Search

The human auditory cortex comprises the supratemporal plane and large parts of the temporal and parietal convexities. We have investigated the relevant intrahemispheric cortico-cortical connections using in vivo DSI tractography combined with landmark-based registration, automatic cortical parcellation and whole-brain structural connection matrices in 20 right-handed male subjects. On the supratemporal plane, the pattern of connectivity was related to the architectonically defined early-stage auditory areas. It revealed a three-tier architecture characterized by a cascade of connections from the primary auditory cortex to six adjacent non-primary areas and from there to the superior temporal gyrus. Graph theory-driven analysis confirmed the cascade-like connectivity pattern and demonstrated a strong degree of segregation and hierarchy within early-stage auditory areas. Putative higher-order areas on the temporal and parietal convexities had more widely spread local connectivity and long-range connections with the prefrontal cortex; analysis of optimal community structure revealed five distinct modules in each hemisphere. The pattern of temporo-parieto-frontal connectivity was partially asymmetrical. In conclusion, the human early-stage auditory cortical connectivity, as revealed by in vivo DSI tractography, has strong similarities with that of non-human primates. The modular architecture and hemispheric asymmetry in higher-order regions is compatible with segregated processing streams and lateralization of cognitive functions.

While Deep Learning (DL) is often considered the state-of-the art for Artificial Intelligence-based medical decision support, it remains sparsely implemented in clinical practice and poorly trusted by clinicians due to insufficient interpretability of neural network models. We have tackled this issue by developing interpretable DL models in the context of online detection of epileptic seizure, based on EEG signal. This has conditioned the preparation of the input signals, the network architecture, and the post-processing of the output in line with the domain knowledge. Specifically, we focused the discussion on three main aspects: 1) how to aggregate the classification results on signal segments provided by the DL model into a larger time scale, at the seizure-level; 2) what are the relevant frequency patterns learned in the first convolutional layer of different models, and their relation with the delta, theta, alpha, beta and gamma frequency bands on which the visual interpretation of EEG is based; and 3) the identification of the signal waveforms with larger contribution towards the ictal class, according to the activation differences highlighted using the DeepLIFT method. Results show that the kernel size in the first layer determines the interpretability of the extracted features and the sensitivity of the trained models, even though the final performance is very similar after post-processing. Also, we found that amplitude is the main feature leading to an ictal prediction, suggesting that a larger patient population would be required to learn more complex frequency patterns. Still, our methodology was successfully able to generalize patient inter-variability for the majority of the studied population with a classification F1-score of 0.873 and detecting 90% of the seizures., Comment: 28 pages, 11 figures, 12 tables

Over the last 10 years, diffusion-weighted imaging (DWI) has become an important tool to investigate white matter (WM) anomalies in schizophrenia. Despite technological improvement and the exponential use of this technique, discrepancies remain and little is known about optimal parameters to apply for diffusion weighting during image acquisition. Specifically, high b-value diffusion-weighted imaging known to be more sensitive to slow diffusion is not widely used, even though subtle myelin alterations as thought to happen in schizophrenia are likely to affect slow-diffusing protons. Schizophrenia patients and healthy controls were scanned with a high b-value (4000 s/mm(2)) protocol. Apparent diffusion coefficient (ADC) measures turned out to be very sensitive in detecting differences between schizophrenia patients and healthy volunteers even in a relatively small sample. We speculate that this is related to the sensitivity of high b-value imaging to the slow-diffusing compartment believed to reflect mainly the intra-axonal and myelin bound water pool. We also compared these results to a low b-value imaging experiment performed on the same population in the same scanning session. Even though the acquisition protocols are not strictly comparable, we noticed important differences in sensitivities in the favor of high b-value imaging, warranting further exploration.

MR connectomics is an emerging framework in neuro-science that combines diffusion MRI and whole brain tractography methodologies with the analytical tools of network science. In the present work we review the current methods enabling structural connectivity mapping with MRI and show how such data can be used to infer new information of both brain structure and function. We also list the technical challenges that should be addressed in the future to achieve high-resolution maps of structural connectivity. From the resulting tremendous amount of data that is going to be accumulated soon, we discuss what new challenges must be tackled in terms of methods for advanced network analysis and visualization, as well data organization and distribution. This new framework is well suited to investigate key questions on brain complexity and we try to foresee what fields will most benefit from these approaches.

It has been shown that the tensor calculation is very sensitive to the presence of noise in the acquired images, yielding to very low quality Diffusion Tensor Images (DTI) data. Recent investigations have shown that the noise present in the Diffusion Weighted Images (DWI) causes bias effects on the DTI data which cannot be corrected if the noise characteristic is not taken into account. One possible solution is to increase the minimum number of acquired measurements (which is 7) to several tens (or even several hundreds). This has the disadvantage of increasing the acquisition time by one (or two) orders of magnitude, making the process inconvenient for a clinical setting. We here proposed a turn-around procedure for which the number of acquisitions is maintained but, the DWI data are filtered prior to determining the DTI. We show a significant reduction on the DTI bias by means of a simple and fast procedure which is based on linear filtering; well-known drawbacks of such filters are circumvented by means of anisotropic neighborhoods and sequential application of the filter itself. Information of the first order probability density function of the raw data, namely, the Rice distribution, is also included. Results are shown both for synthetic and real datasets. Some error measurements are determined in the synthetic experiments, showing how the proposed scheme is able to reduce them. It is worth noting a 50% increase in the linear component for real DTI data, meaning that the bias in the DTI is considerably reduced. A novel fiber smoothness measure is defined to evaluate the resulting tractography for real DWI data. Our findings show that after filtering, fibers are considerably smoother on the average. Execution times are very low as compared to other reported approaches which allows for a real-time implementation.

This work deals with global statistical unsupervised segmentation algorithms. In the context of Magnetic Resonance Image (MRI), an accurate and robust segmentation can be achieved by combining both the Hidden Markov Random Field (HMRF) model and the Expectation-Maximization (EM) algorithm. This EM-HMRF approach is accomplished by taking into account spatial information to improve the segmentation process which, in turn, slows the approach and consequently prevents its adoption for real-time applications such as three-dimensional medical image segmentation. We propose in this paper the use of the Bootstrap resampling to speed up the processing time of the EM-HMRF algorithm. This is accomplished by randomly selecting an optimal representative set of pixels according to some criteria originally defined for the blind segmentation. We will show how to adapt such criteria to the HMRF_EM algorithm context. We validated our proposition through a set of experiments and we proved that the use of the Bootstrap resampling yields the same accuracy and robustness as the basic algorithm, yet it amounts to a considerable processing speed up.

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

Contains fulltext : 109610.pdf (Publisher’s version ) (Open Access) Deep brain stimulation (DBS) for Parkinson's disease often alleviates the motor symptoms, but causes cognitive and emotional side effects in a substantial number of cases. Identification of the motor part of the subthalamic nucleus (STN) as part of the presurgical workup could minimize these adverse effects. In this study, we assessed the STN's connectivity to motor, associative, and limbic brain areas, based on structural and functional connectivity analysis of volunteer data. For the structural connectivity, we used streamline counts derived from HARDI fiber tracking. The resulting tracks supported the existence of the so-called "hyperdirect" pathway in humans. Furthermore, we determined the connectivity of each STN voxel with the motor cortical areas. Functional connectivity was calculated based on functional MRI, as the correlation of the signal within a given brain voxel with the signal in the STN. Also, the signal per STN voxel was explained in terms of the correlation with motor or limbic brain seed ROI areas. Both right and left STN ROIs appeared to be structurally and functionally connected to brain areas that are part of the motor, associative, and limbic circuit. Furthermore, this study enabled us to assess the level of segregation of the STN motor part, which is relevant for the planning of STN DBS procedures.

Researchers working in the field of global connectivity analysis using diffusion magnetic resonance imaging (MRI) can count on a wide selection of software packages for processing their data, with methods ranging from the reconstruction of the local intra-voxel axonal structure to the estimation of the trajectories of the underlying fibre tracts. However, each package is generally task-specific and uses its own conventions and file formats. In this article we present the Connectome Mapper, a software pipeline aimed at helping researchers through the tedious process of organising, processing and analysing diffusion MRI data to perform global brain connectivity analyses. Our pipeline is written in Python and is freely available as open-source at www.cmtk.org., PLoS ONE, 7 (12), ISSN:1932-6203

We study an adaptive statistical approach to analyze brain networks represented by brain connection matrices of interregional connectivity (connectomes). Our approach is at a middle level between a global analysis and single connections analysis by considering subnetworks of the global brain network. These subnetworks represent either the inter-connectivity between two brain anatomical regions or by the intra-connectivity within the same brain anatomical region. An appropriate summary statistic, that characterizes a meaningful feature of the subnetwork, is evaluated. Based on this summary statistic, a statistical test is performed to derive the corresponding p-value. The reformulation of the problem in this way reduces the number of statistical tests in an orderly fashion based on our understanding of the problem. Considering the global testing problem, the p-values are corrected to control the rate of false discoveries. Finally, the procedure is followed by a local investigation within the significant subnetworks. We contrast this strategy with the one based on the individual measures in terms of power. We show that this strategy has a great potential, in particular in cases where the subnetworks are well defined and the summary statistics are properly chosen. As an application example, we compare structural brain connection matrices of two groups of subjects with a 22q11.2 deletion syndrome, distinguished by their IQ scores.

From toddler to late teenager, the macroscopic pattern of axonal projections in the human brain remains largely unchanged while undergoing dramatic functional modifications that lead to network refinement. These functional modifications are mediated by increasing myelination and changes in axonal diameter and synaptic density, as well as changes in neurochemical mediators. Here we explore the contribution of white matter maturation to the development of connectivity between ages 2 and 18 y using high b-value diffusion MRI tractography and connectivity analysis. We measured changes in connection efficacy as the inverse of the average diffusivity along a fiber tract. We observed significant refinement in specific metrics of network topology, including a significant increase in node strength and efficiency along with a decrease in clustering. Major structural modules and hubs were in place by 2 y of age, and they continued to strengthen their profile during subsequent development. Recording resting-state functional MRI from a subset of subjects, we confirmed a positive correlation between structural and functional connectivity, and in addition observed that this relationship strengthened with age. Continuously increasing integration and decreasing segregation of structural connectivity with age suggests that network refinement mediated by white matter maturation promotes increased global efficiency. In addition, the strengthening of the correlation between structural and functional connectivity with age suggests that white matter connectivity in combination with other factors, such as differential modulation of axonal diameter and myelin thickness, that are partially captured by inverse average diffusivity, play an increasingly important role in creating brain-wide coherence and synchrony.

The global structural connectivity of the brain, the human connectome, is now accessible at millimeter scale with the use of MRI. In this paper, we describe an approach to map the connectome by constructing normalized whole-brain structural connection matrices derived from diffusion MRI tractography at 5 different scales. Using a template-based approach to match cortical landmarks of different subjects, we propose a robust method that allows (a) the selection of identical cortical regions of interest of desired size and location in different subjects with identification of the associated fiber tracts (b) straightforward construction and interpretation of anatomically organized whole-brain connection matrices and (c) statistical inter-subject comparison of brain connectivity at various scales. The fully automated post-processing steps necessary to build such matrices are detailed in this paper. Extensive validation tests are performed to assess the reproducibility of the method in a group of 5 healthy subjects and its reliability is as well considerably discussed in a group of 20 healthy subjects.

Lesions of anatomical brain networks result in functional disturbances of brain systems and behavior which depend sensitively, often unpredictably, on the lesion site. The availability of whole-brain maps of structural connections within the human cerebrum and our increased understanding of the physiology and large-scale dynamics of cortical networks allow us to investigate the functional consequences of focal brain lesions in a computational model. We simulate the dynamic effects of lesions placed in different regions of the cerebral cortex by recording changes in the pattern of endogenous (“resting-state”) neural activity. We find that lesions produce specific patterns of altered functional connectivity among distant regions of cortex, often affecting both cortical hemispheres. The magnitude of these dynamic effects depends on the lesion location and is partly predicted by structural network properties of the lesion site. In the model, lesions along the cortical midline and in the vicinity of the temporo-parietal junction result in large and widely distributed changes in functional connectivity, while lesions of primary sensory or motor regions remain more localized. The model suggests that dynamic lesion effects can be predicted on the basis of specific network measures of structural brain networks and that these effects may be related to known behavioral and cognitive consequences of brain lesions., Author Summary Every year, millions of people suffer the consequences of brain damage, as a result of stroke, traumatic brain injury, cancer or degenerative brain disease. The cognitive and behavioral symptoms of focal lesions of the brain are highly variable and in many cases depend on the location of the lesion site. Can we predict the functional impact of such lesions on the basis of a computational model of the brain's structure and dynamics? Numerous other systems that form complex networks have been analyzed for their vulnerability to structural damage. In many cases, the degree to which such systems are perturbed depends on network attributes of the deleted nodes and connections. We apply this network approach to investigate the structural and functional impact of localized lesions of a model of the cerebral cortex. When we delete nodes that occupy, in the intact brain, a highly central position, we find that the dynamic interactions between nodes in the remaining brain are greatly disturbed. In contrast, deletion of less central nodes has relatively little effect. In the model, some of the most disruptive lesion sites correspond to locations in the brain where lesions produce complex cognitive disturbances. Our modeling approach aims towards linking disturbances of structural brain networks to specific clinical outcomes.

This chapter describes a framework for storage of tensor array data, useful to describe regularly sampled tensor fields. The main component of the framework, called Similar Tensor Array Core (STAC), is the result of a collaboration between research groups within the SIMILAR network of excellence. It aims to capture the essence of regularly sampled tensor fields using a minimal set of attributes and can therefore be used as a “greatest common divisor” and interface between tensor array processing algorithms. This is potentially useful in applied fields like medical image analysis, in particular in Diffusion Tensor MRI, where misinterpretation of tensor array data is a common source of errors. By promoting a strictly geometric perspective on tensor arrays, with a close resemblance to the terminology used in differential geometry, (STAC) removes ambiguities and guides the user to define all necessary information. In contrast to existing tensor array file formats, it is minimalistic and based on an intrinsic and geometric interpretation of the array itself, without references to other coordinate systems.

Structurally segregated and functionally specialized regions of the human cerebral cortex are interconnected by a dense network of cortico-cortical axonal pathways. By using diffusion spectrum imaging, we noninvasively mapped these pathways within and across cortical hemispheres in individual human participants. An analysis of the resulting large-scale structural brain networks reveals a structural core within posterior medial and parietal cerebral cortex, as well as several distinct temporal and frontal modules. Brain regions within the structural core share high degree, strength, and betweenness centrality, and they constitute connector hubs that link all major structural modules. The structural core contains brain regions that form the posterior components of the human default network. Looking both within and outside of core regions, we observed a substantial correspondence between structural connectivity and resting-state functional connectivity measured in the same participants. The spatial and topological centrality of the core within cortex suggests an important role in functional integration.

We propose an algorithm for very high-resolution satellite image classification that combines non-supervised segmentation with a supervised classification. Both multi-spectral data and local spatial priors are used in the Gaussian Hidden Markov Random Field (GHMRF) model for the segmentation. Then, two classifiers, Mahalanobis distance classifier and SVM, are studied using intensity, texture and shape features. Validation is done qualitatively and quantitatively by comparison with a manual classification used as a ground truth. Results show very good performance of our approach in comparison to existing techniques. Also, we demonstrate that spectral and spatial features calculated on segmented regions are much more discriminant than the spectral features of the pixels taken individually for the classification task.

The 22q11.2 deletion syndrome (22q11DS) is a widely recognized genetic model allowing the study of neuroanatomical biomarkers that underlie the risk for developing schizophrenia. Recent advances in magnetic resonance image analyses enable the examination of structural connectivity integrity, scarcely used in the 22q11DS field. This framework potentially provides evidence for the disconnectivity hypothesis of schizophrenia in this high-risk population. In the present study, we quantify the whole brain white matter connections in 22q11DS using deterministic tractography. Diffusion Tensor Imaging was acquired in 30 affected patients and 30 age- and gender-matched healthy participants. The Human Connectome technique was applied to register white matter streamlines with cortical anatomy. The number of fibers (streamlines) was used as a measure of connectivity for comparison between groups at the global, lobar and regional level. All statistics were corrected for age and gender. Results showed a 10% reduction of the total number of fibers in patients compared to controls. After correcting for this global reduction, preserved connectivity was found within the right frontal and right parietal lobes. The relative increase in the number of fibers was located mainly in the right hemisphere. Conversely, an excessive reduction of connectivity was observed within and between limbic structures. Finally, a disproportionate reduction was shown at the level of fibers connecting the left fronto-temporal regions. We could therefore speculate that the observed disruption to fronto-temporal connectivity in individuals at risk of schizophrenia implies that fronto-temporal disconnectivity, frequently implicated in the pathogenesis of schizophrenia, could precede the onset of symptoms and, as such, constitutes a biomarker of the vulnerability to develop psychosis. On the contrary, connectivity alterations in the limbic lobe play a role in a wide range of psychiatric disorders and therefore seem to be less specific in defining schizophrenia. ispartof: PLoS One vol:8 issue:3 ispartof: location:United States status: published

Keywords: Diffusion, LTS5 Reference EPFL-CONF-164457doi:10.1016/j.schres.2010.02.324View record in Web of Science Record created on 2011-03-25, modified on 2017-05-10

It has been shown that the tensor calculation is very sensitive to the presence of noise in the acquired images, yielding to very low-quality Diffusion Tensor Images (DTI) data. Recent investigations have shown that the noise present in the Diffusion Weighted Images (DWI) causes bias effects on the DTI data which cannot be corrected if the noise characteristic is not taken into account. One possible solution is to increase the minimum number of acquired measurements (which is 7) to several tens (or even several hundreds). This has the disadvantage of increasing the acquisition time by one (or two) orders of magnitude, making the process inconvenient for a clinical setting. We here proposed a turn-around procedure for which the number of acquisitions is maintained but, the DWI data are filtered prior to determining the DTI. We show a significant reduction on the DTI bias by means of a simple and fast procedure which is based on linear filtering; well-known drawbacks of such filters are circumvented by means of anisotropic neighborhoods and sequential application of the filter itself. Information of the first order probability density function of the raw data, namely, the Rice distribution, is also included. Results are shown both for synthetic and real datasets. Some error measurements are determined in the synthetic experiments, showing how the proposed scheme is able to reduce them. It is worth noting a 50% increase in the linear component for real DTI data, meaning that the bias in the DTI is considerably reduced. A novel fiber smoothness measure is defined to evaluate the resulting tractography for real DWI data. Our findings show that after filtering, fibers are considerably smoother on the average. Execution times are very low as compared to other reported approaches which allows for a real-time implementation.

Keywords: LTS5 Reference EPFL-CONF-138672 Record created on 2009-05-29, modified on 2017-05-10

BackgroundSince the emergence of diffusion tensor imaging, a lot of work has been done to better understand the properties of diffusion MRI tractography. However, the validation of the reconstructed fiber connections remains problematic in many respects. For example, it is difficult to assess whether a connection is the result of the diffusion coherence contrast itself or the simple result of other uncontrolled parameters like for example: noise, brain geometry and algorithmic characteristics.Methodology/principal findingsIn this work, we propose a method to estimate the respective contributions of diffusion coherence versus other effects to a tractography result by comparing data sets with and without diffusion coherence contrast. We use this methodology to assign a confidence level to every gray matter to gray matter connection and add this new information directly in the connectivity matrix.Conclusions/significanceOur results demonstrate that whereas we can have a strong confidence in mid- and long-range connections obtained by a tractography experiment, it is difficult to distinguish between short connections traced due to diffusion coherence contrast from those produced by chance due to the other uncontrolled factors of the tractography methodology.

Keywords: LTS1 ; LTS5 Reference EPFL-ARTICLE-91016 Record created on 2006-10-27, modified on 2017-05-10

Epilepsy is one of the most prevalent paroxystic neurological disorders that can dramatically degrade the quality of life and may even lead to death. Therefore, real-time epilepsy monitoring and seizure detection has become important over the past decades. In this context, wearable technologies offer a promising solution to pervasive epilepsy monitoring by removing the constraints with respect to time and location. In this paper, we propose a self-aware wearable system for real-time detection of epileptic seizures on a long-term basis. First, we propose a multi-parametric machine learning technique to detect seizures by analyzing both cardiac and respiratory responses to seizures, which are obtained using only the ECG signal. Second, in order to enable long-time epilepsy detection, we introduce the notion of self-awareness in our real-time wearable system. We evaluate the performance of our proposed solution based on an epilepsy database of more than 211 hours of recording, provided by the Lausanne University Hospital (CHUV), on the INYU wearable sensor. Our proposed system achieves a sensitivity of 88.66% and a specificity of 85.65% before applying self-awareness. Moreover, by controlling the energy-quality trade-offs using our self-aware energy-management technique, we can tune the battery lifetime of the wearable system to last between 67.55 and 136.91 days while, still outperforming the state-of-the-art techniques for wearable seizure detection, by achieving from 85.54% to 79.33% geometric mean of specificity and sensitivity.

Event detection and classification algorithms are resilient towards aggressive resource-aware optimisations. In this paper, we leverage this characteristic in the context of smart health monitoring systems. In more detail, we study the attainable benefits resulting from tailoring Support Vector Machine (SVM) inference engines devoted to the detection of epileptic seizures from ECG-derived features. We conceive and explore multiple optimisations, each effectively reducing resource budgets while minimally impacting classification performance. These strategies can be seamlessly combined, which results in 12.5X and 16X gains in energy and area, respectively, with a negligible loss, 3.2% in classification performance.

The transition to Exascale computing is going to be characterised by an increased range of application classes. In addition to traditional massively parallel "number crunching" applications, new classes are emerging such as real-time HPC and data-intensive scalable computing. Furthermore, Exascale computing is characterised by a "democratisation" of HPC: to fully exploit the capabilities of Exascale-level facilities, HPC is moving towards enabling access to its resources to a wider range of new players, including SMEs, through cloud-based approaches [1]. Finally, the need for much higher energy efficiency is pushing towards deep heterogeneity, widening the range of options for acceleration, moving from the traditional CPU-only organization, to the CPU plus GPU which currently dominates the Green5001, to more complex options including programmable accelerators and even (reconfigurable) hardware accelerators [2].