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Deep brain stimulation (DBS) surgery has been shown to dramatically improve the quality of life for patients with various motor dysfunctions, such as those afflicted with Parkinson's disease (PD), dystonia, and essential tremor (ET), by relieving motor symptoms associated with such pathologies. The success of DBS procedures is directly related to the proper placement of the electrodes, which requires the ability to accurately detect and identify relevant target structures within the subcortical basal ganglia region. In particular, accurate and reliable segmentation of the globus pallidus (GP) interna is of great interest for DBS surgery for PD and dystonia. In this study, we present a deep‐learning based neural network, which we term GP‐net, for the automatic segmentation of both the external and internal segments of the globus pallidus. High resolution 7 Tesla images from 101 subjects were used in this study; GP‐net is trained on a cohort of 58 subjects, containing patients with movement disorders as well as healthy control subjects. GP‐net performs 3D inference in a patient‐specific manner, alleviating the need for atlas‐based segmentation. GP‐net was extensively validated, both quantitatively and qualitatively over 43 test subjects including patients with movement disorders and healthy control and is shown to consistently produce improved segmentation results compared with state‐of‐the‐art atlas‐based segmentations. We also demonstrate a postoperative lead location assessment with respect to a segmented globus pallidus obtained by GP‐net., Deep brain stimulation (DBS) surgery has been shown to improve the quality of life for patients with various motor dysfunctions. The success of DBS is directly related to the proper placement of the electrodes, which requires accurate detection and identification of the relevant target structures. We present a deep‐learning based automatic, robust and accurate segmentation technique from 7 Tesla MRI acquisitions of the globus pallidus externa and interna for DBS surgery planning and postoperative electrode localization.

To elucidate the role of the basal ganglia during REM sleep movements in Parkinson’s disease (PD) we recorded pallidal neural activity from four PD patients. Unlike desynchronization commonly observed during wakeful movements, beta oscillations (13-35 Hz) synchronized during REM sleep movements; furthermore, high-frequency oscillations (150-350 Hz) synchronized during movement irrespective of sleep-wake states. Our results demonstrate differential engagement of the basal ganglia during REM sleep and awake movements.

This project investigated whether structural changes are present in the subthalamic nucleus (STN) of people with mild-to-moderate severity of Parkinson’s disease (PD). Within-subject measures of STN volume and fractional anisotropy (FA) were derived from high-resolution 7Tesla magnetic resonance imaging (MRI) for 29 subjects with mild-to-moderate PD (median disease duration = 2.3±1.9 years) and 18 healthy matched controls. Manual segmentation of the STN was performed on 0.4 mm in-plane resolution images. FA maps were generated and FA values were averaged over the left and right STN separately for each subject. Motor sign severity was assessed using the Movement Disorders Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Linear effects models showed that STN volume was significantly smaller in the PD subjects compared to controls (p = 0.01). Further, after controlling for differences in STN volumes within or between groups, the PD group had lower FA values in the STN compared to controls (corrected p ≤ 0.008). These findings demonstrate that morphological changes occur in the STN, which likely impact the function of the hyperdirect and indirect pathways of the basal ganglia and movement control.

The mechanisms contributing to increased expression of motor and cognitive impairment in people with Parkinson9s disease who lack muscle atonia during rapid eye movement (REM) sleep compared to those with muscle atonia are poorly understood. This study used tract-based spatial statistics to compare diffusion measures of white matter microstructure between people with Parkinson9s disease with and without REM sleep without atonia as well as the relationships of these measures to motor and cognitive function. Thirty-eight individuals with mild-to-moderate Parkinson9s disease and twenty-one matched control subjects underwent ultra-high-field MRI (7Tesla), quantitative motor assessments of gait and bradykinesia, and neuropsychological testing. The Parkinson9s disease cohort was separated post-hoc into those with and without elevated chin or leg muscle activity during REM sleep based on polysomnography findings. Fractional anisotropy was significantly higher, and radial and mean diffusivity significantly lower, in diffuse regions of the corpus callosum, projection, and association white matter pathways in the Parkinson9s group with normal REM sleep compared to controls. In contrast, there was no significant difference in fractional anisotropy between the Parkinson9s group with elevated muscle tone and controls. Fractional anisotropy was also significantly higher in a subset of pathways in the Parkinson9s disease group with normal REM sleep muscle tone compared to those with elevated REM sleep muscle tone. The group with elevated REM sleep muscle tone had significant impairments in gait and upper arm speed compared to controls and significantly worse scores in specific cognitive domains (executive function, visuospatial memory) compared to the Parkinson9s disease group with normal REM sleep muscle tone. Regression analyses showed that gait speed and step length in the Parkinson9s disease cohort were predicted by measures of mean fractional anisotropy of the anterior corona radiata, whereas elbow flexion velocity was predicted by fractional anisotropy of the superior corona radiata. Visuospatial memory task performance was predicted by the radial diffusivity of the posterior corona radiata. These findings demonstrate that people with mild-to-moderate severity of Parkinson9s disease who have normal muscle tone during REM sleep show alterations in white matter microstructure that are associated with preserved motor and cognitive function, but these adaptations are reduced or absent in those with increased REM sleep motor tone.

BackgroundDeep brain stimulation (DBS) targeting the subthalamic nucleus (STN) often shows variable outcomes on treating gait dysfunction in Parkinson’s disease (PD). Such variability may stem from which specific neuronal pathways are modulated by DBS and the extent to which those pathways are modulated relative to one another.ObjectiveLeveraging ultra-high-field (7T) imaging data and subject-specific computational models, this study investigated how activation of seven distinct pathways in and around STN, including the pallidopeduncular and pedunculopallidal pathways, affect step length at clinically-optimized STN-DBS settings.MethodsPersonalized computational models were developed for 10 subjects with a clinical diagnosis of PD and with bilateral STN-DBS implants.ResultsThe subject-specific pathway activation models showed a significant positive association between activation of the pedunculopallidal pathway and increased step length, and negative association on step length with pallidopeduncular pathway and hyperdirect pathway activation.ConclusionsThe STN region includes multiple pathways, including fibers of passage to and from the mesencephalic locomotor area. Future clinical optimization of STN-DBS should consider these fibers of passage in the context of treating parkinsonian gait.

Background Abnormal oscillatory neural activity in the beta-frequency band (13-35 Hz) is thought to play a role in Parkinson's disease (PD); however, increasing evidence points to alterations in high-frequency ranges (>100 Hz) also having pathophysiological relevance. Objectives Studies have found that power in subthalamic nucleus (STN) high-frequency oscillations is increased with dopaminergic medication and during voluntary movements, implicating these brain rhythms in normal basal ganglia function. The objective of this study was to investigate whether similar signaling occurs in the internal globus pallidus (GPi), a nucleus increasingly used as a target for deep brain stimulation (DBS) for PD. Methods Spontaneous and movement-related GPi field potentials were recorded from DBS leads in 5 externalized PD patients on and off dopaminergic medication, as well as from 3 rhesus monkeys before and after the induction of parkinsonism with the neurotoxin 1-methyl-4-phenyl-1,2,3,6 tetrahydropyridine. Results In the parkinsonian condition, we identified a prominent oscillatory peak centered at 200-300 Hz that increased during movement. In patients the magnitude of high-frequency oscillation modulation was negatively correlated with bradykinesia. In monkeys, high-frequency oscillations were mostly absent in the naive condition but emerged after the neurotoxin 1-methyl-4-phenyl-1,2,3,6 tetrahydropyridine. In patients, spontaneous high-frequency oscillations were significantly attenuated on-medication. Conclusions Our findings provide evidence in support of the hypothesis that exaggerated, movement-modulated high-frequency oscillations in the GPi are pathophysiological features of PD. These findings suggest that the functional role(s) of high-frequency oscillations may differ between the STN and GPi and motivate additional investigations into their relationship to motor control in normal and diseased states.

Subthalamic nucleus (STN) deep brain stimulation (DBS) is an established therapy for Parkinson’s disease motor symptoms. The ideal site for implantation within STN, however, remains controversial. While many argue that placement of a DBS lead within the sensorimotor territory of the STN yields better motor outcomes, others report similar effects with leads placed in the associative or motor territory of the STN, while still others assert that placing a DBS lead “anywhere within a 6-mm-diameter cylinder centered at the presumed middle of the STN (based on stereotactic atlas coordinates) produces similar clinical efficacy.” These discrepancies likely result from methodological differences including targeting preferences, imaging acquisition and the use of brain atlases that do not account for patient-specific anatomic variability. We present a first-in-kind within-patient demonstration of severe mood side effects and minimal motor improvement in a Parkinson’s disease patient following placement of a DBS lead in the limbic/associative territory of the STN who experienced marked improvement in motor benefit and resolution of mood side effects following repositioning the lead within the STN sensorimotor territory. 7 Tesla (7 T) magnetic resonance imaging (MRI) data were used to generate a patient-specific anatomical model of the STN with parcellation into distinct functional territories and computational modeling to assess the relative degree of activation of motor, associative and limbic territories.

Deep brain stimulation (DBS) surgery has been shown to dramatically improve the quality of life for patients with various motor dysfunctions, such as those afflicted with Parkinson’s disease (PD), dystonia, and essential tremor (ET), by relieving motor symptoms associated with such pathologies. The success of DBS procedures is directly related to the proper placement of the electrodes, which requires the ability to accurately detect and identify relevant target structures within the subcortical basal ganglia region. In particular, accurate and reliable segmentation of the globus pallidus (GP) interna is of great interest for DBS surgery for PD and dystonia. In this work, we present a deep-learning based neural network, which we term GP-net, for the automatic segmentation of both the external and internal segments of the globus pallidus. High resolution 7 Tesla images from 101 subjects were used in this study; GP-net is trained on a cohort of 58 subjects, containing patients with movement disorders as well as healthy control subjects. GP-net performs 3D inference in a patient-specific manner, alleviating the need for atlas-based segmentation. GP-net was extensively validated, both quantitatively and qualitatively over 43 test subjects including patients with movement disorders and healthy control and is shown to consistently produce improved segmentation results compared with state-of-the-art atlas-based segmentations. We also demonstrate a post-operative lead location assessment with respect to a segmented globus pallidus obtained by GP-net.

Abnormal oscillatory neural activity in the basal ganglia is thought to play a pathophysiological role in Parkinson’s disease. Many patient studies have focused on beta frequency band (13-35 Hz) local field potential activity in the subthalamic nucleus, however increasing evidence points to alterations in neural oscillations in high frequency ranges (>100 Hz) having pathophysiological relevance. Prior studies have found that power in subthalamic high frequency oscillations (HFOs) is positively correlated with dopamine tone and increased during voluntary movements, implicating these brain rhythms in normal basal ganglia function. Contrary to this idea, in the current study we present a combination of clinical and preclinical data that support the hypothesis that HFOs in the internal globus pallidus (GPi) are a pathophysiological feature of Parkinson’s disease. Spontaneous and movement-related pallidal field potentials were recorded from deep brain stimulation (DBS) leads targeting the GPi in five externalized Parkinson’s disease patients, on and off dopaminergic medication. We identified a prominent oscillatory peak centered at 200-300 Hz in the off-medication rest recordings in all patients. High frequency power increased during movement, and the magnitude of modulation was negatively correlated with bradykinesia. Moreover, high frequency oscillations were significantly attenuated in the on-medication condition, suggesting they are a feature of the parkinsonian condition. To further confirm that GPi high frequency oscillations are characteristic of dopamine depletion, we also collected field potentials from DBS leads chronically implanted in three rhesus monkeys before and after the induction of parkinsonism with the neurotoxin 1-methyl-4-phenyl-1,2,3,6 tetrahydropyridine (MPTP). High frequency oscillations and their modulation during movement were not prominent in the normal condition but emerged in the parkinsonian condition in the monkey model. These data provide the first evidence demonstrating that exaggerated, movement-modulated high frequency oscillations in the internal globus pallidus are a pathophysiological feature of Parkinson’s disease, and motivate additional investigations into the functional roles of high frequency neural oscillations across the basal ganglia-thalamocortical motor circuit and their relationship to motor control in normal and diseased states. These findings also provide rationale for further exploration of these signals for electrophysiological biomarker-based device programming and stimulation strategies in patients receiving deep brain stimulation therapy.

Deep cerebellar nuclei are a key structure of the cerebellum that are involved in processing motor and sensory information. It is thus a crucial step to accurately segment deep cerebellar nuclei for the understanding of the cerebellum system and its utility in deep brain stimulation treatment. However, it is challenging to clearly visualize such small nuclei under standard clinical magnetic resonance imaging (MRI) protocols and therefore precise segmentation is not feasible. Recent advances in 7 Tesla (T) MRI technology and great potential of deep neural networks facilitate automatic patient-specific segmentation. In this paper, we propose a novel deep learning framework (referred to as DCN-Net) for fast, accurate, and robust patient-specific segmentation of deep cerebellar dentate and interposed nuclei on 7T diffusion MRI. DCN-Net effectively encodes contextual information on the patch images without consecutive pooling operations and adding complexity via proposed dilated dense blocks. During the end-to-end training, label probabilities of dentate and interposed nuclei are independently learned with a hybrid loss, handling highly imbalanced data. Finally, we utilize self-training strategies to cope with the problem of limited labeled data. To this end, auxiliary dentate and interposed nuclei labels are created on unlabeled data by using DCN-Net trained on manual labels. We validate the proposed framework using 7T B0 MRIs from 60 subjects. Experimental results demonstrate that DCN-Net provides better segmentation than atlas-based deep cerebellar nuclei segmentation tools and other state-of-the-art deep neural networks in terms of accuracy and consistency. We further prove the effectiveness of the proposed components within DCN-Net in dentate and interposed nuclei segmentation., 56 pages (one column), 13 figures, 5 tables, supplementary materials, Accepted for publication in IEEE Access

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has shown clinical potential for relieving the motor symptoms of advanced Parkinson’s disease. While accurate localization of the STN is critical for consistent across-patients effective DBS, clear visualization of the STN under standard clinical MR protocols is still challenging. Therefore, intraoperative microelectrode recordings (MER) are incorporated to accurately localize the STN. However, MER require significant neurosurgical expertise and lengthen the surgery time. Recent advances in 7 T MR technology facilitate the ability to clearly visualize the STN. The vast majority of centers, however, still do not have 7 T MRI systems, and fewer have the ability to collect and analyze the data. This work introduces an automatic STN localization framework based on standard clinical MRIs without additional cost in the current DBS planning protocol. Our approach benefits from a large database of 7 T MRI and its clinical MRI pairs. We first model in the 7 T database, using efficient machine learning algorithms, the spatial and geometric dependency between the STN and its adjacent structures (predictors). Given a standard clinical MRI, our method automatically computes the predictors and uses the learned information to predict the patient-specific STN. We validate our proposed method on clinical T(2)W MRI of 80 subjects, comparing with experts-segmented STNs from the corresponding 7 T MRI pairs. The experimental results show that our framework provides more accurate and robust patient-specific STN localization than using state-of-the-art atlases. We also demonstrate the clinical feasibility of the proposed technique assessing the post-operative electrode active contact locations.

The success of deep brain stimulation (DBS) surgeries for the treatment of movement disorders relies on the accurate placement of an electrode within the motor portion of subcortical brain targets. However, the high number of electrodes requiring relocation indicates that today's methods do not ensure sufficient accuracy for all patients. Here, with the goal of aiding DBS targeting, we use 7 Tesla (T) MRI data to identify the functional territories and parcellate the globus pallidus pars interna (GPi) into motor, associative and limbic regions in individual subjects. 7 T MRI scans were performed in seventeen patients (prior to DBS surgery) and one healthy control. Tractography-based parcellation of each patient's GPi was performed. The cortex was divided into four masks representing motor, limbic, associative and “other” regions. Given that no direct connections between the GPi and the cortex have been shown to exist, the parcellation was carried out in two steps: 1) The thalamus was parcellated based on the cortical targets, 2) The GPi was parcellated using the thalamus parcels derived from step 1. Reproducibility, via repeated scans of a healthy subject, and validity of the findings, using different anatomical pathways for parcellation, were assessed. Lastly, post-operative imaging data was used to validate and determine the clinical relevance of the parcellation. The organization of the functional territories of the GPi observed in our individual patient population agrees with that previously reported in the literature: the motor territory was located posterolaterally, followed anteriorly by the associative region, and further antero-ventrally by the limbic territory. While this organizational pattern was observed across patients, there was considerable variability among patients. The organization of the functional territories of the GPi was remarkably reproducible in intra-subject scans. Furthermore, the organizational pattern was observed consistently by performing the parcellation of the GPi via the thalamus and via a different pathway, going through the striatum. Finally, the active therapeutic contact of the DBS electrode, identified with a combination of post-operative imaging and post-surgery DBS programming, overlapped with the high-probability “motor” region of the GPi as defined by imaging-based methods. The consistency, validity, and clinical relevance of our findings have the potential for improving DBS targeting, by increasing patient-specific knowledge of subregions of the GPi to be targeted or avoided, at the stage of surgical planning, and later, at the stage when stimulation is adjusted.

The goal of this study was to characterize the spectral characteristics and spatial topography of local field potential (LFP) activity in the internal segment of the globus pallidus (GPi) in patients with Parkinson's disease utilizing directional (segmented) deep brain stimulation (dDBS) leads. Data were collected from externalized dDBS leads of three patients with idiopathic Parkinson's disease after overnight withdrawal of parkinsonian medication at rest and during a cued reach-to-target task. Oscillatory activity across lead contacts/segments was examined in the context of lead locations and contact orientations determined using co-registered preoperative 7 Tesla (T) MRI and postoperative CT scans. Each of the three patients displayed a unique frequency spectrum of oscillatory activity in the pallidum, with prominent peaks ranging from 5 to 35 Hz, that modulated variably across subjects during volitional movement. Despite subject-specific spectral profiles, a consistent finding across patients was that oscillatory power was strongest and had the largest magnitude of modulation during movement in LFPs recorded from segments facing the postero-lateral “sensorimotor” region of GPi, whereas antero-medially-directed segmented contacts facing the internal capsule and/or anterior GPi, had relatively weaker LFP power and less modulation in the 5 to 35 Hz. In each subject, contact configurations chosen for clinically therapeutic stimulation (following data collection and blinded to physiology recordings), were in concordance with the contact pairs showing the largest amplitude of LFP oscillations in the 5–35 Hz range. Although limited to three subjects, these findings provide support for the hypothesis that the sensorimotor territory of the GPi corresponds to the site of maximal power of oscillatory activity in the 5 to 35 Hz and provides the greatest benefit in motor signs during stimulation in the GPi. Variability in oscillatory activity across patients is likely related to Parkinson's disease phenotype as well as small differences in recording location (i.e. lead location), highlighting the importance of lead location for optimizing stimulation efficacy. These data also provide compelling evidence for the use of LFP activity for the development of predictive stimulation models that may optimize patient benefits while reducing clinic time needed for programming.

Background: The introduction of intracranial air (ICA) during deep brain stimulation (DBS) surgery is thought to have a negative influence on targeting and clinical outcomes. Objective: To investigate ICA volumes following surgery and other patient-specific factors as potential variables influencing translocation of the DBS electrode and proximal lead bowing. Methods: High-resolution postoperative computed tomography scans (≤1.0 mm resolution in all directions) within 24 h following DBS surgery and 4–6 weeks of follow-up were acquired. A total of 50 DBS leads in 33 patients were available for analysis. DBS leads included Abbott/St. Jude Medical InfinityTM, Boston Scientific VerciseTM, and Medtronic 3389TM. Results: Both ICA volume and anatomical target were significantly associated with measures of DBS electrode translocation. ICA volume and DBS lead model were found to be significant predictors of proximal lead bowing. Measures of proximal lead bowing and translocation along the electrode trajectory for the Medtronic 3389TM DBS lead were significantly larger than measures for the Abbott/St. Jude Medical InfinityTM and Boston Scientific VerciseTM DBS leads. Conclusion: The association between ICA volume and translocation of the DBS electrode is small in magnitude and not clinically relevant for DBS cases within a normal range of postoperative subdural air volumes. Differences in proximal lead bowing observed between DBS leads may reflect hardware engineering subtleties in the construction of DBS lead models.

This chapter reviews recent advances in neuroimaging methods for the visualization of brain structures, as well as the connectivity patterns that play key roles for deep brain stimulation therapy. The advantages of utilizing ultrahigh field 7 T magnetic resonance imaging (MRI) for visualizing anatomical structures will likely propel our clinical and scientific abilities to investigate, develop, and enhance therapies for patients with neurological disorders such as Parkinson’s disease, essential tremor, dystonia, and Tourette syndrome. Treatment approaches are becoming more patient-specific, thus more therapies will rely on highly accurate treatment delivery capabilities to the target area. As such, 7 T (and stronger fields) MRI systems will provide such abilities by producing enhanced input data with superior resolution and image contrast. Further development of better imaging acquisition strategies and postprocessing methods are needed, as the data being generated will open new possibilities for exploring brain function in both healthy and disease conditions at resolution levels never before observed.

Objective This study investigated stimulation strategies to increase the selectivity of activating axonal pathways within the brain based on their orientations relative to clinical deep brain stimulation (DBS) lead implants. Approach Previous work has shown how varying electrode shape and controlling the primary electric field direction through preclinical electrode arrays can produce orientation-selective axonal stimulation. Here, we significantly extend those results using computational models to evaluate the degree to which clinical DBS leads can direct stimulus-induced electric fields and generate orientation-selective activation of fiber pathways in the brain. Orientation-selective pulse paradigms were evaluated in conceptual models and in patient-specific models of subthalamic nucleus (STN)-DBS for treating Parkinson's disease. Main results Single-contact monopolar or two-contact bipolar stimulation through clinical DBS leads with cylindrical electrodes primarily activated axons orientated parallel to the lead. Conversely, multi-contact monopolar stimulation with a cathode-leading pulse waveform selectively activated axons perpendicular to the DBS lead. Clinical DBS leads with segmented rows of electrodes and a single current source provided additional angular resolution for activating axons oriented 0°, ±22.5°, ±45°, ±67.5°, or 90° relative to the lead shaft. Employing multiple independent current sources to deliver unequal amounts of current through these leads further increased the angular resolution of activation relative to the lead shaft. The patient-specific models indicated that multi-contact cathode configurations, which are rarely used in clinical practice, could increase activation of the hyperdirect pathway collaterals projecting into STN (a putative therapeutic target), while minimizing direct activation of the corticospinal tract of internal capsule, which can elicit sensorimotor side-effects when stimulated. Significance When combined with patient-specific tissue anisotropy and patient-specific anatomical morphologies of neural pathways responsible for therapy and side effects, orientation-selective DBS approaches show potential to significantly improve clinical outcomes of DBS therapy for a range of existing and investigational clinical indications.

BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a proven and effective therapy for the management of the motor symptoms of Parkinson's disease (PD). While accurate positioning of the stimulating electrode is critical for success of this therapy, precise identification of the STN based on imaging can be challenging. We developed a method to accurately visualize the STN on a standard clinical magnetic resonance imaging (MRI). The method incorporates a database of 7-Tesla (T) MRIs of PD patients together with machine-learning methods (hereafter 7 T-ML). OBJECTIVE: To validate the clinical application accuracy of the 7 T-ML method by comparing it with identification of the STN based on intraoperative microelectrode recordings. METHODS: Sixteen PD patients who underwent microelectrode-recordings guided STN DBS were included in this study (30 implanted leads and electrode trajectories). The length of the STN along the electrode trajectory and the position of its contacts to dorsal, inside, or ventral to the STN were compared using microelectrode-recordings and the 7 T-ML method computed based on the patient's clinical 3T MRI. RESULTS: All 30 electrode trajectories that intersected the STN based on microelectrode-recordings, also intersected it when visualized with the 7 T-ML method. STN trajectory average length was 6.2 ± 0.7 mm based on microelectrode recordings and 5.8 ± 0.9 mm for the 7 T-ML method. We observed a 93% agreement regarding contact location between the microelectrode-recordings and the 7 T-ML method. CONCLUSION: The 7 T-ML method is highly consistent with microelectrode-recordings data. This method provides a reliable and accurate patient-specific prediction for targeting the STN.

Deep Brain Stimulation (DBS) of the subthalamic nucleus (STN) has shown clinical potential for relieving the motor symptoms of advanced Parkinson’s disease. While accurate localization of the STN is critical for consistent across-patients effective DBS, clear visualization of the STN under standard clinical MR protocols is still challenging. Therefore, intraoperative microelectrode recordings (MER) are incorporated to accurately localize the STN. However, MER require significant neurosurgical expertise and lengthen the surgery time. Recent advances in 7T MR technology facilitate the ability to clearly visualize the STN. The vast majority of centers, however, still do not have 7T MRI systems, and fewer have the ability to collect and analyze the data. This work introduces an automatic STN localization framework based on standard clinical MRIs without additional cost in the current DBS planning protocol. Our approach benefits from a large database of 7T MRI and its clinical MRI pairs. We first model in the 7T database, using efficient machine learning algorithms, the spatial and geometric dependency between the STN and its adjacent structures (predictors). Given a standard clinical MRI, our method automatically computes the predictors and uses the learned information to predict the patient-specific STN. We validate our proposed method on clinical T2W MRI of 80 subjects, comparing with experts-segmented STNs from the corresponding 7T MRI pairs. The experimental results show that our framework provides more accurate and robust patient-specific STN localization than using state-of-the-art atlases. We also demonstrate the clinical feasibility of the proposed technique assessing the post-operative electrode active contact locations.

Deep brain stimulation of the subthalamic nucleus (STN) is a widely performed surgical treatment for patients with Parkinson's disease. The goal of the surgery is to place an electrode centered in the motor region of the STN while lowering the effects of electrical stimulation on the non-motor regions. However, distinguishing the motor region from the neighboring associative and limbic areas in individual patients using imaging modalities was until recently difficult to obtain in vivo. Here, using ultra-high field MR imaging, we have performed a dissection of the subdivisions of the STN of individual Parkinson's disease patients. We have acquired 7 T diffusion-weighted images of seventeen patients with Parkinson's disease scheduled for deep brain stimulation surgery. Using a structural connectivity-based parcellation protocol, the STN's connections to the motor, limbic, and associative cortical areas were used to map the individual subdivisions of the nucleus. A reproducible patient-specific parcellation of the STN into a posterolateral motor and gradually overlapping central associative area was found in all STNs, taking up on average 55.3% and 55.6% of the total nucleus volume. The limbic area was found in the anteromedial part of the nucleus. Our results suggest that 7T MR imaging may facilitate individualized and highly specific planning of deep brain stimulation surgery of the STN.

Background Deep brain stimulation (DBS) is an established clinical therapy and computational models have played an important role in advancing the technology. Patient-specific DBS models are now common tools in both academic and industrial research, as well as clinical software systems. However, the exact methodology for creating patient-specific DBS models can vary substantially and important technical details are often missing from published reports. Objective Provide a detailed description of the assembly workflow and parameterization of a patient-specific DBS pathway-activation model (PAM) and predict the response of the hyperdirect pathway to clinical stimulation. Methods Integration of multiple software tools (e.g. COMSOL, MATLAB, FSL, NEURON, Python) enables the creation and visualization of a DBS PAM. An example DBS PAM was developed using 7T magnetic resonance imaging data from a single unilaterally implanted patient with Parkinson’s disease (PD). This detailed description implements our best computational practices and most elaborate parameterization steps, as defined from over a decade of technical evolution. Results Pathway recruitment curves and strength-duration relationships highlight the non-linear response of axons to changes in the DBS parameter settings. Conclusion Parameterization of patient-specific DBS models can be highly detailed and constrained, thereby providing confidence in the simulation predictions, but at the expense of time demanding technical implementation steps. DBS PAMs represent new tools for investigating possible correlations between brain pathway activation patterns and clinical symptom modulation.

The utility and success of resting-state functional connectivity MRI (rs-fcMRI) depend critically on the reliability of this technique and the extent to which it accurately reflects neuronal function. One challenge is that rs-fcMRI is influenced by various sources of noise, particularly cardiac- and respiratory-related signal variations. The goal of the current study was to evaluate the impact of various physiological noise correction techniques, specifically those that use independent cardiac and respiration measures, on the test-retest reliability of rs-fcMRI. A group of 25 subjects were each scanned at three time points--two within the same imaging session and another 2-3 months later. Physiological noise corrections accounted for significant variance, particularly in blood vessels, sagittal sinus, cerebrospinal fluid, and gray matter. The fraction of variance explained by each of these corrections was highly similar within subjects between sessions, but variable between subjects. Physiological corrections generally reduced intrasubject (between-session) variability, but also significantly reduced intersubject variability, and thus reduced the test-retest reliability of estimating individual differences in functional connectivity. However, based on known nonneuronal mechanisms by which cardiac pulsation and respiration can lead to MRI signal changes, and the observation that the physiological noise itself is highly stable within individuals, removal of this noise will likely increase the validity of measured connectivity differences. Furthermore, removal of these fluctuations will lead to better estimates of average or group maps of connectivity. It is therefore recommended that studies apply physiological noise corrections but also be mindful of potential correlations with measures of interest.

There has been an increasing use of functional magnetic resonance imaging (fMRI) by the neuroscience community to examine differences in functional connectivity between normal control groups and populations of interest. Understanding the reliability of these functional connections is essential to the study of neurological development and degenerate neuropathological conditions. To date, most research assessing the reliability with which resting-state functional connectivity characterizes the brain's functional networks has been on scans between 3 and 11 min in length. In our present study, we examine the test-retest reliability and similarity of resting-state functional connectivity for scans ranging in length from 3 to 27 min as well as for time series acquired during the same length of time but excluding half the time points via sampling every second image. Our results show that reliability and similarity can be greatly improved by increasing the scan lengths from 5 min up to 13 min, and that both the increase in the number of volumes as well as the increase in the length of time over which these volumes was acquired drove this increase in reliability. This improvement in reliability due to scan length is much greater for scans acquired during the same session. Gains in intersession reliability began to diminish after 9-12 min, while improvements in intrasession reliability plateaued around 12-16 min. Consequently, new techniques that improve reliability across sessions will be important for the interpretation of longitudinal fMRI studies.

Head motion is a significant source of noise in the estimation of functional connectivity from resting-state functional MRI (rs-fMRI). Current strategies to reduce this noise include image realignment, censoring time points corrupted by motion, and including motion realignment parameters and their derivatives as additional nuisance regressors in the general linear model. However, this nuisance regression approach assumes that the motion-induced signal changes are linearly related to the estimated realignment parameters, which is not always the case. In this study we develop an improved model of motion-related signal changes, where nuisance regressors are formed by first rotating and translating a single brain volume according to the estimated motion, re-registering the data, and then performing a principal components analysis (PCA) on the resultant time series of both moved and re-registered data. We show that these "Motion Simulated (MotSim)" regressors account for significantly greater fraction of variance, result in higher temporal signal-to-noise, and lead to functional connectivity estimates that are less affected by motion compared to the most common current approach of using the realignment parameters and their derivatives as nuisance regressors. This improvement should lead to more accurate estimates of functional connectivity, particularly in populations where motion is prevalent, such as patients and young children.

Neuroanatomists posit that the central nucleus of the amygdala (Ce) and bed nucleus of the stria terminalis (BST) comprise two major nodes of a macrostructural forebrain entity termed the extended amygdala. The extended amygdala is thought to play a critical role in adaptive motivational behavior and is implicated in the pathophysiology of maladaptive fear and anxiety. Resting functional connectivity of the Ce was examined in 107 young anesthetized rhesus monkeys and 105 young humans using standard resting-state functional magnetic resonance imaging (fMRI) methods to assess temporal correlations across the brain. The data expand the neuroanatomical concept of the extended amygdala by finding, in both species, highly significant functional coupling between the Ce and the BST. These results support the use of in vivo functional imaging methods in nonhuman and human primates to probe the functional anatomy of major brain networks such as the extended amygdala.

Objective The effectiveness of deep brain stimulation (DBS) therapy strongly depends on precise surgical targeting of intracranial leads and on clinical optimization of stimulation settings. Recent advances in surgical targeting, multi-electrode designs, and multi-channel independent current-controlled stimulation are poised to enable finer control in modulating pathways within the brain. However, the large stimulation parameter space enabled by these technologies also poses significant challenges for efficiently identifying the most therapeutic DBS setting for a given patient. Here, we present a computational approach for programming directional DBS leads that is based on a non-convex optimization framework for neural pathway targeting. Approach The algorithm integrates patient-specific pre-operative 7 T MR imaging, post-operative CT scans, and multi-objective particle swarm optimization (MOPSO) methods using dominance based-criteria and incorporating multiple neural pathways simultaneously. The algorithm was evaluated on eight patient-specific models of subthalamic nucleus (STN) DBS to identify electrode configurations and stimulation amplitudes to optimally activate or avoid six clinically relevant pathways: motor territory of STN, non-motor territory of STN, internal capsule, superior cerebellar peduncle, thalamic fasciculus, and hyperdirect pathway. Main results Across the patient-specific models, single-electrode stimulation showed significant correlations across modeled pathways, particularly for motor and non-motor STN efferents. The MOPSO approach was able to identify multi-electrode configurations that achieved improved targeting of motor STN efferents and hyperdirect pathway afferents than that achieved by any single-electrode monopolar setting at equivalent power levels. Significance These results suggest that pathway targeting with patient-specific model-based optimization algorithms can efficiently identify non-trivial electrode configurations for enhancing activation of clinically relevant pathways. However, the results also indicate that inter-pathway correlations can limit selectivity for certain pathways even with directional DBS leads.

Maser spectra of the long period variable star R Cassiopeia (R Cas) have been observed over approximately four stellar periods in the silicon monoxide v = 1v=1 and v = 2v=2, J = 1 - 0J=1-0 transition. The linear polarization characteristics of the spectra have been determined. The lifetimes and beginning and ending phases of 43 maser features have been extracted from Gaussian fits to the polarized spectra. It has been proposed that the maser emissions of long-period variables are affected by the passage of a shock wave through the circumstellar region in which the masers originate. The proposed shock passage produces a fading or absence of the emission by phase 0.4 (for v = 1v=1 and v = 2v=2). Our study found that five SiO maser features went out of existence between phases 0.3 and 0.4 for v = 1v=1 and v = 2v=2 (total number of features: 19 for v = 1v=1, 24 for v = 2v=2). In no other phase interval did more than three features go out of existence. This result is in accordance with the above propo...

Recent fMRI studies have outlined the critical impact of in-scanner head motion, particularly on estimates of functional connectivity. Common strategies to reduce the influence of motion include realignment as well as the inclusion of nuisance regressors, such as the 6 realignment parameters, their first derivatives, time-shifted versions of the realignment parameters, and the squared parameters. However, these regressors have limited success at noise reduction. We hypothesized that using nuisance regressors consisting of the principal components (PCs) of edge voxel time series would be better able to capture slice-specific and nonlinear signal changes, thus explaining more variance, improving data quality (i.e., lower DVARS and temporal SNR), and reducing the effect of motion on default-mode network connectivity. Functional MRI data from 22 healthy adult subjects were preprocessed using typical motion regression approaches as well as nuisance regression derived from edge voxel time courses. Results were evaluated in the presence and absence of both global signal regression and motion censoring. Nuisance regressors derived from signal intensity time courses at the edge of the brain significantly improved motion correction compared to using only the realignment parameters and their derivatives. Of the models tested, only the edge voxel regression models were able to eliminate significant differences in default-mode network connectivity between high- and low-motion subjects regardless of the use of global signal regression or censoring.

Objective Resting-state functional magnetic resonance imaging (rs-fMRI) studies of adult posttraumatic stress disorder (PTSD) have identified default-mode network (DMN) abnormalities, including reduced within-network connectivity and reduced anticorrelation between the DMN and task-positive network (TPN). However, no prior studies have specifically examined DMN connectivity in pediatric PTSD, which may differ due to neurodevelopmental factors. Method A total of 29 youth with PTSD and 30 nontraumatized healthy youth of comparable age and sex completed rs-fMRI. DMN properties were examined using posterior cingulate cortex (PCC) seed-based connectivity and independent component analysis (ICA). Results Contrary to findings in adult studies, youth with PTSD displayed increased connectivity within the DMN, including increased PCC–inferior parietal gyrus connectivity, and age-related increases in PCC–ventromedial prefrontal cortex connectivity. Strikingly, youth with PTSD also displayed greater anticorrelation between the PCC and multiple nodes within salience and attentional control networks of the TPN. ICA revealed greater anticorrelation between the entire DMN and TPN networks in youth with PTSD. Furthermore, DMN and TPN connectivity strength were positively and negatively associated, respectively, with re-experiencing symptoms of PTSD. Conclusion Pediatric PTSD is characterized by heightened within-DMN connectivity, which may contribute to re-experiencing symptoms of PTSD and is consistent with the role of the DMN in autobiographical memory. At the same time, greater anticorrelation between the DMN and attentional control networks may represent compensatory mechanisms aimed at suppressing trauma-related thought, a notion supported by the inverse relationship between TPN strength and re-experiencing. These findings provide new insights into large-scale network abnormalities underlying pediatric PTSD, which could serve as biomarkers of illness and treatment response.