Purpose: In this work, we introduce a method to classify Multiple Sclerosis (MS) patients into four clinical profiles using structural connectivity information. For the first time, we try to solve this question in a fully automated way using a computer-based method. The main goal is to show how the combination of graph-derived metrics with machine learning techniques constitutes a powerful tool for a better characterization and classification of MS clinical profiles.Materials and Methods: Sixty-four MS patients [12 Clinical Isolated Syndrome (CIS), 24 Relapsing Remitting (RR), 24 Secondary Progressive (SP), and 17 Primary Progressive (PP)] along with 26 healthy controls (HC) underwent MR examination. T1 and diffusion tensor imaging (DTI) were used to obtain structural connectivity matrices for each subject. Global graph metrics, such as density and modularity, were estimated and compared between subjects' groups. These metrics were further used to classify patients using tuned Support Vector Machine (SVM) combined with Radial Basic Function (RBF) kernel.Results: When comparing MS patients to HC subjects, a greater assortativity, transitivity, and characteristic path length as well as a lower global efficiency were found. Using all graph metrics, the best F-Measures (91.8, 91.8, 75.6, and 70.6%) were obtained for binary (HC-CIS, CIS-RR, RR-PP) and multi-class (CIS-RR-SP) classification tasks, respectively. When using only one graph metric, the best F-Measures (83.6, 88.9, and 70.7%) were achieved for modularity with previous binary classification tasks.Conclusion: Based on a simple DTI acquisition associated with structural brain connectivity analysis, this automatic method allowed an accurate classification of different MS patients' clinical profiles.

Benign childhood epilepsy with centro-temporal spikes (BCECTS) is a unique form of non-lesional age-dependent epilepsy with rare seizures, focal electroencepalographic abnormalities affecting the same well delineated cortical region in most patients, and frequent mild to moderate cognitive dysfunctions. In this condition, it is hypothesized that interictal electroencepalographic discharges might interfere with local brain maturation, resulting in altered cognition. Diffusion tensor imaging allows testing of this hypothesis by investigating the white matter microstructure, and has previously proved sensitive to epilepsy-related alterations of fractional anisotropy and diffusivity. However, no diffusion tensor imaging study has yet been performed with a focus on BCECTS. We investigated 25 children suffering from BCECTS and 25 age-matched control subjects using diffusion tensor imaging, 3D-T 1 magnetic resonance imaging, and a battery of neuropsychological tests including Conner's scale and Wechsler Intelligence Scale for Children (fourth revision). Electroencephalography was also performed in all patients within 2 months of the magnetic resonance imaging assessment. Parametric maps of fractional anisotropy, mean-, radial-, and axial diffusivity were extracted from diffusion tensor imaging data. Patients were compared with control subjects using voxel-based statistics and family-wise error correction for multiple comparisons. Each patient was also compared to control subjects. Fractional anisotropy and diffusivity images were correlated to neuropsychological and clinical variables. Group analysis showed significantly reduced fractional anisotropy and increased diffusivity in patients compared with control subjects, predominantly over the left pre-and postcentral gyri and ipsilateral to the electroencephalographic focus. At the individual level, regions of significant differences were observed in 10 patients (40%) for anisotropy (eight reduced fractional anisotropy, one increased fractional anisotropy, one both), and 17 (56%) for diffusivity (13 increased, one reduced, three both). There were significant negative correlations between fractional anisotropy maps and duration of epilepsy in the precentral gyri, bilaterally, and in the left postcentral gyrus. Accordingly, 9 of 12 patients (75%) with duration of epilepsy 412 months showed significantly reduced fractional anisotropy versus none of the 13 patients with duration of epilepsy 412 months. Diffusivity maps positively correlated with duration of epilepsy in the cuneus. Children with BCECTS demonstrate alterations in the microstructure of the white matter, undetectable with conventional magnetic resonance imaging, predominating over the regions displaying chronic interictal epileptiform discharges. The association observed between diffusion tensor imaging changes, duration of epilepsy and cognitive performance appears compatible with the hypothesis that interictal epileptic activity alters brain maturation, which could in turn lead to cognitive dysfunction. Howev...

BACKGROUND AND PURPOSE:MS is an inflammatory demyelinating disease affecting both WM and GM. While WM lesions are easily visualized by conventional MR imaging, the detection of GM alterations remains challenging. This diffusion tensor MR imaging study aimed to detect and characterize diffuse microscopic alterations in 2 deep GM structures, the caudate nucleus and the thalamus, in patients with RR and SP MS. The relationship between diffusivity markers, and atrophy of the caudate and the thalamus, as well as brain lesion load and clinical status of the patients was also explored.

BACKGROUND AND PURPOSE:Brain volume loss is currently a MR imaging marker of neurodegeneration in MS. Available quantification algorithms perform either direct (segmentation-based techniques) or indirect (registration-based techniques) measurements. Because there is no reference standard technique, the assessment of their accuracy and reliability remains a difficult goal. Therefore, the purpose of this work was to assess the robustness of 7 different postprocessing algorithms applied to images acquired from different MR imaging systems.

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Diffusion tensor imaging (DTI) and MR spectroscopic imaging (MRSI) provide greater sensitivity than conventional MRI to detect diffuse alterations in normal appearing white matter (NAWM) of Multiple Sclerosis (MS) patients with different clinical forms. Therefore, the goal of this study is to combine DTI and MRSI measurements to analyze the relation between diffusion and metabolic markers, T2-weighted lesion load (T2-LL) and the patients clinical status. The sensitivity and specificity of both methods were then compared in terms of MS clinical forms differentiation. MR examination was performed on 71 MS patients (27 relapsing remitting (RR), 26 secondary progressive (SP) and 18 primary progressive (PP)) and 24 control subjects. DTI and MRSI measurements were obtained from two identical regions of interest selected in left and right centrum semioval (CSO) WM. DTI metrics and metabolic contents were significantly altered in MS patients with the exception of N-acetyl-aspartate (NAA) and NAA/Choline (Cho) ratio in RR patients. Significant correlations were observed between diffusion and metabolic measures to various degrees in every MS patients group. Most DTI metrics were significantly correlated with the T2-LL while only NAA/Cr ratio was correlated in RR patients. A comparison analysis of MR methods efficiency demonstrated a better sensitivity/specificity of DTI over MRSI. Nevertheless, NAA/Cr ratio could distinguish all MS and SP patients groups from controls, while NAA/Cho ratio differentiated PP patients from controls. This study demonstrated that diffusivity changes related to microstructural alterations were correlated with metabolic changes and provided a better sensitivity to detect early changes, particularly in RR patients who are more subject to inflammatory processes. In contrast, the better specificity of metabolic ratios to detect axonal damage and demyelination may provide a better index for identification of PP patients.

This study aimed to characterize the neural networks involved in patients with chronic low-back pain during hypnoanalgesia. PET was performed in 2 states of consciousness, normal alertness and hypnosis. Two groups of patients received direct or indirect analgesic suggestion. The normal alertness state showed activations in a cognitive-sensory pain modulation network, including frontotemporal cortex, insula, somatosensory cortex, and cerebellum. The hypnotic state activated an emotional pain modulation network, including frontotemporal cortex, insula, caudate, accumbens, lenticular nuclei, and anterior cingulate cortex (ACC). Direct suggestion activated cognitive processes via frontal, prefrontal, and orbitofrontal cortices, while indirect suggestion activated a widespread and more emotional network including frontal cortex, anterior insula, inferior parietal lobule, lenticular nucleus, and ACC. Confirmed by visual analog scale data, these results suggest that chronic pain modulation is greater with hypnosis, which enhances both activated networks.

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