Spinal cord injury disrupts resting-state networks in the human brain

Despite 253,000 spinal cord injury (SCI) patients in the United States, little is known about how SCI affects brain networks. Spinal MRI provides only structural information with no insight into functional connectivity. Resting-state functional MRI (RS-fMRI) quantifies network connectivity through the identification of resting-state networks (RSNs) and allows detection of functionally relevant changes during disease. Given the robust network of spinal cord afferents to the brain, we hypothesized that SCI produces meaningful changes in brain RSNs. RS-fMRIs and functional assessments were performed on 10 SCI subjects. Blood oxygen-dependent RS-fMRI sequences were acquired. Seed-based correlation mapping was performed using five RSNs: default-mode (DMN), dorsal-attention (DAN), salience (SAL), control (CON), and somatomotor (SMN). RSNs were compared with normal control subjects using false-discovery rate-corrected two way t tests. SCI reduced brain network connectivity within the SAL, SMN, and DMN and disrupted anti-correlated connectivity between CON and SMN. When divided into separate cohorts, complete but not incomplete SCI disrupted connectivity within SAL, DAN, SMN and DMN and between CON and SMN. Finally, connectivity changed over time after SCI: the primary motor cortex decreased connectivity with the primary somatosensory cortex, the visual cortex decreased connectivity with the primary motor cortex, and the visual cortex decreased connectivity with the sensory parietal cortex. These unique findings demonstrate the functional network plasticity that occurs in the brain as a result of injury to the spinal cord. Connectivity changes after SCI may serve as biomarkers to predict functional recovery following an SCI and guide future therapy

Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis

Conventional functional connectivity (FC), referred to as low-order FC, estimates temporal correlation of the resting-state functional magnetic resonance imaging (rs-fMRI) time series between any pair of brain regions, simply ignoring the potentially high-level relationship among these brain regions. A high-order FC based on "correlation's correlation" has emerged as a new approach for abnormality detection of brain disease. However, separate construction of the low- and high-order FC networks overlooks information exchange between the two FC levels. Such a higher-level relationship could be more important for brain diseases study. In this paper, we propose a novel framework, namely "hybrid high-order FC networks" by exploiting the higher-level dynamic interaction among brain regions for early mild cognitive impairment (eMCI) diagnosis. For each sliding window-based rs-fMRI sub-series, we construct a whole-brain associated high-order network, by estimating the correlations between the topographical information of the high-order FC sub-network from one brain region and that of the low-order FC sub-network from another brain region. With multi-kernel learning, complementary features from multiple time-varying FC networks constructed at different levels are fused for eMCI classification. Compared with other state-of-the-art methods, the proposed framework achieves superior diagnosis accuracy, and hence could be promising for understanding pathological changes of brain connectome

Functional Brain Networks Develop from a “Local to Distributed” Organization

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward ‘segregation’ (a general decrease in correlation strength) between regions close in anatomical space and ‘integration’ (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more “distributed” architecture in young adults. We argue that this “local to distributed” developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing “small-world”-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways

Deep Neural Networks for Assessing Functional Connectivity: an fNIRS Study

Studies on interactions between brain regions estimate functional connectivity which are usually based on the basis of temporal presence. Functional connectivity derived from resting-state has been attracted by several recent studies as it provides valuable insight into the intrinsic networks of the human brain. Functional near-infrared spectroscopy (fNIRS) has gained attention in resting-state functional connectivity (RSFC) patterns detection because of its advantages compared to other neuroimaging modalities. Several progressive methodologies in detecting RSFC patterns in fNIRS, such as seed-based correlation analysis, and independent component analysis (ICA), were adopted in previous studies. Despite the fact that it is not known which methodology is the most suitable in detecting RSFC patterns, seed-based correlation analysis and ICA-based analysis which are the most widely used methodologies in RSFC studies, have intrinsic disadvantages. Therefore, in this study a method based on artificial neural network(ANN) was introduced to meet the possibilities of overcoming the conventional methods challenges. The RSFC patterns of the sensorimotor system derived from ANN were consistent with the previous findings. Moreover, the results of ANN illustrated the superior performance in the terms of specificity and sensitivity compared to both conventional approaches. The main contribution of the present thesis is to emphasize that ANN can be used as an appropriate method to estimate the temporal relation among brain networks during resting-state. ⓒ 2017 DGISTopenI. INTRODUCTION 1-- II. BASIC CONCEPTS AND BACKGROUND 4-- 1. Functional Near-Infrared Spectroscopy (fNIRS) 4-- 2. Discrete Wavelet Transformation as Band-pass Filter 4-- 3. Seed-based Correlation Analysis using General Linear Model (GLM) 5-- 4. Independent Component Analysis (ICA) 7-- 5. Artificial Neural Network (ANN) 7-- 6. Receiver Operating Characteristic (ROC) Curve 11-- III. METHOD 12-- 1. Experimental Protocol 12-- 2. Concentration Changes of Hemoglobin 13-- 3. RSFC Estimation Using Seed-based Correlation Analysis 14-- 4. RSFC Estimation Using ICA 16-- 5. RSFC Estimation Using Artificial Neural Networks 17-- 6. Performance Evaluation Using ROC Curve 18-- IV. RESULTS 20-- 1. Seed-based Correlation RSFC Results 21-- 2. ICA RSFC Results 22-- 3. ANN RSFC Results 22-- 4. ROC Evaluation Results 24-- V. Discussion and Conclusion 26-- References 28-- Acknowledgments 32-- Curriculum Vitae 33MasterdCollectio