On Frequency Variation of Dynamic Resting-state Functional Brain Network Activation and Connectivity with Applications to both Healthy and Clinical Populations

One of the earliest and fundamental observation in scientific study of the brain was discovering the relation between activities in different local regions of brain and some core functions of the brain. This was later followed by observing that not only local activities of regions but also synchronous activities between distributed brain regions play a key role in high-level brain functions. Synchronous activity related to the functions of the brain is commonly referred to as functional connectivity (FC) and is studied in the form of connectivity states of the brain which measure degree of interactions between distributed parts of the brain. Functional connectivity has been studied with different imaging modalities such as electroencephalogram (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). The latter has the advantage of having relatively higher spatial resolution of the underlying functional regions and is our choice for the source of the data in this work. Functional connectivity analysis of the human brain in fMRI researches focuses on identifying meaningful brain networks that have coherent activity either during a task or in the resting state. These networks are generally identified either as collections of voxels whose time series correlate strongly with a pre-selected region or voxel, or using data-driven methodologies such as independent component analysis (ICA) that compute sets of maximally spatially independent voxel weightings (component spatial maps (SMs)), each associated with a single time course (TC). Recent studies of functional connectivity have shed light on new aspects of functional connectivity. For example, connectivity during a resting state (RS) of the brain had long been know to constitute a single state of connectivity and just recently it is argued that RS-connectivity, varies in time and has a dynamic nature. In this work, we investigate new aspects of RS-connectivity jointly with its dynamic aspect. As part of the new observations, we discuss that RS-connectivity is in fact frequency dependent in addition to be temporally dynamic. This discovery allows to capture RS-coonectivity at a given time as the superposition of multiple connectivity states along frequency dimension. Later, we also show that interaction between fMRI networks is not only frequency dependent and temporally dynamic but also may occur cross different frequency spectra which is the first evidennce of cross-frequency depenence between fMRI functional networks. We also discuss that all of these observations would enable us to design novel measures to explain RS-connectivity variation among different group of subjects such as healthy and diseased or males and females which would have clinical diagnosis applications and could possibly serve as new bio-markers to study underlying functions of the brain

FMRI resting slow fluctuations correlate with the activity of fast cortico-cortical physiological connections

Recording of slow spontaneous fluctuations at rest using functional magnetic resonance imaging (fMRI) allows distinct long-range cortical networks to be identified. The neuronal basis of connectivity as assessed by resting-state fMRI still needs to be fully clarified, considering that these signals are an indirect measure of neuronal activity, reflecting slow local variations in de-oxyhaemoglobin concentration. Here, we combined fMRI with multifocal transcranial magnetic stimulation (TMS), a technique that allows the investigation of the causal neurophysiological interactions occurring in specific cortico-cortical connections. We investigated whether the physiological properties of parieto-frontal circuits mapped with short-latency multifocal TMS at rest may have some relationship with the resting-state fMRI measures of specific resting-state functional networks (RSNs). Results showed that the activity of fast cortico-cortical physiological interactions occurring in the millisecond range correlated selectively with the coupling of fMRI slow oscillations within the same cortical areas that form part of the dorsal attention network, i.e., the attention system believed to be involved in reorientation of attention. We conclude that resting-state fMRI ongoing slow fluctuations likely reflect the interaction of underlying physiological cortico-cortical connections

Towards Patient-Specific Brain Networks Using Functional Magnetic Resonance Imaging

fMRI applications are rare in translational medicine and clinical practice. What can be inferred from a single fMRI scan is often unreliable due to the relative low signal-to-noise ratio compared to other neuroimaging modalities. However, the potential of fMRI is promising. It is one of the few neuroimaging modalities to obtain functional brain organisation of an individual during task engagement and rest. This work extends on current fMRI image processing approaches to obtain robust estimates of functional brain organisation in two resting-state fMRI cohorts. The first cohort comprises of young adults who were born at extremely low gestations and age-matched healthy controls. Group analysis between term- and preterm-born adults revealed differences in functional organisation, which were discovered to be predominantly caused by underlying structural and physiological differences. The second cohort comprises of elderly adults with young onset Alzheimer’s disease and age-matched controls. Their corresponding resting-state fMRI scans are short in scanning time resulting in unreliable spatial estimates with conventional dual regression analysis. This problem was addressed by the development of an ensemble averaging of matrix factorisations approach to compute single subject spatial maps characterised by improved spatial reproducibility compared to maps obtained by dual regression. The approach was extended with a haemodynamic forward model to obtain surrogate neural activations to examine the subject’s task behaviour. This approach applied to two task-fMRI cohorts showed that these surrogate neural activations matched with original task timings in most of the examined fMRI scans but also revealed subjects with task behaviour different than intended by the researcher. It is hoped that both the findings in this work and the novel matrix factorisation approach itself will benefit the fMRI community. To this end, the derived tools are made available online to aid development and validation of methods for resting-state and task fMRI experiments

Temporal dynamics of the default mode network characterise meditation induced alterations in consciousness

Current research suggests that human consciousness is associated with complex, synchronous interactions between multiple cortical networks. In particular, the default mode network (DMN) of the resting brain is thought to be altered by changes in consciousness, including the meditative state. However, it remains unclear how meditation alters the fast and ever-changing dynamics of brain activity within this network. Here we addressed this question using simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to compare the spatial extents and temporal dynamics of the DMN during rest and meditation. Using fMRI, we identified key reductions in the posterior cingulate hub of the DMN, along with increases in right frontal and left temporal areas, in experienced meditators during rest and during meditation, in comparison to healthy controls (HCs). We employed the simultaneously recorded EEG data to identify the topographical microstate corresponding to activation of the DMN. Analysis of the temporal dynamics of this microstate revealed that the average duration and frequency of occurrence of DMN microstate was higher in meditators compared to HCs. Both these temporal parameters increased during meditation, reflecting the state effect of meditation. In particular, we found that the alteration in the duration of the DMN microstate when meditators entered the meditative state correlated negatively with their years of meditation experience. This reflected a trait effect of meditation, highlighting its role in producing durable changes in temporal dynamics of the DMN. Taken together, these findings shed new light on short and long-term consequences of meditation practice on this key brain network

Enhanced dynamic functional connectivity (whole-brain chronnectome) in chess experts

Multidisciplinary approaches have demonstrated that the brain is potentially modulated by the long-term acquisition and practice of specific skills. Chess playing can be considered a paradigm for shaping brain function, with complex interactions among brain networks possibly enhancing cognitive processing. Dynamic network analysis based on resting-state magnetic resonance imaging (rs-fMRI) can be useful to explore the effect of chess playing on whole-brain fluidity/dynamism (the chronnectome). Dynamic connectivity parameters of 18 professional chess players and 20 beginner chess players were evaluated applying spatial independent component analysis (sICA), sliding-time window correlation, and meta-state approaches to rs-fMRI data. Four indexes of meta-state dynamic fluidity were studied: i) the number of distinct meta-states a subject pass through, ii) the number of switches from one meta-state to another, iii) the span of the realized meta-states (the largest distance between two meta-states that subjects occupied), and iv) the total distance travelled in the state space. Professional chess players exhibited an increased dynamic fluidity, expressed as a higher number of occupied meta-states (meta-state numbers, 75.8 ± 7.9 vs 68.8 ± 12.0, p = 0.043 FDR-corrected) and changes from one meta-state to another (meta-state changes, 77.1 ± 7.3 vs 71.2 ± 11.0, p = 0.043 FDR-corrected) than beginner chess players. Furthermore, professional chess players exhibited an increased dynamic range, with increased traveling between successive meta-states (meta-state total distance, 131.7 ± 17.8 vs 108.7 ± 19.7, p = 0.0004 FDR-corrected). Chess playing may induce changes in brain activity through the modulation of the chronnectome. Future studies are warranted to evaluate if these potential effects lead to enhanced cognitive processing and if "gaming" might be used as a treatment in clinical practice

Monitoring Self & World: A Novel Network Model of Hallucinations in Schizophrenia

Schizophrenia (Sz) is a psychotic disorder characterized by multifaceted symptoms including hallucinations (e.g. vivid perceptions that occur in the absence of external stimuli). Auditory hallucinations are the most common type of hallucination in Sz; roughly 70 percent of Sz patients report hearing voices specifically (e.g. auditory verbal hallucinations). Prior functional magnetic resonance imaging (fMRI) studies have provided initial insights into the neural mechanisms underlying hallucinations, implicating an anatomically-distributed network of cortical (sensory, insular, and inferior frontal cortex) and subcortical (hippocampal, striatal) regions. Yet, it remains unclear how this distributed network gives rise to hallucinations impacting different sensory modalities. The insular cortex is a central hub of a larger functional network called the salience network. By regulating default-mode network activity (associated with internally-directed thought), and fronto-parietal network activity (associated with externally-directed attention), the salience network is able to orient our attention to the most pressing matters (e.g. bodily pain, environmental threats, etc.). Abnormal salience monitoring is thought to underlie Sz symptoms; improper monitoring of salient internal events (e.g. auditory-verbal imagery, visual images) plausibly generates hallucinations, but no prior study has directly tested this hypothesis by exploring how sensory networks interact with the salience network in the context of hallucinations in Sz. This dissertation project combined exploratory and hypothesis-driven approaches to delineate functional neural markers of Sz symptoms. The first analysis explored the relationship between Sz symptom expression and altered functional communication between salience and default-mode networks. The second analysis explored fMRI signal fluctuations associated with modality-dependent (e.g. auditory, visual) hallucinations. The final analysis tested the hypothesis that abnormal functional communication between salience and sensory (e.g. auditory, visual) networks underlies hallucinations in Sz. The results suggest that there are three key players in the generation of auditory hallucinations in Sz: auditory cortex, hippocampus, and salience network. A novel functional network model of auditory hallucinations is proposed to account for these findings

Connectivity dynamics from wakefulness to sleep

Interest in time-resolved connectivity in fMRI has grown rapidly in recent years. The most widely used technique for studying connectivity changes over time utilizes a sliding windows approach. There has been some debate about the utility of shorter versus longer windows, the use of fixed versus adaptive windows, as well as whether observed resting state dynamics during wakefulness may be predominantly due to changes in sleep state and subject head motion. In this work we use an independent component analysis (ICA)-based pipeline applied to concurrent EEG/fMRI data collected during wakefulness and various sleep stages and show: 1) connectivity states obtained from clustering sliding windowed correlations of resting state functional network time courses well classify the sleep states obtained from EEG data, 2) using shorter sliding windows instead of longer non-overlapping windows improves the ability to capture transition dynamics even at windows as short as 30 ​s, 3) motion appears to be mostly associated with one of the states rather than spread across all of them 4) a fixed tapered sliding window approach outperforms an adaptive dynamic conditional correlation approach, and 5) consistent with prior EEG/fMRI work, we identify evidence of multiple states within the wakeful condition which are able to be classified with high accuracy. Classification of wakeful only states suggest the presence of time-varying changes in connectivity in fMRI data beyond sleep state or motion. Results also inform about advantageous technical choices, and the identification of different clusters within wakefulness that are separable suggest further studies in this direction.Fil: Damaraju, Eswar. Instituto Tecnológico de Georgia; Estados UnidosFil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Laufs, Helmut. Goethe Universitat Frankfurt; AlemaniaFil: Calhoun, Vince D.. Instituto Tecnológico de Georgia; Estados Unido