans. Because current neural activity data in C. elegans contain many missing data points, it is necessary to merge results from as many animals as possible to obtain more reliable functional modules.ResultsIn this work, we developed a new time-series clustering method, WormTensor, to identify functional modules using whole-brain activity data from C. elegans. WormTensor uses a distance measure, modified shape-based distance to account for the lags and the mutual inhibition of cell–cell interactions and applies the tensor decomposition algorithm multi-view clustering based on matrix integration using the higher orthogonal iteration of tensors (HOOI) algorithm (MC-MI-HOOI), which can estimate both the weight to account for the reliability of data from each animal and the clusters that are common across animals.ConclusionWe applied the method to 24 individual C. elegans and successfully found some known functional modules. Compared with a widely used consensus clustering method to aggregate multiple clustering results, WormTensor showed higher silhouette coefficients. Our simulation also showed that WormTensor is robust to contamination from noisy data. WormTensor is freely available as an R/CRAN package https://cran.r-project.org/web/packages/WormTensor.

An increase of inhibition drives the developmental decorrelation of neural activity.

Throughout development, the brain transits from early highly synchronous activity patterns to a mature state with sparse and decorrelated neural activity, yet the mechanisms underlying this process are poorly understood. The developmental transition has important functional consequences, as the latter state is thought to allow for more efficient storage, retrieval, and processing of information. Here, we show that, in the mouse medial prefrontal cortex (mPFC), neural activity during the first two postnatal weeks decorrelates following specific spatial patterns. This process is accompanied by a concomitant tilting of excitation-inhibition (E-I) ratio toward inhibition. Using optogenetic manipulations and neural network modeling, we show that the two phenomena are mechanistically linked, and that a relative increase of inhibition drives the decorrelation of neural activity. Accordingly, in mice mimicking the etiology of neurodevelopmental disorders, subtle alterations in E-I ratio are associated with specific impairments in the correlational structure of spike trains. Finally, capitalizing on EEG data from newborn babies, we show that an analogous developmental transition takes place also in the human brain. Thus, changes in E-I ratio control the (de)correlation of neural activity and, by these means, its developmental imbalance might contribute to the pathogenesis of neurodevelopmental disorders.

Quantitative evaluation of the functional activity in multilayered brain structures using histological sections

Subject of study. We develop an algorithm and software to estimate optical density as a correlate of neural activity in multilayered brain structures. Aim of study. We develop a method and program algorithm for quantitative evaluation of the optical density in the layers of neural structures from images of histological brain preparations that correlates with the functional activity of the cells. Method. Optical density was measured locally in successive areas of a layer or several layers of the neural structure, which are curvilinear-shaped in the brain slices. An expert researcher selects the trajectory of optical density measurement in the digitized image of the slice in an interactive mode. The results of density measurements were plotted in an orthogonal coordinate system. Capabilities enabling automated curve smoothing, extremum seeking, and calculation of distances between extrema were developed. Results. An algorithm for implementing the developed program is presented. The successive stages of operation are illustrated through the evaluation of the optical density in the visual cortex layer from an image of a brain slice as an example. In addition, the evaluation results of the optical density in layers of a lateral geniculate body are presented. Practical significance. The developed method and program algorithm are required for reconstructing the functional activity of the curved layers of different brain structures to investigate and simulate the organization patterns of the neural networks of the human brain.

Brain kernel: A new spatial covariance function for fMRI data

A key problem in functional magnetic resonance imaging (fMRI) is to estimate spatial activity patterns from noisy high-dimensional signals. Spatial smoothing provides one approach to regularizing such estimates. However, standard smoothing methods ignore the fact that correlations in neural activity may fall off at different rates in different brain areas, or exhibit discontinuities across anatomical or functional boundaries. Moreover, such methods do not exploit the fact that widely separated brain regions may exhibit strong correlations due to bilateral symmetry or the network organization of brain regions. To capture this non-stationary spatial correlation structure, we introduce the brain kernel, a continuous covariance function for whole-brain activity patterns. We define the brain kernel in terms of a continuous nonlinear mapping from 3D brain coordinates to a latent embedding space, parametrized with a Gaussian process (GP). The brain kernel specifies the prior covariance between voxels as a function of the distance between their locations in embedding space. The GP mapping warps the brain nonlinearly so that highly correlated voxels are close together in latent space, and uncorrelated voxels are far apart. We estimate the brain kernel using resting-state fMRI data, and we develop an exact, scalable inference method based on block coordinate descent to overcome the challenges of high dimensionality (10-100K voxels). Finally, we illustrate the brain kernel’s usefulness with applications to brain decoding and factor analysis with multiple task-based fMRI datasets.

Robust and distributed neural representation of action values.

Studies in rats, monkeys, and humans have found action-value signals in multiple regions of the brain. These findings suggest that action-value signals encoded in these brain structures bias choices toward higher expected rewards. However, previous estimates of action-value signals might have been inflated by serial correlations in neural activity and also by activity related to other decision variables. Here, we applied several statistical tests based on permutation and surrogate data to analyze neural activity recorded from the striatum, frontal cortex, and hippocampus. The results show that previously identified action-value signals in these brain areas cannot be entirely accounted for by concurrent serial correlations in neural activity and action value. We also found that neural activity related to action value is intermixed with signals related to other decision variables. Our findings provide strong evidence for broadly distributed neural signals related to action value throughout the brain.

Post-weaning stroking stimuli induce affiliative behavior toward humans and influence brain activity in female rats

Gentle touch contributes to affiliative interactions. We investigated the effects of gentle stroking in female rats on the development of affiliative behaviors toward humans and we exploratively examined brain regions in which activity was influenced by stroking. Rats that had received stroking stimuli repeatedly after weaning emitted 50-kHz calls, an index of positive emotion, and showed affiliative behaviors toward the experimenter. Hypothalamic paraventricular oxytocin neurons were activated in the rats after stroking. The septohypothalamic nucleus (SHy) in the post-weaningly stroked rats showed decreased activity in response to stroking stimuli compared with that in the non-stroked control group. There were negative correlations of neural activity in hypothalamic regions including the SHy with the number of 50-kHz calls. These findings revealed that post-weaning stroking induces an affiliative relationship between female rats and humans, possibly via activation of oxytocin neurons and suppression of the activity of hypothalamic neurons.

Neurovascular coupling and bilateral connectivity during NREM and REM sleep.

To understand how arousal state impacts cerebral hemodynamics and neurovascular coupling, we monitored neural activity, behavior, and hemodynamic signals in un-anesthetized, head-fixed mice. Mice frequently fell asleep during imaging, and these sleep events were interspersed with periods of wake. During both NREM and REM sleep, mice showed large increases in cerebral blood volume ([HbT]) and arteriole diameter relative to the awake state, two to five times larger than those evoked by sensory stimulation. During NREM, the amplitude of bilateral low-frequency oscillations in [HbT] increased markedly, and coherency between neural activity and hemodynamic signals was higher than the awake resting and REM states. Bilateral correlations in neural activity and [HbT] were highest during NREM, and lowest in the awake state. Hemodynamic signals in the cortex are strongly modulated by arousal state, and changes during sleep are substantially larger than sensory-evoked responses.

Blood-based Biomarkers of late Recovery in Severe Traumatic Brain Injury

Background: The incidence of traumatic brain injury (TBI) in the U.S. has been estimated at 1.7 million people each year and results in $60 billion in medical and productivity costs. A TBI can result in severe and potentially chronic cognitive and physical deficits. There has been an increased focus on the use of neurologic biomarkers for both monitoring progression and predicting clinical outcomes; however, the majority of the studies are focused on the acute phase. Objective: In this study, the goal was to longitudinally characterize biochemical correlates of neural activity up to 2 years after injury and determine if these biomarkers correlate with functional outcomes. Methods: Participants (n=13) with severe TBI as defined by the Glasgow Coma Scale (< 8) who are less than 1 year from the time of injury were included in this study. Blood samples and functional outcomes (DRS and RBANS) were collected upon enrollment and at 3, 6, 12, 18, and 24 months following enrollment.

A Moment-Based Maximum Entropy Model for Fitting Higher-Order Interactions in Neural Data.

Correlations in neural activity have been demonstrated to have profound consequences for sensory encoding. To understand how neural populations represent stimulus information, it is therefore necessary to model how pairwise and higher-order spiking correlations between neurons contribute to the collective structure of population-wide spiking patterns. Maximum entropy models are an increasingly popular method for capturing collective neural activity by including successively higher-order interaction terms. However, incorporating higher-order interactions in these models is difficult in practice due to two factors. First, the number of parameters exponentially increases as higher orders are added. Second, because triplet (and higher) spiking events occur infrequently, estimates of higher-order statistics may be contaminated by sampling noise. To address this, we extend previous work on the Reliable Interaction class of models to develop a normalized variant that adaptively identifies the specific pairwise and higher-order moments that can be estimated from a given dataset for a specified confidence level. The resulting “Reliable Moment” model is able to capture cortical-like distributions of population spiking patterns. Finally, we show that, compared with the Reliable Interaction model, the Reliable Moment model infers fewer strong spurious higher-order interactions and is better able to predict the frequencies of previously unobserved spiking patterns.

Correlation of neural activity with behavioral kinematics reveals distinct sensory encoding and evidence accumulation processes during active tactile sensing

Many real-world decisions rely on active sensing, a dynamic process for directing our sensors (e.g. eyes or fingers) across a stimulus to maximize information gain. Though ecologically pervasive, limited work has focused on identifying neural correlates of the active sensing process. In tactile perception, we often make decisions about an object/surface by actively exploring its shape/texture. Here we investigate the neural correlates of active tactile decision-making by simultaneously measuring electroencephalography (EEG) and finger kinematics while subjects interrogated a haptic surface to make perceptual judgments. Since sensorimotor behavior underlies decision formation in active sensing tasks, we hypothesized that the neural correlates of decision-related processes would be detectable by relating active sensing to neural activity. Novel brain-behavior correlation analysis revealed that three distinct EEG components, localizing to right-lateralized occipital cortex (LOC), middle frontal gyrus (MFG), and supplementary motor area (SMA), respectively, were coupled with active sensing as their activity significantly correlated with finger kinematics. To probe the functional role of these components, we fit their single-trial-couplings to decision-making performance using a hierarchical-drift-diffusion-model (HDDM), revealing that the LOC modulated the encoding of the tactile stimulus whereas the MFG predicted the rate of information integration towards a choice. Interestingly, the MFG disappeared from components uncovered from control subjects performing active sensing but not required to make perceptual decisions. By uncovering the neural correlates of distinct stimulus encoding and evidence accumulation processes, this study delineated, for the first time, the functional role of cortical areas in active tactile decision-making.

Interpretation of correlated neural variability from models of feed-forward and recurrent circuits.

Neural populations respond to the repeated presentations of a sensory stimulus with correlated variability. These correlations have been studied in detail, with respect to their mechanistic origin, as well as their influence on stimulus discrimination and on the performance of population codes. A number of theoretical studies have endeavored to link network architecture to the nature of the correlations in neural activity. Here, we contribute to this effort: in models of circuits of stochastic neurons, we elucidate the implications of various network architectures—recurrent connections, shared feed-forward projections, and shared gain fluctuations—on the stimulus dependence in correlations. Specifically, we derive mathematical relations that specify the dependence of population-averaged covariances on firing rates, for different network architectures. In turn, these relations can be used to analyze data on population activity. We examine recordings from neural populations in mouse auditory cortex. We find that a recurrent network model with random effective connections captures the observed statistics. Furthermore, using our circuit model, we investigate the relation between network parameters, correlations, and how well different stimuli can be discriminated from one another based on the population activity. As such, our approach allows us to relate properties of the neural circuit to information processing.

Pupillary Response and Phenotype in ASD: Latency to Constriction Discriminates ASD from Typically Developing Adolescents

Milder forms of autism spectrum disorder (ASD) can be difficult to diagnose based on behavioral testing alone. This study used eye-tracking equipment and a hand-held penlight to measure the pupil reflex in adolescents with "high functioning" ASD and in adolescents without ASD. The ASD group showed a delay in pupil response. This is the first eye-tracking study to conduct this test as typically performed by a clinical provider, demonstrating differences in older individuals with a subtype of ASD.

A Sensorimotor Circuit in Mouse Cortex for Visual Flow Predictions

The cortex is organized as a hierarchical processing structure. Feedback from higher levels of the hierarchy, known as top-down signals, have been shown to be involved in attentional and contextual modulation of sensory responses. Here we argue that top-down input to the primary visual cortex (V1) from A24b and the adjacent secondary motor cortex (M2) signals a prediction of visual flow based on motor output. A24b/M2 sends a dense and topographically organized projection to V1 that targets most neurons in layer 2/3. By imaging the activity of A24b/M2 axons in V1 of mice learning to navigate a 2D virtual environment, we found that their activity was strongly correlated with locomotion and resulting visual flow feedback in an experience-dependent manner. When mice were trained to navigate a left-right inverted virtual environment, correlations of neural activity with behavior reversed to match visual flow. These findings are consistent with a predictive coding interpretation of visual processing.

Fronto-limbic disconnection in depressed patients with suicidal ideation: A resting-state functional connectivity study

BackgroundSuicidal ideation (SI) is highly prevalent and a known symptom of Major Depressive Disorder (MDD), but its underlying biological mechanisms are relatively unknown. Several studies linked suicidal ideation to dysfunctional brain circuits, specifically fronto-limbic connections. The purpose of this work was to investigate fronto-limbic disconnection in MDD patients with or without SI. MethodsMDD patients with SI (SI, n=28) or without SI (NSI, n=20), identified by the Scale for Suicide Ideation and healthy controls (HCs, n=30) underwent resting-state functional MRI scanning. The functional properties of correlations in neural activity (intrinsic functional connectivity, iFC) of the rostral anterior cingulate cortex (rACC) were analyzed among the three groups. Furthermore, correlation analyses between iFC, SI severity and depression severity were performed. ResultsWe found that the SI group exhibited decreased iFC between the rACC, the orbitomedial prefrontal cortex and the right middle temporal pole compared to HCs and NSI groups. The NSI group showed decreased iFC between the rACC and the orbitomedial prefrontal cortex compared to HCs. In the SI group, iFC strength between the right rACC and the middle temporal pole positively correlated with SI severity. ConclusionTransdiagnostic and diagnosis-specific alterations of fronto-limbic iFC were found in MDD patients with or without SI. Disrupted fronto-limbic circuits may impact decision-making and emotional processing in SI. These results provide useful information about the pathophysiological mechanisms of MDD patients with SI.

Thalamocortical dysconnectivity in paroxysmal kinesigenic dyskinesia: Combining functional magnetic resonance imaging and diffusion tensor imaging.

Paroxysmal kinesigenic dyskinesia is associated with macrostructural and microstructural abnormalities in the thalamus. To examine functional and structural connectivity of thalamocortical networks in paroxysmal kinesigenic dyskinesia and to further investigate the effect of mutation of the proline-rich transmembrane protein 2 on thalamocortical networks. Patients with paroxysmal kinesigenic dyskinesia (n = 20), subdivided into proline-rich transmembrane protein 2-mutated (n = 8) and nonmutated patients (n = 12) and healthy controls (n = 20) underwent resting-state functional MRI and diffusion imaging scan. The functional properties of correlations in neural activity (functional connectivity) and the structural properties of white matter probabilistic tractography (structural connectivity) were analyzed to characterize thalamocortical networks. Furthermore, the effect of proline-rich transmembrane protein 2 mutation on functional and structural connectivity of thalamocortical networks were examined using one-way analysis of variance among three groups. Patients had increased functional and structural connectivity between ventral lateral/anterior thalamic nuclei and a lateral motor area, as compared to controls. This functional connectivity positively correlated with disease duration. Interestingly, proline-rich transmembrane protein 2-mutated patients showed decreased functional connectivity and preserved structural connectivity, between mediodorsal nucleus and prefrontal cortex, compared to nonmutated patients and controls. Thalamomotor/premotor hyperconnectivity suggests abnormal communication between thalamus and motor cortex in patients. Furthermore, thalamoprefrontal hypoconnectivity in proline-rich transmembrane protein 2-mutated patients might indicate that proline-rich transmembrane protein 2 mutations result in inefficient thalamoprefrontal integration. Our findings facilitate a deeper understanding of the crucial role of thalamocortical dysconnectivity in the pathophysiological mechanisms of paroxysmal kinesigenic dyskinesia. © 2017 International Parkinson and Movement Disorder Society.

Spatial resolution dependence on spectral frequency in human speech cortex electrocorticography

Objective. Electrocorticography (ECoG) has become an important tool in human neuroscience and has tremendous potential for emerging applications in neural interface technology. Electrode array design parameters are outstanding issues for both research and clinical applications, and these parameters depend critically on the nature of the neural signals to be recorded. Here, we investigate the functional spatial resolution of neural signals recorded at the human cortical surface. We empirically derive spatial spread functions to quantify the shared neural activity for each frequency band of the electrocorticogram. Approach. Five subjects with high-density (4 mm center-to-center spacing) ECoG grid implants participated in speech perception and production tasks while neural activity was recorded from the speech cortex, including superior temporal gyrus, precentral gyrus, and postcentral gyrus. The cortical surface field potential was decomposed into traditional EEG frequency bands. Signal similarity between electrode pairs for each frequency band was quantified using a Pearson correlation coefficient. Main results. The correlation of neural activity between electrode pairs was inversely related to the distance between the electrodes; this relationship was used to quantify spatial falloff functions for cortical subdomains. As expected, lower frequencies remained correlated over larger distances than higher frequencies. However, both the envelope and phase of gamma and high gamma frequencies (30–150 Hz) are largely uncorrelated (<90%) at 4 mm, the smallest spacing of the high-density arrays. Thus, ECoG arrays smaller than 4 mm have significant promise for increasing signal resolution at high frequencies, whereas less additional gain is achieved for lower frequencies. Significance. Our findings quantitatively demonstrate the dependence of ECoG spatial resolution on the neural frequency of interest. We demonstrate that this relationship is consistent across patients and across cortical areas during activity.

Role of expected reward in frontal eye field during natural scene search.

When a saccade is expected to result in a reward, both neural activity in oculomotor areas and the saccade itself (e.g., its vigor and latency) are altered (compared with when no reward is expected). As such, it is unclear whether the correlations of neural activity with reward indicate a representation of reward beyond a movement representation; the modulated neural activity may simply represent the differences in motor output due to expected reward. Here, to distinguish between these possibilities, we trained monkeys to perform a natural scene search task while we recorded from the frontal eye field (FEF). Indeed, when reward was expected (i.e., saccades to the target), FEF neurons showed enhanced responses. Moreover, when monkeys accidentally made eye movements to the target, firing rates were lower than when they purposively moved to the target. Thus, neurons were modulated by expected reward rather than simply the presence of the target. We then fit a model that simultaneously included components related to expected reward and saccade parameters. While expected reward led to shorter latency and higher velocity saccades, these behavioral changes could not fully explain the increased FEF firing rates. Thus, FEF neurons appear to encode motivational factors such as reward expectation, above and beyond the kinematic and behavioral consequences of imminent reward.

Spatial precision of population activity in primate area MT.

The middle temporal (MT) area is a cortical area integral to the "where" pathway of primate visual processing, signaling the movement and position of objects in the visual world. The receptive field of a single MT neuron is sensitive to the direction of object motion but is too large to signal precise spatial position. Here, we asked if the activity of MT neurons could be combined to support the high spatial precision required in the where pathway. With the use of multielectrode arrays, we recorded simultaneously neural activity at 24-65 sites in area MT of anesthetized marmoset monkeys. We found that although individual receptive fields span more than 5° of the visual field, the combined population response can support fine spatial discriminations (<0.2°). This is because receptive fields at neighboring sites overlapped substantially, and changes in spatial position are therefore projected onto neural activity in a large ensemble of neurons. This fine spatial discrimination is supported primarily by neurons with receptive fields flanking the target locations. Population performance is degraded (by 13-22%) when correlations in neural activity are ignored, further reflecting the contribution of population neural interactions. Our results show that population signals can provide high spatial precision despite large receptive fields, allowing area MT to represent both the motion and the position of objects in the visual world.