Sangkyun Lee

Acquired self-control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia

Real‐time functional magnetic resonance imaging (rtfMRI) is a novel technique that has allowed subjects to achieve self‐regulation of circumscribed brain regions. Despite its anticipated therapeutic benefits, there is no report on successful application of this technique in psychiatric populations. The objectives of the present study were to train schizophrenia patients to achieve volitional control of bilateral anterior insula cortex on multiple days, and to explore the effect of learned self‐regulation on face emotion recognition (an extensively studied deficit in schizophrenia) and on brain network connectivity. Nine patients with schizophrenia were trained to regulate the hemodynamic response in bilateral anterior insula with contingent rtfMRI neurofeedback, through a 2‐weeks training. At the end of the training stage, patients performed a face emotion recognition task to explore behavioral effects of learned self‐regulation. A learning effect in self‐regulation was found for bilateral anterior insula, which persisted through the training. Following successful self‐regulation, patients recognized disgust faces more accurately and happy faces less accurately. Improvements in disgust recognition were correlated with levels of self‐activation of right insula. RtfMRI training led to an increase in the number of the incoming and outgoing effective connections of the anterior insula. This study shows for the first time that patients with schizophrenia can learn volitional brain regulation by rtfMRI feedback training leading to changes in the perception of emotions and modulations of the brain network connectivity. These findings open the door for further studies of rtfMRI in severely ill psychiatric populations, and possible therapeutic applications. Hum Brain Mapp, 2013.

Real-time support vector classification and feedback of multiple emotional brain states

An important question that confronts current research in affective neuroscience as well as in the treatment of emotional disorders is whether it is possible to determine the emotional state of a person based on the measurement of brain activity alone. Here, we first show that an online support vector machine (SVM) can be built to recognize two discrete emotional states, such as happiness and disgust from fMRI signals, in healthy individuals instructed to recall emotionally salient episodes from their lives. We report the first application of real-time head motion correction, spatial smoothing and feature selection based on a new method called Effect mapping. The classifier also showed robust prediction rates in decoding three discrete emotional states (happiness, disgust and sadness) in an extended group of participants. Subjective reports ascertained that participants performed emotion imagery and that the online classifier decoded emotions and not arbitrary states of the brain. Offline whole brain classification as well as region-of-interest classification in 24 brain areas previously implicated in emotion processing revealed that the frontal cortex was critically involved in emotion induction by imagery. We also demonstrate an fMRI-BCI based on real-time classification of BOLD signals from multiple brain regions, for each repetition time (TR) of scanning, providing visual feedback of emotional states to the participant for potential applications in the clinical treatment of dysfunctional affect.

Neural mechanisms of brain-computer interface control

Brain-computer interfaces (BCIs) enable people with paralysis to communicate with their environment. Motor imagery can be used to generate distinct patterns of cortical activation in the electroencephalogram (EEG) and thus control a BCI. To elucidate the cortical correlates of BCI control, users of a sensory motor rhythm (SMR)-BCI were classified according to their BCI control performance. In a second session these participants performed a motor imagery, motor observation and motor execution task in a functional magnetic resonance imaging (fMRI) scanner. Group difference analysis between high and low aptitude BCI users revealed significantly higher activation of the supplementary motor areas (SMA) for the motor imagery and the motor observation tasks in high aptitude users. Low aptitude users showed no activation when observing movement. The number of activated voxels during motor observation was significantly correlated with accuracy in the EEG-BCI task (r=0.53). Furthermore, the number of activated voxels in the right middle frontal gyrus, an area responsible for processing of movement observation, correlated (r=0.72) with BCI-performance. This strong correlation highlights the importance of these areas for task monitoring and working memory as task goals have to be activated throughout the BCI session. The ability to regulate behavior and the brain through learning mechanisms involving imagery such as required to control a BCI constitutes the consequence of ideo-motor co-activation of motor brain systems during observation of movements. The results demonstrate that acquisition of a sensorimotor program reflected in SMR-BCI-control is tightly related to the recall of such sensorimotor programs during observation of movements and unrelated to the actual execution of these movement sequences.

Species-specific response to human infant faces in the premotor cortex

The human infant face represents an essential source of communicative signals on the basis of which adults modulate their interactions with infants. Behavioral studies demonstrate that infants faces activate sensitive and attuned responses in adults through their gaze, face expression, voice, and gesture. In this study we aimed to identify brain responses that underlie adults general propensity to respond to infant faces. We recorded fMRI during adults (non-parents) processing of unfamiliar infant faces compared to carefully matched adult faces and infrahuman mammal infant and adult faces. Human infant faces activated several brain systems including the lateral premotor cortex, supplementary motor area, cingulate cortex, anterior insula and the thalamus. Activation of these brain circuits suggests adults preparation for communicative behavior with infants as well as attachment and caregiving. The same brain regions preferentially responded to human infant faces when compared to animal infant faces, indicating species-specific adult brain responses. Moreover, results of support vector machine based classification analysis indicated that these regions allowed above chance-level prediction of brain state during perception of human infant faces. The complex of brain responses to human infant faces appears to include biological mechanisms that underlie responsiveness and a caring inclination toward young children which appear to transcend adults biological relationship to the baby.

Detection of Cerebral Reorganization Induced by Real-Time fMRI Feedback Training of Insula Activation: A Multivariate Investigation

Background. Studies with real-time functional magnetic resonance imaging (fMRI) demonstrate that humans volitionally regulate hemodynamic signals from circumscribed regions of the brain, leading to area-specific behavioral consequences. Methods to better determine the nature of dynamic functional interactions between different brain regions and plasticity due to self-regulation training are still in development. Objective. The authors investigated changes in brain states while training 6 healthy participants to self-regulate insular cortex by real-time fMRI feedback. Methods. The authors used multivariate pattern analysis to observe spatial pattern changes and a multivariate Granger causality model to show changes in temporal interactions in multiple brain areas over the course of 5 repeated scans per subject during positive and negative emotional imagery with feedback about the level of insular activation. Results. Feedback training leads to more spatially focused recruitment of areas relevant for learning and emotion. Effective connectivity analysis reveals that initial training is associated with an increase in network density; further training “prunes” presumably redundant connections and “strengthens” relevant connections. Conclusions. The authors demonstrate the application of multivariate methods for assessing cerebral reorganization during the learning of volitional control of local brain activity. The findings provide insight into mechanisms of training-induced learning techniques for rehabilitation. The authors anticipate that future studies, specifically designed with this hypothesis in mind, may be able to construct a universal index of cerebral reorganization during skill learning based on multiple similar criteria across various skilled tasks. These techniques may be able to discern recovery from compensation, dose–response curves related to training, and ways to determine whether rehabilitation training is actively engaging necessary networks.

The Obese Brain Athlete: Self-Regulation of the Anterior Insula in Adiposity

The anterior insular cortex (AIC) is involved in emotional processes and gustatory functions which can be examined by imaging techniques. Such imaging studies showed increased activation in the insula in response to food stimuli as well as a differential activation in lean and obese people. Additionally, studies investigating lean subjects established the voluntary regulation of the insula by a real-time functional magnetic resonance imaging-brain computer interface (rtfMRI-BCI) approach. In this exploratory study, 11 lean and 10 obese healthy, male participants were investigated in a rtfMRI-BCI protocol. During the training sessions, all obese participants were able to regulate the activity of the AIC voluntarily, while four lean participants were not able to regulate at all. In successful regulators, functional connectivity during regulation vs. relaxation between the AIC and all other regions of the brain was determined by a seed voxel approach. Lean in comparison to obese regulators showed stronger connectivity in cingular and temporal cortices during regulation. We conclude, that obese people possess an improved capacity to self-regulate the anterior insula, a brain system tightly related to bodily awareness and gustatory functions.

A new method for estimating population receptive field topography in visual cortex

We introduce a new method for measuring visual population receptive fields (pRF) with functional magnetic resonance imaging (fMRI). The pRF structure is modeled as a set of weights that can be estimated by solving a linear model that predicts the Blood Oxygen Level-Dependent (BOLD) signal using the stimulus protocol and the canonical hemodynamic response function. This method does not make a priori assumptions about the specific pRF shape and is therefore a useful tool for uncovering the underlying pRF structure at different spatial locations in an unbiased way. We show that our method is more accurate than a previously described method (Dumoulin and Wandell, 2008) which directly fits a 2-dimensional isotropic Gaussian pRF model to predict the fMRI time-series. We demonstrate that direct-fit models do not fully capture the actual pRF shape, and can be prone to pRF center mislocalization when the pRF is located near the border of the stimulus space. A quantitative comparison demonstrates that our method outperforms the direct-fit methods in the pRF center modeling by achieving higher explained variance of the BOLD signal. This was true for direct-fit isotropic Gaussian, anisotropic Gaussian, and difference of isotropic Gaussians model. Importantly, our model is also capable of exploring a variety of pRF properties such as surround suppression, receptive field center elongation, orientation, location and size. Additionally, the proposed method is particularly attractive for monitoring pRF properties in the visual areas of subjects with lesions of the visual pathways, where it is difficult to anticipate what shape the reorganized pRF might take. Finally, the method proposed here is more efficient in computation time than direct-fit methods, which need to search for a set of parameters in an extremely large searching space. Instead, this method uses the pRF topography to constrain the space that needs to be searched for the subsequent modeling.

Effective functional mapping of fMRI data with support-vector machines.

There is a growing interest in using support vector machines (SVMs) to classify and analyze fMRI signals, leading to a wide variety of applications ranging from brain state decoding to functional mapping of spatially and temporally distributed brain activations. Studies so far have generated functional maps using the vector of weight values generated by the SVM classification process, or alternatively by mapping the correlation coefficient between the fMRI signal at each voxel and the brain state determined by the SVM. However, these approaches are limited as they do not incorporate both the information involved in the SVM prediction of a brain state, namely, the BOLD activation at voxels and the degree of involvement of different voxels as indicated by their weight values. An important implication of the above point is that two different datasets of BOLD signals, presumably obtained from two different experiments, can potentially produce two identical hyperplanes irrespective of their differences in data distribution. Yet, the two sets of signal inputs could correspond to different functional maps. With this consideration, we propose a new method called Effect Mapping that is generated as a product of the weight vector and a newly computed vector of mutual information between BOLD activations at each voxel and the SVM output. By applying this method on neuroimaging data of overt motor execution in nine healthy volunteers, we demonstrate higher decoding accuracy indicating the greater efficacy of this method. Hum Brain Mapp, 2010.

Real-Time Regulation and Detection of Brain States from fMRI Signals

Publisher Summary This chapter allows noninvasive assessment of brain function with high spatial resolution and whole brain coverage, by measuring changes of the blood oxygenation level- dependent (BOLD) signal. Although the BOLD response is an indirect measure of neural activity, there is accumulating evidence suggesting the close coupling between BOLD and electrical activity of the neurons. It is postulated that the combined effect of increases and decreases in deoxygenated hemoglobin content resulting from changes in cerebral blood volume, cerebral blood flow, and oxygen metabolism following neural firing results in the BOLD signal. fMRI (functional magnetic resonance imaging) data typically consist of time-series of several hundred 3D images across the brain over a period of time, with each image acquired every few seconds. fMRI usability and applications have been somewhat limited by the offline mode of data analysis; due to the large size of the data, very intensive computation involved in preprocessing and analysis of fMRI images.

A toolbox for real-time subject-independent and subject-dependent classification of brain states from fMRI signals

There is a recent increase in the use of multivariate analysis and pattern classification in prediction and real-time feedback of brain states from functional imaging signals and mapping of spatio-temporal patterns of brain activity. Here we present MANAS, a generalized software toolbox for performing online and offline classification of fMRI signals. MANAS has been developed using MATLAB, LIBSVM, and SVMlight packages to achieve a cross-platform environment. MANAS is targeted for neuroscience investigations and brain rehabilitation applications, based on neurofeedback and brain-computer interface (BCI) paradigms. MANAS provides two different approaches for real-time classification: subject dependent and subject independent classification. In this article, we present the methodology of real-time subject dependent and subject independent pattern classification of fMRI signals; the MANAS software architecture and subsystems; and finally demonstrate the use of the system with experimental results.