Plasma and cerebrospinal fluid pharmacokinetics of vodobatinib, a neuroprotective c-Abl tyrosine kinase inhibitor for the treatment of Parkinson's disease.

BACKGROUND: Preclinical evidence suggests that c-Abl is critical in the pathogenesis of Parkinson's Disease (PD). Vodobatinib (K0706) is a potent, specific Abl kinase inhibitor currently being developed for the treatment of PD. In previously reported studies, nilotinib, a multikinase c-Abl inhibitor, did not show clinical activity as evidenced by no improvement of symptoms or the rate of decline after one to six months of treatment at the maximum permissible dose, presumably because of insufficient CNS penetration. Here we report clinical PK and safety data for vodobatinib. OBJECTIVES: To determine safety, plasma PK, and CSF penetration of vodobatinib in healthy volunteers and PD subjects following oral administration, and compare CSF levels to in vitro concentrations required for c-Abl inhibition relative to data reported for nilotinib. METHODS: Inhibition of c-Abl kinase activity and c-Abl binding affinity were first assessed in vitro. Healthy human volunteers and PD patients received various oral doses of vodobatinib once-daily for seven and fourteen days respectively, to assess safety, and plasma and CSF PK. RESULTS: In in vitro assays, vodobatinib was more potent (kinase IC50 = 0.9 nM) than nilotinib (kinase IC50 = 15-45 nM). Administration of vodobatinib 48, 192 and 384 mg to healthy subjects for 7 days yielded mean Cmax, CSF values of 1.8, 11.6, and 12.2 nM respectively, with the two highest doses exceeding the IC50 over the entire dosing interval. Cavg, CSF values were 6-8 times greater than the IC50. Comparable CSF levels were observed in PD patients. All doses were well tolerated in both cohorts. CONCLUSION: Based on achieved CSF concentrations, the potential for c-Abl inhibition in the brain is substantially higher with vodobatinib than with nilotinib. The CSF PK profile of vodobatinib is suitable for determining if c-Abl inhibition will be neuroprotective in PD patients.

Analysis of Brain Structural Connectivity Networks and White Matter Integrity in Patients With Mild Cognitive Impairment.

White matter integrity and structural connectivity may be altered in mild cognitive impairment (MCI), and these changes may closely reflect decline in specific cognitive domains. Multi-shell diffusion data in healthy control (HC, n = 31) and mild cognitive impairment (MCI, n = 19) cohorts were downloaded from the ADNI3 database. The data were analyzed using an advanced approach to assess both white matter microstructural integrity and structural connectivity. Compared with HC, lower intracellular compartment (IC) and higher isotropic (ISO) values were found in MCI. Additionally, significant correlations were found between IC and Montreal Cognitive Assessment (MoCA) scores in the MCI cohort. Network analysis detected structural connectivity differences between the two groups, with lower connectivity in MCI. Additionally, significant differences between HC and MCI were observed for global network efficiency. Our results demonstrate the potential of advanced diffusion MRI biomarkers for understanding brain changes in MCI.

Sex Differences in Alzheimer's Disease Revealed by Free-Water Diffusion Tensor Imaging and Voxel-Based Morphometry.

BACKGROUND: Imaging biomarkers are increasingly used in Alzheimer's disease (AD), and the identification of sex differences using neuroimaging may provide insight into disease heterogeneity, progression, and therapeutic targets. OBJECTIVE: The purpose of this study was to investigate differences in grey matter (GM) volume and white matter (WM) microstructural disorganization between males and females with AD using voxel-based morphometry (VBM) and free-water-corrected diffusion tensor imaging (FW-DTI). METHODS: Data were downloaded from the OASIS-3 database, including 158 healthy control (HC; 86 females) and 46 mild AD subjects (24 females). VBM and FW-DTI metrics (fractional anisotropy (FA), axial and radial diffusivities (AxD and RD, respectively), and FW index) were compared using effect size for the main effects of group, sex, and their interaction. RESULTS: Significant group and sex differences were observed, with no significant interaction. Post-hoc comparisons showed that AD is associated with reduced GM volume, reduced FW-FA, and higher FW-RD/FW-index, consistent with neurodegeneration. Females in both groups exhibited higher GM volume than males, while FW-DTI metrics showed sex differences only in the AD group. Lower FW, lower FW-FA and higher FW-RD were observed in females relative to males in the AD group. CONCLUSION: The combination of VBM and DTI may reveal complementary sex-specific changes in GM and WM associated with AD and aging. Sex differences in GM volume were observed for both groups, while FW-DTI metrics only showed significant sex differences in the AD group, suggesting that WM tract disorganization may play a differential role in AD pathophysiology between females and males.

Energy-Period Profiles of Brain Networks in Group fMRI Resting-State Data: A Comparison of Empirical Mode Decomposition With the Short-Time Fourier Transform and the Discrete Wavelet Transform.

Traditionally, functional networks in resting-state data were investigated with linear Fourier and wavelet-related methods to characterize their frequency content by relying on pre-specified frequency bands. In this study, Empirical Mode Decomposition (EMD), an adaptive time-frequency method, is used to investigate the naturally occurring frequency bands of resting-state data obtained by Group Independent Component Analysis. Specifically, energy-period profiles of Intrinsic Mode Functions (IMFs) obtained by EMD are created and compared for different resting-state networks. These profiles have a characteristic distribution for many resting-state networks and are related to the frequency content of each network. A comparison with the linear Short-Time Fourier Transform (STFT) and the Maximal Overlap Discrete Wavelet Transform (MODWT) shows that EMD provides a more frequency-adaptive representation of different types of resting-state networks. Clustering of resting-state networks based on the energy-period profiles leads to clusters of resting-state networks that have a monotone relationship with frequency and energy. This relationship is strongest with EMD, intermediate with MODWT, and weakest with STFT. The identification of these relationships suggests that EMD has significant advantages in characterizing brain networks compared to STFT and MODWT. In a clinical application to early Parkinson's disease (PD) vs. normal controls (NC), energy and period content were studied for several common resting-state networks. Compared to STFT and MODWT, EMD showed the largest differences in energy and period between PD and NC subjects. Using a support vector machine, EMD achieved the highest prediction accuracy in classifying NC and PD subjects among STFT, MODWT, and EMD.

Amantadine ER (Gocovri®) Significantly Increases ON Time Without Any Dyskinesia: Pooled Analyses From Pivotal Trials in Parkinson's Disease.

Background: Clinical trials for antiparkinsonian drugs aimed at managing motor complications typically use patient diaries to divide levodopa-induced dyskinesias (LID) into "troublesome" and "non-troublesome" categories. Yet, given the choice, most patients would prefer to live without experiencing any dyskinesia. However, the concept of evaluating time spent ON without any dyskinesia as an outcome has never been tested. We conducted analyses of pooled Gocovri pivotal trial data in order to evaluate the extent to which Gocovri increased the time PD patients spent ON without dyskinesia (troublesome or non-troublesome), beyond its already identified improvement in reducing troublesome dyskinesia. Methods: Patients enrolled in phase 3 trials (EASE LID [NCT02136914] or EASE LID 3 [NCT02274766]) recorded time spent in the following PD diary states at baseline and Week 12 (endpoint): asleep, OFF, ON with troublesome dyskinesia, ON with non-troublesome dyskinesia, and ON without dyskinesia. Mixed model repeated measures analyses with estimated Cohen D effect sizes were performed on the modified intent to treat population to evaluate changes in time spent in these states. Results: Patients randomized to receive Gocovri showed an increase in ON time without dyskinesia and corresponding decreases in ON time with dyskinesia and OFF time vs. placebo. Treatment effects were statistically significant for Gocovri vs. placebo starting at Week 2 and were sustained until Week 12. On MMRM analysis at Week 12, patients in the Gocovri group showed an adjusted mean ± SE increase over placebo of 2.9 ± 0.6 h in ON time without dyskinesia (Cohen D effect size 0.79) and an adjusted mean ± SE decrease of -1.9 ± 0.6 h in ON time with dyskinesia (troublesome + non-troublesome) (Cohen D effect size 0.49), that included a -1.5 ± 0.4 h placebo-adjusted reduction in ON time with troublesome dyskinesia and a -0.6 ± 0.4 h reduction in ON time with non-troublesome dyskinesia. OFF time was reduced by -1.0 ± 0.3 h compared to placebo. Conclusions: Gocovri treatment more than doubled the daily time patients spent ON without dyskinesia. These results suggest that the Gocovri treatment effect was driven by a reduction in overall motor complications including ON time with both troublesome and non-troublesome dyskinesia as well as time spent OFF.

Free-water diffusion tensor imaging improves the accuracy and sensitivity of white matter analysis in Alzheimer's disease.

Magnetic resonance imaging (MRI) based diffusion tensor imaging (DTI) can assess white matter (WM) integrity through several metrics, such as fractional anisotropy (FA), axial/radial diffusivities (AxD/RD), and mode of anisotropy (MA). Standard DTI is susceptible to the effects of extracellular free water (FW), which can be removed using an advanced free-water DTI (FW-DTI) model. The purpose of this study was to compare standard and FW-DTI metrics in the context of Alzheimer's disease (AD). Data were obtained from the Open Access Series of Imaging Studies (OASIS-3) database and included both healthy controls (HC) and mild-to-moderate AD. With both standard and FW-DTI, decreased FA was found in AD, mainly in the corpus callosum and fornix, consistent with neurodegenerative mechanisms. Widespread higher AxD and RD were observed with standard DTI; however, the FW index, indicative of AD-associated neurodegeneration, was significantly elevated in these regions in AD, highlighting the potential impact of free water contributions on standard DTI in neurodegenerative pathologies. Using FW-DTI, improved consistency was observed in FA, AxD, and RD, and the complementary FW index was higher in the AD group as expected. With both standard and FW-DTI, higher values of MA coupled with higher values of FA in AD were found in the anterior thalamic radiation and cortico-spinal tract, most likely arising from a loss of crossing fibers. In conclusion, FW-DTI better reflects the underlying pathology of AD and improves the accuracy of DTI metrics related to WM integrity in Alzheimer's disease.

Systematic Assessment of the Impact of DTI Methodology on Fractional Anisotropy Measures in Alzheimer's Disease.

White matter microstructural changes in Alzheimer's disease (AD) are often assessed using fractional anisotropy (FA) obtained from diffusion tensor imaging (DTI). FA depends on the acquisition and analysis methods, including the fitting algorithm. In this study, we compared FA maps from different acquisitions and fitting algorithms in AD, mild cognitive impairment (MCI), and healthy controls (HCs) using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Three acquisitions from two vendors were compared (Siemens 30, GE 48, and Siemens 54 directions). DTI data were fit using nine fitting algorithms (four linear least squares (LLS), two weighted LLS (WLLS), and three non-linear LLS (NLLS) from four software tools (FSL, DSI-Studio, CAMINO, and AFNI). Different cluster volumes and effect-sizes were observed across acquisitions and fits, but higher consistency was observed as the number of diffusion directions increased. Significant differences were observed between HC and AD groups for all acquisitions, while significant differences between HC and MCI groups were only observed for GE48 and SI54. Using the intraclass correlation coefficient, AFNI-LLS and CAMINO-RESTORE were the least consistent with the other algorithms. By combining data across all three acquisitions and nine fits, differences between AD and HC/MCI groups were observed in the fornix and corpus callosum, indicating FA differences in these regions may be robust DTI-based biomarkers. This study demonstrates that comparisons of FA across aging populations could be confounded by variability in acquisitions and fit methodologies and that identifying the most robust DTI methodology is critical to provide more reliable DTI-based neuroimaging biomarkers for assessing microstructural changes in AD.

Central airway collapse is related to obesity independent of asthma phenotype.

BACKGROUND AND OBJECTIVE: Late-onset non-allergic asthma in obesity is characterized by an abnormally compliant, collapsible lung periphery; it is not known whether this abnormality exists in proximal airways. We sought to compare collapsibility of central airways between lean and obese individuals with and without asthma. METHODS: A cross-sectional study comparing luminal area and shape (circularity) of the trachea, left mainstem bronchus, right bronchus intermedius and right inferior lobar bronchus at RV and TLC by CT was conducted. RESULTS: In 11 lean controls (BMI: 22.4 (21.5, 23.8) kg/m2 ), 10 lean individuals with asthma (23.6 (22.0, 24.8) kg/m2 ), 10 obese controls (45.5 (40.3, 48.5) kg/m2 ) and 21 obese individuals with asthma (39.2 (35.8, 42.9) kg/m2 ), lumen area and circularity increased significantly with an increase in lung volume from RV to TLC for all four airways (P < 0.05 for all). Changes in area and circularity with lung volume were similar in obese individuals with and without asthma, and both obese groups had severe airway collapse at RV. In multivariate analysis, change in lumen area was related to BMI and change in circularity to waist circumference, but neither was related to asthma diagnosis. CONCLUSION: Excessive collapse of the central airways is related to obesity, and occurs in both obese controls and obese asthma. Increased airway collapse could contribute to ventilation abnormalities in obese individuals particularly at lower lung volumes, and complicate asthma in obese individuals.

Fluid and Tissue Biomarkers of Lewy Body Dementia: Report of an LBDA Symposium.

The Lewy Body Dementia Association (LBDA) held a virtual event, the LBDA Biofluid/Tissue Biomarker Symposium, on January 25, 2021, to present advances in biomarkers for Lewy body dementia (LBD), which includes dementia with Lewy bodies (DLBs) and Parkinson's disease dementia (PDD). The meeting featured eight internationally known scientists from Europe and the United States and attracted over 200 scientists and physicians from academic centers, the National Institutes of Health, and the pharmaceutical industry. Methods for confirming and quantifying the presence of Lewy body and Alzheimer's pathology and novel biomarkers were discussed.

Preliminary Assessment of Intravoxel Incoherent Motion Diffusion-Weighted MRI (IVIM-DWI) Metrics in Alzheimer's Disease.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disease that affects aging populations. Current MRI techniques are often limited in their sensitivity to underlying neuropathological changes. PURPOSE: To characterize differences in voxel-based morphometry (VBM), apparent diffusion coefficient (ADC), and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) metrics in aging populations. Additionally, to investigate the connection between cognitive assessments and neuroimaging metrics. STUDY TYPE: Prospective/cross-sectional. POPULATION: In all, 49 subjects, including 13 with AD dementia, 12 with mild cognitive impairment (MCI), and 24 healthy controls (HC). FIELD STRENGTH/SEQUENCE: 3T/magnetization-prepared rapid acquisition gradient echo (MP-RAGE) and IVIM-DWI ASSESSMENT: All participants completed a cognitive screening battery prior to MRI. IVIM-DWI maps (pure diffusion coefficient [D], pseudodiffusion coefficient [D*], and perfusion fraction [f]) were generated from a biexponential fit of diffusion MRI data. VBM was performed on the standard T1 -weighted MP-RAGE structural images. Group-wise templates were used to compare across groups. STATISTICAL TESTS: Analysis of covariance (ANCOVA) with gender and age as covariates (familywise error [FWE] corrected, post-hoc comparisons using Bonferroni correction) for group comparisons. Partial-η2 and Hedges' g were used for effect-size analysis. Spearman's correlations (false discovery rate [FDR]-corrected) for the relationship between cognitive scores and imaging. RESULTS: Clusters of significant group-wise differences were found mainly in the temporal lobe, hippocampus, and amygdala using all VBM and IVIM methods (P < 0.05 FWE). While VBM showed significant changes between MCI and AD groups and between HC and AD groups, no significant clusters were observed between HC and MCI using VBM. ADC and IVIM-D demonstrated significant changes, at P < 0.05 FWE, between HC and MCI, notably in the amygdala and hippocampus. Several voxel-based correlations were observed between neuroimaging metrics and cognitive tests within the cognitively impaired groups (P < 0.05 FDR). DATA CONCLUSION: These findings suggest that IVIM-DWI metrics may be earlier biomarkers for AD-related changes than VBM. The use of these techniques may provide novel insight into subvoxel neurodegenerative processes. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1811-1826.

Assessing White Matter Pathology in Early-Stage Parkinson Disease Using Diffusion MRI: A Systematic Review.

Structural brain white matter (WM) changes such as axonal caliber, density, myelination, and orientation, along with WM-dependent structural connectivity, may be impacted early in Parkinson disease (PD). Diffusion magnetic resonance imaging (dMRI) has been used extensively to understand such pathological WM changes, and the focus of this systematic review is to understand both the methods utilized and their corresponding results in the context of early-stage PD. Diffusion tensor imaging (DTI) is the most commonly utilized method to probe WM pathological changes. Previous studies have suggested that DTI metrics are sensitive in capturing early disease-associated WM changes in preclinical symptomatic regions such as olfactory regions and the substantia nigra, which is considered to be a hallmark of PD pathology and progression. Postprocessing analytic approaches include region of interest-based analysis, voxel-based analysis, skeletonized approaches, and connectome analysis, each with unique advantages and challenges. While DTI has been used extensively to study WM disorganization in early-stage PD, it has several limitations, including an inability to resolve multiple fiber orientations within each voxel and sensitivity to partial volume effects. Given the subtle changes associated with early-stage PD, these limitations result in inaccuracies that severely impact the reliability of DTI-based metrics as potential biomarkers. To overcome these limitations, advanced dMRI acquisition and analysis methods have been employed, including diffusion kurtosis imaging and q-space diffeomorphic reconstruction. The combination of improved acquisition and analysis in DTI may yield novel and accurate information related to WM-associated changes in early-stage PD. In the current article, we present a systematic and critical review of dMRI studies in early-stage PD, with a focus on recent advances in DTI methodology. Yielding novel metrics, these advanced methods have been shown to detect diffuse WM changes in early-stage PD. These findings support the notion of early axonal damage in PD and suggest that WM pathology may go unrecognized until symptoms appear. Finally, the advantages and disadvantages of different dMRI techniques, analysis methods, and software employed are discussed in the context of PD-related pathology.

Unique white matter structural connectivity in early-stage drug-naive Parkinson disease.

OBJECTIVE: To investigate the topographic arrangement and strength of whole-brain white matter (WM) structural connectivity in patients with early-stage drug-naive Parkinson disease (PD). METHODS: We employed a model-free data-driven approach for computing whole-brain WM topologic arrangement and connectivity strength between brain regions by utilizing diffusion MRI of 70 participants with early-stage drug-naive PD and 41 healthy controls. Subsequently, we generated a novel group-specific WM anatomical network by minimizing variance in anatomical connectivity of each group. Global WM connectivity strength and network measures were computed on this group-specific WM anatomical network and were compared between the groups. We tested correlations of these network measures with clinical measures in PD to assess their pathophysiologic relevance. RESULTS: PD-relevant cortical and subcortical regions were identified in the novel PD-specific WM anatomical network. Impaired modular organization accompanied by a correlation of network measures with multiple clinical variables in early PD were revealed. Furthermore, disease duration was negatively correlated with global connectivity strength of the PD-specific WM anatomical network. CONCLUSION: By minimizing variance in anatomical connectivity, this study found the presence of a novel WM structural connectome in early PD that correlated with clinical symptoms, despite the lack of a priori analytic assumptions. This included the novel finding of increased structural connectivity between known PD-relevant brain regions. The current study provides a framework for further investigation of WM structural changes underlying the clinical and pathologic heterogeneity of PD.

Prevalence of Dyskinesia and OFF by 30-Minute Intervals Through the Day and Assessment of Daily Episodes of Dyskinesia and OFF: Novel Analyses of Diary Data from Gocovri Pivotal Trials.

BACKGROUND: Parkinson's disease (PD) patients using levodopa commonly develop dyskinesia and OFF episodes that reduce quality of life. OBJECTIVE: Evaluate prevalence of troublesome dyskinesia and OFF through the day, assessed by 30-minute intervals, as well as the mean number and duration of troublesome dyskinesia and OFF episodes, transitions between PD states, and effects of Gocovri® (amantadine) extended release capsules on these episodes. METHODS: Evaluate diary data from pooled Gocovri phase 3, placebo-controlled trials-analyzed for 17 hours following wake-up-at baseline and week 12. RESULTS: Diaries were evaluable for 162 patients. At baseline, 67% of patients woke up OFF, with prevalence decreasing to 13% at 2 hours and then remaining relatively steady at ∼12% (range, 6-17%) across half-hour intervals thereafter. Troublesome dyskinesia prevalence rose steadily from 5% to 24% over the first 2 hours, then fluctuated between 20% and 44% through the rest of the waking day. At baseline, patients experienced a mean of 3.0 daily episodes of troublesome dyskinesia (average duration 2.0 hours each), and 2.2 daily episodes of OFF (average duration 1.1 hour each). At week 12, Gocovri-treated patients showed greater reductions than placebo in troublesome dyskinesia and OFF episodes per day (treatment difference: -1.0 episodes and -0.4 episodes, respectively) and average episode duration (treatment difference: -0.6 hours and -0.3 hours, respectively). Mean duration of individual episodes of ON without troublesome dyskinesia (Good ON) increased by 5.0 hours for Gocovri, compared with 2.0 hours for placebo. Patients taking Gocovri experienced 2.2 fewer transitions between states than patients taking placebo. CONCLUSIONS: Troublesome dyskinesia and OFF occurred in the morning and throughout the waking day. Gocovri-treated patients experienced fewer, shorter episodes of both troublesome dyskinesia and OFF, thereby increasing the duration of continuous Good ON episodes and reducing the frequency of transitions between motor states.

Influence of analytic techniques on comparing DTI-derived measurements in early stage Parkinson's disease.

Diffusion tensor imaging (DTI) studies in early Parkinson's disease (PD) to understand pathologic changes in white matter (WM) organization are variable in their findings. Evaluation of different analytic techniques frequently employed to understand the DTI-derived change in WM organization in a multisite, well-characterized, early stage PD cohort should aid the identification of the most robust analytic techniques to be used to investigate WM pathology in this disease, an important unmet need in the field. Thus, region of interest (ROI)-based analysis, voxel-based morphometry (VBM) analysis with varying spatial smoothing, and the two most widely used skeletonwise approaches (tract-based spatial statistics, TBSS, and tensor-based registration, DTI-TK) were evaluated in a DTI dataset of early PD and Healthy Controls (HC) from the Parkinson's Progression Markers Initiative (PPMI) cohort. Statistical tests on the DTI-derived metrics were conducted using a nonparametric approach from this cohort of early PD, after rigorously controlling for motion and signal artifacts during DTI scan which are frequent confounds in this disease population. Both TBSS and DTI-TK revealed a significantly negative correlation of fractional anisotropy (FA) with disease duration. However, only DTI-TK revealed radial diffusivity (RD) to be driving this FA correlation with disease duration. HC had a significantly positive correlation of MD with cumulative DaT score in the right middle-frontal cortex after a minimum smoothing level (at least 13mm) was attained. The present study found that scalar DTI-derived measures such as FA, MD, and RD should be used as imaging biomarkers with caution in early PD as the conclusions derived from them are heavily dependent on the choice of the analysis used. This study further demonstrated DTI-TK may be used to understand changes in DTI-derived measures with disease progression as it was found to be more accurate than TBSS. In addition, no singular region was identified that could explain both disease duration and severity in early PD. The results of this study should help standardize the utilization of DTI-derived measures in PD in an effort to improve comparability across studies and time, and to minimize variability in reported results due to variation in techniques.

Altered functional network topology correlates with clinical measures in very early-stage, drug-naïve Parkinson's disease.

INTRODUCTION: The aim of the study was to identify abnormalities of whole-brain network functional organization and their relation to clinical measures in a well-characterized, multi-site cohort of very early-stage, drug-naïve Parkinson's Disease (PD) patients. METHODS: Functional-MRI data for 16 healthy controls and 20 very early-stage, drug-naïve patients with PD were obtained from the Parkinson's Progression Markers Initiative database after controlling for strict inclusion/exclusion imaging criteria. Connectivity between regions of interest was estimated using Pearson's correlation between averaged time-series, and subsequently a connectivity matrix was obtained for each subject. These connectivity matrices were then used in an unbiased, whole-brain graph theoretical approach to investigate the functional connectome and its correlation with disease severity in very early PD. RESULTS: The current study revealed altered network topology which correlated with multiple clinical measures in very early drug-naïve PD. Decreased functional segregation and integration (both globally and locally) were evident in PD. Importantly, our results demonstrated that most of the cortical regions hypothesized to be involved early in PD manifested decreased graph theoretical measures, despite utilizing a whole-brain analytic approach that is free from prior assumptions regarding cortical region involvement. CONCLUSION: Graph theoretical investigation of very early drug-naïve PD revealed disrupted topological organization. These findings are evident in a stringently homogeneous group of very early-stage, medication-naive, and non-tremor dominant PD patients by using a whole-brain unbiased approach. These results provide an important unbiased and rigorously controlled baseline for understanding further studies of PD functional connectivity investigating response to treatment, symptom development, and disease progression.

Incorporating spatial constraint in co-activation pattern analysis to explore the dynamics of resting-state networks: An application to Parkinson's disease.

The dynamics of the brain's intrinsic networks have been recently studied using co-activation pattern (CAP) analysis. The CAP method relies on few model assumptions and CAP-based measurements provide quantitative information of network temporal dynamics. One limitation of existing CAP-related methods is that the computed CAPs share considerable spatial overlap that may or may not be functionally distinct relative to specific network dynamics. To more accurately describe network dynamics with spatially distinct CAPs, and to compare network dynamics between different populations, a novel data-driven CAP group analysis method is proposed in this study. In the proposed method, a dominant-CAP (d-CAP) set is synthesized across CAPs from multiple clustering runs for each group with the constraint of low spatial similarities among d-CAPs. Alternating d-CAPs with less overlapping spatial patterns can better capture overall network dynamics. The number of d-CAPs, the temporal fraction and spatial consistency of each d-CAP, and the subject-specific switching probability among all d-CAPs are then calculated for each group and used to compare network dynamics between groups. The spatial dissimilarities among d-CAPs computed with the proposed method were first demonstrated using simulated data. High consistency between simulated ground-truth and computed d-CAPs was achieved, and detailed comparisons between the proposed method and existing CAP-based methods were conducted using simulated data. In an effort to physiologically validate the proposed technique and investigate network dynamics in a relevant brain network disorder, the proposed method was then applied to data from the Parkinson's Progression Markers Initiative (PPMI) database to compare the network dynamics in Parkinson's disease (PD) and normal control (NC) groups. Fewer d-CAPs, skewed distribution of temporal fractions of d-CAPs, and reduced switching probabilities among final d-CAPs were found in most networks in the PD group, as compared to the NC group. Furthermore, an overall negative association between switching probability among d-CAPs and disease severity was observed in most networks in the PD group as well. These results expand upon previous findings from in vivo electrophysiological recording studies in PD. Importantly, this novel analysis also demonstrates that changes in network dynamics can be measured using resting-state fMRI data from subjects with early stage PD.

Recommendations of the Global Multiple System Atrophy Research Roadmap Meeting.

Multiple system atrophy (MSA) is a rare neurodegenerative disorder with substantial knowledge gaps despite recent gains in basic and clinical research. In order to make further advances, concerted international collaboration is vital. In 2014, an international meeting involving leaders in the field and MSA advocacy groups was convened in Las Vegas, Nevada, to identify critical research areas where consensus and progress was needed to improve understanding, diagnosis, and treatment of the disease. Eight topic areas were defined: pathogenesis, preclinical modeling, target identification, endophenotyping, clinical measures, imaging biomarkers, nonimaging biomarkers, treatments/trial designs, and patient advocacy. For each topic area, an expert served as a working group chair and each working group developed priority-ranked research recommendations with associated timelines and pathways to reach the intended goals. In this report, each groups' recommendations are provided.