Validation of an automatic tool for the rapid measurement of brain atrophy and white matter hyperintensity: QyScore®.

OBJECTIVES: QyScore® is an imaging analysis tool certified in Europe (CE marked) and the US (FDA cleared) for the automatic volumetry of grey and white matter (GM and WM respectively), hippocampus (HP), amygdala (AM), and white matter hyperintensity (WMH). Here we compare QyScore® performances with the consensus of expert neuroradiologists. METHODS: Dice similarity coefficient (DSC) and the relative volume difference (RVD) for GM, WM volumes were calculated on 50 3DT1 images. DSC and the F1 metrics were calculated for WMH on 130 3DT1 and FLAIR images. For each index, we identified thresholds of reliability based on current literature review results. We hypothesized that DSC/F1 scores obtained using QyScore® markers would be higher than the threshold. In contrast, RVD scores would be lower. Regression analysis and Bland-Altman plots were obtained to evaluate QyScore® performance in comparison to the consensus of three expert neuroradiologists. RESULTS: The lower bound of the DSC/F1 confidence intervals was higher than the threshold for the GM, WM, HP, AM, and WMH, and the higher bounds of the RVD confidence interval were below the threshold for the WM, GM, HP, and AM. QyScore®, compared with the consensus of three expert neuroradiologists, provides reliable performance for the automatic segmentation of the GM and WM volumes, and HP and AM volumes, as well as WMH volumes. CONCLUSIONS: QyScore® represents a reliable medical device in comparison with the consensus of expert neuroradiologists. Therefore, QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases. KEY POINTS: • QyScore® provides reliable automatic segmentation of brain structures in comparison with the consensus of three expert neuroradiologists. • QyScore® automatic segmentation could be performed on MRI images using different vendors and protocols of acquisition. In addition, the fast segmentation process saves time over manual and semi-automatic methods. • QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.

Dopaminergic denervation using [123I]-FPCIT and pain in Parkinson's disease: a correlation study.

In patients with Parkinson's disease (PD), abnormal activations of nociceptive brain areas and lowered pain thresholds were reported, probably reflecting a central modification of pain processing. The aim of this study was to investigate the possible correlation between the striatal and extrastriatal dopaminergic system and pain threshold in PD patients. We included 25 PD patients with various intensities of central pain (visual analog scale). Subjective pain threshold (thermotest) and a motor examination (UPDRS III) were performed. Patients underwent SPECT imaging with [123I]-FP-CIT. We analyzed the correlation between [123I]-FP-CIT binding and subjective pain threshold, using a simple linear regression model for striatal uptake and a voxel-based approach for extrastriatal uptake. The covariables were age, sex, duration of PD, and UPDRS motor score. A pain matrix mask was also used to identify clusters in relation with pain matrix. Striatal analysis revealed that [123I]-FP-CIT binding was negatively correlated with age (p = 0.02), duration of PD (p = 0.0002) and UPDRS motor score (p = 0.006), but no correlation with pain threshold was observed. The extrastriatal analysis showed a positive correlation between [123I]-FP-CIT binding and subjective heat pain threshold for the left posterior cingulate cortex (PCC) (p < 0.001) and negative correlations for the right secondary visual cortex (p < 0.001) and left insula (p < 0.001). When applying the pain matrix mask, correlations remained significant only in the left PCC and the left insula. We suggest that pain perception abnormalities in PD are not directly related to striatal dopaminergic dysfunction. Painful sensations may be related to extrastriatal monoaminergic systems.

A cortical-subcortical syntax pathway linking Broca's area and the striatum.

Combinatorial syntax has been shown to be underpinned by cortical key regions such as Broca's area and temporal cortices, and by subcortical structures such as the striatum. The cortical regions are connected via several cortico-to-cortical tracts impacting syntactic processing (e.g., the arcuate) but it remains unclear whether and how the striatum can be integrated into this cortex-centered syntax network. Here, we used a systematic stepwise approach to investigate the existence and syntactic function of an additional deep Broca-striatum pathway. We first asked 15 healthy controls and 12 patients with frontal/striatal lesions to perform three syntax tests. The results obtained were subjected to voxel-based lesion-symptom mapping (VLSM) to provide an anatomo-functional approximation of the pathway. The significant VLSM clusters were then overlapped with the probability maps of four cortico-cortical language tracts generated for 12 healthy participants (arcuate, extreme capsule fiber system, uncinate, aslant), including a probabilistic Broca-striatum tract. Finally, we carried out quantitative analyses of the relationship between the lesion load along the tracts and syntactic processing, by calculating tract-lesion overlap for each patient and analyzing the correlation with syntactic data. Our findings revealed a Broca-striatum tract linking BA45 with the left caudate head and overlapping with VLSM voxel clusters relating to complex syntax. The lesion load values for this tract were correlated with complex syntax scores, whereas no such correlation was observed for the other tracts. These results extend current syntax-network models, by adding a deep "Broca-caudate pathway," and are consistent with functional accounts of frontostriatal circuits.

Automatic segmentation of white matter hyperintensities: validation and comparison with state-of-the-art methods on both Multiple Sclerosis and elderly subjects.

Different types of white matter hyperintensities (WMH) can be observed through MRI in the brain and spinal cord, especially Multiple Sclerosis (MS) lesions for patients suffering from MS and age-related WMH for subjects with cognitive disorders and/or elderly people. To better diagnose and monitor the disease progression, the quantitative evaluation of WMH load has proven to be useful for clinical routine and trials. Since manual delineation for WMH segmentation is highly time-consuming and suffers from intra and inter observer variability, several methods have been proposed to automatically segment either MS lesions or age-related WMH, but none is validated on both WMH types. Here, we aim at proposing the White matter Hyperintensities Automatic Segmentation Algorithm adapted to 3D T2-FLAIR datasets (WHASA-3D), a fast and robust automatic segmentation tool designed to be implemented in clinical practice for the detection of both MS lesions and age-related WMH in the brain, using both 3D T1-weighted and T2-FLAIR images. In order to increase its robustness for MS lesions, WHASA-3D expands the original WHASA method, which relies on the coupling of non-linear diffusion framework and watershed parcellation, where regions considered as WMH are selected based on intensity and location characteristics, and finally refined with geodesic dilation. The previous validation was performed on 2D T2-FLAIR and subjects with cognitive disorders and elderly subjects. 60 subjects from a heterogeneous database of dementia patients, multiple sclerosis patients and elderly subjects with multiple MRI scanners and a wide range of lesion loads were used to evaluate WHASA and WHASA-3D through volume and spatial agreement in comparison with consensus reference segmentations. In addition, a direct comparison on the MS database with six available supervised and unsupervised state-of-the-art WMH segmentation methods (LST-LGA and LPA, Lesion-TOADS, lesionBrain, BIANCA and nicMSlesions) with default and optimised settings (when feasible) was conducted. WHASA-3D confirmed an improved performance with respect to WHASA, achieving a better spatial overlap (Dice) (0.67 vs 0.63), a reduced absolute volume error (AVE) (3.11 vs 6.2 mL) and an increased volume agreement (intraclass correlation coefficient, ICC) (0.96 vs 0.78). Compared to available state-of-the-art algorithms on the MS database, WHASA-3D outperformed both unsupervised and supervised methods when used with their default settings, showing the highest volume agreement (ICC = 0.95) as well as the highest average Dice (0.58). Optimising and/or retraining LST-LGA, BIANCA and nicMSlesions, using a subset of the MS database as training set, resulted in improved performances on the remaining testing set (average Dice: LST-LGA default/optimized = 0.41/0.51, BIANCA default/optimized = 0.22/0.39, nicMSlesions default/optimized = 0.17/0.63, WHASA-3D = 0.58). Evaluation and comparison results suggest that WHASA-3D is a reliable and easy-to-use method for the automated segmentation of white matter hyperintensities, for both MS lesions and age-related WMH. Further validation on larger datasets would be useful to confirm these first findings.

Optimization of brain PET imaging for a multicentre trial: the French CATI experience.

BACKGROUND: CATI is a French initiative launched in 2010 to handle the neuroimaging of a large cohort of subjects recruited for an Alzheimer's research program called MEMENTO. This paper presents our test protocol and results obtained for the 22 PET centres (overall 13 different scanners) involved in the MEMENTO cohort. We determined acquisition parameters using phantom experiments prior to patient studies, with the aim of optimizing PET quantitative values to the highest possible per site, while reducing, if possible, variability across centres. METHODS: Jaszczak's and 3D-Hoffman's phantom measurements were used to assess image spatial resolution (ISR), recovery coefficients (RC) in hot and cold spheres, and signal-to-noise ratio (SNR). For each centre, the optimal reconstruction parameters were chosen as those maximizing ISR and RC without a noticeable decrease in SNR. Point-spread-function (PSF) modelling reconstructions were discarded. The three figures of merit extracted from the images reconstructed with optimized parameters and routine schemes were compared, as were volumes of interest ratios extracted from Hoffman acquisitions. The net effect of the 3D-OSEM reconstruction parameter optimization was investigated on a subset of 18 scanners without PSF modelling reconstruction. RESULTS: Compared to the routine parameters of the 22 PET centres, average RC in the two smallest hot and cold spheres and average ISR remained stable or were improved with the optimized reconstruction, at the expense of slight SNR degradation, while the dispersion of values was reduced. For the subset of scanners without PSF modelling, the mean RC of the smallest hot sphere obtained with the optimized reconstruction was significantly higher than with routine reconstruction. The putamen and caudate-to-white matter ratios measured on 3D-Hoffman acquisitions of all centres were also significantly improved by the optimization, while the variance was reduced. CONCLUSIONS: This study provides guidelines for optimizing quantitative results for multicentric PET neuroimaging trials.

Dopaminergic denervation severity depends on COMT Val158Met polymorphism in Parkinson's disease.

BACKGROUND: Catecholamine-O-methyl-tranferase (COMT) initiates dopamine degradation. Its activity is mainly determined by a single nucleotide polymorphism in the COMT gene (Val158Met, rs4680) separating high (Val/Val, COMT(HH)), intermediate (Val/Met, COMT(HL)) and low metabolizers (Met/Met, COMT(LL)). We investigated dopaminergic denervation in the striatum in PD patients according to COMT rs4680 genotype. METHODS: Patients with idiopathic PD were assessed for motor severity (UPDRS-III rating scale in OFF-state), dopaminergic denervation using [123I]-FP-CIT SPECT imaging, and genotyped for the COMT rs4680 enzyme. [123I]-FP-CIT binding potential (BP) for each voxel was defined by the ratio of tracer-binding in the region of interest (striatum, caudate nucleus and putamen) to that in a region of non-specific activity. Genotyping was performed using TaqMan(®) SNP genotyping assay. We used a regression model to evaluate the effect of COMT genotype on the BP in the striatum and its sub-regions. RESULTS: Genotype distribution was: 11 (27.5%) COMT(HH), 26 (65%) COMT(HL) and 3 (7.5%) COMT(LL). There were no significant differences in disease severity, treatments, or motor scores between genotypes. When adjusted to clinical severity, gender and age, low and intermediate metabolizers showed significantly higher rates of striatal denervation (COMT(HL+LL) BP = 1.32 ± 0.04) than high metabolizers (COMT(HH), BP = 1.6 ± 0.08; F(1.34) = 9.0, p = 0.005). Striatal sub-regions showed similar results. BP and UPDRS-III motor scores (r = 0.44, p = 0.04) (p < 0.001) were highly correlated. There was a gender effect, but no gender-genotype interaction. CONCLUSIONS: Striatal denervation differs according to COMT-Val158Met polymorphism. COMT activity may play a role as a compensatory mechanism in PD motor symptoms.