A systematic review with meta-analysis of Yoga's contributions to neuropsychiatric aspects of aging.

Yoga is one of the most common Complementary and Alternative Medicines (CAM) for mind-body approaches to psychological and stress-related conditions in aging. Such wide usage demands the review and systematization of the scientific literature, searching for accumulated evidence of its effectiveness. We reviewed the literature to assess whether Yoga would offer significant improvements to neuropsychiatric aspects of the elderly: anxiety, depression, stress, memory and executive functions. METHODS: This systematic review with meta-analyses organized the results from all analyzed articles, comparing them between the experimental and either the control or waiting groups, calculating the effect size (Cohen-d) and the p-value of a two-tailed T-test. We presented the transformed metadata in forest graphs. RESULTS AND DISCUSSION: Given the heterogeneity of methods, results, and effect sizes of each study and due to the number of articles found, this meta-analysis indicates that it is not possible to state that Yoga reduces anxiety and stress in the elderly or improves cognition. However, this meta-analysis found significant results of Yoga in reducing depression with small to medium effect sizes. CONCLUSION: According to the currently available literature on Yoga and aspects of aging, we concluded that yoga was effective in most studies on reducing depression.

Time encoding migrates from prefrontal cortex to dorsal striatum during learning of a self-timed response duration task.

Although time is a fundamental dimension of life, we do not know how brain areas cooperate to keep track and process time intervals. Notably, analyses of neural activity during learning are rare, mainly because timing tasks usually require training over many days. We investigated how the time encoding evolves when animals learn to time a 1.5 s interval. We designed a novel training protocol where rats go from naive- to proficient-level timing performance within a single session, allowing us to investigate neuronal activity from very early learning stages. We used pharmacological experiments and machine-learning algorithms to evaluate the level of time encoding in the medial prefrontal cortex and the dorsal striatum. Our results show a double dissociation between the medial prefrontal cortex and the dorsal striatum during temporal learning, where the former commits to early learning stages while the latter engages as animals become proficient in the task.

Directed functional and structural connectivity in a large-scale model for the mouse cortex.

Inferring the structural connectivity from electrophysiological measurements is a fundamental challenge in systems neuroscience. Directed functional connectivity measures, such as the generalized partial directed coherence (GPDC), provide estimates of the causal influence between areas. However, the relation between causality estimates and structural connectivity is still not clear. We analyzed this problem by evaluating the effectiveness of GPDC to estimate the connectivity of a ground-truth, data-constrained computational model of a large-scale network model of the mouse cortex. The model contains 19 cortical areas composed of spiking neurons, with areas connected by long-range projections with weights obtained from a tract-tracing cortical connectome. We show that GPDC values provide a reasonable estimate of structural connectivity, with an average Pearson correlation over simulations of 0.74. Moreover, even in a typical electrophysiological recording scenario containing five areas, the mean correlation was above 0.6. These results suggest that it may be possible to empirically estimate structural connectivity from functional connectivity even when detailed whole-brain recordings are not achievable.

Hippocampal and lateral entorhinal cortex physiological activity during trace conditioning under urethane anesthesia.

Significant evidence shows that the acquisition of delay conditioning can occur in out-of-awareness states, such as under anesthesia. However, it is unclear to what extent and what type of conditioning animals may achieve during nonawake states. Trace conditioning is an appealing protocol to study under anesthesia, given the long empty gap separating the conditioned and unconditioned stimuli, which must be bridged for acquisition to happen. Here, we show evidence that rats develop physiological responses during the trace conditioning paradigm under anesthesia. We recorded the activity of the hippocampus (HPC) and lateral entorhinal cortex (LEC) in urethane-anesthetized rats, along with an electromyogram and an electrocardiogram. The protocol consisted of randomly presenting two distinct sound stimuli (CS- and CS+), where only one stimulus (CS+) was assigned to be trace-paired with a footshock. A trial-average analysis revealed that animals developed significant climbing heart rate activity initiating at the CS onset and persisting during the trace period. Such climbing arose for both CS- and CS+ with similar slopes but different intercepts, suggesting CS+ heart rates were typically above CS-. The power and coherence of HPC and LEC high-frequency bands (>100 Hz) significantly increased during CS presentation and trace, similarly to CS- and CS+ and insensitive to either activated or deactivated states. To the best of our knowledge, this is the first attempt to perform a trace conditioning protocol under anesthesia. Confirmation of this procedure acquisition can allow a new preparation for the exploration of brain mechanisms that bind time-discontinuous events.NEW & NOTEWORTHY Some forms of learning, such as some types of conditioning, can occur in anesthetized states. However, the extent to which memories can be formed in these states is still an open question. Here, we investigated the trace conditioning under urethane anesthesia and found heart rate, hippocampus, and lateral entorhinal cortex physiological changes to stimuli presentation. This new preparation may allow for exploration of memory acquisition of time-discontinuous events in the nonawake brain.

Low-cost open hardware system for behavioural experiments simultaneously with electrophysiological recordings.

A major frontier in neuroscience is to find neural correlates of perception, learning, decision making, and a variety of other types of behavior. In the last decades, modern devices allow simultaneous recordings of different operant responses and the electrical activity of large neuronal populations. However, the commercially available instruments for studying operant conditioning are expensive, and the design of low-cost chambers has emerged as an appealing alternative to resource-limited laboratories engaged in animal behavior. In this article, we provide a full description of a platform that records the operant behavior and synchronizes it with the electrophysiological activity. The programming of this platform is open source, flexible, and adaptable to a wide range of operant conditioning tasks. We also show results of operant conditioning experiments with freely moving rats with simultaneous electrophysiological recordings.

Temporal regularity and stimulus control in multiple fixed interval schedules.

In multiple fixed interval schedules of reinforcement, different time intervals are signaled by different environmental stimuli which acquire control over behavior. Previous work has shown that temporal performance is controlled not only by external stimuli but also by temporal aspects of the task, depending on the order in which the different intervals are trained - intermixed across trials or in blocks of several trials. The aim of this study was to further describe the training conditions under which the stimuli acquire control over temporal performance. We manipulated the number of consecutive trials of each fixed interval (FI) per training block (Experiment I) and the number of FIs trained (Experiment II). The results suggest that when trained in blocks of several consecutive trials of the same FI, temporal performance is controlled by temporal regularities across trials and not by the visual stimuli that signal the FIs. One possible account for those data is that the temporal cues overshadow the visual stimuli for the control of temporal performance. Similar results have also been observed with humans, which suggest that temporal regularity overcomes the stimuli in the control of behavior in temporal tasks across species.

Rats can learn a temporal task in a single session.

Fixed interval, peak interval, and temporal bisection procedures have been used to assess cognitive functions and address questions such as how animals perceive, represent, and reproduce time intervals. They have also been extensively used to test the effects of drugs on behavior, and to describe the neural correlates of interval timing. However, those procedures usually require several weeks of training for behavior to stabilize. Here, we investigated a variation of the Differential Reinforcement of Response Duration (DRRD) task with a target time of 1.2 s. We compared three types of training protocols and reported a procedure in which performance by the end of the very first session nearly matches the performance of long-term training. We also showed that the initial distribution of the responses is uni-modal and, as training evolves (and rats improve their performance), a second peak emerges and progressively shifts toward longer times. This one-day training protocol can be used to investigate temporal learning and may be especially useful to electrophysiological and neuropharmacological studies.

A model for the peak-interval task based on neural oscillation-delimited states.

Specific mechanisms underlying how the brain keeps track of time are largely unknown. Several existing computational models of timing reproduce behavioral results obtained with experimental psychophysical tasks, but only a few tackle the underlying biological mechanisms, such as the synchronized neural activity that occurs throughout brain areas. In this paper, we introduce a model for the peak-interval task based on neuronal network properties. We consider that Local Field Potential (LFP) oscillation cycles specify a sequence of states, represented as neuronal ensembles. Repeated presentation of time intervals during training reinforces the connections of specific ensembles to downstream networks - sets of neurons connected to the sequence of states. Later, during the peak-interval procedure, these downstream networks are reactivated by previously experienced neuronal ensembles, triggering behavioral responses at the learned time intervals. The model reproduces experimental response patterns from individual rats in the peak-interval procedure, satisfying relevant properties such as the Weber law. Finally, we provide a biological interpretation of the parameters of the model.

Evaluation of connectivity estimates using spiking neuronal network models.

The flow of information between different regions of the cortex is fundamental for brain function. Researchers use causality detection techniques, such as Granger causality, to infer connectivity among brain areas from time series. Generalized partial directed coherence (GPDC) is a frequency domain linear method based on vector autoregressive model, which has been applied in electroencephalography, local field potential, and blood oxygenation level-dependent signals. Despite its widespread usage, previous attempts to validate GPDC use oversimplified simulated data, which do not reflect the nonlinearities and network couplings present in biological signals. In this work, we evaluated the GPDC performance when applied to simulated LFP signals, i.e., generated from networks of spiking neuronal models. We created three models, each containing five interacting networks, and evaluated whether the GPDC method could accurately detect network couplings. When using a stronger coupling, we showed that GPDC correctly detects all existing connections from simulated LFP signals in the three models, without false positives. Varying the coupling strength between networks, by changing the number of connections or synaptic strengths, and adding noise in the times series, altered the receiver operating characteristic (ROC) curve, ranging from perfect to chance level retrieval. We also showed that GPDC values correlated with coupling strength, indicating that GPDC values can provide useful information regarding coupling strength. These results reinforce that GPDC can be used to detect causality relationships over neural signals.

Inactivation of the Medial-Prefrontal Cortex Impairs Interval Timing Precision, but Not Timing Accuracy or Scalar Timing in a Peak-Interval Procedure in Rats.

Motor sequence learning, planning and execution of goal-directed behaviors, and decision making rely on accurate time estimation and production of durations in the seconds-to-minutes range. The pathways involved in planning and execution of goal-directed behaviors include cortico-striato-thalamo-cortical circuitry modulated by dopaminergic inputs. A critical feature of interval timing is its scalar property, by which the precision of timing is proportional to the timed duration. We examined the role of medial prefrontal cortex (mPFC) in timing by evaluating the effect of its reversible inactivation on timing accuracy, timing precision and scalar timing. Rats were trained to time two durations in a peak-interval (PI) procedure. Reversible mPFC inactivation using GABA agonist muscimol resulted in decreased timing precision, with no effect on timing accuracy and scalar timing. These results are partly at odds with studies suggesting that ramping prefrontal activity is crucial to timing but closely match simulations with the Striatal Beat Frequency (SBF) model proposing that timing is coded by the coincidental activation of striatal neurons by cortical inputs. Computer simulations indicate that in SBF, gradual inactivation of cortical inputs results in a gradual decrease in timing precision with preservation of timing accuracy and scalar timing. Further studies are needed to differentiate between timing models based on coincidence detection and timing models based on ramping mPFC activity, and clarify whether mPFC is specifically involved in timing, or more generally involved in attention, working memory, or response selection/inhibition.

Heteroassociative storage of hippocampal pattern sequences in the CA3 subregion.

BACKGROUND: Recent research suggests that the CA3 subregion of the hippocampus has properties of both autoassociative network, due to its ability to complete partial cues, tolerate noise, and store associations between memories, and heteroassociative one, due to its ability to store and retrieve sequences of patterns. Although there are several computational models of the CA3 as an autoassociative network, more detailed evaluations of its heteroassociative properties are missing. METHODS: We developed a model of the CA3 subregion containing 10,000 integrate-and-fire neurons with both recurrent excitatory and inhibitory connections, and which exhibits coupled oscillations in the gamma and theta ranges. We stored thousands of pattern sequences using a heteroassociative learning rule with competitive synaptic scaling. RESULTS: We showed that a purely heteroassociative network model can (i) retrieve pattern sequences from partial cues with external noise and incomplete connectivity, (ii) achieve homeostasis regarding the number of connections per neuron when many patterns are stored when using synaptic scaling, (iii) continuously update the set of retrievable patterns, guaranteeing that the last stored patterns can be retrieved and older ones can be forgotten. DISCUSSION: Heteroassociative networks with synaptic scaling rules seem sufficient to achieve many desirable features regarding connectivity homeostasis, pattern sequence retrieval, noise tolerance and updating of the set of retrievable patterns.

Dynamic representation of time in brain states.

The ability to process time on the scale of milliseconds and seconds is essential for behaviour. A growing number of studies have started to focus on brain dynamics as a mechanism for temporal encoding. Although there is growing evidence in favour of this view from computational and in vitro studies, there is still a lack of results from experiments in humans. We show that high-dimensional brain states revealed by multivariate pattern analysis of human EEG are correlated to temporal judgements. First, we show that, as participants estimate temporal intervals, the spatiotemporal dynamics of their brain activity are consistent across trials. Second, we present evidence that these dynamics exhibit properties of temporal perception, such as scale invariance. Lastly, we show that it is possible to predict temporal judgements based on brain states. These results show how scalp recordings can reveal the spatiotemporal dynamics of human brain activity related to temporal processing.

Low-frequency cortical oscillations are modulated by temporal prediction and temporal error coding.

Monitoring and updating temporal predictions are critical abilities for adaptive behavior. Here, we investigated whether neural oscillations are related to violation and updating of temporal predictions. Human participants performed an experiment in which they had to generate a target at an expected time point, by pressing a button while taking into account a variable delay between the act and the stimulus occurrence. Our behavioral results showed that participants quickly adapted their temporal predictions in face of an error. Concurrent electrophysiological (EEG) data showed that temporal errors elicited markers that are classically related to error coding. Furthermore, intertrial phase coherence of frontal theta oscillations was modulated by error magnitude, possibly indexing the degree of surprise. Finally, we found that delta phase at stimulus onset was correlated with future behavioral adjustments. Together, our findings suggest that low frequency oscillations play a key role in monitoring and in updating temporal predictions.

Visual Causality Judgments Correlate with the Phase of Alpha Oscillations.

The detection of causality is essential for our understanding of whether distinct events relate. A central requirement for the sensation of causality is temporal contiguity: As the interval between events increases, causality ratings decrease; for intervals longer than approximately 100 msec, the events start to appear independent. It has been suggested that this effect might be due to perception relying on discrete processing. According to this view, two events may be judged as sequential or simultaneous depending on their temporal relationship within a discrete neuronal process. To assess if alpha oscillations underlie this discrete neuronal process, we investigated how these oscillations modulate the judgment of causality. We used the classic launching effect with concurrent recording of EEG signal. In each trial, a disk moved horizontally toward a second disk at the center of the screen and stopped when they touched each other. After a delay that varied between 0 and 400 msec after contact, the right disk began to move. Participants were instructed to judge whether or not they had a feeling that the first disk caused the movement of the second disk. We found that frontocentral alpha phase significantly biased causality estimates. Moreover, we found that alpha phase was concentrated around different angles for trials in which participants judged events as causally related versus not causally related. We conclude that alpha phase plays a key role in biasing causality judgments.

A modeling approach on why simple central pattern generators are built of irregular neurons.

The crustacean pyloric Central Pattern Generator (CPG) is a nervous circuit that endogenously provides periodic motor patterns. Even after about 40 years of intensive studies, the rhythm genesis is still not rigorously understood in this CPG, mainly because it is made of neurons with irregular intrinsic activity. Using mathematical models we addressed the question of using a network of irregularly behaving elements to generate periodic oscillations, and we show some advantages of using non-periodic neurons with intrinsic behavior in the transition from bursting to tonic spiking (as found in biological pyloric CPGs) as building components. We studied two- and three-neuron model CPGs built either with Hindmarsh-Rose or with conductance-based Hodgkin-Huxley-like model neurons. By changing a model's parameter we could span the neuron's intrinsic dynamical behavior from slow periodic bursting to fast tonic spiking, passing through a transition where irregular bursting was observed. Two-neuron CPG, half center oscillator (HCO), was obtained for each intrinsic behavior of the neurons by coupling them with mutual symmetric synaptic inhibition. Most of these HCOs presented regular antiphasic bursting activity and the changes of the bursting frequencies was studied as a function of the inhibitory synaptic strength. Among all HCOs, those made of intrinsic irregular neurons presented a wider burst frequency range while keeping a reliable regular oscillatory (bursting) behavior. HCOs of periodic neurons tended to be either hard to change their behavior with synaptic strength variations (slow periodic burster neurons) or unable to perform a physiologically meaningful rhythm (fast tonic spiking neurons). Moreover, 3-neuron CPGs with connectivity and output similar to those of the pyloric CPG presented the same results.

What is learned during simultaneous temporal acquisition? An individual-trials analysis.

The processes involved in the acquisition of simultaneous temporal processing are currently less understood. For example, it is unclear whether scalar property emerges early during simultaneous temporal acquisition. Using an information-processing model which accounts for the amount of information that each temporal process provides in regard to reward time, we predicted that scalar property would emerge early during the acquisition process, but that subjects should take about 27% longer (more trials) to acquire the long duration than the short duration. To evaluate these predictions, we performed individual-trials analyses to identify changes in timing behavior when rats simultaneously acquire two criterion durations, either 10s and 20s (group 10/20) or 20s and 40s (group 20/40). To analyze the individual trials we used a change-point algorithm to identify changes in rats' wait time. For each individual rat, and for each criterion duration, analyses indicated that simultaneous temporal acquisition is characterized by a sudden change in waiting to a wait-time proportional to the associated criterion. The results failed to indicate group differences in regard to the number of trials it takes for the change in wait-time to occur, but that in both groups, it took longer (more trials) to acquire the long duration than the shorter one, not significantly different from the theoretical prediction. These results are discussed in the framework of an information-processing model informing both associative and temporal learning, thus providing a bridge between the two fields. This article is part of a Special Issue entitled: Associative and Temporal Learning.

Effects of chronic video game use on time perception: differences between sub- and multi-second intervals.

Even though video game players frequently report losing track of time while playing, few studies have addressed whether there are long-lasting effects of such activity on time perception. We compared the performance of chronic and occasional video game players in sub- and multi-second time perception tasks. Temporal Discrimination and Temporal Bisection tasks, in the range of 100 to 1,000 milliseconds, and Time estimation and Time production tasks, in the range of 5 to 60 seconds, were used to assess sub- and multi-second time perceptions, respectively. Chronic video game players performed significantly better than occasional players on sub-second tasks, but no group difference was found for the multi-second tasks used. Sub- and multi-second time perceptions are associated to different underlying systems: automatic and cognitive controlled for sub- and multi-second tasks, respectively. We argue that video game use seems to induce more efficient implicit, rather than cognitive controlled, processing of time.

Lymph node T cell responses predict the efficacy of live attenuated SIV vaccines.

Live attenuated simian immunodeficiency virus (SIV) vaccines (LAVs) remain the most efficacious of all vaccines in nonhuman primate models of HIV and AIDS, yet the basis of their robust protection remains poorly understood. Here we show that the degree of LAV-mediated protection against intravenous wild-type SIVmac239 challenge strongly correlates with the magnitude and function of SIV-specific, effector-differentiated T cells in the lymph node but not with the responses of such T cells in the blood or with other cellular, humoral and innate immune parameters. We found that maintenance of protective T cell responses is associated with persistent LAV replication in the lymph node, which occurs almost exclusively in follicular helper T cells. Thus, effective LAVs maintain lymphoid tissue-based, effector-differentiated, SIV-specific T cells that intercept and suppress early wild-type SIV amplification and, if present in sufficient frequencies, can completely control and perhaps clear infection, an observation that provides a rationale for the development of safe, persistent vectors that can elicit and maintain such responses.