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Deposition of beta-amyloid (Aβ) deposits is one major histopathological hallmark of Alzheimer’s disease (AD). Here, we introduce volumetric multi-spectral optoacoustic tomography (vMSOT), which covers 10×10×10 mm3 field-of-view, capable of 3D whole mouse brain imaging. We show for the first time the optoacoustic properties of oxazine-derivative AOI987 probe, which binds to Aβ, and the application of vMSOT for the quantification of brain-wide Aβ deposition. Administration of AOI987 to two common transgenic mouse strains of AD amyloidosis led to a retention of the probe in Aβ-laden brain regions. Co-registered of vMSOT data to a brain atlas revealed strain-specific pattern of AOI987 uptake. A comparison with ex vivo light-sheet microscopy in cleared mouse brains showed a good correspondence in Aβ distribution. Lastly, we demonstrate the specificity of the AOI987 probe by immunohistochemistry. vMSOT with AOI987 facilitates preclinical brain region-specific studies of Aβ spread and accumulation, and the monitoring of putative treatments targeting Aβ.

The identification of genetic variants associated with complex brain diseases has evolved in the past decades. Studies in the field have taken different approaches and study designs including genome-wide association studies. Neuroimaging and connectomics have also improved our understanding of the structural connectivity of the human brain and produced reliable measurements. Combining both neuroimaging and genetic characteristics significantly contributes to understand their complex relationship in affecting behaviour and cognition. Throughout our previous work, we proposed analysis pipeline to study the association between imaging and genetics of two different types of brain disease, which is, Alzheimer9s disease and glioblastoma. We observe the need for a unified model to study the complex interplay between genetic, environmental and clinical, neuroimaging and phenotype features. In this chapter, we developed BiGen, a mathematical model to measure the inter-correlation structure through the integration of genetic, environmental, neuroimaging and disease measurements. We utilised the structural equation model and used a path construct of latent variables to study the hidden association between genes and brain-related diseases, mediated by connectivity characteristics. We applied BiGen to simulated data and to a dataset from the Alzheimer9s Disease Neuroimaging Initiative. % Our proposed methodology has successfully identified risk genes in a simulated dataset, and replicated risk genes in the literature of Alzheimer9s disease and dementia.

SummaryNeuropathological and experimental evidence suggests that the cell-to-cell transfer of a-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 an siRNA, genome-wide high throughput screen to identify genes regulating the cell-to-cell transfer of a-synuclein. We transiently transfected HEK cells stably overexpressing a-synuclein with a construct encoding GFP-2a-aSynuclein-RFP. The cells expressing a-synuclein-RFP through transfection were double positive for GFP and RFP fluorescence, whereas the cells receiving it through transfer were positive only for RFP fluorescence. The amount of a-synuclein transfer was quantified by high content microscopy. A series of unbiased screens confirmed the involvement of 38 genes in the regulation of a-synuclein-RFP transfer. One of those hits was ITGA8, a candidate gene recently identified through a large PD genome wide association study (GWAS). Weighted gene co-expression network analysis (WGCNA) and weighted protein-protein network interaction analysis (WPPNIA) showed that the hits clustered in networks that included known PD Mendelian and GWAS risk genes more frequently than expected than random chance. Given the genetic overlap between a-synuclein transfer and PD, those findings provide supporting evidence for the importance of the cell-to-cell transfer of a-synuclein in the pathogenesis of PD, and expand our understanding of the mechanism of a-synuclein spread.

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. Indeed, there is still room for investigation of the genetic contribution to brain connectivity. The integration of neuroimaging and genetics allows a better understanding of the effects of the genetic variations on brain structural and functional connections, but few studies have successfully investigated the longitudinal association of such a mutual interplay. Nevertheless, several Alzheimer’s disease-associated genetic variants have been identified through omic studies, and the current work uses whole-brain tractography in a longitudinal case-control study design and measures the structural connectivity changes of brain networks to study the neurodegeneration of Alzheimer’s. This is performed by examining the effect of targeted genetic risk factors on local and global brain connectivity. We investigated the degree to which changes in brain connectivity are affected by gene expression. More specifically, we used the most common brain connectivity measures such as efficiency, characteristic path length, betweenness centrality, Louvain modularity and transitivity (a variation of clustering coefficient). Furthermore, we examined the extent to which Clinical Dementia Rating reflects brain connections longitudinally and genetic variation. Here, we show that the expression of PLAU and HFE genes increases the change in betweenness centrality related to the fusiform gyrus and clustering coefficient of cingulum bundle over time, respectively. APP and BLMH gene expression associates with local connectivity. We also show that betweenness centrality has a high contribution to dementia in distinct brain regions. Our findings provide insights into the complex longitudinal interplay between genetics and neuroimaging characteristics and highlight the role of Alzheimer’s genetic risk factors in the estimation of regional brain connection alterations. These regional relationship patterns can be useful for early disease treatment and neurodegeneration prediction.

Parkinsons disease is a neurodegenerative disease characterized by the progressive development of $\alpha$-synuclein pathology across the brain. To better understand the disruption of neuronal networks in Parkinsons disease and its relation to the spread of $\alpha$-synuclein, advanced descriptors from neuroimaging can be used to complement histopathological analyses and in vitro and mouse experimental models. It is yet to be understood whether the course of Parkinson’s disease affects the structural brain network, or, conversely, if some subjects have specific structural connections which facilitate the transmission of the pathology. In this paper we investigate whether there are differences between the connectomes of Parkinson’s disease patients and healthy controls. Moreover, we evaluate a computational model to simulate the spread of $\alpha$-synuclein across neuronal networks in patients with Parkinson’s disease, quantifying which areas could be the most affected by the disease.

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 genome-wide 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.

Recent advances in neuroimaging have allowed the use of network analysis to study the brain in a system-based approach. A system-based analysis of gliomas can shed light on mechanisms underlying neuronal connectivity and plasticity and the recovery process, and it could support surgical decision-making. Surgery has been shifting from image-guided surgery to a functional mapping-guided resection where several structural and functional modalities are used. However, reliable identification of eloquent areas during planning of surgical resection is still a challenge. Pre-operative language mapping performed by navigated transcranial magnetic stimulation (nTMS) is of great value as it elucidates functional cortical organization, which might be different from patient to patient due to the heterogeneity of the lesions and individual plasticity. In this paper we propose the construction of an “effective” speech network used for surgical decision making. This is achieved by mapping functionally relevant areas identified by nTMS on tractography-based connectomics. Subsequently we compute graph metrics on the identified speech networks of patients who show preoperative aphasia aiming to identify relevant differences between graph metrics in patients with and without preoperative aphasia. Lastly, the validity of the speech networks is examined by checking the involved graph communities.

The underlying neural mechanisms of autism spectrum disorders (ASD) remains unclear. Most of the previous studies based on connectomics to discriminate ASD from typically developing (TD) subjects focused either on global graph metrics or specific discriminant connections. In this paper we investigate whether there is a correlation between local and global features, and whether the characterization that discriminates ASD from TD subjects is primarily given by widespread network differences, or the difference lies in specific local connections which are just captured by global metrics. Namely, whether miswiring of brain connections related to ASD is localized or diffuse. The presented results suggest that the widespread hypothesis is more likely.

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.

BackgroundDiabetes 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. Pre-diabetes condition occurs when blood sugar levels are higher than normal but are not high enough to be classified as diabetes, and it is still reversible.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.MethodsThe screening 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.ResultsThe 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. The need for further campaigns about alcohol consumption and physical activities has emerged even in rural areas.ConclusionsPolicies 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.

Schizophrenia and autism share some genotipic and phenotypic aspects as connectome miswiring and common cognitive deficits. Currently, there are no medical tests available for either disorders, and diagnostics for both of them include direct reports of relatives and clinical evaluation by a psychiatrist. Despite several medical imaging biomarkers have been proposed in the past, novel effective biomarkers or improvements of the existing ones is still need. This work proposes a dynamic functional connectome analysis combined with machine learning techniques to complement the present diagnostic procedure. We used the moving window technique to locate a set of dynamic functional connectivity states, and then use them as features to classify subjects as autism/schizophrenia or control. Moreover, by using dynamic functional connectivity measures we investigate the question whether those two disorders overlap, namely whether schizophrenia is part of the autism spectrum and which brain region could be involved in both disorders. The results reveal that both static and dynamic functional connectivity can be used to classify subjects with schizophrenia or autism. Lastly, some brain regions show similar functional flexibility in both autism and schizophrenia cohorts giving further possible proofs of their overlaps. Keywords: Brain imaging, Schizophrenia, Autism spectrum disorder, Connectome, Functional connectivity, FMRI

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., BMC Pregnancy and Childbirth, 16, ISSN:1471-2393

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.

A brain lesion is a brain tissue abnormality which can be seen on a neurological scan, such as magnetic resonance imaging or computerized tomography. Brain tumor, multiple sclerosis, stroke and traumatic brain injuries are different diseases and accidents affecting in different ways the brain. Their unpredictable appearance and shape make them challenging to be segmented in multi-modal brain imaging. Nevertheless, they share similarities in the way they appear in medical images.

Recent advancements in neuroimaging have allowed the use of network analysis to study the brain in a system-based approach. In fact, several neurological disorders have been investigated from a network perspective. These include Alzheimer’s disease, autism spectrum disorder, stroke, and traumatic brain injury. So far, few studies have been conducted on glioma by using connectome techniques. A connectome-based approach might be useful in quantifying the status of patients, in supporting surgical procedures, and ultimately shedding light on the underlying mechanisms and the recovery process.

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.

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.

International audience; 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 [Formula: see text] score of [Formula: see text]. 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.

International audience; Multiple sclerosis (MS) is neuro-degenerative disease of the Central Nervous System characterized by the loss of myelin. A Clinically Isolated Syndrome (CIS) is a first neurological episode caused by inflammation/demyelination in the central nervous system which may lead to MS. Better understanding of the disease at its onset will lead to a better discovery of pathogenic mechanisms, allowing suitable therapies at an early stage. We propose an automatic segmentation algorithm for two different contrast agents, used within a framework for early characterization of CIS patients according to lesion patterns, and more specifically according to the nature of the inflammatory patterns of these lesions. We expect that the proposed framework can infer new prospective figures from the earliest imaging signs of MS since it can provide a classification of different types of lesions across patients. The lesion detection algorithm based on intensity normalization and subtraction of the used MRI data is a pivotal step, since it avoids the time-demanding task of manual delineation.

International audience; Multiple sclerosis (MS) is a disease with heterogeneous evolution among the patients. Some classifications have been carried out according to either the clinical course or the immunopathological profiles. Epidemiological data and imaging are showing that MS is a two-phase neurodegenerative inflammatory disease. At the early stage it is dominated by focal inflammation of the white matter (WM), and at a latter stage it is dominated by diffuse lesions of the grey matter and spinal cord. A Clinically Isolated Syndrome (CIS) is a first neurological episode caused by inflammation/demyelination in the central nervous system which may lead to MS. Few studies have been carried out so far about this initial stage. Better understanding of the disease at its onset will lead to a better discovery of pathogenic mechanisms, allowing suitable therapies at an early stage. We propose a new data processing framework able to provide an early characterization of CIS patients according to lesion patterns, and more specifically according to the nature of the inflammatory patterns of these lesions. The method is based on a two layers classification. Initially, the spatio-temporal lesion patterns are classified using a tensor-like representation. The discovered lesion patterns are then used to identify group of patients and their correlation to 15 months follow-up total lesion loads (TLL), which is so far the only image-based figure that can potentially infer future evolution of the pathology. We expect that the proposed framework can infer new prospective figures from the earliest imaging sign of MS since it can provide a classification of different types of lesion across patients.

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.

Information concerning central venous pressure (CVP) is crucial in clinical situations, such as cardiac failure, volume overload, and sepsis. The measurement of CVP, however, requires insertion of a catheter through a vein up a vena cava - close to the heart - with related cost and risk of complications. Peripheral venous pressure (PVP) measurement is a technique which allows indirect assess-ment of CVP without catheterization. However, PVP measurement is cumbersome since it requires several devices, trained medical per-sonnel, and is difficult to perform repeatably. Aiming at an automatic venous pressure measurement system via image-processing, we in-troduce in this paper a robust vessel tracking algorithm fit for this purpose. The proposed algorithm addresses the challenge of track-ing compressed vessels, which is essential for this venous pressure measurement technique. Given this tracking algorithm, initial PVP measurements on healthy volunteers are reported.

Functional and structural connectivity convey different information about the brain. The integration of these different approaches is receiving growing attention from the research community, as it can shed new light on brain functions. This manuscript proposes a constrained autoregressive model with different lag-orders generating an “effective” connectivity matrix which models the structural connectivity integrating the functional activity. Multiple orders are investigated to observe how different time dependencies influence the effective connectivity. The proposed approach alters an initial structural connectivity representation according to functional data, by minimizing the reconstruction error of an autoregressive model constrained by the structural prior. The model is further validated in a case-control experiment, which aims at differentiating healthy subject and young patients affected by autism spectrum disorder.

International audience; We introduce a new framework for the automated and un-supervised segmentation of Multiple Sclerosis lesions from multimodal Magnetic Resonance images. It relies on a voxel-wise approach to detect local white matter abnormalities, with an a-contrario analysis, which takes into account local information. First, a voxel-wise comparison of multimodal patient images to a set of controls is performed. Then, region-based probabilities are estimated using an a-contrario approach. Finally, correction for multiple testing is performed. Validation was undertaken on a multi-site clinical dataset of 53 MS patients with various number and volume of lesions. We showed that the proposed framework outperforms the widely used FDR-correction for this type of analysis, particularly for low lesion loads.