Internal states as a source of subject-dependent movement variability are represented by large-scale brain networks.

Humans' ability to adapt and learn relies on reflecting on past performance. These experiences form latent representations called internal states that induce movement variability that improves how we interact with our environment. Our study uncovered temporal dynamics and neural substrates of two states from ten subjects implanted with intracranial depth electrodes while they performed a goal-directed motor task with physical perturbations. We identified two internal states using state-space models: one tracking past errors and the other past perturbations. These states influenced reaction times and speed errors, revealing how subjects strategize from trial history. Using local field potentials from over 100 brain regions, we found large-scale brain networks such as the dorsal attention and default mode network modulate visuospatial attention based on recent performance and environmental feedback. Notably, these networks were more prominent in higher-performing subjects, emphasizing their role in improving motor performance by regulating movement variability through internal states.

Intraoperative neural signals predict rapid antidepressant effects of deep brain stimulation.

Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) is a promising intervention for treatment-resistant depression (TRD). Despite the failure of a clinical trial, multiple case series have described encouraging results, especially with the introduction of improved surgical protocols. Recent evidence further suggests that tractography targeting and intraoperative exposure to stimulation enhances early antidepressant effects that further evolve with ongoing chronic DBS. Accelerating treatment gains is critical to the care of this at-risk population, and identification of intraoperative electrophysiological biomarkers of early antidepressant effects will help guide future treatment protocols. Eight patients underwent intraoperative electrophysiological recording when bilateral DBS leads were implanted in the SCC using a connectomic approach at the site previously shown to optimize 6-month treatment outcomes. A machine learning classification method was used to discriminate between intracranial local field potentials (LFPs) recorded at baseline (stimulation-naïve) and after the first exposure to SCC DBS during surgical procedures. Spectral inputs (theta, 4-8 Hz; alpha, 9-12 Hz; beta, 13-30 Hz) to the model were then evaluated for importance to classifier success and tested as predictors of the antidepressant response. A decline in depression scores by 45.6% was observed after 1 week and this early antidepressant response correlated with a decrease in SCC LFP beta power, which most contributed to classifier success. Intraoperative exposure to therapeutic stimulation may result in an acute decrease in symptoms of depression following SCC DBS surgery. The correlation of symptom improvement with an intraoperative reduction in SCC beta power suggests this electrophysiological finding as a biomarker for treatment optimization.

Clinical outcomes of globus pallidus deep brain stimulation for Parkinson disease: a comparison of intraoperative MRI- and MER-guided lead placement.

OBJECTIVE: Deep brain stimulation (DBS) lead placement is increasingly performed with the patient under general anesthesia by surgeons using intraoperative MRI (iMRI) guidance without microelectrode recording (MER) or macrostimulation. The authors assessed the accuracy of lead placement, safety, and motor outcomes in patients with Parkinson disease (PD) undergoing DBS lead placement into the globus pallidus internus (GPi) using iMRI or MER guidance. METHODS: The authors identified all patients with PD who underwent either MER- or iMRI-guided GPi-DBS lead placement at Emory University between July 2007 and August 2016. Lead placement accuracy and adverse events were determined for all patients. Clinical outcomes were assessed using the Unified Parkinson's Disease Rating Scale (UPDRS) part III motor scores for patients completing 12 months of follow-up. The authors also assessed the levodopa-equivalent daily dose (LEDD) and stimulation parameters. RESULTS: Seventy-seven patients were identified (MER, n = 28; iMRI, n = 49), in whom 131 leads were placed. The stereotactic accuracy of the surgical procedure with respect to the planned lead location was 1.94 ± 0.21 mm (mean ± SEM) (95% CI 1.54-2.34) with frame-based MER and 0.84 ± 0.007 mm (95% CI 0.69-0.98) with iMRI. The rate of serious complications was similar, at 6.9% for MER-guided DBS lead placement and 9.4% for iMRI-guided DBS lead placement (RR 0.71 [95% CI 0.13%-3.9%]; p = 0.695). Fifty-seven patients were included in clinical outcome analyses (MER, n = 16; iMRI, n = 41). Both groups had similar characteristics at baseline, although patients undergoing MER-guided DBS had a lower response on their baseline levodopa challenge (44.8% ± 5.4% [95% CI 33.2%-56.4%] vs 61.6% ± 2.1% [95% CI 57.4%-65.8%]; t = 3.558, p = 0.001). Greater improvement was seen following iMRI-guided lead placement (43.2% ± 3.5% [95% CI 36.2%-50.3%]) versus MER-guided lead placement (25.5% ± 6.7% [95% CI 11.1%-39.8%]; F = 5.835, p = 0.019). When UPDRS III motor scores were assessed only in the contralateral hemibody (per-lead analyses), the improvements remained significantly different (37.1% ± 7.2% [95% CI 22.2%-51.9%] and 50.0% ± 3.5% [95% CI 43.1%-56.9%] for MER- and iMRI-guided DBS lead placement, respectively). Both groups exhibited similar reductions in LEDDs (21.2% and 20.9%, respectively; F = 0.221, p = 0.640). The locations of all active contacts and the 2D radial distance from these to consensus coordinates for GPi-DBS lead placement (x, ±20; y, +2; and z, -4) did not differ statistically by type of surgery. CONCLUSIONS: iMRI-guided GPi-DBS lead placement in PD patients was associated with significant improvement in clinical outcomes, comparable to those observed following MER-guided DBS lead placement. Furthermore, iMRI-guided DBS implantation produced a similar safety profile to that of the MER-guided procedure. As such, iMRI guidance is an alternative to MER guidance for patients undergoing GPi-DBS implantation for PD.

Clinical outcomes of pallidal deep brain stimulation for dystonia implanted using intraoperative MRI.

OBJECTIVE: Lead placement for deep brain stimulation (DBS) using intraoperative MRI (iMRI) relies solely on real-time intraoperative neuroimaging to guide electrode placement, without microelectrode recording (MER) or electrical stimulation. There is limited information, however, on outcomes after iMRI-guided DBS for dystonia. The authors evaluated clinical outcomes and targeting accuracy in patients with dystonia who underwent lead placement using an iMRI targeting platform. METHODS: Patients with dystonia undergoing iMRI-guided lead placement in the globus pallidus pars internus (GPi) were identified. Patients with a prior ablative or MER-guided procedure were excluded from clinical outcomes analysis. Burke-Fahn-Marsden Dystonia Rating Scale (BFMDRS) scores and Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) scores were assessed preoperatively and at 6 and 12 months postoperatively. Other measures analyzed include lead accuracy, complications/adverse events, and stimulation parameters. RESULTS: A total of 60 leads were implanted in 30 patients. Stereotactic lead accuracy in the axial plane was 0.93 ± 0.12 mm from the intended target. Nineteen patients (idiopathic focal, n = 7; idiopathic segmental, n = 5; DYT1, n = 1; tardive, n = 2; other secondary, n = 4) were included in clinical outcomes analysis. The mean improvement in BFMDRS score was 51.9% ± 9.7% at 6 months and 63.4% ± 8.0% at 1 year. TWSTRS scores in patients with predominant cervical dystonia (n = 13) improved by 53.3% ± 10.5% at 6 months and 67.6% ± 9.0% at 1 year. Serious complications occurred in 6 patients (20%), involving 8 of 60 implanted leads (13.3%). The rate of serious complications across all patients undergoing iMRI-guided DBS at the authors' institution was further reviewed, including an additional 53 patients undergoing GPi-DBS for Parkinson disease. In this expanded cohort, serious complications occurred in 11 patients (13.3%) involving 15 leads (10.1%). CONCLUSIONS: Intraoperative MRI-guided lead placement in patients with dystonia showed improvement in clinical outcomes comparable to previously reported results using awake MER-guided lead placement. The accuracy of lead placement was high, and the procedure was well tolerated in the majority of patients. However, a number of patients experienced serious adverse events that were attributable to the introduction of a novel technique into a busy neurosurgical practice, and which led to the revision of protocols, product inserts, and on-site training.

Non-motor Brain Regions in Non-dominant Hemisphere Are Influential in Decoding Movement Speed.

Sensorimotor control studies have predominantly focused on how motor regions of the brain relay basic movement-related information such as position and velocity. However, motor control is often complex, involving the integration of sensory information, planning, visuomotor tracking, spatial mapping, retrieval and storage of memories, and may even be emotionally driven. This suggests that many more regions in the brain are involved beyond premotor and motor cortices. In this study, we exploited an experimental setup wherein activity from over 87 non-motor structures of the brain were recorded in eight human subjects executing a center-out motor task. The subjects were implanted with depth electrodes for clinical purposes. Using training data, we constructed subject-specific models that related spectral power of neural activity in six different frequency bands as well as a combined model containing the aggregation of multiple frequency bands to movement speed. We then tested the models by evaluating their ability to decode movement speed from neural activity in the test data set. The best models achieved a correlation of 0.38 ± 0.03 (mean ± standard deviation). Further, the decoded speeds matched the categorical representation of the test trials as correct or incorrect with an accuracy of 70 ± 2.75% across subjects. These models included features from regions such as the right hippocampus, left and right middle temporal gyrus, intraparietal sulcus, and left fusiform gyrus across multiple frequency bands. Perhaps more interestingly, we observed that the non-dominant hemisphere (ipsilateral to dominant hand) was most influential in decoding movement speed.

Modulations in Oscillatory Activity of Globus Pallidus Internus Neurons During a Directed Hand Movement Task-A Primary Mechanism for Motor Planning.

Globus pallidus internus (GPi) neurons in the basal ganglia are traditionally thought to play a significant role in the promotion and suppression of movement via a change in firing rates. Here, we hypothesize that a primary mechanism of movement control by GPi neurons is through specific modulations in their oscillatory patterns. We analyzed neuronal spiking activity of 83 GPi neurons recorded from two healthy nonhuman primates executing a radial center-out motor task. We found that, in directionally tuned neurons, the power in the gamma band is significantly (p < 0.05) greater than that in the beta band (a "cross-over" effect), during the planning stages of movements in their preferred direction. This cross-over effect is not observed in the non-directionally tuned neurons. These data suggest that, during movement planning, information encoding by GPi neurons may be governed by a sudden emergence and suppression of oscillatory activities, rather than simply by a change in average firing rates.

BrdU-induced hyperlocomotion in the stroked rat.

5-bromo-2'-dexoyuridine (BrdU) is often used in neuroscience research as a marker of newly-divided cells. However, several studies suggest that BrdU can produce unwanted side effects, including changes in animal behavior and cellular function. In this study, we investigated the effect of BrdU injections on locomotor behavior in a rodent model of ischemic stroke. Ischemic strokes were induced in adult rats, and 50 mg/kg BrdU was intraperitoneally injected over 5 days beginning 2 weeks post-stroke, while control animals received vehicle. Locomotor activity was evaluated by videotaping the rats in their home cages for 30 min, beginning one hour after BrdU injection. BrdU-injected rats showed a nearly three-fold increase in locomotor activity compared to control animals. These findings suggest that BrdU induces a hyperlocomotor effect in rats following brain injury, pointing to the need for caution when interpreting behavioral results in such studies.

Risk-taking bias in human decision-making is encoded via a right-left brain push-pull system.

A person's decisions vary even when options stay the same, like when a gambler changes bets despite constant odds of winning. Internal bias (e.g., emotion) contributes to this variability and is shaped by past outcomes, yet its neurobiology during decision-making is not well understood. To map neural circuits encoding bias, we administered a gambling task to 10 participants implanted with intracerebral depth electrodes in cortical and subcortical structures. We predicted the variability in betting behavior within and across patients by individual bias, which is estimated through a dynamical model of choice. Our analysis further revealed that high-frequency activity increased in the right hemisphere when participants were biased toward risky bets, while it increased in the left hemisphere when participants were biased away from risky bets. Our findings provide electrophysiological evidence that risk-taking bias is a lateralized push-pull neural system governing counterintuitive and highly variable decision-making in humans.

Neural Correlates of Internal States that Capture Movement Variability.

The brain lacks the ability to perfectly replicate movements. In particular, if specific movements are cued sequentially, how you perform on past trials may influence how you move on current and future trials. Past trial outcomes may, for example, modulate motivation or attention which can play a significant role in how one moves, yet variability due to such internal factors are often ignored when modeling the sensorimotor control system. In this study, we wish to extract such internal factors by modeling variability in movements during a motor task riddled with unpredictable perturbations. Four subjects performed the task, and we simultaneously obtained Local Field Potential (LFP) activity from nonmotor brain regions via depth electrodes implanted for clinical purposes. We first show that motor behavior depends not only on current trial conditions, but also on internal state variables that accumulate past outcomes involving movement performance, movement speed, and whether or not perturbations have occurred. We further show that these internal states modulate with beta band activity in specific brain regions on a trial-by-trial basis. These results suggest a nontraditional role of nonmotor brain regions and prompt a need for further exploration.

Neural Activity from Attention Networks Predicts Movement Errors.

Traditionally, movement-related behavior is estimated using activity from motor regions in the brain. This predictive capability of interpreting neural signals into tangible outputs has led to the emergence of Brain-Computer Interface (BCI) systems. However, nonmotor regions can play a significant role in shaping how movements are executed. Our goal was to explore the contribution of nonmotor brain regions to movement using a unique experimental paradigm in which local field potential recordings of several cortical and subcortical regions were obtained from eight epilepsy patients implanted with depth electrodes as they performed goal-directed reaching movements. The instruction of the task was to move a cursor with a robotic arm to the indicated target with a specific speed, where correct trials were ones in which the subject achieved the instructed speed. We constructed subject-specific models that predict the speed error of each trial from neural activity in nonmotor regions. Neural features were found by averaging spectral power of activity in multiple frequency bands produced during the planning or execution of movement. Features with high predictive power were selected using a forward selection greedy search. Using our modeling framework, we were able to identify networks of regions related to attention that significantly contributed to predicting trial errors. Our results suggest that nonmotor brain regions contain relevant information about upcoming movements and should be further studied.

An exploratory data analysis method for identifying brain regions and frequencies of interest from large-scale neural recordings.

High-resolution whole brain recordings have the potential to uncover unknown functionality but also present the challenge of how to find such associations between brain and behavior when presented with a large number of regions and spectral frequencies. In this paper, we propose an exploratory data analysis method that sorts through a massive quantity of multivariate neural recordings to quickly extract a subset of brain regions and frequencies that encode behavior. This approach combines existing tools and exploits low-rank approximation of matrices without a priori selection of regions and frequency bands for analysis. In detail, the spectral content of neural activity across all frequencies of each recording contact is computed and represented as a matrix. Then, the rank-1 approximation of the matrix is computed using singular value decomposition and the associated singular vectors are extracted. The temporal singular vector, which captures the salient features of the spectrogram, is then correlated to the trial-varying behavioral signal. The distribution of correlations for each brain region is efficiently computed and used to find a subset of regions and frequency bands of interest for further examination. As an illustration, we apply this approach to a data set of local field potentials collected using stereoelectroencephalography from a human subject performing a reaching task. Using the proposed procedure, we produced a comprehensive set of brain regions and frequencies related to our specific behavior. We demonstrate how this tool can produce preliminary results that capture neural patterns related to behavior and aid in formulating data-driven hypotheses, hence reducing the time it takes for any scientist to transition from the exploratory to the confirmatory phase.

Using network analysis to localize the epileptogenic zone from invasive EEG recordings in intractable focal epilepsy.

Treatment of medically intractable focal epilepsy (MIFE) by surgical resection of the epileptogenic zone (EZ) is often effective provided the EZ can be reliably identified. Even with the use of invasive recordings, the clinical differentiation between the EZ and normal brain areas can be quite challenging, mainly in patients without MRI detectable lesions. Consequently, despite relatively large brain regions being removed, surgical success rates barely reach 60-65%. Such variable and unfavorable outcomes associated with high morbidity rates are often caused by imprecise and/or inaccurate EZ localization. We developed a localization algorithm that uses network-based data analytics to process invasive EEG recordings. This network algorithm analyzes the centrality signatures of every contact electrode within the recording network and characterizes contacts into susceptible EZ based on the centrality trends over time. The algorithm was tested in a retrospective study that included 42 patients from four epilepsy centers. Our algorithm had higher agreement with EZ regions identified by clinicians for patients with successful surgical outcomes and less agreement for patients with failed outcomes. These findings suggest that network analytics and a network systems perspective of epilepsy may be useful in assisting clinicians in more accurately localizing the EZ.

Solenoidal Micromagnetic Stimulation Enables Activation of Axons With Specific Orientation.

Electrical stimulation of the central and peripheral nervous systems - such as deep brain stimulation, spinal cord stimulation, and epidural cortical stimulation are common therapeutic options increasingly used to treat a large variety of neurological and psychiatric conditions. Despite their remarkable success, there are limitations which if overcome, could enhance outcomes and potentially reduce common side-effects. Micromagnetic stimulation (µMS) was introduced to address some of these limitations. One of the most remarkable properties is that µMS is theoretically capable of activating neurons with specific axonal orientations. Here, we used computational electromagnetic models of the µMS coils adjacent to neuronal tissue combined with axon cable models to investigate µMS orientation-specific properties. We found a 20-fold reduction in the stimulation threshold of the preferred axonal orientation compared to the orthogonal direction. We also studied the directional specificity of µMS coils by recording the responses evoked in the inferior colliculus of rodents when a pulsed magnetic stimulus was applied to the surface of the dorsal cochlear nucleus. The results confirmed that the neuronal responses were highly sensitive to changes in the µMS coil orientation. Accordingly, our results suggest that µMS has the potential of stimulating target nuclei in the brain without affecting the surrounding white matter tracts.

Systems approaches to optimizing deep brain stimulation therapies in Parkinson's disease.

Over the last 30 years, deep brain stimulation (DBS) has been used to treat chronic neurological diseases like dystonia, obsessive-compulsive disorders, essential tremor, Parkinson's disease, and more recently, dementias, depression, cognitive disorders, and epilepsy. Despite its wide use, DBS presents numerous challenges for both clinicians and engineers. One challenge is the design of novel, more efficient DBS therapies, which are hampered by the lack of complete understanding about the cellular mechanisms of therapeutic DBS. Another challenge is the existence of redundancy in clinical outcomes, that is, different DBS programs can result in similar clinical benefits but very little information (e.g., predictive models, longitudinal data, metrics, etc.) is available to select one program over another. Finally, there is high variability in patients' responses to DBS, which forces clinicians to carefully adjust the stimulation settings to each patient via lengthy programming sessions. Researchers in neural engineering and systems biology have been tackling these challenges over the past few years with the specific goal of developing novel DBS therapies, design methodologies, and computational tools that optimize the therapeutic effects of DBS in each patient. Furthermore, efforts are being made to automatically adapt the DBS treatment to the fluctuations of disease symptoms. A review of the quantitative approaches currently available for the treatment of Parkinson's disease is presented here with an emphasis on the contributions that systems theoretical approaches have provided to understand the global dynamics of complex neuronal circuits in the brain under DBS. This article is categorized under: Translational, Genomic, and Systems Medicine > Therapeutic Methods Analytical and Computational Methods > Computational Methods Analytical and Computational Methods > Dynamical Methods Physiology > Mammalian Physiology in Health and Disease.

Deep brain stimulation of the ventral striatal area for poststroke pain syndrome: a magnetoencephalography study.

Poststroke pain syndrome (PSPS) is an often intractable disorder characterized by hemiparesis associated with unrelenting chronic pain. Although traditional analgesics have largely failed, integrative approaches targeting affective-cognitive spheres have started to show promise. Recently, we demonstrated that deep brain stimulation (DBS) of the ventral striatal area significantly improved the affective sphere of pain in patients with PSPS. In the present study, we examined whether electrophysiological correlates of pain anticipation were modulated by DBS that could serve as signatures of treatment effects. We recorded event-related fields (ERFs) of pain anticipation using magnetoencephalography (MEG) in 10 patients with PSPS preoperatively and postoperatively in DBS OFF and ON states. Simple visual cues evoked anticipation as patients awaited a painful (PS) or nonpainful stimulus (NPS) to the nonaffected or affected extremity. Preoperatively, ERFs showed no difference between PS and NPS anticipation to the affected extremity, possibly due to loss of salience in a network saturated by pain experience. DBS significantly modulated the early N1, consistent with improvements in affective networks involving restoration of salience and discrimination capacity. Additionally, DBS suppressed the posterior P2 (aberrant anticipatory anxiety) while enhancing the anterior N1 (cognitive and emotional regulation) in responders. DBS-induced changes in ERFs could potentially serve as signatures for clinical outcomes. NEW & NOTEWORTHY We examined the electrophysiological correlates of pain affect in poststroke pain patients who underwent deep brain stimulation (DBS) targeting the ventral striatal area under a randomized, controlled trial. DBS significantly modulated early event-related components, particularly N1 and P2, measured with magnetoencephalography during a pain anticipatory task, compared with baseline and the DBS-OFF condition, pointing to possible mechanisms of action. DBS-induced changes in event-related fields could potentially serve as biomarkers for clinical outcomes.

Lateral Cerebellar Nucleus Stimulation has Selective Effects on Glutamatergic and GABAergic Perilesional Neurogenesis After Cortical Ischemia in the Rodent Model.

BACKGROUND: Chronic deep brain stimulation of the rodent lateral cerebellar nucleus (LCN) has been demonstrated to enhance motor recovery following cortical ischemia. This effect is concurrent with synaptogenesis and expression of long-term potentiation markers in the perilesional cerebral cortex. OBJECTIVE: To further investigate the cellular changes associated with chronic LCN stimulation in the ischemic rodent by examining neurogenesis along the cerebellothalamocortical pathway. METHODS: Rats were trained on the pasta matrix task, followed by induction of cortical ischemia and electrode implantation in the contralesional LCN. Electrical stimulation was initiated 6 wk after stroke induction and continued for 4 wk prior to sacrifice. Neurogenesis was examined using immunohistochemistry. RESULTS: Treated animals showed enhanced performance on the pasta matrix task relative to sham controls. Increased cell proliferation colabeled with 5'-Bromo-2'-deoxyuridine and neurogenic markers (doublecortin) was observed in the perilesional cortex as well as bilateral mediodorsal and ventrolateral thalamic subnuclei in treated vs untreated animals. The neurogenic effect at the level of motor cortex was selective, with stimulation-treated animals showing greater glutamatergic neurogenesis but significantly less GABAergic neurogenesis. CONCLUSION: These findings suggest that LCN deep brain stimulation modulates postinjury neurogenesis, providing a possible mechanistic foundation for the associated enhancement in poststroke motor recovery.

The influences and neural correlates of past and present during gambling in humans.

During financial decision-making tasks, humans often make "rational" decisions, where they maximize expected reward. However, this rationality may compete with a bias that reflects past outcomes. That is, if one just lost money or won money, this may impact future decisions. It is unclear how past outcomes influence future decisions in humans, and how neural circuits encode present and past information. In this study, six human subjects performed a financial decision-making task while we recorded local field potentials from multiple brain structures. We constructed a model for each subject characterizing bets on each trial as a function of present and past information. The models suggest that some patients are more influenced by previous trial outcomes (i.e., previous return and risk) than others who stick to more fixed decision strategies. In addition, past return and present risk modulated with the activity in the cuneus; while present return and past risk modulated with the activity in the superior temporal gyrus and the angular gyrus, respectively. Our findings suggest that these structures play a role in decision-making beyond their classical functions by incorporating predictions and risks in humans' decision strategy, and provide new insight into how humans link their internal biases to decisions.

The role of nonmotor brain regions during human motor control.

Neural prostheses have generally relied on signals from cortical motor regions to control reaching movements of a robotic arm. However, little work has been done in exploring the involvement of nonmotor cortical and associative regions during motor tasks. In this study, we identify regions which may encode direction during planning and movement of a center-out motor task. Local field potentials were collected using stereoelectroencephalography (SEEG) from nine epilepsy patients implanted with multiple depth electrodes for clinical purposes. Spectral analysis of the recorded data was performed using nonparametric statistical techniques to identify regions that may encode direction of movements during the motor task. The analysis revealed several nonmotor regions; including the right insular cortex, right temporal pole, right superior parietal lobule, and the right lingual gyrus, that encode directionality before and after movement onset. We observed that each of these regions encode direction in different frequency bands. This preliminary study suggests that nonmotor regions may be useful in assisting in neural prosthetic control.

Nonmotor regions encode path-related information during movements.

Sensorimotor control and the involvement of motor brain regions has been extensively studied, but the role nonmotor brain regions play during movements has been overlooked. This is particularly due to the difficulty of recording from multiple regions in the brain during motor control. In this study, we utilize stereoelectroencephalography (SEEG) recording techniques to explore the role nonmotor brain areas have on the way we move. Nine humans were implanted with SEEG depth electrodes for clinical purposes, which rendered access to local field potential (LFP) activity in deep and peripheral nonmotor structures. Participants performed fast and slow arm reaching movements using a robotic manipulandum. In this study, we explored whether neural activity in a given nonmotor brain structure correlated to movement path metrics including: path length, path deviation, and path speed. Statistical analysis revealed correlations between averaged neural activity in middle temporal gyrus, supramarginal gyrus, and fusiform gyrus and our path metrics both within and across the subjects. Furthermore, we split trials across subjects into two groups: one group consisted of trials with high values of each path metric and the other with low values. We then found significant differences in LFP power in specific frequency bands (e.g. beta) during movement between each group. These results suggest that nonmotor regions may dynamically encode path-related information during movement.