Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes.

The INTERPRET project was a multicentre European collaboration, carried out from 2000 to 2002, which developed a decision-support system (DSS) for helping neuroradiologists with no experience of MRS to utilize spectroscopic data for the diagnosis and grading of human brain tumours. INTERPRET gathered a large collection of MR spectra of brain tumours and pseudo-tumoural lesions from seven centres. Consensus acquisition protocols, a standard processing pipeline and strict methods for quality control of the aquired data were put in place. Particular emphasis was placed on ensuring the diagnostic certainty of each case, for which all cases were evaluated by a clinical data validation committee. One outcome of the project is a database of 304 fully validated spectra from brain tumours, pseudotumoural lesions and normal brains, along with their associated images and clinical data, which remains available to the scientific and medical community. The second is the INTERPRET DSS, which has continued to be developed and clinically evaluated since the project ended. We also review here the results of the post-INTERPRET period. We evaluate the results of the studies with the INTERPRET database by other consortia or research groups. A summary of the clinical evaluations that have been performed on the post-INTERPRET DSS versions is also presented. Several have shown that diagnostic certainty can be improved for certain tumour types when the INTERPRET DSS is used in conjunction with conventional radiological image interpretation. About 30 papers concerned with the INTERPRET single-voxel dataset have so far been published. We discuss stengths and weaknesses of the DSS and the lessons learned. Finally we speculate on how the INTERPRET concept might be carried into the future.Funding from project MARESCAN (SAF2011-23870) from Ministerio de Economia y Competitividad in Spain. This work was also partially funded by CIBER-BBN, which is an initiative of the VI National R&D&i Plan 2008-2011, CIBER Actions and financed by the Instituto de Salud Carlos III with assistance from the European Regional Development Fund. JRG acknowledges support from Cancer Research UK, the University of Cambridge and Hutchison Whampoa Ltd.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1002/nbm.343

Functional neurochemical imaging of the human striatal cholinergic system during reversal learning

Animal studies have shown that acetylcholine (ACh) levels in the dorsal striatum play a role in reversal learning. However, this has not been studied in humans due to a lack of appropriate non-invasive techniques. Proton magnetic resonance spectroscopy (1H-MRS) can be used to measure metabolite levels in humans in vivo. Although it cannot be used to study ACh directly, 1H-MRS can be used to study choline, an ACh precursor which is linked to activity-dependent ACh release. The aim of this study was to use functional-1H-MRS (fMRS) to measure changes in choline levels in the human dorsal striatum during performance of a probabilistic reversal learning task. We demonstrate a task-dependent decrease in choline, specifically during reversal, but not initial, learning. We interpret this to reflect a sustained increase in ACh levels, which is in line with findings from the animal literature. This task-dependent change was specific to choline and was not observed in control metabolites. These findings provide support for the use of fMRS in the in vivo study of the human cholinergic system

Comparison of MRI Spectroscopy software packages performance and application on HCV-infected patients’ real data

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Sala Llonch, Roser, Laredo Gregorio, Carlos1H MRS is conceived as a pioneer methodology for brain metabolism inspection and health status appraisal. Post-processing interventions are required to obtain explicit metabolite quantification values from which to derive diagnosis. On the grounds of addressing and covering such operation, multiple software packages have been recently developed and launched leading to an amorphous assortment of spectroscopic image processing tools, with lack of standardization and regulation. The current study thereby intends to judge the coherence and consistency of compound estimation outputs in terms of result variability by intercorrelation and intracorrelation analyses between appointed programs, being LCModel, Osprey, TARQUIN, and spant toolbox. The examination is performed on a 83-subject SVS short-TE 3T SIEMENS PRESS spectroscopic acquisitions’ collection, including healthy controls and HCV-infected patients assisted with DAA treatment. The analytical core of the project assesses software performance through the creation of a Python script in order to automatically compute and display the results sought. The statistical tests providing enough information to draw substantial conclusions stem from extraction of coefficient of determination (R2 ), Pearson’s coefficient (r), and intraclass correlation coefficient (ICC) together with representation of boxplots, rainclouds, and scatter plots easing data visualization. A clinical implementation is also entailed on the same basis, whose purpose is to reveal actual DAA treatment effect on HCV-infected patients by means of metabolite concentration alteration and hypothetical restoration. Conclusions declare evident and alarming variability among MRS platforms compromising the rigor, sharpness and systematization demanded in this discipline since quantification results hold incoherences, although they do not seem to affect or oppose medical determinations jeopardizing patient’s health. However, it would be interesting to extend the analysis to a greater cohort of subjects to reinforce and get to more solid resolutions

Regional striatal cholinergic involvement in human behavioural flexibility

Animal studies have shown that the striatal cholinergic system plays a role in behavioural flexibility but, until recently, this system could not be studied in humans due to a lack of appropriate non-invasive techniques. Using proton magnetic resonance spectroscopy (1H-MRS) we recently showed that the concentration of dorsal striatal choline (an acetylcholine precursor) changes during reversal learning (a measure of behavioural flexibility) in humans. The aim of the present study was to examine whether regional average striatal choline was associated with reversal learning. 36 participants (mean age = 24.8, range = 18-32, 20 female) performed a probabilistic learning task with a reversal component. We measured choline at rest in both the dorsal and ventral striatum using 1H-MRS. Task performance was described using a simple reinforcement learning model that dissociates the contributions of positive and negative prediction errors to learning. Average levels of choline in the dorsal striatum were associated with performance during reversal, but not during initial learning. Specifically, lower levels of choline in the dorsal striatum were associated with a lower number of perseverative trials. Moreover, choline levels explained inter-individual variance in perseveration over and above that explained by learning from negative prediction errors. These findings suggest that the dorsal striatal cholinergic system plays an important role in behavioural flexibility, in line with evidence from the animal literature and our previous work in humans. Additionally, this work provides further support for the idea of measuring choline with 1H-MRS as a non-invasive way of studying human cholinergic neurochemistr

Improving the Clinical Use of Magnetic Resonance Spectroscopy for the Analysis of Brain Tumours using Machine Learning and Novel Post-Processing Methods

Magnetic Resonance Spectroscopy (MRS) provides unique and clinically relevant information for the assessment of several diseases. However, using the currently available tools, MRS processing and analysis is time-consuming and requires profound expert knowledge. For these two reasons, MRS did not gain general acceptance as a mainstream diagnostic technique yet, and the currently available clinical tools have seen little progress during the past years. MRS provides localized chemical information non-invasively, making it a valuable technique for the assessment of various diseases and conditions, namely brain, prostate and breast cancer, and metabolic diseases affecting the brain. In brain cancer, MRS is normally used for: (1.) differentiation between tumors and non-cancerous lesions, (2.) tumor typing and grading, (3.) differentiation between tumor-progression and radiation necrosis, and (4.) identification of tumor infiltration. Despite the value of MRS for these tasks, susceptibility differences associated with tissue-bone and tissue-air interfaces, as well as with the presence of post-operative paramagnetic particles, affect the quality of brain MR spectra and consequently reduce their clinical value. Therefore, the proper quality management of MRS acquisition and processing is essential to achieve unambiguous and reproducible results. In this thesis, special emphasis was placed on this topic. This thesis addresses some of the major problems that limit the use of MRS in brain tumors and focuses on the use of machine learning for the automation of the MRS processing pipeline and for assisting the interpretation of MRS data. Three main topics were investigated: (1.) automatic quality control of MRS data, (2.) identification of spectroscopic patterns characteristic of different tissue-types in brain tumors, and (3.) development of a new approach for the detection of tumor-related changes in GBM using MRSI data. The first topic tackles the problem of MR spectra being frequently affected by signal artifacts that obscure their clinical information content. Manual identification of these artifacts is subjective and is only practically feasible for single-voxel acquisitions and in case the user has an extensive experience with MRS. Therefore, the automatic distinction between data of good or bad quality is an essential step for the automation of MRS processing and routine reporting. The second topic addresses the difficulties that arise while interpreting MRS results: the interpretation requires expert knowledge, which is not available at every site. Consequently, the development of methods that enable the easy comparison of new spectra with known spectroscopic patterns is of utmost importance for clinical applications of MRS. The third and last topic focuses on the use of MRSI information for the detection of tumor-related effects in the periphery of brain tumors. Several research groups have shown that MRSI information enables the detection of tumor infiltration in regions where structural MRI appears normal. However, many of the approaches described in the literature make use of only a very limited amount of the total information contained in each MR spectrum. Thus, a better way to exploit MRSI information should enable an improvement in the detection of tumor borders, and consequently improve the treatment of brain tumor patients. The development of the methods described was made possible by a novel software tool for the combined processing of MRS and MRI: SpectrIm. This tool, which is currently distributed as part of the jMRUI software suite (www.jmrui.eu), is ubiquitous to all of the different methods presented and was one of the main outputs of the doctoral work. Overall, this thesis presents different methods that, when combined, enable the full automation of MRS processing and assist the analysis of MRS data in brain tumors. By allowing clinical users to obtain more information from MRS with less effort, this thesis contributes to the transformation of MRS into an important clinical tool that may be available whenever its information is of relevance for patient management

Refined electrophysiological recording and processing of neural signals from the retina and ascending visual pathways

The purpose of this thesis was the development of refined methods for recording and processing of neural signals of the retina and ascending visual pathways. The first chapter describes briefly the fundamentals of the human visual system and the basics of the functional testing of the retina and the visual pathways. The second and third chapters are dedicated to the processing of visual electrophysiological data using the newly developed software ERG Explorer, and present a proposal for an open and standardized data format, ElVisML, for future proof storage of visual electrophysiological data. The fourth chapter describes the development and application of two novel electrodes: First a contact lens electrode for the recording of electrical potentials of the ciliary muscle during accommodation, and second, the marble electrode, which is made of a super-absorbant polymer and allows for a preparation-free recording of visual evoked potentials. Results obtained in studies using the both electrodes are presented. The fifths and last chapter of the thesis presents the results from four studies within the field of visual electrophysiology. The first study examines the ophthalmological assessment of cannabis-induced perception disorder using electrophysiological methods. The second study presents a refined method for the objective assessment of the visual acuity using visual evoked potentials and introduces therefore, a refined stimulus paradigm and a novel method for the analysis of the sweep VEP. The third study presents the results of a newly developed stimulus design for full-field electrophysiology, which allows to assess previously non-recordable electroretinograms. The last study describes a relation of the spatial frequency of a visual stimulus to the amplitudes of visual evoked potentials in comparison to the BOLD response obtained using functional near-infrared spectroscopy and functional magnetic resonance imaging

New methods in quantification and RF pulse optimisation for magnetic resonance spectroscopy

Magnetic Resonance Spectroscopy (MRS) is a powerful medical diagnostic and research tool that enables us to identify metabolite concentrations in a region of interest (ROI) in-vivo. This non-invasive diagnostic technique provides a large amount of information about a certain region in the body, such as the brain or spinal cord, with no impact on patient wellbeing. MRS is readily available in many clinical units across the UK with an MRI machine and no additional hardware is needed. MRS has a number of challenges, including the requirement of a much higher level of magnetic field calibration compared to MRI, and detecting and analysing a substantially weaker signal per metabolite. To complicate the matter, there is a broad range of metabolites found in-vivo with overlapping proton spectra, obscuring signals and making spectral analysis very challenging. The primary focus of this thesis is to explore methods to aid quantification of metabolites by exploring two ends of the issue, focusing specifically on GABA, NAA, Creatine quantification, of interest to a range of neuroscience studies. Firstly, the focus is on the analysis of the acquired spectral data utilizing the MEGA-PRESS pulse sequence, specifically aimed at GABA. Comprehensively benchmarking the current state-of-the-art spectral quantification methods with experimental data from phantoms of known composition lays the foundation for devising an improved quantification technique. This novel quantification method utilises a convolutional neural network for MEGA-PRESS spectra and can outperform the state-of-the-art. Secondly, an optimisation method to find RF pulses that create specific excitations in the metabolites is devised, leading to spectra that are simpler to analyse. Such customisation of the spectra allows the removal of overlapping or obscuring features, creating chemically selective spectral acquisition methods. Moreover, the RF pulses are optimised over a range of scanner uncertainties to improve robustness. Simulations demonstrate that this approach can separate GABA, NAA and Creatine as well as Glutamine and Glutamate at 3 Tesla