Resting state functional connectivity in the default mode network and aerobic exercise in young adults

Around the world Alzheimer’s Disease (AD) is on the rise. Previous studies have shown the default mode network (DMN) sees changes with AD progression as the disease erodes away cortical areas. Aerobic exercise with significant increases to cardiorespiratory fitness could show neuro-protective changes to delay AD. This study will explore if functional connectivity changes in the DMN can be seen in a young adult sample by using group independent component analysis through FSL MELODIC. The young adult sample of 19 were selected from a larger study at the Brain Plasticity and Neuroimaging Laboratory at Boston University. The participants engaged in a twelve-week exercise intervention in either a strength training or aerobic training group. They also completed pre-intervention and post-intervention resting-state fMRI scans to evaluate change in functional connectivity in the default mode network. Cardiorespiratory fitness was assessed using a modified Balke protocol with pre-intervention and post-intervention VO2 max percentiles being used. Through two repeated-measure ANOVA analyses, this study found no significant increase in mean functional connectivity or cardiorespiratory fitness in the young adult sample. While improvements in mean VO2 max percentile and functional connectivity would have been seen with a larger sample size, this study adds to the literature by suggesting if fitness does not improve significantly, neither will functional connectivity in the default mode network

EEG Source Imaging Indices of Cognitive Control Show Associations with Dopamine System Genes.

Cognitive or executive control is a critical mental ability, an important marker of mental illness, and among the most heritable of neurocognitive traits. Two candidate genes, catechol-O-methyltransferase (COMT) and DRD4, which both have a roles in the regulation of cortical dopamine, have been consistently associated with cognitive control. Here, we predicted that individuals with the COMT Met/Met allele would show improved response execution and inhibition as indexed by event-related potentials in a Go/NoGo task, while individuals with the DRD4 7-repeat allele would show impaired brain activity. We used independent component analysis (ICA) to separate brain source processes contributing to high-density EEG scalp signals recorded during the task. As expected, individuals with the DRD4 7-repeat polymorphism had reduced parietal P3 source and scalp responses to response (Go) compared to those without the 7-repeat. Contrary to our expectation, the COMT homozygous Met allele was associated with a smaller frontal P3 source and scalp response to response-inhibition (NoGo) stimuli, suggesting that while more dopamine in frontal cortical areas has advantages in some tasks, it may also compromise response inhibition function. An interaction effect emerged for P3 source responses to Go stimuli. These were reduced in those with both the 7-repeat DRD4 allele and either the COMT Val/Val or the Met/Met homozygous polymorphisms but not in those with the heterozygous Val/Met polymorphism. This epistatic interaction between DRD4 and COMT replicates findings that too little or too much dopamine impairs cognitive control. The anatomic and functional separated maximally independent cortical EEG sources proved more informative than scalp channel measures for genetic studies of brain function and thus better elucidate the complex mechanisms in psychiatric illness

Approaches For Capturing Time-Varying Functional Network Connectivity With Application to Normative Development and Mental Illness

Since the beginning of medical science, the human brain has remained an unsolved puzzle; an illusive organ that controls everything- from breathing to heartbeats, from emotion to anger, and more. With the power of advanced neuroimaging techniques, scientists have now started to solve this nearly impossible puzzle, piece by piece. Over the past decade, various in vivo techniques, including functional magnetic resonance imaging (fMRI), have been increasingly used to understand brain functions. fMRI is extensively being used to facilitate the identification of various neuropsychological disorders such as schizophrenia (SZ), bipolar disorder (BP) and autism spectrum disorder (ASD). These disorders are currently diagnosed based on patients’ self-reported experiences, and observed symptoms and behaviors over the course of the illnesses. Therefore, efficient identification of biological-based markers (biomarkers) can lead to early diagnosis of these mental disorders, and provide a trajectory for disease progression. By applying advanced machine learning techniques on fMRI data, significant differences in brain function among patients with mental disorders and healthy controls can be identified. Moreover, by jointly estimating information from multiple modalities, such as, functional brain data and genetic factors, we can now investigate the relationship between brain function and genes. Functional connectivity (FC) has become a very common measure to characterize brain functions, where FC is defined as the temporal covariance of neural signals between multiple spatially distinct brain regions. Recently, researchers are studying the FC among functionally specialized brain networks which can be defined as a higher level of FC, and is termed as functional network connectivity (FNC, defined as the correlation value that summarizes the overall connection between brain ‘networks’ over time). Most functional connectivity studies have made the limiting assumption that connectivity is stationary over multiple minutes, and ignore to identify the time-varying and reoccurring patterns of FNC among brain regions (known as time-varying FNC). In this dissertation, we demonstrate the use of time-varying FNC features as potential biomarkers to differentiate between patients with mental disorders and healthy subjects. The developmental characteristics of time-varying FNC in children with typically developing brain and ASD have been extensively studies in a cross-sectional framework, and age-, sex- and disease-related FNC profiles have been proposed. Also, time-varying FNC is characterized in healthy adults and patients with severe mental disorders (SZ and BP). Moreover, an efficient classification algorithm is designed to identify patients and controls at individual level. Finally, a new framework is proposed to jointly utilize information from brain’s functional network connectivity and genetic features to find the associations between them. The frameworks that we presented here can help us understand the important role played by time-varying FNC to identify potential biomarkers for the diagnosis of severe mental disorders

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

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

Application of resting-state fMRI methods to acute ischemic stroke

Diffusion weighted imaging (DWI) and dynamic susceptibility contrast-enhanced (DSC) perfusion-weighted imaging (PWI) are commonly employed in clinical practice and in research to give pathophysiological information for patients with acute ischemic stroke. DWI is thought to roughly reflect the severely damaged infarct core, while DSC-PWI reflects the area of hypoperfusion. The volumetric difference between DWI and DSC-PWI is termed the PWI/DWI-mismatch, and has been suggested as an MRI surrogate of the ischemic penumbra. However, due to the application of a contrast agent, which has potentially severe side-effects (e.g., nephrogenic systemic fibrosis), the DSC-PWI precludes repetitive examinations for monitoring purposes. New approaches are being sought to overcome this shortcoming. BOLD (blood oxygen-level dependent) signal can reflect the metabolism of blood oxygen in the brain and hemodynamics can be assessed with resting-state fMRI. The aim of this thesis was to use resting-state fMRI as a new approach to give similar information as DSC-PWI. This thesis comprises two studies: In the first study (see Chapter 2), two resting-state fMRI methods, local methods which compare low frequency amplitudes between two hemispheres and a k-means clustering approach, were applied to investigate the functional damage of patients with acute ischemic stroke both in the time domain and frequency domain. We found that the lesion areas had lower amplitudes than contralateral homotopic healthy tissues. We also differentiated the lesion areas from healthy tissues using a k-means clustering approach. In the second study (see Chapter 3), time-shift analysis (TSA), which assesses time delays of the spontaneous low frequency fluctuations of the resting-state BOLD signal, was applied to give similar pathophysiological information as DSC-PWI in the acute phase of stroke. We found that areas which showed a pronounced time delay to the respective mean time course were very similar to the hypoperfusion area. In summary, we suggest that the resting-state fMRI methods, especially the time-shift analysis (TSA), may provide comparable information to DSC-PWI and thus serve as a useful diagnostic tool for stroke MRI without the need for the application of a contrast agent

A group model for stable multi-subject ICA on fMRI datasets

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract sets of mutually correlated brain regions without prior information on the time course of these regions. Some of these sets of regions, interpreted as functional networks, have recently been used to provide markers of brain diseases and open the road to paradigm-free population comparisons. Such group studies raise the question of modeling subject variability within ICA: how can the patterns representative of a group be modeled and estimated via ICA for reliable inter-group comparisons? In this paper, we propose a hierarchical model for patterns in multi-subject fMRI datasets, akin to mixed-effect group models used in linear-model-based analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based on i) probabilistic dimension reduction of the individual data, ii) canonical correlation analysis to identify a data subspace common to the group iii) ICA-based pattern extraction. In addition, we introduce a procedure based on cross-validation to quantify the stability of ICA patterns at the level of the group. We compare our method with state-of-the-art multi-subject fMRI ICA methods and show that the features extracted using our procedure are more reproducible at the group level on two datasets of 12 healthy controls: a resting-state and a functional localizer study

Mind over chatter: plastic up-regulation of the fMRI alertness network by EEG neurofeedback

EEG neurofeedback (NFB) is a brain-computer interface (BCI) approach used to shape brain oscillations by means of real-time feedback from the electroencephalogram (EEG), which is known to reflect neural activity across cortical networks. Although NFB is being evaluated as a novel tool for treating brain disorders, evidence is scarce on the mechanism of its impact on brain function. In this study with 34 healthy participants, we examined whether, during the performance of an attentional auditory oddball task, the functional connectivity strength of distinct fMRI networks would be plastically altered after a 30-min NFB session of alpha-band reduction (n=17) versus a sham-feedback condition (n=17). Our results reveal that compared to sham, NFB induced a specific increase of functional connectivity within the alertness/salience network (dorsal anterior and mid cingulate), which was detectable 30 minutes after termination of training. Crucially, these effects were significantly correlated with reduced mind-wandering 'on-task' and were coupled to NFB-mediated resting state reductions in the alpha-band (8-12 Hz). No such relationships were evident for the sham condition. Although group default-mode network (DMN) connectivity was not significantly altered following NFB, we observed a positive association between modulations of resting alpha amplitude and precuneal connectivity, both correlating positively with frequency of mind-wandering. Our findings demonstrate a temporally direct, plastic impact of NFB on large-scale brain functional networks, and provide promising neurobehavioral evidence supporting its use as a noninvasive tool to modulate brain function in health and disease