Neural networks associated with eye movements in congenital blindness.

Recent studies have shown that during the typical resting-state, echo planar imaging (EPI) time series obtained from the eye orbit area correlate with brain regions associated with oculomotor control and lower-level visual cortex. Here, we asked whether congenitally blind (CB) shows similar patterns, suggesting a hard-wired constraint on connectivity. We find that orbital EPI signals in CB do correlate with activity in the motor cortex, but less so with activity in the visual cortex. However, the temporal patterns of this eye movement-related signal differed strongly between CB and sighted controls. Furthermore, in CB, a few participants showed uncoordinated orbital EPI signals between the two eyes, each correlated with activity in different brain networks. Our findings suggest a retained circuitry between motor cortex and eye movements in blind, but also a moderate reorganization due to the absence of visual input, and the inability of CB to control their eye movements or sense their positions.

Functional and structural reorganization in brain tumors: a machine learning approach using desynchronized functional oscillations.

Neuroimaging studies have allowed for non-invasive mapping of brain networks in brain tumors. Although tumor core and edema are easily identifiable using standard MRI acquisitions, imaging studies often neglect signals, structures, and functions within their presence. Therefore, both functional and diffusion signals, as well as their relationship with global patterns of connectivity reorganization, are poorly understood. Here, we explore the functional activity and the structure of white matter fibers considering the contribution of the whole tumor in a surgical context. First, we find intertwined alterations in the frequency domain of local and spatially distributed resting-state functional signals, potentially arising within the tumor. Second, we propose a fiber tracking pipeline capable of using anatomical information while still reconstructing bundles in tumoral and peritumoral tissue. Finally, using machine learning and healthy anatomical information, we predict structural rearrangement after surgery given the preoperative brain network. The generative model also disentangles complex patterns of connectivity reorganization for different types of tumors. Overall, we show the importance of carefully designing studies including MR signals within damaged brain tissues, as they exhibit and relate to non-trivial patterns of both structural and functional (dis-)connections or activity.

Prediction of misfolded proteins spreading in Alzheimer's disease using machine learning and spreading models.

The pervasive impact of Alzheimer's disease on aging society represents one of the main challenges at this time. Current investigations highlight 2 specific misfolded proteins in its development: Amyloid-$\beta$ and tau. Previous studies focused on spreading for misfolded proteins exploited simulations, which required several parameters to be empirically estimated. Here, we provide an alternative view based on 2 machine learning approaches which we compare with known simulation models. The first approach applies an autoregressive model constrained by structural connectivity, while the second is based on graph convolutional networks. The aim is to predict concentrations of Amyloid-$\beta$ 2 yr after a provided baseline. We also evaluate its real-world effectiveness and suitability by providing a web service for physicians and researchers. In experiments, the autoregressive model generally outperformed state-of-the-art models resulting in lower prediction errors. While it is important to note that a comprehensive prognostic plan cannot solely rely on amyloid beta concentrations, their prediction, achieved by the discussed approaches, can be valuable for planning therapies and other cures, especially when dealing with asymptomatic patients for whom novel therapies could prove effective.

Community detection in brain connectomes with hybrid quantum computing.

Recent advancements in network neuroscience are pointing in the direction of considering the brain as a small-world system with an efficient integration-segregation balance that facilitates different cognitive tasks and functions. In this context, community detection is a pivotal issue in computational neuroscience. In this paper we explored community detection within brain connectomes using the power of quantum annealers, and in particular the Leap's Hybrid Solver in D-Wave. By reframing the modularity optimization problem into a Discrete Quadratic Model, we show that quantum annealers achieved higher modularity indices compared to the Louvain Community Detection Algorithm without the need to overcomplicate the mathematical formulation. We also found that the number of communities detected in brain connectomes slightly differed while still being biologically interpretable. These promising preliminary results, together with recent findings, strengthen the claim that quantum optimization methods might be a suitable alternative against classical approaches when dealing with community assignment in networks.

Multiscale optical and optoacoustic imaging of amyloid-ß deposits in mice.

Deposits of amyloid-ß (Aß) in the brains of rodents can be analysed by invasive intravital microscopy on a submillimetre scale, or via whole-brain images from modalities lacking the resolution or molecular specificity to accurately characterize Aß pathologies. Here we show that large-field multifocal illumination fluorescence microscopy and panoramic volumetric multispectral optoacoustic tomography can be combined to longitudinally assess Aß deposits in transgenic mouse models of Alzheimer's disease. We used fluorescent Aß-targeted probes (the luminescent conjugated oligothiophene HS-169 and the oxazine-derivative AOI987) to transcranially detect Aß deposits in the cortex of APP/PS1 and arcAß mice with single-plaque resolution (8 µm) and across the whole brain (including the hippocampus and the thalamus, which are inaccessible by conventional intravital microscopy) at sub-150 µm resolutions. Two-photon microscopy, light-sheet microscopy and immunohistochemistry of brain-tissue sections confirmed the specificity and regional distributions of the deposits. High-resolution multiscale optical and optoacoustic imaging of Aß deposits across the entire brain in rodents thus facilitates the in vivo study of Aß accumulation by brain region and by animal age and strain.

Structurally constrained effective brain connectivity.

The relationship between structure and function is of interest in many research fields involving the study of complex biological processes. In neuroscience in particular, the fusion of structural and functional data can help to understand the underlying principles of the operational networks in the brain. To address this issue, this paper proposes a constrained autoregressive model leading to a representation of effective connectivity that can be used to better understand how the structure modulates the function. Or simply, it can be used to find novel biomarkers characterizing groups of subjects. In practice, an initial structural connectivity representation is re-weighted to explain the functional co-activations. This is obtained by minimizing the reconstruction error of an autoregressive model constrained by the structural connectivity prior. The model has been designed to also include indirect connections, allowing to split direct and indirect components in the functional connectivity, and it can be used with raw and deconvoluted BOLD signal. The derived representation of dependencies was compared to the well known dynamic causal model, giving results closer to known ground-truth. Further evaluation of the proposed effective network was performed on two typical tasks. In a first experiment the direct functional dependencies were tested on a community detection problem, where the brain was partitioned using the effective networks across multiple subjects. In a second experiment the model was validated in a case-control task, which aimed at differentiating healthy subjects from individuals with autism spectrum disorder. Results showed that using effective connectivity leads to clusters better describing the functional interactions in the community detection task, while maintaining the original structural organization, and obtaining a better discrimination in the case-control classification task.

An integrated genomic approach to dissect the genetic landscape regulating the cell-to-cell transfer of α-synuclein.

Neuropathological and experimental evidence suggests that the cell-to-cell transfer of α-synuclein has an important role in the pathogenesis of Parkinson's disease (PD). However, the mechanism underlying this phenomenon is not fully understood. We undertook a small interfering RNA (siRNA), genome-wide screen to identify genes regulating the cell-to-cell transfer of α-synuclein. A genetically encoded reporter, GFP-2A-αSynuclein-RFP, suitable for separating donor and recipient cells, was transiently transfected into HEK cells stably overexpressing α-synuclein. We find that 38 genes regulate the transfer of α-synuclein-RFP, one of which is ITGA8, a candidate gene identified through a recent PD genome-wide association study (GWAS). Weighted gene co-expression network analysis (WGCNA) and weighted protein-protein network interaction analysis (WPPNIA) show that those hits cluster in networks that include known PD genes more frequently than expected by random chance. The findings expand our understanding of the mechanism of α-synuclein spread.

Relating Global and Local Connectome Changes to Dementia and Targeted Gene Expression in Alzheimer's Disease.

Networks are present in many aspects of our lives, and networks in neuroscience have recently gained much attention leading to novel representations of brain connectivity. The integration of neuroimaging characteristics and genetics data allows a better understanding of the effects of the gene expression on brain structural and functional connections. The current work uses whole-brain tractography in a longitudinal setting, and by measuring the brain structural connectivity changes studies the neurodegeneration of Alzheimer's disease. This is accomplished by examining the effect of targeted genetic risk factors on the most common local and global brain connectivity measures. Furthermore, we examined the extent to which Clinical Dementia Rating relates to brain connections longitudinally, as well as to gene expression. For instance, here we show that the expression of PLAU gene increases the change over time in betweenness centrality related to the fusiform gyrus. We also show that the betweenness centrality metric impact dementia-related changes in distinct brain regions. Our findings provide insights into the complex longitudinal interplay between genetics and brain characteristics and highlight the role of Alzheimer's genetic risk factors in the estimation of regional brain connectivity alterations.

Genome-Wide Association Study of Brain Connectivity Changes for Alzheimer's Disease.

Variations in the human genome have been found to be an essential factor that affects susceptibility to Alzheimer's disease. Genome-wide association studies (GWAS) have identified genetic loci that significantly contribute to the risk of Alzheimers. The availability of genetic data, coupled with brain imaging technologies have opened the door for further discoveries, by using data integration methodologies and new study designs. Although methods have been proposed for integrating image characteristics and genetic information for studying Alzheimers, the measurement of disease is often taken at a single time point, therefore, not allowing the disease progression to be taken into consideration. In longitudinal settings, we analyzed neuroimaging and single nucleotide polymorphism datasets obtained from the Alzheimer's Disease Neuroimaging Initiative for three clinical stages of the disease, including healthy control, early mild cognitive impairment and Alzheimer's disease subjects. We conducted a GWAS regressing the absolute change of global connectivity metrics on the genetic variants, and used the GWAS summary statistics to compute the gene and pathway scores. We observed significant associations between the change in structural brain connectivity defined by tractography and genes, which have previously been reported to biologically manipulate the risk and progression of certain neurodegenerative disorders, including Alzheimer's disease.

Boosting diabetes and pre-diabetes detection in rural Ghana.

Background: Diabetes is a growing worldwide disease with serious consequences to health and with a high financial burden. Ghana is one of the developing African countries where the prevalence of diabetes is increasing. Moreover, many cases remain undiagnosed, when along with pre-diabetic cases they can be easily detected. The main objective of this study is to propose a novel method to increase diabetes and pre-diabetes early detection in rural areas. A secondary aim is to look for new related behavioral determinants specific to rural Ghana, by comparing subjects at risk with those already diagnosed as diabetic. Methods: The detection approach was based on tests performed pro-actively by community nurses using glucometers and mobile phone apps. As a pilot for future policies, glycemic tests were carried out on 101 subjects from rural communities in Ghana deemed at risk and unaware of their diabetic/pre-diabetic status. A comparison of dietary and lifestyle habits of the screened people was conducted in regards to a cohort of 103 diabetic patients from the same rural communities. Participants for both groups were found through snow-ball sampling. Results: The pilot screening detected 2 diabetic subjects (2% of the cohort) showing WHO diabetic glycemic values, and 20 pre-diabetic subjects (19.8% of the cohort) which showed the effectiveness of the user-friendly approach. Conclusions: Policies based on prevention screening as reported in the manuscript have the potential to reduce diabetes incidence, if actions are taken while patients are pre-diabetic, reduce complication related to late diagnosis and indirectly related health-care costs in the country. The need for further campaigns on alcohol consumption and physical activity has emerged, even in rural areas.

Multi-stage Association Analysis of Glioblastoma Gene Expressions with Texture and Spatial Patterns.

Glioblastoma is the most aggressive malignant primary brain tumor with a poor prognosis. Glioblastoma heterogeneous neuroimaging, pathologic, and molecular features provide opportunities for subclassification, prognostication, and the development of targeted therapies. Magnetic resonance imaging has the capability of quantifying specific phenotypic imaging features of these tumors. Additional insight into disease mechanism can be gained by exploring genetics foundations. Here, we use the gene expressions to evaluate the associations with various quantitative imaging phenomic features extracted from magnetic resonance imaging. We highlight a novel correlation by carrying out multi-stage genomewide association tests at the gene-level through a non-parametric correlation framework that allows testing multiple hypotheses about the integrated relationship of imaging phenotype-genotype more efficiently and less expensive computationally. Our result showed several novel genes previously associated with glioblastoma and other types of cancers, as the LRRC46 (chromosome 17), EPGN (chromosome 4) and TUBA1C (chromosome 12), all associated with our radiographic tumor features.

MultiLink Analysis: Brain Network Comparison via Sparse Connectivity Analysis.

The analysis of the brain from a connectivity perspective is revealing novel insights into brain structure and function. Discovery is, however, hindered by the lack of prior knowledge used to make hypotheses. Additionally, exploratory data analysis is made complex by the high dimensionality of data. Indeed, to assess the effect of pathological states on brain networks, neuroscientists are often required to evaluate experimental effects in case-control studies, with hundreds of thousands of connections. In this paper, we propose an approach to identify the multivariate relationships in brain connections that characterize two distinct groups, hence permitting the investigators to immediately discover the subnetworks that contain information about the differences between experimental groups. In particular, we are interested in data discovery related to connectomics, where the connections that characterize differences between two groups of subjects are found. Nevertheless, those connections do not necessarily maximize the accuracy in classification since this does not guarantee reliable interpretation of specific differences between groups. In practice, our method exploits recent machine learning techniques employing sparsity to deal with weighted networks describing the whole-brain macro connectivity. We evaluated our technique on functional and structural connectomes from human and murine brain data. In our experiments, we automatically identified disease-relevant connections in datasets with supervised and unsupervised anatomy-driven parcellation approaches and by using high-dimensional datasets.

Boosting antenatal care attendance and number of hospital deliveries among pregnant women in rural communities: a community initiative in Ghana based on mobile phones applications and portable ultrasound scans.

BACKGROUND: The World Health Organization has recommended at least four antenatal care (ANC) visits and skilled attendants at birth. Most pregnant women in rural communities in low-income countries do not achieve the minimum recommended visits and deliver without skilled attendants. With the aim of increasing number of ANC visits, reducing home deliveries, and supplementing care given by ANC clinics, a proposed system based on low-cost mobile phones and portable ultrasound scan machines was piloted. METHODS: A sample of 323 pregnant women from four rural communities in the Central Region of Ghana were followed within a 11-month project. In each community, at least one health worker was trained and equipped with a mobile phone to promote ANC and hospital deliveries in her own community. If women cannot attend ANC, technicians acquired scans by using portable ultrasound machines in her community directly and sent them almost in real time to be analyzed by a gynecologist in an urban hospital. A preliminary survey to assess ANC status preceding the pilot study was conducted. During this, one hundred women who had had pregnancies within five years prior to the study were interviewed. RESULTS: The preliminary survey showed that women who attended ANC were less likely to have a miscarriage and more likely to have delivery at hospital or clinic than those who did not, and women who attained at least four ANC visits were less likely to practice self-medication. Among the women involved in the project, 40 gave birth during the period of observation. The proposed prenatal care approach showed that 62.5 % of pregnant women who gave birth during the observation period included in the project (n=40) had their labor attended in clinics or hospitals as against 37.5 % among the cases reported in the pre-survey. One case of ectopic and two cases of breech pregnancies were detected during the pilot through the proposed approach, and appropriate medical interventions were sought. CONCLUSION: Our results show that the proposed prenatal care approach can make quality ANC accessible in rural communities where pregnant women have not been able to access proper ANC.

Automatic Measurement of Venous Pressure Using B-Mode Ultrasound.

Central venous pressure (CVP) information is crucial in clinical situations, such as cardiac failure, intravascular volume overload, and sepsis. The measurement of CVP, however, requires the catheterization of vena cava through the subclavian or internal jugular veins, which is an impractical and costly procedure with related risk of complications. Peripheral venous pressure (PVP), which correlates with CVP under certain patient positioning, can be measured noninvasively using ultrasound via controlled compressions of a superficial vein. This paper presents an automatic system for acquiring such noninvasive measurements. Robust signal and image processing techniques developed for this purpose are introduced in this paper. The proposed standalone mobile platform collects images in real time from the display output of any ultrasound machine, meanwhile measuring the pressure on the skin underneath the ultrasound transducer via a liquid-filled pouch. The image and pressure data are synchronized through an automated temporal calibration procedure. During forearm compressions, blood vessels are detected and tracked in the images using robust geometric (ellipse) models, the parameters of which are used further in the model-based estimation of PVP. The proposed system was tested in 56 image sequences on 14 healthy volunteers, and was shown to achieve measurements with errors comparable to or lower than the interoperator variability of expert manual assessments.

Automatic fetal measurements for low-cost settings by using Local Phase Bone detection.

The estimation of gestational age is done mostly by measurements of fetal anatomical structures such as the head and femur. These measurement are also used in diagnosis and growth assessment. Manual measurements is operator dependent and hence subject to variability.

Segmentation of ultrasound images of fetal anatomic structures using random forest for low-cost settings.

In ultrasound imaging, manual extraction of contours of fetal anatomic structures from echographic images have been found to be very challenging due to speckles and low contrast characteristic features. Contours extracted are therefore associated with variability of human observers. In this case, the contours that are extracted are not reproducible and hence not reliable. This challenge has called for the need to develop a method that can accurately segment the fetal anatomic structures. This will help to estimate and measure the contours of the structures of fetal bodies such as the head circumference, femur length, etc. Most recent methods are able to integrate global shape and appearance. The drawback to most of these methods is that, they are not able to handle localized appearance variations. They only rely on an assumption of Gaussian gray value distribution and also require initialization near the optimal solution. In this manuscript random forest is used to segment head contour in fetal ultrasound scans acquired in low-cost settings, such as acquisition performed in rural areas of low-income countries using low-cost portable machines.

Predictive value of imaging markers at multiple sclerosis disease onset based on gadolinium- and USPIO-enhanced MRI and machine learning.

OBJECTIVES: A novel characterization of Clinically Isolated Syndrome (CIS) patients according to lesion patterns is proposed. More specifically, patients are classified according to the nature of inflammatory lesions patterns. It is expected that this characterization can infer new prospective figures from the earliest imaging signs of Multiple Sclerosis (MS), since it can provide a classification of different types of lesions across patients. METHODS: The method is based on a two-tiered classification. Initially, the spatio-temporal lesion patterns are classified. The discovered lesion patterns are then used to characterize groups of patients. The patient groups are validated using statistical measures and by correlations at 24-month follow-up with hypointense lesion loads. RESULTS: The methodology identified 3 statistically significantly different clusters of lesion patterns showing p-values smaller than 0.01. Moreover, these patterns defined at baseline correlated with chronic hypointense lesion volumes by follow-up with an R(2) score of 0.90. CONCLUSIONS: The proposed methodology is capable of identifying three major different lesion patterns that are heterogeneously present in patients, allowing a patient classification using only two MRI scans. This finding may lead to more accurate prognosis and thus to more suitable treatments at early stage of MS.

Shape-based assessment of vertebral fracture risk in postmenopausal women using discriminative shape alignment.

RATIONALE AND OBJECTIVES: Risk assessment of future osteoporotic vertebral fractures is currently based mainly on risk factors, such as bone mineral density, age, prior fragility fractures, and smoking. It can be argued that an osteoporotic vertebral fracture is not exclusively an abrupt event but the result of a decaying process. To evaluate fracture risk, a shape-based classifier, identifying possible small prefracture deformities, may be constructed. MATERIALS AND METHODS: During a longitudinal case-control study, a large population of postmenopausal women, fracture free at baseline, were followed. The 22 women who sustained at least one lumbar fracture on follow-up represented the case group. The control group comprised 91 women who maintained skeletal integrity and matched the case group according to the standard osteoporosis risk factors. On radiographs, a radiologist and two technicians independently performed manual annotations of the vertebrae, and fracture prediction using shape features extracted from the baseline annotations was performed. This was implemented using posterior probabilities from a standard linear classifier. RESULTS: The classifier tested on the study population quantified vertebral fracture risk, giving statistically significant results for the radiologist annotations (area under the curve, 0.71 ± 0.013; odds ratio, 4.9; 95% confidence interval, 2.94-8.05). CONCLUSIONS: The shape-based classifier provided meaningful information for the prediction of vertebral fractures. The approach was tested on case and control groups matched for osteoporosis risk factors. Therefore, the method can be considered an additional biomarker, which combined with traditional risk factors can improve population selection (eg, in clinical trials), identifying patients with high fracture risk.

A Bayesian framework for automated cardiovascular risk scoring on standard lumbar radiographs.

We present a fully automated framework for scoring a patient's risk of cardiovascular disease (CVD) and mortality from a standard lateral radiograph of the lumbar aorta. The framework segments abdominal aortic calcifications for computing a CVD risk score and performs a survival analysis to validate the score. Since the aorta is invisible on X-ray images, its position is reasoned from 1) the shape and location of the lumbar vertebrae and 2) the location, shape, and orientation of potential calcifications. The proposed framework follows the principle of Bayesian inference, which has several advantages in the complex task of segmenting aortic calcifications. Bayesian modeling allows us to compute CVD risk scores conditioned on the seen calcifications by formulating distributions, dependencies, and constraints on the unknown parameters. We evaluate the framework on two datasets consisting of 351 and 462 standard lumbar radiographs, respectively. Promising results indicate that the framework has potential applications in diagnosis, treatment planning, and the study of drug effects related to CVD.

Maximum a posteriori estimation of linear shape variation with application to vertebra and cartilage modeling.

The estimation of covariance matrices is a crucial step in several statistical tasks. Especially when using few samples of a high dimensional representation of shapes, the standard maximum likelihood estimation (ML) of the covariance matrix can be far from the truth, is often rank deficient, and may lead to unreliable results. In this paper, we discuss regularization by prior knowledge using maximum a posteriori (MAP) estimates. We compare ML to MAP using a number of priors and to Tikhonov regularization. We evaluate the covariance estimates on both synthetic and real data, and we analyze the estimates' influence on a missing-data reconstruction task, where high resolution vertebra and cartilage models are reconstructed from incomplete and lower dimensional representations. Our results demonstrate that our methods outperform the traditional ML method and Tikhonov regularization.