Growth charts for small sample sizes using unsupervised clustering: Application to canine early growth.

Breed-specific growth curves (GCs) are needed for neonatal puppies, but breed-specific data may be insufficient. We investigated an unsupervised clustering methodology for modeling GCs by augmenting breed-specific data with data from breeds having similar growth profiles. Puppy breeds were grouped by median growth profiles (bodyweights between birth and Day 20) using hierarchical clustering on principal components. Median bodyweights for breeds in a cluster were centered to that cluster's median and used to model cluster GCs by Generalized Additive Models for Location, Shape and Scale. These were centered back to breed growth profiles to produce cluster-scale breed GCs. The accuracy of breed-scale GCs modeled with breed-specific data only and cluster-scale breed GCs were compared when modeled from diminishing sample sizes. A complete dataset of Labrador Retriever bodyweights (birth to Day 20) was split into training (410 puppies) and test (460 puppies) datasets. Cluster-scale breed and breed-scale GCs were modelled from defined sample sizes from the training dataset. Quality criteria were the percentages of observed data in the test dataset outside the target growth centiles of simulations. Accuracy of cluster-scale breed GCs remained consistently high down to sampling sizes of three. They slightly overestimated breed variability, but centile curves were smooth and consistent with breed-scale GCs modeled from the complete Labrador Retriever dataset. At sampling sizes ≤ 20, the quality of breed-scale GCs reduced notably. In conclusion, GCs for neonatal puppies generated using a breed-cluster hybrid methodology can be more satisfactory than GCs at purely the breed level when sample sizes are small.

Case Report: True Motor Recovery of Upper Limb Beyond 5 Years Post-stroke.

Most of motor recovery usually occurs within the first 3 months after stroke. Herein is reported a remarkable late recovery of the right upper-limb motor function after a left middle cerebral artery stroke. This recovery happened progressively, from two to 12 years post-stroke onset, and along a proximo-distal gradient, including dissociated finger movements after 5 years. Standardized clinical assessment and quantified analysis of the reach-to-grasp movement were repeated over time to characterize the recovery. Twelve years after stroke onset, diffusion tensor imaging (DTI), functional magnetic resonance imaging (fMRI), and transcranial magnetic stimulation (TMS) analyses of the corticospinal tracts were carried out to investigate the plasticity mechanisms and efferent pathways underlying motor control of the paretic hand. Clinical evaluations and quantified movement analysis argue for a true neurological recovery rather than a compensation mechanism. DTI showed a significant decrease of fractional anisotropy, associated with a severe atrophy, only in the upper part of the left corticospinal tract (CST), suggesting an alteration of the CST at the level of the infarction that is not propagated downstream. The finger opposition movement of the right paretic hand was associated with fMRI activations of a broad network including predominantly the contralateral sensorimotor areas. Motor evoked potentials were normal and the selective stimulation of the right hemisphere did not elicit any response of the ipsilateral upper limb. These findings support the idea that the motor control of the paretic hand is mediated mainly by the contralateral sensorimotor cortex and the corresponding CST, but also by a plasticity of motor-related areas in both hemispheres. To our knowledge, this is the first report of a high quality upper-limb recovery occurring more than 2 years after stroke with a genuine insight of brain plasticity mechanisms.

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.

Signal Intensity Evaluation in the Dentate Nucleus and Subcortical Gray Matter : Effect of Several Administrations of Gadoterate Meglumine in Multiple Sclerosis.

PURPOSE: Several studies reported gadolinium deposition in the dentate nuclei (DN) and the globus pallidus (GP) that was associated to linear GBCA administrations rather than macrocyclic. It is therefore imperative to evaluate and assess the safety of cumulative administration of gadoterate meglumine (macrocyclic). Thus, T1-weighted images (T1WI) of multiple sclerosis (MS) patients longitudinally followed for 4 years were retrospectively analyzed. METHODS: In this study 44 patients, 10 with clinically isolated syndrome (CIS), 24 relapsing-remitting MS (RRMS) and 10 primary-progressive MS (PPMS) were examined every 6 months (first four scans) and then with a 1-year interval (last two scans). Image processing consisted in reorienting unenhanced T1WI to standard space, followed by B1 inhomogeneity correction. A patient-specific template was then generated to normalize T1WI signal intensity (SI) and segment the DN and subcortical GM structures. All structures were then transformed to each patient space in order to measure the SI in each region. The cerebellar peduncles (CP) and semi-oval (SO) white matter were then manually delineated and used as reference to calculate SI ratios in the DN and subcortical GM structures. A linear mixed-effect model was finally applied to longitudinally analyze SI variations. RESULTS: The SI measurements performed in all structures showed no significant increases with the cumulative GBCA administration. CONCLUSION: This study showed no significant SI increases within the DN and subcortical GM structures of longitudinally followed MS patients even with the cumulative administration of the macrocyclic GBCA gadoterate meglumine.

Intracellular Phosphate and ATP Depletion Measured by Magnetic Resonance Spectroscopy in Patients Receiving Maintenance Hemodialysis.

BACKGROUND: The precise origin of phosphate that is removed during hemodialysis remains unclear; only a minority comes from the extracellular space. One possibility is that the remaining phosphate originates from the intracellular compartment, but there have been no available data from direct assessment of intracellular phosphate in patients undergoing hemodialysis. METHODS: We used phosphorus magnetic resonance spectroscopy to quantify intracellular inorganic phosphate (Pi), phosphocreatine (PCr), and ßATP. In our pilot, single-center, prospective study, 11 patients with ESKD underwent phosphorus (31P) magnetic resonance spectroscopy examination during a 4-hour hemodialysis treatment. Spectra were acquired every 152 seconds during the hemodialysis session. The primary outcome was a change in the PCr-Pi ratio during the session. RESULTS: During the first hour of hemodialysis, mean phosphatemia decreased significantly (-41%; P<0.001); thereafter, it decreased more slowly until the end of the session. We found a significant increase in the PCr-Pi ratio (+23%; P=0.001) during dialysis, indicating a reduction in intracellular Pi concentration. The PCr-ßATP ratio increased significantly (+31%; P=0.001) over a similar time period, indicating a reduction in ßATP. The change of the PCr-ßATP ratio was significantly correlated to the change of depurated Pi. CONCLUSIONS: Phosphorus magnetic resonance spectroscopy examination of patients with ESKD during hemodialysis treatment confirmed that depurated Pi originates from the intracellular compartment. This finding raises the possibility that excessive dialytic depuration of phosphate might adversely affect the intracellular availability of high-energy phosphates and ultimately, cellular metabolism. Further studies are needed to investigate the relationship between objective and subjective effects of hemodialysis and decreases of intracellular Pi and ßATP content. CLINICAL TRIAL REGISTRY NAME AND REGISTRATION NUMBER: Intracellular Phosphate Concentration Evolution During Hemodialysis by MR Spectroscopy (CIPHEMO), NCT03119818.

White matter microarchitecture and structural network integrity correlate with children intelligence quotient.

The neural substrate of high intelligence performances remains not well understood. Based on diffusion tensor imaging (DTI) which provides microstructural information of white matter fibers, we proposed in this work to investigate the relationship between structural brain connectivity and intelligence quotient (IQ) scores. Fifty-seven children (8-12 y.o.) underwent a MRI examination, including conventional T1-weighted and DTI sequences, and neuropsychological testing using the fourth edition of Wechsler Intelligence Scale for Children (WISC-IV), providing an estimation of the Full-Scale Intelligence Quotient (FSIQ) based on four subscales: verbal comprehension index (VCI), perceptual reasoning index (PRI), working memory index (WMI), and processing speed index (PSI). Correlations between the IQ scores and both graphs and diffusivity metrics were explored. First, we found significant correlations between the increased integrity of WM fiber-bundles and high intelligence scores. Second, the graph theory analysis showed that integration and segregation graph metrics were positively and negatively correlated with WISC-IV scores, respectively. These results were mainly driven by significant correlations between FSIQ, VCI, and PRI and graph metrics in the temporal and parietal lobes. In conclusion, these findings demonstrated that intelligence performances are related to the integrity of WM fiber-bundles as well as the density and homogeneity of WM brain networks.

Between-Batch Bioequivalence (BBE): a Statistical Test to Evaluate In Vitro Bioequivalence Considering the Between-Batch Variability.

Bioequivalence testing is an essential step during the development of generic drugs. Regulatory agencies have drafted recommendations and guidelines to frame this step but without finding any consensus. Different methodologies are applied depending on the geographical region. For instance, in the EU, EMA recommends using average bioequivalence test (ABE), while in the USA, FDA recommends using population bioequivalence (PBE) test. Both methods present some limitations (e.g., when batch variability is non-negligible) making it difficult to conclude to equivalence without subsequently increasing the sample size. This article proposes an alternative method to evaluate bioequivalence: between-batch bioequivalence (BBE). It is based on the comparison between the mean difference (Reference - Test) and the Reference between-batch variability. After presenting the theoretical concepts, BBE relevance is evaluated through simulation and real case (nasal spray) studies. Simulation results showed high performance of the method based on false positive and false negative rate estimations (type I and type II errors respectively). Especially, BBE has shown significantly greater true positive rates than ABE and PBE when the Reference residual standard deviation is higher than 15%, depending on the between-batch variability and the number of batches. Finally, real case applications revealed that BBE is more efficient than ABE and PBE to demonstrate equivalence, in some well-known situations where the between-batch variability is not negligible. These results suggest that BBE could be considered as an alternative to the state-of-the-art methods allowing costless development. Graphical abstract.

Topological Modification of Brain Networks Organization in Children With High Intelligence Quotient: A Resting-State fMRI Study.

The idea that intelligence is embedded not only in a single brain network, but instead in a complex, well-optimized system of complementary networks, has led to the development of whole brain network analysis. Using graph theory to analyze resting-state functional MRI data, we investigated the brain graph networks (or brain networks) of high intelligence quotient (HIQ) children. To this end, we computed the "hub disruption index κ," an index sensitive to graph network modifications. We found significant topological differences in the integration and segregation properties of brain networks in HIQ compared to standard IQ children, not only for the whole brain graph, but also for each hemispheric graph, and for the homotopic connectivity. Moreover, two profiles of HIQ children, homogenous and heterogeneous, based on the differences between the two main IQ subscales [verbal comprehension index (VCI) and perceptual reasoning index (PRI)], were compared. Brain network changes were more pronounced in the heterogeneous than in the homogeneous HIQ subgroups. Finally, we found significant correlations between the graph networks' changes and the full-scale IQ (FSIQ), as well as the subscales VCI and PRI. Specifically, the higher the FSIQ the greater was the brain organization modification in the whole brain, the left hemisphere, and the homotopic connectivity. These results shed new light on the relation between functional connectivity topology and high intelligence, as well as on different intelligence profiles.

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.

Corrigendum: Topological Modification of Brain Networks Organization in Children With High Intelligence Quotient: A Resting-State fMRI Study.

[This corrects the article DOI: 10.3389/fnhum.2019.00241.].

Prediction of Multiple Sclerosis Patient Disability from Structural Connectivity using Convolutional Neural Networks.

Prediction of disability progression in multiple sclerosis patients is a critical component of their management. In particular, one challenge is to identify and characterize a patient profile who may benefit of efficient treatments. However, it is not yet clear whether a particular relation exists between the brain structure and the disability status.This work aims at producing a fully automatic model for the expanded disability status score estimation, given the brain structural connectivity representation of a multiple sclerosis patient. The task is addressed by first extracting the connectivity graph, obtained by combining brain grey matter parcellation and tractography extracted from Diffusion and T1-weighted Magnetic Resonance (MR) images, and then processing it via a convolutional neural network (CNN) in order to compute the predicted score. Experiments show that the herein proposed approach achieves promising results, thus resulting as an important step forward on the road to better predict the evolution of the disease.

Overcoming Challenges of Cranial Nerve Tractography: A Targeted Review.

BACKGROUND: Diffusion imaging tractography caught the attention of the scientific community by describing the white matter architecture in vivo and noninvasively, but its application to small structures such as cranial nerves remains difficult. The few attempts to track cranial nerves presented highly variable acquisition and tracking settings. OBJECTIVE: To conduct and present a targeted review collecting all technical details and pointing out challenges and solutions in cranial nerve tractography. METHODS: A "targeted" review of the scientific literature was carried out using the MEDLINE database. We selected studies that reported how to perform the tractography of cranial nerves, and extracted the following: clinical context; imaging acquisition settings; tractography parameters; regions of interest (ROIs) design; and filtering methods. RESULTS: Twenty-one published articles were included. These studied the optic nerves in suprasellar tumors, the trigeminal nerve in neurovascular conflicts, the facial nerve position around vestibular schwannomas, or all cranial nerves. Over time, the number of MRI diffusion gradient directions increased from 6 to 101. Nine tracking software packages were used which offered various types of tridimensional display. Tracking parameters were disparately detailed except for fractional anisotropy, which ranged from 0.06 to 0.5, and curvature angle, which was set between 20° and 90°. ROI design has evolved towards a multi-ROI strategy. Furthermore, new algorithms are being developed to avoid spurious tracts and improve angular resolution. CONCLUSION: This review highlights the variability in the settings used for cranial nerve tractography. It points out challenges that originate both from cranial nerve anatomy and the tractography technology, and allows a better understanding of cranial nerve tractography.

Evidence of axonal damage in cerebellar peduncles without T2-lesions in multiple sclerosis.

BACKGROUND AND AIM: Cerebellar peduncles (CP) can be probed by diffusion tensor imaging (DTI) to evaluate the integrity of cerebellar afferent and efferent networks. Damage to the CP in multiple sclerosis (MS) could lead to serious cognitive and mobility impairment. The aim of this study was to investigate the extent and the clinical impact of CP damage in MS. METHODS: Sixty-eight MS patients were included in this study along with 27 healthy controls (HC) and underwent an MRI on a 1.5T including T1, T2, FLAIR and DTI. Using DTI, the microstructural integrity within the CP regions (superior (SCP), inferior (ICP) and middle (MCP)) was probed while controlling for focal T2-lesions presence or absence. A general linear model was performed to test for associations between clinical scores and DTI metrics for each CP. RESULTS: Significantly decreased fractional anisotropy (FA) and increased radial diffusivity (RD) were found in the CP of all MS patients compared to those of HC, but to a lesser extent in non-lesioned CP than those with lesions. Axial diffusivity (AD) was significantly and similarly increased in both non-lesioned and lesioned CP, but only in the SCP and ICP. Expanded disability status scale (EDSS) significantly correlated with MCP's FA (p < 0.05) and RD (p < 0.05), while MS functional composite (MSFC) significantly correlated with SCP's FA (p < 0.01) and RD (p < 0.01). CONCLUSION: The diffusion changes (FA and RD) measured in lesioned CP are probably directly related to the presence of inflammatory and/or demyelinating lesions. In contrast, the microstructural alterations reflected by AD increase in non-lesioned CP may result either from remote effects of cerebral white matter injury (diaschisis) or primary axonal degeneration, that are associated with cognitive, sensory and motor impairments of MS patients.

Weekly enhanced T1-weighted MRI with Gadobutrol injections in MS patients: Is there a signal intensity increase in the dentate nucleus and the globus pallidus?

BACKGROUND AND PURPOSE: Gadolinium-based contrast agents (GBCAs) administration have drastically improved the accuracy of Multiple Sclerosis (MS) diagnosis by highlighting any damage to the brain blood barrier, thereby differentiating between active and non-active lesions. Following multiple administrations of GBCAs, several MS studies have reported a signal intensity (SI) increase on unenhanced T1-weighted images in certain brain regions such as the dentate nucleus (DN) and the globus pallidus (GP). Our aim was therefore to determine the accumulation of macrocyclic GBCAs on enhanced T1-weighted images SI in the DN and the GP of MS patients injected eight times. MATERIALS AND METHODS: Five MS patients underwent eight weekly consecutive MRI scans. Enhanced 3D T1-weighted images with Gadobutrol as a macrocyclic GBCA, were acquired. A ROI-based approach was applied for the evaluation of SI in the DN to middle cerebellar peduncle (DN-MCP) and GP to semi-oval white matter (GP-SOWM) ratios. An analysis of variance on repeated measures was used for the statistical analysis of each ratio. RESULTS: No DN-MCP and GP-SOWM SI ratio differences were observed over the eight-weeks period using the macrocyclic GBCA. CONCLUSION: Iterative and weekly injections of macrocyclic GBCAs are not associated with T1 signal increase in the DN and GP of MS patients. These results would suggest a no gadolinium accumulation in the brain using macrocyclic GBCA even after several close injections and promote the use of a macrocylcic GBCA rather than linear agents for MS patients.

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.

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.

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.

Hemispheric Differences in White Matter Microstructure between Two Profiles of Children with High Intelligence Quotient vs. Controls: A Tract-Based Spatial Statistics Study.

Objectives: The main goal of this study was to investigate and compare the neural substrate of two children's profiles of high intelligence quotient (HIQ). Methods: Two groups of HIQ children were included with either a homogeneous (Hom-HIQ: n = 20) or a heterogeneous IQ profile (Het-HIQ: n = 24) as defined by a significant difference between verbal comprehension index and perceptual reasoning index. Diffusion tensor imaging was used to assess white matter (WM) microstructure while tract-based spatial statistics (TBSS) analysis was performed to detect and localize WM regional differences in fractional anisotropy (FA), mean diffusivity, axial (AD), and radial diffusivities. Quantitative measurements were performed on 48 regions and 21 fiber-bundles of WM. Results: Hom-HIQ children presented higher FA than Het-HIQ children in widespread WM regions including central structures, and associative intra-hemispheric WM fasciculi. AD was also greater in numerous WM regions of Total-HIQ, Hom-HIQ, and Het-HIQ groups when compared to the Control group. Hom-HIQ and Het-HIQ groups also differed by their hemispheric lateralization in AD differences compared to Controls. Het-HIQ and Hom-HIQ groups showed a lateralization ratio (left/right) of 1.38 and 0.78, respectively. Conclusions: These findings suggest that both inter- and intra-hemispheric WM integrity are enhanced in HIQ children and that neural substrate differs between Hom-HIQ and Het-HIQ. The left hemispheric lateralization of Het-HIQ children is concordant with their higher verbal index while the relative right hemispheric lateralization of Hom-HIQ children is concordant with their global brain processing and adaptation capacities as evidenced by their homogeneous IQ.

Multiparametric Non-Negative Matrix Factorization for Longitudinal Variations Detection in White-Matter Fiber Bundles.

Processing of longitudinal diffusion tensor imaging (DTI) data is a crucial challenge to better understand pathological mechanisms of complex brain diseases such as multiple sclerosis (MS) where white-matter (WM) fiber bundles are variably altered by inflammatory events. In this study, we propose a new fully automated method to detect longitudinal changes in diffusivity metrics along WM fiber bundles. The proposed method is divided in three main parts: 1) preprocessing of longitudinal diffusion acquisitions, 2) WM fiber-bundle extraction, and 3) application of nonnegative matrix factorization and density-based local outliers algorithms to detect and delineate longitudinal variations appearing in the cross section of the WM fiber bundle. In order to validate our method, we introduce a new model to simulate real longitudinal changes based on a generalized Gaussian probability density function. Moreover, we applied our method on longitudinal data. High level of performances were obtained for the detection of small longitudinal changes along the WM fiber bundles in MS patients.

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.