Mapping dopaminergic projections in the human brain with resting-state fMRI.

The striatum receives dense dopaminergic projections, making it a key region of the dopaminergic system. Its dysfunction has been implicated in various conditions including Parkinson's disease (PD) and substance use disorder. However, the investigation of dopamine-specific functioning in humans is problematic as current MRI approaches are unable to differentiate between dopaminergic and other projections. Here, we demonstrate that 'connectopic mapping' - a novel approach for characterizing fine-grained, overlapping modes of functional connectivity - can be used to map dopaminergic projections in striatum. We applied connectopic mapping to resting-state functional MRI data of the Human Connectome Project (population cohort; N = 839) and selected the second-order striatal connectivity mode for further analyses. We first validated its specificity to dopaminergic projections by demonstrating a high spatial correlation (r = 0.884) with dopamine transporter availability - a marker of dopaminergic projections - derived from DaT SPECT scans of 209 healthy controls. Next, we obtained the subject-specific second-order modes from 20 controls and 39 PD patients scanned under placebo and under dopamine replacement therapy (L-DOPA), and show that our proposed dopaminergic marker tracks PD diagnosis, symptom severity, and sensitivity to L-DOPA. Finally, across 30 daily alcohol users and 38 daily smokers, we establish strong associations with self-reported alcohol and nicotine use. Our findings provide evidence that the second-order mode of functional connectivity in striatum maps onto dopaminergic projections, tracks inter-individual differences in PD symptom severity and L-DOPA sensitivity, and exhibits strong associations with levels of nicotine and alcohol use, thereby offering a new biomarker for dopamine-related (dys)function in the human brain.

Exploration of High- and Low-Frequency Options for Subperception Spinal Cord Stimulation Using Neural Dosing Parameter Relationships: The HALO Study.

OBJECTIVES: Subperception spinal cord stimulation (SCS) is described mostly utilizing waveforms that require high energy. However, the necessity of these waveforms for effective subperception has not been established. We aimed to explore whether effective subperception pain relief can be achieved using frequencies below 1 kHz. MATERIALS AND METHODS: Thirty chronic pain patients implanted with SCS were enrolled as part of a multicenter, real-world, consecutive, observational case series. An effective stimulation location was determined using a novel electric field shape designed to preferentially modulate dorsal horn elements. Subsequently, programs at lower frequencies (600, 400, 200, 100, 50, and 10 Hz) were provided with pulse-width and amplitude adjusted to optimize response. RESULTS: All tested frequencies (1 kHz down to 10 Hz) provided effective subperception relief, yielding a mean of 66-72% reduction in back, leg, and overall pain. It was found that to maintain analgesia, as frequency was decreased, the electrical or "neural" dose had to be adjusted according to parameter relationships described herein. With the reduction of frequency, we observed a net reduction of charge-per-second, which enabled energy savings of 74% (200 Hz) and 97% (10 Hz) relative to 1 kHz. Furthermore, pain reduction was sustained out to one year, with 85% of patients reporting a preference for frequencies of 400 Hz or below. CONCLUSIONS: We have derived an electric field configuration and, along with previous learnings in the kHz range, a set of neural dosing parameter relationships (10-10,000 Hz), which enable the expansion of effective subperception SCS to low frequency and achieve major energy savings.

Levodopa-Induced Dyskinesia in Parkinson Disease Specifically Associates With Dopaminergic Depletion in Sensorimotor-Related Functional Subregions of the Striatum.

PURPOSE: To determine whether the development of levodopa-induced dyskinesia (LID) in Parkinson disease (PD) specifically relates to dopaminergic depletion in sensorimotor-related subregions of the striatum. METHODS: Our primary study sample consisted of 185 locally recruited PD patients, of which 73 (40%) developed LID. Retrospective 123I-FP-CIT SPECT data were used to quantify the specific dopamine transporter (DAT) binding ratio within distinct functionally defined striatal subregions related to limbic, executive, and sensorimotor systems. Regional DAT levels were contrasted between patients who developed LID (PD + LID) and those who did not (PD-LID) using analysis of covariance models controlled for demographic and clinical features. For validation of the findings and assessment of the evolution of LID-associated DAT changes from an early disease stage, we also studied serial 123I-FP-CIT SPECT data from 343 de novo PD patients enrolled in the Parkinson Progression Marker's Initiative using mixed linear model analysis. RESULTS: Compared with PD-LID, DAT level reductions in PD + LID patients were most pronounced in the sensorimotor striatal subregion (F = 5.99, P = 0.016) and also significant in the executive-related subregion (F = 5.30, P = 0.023). In the Parkinson Progression Marker's Initiative cohort, DAT levels in PD + LID (n = 161, 47%) were only significantly reduced compared with PD-LID in the sensorimotor striatal subregion (t = -2.05, P = 0.041), and this difference was already present at baseline and remained largely constant over time. CONCLUSION: Measuring DAT depletion in functionally defined sensorimotor-related striatal regions of interest may provide a more sensitive tool to detect LID-associated dopaminergic changes at an early disease stage and could improve individual prognosis of this common clinical complication in PD.

Quantitative Intensity Harmonization of Dopamine Transporter SPECT Images Using Gamma Mixture Models.

PURPOSE: Differences in site, device, and/or settings may cause large variations in the intensity profile of dopamine transporter (DAT) single-photon emission computed tomography (SPECT) images. However, the current standard to evaluate these images, the striatal binding ratio (SBR), does not efficiently account for this heterogeneity and the assessment can be unequivalent across distinct acquisition pipelines. In this work, we present a voxel-based automated approach to intensity normalize such type of data that improves on cross-session interpretation. PROCEDURES: The normalization method consists of a reparametrization of the voxel values based on the cumulative density function (CDF) of a Gamma distribution modeling the specific region intensity. The harmonization ability was tested in 1342 SPECT images from the PPMI repository, acquired with 7 distinct gamma camera models and at 24 different sites. We compared the striatal quantification across distinct cameras for raw intensities, SBR values, and after applying the Gamma CDF (GDCF) harmonization. As a proof-of-concept, we evaluated the impact of GCDF normalization in a classification task between controls and Parkinson disease patients. RESULTS: Raw striatal intensities and SBR values presented significant differences across distinct camera models. We demonstrate that GCDF normalization efficiently alleviated these differences in striatal quantification and with values constrained to a fixed interval [0, 1]. Also, our method allowed a fully automated image assessment that provided maximal classification ability, given by an area under the curve (AUC) of AUC = 0.94 when used mean regional variables and AUC = 0.98 when used voxel-based variables. CONCLUSION: The GCDF normalization method is useful to standardize the intensity of DAT SPECT images in an automated fashion and enables the development of unbiased algorithms using multicenter datasets. This method may constitute a key pre-processing step in the analysis of this type of images.

A Bayesian spatial model for neuroimaging data based on biologically informed basis functions.

The dominant approach to neuroimaging data analysis employs the voxel as the unit of computation. While convenient, voxels lack biological meaning and their size is arbitrarily determined by the resolution of the image. Here, we propose a multivariate spatial model in which neuroimaging data are characterised as a linearly weighted combination of multiscale basis functions which map onto underlying brain nuclei or networks or nuclei. In this model, the elementary building blocks are derived to reflect the functional anatomy of the brain during the resting state. This model is estimated using a Bayesian framework which accurately quantifies uncertainty and automatically finds the most accurate and parsimonious combination of basis functions describing the data. We demonstrate the utility of this framework by predicting quantitative SPECT images of striatal dopamine function and we compare a variety of basis sets including generic isotropic functions, anatomical representations of the striatum derived from structural MRI, and two different soft functional parcellations of the striatum derived from resting-state fMRI (rfMRI). We found that a combination of ∼50 multiscale functional basis functions accurately represented the striatal dopamine activity, and that functional basis functions derived from an advanced parcellation technique known as Instantaneous Connectivity Parcellation (ICP) provided the most parsimonious models of dopamine function. Importantly, functional basis functions derived from resting fMRI were more accurate than both structural and generic basis sets in representing dopamine function in the striatum for a fixed model order. We demonstrate the translational validity of our framework by constructing classification models for discriminating parkinsonian disorders and their subtypes. Here, we show that ICP approach is the only basis set that performs well across all comparisons and performs better overall than the classical voxel-based approach. This spatial model constitutes an elegant alternative to voxel-based approaches in neuroimaging studies; not only are their atoms biologically informed, they are also adaptive to high resolutions, represent high dimensions efficiently, and capture long-range spatial dependencies, which are important and challenging objectives for neuroimaging data.

Genetic factors influencing frontostriatal dysfunction and the development of dementia in Parkinson's disease.

The dual syndrome hypothesis for cognitive impairment in Parkinson's disease (PD) establishes a dichotomy between a frontrostriatal dopamine-mediated syndrome, which leads to executive deficits, and a posterior cortical syndrome, which leads to dementia. Certain genes have been linked to these syndromes although the exact contribution is still controversial. The study's objective was to investigate the role of APOE, MAPT, COMT, SNCA and GBA genes in the dual syndromes. We genotyped APOE (rs429358 and rs7412), MAPT (rs9468), COMT (rs4680) and SNCA (rs356219) risk polymorphisms and sequenced GBA in a cohort of 298 PD patients. The degree of dopaminergic depletion was investigated with [123I]FP-CIT SPECTs and the presence of dementia was ascertained with a long-term review based on established criteria. The association between genetic and imaging parameters was studied with linear regression, and the relationship with dementia onset with Cox regression. We found that APOE2 allele (Pput = 0.002; Pcau = 0.01), the minor allele 'G' in SNCA polymorphism (Pput = 0.02; Pcau = 0.006) and GBA deleterious variants in (Pput = 0.01; Pcau = 0.001) had a detrimental effect on striatal [123I]FP-CIT uptake in PD. Conversely, Met/Met carriers in COMT polymorphism had increased caudate uptake (Pcau = 0.03). The development of dementia was influenced by APOE4 allele (HR = 1.90; P = 0.03) and GBA deleterious variants (HR = 2.44; P = 0.01). Finally, we observed no role of MAPT locus in any of the syndromes. As a conclusion, APOE2, SNCA, COMT and GBA influence frontostriatal dysfunction whereas APOE4 and GBA influence the development of dementia, suggesting a double-edged role of GBA. The dichotomy of the dual syndromes may be driven by a broad dichotomy in these genetic factors.

Lower levels of uric acid and striatal dopamine in non-tremor dominant Parkinson's disease subtype.

Parkinson's disease (PD) patients who present with tremor and maintain a predominance of tremor have a better prognosis. Similarly, PD patients with high levels of uric acid (UA), a natural neuroprotectant, have also a better disease course. Our aim was to investigate whether PD motor subtypes differ in their levels of UA, and if these differences correlate with the degree of dopamine transporter (DAT) availability. We included 75 PD patients from whom we collected information about their motor symptoms, DAT imaging and UA concentration levels. Based on the predominance of their motor symptoms, patients were classified into postural instability and gait disorder (PIGD, n = 36), intermediate (I, n = 22), and tremor-dominant (TD, n = 17) subtypes. The levels of UA and striatal DAT were compared across subtypes and the correlation between these two measures was also explored. We found that PIGD patients had lower levels of UA (3.7 vs 4.5 vs 5.3 mg/dL; P<0.001) and striatal DAT than patients with an intermediate or TD phenotype. Furthermore, UA levels significantly correlated with the levels of striatal DAT. We also observed that some PIGD (25%) and I (45%) patients had a predominance of tremor at disease onset. We speculate that UA might be involved in the maintenance of the less damaging TD phenotype and thus also in the conversion from TD to PIGD. Low levels of this natural antioxidant could lead to a major neuronal damage and therefore influence the conversion to a more severe motor phenotype.

GBA Variants Influence Motor and Non-Motor Features of Parkinson's Disease.

The presence of mutations in glucocerebrosidase (GBA) gene is a known factor increasing the risk of developing Parkinson's disease (PD). Mutations carriers have earlier disease onset and are more likely to develop neuropsychiatric symptoms than other sporadic PD cases. These symptoms have primarily been observed in Parkinson's patients carrying the most common pathogenic mutations L444P and N370S. However, recent findings suggest that other variants across the gene may have a different impact on the phenotype as well as on the disease progression. We aimed to explore the influence of variants across GBA gene on the clinical features and treatment related complications in PD. In this study, we screened the GBA gene in a cohort of 532 well-characterised PD patients and 542 controls from southern Spain. The potential pathogeniticy of the identified variants was assessed using in-silico analysis and subsequently classified as benign or deleterious. As a result, we observed a higher frequency of GBA variants in PD patients (12.2% vs. 7.9% in controls, p = 0.021), earlier mean age at disease onset in GBA variant carriers (50.6 vs. 56.6 years; p = 0.013), as well as more prevalent motor and non-motor symptoms in patients carrying deleterious variants. In addition, we found that dopaminergic motor complications are influenced by both benign and deleterious variants. Our results highlight the fact that the impact on the phenotype highly depends on the potential pathogenicity of the carried variants. Therefore, the course of motor and non-motor symptoms as well as treatment-related motor complications could be influenced by GBA variants.