Correlation of diffusion and metabolic alterations in different clinical forms of multiple sclerosis. PLoS One. 2012;7:e32525. [PMID: 22479330 PMCID: PMC3316537 DOI: 10.1371/journal.pone.0032525]

Associations between N-Acetylaspartate and white matter integrity in individuals with schizophrenia and unaffected relatives

Compromised white matter has been reported in schizophrenia; however, few studies have investigated neurochemical abnormalities underlying microstructural differences. N-acetylaspartate (NAA) is used to synthesize myelin and is often reduced in persons with schizophrenia (PSZ) and their unaffected first-degree relatives (REL). Low levels of NAA could affect white matter by preventing the synthesis or repair of myelin. We used magnetic resonance spectroscopy and diffusion tensor imaging to investigate the relationship between NAA and white matter integrity in PSZ. REL were included to examine whether putative relationships are associated with symptom expression or illness liability. 52 controls, 23 REL and 25 PSZ underwent 7T proton magnetic resonance spectroscopy and/or 3T diffusion tensor imaging. NAA in the visual cortex and basal ganglia were measured and compared across groups. Diffusivity measures were compared across groups using tract-based spatial statistics and related to NAA concentrations. Visual cortex NAA was significantly reduced in PSZ compared to controls. White matter integrity did not differ between groups. Reduced cortical and subcortical NAA were associated with diffusivity measures of poor white matter microstructure. These data suggest that levels of neural NAA may be related to white matter integrity similarly across individuals with schizophrenia, those at genetic risk, and controls.

Magnetic Resonance Spectroscopy Metabolites as Biomarkers of Disease Status in Pediatric Diffuse Intrinsic Pontine Gliomas (DIPG) Treated with Glioma-Associated Antigen Peptide Vaccines

PURPOSE

Diffuse intrinsic pontine gliomas (DIPG) are highly aggressive tumors with no currently available curative therapy. This study evaluated whether measurements of in vivo cell metabolites using magnetic resonance spectroscopy (MRS) may serve as biomarkers of response to therapy, including progression.

METHODS

Single-voxel MR spectra were serially acquired in two cohorts of patients with DIPG treated with radiation therapy (RT) with or without concurrent chemotherapy and prior to progression: 14 participants were enrolled in a clinical trial of adjuvant glioma-associated antigen peptide vaccines and 32 patients were enrolled who did not receive adjuvant vaccine therapy. Spearman correlations measured overall survival associations with absolute metabolite concentrations of myo-inositol (mI), creatine (Cr), and n-acetyl-aspartate (NAA) and their ratios relative to choline (Cho) during three specified time periods following completion of RT. Linear mixed-effects regression models evaluated the longitudinal associations between metabolite ratios and time from death (terminal decline).

RESULTS

Overall survival was not associated with metabolite ratios obtained shortly after RT (1.9-3.8 months post-diagnosis) in either cohort. In the vaccine cohort, an elevated mI/Cho ratio after 2-3 doses (3.9-5.2 months post-diagnosis) was associated with longer survival (rho = 0.92, 95% CI 0.67-0.98). Scans performed up to 6 months before death showed a terminal decline in the mI/Cho ratio, with an average of 0.37 ratio/month in vaccine patients (95% CI 0.11-0.63) and 0.26 (0.04-0.48) in the non-vaccine cohort.

CONCLUSION

Higher mI/Cho ratios following RT, consistent with less proliferate tumors and decreased cell turnover, were associated with longer survival, suggesting that this ratio can serve as a biomarker of prognosis following RT. This finding was seen in both cohorts, although the association with OS was detected earlier in the vaccine cohort. Increased mI/Cho (possibly reflecting immune-effector cell influx into the tumor as a mechanism of tumor response) requires further study.

Neural diffusion tensor imaging metrics correlate with clinical measures in people with relapsing-remitting MS

BACKGROUND AND PURPOSE

Diffusion tensor imaging (DTI) can detect microstructural changes of white matter in multiple sclerosis (MS) and might clarify mechanisms responsible for disability. Thus, we aimed to compare DTI metrics in relapsing-remitting MS patients (RRMS) with healthy controls (HCs), and explore the correlations between DTI metrics, total brain white matter (TBWM) and white matter lesion (WML) with clinical parameters compared to volumetric measures.

MATERIAL AND METHODS

37 RRMS patients and 19 age/sex-matched HCs were included. All participants had clinical assessments, structural and diffusion scans on a 3T MRI. Volumetric and white matter DTI metrics; fractional anisotropy (FA), mean, radial and axial diffusivities (MD, RD and AD) were estimated and correlated with clinical parameters. The mean group differences were calculated using t-tests, and univariate correlations with Pearson correlation coefficients.

RESULTS

Compared to HCs, statistically significant increases in MD (+3.6%), RD (+4.8%), AD (+2.7%) and a decrease in FA (-4.3%) for TBWM in RRMS was observed (p < .01). MD and RD in TBWM and AD in WML correlated moderately with disability status. Volumetric segmentation indicated a decrease in the total brain volume, GM and WM(-5%) with a reciprocal increase in CSF(+26%) in RRMS(p < .01). Importantly, DTI parameters showed a medium correlation with cognitive domains in contrast to white matter-related volumetric measurements in RRMS(Pearson correlation, p < .05).

CONCLUSIONS

Our study shows a correlation of DTI metrics with clinical symptoms of MS, in particular cognition. More generally, these findings indicated that DTI is a useful and unique technique for evaluating the clinical features of white matter disease and warrants further investigation into its clinical role.

T1/T2 ratio: A quantitative sensitive marker of brain tissue integrity in multiple sclerosis

BACKGROUND AND PURPOSE

The aim of this study is to determine whether cerebral white matter (WM) microstructural damage, defined by decreased fractional anisotropy (FA) and increased axial (AD) and radial (RD) diffusivities, could be detected as accurately by measuring the T1/T2 ratio, in relapsing-remitting multiple sclerosis (RRMS) patients compared to healthy control (HC) subjects.

METHODS

Twenty-eight RRMS patients and 24 HC subjects were included in this study. Region-based analysis based on the ICBM-81 diffusion tensor imaging (DTI) atlas WM labels was performed to compare T1/T2 ratio to DTI values in normal-appearing WM (NAWM) regions of interest. Lesions segmentation was also performed and compared to the HC global WM.

RESULTS

A significant 19.65% decrease of T1/T2 ratio values was observed in NAWM regions of RRMS patients compared to HC. A significant 6.30% decrease of FA, as well as significant 4.76% and 10.27% increases of AD and RD, respectively, were observed in RRMS compared to the HC group in various NAWM regions. Compared to the global WM HC mask, lesions have significantly decreased T1/T2 ratio and FA and increased AD and RD (p < . 001).

CONCLUSIONS

Results showed significant differences between RRMS and HC in both DTI and T1/T2 ratio measurements. T1/T2 ratio even demonstrated extensive WM abnormalities when compared to DTI, thereby highlighting the ratio's sensitivity to subtle differences in cerebral WM structural integrity using only conventional MRI sequences.

Alterations in Sensorimotor and Mesiotemporal Cortices and Diffuse White Matter Changes in Primary Progressive Multiple Sclerosis Detected by Adiabatic Relaxometry

Background: The research of primary progressive multiple sclerosis (PPMS) has not been able to capitalize on recent progresses in advanced magnetic resonance imaging (MRI) protocols.

Objective: The presented cross-sectional study evaluated the utility of four different MRI relaxation metrics and diffusion-weighted imaging in PPMS.

Methods: Conventional free precession T1 and T2, and rotating frame adiabatic T1ρ and T2ρ in combination with diffusion-weighted parameters were acquired in 13 PPMS patients and 13 age- and sex-matched controls.

Results: T1ρ, a marker of crucial relevance for PPMS due to its sensitivity to neuronal loss, revealed large-scale changes in mesiotemporal structures, the sensorimotor cortex, and the cingulate, in combination with diffuse alterations in the white matter and cerebellum. T2ρ, particularly sensitive to local tissue background gradients and thus an indicator of iron accumulation, concurred with similar topography of damage, but of lower extent. Moreover, these adiabatic protocols outperformed both conventional T1 and T2 maps and diffusion tensor/kurtosis approaches, methods previously used in the MRI research of PPMS.

Conclusion: This study introduces adiabatic T1ρ and T2ρ as elegant markers confirming large-scale cortical gray matter, cerebellar, and white matter alterations in PPMS invisible to other in vivo biomarkers.

NAA is a Marker of Disability in Secondary-Progressive MS: A Proton MR Spectroscopic Imaging Study

BACKGROUND AND PURPOSE

The secondary progressive phase of multiple sclerosis is characterised by disability progression due to processes that lead to neurodegeneration. Surrogate markers such as those derived from MRI are beneficial in understanding the pathophysiology that drives disease progression and its relationship to clinical disability. We undertook a 1H-MRS imaging study in a large secondary progressive MS (SPMS) cohort, to examine whether metabolic markers of brain injury are associated with measures of disability, both physical and cognitive.

MATERIALS AND METHODS

A cross-sectional analysis of individuals with secondary-progressive MS was performed in 119 participants. They underwent ¹H-MR spectroscopy to obtain estimated concentrations and ratios to total Cr for total NAA, mIns, Glx, and total Cho in normal-appearing WM and GM. Clinical outcome measures chosen were the following: Paced Auditory Serial Addition Test, Symbol Digit Modalities Test, Nine-Hole Peg Test, Timed 25-foot Walk Test, and the Expanded Disability Status Scale. The relationship between these neurometabolites and clinical disability measures was initially examined using Spearman rank correlations. Significant associations were then further analyzed in multiple regression models adjusting for age, sex, disease duration, T2 lesion load, normalized brain volume, and occurrence of relapses in 2 years preceding study entry.

RESULTS

Significant associations, which were then confirmed by multiple linear regression, were found in normal-appearing WM for total NAA (tNAA)/total Cr (tCr) and the Nine-Hole Peg Test (ρ = 0.23; 95% CI, 0.06-0.40); tNAA and tNAA/tCr and the Paced Auditory Serial Addition Test (ρ = 0.21; 95% CI, 0.03-0.38) (ρ = 0.19; 95% CI, 0.01-0.36); mIns/tCr and the Paced Auditory Serial Addition Test, (ρ = -0.23; 95% CI, -0.39 to -0.05); and in GM for tCho and the Paced Auditory Serial Addition Test (ρ = -0.24; 95% CI, -0.40 to -0.06). No other GM or normal-appearing WM relationships were found with any metabolite, with associations found during initial correlation testing losing significance after multiple linear regression analysis.

CONCLUSIONS

This study suggests that metabolic markers of neuroaxonal integrity and astrogliosis in normal-appearing WM and membrane turnover in GM may act as markers of disability in secondary-progressive MS.

Quantifying the Metabolic Signature of Multiple Sclerosis by in vivo Proton Magnetic Resonance Spectroscopy: Current Challenges and Future Outlook in the Translation From Proton Signal to Diagnostic Biomarker

Proton magnetic resonance spectroscopy (¹H-MRS) offers a growing variety of methods for querying potential diagnostic biomarkers of multiple sclerosis in living central nervous system tissue. For the past three decades, ¹H-MRS has enabled the acquisition of a rich dataset suggestive of numerous metabolic alterations in lesions, normal-appearing white matter, gray matter, and spinal cord of individuals with multiple sclerosis, but this body of information is not free of seeming internal contradiction. The use of ¹H-MRS signals as diagnostic biomarkers depends on reproducible and generalizable sensitivity and specificity to disease state that can be confounded by a multitude of influences, including experiment group classification and demographics; acquisition sequence; spectral quality and quantifiability; the contribution of macromolecules and lipids to the spectroscopic baseline; spectral quantification pipeline; voxel tissue and lesion composition; T₁ and T₂ relaxation; B₁ field characteristics; and other features of study design, spectral acquisition and processing, and metabolite quantification about which the experimenter may possess imperfect or incomplete information. The direct comparison of ¹H-MRS data from individuals with and without multiple sclerosis poses a special challenge in this regard, as several lines of evidence suggest that experimental cohorts may differ significantly in some of these parameters. We review the existing findings of in vivo¹H-MRS on central nervous system metabolic abnormalities in multiple sclerosis and its subtypes within the context of study design, spectral acquisition and processing, and metabolite quantification and offer an outlook on technical considerations, including the growing use of machine learning, by future investigations into diagnostic biomarkers of multiple sclerosis measurable by ¹H-MRS.

Classification of Multiple Sclerosis Clinical Profiles via Graph Convolutional Neural Networks

Recent advances in image acquisition and processing techniques, along with the success of novel deep learning architectures, have given the opportunity to develop innovative algorithms capable to provide a better characterization of neurological related diseases. In this work, we introduce a neural network based approach to classify Multiple Sclerosis (MS) patients into four clinical profiles. Starting from their structural connectivity information, obtained by diffusion tensor imaging and represented as a graph, we evaluate the classification performances using unweighted and weighted connectivity matrices. Furthermore, we investigate the role of graph-based features for a better characterization and classification of the pathology. Ninety MS patients (12 clinically isolated syndrome, 30 relapsing-remitting, 28 secondary-progressive, and 20 primary-progressive) along with 24 healthy controls, were considered in this study. This work shows the great performances achieved by neural networks methods in the classification of the clinical profiles. Furthermore, it shows local graph metrics do not improve the classification results suggesting that the latent features created by the neural network in its layers have a much important informative content. Finally, we observe that graph weights representation of brain connections preserve important information to discriminate between clinical forms.

Bu Shen Yi Sui capsule promotes remyelination correlating with Sema3A/NRP-1, LIF/LIFR and Nkx6.2 in mice with experimental autoimmune encephalomyelitis

ETHNOPHARMACOLOGICAL RELEVANCE

Bu Shen Yi Sui capsule (BSYSC), based on traditional Chinese formula Liu Wei Di Huang pill, is effective for the treatment of multiple sclerosis (MS) in clinical experience and trials. Our previous studies confirmed that BSYSC had the neuroprotective effect in MS and its animal model, experimental autoimmune encephalomyelitis (EAE); however, its mechanism of action was not clear. Thus, the effect of BSYSC on remyelination and the underlying mechanisms were investigated in the EAE mice.

MATERIALS AND METHODS

The EAE model was established by injecting subcutaneously myelin oligodendrocyte protein (MOG) _35-55 in mice. Mice were treated with BSYSC (3.02 g/kg) or vehicle daily by oral gavage for 40 days. The body weight and clinical score of mice were evaluated. Brain was observed by magnetic resonance imaging. The inflammation infiltrate of brain and spinal cord was determined by hematoxylin-eosin staining, while the structure of myelin sheath was visualized by transmission electron microscopy on days 23 and 40 post immunization (dpi), respectively. The protein and mRNA levels of platelets-derived growth factor receptor (PDGFR) α and 2', 3'-cyclic nucleotide-3'-phosphodiesterase (CNPase) were measured by immunohistochemistry, western blot and quantitative real-time polymerase chain reaction. The protein expressions of semaphorins (Sema) 3A, Neuropilin (NRP) - 1, leukemia inhibitory factor (LIF), LIF receptor (LIFR) and Nkx6.2 were further investigated by western blot.

RESULTS

BSYSC treatment improved the body weight and clinical score of EAE mice, alleviated inflammatory infiltration and nerve fiber injuries. It also protected the ultrastructural integrity of myelin sheath. BSYSC significantly increased expressions of PDGFRα and CNPase in mice with EAE on 40 dpi. Furthermore, BSYSC treatment increased the expressions of LIF, LIFR and Nkx6.2 and reduced Sema3A and NRP-1 in EAE mice on 40 dpi.

CONCLUSIONS

The data demonstrated that BSYSC exhibited the neuroprotective effect against EAE by promoting oligodendrocyte progenitor cells (OPCs) proliferation and differentiation, thus facilitating remyelination. Sema3A/NRP-1, LIF/LIFR and Nkx6.2 are likely contributed to the effects of BSYSC on OPCs.

Weekly follow up of acute lesions in three early multiple sclerosis patients using MR spectroscopy and diffusion

OBJECT

Pathophysiological mechanisms underlying multiple sclerosis (MS) lesion formation, including inflammation, demyelination/remyelination and axonal damage, and their temporal evolution are still not clearly understood. To this end, three acute white matter lesions were monitored using a weekly multimodal magnetic resonance (MR) protocol.

MATERIALS AND METHODS

Three untreated patients with early relapsing-remitting MS and one healthy control subject were followed weekly for two months. MR protocol included conventional MR imaging (MRI), diffusion tensor imaging (DTI), and localized MR spectroscopy (MRS), performed on the largest gadolinium-enhancing lesion, selected at the first exam.

RESULTS

Mean diffusivity increased and fractional anisotropy decreased in lesions compared to healthy control. Cho/Cr ratios remained elevated in lesions throughout the follow-up. In contrast, temporal profiles of mI/Cr ratios varied between patients' lesions. For patient 1, mI/Cr ratios were already elevated at the beginning of the follow-up. Patients 2 and 3 ratios increase was delayed by two and five weeks. Blood-brain barrier (BBB) recovery occurred after three weeks.

CONCLUSION

This multimodal MR follow-up highlighted the complementary role of DTI and MRS in identifying temporal relationships between BBB disruption, inflammation, and demyelination. Diffusion metrics showed high sensitivity to detect inflammatory processes. The different temporal profiles of mI suggested a potential better specificity to monitor pathological mechanisms occurring after lesion formation, such as glial proliferation and remyelination.

Calibrated imaging reveals altered grey matter metabolism related to white matter microstructure and symptom severity in multiple sclerosis

Multiple sclerosis (MS) involves damage to white matter microstructures. This damage has been related to grey matter function as measured by standard, physiologically-nonspecific neuroimaging indices (i.e., blood-oxygen-level dependent signal [BOLD]). Here, we used calibrated functional magnetic resonance imaging and diffusion tensor imaging to examine the extent to which specific, evoked grey matter physiological processes were associated with white matter diffusion in MS. Evoked changes in BOLD, cerebral blood flow (CBF), and oxygen metabolism (CMRO₂ ) were measured in visual cortex. Individual differences in the diffusion tensor measure, radial diffusivity, within occipital tracts were strongly associated with MS patients' BOLD and CMRO₂ . However, these relationships were in opposite directions, complicating the interpretation of the relationship between BOLD and white matter microstructural damage in MS. CMRO₂ was strongly associated with individual differences in patients' fatigue and neurological disability, suggesting that alterations to evoked oxygen metabolic processes may be taken as a marker for primary symptoms of MS. This work demonstrates the first application of calibrated and diffusion imaging together and details the first application of calibrated functional MRI in a neurological population. Results lend support for neuroenergetic hypotheses of MS pathophysiology and provide an initial demonstration of the utility of evoked oxygen metabolism signals for neurology research. Hum Brain Mapp 38:5375-5390, 2017. © 2017 Wiley Periodicals, Inc.

Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features

Purpose: The purpose of this study is classifying multiple sclerosis (MS) patients in the four clinical forms as defined by the McDonald criteria using machine learning algorithms trained on clinical data combined with lesion loads and magnetic resonance metabolic features.

Materials and Methods: Eighty-seven MS patients [12 Clinically Isolated Syndrome (CIS), 30 Relapse Remitting (RR), 17 Primary Progressive (PP), and 28 Secondary Progressive (SP)] and 18 healthy controls were included in this study. Longitudinal data available for each MS patient included clinical (e.g., age, disease duration, Expanded Disability Status Scale), conventional magnetic resonance imaging and spectroscopic imaging. We extract N-acetyl-aspartate (NAA), Choline (Cho), and Creatine (Cre) concentrations, and we compute three features for each spectroscopic grid by averaging metabolite ratios (NAA/Cho, NAA/Cre, Cho/Cre) over good quality voxels. We built linear mixed-effects models to test for statistically significant differences between MS forms. We test nine binary classification tasks on clinical data, lesion loads, and metabolic features, using a leave-one-patient-out cross-validation method based on 100 random patient-based bootstrap selections. We compute F1-scores and BAR values after tuning Linear Discriminant Analysis (LDA), Support Vector Machines with gaussian kernel (SVM-rbf), and Random Forests.

Results: Statistically significant differences were found between the disease starting points of each MS form using four different response variables: Lesion Load, NAA/Cre, NAA/Cho, and Cho/Cre ratios. Training SVM-rbf on clinical and lesion loads yields F1-scores of 71–72% for CIS vs. RR and CIS vs. RR+SP, respectively. For RR vs. PP we obtained good classification results (maximum F1-score of 85%) after training LDA on clinical and metabolic features, while for RR vs. SP we obtained slightly higher classification results (maximum F1-score of 87%) after training LDA and SVM-rbf on clinical, lesion loads and metabolic features.

Conclusions: Our results suggest that metabolic features are better at differentiating between relapsing-remitting and primary progressive forms, while lesion loads are better at differentiating between relapsing-remitting and secondary progressive forms. Therefore, combining clinical data with magnetic resonance lesion loads and metabolic features can improve the discrimination between relapsing-remitting and progressive forms.

Evaluation of Visual-Evoked Cerebral Metabolic Rate of Oxygen as a Diagnostic Marker in Multiple Sclerosis

A multiple sclerosis (MS) diagnosis often relies upon clinical presentation and qualitative analysis of standard, magnetic resonance brain images. However, the accuracy of MS diagnoses can be improved by utilizing advanced brain imaging methods. We assessed the accuracy of a new neuroimaging marker, visual-evoked cerebral metabolic rate of oxygen (veCMRO₂), in classifying MS patients and closely age- and sex-matched healthy control (HC) participants. MS patients and HCs underwent calibrated functional magnetic resonance imaging (cfMRI) during a visual stimulation task, diffusion tensor imaging, T₁- and T₂-weighted imaging, neuropsychological testing, and completed self-report questionnaires. Using resampling techniques to avoid bias and increase the generalizability of the results, we assessed the accuracy of veCMRO₂ in classifying MS patients and HCs. veCMRO₂ classification accuracy was also examined in the context of other evoked visuofunctional measures, white matter microstructural integrity, lesion-based measures from T₂-weighted imaging, atrophy measures from T₁-weighted imaging, neuropsychological tests, and self-report assays of clinical symptomology. veCMRO₂ was significant and within the top 16% of measures (43 total) in classifying MS status using both within-sample (82% accuracy) and out-of-sample (77% accuracy) observations. High accuracy of veCMRO₂ in classifying MS demonstrated an encouraging first step toward establishing veCMRO₂ as a neurodiagnostic marker of MS.

A genetic algorithm-based model for longitudinal changes detection in white matter fiber-bundles of patient with multiple sclerosis

Analysis of white matter (WM) tissue is essential to understand the mechanisms of neurodegenerative pathologies like multiple sclerosis (MS). Recently longitudinal studies started to show how the temporal component is important to investigate temporal diffuse effects of neurodegenerative pathologies. Diffusion tensor imaging (DTI) constitutes one of the most sensitive techniques for the detection and characterization of brain related pathological processes and allows also the reconstruction of WM fibers. The analysis of spatial and temporal pathological changes along the fibers are thus possible by merging quantitative maps with structural information provided by DTI. In this work, we present a new genetic algorithm (GA) based method to analyze longitudinal changes occurring along WM fiber-bundles. In the first part of this paper, we describe the data processing pipeline, including data registration and fiber tract post-processing. In the second part, we focus our attention to the description of our GA model. In the last part, we show the tests we performed on simulated and real MS longitudinal data. Our method reached a high level of precision, recall and F-Measure in the detection of longitudinal pathological alterations occurring along different WM fiber-bundles.

Graph Theory-Based Brain Connectivity for Automatic Classification of Multiple Sclerosis Clinical Courses

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.

A Sensitive and Automatic White Matter Fiber Tracts Model for Longitudinal Analysis of Diffusion Tensor Images in Multiple Sclerosis

Diffusion tensor imaging (DTI) is a sensitive tool for the assessment of microstructural alterations in brain white matter (WM). We propose a new processing technique to detect, local and global longitudinal changes of diffusivity metrics, in homologous regions along WM fiber-bundles. To this end, a reliable and automatic processing pipeline was developed in three steps: 1) co-registration and diffusion metrics computation, 2) tractography, bundle extraction and processing, and 3) longitudinal fiber-bundle analysis. The last step was based on an original Gaussian mixture model providing a fine analysis of fiber-bundle cross-sections, and allowing a sensitive detection of longitudinal changes along fibers. This method was tested on simulated and clinical data. High levels of F-Measure were obtained on simulated data. Experiments on cortico-spinal tract and inferior fronto-occipital fasciculi of five patients with Multiple Sclerosis (MS) included in a weekly follow-up protocol highlighted the greater sensitivity of this fiber scale approach to detect small longitudinal alterations.

Functional topography of the corpus callosum investigated by DTI and fMRI

This short review examines the most recent functional studies of the topographic organization of the human corpus callosum, the main interhemispheric commissure. After a brief description of its anatomy, development, microstructure, and function, it examines and discusses the latest findings obtained using diffusion tensor imaging (DTI) and tractography (DTT) and functional magnetic resonance imaging (fMRI), three recently developed imaging techniques that have significantly expanded and refined our knowledge of the commissure. While DTI and DTT have been providing insights into its microstructure, integrity and level of myelination, fMRI has been the key technique in documenting the activation of white matter fibers, particularly in the corpus callosum. By combining DTT and fMRI it has been possible to describe the trajectory of the callosal fibers interconnecting the primary olfactory, gustatory, motor, somatic sensory, auditory and visual cortices at sites where the activation elicited by peripheral stimulation was detected by fMRI. These studies have demonstrated the presence of callosal fiber tracts that cross the commissure at the level of the genu, body, and splenium, at sites showing fMRI activation. Altogether such findings lend further support to the notion that the corpus callosum displays a functional topographic organization that can be explored with fMRI.

Comparison of sagittal and transverse echo planar spectroscopic imaging on the quantification of brain metabolites

PURPOSE

We quantitatively compared sagittal and transverse echo planar spectroscopic imaging (EPSI) on the quantification of metabolite concentrations with consideration of tissue variation. A quantification strategy is proposed to collect the necessary information for quantification of concentrations in a minimized acquisition time.

METHODS

Six transverse and six sagittal EPSI data were collected on healthy volunteers. Metabolite concentrations of N-acetyl-aspartate (NAA), total creatine (tCr), total choline (tCho), myo-inositol (mI), and glutamate and glutamine complex (Glx) were quantified using water scaling with partial volume and relaxation correction. Linear regression analysis was performed to extract concentrations in gray matter (GM) and white matter (WM). The inter- and intrasubject coefficients of variance (CV) were estimated.

RESULTS

Concentrations and fitting errors of sagittal and transverse EPSI were at same level. GM to WM contrast of concentrations was found in NAA, tCr, and tCho. The intersubject CVs revealed greater variability in the sagittal EPSI than in the transverse EPSI. The intrasubject CVs of the transverse EPSI were below 5% for NAA, tCr, and tCho.

CONCLUSION

We showed that quantified concentrations of sagittal and transverse EPSI after partial volume correction are comparable and reproducible. The proposed quantification strategy can be conveniently adapted into various MRI protocols.

Clinical proton MR spectroscopy in central nervous system disorders

A large body of published work shows that proton (hydrogen 1 [(1)H]) magnetic resonance (MR) spectroscopy has evolved from a research tool into a clinical neuroimaging modality. Herein, the authors present a summary of brain disorders in which MR spectroscopy has an impact on patient management, together with a critical consideration of common data acquisition and processing procedures. The article documents the impact of (1)H MR spectroscopy in the clinical evaluation of disorders of the central nervous system. The clinical usefulness of (1)H MR spectroscopy has been established for brain neoplasms, neonatal and pediatric disorders (hypoxia-ischemia, inherited metabolic diseases, and traumatic brain injury), demyelinating disorders, and infectious brain lesions. The growing list of disorders for which (1)H MR spectroscopy may contribute to patient management extends to neurodegenerative diseases, epilepsy, and stroke. To facilitate expanded clinical acceptance and standardization of MR spectroscopy methodology, guidelines are provided for data acquisition and analysis, quality assessment, and interpretation. Finally, the authors offer recommendations to expedite the use of robust MR spectroscopy methodology in the clinical setting, including incorporation of technical advances on clinical units.

Associations of CD6, TNFRSF1A and IRF8 polymorphisms with risk of inflammatory demyelinating diseases

AIMS

Multiple sclerosis (MS) and neuromyelitis optica (NMO) are inflammatory autoimmune diseases that affect the central nervous system. Several genome-wide and candidate gene studies have identified genetic polymorphisms associated with the risk of MS or NMO. In particular, two recently published studies of meta-analysis in European-origin populations have suggested associations of single-nucleotide polymorphisms (SNPs) in CD6, TNFRSF1A and IRF8 with MS. The aim of our study was to assess the associations between SNPs in these three genes and the risk of inflammatory demyelinating disease (IDD) including MS and NMO. To the best of our knowledge, this is the first time such a study has been performed in an Asian population.

METHODS

A total of 21 SNPs of CD6, TNFRSF1A and IRF8 were genotyped in 178 IDD cases (79 MS and 99 NMO patients) and 237 normal controls in a Korean population.

RESULTS

Logistic analyses revealed that one SNP in CD6 (rs12288280, P = 0.04) and three SNPs in TNFRSF1A (rs767455, rs4149577 and rs1800693, P = 0.01-0.03) were associated with NMO. However, there was no association of IRF8 polymorphisms with IDD, including MS and NMO. Using further information from the SNP Function Prediction website, two exonic splicing enhancers (ESEs), including the polymorphic site of rs767455, were predicted to be binding sites for splicing factors (SRp55, SF2/ASF2 and SF2/ASF1).

CONCLUSION

Although additional studies are needed, our findings could provide information regarding the genetic aetiology of IDD in the Korean population.

Diffusion Tensor Imaging in NAWM and NADGM in MS and CIS: Association with Candidate Biomarkers in Sera

The aim of this study was to evaluate diffusion tensor imaging (DTI) indices in the corpus callosum and pyramidal tract in normal-appearing white matter (NAWM) and the caudate nucleus and thalamus in deep grey matter (NADGM) in all MS subtypes and clinically isolated syndrome (CIS). Furthermore, it was determined whether these metrics are associated with clinical measures and the serum levels of candidate immune biomarkers. Apparent diffusion coefficients (ADC) values were significantly higher than in controls in all six studied NAWM regions in SPMS, 4/6 regions in RRMS and PPMS and 2/6 regions in CIS. In contrast, decreased fractional anisotropy (FA) values in comparison to controls were detected in 2/6 NAWM regions in SPMS and 1/6 in RRMS and PPMS. In RRMS, the level of neurological disability correlated with thalamic FA values (r = 0.479, P = 0.004). In chronic progressive subtypes and CIS, ADC values of NAWM and NADGM were associated with the levels of MIF, sFas, and sTNF-α. Our data indicate that DTI may be useful in detecting pathological changes in NAWM and NADGM in MS patients and that these changes are related to neurological disability.

Tracking cerebral white matter changes across the lifespan: insights from diffusion tensor imaging studies

Delineating the normal development of brain white matter (WM) over the human lifespan is crucial to improved understanding of underlying WM pathology in neuropsychiatric and neurological conditions. We review the extant literature concerning diffusion tensor imaging studies of brain WM development in healthy individuals available until October 2012, summarise trends of normal development of human brain WM and suggest possible future research directions. Temporally, brain WM maturation follows a curvilinear pattern with an increase in fractional anisotropy (FA) from newborn to adolescence, decelerating in adulthood till a plateau around mid-adulthood, and a more rapid decrease of FA from old age onwards. Spatially, brain WM tracts develop from central to peripheral regions, with evidence of anterior-to-posterior maturation in commissural and projection fibres. The corpus callosum and fornix develop first and decline earlier, whilst fronto-temporal WM tracts like cingulum and uncinate fasciculus have protracted maturation and decline later. Prefrontal WM is most vulnerable with greater age-related FA reduction compared with posterior WM. Future large scale studies adopting longitudinal design will better clarify human brain WM changes over time.

The role of mitochondria in axonal degeneration and tissue repair in MS

Axonal injury is a key feature of multiple sclerosis (MS) pathology and is currently seen as the main correlate for permanent clinical disability. Although little is known about the pathogenetic mechanisms that drive axonal damage and loss, there is accumulating evidence highlighting the central role of mitochondrial dysfunction in axonal degeneration and associated neurodegeneration. The aim of this topical review is to provide a concise overview on the involvement of mitochondrial dysfunction in axonal damage and destruction in MS. Hereto, we will discuss putative pathological mechanisms leading to mitochondrial dysfunction and recent imaging studies performed in vivo in patients with MS. Moreover, we will focus on molecular mechanisms and novel imaging studies that address the role of mitochondrial metabolism in tissue repair. Finally, we will briefly review therapeutic strategies aimed at improving mitochondrial metabolism and function under neuroinflammatory conditions.