Banner Alzheimers Institute BAI

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Banner Alzheimers Institute BAI

Phoenix, AZ, United States
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Li J.,Beijing Normal University | Li R.,CAS Institute of Psychology | Chen K.,Beijing Normal University | Chen K.,Banner Alzheimers Institute BAI | And 2 more authors.
Neuroscience Letters | Year: 2012

By probing its functional anatomy, the default mode network (DMN) can be considered consisting of two interacting hub and non-hub subsystems. The hub subsystem includes posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC) and bilateral inferior parietal cortex (IPC). The non-hub subsystem contains inferior temporal cortex (ITC) and (para) hippocampus (HC). In this study, Gaussian Bayesian Network (BN) and Gaussian Dynamic Bayesian Network (DBN) were applied separately to detect the instantaneous and temporal connection relationship within each and between the two DMN subsystems. It was found that the directional instantaneous interactions between the two subsystems were primarily " from non-hub to hub" The temporal interactions between hub and non-hub regions, on the other hand, are less presented between the two subsystems. The hub subsystem demonstrated both strong instantaneous and temporal interactions among the hub regions, while the non-hub regions were only strongly inter-connected instantaneously but temporally isolated with each other. In addition, one of the hub regions, PCC, appears to be a confluent node and important in the functional integration within the network. © 2012 Elsevier Ireland Ltd.

Li R.,Beijing Normal University | Chen K.,Banner Alzheimers Institute BAI | Fleisher A.S.,Banner Alzheimers Institute BAI | Fleisher A.S.,University of California at San Diego | And 3 more authors.
NeuroImage | Year: 2011

This study examined the large-scale connectivity among multiple resting-state networks (RSNs) in the human brain. Independent component analysis was first applied to the resting-state functional MRI (fMRI) data acquired from 12 healthy young subjects for the separation of RSNs. Four sensory (lateral and medial visual, auditory, and sensory-motor) RSNs and four cognitive (default-mode, self-referential, dorsal and ventral attention) RSNs were identified. Gaussian Bayesian network (BN) learning approach was then used for the examination of the conditional dependencies among these RSNs and the construction of the network-to-network directional connectivity patterns. The BN based results demonstrated that sensory networks and cognitive networks were hierarchically organized. Specially, we found the sensory networks were highly intra-dependent and the cognitive networks were strongly intra-influenced. In addition, the results depicted dominant bottom-up connectivity from sensory networks to cognitive networks in which the self-referential and the default-mode networks might play respectively important roles in the process of resting-state information transfer and integration. The present study characterized the global connectivity relations among RSNs and delineated more characteristics of spontaneous activity dynamics. © 2011 Elsevier Inc.

Li R.,Beijing Normal University | Wu X.,Beijing Normal University | Fleisher A.S.,Banner Alzheimers Institute BAI | Fleisher A.S.,University of California at San Diego | And 3 more authors.
Human Brain Mapping | Year: 2012

In addition to memory deficits, attentional impairment is a common manifestation of Alzheimer's disease (AD). The present study examines the abnormalities of attention-related functional networks in AD using resting functional MRI (fMRI) technique and evaluates the sensitivity and specificity of these networks as potential biomarkers compared with the default mode network (DMN). Group independent component analysis (Group ICA) was applied to fMRI data from 15 AD patients and 16 normal healthy elderly controls (NC) to derive the dorsal attention network (DAN) and the ventral attention network (VAN) which are respectively responsible for the endogenous attention orienting ("top-down") process and the exogenous attention re-orienting ("bottom-up") process. Receiver operating characteristic (ROC) curve analysis was performed for activity in core regions within each of these networks. Functional connectivity analysis revealed disrupted DAN and preserved (less impaired) VAN in AD patients compared with NC, which might indicate impairment of a "top-down" and intact "bottom-up" attentional processing mechanisms in AD. ROC curve analysis suggested that activity in the left intraparietal sulcus and left frontal eye field from DAN as well as the posterior cingulate cortex from the DMN could serve as sensitive and specific biomarkers distinguishing AD from NC. © 2011 Wiley-Liss, Inc.

Wu X.,Beijing Normal University | Li R.,Beijing Normal University | Fleisher A.S.,Banner Alzheimers Institute BAI | Reiman E.M.,Banner Alzheimers Institute BAI | And 4 more authors.
Human Brain Mapping | Year: 2011

A number of functional magnetic resonance imaging (fMRI) studies reported the existence of default mode network (DMN) and its disruption due to the presence of a disease such as Alzheimer's disease (AD). In this investigation, first, we used the independent component analysis (ICA) technique to confirm the DMN difference between patients with AD and normal control (NC) reported in previous studies. Consistent with the previous studies, the decreased resting-state functional connectivity of DMN in AD was identified in posterior cingulated cortex (PCC), medial prefrontal cortex (MPFC), inferior parietal cortex (IPC), inferior temporal cortex (ITC), and hippocampus (HC). Moreover, we introduced Bayesian network (BN) to study the effective connectivity of DMN and the difference between AD and NC. When compared the DMN effective connectivity in AD with the one in NC using a nonparametric random permutation test, we found that connections from left HC to left IPC, left ITC to right HC, right HC to left IPC, to MPFC and to PCC were all lost. In addition, in AD group, the connection directions between right HC and left HC, between left HC and left ITC, and between right IPC and right ITC were opposite to those in NC group. The connections of right HC to other regions, except left HC, within the BN were all statistically in-distinguishable from 0, suggesting an increased right hippocampal pathological and functional burden in AD. The altered effective connectivity in patients with AD may reveal more characteristics of the disease and may serve as a potential biomarker. © 2011 Wiley-Liss, Inc.

Miao X.,Beijing Normal University | Miao X.,Beijing Institute Information and Control | Wu X.,Beijing Normal University | Li R.,Beijing Normal University | And 2 more authors.
PLoS ONE | Year: 2011

Background: Evidences from normal subjects suggest that the default-mode network (DMN) has posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC) and inferior parietal cortex (IPC) as its hubs; meanwhile, these DMN nodes are often found to be abnormally recruited in Alzheimer's disease (AD) patients. The issues on how these hubs interact to each other, with the rest nodes of the DMN and the altered pattern of hubs with respect to AD, are still on going discussion for eventual final clarification. Principal Findings: To address these issues, we investigated the causal influences between any pair of nodes within the DMN using Granger causality analysis and graph-theoretic methods on resting-state fMRI data of 12 young subjects, 16 old normal controls and 15 AD patients respectively. We found that: (1) PCC/MPFC/IPC, especially the PCC, showed the widest and distinctive causal effects on the DMN dynamics in young group; (2) the pattern of DMN hubs was abnormal in AD patients compared to old control: MPFC and IPC had obvious causal interaction disruption with other nodes; the PCC showed outstanding performance for it was the only region having causal relation with all other nodes significantly; (3) the altered relation between hubs and other DMN nodes held potential as a noninvasive biomarker of AD. Conclusions: Our study, to the best of our knowledge, is the first to support the hub configuration of the DMN from the perspective of causal relationship, and reveal abnormal pattern of the DMN hubs in AD. Findings from young subjects provide additional evidence for the role of PCC/MPFC/IPC acting as hubs in the DMN. Compared to old control, MPFC and IPC lost their roles as hubs owing to the obvious causal interaction disruption, and PCC was preserved as the only hub showing significant causal relations with all other nodes. © 2011 Miao et al.

Wu X.,Beijing Normal University | Li J.,Beijing Normal University | Ayutyanont N.,Banner Alzheimers Institute BAI | Protas H.,Banner Alzheimers Institute BAI | And 5 more authors.
IEEE/ACM Transactions on Computational Biology and Bioinformatics | Year: 2013

Given a single index, the receiver operational characteristic (ROC) curve analysis is routinely utilized for characterizing performances in distinguishing two conditions/groups in terms of sensitivity and specificity. Given the availability of multiple data sources (referred to as multi-indices), such as multimodal neuroimaging data sets, cognitive tests, and clinical ratings and genomic data in Alzheimer's disease (AD) studies, the single-index-based ROC underutilizes all available information. For a long time, a number of algorithmic/analytic approaches combining multiple indices have been widely used to simultaneously incorporate multiple sources. In this study, we propose an alternative for combining multiple indices using logical operations, such as 'AND," 'OR," and 'at least (n)" (where (n) is an integer), to construct multivariate ROC (multiV-ROC) and characterize the sensitivity and specificity statistically associated with the use of multiple indices. With and without the 'leave-one-out" cross-validation, we used two data sets from AD studies to showcase the potentially increased sensitivity/specificity of the multiV-ROC in comparison to the single-index ROC and linear discriminant analysis (an analytic way of combining multi-indices). We conclude that, for the data sets we investigated, the proposed multiV-ROC approach is capable of providing a natural and practical alternative with improved classification accuracy as compared to univariate ROC and linear discriminant analysis. © 2004-2012 IEEE.

Li R.,Beijing Normal University | Chen K.,Banner Alzheimers Institute BAI | Li J.,Beijing Normal University | Fleisher A.S.,Banner Alzheimers Institute BAI | And 3 more authors.
2010 IEEE/ICME International Conference on Complex Medical Engineering, CME2010 | Year: 2010

Recently introduced in analyzing data from functional MRI (fMRI) and other neuroimaging techniques, Bayesian networks (BN) is a method to characterize effective connectivity patterns among multiple brain regions. So far, interests of using BN have been primarily on learning the connectivity pattern for each single group with well investigated computational algorithms. Examination of the connectivity pattern differences between groups, on the other hand, lacks rigorous statistical inference procedure. In this study, we propose using random permutation, a type of non-parametric statistical significance test in which a reference distribution is obtained by calculating all possible values of the test statistic under re-arrangements of the group labels on the observed data points, to infer whether the difference is significant. Two different approaches to perform the permutation test are introduced, compared to each other and both compared to the routinely used parametric t-test. Permutation approach 1 permutes the group labels first followed by learning BN pattern for each of the newly formed groups. Approach 2 learns BN pattern for each individual and connection parameters are then subjected to the group label permutations. Synthetic data generated under varying signal-to-noise ratios are used to investigate the performances of the proposed methods. Our results demonstrated that permutation approach 1 in detecting the effective connectivity pattern difference between two groups is superior to permutation approach 2 and to the common-sense two sample t-test. © 2010 IEEE.

Miao X.,Beijing Normal University | Chen K.,Banner Alzheimers Institute BAI | Li R.,Beijing Normal University | Wen X.,University of Florida | And 2 more authors.
2010 IEEE/ICME International Conference on Complex Medical Engineering, CME2010 | Year: 2010

The default-mode network (DMN), which is suggested to have important functions related to internal modes of cognition and increasingly implicated in brain disorders, has attracted much attention in the past few years. Effective connectivity, defined as the influence one neuronal system exerts over another, can provide deep understanding of directed influence between brain regions in the network from the view of functional integration. Granger causality analysis is one of the conventional approaches to explore the effective connectivity in brain imaging researches. In this study, we applied Granger causality analysis to resting-state functional Magnetic Resonance Imaging (fMRI) data from 12 young subjects to explore the effective connectivity pattern of the DMN. The results demonstrated that posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC) and inferior parietal cortex (IPC) were the only three regions had significant causal relationship with all other regions in more than 50% subjects and PCC was the only brain area influenced by all others while had no directed influence to others. The strong effective connectivity pattern demonstrated that PCC, MPFC and IPC were the three key regions and PCC was the convergence hub in the network. These results provide further understanding of physiological mechanism of DMN underlying internal modes of cognition. © 2010 IEEE.

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