Muscle diffusion tensor imaging in facioscapulohumeral muscular dystrophy.

INTRODUCTION/AIMS: Muscle diffusion tensor imaging has not yet been explored in facioscapulohumeral muscular dystrophy (FSHD). We assessed diffusivity parameters in FSHD subjects compared with healthy controls (HCs), with regard to their ability to precede any fat replacement or edema. METHODS: Fat fraction (FF), water T2 (wT2), mean, radial, axial diffusivity (MD, RD, AD), and fractional anisotropy (FA) of thigh muscles were calculated in 10 FSHD subjects and 15 HCs. All parameters were compared between FSHD and controls, also exploring their gradient along the main axis of the muscle. Diffusivity parameters were tested in a subgroup analysis as predictors of disease involvement in muscle compartments with different degrees of FF and wT2 and were also correlated with clinical severity scores. RESULTS: We found that MD, RD, and AD were significantly lower in FSHD subjects than in controls, whereas we failed to find a difference for FA. In contrast, we found a significant positive correlation between FF and FA and a negative correlation between MD, RD, and AD and FF. No correlation was found with wT2. In our subgroup analysis we found that muscle compartments with no significant fat replacement or edema (FF < 10% and wT2 < 41 ms) showed a reduced AD and FA compared with controls. Less involved compartments showed different diffusivity parameters than more involved compartments. DISCUSSION: Our exploratory study was able to demonstrate diffusivity parameter abnormalities even in muscles with no significant fat replacement or edema. Larger cohorts are needed to confirm these preliminary findings.

Autoimmune encephalitis: what the radiologist needs to know.

Autoimmune encephalitis is a relatively novel nosological entity characterized by an immune-mediated damage of the central nervous system. While originally described as a paraneoplastic inflammatory phenomenon affecting limbic structures, numerous instances of non-paraneoplastic pathogenesis, as well as extra-limbic involvement, have been characterized. Given the wide spectrum of insidious clinical presentations ranging from cognitive impairment to psychiatric symptoms or seizures, it is crucial to raise awareness about this disease category. In fact, an early diagnosis can be dramatically beneficial for the prognosis both to achieve an early therapeutic intervention and to detect a potential underlying malignancy. In this scenario, the radiologist can be the first to pose the hypothesis of autoimmune encephalitis and refer the patient to a comprehensive diagnostic work-up - including clinical, serological, and neurophysiological assessments.In this article, we illustrate the main radiological characteristics of autoimmune encephalitis and its subtypes, including the typical limbic presentation, the features of extra-limbic involvement, and also peculiar imaging findings. In addition, we review the most relevant alternative diagnoses that should be considered, ranging from other encephalitides to neoplasms, vascular conditions, and post-seizure alterations. Finally, we discuss the most appropriate imaging diagnostic work-up, also proposing a suggested MRI protocol.

Magnetic resonance advanced imaging analysis in adolescents: cortical thickness study to identify attenuated psychosis syndrome.

PURPOSE: Psychosis is a symptom common to several mental illnesses and a defining feature of schizophrenia spectrum disorders, whose onset typically occurs in adolescence. Neuroradiological studies have reported evidence of brain structural abnormalities in patients with overt psychosis. However, early identification of brain structural changes in young subjects at risk for developing psychosis (such as those with Attenuated Psychosis Syndrome -APS) is currently lacking. METHODS: Brain 3D T1-weighted and 64 directions diffusion-weighted images were acquired on 55 help-seeking adolescents (12-17 years old) with psychiatric disorders who referred to our Institute. Patients were divided into three groups: non-APS (n = 20), APS (n = 20), and Early-Onset Psychosis (n = 15). Cortical thickness was calculated from T1w images, and Tract-Based Spatial Statistics analysis was performed to study the distribution of white matter fractional anisotropy and all diffusivity metrics. A thorough neuropsychological test battery was adopted to investigate cognitive performance in several domains. RESULTS: In patients with Attenuated Psychotic Syndrome, the left superior frontal gyrus was significantly thinner compared to patients with non-APS (p = 0.048), and their right medial orbitofrontal cortex thickness was associated with lower working memory scores (p = 0.0025, r = -0.668 for the working memory index and p = 0.001, r = -0.738 for the digit span). Early-Onset Psychosis patients showed thinner left pars triangularis compared to non-APS individuals (p = 0.024), and their left pars orbitalis was associated with impaired performance at the symbol search test (p = 0.005, r = -0.726). No differences in diffusivity along main tracts were found between sub-groups (p > 0.05). CONCLUSION: This study showed specific associations between structural imaging features and cognitive performance in patients with APS. Characterizing this disorder using neuroimaging could reveal useful information that may aid in the development and evaluation of preventive strategies in these individuals.

Default Mode Network quantitative diffusion and resting-state functional magnetic resonance imaging correlates in sporadic Creutzfeldt-Jakob disease.

Grey matter involvement is a well-known feature in sporadic Creutzfeldt-Jakob disease (sCJD), yet precise anatomy-based quantification of reduced diffusivity is still not fully understood. Default Mode Network (DMN) areas have been recently demonstrated as selectively involved in sCJD, and functional connectivity has never been investigated in prion diseases. We analyzed the grey matter involvement using a quantitatively multi-parametric MRI approach. Specifically, grey matter mean diffusivity of 37 subjects with sCJD was compared with that of 30 age-matched healthy controls with a group-wise approach. Differences in mean diffusivity were also examined between the cortical (MM(V)1, MM(V)2C, and VV1) and subcortical (VV2 and MV2K) subgroups of sCJD for those with autopsy data available (n = 27, 73%). We also assessed resting-state functional connectivity of both ventral and dorsal components of DMN in a subset of subject with a rs-fMRI dataset available (n = 17). Decreased diffusivity was predominantly present in posterior cortical regions of the DMN, but also outside of the DMN in temporal areas and in a few limbic and frontal areas, in addition to extensive deep nuclei involvement. Both subcortical and cortical sCJD subgroups showed decreased diffusivity subcortically, whereas only the cortical type expressed significantly decreased diffusivity cortically, mainly in parietal, occipital, and medial-inferior temporal cortices bilaterally. Interestingly, we found abnormally increased connectivity in both dorsal and ventral components of the DMN in sCJD subjects compared with healthy controls. The significance and possible utility of functional imaging as a biomarker for tracking disease progression in prion disease needs to be explored further.

Adoptive Transfer of JC Virus-Specific T Lymphocytes for the Treatment of Progressive Multifocal Leukoencephalopathy.

OBJECTIVE: Progressive multifocal leukoencephalopathy (PML) is still burdened by high mortality in a subset of patients, such as those affected by hematological malignancies. The aim of this study was to analyze the safety and carry out preliminary evaluation of the efficacy of polyomavirus JC (JCPyV)-specific T cell therapy in a cohort of hematological patients with PML. METHODS: Between 2014 and 2019, 9 patients with a diagnosis of "definite PML" according to the 2013 consensus who were showing progressive clinical deterioration received JCPyV-specific T cells. Cell lines were expanded from autologous or allogenic peripheral blood mononuclear cells by stimulation with JCPyV antigen-derived peptides. RESULTS: None of the patients experienced treatment-related adverse events. In the evaluable patients, an increase in the frequency of circulating JCPyV-specific lymphocytes was observed, with a decrease or clearance of JCPyV viral load in cerebrospinal fluid. In responsive patients, transient appearance of punctate areas of contrast enhancement within, or close to, PML lesions was observed, which was interpreted as a sign of immune control and which regressed spontaneously without the need for steroid treatment. Six of 9 patients achieved PML control, with 5 alive and in good clinical condition at their last follow-up. INTERPRETATION: Among other novel treatments, T cell therapy is emerging as a viable treatment option in patients with PML, particularly for those not amenable to restoration of specific immunity. Neurologists should be encouraged to refer PML patients to specialized centers to allow access to this treatment strategy. ANN NEUROL 2021;89:769-779.

Deep learning for automatic segmentation of thigh and leg muscles.

OBJECTIVE: In this study we address the automatic segmentation of selected muscles of the thigh and leg through a supervised deep learning approach. MATERIAL AND METHODS: The application of quantitative imaging in neuromuscular diseases requires the availability of regions of interest (ROI) drawn on muscles to extract quantitative parameters. Up to now, manual drawing of ROIs has been considered the gold standard in clinical studies, with no clear and universally accepted standardized procedure for segmentation. Several automatic methods, based mainly on machine learning and deep learning algorithms, have recently been proposed to discriminate between skeletal muscle, bone, subcutaneous and intermuscular adipose tissue. We develop a supervised deep learning approach based on a unified framework for ROI segmentation. RESULTS: The proposed network generates segmentation maps with high accuracy, consisting in Dice Scores ranging from 0.89 to 0.95, with respect to "ground truth" manually segmented labelled images, also showing high average performance in both mild and severe cases of disease involvement (i.e. entity of fatty replacement). DISCUSSION: The presented results are promising and potentially translatable to different skeletal muscle groups and other MRI sequences with different contrast and resolution.

Variable echo time imaging for detecting the short T2* components of the sciatic nerve: a validation study.

OBJECTIVE: The aim of this study was to develop and validate an MRI protocol based on a variable echo time (vTE) sensitive to the short T2* components of the sciatic nerve. MATERIALS AND METHODS: 15 healthy subjects (M/F: 9/6; age: 21-62) were scanned at 3T targeting the sciatic nerve at the thigh bilaterally, using a dual echo variable echo time (vTE) sequence (based on a spoiled gradient echo acquisition) with echo times of 0.98/5.37 ms. Apparent T2* (aT2*) values of the sciatic nerves were calculated with a mono-exponential fit and used for data comparison. RESULTS: There were no significant differences in aT2* related to side, sex, age, and BMI, even though small differences for side were reported. Good-to-excellent repeatability and reproducibility were found for geometry of ROIs (Dice indices: intra-rater 0.68-0.7; inter-rater 0.70-0.72) and the related aT2* measures (intra-inter reader ICC 0.95-0.97; 0.66-0.85) from two different operators. Side-related signal-to-noise-ratio non-significant differences were reported, while contrast-to-noise-ratio measures were excellent both for side and echo. DISCUSSION: Our study introduces a novel MR sequence sensitive to the short T2* components of the sciatic nerve and may be used for the study of peripheral nerve disorders.

Differential imaging of atypical demyelinating lesions of the central nervous system.

The detection of atypical and sometimes aggressive or tumefactive demyelinating lesions of the central nervous system often poses difficulties in the differential diagnosis. The clinical presentation is generally aspecific, related to the location and similar to a number of different lesions, including neoplasms and other intracranial lesions with mass effect. CSF analysis may also be inconclusive, especially for lesions presenting as a single mass at onset. As a consequence, a brain biopsy is frequently performed for characterization. Advanced MRI imaging plays an important role in directing the diagnosis, reducing the rate of unnecessary biopsies and allowing a prompt start of therapy that is often crucial, especially in the case of infratentorial lesions. In this review, the main pattern of presentation of atypical inflammatory demyelinating diseases is discussed, with particular attention on the differential diagnosis and how to adequately define the correct etiology.

Diagnosing acute encephalitis in patients with hematological disorders: caveats and pitfalls.

The aim of this study was to review the quality of the diagnostic work-up for acute encephalitis carried out at our center in a cohort of patients with hematological disorders. Our data showed substantial heterogeneity in investigating patients. Not all patients had their CSF tested for viruses commonly responsible for encephalitis in immunocompetent individuals (e.g., VZV, enterovirus). A blood sample for the calculation of the CSF/blood replication ratio was collected in 74% of cases. CSF cultures and immunophenotyping of CSF cells were performed in 77% and 21% of patients, respectively. A multidisciplinary consensus is needed to improve current guidelines and standardize diagnostic protocols.

Validation of a method to assess emphysema severity by spirometry in the COPDGene study.

BACKGROUND: Standard spirometry cannot identify the predominant mechanism underlying airflow obstruction in COPD, namely emphysema or airway disease. We aimed at validating a previously developed methodology to detect emphysema by mathematical analysis of the maximal expiratory flow-volume (MEFV) curve in standard spirometry. METHODS: From the COPDGene population we selected those 5930 subjects with MEFV curve and inspiratory-expiratory CT obtained on the same day. The MEFV curve descending limb was fit real-time using forced vital capacity (FVC), peak expiratory flow, and forced expiratory flows at 25, 50 and 75% of FVC to derive an emphysema severity index (ESI), expressed as a continuous positive numeric parameter ranging from 0 to 10. According to inspiratory CT percent lung attenuation area below - 950 HU we defined three emphysema severity subgroups (%LAA-950insp < 6, 6-14, ≥14). By co-registration of inspiratory-expiratory CT we quantified persistent (%pLDA) and functional (%fLDA) low-density areas as CT metrics of emphysema and airway disease, respectively. RESULTS: ESI differentiated CT emphysema severity subgroups increasing in parallel with GOLD stages (p < .001), but with high variability within each stage. ESI had significantly higher correlations (p < .001) with emphysema than with airway disease CT metrics, explaining 67% of %pLDA variability. Conversely, standard spirometric variables (FEV1, FEV1/FVC) had significantly lower correlations than ESI with emphysema CT metrics and did not differentiate between emphysema and airways CT metrics. CONCLUSIONS: ESI adds to standard spirometry the power to discriminate whether emphysema is the predominant mechanism of airway obstruction. ESI methodology has been validated in the large multiethnic population of smokers of the COPDGene study and therefore it could be applied for clinical and research purposes in the general population of smokers, using a readily available online website.

Spirometric assessment of emphysema presence and severity as measured by quantitative CT and CT-based radiomics in COPD.

BACKGROUND: The mechanisms underlying airflow obstruction in COPD cannot be distinguished by standard spirometry. We ascertain whether mathematical modeling of airway biomechanical properties, as assessed from spirometry, could provide estimates of emphysema presence and severity, as quantified by computed tomography (CT) metrics and CT-based radiomics. METHODS: We quantified presence and severity of emphysema by standard CT metrics (VIDA) and co-registration analysis (ImbioLDA) of inspiratory-expiratory CT in 194 COPD patients who underwent pulmonary function testing. According to percentages of low attenuation area below - 950 Hounsfield Units (%LAA-950insp) patients were classified as having no emphysema (NE) with %LAA-950insp < 6, moderate emphysema (ME) with %LAA-950insp ≥ 6 and < 14, and severe emphysema (SE) with %LAA-950insp ≥ 14. We also obtained stratified clusters of emphysema CT features by an automated unsupervised radiomics approach (CALIPER). An emphysema severity index (ESI), derived from mathematical modeling of the maximum expiratory flow-volume curve descending limb, was compared with pulmonary function data and the three CT classifications of emphysema presence and severity as derived from CT metrics and radiomics. RESULTS: ESI mean values and pulmonary function data differed significantly in the subgroups with different emphysema degree classified by VIDA, ImbioLDA and CALIPER (p < 0.001 by ANOVA). ESI differentiated NE from ME/SE CT-classified patients (sensitivity 0.80, specificity 0.85, AUC 0.86) and SE from ME CT-classified patients (sensitivity 0.82, specificity 0.87, AUC 0.88). CONCLUSIONS: Presence and severity of emphysema in patients with COPD, as quantified by CT metrics and radiomics can be estimated by mathematical modeling of airway function as derived from standard spirometry.

Emphysematous and Nonemphysematous Gas Trapping in Chronic Obstructive Pulmonary Disease: Quantitative CT Findings and Pulmonary Function.

Purpose To identify a prevalent computed tomography (CT) subtype in patients with chronic obstructive pulmonary disease (COPD) by separating emphysematous from nonemphysematous contributions to total gas trapping and to attempt to predict and grade the emphysematous gas trapping by using clinical and functional data. Materials and Methods Two-hundred and two consecutive eligible patients (159 men and 43 women; mean age, 70 years [age range, 41-85 years]) were prospectively studied. Pulmonary function and CT data were acquired by pulmonologists and radiologists. Noncontrast agent-enhanced thoracic CT scans were acquired at full inspiration and expiration, and were quantitatively analyzed by using two software programs. CT parameters were set as follows: 120 kVp; 200 mAs; rotation time, 0.5 second; pitch, 1.1; section thickness, 0.75 mm; and reconstruction kernels, b31f and b70f. Gas trapping obtained by difference of inspiratory and expiratory CT density thresholds (percentage area with CT attenuation values less than -950 HU at inspiration and percentage area with CT attenuation values less than -856 HU at expiration) was compared with that obtained by coregistration analysis. A logistic regression model on the basis of anthropometric and functional data was cross-validated and trained to classify patients with COPD according to the relative contribution of emphysema to total gas trapping, as assessed at CT. Results Gas trapping obtained by difference of inspiratory and expiratory CT density thresholds was highly correlated (r = 0.99) with that obtained by coregistration analysis. Four groups of patients were distinguished according to the prevalent CT subtype: prevalent emphysematous gas trapping, prevalent functional gas trapping, mixed severe, and mixed mild. The predictive model included predicted forced expiratory volume in 1 second/vital capacity, percentage of predicted forced expiratory volume in 1 second, percentage of diffusing capacity for carbon monoxide, and body mass index as emphysema regressors at CT, with 81% overall accuracy in classifying patients according to its extent. Conclusion The relative contribution of emphysematous and nonemphysematous gas trapping obtained by coregistration of inspiratory and expiratory CT scanning can be determined accurately by difference of CT inspiratory and expiratory density thresholds. CT extent of emphysema can be predicted with accuracy suitable for clinical purposes by pulmonary function data and body mass index. © RSNA, 2018 Online supplemental material is available for this article.

Reference Values for Central Airway Dimensions on CT Images of Children and Adolescents.

OBJECTIVE: The purpose of this study was to acquire normative data on central airway dimensions on chest CT scans in the pediatric population. MATERIALS AND METHODS: Chest CT findings reported as normal by a radiologist were collected retrospectively at 10 international centers. An experienced and independent thoracic radiologist reevaluated all CT scans for image quality and for normal findings. Semiautomated image analysis was performed to measure dimensions of the trachea and right and left main bronchi at inspiration. Intrathoracic tracheal length was measured from carina to thorax inlet. Cross-sectional area and short and long axes were measured perpendicular to the longitudinal airway axis starting from the carina every centimeter upward for the trachea and every 0.5 cm downward for the main bronchi. The effects on airway diameters of age, sex, intrathoracic tracheal length, and distance from the carina were investigated by use of mixed-effects models analysis. RESULTS: Among 1160 CT scans collected, 388 were evaluated as normal by the independent radiologist with sufficient image quality and adequate inspiratory volume level. Central airways were successfully semiautomatically analyzed in 294 of 388 CT studies. Age, sex, intrathoracic tracheal length, and distance from carina were all significant predictors in the models for tracheal and right and left main bronchial diameters (p < 0.001). The central airway dimensions increased with age up to 20 years, and dimensions were larger in male than in female adolescents. CONCLUSION: Normative data were determined for the central airways of children and adolescents. Central airway dimensions depended on distance from the carina and on intrathoracic tracheal length.

Do COPD subtypes really exist? COPD heterogeneity and clustering in 10 independent cohorts.

BACKGROUND: COPD is a heterogeneous disease, but there is little consensus on specific definitions for COPD subtypes. Unsupervised clustering offers the promise of 'unbiased' data-driven assessment of COPD heterogeneity. Multiple groups have identified COPD subtypes using cluster analysis, but there has been no systematic assessment of the reproducibility of these subtypes. OBJECTIVE: We performed clustering analyses across 10 cohorts in North America and Europe in order to assess the reproducibility of (1) correlation patterns of key COPD-related clinical characteristics and (2) clustering results. METHODS: We studied 17 146 individuals with COPD using identical methods and common COPD-related characteristics across cohorts (FEV1, FEV1/FVC, FVC, body mass index, Modified Medical Research Council score, asthma and cardiovascular comorbid disease). Correlation patterns between these clinical characteristics were assessed by principal components analysis (PCA). Cluster analysis was performed using k-medoids and hierarchical clustering, and concordance of clustering solutions was quantified with normalised mutual information (NMI), a metric that ranges from 0 to 1 with higher values indicating greater concordance. RESULTS: The reproducibility of COPD clustering subtypes across studies was modest (median NMI range 0.17-0.43). For methods that excluded individuals that did not clearly belong to any cluster, agreement was better but still suboptimal (median NMI range 0.32-0.60). Continuous representations of COPD clinical characteristics derived from PCA were much more consistent across studies. CONCLUSIONS: Identical clustering analyses across multiple COPD cohorts showed modest reproducibility. COPD heterogeneity is better characterised by continuous disease traits coexisting in varying degrees within the same individual, rather than by mutually exclusive COPD subtypes.

Chronic Obstructive Pulmonary Disease: Pulmonary Function and CT Lung Attenuation Do Not Show Linear Correlation.

PURPOSE: To determine whether the relationship between pulmonary function and computed tomographic (CT) lung attenuation in chronic obstructive pulmonary disease (COPD), which is traditionally described with single univariate and multivariate statistical models, could be more accurately described with a multiple model estimation approach. MATERIALS AND METHODS: The study was approved by the local ethics committee. All participants provided written informed consent. The prediction of the percentage area with CT attenuation values less than -950 HU at inspiration (%LAA-950insp) and less than -910 HU at expiration (%LAA-910exp) obtained with single univariate and multivariate models was compared with that obtained with a multiple model estimation approach in 132 patients with COPD. RESULTS: At univariate analysis, %LAA-950insp and %LAA-910exp values higher than the mean value of this cohort (19.1% and 22.0%) showed better correlation with percentage of predicted diffusing capacity of lung for carbon monoxide (Dlco%) than with airflow obstruction (forced expiratory volume in 1 second [FEV1]/vital capacity [VC]). Conversely, %LAA-950insp and %LAA-910exp values lower than the mean value were correlated with FEV1/VC but not with Dlco%. Multiple model estimation performed with two multivariate regressions, each selecting the most appropriate functional variables (FEV1/VC for mild parenchymal destruction, Dlco% and functional residual capacity for severe parenchymal destruction), predicted better than single multivariate regression both %LAA-950insp (R(2) = 0.75 vs 0.46) and %LAA-910exp (R(2) = 0.83 vs 0.63). CONCLUSION: The relationship between pulmonary function data and CT densitometric changes in COPD varies with the level of lung attenuation impairment. The nonlinear profile of this relationship is accurately predicted with a multiple model estimation approach.

Pulmonary function and sputum characteristics predict computed tomography phenotype and severity of COPD.

Airway obstruction and parenchymal destruction underlie phenotype and severity in chronic obstructive pulmonary disease (COPD). We aimed to predict, by clinical and pulmonary function data, the predominant type and severity of pathological changes quantitatively assessed by computed tomography (CT). Airway wall thickness (AWT-Pi10) and percentage of lung area with X-ray attenuation values <-950 HU (%LAA-950) were measured in 100 (learning set) out of 473 COPD outpatients undergoing clinical and functional evaluation. Original CT measurements were translated by principal component analysis onto a plane with the novel coordinates CT1 and CT2, depending on the difference (prevalent mechanism of airflow limitation) and on the sum (severity) of AWT-Pi10 and %LAA-950, respectively. CT1 and CT2, estimated in the learning set by cross-validated models of clinical and functional variables, were used to classify 373 patients in the testing set. A model based on diffusing capacity of the lung for carbon monoxide, total lung capacity and purulent sputum predicted CT1 (r = 0.64; p<0.01). A model based on forced expiratory volume in 1 s/vital capacity, functional residual capacity and purulent sputum predicted CT2 (r = 0.77; p<0.01). Classification of patients in the testing set obtained by model-predicted CT1 and CT2 reflected, according to correlations with clinical and functional variables, both COPD phenotype and severity. Multivariate models based on pulmonary function variables and sputum purulence classify patients according to overall severity and predominant phenotype of COPD as assessed quantitatively by CT.

Treatment Dilemma in Children with Late-Onset Pompe Disease.

In recent years, there has been a significant increase in the diagnosis of asymptomatic Late-Onset Pompe Disease (LOPD) patients, who are detected via family screening or Newborn Screening (NBS). The dilemma is when to start Enzyme Replacement Therapy (ERT) in patients without any clinical sign of the disease, considering its important benefits in terms of loss of muscle but also its very high cost, risk of side effects, and long-term immunogenicity. Muscle Magnetic Resonance Imaging (MRI) is accessible, radiation-free, and reproducible; therefore, it is an important instrument for the diagnosis and follow-up of patients with LOPD, especially in asymptomatic cases. European guidelines suggest monitoring in asymptomatic LOPD cases with minimal MRI findings, although other guidelines consider starting ERT in apparently asymptomatic cases with initial muscle involvement (e.g., paraspinal muscles). We describe three siblings affected by LOPD who present compound heterozygosis and wide phenotypic variability. The three cases differ in age at presentation, symptoms, urinary tetrasaccharide levels, and MRI findings, confirming the significant phenotypic variability of LOPD and the difficulty in deciding when to start therapy.

PRNP expression predicts imaging findings in sporadic Creutzfeldt-Jakob disease.

OBJECTIVE: We explored the relationship between regional PRNP expression from healthy brain tissue and patterns of increased and decreased diffusion and regional brain atrophy in patients with sporadic Creutzfeldt-Jakob disease (sCJD). METHODS: We used PRNP microarray data from 6 healthy adult brains from Allen Brain Institute and T1-weighted and diffusion-weighted MRIs from 34 patients diagnosed with sCJD and 30 age- and sex-matched healthy controls to construct partial correlation matrices across brain regions for specific measures of interest: PRNP expression, mean diffusivity, volume, cortical thickness, and local gyrification index, a measure of cortical folding. RESULTS: Regional patterns of PRNP expression in the healthy brain correlated with regional patterns of diffusion signal abnormalities and atrophy in sCJD. Among different measures of cortical morphology, regional patterns of local gyrification index in sCJD most strongly correlated with regional patterns of PRNP expression. At the vertex-wise level, different molecular subtypes of sCJD showed distinct regional correlations in local gyrification index across the cortex. Local gyrification index correlation patterns most closely matched patterns of PRNP expression in sCJD subtypes known to have greatest pathologic involvement of the cerebral cortex. INTERPRETATION: These results suggest that the specific genetic and molecular environment in which the prion protein is expressed confer variable vulnerability to misfolding across different brain regions that is reflected in patterns of imaging findings in sCJD. Further work in larger samples will be needed to determine whether these regional imaging patterns can serve as reliable markers of distinct disease subtypes to improve diagnosis and treatment targeting.

Radiomics and machine learning applied to STIR sequence for prediction of quantitative parameters in facioscapulohumeral disease.

Purpose: Quantitative Muscle MRI (qMRI) is a valuable and non-invasive tool to assess disease involvement and progression in neuromuscular disorders being able to detect even subtle changes in muscle pathology. The aim of this study is to evaluate the feasibility of using a conventional short-tau inversion recovery (STIR) sequence to predict fat fraction (FF) and water T2 (wT2) in skeletal muscle introducing a radiomic workflow with standardized feature extraction combined with machine learning algorithms. Methods: Twenty-five patients with facioscapulohumeral muscular dystrophy (FSHD) were scanned at calf level using conventional STIR sequence and qMRI techniques. We applied and compared three different radiomics workflows (WF1, WF2, WF3), combined with seven Machine Learning regression algorithms (linear, ridge and lasso regression, tree, random forest, k-nearest neighbor and support vector machine), on conventional STIR images to predict FF and wT2 for six calf muscles. Results: The combination of WF3 and K-nearest neighbor resulted to be the best predictor model of qMRI parameters with a mean absolute error about ± 5 pp for FF and ± 1.8 ms for wT2. Conclusion: This pilot study demonstrated the possibility to predict qMRI parameters in a cohort of FSHD subjects starting from conventional STIR sequence.