Biology subjects

Peterson, V.

Publications and source records attributed to Peterson, V..

4 recordsLinked to original sources

bioRxiv2021

A spatio-spectral approach for movement decoding from cortical and subcortical recordings in patients with Parkinson's disease

The application of machine learning to intracranial signal analysis has the potential to revolutionize deep brain stimulation (DBS) by personalizing therapy to dynamic brain states, specific to symptoms and behaviors. Most decoding pipelines for movement decoding in the context of adaptive DBS are based on single channel frequency domain features, neglecting spatial information available in multichannel recordings. Such features are extracted either from DBS lead recordings in the subcortical target and/or from electrocorticography (ECoG). To optimize the simultaneous use of both types of signals, we developed a supervised online-compatible movement decoding pipeline based on multichannel and multiple site recordings. We found that adding spatial information to the model has the potential to improve decoding. In addition, we demonstrate movement decoding from spatio-spectral features derived from cortical and subcortical oscillations. We demonstrate between-patients variability of the spatial neural maps and its relationship to feature decoding performance. This application of spatial filters to decode movement from combined cortical and subcortical recordings is an important step in developing machine learning approaches for intelligent DBS systems.

neuroscience↗

bioRxiv2021

Differentiation of speech-induced artifacts from physiological high gamma activity in intracranial recordings

There is great interest in identifying the neurophysiological underpinnings of speech production. Deep brain stimulation (DBS) surgery is unique in that it allows intracranial recordings from both cortical and subcortical regions in patients who are awake and speaking. The quality of these recordings, however, may be affected to various degrees by mechanical forces resulting from speech itself. Here we describe the presence of speech-induced artifacts in local-field potential (LFP) recordings obtained from mapping electrodes, DBS leads, and cortical electrodes. In addition to expected physiological increases in high gamma (60-200 Hz) activity during speech production, time-frequency analysis in many channels revealed a narrowband gamma component that exhibited a pattern similar to that observed in the speech audio spectrogram. This component was present to different degrees in multiple types of neural recordings. We show that this component tracks the fundamental frequency of the participants voice, correlates with the power spectrum of speech and has coherence with the produced speech audio. A vibration sensor attached to the stereotactic frame recorded speech-induced vibrations with the same pattern observed in the LFPs. No corresponding component was identified in any neural channel during the listening epoch of a syllable repetition task. These observations demonstrate how speech-induced vibrations can create artifacts in the primary frequency band of interest. Identifying and accounting for these artifacts is crucial for establishing the validity and reproducibility of speech-related data obtained from intracranial recordings during DBS surgery.

neuroscience↗

bioRxiv2021

Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's disease

Brain signal decoding promises significant advances in the development of clinical brain computer interfaces (BCI). In Parkinsons disease (PD), first bidirectional BCI implants for adaptive deep brain stimulation (DBS) are now available. Brain signal decoding can extend the clinical utility of adaptive DBS but the impact of neural source, computational methods and PD pathophysiology on decoding performance are unknown. This represents an unmet need for the development of future neurotechnology. To address this, we developed an invasive brain-signal decoding approach based on intraoperative sensorimotor electrocorticography (ECoG) and subthalamic LFP to predict grip-force, a representative movement decoding application, in 11 PD patients undergoing DBS. We demonstrate that ECoG is superior to subthalamic LFP for accurate grip-force decoding. Gradient boosted decision trees (XGBOOST) outperformed other model architectures. ECoG based decoding performance negatively correlated with motor impairment, which could be attributed to subthalamic beta bursts in the motor preparation and movement period. This highlights the impact of PD pathophysiology on the neural capacity to encode movement kinematics. Finally, we developed a connectomic analysis that could predict grip-force decoding performance of individual ECoG channels across patients by using their connectomic fingerprints. Our study provides a neurophysiological and computational framework for invasive brain signal decoding to aid the development of an individualized precision-medicine approach to intelligent adaptive DBS. Significance StatementNeurotechnology will revolutionize the treatment of neurological and psychiatric patients, promising novel treatment avenues for previously intractable brain disorders. However, optimal surgical and computational approaches and their interactions with neurological disorders are unknown. How can recent advances in machine learning and connectomics aid the precision and performance of invasive brain signal decoding strategies? Do the brain disorders treated with such approaches have impact on decoding performance? We propose a real time compatible advanced machine learning pipeline for invasively recorded brain signals in Parkinsons disease (PD) patients. We report optimal movement decoding strategies with respect to signal source, model architecture and connectomic fingerprint and demonstrate that PD pathophysiology significantly and negatively impacts movement decoding. Our study has broad impacts for the development of smart brain implants for the treatment of PD and other brain disorders.

neuroscience↗

bioRxiv2021

"Thinking out loud": an open-access EEG-based BCI dataset for inner speech recognition

Surface electroencephalography is a standard and noninvasive way to measure electrical brain activity. Recent advances in artificial intelligence led to significant improvements in the automatic detection of brain patterns, allowing increasingly faster, more reliable and accessible Brain-Computer Interfaces. Different paradigms have been used to enable the human-machine interaction and the last few years have broad a mark increase in the interest for interpreting and characterizing the "inner voice" phenomenon. This paradigm, called inner speech, raises the possibility of executing an order just by thinking about it, allowing a "natural" way of controlling external devices. Unfortunately, the lack of publicly available electroencephalography datasets, restricts the development of new techniques for inner speech recognition. A ten-subjects dataset acquired under this and two others related paradigms, obtained with an acquisition system of 136 channels, is presented. The main purpose of this work is to provide the scientific community with an open-access multiclass electroencephalography database of inner speech commands that could be used for better understanding of the related brain mechanisms.

neuroscience↗