712 research outputs found
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Exposing Internal Attentional Brain States using Single-Trial EEG Analysis with Combined Imaging Modalities
The goal of this dissertation is to explore the neural correlates of endogenous task-related attentional modulations. Natural fluctuations in task engagement are challenging to study, primarily because they are by nature not event related and thus cannot be controlled experimentally. Here we exploit well-accepted links between attention and various measures of neural activity while subjects perform simple target detection tasks that leave their minds free to wander. We use multimodal neuroimaging, specifically simultaneous electroencephalograpy and functional magnetic resonance imaging (EEG-fMRI) and EEG-pupillometry, with data-driven machine learning methods and study activity across the whole brain.
We investigate BOLD fMRI correlates of EEG variability spanning each trial, enabling us to unravel a cascade of attention-related activations and determine their temporal ordering. We study activity during auditory and visual paradigms independently, and we also combine data to investigate supra modal attention systems. Without aiming to study known attention-related functional brain networks, we found correlates of attentional modulations in areas representative of the default mode network (DMN), ventral attention network (VAN), locus coeruleus norepinephrine (LC-NE) system, and regions implicated in generation of the extensively-studied P300 EEG response to target stimuli. Our results reveal complex interactions between known attentional systems, and do so non-invasively to study normal fluctuations of task engagement in the human brain
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Abnormal Frontoparietal Synaptic Gain Mediating the P300 in Patients with Psychotic Disorder and Their Unaffected Relatives
The âdysconnection hypothesisâ of psychosis suggests that a disruption of functional integration underlies cognitive deficits and clinical symptoms. Impairments in the P300 potential are well documented in psychosis. Intrinsic (self-)connectivity in a frontoparietal cortical hierarchy during a P300 experiment was investigated. Dynamic Causal Modeling was used to estimate how evoked activity results from the dynamics of coupled neural populations and how neural coupling changes with the experimental factors. Twenty-four patients with psychotic disorder, twenty-four unaffected relatives, and twenty-five controls underwent EEG recordings during an auditory oddball paradigm. Sixteen frontoparietal network models (including primary auditory, superior parietal, and superior frontal sources) were analyzed and an optimal model of neural coupling, explaining diagnosis and genetic risk effects, as well as their interactions with task condition were identified. The winning model included changes in connectivity at all three hierarchical levels. Patients showed decreased self-inhibitionâthat is, increased cortical excitabilityâin left superior frontal gyrus across task conditions, compared with unaffected participants. Relatives had similar increases in excitability in left superior frontal and right superior parietal sources, and a reversal of the normal synaptic gain changes in response to targets relative to standard tones. It was confirmed that both subjects with psychotic disorder and their relatives show a context-independent loss of synaptic gain control at the highest hierarchy levels. The relatives also showed abnormal gain modulation responses to task-relevant stimuli. These may be caused by NMDA-receptor and/or GABAergic pathologies that change the excitability of superficial pyramidal cells and may be a potential biological marker for psychosis
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
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The role of HG in the analysis of temporal iteration and interaural correlation
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Multimodal Investigation of Brain Network Systems: From Brain Structure and Function to Connectivity and Neuromodulation
The field of cognitive neuroscience has benefited greatly from multimodal investigations of the human brain, which integrate various tools and neuroimaging data to understand brain functions and guide treatments for brain disorders. In this dissertation, we present a series of studies that illustrate the use of multimodal approaches to investigate brain structure and function, brain connectivity, and neuromodulation effects.
Firstly, we propose a novel landmark-guided region-based spatial normalization technique to accurately quantify brain morphology, which can improve the sensitivity and specificity of functional imaging studies. Subsequently, we shift the investigation to the characteristics of functional brain activity due to visual stimulations. Our findings reveal that the task-evoked positive blood-oxygen-level dependent (BOLD) response is accompanied by sustained negative BOLD responses in the visual cortex. These negative BOLD responses are likely generated through subcortical neuromodulatory systems with distributed ascending projections to the cortex.
To further explore the cortico-subcortical relationship, we conduct a multimodal investigation that involves simultaneous data acquisition of pupillometry, electroencephalography (EEG), and functional magnetic resonance imaging (fMRI). This investigation aims to examine the connectivity of brain circuits involved in the cognitive processes of salient stimuli. Using pupillary response as a surrogate measure of activity in the locus coeruleus-norepinephrine system, we find that the pupillary response is associated with the reorganization of functional brain networks during salience processing.
In addition, we propose a cortico-subcortical integrated network reorganization model with potential implications for understanding attentional processing and network switching. Lastly, we employ a multimodal investigation that involves concurrent transcranial magnetic stimulation (TMS), EEG, and fMRI to explore network perturbations and measurements of the propagation effects. In a preliminary exploration on brain-state dependency of TMS-induced effects, we find that the propagation of left dorsolateral prefrontal cortex TMS to regions in the lateral frontoparietal network might depend on the brain-state, as indexed by the EEG prefrontal alpha phase.
Overall, the studies in this dissertation contribute to the understanding of the structural and functional characteristics of brain network systems, and may inform future investigations that use multimodal methodological approaches, such as pupillometry, brain connectivity, and neuromodulation tools. The work presented in this dissertation has potential implications for the development of efficient and personalized treatments for major depressive disorder, attention deficit hyperactivity disorder, and Alzheimer's disease
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
Mismatch responses: Probing probabilistic inference in the brain
Sensory signals are governed by statistical regularities and carry valuable information about the unfolding of environmental events. The brain is thought to capitalize on the probabilistic nature of sequential inputs to infer on the underlying (hidden) dynamics driving sensory stimulation. Mis-match responses (MMRs) such as the mismatch negativity (MMN) and the P3 constitute prominent neuronal signatures which are increasingly interpreted as reflecting a mismatch between the current sensory input and the brainâs generative model of incoming stimuli. As such, MMRs might be viewed as signatures of probabilistic inference in the brain and their response dynamics can provide insights into the underlying computational principles. However, given the dominance of the auditory modality in MMR research, the specifics of brain responses to probabilistic sequences across sensory modalities and especially in the somatosensory domain are not well characterized.
The work presented here investigates MMRs across the auditory, visual and somatosensory modality by means of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). We designed probabilistic stimulus sequences to elicit and characterize MMRs and employed computational modeling of response dynamics to inspect different aspects of the brainâs generative model of the sensory environment. In the first study, we used a volatile roving stimulus paradigm to elicit somatosensory MMRs and performed single-trial modeling of EEG signals in sensor and source space. Model comparison suggested that responses reflect Bayesian inference based on the estimation of transition probability and limited information integration of the recent past in order to adapt to a changing environment. The results indicated that somatosensory MMRs reflect an initial mismatch between sensory input and model beliefs represented by confidence-corrected surprise (CS) followed by model adjustment dynamics represented by Bayesian surprise (BS). For the second and third study we designed a tri-modal roving stimulus paradigm to delineate modality specific and modality general features of mismatch processing. Computational modeling of EEG signals in study 2 suggested that single-trial dynamics reflect Bayesian inference based on estimation of uni-modal transition probabilities as well as cross-modal conditional dependencies. While early mismatch processing around the MMN tended to reflect CS, later MMRs around the P3 rather reflect BS, in correspondence to the somatosensory study. Finally, the fMRI results of study 3 showed that MMRs are generated by an interaction of modality specific regions in higher order sensory cortices and a modality general fronto-parietal network. Inferior parietal regions in particular were sensitive to expectation violations with respect to the cross-modal contingencies in the stimulus sequences. Overall, our results indicate that MMRs across the senses reflect processes of probabilistic inference in a complex and inherently multi-modal environment.Sensorische Signale sind durch statistische RegularitĂ€ten bestimmt und beinhalten wertvolle Informationen ĂŒber die Entwicklung von Umweltereignissen. Es wird angenommen, dass das Gehirn die Wahrscheinlichkeitseigenschaften sequenzieller Reize nutzt um auf die zugrundeliegenden (verborgenen) Dynamiken zu schlieĂen, welche sensorische Stimulation verursachen. Diskrepanz-Reaktionen ("Mismatch responses"; MMRs) wie die "mismatch negativity" (MMN) und die P3 sind bekannte neuronale Signaturen die vermehrt als Signale einer Diskrepanz zwischen der momentanen sensorischen Einspeisung und dem generativen Modell, welches das Gehirn von den eingehenden Reizen erstellt angesehen werden. Als solche können MMRs als Signaturen von wahrscheinlichkeitsbasierter Inferenz im Gehirn betrachtet werden und ihre Reaktionsdynamiken können Einblicke in die zugrundeliegenden komputationalen Prinzipien geben. Angesichts der Dominanz der auditorischen ModalitĂ€t in der MMR-Forschung, sind allerdings die spezifischen Eigenschaften von Hirn-Reaktionen auf Wahrscheinlichkeitssequenzen ĂŒber sensorische ModalitĂ€ten hinweg und vor allem in der somatosensorischen ModalitĂ€t nicht gut charakterisiert.
Die hier vorgestellte Arbeit untersucht MMRs ĂŒber die auditorische, visuelle und somatosensorische ModalitĂ€t hinweg anhand von Elektroenzephalographie (EEG) und funktioneller Magnetresonanztomographie (fMRT). Wir gestalteten wahrscheinlichkeitsbasierte Reizsequenzen, um MMRs auszulösen und zu charakterisieren und verwendeten komputationale Modellierung der Reaktionsdynamiken, um verschiedene Aspekte des generativen Modells des Gehirns von der sensorischen Umwelt zu untersuchen. In der ersten Studie verwendeten wir ein volatiles "Roving-Stimulus"-Paradigma, um somatosensorische MMRs auszulösen und modellierten die Einzel-Proben der EEG-Signale im sensorischen und Quell-Raum. Modellvergleiche legten nahe, dass die Reaktionen Bayesâsche Inferenz abbilden, basierend auf der SchĂ€tzung von Transitionswahrscheinlichkeiten und limitierter Integration von Information der jĂŒngsten Vergangenheit, welche eine Anpassung an UmweltĂ€nderungen ermöglicht. Die Ergebnisse legen nahe, dass somatosen-sorische MMRs eine initiale Diskrepanz zwischen sensorischer Einspeisung und ModellĂŒberzeugung reflektieren welche durch "confidence-corrected surprise" (CS) reprĂ€sentiert ist, gefolgt von Modelanpassungsdynamiken reprĂ€sentiert von "Bayesian surprise" (BS). FĂŒr die zweite und dritte Studie haben wir ein Tri-Modales "Roving-Stimulus"-Paradigma gestaltet, um modalitĂ€tsspezifische und modalitĂ€tsĂŒbergreifende Eigenschaften von Diskrepanzprozessierung zu umreiĂen. Komputationale Modellierung von EEG-Signalen in Studie 2 legte nahe, dass Einzel-Proben Dynamiken Bayesâsche Inferenz abbilden, basierend auf der SchĂ€tzung von unimodalen Transitionswahrscheinlichkeiten sowie modalitĂ€tsĂŒbergreifenden bedingten AbhĂ€ngigkeiten. WĂ€hrend frĂŒhe Diskrepanzprozessierung um die MMN dazu tendierten CS zu reflektieren, so reflektierten spĂ€tere MMRs um die P3 eher BS, in Ăbereinstimmung mit der somatosensorischen Studie. AbschlieĂend zeigten die fMRT-Ergebnisse der Studie 3 dass MMRs durch eine Interaktion von modalitĂ€tsspezifischen Regionen in sensorischen Kortizes höherer Ordnung mit einem modalitĂ€tsĂŒbergreifenden fronto-parietalen Netzwerk generiert werden. Inferior parietale Regionen im Speziellen waren sensitiv gegenĂŒber ErwartungsverstoĂ in Bezug auf die modalitĂ€tsĂŒbergreifenden Wahrscheinlichkeiten in den Reizsequenzen. Insgesamt weisen unsere Ergebnisse darauf hin, dass MMRs ĂŒber die Sinne hinweg Prozesse von wahrscheinlichkeitsbasierter Inferenz in einer komplexen und inhĂ€rent multi-modalen Umwelt darstellen
Gender modulates the development of Theta Event Related Oscillations in Adolescents and Young Adults.
The developmental trajectories of theta band (4-7 Hz) event-related oscillations (EROs), a key neurophysiological constituent of the P3 response, were assessed in 2170 adolescents and young adults ages 12 to 25. The theta EROs occurring in the P3 response, important indicators of neurocognitive function, were elicited during the evaluation of task-relevant target stimuli in visual and auditory oddball tasks. These tasks call upon attentional and working memory resources. Large differences in developmental rates between males and females were found; scalp location and task modality (visual or auditory) differences within males and females were small compared to gender differences. Trajectories of interregional and intermodal correlations between ERO power values exhibited increases with age in both genders, but showed a divergence in development between auditory and visual systems during ages 16 to 21. These results are consistent with previous electrophysiological and imaging studies and provide additional temporal detail about the development of neurophysiological indices of cognitive activity. Since measures of the P3 response has been found to be a useful endophenotypes for the study of a number of clinical and behavioral disorders, studies of its development in adolescents and young adults may illuminate neurophysiological factors contributing to the onset of these conditions
A Novel Synergistic Model Fusing Electroencephalography and Functional Magnetic Resonance Imaging for Modeling Brain Activities
Study of the human brain is an important and very active area of research. Unraveling the way the human brain works would allow us to better understand, predict and prevent brain related diseases that affect a significant part of the population. Studying the brain response to certain input stimuli can help us determine the involved brain areas and understand the mechanisms that characterize behavioral and psychological traits.
In this research work two methods used for the monitoring of brain activities, Electroencephalography (EEG) and functional Magnetic Resonance (fMRI) have been studied for their fusion, in an attempt to bridge together the advantages of each one. In particular, this work has focused in the analysis of a specific type of EEG and fMRI recordings that are related to certain events and capture the brain response under specific experimental conditions.
Using spatial features of the EEG we can describe the temporal evolution of the electrical field recorded in the scalp of the head. This work introduces the use of Hidden Markov Models (HMM) for modeling the EEG dynamics. This novel approach is applied for the discrimination of normal and progressive Mild Cognitive Impairment patients with significant results.
EEG alone is not able to provide the spatial localization needed to uncover and understand the neural mechanisms and processes of the human brain. Functional Magnetic Resonance imaging (fMRI) provides the means of localizing functional activity, without though, providing the timing details of these activations. Although, at first glance it is apparent that the strengths of these two modalities, EEG and fMRI, complement each other, the fusion of information provided from each one is a challenging task. A novel methodology for fusing EEG spatiotemporal features and fMRI features, based on Canonical Partial Least Squares (CPLS) is presented in this work. A HMM modeling approach is used in order to derive a novel feature-based representation of the EEG signal that characterizes the topographic information of the EEG. We use the HMM model in order to project the EEG data in the Fisher score space and use the Fisher score to describe the dynamics of the EEG topography sequence. The correspondence between this new feature and the fMRI is studied using CPLS. This methodology is applied for extracting features for the classification of a visual task. The results indicate that the proposed methodology is able to capture task related activations that can be used for the classification of mental tasks. Extensions on the proposed models are examined along with future research directions and applications
Functional network analyses and dynamical modeling of proprioceptive updating of the body schema
Proprioception is an ability to perceive the position and speed of body parts that is important for construction of the body schema in the brain. Proper updating of the body schema is necessary for appropriate voluntary movement. However, the mechanisms mediating such an updating are not well understood. To study these mechanisms when the body part was at rest, electroencephalography (EEG) and evoked potentials studies were employed, and when the body was in motion, kinematic studies were performed. An experimental approach to elicit proprioceptive P300 evoked potentials was developed providing evidence that processing of novel passive movements is similar to processing of novel visual and auditory stimuli. The latencies of the proprioceptive P300 potentials were found to be greater than those elicited by auditory, but not different from those elicited by the visual stimuli. The features of the functional networks that generated the P300s were analyzed for each modality. Cross-correlation networks showed both common features, e.g. connections between frontal and parietal areas, and the stimulus-specific features, e.g. increases of the connectivity for temporal electrodes in the visual and auditory networks, but not in the proprioceptive ones. The magnitude of coherency networks showed a reduction in alpha band connectivity for most of the electrodes groupings for all stimuli modalities, but did not demonstrate modality-specific features. Kinematic study compared performances of 19 models previously proposed in the literature for movements at the shoulder and elbow joints in terms of their ability to reconstruct the speed profiles of the wrist pointing movements. It was found that lognormal and beta function models are most suitable for wrist speed profile modeling. In addition, an investigation of the blinking rates during the P300 potentials recordings revealed significantly lower rates in left-handed participants, compared to the right-handed ones. Future work will include expanding the experimental and analytical methodologies to different kinds of proprioceptive stimuli (displacements and speeds) and experimental paradigms (error-related negativity potentials), and comparing the models of the speed profiles produced by the feet to those of the wrists, as well as replicating the observations made on the blinking rates in a larger scale study
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