State-Dependent Architecture of Thalamic Reticular Subnetworks

Behavioral state is known to influence interactions between thalamus and cortex, which are important for sensation, action, and cognition. The thalamic reticular nucleus (TRN) is hypothesized to regulate thalamo-cortical interactions, but the underlying functional architecture of this process and its state dependence are unknown. By combining the first TRN ensemble recording with psychophysics and connectivity-based optogenetic tagging, we found reticular circuits to be composed of distinct subnetworks. While activity of limbic-projecting TRN neurons positively correlates with arousal, sensory-projecting neurons participate in spindles and show elevated synchrony by slow waves during sleep. Sensory-projecting neurons are suppressed by attentional states, demonstrating that their gating of thalamo-cortical interactions is matched to behavioral state. Bidirectional manipulation of attentional performance was achieved through subnetwork-specific optogenetic stimulation. Together, our findings provide evidence for differential inhibition of thalamic nuclei across brain states, where the TRN separately controls external sensory and internal limbic processing facilitating normal cognitive function.National Institute of Neurological Disorders and Stroke (U.S.) (NIH Pathway to Independence Career Award K99 NS 078115)Brain & Behavior Research Foundation (Young Investigator Award)National Institutes of Health (U.S.) ( Transformative R01 Award TR01-GM10498)National Institutes of Health (U.S.) (Grant R01-MH061976

Cell

Behavioral state is known to influence interactions between thalamus and cortex, which are important for sensation, action, and cognition. The thalamic reticular nucleus (TRN) is hypothesized to regulate thalamo-cortical interactions, but the underlying functional architecture of this process and its state dependence are unknown. By combining the first TRN ensemble recording with psychophysics and connectivity-based optogenetic tagging, we found reticular circuits to be composed of distinct subnetworks. While activity of limbic-projecting TRN neurons positively correlates with arousal, sensory-projecting neurons participate in spindles and show elevated synchrony by slow waves during sleep. Sensory-projecting neurons are suppressed by attentional states, demonstrating that their gating of thalamo-cortical interactions is matched to behavioral state. Bidirectional manipulation of attentional performance was achieved through subnetwork-specific optogenetic stimulation. Together, our findings provide evidence for differential inhibition of thalamic nuclei across brain states, where the TRN separately controls external sensory and internal limbic processing facilitating normal cognitive function. PAPERFLICK:DP1 MH103908/MH/NIMH NIH HHS/United StatesDP1MH103908/DP/NCCDPHP CDC HHS/United StatesK99 NS 078115/NS/NINDS NIH HHS/United StatesK99 NS078115/NS/NINDS NIH HHS/United StatesR00 NS078115/NS/NINDS NIH HHS/United StatesR01 GM104948/GM/NIGMS NIH HHS/United StatesR01 MH061976/MH/NIMH NIH HHS/United StatesR01 NS077986/NS/NINDS NIH HHS/United StatesR01-MH061976/MH/NIMH NIH HHS/United StatesR01MH057414/MH/NIMH NIH HHS/United StatesR01MH101209/MH/NIMH NIH HHS/United StatesR01NS077986/NS/NINDS NIH HHS/United StatesTR01-GM10498/GM/NIGMS NIH HHS/United States2015-08-14T00:00:00Z25126786PMC420548

Cortical Components of Reaction-Time during Perceptual Decisions in Humans

The mechanisms of perceptual decision-making are frequently studied through measurements of reaction time (RT). Classical sequential-sampling models (SSMs) of decision-making posit RT as the sum of non-overlapping sensory, evidence accumulation, and motor delays. In contrast, recent empirical evidence hints at a continuous-flow paradigm in which multiple motor plans evolve concurrently with the accumulation of sensory evidence. Here we employ a trial-to-trial reliability-based component analysis of encephalographic data acquired during a random-dot motion task to directly image continuous flow in the human brain. We identify three topographically distinct neural sources whose dynamics exhibit contemporaneous ramping to time-of-response, with the rate and duration of ramping discriminating fast and slow responses. Only one of these sources, a parietal component, exhibits dependence on strength-of-evidence. The remaining two components possess topographies consistent with origins in the motor system, and their covariation with RT overlaps in time with the evidence accumulation process. After fitting the behavioral data to a popular SSM, we find that the model decision variable is more closely matched to the combined activity of the three components than to their individual activity. Our results emphasize the role of motor variability in shaping RT distributions on perceptual decision tasks, suggesting that physiologically plausible computational accounts of perceptual decision-making must model the concurrent nature of evidence accumulation and motor planning

A low-frequency oscillatory neural signal in humans encodes a developing decision variable

We often make decisions based on sensory evidence that is accumulated over a period of time. How the evidence for such decisions is represented in the brain and how such a neural representation is used to guide a subsequent action are questions of considerable interest to decision sciences. The neural correlates of developing perceptual decisions have been thoroughly investigated in the oculomotor system of macaques who communicated their decisions using an eye movement. It has been found that the evidence informing a decision to make an eye movement is in part accumulated within the same oculomotor circuits that signal the upcoming eye movement. Recent evidence suggests that the somatomotor system may exhibit an analogous property for choices made using a hand movement. To investigate this possibility, we engaged humans in a decision task in which they integrated discrete quanta of sensory information over a period of time and signaled their decision using a hand movement or an eye movement. The discrete form of the sensory evidence allowed us to infer the decision variable on which subjects base their decision on each trial and to assess the neural processes related to each quantum of the incoming decision evidence. We found that a low-frequency electrophysiological signal recorded over centroparietal regions strongly encodes the decision variable inferred in this task, and that it does so specifically for hand movement choices. The signal ramps up with a rate that is proportional to the decision variable, remains graded by the decision variable throughout the delay period, reaches a common peak shortly before a hand movement, and falls off shortly after the hand movement. Furthermore, the signal encodes the polarity of each evidence quantum, with a short latency, and retains the response level over time. Thus, this neural signal shows properties of evidence accumulation. These findings suggest that the decision-related effects observed in the oculomotor system of the monkey during eye movement choices may share the same basic properties with the decision-related effects in the somatomotor system of humans during hand movement choices

A low-frequency oscillatory neural signal in humans encodes a developing decision variable

We often make decisions based on sensory evidence that is accumulated over a period of time. How the evidence for such decisions is represented in the brain and how such a neural representation is used to guide a subsequent action are questions of considerable interest to decision sciences. The neural correlates of developing perceptual decisions have been thoroughly investigated in the oculomotor system of macaques who communicated their decisions using an eye movement. It has been found that the evidence informing a decision to make an eye movement is in part accumulated within the same oculomotor circuits that signal the upcoming eye movement. Recent evidence suggests that the somatomotor system may exhibit an analogous property for choices made using a hand movement. To investigate this possibility, we engaged humans in a decision task in which they integrated discrete quanta of sensory information over a period of time and signaled their decision using a hand movement or an eye movement. The discrete form of the sensory evidence allowed us to infer the decision variable on which subjects base their decision on each trial and to assess the neural processes related to each quantum of the incoming decision evidence. We found that a low-frequency electrophysiological signal recorded over centroparietal regions strongly encodes the decision variable inferred in this task, and that it does so specifically for hand movement choices. The signal ramps up with a rate that is proportional to the decision variable, remains graded by the decision variable throughout the delay period, reaches a common peak shortly before a hand movement, and falls off shortly after the hand movement. Furthermore, the signal encodes the polarity of each evidence quantum, with a short latency, and retains the response level over time. Thus, this neural signal shows properties of evidence accumulation. These findings suggest that the decision-related effects observed in the oculomotor system of the monkey during eye movement choices may share the same basic properties with the decision-related effects in the somatomotor system of humans during hand movement choices

There is more to decisions than meets the eye: Cortical motor activity and previous motor responses predict sensorimotor decisions

Abstract Human behavior is largely guided by sensory information about our environment. The process of transforming sensory evidence into appropriate behavior is called sensorimotor decision making. Despite the many advances in uncovering its neural basis, it remains unclear which role cortical motor areas play in the functional architecture enabling sensorimotor decision making. Specifically, it is unknown whether cortical motor areas actually contribute to the decision making process, e.g. by casting a vote on the response alternatives, or whether they alternatively simply produce the behavior selected elsewhere. To investigate the involvement of cortical motor areas in sensorimotor decision making, we conducted two experiments in which human participants made choices about motion in visual stimuli and reported the choice with one of two manual responses, i.e. button presses with the left or the right index finger. Using magnetoencephalography to measure neural activity during decision making, in the first experiment we showed that activity in sensorimotor areas was predictive of upcoming choices several seconds before the button press and even before stimulus presentation. In part, this activity could be linked to the neural aftermath of the previous trial’s choice report, which shifted a measure of cortical activity in sensorimotor areas towards the previously unchosen response alternative in the current trial. This previously unknown tendency to alternate between hands when reporting sensorimotor decisions was significant and varied in size with the size of the neural aftermath of the previous button press over sensorimotor areas across several independent statistics. The results show that beyond the current stimulus, i.e. beyond what meets the eye, other factors like the previous motor act may influence response selection in sensorimotor decision making. Additionally, the results suggest that this is driven by the neural aftermath of previous responses in cortical motor areas. More generally, this suggest that neural fluctuations in cortical motor areas can influence response selection in sensorimotor decision making. This means that cortical motor areas may be more than an output stage in sensorimotor decision making. Consistent with this interpretation, we showed that response alternation in sensorimotor decision making can be manipulated in a directed fashion through instructed and non-choice-related simple button 12 | Abstract presses in an independent group of participants in our second study. This result establishes that previous motor acts can influence response selection in sensorimotor decision making, independent of whether they are choice-related or simply instructed. Given this generalization beyond choice-driven button presses, the results of the second experiment are consistent with the interpretation that response alternation is at least partly driven by neural correlates of previous motor acts. In summary, our results suggest that neural fluctuations in cortical motor areas can influence response selection in sensorimotor decision making, in turn suggesting that motor areas may be more than an output stage of the brain during sensorimotor decision making

Neural Substrates of Human Perceptual Decision-Making

Perceptual decision-making describes the process of choosing one of at least two response alternatives based on sensory evidence. This sensorimotor process underlies a range of human behaviours and has been studied extensively by both psychologists and neuroscientists. There is now a consensus, that perceptual decision-making can be explained by sequential sampling models, which assume that we make decisions by accumulating sensory evidence over time until a decision threshold is reached and the response is executed. Although these models are designed to explain behavioural data, the accumulation-to-bound processes they predict have recently been shown to occur in the brain. In this project, we set out to explore these neural correlates of decision-making in the human brain by combining mathematical modelling with neuroimaging. We fitted sequential sampling models to human decision-making data collected in a number of paradigms and directly compared the associated accumulation profiles with neural signals, which were generated either by using electroencephalographic (EEG) recordings or through transcranial magnetic stimulation (TMS). We found that decision-related accumulation profiles can be observed using a parietal EEG signal, namely the event-related potential centroparietal positivity (CPP). Additionally, we showed that accumulation is fed forward to the motor system, where it can be measured using TMS-induced motor evoked potentials. We demonstrated that, under a number of manipulations, namely difficulty, response speed instructions, non-stationary evidence, decision biases, and number of alternatives, these signals display profiles similar to those predicted by sequential sampling models. Our findings support the notion that sequential sampling occurs in the human brain and demonstrate that a model-based approach in which sequential sampling models and neuroimaging are combined and inform each other, can shed light on the underlying mechanisms of human perceptual decision-making