Parabrachial nucleus activity in nociception and pain in awake mice.

The parabrachial nuclear complex (PBN) is a nexus for aversion, and for the sensory and affective components of pain perception. We have previously shown that, during chronic pain, PBN neurons in anesthetized rodents have amplified activity. We report a method to record from PBN neurons of behaving, head-restrained mice, while applying reproducible noxious stimuli. We find that both spontaneous and evoked activity are higher in awake animals, compared to urethane anesthetized mice. Fiber photometry of calcium responses from CGRP-expressing PBN neurons demonstrates that these neurons respond to nociceptive stimuli. In both males and females with neuropathic or inflammatory pain, responses of PBN neurons remain amplified for at least 5 weeks, in parallel with increased pain metrics. We also show that PBN neurons can be rapidly conditioned to respond to innocuous stimuli, after pairing with nociceptive stimuli. Finally, we demonstrate that changes in PBN neuronal activity are correlated with changes in arousal, measured as changes in pupil diameter. Significance Statement: The parabrachial complex is a nexus of aversion, including pain. We report a method to record from parabrachial nucleus neurons of behaving mice, while applying reproducible noxious stimuli. This allowed, for the first time, tracking the activity of these neurons over time in animals with neuropathic or inflammatory pain. It also allowed us to show that the activity of these neurons correlates with arousal states, and that these neurons can be conditioned to respond to innocuous stimuli.

Internal states as a source of subject-dependent movement variability are represented by large-scale brain networks.

Humans' ability to adapt and learn relies on reflecting on past performance. These experiences form latent representations called internal states that induce movement variability that improves how we interact with our environment. Our study uncovered temporal dynamics and neural substrates of two states from ten subjects implanted with intracranial depth electrodes while they performed a goal-directed motor task with physical perturbations. We identified two internal states using state-space models: one tracking past errors and the other past perturbations. These states influenced reaction times and speed errors, revealing how subjects strategize from trial history. Using local field potentials from over 100 brain regions, we found large-scale brain networks such as the dorsal attention and default mode network modulate visuospatial attention based on recent performance and environmental feedback. Notably, these networks were more prominent in higher-performing subjects, emphasizing their role in improving motor performance by regulating movement variability through internal states.

Alpha Power in the Cingulate Cortex reflects Accumulated Winnings During Gambling in Humans.

When making bets one's level of attention determines how much they may win. The cingulate cortex is a brain region associated with attention and may influence behaviors during gambling. With data gathered from the cingulate cortex in humans implanted with depth electrodes for clinical purposes while performing a gambling task of high card, we determine a relationship between neural correlates of attention and accumulated winnings. Specifically, we analyze how changes in alpha power (8-12 Hz) in the CC relate to accumulated winnings. We compared three subjects with different betting strategies: Reflexive (betting low on cards 2, 4, and 6), Logical (varying how they bet on card 6), and Illogical (betting randomly on all cards). We found that alpha power encodes attention in the cingulate cortex and relates to their accumulated winnings, especially in the illogical subject who had the least winning.

Correlates of Attention in the Cingulate Cortex During Gambling in Humans.

People make decisions multiple times on a daily basis. However, some decisions are easier to make than others and perhaps require more attention to ensure a positive outcome. During gambling, one should attempt to compute the expected rewards and risks associated with decisions. Failing to allocate attention and neural resources to estimate these values can be costly, and in some cases can lead to bankruptcy. Alpha-band (8-12 Hz) oscillatory power in the brain is thought to reflect attention, but how this influences financial decision making is not well understood. Using local field potential recordings in nine human subjects performing a gambling task, we compared alpha-band power from the cingulate cortex (CC) during trials of low and high attention. We found that alpha-band power tended to be higher during a 2 second window after a fixation cue was shown in low attention trials.

Neural Correlates of Internal States that Capture Movement Variability.

The brain lacks the ability to perfectly replicate movements. In particular, if specific movements are cued sequentially, how you perform on past trials may influence how you move on current and future trials. Past trial outcomes may, for example, modulate motivation or attention which can play a significant role in how one moves, yet variability due to such internal factors are often ignored when modeling the sensorimotor control system. In this study, we wish to extract such internal factors by modeling variability in movements during a motor task riddled with unpredictable perturbations. Four subjects performed the task, and we simultaneously obtained Local Field Potential (LFP) activity from nonmotor brain regions via depth electrodes implanted for clinical purposes. We first show that motor behavior depends not only on current trial conditions, but also on internal state variables that accumulate past outcomes involving movement performance, movement speed, and whether or not perturbations have occurred. We further show that these internal states modulate with beta band activity in specific brain regions on a trial-by-trial basis. These results suggest a nontraditional role of nonmotor brain regions and prompt a need for further exploration.

Neural Activity from Attention Networks Predicts Movement Errors.

Traditionally, movement-related behavior is estimated using activity from motor regions in the brain. This predictive capability of interpreting neural signals into tangible outputs has led to the emergence of Brain-Computer Interface (BCI) systems. However, nonmotor regions can play a significant role in shaping how movements are executed. Our goal was to explore the contribution of nonmotor brain regions to movement using a unique experimental paradigm in which local field potential recordings of several cortical and subcortical regions were obtained from eight epilepsy patients implanted with depth electrodes as they performed goal-directed reaching movements. The instruction of the task was to move a cursor with a robotic arm to the indicated target with a specific speed, where correct trials were ones in which the subject achieved the instructed speed. We constructed subject-specific models that predict the speed error of each trial from neural activity in nonmotor regions. Neural features were found by averaging spectral power of activity in multiple frequency bands produced during the planning or execution of movement. Features with high predictive power were selected using a forward selection greedy search. Using our modeling framework, we were able to identify networks of regions related to attention that significantly contributed to predicting trial errors. Our results suggest that nonmotor brain regions contain relevant information about upcoming movements and should be further studied.

Non-motor Brain Regions in Non-dominant Hemisphere Are Influential in Decoding Movement Speed.

Sensorimotor control studies have predominantly focused on how motor regions of the brain relay basic movement-related information such as position and velocity. However, motor control is often complex, involving the integration of sensory information, planning, visuomotor tracking, spatial mapping, retrieval and storage of memories, and may even be emotionally driven. This suggests that many more regions in the brain are involved beyond premotor and motor cortices. In this study, we exploited an experimental setup wherein activity from over 87 non-motor structures of the brain were recorded in eight human subjects executing a center-out motor task. The subjects were implanted with depth electrodes for clinical purposes. Using training data, we constructed subject-specific models that related spectral power of neural activity in six different frequency bands as well as a combined model containing the aggregation of multiple frequency bands to movement speed. We then tested the models by evaluating their ability to decode movement speed from neural activity in the test data set. The best models achieved a correlation of 0.38 ± 0.03 (mean ± standard deviation). Further, the decoded speeds matched the categorical representation of the test trials as correct or incorrect with an accuracy of 70 ± 2.75% across subjects. These models included features from regions such as the right hippocampus, left and right middle temporal gyrus, intraparietal sulcus, and left fusiform gyrus across multiple frequency bands. Perhaps more interestingly, we observed that the non-dominant hemisphere (ipsilateral to dominant hand) was most influential in decoding movement speed.