Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis

We propose a novel denoising framework for task functional Magnetic Resonance Imaging (tfMRI) data to delineate the high-resolution spatial pattern of the brain functional connectivity via dictionary learning and sparse coding (DLSC). In order to address the limitations of the unsupervised DLSC-based fMRI studies, we utilize the prior knowledge of task paradigm in the learning step to train a data-driven dictionary and to model the sparse representation. We apply the proposed DLSC-based method to Human Connectome Project (HCP) motor tfMRI dataset. Studies on the functional connectivity of cerebrocerebellar circuits in somatomotor networks show that the DLSC-based denoising framework can significantly improve the prominent connectivity patterns, in comparison to the temporal non-local means (tNLM)-based denoising method as well as the case without denoising, which is consistent and neuroscientifically meaningful within motor area. The promising results show that the proposed method can provide an important foundation for the high-resolution functional connectivity analysis, and provide a better approach for fMRI preprocessing.Comment: 8 pages, 3 figures, MLMI201

A Cortical Folding Pattern-Guided Model of Intrinsic Functional Brain Networks in Emotion Processing

There have been increasing studies demonstrating that emotion processing in humans is realized by the interaction within or among the large-scale intrinsic functional brain networks. Identifying those meaningful intrinsic functional networks based on task-based functional magnetic resonance imaging (task fMRI) with specific emotional stimuli and responses, and exploring the underlying functional working mechanisms of interregional neural communication within the intrinsic functional networks are thus of great importance to understand the neural basis of emotion processing. In this paper, we propose a novel cortical folding pattern-guided model of intrinsic networks in emotion processing: gyri serve as global functional connection centers that perform interregional neural communication among distinct regions via long distance dense axonal fibers, and sulci serve as local functional units that directly communicate with neighboring gyri via short distance fibers and indirectly communicate with other distinct regions via the neighboring gyri. We test the proposed model by adopting a computational framework of dictionary learning and sparse representation of emotion task fMRI data of 68 subjects in the publicly released Human Connectome Project. The proposed model provides novel insights of functional mechanisms in emotion processing

Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes

Opening the “Black Box”: Functions of the Frontal Lobes and Their Implications for Sociology

Previous research has provided theoretical frameworks for building inter-disciplinary bridges between sociology and the neurosciences; yet, more anatomically, or functionally focused perspectives offering detailed information to sociologists are largely missing from the literature. This manuscript addresses this gap by offering a comprehensive review of the functions of the frontal lobes, arguably the most important brain region involved in various “human” skills ranging from abstract thinking to language. The paper proposes that the functions of the frontal lobe sub-regions can be divided into three inter-related hierarchical systems with varying degrees of causal proximity in regulating human behavior and social connectedness: (a) the most proximate, voluntary, controlled behavior—including motor functions underlying action-perception and mirror neurons, (b) more abstract motivation and emotional regulation—such as Theory of Mind and empathy, and (c) the higher-order executive functioning—e.g., inhibition of racial bias. The paper offers insights from the social neuroscience literature on phenomena that lie at the core of social theory and research including moral cognition and behavior, and empathy and inter-group attitudes and provides future research questions for interdisciplinary research

Mapping the time-varying functional brain networks in response to naturalistic movie stimuli

One of human brain’s remarkable traits lies in its capacity to dynamically coordinate the activities of multiple brain regions or networks, adapting to an externally changing environment. Studying the dynamic functional brain networks (DFNs) and their role in perception, assessment, and action can significantly advance our comprehension of how the brain responds to patterns of sensory input. Movies provide a valuable tool for studying DFNs, as they offer a naturalistic paradigm that can evoke complex cognitive and emotional experiences through rich multimodal and dynamic stimuli. However, most previous research on DFNs have predominantly concentrated on the resting-state paradigm, investigating the topological structure of temporal dynamic brain networks generated via chosen templates. The dynamic spatial configurations of the functional networks elicited by naturalistic stimuli demand further exploration. In this study, we employed an unsupervised dictionary learning and sparse coding method combing with a sliding window strategy to map and quantify the dynamic spatial patterns of functional brain networks (FBNs) present in naturalistic functional magnetic resonance imaging (NfMRI) data, and further evaluated whether the temporal dynamics of distinct FBNs are aligned to the sensory, cognitive, and affective processes involved in the subjective perception of the movie. The results revealed that movie viewing can evoke complex FBNs, and these FBNs were time-varying with the movie storylines and were correlated with the movie annotations and the subjective ratings of viewing experience. The reliability of DFNs was also validated by assessing the Intra-class coefficient (ICC) among two scanning sessions under the same naturalistic paradigm with a three-month interval. Our findings offer novel insight into comprehending the dynamic properties of FBNs in response to naturalistic stimuli, which could potentially deepen our understanding of the neural mechanisms underlying the brain’s dynamic changes during the processing of visual and auditory stimuli

Cerebral white matter status and resting state functional MRI.

White Matter (WM) is a pivotal component of the Central Nervous System (CNS), and its main role is the transmission of the neural impulses within the CNS and between CNS and Peripheral Nervous System (PNS). It is note from literature that changes in the WM affects the function of the CNS with effects on the higher neurological function, included cognition. Further, it has been theorized in the last decades that ageing-associated decline in higher neurological functions, in particular in the neurocognitive sphere, could be at least partly explained by the “disconnection” of the cortical areas of the brain due to the WM degeneration. Although standard “in-vivo” imaging biomarkers of WM integrity have not been validated yet for clinical purposes, several researches have demonstrated the correlation between different potential imaging biomarkers and WM integrity. The aim of the PhD project is to explore and better understanding the effects of WM status on the brain structure, networking and cognition. In particular, we designed three distinct explorative and cross-sectional studies; more specifically, we analyzed the effects of two Magnetic Resonance Imaging (MRI) markers of WM degeneration (the global Fractional Anisotropy (gFA) and the white matter hyperintensities burden (WMHb), respectively) on the brain activity measured with the Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) technique. The project was conducted by analyzing a human population of healthy subjects extracted from the public available dataset “Leipzig Study for Mind-Body-Emotion Interactions” (LEMON). The results of these studies have been published during the PhD course on three distinct international scientific papers

Brain Connectivity After Concussion

Mild traumatic brain injury (mTBI) accounts for over one million emergency visits in the United States each year. While most mTBI patients have normal findings in clinical neuroimaging, alterations in brain structure and functional connectivity have frequently been reported. In this study, we investigated the large-scale brain structural and functional connectivity using diffusion MRI and resting-state fMRI data. Data from 40 mTBI patients was acquired at the acute stage (within 24 hrs after injury). 35 patients returned for data acquisition at a follow-up (4-6 weeks after injury). Data was also collected from a cohort of 58 healthy subjects, 36 of whom returned for data acquisition at the second time point, 4-6 weeks later. All data was collected at Wayne State University, Detroit, Michigan, USA. We also evaluated the relationship between functional connectivity findings at the acute stage and neurocognitive symptoms at follow up to assess the feasibility of using neuroimaging data to predict neurocognitive complications after mTBI. Moreover, we developed the connectivity domain, a new analysis method which can potentially improve reproducibility and ability to compare findings across datasets