84 research outputs found

    Frameworks to Investigate Robustness and Disease Characterization/Prediction Utility of Time-Varying Functional Connectivity State Profiles of the Human Brain at Rest

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    Neuroimaging technologies aim at delineating the highly complex structural and functional organization of the human brain. In recent years, several unimodal as well as multimodal analyses of structural MRI (sMRI) and functional MRI (fMRI) neuroimaging modalities, leveraging advanced signal processing and machine learning based feature extraction algorithms, have opened new avenues in diagnosis of complex brain syndromes and neurocognitive disorders. Generically regarding these neuroimaging modalities as filtered, complimentary insights of brain’s anatomical and functional organization, multimodal data fusion efforts could enable more comprehensive mapping of brain structure and function. Large scale functional organization of the brain is often studied by viewing the brain as a complex, integrative network composed of spatially distributed, but functionally interacting, sub-networks that continually share and process information. Such whole-brain functional interactions, also referred to as patterns of functional connectivity (FC), are typically examined as levels of synchronous co-activation in the different functional networks of the brain. More recently, there has been a major paradigm shift from measuring the whole-brain FC in an oversimplified, time-averaged manner to additional exploration of time-varying mechanisms to identify the recurring, transient brain configurations or brain states, referred to as time-varying FC state profiles in this dissertation. Notably, prior studies based on time-varying FC approaches have made use of these relatively lower dimensional fMRI features to characterize pathophysiology and have also been reported to relate to demographic characterization, consciousness levels and cognition. In this dissertation, we corroborate the efficacy of time-varying FC state profiles of the human brain at rest by implementing statistical frameworks to evaluate their robustness and statistical significance through an in-depth, novel evaluation on multiple, independent partitions of a very large rest-fMRI dataset, as well as extensive validation testing on surrogate rest-fMRI datasets. In the following, we present a novel data-driven, blind source separation based multimodal (sMRI-fMRI) data fusion framework that uses the time-varying FC state profiles as features from the fMRI modality to characterize diseased brain conditions and substantiate brain structure-function relationships. Finally, we present a novel data-driven, deep learning based multimodal (sMRI-fMRI) data fusion framework that examines the degree of diagnostic and prognostic performance improvement based on time-varying FC state profiles as features from the fMRI modality. The approaches developed and tested in this dissertation evince high levels of robustness and highlight the utility of time-varying FC state profiles as potential biomarkers to characterize, diagnose and predict diseased brain conditions. As such, the findings in this work argue in favor of the view of FC investigations of the brain that are centered on time-varying FC approaches, and also highlight the benefits of combining multiple neuroimaging data modalities via data fusion

    FUNCTIONAL NETWORK CONNECTIVITY IN HUMAN BRAIN AND ITS APPLICATIONS IN AUTOMATIC DIAGNOSIS OF BRAIN DISORDERS

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    The human brain is one of the most complex systems known to the mankind. Over the past 3500 years, mankind has constantly investigated this remarkable system in order to understand its structure and function. Emerging of neuroimaging techniques such as functional magnetic resonance imaging (fMRI) have opened a non-invasive in-vivo window into brain function. Moreover, fMRI has made it possible to study brain disorders such as schizophrenia from a different angle unknown to researchers before. Human brain function can be divided into two categories: functional segregation and integration. It is well-understood that each region in the brain is specialized in certain cognitive or motor tasks. The information processed in these specialized regions in different temporal and spatial scales must be integrated in order to form a unified cognition or behavior. One way to assess functional integration is by measuring functional connectivity (FC) among specialized regions in the brain. Recently, there is growing interest in studying the FC among brain functional networks. This type of connectivity, which can be considered as a higher level of FC, is termed functional network connectivity (FNC) and measures the statistical dependencies among brain functional networks. Each functional network may consist of multiple remote brain regions. Four studies related to FNC are presented in this work. First FNC is compared during the resting-state and auditory oddball task (AOD). Most previous FNC studies have been focused on either resting-state or task-based data but have not directly compared these two. Secondly we propose an automatic diagnosis framework based on resting-state FNC features for mental disorders such as schizophrenia. Then, we investigate the proper preprocessing for fMRI time-series in order to conduct FNC studies. Specifically the impact of autocorrelated time-series on FNC will be comprehensively assessed in theory, simulation and real fMRI data. At the end, the notion of autoconnectivity as a new perspective on human brain functionality will be proposed. It will be shown that autoconnectivity is cognitive-state and mental-state dependent and we discuss how this source of information, previously believed to originate from physical and physiological noise, can be used to discriminate schizophrenia patients with high accuracy

    A multimodal approach to the study of self and others’ awareness in prodromal to mild Alzheimer’s disease

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    Patients in the early stage of Alzheimer’s disease (AD) can manifest disorders of cognitive awareness such as a lack of/limited self-awareness of their own cognitive deficits (anosognosia) or as a reduction in the ability to be aware of others, i.e., social cognition; more specifically in the ability to recognise emotions or attribute mental states to others (also known as Theory of Mind, ToM). The present thesis intended to explain the behavioural, brain neuroanatomical, structural connectivity and resting-state functional relationship between the presence of multi-domain alterations of self-awareness/anosognosia and others awareness/social cognition to understand the cognitive and neural substrates that shape conscious awareness in early AD. Behavioural findings evidenced an association between the presence of anosognosia and reduced ToM. Individually, memory anosognosia was associated with memory proxies and total anosognosia with visuospatial abilities, while social cognition was associated with language, memory, attention and most importantly, executive functions. Neuroanatomical structural findings of non-memory and total anosognosia showed reduced grey matter volume in the anterior cingulate cortex (ACC), fusiform, lingual and precentral gyri. Conversely, ToM showed reduced grey matter volume also in the ACC, but reduction extended to encompass temporoparietal junction, orbitofrontal, superior temporal and cerebellar cortices. The ACC showed a statistical shared neural overlap between self-other awareness. At the functional level, both anosognosia and social cognition were associated with reduced internetwork connectivity between the default mode network (DMN) and the executive frontoparietal network (FPN), as well as higher connectivity between the DMN and the salience network, in which the insula seems to have an essential role. Subcortical contributions to large-scale network connectivity were also found. We propose that medial frontal executive mechanisms, such as those subserved by the ACC, might support awareness in the presence of an inherently damaged DMN in early-AD. Functional adaptive reorganisation of network dynamics might increase the strain to salient system hubs (ACC) by redirecting network traffic of executive resources to cope with the progressive decline of conscious awareness

    An Examination of Brain Network Organization and the Analgesic Mechanisms of a Non-Pharmacological Treatment in Chronic Centralized Pain

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    Chronic pain is a global public health challenge, affecting nearly one third of adults worldwide. Current treatments are inadequate, especially since some of the mainstay therapies (e.g. opioids, NSAIDs) are often ineffective and/or associated with significant toxicity. The solution to these problems requires an improved understanding of chronic pain pathology, particularly the role that the brain plays in causing or amplifying pain perception, and how analgesic intervention might target these brain-based mechanisms. This dissertation aims to identify brain network alterations in fibromyalgia (FM), a common and canonical chronic pain condition with presumed CNS pathology, and determine how non-invasive brain stimulation may target aberrant brain network connectivity to promote analgesia. Across a wide range of diverse neurological disorders, hubs (i.e. highly connected brain regions) appear to be disrupted and the character of this disruption can yield insights into the pathophysiology of these disorders. In Chapter 2, we describe the application of a brain network based approach to examine hub topology in FM patients compared to healthy volunteers. We identified significant disruptions in hub rank order in FM patients. In FM, but not controls, the anterior insula was a hub with significantly higher inter-modular connectivity and membership in the rich club (a functional backbone of connectivity formed by highly interconnected hubs). Among FM patients, rich club membership varied with the intensity of clinical pain: the posterior insula, primary somatosensory and motor cortices belonged to the rich club only in FM patients with the highest pain. Further, we found that the eigenvector centrality (a measure of how connected a brain region is to other highly connected regions) of the posterior insula positively correlated with clinical pain, and mediated the relationship between levels of glutamate + glutamine within this structure and the patient’s subjective clinical pain report. Together, these findings demonstrate an altered hub topology in FM and are the first to suggest that disruptions in the excitatory tone within the insula could alter the strength of the insula as a hub and subsequently lead to increased clinical pain. Transcranial direct current stimulation (tDCS) has emerged as an attractive noninvasive treatment for pain, given its ability to target specific cortical regions with relatively few side effects. Motor cortex (M1) tDCS relieves pain in FM, but the analgesic mechanism remains unknown. In Chapter 3, we measured changes in resting state functional connectivity after sham and real M1 tDCS in twelve FM patients and examined if these changes were related to subsequent analgesia. M1 tDCS (compared to sham) reduced pro-nociceptive functional connectivity, specifically between the motor and sensory nuclei of the thalamus and multiple cortical regions, including primary motor and somatosensory areas. Interestingly, decreased connectivity between the thalamus and posterior insula, M1 and somatosensory cortices correlated with reductions in clinical pain after both sham and active treatment. These results suggest that while there may be a placebo response common to both sham and real tDCS, repetitive M1 tDCS causes distinct changes in functional connectivity that last beyond the stimulation period and may produce analgesia by inhibiting pro-nociceptive thalamic connectivity. This research offers new insight into the neurobiology of chronic centralized pain conditions and contributes to the understanding of how non-invasive brain stimulation causes analgesia. This knowledge could lead to more informed stimulation sites and personalized treatment based on network connectivity in each individual patient.PHDNeuroscienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143930/1/chelsmar_1.pd

    AGE- AND SEX-DEPENDENT ALTERATIONS IN PRIMARY SOMATOSENSORY NEURONAL CALCIUM NETWORK DYNAMICS DURING LOCOMOTION

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    Over the past 30 years, the calcium (Ca2+) hypothesis of brain aging has provided clear evidence that hippocampal neuronal Ca2+ dysregulation is a key biomarker of aging. Indeed, age-dependent Ca2+-mediated changes in intrinsic excitability, synaptic plasticity, and activity have helped identify some of the mechanisms engaged in memory and cognitive decline. However, much of this work has been done at the single-cell level, mostly in slice preparations, and in restricted structures of the brain. Recently, our lab identified age- and Ca2+-related neuronal network dysregulation in the cortex of the anesthetized animal. Still, investigations in the awake animal are needed to test the generalizability of the Ca2+ hypothesis of brain aging and dementia. Here, we used in vigilo two-photon (2P) imaging in ambulating mice, to image GCaMP8f in the primary somatosensory cortex (S1), during ambulation and at rest. In order to investigate aging- and sex- related changes in the neuronal Ca2+ network, a continuous wavelet transform (CWT) analysis was developed (MATLAB) to extract measures of network communication while also addressing pair-wise correlations at single-cell resolution. Following imaging, gait behavior was characterized to test for changes in locomotor stability. During ambulation and compared to rest, in both young (4 months) and aged mice (22 months), an increase in connectivity and synchronicity was noted. An age-dependent increase in network synchronicity was seen in ambulating aged males only. Additionally, females displayed a greater number of active neurons, area-under-curve, and neuronal activity compared to males, particularly during ambulation. These results suggest S1 Ca2+ dynamics and network synchronicity are likely contributors of locomotor stability. We believe this work raises awareness of central elements at play in S1 where neuronal Ca2+ network dysregulation is seen with aging, perhaps highlighting potential therapeutic targets that may help offset age-dependent increases in falls
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