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Atomic dynamic functional interaction patterns for characterization of ADHD.

ABSTRACT: Modeling abnormal temporal dynamics of functional interactions in psychiatric disorders has been of great interest in the neuroimaging field, and thus a variety of methods have been proposed so far. However, the temporal dynamics and disease-related abnormalities of functional interactions within specific data-driven discovered subnetworks have been rarely explored yet. In this work, we propose a novel computational framework composed of an effective Bayesian connectivity change point model for modeling functional brain interactions and their dynamics simultaneously and an effective variant of nonnegative matrix factorization for assessing the functional interaction abnormalities within subnetworks. This framework has been applied on the resting state fmagnetic resonance imaging (fMRI) datasets of 23 children with attention-deficit/hyperactivity disorder (ADHD) and 45 normal control (NC) children, and has revealed two atomic functional interaction patterns (AFIPs) discovered for ADHD and another two AFIPs derived for NC. Together, these four AFIPs could be grouped into two pairs, one common pair representing the common AFIPs in ADHD and NC, and the other abnormal pair representing the abnormal AFIPs in ADHD. Interestingly, by comparing the abnormal AFIP pair, two data-driven abnormal functional subnetworks are derived. Strikingly, by evaluating the approximation based on the four AFIPs, all of the ADHD children were successfully differentiated from NCs without any false positive.

SUBMITTER: Ou J

PROVIDER: S-EPMC6869011 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Atomic dynamic functional interaction patterns for characterization of ADHD.

Ou Jinli J Lian Zhichao Z Xie Li L Li Xiang X Wang Peng P Hao Yun Y Zhu Dajiang D Jiang Rongxin R Wang Yufeng Y Chen Yaowu Y Zhang Jing J Liu Tianming T

Human brain mapping 20140523 10

Modeling abnormal temporal dynamics of functional interactions in psychiatric disorders has been of great interest in the neuroimaging field, and thus a variety of methods have been proposed so far. However, the temporal dynamics and disease-related abnormalities of functional interactions within specific data-driven discovered subnetworks have been rarely explored yet. In this work, we propose a novel computational framework composed of an effective Bayesian connectivity change point model for ...[more]

PMID: 24861961

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Project description:MotivationIdentifying functional modules in protein-protein interaction (PPI) networks may shed light on cellular functional organization and thereafter underlying cellular mechanisms. Many existing module identification algorithms aim to detect densely connected groups of proteins as potential modules. However, based on this simple topological criterion of 'higher than expected connectivity', those algorithms may miss biologically meaningful modules of functional significance, in which proteins have similar interaction patterns to other proteins in networks but may not be densely connected to each other. A few blockmodel module identification algorithms have been proposed to address the problem but the lack of global optimum guarantee and the prohibitive computational complexity have been the bottleneck of their applications in real-world large-scale PPI networks.ResultsIn this article, we propose a novel optimization formulation LCP(2) (low two-hop conductance sets) using the concept of Markov random walk on graphs, which enables simultaneous identification of both dense and sparse modules based on protein interaction patterns in given networks through searching for LCP(2) by random walk. A spectral approximate algorithm SLCP(2) is derived to identify non-overlapping functional modules. Based on a bottom-up greedy strategy, we further extend LCP(2) to a new algorithm (greedy algorithm for LCP(2)) GLCP(2) to identify overlapping functional modules. We compare SLCP(2) and GLCP(2) with a range of state-of-the-art algorithms on synthetic networks and real-world PPI networks. The performance evaluation based on several criteria with respect to protein complex prediction, high level Gene Ontology term prediction and especially sparse module detection, has demonstrated that our algorithms based on searching for LCP(2) outperform all other compared algorithms.Availability and implementationAll data and code are available at http://www.cse.usf.edu/~xqian/fmi/slcp2hop/.

Project description:Introduction: Mild cognitive impairment (MCI) is a heterogenous syndrome considered as a risk factor for developing dementia. Previous work examining morphological brain changes in MCI has identified a temporo-parietal atrophy pattern that suggests a common neuroanatomical denominator of cognitive impairment. Using functional connectivity analyses of structurally affected regions in MCI, we aimed to investigate and characterize functional networks formed by these regions that appear to be particularly vulnerable to disease-related disruptions. Methods: Areas of convergent atrophy in MCI were derived from a quantitative meta-analysis and encompassed left and right medial temporal (i.e., hippocampus, amygdala), as well as parietal regions (precuneus), which were defined as seed regions for connectivity analyses. Both task-based meta-analytical connectivity modeling (MACM) based on the BrainMap database and task-free resting-state functional MRI in a large cohort of older adults from the 1000BRAINS study were applied. We additionally assessed behavioral characteristics associated with the seed regions using BrainMap meta-data and investigated correlations of resting-state connectivity with age. Results: The left temporal seed showed stronger associations with a fronto-temporal network, whereas the right temporal atrophy cluster was more linked to cortico-striatal regions. In accordance with this, behavioral analysis indicated an emphasis of the left temporal seed on language generation, and the right temporal seed was associated with the domains of emotion and attention. Task-independent co-activation was more pronounced in the parietal seed, which demonstrated stronger connectivity with a frontoparietal network and associations with introspection and social cognition. Correlation analysis revealed both decreasing and increasing functional connectivity with higher age that may add to pathological processes but also indicates compensatory mechanisms of functional reorganization with increasing age. Conclusion: Our findings provide an important pathophysiological link between morphological changes and the clinical relevance of major structural damage in MCI. Multimodal analysis of functional networks related to areas of MCI-typical atrophy may help to explain cognitive decline and behavioral alterations not tractable by a mere anatomical interpretation and therefore contribute to prognostic evaluations.

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Atomic dynamic functional interaction patterns for characterization of ADHD.

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Atomic dynamic functional interaction patterns for characterization of ADHD.

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