The Structural and Functional Connectome and Prediction of Risk for Cognitive Impairment in Older Adults

The human connectome refers to a comprehensive description of the brain's structural and functional connections in terms of brain networks. As the field of brain connectomics has developed, data acquisition, subsequent processing and modeling, and ultimately the representation of the connectome have become better defined and integrated with network science approaches. In this way, the human connectome has provided a way to elucidate key features of not only the healthy brain but also diseased brains. The field has quickly evolved, offering insights into network disruptions that are characteristic for specific neurodegenerative disorders. In this paper, we provide a brief review of the field of brain connectomics, as well as a more in-depth survey of recent studies that have provided new insights into brain network pathologies, including those found in Alzheimer's disease (AD), patients with mild cognitive impairment (MCI), and finally in people classified as being "at risk". Until the emergence of brain connectomics, most previous studies had assessed neurodegenerative diseases mainly by focusing on specific and dispersed locales in the brain. Connectomics-based approaches allow us to model the brain as a network, which allows for inferences about how dynamic changes in brain function would be affected in relation to structural changes. In fact, looking at diseases using network theory gives rise to new hypotheses on mechanisms of pathophysiology and clinical symptoms. Finally, we discuss the future of this field and how understanding both the functional and structural connectome can aid in gaining sharper insight into changes in biological brain networks associated with cognitive impairment and dementia

Aberrations of anterior insular cortex functional connectivity in nontreatment-seeking alcoholics

An emergent literature suggests that resting state functional magnetic resonance imaging (rsfMRI) functional connectivity (FC) patterns are aberrant in alcohol use disorder (AUD) populations. The salience network (SAL) is an established set of brain regions prominent in salience attribution and valuation, and includes the anterior insular cortex (AIC). The SAL is thought to play a role in AUD through directing increased attention to interoceptive cues of intoxication. There is very little information on the salience network (SAL) in AUD, and, in particular, there are no data on SAL FC in currently drinking, nontreatment seeking individuals with AUD (NTS). rsfMRI data from 16 NTS and 21 social drinkers (SD) were compared using FC correlation maps from ten seed regions of interest in the bilateral AIC. As anticipated, SD subjects demonstrated greater insular FC with frontal and parietal regions. We also found that, compared to SD, NTS had higher insular FC with hippocampal and medial orbitofrontal regions. The apparent overactivity in brain networks involved in salience, learning, and behavioral control in NTS suggests possible mechanisms in the development and maintenance of AUD

Differences in White Matter Microstructure and Connectivity in Nontreatment‐Seeking Individuals with Alcohol Use Disorder

Background Diffusion‐weighted imaging (DWI) has been widely used to investigate the integrity of white matter (WM; indexed by fractional anisotropy [FA]) in alcohol dependence and cigarette smoking. These disorders are highly comorbid, yet cigarette use has often not been adequately controlled in neuroimaging studies of alcohol‐dependent populations. In addition, information on WM deficits in currently drinking, nontreatment‐seeking (NTS) individuals with alcohol dependence is limited. Therefore, the aim of this work was to investigate WM microstructural integrity in alcohol use disorder by comparing matched samples of cigarette smoking NTS and social drinkers (SD). Methods Thirty‐eight smoking NTS and 19 smoking SD subjects underwent DWI as well as structural magnetic resonance imaging. After an in‐house preprocessing of the DWI data, FA images were analyzed with tract‐based spatial statistics (TBSS). FA obtained from the TBSS skeleton was tested for correlation with recent alcohol consumption. Results Smoking NTS had lower FA relative to smoking SD, predominantly in the left hemisphere (p < 0.05, family‐wise error rate corrected across FA skeleton). Across the full sample, FA and number of drinks per week were negatively related (ρ = −0.348, p = 0.008). Qualitative analyses of the structural connections through compromised WM as identified by TBSS showed differential connectivity of gray matter in NTS compared to SD subjects of left frontal, temporal, and parietal regions. Conclusions NTS subjects had lower WM FA than SD, indicating compromised WM integrity in the NTS population. The inverse relationship of entire WM skeleton FA with self‐reported alcohol consumption supports previous evidence of a continuum of detrimental effects of alcohol consumption on WM. These results provide additional evidence that alcohol dependence is associated with reduced WM integrity in currently drinking NTS alcohol‐dependent individuals, after controlling for the key variable of cigarette smoking

A computational analysis of protein-protein interaction networks in neurodegenerative diseases

<p>Abstract</p> <p>Background</p> <p>Recent developments have meant that network theory is making an important contribution to the topological study of biological networks, such as protein-protein interaction (PPI) networks. The identification of differentially expressed genes in DNA array experiments is a source of information regarding the molecular pathways involved in disease. Thus, considering PPI analysis and gene expression studies together may provide a better understanding of multifactorial neurodegenerative diseases such as Multiple Sclerosis (MS) and Alzheimer disease (AD). The aim of this study was to assess whether the parameters of degree and betweenness, two fundamental measures in network theory, are properties that differentiate between implicated (seed-proteins) and non-implicated nodes (neighbors) in MS and AD. We used experimentally validated PPI information to obtain the neighbors for each seed group and we studied these parameters in four networks: MS-blood network; MS-brain network; AD-blood network; and AD-brain network.</p> <p>Results</p> <p>Specific features of seed-proteins were revealed, whereby they displayed a lower average degree in both diseases and tissues, and a higher betweenness in AD-brain and MS-blood networks. Additionally, the heterogeneity of the processes involved indicate that these findings are not pathway specific but rather that they are spread over different pathways.</p> <p>Conclusion</p> <p>Our findings show differential centrality properties of proteins whose gene expression is impaired in neurodegenerative diseases.</p

Exploring the randomness of Directed Acyclic Networks

The feed-forward relationship naturally observed in time-dependent processes and in a diverse number of real systems -such as some food-webs and electronic and neural wiring- can be described in terms of so-called directed acyclic graphs (DAGs). An important ingredient of the analysis of such networks is a proper comparison of their observed architecture against an ensemble of randomized graphs, thereby quantifying the {\em randomness} of the real systems with respect to suitable null models. This approximation is particularly relevant when the finite size and/or large connectivity of real systems make inadequate a comparison with the predictions obtained from the so-called {\em configuration model}. In this paper we analyze four methods of DAG randomization as defined by the desired combination of topological invariants (directed and undirected degree sequence and component distributions) aimed to be preserved. A highly ordered DAG, called \textit{snake}-graph and a Erd\:os-R\'enyi DAG were used to validate the performance of the algorithms. Finally, three real case studies, namely, the \textit{C. elegans} cell lineage network, a PhD student-advisor network and the Milgram's citation network were analyzed using each randomization method. Results show how the interpretation of degree-degree relations in DAGs respect to their randomized ensembles depend on the topological invariants imposed. In general, real DAGs provide disordered values, lower than the expected by chance when the directedness of the links is not preserved in the randomization process. Conversely, if the direction of the links is conserved throughout the randomization process, disorder indicators are close to the obtained from the null-model ensemble, although some deviations are observed.Comment: 13 pages, 5 figures and 5 table

Fractal dimension analysis of grey matter in multiple sclerosis

The fractal dimension (FD) is a quantitative parameter that characterizes the morphometric variability of a complex object. Among other applications, FD has been used to identify abnormalities of the human brain in conventional magnetic resonance imaging (MRI), including white matter abnormalities in patients with Multiple Sclerosis (MS). Extensive grey matter (GM) pathology has been recently identified in MS and it appears to be a key factor in long-term disability. The aim of the present work was to assess whether FD measurement of GM in T1 MRI sequences can identify GM abnormalities in patients with MS in the early phase of the disease. A voxel-based morphometry approach optimized for MS was used to obtain the segmented brain, where we later calculated the three-dimensional FD of the GM in MS patients and healthy controls.We found that patients with MS had a significant increase in the FD of the GM compared to controls. Such differences were present even in patients with short disease durations, including patients with first attacks of MS. In addition, the FD of the GM correlated with T1 and T2 lesion load, but not with GM atrophy or disability. The FD abnormalities of the GM here detected differed from the previously published FD of the white matter in MS, suggesting that different pathological processes were taking place in each structure. These results indicate that GM morphology is abnormal in patients with MS and that this alteration appears early in the course of the disease

Brain explorer for connectomic analysis

Visualization plays a vital role in the analysis of multimodal neuroimaging data. A major challenge in neuroimaging visualization is how to integrate structural, functional, and connectivity data to form a comprehensive visual context for data exploration, quality control, and hypothesis discovery. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomical structure. In this paper, new surface texture techniques are developed to map non-spatial attributes onto both 3D brain surfaces and a planar volume map which is generated by the proposed volume rendering technique, spherical volume rendering. Two types of non-spatial information are represented: (1) time series data from resting-state functional MRI measuring brain activation; (2) network properties derived from structural connectivity data for different groups of subjects, which may help guide the detection of differentiation features. Through visual exploration, this integrated solution can help identify brain regions with highly correlated functional activations as well as their activation patterns. Visual detection of differentiation features can also potentially discover image-based phenotypic biomarkers for brain diseases

Joint Exploration and Mining of Memory-Relevant Brain Anatomic and Connectomic Patterns via a Three-Way Association Model

Early change in memory performance is a key symptom of many brain diseases, but its underlying mechanism remains largely unknown. While structural MRI has been playing an essential role in revealing potentially relevant brain regions, increasing availability of diffusion MRI data (e.g., Human Connectome Project (HCP)) provides excellent opportunities for exploration of their complex coordination. Given the complementary information held in these two imaging modalities, we hypothesize that studying them as a whole, rather than individually, and exploring their association will provide us valuable insights of the memory mechanism. However, many existing association methods, such as sparse canonical correlation analysis (SCCA), only manage to handle two-way association and thus cannot guarantee the selection of biomarkers and associations to be memory relevant. To overcome this limitation, we propose a new outcome-relevant SCCA model (OSCCA) together with a new algorithm to enable the three-way associations among brain connectivity, anatomic structure and episodic memory performance. In comparison with traditional SCCA, we demonstrate the effectiveness of our model with both synthetic and real data from the HCP cohort

Topological reversibility and causality in feed-forward networks

Systems whose organization displays causal asymmetry constraints, from evolutionary trees to river basins or transport networks, can be often described in terms of directed paths (causal flows) on a discrete state space. Such a set of paths defines a feed-forward, acyclic network. A key problem associated with these systems involves characterizing their intrinsic degree of path reversibility: given an end node in the graph, what is the uncertainty of recovering the process backwards until the origin? Here we propose a novel concept, \textit{topological reversibility}, which rigorously weigths such uncertainty in path dependency quantified as the minimum amount of information required to successfully revert a causal path. Within the proposed framework we also analytically characterize limit cases for both topologically reversible and maximally entropic structures. The relevance of these measures within the context of evolutionary dynamics is highlighted.Comment: 9 pages, 3 figure