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Slow fluctuations in eye position and resting-state functional magnetic resonance imaging brain activity during visual fixation.

ABSTRACT: The neuronal circuitry that supports voluntary changes in eye position in tasks that require attention-driven oculo-motor control is well known. However, less is known about the neuronal basis for eye control during visual fixation. This, together with the fact that visual fixation is one of the most commonly used baseline conditions in resting-state functional magnetic resonance imaging (fMRI) studies, prompted us to conduct a study in which we employed resting-state fMRI and concurrent recordings of eye gaze to investigate the relationship between spontaneous changes in eye position during passive visual fixation and intrinsic brain activity. As a control experiment, we recorded fMRI brain activity related to cued voluntary vertical and horizontal changes in eye position in a block-related task-evoked fMRI experiment. Our results for the voluntarily performed changes in eye position elicited brain activity in the bilateral occipitotemporal cortex, supplementary motor cortex and frontal eye fields. In contrast, we show that slow fluctuations in eye position during passive visual fixation are linked to intrinsic brain activity, foremost in midline cortical brain regions located in the posteromedial parietal cortex and the medial prefrontal cortex, brain regions that act as core cortical hubs in the brain's default mode network. Our results suggest that subconscious and sustained changes in behavior are tied to intrinsic brain activity on a moment-by-moment basis.

SUBMITTER: Fransson P

PROVIDER: S-EPMC4309478 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Slow fluctuations in eye position and resting-state functional magnetic resonance imaging brain activity during visual fixation.

Fransson Peter P Flodin Pär P Seimyr Gustaf Öqvist GÖ Pansell Tony T

The European journal of neuroscience 20141010 12

The neuronal circuitry that supports voluntary changes in eye position in tasks that require attention-driven oculo-motor control is well known. However, less is known about the neuronal basis for eye control during visual fixation. This, together with the fact that visual fixation is one of the most commonly used baseline conditions in resting-state functional magnetic resonance imaging (fMRI) studies, prompted us to conduct a study in which we employed resting-state fMRI and concurrent recordi ...[more]

PMID: 25302817

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Project description:Functional magnetic resonance imaging (fMRI) during a resting-state condition can reveal the co-activation of specific brain regions in distributed networks, called resting-state networks, which are selected by independent component analysis (ICA) of the fMRI data. One of the major difficulties with component analysis is the automatic selection of the ICA features related to brain activity. In this study we describe a method designed to automatically select networks of potential functional relevance, specifically, those regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the default-mode network. To do this, image analysis was based on probabilistic ICA as implemented in FSL software. After decomposition, the optimal number of components was selected by applying a novel algorithm which takes into account, for each component, Pearson's median coefficient of skewness of the spatial maps generated by FSL, followed by clustering, segmentation, and spectral analysis. To evaluate the performance of the approach, we investigated the resting-state networks in 25 subjects. For each subject, three resting-state scans were obtained with a Siemens Allegra 3 T scanner (NYU data set). Comparison of the visually and the automatically identified neuronal networks showed that the algorithm had high accuracy (first scan: 95%, second scan: 95%, third scan: 93%) and precision (90%, 90%, 84%). The reproducibility of the networks for visual and automatic selection was very close: it was highly consistent in each subject for the default-mode network (?92%) and the occipital network, which includes the medial visual cortical areas (?94%), and consistent for the attention network (?80%), the right and/or left lateralized frontoparietal attention networks, and the temporal-motor network (?80%). The automatic selection method may be used to detect neural networks and reduce subjectivity in ICA component assessment.

Project description:Background/Introduction: Concurrent electroencephalography and resting-state functional magnetic resonance imaging (rsfMRI) have been widely used for studying the (presumably) awake and alert human brain with high temporal/spatial resolution. Although rsfMRI scans are typically collected while individuals are instructed to focus their eyes on a fixated cross, objective and verified experimental measures to quantify degree of vigilance are not readily available. Electroencephalography (EEG) is the modality extensively used for estimating vigilance, especially during eyes-closed resting state. However, pupil size measured using an eye-tracker device could provide an indirect index of vigilance. Methods: Three 12-min resting scans (eyes open, fixating on the cross) were collected from 10 healthy control participants. We simultaneously collected EEG, fMRI, physiological, and eye-tracker data and investigated the correlation between EEG features, pupil size, and heart rate. Furthermore, we used pupil size and EEG features as regressors to find their correlations with blood-oxygen-level-dependent fMRI measures. Results: EEG frontal and occipital beta power (FOBP) correlates with pupil size changes, an indirect index for locus coeruleus activity implicated in vigilance regulation (r = 0.306, p < 0.001). Moreover, FOBP also correlated with heart rate (r = 0.255, p < 0.001), as well as several brain regions in the anticorrelated network, including the bilateral insula and inferior parietal lobule. Discussion: In this study, we investigated whether simultaneous EEG-fMRI combined with eye-tracker measurements can be used to determine EEG signal feature associated with vigilance measures during eyes-open rsfMRI. Our results support the conclusion that FOBP is an objective measure of vigilance in healthy human subjects. Impact statement We revealed an association between electroencephalography frontal and occipital beta power (FOBP) and pupil size changes during an eyes-open resting state, which supports the conclusion that FOBP could serve as an objective measure of vigilance in healthy human subjects. The results were validated by using simultaneously recorded heart rate and functional magnetic resonance imaging (fMRI). Interestingly, independently verified heart rate changes can also provide an easy-to-determine measure of vigilance during resting-state fMRI. These findings have important implications for an analysis and interpretation of dynamic resting-state fMRI connectivity studies in health and disease.

Project description:Introduction: Functional movement disorder (FMD) is a type of functional neurological disorder characterized by abnormal movements that patients do not perceive as self-generated. Prior imaging studies show a complex pattern of altered activity, linking regions of the brain involved in emotional responses, motor control, and agency. This study aimed to better characterize these relationships by building a classifier using a support vector machine to accurately distinguish between 61 FMD patients and 59 healthy controls using features derived from resting-state functional magnetic resonance imaging. Materials and Methods: First, we selected 66 seed regions based on prior related studies, then we calculated the full correlation matrix between them before performing recursive feature elimination to winnow the feature set to the most predictive features and building the classifier. Results: We identified 29 features of interest that were highly predictive of the FMD condition, classifying patients and controls with 80% accuracy. Several key features included regions in the right sensorimotor cortex, left dorsolateral prefrontal cortex, left cerebellum, and left posterior insula. Conclusions: The features selected by the model highlight the importance of the interconnected relationship between areas associated with emotion, reward, and sensorimotor integration, potentially mediating communication between regions associated with motor function, attention, and executive function. Exploratory machine learning was able to identify this distinctive abnormal pattern, suggesting that alterations in functional linkages between these regions may be a consistent feature of the condition in many FMD patients. Clinical-Trials.gov ID: NCT00500994 Impact statement Our research presents novel results that further elucidate the pathophysiology of functional movement disorder (FMD) with a machine learning model that classifies FMD and healthy controls correctly 80% of the time. Herein, we demonstrate how known differences in resting-state functional magnetic resonance imaging connectivity in FMD patients can be leveraged to better understand the complex pattern of neural changes in these patients. Knowing that there are measurable predictable differences in brain activity in patients with FMD may help both clinicians and patients conceptualize and better understand the illness at the point of diagnosis and during treatment. Our methods demonstrate how an effective combination of machine learning and qualitative approaches to analyzing functional brain connectivity can enhance our understanding of abnormal patterns of brain activity in FMD patients.

Dataset Information

Slow fluctuations in eye position and resting-state functional magnetic resonance imaging brain activity during visual fixation.

Publications

Slow fluctuations in eye position and resting-state functional magnetic resonance imaging brain activity during visual fixation.

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