Biased competition in the absence of input bias revealed through corticostriatal computation

Modeling and dissociation of intrinsic and input-driven neural population dynamics underlying behavior

Neural dynamics can reflect intrinsic dynamics or dynamic inputs, such as sensory inputs or inputs from other brain regions. To avoid misinterpreting temporally structured inputs as intrinsic dynamics, dynamical models of neural activity should account for measured inputs. However, incorporating measured inputs remains elusive in joint dynamical modeling of neural-behavioral data, which is important for studying neural computations of behavior. We first show how training dynamical models of neural activity while considering behavior but not input or input but not behavior may lead to misinterpretations. We then develop an analytical learning method for linear dynamical models that simultaneously accounts for neural activity, behavior, and measured inputs. The method provides the capability to prioritize the learning of intrinsic behaviorally relevant neural dynamics and dissociate them from both other intrinsic dynamics and measured input dynamics. In data from a simulated brain with fixed intrinsic dynamics that performs different tasks, the method correctly finds the same intrinsic dynamics regardless of the task while other methods can be influenced by the task. In neural datasets from three subjects performing two different motor tasks with task instruction sensory inputs, the method reveals low-dimensional intrinsic neural dynamics that are missed by other methods and are more predictive of behavior and/or neural activity. The method also uniquely finds that the intrinsic behaviorally relevant neural dynamics are largely similar across the different subjects and tasks, whereas the overall neural dynamics are not. These input-driven dynamical models of neural-behavioral data can uncover intrinsic dynamics that may otherwise be missed.

Modeling and dissociation of intrinsic and input-driven neural population dynamics underlying behavior

Neural dynamics can reflect intrinsic dynamics or dynamic inputs, such as sensory inputs or inputs from other regions. To avoid misinterpreting temporally-structured inputs as intrinsic dynamics, dynamical models of neural activity should account for measured inputs. However, incorporating measured inputs remains elusive in joint dynamical modeling of neural-behavioral data, which is important for studying neural computations of a specific behavior. We first show how training dynamical models of neural activity while considering behavior but not input, or input but not behavior may lead to misinterpretations. We then develop a novel analytical learning method that simultaneously accounts for neural activity, behavior, and measured inputs. The method provides the new capability to prioritize the learning of intrinsic behaviorally relevant neural dynamics and dissociate them from both other intrinsic dynamics and measured input dynamics. In data from a simulated brain with fixed intrinsic dynamics that performs different tasks, the method correctly finds the same intrinsic dynamics regardless of task while other methods can be influenced by the change in task. In neural datasets from three subjects performing two different motor tasks with task instruction sensory inputs, the method reveals low-dimensional intrinsic neural dynamics that are missed by other methods and are more predictive of behavior and/or neural activity. The method also uniquely finds that the intrinsic behaviorally relevant neural dynamics are largely similar across the three subjects and two tasks whereas the overall neural dynamics are not. These input-driven dynamical models of neural-behavioral data can uncover intrinsic dynamics that may otherwise be missed.

Bi-directional regulation of cognitive control by distinct prefrontal cortical output neurons to thalamus and striatum

The medial prefrontal cortex (mPFC) steers goal-directed actions and withholds inappropriate behavior. Dorsal and ventral mPFC (dmPFC/vmPFC) circuits have distinct roles in cognitive control, but underlying mechanisms are poorly understood. Here we use neuroanatomical tracing techniques, in vitro electrophysiology, chemogenetics and fiber photometry in rats engaged in a 5-choice serial reaction time task to characterize dmPFC and vmPFC outputs to distinct thalamic and striatal subdomains. We identify four spatially segregated projection neuron populations in the mPFC. Using fiber photometry we show that these projections distinctly encode behavior. Postsynaptic striatal and thalamic neurons differentially process synaptic inputs from dmPFC and vmPFC, highlighting mechanisms that potentially amplify distinct pathways underlying cognitive control of behavior. Chemogenetic silencing of dmPFC and vmPFC projections to lateral and medial mediodorsal thalamus subregions oppositely regulate cognitive control. In addition, dmPFC neurons projecting to striatum and thalamus divergently regulate cognitive control. Collectively, we show that mPFC output pathways targeting anatomically and functionally distinct striatal and thalamic subregions encode bi-directional command of cognitive control.

Non-invasive suppression of essential tremor via phase-locked disruption of its temporal coherence

Aberrant neural oscillations hallmark numerous brain disorders. Here, we first report a method to track the phase of neural oscillations in real-time via endpoint-corrected Hilbert transform (ecHT) that mitigates the characteristic Gibbs distortion. We then used ecHT to show that the aberrant neural oscillation that hallmarks essential tremor (ET) syndrome, the most common adult movement disorder, can be transiently suppressed via transcranial electrical stimulation of the cerebellum phase-locked to the tremor. The tremor suppression is sustained shortly after the end of the stimulation and can be phenomenologically predicted. Finally, we use feature-based statistical-learning and neurophysiological-modelling to show that the suppression of ET is mechanistically attributed to a disruption of the temporal coherence of the aberrant oscillations in the olivocerebellar loop, thus establishing its causal role. The suppression of aberrant neural oscillation via phase-locked driven disruption of temporal coherence may in the future represent a powerful neuromodulatory strategy to treat brain disorders.

Striatal bilateral control of skilled forelimb movement

Skilled motor behavior requires bihemispheric coordination, and participation of striatal outputs originating from two neuronal groups identified by distinctive expression of D1 or D2 dopamine receptors. We trained mice to reach for and grasp a single food pellet and determined how the output pathways differently affected forelimb trajectory and task efficiency. We found that inhibition and excitation of D1-expressing spiny projection neurons (D1SPNs) have a similar effect on kinematics results, as if excitation and inhibition disrupt the whole ensemble dynamics and not exclusively one kind of output. In contrast, D2SPNs participate in control of target accuracy. Further, ex vivo electrophysiological comparison of naive mice and mice exposed to the task showed stronger striatal neuronal connectivity for ipsilateral D1 and contralateral D2 neurons in relation to the paw used. In summary, while the output pathways work together to smoothly execute skill movements, practice of the movement itself changes synaptic patterns.

Fast spiking interneuron activity in primate striatum tracks learning of attention cues

Cognitive flexibility depends on a fast neural learning mechanism for enhancing momentary relevant over irrelevant information. A possible neural mechanism realizing this enhancement uses fast spiking interneurons (FSIs) in the striatum to train striatal projection neurons to gate relevant and suppress distracting cortical inputs. We found support for such a mechanism in nonhuman primates during the flexible adjustment of visual attention in a reversal learning task. FSI activity was modulated by visual attention cues during feature-based learning. One FSI subpopulation showed stronger activation during learning, while another FSI subpopulation showed response suppression after learning, which could indicate a disinhibitory effect on the local circuit. Additionally, FSIs that showed response suppression to learned attention cues were activated by salient distractor events, suggesting they contribute to suppressing bottom-up distraction. These findings suggest that striatal fast spiking interneurons play an important role when cues are learned that redirect attention away from previously relevant to newly relevant visual information. This cue-specific activity was independent of motor-related activity and thus tracked specifically the learning of reward predictive visual features.

Prefrontal oscillations modulate the propagation of neuronal activity required for working memory

Cognition involves using attended information, maintained in working memory (WM), to guide action. During a cognitive task, a correct response requires flexible, selective gating so that only the appropriate information flows from WM to downstream effectors that carry out the response. In this work, we used biophysically-detailed modeling to explore the hypothesis that network oscillations in prefrontal cortex (PFC), leveraging local inhibition, can independently gate responses to items in WM. The key role of local inhibition was to control the period between spike bursts in the outputs, and to produce an oscillatory response no matter whether the WM item was maintained in an asynchronous or oscillatory state. We found that the WM item that induced an oscillatory population response in the PFC output layer with the shortest period between spike bursts was most reliably propagated. The network resonant frequency (i.e., the input frequency that produces the largest response) of the output layer can be flexibly tuned by varying the excitability of deep layer principal cells. Our model suggests that experimentally-observed modulation of PFC beta-frequency (15-30 Hz) and gamma-frequency (30-80 Hz) oscillations could leverage network resonance and local inhibition to govern the flexible routing of signals in service to cognitive processes like gating outputs from working memory and the selection of rule-based actions. Importantly, we show for the first time that nonspecific changes in deep layer excitability can tune the output gate's resonant frequency, enabling the specific selection of signals encoded by populations in asynchronous or fast oscillatory states. More generally, this represents a dynamic mechanism by which adjusting network excitability can govern the propagation of asynchronous and oscillatory signals throughout neocortex.

Inhibitory control is associated with the activation of output-driven competitors in a spoken word recognition task

Although lexical competition has been ubiquitously observed in spoken word recognition, less has been known about whether the lexical competitors interfere with the recognition of the target and how lexical interference is resolved. The present study examined whether lexical competitors overlapping in output with the target would interfere with its recognition, and tested an underestimated hypothesis that the domain-general inhibitory control contributes to the resolution of lexical interference. Specifically, in this study, a Visual World Paradigm was used to access the temporal dynamics of lexical activations when participants were moving the mouse cursor to the written word form of the spoken word they heard. By using Chinese characters, the orthographic similarity between the lexical competitor and target was manipulated independently of their phonological overlap. The results demonstrated that behavioral performance in the similar condition was poorer compared to that in the control condition, and that individuals with better inhibitory control (having smaller Stroop interference effect) exhibited weaker activation of orthographic competitors (mouse trajectories less attracted by the orthographic competitors). The implications of these findings for our understanding of lexical interference and its resolution in spoken word recognition were discussed.

A biophysical model of striatal microcircuits suggests gamma and beta oscillations interleaved at delta/theta frequencies mediate periodicity in motor control

Striatal oscillatory activity is associated with movement, reward, and decision-making, and observed in several interacting frequency bands. Local field potential recordings in rodent striatum show dopamine- and reward-dependent transitions between two states: a "spontaneous" state involving β (∼15-30 Hz) and low γ (∼40-60 Hz), and a state involving θ (∼4-8 Hz) and high γ (∼60-100 Hz) in response to dopaminergic agonism and reward. The mechanisms underlying these rhythmic dynamics, their interactions, and their functional consequences are not well understood. In this paper, we propose a biophysical model of striatal microcircuits that comprehensively describes the generation and interaction of these rhythms, as well as their modulation by dopamine. Building on previous modeling and experimental work suggesting that striatal projection neurons (SPNs) are capable of generating β oscillations, we show that networks of striatal fast-spiking interneurons (FSIs) are capable of generating δ/θ (ie, 2 to 6 Hz) and γ rhythms. Under simulated low dopaminergic tone our model FSI network produces low γ band oscillations, while under high dopaminergic tone the FSI network produces high γ band activity nested within a δ/θ oscillation. SPN networks produce β rhythms in both conditions, but under high dopaminergic tone, this β oscillation is interrupted by δ/θ-periodic bursts of γ-frequency FSI inhibition. Thus, in the high dopamine state, packets of FSI γ and SPN β alternate at a δ/θ timescale. In addition to a mechanistic explanation for previously observed rhythmic interactions and transitions, our model suggests a hypothesis as to how the relationship between dopamine and rhythmicity impacts motor function. We hypothesize that high dopamine-induced periodic FSI γ-rhythmic inhibition enables switching between β-rhythmic SPN cell assemblies representing the currently active motor program, and thus that dopamine facilitates movement in part by allowing for rapid, periodic shifts in motor program execution.

Threat Prediction from Schemas as a Source of Bias in Pain Perception

Our sensory impressions of pain are generally thought to represent the noxious properties of an agent but can be influenced by the predicted level of threat. Predictions can be sourced from higher-order cognitive processes, such as schemas, but the extent to which schemas can influence pain perception relative to bottom-up sensory inputs and the underlying neural underpinnings of such a phenomenon are unclear. Here, we investigate how threat predictions generated from learning a cognitive schema lead to inaccurate sensory impressions of the pain stimulus. Healthy male and female participants first detected a linear association between cue values and stimulus intensity and rated pain to reflect the linear schema when compared with uncued heat stimuli. The effect of bias on pain ratings was reduced when prediction errors (PEs) increased, but pain perception was only partially updated when measured against stepped increases in PEs. Cognitive, striatal, and sensory regions graded their responses to changes in predicted threat despite the PEs (p < 0.05, corrected). Individuals with more catastrophic thinking about pain and with low mindfulness were significantly more reliant on the schema than on the sensory evidence from the pain stimulus. These behavioral differences mapped to variability in responses of the striatum and ventromedial prefrontal cortex. Thus, this study demonstrates a significant role of higher-order schemas in pain perception and indicates that pain perception is biased more toward predictions and less toward nociceptive inputs in individuals who report less mindfulness and more fear of pain.SIGNIFICANCE STATEMENT This study demonstrates that threat predictions generated from cognitive schemas continue to influence pain perception despite increasing prediction errors arising in pain pathways. Individuals first formed a cognitive schema of linearity in the relationship between the cued threat value and the stimulus intensity. Subsequently, the linearity was reduced gradually, and participants partially updated their evaluations of pain in relation to the stepped increases in prediction errors. Individuals who continued to rate pain based more on the predicted threat than on changes in nociceptive inputs reported high pain catastrophizing and less mindful-awareness scores. These two affects mapped to activity in the ventral and dorsal striatum, respectively. These findings direct us to a significant role of top-down processes in pain perception.