Temporal spatial differences observed by functional MRI and human intraoperative optical imaging

Blind source separation (BSS) is commonly used in functional magnetic resonance imaging (fMRI) data analysis. Recently, BSS models based on restricted Boltzmann machine (RBM), one of the building blocks of deep learning models, have been shown to improve brain network identification compared to conventional single matrix factorization models such as independent component analysis (ICA). These models, however, trained RBM on fMRI volumes, and are hence challenged by model complexity and limited training set. In this article, we propose to apply RBM to fMRI time courses instead of volumes for BSS. The proposed method not only interprets fMRI time courses explicitly to take advantages of deep learning models in latent feature learning but also substantially reduces model complexity and increases the scale of training set to improve training efficiency. Our experimental results based on Human Connectome Project (HCP) datasets demonstrated the superiority of the proposed method over ICA and the one that applied RBM to fMRI volumes in identifying task-related components, resulted in more accurate and specific representations of task-related activations. Moreover, our method separated out components representing intermixed effects between task events, which could reflect inherent interactions among functionally connected brain regions. Our study demonstrates the value of RBM in mining complex structures embedded in large-scale fMRI data and its potential as a building block for deeper models in fMRI data analysis.

Cortical neurons with similar properties are grouped in columnar structures and supplied by matching vascular networks. The hemodynamic response to neuronal activation, however, is not well described on a fine spatial scale. We investigated the spatiotemporal characteristics of microvascular responses to neuronal activation in rat barrel cortex using optical intrinsic signal imaging and spectroscopy. Imaging was performed at 570 nm to provide functional maps of cerebral blood volume (CBV) changes and at 610 nm to estimate oxygenation changes. To emphasize parenchymal rather than large vessel contributions to the functional hemodynamic responses, we developed an ANOVA-based statistical analysis technique. Perfusion-based maps were compared with underlying neuroanatomy with cytochrome oxidase staining. Statistically determined CBV responses localized accurately to individually stimulated barrel columns and could resolve neighboring columns with a resolution better than 400 microm. Both CBV and early oxygenation responses extended beyond anatomical boundaries of single columns, but this vascular point spread did not preclude spatial specificity. These results indicate that microvascular flow control structures providing targeted flow increases to metabolically active neuronal columns also produce finely localized changes in CBV. This spatial specificity, along with the high contrast/noise ratio, makes the CBV response an attractive mapping signal. We also found that functional oxygenation changes can achieve submillimeter specificity not only during the transient deoxygenation ("initial dip") but also during the early part of the hyperoxygenation. We, therefore, suggest that to optimize hemodynamic spatial specificity, appropriate response timing (using < or =2-3 sec changes) is more important than etiology (oxygenation or volume).

3D spatially-adaptive canonical correlation analysis: Local and global methods

Previous studies have shown that hemodynamic response overlap severely limits the maximum presentation rate with event-related functional MRI (fMRI) using fixed intertrial experimental designs. Here we demonstrate that the use of randomized experimental designs can largely overcome this limitation, thereby allowing for event-related fMRI experiments with extremely rapid presentation rates. In the first experiment, fMRI time courses were simulated using a fixed intertrial interval design with intervals of 16, 3, and 1 s, and using a randomized design having the same mean intertrial intervals. We found that using fixed intertrial interval designs the transient information decreased with decreasing intertrial intervals, whereas using randomized designs the transient information increased with decreasing mean intertrial intervals. In a second experiment, fMRI data were collected from two subjects using a randomized paradigm with visual hemifield stimuli presented randomly every 500 ms. Robust event-related activation maps and hemodynamic response estimates were obtained. These results demonstrate the feasibility of performing event-related fMRI experiments with rapid, randomized paradigms identical to those used in electrophysiological and behavioral studies, thereby expanding the applicability of event-related fMRI to a whole new range of cognitive neurosciences questions and paradigms.

Functional MRI evidence for disparate developmental processes underlying intelligence in boys and girls

RGB optical imaging is a marker-free, contactless, and non-invasive technique that is able to monitor hemodynamic brain response following neuronal activation using task-based and resting-state procedures. As opposed to functional task-based analyses, resting-state functional connectivity aims to identify the low frequency cortical hemodynamic fluctuations during patient rest that are linked to resting-state networks. Using intraoperative optical imaging, the main issues of using resting-state procedures come from the partial access to the brain cortex, whereas fMRI or fNIRS resting-state models used whole brain imaging. Task-based fMRI brain maps were compared to intraoperative optical functional brain maps by registering these maps to a preoperative anatomical MRI volume. The objective is to improve the patient care process before, during and after neurosurgery. With the task-based procedure, the RGB brain map showed a good correspondence with task-based fMRI (DICE = 0:75). With the resting-state procedure, the RGB brain map showed a good correspondence with task-based fMRI (seed correlation method: DICE = 0:58 and ICA method: DICE = 0:75).

In this paper, a classical blind source separation (BSS) method is applied to optical imaging of intrinsic signals for separation of arteries and veins in the cerebral cortex. The separation method is based on the fact that the physiological sources are different between arterial regions and venous regions. After segmenting the vascular network, independent component analysis (ICA) was applied to the vascular images for separating the mixed sources into independent components. We selected heartbeat corresponding time course, then founded coherence with every pixel time course at heartbeat frequency. The resulting value constructed a new heartbeat corresponding feature map. A new respiration corresponding feature map was obtained in the same way. Two feature maps were combined as feature vector for the fuzzy c-means clustering method. Determination of vessel type was conducted on each vessel segments divided by morphological intersection points. Finally, it was able to separate arteries and veins by optical gray image of intrinsic signal.

Investigating the enhancement of template-free activation detection of event-related fMRI data using wavelet shrinkage and figures of merit

Current trends in intraoperative optical imaging for functional brain mapping and delineation of lesions of language cortex

Time-resolved fMRI seeks to elucidate neuronal activity during a single execution of a mental task, which corresponds typically to a timescale of seconds. However, this is also the timescale of the hemodynamic response, which delays and blurs the signal in time. In order to distinguish the temporal characteristics of the neuronal activity from that of the hemodynamic response, which is often vaguely known, we recorded a set of fMRI time courses under conditions of a varying behavioral parameter, and correlated this parameter to the width of the fMRI response. For the task under investigation, the mental rotation of three-dimensional objects, we found that the activation in the parietal lobe is related to an aspect of the task that is described by the reaction time (for example, the very act of mental rotation), and not only to aspects of the task that are constant from trial to trial, such as the visual presentation at the beginning or the decision at the end of the task.

Event Abstract Back to Event A combination model of ICA and sparsity prior with respect to fMRI signal analysis Nizhuan Wang1* and Weiming Zeng1 1 Shanghai maritime University, College of Information Engineering, China The blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) data effective analysis is a challenging task because of the complexity of neuron activity in human brain. Independent component analysis (ICA) has been widely used to investigate the functional connectivity of fMRI data, which assumes that the sources of functional regions are independent statistically. However, in the method, the spatial sparsity feature has usually been ignored, which is intrinsic property of the fMRI sources. In 2001, Alexander et al. demonstrated that the optimal sparse representation could significantly benefit the de-noising of fMRI time courses [1]. Recently, Daubechies et al. also demonstrated that the sparsity was more general, intuitive and promising assumption for BOLD fMRI signals [2], whose sources were highly clustering and centering located. In this study, a combination model of FastICA and sparsity prior with respect to fMRI signal analysis, named SFICA, is presented. In this model, the sparse decomposition is performed on the fMRI signal using the wavelet packet decomposition, which yields wavelet tree nodes with different degree of sparsity [3]. Based on these wavelet tree nodes, a common fuzzy C-means clustering is applied to form the optimal sparse representation set of fMRI signal, which can be as an input of FastICA. Compared with FastICA, hybrid data experiment in our study demonstrated this combination model had better spatial source recovery performance on the ground of receiver operating characteristic (ROC) analysis (shown in Fig.1A). For task-related and resting state experimental tests, according to the skewness analysis and correlation variance analysis, almost all the functional networks discovered by this combination model were more symmetric with less skewness values in spatial domain and more consistent in temporal domain than the ones by FastICA. For simplicity, the partial experimental results corresponding to task-related experimental tests were showed in the Fig.1B. Fig. 1: (A) Hybrid data experimental results: ROC curves of simulated signals S1 and S2 corresponding to anterior regions and the more posterior regions respectively, where the z-score ranges from 0.5 to 2.0; (B) Task-related data experimental results: the visual networks of one subject detected by the FastICA and SFICA methods corresponding to the task-related visual stimulus. The corresponding skewness values are labeled and the spatial differences of the visual networks corresponding to the two method are marked by circle shapes with distinct colors in this figure. Figure 1 Acknowledgements Research supported by the National Natural Science Foundation of China (Grant No.31170952), the Innovation Program of Shanghai Municipal Education Commission (Grant No.11ZZ143) , the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning and the Program for Graduate Special Endowment Fund for Innovative developing of Shanghai Maritime University (Grant No. yc2011021).

In this paper we construct an atlas that captures functional characteristics of a cognitive process from a population of individuals. The functional connectivity is encoded in a low-dimensional embedding space derived from a diffusion process on a graph that represents correlations of fMRI time courses. The atlas is represented by a common prior distribution for the embedded fMRI signals of all subjects. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution. We derive an algorithm for fitting this generative model to the observed data in a population. Our results in a language fMRI study demonstrate that the method identifies coherent and functionally equivalent regions across subjects.

Detection of signal synchronizations in resting-state fMRI datasets

Bayesian longitudinal spectral estimation with application to resting-state fMRI data analysis

The contribution of different frequency bands of fMRI data to the correlation with EEG alpha rhythm