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 Parkinson's 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 vigor. 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

Subthalamic Nucleus and Sensorimotor Cortex Activity During Speech Production

The sensorimotor cortex is somatotopically organized to represent the vocal tract articulators such as lips, tongue, larynx, and jaw. How speech and articulatory features are encoded at the subcortical level, however, remains largely unknown. We analyzed LFP recordings from the subthalamic nucleus (STN) and simultaneous electrocorticography recordings from the sensorimotor cortex of 11 human subjects (1 female) with Parkinson´s disease during implantation of deep-brain stimulation (DBS) electrodes while they read aloud three-phoneme words. The initial phonemes involved either articulation primarily with the tongue (coronal consonants) or the lips (labial consonants). We observed significant increases in high-gamma (60?150 Hz) power in both the STN and the sensorimotor cortex that began before speech onset and persisted for the duration of speech articulation. As expected from previous reports, in the sensorimotor cortex, the primary articulators involved in the production of the initial consonants were topographically represented by high-gamma activity. We found that STN high-gamma activity also demonstrated specificity for the primary articulator, although no clear topography was observed. In general, subthalamic high-gamma activity varied along the ventral?dorsal trajectory of the electrodes, with greater high-gamma power recorded in the dorsal locations of the STN. Interestingly, the majority of significant articulator-discriminative activity in the STN occurred before that in sensorimotor cortex. These results demonstrate that articulator-specific speech information is contained within high-gamma activity of the STN, but with different spatial and temporal organization compared with similar information encoded in the sensorimotor cortex.Fil: Chrabaszcz, Anna. University of Pittsburgh; Estados UnidosFil: Neumann, Wolf Julian. Universität zu Berlin; AlemaniaFil: Stretcu, Otilia. University of Pittsburgh; Estados UnidosFil: Lipski, Witold J.. University of Pittsburgh; Estados UnidosFil: Dastolfo Hromack, Christina A.. University of Pittsburgh; Estados UnidosFil: Bush, Alan. University of Pittsburgh; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Wang, Dengyu. Tsinghua University; China. University of Pittsburgh; Estados UnidosFil: Crammond, Donald J.. University of Pittsburgh; Estados UnidosFil: Shaiman, Susan. University of Pittsburgh; Estados UnidosFil: Dickey, Michael W.. University of Pittsburgh; Estados UnidosFil: Holt, Lori L.. University of Pittsburgh; Estados UnidosFil: Turner, Robert S.. University of Pittsburgh; Estados UnidosFil: Fiez, Julie A.. University of Pittsburgh; Estados UnidosFil: Richardson, R. Mark. University of Pittsburgh; Estados Unido

High frequency activation data used to validate localization of cortical electrodes during surgery for deep brain stimulation

Movement related synchronization of high frequency activity (HFA, 76–100 Hz) is a somatotopic process with spectral power changes occurring during movement in the sensorimotor cortex (Miller et al., 2007) [1]. These features allowed movement-related changes in HFA to be used to functionally validate the estimations of subdural electrode locations, which may be placed temporarily for research in deep brain stimulation surgery, using the novel tool described in Randazzo et al. (2015) [2]. We recorded electrocorticography (ECoG) signals and localized electrodes in the region of the sensorimotor cortex during an externally cued hand grip task in 8 subjects. Movement related HFA was determined for each trial by comparing HFA spectral power during movement epochs to pre-movement baseline epochs. Significant movement related HFA was found to be focal in time and space, occurring only during movement and only in a subset of electrodes localized to the pre- and post-central gyri near the hand knob. To further demonstrate the use of movement related HFA to aid electrode localization, we provide a sample of the electrode localization tool, with data loaded to allow readers to map movement related HFA onto the cortical surface of a sample patient

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 participant's 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.Fil: Bush, Alan. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Massachusetts General Hospital; Estados Unidos. Harvard Medical School; Estados UnidosFil: Chrabaszcz, Anna. University of Pittsburgh; Estados UnidosFil: Peterson, Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Massachusetts General Hospital; Estados Unidos. Harvard Medical School; Estados UnidosFil: Saravanan, Varun. Massachusetts General Hospital; Estados Unidos. Massachusetts Institute of Technology; Estados UnidosFil: Dastolfo Hromack, Christina. West Virginia University; Estados Unidos. University of Pittsburgh; Estados UnidosFil: Lipski, Witold J.. Univeristy of Pittsburgh. School of Medicine; Estados UnidosFil: Richardson, R. Mark. Massachusetts General Hospital; Estados Unidos. Harvard Medical School; Estados Unido