Auditory cues modulate the short timescale dynamics of STN activity during stepping in Parkinson's disease.

BACKGROUND: Gait impairment has a major impact on quality of life in patients with Parkinson's disease (PD). It is believed that basal ganglia oscillatory activity at ß frequencies (15-30 Hz) may contribute to gait impairment, but the precise dynamics of this oscillatory activity during gait remain unclear. Additionally, auditory cues are known to lead to improvements in gait kinematics in PD. If the neurophysiological mechanisms of this cueing effect were better understood they could be leveraged to treat gait impairments using adaptive Deep Brain Stimulation (aDBS) technologies. OBJECTIVE: We aimed to characterize the dynamics of subthalamic nucleus (STN) oscillatory activity during stepping movements in PD and to establish the neurophysiological mechanisms by which auditory cues modulate gait. METHODS: We studied STN local field potentials (LFPs) in eight PD patients while they performed stepping movements. Hidden Markov Models (HMMs) were used to discover transient states of spectral activity that occurred during stepping with and without auditory cues. RESULTS: The occurrence of low and high ß bursts was suppressed during and after auditory cues. This manifested as a decrease in their fractional occupancy and state lifetimes. Interestingly, α transients showed the opposite effect, with fractional occupancy and state lifetimes increasing during and after auditory cues. CONCLUSIONS: We show that STN oscillatory activity in the α and ß frequency bands are differentially modulated by gait-promoting oscillatory cues. These findings suggest that the enhancement of α rhythms may be an approach for ameliorating gait impairments in PD.

Evaluation of 3D C-Arm Fluoroscopy versus Diagnostic CT for Deep Brain Stimulation Stereotactic Registration and Post-Operative Lead Localization.

INTRODUCTION: DBS efficacy depends on accuracy. CT-MRI fusion is established for both stereotactic registration and electrode placement verification. The desire to streamline DBS workflows, reduce operative time, and minimize patient transfers has increased interest in portable imaging modalities such as the Medtronic O-arm® and mobile CT. However, these remain expensive and bulky. 3D C-arm fluoroscopy (3DXT) units are a smaller and less costly alternative, albeit incompatible with traditional frame-based localization and without useful soft tissue resolution. We aimed to compare fusion of 3DXT and CT with pre-operative MRI to evaluate if 3DXT-MRI fusion alone is sufficient for accurate registration and reliable targeting verification. We further assess DBS targeting accuracy using a 3DXT workflow and compare radiation dosimetry between modalities. METHODS: Patients underwent robot-assisted DBS implantation using a workflow incorporating 3DXT which we describe. Two intra-operative 3DXT spins were performed for registration and accuracy verification followed by conventional CT post-operatively. Post-operative 3DXT and CT images were independently fused to the same pre-operative MRI sequence and co-ordinates generated for comparison. Registration accuracy was compared to 15 consecutive controls who underwent CT-based registration. Radial targeting accuracy was calculated and radiation dosimetry recorded. RESULTS: Data were obtained from 29 leads in 15 consecutive patients. 3DXT registration accuracy was significantly superior to CT with mean error 0.22 ± 0.03 mm (p < 0.0001). Mean Euclidean electrode tip position variation for CT to MRI versus 3DXT to MRI fusion was 0.62 ± 0.40 mm (range 0.0 mm-1.7 mm). In comparison, direct CT to 3DXT fusion showed electrode tip Euclidean variance of 0.23 ± 0.09 mm. Mean radial targeting accuracy assessed on 3DXT was 0.97 ± 0.54 mm versus 1.15 ± 0.55 mm on CT with differences insignificant (p = 0.30). Mean patient radiation doses were around 80% lower with 3DXT versus CT (p < 0.0001). DISCUSSION: Mobile 3D C-arm fluoroscopy can be safely incorporated into DBS workflows for both registration and lead verification. For registration, the limited field of view requires the use of frameless transient fiducials and is highly accurate. For lead position verification based on MRI co-registration, we estimate there is around a 0.4 mm discrepancy between lead position seen on 3DXT versus CT when corrected for brain shift. This is similar to that described in O-arm® or mobile CT series. For units where logistical or financial considerations preclude the acquisition of a cone beam CT or mobile CT scanner, our data support portable 3D C-arm fluoroscopy as an acceptable alternative with significantly lower radiation exposure.

Closed-loop parameter optimization for patient-specific phrenic nerve stimulation.

BACKGROUND: Ventilator-induced diaphragm dysfunction occurs rapidly following the onset of mechanical ventilation and has significant clinical consequences. Phrenic nerve stimulation has shown promise in maintaining diaphragm function by inducing diaphragm contractions. Non-invasive stimulation is an attractive option as it minimizes the procedural risks associated with invasive approaches. However, this method is limited by sensitivity to electrode position and inter-individual variability in stimulation thresholds. This makes clinical application challenging due to potentially time-consuming calibration processes to achieve reliable stimulation. METHODS: We applied non-invasive electrical stimulation to the phrenic nerve in the neck in healthy volunteers. A closed-loop system recorded the respiratory flow produced by stimulation and automatically adjusted the electrode position and stimulation amplitude based on the respiratory response. By iterating over electrodes, the optimal electrode was selected. A binary search method over stimulation amplitudes was then employed to determine an individualized stimulation threshold. Pulse trains above this threshold were delivered to produce diaphragm contraction. RESULTS: Nine healthy volunteers were recruited. Mean threshold stimulation amplitude was 36.17 ± 14.34 mA (range 19.38-59.06 mA). The threshold amplitude for reliable nerve capture was moderately correlated with BMI (Pearson's r = 0.66, p = 0.049). Repeating threshold measurements within subjects demonstrated low intra-subject variability of 2.15 ± 1.61 mA between maximum and minimum thresholds on repeated trials. Bilateral stimulation with individually optimized parameters generated reliable diaphragm contraction, resulting in significant inhaled volumes following stimulation. CONCLUSION: We demonstrate the feasibility of a system for automatic optimization of electrode position and stimulation parameters using a closed-loop system. This opens the possibility of easily deployable individualized stimulation in the intensive care setting to reduce ventilator-induced diaphragm dysfunction.

Seeing Is Believing: Photon Counting Computed Tomography Clearly Images Directional Deep Brain Stimulation Lead Segments and Markers After Implantation.

BACKGROUND AND OBJECTIVES: Directional deep brain stimulation (DBS) electrodes are increasingly used, but conventional computed tomography (CT) is unable to directly image segmented contacts owing to physics-based resolution constraints. Postoperative electrode segment orientation assessment is necessary because of the possibility of significant deviation during or immediately after insertion. Photon-counting detector (PCD) CT is a relatively novel technology that enables high resolution imaging while addressing several limitations intrinsic to CT. We show how PCD CT can enable clear in vivo imaging of DBS electrodes, including segmented contacts and markers for all major lead manufacturers. MATERIALS AND METHODS: We describe postoperative imaging and reconstruction protocols we have developed to enable optimal lead visualization. PCD CT images were obtained of directional leads from the three major manufacturers and fused with preoperative 3T magnetic resonance imaging (MRI). Radiation dosimetry also was evaluated and compared with conventional imaging controls. Orientation estimates from directly imaged leads were compared with validated software-based reconstructions (derived from standard CT imaging artifact analysis) to quantify congruence in alignment and directional orientation. RESULTS: High-fidelity images were obtained for 15 patients, clearly indicating the segmented contacts and directional markers both on CT alone and when fused to MRI. Our routine imaging protocol is described. Ionizing radiation doses were significantly lower than with conventional CT. For most leads, the directly imaged lead orientations and depths corresponded closely to those predicted by CT artifact-based reconstructions. However, unlike direct imaging, the software reconstructions were susceptible to 180° error in orientation assessment. CONCLUSIONS: High-resolution photon-counting CT can very clearly image segmented DBS electrode contacts and directional markers and unambiguously determine lead orientation, with lower radiation than in conventional imaging. This obviates the need for further imaging and may facilitate anatomically tailored directional programming.

Developing RPC-Net: Leveraging High-Density Electromyography and Machine Learning for Improved Hand Position Estimation.

OBJECTIVE: The purpose of this study was to develop and evaluate the performance of RPC-Net (Recursive Prosthetic Control Network), a novel method using simple neural network architectures to translate electromyographic activity into hand position with high accuracy and computational efficiency. METHODS: RPC-Net uses a regression-based approach to convert forearm electromyographic signals into hand kinematics. We tested the adaptability of the algorithm to different conditions and compared its performance with that of solutions from the academic literature. RESULTS: RPC-Net demonstrated a high degree of accuracy in predicting hand position from electromyographic activity, outperforming other solutions with the same computational cost. Including previous position data consistently improved results across subjects and conditions. RPC-Net showed robustness against a reduction in the number of electromyography electrodes used and shorter input signals, indicating potential for further reduction in computational cost. CONCLUSION: The results demonstrate that RPC-Net is capable of accurately translating forearm electromyographic activity into hand position, offering a practical and adaptable tool that may be accessible in clinical settings. SIGNIFICANCE: The development of RPC-Net represents a significant advancement. In clinical settings, its application could enable prosthetic devices to be controlled in a way that feels more natural, improving the quality of life for individuals with limb loss.

Identification of motor progression in Parkinson's disease using wearable sensors and machine learning.

Wearable devices offer the potential to track motor symptoms in neurological disorders. Kinematic data used together with machine learning algorithms can accurately identify people living with movement disorders and the severity of their motor symptoms. In this study we aimed to establish whether a combination of wearable sensor data and machine learning algorithms with automatic feature selection can estimate the clinical rating scale and whether it is possible to monitor the motor symptom progression longitudinally, for people with Parkinson's Disease. Seventy-four patients visited the lab seven times at 3-month intervals. Their walking (2-minutes) and postural sway (30-seconds,eyes-closed) were recorded using six Inertial Measurement Unit sensors. Simple linear regression and Random Forest algorithms were utilised together with different routines of automatic feature selection or factorisation, resulting in seven different machine learning algorithms to estimate the clinical rating scale (Movement Disorder Society- Unified Parkinson's Disease Rating Scale part III; MDS-UPDRS-III). Twenty-nine features were found to significantly progress with time at group level. The Random Forest model revealed the most accurate estimation of the MDS-UPDRS-III among the seven models. The model estimations detected a statistically significant progression of the motor symptoms within 15 months when compared to the first visit, whereas the MDS-UPDRS-III did not capture any change. Wearable sensors and machine learning can track the motor symptom progression in people with PD better than the conventionally used clinical rating scales. The methods described in this study can be utilised complimentary to the clinical rating scales to improve the diagnostic and prognostic accuracy.

Antiparkinsonian medication masks motor signal progression in de novo patients.

Patients not yet receiving medication provide insight to drug-naïve early physiology of Parkinson's Disease (PD). Wearable sensors can measure changes in motor features before and after introduction of antiparkinsonian medication. We aimed to identify features of upper limb bradykinesia, postural stability, and gait that measurably progress in de novo PD patients prior to the start of medication, and determine whether these features remain sensitive to progression in the period after commencement of antiparkinsonian medication. Upper limb motion was measured using an inertial sensor worn on a finger, while postural stability and gait were recorded using an array of six wearable sensors. Patients were tested over nine visits at three monthly intervals. The timepoint of start of medication was noted. Three upper limb bradykinetic features (finger tapping speed, pronation supination speed, and pronation supination amplitude) and three gait features (gait speed, arm range of motion, duration of stance phase) were found to progress in unmedicated early-stage PD patients. In all features, progression was masked after the start of medication. Commencing antiparkinsonian medication is known to lead to masking of progression signals in clinical measures in de novo PD patients. In this study, we show that this effect is also observed with digital measures of bradykinetic and gait motor features.

Deep Brain Stimulation and Levodopa Affect Gait Variability in Parkinson Disease Differently.

BACKGROUND: Both dopaminergic medication and subthalamic nucleus (STN) deep brain stimulation (DBS) can improve the amplitude and speed of gait in Parkinson disease (PD), but relatively little is known about their comparative effects on gait variability. Gait irregularity has been linked to the degeneration of cholinergic neurons in the pedunculopontine nucleus (PPN). OBJECTIVES: The STN and PPN have reciprocal connections, and we hypothesized that STN DBS might improve gait variability by modulating PPN function. Dopaminergic medication should not do this, and we therefore sought to compare the effects of medication and STN DBS on gait variability. MATERIALS AND METHODS: We studied 11 patients with STN DBS systems on and off with no alteration to their medication, and 15 patients with PD without DBS systems on and off medication. Participants walked for two minutes in each state, wearing six inertial measurement units. Variability has previously often been expressed in terms of SD or coefficient of variation over a testing session, but these measures conflate long-term variability (eg, gradual slowing, which is not necessarily pathological) with short-term variability (true irregularity). We used Poincaré analysis to separate the short- and long-term variability. RESULTS: DBS decreased short-term variability in lower limb gait parameters, whereas medication did not have this effect. In contrast, STN DBS had no effect on arm swing and trunk motion variability, whereas medication increased them, without obvious dyskinesia. CONCLUSIONS: Our results suggest that STN DBS acts through a nondopaminergic mechanism to reduce gait variability. We believe that the most likely explanation is the retrograde activation of cholinergic PPN projection neurons.

Decomposition into dynamic features reveals a conserved temporal structure in hand kinematics.

The human hand is a unique and highly complex effector. The ability to describe hand kinematics with a small number of features suggests that complex hand movements are composed of combinations of simpler movements. This would greatly simplify the neural control of hand movements. If such movement primitives exist, a dimensionality reduction approach designed to exploit these features should outperform existing methods. We developed a deep neural network to capture the temporal dynamics of movements and demonstrate that the features learned allow accurate representation of functional hand movements using lower-dimensional representations than previously reported. We show that these temporal features are highly conserved across individuals and can interpolate previously unseen movements, indicating that they capture the intrinsic structure of hand movements. These results indicate that functional hand movements are defined by a low-dimensional basis set of movement primitives with important temporal dynamics and that these features are common across individuals.

Parkinson's disease deficits in time perception to auditory as well as visual stimuli - A large online study.

Cognitive deficits are common in Parkinson's disease (PD) and range from mild cognitive impairment to dementia, often dramatically reducing quality of life. Physiological models have shown that attention and memory are predicated on the brain's ability to process time. Perception has been shown to be increased or decreased by activation or deactivation of dopaminergic neurons respectively. Here we investigate differences in time perception between patients with PD and healthy controls. We have measured differences in sub-second- and second-time intervals. Sensitivity and error in perception as well as the response times are calculated. Additionally, we investigated intra-individual response variability and the effect of participant devices on both reaction time and sensitivity. Patients with PD have impaired sensitivity in discriminating between durations of both visual and auditory stimuli compared to healthy controls. Though initially designed as an in-person study, because of the pandemic the experiment was adapted into an online study. This adaptation provided a unique opportunity to enroll a larger number of international participants and use this study to evaluate the feasibility of future virtual studies focused on cognitive impairment. To our knowledge this is the only time perception study, focusing on PD, which measures the differences in perception using both auditory and visual stimuli. The cohort involved is the largest to date, comprising over 800 participants.

Computation of Activating Fields for Approximation of the Orientation-Specific Neural Response to Electrical Stimulation.

Computational methods of determining the response of neural tissue to electrical stimulation have demonstrated value for the development of novel devices and the programming of neuromodulation therapies. Detailed biophysical models are excessively computationally intensive for many applications; simple metrics to approximate activation can speed up progress in this area. The activating function provides such a useful metric. However, this measure, defined for a specific axon orientation, is not immediately applicable to computed electric fields to assess their effects. We demonstrate a method for computation of the activating function generalized to a field in order to allow rapid computation of the effects of stimulation on neural tissue while preserving information on axon orientation. Clinical Relevance- This demonstrates a useful method of approximating the effect of electrical stimulation on nervous tissue for the development of devices and the optimization of parameters for electrical neuromodulation.

Longitudinal Monitoring of Progressive Supranuclear Palsy using Body-Worn Movement Sensors.

BACKGROUND: We have previously shown that wearable technology and machine learning techniques can accurately discriminate between progressive supranuclear palsy (PSP), Parkinson's disease, and healthy controls. To date these techniques have not been applied in longitudinal studies of disease progression in PSP. OBJECTIVES: We aimed to establish whether data collected by a body-worn inertial measurement unit (IMU) network could predict clinical rating scale scores in PSP and whether it could be used to track disease progression. METHODS: We studied gait and postural stability in 17 participants with PSP over five visits at 3-month intervals. Participants performed a 2-minute walk and an assessment of postural stability by standing for 30 seconds with their eyes closed, while wearing an array of six IMUs. RESULTS: Thirty-two gait and posture features were identified, which progressed significantly with time. A simple linear regression model incorporating the three features with the clearest progression pattern was able to detect statistically significant progression 3 months in advance of the clinical scores. A more complex linear regression and a random forest approach did not improve on this. CONCLUSIONS: The reduced variability of the models, in comparison to clinical rating scales, allows a significant change in disease status from baseline to be observed at an earlier stage. The current study sheds light on the individual features that are important in tracking disease progression. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Oculomotor deficits in Parkinson's disease: Increasing sensitivity using multivariate approaches.

Parkinson's disease (PD) affects several domains of neurological function, from lower-level motor programs to higher cognitive processing. As certain types of eye movements (saccades) are fast, non-fatiguing, and can be measured objectively and non-invasively, they are a promising candidate for quantifying motor and cognitive dysfunction in PD, as well as other movement disorders. In this pilot study, we evaluate the latency (reaction time), damping (resistance to oscillation), and amplitude of saccadic movements in two tasks performed by 25 PD patients with mild to moderate disease and 26 age-matched healthy controls. As well as general increases in reaction time caused by PD, the damping of saccadic eye movements was found to be task-dependent and affected by disease. Finally, we introduce a proof-of-concept multivariate model to demonstrate how information from saccadometry can be combined to infer disease status.

Non-invasive phrenic nerve stimulation to avoid ventilator-induced diaphragm dysfunction in critical care.

BACKGROUND: Diaphragm muscle atrophy during mechanical ventilation begins within 24 h and progresses rapidly with significant clinical consequences. Electrical stimulation of the phrenic nerves using invasive electrodes has shown promise in maintaining diaphragm condition by inducing intermittent diaphragm muscle contraction. However, the widespread application of these methods may be limited by their risks as well as the technical and environmental requirements of placement and care. Non-invasive stimulation would offer a valuable alternative method to maintain diaphragm health while overcoming these limitations. METHODS: We applied non-invasive electrical stimulation to the phrenic nerve in the neck in healthy volunteers. Respiratory pressure and flow, diaphragm electromyography and mechanomyography, and ultrasound visualization were used to assess the diaphragmatic response to stimulation. The electrode positions and stimulation parameters were systematically varied in order to investigate the influence of these parameters on the ability to induce diaphragm contraction with non-invasive stimulation. RESULTS: We demonstrate that non-invasive capture of the phrenic nerve is feasible using surface electrodes without the application of pressure, and characterize the stimulation parameters required to achieve therapeutic diaphragm contractions in healthy volunteers. We show that an optimal electrode position for phrenic nerve capture can be identified and that this position does not vary as head orientation is changed. The stimulation parameters required to produce a diaphragm response at this site are characterized and we show that burst stimulation above the activation threshold reliably produces diaphragm contractions sufficient to drive an inspired volume of over 600 ml, indicating the ability to produce significant diaphragmatic work using non-invasive stimulation. CONCLUSION: This opens the possibility of non-invasive systems, requiring minimal specialist skills to set up, for maintaining diaphragm function in the intensive care setting.

Spatial and Temporal Distribution of Information Processing in the Human Dorsal Anterior Cingulate Cortex.

The dorsal anterior cingulate cortex (dACC) is a key node in the human salience network. It has been ascribed motor, pain-processing and affective functions. However, the dynamics of information flow in this complex region and how it responds to inputs remain unclear and are difficult to study using non-invasive electrophysiology. The area is targeted by neurosurgery to treat neuropathic pain. During deep brain stimulation surgery, we recorded local field potentials from this region in humans during a decision-making task requiring motor output. We investigated the spatial and temporal distribution of information flow within the dACC. We demonstrate the existence of a distributed network within the anterior cingulate cortex where discrete nodes demonstrate directed communication following inputs. We show that this network anticipates and responds to the valence of feedback to actions. We further show that these network dynamics adapt following learning. Our results provide evidence for the integration of learning and the response to feedback in a key cognitive region.

Longitudinal changes of early motor and cognitive symptoms in progressive supranuclear palsy: the OxQUIP study.

BACKGROUND: Progressive supranuclear palsy (PSP) is a rare neurodegenerative condition characterised by a range of motor and cognitive symptoms. Very little is known about the longitudinal change in these symptoms over time. Moreover, the effectiveness of clinical scales to detect early changes in PSP is still a matter of debate. OBJECTIVE: We aimed to determine longitudinal changes in PSP features using multiple closely spaced follow-up time points over a period of 2 years. Methods 28 healthy control and 28 PSP participants, with average time since onset of symptoms of 1.9 years, were prospectively studied every 3 months for up to 24 months. Changes from baseline scores were calculated at each follow-up time point using multiple clinical scales to identify longitudinal progression of motor and cognitive symptoms. RESULTS: The Montreal Cognitive Assessment, but not the Mini-Mental State Examination, detected cognitive decline at baseline. Both scales revealed poor longitudinal sensitivity to clinical change in global cognitive symptoms. Conversely, the Movement Disorders Society Unified Parkinson's disease Rating Scale - part III and the PSP Rating Scale (PSPRS) reliably detected motor decline less than 2 years after disease onset. The 'Gait/Midline' PSPRS subscore consistently declined over time, with the earliest change being observed 6 months after baseline assessment. CONCLUSION: While better cognitive screening tools are still needed to monitor cognitive decline in PSP, motor decline is consistently captured by clinical rating scales. These results support the inclusion of multiple follow-up time points in longitudinal studies in the early stages of PSP.

Paired Acute Invasive/Non-invasive Stimulation (PAINS) study: A phase I/II randomized, sham-controlled crossover trial in chronic neuropathic pain.

BACKGROUND: Dorsal root ganglion (DRG) stimulation, an invasive method of neuromodulation, and transcranial direct current stimulation (tDCS), a non-invasive method of altering cortical excitability, have both proven effective in relieving chronic pain. OBJECTIVE: We employed a randomized, sham-controlled crossover study design to investigate whether single-session tDCS would have an additive therapeutic effect alongside DRG stimulation (DRGS) in the treatment of chronic pain. METHODS: Sixteen neuropathic pain patients who were previously implanted with DRG stimulators were recruited. Baseline pain scores were established with DRGS-OFF. Pain scores were then recorded with DRGS-ON, after paired sham tDCS stimulation, and after paired active anodal tDCS (a-tDCS) stimulation. For active tDCS, patients were randomized to 'MEG (magnetoencephalography) localized' tDCS or contralateral motor cortex (M1) tDCS for 30 min. EEG recordings and evaluations of tDCS adverse effects were also collected. RESULTS: All participants reported the interventions to be tolerable with no significant adverse effects during the session. Paired DRGS/active tDCS resulted in a significant reduction in pain scores compared to paired DRGS-ON/sham tDCS or DRGS alone. There was no difference in the additive effect of M1 vs. MEG-localized tDCS. Significant augmentation of beta activity was observed between DRGS-OFF and DRGS-ON conditions, as well as between paired DRGS-ON/sham tDCS and paired DRGS-ON/active tDCS. CONCLUSION: Our results indicate that a single session of tDCS alongside DRGS is safe and can significantly reduce pain acutely in neuropathic pain patients. Paired invasive/non-invasive neuromodulation is a promising new treatment strategy for pain management and should be evaluated further to assess long-term benefits.

Pallido-putaminal connectivity predicts outcomes of deep brain stimulation for cervical dystonia.

Cervical dystonia is a non-degenerative movement disorder characterized by dysfunction of both motor and sensory cortico-basal ganglia networks. Deep brain stimulation targeted to the internal pallidum is an established treatment, but its specific mechanisms remain elusive, and response to therapy is highly variable. Modulation of key dysfunctional networks via axonal connections is likely important. Fifteen patients underwent preoperative diffusion-MRI acquisitions and then progressed to bilateral deep brain stimulation targeting the posterior internal pallidum. Severity of disease was assessed preoperatively and later at follow-up. Scans were used to generate tractography-derived connectivity estimates between the bilateral regions of stimulation and relevant structures. Connectivity to the putamen correlated with clinical improvement, and a series of cortical connectivity-based putaminal parcellations identified the primary motor putamen as the key node (r = 0.70, P = 0.004). A regression model with this connectivity and electrode coordinates explained 68% of the variance in outcomes (r = 0.83, P = 0.001), with both as significant explanatory variables. We conclude that modulation of the primary motor putamen-posterior internal pallidum limb of the cortico-basal ganglia loop is characteristic of successful deep brain stimulation treatment of cervical dystonia. Preoperative diffusion imaging contains additional information that predicts outcomes, implying utility for patient selection and/or individualized targeting.