Degradation of Neuronal Encoding of Speech in the Subthalamic Nucleus in Parkinson's Disease

Spike-phase coupling of subthalamic neurons to posterior perisylvian cortex predicts speech sound accuracy

Speech provides a rich context for understanding how cortical interactions with the basal ganglia contribute to unique human behaviors, but opportunities for direct human intracranial recordings across cortical-basal ganglia networks are rare. Here we have recorded electrocorticographic signals in the cortex synchronously with single units in the basal ganglia during awake neurosurgeries where participants spoke syllable repetitions. We have discovered that individual subthalamic nucleus (STN) neurons have transient (200 ms) spike-phase coupling (SPC) events with multiple cortical regions. The spike timing of STN neurons is locked to the phase of theta-alpha oscillations in the supramarginal and posterior superior temporal gyrus during speech planning and production. Speech sound errors occur when this STN-cortical interaction is delayed. Our results suggest that timely interactions between the STN and the posterior perisylvian cortex support auditory-motor coordinate transformation or phonological working memory during speech planning. These findings establish a framework for understanding cortical-basal ganglia interaction in other human behaviors, and additionally indicate that firing-rate based models are insufficient for explaining basal ganglia circuit behavior.

Abnormal neuronal activity in the subthalamic nucleus contributes to dysarthria in patients with Parkinson's disease

BACKGROUND

This study investigated the subthalamic nucleus (STN) function and deep brain stimulation (DBS) effects on single-unit activity (SUA) in Parkinson's disease (PD) patients with dysarthria.

METHODS

After presurgical speech analysis, we recorded STN neuronal activities while PD patients (n = 16) articulated Chinese Pinyin consonants. The Pinyin consonants were categorized by the manner and place of articulation for SUA cluster analysis. The cohort was then divided into normal articulation and dysarthria groups based on diadochokinetic (DDK) assessments. The STN SUA patterns, represented by the mean firing rate (FR), peak time, and response intensity during articulation, were analyzed and compared between the two groups. Finally, a stimulation cohort of 7 PD patients was included to test articulation and SUA pattern changes following intraoperative DBS.

RESULTS

Clustering analysis of STN neuronal firing patterns demonstrated that neurons encode articulation by grouping consonants with the same manner of articulation into distinct clusters. Using k-means clustering, we further classified SUAs into two waveform types: negative spikes (type 1) and positive spikes (type 2). Dysarthria patients exhibited an increased mean FR of type 1 spikes and a reduced response intensity of type 2 spikes. During intraoperative stimulation, PD patients showed accelerated DDK, accompanied by a decrease in type 1 mean FR and an increase in type 2 mean FR.

CONCLUSION

Our findings indicate the crucial role of the STN in consonant encoding and dysarthria at the single-unit level. Both SUA firing patterns in the STN and DDK performance can be modulated by DBS.

A Speech Neuroprosthesis in the Frontal Lobe and Hippocampus: Decoding High-Frequency Activity into Phonemes

BACKGROUND AND OBJECTIVES

Loss of speech due to injury or disease is devastating. Here, we report a novel speech neuroprosthesis that artificially articulates building blocks of speech based on high-frequency activity in brain areas never harnessed for a neuroprosthesis before: anterior cingulate and orbitofrontal cortices, and hippocampus.

METHODS

A 37-year-old male neurosurgical epilepsy patient with intact speech, implanted with depth electrodes for clinical reasons only, silently controlled the neuroprosthesis almost immediately and in a natural way to voluntarily produce 2 vowel sounds.

RESULTS

During the first set of trials, the participant made the neuroprosthesis produce the different vowel sounds artificially with 85% accuracy. In the following trials, performance improved consistently, which may be attributed to neuroplasticity. We show that a neuroprosthesis trained on overt speech data may be controlled silently.

CONCLUSION

This may open the way for a novel strategy of neuroprosthesis implantation at earlier disease stages (eg, amyotrophic lateral sclerosis), while speech is intact, for improved training that still allows silent control at later stages. The results demonstrate clinical feasibility of direct decoding of high-frequency activity that includes spiking activity in the aforementioned areas for silent production of phonemes that may serve as a part of a neuroprosthesis for replacing lost speech control pathways.

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 encode 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 Parkinson's 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 females). 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 copy- or sensory-related processing, rather than speech motor planning (-50 ms and +175 ms 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.NEW & NOTEWORTHY Speech is a unique human behavior that requires dynamic execution of precisely timed and coordinated movements, resulting in intelligible vocalizations. Here, we demonstrate that activity of individual neurons in the subthalamic nucleus (STN) of the basal ganglia encode syllable sequence order and phoneme identity during a speech production task. These findings advance our understanding of neural substrates of human speech and shed light on potential involvement of the STN in complex human behaviors.

Subthalamic nucleus-language network connectivity predicts dopaminergic modulation of speech function in Parkinson's disease

Speech impediments are a prominent yet understudied symptom of Parkinson's disease (PD). While the subthalamic nucleus (STN) is an established clinical target for treating motor symptoms, these interventions can lead to further worsening of speech. The interplay between dopaminergic medication, STN circuitry, and their downstream effects on speech in PD is not yet fully understood. Here, we investigate the effect of dopaminergic medication on STN circuitry and probe its association with speech and cognitive functions in PD patients. We found that changes in intrinsic functional connectivity of the STN were associated with alterations in speech functions in PD. Interestingly, this relationship was characterized by altered functional connectivity of the dorsolateral and ventromedial subdivisions of the STN with the language network. Crucially, medication-induced changes in functional connectivity between the STN's dorsolateral subdivision and key regions in the language network, including the left inferior frontal cortex and the left superior temporal gyrus, correlated with alterations on a standardized neuropsychological test requiring oral responses. This relation was not observed in the written version of the same test. Furthermore, changes in functional connectivity between STN and language regions predicted the medication's downstream effects on speech-related cognitive performance. These findings reveal a previously unidentified brain mechanism through which dopaminergic medication influences speech function in PD. Our study sheds light into the subcortical-cortical circuit mechanisms underlying impaired speech control in PD. The insights gained here could inform treatment strategies aimed at mitigating speech deficits in PD and enhancing the quality of life for affected individuals.

A neurocomputational view of the effects of Parkinson's disease on speech production

The purpose of this article is to review the scientific literature concerning speech in Parkinson's disease (PD) with reference to the DIVA/GODIVA neurocomputational modeling framework. Within this theoretical view, the basal ganglia (BG) contribute to several different aspects of speech motor learning and execution. First, the BG are posited to play a role in the initiation and scaling of speech movements. Within the DIVA/GODIVA framework, initiation and scaling are carried out by initiation map nodes in the supplementary motor area acting in concert with the BG. Reduced support of the initiation map from the BG in PD would result in reduced movement intensity as well as susceptibility to early termination of movement. A second proposed role concerns the learning of common speech sequences, such as phoneme sequences comprising words; this view receives support from the animal literature as well as studies identifying speech sequence learning deficits in PD. Third, the BG may play a role in the temporary buffering and sequencing of longer speech utterances such as phrases during conversational speech. Although the literature does not support a critical role for the BG in representing sequence order (since incorrectly ordered speech is not characteristic of PD), the BG are posited to contribute to the scaling of individual movements in the sequence, including increasing movement intensity for emphatic stress on key words. Therapeutic interventions for PD have inconsistent effects on speech. In contrast to dopaminergic treatments, which typically either leave speech unchanged or lead to minor improvements, deep brain stimulation (DBS) can degrade speech in some cases and improve it in others. However, cases of degradation may be due to unintended stimulation of efferent motor projections to the speech articulators. Findings of spared speech after bilateral pallidotomy appear to indicate that any role played by the BG in adult speech must be supplementary rather than mandatory, with the sequential order of well-learned sequences apparently represented elsewhere (e.g., in cortico-cortical projections).

Machine learning decoding of single neurons in the thalamus for speech brain-machine interfaces

Objective. Our goal is to decode firing patterns of single neurons in the left ventralis intermediate nucleus (Vim) of the thalamus, related to speech production, perception, and imagery. For realistic speech brain-machine interfaces (BMIs), we aim to characterize the amount of thalamic neurons necessary for high accuracy decoding.Approach. We intraoperatively recorded single neuron activity in the left Vim of eight neurosurgical patients undergoing implantation of deep brain stimulator or RF lesioning during production, perception and imagery of the five monophthongal vowel sounds. We utilized the Spade decoder, a machine learning algorithm that dynamically learns specific features of firing patterns and is based on sparse decomposition of the high dimensional feature space.Main results. Spade outperformed all algorithms compared with, for all three aspects of speech: production, perception and imagery, and obtained accuracies of 100%, 96%, and 92%, respectively (chance level: 20%) based on pooling together neurons across all patients. The accuracy was logarithmic in the amount of neurons for all three aspects of speech. Regardless of the amount of units employed, production gained highest accuracies, whereas perception and imagery equated with each other.Significance. Our research renders single neuron activity in the left Vim a promising source of inputs to BMIs for restoration of speech faculties for locked-in patients or patients with anarthria or dysarthria to allow them to communicate again. Our characterization of how many neurons are necessary to achieve a certain decoding accuracy is of utmost importance for planning BMI implantation.

Neuronal Encoding of Speech Features in the Human Thalamus in Parkinson's Disease and Essential Tremor Patients

BACKGROUND AND OBJECTIVES

The human thalamus is known, from stimulation studies and functional imaging, to participate in high-level language tasks. The goal of this study is to find whether and how speech features, in particular, vowel phonemes, are encoded in the neuronal activity of the thalamus, and specifically of the left ventralis intermediate nucleus (Vim), during speech production, perception, and imagery.

METHODS

In this cross-sectional study, we intraoperatively recorded single neuron activity in the left Vim of eight neurosurgical patients with Parkinson's disease (PD) (n = 4) or essential tremor (n = 4) undergoing implantation of deep brain stimulation (n = 3) or radiofrequency lesioning (n = 5) while patients articulated the five monophthongal vowel sounds.

RESULTS

In this article, we report that single neurons in the left Vim encode individual vowel phonemes mainly during speech production but also during perception and imagery. They mainly use one of two encoding schemes: broad or sharp tuning, with a similar percentage of units each. Sinusoidal tuning has been demonstrated in almost half of the broadly tuned units. Patients with PD had a lower percentage of speech-related units in each aspect of speech (production, perception, and imagery), a significantly lower percentage of broadly tuned units, and significantly lower median firing rates during speech production and perception, but significantly higher rates during imagery, than patients with essential tremor.

CONCLUSION

The results suggest that the left Vim uses mixed encoding schemes for speech features. Our findings explain, at the single neuron level, why deep brain stimulation and radiofrequency lesioning of the left Vim are likely to cause speech side effects. Moreover, they may indicate that speech-related units in the left Vim of patients with PD may be degraded even in the subclinical phase.

Intelligibility of speech in Parkinson's disease relies on anatomically segregated subthalamic beta oscillations

BACKGROUND

Speech impairment is commonly reported in Parkinson's disease and is not consistently improved by available therapies - including deep brain stimulation of the subthalamic nucleus (STN-DBS), which can worsen communication performance in some patients. Improving the outcome of STN-DBS on speech is difficult due to our incomplete understanding of the contribution of the STN to fluent speaking.

OBJECTIVE

To assess the relationship between subthalamic neural activity and speech production and intelligibility.

METHODS

We investigated bilateral STN local field potentials (LFPs) in nine parkinsonian patients chronically implanted with DBS during overt reading. LFP spectral features were correlated with clinical scores and measures of speech intelligibility.

RESULTS

Overt reading was associated with increased beta-low ([1220) Hz) power in the left STN, whereas speech intelligibility correlated positively with beta-high ([2030) Hz) power in the right STN.

CONCLUSION

We identified separate contributions from frequency and brain lateralization of the STN in the execution of an overt reading motor task and its intelligibility. This subcortical organization could be exploited for new adaptive stimulation strategies capable of identifying the occurrence of speaking behavior and facilitating its functional execution.

Human subthalamic nucleus neurons differentially encode speech and limb movement

Deep brain stimulation (DBS) of the subthalamic nucleus (STN), which consistently improves limb motor functions, shows mixed effects on speech functions in Parkinson's disease (PD). One possible explanation for this discrepancy is that STN neurons may differentially encode speech and limb movement. However, this hypothesis has not yet been tested. We examined how STN is modulated by limb movement and speech by recording 69 single- and multi-unit neuronal clusters in 12 intraoperative PD patients. Our findings indicated: (1) diverse patterns of modulation in neuronal firing rates in STN for speech and limb movement; (2) a higher number of STN neurons were modulated by speech vs. limb movement; (3) an overall increase in neuronal firing rates for speech vs. limb movement; and (4) participants with longer disease duration had higher firing rates. These data provide new insights into the role of STN neurons in speech and limb movement.

Machine learning algorithm for decoding multiple subthalamic spike trains for speech brain-machine interfaces

Objective. The goal of this study is to decode the electrical activity of single neurons in the human subthalamic nucleus (STN) to infer the speech features that a person articulated, heard or imagined. We also aim to evaluate the amount of subthalamic neurons required for high accuracy decoding suitable for real-life speech brain-machine interfaces (BMI).Approach. We intraoperatively recorded single-neuron activity in the STN of 21 neurosurgical patients with Parkinson's disease undergoing implantation of deep brain stimulator while patients produced, perceived or imagined the five monophthongal vowel sounds. Our decoder is based on machine learning algorithms that dynamically learn specific features of the speech-related firing patterns.Main results. In an extensive comparison of algorithms, our sparse decoder ('SpaDe'), based on sparse decomposition of the high dimensional neuronal feature space, outperformed the other algorithms in all three conditions: production, perception and imagery. For speech production, our algorithm, Spade, predicted all vowels correctly (accuracy: 100%; chance level: 20%). For perception accuracy was 96%, and for imagery: 88%. The accuracy of Spade showed a linear behavior in the amount of neurons for the perception data, and even faster for production or imagery.Significance. Our study demonstrates that the information encoded by single neurons in the STN about the production, perception and imagery of speech is suitable for high-accuracy decoding. It is therefore an important step towards BMIs for restoration of speech faculties that bears an enormous potential to alleviate the suffering of completely paralyzed ('locked-in') patients and allow them to communicate again with their environment. Moreover, our research indicates how many subthalamic neurons may be necessary to achieve each level of decoding accuracy, which is of supreme importance for a neurosurgeon planning the implantation of a speech BMI.

Articulatory Gain Predicts Motor Cortex and Subthalamic Nucleus Activity During Speech

Speaking precisely is important for effective verbal communication, and articulatory gain is one component of speech motor control that contributes to achieving this goal. Given that the basal ganglia have been proposed to regulate the speed and size of limb movement, that is, movement gain, we explored the basal ganglia contribution to articulatory gain, through local field potentials (LFP) recorded simultaneously from the subthalamic nucleus (STN), precentral gyrus, and postcentral gyrus. During STN deep brain stimulation implantation for Parkinson's disease, participants read aloud consonant-vowel-consonant syllables. Articulatory gain was indirectly assessed using the F2 Ratio, an acoustic measurement of the second formant frequency of/i/vowels divided by/u/vowels. Mixed effects models demonstrated that the F2 Ratio correlated with alpha and theta activity in the precentral gyrus and STN. No correlations were observed for the postcentral gyrus. Functional connectivity analysis revealed that higher phase locking values for beta activity between the STN and precentral gyrus were correlated with lower F2 Ratios, suggesting that higher beta synchrony impairs articulatory precision. Effects were not related to disease severity. These data suggest that articulatory gain is encoded within the basal ganglia-cortical loop.

Impaired Timing of Speech-Related Neurons in the Subthalamic Nucleus of Parkinson Disease Patients Suffering Speech Disorders

BACKGROUND

Our previous study found degradation to subthalamic neuronal encoding of speech features in Parkinson disease (PD) patients suffering from speech disorders.

OBJECTIVE

To find how timing of speech-related neuronal firing changes in PD patients with speech disorders compared to PD patients without speech disorders.

METHODS

During the implantation of deep brain stimulator (DBS), we recorded the activity of single neurons in the subthalamic nucleus (STN) of 18 neurosurgical patients with PD while they articulated, listened to, or imagined articulation of 5 vowel sounds, each following a beep. We compared subthalamic activity of PD patients with (n = 10) vs without speech disorders.

RESULTS

In this comparison, patients with speech disorders had longer reaction times and shorter lengths of articulation. Their speech-related neuronal activity preceding speech onset (planning) was delayed relative to the beep, but the time between this activity and the emission of speech sound was similar. Notwithstanding, speech-related neuronal activity following the onset of speech (feedback) was delayed when computed relative to the onset. Only in these patients was the time lag of planning neurons significantly correlated with the reaction time. Neuronal activity in patients with speech disorders was delayed during imagined articulation of vowel sounds but earlier during speech perception.

CONCLUSION

Our findings indicate that longer reaction times in patients with speech disorders are due to STN or earlier activity of the speech control network. This is a first step in locating the source(s) of PD delays within this network and is therefore of utmost importance for future treatment of speech disorders.

Decoding spoken English from intracortical electrode arrays in dorsal precentral gyrus

OBJECTIVE

To evaluate the potential of intracortical electrode array signals for brain-computer interfaces (BCIs) to restore lost speech, we measured the performance of decoders trained to discriminate a comprehensive basis set of 39 English phonemes and to synthesize speech sounds via a neural pattern matching method. We decoded neural correlates of spoken-out-loud words in the 'hand knob' area of precentral gyrus, a step toward the eventual goal of decoding attempted speech from ventral speech areas in patients who are unable to speak.

APPROACH

Neural and audio data were recorded while two BrainGate2 pilot clinical trial participants, each with two chronically-implanted 96-electrode arrays, spoke 420 different words that broadly sampled English phonemes. Phoneme onsets were identified from audio recordings, and their identities were then classified from neural features consisting of each electrode's binned action potential counts or high-frequency local field potential power. Speech synthesis was performed using the 'Brain-to-Speech' pattern matching method. We also examined two potential confounds specific to decoding overt speech: acoustic contamination of neural signals and systematic differences in labeling different phonemes' onset times.

MAIN RESULTS

A linear decoder achieved up to 29.3% classification accuracy (chance = 6%) across 39 phonemes, while an RNN classifier achieved 33.9% accuracy. Parameter sweeps indicated that performance did not saturate when adding more electrodes or more training data, and that accuracy improved when utilizing time-varying structure in the data. Microphonic contamination and phoneme onset differences modestly increased decoding accuracy, but could be mitigated by acoustic artifact subtraction and using a neural speech onset marker, respectively. Speech synthesis achieved r = 0.523 correlation between true and reconstructed audio.

SIGNIFICANCE

The ability to decode speech using intracortical electrode array signals from a nontraditional speech area suggests that placing electrode arrays in ventral speech areas is a promising direction for speech BCIs.

Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis

Speaking is a sensorimotor behavior whose neural basis is difficult to study with single neuron resolution due to the scarcity of human intracortical measurements. We used electrode arrays to record from the motor cortex 'hand knob' in two people with tetraplegia, an area not previously implicated in speech. Neurons modulated during speaking and during non-speaking movements of the tongue, lips, and jaw. This challenges whether the conventional model of a 'motor homunculus' division by major body regions extends to the single-neuron scale. Spoken words and syllables could be decoded from single trials, demonstrating the potential of intracortical recordings for brain-computer interfaces to restore speech. Two neural population dynamics features previously reported for arm movements were also present during speaking: a component that was mostly invariant across initiating different words, followed by rotatory dynamics during speaking. This suggests that common neural dynamical motifs may underlie movement of arm and speech articulators.