Dopamine and the interdependency of time perception and reward

Parallel processes of temporal control in the supplementary motor area and the frontoparietal circuit

Durations in the several seconds' range are cognitively accessible during active timing. Functional neuroimaging studies suggest the engagement of the basal ganglia (BG) and supplementary motor area (SMA). However, their functional relevance and arrangement remain unclear because non-timing cognitive processes temporally coincide with the active timing. To examine the potential contamination by parallel processes, we introduced a sensory control and a motor control to the duration-reproduction task. By comparing their hemodynamic functions, we decomposed the neural activities in multiple brain loci linked to different cognitive processes. Our results show a dissociation of two cortical neural circuits: the SMA for both active timing and motor preparation, followed by a prefrontal-parietal circuit related to duration working memory. We argue that these cortical processes represent duration as the content but at different levels of abstraction, while the subcortical structures, including the BG and thalamus, provide the logistic basis of timing by coordinating the temporal framework across brain structures.

Unified control of temporal and spatial scales of sensorimotor behavior through neuromodulation of short-term synaptic plasticity

Neuromodulators have been shown to alter the temporal profile of short-term synaptic plasticity (STP); however, the computational function of this neuromodulation remains unexplored. Here, we propose that the neuromodulation of STP provides a general mechanism to scale neural dynamics and motor outputs in time and space. We trained recurrent neural networks that incorporated STP to produce complex motor trajectories-handwritten digits-with different temporal (speed) and spatial (size) scales. Neuromodulation of STP produced temporal and spatial scaling of the learned dynamics and enhanced temporal or spatial generalization compared to standard training of the synaptic weights in the absence of STP. The model also accounted for the results of two experimental studies involving flexible sensorimotor timing. Neuromodulation of STP provides a unified and biologically plausible mechanism to control the temporal and spatial scales of neural dynamics and sensorimotor behaviors.

Modulation of time in Parkinson's disease: a review and perspective on cognitive rehabilitation

Time cognition is an essential function of human life, and the impairment affects a variety of behavioral patterns. Neuropsychological approaches have been widely demonstrated that Parkinson's disease (PD) impairs time cognitive processing. Many researchers believe that time cognitive deficits are due to the basal ganglia, including the striatum or subthalamic nucleus, which is the pathomechanism of PD, and are considered to produce only transient recovery due to medication effects. In this perspective, we focus on a compensatory property of brain function based on the improved time cognition independent of basal ganglia recovery and an overlapping structure on the neural network based on an improved inhibitory system by time cognitive training, in patients with PD. This perspective may lead to restoring multiple functions through single function training.

A Cortico-Striatal Circuit for Sound-Triggered Prediction of Reward Timing

A crucial aspect of auditory perception is the ability to use sound cues to predict future events and to time actions accordingly. For example, distinct smartphone notification sounds reflect a call that needs to be answered within a few seconds, or a text that can be read later; the sound of an approaching vehicle signals when it is safe to cross the street. Other animals similarly use sounds to plan, time and execute behaviors such as hunting, evading predation and tending to offspring. However, the neural mechanisms that underlie sound-guided prediction of upcoming salient event timing are not well understood. To address this gap, we employed an appetitive sound-triggered reward time prediction behavior in head-fixed mice. We find that mice trained on this task reliably estimate the time from a sound cue to upcoming reward on the scale of a few seconds, as demonstrated by learning-dependent well-timed increases in reward-predictive licking. Moreover, mice showed a dramatic impairment in their ability to use sound to predict delayed reward when the auditory cortex was inactivated, demonstrating its causal involvement. To identify the neurophysiological signatures of auditory cortical reward-timing prediction, we recorded local field potentials during learning and performance of this behavior and found that the magnitude of auditory cortical responses to the sound prospectively encoded the duration of the anticipated sound-reward time interval. Next, we explored how and where these sound-triggered time interval prediction signals propagate from the auditory cortex to time and initiate consequent action. We targeted the monosynaptic projections from the auditory cortex to the posterior striatum and found that chemogenetic inactivation of these projections impairs animal's ability to predict sound-triggered delayed reward. Simultaneous neural recordings in the auditory cortex and posterior striatum during task performance revealed coordination of neural activity across these regions during the sound cue predicting the time interval to reward. Collectively, our findings identify an auditory cortical-striatal circuit supporting sound-triggered timing-prediction behaviors.

The effect of 24-hour sleep deprivation on subjective time perception

In this study, we investigated the effect of 24-h total sleep deprivation on subjective time perception. Twenty-five participants aged 18-35 years (13 female and 12 male) were recruited. Time perception and cognitive assessments were performed twice: after a regular night's sleep and following a 24-h sleep deprivation. The retrospective and prospective tasks were used to measure time perception. In order to prevent order effect, the test orders were randomized. The Stroop test and the Wechsler Memory Scale-III were used to evaluate attention, processing speed, and memory. The repeated measures ANOVA was used to examine gender-by-sleep deprivation interactions on time perception. We found that retrospective time perception was significantly prolonged after sleep deprivation (p < 0.05). Women had a shorter prospective time estimation rate after adequate sleep than men, but this difference disappeared after sleep deprivation. The Stroop test showed improvement in cognitive flexibility after sleep deprivation (p < 0.05), and short-term or working memory appeared unaffected by one night of sleep deprivation. There was a negative correlation between sleepiness rate and working memory function in female subgroup. The results suggest that even short-term sleep deprivation can significantly affect time perception, which may have important implications in critical situations.

Altered delay discounting in neurodegeneration: insight into the underlying mechanisms and perspectives for clinical applications

Steeper delay discounting (i.e., the extent to which future rewards are perceived as less valuable than immediate ones) has been proposed as a transdiagnostic process across different health conditions, in particular psychiatric disorders. Impulsive decision-making is a hallmark of different neurodegenerative conditions but little is known about delay discounting in the domain of neurodegenerative conditions. We reviewed studies on delay discounting in patients with Parkinson's disease (PD) and in patients with dementia (Alzheimer's disease / AD or frontotemporal dementia / FTD). We proposed that delay discounting could be an early marker of the neurodegenerative process. We developed the idea that altered delay discounting is associated with overlapping but distinct neurocognitive mechanisms across neurodegenerative diseases: dopaminergic-related disorders of reward processing in PD, memory/projection deficits due to medial temporal atrophy in AD, modified reward processing due to orbitofrontal atrophy in FTD. Neurodegeneration could provide a framework to decipher the neuropsychological mechanisms of value-based decision-making. Further, delay discounting could become a marker of interest in clinical practice, in particular for differential diagnosis.

Neural population clocks: Encoding time in dynamic patterns of neural activity

The ability to predict and prepare for near- and far-future events is among the most fundamental computations the brain performs. Because of the importance of time for prediction and sensorimotor processing, the brain has evolved multiple mechanisms to tell and encode time across scales ranging from microseconds to days and beyond. Converging experimental and computational data indicate that, on the scale of seconds, timing relies on diverse neural mechanisms distributed across different brain areas. Among the different encoding mechanisms on the scale of seconds, we distinguish between neural population clocks and ramping activity as distinct strategies to encode time. One instance of neural population clocks, neural sequences, represents in some ways an optimal and flexible dynamic regime for the encoding of time. Specifically, neural sequences comprise a high-dimensional representation that can be used by downstream areas to flexibly generate arbitrarily simple and complex output patterns using biologically plausible learning rules. We propose that high-level integration areas may use high-dimensional dynamics such as neural sequences to encode time, providing downstream areas information to build low-dimensional ramp-like activity that can drive movements and temporal expectation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Encoding time in neural dynamic regimes with distinct computational tradeoffs

Converging evidence suggests the brain encodes time in dynamic patterns of neural activity, including neural sequences, ramping activity, and complex dynamics. Most temporal tasks, however, require more than just encoding time, and can have distinct computational requirements including the need to exhibit temporal scaling, generalize to novel contexts, or robustness to noise. It is not known how neural circuits can encode time and satisfy distinct computational requirements, nor is it known whether similar patterns of neural activity at the population level can exhibit dramatically different computational or generalization properties. To begin to answer these questions, we trained RNNs on two timing tasks based on behavioral studies. The tasks had different input structures but required producing identically timed output patterns. Using a novel framework we quantified whether RNNs encoded two intervals using either of three different timing strategies: scaling, absolute, or stimulus-specific dynamics. We found that similar neural dynamic patterns at the level of single intervals, could exhibit fundamentally different properties, including, generalization, the connectivity structure of the trained networks, and the contribution of excitatory and inhibitory neurons. Critically, depending on the task structure RNNs were better suited for generalization or robustness to noise. Further analysis revealed different connection patterns underlying the different regimes. Our results predict that apparently similar neural dynamic patterns at the population level (e.g., neural sequences) can exhibit fundamentally different computational properties in regards to their ability to generalize to novel stimuli and their robustness to noise—and that these differences are associated with differences in network connectivity and distinct contributions of excitatory and inhibitory neurons. We also predict that the task structure used in different experimental studies accounts for some of the experimentally observed variability in how networks encode time.

The ability to tell time and anticipate when external events will occur are among the most fundamental computations the brain performs. Converging evidence suggests the brain encodes time through changing patterns of neural activity. Different temporal tasks, however, have distinct computational requirements, such as the need to flexibly scale temporal patterns or generalize to novel inputs. To understand how networks can encode time and satisfy different computational requirements we trained recurrent neural networks (RNNs) on two timing tasks that have previously been used in behavioral studies. Both tasks required producing identically timed output patterns. Using a novel framework to quantify how networks encode different intervals, we found that similar patterns of neural activity—neural sequences—were associated with fundamentally different underlying mechanisms, including the connectivity patterns of the RNNs. Critically, depending on the task the RNNs were trained on, they were better suited for generalization or robustness to noise. Our results predict that similar patterns of neural activity can be produced by distinct RNN configurations, which in turn have fundamentally different computational tradeoffs. Our results also predict that differences in task structure account for some of the experimentally observed variability in how networks encode time.

Time Perception in Prodromal Alzheimer's Dementia and in Prodromal Dementia With Lewy Bodies

Background: Time perception is a subjective experience or sense of time. Previous studies have shown that Alzheimer's dementia (AD) patients have time perception deficits compared to a cognitively unimpaired control group (CU). There are only a few studies on dementia with Lewy bodies (DLB) patients' time perception in comparison with CU and AD patients. Early intervention and prescription of the right medicine may delay the deterioration of AD and DLB, moreover, knowing how prodromal AD (prAD) and prodromal DLB's (prDLB) time perception differ from each other might be helpful for future understanding of these two dementias. Therefore, the purpose of this study is to explore the difference in time perception performance between prodromal AD and prodromal DLB. Methods: We invited people diagnosed with prAD, prDLB, and CU to participate in this study. Tests of verbal estimation of time and time interval production were used to assess their time perception. We analyzed the average time estimation (ATE), absolute error score (ABS), coefficient of variance (CV), and subjective temporal unit (STU) within the three groups. Results: A total of 40 prAD, 30 prDLB, and 47 CU completed the study. In the verbal estimation test, the CV for the prAD was higher than both prDLB and CU at the 9 s interval, and the CV of prAD was higher than CU at the 27 s interval. In the time interval production test, the subjective time units of prDLB were higher than prAD at the 10 s interval, while those of both prDLB and CU were higher than prAD at the 30 s interval. The percentage of subjects with STU < 1.0 s, indicating overestimation, was higher in prAD than both prDLB and CU. Conclusion: Time perception of prAD patients showed imprecision and overestimation of time, while prDLB tended to underestimate time intervals. No significant difference was found in accuracy among the three groups. It is speculated that the clinical and pathological severity of the two prodromal dementia stages may be different, and some patients have not yet had their time perception affected.