Biology subjects

Lipski, W.

Publications and source records attributed to Lipski, W..

5 recordsLinked to original sources

bioRxiv2023

Subthalamic nucleus neurons encode syllable sequence and phonetic characteristics during speech

Speech is a complex behavior that can be used to study unique contributions of the basal ganglia to motor control in the human brain. Computational models suggest that the basal ganglia encodes either the phonetic content or the sequence of speech elements. To explore this question, we investigated the relationship between phoneme and sequence features of a spoken syllable triplet and the firing rate of subthalamic nucleus (STN) neurons recorded during the implantation of deep brain stimulation (DBS) electrodes in individuals with Parkinsons disease. Patients repeated aloud a random sequence of three consonant-vowel (CV) syllables in response to audio cues. Single-unit extracellular potentials were sampled from the sensorimotor STN; a total of 227 unit recordings were obtained from the left STN of 25 subjects (4 female). Of these, 113 (50%) units showed significant task-related increased firing and 53 (23%) showed decreased firing (t-test relative to inter-trial period baseline, p<0.05). Linear regression analysis revealed that both populations of STN neurons encode phoneme and sequence features of produced speech. Maximal phoneme encoding occurred at the time of phoneme production, suggesting efference copyor sensory-related processing, rather than speech motor planning (-50ms and +175ms relative to CV transition for consonant and vowel encoding, respectively). These findings demonstrate that involvement of the basal ganglia in speaking includes separate single unit representations of speech sequencing and phoneme selection in the STN.

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↗

bioRxiv2020

Thalamic encoding of lexical status is lateralized during reading aloud

To explore whether the thalamus participates in lexical status encoding, local field potentials were recorded in patients undergoing deep brain stimulation lead implantation while they read aloud single-syllable words and nonwords. Bilateral decreases in thalamic beta (12-30Hz) activity were preferentially locked to stimulus presentation, and these decreases were greater when nonwords were read. Increased broadband gamma (70-150Hz) activity also was locked preferentially to speech onset bilaterally, but greater nonword-related increases in this activity were observed only on the left, demonstrating lateralization of thalamic gamma selectivity for lexical status. In addition, this lexical status effect was strongest in more anterior thalamic locations, regions which are more likely to receive pallidal than cerebellar afferents. These results provide evidence from intracranial thalamic recordings for the lateralization and topography of subcortical lexical status processing.

neuroscience↗

bioRxiv2018

Cortico-basal-ganglia communication: Temporally structured activity for selective motor control

Despite the hard-wired structural connectivity of neural pathways, neural circuits allow context-dependent reactions to sensory cues by triggering the desired movement. Cortico-basal-ganglia circuits seem particularly important for flexible motor control as this is impaired in Parkinsons disease (PD). We analysed subthalamic nucleus (STN) spike and cortical ECoG activity from PD patients performing a visually-cued hand grip task. Fast reaction times were preceded by enhanced STN spike-to-cortical gamma phase coupling irrespective of firing rate changes, suggesting a role of gamma coupling in motor preparation. STN spike timing was offset by half a cycle when comparing ipsilateral with contralateral movements. Additionally, cortical high-frequency activity increased more steeply within each gamma cycle at the sites that showed the strongest coupling with STN spikes. Cortico-basal-ganglia gamma coupling may thus help shape neural activity to facilitate selective motor control. The observation that this effect occurs independent of changes in mean firing rate has far-reaching implications.\n\nHighlightsO_LIFast RTs were preceded by enhanced STN spike-to-cortical gamma phase coupling\nC_LIO_LISTN spike probability was significantly modulated relative to the gamma cycle\nC_LIO_LIDuring ipsilateral movement, spikes were more likely at the opposite part of the cycle\nC_LIO_LISTN output may thus help shape cortical gamma for selective motor control\nC_LI

neuroscience↗

bioRxiv2018

Sensorimotor cortical-subthalamic network dynamics during force generation

The subthalamic nucleus (STN) is proposed to participate in pausing, or alternately, in dynamic scaling of behavioral responses, roles that have conflicting implications for understanding STN function in the context of deep brain stimulation (DBS) therapy. To examine the nature of event-related STN activity and subthalamic-cortical dynamics, we performed primary motor and somatosensory electrocorticography while subjects (n=10) performed a grip force task during DBS implantation surgery. The results provide the first evidence from humans that STN gamma activity can predict activity in the cortex both prior to and during movement, consistent with the idea that the STN participates in both motor planning and execution. We observed that STN activity appeared to facilitate movement: while both movement onset and termination both coincided with STN-cortical phase-locking, narrow-band gamma power was positively correlated with grip force, and event-related causality measures demonstrated that STN gamma activity predicted cortical gamma activity during movement. STN participation in somatosensory integration also was demonstrated by casual analysis. Information flow from the STN to somatosensory cortex was observed for both beta and gamma range frequencies, specific to particular movement periods and kinematics. Interactions in beta activity between the STN and somatosensory cortex, rather than motor cortex, predicted PD symptom severity. Thus, the STN contributes to multiple aspects of sensorimotor behavior dynamically across time.

neuroscience↗