Innate immune cells and myelin profile in multiple sclerosis: a multi-tracer PET/MR study.

PURPOSE: Neuropathological studies have demonstrated distinct profiles of microglia activation and myelin injury among different multiple sclerosis (MS) phenotypes and disability stages. PET imaging using specific tracers may uncover the in vivo molecular pathology and broaden the understanding of the disease heterogeneity. METHODS: We used the 18-kDa translocator protein (TSPO) tracer (R)-[11C]PK11195 and [11C]PIB PET images acquired in a hybrid PET/MR 3 T system to characterize, respectively, the profile of innate immune cells and myelin content in 47 patients with MS compared to 18 healthy controls (HC). For the volume of interest (VOI)-based analysis of the dynamic data, (R)-[11C]PK11195 distribution volume (VT) was determined for each subject using a metabolite-corrected arterial plasma input function while [11C]PIB distribution volume ratio (DVR) was estimated using a reference region extracted by a supervised clustering algorithm. A voxel-based analysis was also performed using Statistical Parametric Mapping. Functional disability was evaluated by the Expanded Disability Status Scale (EDSS), Multiple Sclerosis Functional Composite (MSFC), and Symbol Digit Modality Test (SDMT). RESULTS: In the VOI-based analysis, [11C]PIB DVR differed between patients and HC in the corpus callosum (P = 0.019) while no differences in (R)-[11C]PK11195 VT were observed in patients relative to HC. Furthermore, no correlations or associations were observed between both tracers within the VOI analyzed. In the voxel-based analysis, high (R)-[11C]PK11195 uptake was observed diffusively in the white matter (WM) when comparing the progressive phenotype and HC, and lower [11C]PIB uptake was observed in certain WM regions when comparing the relapsing-remitting phenotype and HC. None of the tracers were able to differentiate phenotypes at voxel or VOI level in our cohort. Linear regression models adjusted for age, sex, and phenotype demonstrated that higher EDSS was associated with an increased (R)-[11C]PK11195 VT and lower [11C]PIB DVR in corpus callosum (P = 0.001; P = 0.023), caudate (P = 0.015; P = 0.008), and total T2 lesion (P = 0.007; P = 0.012), while better cognitive scores in SDMT were associated with higher [11C]PIB DVR in the corpus callosum (P = 0.001), and lower (R)-[11C]PK11195 VT (P = 0.013). CONCLUSIONS: Widespread innate immune cells profile and marked loss of myelin in T2 lesions and regions close to the ventricles may occur independently and are associated with disability, in both WM and GM structures.

Substantia nigra fractional anisotropy is not a diagnostic biomarker of Parkinson's disease: A diagnostic performance study and meta-analysis.

OBJECTIVES: Our goal was to estimate the diagnostic accuracy of substantia nigra fractional anisotropy (SN-FA) for Parkinson's disease (PD) diagnosis in a sample similar to the clinical setting, including patients with essential tremor (ET) and healthy controls (HC). We also performed a systematic review and meta-analysis to estimate mean change in SN-FA induced by PD and its diagnostic accuracy. METHODS: Our sample consisted of 135 subjects: 72 PD, 21 ET and 42 HC. To address inter-scanner variability, two 3.0-T MRI scans were performed. MRI results of this sample were pooled into a meta-analysis that included 1,432 subjects (806 PD and 626 HC). A bivariate model was used to evaluate diagnostic accuracy measures. RESULTS: In our sample, we did not observe a significant effect of disease on SN-FA and it was uninformative for diagnosis. The results of the meta-analysis estimated a 0.03 decrease in mean SN-FA in PD relative to HC (CI: 0.01-0.05). However, the discriminatory capability of SN-FA to diagnose PD was low: pooled sensitivity and specificity were 72 % (CI: 68-75) and 63 % (CI: 58-70), respectively. There was high heterogeneity between studies (I2 = 91.9 %). CONCLUSIONS: SN-FA cannot be used as an isolated measure to diagnose PD. KEY POINTS: • SN-FA appears insufficiently sensitive and specific to diagnose PD. • Radiologists must be careful when translating mean group results to clinical practice. • Imaging protocol and analysis standardization is necessary for developing reproducible quantitative biomarkers.

The effect of lesion filling on brain network analysis in multiple sclerosis using structural magnetic resonance imaging.

BACKGROUND: Graph theoretical network analysis with structural magnetic resonance imaging (MRI) of multiple sclerosis (MS) patients can be used to assess subtle changes in brain networks. However, the presence of multiple focal brain lesions might impair the accuracy of automatic tissue segmentation methods, and hamper the performance of graph theoretical network analysis. Applying "lesion filling" by substituting the voxel intensities of a lesion with the voxel intensities of nearby voxels, thus creating an image devoid of lesions, might improve segmentation and graph theoretical network analysis. This study aims to determine if brain networks are different between MS subtypes and healthy controls (HC) and if the assessment of these differences is affected by lesion filling. METHODS: The study included 49 MS patients and 19 HC that underwent a T1w, and T2w-FLAIR MRI scan. Graph theoretical network analysis was performed from grey matter fractions extracted from the original T1w-images and T1w-images after lesion filling. RESULTS: Artefacts in lesion-filled T1w images correlated positively with total lesion volume (r = 0.84, p < 0.001) and had a major impact on grey matter segmentation accuracy. Differences in sensitivity for network alterations were observed between original T1w data and after application of lesion filling: graph theoretical network analysis obtained from lesion-filled T1w images produced more differences in network organization in MS patients. CONCLUSION: Lesion filling might reduce variability across subjects resulting in an increased detection rate of network alterations in MS, but also induces significant artefacts, and therefore should be applied cautiously especially in individuals with higher lesions loads.

Altered Functional Connectivity Between Precuneus and Motor Systems in Parkinson's Disease Patients.

Parkinson's disease (PD) is a neurodegenerative disorder that affects motor skills and cognition. As brain structure and function are compromised, functional magnetic resonance imaging (fMRI) can be a helpful tool to further investigate how intrinsic connectivity is impaired on the disease. The precuneus and medial prefrontal cortex (mPFC) are hub regions involved on the default mode network (DMN), a system that is active during rest and related to cognitive processes. We hypothesized that PD patients would present a decrease in functional connectivity among these two regions and the rest of the brain. Our goal was to identify regions in which functional connectivity to precuneus and mPFC was altered in PD. This study was based on resting-state fMRI data from 37 healthy subjects and 55 PD patients. Precuneus and mPFC were selected as seed regions in a whole brain functional connectivity mapping. As expected, we found abnormal connectivity from precuneus to motor system regions in PD patients, pointing toward a decreased connectivity in the disease. No significant group effects were found for the mPFC. Our findings suggest that internetwork connectivity from DMN to motor system is impaired in PD.