114 research outputs found

    Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training

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    Multiple sclerosis (MS) is a demyelinating disease of the central nervous system (CNS). A reliable measure of the tissue myelin content is therefore essential for the understanding of the physiopathology of MS, tracking progression and assessing treatment efficacy. Positron emission tomography (PET) with [^{11} \mbox{C}] \mbox{PIB} has been proposed as a promising biomarker for measuring myelin content changes in-vivo in MS. However, PET imaging is expensive and invasive due to the injection of a radioactive tracer. On the contrary, magnetic resonance imaging (MRI) is a non-invasive, widely available technique, but existing MRI sequences do not provide, to date, a reliable, specific, or direct marker of either demyelination or remyelination. In this work, we therefore propose Sketcher-Refiner Generative Adversarial Networks (GANs) with specifically designed adversarial loss functions to predict the PET-derived myelin content map from a combination of MRI modalities. The prediction problem is solved by a sketch-refinement process in which the sketcher generates the preliminary anatomical and physiological information and the refiner refines and generates images reflecting the tissue myelin content in the human brain. We evaluated the ability of our method to predict myelin content at both global and voxel-wise levels. The evaluation results show that the demyelination in lesion regions and myelin content in normal-appearing white matter (NAWM) can be well predicted by our method. The method has the potential to become a useful tool for clinical management of patients with MS.Comment: Accepted by MICCAI201

    Modeling and analysis of mechanisms underlying high-resolution functional MRI of cortical columns

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    High spatial resolution functional MRI (fMRI) and advanced multivariate analysis techniques are promising tools for studying the cortical basis of human cognitive processes at the level of columns and layers. However the true spatial specificity of high-resolution fMRI has not been quantified, and the basis for decoding from fine scale structures using large voxels and relatively low magnetic field strength is unknown. It is also not yet known what method and voxel size is optimal for decoding and what voxel size is optimal for high-resolution imaging. In this thesis we present four studies that answer part of these questions using a model-based approach of imaging cortical columns. We started our investigation of model-based analysis of high-resolution fMRI of cortical columns by addressing the specific problem of how it is possible to decode information thought to be mediated by cortical columns using large voxels at low field strength. Multivariate machine learning algorithms applied to human functional MRI (fMRI) data can decode information conveyed by cortical columns, despite the voxel-size being large relative to the width of columns. Several mechanisms have been proposed to underlie decoding of stimulus orientation or the stimulated eye. These include: (I) aliasing of high spatial-frequency components, including the main frequency component of the columnar organization, (II) contributions from local irregularities in the columnar organization, (III) contributions from large-scale non-columnar organizations, (IV) functionally selective veins with biased draining regions, and (V) complex spatio-temporal filtering of neuronal activity by fMRI voxels. Here we sought to assess the plausibility of two of the suggested mechanisms: (I) aliasing and (II) local irregularities, using a naive model of BOLD as blurring and MRI voxel sampling. To this end, we formulated a mathematical model that encompasses both the processes of imaging ocular dominance (OD) columns and the subsequent linear classification analysis. Through numerical simulations of the model, we evaluated the distribution of functional differential contrasts that can be expected when considering the pattern of cortical columns, the hemodynamic point spread function, the voxel size, and the noise. We found that with data acquisition parameters used at 3 Tesla, sub-voxel supra-Nyquist frequencies, including frequencies near the main frequency of the OD organization (0.5 cycles per mm), cannot contribute to the differential contrast. The differential functional contrast of local origin is dominated by low-amplitude contributions from low frequencies, associated with irregularities of the cortical pattern. Realizations of the model with parameters that reflected a best-case scenario and the reported BOLD point-spread at 3 Tesla (3.5 mm) predicted decoding performances lower than those that have been previously obtained at this magnetic field strength. We conclude that low frequency components that underlie local irregularities in the columnar organization are likely to play a role in decoding. We further expect that fMRI-based decoding relies, in part, on signal contributions from large-scale, non-columnar functional organizations, and from complex spatio-temporal filtering of neuronal activity by fMRI voxels, involving biased venous responses. Our model can potentially be used for evaluating and optimizing data-acquisition parameters for decoding information conveyed by cortical columns. Having developed a model of imaging ODCs we then used this model to estimate the spatial specificity of BOLD fMRI, specifically at high field (7 T). Previous attempts at characterizing the spatial specificity of the blood oxygenation level dependent functional MRI (BOLD fMRI) response by estimating its point-spread function (PSF) have conventionally relied on spatial representations of visual stimuli in area V1. Consequently, their estimates were confounded by the width and scatter of receptive fields of V1 neurons. Here, we circumvent these limits by instead using the inherent cortical spatial organization of ocular dominance columns (ODCs) to determine the PSF for both Gradient Echo (GE) and Spin Echo (SE) BOLD imaging at 7 Tesla. By applying Markov Chain Monte Carlo sampling on a probabilistic generative model of imaging ODCs, we quantified the PSFs that best predict the spatial structure and magnitude of differential ODCs’ responses. Prior distributions for the ODC model parameters were determined by analyzing published data of cytochrome oxidase patterns from post-mortem histology of human V1 and of neurophysiological ocular dominance indices. The most probable PSF full-widths at half-maximum were 0.82 mm (SE) and 1.02 mm (GE). Our results provide a quantitative basis for the spatial specificity of BOLD fMRI at ultra-high fields, which can be used for planning and interpretation of high-resolution differential fMRI of fine-scale cortical organizations. Our BOLD fMRI PSF findings show that the PSF is considerably smaller than what was reported previously. This in turn raised the question of the role of the imaging PSF, which now has become relevant. Next, we show that the commonly used magnitude point-spread function fails to accurately represent the true effects of k-space sampling and signal decay, and propose an alternative model that accounts more accurately for these effects. The effects of k-space sampling and signal decay on the effective spatial resolution of MRI and functional MRI (fMRI) are commonly assessed by means of the magnitude point-spread function (PSF), defined as the absolute values (magnitudes) of the complex MR imaging PSF. It is commonly assumed that this magnitude PSF signifies blurring, which can be quantified by its full-width at half-maximum (FWHM). Here we show that the magnitude PSF fails to accurately represent the true effects of k-space sampling and signal decay. Firstly, a substantial part of the width of the magnitude PSF is due to MRI sampling per se. This part is independent of any signal decay and its effect depends on the spatial frequency composition of the imaged object. Therefore, it cannot always be expected to introduce blurring. Secondly, MRI reconstruction is typically followed by taking the absolute values (magnitude image) of the reconstructed complex image. This introduces a non-linear stage into the process of image formation. The complex imaging PSF does not fully describe this process, since it does not reflect the stage of taking the magnitude image. Its corresponding magnitude PSF fails to correctly describe this process, since convolving the original pattern with the magnitude PSF is different from the true process of taking the absolute following a convolution with the complex imaging PSF. Lastly, signal decay can have not only a blurring, but also a high-pass filtering effect. This cannot be reflected by the strictly positive width of the magnitude PSF. As an alternative, we propose to model the imaging process by decomposing it into a signal decay-independent MR sampling part and an approximation of the signal decay effect. We approximate the latter as a convolution with a Gaussian PSF or, if the effect is that of high-pass filtering, as reversing the effect of a convolution with a Gaussian PSF. We show that for typical high-resolution fMRI at 7 Tesla, signal decay in Spin-Echo has a moderate blurring effect (FWHM = 0.89 voxels, corresponds to 0.44 mm for 0.5 mm wide voxels). In contrast, Gradient-Echo acts as a moderate high-pass filter that can be interpreted as reversing a Gaussian blurring with FWHM = 0.59 voxels (0.30 mm for 0.5 mm wide voxels). Our improved approximations and findings hold not only for Gradient-Echo and Spin-Echo fMRI but also for GRASE and VASO fMRI. Our findings support the correct planning, interpretation, and modeling of high-resolution fMRI. In our first study we used our model to analyze imaging of cortical columns under a very specific scenario. We studied a best case scenario for decoding the stimulated eye from ODCs imaged at 3T using large voxels. In order to do so, we formalized available knowledge about fMRI of cortical columns. In particular, the ability of fMRI to resolve cortical columnar organization depends on several interdependent factors, e.g. the spatial scale of the columnar pattern, the point-spread of the BOLD response, voxel size and the signal-to-noise ratio. In our fourth study we aim to analyze how these factors contribute and combine in imaging of arbitrary cortical columnar patterns at varying field strengths and voxel sizes. In addition, we compared different pattern imaging approaches. We show how detection, decoding and reconstruction of a fine scale organization depend on the parameters of the model, and we predict optimal voxel sizes for each approach under various scenario. The capacity of fMRI to resolve cortical columnar organizations depends on several factors, e.g. the spatial scale of the columnar pattern, the point-spread of the fMRI response, the voxel size, and the SNR considering thermal and physiological noise. How these factors combine, and what is the voxel size that optimizes fMRI of cortical columns remain unknown. Here we combine current knowledge into a quantitative model of fMRI of patterns of cortical columns. We compare different approaches for imaging patterns of cortical columns, including univariate and multivariate based detection, multi-voxel pattern analysis (MVPA) based decoding, and reconstruction of the pattern of cortical columns. We present the dependence of their performance on the parameters of the imaged pattern and the data acquisition, and predict voxel sizes that optimize fMRI under various scenarios. To this end, we modeled differential imaging of realistic patterns of cortical columns with different spatial scales and degrees of irregularity. We quantified the capacity to detect and decode stimulus-specific responses by analyzing the distribution of voxel-wise differential responses relative to noise. We quantified the accuracy with which the spatial pattern of cortical columns can be reconstructed as the correlation between the underlying columnar pattern and the imaged pattern. For regular patterns, optimal voxel widths for detection, decoding and reconstruction were close to half the main cycle length of the organization. Optimal voxel widths for irregular patterns were less dependent on the main cycle length, and differed between univariate detection, multivariate detection and decoding, and reconstruction. We compared the effects of different factors of Gradient Echo fMRI at 3 Tesla (T), Gradient Echo fMRI at 7T and Spin-Echo fMRI at 7T, and found that for all measures (detection, decoding, and reconstruction), the width of the fMRI point-spread has the most significant effect. In contrast, different response amplitudes and noise characteristics played a comparatively minor role. We recommend specific voxel widths for optimal univariate detection, for multivariate detection and decoding, and for reconstruction under these three data-acquisition scenarios. Our study supports the planning, optimization, and interpretation of fMRI of cortical columns and the decoding of information conveyed by these columns

    Deep learning for brain disorders: from data processing to disease treatment

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    International audienceIn order to reach precision medicine and improve patients' quality of life, machine learning is increasingly used in medicine. Brain disorders are often complex and heterogeneous, and several modalities such as demographic, clinical, imaging, genetics and environmental data have been studied to improve their understanding. Deep learning, a subpart of machine learning, provides complex algorithms that can learn from such various data. It has become state of the art in numerous fields, including computer vision and natural language processing, and is also growingly applied in medicine. In this article, we review the use of deep learning for brain disorders. More specifically, we identify the main applications, the concerned disorders and the types of architectures and data used. Finally, we provide guidelines to bridge the gap between research studies and clinical routine

    An interplay of feedforward and feedback signals supporting visual cognition

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    Vast majority of visual cognitive functions from low to high level rely not only on feedforward signals carrying sensory input to downstream brain areas but also on internally-generated feedback signals traversing the brain in the opposite direction. The feedback signals underlie our ability to conjure up internal representations regardless of sensory input – when imagining an object or directly perceiving it. Despite ubiquitous implications of feedback signals in visual cognition, little is known about their functional organization in the brain. Multiple studies have shown that within the visual system the same brain region can concurrently represent feedforward and feedback contents. Given this spatial overlap, (1) how does the visual brain separate feedforward and feedback signals thus avoiding a mixture of the perceived and the imagined? Confusing the two information streams could lead to potentially detrimental consequences. Another body of research demonstrated that feedback connections between two different sensory systems participate in a rapid and effortless signal transmission across them. (2) How do nonvisual signals elicit visual representations? In this work, we aimed to scrutinize the functional organization of directed signal transmission in the visual brain by interrogating these two critical questions. In Studies I and II, we explored the functional segregation of feedforward and feedback signals in grey matter depth of early visual area V1 using 7T fMRI. In Study III we investigated the mechanism of cross-modal generalization using EEG. In Study I, we hypothesized that functional segregation of external and internally-generated visual contents follows the organization of feedforward and feedback anatomical projections revealed in primate tracing anatomy studies: feedforward projections were found to terminate in the middle cortical layer of primate area V1, whereas feedback connections project to the superficial and deep layers. We used high-resolution layer-specific fMRI and multivariate pattern analysis to test this hypothesis in a mental rotation task. We found that rotated contents were predominant at outer cortical depth compartments (i.e. superficial and deep). At the same time perceived contents were more strongly represented at the middle cortical compartment. These results correspond to the previous neuroanatomical findings and identify how through cortical depth compartmentalization V1 functionally segregates rather than confuses external from internally-generated visual contents. For the more precise estimation of signal-by-depth separation revealed in Study I, next we benchmarked three MR-sequences at 7T - gradient-echo, spin-echo, and vascular space occupancy - in their ability to differentiate feedforward and feedback signals in V1. The experiment in Study II consisted of two complementary tasks: a perception task that predominantly evokes feedforward signals and a working memory task that relies on feedback signals. We used multivariate pattern analysis to read out the perceived (feedforward) and memorized (feedback) grating orientation from neural signals across cortical depth. Analyses across all the MR-sequences revealed perception signals predominantly in the middle cortical compartment of area V1 and working memory signals in the deep compartment. Despite an overall consistency across sequences, spin-echo was the only sequence where both feedforward and feedback information were differently pronounced across cortical depth in a statistically robust way. We therefore suggest that in the context of a typical cognitive neuroscience experiment manipulating feedforward and feedback signals at 7T fMRI, spin-echo method may provide a favorable trade-off between spatial specificity and signal sensitivity. In Study III we focused on the second critical question - how are visual representations activated by signals belonging to another sensory modality? Here we built our hypothesis following the studies in the field of object recognition, which demonstrate that abstract category-level representations emerge in the brain after a brief stimuli presentation in the absence of any explicit categorization task. Based on these findings we assumed that two sensory systems can reach a modality-independent representational state providing a universal feature space which can be read out by both sensory systems. We used EEG and a paradigm in which participants were presented with images and spoken words while they were conducting an unrelated task. We aimed to explore whether categorical object representations in both modalities reflect a convergence towards modality-independent representations. We obtained robust representations of objects and object categories in visual and auditory modalities; however, we did not find a conceptual representation shared across modalities at the level of patterns extracted from EEG scalp electrodes in our study. Overall, our results show that feedforward and feedback signals are spatially segregated in the grey matter depth, possibly reflecting a general strategy for implementation of multiple cognitive functions within the same brain region. This differentiation can be revealed with diverse MR-sequences at 7T fMRI, where spin-echo sequence could be particularly suitable for establishing cortical depth-specific effects in humans. We did not find modality-independent representations which, according to our hypothesis, may subserve the activation of visual representations by the signals from another sensory system. This pattern of results indicates that identifying the mechanisms bridging different sensory systems is more challenging than exploring within-modality signal circuitry and this challenge requires further studies. With this, our results contribute to a large body of research interrogating how feedforward and feedback signals give rise to complex visual cognition

    Super-resolution reconstruction of brain magnetic resonance images via lightweight autoencoder

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    Magnetic Resonance Imaging (MRI) is useful to provide detailed anatomical information such as images of tissues and organs within the body that are vital for quantitative image analysis. However, typically the MR images acquired lacks adequate resolution because of the constraints such as patients’ comfort and long sampling duration. Processing the low resolution MRI may lead to an incorrect diagnosis. Therefore, there is a need for super resolution techniques to obtain high resolution MRI images. Single image super resolution (SR) is one of the popular techniques to enhance image quality. Reconstruction based SR technique is a category of single image SR that can reconstruct the low resolution MRI images to high resolution images. Inspired by the advanced deep learning based SR techniques, in this paper we propose an autoencoder based MRI image super resolution technique that performs reconstruction of the high resolution MRI images from low resolution MRI images. Experimental results on synthetic and real brain MRI images show that our autoencoder based SR technique surpasses other state-of-the-art techniques in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), Information Fidelity Criterion (IFC), and computational time

    Echoes of Vision: Mental Imagery in the Human Brain

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    When you picture the face of a friend or imagine your dream house, you are using the same parts of your brain that you use to see. How does the same system manage to both accurately analyze the world around it and synthesize visual experiences without any external input at all? We approach this question and others by extending the well-established theory that the human visual system embodies a probabilistic generative model of the visual world. That is, just as visual features co-occur with one another in the real world with a certain probability (the feature “tree” has a high probability of occurring with the feature “green”), so do the patterns of activity that encode those features in the brain. With such a joint probability distribution at its disposal, the brain can not only infer the cause of a given activity pattern on the retina (vision), but can also generate the probable visual consequence of an assumed or remembered cause (imagery). The formulation of this model predicts that the encoding of imagined stimuli in low-level visual areas resemble the encoding of seen stimuli in higher areas. To test this prediction we developed imagery encoding models-a novel tool that reveals how the features of imagined stimuli are encoded in brain activity. We estimated imagery encoding models from brain activity measured while subjects imagined complex visual stimuli, and then compared these to visual encoding models estimated from a matched viewing experiment. Consistent with our proposal, imagery encoding models revealed changes in spatial frequency tuning and receptive field properties that made early visual areas during imagery more functionally similar to higher visual areas during vision. Likewise, signal and noise properties of the voxel activation between vision and imagery favor the generative model interpretation. Our results provide new evidence for an internal generative model of the visual world, while demonstrating that vision is just one of many possible forms of inference that this putative internal model may support

    Developing an Imaging Biomarker to Detect Aberrant Brain Connectivity in Individual Patients

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    Resting state functional MRI (rsfMRI) has been proven to be a valuable tool in clinical applications such as pre-surgical mapping, but there is not yet a functional and usable algorithm that can be used by physicians in a clinical setting to evaluate an individual patient for diseases and aberrant brain connectivity. If a physician wants to evaluate a patient in this way, the rsfMRI data must be looked at “by hand,” i.e. the physician must manually evaluate the data and identify the functional ICN’s and whether they are normal or aberrant. An algorithm that would automate this process and supplement the physician’s evaluation would be very valuable and would decrease the time needed while increasing accuracy of the data analysis. The algorithm could be used in clinical applications as discussed, or academic and research applications to explore the neural basis of neurological disorders and deficits (epilepsy, etc). rsfMRI data is significant for the proposed solution as it provides maps of functional brain connectivity within functionally specific neural networks, and those connectivity maps can help identify normal and abnormal brain conditions. Whether an ICA approach based on standard networks or an ROI seed based approach which utilizes temporal correlation is used, the end goal of this research is to develop and refine an imaging biomarker for aberrant brain connectivity. The biomarker algorithm should be able to detect the two main types of aberrant connectivity: increased (when abnormal brain connections are present) and decreased (when normal brain connections are missing). The algorithm should then correlate the connectivity patterns to a normative reference data set and create prioritized classification matches to that reference data set. This will allow identification of the aberrant connectivity patterns. Data from the Human Connectome Project (HCP) will be used to create the normative reference data set. The algorithm will finally be verified using simulated test data and test statistics

    4D Flow cardiovascular magnetic resonance consensus statement: 2023 update

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    4D Flow MRI; Hemodynamics; RecommendationsRessonància magnètica de flux 4D; Hemodinàmica; RecomanacionsResonancia magnética de flujo 4D; Hemodinámica; RecomendacionesHemodynamic assessment is an integral part of the diagnosis and management of cardiovascular disease. Four-dimensional cardiovascular magnetic resonance flow imaging (4D Flow CMR) allows comprehensive and accurate assessment of flow in a single acquisition. This consensus paper is an update from the 2015 ‘4D Flow CMR Consensus Statement’. We elaborate on 4D Flow CMR sequence options and imaging considerations. The document aims to assist centers starting out with 4D Flow CMR of the heart and great vessels with advice on acquisition parameters, post-processing workflows and integration into clinical practice. Furthermore, we define minimum quality assurance and validation standards for clinical centers. We also address the challenges faced in quality assurance and validation in the research setting. We also include a checklist for recommended publication standards, specifically for 4D Flow CMR. Finally, we discuss the current limitations and the future of 4D Flow CMR. This updated consensus paper will further facilitate widespread adoption of 4D Flow CMR in the clinical workflow across the globe and aid consistently high-quality publication standards.1R01HL149787-01A1 (S. Schnell, M. Markl), 1R21NS122511-01 (S. Schnell), 1R01CA233878-01 (J.Collins) J.Sotelo thanks to ANID–Millennium Science Initiative Program–ICN2021_004 and FONDECYT de iniciación en investigación #11200481. Dr. Oechtering receives funding from the German Research Foundation (OE 746/1-1)
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