Deep brain stimulation and its effects on Parkinson disease spatiotemporal gait parameters.

Subthalamic (STN) deep brain stimulation (DBS) alleviates common appendicular PD symptoms, such as: tremor, rigidity and bradykinesia. However, the effect STN-DBS has on modulating axial gait features has not been properly quantified objectively. The purpose of the present thesis was to investigate the role STN-DBS plays in modulating specific gait features such as pace, asymmetry, variability, rhythm and postural control. It is hypothesized that axial gait function is regulated predominantly by non-dopaminergic control systems. In the acute immediate post-operative phase a surgical effect, named the microlesion effect (MLE), is thought to produce a transient improvement of appendicular and axial symptoms. It was hypothesized the MLE is a surgical effect, having a non-specific influence on both appendicular and axial symptoms. Following surgical recovery and 6 months of clinically optimized STN-DBS, it was expected that the true STN-DBS effects would be presented. It was hypothesized that STN-DBS plays an important role in the dopaminergic basal ganglia circuit and a lesser role in the non-dopaminergic system. 10 individuals with PD who were approved for STN-DBS along with 11 healthy age-matched controls were used in the study. The participants were asked to walk across a 7 metre long gait analysis carpet at a self-selected paced walk (SELF) and a fast-as-possible walk (FAST). However, in the current study we found no improvement on Unified Parkinson’s Disease Rating scale (UPDRS) appendicular scores and axial gait features at baseline, 1 week post-operation and 2 weeks post-operation. At 6 months, it was found that UPDRS scores improved for appendicular items but remained unchanged in the axial items. Furthermore, axial gait features remained unchanged in the SELF and FAST walks. Overall, axial gait function failed to improve from the MLE and STN-DBS. While the sample size was small, this finding may suggest an influence of regions outside the STN on axial function. Further analysis with more subjects should be conducted to verify the current findings

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

Towards A Comprehensive Software Suite for Stereotactic Neurosurgery

Stereotactic neurosurgery is a subspeciality within neurosurgery that relies on accurately targeting structures within the brain for diagnostic and therapeutic purposes. Surgical implantation of electrodes is a common procedure in stereotactic neurosurgery. For instance, deep brain stimulation (DBS) is an effective treatment option for individuals living with movement disorders while stereoelectroencephalography (SEEG) provides invaluable information from individuals living with drug-resistant epilepsy. In both procedures, electrophysiology data is acquired and used to identify brain characteristics such as surgical target location in DBS surgery and the seizure onset zone in SEEG surgery. Accurate surgical targeting of the electrodes is crucial, with millimeter deviations resulting in unwanted side effects and minimal clinical benefit. Among the medical centres that perform stereotactic neurosurgery, most employ preoperative magnetic resonance imaging (MRI) visualization of the surgical target for preoperative surgical planning. Neuronavigation is the term given to a set of tools or software that a neurosurgeon can use to navigate the brain. Currently, closed-source commercial neuronavigation software is used for preoperative trajectory planning. To ensure optimal positioning of DBS electrodes, intraoperative electrophysiology recording and test stimulation are performed to accurately place the electrode(s) within the surgical target nucleus. The preoperative MRI provides the anatomical border of the selected target nucleus while the intraoperative recordings provide the electrophysiological border. Once the final electrodes are implanted, the commercial neuronavigation software can be used to qualitatively assess the postoperative position of the implanted electrode(s) based on postoperative medical imaging data. Unfortunately, the commercial neuronavigation software is costly, is not easily customized, employs proprietary data formats, and often does not implement the most cutting-edge algorithms that may improve targeting accuracy. Few open-source tools have been developed that perform similar functions to the commercial neuronavigation software while also maintaining the flexibility for users to modify and improve the tool. The work in this thesis explores open-source solutions to stereotactic neurosurgery planning, data storage, and data visualization. In Chapter 3, a anatomical fiducial placement protocol is validated in a set of clinical imaging data for potential use in surgical planning. Chapter 4 explores an open-source pipeline for detecting and navigating stereotactic space using several common head frame systems. In Chapter 5, an open-source data storage structure for electrophysiology data is described and a preprocessing pipeline is introduced, called ephysPrep, which extracts electrophysiology signal features for machine learning applications. Finally, in Chapter 6, an open-source neuronavigation software called trajectoryGuide is introduced, which provides modules for preoperative surgical planning, data visualization, and postoperative electrode localization with electrode stimulation field modelling. Overall, the goal of this work is to provide a foundation of open and transparent tools that can be used and built upon by members of the clinical neuroscience community

Forward and backward walking in Parkinson disease: A factor analysis

International audienceBackgroundForward 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 questionGiven the difference in levodopa response between forward and backward walking, what is the additive value of testing backwards walking in a clinical setting.MethodsSixty-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.ResultsIn 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%).SignificanceThis 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

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

International audiencePurpose - 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

Extending Convolutional Neural Networks for Localizing the Subthalamic Nucleus from Micro-Electrode Recordings in Parkinson's Disease

International audienceDeep brain stimulation (DBS) is an interventional treatment for Parkinson's disease which involves the precise positioning of stimulated electrodes within deep brain structures, such as the SubThalamic Nucleus (STN). Although originally identified via imaging, additional inter-operative guidance is necessary to localize the target anatomy. Analysis of Micro-Electrode Recordings (MERs) allows for a trained neurophysiologist to infer the underlying anatomy at a particular electrode position using human audition, although it is subjective and requires a high degree of expertise. Various approaches to assist MER analysis during DBS are proposed in the literature, including deep learning methods, which rely on a static input description, that is, a pre-defined number of features or input size. In this paper, we propose two dynamic deep learning approaches adaptable to the complexity of MERs signal, by using an arbitrary long listening time (in 1s chunks), while providing feedback to the neurophysiologist as to the model's certainty. We evaluated five different deep learning based classifiers which can use arbitrary length MERs for STN segmentation. We found that a Bayesian extension using the highlevel features from SepaConvNet performed the best, increasing the balanced accuracy to 83.5%. This work represents a step forward in integrating automated analysis of MERs into the DBS surgical workflow by automatically finding and exploiting possible efficiencies in MER acquisition