Multi-contrast unbiased MRI atlas of a Parkinson’s disease population

Unlocking the potential: T1-weighed MRI as a powerful predictor of levodopa response in Parkinson's disease

BACKGROUND

The efficacy of levodopa, the most crucial metric for Parkinson's disease diagnosis and treatment, is traditionally gauged through the levodopa challenge test, which lacks a predictive model. This study aims to probe the predictive power of T1-weighted MRI, the most accessible modality for levodopa response.

METHODS

This retrospective study used two datasets: from the Parkinson's Progression Markers Initiative (219 records) and the external clinical dataset from Ruijin Hospital (217 records). A novel feature extraction method using MedicalNet, a pre-trained deep learning network, along with three previous approaches was applied. Three machine learning models were trained and tested on the PPMI dataset and included clinical features, imaging features, and their union set, using the area under the curve (AUC) as the metric. The most significant brain regions were visualized. The external clinical dataset was further evaluated using trained models. A paired one-tailed t-test was performed between the two sets; statistical significance was set at p < 0.001.

RESULTS

For 46 test set records (mean age, 62 ± 9 years, 28 men), MedicalNet-extracted features demonstrated a consistent improvement in all three machine learning models (SVM 0.83 ± 0.01 versus 0.73 ± 0.01, XgBoost 0.80 ± 0.04 versus 0.74 ± 0.02, MLP 0.80 ± 0.03 versus 0.70 ± 0.07, p < 0.001). Both feature sets were validated on the clinical dataset using SVM, where MedicalNet features alone achieved an AUC of 0.64 ± 0.03. Key responsible brain regions were visualized.

CONCLUSION

The T1-weighed MRI features were more robust and generalizable than the clinical features in prediction; their combination provided the best results. T1-weighed MRI provided insights on specific regions responsible for levodopa response prediction.

CRITICAL RELEVANCE STATEMENT

This study demonstrated that T1w MRI features extracted by a deep learning model have the potential to predict the levodopa response of PD patients and are more robust than widely used clinical information, which might help in determining treatment strategy.

KEY POINTS

This study investigated the predictive value of T1w features for levodopa response. MedicalNet extractor outperformed all other previously published methods with key region visualization. T1w features are more effective than clinical information in levodopa response prediction.

Motor network gamma oscillations in chronic home recordings predict dyskinesia in Parkinson's disease

In Parkinson's disease, imbalances between 'antikinetic' and 'prokinetic' patterns of neuronal oscillatory activity are related to motor dysfunction. Invasive brain recordings from the motor network have suggested that medical or surgical therapy can promote a prokinetic state by inducing narrowband gamma rhythms (65-90 Hz). Excessive narrowband gamma in the motor cortex promotes dyskinesia in rodent models, but the relationship between narrowband gamma and dyskinesia in humans has not been well established. To assess this relationship, we used a sensing-enabled deep brain stimulator system, attached to both motor cortex and basal ganglia (subthalamic or pallidal) leads, paired with wearable devices that continuously tracked motor signs in the contralateral upper limbs. We recorded 984 h of multisite field potentials in 30 hemispheres of 16 subjects with Parkinson's disease (2/16 female, mean age 57 ± 12 years) while at home on usual antiparkinsonian medications. Recordings were done 2-4 weeks after implantation, prior to starting therapeutic stimulation. Narrowband gamma was detected in the precentral gyrus, subthalamic nucleus or both structures on at least one side of 92% of subjects with a clinical history of dyskinesia. Narrowband gamma was not detected in the globus pallidus. Narrowband gamma spectral power in both structures co-fluctuated similarly with contralateral wearable dyskinesia scores (mean correlation coefficient of ρ = 0.48 with a range of 0.12-0.82 for cortex, ρ = 0.53 with a range of 0.5-0.77 for subthalamic nucleus). Stratification analysis showed the correlations were not driven by outlier values, and narrowband gamma could distinguish 'on' periods with dyskinesia from 'on' periods without dyskinesia. Time lag comparisons confirmed that gamma oscillations herald dyskinesia onset without a time lag in either structure when using 2-min epochs. A linear model incorporating the three oscillatory bands (beta, theta/alpha and narrowband gamma) increased the predictive power of dyskinesia for several subject hemispheres. We further identified spectrally distinct oscillations in the low gamma range (40-60 Hz) in three subjects, but the relationship of low gamma oscillations to dyskinesia was variable. Our findings support the hypothesis that excessive oscillatory activity at 65-90 Hz in the motor network tracks with dyskinesia similarly across both structures, without a detectable time lag. This rhythm may serve as a promising control signal for closed-loop deep brain stimulation using either cortical or subthalamic detection.

Magnetic Resonance-Guided Focused Ultrasound for Treatment of Essential Tremor: Ventral Intermediate Nucleus Ablation Alone or Additional Posterior Subthalamic Area Lesioning?

BACKGROUND

Magnetic resonance-guided focused ultrasound (MRgFUS) for treatment of essential tremor (ET) traditionally targets the ventral intermediate (Vim) nucleus. Recent strategies include a secondary lesion to the posterior subthalamic area (PSA).

OBJECTIVE

The aim was to compare lesion characteristics, tremor improvement, and adverse events (AE) between patients in whom satisfactory tremor suppression was achieved with lesioning of the Vim alone and patients who required additional lesioning of the PSA.

METHODS

Retrospective analysis of data collected from ET patients treated with MRgFUS at St Vincent's Hospital Sydney was performed. Clinical Rating Scale for Tremor (CRST), hand tremor score (HTS), and Quality of Life in Essential Tremor Questionnaire (QUEST) were collected pre- and posttreatment in addition to the prevalence of AEs. The lesion coordinates and overlap with the dentatorubrothalamic tract (DRTT) were evaluated using magnetic resonance imaging.

RESULTS

Twenty-one patients were treated in Vim only, and 14 were treated with dual Vim-PSA lesions. Clinical data were available for 29 of the 35 patients (19 single target and 10 dual target). At follow-up (mean: 18.80 months) HTS, CRST, and QUEST in single-target patients improved by 57.97% (P < 0.001), 36.71% (P < 0.001), and 58.26% (P < 0.001), whereas dual-target patients improved by 68.34% (P < 0.001), 35.37% (P < 0.003), and 46.97% (P < 0.005), respectively. The Vim lesion of dual-target patients was further anterior relative to the posterior commissure (PC) (7.84 mm), compared with single-target patients (6.92 mm), with less DRTT involvement (14.85% vs. 23.21%). Dual-target patients exhibited a greater proportion of patients with acute motor AEs (100% vs. 58%); however, motor AE prevalence was similar in both groups at long-term follow-up (33% vs. 38%).

CONCLUSION

Posterior placement of lesions targeting the Vim may confer greater tremor suppression. The addition of a PSA lesion, in patients with inadequate tremor control despite Vim lesioning, had a trend toward better long-term tremor suppression; however, this approach was associated with greater prevalence of gait disturbance in the short term.

7T MRI detects widespread brain iron deposition in neuroferritinopathy

Neuroferritinopathy is a disorder of neurodegeneration with brain iron accumulation that has no proven disease-modifying treatments. Clinical trials require biomarkers of iron deposition. We examined brain iron accumulation in one presymptomatic FTL mutation carrier, two individuals with neuroferritinopathy and one healthy control using ultra-high-field 7T MRI. There was increased magnetic susceptibility, suggestive of iron deposition, in superficial and deep gray matter in both presymptomatic and symptomatic neuroferritinopathy. Cavitation of the putamen and globus pallidus increased with disease stage and at follow up. The widespread brain iron deposition in presymptomatic and early disease provides an opportunity for monitoring disease-modifying intervention.

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.

Quantitative Susceptibility Mapping MRI in Deep-Brain Nuclei in First-Episode Psychosis

BACKGROUND

Psychosis is related to neurochemical changes in deep-brain nuclei, particularly suggesting dopamine dysfunctions. We used an magnetic resonance imaging-based technique called quantitative susceptibility mapping (QSM) to study these regions in psychosis. QSM quantifies magnetic susceptibility in the brain, which is associated with iron concentrations. Since iron is a cofactor in dopamine pathways and co-localizes with inhibitory neurons, differences in QSM could reflect changes in these processes.

METHODS

We scanned 83 patients with first-episode psychosis and 64 healthy subjects. We reassessed 22 patients and 21 control subjects after 3 months. Mean susceptibility was measured in 6 deep-brain nuclei. Using linear mixed models, we analyzed the effect of case-control differences, region, age, gender, volume, framewise displacement (FD), treatment duration, dose, laterality, session, and psychotic symptoms on QSM.

RESULTS

Patients showed a significant susceptibility reduction in the putamen and globus pallidus externa (GPe). Patients also showed a significant R2* reduction in GPe. Age, gender, FD, session, group, and region are significant predictor variables for QSM. Dose, treatment duration, and volume were not predictor variables of QSM.

CONCLUSIONS

Reduction in QSM and R2* suggests a decreased iron concentration in the GPe of patients. Susceptibility reduction in putamen cannot be associated with iron changes. Since changes observed in putamen and GPe were not associated with symptoms, dose, and treatment duration, we hypothesize that susceptibility may be a trait marker rather than a state marker, but this must be verified with long-term studies.

A dataset of multi-contrast unbiased average MRI templates of a Parkinson's disease population

Parkinson's disease (PD) is a complex neurodegenerative disorder affecting regions such as the substantia nigra (SN), red nucleus (RN) and locus coeruleus (LC). Processing MRI data from patients with PD requires anatomical structural references for spatial normalization and structural segmentation. Extending our previous work, we present multi-contrast unbiased MRI templates using nine 3T MRI modalities: T1w, T2*w, T1-T2* fusion, R2*, T2w, PDw, fluid-attenuated inversion recovery (FLAIR), susceptibility-weighted imaging, and neuromelanin-sensitive MRI (NM). One mm isotropic voxel size templates were created, along with 0.5 mm isotropic whole brain templates and 0.3 mm isotropic templates of the midbrain. All templates were created from 126 PD patients (44 female; ages=40-87), and 17 healthy controls (13 female; ages=39-84), except the NM template, which was created from 85 PD patients and 13 controls, respectively. The dataset is available on the NIST MNI Repository via the following link: http://nist.mni.mcgill.ca/multi-contrast-pd126-and-ctrl17-templates/. The data is also available on NITRC at the following link: https://www.nitrc.org/projects/pd126/.

Oscillatory beta dynamics inform biomarker-driven treatment optimization for Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by loss of dopaminergic neurons and dysregulation of the basal ganglia. Cardinal motor symptoms include bradykinesia, rigidity, and tremor. Deep brain stimulation (DBS) of select subcortical nuclei is standard of care for medication-refractory PD. Conventional open-loop DBS delivers continuous stimulation with fixed parameters that do not account for a patient's dynamic activity state or medication cycle. In comparison, closed-loop DBS, or adaptive DBS (aDBS), adjusts stimulation based on biomarker feedback that correlates with clinical state. Recent work has identified several neurophysiological biomarkers in local field potential recordings from PD patients, the most promising of which are 1) elevated beta (∼13-30 Hz) power in the subthalamic nucleus (STN), 2) increased beta synchrony throughout basal ganglia-thalamocortical circuits, notably observed as coupling between the STN beta phase and cortical broadband gamma (∼50-200 Hz) amplitude, and 3) prolonged beta bursts in the STN and cortex. In this review, we highlight relevant frequency and time domain features of STN beta measured in PD patients and summarize how spectral beta power, oscillatory beta synchrony, phase-amplitude coupling, and temporal beta bursting inform PD pathology, neurosurgical targeting, and DBS therapy. We then review how STN beta dynamics inform predictive, biomarker-driven aDBS approaches for optimizing PD treatment. We therefore provide clinically useful and actionable insight that can be applied toward aDBS implementation for PD.

Concurrent decoding of distinct neurophysiological fingerprints of tremor and bradykinesia in Parkinson's disease

Parkinson's disease (PD) is characterized by distinct motor phenomena that are expressed asynchronously. Understanding the neurophysiological correlates of these motor states could facilitate monitoring of disease progression and allow improved assessments of therapeutic efficacy, as well as enable optimal closed-loop neuromodulation. We examined neural activity in the basal ganglia and cortex of 31 subjects with PD during a quantitative motor task to decode tremor and bradykinesia - two cardinal motor signs of PD - and relatively asymptomatic periods of behavior. Support vector regression analysis of microelectrode and electrocorticography recordings revealed that tremor and bradykinesia had nearly opposite neural signatures, while effective motor control displayed unique, differentiating features. The neurophysiological signatures of these motor states depended on the signal type and location. Cortical decoding generally outperformed subcortical decoding. Within the subthalamic nucleus (STN), tremor and bradykinesia were better decoded from distinct subregions. These results demonstrate how to leverage neurophysiology to more precisely treat PD.

Interactive mobile application for Parkinson's disease deep brain stimulation (MAP DBS): An open-label, multicenter, randomized, controlled clinical trial

INTRODUCTION

Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease (PD), but its efficacy is tied to DBS programming, which is often time consuming and burdensome for patients, caregivers, and clinicians. Our aim is to test whether the Mobile Application for PD DBS (MAP DBS), a clinical decision support system, can improve programming.

METHODS

We conducted an open-label, 1:1 randomized, controlled, multicenter clinical trial comparing six months of SOC standard of care (SOC) to six months of MAP DBS-aided programming. We enrolled patients between 30 and 80 years old who received DBS to treat idiopathic PD at six expert centers across the United States. The primary outcome was time spent DBS programming and secondary outcomes measured changes in motor symptoms, caregiver strain and medication requirements.

RESULTS

We found a significant reduction in initial visit time (SOC: 43.8 ± 28.9 min n = 37, MAP DBS: 27.4 ± 13.0 min n = 35, p = 0.001). We did not find a significant difference in total programming time between the groups over the 6-month study duration. MAP DBS-aided patients experienced a significantly larger reduction in UPDRS III on-medication scores (-7.0 ± 7.9) compared to SOC (-2.7 ± 6.9, p = 0.01) at six months.

CONCLUSION

MAP DBS was well tolerated and improves key aspects of DBS programming time and clinical efficacy.

Fusion of quantitative susceptibility maps and T1-weighted images improve brain tissue contrast in primates

Recent progress in quantitative susceptibility mapping (QSM) has enabled the accurate delineation of submillimeter-scale subcortical brain structures in humans. However, the simultaneous visualization of cortical, subcortical, and white matter structure remains challenging, utilizing QSM data solely. Here we present TQ-SILiCON, a fusion method that enhances the contrast of cortex and subcortical structures and provides an excellent white matter delineation by combining QSM and conventional T1-weighted (T1w) images. In this study, we first applied QSM in the macaque monkey to map iron-rich subcortical structures. Implementing the same QSM acquisition and analysis methods allowed a similar accurate delineation of subcortical structures in humans. However, the QSM contrast of white and cortical gray matter was not sufficient for appropriate segmentation. Applying automatic brain tissue segmentation to TQ-SILiCON images of the macaque improved the classification of subcortical brain structures as compared to the single T1 contrast by maintaining an excellent white to cortical gray matter contrast. Furthermore, we validated our dual-contrast fusion approach in humans and similarly demonstrated improvements in automated segmentation of the cortex and subcortical structures. We believe the proposed contrast will facilitate translational studies in nonhuman primates to investigate the pathophysiology of neurodegenerative diseases that affect subcortical structures such as the basal ganglia in humans.

Treadmill training in Parkinson’s disease is underpinned by the interregional connectivity in cortical-subcortical network

Treadmill training (TT) has been extensively used as an intervention to improve gait and mobility in patients with Parkinson’s disease (PD). Regional and global effects on brain activity could be induced through TT. Training effects can lead to a beneficial shift of interregional connectivity towards a physiological range. The current work investigates the effects of TT on brain activity and connectivity during walking and at rest by using both functional near-infrared spectroscopy and functional magnetic resonance imaging. Nineteen PD patients (74.0 ± 6.59 years, 13 males, disease duration 10.45 ± 6.83 years) before and after 6 weeks of TT, along with 19 age-matched healthy controls were assessed. Interregional effective connectivity (EC) between cortical and subcortical regions were assessed and its interrelation to prefrontal cortex (PFC) activity. Support vector regression (SVR) on the resting-state ECs was used to predict prefrontal connectivity. In response to TT, EC analysis indicated modifications in the patients with PD towards the level of healthy controls during walking and at rest. SVR revealed cerebellum related connectivity patterns that were associated with the training effect on PFC. These findings suggest that the potential therapeutic effect of training on brain activity may be facilitated via changes in compensatory modulation of the cerebellar interregional connectivity.

Multi sequence average templates for aging and neurodegenerative disease populations

Magnetic resonance image (MRI) processing pipelines use average templates to enable standardization of individual MRIs in a common space. MNI-ICBM152 is currently used as the standard template by most MRI processing tools. However, MNI-ICBM152 represents an average of 152 healthy young adult brains and is vastly different from brains of patients with neurodegenerative diseases. In those populations, extensive atrophy might cause inevitable registration errors when using an average template of young healthy individuals for standardization. Disease-specific templates that represent the anatomical characteristics of the populations can reduce such errors and improve downstream driven estimates. We present multi-sequence average templates for Alzheimer’s Dementia (AD), Fronto-temporal Dementia (FTD), Lewy Body Dementia (LBD), Mild Cognitive Impairment (MCI), cognitively intact and impaired Parkinson’s Disease patients (PD-CIE and PD-CI, respectively), individuals with Subjective Cognitive Impairment (SCI), AD with vascular contribution (V-AD), Vascular Mild Cognitive Impairment (V-MCI), Cognitively Intact Elderly (CIE) individuals, and a human phantom. We also provide separate templates for males and females to allow better representation of the diseases in each sex group.

Measurement(s)	Human Brain
Technology Type(s)	Magnetic resonance imaging
Sample Characteristic - Organism	Homo Sapiens
Sample Characteristic - Location	Canada

Quantifying Differences Between Affine and Nonlinear Spatial Normalization of FP-CIT Spect Images

Spatial normalization helps us to compare quantitatively two or more input brain scans. Although using an affine normalization approach preserves the anatomical structures, the neuroimaging field is more common to find works that make use of nonlinear transformations. The main reason is that they facilitate a voxel-wise comparison, not only when studying functional images but also when comparing MRI scans given that they fit better to a reference template. However, the amount of bias introduced by the nonlinear transformations can potentially alter the final outcome of a diagnosis especially when studying functional scans for neurological disorders like Parkinson's Disease. In this context, we have tried to quantify the bias introduced by the affine and the nonlinear spatial registration of FP-CIT SPECT volumes of healthy control subjects and patients with PD. For that purpose, we calculated the deformation fields of each participant and applied these deformation fields to a 3D-grid. As the space between the edges of small cubes comprising the grid change, we can quantify which parts from the brain have been enlarged, compressed or just remain the same. When the nonlinear approach is applied, scans from PD patients show a region near their striatum very similar in shape to that of healthy subjects. This artificially increases the interclass separation between patients with PD and healthy subjects as the local intensity is decreased in the latter region, and leads machine learning systems to biased results due to the artificial information introduced by these deformations.

An atlas of white matter anatomy, its variability, and reproducibility based on constrained spherical deconvolution of diffusion MRI

Virtual dissection of white matter (WM) using diffusion MRI tractography is confounded by its poor reproducibility. Despite the increased adoption of advanced reconstruction models, early region-of-interest driven protocols based on diffusion tensor imaging (DTI) remain the dominant reference for virtual dissection protocols. Here we bridge this gap by providing a comprehensive description of typical WM anatomy reconstructed using a reproducible automated subject-specific parcellation-based approach based on probabilistic constrained-spherical deconvolution (CSD) tractography. We complement this with a WM template in MNI space comprising 68 bundles, including all associated anatomical tract selection labels and associated automated workflows. Additionally, we demonstrate bundle inter- and intra-subject variability using 40 (20 test-retest) datasets from the human connectome project (HCP) and 5 sessions with varying b-values and number of b-shells from the single-subject Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation (MASSIVE) dataset. The most reliably reconstructed bundles were the whole pyramidal tracts, primary corticospinal tracts, whole superior longitudinal fasciculi, frontal, parietal and occipital segments of the corpus callosum and middle cerebellar peduncles. More variability was found in less dense bundles, e.g., the fornix, dentato-rubro-thalamic tract (DRTT), and premotor pyramidal tract. Using the DRTT as an example, we show that this variability can be reduced by using a higher number of seeding attempts. Overall inter-session similarity was high for HCP test-retest data (median weighted-dice = 0.963, stdev = 0.201 and IQR = 0.099). Compared to the HCP-template bundles there was a high level of agreement for the HCP test-retest data (median weighted-dice = 0.747, stdev = 0.220 and IQR = 0.277) and for the MASSIVE data (median weighted-dice = 0.767, stdev = 0.255 and IQR = 0.338). In summary, this WM atlas provides an overview of the capabilities and limitations of automated subject-specific probabilistic CSD tractography for mapping white matter fasciculi in healthy adults. It will be most useful in applications requiring a reproducible parcellation-based dissection protocol, and as an educational resource for applied neuroimaging and clinical professionals.

Subthalamic-Cortical Network Reorganization during Parkinson's Tremor

Tremor, a common and often primary symptom of Parkinson's disease, has been modeled with distinct onset and maintenance dynamics. To identify the neurophysiologic correlates of each state, we acquired intraoperative cortical and subthalamic nucleus recordings from 10 patients (9 male, 1 female) performing a naturalistic visual-motor task. From this task, we isolated short epochs of tremor onset and sustained tremor. Comparing these epochs, we found that the subthalamic nucleus was central to tremor onset, as it drove both motor cortical activity and tremor output. Once tremor became sustained, control of tremor shifted to cortex. At the same time, changes in directed functional connectivity across sensorimotor cortex further distinguished the sustained tremor state.SIGNIFICANCE STATEMENT Tremor is a common symptom of Parkinson's disease (PD). While tremor pathophysiology is thought to involve both basal ganglia and cerebello-thalamic-cortical circuits, it is unknown how these structures functionally interact to produce tremor. In this article, we analyzed intracranial recordings from the subthalamic nucleus and sensorimotor cortex in patients with PD undergoing deep brain stimulation surgery. Using an intraoperative task, we examined tremor in two separate dynamic contexts: when tremor first emerged, and when tremor was sustained. We believe that these findings reconcile several models of Parkinson's tremor, while describing the short-timescale dynamics of subcortical-cortical interactions during tremor for the first time. These findings may describe a framework for developing proactive and responsive neurostimulation models for specifically treating tremor.

HybraPD atlas: Towards precise subcortical nuclei segmentation using multimodality medical images in patients with Parkinson disease

Human brain atlases are essential for research and surgical treatment of Parkinson's disease (PD). For example, deep brain stimulation for PD often requires human brain atlases for brain structure identification. However, few atlases can provide disease-specific subcortical structures for PD, and most of them are based on T1w and T2w images. In this work, we construct a HybraPD atlas using fused quantitative susceptibility mapping (QSM) and T1w images from 87 patients with PD. The constructed HybraPD atlas provides a series of templates, that is, T1w, GRE magnitude, QSM, R2*, and brain tissue probabilistic maps. Then, we manually delineate a parcellation map with 12 bilateral subcortical nuclei, which are highly related to PD pathology, such as sub-regions in globus pallidus and substantia nigra. Furthermore, we build a whole-brain parcellation map by combining existing cortical parcellation and white-matter segmentation with the proposed subcortical nuclei map. Considering the multimodality of the HybraPD atlas, the segmentation accuracy of each nucleus is evaluated using T1w and QSM templates, respectively. The results show that the HybraPD atlas provides more accurate segmentation than existing atlases. Moreover, we analyze the metabolic difference in subcortical nuclei between PD patients and healthy control subjects by applying the HybraPD atlas to calculate uptake values of contrast agents on positron emission tomography (PET) images. The atlas-based analysis generates accurate disease-related brain nuclei segmentation on PET images. The newly developed HybraPD atlas could serve as an efficient template to study brain pathological alterations in subcortical regions for PD research.

The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging

Neuroimaging stands to benefit from emerging ultrahigh-resolution 3D histological atlases of the human brain; the first of which is 'BigBrain'. Here, we review recent methodological advances for the integration of BigBrain with multi-modal neuroimaging and introduce a toolbox, 'BigBrainWarp', that combines these developments. The aim of BigBrainWarp is to simplify workflows and support the adoption of best practices. This is accomplished with a simple wrapper function that allows users to easily map data between BigBrain and standard MRI spaces. The function automatically pulls specialised transformation procedures, based on ongoing research from a wide collaborative network of researchers. Additionally, the toolbox improves accessibility of histological information through dissemination of ready-to-use cytoarchitectural features. Finally, we demonstrate the utility of BigBrainWarp with three tutorials and discuss the potential of the toolbox to support multi-scale investigations of brain organisation.

Edge-enhancing gradient echo with multi-image co-registration and averaging (EDGE-MICRA) for targeting thalamic centromedian and parafascicular nuclei

BACKGROUND AND PURPOSE

Deep brain stimulation of the thalamus is an effective treatment for multiple neurological disorders. The centromedian and parafascicular nuclei are recently emerging targets for multiple conditions, such as epilepsy and Tourette syndrome; however, their limited visibility on conventional magnetic resonance imaging sequences has been a major obstacle. The goal of this study was to demonstrate the feasibility of a high-resolution and high-contrast targeting sequence for centromedian-parafascicular deep brain stimulation using a recently described magnetic resonance imaging sequence, three-dimensional edge-enhancing gradient echo.

METHODS

The three-dimensional edge-enhancing gradient echo sequence was performed on a normal volunteer for a total of six acquisitions. Multi-image co-registration and averaging was performed by first co-registering each of the six scans and then averaging to produce an edge-enhancing gradient echo-multi-image co-registration and averaging scan. The averaging was also performed for two, three, four and five scans to assess the change in the signal-to-noise ratio and identify the ideal balance of image quality and scan time.

RESULTS

The edge-enhancing gradient echo-multi-image co-registration and averaging scan allowed clear boundary delineation of the centromedian and parafascicular nuclei. The signal-to-noise ratio increased as a function of increasing scan number, but the added gain was small beyond four scans for the imaging parameters used in this study.

CONCLUSIONS

The recently described three-dimensional edge-enhancing gradient echo sequence provides an easily implementable approach, using widely available magnetic resonance imaging technology without complex post-processing techniques, to delineate centromedian and parafascicular nuclei for deep brain stimulation targeting.

Convergent patterns of structural brain changes in rapid eye movement sleep behavior disorder and Parkinson's disease on behalf of the German rapid eye movement sleep behavior disorder study group

STUDY OBJECTIVES

Rapid eye movement sleep behavior disorder (RBD) is considered a prodromal state of Parkinson's disease (PD). We aimed to characterize patterns of structural brain changes in RBD and PD patients using multimodal MRI.

METHODS

A total of 30 patients with isolated RBD, 29 patients with PD, and 56 age-matched healthy controls (HC) underwent MRI at 3T, including tensor-based morphometry, diffusion tensor imaging, and assessment of cortical thickness.

RESULTS

RBD individuals showed increased volume of the right caudate nucleus compared with HC, and higher cerebellar volume compared with both PD subjects and HC. Similar to PD subjects, RBD patients displayed increased fractional anisotropy (FA) in the corticospinal tracts, several tracts mainly related to non-motor function, and reduced FA of the corpus callosum compared with HC. Further, RBD subjects showed higher FA in the cerebellar peduncles and brainstem compared with both, PD patients and HC. PD individuals exhibited lower than normal volume in the basal ganglia, midbrain, pedunculopontine nuclei, and cerebellum. In contrast, volume in PD subjects was increased in the thalamus compared with both HC and RBD subjects.

CONCLUSIONS

We found convergent patterns of structural brain alterations in RBD and PD patients compared with HC. The changes observed suggest a co-occurrence of neurodegeneration and compensatory mechanisms that fail with emerging PD pathology. Our findings strengthen the hypothesis of RBD and PD constituting a continuous disease spectrum.

A literature review of magnetic resonance imaging sequence advancements in visualizing functional neurosurgery targets

OBJECTIVE

Historically, preoperative planning for functional neurosurgery has depended on the indirect localization of target brain structures using visible anatomical landmarks. However, recent technological advances in neuroimaging have permitted marked improvements in MRI-based direct target visualization, allowing for refinement of "first-pass" targeting. The authors reviewed studies relating to direct MRI visualization of the most common functional neurosurgery targets (subthalamic nucleus, globus pallidus, and thalamus) and summarize sequence specifications for the various approaches described in this literature.

METHODS

The peer-reviewed literature on MRI visualization of the subthalamic nucleus, globus pallidus, and thalamus was obtained by searching MEDLINE. Publications examining direct MRI visualization of these deep brain stimulation targets were included for review.

RESULTS

A variety of specialized sequences and postprocessing methods for enhanced MRI visualization are in current use. These include susceptibility-based techniques such as quantitative susceptibility mapping, which exploit the amount of tissue iron in target structures, and white matter attenuated inversion recovery, which suppresses the signal from white matter to improve the distinction between gray matter nuclei. However, evidence confirming the superiority of these sequences over indirect targeting with respect to clinical outcome is sparse. Future targeting may utilize information about functional and structural networks, necessitating the use of resting-state functional MRI and diffusion-weighted imaging.

CONCLUSIONS

Specialized MRI sequences have enabled considerable improvement in the visualization of common deep brain stimulation targets. With further validation of their ability to improve clinical outcomes and advances in imaging techniques, direct visualization of targets may play an increasingly important role in preoperative planning.

Multi-contrast anatomical subcortical structures parcellation

The human subcortex is comprised of more than 450 individual nuclei which lie deep in the brain. Due to their small size and close proximity, up until now only 7% have been depicted in standard MRI atlases. Thus, the human subcortex can largely be considered as terra incognita. Here, we present a new open-source parcellation algorithm to automatically map the subcortex. The new algorithm has been tested on 17 prominent subcortical structures based on a large quantitative MRI dataset at 7 Tesla. It has been carefully validated against expert human raters and previous methods, and can easily be extended to other subcortical structures and applied to any quantitative MRI dataset. In sum, we hope this novel parcellation algorithm will facilitate functional and structural neuroimaging research into small subcortical nuclei and help to chart terra incognita.

Cortico-Subcortical Functional Connectivity Profiles of Resting-State Networks in Marmosets and Humans

Understanding the similarity of cortico-subcortical networks topologies between humans and nonhuman primate species is critical to study the origin of network alternations underlying human neurologic and neuropsychiatric diseases. The New World common marmoset (Callithrix jacchus) has become popular as a nonhuman primate model for human brain function. Most marmoset connectomic research, however, has exclusively focused on cortical areas, with connectivity to subcortical networks less extensively explored. Here, we aimed to first isolate patterns of subcortical connectivity with cortical resting-state networks in awake marmosets using resting-state fMRI, then to compare these networks with those in humans using connectivity fingerprinting. In this study, we used 5 marmosets (4 males, 1 female). While we could match several marmoset and human resting-state networks based on their functional fingerprints, we also found a few striking differences, for example, strong functional connectivity of the default mode network with the superior colliculus in marmosets that was much weaker in humans. Together, these findings demonstrate that many of the core cortico-subcortical networks in humans are also present in marmosets, but that small, potentially functionally relevant differences exist.SIGNIFICANCE STATEMENT The common marmoset is becoming increasingly popular as an additional preclinical nonhuman primate model for human brain function. Here we compared the functional organization of cortico-subcortical networks in marmosets and humans using ultra-high field fMRI. We isolated the patterns of subcortical connectivity with cortical resting-state networks (RSNs) in awake marmosets using resting-state fMRI and then compared these networks with those in humans using connectivity fingerprinting. While we could match several marmoset and human RSNs based on their functional fingerprints, we also found several striking differences. Together, these findings demonstrate that many of the core cortico-subcortical RSNs in humans are also present in marmosets, but that small, potentially functionally relevant differences exist.

Rapid motor fluctuations reveal short-timescale neurophysiological biomarkers of Parkinson's disease

Objective.

Identifying neural activity biomarkers of brain disease is essential to provide objective estimates of disease burden, obtain reliable feedback regarding therapeutic efficacy, and potentially to serve as a source of control for closed-loop neuromodulation. In Parkinson’s disease (PD), microelectrode recordings (MER) are routinely performed in the basal ganglia to guide electrode implantation for deep brain stimulation (DBS). While pathologically-excessive oscillatory activity has been observed and linked to PD motor dysfunction broadly, the extent to which these signals provide quantitative information about disease expression and fluctuations, particularly at short timescales, is unknown. Furthermore, the degree to which informative signal features are similar or different across patients has not been rigorously investigated. We sought to determine the extent to which motor error in PD across patients can be decoded on a rapid timescale using spectral features of neural activity.

Approach.

Here, we recorded neural activity from the subthalamic nucleus (STN) of subjects with PD undergoing awake DBS surgery while they performed an objective, continuous behavioral assessment that synthesized heterogenous PD motor manifestations to generate a scalar measure of motor dysfunction at short timescales. We then leveraged natural motor performance variations as a ‘ground truth’ to identify corresponding neurophysiological biomarkers.

Main results.

Support vector machines using multi-spectral decoding of neural signals from the STN succeeded in tracking the degree of motor impairment at short timescales (as short as one second). Spectral power across a wide range of frequencies, beyond the classic ‘β’ oscillations, contributed to this decoding, and multi-spectral models consistently outperformed those generated using more isolated frequency bands. While generalized decoding models derived across subjects were able to estimate motor impairment, patient-specific models typically performed better.

Significance.

These results demonstrate that quantitative information about short-timescale PD motor dysfunction is available in STN neural activity, distributed across various patient-specific spectral components, such that an individualized approach will be critical to fully harness this information for optimal disease tracking and closed-loop neuromodulation.

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.

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.

Detection of Parkinson's Disease from 3T T1 Weighted MRI Scans Using 3D Convolutional Neural Network

Parkinson's Disease is a neurodegenerative disease that affects the aging population and is caused by a progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). With the onset of the disease, the patients suffer from mobility disorders such as tremors, bradykinesia, impairment of posture and balance, etc., and it progressively worsens in the due course of time. Additionally, as there is an exponential growth of the aging population in the world the number of people suffering from Parkinson's Disease is increasing and it levies a huge economic burden on governments. However, until now no therapeutic method has been discovered for completely eradicating the disease from a person's body after it's onset. Therefore, the early detection of Parkinson's Disease is of paramount importance to tackle the progressive loss of dopaminergic neurons in patients to serve them with a better life. In this study, 3T T1-weighted MRI scans were acquired from the Parkinson's Progression Markers Initiative (PPMI) database of 406 subjects from baseline visit, where 203 were healthy and 203 were suffering from Parkinson's Disease. Following data pre-processing, a 3D convolutional neural network (CNN) architecture was developed for learning the intricate patterns in the Magnetic Resonance Imaging (MRI) scans for the detection of Parkinson's Disease. In the end, it was observed that the developed 3D CNN model performed superiorly by completely aligning with the hypothesis of the study and plotted an overall accuracy of 95.29%, average recall of 0.943, average precision of 0.927, average specificity of 0.9430, f1-score of 0.936, and Receiver Operating Characteristic-Area Under Curve (ROC-AUC) score of 0.98 for both the classes respectively.

Regional SUV quantification in hybrid PET/MR, a comparison of two atlas-based automatic brain segmentation methods

BACKGROUND

Quantitative analysis of brain positron-emission tomography (PET) depends on structural segmentation, which can be time-consuming and operator-dependent when performed manually. Previous automatic segmentation usually registered subjects' images onto an atlas template (defined as RSIAT here) for group analysis, which changed the individuals' images and probably affected regional PET segmentation. In contrast, we could register atlas template to subjects' images (RATSI), which created an individual atlas template and may be more accurate for PET segmentation. We segmented two representative brain areas in twenty Parkinson disease (PD) and eight multiple system atrophy (MSA) patients performed in hybrid positron-emission tomography/magnetic resonance imaging (PET/MR). The segmentation accuracy was evaluated using the Dice coefficient (DC) and Hausdorff distance (HD), and the standardized uptake value (SUV) measurements of these two automatic segmentation methods were compared, using manual segmentation as a reference.

RESULTS

The DC of RATSI increased, and the HD decreased significantly (P < 0.05) compared with the RSIAT in PD, while the results of one-way analysis of variance (ANOVA) found no significant differences in the SUVmean and SUVmax among the two automatic and the manual segmentation methods. Further, RATSI was used to compare regional differences in cerebral metabolism pattern between PD and MSA patients. The SUVmean in the segmented cerebellar gray matter for the MSA group was significantly lower compared with the PD group (P < 0.05), which is consistent with previous reports.

CONCLUSION

The RATSI was more accurate for the caudate nucleus and putamen automatic segmentation and can be used for regional PET analysis in hybrid PET/MR.

3D Textural, Morphological and Statistical Analysis of Voxel of Interests in 3T MRI Scans for the Detection of Parkinson's Disease Using Artificial Neural Networks

Parkinson's disease is caused due to the progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). Presently, with the exponential growth of the aging population across the world the number of people being affected by the disease is also increasing and it imposes a huge economic burden on the governments. However, to date, no therapy or treatment has been found that can completely eradicate the disease. Therefore, early detection of Parkinson's disease is very important so that the progressive loss of dopaminergic neurons can be controlled to provide the patients with a better life. In this study, 3T T1-MRI scans were collected from 906 subjects, out of which, 203 are control subjects, 66 are prodromal subjects and 637 are Parkinson's disease patients. To analyze the MRI scans for the detection of neurodegeneration and Parkinson's disease, eight subcortical structures were segmented from the acquired MRI scans using atlas based segmentation. Further, on the extracted eight subcortical structures, feature extraction was performed to extract textural, morphological and statistical features, respectively. After the feature extraction process, an exhaustive set of 107 features were generated for each MRI scan. Therefore, a two-level feature extraction process was implemented for finding the best possible feature set for the detection of Parkinson's disease. The two-level feature extraction procedure leveraged correlation analysis and recursive feature elimination, which at the end provided us with 20 best performing features out of the extracted 107 features. Further, all the features were trained using machine learning algorithms and a comparative analysis was performed between four different machine learning algorithms based on the selected performance metrics. And at the end, it was observed that artificial neural network (multi-layer perceptron) performed the best by providing an overall accuracy of 95.3%, overall recall of 95.41%, overall precision of 97.28% and f1-score of 94%, respectively.

Interleaved deep brain stimulation for dyskinesia management in Parkinson's disease

BACKGROUND

In patients with Parkinson's disease, stimulation above the subthalamic nucleus (STN) may engage the pallidofugal fibers and directly suppress dyskinesia.

OBJECTIVES

The objective of this study was to evaluate the effect of interleaving stimulation through a dorsal deep brain stimulation contact above the STN in a cohort of PD patients and to define the volume of tissue activated with antidyskinesia effects.

METHODS

We analyzed the Core Assessment Program for Surgical Interventional Therapies dyskinesia scale, Unified Parkinson's Disease Rating Scale parts III and IV, and other endpoints in 20 patients with interleaving stimulation for management of dyskinesia. Individual models of volume of tissue activated and heat maps were used to identify stimulation sites with antidyskinesia effects.

RESULTS

The Core Assessment Program for Surgical Interventional Therapies dyskinesia score in the on medication phase improved 70.9 ± 20.6% from baseline with noninterleaved settings (P < 0.003). With interleaved settings, dyskinesia improved 82.0 ± 27.3% from baseline (P < 0.001) and 61.6 ± 39.3% from the noninterleaved phase (P = 0.006). The heat map showed a concentration of volume of tissue activated dorsally to the STN during the interleaved setting with an antidyskinesia effect.

CONCLUSION

Interleaved deep brain stimulation using the dorsal contacts can directly suppress dyskinesia, probably because of the involvement of the pallidofugal tract, allowing more conservative medication reduction. © 2019 International Parkinson and Movement Disorder Society.

An accurate registration of the BigBrain dataset with the MNI PD25 and ICBM152 atlases

Brain atlases that encompass detailed anatomical or physiological features are instrumental in the research and surgical planning of various neurological conditions. Magnetic resonance imaging (MRI) has played important roles in neuro-image analysis while histological data remain crucial as a gold standard to guide and validate such analyses. With cellular-scale resolution, the BigBrain atlas offers 3D histology of a complete human brain, and is highly valuable to the research and clinical community. To bridge the insights at macro- and micro-levels, accurate mapping of BigBrain and established MRI brain atlases is necessary, but the existing registration is unsatisfactory. The described dataset includes co-registration of the BigBrain atlas to the MNI PD25 atlas and the ICBM152 2009b atlases (symmetric and asymmetric versions) in addition to manual segmentation of the basal ganglia, red nucleus, amygdala, and hippocampus for all mentioned atlases. The dataset intends to provide a bridge between insights from histological data and MRI studies in research and neurosurgical planning. The registered atlases, anatomical segmentations, and deformation matrices are available at: https://osf.io/xkqb3/ .

Combined Assessment of Diffusion Parameters and Cerebral Blood Flow Within Basal Ganglia in Early Parkinson's Disease

Diffusion tensor imaging (DTI) is a sensitive tool for detecting brain tissue microstructural alterations in Parkinson’s disease (PD). Abnormal cerebral perfusion patterns have also been reported in PD patients using arterial spin labeling (ASL) MRI. In this study we aimed to perform a combined DTI and ASL assessment in PD patients within the basal ganglia, in order to test the relationship between microstructural and perfusion alterations. Fifty-two subjects participated in this study. Specifically, 26 PD patients [mean age (SD) = 66.7 (8.9) years, 21 males, median (IQR) Modified Hoehn and Yahr = 1.5 (1–1.6)] and twenty-six healthy controls [HC, mean age (SD) = 65.2 (7.5), 15 males] were scanned with 1.5T MRI. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD) maps were derived from diffusion-weighted images, while cerebral blood flow (CBF) maps were computed from ASL data. After registration to Montreal Neurological Institute standard space, FA, MD, AD, RD and CBF median values were extracted within specific regions of interest: substantia nigra, caudate, putamen, globus pallidus, thalamus, red nucleus and subthalamic nucleus. DTI measures and CBF were compared between the two groups. The relationship between diffusion parameters and CBF was tested with Spearman’s correlations. False discovery rate (FDR)-corrected p-values lower than 0.05 were considered significant, while uncorrected p-values <0.05 were considered a trend. No significant FA, MD and RD differences were observed. AD was significantly increased in PD patients compared with HC in the putamen (p = 0.005, p_FDR = 0.035). No significant CBF differences were found between PD patients and HC. Diffusion parameters were not significantly correlated with CBF in the HC group, while a significant correlation emerged for PD patients in the caudate nucleus, for all DTI measures (with FA: r = 0.543, p_FDR = 0.028; with MD: r = −0.661, p_FDR = 0.002; with AD: r = −0.628, p_FDR = 0.007; with RD: r = −0.635, p_FDR = 0.003). This study showed that DTI is a more sensitive technique than ASL to detect alterations in the basal ganglia in the early phase of PD. Our results suggest that, although DTI and ASL convey different information, a relationship between microstructural integrity and perfusion changes in the caudate may be present.

Are Magnetic Resonance Imaging Technologies Crucial to Our Understanding of Spinal Conditions?

Persistent spinal (traumatic and nontraumatic) pain is common and contributes to high societal and personal costs globally. There is an acknowledged urgency for new and interdisciplinary approaches to the condition, and soft tissues, including skeletal muscles, the spinal cord, and the brain, are rightly receiving increased attention as important biological contributors. In reaction to the recent suspicion and questioned value of imaging-based findings, this paper serves to recognize the promise that the technological evolution of imaging techniques, and particularly magnetic resonance imaging, is allowing in characterizing previously less visible morphology. We emphasize the value of quantification and data analysis of several contributors in the biopsychosocial model for understanding spinal pain. Further, we highlight emerging evidence regarding the pathobiology of changes to muscle composition (eg, atrophy, fatty infiltration), as well as advancements in neuroimaging and musculoskeletal imaging techniques (eg, fat-water imaging, functional magnetic resonance imaging, diffusion imaging, magnetization transfer imaging) for these important soft tissues. These noninvasive and objective data sources may complement known prognostic factors of poor recovery, patient self-report, diagnostic tests, and the "-omics" fields. When combined, advanced "big-data" analyses may assist in identifying associations previously not considered. Our clinical commentary is supported by empirical findings that may orient future efforts toward collaborative conversation, hypothesis generation, interdisciplinary research, and translation across a number of health fields. Our emphasis is that magnetic resonance imaging technologies and research are crucial to the advancement of our understanding of the complexities of spinal conditions. J Orthop Sports Phys Ther 2019;49(5):320-329. Epub 26 Mar 2019. doi:10.2519/jospt.2019.8793.

Cerebral blood flow and cerebrovascular reactivity correlate with severity of motor symptoms in Parkinson's disease

Background:

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that is mainly characterized by movement dysfunction. Neurovascular unit (NVU) disruption has been proposed to be involved in the disease, but its role in PD neurodegenerative mechanisms is still unclear. The aim of this study was to investigate cerebral blood flow (CBF) and cerebrovascular reactivity (CVR) within the regions belonging to the motor network, in patients with mild to moderate stages of PD.

Methods:

Twenty-eight PD patients (66.6 ± 8.6 years, 22 males, median [interquartile range, IQR] Hoehn & Yahr = 1.5 [1–1.9]) and 32 age- and sex-matched healthy controls (HCs) were scanned with arterial spin labeling (ASL) magnetic resonance imaging (MRI) for CBF assessment. ASL MRI was also acquired in hypercapnic conditions to induce vasodilation and subsequently allow for CVR measurement in a subgroup of 13 PD patients and 13 HCs. Median CBF and CVR were extracted from cortical and subcortical regions belonging to the motor network and compared between PD patients and HCs. In addition, the correlation between these parameters and the severity of PD motor symptoms [quantified with Unified Parkinson’s Disease Rating Scale part III (UPDRS III)] was assessed. The false discovery rate (FDR) method was used to correct for multiple comparisons.

Results:

No significant differences in terms of CBF and CVR were found between PD patients and HCs. Positive significant correlations were observed between CBF and UPDRS III within the precentral gyrus, postcentral gyrus, supplementary motor area, striatum, pallidum, thalamus, red nucleus, and substantia nigra (pFDR < 0.05). Conversely, significant negative correlation between CVR and UPDRS III was found in the corpus striatum (pFDR < 0.05).

Conclusion:

CBF and CVR assessment provides information about NVU integrity in an indirect and noninvasive way. Our findings support the hypothesis of NVU involvement at the mild to moderate stages of PD, suggesting that CBF and CVR within the motor network might be used as either diagnostic or prognostic markers for PD.

Anodic stimulation misunderstood: preferential activation of fiber orientations with anodic waveforms in deep brain stimulation

OBJECTIVE

During deep brain stimulation (DBS), it is well understood that extracellular cathodic stimulation can cause activation of passing axons. Activation can be predicted from the second derivative of the electric potential along an axon, which depends on axonal orientation with respect to the stimulation source. We hypothesize that fiber orientation influences activation thresholds and that fiber orientations can be selectively targeted with DBS waveforms.

APPROACH

We used bioelectric field and multicompartment NEURON models to explore preferential activation based on fiber orientation during monopolar or bipolar stimulation. Preferential fiber orientation was extracted from the principal eigenvectors and eigenvalues of the Hessian matrix of the electric potential. We tested cathodic, anodic, and charge-balanced pulses to target neurons based on fiber orientation in general and clinical scenarios.

MAIN RESULTS

Axons passing the DBS lead have positive second derivatives around a cathode, whereas orthogonal axons have positive second derivatives around an anode, as indicated by the Hessian. Multicompartment NEURON models confirm that passing fibers are activated by cathodic stimulation, and orthogonal fibers are activated by anodic stimulation. Additionally, orthogonal axons have lower thresholds compared to passing axons. In a clinical scenario, fiber pathways associated with therapeutic benefit can be targeted with anodic stimulation at 50% lower stimulation amplitudes.

SIGNIFICANCE

Fiber orientations can be selectively targeted with simple changes to the stimulus waveform. Anodic stimulation preferentially activates orthogonal fibers, approaching or leaving the electrode, at lower thresholds for similar therapeutic benefit in DBS with decreased power consumption.

Accuracy of different three-dimensional subcortical human brain atlases for DBS -lead localisation

BACKGROUND

Accurate interindividual comparability of deep brain stimulation (DBS) lead locations in relation to the surrounding anatomical structures is of eminent importance to define and understand effective stimulation areas. The objective of the current work is to compare the accuracy of the DBS lead localisation relative to the STN in native space with four recently developed three-dimensional subcortical brain atlases in the MNI template space. Accuracy is reviewed by anatomical and volumetric analysis as well as intraoperative electrophysiological data.

METHODS

Postoperative lead localisations of 10 patients (19 hemispheres) were analysed in each individual patient based on Brainlab software (native space) and after normalization into the MNI space and application of 4 different human brain atlases using Lead-DBS toolbox within Matlab (template space). Each patient's STN was manually segmented and the relation between the reconstructed lead and the STN was compared to the 4 atlas-based STN models by applying the Dice coefficient. The length of intraoperative electrophysiological STN activity along different microelectrode recording tracks was measured and compared to reconstructions in native and template space. Descriptive non-parametric statistical tests were used to calculate differences between the 4 different atlases.

RESULTS

The mean STN volume of the study cohort was 153.3 ± 40.3 mm3 (n = 19). This is similar to the STN volume of the DISTAL atlas (166 mm3; p = .22), but significantly larger compared to the other atlases tested in this study. The anatomical overlap of the lead-STN-reconstruction was highest for the DISTAL atlas (0.56 ± 0.18) and lowest for the PD25 atlas (0.34 ± 0.17). A total number of 47 MER trajectories through the STN were analysed. There was a statistically significant discrepancy of the electrophysiogical STN activity compared to the reconstructed STN of all four atlases (p < .0001).

CONCLUSION

Lead reconstruction after normalization into the MNI template space and application of four different atlases led to different results in terms of the DBS lead position relative to the STN. Based on electrophysiological and imaging data, the DISTAL atlas led to the most accurate display of the reconstructed DBS lead relative to the DISTAL-based STN.

Population-averaged MRI atlases for automated image processing and assessments of lumbar paraspinal muscles

PURPOSE

Growing evidence suggests an association between lumbar paraspinal muscle degeneration and low back pain (LBP). Currently, time-consuming and laborious manual segmentations of paraspinal muscles are commonly performed on magnetic resonance imaging (MRI) axial scans. Automated image analysis algorithms can mitigate these drawbacks, but they often require individual MRIs to be aligned to a standard "reference" atlas. Such atlases are well established in automated neuroimaging analysis. Our aim was to create atlases of similar nature for automated paraspinal muscle measurements.

METHODS

Lumbosacral T2-weighted MRIs were acquired from 117 patients who experienced LBP, stratified by gender and age group (30-39, 40-49, and 50-59 years old). Axial MRI slices of the L4-L5 and L5-S1 levels at mid-disc were obtained and aligned using group-wise linear and nonlinear image registration to produce a set of unbiased population-averaged atlases for lumbar paraspinal muscles.

RESULTS

The resulting atlases represent the averaged morphology and MRI intensity features of the corresponding cohorts. Differences in paraspinal muscle shapes and fat infiltration levels with respect to gender and age can be visually identified from the population-averaged data from both linear and nonlinear registrations.

CONCLUSION

We constructed a set of population-averaged atlases for developing automated algorithms to help analyze paraspinal muscle morphometry from axial MRI scans. Such an advancement could greatly benefit the fields of paraspinal muscle and LBP research. These slides can be retrieved under Electronic Supplementary Material.

Warping an atlas derived from serial histology to 5 high-resolution MRIs

Previous work from our group demonstrated the use of multiple input atlases to a modified multi-atlas framework (MAGeT-Brain) to improve subject-based segmentation accuracy. Currently, segmentation of the striatum, globus pallidus and thalamus are generated from a single high-resolution and -contrast MRI atlas derived from annotated serial histological sections. Here, we warp this atlas to five high-resolution MRI templates to create five de novo atlases. The overall goal of this work is to use these newly warped atlases as input to MAGeT-Brain in an effort to consolidate and improve the workflow presented in previous manuscripts from our group, allowing for simultaneous multi-structure segmentation. The work presented details the methodology used for the creation of the atlases using a technique previously proposed, where atlas labels are modified to mimic the intensity and contrast profile of MRI to facilitate atlas-to-template nonlinear transformation estimation. Dice's Kappa metric was used to demonstrate high quality registration and segmentation accuracy of the atlases. The final atlases are available at https://github.com/CobraLab/atlases/tree/master/5-atlas-subcortical.

Value encoding in the globus pallidus: fMRI reveals an interaction effect between reward and dopamine drive

The external part of the globus pallidus (GPe) is a core nucleus of the basal ganglia (BG) whose activity is disrupted under conditions of low dopamine release, as in Parkinson's disease. Current models assume decreased dopamine release in the dorsal striatum results in deactivation of dorsal GPe, which in turn affects motor expression via a regulatory effect on other nuclei of the BG. However, recent studies in healthy and pathological animal models have reported neural dynamics that do not match with this view of the GPe as a relay in the BG circuit. Thus, the computational role of the GPe in the BG is still to be determined. We previously proposed a neural model that revisits the functions of the nuclei of the BG, and this model predicts that GPe encodes values which are amplified under a condition of low striatal dopaminergic drive. To test this prediction, we used an fMRI paradigm involving a within-subject placebo-controlled design, using the dopamine antagonist risperidone, wherein healthy volunteers performed a motor selection and maintenance task under low and high reward conditions. ROI-based fMRI analysis revealed an interaction between reward and dopamine drive manipulations, with increased BOLD activity in GPe in a high compared to low reward condition, and under risperidone compared to placebo. These results confirm the core prediction of our computational model, and provide a new perspective on neural dynamics in the BG and their effects on motor selection and cognitive disorders.

•

We investigate the representation of action-state values in the basal ganglia.

•

Value representation is enhanced in the GPe under reduced dopaminergic drive.

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Value representation is enhanced in the SNr under basal dopaminergic drive.

•

The results validate a proposed neural model of the basal ganglia.

Levodopa improves response inhibition and enhances striatal activation in early-stage Parkinson's disease

Dopaminergic medications improve the motor symptoms of Parkinson's disease (PD), but their effect on response inhibition, a critical executive function, remains unclear. Previous studies primarily enrolled patients in more advanced stages of PD, when dopaminergic medication loses efficacy, and patients were typically on multiple medications. Here, we recruited 21 patients in early-stage PD on levodopa monotherapy and 37 age-matched controls to perform the stop-signal task during functional magnetic resonance imaging. In contrast to previous studies reporting null effects in more advanced PD, levodopa significantly improved response inhibition performance in our sample. No significant group differences were found in brain activations to pure motor inhibition or error processing (stop success vs. error trials). However, relative to controls, the PD group showed weaker striatal activations to salient events (infrequent vs. frequent events: stop vs. go trials) and fronto-striatal task-residual functional connectivity; both were restored with levodopa. Thus, levodopa appears to improve an important executive function in early-stage PD via enhanced salient signal processing, shedding new light on the role of dopaminergic signaling in response inhibition.

7T MRI subthalamic nucleus atlas for use with 3T MRI

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) reduces motor symptoms in most patients with Parkinson disease (PD), yet may produce untoward effects. Investigation of DBS effects requires accurate localization of the STN, which can be difficult to identify on magnetic resonance images collected with clinically available 3T scanners. The goal of this study is to develop a high-quality STN atlas that can be applied to standard 3T images. We created a high-definition STN atlas derived from seven older participants imaged at 7T. This atlas was nonlinearly registered to a standard template representing 56 patients with PD imaged at 3T. This process required development of methodology for nonlinear multimodal image registration. We demonstrate mm-scale STN localization accuracy by comparison of our 3T atlas with a publicly available 7T atlas. We also demonstrate less agreement with an earlier histological atlas. STN localization error in the 56 patients imaged at 3T was less than 1 mm on average. Our methodology enables accurate STN localization in individuals imaged at 3T. The STN atlas and underlying 3T average template in MNI space are freely available to the research community. The image registration methodology developed in the course of this work may be generally applicable to other datasets.

Toward defining deep brain stimulation targets in MNI space: A subcortical atlas based on multimodal MRI, histology and structural connectivity

Three-dimensional atlases of subcortical brain structures are valuable tools to reference anatomy in neuroscience and neurology. For instance, they can be used to study the position and shape of the three most common deep brain stimulation (DBS) targets, the subthalamic nucleus (STN), internal part of the pallidum (GPi) and ventral intermediate nucleus of the thalamus (VIM) in spatial relationship to DBS electrodes. Here, we present a composite atlas based on manual segmentations of a multimodal high resolution brain template, histology and structural connectivity. In a first step, four key structures were defined on the template itself using a combination of multispectral image analysis and manual segmentation. Second, these structures were used as anchor points to coregister a detailed histological atlas into standard space. Results show that this approach significantly improved coregistration accuracy over previously published methods. Finally, a sub-segmentation of STN and GPi into functional zones was achieved based on structural connectivity. The result is a composite atlas that defines key nuclei on the template itself, fills the gaps between them using histology and further subdivides them using structural connectivity. We show that the atlas can be used to segment DBS targets in single subjects, yielding more accurate results compared to priorly published atlases. The atlas will be made publicly available and constitutes a resource to study DBS electrode localizations in combination with modern neuroimaging methods.

Ultra-High Field Template-Assisted Target Selection for Deep Brain Stimulation Surgery

BACKGROUND

Template and atlas guidance are fundamental aspects of stereotactic neurosurgery. The recent availability of ultra-high field (7 Tesla) magnetic resonance imaging has enabled in vivo visualization at the submillimeter scale. In this Doing More with Less article, we describe our experiences with integrating ultra-high field template data into the clinical workflow to assist with target selection in deep brain stimulation (DBS) surgical planning.

METHODS

The creation of a high-resolution 7T template is described, generated from group data acquired at our center. A computational workflow was developed for spatially aligning the 7T template with standard clinical data and furthermore, integrating the derived imaging volumes into the surgical planning workstation.

RESULTS

We demonstrate that our methodology can be effective for assisting with target selection in 2 cases: unilateral internal pallidum DBS for painful dystonia and bilateral subthalamic nucleus DBS for Parkinson's disease.

CONCLUSIONS

In this article, we have described a workflow for the integration of high-resolution in vivo ultra-high field templates into the surgical navigation system as a means to assist with DBS planning. The method does not require any additional cost or time to the patient. Future work will include prospectively evaluating different templates and their impact on target selection.

A dataset of multi-contrast population-averaged brain MRI atlases of a Parkinson׳s disease cohort

Parkinson׳s disease (PD) is a neurodegenerative disease that primarily affects the motor functions of the patients. Research and surgical treatment of PD (e.g., deep brain stimulation) often require human brain atlases for structural identification or as references for anatomical normalization. However, two pitfalls exist for many current atlases used for PD. First, most atlases do not represent the disease-specific anatomy as they are based on healthy young subjects. Second, subcortical structures, such as the subthalamic nucleus (STN) used in deep brain stimulation procedures, are often not well visualized. The dataset described in this Data in Brief is a population-averaged atlas that was made with 3 T MRI scans of 25 PD patients, and contains 5 image contrasts: T1w (FLASH & MPRAGE), T2*w, T1–T2* fusion, phase, and an R2* map. While the T1w, T2*w, and T1–T2* fusion templates provide excellent anatomical details for both cortical and sub-cortical structures, the phase and R2* map contain bio-chemical features. Probabilistic tissue maps of whiter matter, grey matter, and cerebrospinal fluid are provided for the atlas. We also manually segmented eight subcortical structures: caudate nucleus, putamen, globus pallidus internus and externus (GPi & GPe), thalamus, STN, substantia nigra (SN), and the red nucleus (RN). Lastly, a co-registered histology-derived digitized atlas containing 123 anatomical structures is included. The dataset is made freely available at the MNI data repository accessible through the link http://nist.mni.mcgill.ca/?p=1209.

Patch-based label fusion segmentation of brainstem structures with dual-contrast MRI for Parkinson's disease

PURPOSE

Parkinson's disease (PD) is a neurodegenerative disorder that impairs the motor functions. Both surgical treatment and study of PD require delineation of basal ganglia nuclei morphology. While many automatic volumetric segmentation methods have been proposed for the lentiform nucleus, few have attempted to identify the key brainstem substructures including the subthalamic nucleus (STN), substantia nigra (SN), and red nucleus (RN) due to their small size and poor contrast in conventional T1W MRI.

METHODS

A dual-contrast patch-based label fusion method was developed to segment the SN, STN, and RN using multivariate cross-correlation. Two different MRI contrasts (T2*w and phase) are produced from a multi-contrast multi-echo FLASH MRI sequence, enabling visualization of these nuclei. T1-T2* fusion MRI was used to resolve the issue of poor nuclei (i.e., the STN, SN, and RN) contrast on T1w MRI, and to mitigate susceptibility artifacts that may hinder accurate nonlinear registration on T2*w MRI. Unbiased group-wise registration was used for anatomical normalization between the atlas library and the target subject. The performance of the proposed method was compared with a state-of-the-art single-contrast label fusion technique.

RESULTS

The proposed method outperformed a state-of-the-art single-contrast patch-based method in segmenting the STN, RN and SN, and the results were better than those reported in previous literature.

CONCLUSION

Our dual-contrast patch-based label fusion method was superior to a single-contrast method for segmenting brainstem nuclei using a multi-contrast multi-echo FLASH MRI sequence. The method is promising for the treatment and research of Parkinson's disease. This method can be extended for multiple alternative image contrasts and other fields of applications.