Connectomics data from animal models provide an invaluable opportunity to reveal the complex interplay between structure and function in the mammalian brain. In this work, we investigate the relationship between structural and functional connectivity in the rat brain cortex using a directed anatomical network generated from a carefully curated meta-analysis of published tracing data, along with resting-state functional MRI data obtained from a group of 14 anesthetized Wistar rats. We found a high correspondence between the strength of functional connections, measured as blood oxygen level dependent (BOLD) signal correlations between cortical regions, and the weight of the corresponding anatomical links in the connectome graph (maximum Spearman rank-order correlation ρ = 0.48). At the network-level, regions belonging to the same functionally defined community tend to form more mutual weighted connections between each other compared to regions located in different communities. We further found that functional communities in resting-state networks are enriched in densely connected anatomical motifs. Importantly, these higher-order structural subgraphs cannot be explained by lower-order topological properties, suggesting that dense structural patterns support functional associations in the resting brain. Simulations of brain-wide resting-state activity based on neural mass models implemented on the empirical rat anatomical connectome demonstrated high correlation between the simulated and the measured functional connectivity (maximum Pearson correlation ρ = 0.53), further suggesting that the topology of structural connections plays an important role in shaping functional cortical networks.

Introduction The pathophysiological process of Alzheimer's disease is thought to begin years before clinical decline, with evidence suggesting prion-like spreading processes of neurofibrillary tangles and amyloid plaques. Methods Using diffusion magnetic resonance imaging data from the Alzheimer's Disease Neuroimaging Initiative database, we first identified relevant features for dementia diagnosis. We then created dynamic models with the Nathan Kline Institute-Rockland Sample database to estimate the earliest detectable stage associated with dementia in the simulated disease progression. Results A classifier based on centrality measures provides informative predictions. Strength and closeness centralities are the most discriminative features, which are associated with the medial temporal lobe and subcortical regions, together with posterior and occipital brain regions. Our model simulations suggest that changes associated with dementia begin to manifest structurally at early stages. Discussion Our analyses suggest that diffusion magnetic resonance imaging–based centrality measures can offer a tool for early disease detection before clinical dementia onset.

Spine is a structure commonly involved in several diseases. Identification and segmentation of the vertebral structures are of relevance to many medical applications related to the spine such as diagnosis, therapy or surgical intervention. However, the development of automatic and reliable methods are an unmet need. This work presents a fully automatic segmentation method of thoracic and lumbar vertebral bodies from Computed Tomography images. The procedure can be divided into four main stages: firstly, seed points were detected in the spinal canal in order to generate initial contours in the segmentation process, automating the whole process. Secondly, a processing step is performed to improve image quality. Third step was to carry out the segmentation using the Selective Binary Gaussian Filtering Regularized Level Set method and, finally, two morphological operations were applied in order to refine the segmentation result. The method was tested in clinical data coming from 10 trauma patients. To evaluate the result the average value of the DICE coefficient was calculated, obtaining a 90.86 ± 1.87% in the whole spine (thoracic and lumbar regions), a 86.08 ± 1.73% in the thoracic region and a 95,61 ±2,25% in the lumbar region. The results are highly competitive when compared to the results obtained in previous methods, especially for the lumbar region.

The study of resting-state functional brain networks is a powerful tool to understand the neurological bases of a variety of disorders such as Autism Spectrum Disorder (ASD). In this work, we have studied the differences in functional brain connectivity between a group of 74 ASD subjects and a group of 82 typical-development (TD) subjects using functional magnetic resonance imaging (fMRI). We have used a network approach whereby the brain is divided into discrete regions or nodes that interact with each other through connections or edges. Functional brain networks were estimated using the Pearson’s correlation coefficient and compared by means of the Network-Based Statistic (NBS) method. The obtained results reveal a combination of both overconnectivity and underconnectivity, with the presence of networks in which the connectivity levels differ significantly between ASD and TD groups. The alterations mainly affect the temporal and frontal lobe, as well as the limbic system, especially those regions related with social interaction and emotion management functions. These results are concordant with the clinical profile of the disorder and can contribute to the elucidation of its neurological basis, encouraging the development of new clinical approaches.

This work presents a new automated method for spinal canal detection in Computed Tomography (CT) images. It uses both 2D and 3D information and the algorithm extracts the spinal canal automatically. The procedure can be divided into three main steps. Firstly, a thresholding and a set of morphological operations were applied. Secondly, 3D connectivity analysis was defined to extract the objects forming part of the spinal canal. Finally, the centroid of each slice constituting the spinal canal object was computed. Furthermore, interpolation and extrapolation of data were performed, if required. The method was applied on two different groups, each one coming from different acquisition systems. A total of 25 patients and 8704 images were used. An experienced radiologist evaluated the method qualitatively supporting the utility of it, as all extracted points fell into the spinal canal. Therefore, our method was able to reduce the workload and detect spinal canal objectively. We expect to carry out a quantitative evaluation in our future research. The qualitative outcome of this work suggests promising results.