Magnetic resonance imaging scoring system of the lower limbs in adult patients with suspected idiopathic inflammatory myopathy.

PURPOSE: We aim to propose a visual quantitative score for muscle edema in lower limb MRI to contribute to the diagnosis of idiopathic inflammatory myopathy (IIM). MATERIAL AND METHODS: We retrospectively evaluated 85 consecutive patients (mean age 57.4 ± 13.9 years; 56.5% female) with suspected IIM (muscle weakness and/or persistent hyper-CPK-emia with/without myalgia) who underwent MRI of lower limbs using T2-weighted fast recovery-fast spin echo images and fat-sat T2 echo planar images. Muscle inflammation was evaluated bilaterally in 11 muscles of the thigh and eight muscles of the leg. Edema in each muscle was graded according to a four-point Likert-type scale adding up to 114 points ([11 + 8)] × 3 × 2). Diagnostic accuracy of the total edema score was explored by assessing sensitivity and specificity using the area under the ROC curve. Final diagnoses were made by a multidisciplinary Expert Consensus Panel applying the Bohan and Peter diagnostic criteria whenever possible. RESULTS: Of the 85 included patients, 34 (40%) received a final diagnosis of IIM (IIM group) while 51 (60%) received an alternative diagnosis (non-IIM group). A cutoff score ≥ 18 was able to correctly classify patients having an IIM with an area under the curve of 0.85, specificity of 96%, and sensitivity of 52.9%. CONCLUSION: Our study demonstrates that a quantitative MRI score for muscle edema in the lower limbs (thighs and legs) aids in distinguishing IIM from conditions that mimic it.

Molecular biomarkers correlate with brain grey and white matter changes in patients with mitochondrial m.3243A > G mutation.

INTRODUCTION: The mitochondrial DNA (mtDNA) m.3243A > G mutation in the MT-TL1 gene results in a multi-systemic disease, that is commonly associated with neurodegenerative changes in the brain. METHODS: Seventeen patients harboring the m3243A > G mutation were enrolled (age 43.1 ± 11.4 years, 10 M/7F). A panel of plasma biomarkers including lactate acid, alanine, L-arginine, fibroblast growth factor 21 (FGF-21), growth/differentiation factor 15 (GDF-15) and circulating cell free -mtDNA (ccf-mtDNA), as well as blood, urine and muscle mtDNA heteroplasmy were evaluated. Patients also underwent a brain standardized MR protocol that included volumetric T1-weighted images and diffusion-weighted MRI. Twenty sex- and age-matched healthy controls were included. Voxel-wise analysis was performed on T1-weighted and diffusion imaging, respectively with VBM (voxel-based morphometry) and TBSS (Tract-based Spatial Statistics). Ventricular lactate was also evaluated by 1H-MR spectroscopy. RESULTS: A widespread cortical gray matter (GM) loss was observed, more severe (p < 0.001) in the bilateral calcarine, insular, frontal and parietal cortex, along with infratentorial cerebellar cortex. High urine mtDNA mutation load, high levels of plasma lactate and alanine, low levels of plasma arginine, high levels of serum FGF-21 and ventricular lactate accumulation significantly (p < 0.05) correlated with the reduced brain GM density. Widespread microstructural alterations were highlighted in the white matter, significantly (p < 0.05) correlated with plasma alanine and arginine levels, with mtDNA mutation load in urine, with high level of serum GDF-15 and with high content of plasma ccf-mtDNA. CONCLUSIONS: Our results suggest that the synergy of two pathogenic mechanisms, mtDNA-related mitochondrial respiratory deficiency and defective nitric oxide metabolism, contributes to the brain neurodegeneration in m.3243A > G patients.

Combined Cerebellar Proton MR Spectroscopy and DWI Study of Patients with Friedreich's Ataxia.

Friedreich's ataxia (FRDA) is the commonest autosomal recessive ataxia, caused by GAA triplet expansion in the frataxin gene. Neuropathological studies in FRDA demonstrate that besides the primary neurodegeneration of the dorsal root ganglia, there is a progressive atrophy of the cerebellar dentate nucleus. Diffusion-weighted imaging (DWI) detected microstructural alterations in the cerebellum of FRDA patients. To investigate the biochemical basis of these alterations, we used both DWI and proton MR spectroscopy (1H-MRS) to study the same cerebellar volume of interest (VOI) including the dentate nucleus. DWI and 1H-MRS study of the left cerebellar hemisphere was performed in 28 genetically proven FRDA patients and 35 healthy controls. In FRDA mean diffusivity (MD) values were calculated for the same 1H-MRS VOI. Clinical severity was evaluated using the International Cooperative Ataxia Rating Scale (ICARS). FRDA patients showed a significant reduction of N-acetyl-aspartate (NAA), a neuroaxonal marker, and choline (Cho), a membrane marker, both expressed relatively to creatine (Cr), and increased MD values. In FRDA patients NAA/Cr negatively correlated with MD values (r = -0.396, p = 0.037) and with ICARS score (r = -0.669, p < 0.001). Age-normalized NAA/Cr loss correlated with the GAA expansion (r = -0.492, p = 0.008). The reduced cerebellar NAA/Cr in FRDA suggests that neuroaxonal loss is related to the microstructural changes determining higher MD values. The correlation between NAA/Cr and the severity of disability suggests that this biochemical in vivo MR parameter might be a useful biomarker to evaluate therapeutic interventions.

The effect of diffusion gradient direction number on corticospinal tractography in the human brain: an along-tract analysis.

OBJECTIVES: We evaluated diffusion imaging measures of the corticospinal tract obtained with a probabilistic tractography algorithm applied to data of two acquisition protocols based on different numbers of diffusion gradient directions (NDGDs). MATERIALS AND METHODS: The corticospinal tracts (CST) of 18 healthy subjects were delineated using 22 and 66-NDGD data. An along-tract analysis of diffusion metrics was performed to detect possible local differences due to NDGD. RESULTS: FA values at 22-NDGD showed an increase along the central portion of the CST. The mean of partial volume fraction of the orientation of the second fiber (f2) was higher at 66-NDGD bilaterally, because for 66-NDGD data the algorithm more readily detects dominant fiber directions beyond the first, thus the increase in FA at 22-NDGD is due to a substantially reduced detection of crossing fiber volume. However, the good spatial correlation between the tracts drawn at 22 and 66 NDGD shows that the extent of the tract can be successfully defined even at lower NDGD. CONCLUSIONS: Given the spatial tract localization obtained even at 22-NDGD, local analysis of CST can be performed using a NDGD compatible with clinical protocols. The probabilistic approach was particularly powerful in evaluating crossing fibers when present.

Precuneal Thickness and Depression in Parkinson Disease.

BACKGROUND: Depression-related gray matter changes in Parkinson disease (PD) patients have been reported, although studies investigating cortical thickness in early-stage disease are lacking. OBJECTIVE: We aimed to evaluate cortical changes related to depression in early-stage PD patients with an extensive neuropsychological evaluation. METHODS: 17 PD patients and 22 healthy controls underwent a 1.5-T brain MR protocol, and voxel-wise differences in cortical thickness among patients with (n = 6) and without (n = 11) depression and controls were evaluated using FreeSurfer software. RESULTS: Cortical thickness was increased in the precuneus bilaterally in PD patients with depression compared to the other groups (number of vertices >100; p < 0.001, uncorrected) with a direct correlation with the Beck Depression Inventory score (p < 0.001, uncorrected). CONCLUSION: Precuneal cortical thickening is evident in PD patients with mild-moderate depression even in the early stages of the disease. This finding may reflect the early involvement of this region in the development of PD-related depression.

IMPA-Net: Interpretable Multi-Part Attention Network for Trustworthy Brain Tumor Classification from MRI.

Deep learning (DL) networks have shown attractive performance in medical image processing tasks such as brain tumor classification. However, they are often criticized as mysterious "black boxes". The opaqueness of the model and the reasoning process make it difficult for health workers to decide whether to trust the prediction outcomes. In this study, we develop an interpretable multi-part attention network (IMPA-Net) for brain tumor classification to enhance the interpretability and trustworthiness of classification outcomes. The proposed model not only predicts the tumor grade but also provides a global explanation for the model interpretability and a local explanation as justification for the proffered prediction. Global explanation is represented as a group of feature patterns that the model learns to distinguish high-grade glioma (HGG) and low-grade glioma (LGG) classes. Local explanation interprets the reasoning process of an individual prediction by calculating the similarity between the prototypical parts of the image and a group of pre-learned task-related features. Experiments conducted on the BraTS2017 dataset demonstrate that IMPA-Net is a verifiable model for the classification task. A percentage of 86% of feature patterns were assessed by two radiologists to be valid for representing task-relevant medical features. The model shows a classification accuracy of 92.12%, of which 81.17% were evaluated as trustworthy based on local explanations. Our interpretable model is a trustworthy model that can be used for decision aids for glioma classification. Compared with black-box CNNs, it allows health workers and patients to understand the reasoning process and trust the prediction outcomes.

Generalizing the Enhanced-Deep-Super-Resolution Neural Network to Brain MR Images: A Retrospective Study on the Cam-CAN Dataset.

The Enhanced-Deep-Super-Resolution (EDSR) model is a state-of-the-art convolutional neural network suitable for improving image spatial resolution. It was previously trained with general-purpose pictures and then, in this work, tested on biomedical magnetic resonance (MR) images, comparing the network outcomes with traditional up-sampling techniques. We explored possible changes in the model response when different MR sequences were analyzed. T1w and T2w MR brain images of 70 human healthy subjects (F:M, 40:30) from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) repository were down-sampled and then up-sampled using EDSR model and BiCubic (BC) interpolation. Several reference metrics were used to quantitatively assess the performance of up-sampling operations (RMSE, pSNR, SSIM, and HFEN). Two-dimensional and three-dimensional reconstructions were evaluated. Different brain tissues were analyzed individually. The EDSR model was superior to BC interpolation on the selected metrics, both for two- and three- dimensional reconstructions. The reference metrics showed higher quality of EDSR over BC reconstructions for all the analyzed images, with a significant difference of all the criteria in T1w images and of the perception-based SSIM and HFEN in T2w images. The analysis per tissue highlights differences in EDSR performance related to the gray-level values, showing a relative lack of outperformance in reconstructing hyperintense areas. The EDSR model, trained on general-purpose images, better reconstructs MR T1w and T2w images than BC, without any retraining or fine-tuning. These results highlight the excellent generalization ability of the network and lead to possible applications on other MR measurements.

Multiparametric MRI dataset for susceptibility-based radiomic feature extraction and analysis.

Multiple sclerosis (MS) is a progressive demyelinating disease impacting the central nervous system. Conventional Magnetic Resonance Imaging (MRI) techniques (e.g., T2w images) help diagnose MS, although they sometimes reveal non-specific lesions. Quantitative MRI techniques are capable of quantifying imaging biomarkers in vivo, offering the potential to identify specific signs related to pre-clinical inflammation. Among those techniques, Quantitative Susceptibility Mapping (QSM) is particularly useful for studying processes that influence the magnetic properties of brain tissue, such as alterations in myelin concentration. Because of its intrinsic quantitative nature, it is particularly well-suited to be analyzed through radiomics, including techniques that extract a high number of complex and multi-dimensional features from radiological images. The dataset presented in this work provides information about normal-appearing white matter (NAWM) in a cohort of MS patients and healthy controls. It includes QSM-based radiomic features from NAWM and its tracts, and MR sequences necessary to implement the pipeline: T1w, T2w, QSM, DWI. The workflow is outlined in this article, along with an application showing feature reliability assessment.

Methods of diffusion MRI tractography for localization of the anterior optic pathway: A systematic review of validated methods.

The anterior optic pathway (AOP) is a system of three structures (optic nerves, optic chiasma, and optic tracts) that convey visual stimuli from the retina to the lateral geniculate nuclei. A successful reconstruction of the AOP using tractography could be helpful in several clinical scenarios, from presurgical planning and neuronavigation of sellar and parasellar surgery to monitoring the stage of fiber degeneration both in acute (e.g., traumatic optic neuropathy) or chronic conditions that affect AOP structures (e.g., amblyopia, glaucoma, demyelinating disorders or genetic optic nerve atrophies). However, its peculiar anatomy and course, as well as its surroundings, pose a serious challenge to obtaining successful tractographic reconstructions. Several AOP tractography strategies have been adopted but no standard procedure has been agreed upon. We performed a systematic review of the literature according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) 2020 guidelines in order to find the combinations of acquisition and reconstruction parameters that have been performed previously and have provided the highest rate of successful reconstruction of the AOP, in order to promote their routine implementation in clinical practice. For this purpose, we reviewed data regarding how the process of anatomical validation of the tractographies was performed. The Cochrane Handbook for Systematic Reviews of Interventions was used to assess the risk of bias and thus the study quality We identified thirty-nine studies that met our inclusion criteria, and only five were considered at low risk of bias and achieved over 80% of successful reconstructions. We found a high degree of heterogeneity in the acquisition and analysis parameters used to perform AOP tractography and different combinations of them can achieve satisfactory levels of anterior optic tractographic reconstruction both in real-life research and clinical scenarios. One thousand s/mm2 was the most frequently used b value, while both deterministic and probabilistic tractography algorithms performed morphological reconstruction of the tract satisfactorily, although probabilistic algorithms estimated a more realistic percentage of crossing fibers (45.6%) in healthy subjects. A wide heterogeneity was also found regarding the method used to assess the anatomical fidelity of the AOP reconstructions. Three main strategies can be found: direct visual direct visual assessment of the tractography superimposed to a conventional MR image, surgical evaluation, and computational methods. Because the latter is less dependent on a priori knowledge of the anatomy by the operator, computational methods of validation of the anatomy should be considered whenever possible.

Assessing robustness of quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis.

Multiple Sclerosis (MS) is an autoimmune demyelinating disease characterised by changes in iron and myelin content. These biomarkers are detectable by Quantitative Susceptibility Mapping (QSM), an advanced Magnetic Resonance Imaging technique detecting magnetic properties. When analysed with radiomic techniques that exploit its intrinsic quantitative nature, QSM may furnish biomarkers to facilitate early diagnosis of MS and timely assessment of progression. In this work, we explore the robustness of QSM radiomic features by varying the number of grey levels (GLs) and echo times (TEs), in a sample of healthy controls and patients with MS. We analysed the white matter in total and within six clinically relevant tracts, including the cortico-spinal tract and the optic radiation. After optimising the number of GLs (n = 64), at least 65% of features were robust for each Volume of Interest (VOI), with no difference (p > .05) between left and right hemispheres. Different outcomes in feature robustness among the VOIs depend on their characteristics, such as volume and variance of susceptibility values. This study validated the processing pipeline for robustness analysis and established the reliability of QSM-based radiomics features against GLs and TEs. Our results provide important insights for future radiomics studies using QSM in clinical applications.

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives.

Convolutional neural networks (CNNs) constitute a widely used deep learning approach that has frequently been applied to the problem of brain tumor diagnosis. Such techniques still face some critical challenges in moving towards clinic application. The main objective of this work is to present a comprehensive review of studies using CNN architectures to classify brain tumors using MR images with the aim of identifying useful strategies for and possible impediments in the development of this technology. Relevant articles were identified using a predefined, systematic procedure. For each article, data were extracted regarding training data, target problems, the network architecture, validation methods, and the reported quantitative performance criteria. The clinical relevance of the studies was then evaluated to identify limitations by considering the merits of convolutional neural networks and the remaining challenges that need to be solved to promote the clinical application and development of CNN algorithms. Finally, possible directions for future research are discussed for researchers in the biomedical and machine learning communities. A total of 83 studies were identified and reviewed. They differed in terms of the precise classification problem targeted and the strategies used to construct and train the chosen CNN. Consequently, the reported performance varied widely, with accuracies of 91.63-100% in differentiating meningiomas, gliomas, and pituitary tumors (26 articles) and of 60.0-99.46% in distinguishing low-grade from high-grade gliomas (13 articles). The review provides a survey of the state of the art in CNN-based deep learning methods for brain tumor classification. Many networks demonstrated good performance, and it is not evident that any specific methodological choice greatly outperforms the alternatives, especially given the inconsistencies in the reporting of validation methods, performance metrics, and training data encountered. Few studies have focused on clinical usability.

Multishell Diffusion MR Tractography Yields Morphological and Microstructural Information of the Anterior Optic Pathway: A Proof-of-Concept Study in Patients with Leber's Hereditary Optic Neuropathy.

Tractography based on multishell diffusion-weighted magnetic resonance imaging (DWI) can be used to estimate the course of myelinated white matter tracts and nerves, yielding valuable information regarding normal anatomy and variability. DWI is sensitive to the local tissue microstructure, so tractography can be used to estimate tissue properties within nerve tracts at a resolution of millimeters. This study aimed to test the applicability of the method using a disease with a well-established pattern of myelinated nerve involvement. Eight patients with LHON and 13 age-matched healthy controls underwent tractography of the anterior optic pathway. Diffusion parameters were compared between groups, and for the patient group correlated with clinical/ophthalmological parameters. Tractography established the course of the anterior optic pathway in both patients and controls. Localized changes in fractional anisotropy were observed, and related to estimates of different tissue compartments within the nerve and tract. The proportion of different compartments correlated with markers of disease severity. The method described allows both anatomical localization and tissue characterization in vivo, permitting both visualization of variation at the individual level and statistical inference at the group level. It provides a valuable adjunct to ex vivo anatomical and histological study of normal variation and disease processes.

A deep learning approach for magnetic resonance fingerprinting: Scaling capabilities and good training practices investigated by simulations.

MR fingerprinting (MRF) is an innovative approach to quantitative MRI. A typical disadvantage of dictionary-based MRF is the explosive growth of the dictionary as a function of the number of reconstructed parameters, an instance of the curse of dimensionality, which determines an explosion of resource requirements. In this work, we describe a deep learning approach for MRF parameter map reconstruction using a fully connected architecture. Employing simulations, we have investigated how the performance of the Neural Networks (NN) approach scales with the number of parameters to be retrieved, compared to the standard dictionary approach. We have also studied optimal training procedures by comparing different strategies for noise addition and parameter space sampling, to achieve better accuracy and robustness to noise. Four MRF sequences were considered: IR-FISP, bSSFP, IR-FISP-B1, and IR-bSSFP-B1. A comparison between NN and the dictionary approaches in reconstructing parameter maps as a function of the number of parameters to be retrieved was performed using a numerical brain phantom. Results demonstrated that training with random sampling and different levels of noise variance yielded the best performance. NN performance was at least as good as the dictionary-based approach in reconstructing parameter maps using Gaussian noise as a source of artifacts: the difference in performance increased with the number of estimated parameters because the dictionary method suffers from the coarse resolution of the parameter space sampling. The NN proved to be more efficient in memory usage and computational burden, and has great potential for solving large-scale MRF problems.

Stridor-related gray matter alterations in multiple system atrophy: A pilot study.

INTRODUCTION: The neuroanatomical substrate of stridor associated with Multiple System Atrophy (MSA) remains unclear. We evaluated stridor-related gray matter (GM) changes in MSA. METHODS: 36 MSA patients underwent standardized nocturnal video-polysomnography and brain MRI. Differences in GM density between MSA patients with and without stridor and a sample of 22 matched healthy controls were evaluated with Voxel Based Morphometry protocol supplemented by a specific tool (SUIT) for analysing infratentorial structures. RESULTS: Stridor was confirmed in 14 patients (10 MSA-cerebellar variant; 10 M; mean ±â€¯SD age = 61.6 ±â€¯8.9years; disease duration = 5.2 ±â€¯2.9years) and absent in 22 (11 MSA-cerebellar variant; 18 M; age = 61.4 ±â€¯9.9years; disease duration = 4.8 ±â€¯3.4years). Compared to MSA without stridor, patients with stridor showed higher GM density in the cerebellum (p < 0.05, corrected for the MSA-cerebellar variant and uncorrected when considering both MSA-variants) and lower in the striatum (p < 0.05, uncorrected). CONCLUSIONS: This preliminary study has demonstrated for the first time in MSA stridor-related GM changes in striatal and cerebellar regions. Abnormalities in these regions were previously reported in dystonic disorders affecting laryngeal muscles, suggesting the hypothesis that stridor pathophysiology is dystonia-related. These results need however to be confirmed in a larger sample of patients.

L-Dopa Modulation of Brain Connectivity in Parkinson's Disease Patients: A Pilot EEG-fMRI Study.

Studies of functional neurosurgery and electroencephalography in Parkinson's disease have demonstrated abnormally synchronous activity between basal ganglia and motor cortex. Functional neuroimaging studies investigated brain dysfunction during motor task or resting state and primarily have shown altered patterns of activation and connectivity for motor areas. L-dopa administration relatively normalized these functional alterations. The aim of this pilot study was to examine the effects of L-dopa administration on functional connectivity in early-stage PD, as revealed by simultaneous recording of functional magnetic resonance imaging (fMRI) and electroencephalographic (EEG) data. Six patients with diagnosis of probable PD underwent EEG-fMRI acquisitions (1.5 T MR scanner and 64-channel cap) before and immediately after the intake of L-dopa. Regions of interest in the primary motor and sensorimotor regions were used for resting state fMRI analysis. From the EEG data, weighted partial directed coherence was computed in the inverse space after the removal of gradient and cardioballistic artifacts. fMRI results showed that the intake of L-dopa increased functional connectivity within the sensorimotor network, and between motor areas and both attention and default mode networks. EEG connectivity among regions of the motor network did not change significantly, while regions of the default mode network showed a strong tendency to increase their outflow toward the rest of the brain. This pilot study provided a first insight into the potentiality of simultaneous EEG-fMRI acquisitions in PD patients, showing for both techniques the analogous direction of increased connectivity after L-dopa intake, mainly involving motor, dorsal attention and default mode networks.

Along-tract analysis of the arcuate fasciculus using the Laplacian operator to evaluate different tractography methods.

PURPOSE: We propose a new along-tract algorithm to compare different tractography algorithms in tract curvature mapping and along-tract analysis of the arcuate fasciculus (AF). In particular, we quantified along-tract diffusion parameters and AF spatial distribution evaluating hemispheric asymmetries in a group of healthy subjects. METHODS: The AF was bilaterally reconstructed in a group of 29 healthy subjects using the probabilistic ball-and-sticks model, and both deterministic and probabilistic constrained spherical deconvolution. We chose cortical ROIs as tractography targets and the developed along-tract algorithm used the Laplacian operator to parameterize the volume of the tract, allowing along-tract analysis and tract curvature mapping independent of the tractography algorithm used. RESULTS: The Laplacian parameterization successfully described the tract geometry underlying hemispheric asymmetries in the AF curvature. Using the probabilistic tractography methods, we found more tracts branching towards cortical terminations in the left hemisphere. This influenced the left AF curvature and its diffusion parameters, which were significantly different with respect to the right. In particular, we detected projections towards the middle temporal and inferior frontal gyri bilaterally, and towards the superior temporal and precentral gyri in the left hemisphere, with a significantly increased volume and connectivity. CONCLUSIONS: The approach we propose is useful to evaluate brain asymmetries, assessing the volume, the diffusion properties and the quantitative spatial localization of the AF.

Mitochondrial dysfunction in myotonic dystrophy type 1.

The pathophysiological mechanism linking the nucleotide expansion in the DMPK gene to the clinical manifestations of myotonic dystrophy type 1 (DM1) is still unclear. In vitro studies demonstrate DMPK involvement in the redox homeostasis of cells and the mitochondrial dysfunction in DM1, but in vivo investigations of oxidative metabolism in skeletal muscle have provided ambiguous results and have never been performed in the brain. Twenty-five DM1 patients (14M, 39 ± 11years) underwent brain proton MR spectroscopy (1H-MRS), and sixteen cases (9M, 40 ± 13 years old) also calf muscle phosphorus MRS (31P-MRS). Findings were compared to those of sex- and age-matched controls. Eight DM1 patients showed pathological increase of brain lactate and, compared to those without, had larger lateral ventricles (p < 0.01), smaller gray matter volumes (p < 0.05) and higher white matter lesion load (p < 0.05). A reduction of phosphocreatine/inorganic phosphate (p < 0.001) at rest and, at first minute of exercise, a lower [phosphocreatine] (p = 0.003) and greater [ADP] (p = 0.004) were found in DM1 patients compared to controls. The post-exercise indices of muscle oxidative metabolism were all impaired in DM1, including the increase of time constant of phosphocreatine resynthesis (TC PCr, p = 0.038) and the reduction of the maximum rate of mitochondrial ATP synthesis (p = 0.033). TC PCr values correlated with the myotonic area score (ρ = 0.74, p = 0.01) indicating higher impairment of muscle oxidative metabolism in clinically more affected patients. Our findings provide clear in vivo evidence of multisystem impairment of oxidative metabolism in DM1 patients, providing a rationale for targeted treatment enhancing energy metabolism.

Brain functional connectivity in sleep-related hypermotor epilepsy.

Objectives: To evaluate functional connectivity (FC) in patients with sleep-related hypermotor epilepsy (SHE) compared to healthy controls. Methods: Resting state fMRI was performed in 13 patients with a clinical diagnosis of SHE (age = 38.3 ± 11.8 years, 6 M) and 13 matched healthy controls (age = 38.5 ± 10.8 years, 6 M).Data were first analysed using probabilistic independent component analysis (ICA), then a graph theoretical approach was applied to assess topological and organizational properties at the whole brain level. We evaluated node degree (ND), betweenness centrality (BC), clustering coefficient (CC), local efficiency (LE) and global efficiency (GE). The differences between the two groups were evaluated non-parametrically. Results: At the group level, we distinguished 16 RSNs (Resting State Networks). Patients showed a significantly higher FC in sensorimotor and thalamic regions (p < 0.05 corrected). Compared to controls, SHE patients showed no significant differences in network global efficiency, while ND and BC were higher in regions of the limbic system and lower in the occipital cortex, while CC and LE were higher in regions of basal ganglia and lower in limbic areas (p < 0.05 uncorrected). Discussion and conclusions: The higher FC of the sensorimotor cortex and thalamus might be in agreement with the hypothesis of a peculiar excitability of the motor cortex during thalamic K-complexes. This sensorimotor-thalamic hyperconnection might be regarded as a consequence of an alteration of the arousal regulatory system in SHE. An altered topology has been found in structures like basal ganglia and limbic system, hypothesized to be involved in the pathophysiology of the disease as suggested by the dystonic-dyskinetic features and primitive behaviours observed during the seizures.

Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines.

BACKGROUND AND PURPOSE: In this study we attempt to automatically classify individual patients with different parkinsonian disorders, making use of pattern recognition techniques to distinguish among several forms of parkinsonisms (multi-class classification), based on a set of binary classifiers that discriminate each disorder from all others. METHODS: We combine diffusion tensor imaging, proton spectroscopy and morphometric-volumetric data to obtain MR quantitative markers, which are provided to support vector machines with the aim of recognizing the different parkinsonian disorders. Feature selection is used to find the most important features for classification. We also exploit a graph-based technique on the set of quantitative markers to extract additional features from the dataset, and increase classification accuracy. RESULTS: When graph-based features are not used, the MR markers that are most frequently automatically extracted by the feature selection procedure reflect alterations in brain regions that are also usually considered to discriminate parkinsonisms in routine clinical practice. Graph-derived features typically increase the diagnostic accuracy, and reduce the number of features required. CONCLUSIONS: The results obtained in the work demonstrate that support vector machines applied to multimodal brain MR imaging and using graph-based features represent a novel and highly accurate approach to discriminate parkinsonisms, and a useful tool to assist the diagnosis.