Connectomic Basis for Tremor Control in Stereotactic Radiosurgical Thalamotomy.

BACKGROUND AND PURPOSE: Given the increased use of stereotactic radiosurgical thalamotomy and other ablative therapies for tremor, new biomarkers are needed to improve outcomes. Using resting-state fMRI and MR tractography, we hypothesized that a "connectome fingerprint" can predict tremor outcomes and potentially serve as a targeting biomarker for stereotactic radiosurgical thalamotomy. MATERIALS AND METHODS: We evaluated 27 patients who underwent unilateral stereotactic radiosurgical thalamotomy for essential tremor or tremor-predominant Parkinson disease. Percentage postoperative improvement in the contralateral limb Fahn-Tolosa-Marin Clinical Tremor Rating Scale (TRS) was the primary end point. Connectome-style resting-state fMRI and MR tractography were performed before stereotactic radiosurgery. Using the final lesion volume as a seed, "connectivity fingerprints" representing ideal connectivity maps were generated as whole-brain R-maps using a voxelwise nonparametric Spearman correlation. A leave-one-out cross-validation was performed using the generated R-maps. RESULTS: The mean improvement in the contralateral tremor score was 55.1% (SD, 38.9%) at a mean follow-up of 10.0 (SD, 5.0) months. Structural connectivity correlated with contralateral TRS improvement (r = 0.52; P = .006) and explained 27.0% of the variance in outcome. Functional connectivity correlated with contralateral TRS improvement (r = 0.50; P = .008) and explained 25.0% of the variance in outcome. Nodes most correlated with tremor improvement corresponded to areas of known network dysfunction in tremor, including the cerebello-thalamo-cortical pathway and the primary and extrastriate visual cortices. CONCLUSIONS: Stereotactic radiosurgical targets with a distinct connectivity profile predict improvement in tremor after treatment. Such connectomic fingerprints show promise for developing patient-specific biomarkers to guide therapy with stereotactic radiosurgical thalamotomy.

Neural control of finger movement via intracortical brain-machine interface.

OBJECTIVE: Intracortical brain-machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques. APPROACH: In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets. In the physical control condition, four rhesus macaques performed this task by moving all four fingers together in order to acquire a single target. This movement was equivalent to controlling the aperture of a power grasp. During this task performance, we recorded neural spikes from intracortical electrode arrays in primary motor cortex. MAIN RESULTS: Using a standard Kalman filter, we could reconstruct continuous finger movement offline with an average correlation of ρ = 0.78 between actual and predicted position across four rhesus macaques. For two of the monkeys, this movement prediction was performed in real-time to enable direct brain control of the virtual hand. Compared to physical control, neural control performance was slightly degraded; however, the monkeys were still able to successfully perform the task with an average target acquisition rate of 83.1%. The monkeys' ability to arbitrarily specify fingertip position was also quantified using an information throughput metric. During brain control task performance, the monkeys achieved an average 1.01 bits s-1 throughput, similar to that achieved in previous studies which decoded upper-arm movements to control computer cursors using a standard Kalman filter. SIGNIFICANCE: This is, to our knowledge, the first demonstration of brain control of finger-level fine motor skills. We believe that these results represent an important step towards full and dexterous control of neural prosthetic devices.