Disrupting the epileptogenic network with stereoelectroencephalography-guided radiofrequency thermocoagulation.

Stereoelectroencephalography-guided radiofrequency thermocoagulation (SEEG-guided RF-TC) is a treatment option for focal drug-resistant epilepsy. In previous studies, this technique has shown seizure reduction by ≥50% in 50% of patients at 1 year. However, the relationship between the location of the ablation within the epileptogenic network and clinical outcomes remains poorly understood. Seizure outcomes were analyzed for patients who underwent SEEG-guided RF-TC and across subgroups depending on the location of the ablation within the epileptogenic network, defined as SEEG sites involved in seizure generation and spread. Eighteen patients who had SEEG-guided RF-TC were included. SEEG-guided seizure-onset zone ablation (SEEG-guided SOZA) was performed in 12 patients, and SEEG-guided partial seizure-onset zone ablation (SEEG-guided P-SOZA) in 6 patients. The early spread was ablated in three SEEG-guided SOZA patients. Five patients had ablation of a lesion. The seizure freedom rate in the cohort ranged between 22% and 50%, and the responder rate between 67% and 85%. SEEG-guided SOZA demonstrated superior results for both outcomes compared to SEEG-guided P-SOZA at 6 months (seizure freedom p = .294, responder rate p = .014). Adding the early spread ablation to SEEG-guided SOZA did not increase seizure freedom rates but exhibited comparable effectiveness regarding responder rates, indicating a potential network disruption.

Direct visualization and characterization of the human zona incerta and surrounding structures.

The zona incerta (ZI) is a small gray matter region of the deep brain first identified in the 19th century, yet direct in vivo visualization and characterization has remained elusive. Noninvasive detection of the ZI and surrounding region could be critical to further our understanding of this widely connected but poorly understood deep brain region and could contribute to the development and optimization of neuromodulatory therapies. We demonstrate that high resolution (submillimetric) longitudinal (T1) relaxometry measurements at high magnetic field strength (7 T) can be used to delineate the ZI from surrounding white matter structures, specifically the fasciculus cerebellothalamicus, fields of Forel (fasciculus lenticularis, fasciculus thalamicus, and field H), and medial lemniscus. Using this approach, we successfully derived in vivo estimates of the size, shape, location, and tissue characteristics of substructures in the ZI region, confirming observations only previously possible through histological evaluation that this region is not just a space between structures but contains distinct morphological entities that should be considered separately. Our findings pave the way for increasingly detailed in vivo study and provide a structural foundation for precise functional and neuromodulatory investigation.

Effects of Deep Brain Stimulation of the Subthalamic Nucleus Settings on Voice Quality, Intensity, and Prosody in Parkinson's Disease: Preliminary Evidence for Speech Optimization.

OBJECTIVE: To systematically evaluate how different deep brain stimulation of the subthalamic nucleus (STN-DBS) amplitude, frequency, and pulse-width electrical parameter settings impact speech intensity, voice quality, and prosody of speech in Parkinson's disease (PD). METHODS: Ten individuals with PD receiving bilateral STN-DBS treatments were seen for three baseline and five treatment visits. The five treatment visits involved an examination of the standard clinical settings as well as manipulation of different combinations of frequency (low, mid, and high), pulse width (low, mid, and high), and voltage (low, mid, and high) of stimulation. Measures of speech intensity, jitter, shimmer, harmonics-noise ratio, semitone standard deviation, and listener ratings of voice quality and prosody were obtained for each STN-DBS manipulation. RESULTS: The combinations of lower frequency, lower pulse width, and higher voltage settings were associated with improved speech outcomes compared to the current standard clinical settings. In addition, decreased total electrical energy delivered to the STN appears to be associated with speech improvements. CONCLUSIONS: This study provides preliminary evidence that STN-DBS may be optimized for Parkinson-related problems with voice quality, speech intensity, and prosody of speech.

Statistical segmentation model for accurate electrode positioning in Parkinson's deep brain stimulation based on clinical low-resolution image data and electrophysiology.

BACKGROUND: Deep Brain Stimulation (DBS), applying chronic electrical stimulation of subcortical structures, is a clinical intervention applied in major neurologic disorders. In order to achieve a good clinical effect, accurate electrode placement is necessary. The primary localisation is typically based on presurgical MRI imaging, often followed by intra-operative electrophysiology recording to increase the accuracy and to compensate for brain shift, especially in cases where the surgical target is small, and there is low contrast: e.g., in Parkinson's disease (PD) and in its common target, the subthalamic nucleus (STN). METHODS: We propose a novel, fully automatic method for intra-operative surgical navigation. First, the surgical target is segmented in presurgical MRI images using a statistical shape-intensity model. Next, automated alignment with intra-operatively recorded microelectrode recordings is performed using a probabilistic model of STN electrophysiology. We apply the method to a dataset of 120 PD patients with clinical T2 1.5T images, of which 48 also had available microelectrode recordings (MER). RESULTS: The proposed segmentation method achieved STN segmentation accuracy around dice = 0.60 compared to manual segmentation. This is comparable to the state-of-the-art on low-resolution clinical MRI data. When combined with electrophysiology-based alignment, we achieved an accuracy of 0.85 for correctly including recording sites of STN-labelled MERs in the final STN volume. CONCLUSION: The proposed method combines image-based segmentation of the subthalamic nucleus with microelectrode recordings to estimate their mutual location during the surgery in a fully automated process. Apart from its potential use in clinical targeting, the method can be used to map electrophysiological properties to specific parts of the basal ganglia structures and their vicinity.

Correction: Statistical segmentation model for accurate electrode positioning in Parkinson's deep brain stimulation based on clinical low-resolution image data and electrophysiology.

[This corrects the article DOI: 10.1371/journal.pone.0298320.].

A population-averaged structural connectomic brain atlas dataset from 422 HCP-aging subjects.

Population-averaged brain atlases, that are represented in a standard space with anatomical labels, are instrumental tools in neurosurgical planning and the study of neurodegenerative conditions. Traditional brain atlases are primarily derived from anatomical scans and contain limited information regarding the axonal organization of the white matter. With the advance of diffusion MRI that allows the modeling of fiber orientation distribution (FOD) in the brain tissue, there is an increasing interest for a population-averaged FOD template, especially based on a large healthy aging cohort, to offer structural connectivity information for connectomic surgery and analysis of neurodegeneration. The dataset described in this article contains a set of multi-contrast structural connectomic MRI atlases, including T1w, T2w, and FOD templates, along with the associated whole brain tractograms. The templates were made using multi-contrast group-wise registration based on 3T MRIs of 422 Human Connectome Project in Aging (HCP-A) subjects. To enhance the usability, probabilistic tissue maps and segmentation of 22 subcortical structures are provided. Finally, the subthalamic nucleus shown in the atlas is parcellated into sensorimotor, limbic, and associative sub-regions based on their structural connectivity to facilitate the analysis and planning of deep brain stimulation procedures. The dataset is available on the OSF Repository: https://osf.io/p7syt.

Lack of spontaneous typical seizures during intracranial monitoring with stereo-electroencephalography.

OBJECTIVE: In the presurgical evaluation of patients with drug-resistant epilepsy (DRE), occasionally, patients do not experience spontaneous typical seizures (STS) during a stereo-electroencephalography (SEEG) study, which limits its effectiveness. We sought to identify risk factors for patients who did not have STS during SEEG and to analyze the clinical outcomes for this particular set of patients. METHODS: We conducted a retrospective analysis of all patients with DRE who underwent depth electrode implantation and SEEG recordings between January 2013 and December 2018. RESULTS: SEEG was performed in 155 cases during this period. 11 (7.2%) did not experience any clinical seizures (non-STS group), while 143 experienced at least one patient-typical seizure during admission (STS group). No significant differences were found between STS and non-STS groups in terms of patient demographics, lesional/non-lesional epilepsy ratio, pre-SEEG seizure frequency, number of ASMs used, electrographic seizures or postoperative seizure outcome in those who underwent resective surgery. Statistically significant differences were found in the average number of electrodes implanted (7.0 in the non-STS group vs. 10.2 in STS), days in Epilepsy Monitoring Unit (21.8 vs. 12.8 days) and the number of cases that underwent resective surgery following SEEG (27.3% vs. 60.8%), respectively. The three non-STS patients (30%) who underwent surgery, all had their typical seizures triggered during ECS studies. Three cases were found to have psychogenic non-epileptic seizures. None of the patients in the non-STS group were offered neurostimulation devices. Five of the non-STS patients experienced transient seizure improvement following SEEG. SIGNIFICANCE: We were unable to identify any factors that predicted lack of seizures during SEEG recordings. Resective surgery was only offered in cases where ECS studies replicated patient-typical seizures. Larger datasets are required to be able to identify factors that predict which patients will fail to develop seizures during SEEG.

Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration.

Tools available for reproducible, quantitative assessment of brain correspondence have been limited. We previously validated the anatomical fiducial (AFID) placement protocol for point-based assessment of image registration with millimetric (mm) accuracy. In this data descriptor, we release curated AFID placements for some of the most commonly used structural magnetic resonance imaging datasets and templates. The release of our accurate placements allows for rapid quality control of image registration, teaching neuroanatomy, and clinical applications such as disease diagnosis and surgical targeting. We release placements on individual subjects from four datasets (N = 132 subjects for a total of 15,232 fiducials) and 14 brain templates (4,288 fiducials), totalling more than 300 human rater hours of annotation. We also validate human rater accuracy of released placements to be within 1 - 2 mm (using more than 45,000 Euclidean distances), consistent with prior studies. Our data is compliant with the Brain Imaging Data Structure allowing for facile incorporation into neuroimaging analysis pipelines.

Optimizing the selection of Parkinson's disease patients for neuromodulation using the levodopa challenge test.

BACKGROUND: In Parkinson's disease (PD), early stages are associated with a good long-duration response and as the disease advances, the short-duration response predominates. The transition between the long-duration and short-duration responses may be an important and measurable intermediate stage. A critical criterion in determining the candidature for neuromodulation is a beneficial response to an 'off-on' levodopa challenge test. This test is usually reserved for those that have already developed marked short-duration response and are candidates for deep brain stimulation (DBS) surgery. However, identifying those that are in transition may allow DBS to be offered earlier. OBJECTIVE: The objective of the study was to determine if the transition from a long-duration to a short-duration response can be assessed on a levodopa challenge test. METHODS: An 'off-on" levodopa challenge test was done in sixty-five PD patients divided into four groups based on the disease duration. RESULTS: OFF motor scores increased in all groups [Mean ± STD; 22.94 ± 8.52, 31.53 ± 9.87, 34.05 ± 9.50, and 33.92 ± 10.15 in groups 1-4, respectively] while a significant response to medication was maintained on 'off-on' testing. The mean levodopa equivalency dose in groups 1 and 2 was significantly less than in groups 3 and 4. This transition occurred between years 7 and 9 of disease duration. CONCLUSION: Performing a regular levodopa challenge test, when levodopa dose increases substantially, should be considered to determine the ideal time for DBS in patients with Parkinson's disease.

Application of the anatomical fiducials framework to a clinical dataset of patients with Parkinson's disease.

Establishing spatial correspondence between subject and template images is necessary in neuroimaging research and clinical applications such as brain mapping and stereotactic neurosurgery. Our anatomical fiducial (AFID) framework has recently been validated to serve as a quantitative measure of image registration based on salient anatomical features. In this study, we sought to apply the AFIDs protocol to the clinic, focusing on structural magnetic resonance images obtained from patients with Parkinson's disease (PD). We confirmed AFIDs could be placed to millimetric accuracy in the PD dataset with results comparable to those in normal control subjects. We evaluated subject-to-template registration using this framework by aligning the clinical scans to standard template space using a robust open preprocessing workflow. We found that registration errors measured using AFIDs were higher than previously reported, suggesting the need for optimization of image processing pipelines for clinical grade datasets. Finally, we examined the utility of using point-to-point distances between AFIDs as a morphometric biomarker of PD, finding evidence of reduced distances between AFIDs that circumscribe regions known to be affected in PD including the substantia nigra. Overall, we provide evidence that AFIDs can be successfully applied in a clinical setting and utilized to provide localized and quantitative measures of registration error. AFIDs provide clinicians and researchers with a common, open framework for quality control and validation of spatial correspondence and the location of anatomical structures, facilitating aggregation of imaging datasets and comparisons between various neurological conditions.

Waveform detection by deep learning reveals multi-area spindles that are selectively modulated by memory load.

Sleep is generally considered to be a state of large-scale synchrony across thalamus and neocortex; however, recent work has challenged this idea by reporting isolated sleep rhythms such as slow oscillations and spindles. What is the spatial scale of sleep rhythms? To answer this question, we adapted deep learning algorithms initially developed for detecting earthquakes and gravitational waves in high-noise settings for analysis of neural recordings in sleep. We then studied sleep spindles in non-human primate electrocorticography (ECoG), human electroencephalogram (EEG), and clinical intracranial electroencephalogram (iEEG) recordings in the human. Within each recording type, we find widespread spindles occur much more frequently than previously reported. We then analyzed the spatiotemporal patterns of these large-scale, multi-area spindles and, in the EEG recordings, how spindle patterns change following a visual memory task. Our results reveal a potential role for widespread, multi-area spindles in consolidation of memories in networks widely distributed across primate cortex.

The brain processes memories as we sleep, generating rhythms of electrical activity called 'sleep spindles'. Sleep spindles were long thought to be a state where the entire brain was fully synchronized by this rhythm. This was based on EEG recordings, short for electroencephalogram, a technique that uses electrodes on the scalp to measure electrical activity in the outermost layer of the brain, the cortex. But more recent intracranial recordings of people undergoing brain surgery have challenged this idea and suggested that sleep spindles may not be a state of global brain synchronization, but rather localised to specific areas. Mofrad et al. sought to clarify the extent to which spindles co-occur at multiple sites in the brain, which could shed light on how networks of neurons coordinate memory storage during sleep. To analyse highly variable brain wave recordings, Mofrad et al. adapted deep learning algorithms initially developed for detecting earthquakes and gravitational waves. The resulting algorithm, designed to more sensitively detect spindles amongst other brain activity, was then applied to a range of sleep recordings from humans and macaque monkeys. The analyses revealed that widespread and complex patterns of spindle rhythms, spanning multiple areas in the cortex of the brain, actually appear much more frequently than previously thought. This finding was consistent across all the recordings analysed, even recordings under the skull, which provide the clearest window into brain circuits. Further analyses found that these multi-area spindles occurred more often in sleep after people had completed tasks that required holding many visual scenes in memory, as opposed to control conditions with fewer visual scenes. In summary, Mofrad et al. show that neuroscientists had previously not appreciated the complex and dynamic patterns in this sleep rhythm. These patterns in sleep spindles may be able to adapt based on the demands needed for memory storage, and this will be the subject of future work. Moreover, the findings support the idea that sleep spindles help coordinate the consolidation of memories in brain circuits that stretch across the cortex. Understanding this mechanism may provide insights into how memory falters in aging and sleep-related diseases, such as Alzheimer's disease. Lastly, the algorithm developed by Mofrad et al. stands to be a useful tool for analysing other rhythmic waveforms in noisy recordings.

Adapting the listening time for micro-electrode recordings in deep brain stimulation interventions.

PURPOSE: Deep brain stimulation (DBS) is a common treatment for a variety of neurological disorders which involves the precise placement of electrodes at particular subcortical locations such as the subthalamic nucleus. This placement is often guided by auditory analysis of micro-electrode recordings (MERs) which informs the clinical team as to the anatomic region in which the electrode is currently positioned. Recent automation attempts have lacked flexibility in terms of the amount of signal recorded, not allowing them to collect more signal when higher certainty is needed or less when the anatomy is unambiguous. METHODS: We have addressed this problem by evaluating a simple algorithm that allows for MER signal collection to terminate once the underlying model has sufficient confidence. We have parameterized this approach and explored its performance using three underlying models composed of one neural network and two Bayesian extensions of said network. RESULTS: We have shown that one particular configuration, a Bayesian model of the underlying network's certainty, outperforms the others and is relatively insensitive to parameterization. Further investigation shows that this model also allows for signals to be classified earlier without increasing the error rate. CONCLUSION: We have presented a simple algorithm that records the confidence of an underlying neural network, thus allowing for MER data collection to be terminated early when sufficient confidence is reached. This has the potential to improve the efficiency of DBS electrode implantation by reducing the time required to identify anatomical structures using MERs.

Image Guidance in Deep Brain Stimulation Surgery to Treat Parkinson's Disease: A Comprehensive Review.

Deep brain stimulation (DBS) is an effective therapy as an alternative to pharmaceutical treatments for Parkinson's disease (PD). Aside from factors such as instrumentation, treatment plans, and surgical protocols, the success of the procedure depends heavily on the accurate placement of the electrode within the optimal therapeutic targets while avoiding vital structures that can cause surgical complications and adverse neurologic effects. Although specific surgical techniques for DBS can vary, interventional guidance with medical imaging has greatly contributed to the development, outcomes, and safety of the procedure. With rapid development in novel imaging techniques, computational methods, and surgical navigation software, as well as growing insights into the disease and mechanism of action of DBS, modern image guidance is expected to further enhance the capacity and efficacy of the procedure in treating PD. This article surveys the state-of-the-art techniques in image-guided DBS surgery to treat PD, and discusses their benefits and drawbacks, as well as future directions on the topic.

Intraoperative Localization of STN During DBS Surgery Using a Data-Driven Model.

A new approach is presented for localizing the Subthalamic Nucleus (STN) during Deep Brain Stimulation (DBS) surgery based on microelectrode recordings (MERs). DBS is an accepted treatment for individuals living with Parkinson's Disease (PD). This surgery involves implantation of a permanent electrode inside the STN to deliver electrical current. Since the STN is a very small region inside the brain, accurate placement of an electrode is a challenging task for the surgical team. Prior to placement of the permanent electrode, microelectrode recordings of brain activity are used intraoperatively to localize the STN. The placement of the electrode and the success of the therapy depend on this location. In this paper, an objective approach is implemented to help the surgical team in localizing the STN. This is achieved by processing the MER signals and extracting features during the surgery to be used in a Machine Learning (ML) algorithm for defining the neurophysiological borders of the STN. For this purpose, a new classification approach is proposed with the goal of detecting both the dorsal and the ventral borders of the STN during the surgical procedure. Results collected from 100 PD patients in this study, show that by calculating and extracting wavelet transformation features from MER signals and using a data-driven computational deep neural network model, it is possible to detect the borders of the STN with an accuracy of 92%. The proposed method can be implemented in real-time during the surgery to model the neurophysiological nonlinearity along the path of the electrode trajectory during insertion.

SepaConvNet for Localizing the Subthalamic Nucleus Using One Second Micro-electrode Recordings.

Micro-electrode recording (MER) is a powerful way of localizing target structures during neurosurgical procedures such as the implantation of deep brain stimulation electrodes, which is a common treatment for Parkinson's disease and other neurological disorders. While Micro-electrode Recording (MER) provides adjunctive information to guidance assisted by pre-operative imaging, it is not unanimously used in the operating room. The lack of standard use of MER may be in part due to its long duration, which can lead to complications during the operation, or due to high degree of expertise required for their interpretation. Over the past decade, various approaches addressing automating MER analysis for target localization have been proposed, which have mainly focused on feature engineering. While the accuracies obtained are acceptable in certain configurations, one issue with handcrafted MER features is that they do not necessarily capture more subtle differences in MER that could be detected auditorily by an expert neurophysiologist. In this paper, we propose and validate a deep learning-based pipeline for subthalamic nucleus (STN) localization with micro-electrode recordings motivated by the human auditory system. Our proposed Convolutional Neural Network (CNN), referred as SepaConvNet, shows improved accuracy over two comparative networks for locating the STN from one second MER samples.

Assessing the effect of current steering on the total electrical energy delivered and ambulation in Parkinson's disease.

Vertical current steering (vCS) divides current between multiple contacts, which reduces radial spread to fine-tune the electric field shape and improves neuroanatomical targeting. vCS may improve the variable responsiveness of Parkinsonian gait to conventional deep brain stimulation. We hypothesized that vCS elicits greater improvement in ambulation in Parkinson's disease patients compared to conventional, single-contact stimulation. vCS was implemented with divisions of 70%/30% and 50%/50% and compared to single-contact stimulation with four therapeutic window amplitudes in current-controlled systems. Walking at a self-selected pace was evaluated in seven levodopa-responsive patients. Integrative measures of gait and stimulation parameters were assessed with the functional ambulation performance (FAP) score and total electrical energy delivered (TEED), respectively. A two-tailed Wilcoxon matched-pairs signed rank test assessed the effect of each stimulation condition on FAP and TEED and compared regression slopes; further, a two-tailed Spearman test identified correlations. vCS significantly lowered the TEED (P < 0.0001); however, FAP scores were not different between conditions (P = 0.786). Compared to single-contact stimulation, vCS elicited higher FAP scores with lower TEED (P = 0.031). FAP and TEED were positively correlated in vCS (P = 2.000 × 10-5, r = 0.397) and single-contact stimulation (P = 0.034, r = 0.205). Therefore, vCS and single-contact stimulation improved ambulation similarly but vCS reduced the TEED and side-effects at higher amplitudes.

Zona incerta deep-brain stimulation in orthostatic tremor: efficacy and mechanism of improvement.

BACKGROUND: Orthostatic tremor is a rare hyperkinetic movement disorder that is characterized by a 13-18 Hz tremor in both legs while standing. Deep-brain stimulation of the caudal zona incerta has re-emerged as an alternate target for tremor control in various etiologies. OBJECT: Explore the clinical efficacy and mechanism of action of caudal zona incerta deep-brain stimulation in orthostatic tremor. METHODS: Four patients (63.1 ± 4.1 years, female = 50%) with orthostatic tremor were recruited for this open label study (63.1 ± 4.1 years, female = 50%). In two patients, the electrodes were externalized to determine the effectiveness of caudal zona incerta as a target. Surface EMG (leg muscles), EEG (leg motor cortex) and caudal zona incerta local field potential recordings were recorded. Data were recorded in sitting and standing positions with stimulation OFF and ON. RESULTS: EMG frequency analysis showed tremor frequency at 13-17 Hz. EMG-EEG coherence was found in the tremor frequency band and double tremor frequency band. EMG-caudal zona incerta coherence was higher in the tremor frequency band, while EEG coherence was higher in the double tremor frequency band. Upon stimulation, there was a selective reduction in tremor frequency band EEG-EMG coherence in all patients. All the patients had reduction in feeling of unsteadiness and increase in the stance duration. CONCLUSIONS: Bilateral caudal zona incerta deep-brain stimulation is effective in refractory orthostatic tremor. Two independent central oscillations were found at tremor and double tremor frequency. Zona incerta DBS produces improvement in OT patients possibly by modifying the abnormal oscillatory proprioceptive input from leg muscles. Frequent changes in deep-brain stimulation settings were required for maintaining the clinical benefit.

Effect of Levodopa on Speech Dysfluency in Parkinson's Disease.

Objective: To examine the effect of levodopa medication on speech dysfluency in Parkinson's disease. Methods: Fifty-one individuals with Parkinson's disease (IWPD) read aloud during off- and on- medication states. Total speech dysfluencies were calculated from transcriptions of recorded speech samples. Results: Severity of speech dysfluency was not significantly related to the severity of motor symptoms, duration of disease, levodopa equivalent dosage, or age. When the IWPD were divided into two groups based on dysfluency severity, there was a significant group-by-medication state interaction. There was a significant correlation between the medication-related change in speech dysfluency and the off-medication severity of speech dysfluency measure (r = -0.46). Conclusions: The results of this study indicate that levodopa medication can have a significant effect on speech dysfluency. The beneficial levodopa effect appears to be related to the severity of the off-medication speech dysfluency. Results did not provide strong support for the excess dopamine theory of stuttering in IWPD. A dualistic model of the effects of dopamine on speech fluency in PD is proposed.

Forward and backward walking in Parkinson disease: A factor analysis.

BACKGROUND: Forward and backward walking are both impaired in Parkinson disease (PD). In this study, an exploratory factor analysis was performed to investigate the relationship between forward and backward walking in PD. RESEARCH QUESTION: Given the difference in levodopa response between forward and backward walking, what is the additive value of testing backwards walking in a clinical setting. METHODS: Sixty-two patients with PD (65.29 ± 7.17 yrs, UPDRS OFF = 29.68 ± 9.88, UPDRS ON = 16.40 ± 8.21) and eleven healthy age-matched controls (63.09 ± 8.09 yrs) were recruited. PD participants completed forward (F) and backward (B) walking tasks on a 6.1 m instrumented walkway (OFF and ON levodopa). Factor analysis was used to derive models for both walking tasks/medication states. RESULTS: In both OFF and ON, four factors were identified: Variability (OFF: F = 30.0%, B = 17.8%, ON: F = 21.6%, B = 25.0%), Rhythm (OFF: F = 14.5%, B = 17.0%, ON: F = 17.4%, B = 19.0%), Asymmetry (OFF: F = 13.7%, B = 14.3%, ON: F = 16.1%, B = 15.2%), and Pace (OFF: F = 12.2%, B = 17.0%, ON: F = 13.9%, B = 8.7%). In the ON state, a fifth factor was identified: Posture (ON: F = 3.8%, B = 7.7%). SIGNIFICANCE: This study demonstrates the similarity in gait domain factors in both forward and backward walking. While domains of gait are similar in both walking tasks, levodopa response is reduced in backward walking. This could be a result of the increased complexity of backward walking. This study provides a normative dataset that can be used when assessing forward and backward walking in individuals with PD.