Coordination Of Neural Activity Research Articles

Modeling statistical dependencies in multi-region spike train data.

Neural computations underlying cognition and behavior rely on the coordination of neural activity across multiple brain areas. Understanding how brain areas interact to process information or generate behavior is thus a central question in neuroscience. Here we provide an overview of statistical approaches for characterizing statistical dependencies in multi-region spike train recordings. We focus on two classes of models in particular: regression-based models and shared latent variable models. Regression-based models describe interactions in terms of a directed transformation of information from one region to another. Shared latent variable models, on the other hand, seek to describe interactions in terms of sources that capture common fluctuations in spiking activity across regions. We discuss the advantages and limitations of each of these approaches and future directions for the field. We intend this review to be an introduction to the statistical methods in multi-region models for computational neuroscientists and experimentalists alike.

Reduced resting-state brain functional network connectivity and poor regional homogeneity in patients with CADASIL

BackgroundCerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) manifests principally as a suite of cognitive impairments, particularly in the executive domain. Executive functioning requires the dynamic coordination of neural activity over large-scale networks. It remains unclear whether changes in resting-state brain functional network connectivity and regional homogeneities (ReHos) underly the mechanisms of executive dysfunction evident in CADASIL patients.MethodsIn this study, 22 CADASIL patients and 44 matched healthy controls underwent resting-state functional magnetic resonance imaging (fMRI). Independent component analysis (ICA) was used to measure functional brain network connectivity, and ReHos were calculated to evaluate local brain activities. We used seed-based functional connectivity (FC) analyses to determine whether dysfunctional areas (as defined by ReHos) exhibited abnormal FC with other brain areas. Relationships among the mean intra-network connectivity z-scores of dysfunctional areas within functional networks, and cognitive scores were evaluated using Pearson correlation analyses.ResultsCompared to the controls, CADASIL patients exhibited decreased intra-network connectivity within the bilateral lingual gyrus (LG) and the right cuneus (CU) (thus within the visual network [VIN)], and within the right precuneus (Pcu), inferior frontal gyrus (IFG), and precentral gyrus (thus within the frontal network [FRN]). Compared to the controls, patients also exhibited significantly lower ReHos in the right precuneus and cuneus (Pcu/CU), visual association cortex, calcarine gyri, posterior cingulate, limbic lobe, and weaker FC between the right Pcu/CU and the bilateral parahippocampal gyrus (PHG), and between the right Pcu/CU and the right postcentral gyrus. Notably, the mean connectivity z-scores of the bilateral LG and the right CU within the VIN were positively associated with compromised attention, calculation and delayed recall as revealed by tests of the various cognitive domains explored by the Mini-Mental State Examination.ConclusionsThe decreases in intra-network connectivity within the VIN and FRN and reduced local brain activity in the posterior parietal area suggest that patients with CADASIL may exhibit dysfunctional visuomotor behaviors (a hallmark of executive function), and that all visual information processing, visuomotor planning, and movement execution may be affected.

Attention decouples action potentials from the phase of local field potentials in macaque visual cortical area MT

BackgroundThe timing of action potentials (“spikes”) of cortical neurons has been shown to be aligned to the phase of low-frequency (< 10 Hz) local field potentials (LFPs) in several cortical areas. However, across the areas, this alignment varies and the role of this spike-phase coupling (SPC) in cognitive functions is not well understood.ResultsHere, we propose a role in the coordination of neural activity by selective attention. After refining previous analytical methods for measuring SPC, we show that first, SPC is present along the dorsal processing pathway in macaque visual cortex (area MT); second, spikes occur in falling phases of the low-frequency LFP independent of the location of spatial attention; third, switching spatial attention into the receptive field (RF) of MT neurons decreases this coupling; and finally, the LFP phase causally influences the spikes.ConclusionsHere, we show that spikes are coupled to the phase of low-frequency LFP along the dorsal visual pathway. Our data suggest that attention harnesses this spike-LFP coupling to de-synchronize neurons and thereby enhance the neural representation of the attended stimuli.

Embedded ensemble encoding hypothesis: The role of the “Prepared” cell

We here reconsider current theories of neural ensembles in the context of recent discoveries about neuronal dendritic physiology. The key physiological observation is that the dendritic plateau potential produces sustained depolarization of the cell body (amplitude 10-20 mV, duration 200-500 ms). Our central hypothesis is that synaptically-evoked dendritic plateau potentials lead to a prepared state of a neuron that favors spike generation. The plateau both depolarizes the cell toward spike threshold, and provides faster response to inputs through a shortened membrane time constant. As a result, the speed of synaptic-to-action potential (AP) transfer is faster during the plateau phase. Our hypothesis relates the changes from "resting" to "depolarized" neuronal state to changes in ensemble dynamics and in network information flow. The plateau provides the Prepared state (sustained depolarization of the cell body) with a time window of 200-500 ms. During this time, a neuron can tune into ongoing network activity and synchronize spiking with other neurons to provide a coordinated Active state (robust firing of somatic APs), which would permit "binding" of signals through coordination of neural activity across a population. The transient Active ensemble of neurons is embedded in the longer-lasting Prepared ensemble of neurons. We hypothesize that "embedded ensemble encoding" may be an important organizing principle in networks of neurons.

Age-Related Changes in Task Related Functional Network Connectivity

Aging has a multi-faceted impact on brain structure, brain function and cognitive task performance, but the interaction of these different age-related changes is largely unexplored. We hypothesize that age-related structural changes alter the functional connectivity within the brain, resulting in altered task performance during cognitive challenges. In this neuroimaging study, we used independent components analysis to identify spatial patterns of coordinated functional activity involved in the performance of a verbal delayed item recognition task from 75 healthy young and 37 healthy old adults. Strength of functional connectivity between spatial components was assessed for age group differences and related to speeded task performance. We then assessed whether age-related differences in global brain volume were associated with age-related differences in functional network connectivity. Both age groups used a series of spatial components during the verbal working memory task and the strength and distribution of functional network connectivity between these components differed across the age groups. Poorer task performance, i.e. slower speed with increasing memory load, in the old adults was associated with decreases in functional network connectivity between components comprised of the supplementary motor area and the middle cingulate and between the precuneus and the middle/superior frontal cortex. Advancing age also led to decreased brain volume; however, there was no evidence to support the hypothesis that age-related alterations in functional network connectivity were the result of global brain volume changes. These results suggest that age-related differences in the coordination of neural activity between brain regions partially underlie differences in cognitive performance.

Analyzing possible pitfalls of cross-frequency analysis

One of the central questions in neuroscience is how neural activity is organized across different spatial and temporal scales. As larger populations oscillate and synchronize at lower frequencies and smaller ensembles are active at higher frequencies, a cross-frequency coupling would facilitate flexible coordination of neural activity simultaneously in time and space. Although various experiments have revealed amplitude-to-amplitude and phase-to-phase coupling, the most common and most celebrated result is that the phase of the lower frequency component modulates the amplitude of the higher frequency component. Over the recent 5 years the amount of experimental works finding such phase-amplitude coupling in LFP, ECoG, EEG and MEG has been tremendous (summarized in [1]). We suggest that although the mechanism of cross-frequency-coupling (CFC) is theoretically very tempting, the current analysis methods might overestimate any physiological CFC actually evident in the signals of LFP, ECoG, EEG and MEG. In particular, we point out three conceptual problems in assessing the components and their correlations of a time series. Although we focus on phase-amplitude coupling, most of our argument is relevant for any type of coupling. 1) The first conceptual problem is related to isolating physiological frequency components of the recorded signal. The key point is to notice that there are many different mathematical representations for a time series but the physical interpretation we make out of them is dependent on the choice of the components to be analyzed. In particular, when one isolates the components by Fourier-representation based filtering, it is the width of the filtering bands what defines what we consider as our components and how their power or group phase change in time. We will discuss clear cut examples where the interpretation of the existence of CFC depends on the width of the filtering process. 2) A second problem deals with the origin of spectral correlations as detected by current cross-frequency analysis. It is known that non-stationarities are associated with spectral correlations in the Fourier space. Therefore, there are two possibilities regarding the interpretation of any observed CFC. One scenario is that basic neuronal mechanisms indeed generate an interaction across different time scales (or frequencies) resulting in processes with non-stationary features. The other and problematic possibility is that unspecific non-stationarities can also be associated with spectral correlations which in turn will be detected by cross frequency measures even if physiologically there is no causal interaction between the frequencies. 3) We discuss on the role of non-linearities as generators of cross frequency interactions. As an example we performed a phase-amplitude coupling analysis of two nonlinearly related signals: atmospheric noise and the square of it (Figure ​(Figure1)1) observing an enhancement of phase-amplitude coupling in the second signal while no pattern is observed in the first. Figure 1 Time-evolution of the amplitudes of high frequency components locked to the trough of a low frequency component (from 4 to 8 Hz) for random noise from an atmospheric source (left) and its square (right). Finally, we discuss some minimal conditions need to be tested to solve some of the ambiguities here noted. In summary, we simply want to point out that finding a significant cross frequency pattern does not always have to imply that there indeed is physiological cross frequency interaction in the brain.

Abnormalities in task‐related neural network formation in schizophrenia

The study reported by Sharma et al. in this issue of Acta Psychiatrica Scandinavica (1) adds to increasing evidence that the cognitive deficits in schizophrenia are partially attributable to impairments in the coordination of neural activity across the brain, which is necessary to link networks of neurons collectively performing a particular task. The authors used an emotional Stroop task requiring participants to name the colour of a word that has either a positive, negative or neutral emotional valence. Patients were significantly slower than controls, but neither group showed the typical slowing in reaction times to negative words. Interestingly, however, they also examined the coherence (or synchronisation) of activity between two electrodes (at frontal and posterior locations), which reflects the degree to which activity at specific electrode sites form the part of common functional network of activity. The authors report that immediately following stimulus onset in comparison with controls, patients showed reduced connectivity in the delta (1–4 Hz) and theta (4–8 Hz) frequency range, which some researchers have attributed to long-range synchronisation in activity across brain areas. Interestingly, however, during the inter-trial interval (ITI) period when not directly performing the task patients had significantly higher coherence across all frequency ranges. The results from the study highlight a number of issues that need to be considered in future studies. First, the patients’ enhanced ITI activity indicates that researchers should exercise caution in the common practise of using this as a baseline from which to measure performance. Second, the meaning of the ITI enhancement would warrant further investigations. Specifically, it would be important to look at coherence across a wider range of electrodes using data-driven measures (i.e. cluster statistics) during all periods of the task. In this way, the specific networks associated with the task could be identified, and it would be then possible to distinguish whether the ITI activity was related to either (i) compensatory maintenance of the specific task-related network (see for example 2, 3) or (ii) increases in activity in the default-mode network. The latter is a hypothesised network of areas reflecting a state into which the brain slips when not actively engaged in a specific task, which has shown to be increased in patients with schizophrenia and possibly reflect a difficulty in maintaining task-specific activity. Third, the attenuation of low frequency (delta and theta) coherence in the early stimulus period in patients compared with controls might reflect the early sensory processing deficit reported in the disease (4). This could be investigated further by looking at measures that determine the direction of the connectivity (dynamic causal modelling), i.e. is the coherence driven by early posterior or later frontal activity. Finally, the study highlights the importance of neurophysiological measurements for probing the aetiology of schizophrenia by allowing a more sophisticated understanding of the underlying causes of behaviour deficits. Importantly, a considerable body of research is uncovering the neurochemical basis of oscillatory activity [i.e. network oscillation rely on GABAergic interneuron circuits (5)] which means oscillatory activity may provide a link between behavioural and neurochemical dysfunctions. As a consequence, this area of research holds considerable promise for the development and testing of targeted drug treatments.

Learning to control a brain-machine interface for reaching and grasping by primates.

Reaching and grasping in primates depend on the coordination of neural activity in large frontoparietal ensembles. Here we demonstrate that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain–machine interface (BMIc) that uses multiple mathematical models to extract several motor parameters (i.e., hand position, velocity, gripping force, and the EMGs of multiple arm muscles) from the electrical activity of frontoparietal neuronal ensembles. As single neurons typically contribute to the encoding of several motor parameters, we observed that high BMIc accuracy required recording from large neuronal ensembles. Continuous BMIc operation by monkeys led to significant improvements in both model predictions and behavioral performance. Using visual feedback, monkeys succeeded in producing robot reach-and-grasp movements even when their arms did not move. Learning to operate the BMIc was paralleled by functional reorganization in multiple cortical areas, suggesting that the dynamic properties of the BMIc were incorporated into motor and sensory cortical representations.

Postnatal development of rat hippocampal gamma rhythm in vivo.

Network oscillations in the gamma-frequency band (20-100 Hz) may have a central role in the timing and coordination of neural activity in the adult brain, yet their appearance in the course of development has remained unexplored. Moreover, electroencephalogram (EEG)-based classification of the vigilance states [active sleep (AS), quiet sleep (QS), or awake (W)] has been thought to be possible only after the second postnatal week. We now report the presence of spontaneous hippocampal gamma oscillations in the area CA3 of freely moving rats at postnatal days (P) 5-10. Initially, at P5, the gamma oscillations were seen in time-frequency analyses of intrahippocampal EEG recordings as brief (<500 ms) bursts at 20-30 Hz. The early gamma rhythmicity was most pronounced during periods of AS but was occasionally detected also during QS. Toward P10, the gamma oscillations gained amplitude and extended also to higher (<or=60 Hz) frequencies. In parallel, the gamma oscillations were progressively more and more confined to AS. To further consolidate these findings, we compared amplitude spectra averaged within the behavioral categories. AS was characterized by the appearances of gamma (20-30 Hz) and theta (3-5 Hz) peaks at P6 and at P8, respectively. QS, on the other hand, had considerably smoother amplitude distributions between 1 and 100 Hz for P5-P10, with no peaks in gamma or theta bands. Hippocampal gamma rhythm thus seems to hallmark early AS. Our data provide the first in vivo evidence for both the presence and the behavioral correlate of spontaneous gamma oscillations in the newborn rat.