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Background: Use proton magnetic resonance spectroscopy (1H-MRS) non invasive technique to assess the modifications of glutamate-glutamine (Glx) and gammaaminobutyric acid (GABA) brain levels in patients reporting a cognitive complain METHODS: Posterior cingular cortex 1H-MRS spectra of 46 patients (19 male, 27 female) aged 57 to 87 years (mean : 73.32 ± 7.33 years) with a cognitive complaint were examined with a MEGA PRESS sequence at 3T, and compounds Glutamateglutamine (Glx), GABA, Creatine (Cr) and NAA were measured. From this data the metabolite ratios Glx/Cr, GABA/Cr and NAA/Cr were calculated. In addition, all patient performed the Mini Mental State Evaluation (MMSE) and 2 groups were realized with the clinical threshold of 24., Results: 16 patients with MMSE 〈 24 and 30 patients with MMSE 〉 24. Significant increase of Glx/Cr in PCC of patients with MMSE 〈 24 compared to patients with MMSE 〉 24. Moreover, GABA/Cr ratio exhibited a trend for a decrease in PCC between the two groups, while they showed a significant decrease NAA/Cr ratio., Conclusion: Our results concerning Glx are in agreement with a physiopathological hypothesis involving a biphasic variation of glutamate levels associated with excitotoxicity, correlated with the clinical evolution of the disease. These observations suggest that MRS assessment of glutamate levels could be helpful for both diagnosis and classification of cognitive impairment in stage., Competing Interests: Declaration of Competing Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023. Published by Elsevier Masson SAS.)

Glial tumors represent the leading etiology of primary brain tumors. Their particularities lie in (i) their location in a highly functional organ that is difficult to access surgically, including for biopsy, and (ii) their rapid, anisotropic mode of extension, notably via the fiber bundles of the white matter, which further limits the possibilities of resection. The use of mathematical tools enables the development of numerical models representative of the oncotype, genotype, evolution, and therapeutic response of lesions. The significant development of digital technologies linked to high-resolution NMR exploration, coupled with the possibilities offered by AI, means that we can envisage the creation of digital twins of tumors and their host organs, thus reducing the use of physical sampling.

Background: To investigate the contribution of machine learning decision tree models applied to perfusion and spectroscopy MRI for multiclass classification of lymphomas, glioblastomas, and metastases, and then to bring out the underlying key pathophysiological processes involved in the hierarchization of the decision-making algorithms of the models., Methods: From 2013 to 2020, 180 consecutive patients with histopathologically proved lymphomas (n = 77), glioblastomas (n = 45), and metastases (n = 58) were included in machine learning analysis after undergoing MRI. The perfusion parameters (rCBV max , PSR max ) and spectroscopic concentration ratios (lac/Cr, Cho/NAA, Cho/Cr, and lip/Cr) were applied to construct Classification and Regression Tree (CART) models for multiclass classification of these brain tumors. A 5-fold random cross validation was performed on the dataset., Results: The decision tree model thus constructed successfully classified all 3 tumor types with a performance (AUC) of 0.98 for PCNSLs, 0.98 for GBM and 1.00 for METs. The model accuracy was 0.96 with a RSquare of 0.887. Five rules of classifier combinations were extracted with a predicted probability from 0.907 to 0.989 for that end nodes of the decision tree for tumor multiclass classification. In hierarchical order of importance, the root node (Cho/NAA) in the decision tree algorithm was primarily based on the proliferative, infiltrative, and neuronal destructive characteristics of the tumor, the internal node (PSRmax), on tumor tissue capillary permeability characteristics, and the end node (Lac/Cr or Cho/Cr), on tumor energy glycolytic (Warburg effect), or on membrane lipid tumor metabolism., Conclusion: Our study shows potential implementation of machine learning decision tree model algorithms based on a hierarchical, convenient, and personalized use of perfusion and spectroscopy MRI data for multiclass classification of these brain tumors., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Vallée, Vallée, Guillevin, Lallouette, Thomas, Rittano, Wager, Guillevin and Vallée.)

Our aim in this paper is to study a mathematical model for high grade gliomas, taking into account lactates kinetics, as well as chemotherapy and antiangiogenic treatment. In particular, we prove the existence and uniqueness of biologically relevant solutions. We also perform numerical simulations based on different therapeutical situations that can be found in the literature. These simulations are consistent with what is expected in these situations., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.)

Imaging bio-markers have been widely used for Computer-Aided Diagnosis (CAD) of Alzheimer's Disease (AD) with Deep Learning (DL). However, the structural brain atrophy is not detectable at an early stage of the disease (namely for Mild Cognitive Impairment (MCI) and Mild Alzheimer's Disease (MAD)). Indeed, potential biological bio-markers have been proved their ability to early detect brain abnormalities related to AD before brain structural damage and clinical manifestation. Proton Magnetic Resonance Spectroscopy ( 1 H-MRS) provides a promising solution for biological brain changes detection in a no invasive manner. In this paper, we propose an attention-guided supervised DL framework for early AD detection using 1 H-MRS data. In the early stages of AD, features may be closely related and often complex to delineate between subjects. Hence, we develop a 1D attention mechanism that explicitly guides the classifier to focus on diagnostically relevant metabolites for classes discrimination. Synthetic data are used to tackle the lack of data problem and to help in learning the feature space. Data used in this paper are collected in the University Hospital of Poitiers, which contained 111 1 H-MRS samples extracted from the Posterior Cingulate Cortex (PCC) brain region. The data contain 33 Normal Control (NC), 49 MCI due to AD, and 29 MAD subjects. The proposed model achieves an average classification accuracy of 95.23%. Our framework outperforms state of the art imaging-based approaches, proving the robustness of learning metabolites features against traditional imaging bio-markers for early AD detection., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Three-dimensional surface reconstruction is a well-known task in medical imaging. In procedures for intervention or radiation treatment planning, the generated models should be accurate and reflect the natural appearance. Traditional methods for this task, such as Marching Cubes, use smoothing post processing to reduce staircase artifacts from mesh generation and exhibit the natural look. However, smoothing algorithms often reduce the quality and degrade the accuracy. Other methods, such as MPU implicits, based on adaptive implicit functions, inherently produce smooth 3D models. However, the integration in the implicit functions of both smoothness and accuracy of the shape approximation may impact the precision of the reconstruction. Having these limitations in mind, we propose a hybrid method for 3D reconstruction of MR images. This method is based on a parallel Marching Cubes algorithm called Flying Edges (FE) and Multi-level Partition of Unity (MPU) implicits. We aim to combine the robustness of the Marching Cubes algorithm with the smooth implicit curve tracking enabled by the use of implicit models in order to provide higher geometry precision. Towards this end, the regions that closely fit to the segmentation data, and thus regions that are not impacted by reconstruction issues, are first extracted from both methods. These regions are then merged and used to reconstruct the final model. Experimental studies were performed on a number of MRI datasets, providing images and error statistics generated from our results. The results obtained show that our method reduces the geometric errors of the reconstructed surfaces when compared to the MPU and FE approaches, producing a more accurate 3D reconstruction.

Introduction: Cerebral metabolism in obsessive-compulsive-disorder(OCD) has been the subject of numerous studies using proton magnetic resonance spectroscopy(MRS). Despite heterogeneous results, some studies have unraveled membrane turnover and energy metabolism abnormalities in different brain regions, suggesting that alterations in these processes may contribute to the pathophysiology. So far, no authors have explored phospholipids and high-energy phosphate metabolism using 31 P-MRS, which allows in vivo quantification of phosphorus metabolites that are considered to be related to membrane turnover and energy metabolism., Materials and Methods: The aim of our study was to describe and compare brain metabolic changes using 31 P-MRS in the striatum and the thalamus, between 23 severe OCD patients and 22 healthy controls. All subject underwent a clinical examination and a same 31 P-MRS protocol., Results: Significantly, increased concentrations of PC, PDE,PME,GPC,PME/PCr,PDE/PCr were found in patients compared to controls in the striatum and the thalamus. PCr and tATP were decreased in the striatum. Finally, significant correlations were found in the striatum and the thalamus between illness duration and some specific measured parameters., Conclusion: Our results showed significant modifications of the membrane and energy metabolism in the basal ganglia of severe OCD patients and suggests a link between energetic buffer and serotonin metabolism disorder., (Copyright © 2020. Published by Elsevier B.V.)

Biotin is thought to improve functional impairment in progressive multiple sclerosis (MS) by upregulating bioenergetic metabolism. We enrolled 19 patients suffering from progressive MS (5 primary and 14 secondary Progressive-MS). Using cerebral multinuclear magnetic resonance spectroscopy (MMRS) and clinical evaluation before and after 6 months of biotin cure, we showed significant modifications of: PME/PDE, ATP, and lactate resonances; an improvement of EDSS Neuroscore. Our results are consistent with metabolic pathways concerned with biotin action and could suggest the usefulness of MMRS for monitoring., (© 2019 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals, Inc on behalf of American Neurological Association.)

The aim of this article is to show how a tumor can modify energy substrates fluxes in the brain to support its own growth. To address this question we use a modeling approach to explain brain nutrient kinetics. In particular we set up a system of 17 equations for oxygen, lactate, glucose concentrations and cells number in the brain. We prove the existence and uniqueness of nonnegative solutions and give bounds on the solutions. We also provide numerical simulations.

The aim of this article is to study the well-posedness and properties of a fast-slow system which is related with brain lactate kinetics. In particular, we prove the existence and uniqueness of nonnegative solutions and obtain linear stability results. We also give numerical simulations with different values of the small parameter ε and compare them with experimental data.

Background and Purpose: Perfusion and spectroscopic MR imaging provide noninvasive physiologic and metabolic characterization of tissues, which can help in differentiating brain tumors. We investigated the diagnostic role of perfusion and spectroscopic MR imaging using individual and combined classifiers of these modalities and assessed the added performance value that spectroscopy can provide to perfusion using optimal combined classifiers that have the highest differential diagnostic performance to discriminate lymphomas, glioblastomas, and metastases., Materials and Methods: From January 2013 to January 2016, fifty-five consecutive patients with histopathologically proved lymphomas, glioblastomas, and metastases were included after undergoing MR imaging. The perfusion parameters (maximum relative CBV, maximum percentage of signal intensity recovery) and spectroscopic concentration ratios (lactate/Cr, Cho/NAA, Cho/Cr, and lipids/Cr) were analyzed individually and in optimal combinations. Differences among tumor groups, differential diagnostic performance, and differences in discriminatory performance of models with quantification of the added performance value of spectroscopy to perfusion were tested using 1-way ANOVA models, receiver operating characteristic analysis, and comparisons between receiver operating characteristic analysis curves using a bivariate χ 2 , respectively., Results: The highest differential diagnostic performance was obtained with the following combined classifiers: maximum percentage of signal intensity recovery-Cho/NAA to discriminate lymphomas from glioblastomas and metastases, significantly increasing the sensitivity from 82.1% to 95.7%; relative CBV-Cho/NAA to discriminate glioblastomas from lymphomas and metastases, significantly increasing the specificity from 92.7% to 100%; and maximum percentage of signal intensity recovery-lactate/Cr and maximum percentage of signal intensity recovery-Cho/Cr to discriminate metastases from lymphomas and glioblastomas, significantly increasing the specificity from 83.3% to 97.0% and 100%, respectively., Conclusions: Spectroscopy yielded an added performance value to perfusion using optimal combined classifiers of these modalities, significantly increasing the differential diagnostic performances for these common brain tumors., (© 2018 by American Journal of Neuroradiology.)

Introduction: Despite extensive testing, the efficacy of low-frequency (1 Hz) repetitive transcranial magnetic stimulation (rTMS) of temporo-parietal targets for the treatment of auditory verbal hallucinations (AVH) in patients with schizophrenia is still controversial, but promising results have been reported with both high-frequency and neuronavigated rTMS. Here, we report a double-blind sham-controlled study to assess the efficacy of high-frequency (20 Hz) rTMS applied over a precise anatomical site in the left temporal region using neuronavigation., Methods: Fifty-nine of 74 randomized patients with schizophrenia or schizoaffective disorders (DSM-IV R) were treated with rTMS or sham treatment and fully evaluated over 4 weeks. The rTMS target was determined by morphological MRI at the crossing between the projection of the ascending branch of the left lateral sulcus and the superior temporal sulcus (STS)., Results: The primary outcome was response to treatment, defined as a 30% decrease of the Auditory Hallucinations Rating Scale (AHRS) frequency item, observed at 2 successive evaluations. While there was no difference in primary outcome between the treatment groups, the percentages of patients showing a decrease of more than 30% of AHRS score (secondary outcome) did differ between the active (34.6%) and sham groups (9.1%) (P = .016) at day 14., Discussion: This controlled study reports negative results on the primary outcome but demonstrates a transient effect of 20 Hz rTMS guided by neuronavigation and targeted on an accurate anatomical site for the treatment of AVHs in schizophrenia patients.