Biology subjects

Kellis, S.

Publications and source records attributed to Kellis, S..

6 recordsLinked to original sources

bioRxiv2023

Neural subspaces of imagined movements in parietal cortex remain stable over several years in humans.

A crucial goal in brain-machine interfacing is long-term stability of neural decoding performance, ideally without regular retraining. Here we demonstrate stable neural decoding over several years in two human participants, achieved by latent subspace alignment of multi-unit intracortical recordings in posterior parietal cortex. These results can be practically applied to significantly expand the longevity and generalizability of future movement decoding devices.

neuroscience↗

bioRxiv2023

Neural representations of economic decision variables in human posterior parietal cortex

Decision making has been intensively studied in the posterior parietal cortex in non-human primates on a single neuron level. In humans decision making has mainly been studied with psychophysical tools or with fMRI. Here, we investigated how single neurons from human posterior parietal cortex represent numeric values informing future decisions during a complex two-player game. The tetraplegic study participant was implanted with a Utah electrode array in the anterior intraparietal area (AIP). We played a simplified variant of Black Jack with the participant while neuronal data was recorded. During the game two players are presented with numbers which are added up. Each time a number is presented the player has to decide to proceed or to stop. Once the first player stops or the score reaches a limit the turn passes on to the second player who tries to beat the score of the first player. Whoever is closer to the limit (without overshooting) wins the game. We found that many AIP neurons selectively responded to the face value of the presented number. Other neurons tracked the cumulative score or were selectively active for the upcoming decision of the study participant. Interestingly, some cells also kept track of the opponents score. Our findings show that parietal regions engaged in hand action control also represent numbers and their complex transformations. This is also the first demonstration of complex economic decisions being possible to track in single neuron activity in human AIP. Our findings show how tight are the links between parietal neural circuits underlying hand control, numerical cognition and complex decision-making.

neuroscience↗

bioRxiv2022

S1 represents multisensory contexts and somatotopic locations within and outside the bounds of the cortical homunculus

The responsiveness of primary somatosensory cortex (S1) to physical tactile stimuli is well documented but the extent to which it is modulated by vision is unresolved. Additionally, recent literature has suggested that tactile events are represented in S1 in a more complex, generalized manner than its long-established topographic organization. To better characterize S1 function, neural activity was recorded from a tetraplegic patient implanted with microelectrode arrays in S1 during 1s stroking touches to the forearm (evoking numb sensation) or finger (naturalistic sensation). Touch conditions included visually observed first person physical touches, physical touches without vision, and visual touches without physical contact which occurred either to a third person, an inanimate object, or the patients own body in virtual reality. Two major findings emerged from this dataset. The first was that vision strongly modulates S1 activity, but only if there is a physical element to the touch, suggesting that passive observation of touches is not sufficient to recruit S1 neurons. The second was that despite the location of the recording arrays in a putative arm area of S1, neural activity was able to represent both arm and finger touches in physical touch conditions. Arm touches were encoded more strongly and specifically, supporting the idea that S1 encodes tactile events primarily through its topographic organization, as well as in a more general manner encompassing larger areas of the body.

neuroscience↗

bioRxiv2021

Decoding grasp and speech signals from the cortical grasp circuit in a tetraplegic human

Tetraplegia from spinal cord injury leaves many patients paralyzed below the neck, leaving them unable to perform most activities of daily living. Brain-machine interfaces (BMIs) could give tetraplegic patients more independence by directly utilizing brain signals to control external devices such as robotic arms or hands. The cortical grasp network has been of particular interest because of its potential to facilitate the restoration of dexterous object manipulation. However, a network that involves such high-level cortical areas may also provide additional information, such as the encoding of speech. Towards understanding the role of different brain areas in the human cortical grasp network, neural activity related to motor intentions for grasping and performing speech was recorded in a tetraplegic patient in the supramarginal gyrus (SMG), the ventral premotor cortex (PMv), and the somatosensory cortex (S1). We found that in high-level brain areas SMG and PMv, grasps were well represented by firing rates of neuronal populations already at visual cue presentation. During motor imagery, grasps could be significantly decoded from all brain areas. At identical neuronal population sizes, SMG and PMv achieved similar highly-significant decoding abilities, demonstrating their potential for grasp BMIs. During speech, SMG encoded both spoken grasps and colors, in contrast to PMv and S1, which were not able to significantly decode speech.These findings suggest that grasp signals can robustly be decoded at a single unit level from the cortical grasping circuit in human. Data from PMv suggests a specialized role in grasping, while SMGs role is broader and extends to speech. Together, these results indicate that brain signals from high-level areas of the human cortex can be exploited for a variety of different BMI applications.

neuroscience↗

bioRxiv2020

Neural correlates of cognitive motor signals in primary somatosensory cortex.

Classical systems neuroscience positions primary sensory areas as early feed-forward processing stations for refining incoming sensory information. This view may oversimplify their role given extensive bi-directional connectivity with multimodal cortical and subcortical regions. Here we show that single units in human primary somatosensory cortex encode imagined reaches centered on imagined limb positions in a cognitive motor task. This result suggests a broader role of primary somatosensory cortex in cortical function than previously demonstrated.

neuroscience↗

bioRxiv2019

Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces

We present a new deep multi-state Dynamic Recurrent Neural Network (DRNN) architecture for Brain Machine Interface (BMI) applications. Our DRNN is used to predict Cartesian representation of a computer cursor movement kinematics from open-loop neural data recorded from the posterior parietal cortex (PPC) of a human subject in a BMI system. We design the algorithm to achieve a reasonable trade-off between performance and robustness, and we constrain memory usage in favor of future hardware implementation. We feed the predictions of the network back to the input to improve prediction performance and robustness. We apply a scheduled sampling approach to the model in order to solve a statistical distribution mismatch between the ground truth and predictions. Additionally, we configure a small DRNN to operate with a short history of input, reducing the required buffering of input data and number of memory accesses. This configuration lowers the expected power consumption in a neural network accelerator. Operating on wavelet-based neural features, we show that the average performance of DRNN surpasses other state-of-the-art methods in the literature on both single- and multi-day data recorded over 43 days. Results show that multi-state DRNN has the potential to model the nonlinear relationships between the neural data and kinematics for robust BMIs.

neuroscience↗