Spatiotemporal wavelet analysis for functional MRI

Characterizing the spatiotemporal behavior of the BOLD signal in functional Magnetic Resonance Imaging (fMRI) is a central issue in understanding brain function. While the nature of functional activation clusters is fundamentally heterogeneous, many current analysis approaches use spatially invariant models that can degrade anatomic boundaries and distort the underlying spatiotemporal signal. Furthermore, few analysis approaches use true spatiotemporal continuity in their statistical formulations. To address these issues, we present a novel spatiotemporal wavelet procedure that uses a stimulus-convolved hemodynamic signal plus correlated noise model. The wavelet fits, computed by spatially constrained maximum-likelihood estimation, provide efficient multiscale representations of heterogeneous brain structures and give well-identified, parsimonious spatial activation estimates that are modulated by the temporal fMRI dynamics. In a study of both simulated data and actual fMRI memory task experiments, our new method gave lower mean-squared error and seemed to result in more localized fMRI activation maps compared to models using standard wavelet or smoothing techniques. Our spatiotemporal wavelet framework suggests a useful tool for the analysis of fMRI studies.     

Determining significant connectivity by 4D spatiotemporal wavelet packet resampling of functional neuroimaging data

An active area of neuroimaging research involves examining functional relationships between spatially remote brain regions. When determining whether two brain regions exhibit significant correlation due to true functional connectivity, one must account for the background spatial correlation inherent in neuroimaging data. We define background correlation as spatiotemporal correlation in the data caused by factors other than neurophysiologically based functional associations such as scanner induced correlations and image preprocessing. We develop a 4D spatiotemporal wavelet packet resampling method which generates surrogate data that preserves only the average background spatial correlation within an axial slice, across axial slices, and through each voxel time series, while excluding the specific correlations due to true functional relationships. We also extend an amplitude adjustment algorithm which adjusts our surrogate data to closely match the amplitude distribution of the original data. Our method improves upon existing wavelet-based methods and extends them to 4D. We apply our resampling technique to determine significant functional connectivity from resting state and motor task fMRI datasets.     

Wavelet variance components in image space for spatiotemporal neuroimaging data

Neuroimaging studies place great emphasis on not only the estimation but also the standard error estimates of underlying parameters derived from a temporal model. This allows inferences to be made about the signal estimates and resulting conclusions to be drawn about the underlying data. It can often be advantageous to interrogate temporal models after spatial transformation of the data into the wavelet domain. Wavelet bases provide a multiresolution decomposition of the spatial data dimension and an ensuing reduction in spatial correlation. However, widespread acceptance of these wavelet techniques has been hampered by the limited ability to reconstruct both parametric and error estimates into the image domain after analysis of temporal models in the wavelet domain. This paper introduces a derivation and a fast implementation of a method for the calculation of the variance of the parametric images obtained from wavelet filters. The technique is proposed for a class of estimators that have been shown to be useful in neuroimaging studies. The techniques are demonstrated for both functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) data sets.     

The multiscale character of evoked cortical activity

Both the architecture and the dynamics of the brain have characteristic features at different spatial scales. However, the existence, nature and function of dynamical interdependencies between such scales have not been investigated. We studied the multiscale properties of functional magnetic resonance imaging (fMRI) data acquired while human subjects viewed a visual image. Traditional "region of interest" analysis of this data set revealed evoked activity in primary and extrastriate visual cortex. Wavelet transform in the spatial domain provides a multiscale representation of this evoked brain activity. Studying the correlation structure of this representation revealed strong and novel interdependencies in these data within and between different spatial scales. We found that such correlations are stronger than those evident in the original data and comparable in magnitude to those obtained after Gaussian smoothing. However, analysis of the data in the wavelet domain revealed additional structure such as positive correlations, strong anti-correlations and phase-lagged interdependencies. Statistical significance of these effects was inferred through nonparametric bootstrap techniques. We conclude that the spatial analysis of functional neuroimaging data in the wavelet domain provides novel information which may reflect complex spatiotemporal neuronal activity and information encoding. It also affords a quantitative means of testing hierarchical and multiscale models of cortical activity.     

Determining functional connectivity using fMRI data with diffusion-based anatomical weighting

There is strong interest in investigating both functional connectivity (FC) using functional magnetic resonance imaging (fMRI) and structural connectivity (SC) using diffusion tensor imaging (DTI). There is also emerging evidence of correspondence between functional and structural pathways within many networks (Greicius, et al., 2009; Skudlarski et al., 2008; van den Heuvel et al., 2009), although some regions without SC exhibit strong FC (Honey et al., 2008). These findings suggest that FC may be mediated by (direct or indirect) anatomical connections, offering an opportunity to supplement fMRI data with DTI data when determining FC. We develop a novel statistical method for determining FC, called anatomically weighted FC (awFC), which combines fMRI and DTI data. Our awFC approach implements a hierarchical clustering algorithm that establishes neural processing networks using a new distance measure consisting of two components, a primary functional component that captures correlations between fMRI signals from different regions and a secondary anatomical weight reflecting probabilities of SC. The awFC approach defaults to conventional unweighted clustering for specific parameter settings. We optimize awFC parameters using a strictly functional criterion, therefore our approach will generally perform at least as well as an unweighted analysis, with respect to intracluster coherence or autocorrelation. AwFC also yields more informative results since it provides structural properties associated with identified functional networks. We apply awFC to two fMRI data sets: resting-state data from 6 healthy subjects and data from 17 subjects performing an auditory task. In these examples, awFC leads to more highly autocorrelated networks than a conventional analysis. We also conduct a simulation study, which demonstrates accurate performance of awFC and confirms that awFC generally yields comparable, if not superior, accuracy relative to a standard approach.     

Diagnosis and exploration of massively univariate neuroimaging models

The goal of this work is to establish the validity of neuroimaging models and inferences through diagnosis and exploratory data analysis. While model diagnosis and exploration are integral parts of any statistical modeling enterprise, these aspects have been mostly neglected in functional neuroimaging. We present methods that make diagnosis and exploration of neuroimaging data feasible. We use three- and one-dimensional summaries that characterize the model fit and the four-dimensional residuals. The statistical tools are diagnostic summary statistics with tractable null distributions and the dynamic graphical tools which allow the exploration of multiple summaries in both spatial and temporal/interscan aspects, with the ability to quickly jump to spatiotemporal detail. We apply our methods to a fMRI data set, demonstrating their ability to localize subtle artifacts and to discover systematic experimental variation not captured by the model.     

A method to produce evolving functional connectivity maps during the course of an fMRI experiment using wavelet-based time-varying Granger causality

Functional magnetic resonance imaging (fMRI) is widely used to identify neural correlates of cognitive tasks. However, the analysis of functional connectivity is crucial to understanding neural dynamics. Although many studies of cerebral circuitry have revealed adaptative behavior, which can change during the course of the experiment, most of contemporary connectivity studies are based on correlational analysis or structural equations analysis, assuming a time-invariant connectivity structure. In this paper, a novel method of continuous time-varying connectivity analysis is proposed, based on the wavelet expansion of functions and vector autoregressive model (wavelet dynamic vector autoregressive-DVAR). The model also allows identification of the direction of information flow between brain areas, extending the Granger causality concept to locally stationary processes. Simulation results show a good performance of this approach even using short time intervals. The application of this new approach is illustrated with fMRI data from a simple AB motor task experiment.     

Answering the call: the influence of neuroimaging and electrophysiological evidence on rehabilitation

Functional recovery after brain damage or disease is dependent on the neuroplastic capability of the cortex and the nonaffected brain. Following cortical injury in the motor and sensory regions, the adjacent spared neural tissues and related areas undergo modifications that are required in order to drive more normal motor control. Current rehabilitation models seek to stimulate functional recovery by capitalizing on the inherent potential of the brain for positive reorganization after neurological injury or disease. This article discusses how neuroimaging and electrophysiological data can inform clinical practice; representative data from the modalities of functional magnetic resonance imaging, diffusion tensor imaging, magnetoencephalography, electroencephalography, and positron emission tomography are cited. Data from a variety of central nervous system disease and damage models are presented to illustrate how rehabilitation practices are beginning to be shaped and informed by neuroimaging and electrophysiological data.     

Predicting cortical ROIs via joint modeling of anatomical and connectional profiles

Localization of cortical regions of interests (ROIs) in structural neuroimaging data such as diffusion tensor imaging (DTI) and T1-weighted MRI images has significant importance in basic and clinical neurosciences. However, this problem is considerably challenging due to the lack of quantitative mapping between brain structure and function, which relies on the availability of multimodal training data including benchmark task-based functional MRI (fMRI) images and effective machine learning algorithms. This paper presents a novel joint modeling approach that learns predictive models of ROIs from concurrent task-based fMRI, DTI, and T1-weighted MRI datasets. In particular, the effective generalized multiple kernel learning (GMKL) algorithm and ROI coordinate principal component analysis (PCA) model are employed to infer the intrinsic relationships between anatomical T1-weighted MRI/connectional DTI features and task-based fMRI-derived functional ROIs. Then, these predictive models of cortical ROIs are evaluated by cross-validation studies, independent datasets, and reproducibility studies. Experimental results are promising. We envision that these predictive models can be potentially applied in many scenarios that have only DTI and/or T1-weighted MRI data, but without task-based fMRI data.     

Visual analytics of brain networks

Identification of regions of interest (ROIs) is a fundamental issue in brain network construction and analysis. Recent studies demonstrate that multimodal neuroimaging approaches and joint analysis strategies are crucial for accurate, reliable and individualized identification of brain ROIs. In this paper, we present a novel approach of visual analytics and its open-source software for ROI definition and brain network construction. By combining neuroscience knowledge and computational intelligence capabilities, visual analytics can generate accurate, reliable and individualized ROIs for brain networks via joint modeling of multimodal neuroimaging data and an intuitive and real-time visual analytics interface. Furthermore, it can be used as a functional ROI optimization and prediction solution when fMRI data is unavailable or inadequate. We have applied this approach to an operation span working memory fMRI/DTI dataset, a schizophrenia DTI/resting state fMRI (R-fMRI) dataset, and a mild cognitive impairment DTI/R-fMRI dataset, in order to demonstrate the effectiveness of visual analytics. Our experimental results are encouraging.     

Dynamical cluster analysis of cortical fMRI activation.

Localized changes in cortical blood oxygenation during voluntary movements were examined with functional magnetic resonance imaging (fMRI) and evaluated with a new dynamical cluster analysis (DCA) method. fMRI was performed during finger movements with eight subjects on a 1.5-T scanner using single-slice echo planar imaging with a 107-ms repetition time. Clustering based on similarity of the detailed signal time courses requires besides the used distance measure no assumptions about spatial location and extension of activation sites or the shape of the expected activation time course. We discuss the basic requirements on a clustering algorithm for fMRI data. It is shown that with respect to easy adjustment of the quantization error and reproducibility of the results DCA outperforms the standard k-means algorithm. In contrast to currently used clustering methods for fMRI, like k-means or fuzzy k-means, DCA extracts the appropriate number and initial shapes of representative signal time courses from data properties during run time. With DCA we simultaneously calculate a two-dimensional projection of cluster centers (MDS) and data points for online visualization of the results. We describe the new DCA method and show for the well-studied motor task that it detects cortical activation loci and provides additional information by discriminating different shapes and phases of hemodynamic responses. Robustness of activity detection is demonstrated with respect to repeated DCA runs and effects of different data preprocessing are shown. As an example of how DCA enables further analysis we examined activation onset times. In areas SMA, M1, and S1 simultaneous and sequential activation (in the given order) was found.     

神经精神疾病的功能磁共振成像研究进展

脑功能磁共振成像(fMRI)能在活体人脑定位各功能脑区,研究脑的功能连接.本文基于任务态和静息态的应用模式,自局部脑区功能活动、脑区间功能连接及脑功能网络层面,综述了脑功能磁共振成像在神经精神疾病发病机制、诊断、治疗及预后等方面的临床研究现状.今后需进行多学科、多方法 相结合的纵向研究,探索神经精神疾病特异的功能影像学...     

Wavelets and functional magnetic resonance imaging of the human brain

The discrete wavelet transform (DWT) is widely used for multiresolution analysis and decorrelation or "whitening" of nonstationary time series and spatial processes. Wavelets are naturally appropriate for analysis of biological data, such as functional magnetic resonance images of the human brain, which often demonstrate scale invariant or fractal properties. We provide a brief formal introduction to key properties of the DWT and review the growing literature on its application to fMRI. We focus on three applications in particular: (i) wavelet coefficient resampling or "wavestrapping" of 1-D time series, 2- to 3-D spatial maps and 4-D spatiotemporal processes; (ii) wavelet-based estimators for signal and noise parameters of time series regression models assuming the errors are fractional Gaussian noise (fGn); and (iii) wavelet shrinkage in frequentist and Bayesian frameworks to support multiresolution hypothesis testing on spatially extended statistic maps. We conclude that the wavelet domain is a rich source of new concepts and techniques to enhance the power of statistical analysis of human fMRI data.     

A generalized regression model for region of interest analysis of fMRI data

In this study functional Magnetic Resonance Imaging (fMRI) was used to evaluate cortical motor network adaptation after a rehabilitation program for upper extremity motor function in chronic stroke patients. Patients and healthy controls were imaged when they attempted to perform shoulder-elbow and wrist-hand movements in a 1.5 T Siemens scanner. We perform fMRI analysis at both single- and group-subject levels. Activated voxel counts are calculated to quantify brain activation in regions of interest. We discuss several candidate regression models for making inference on the count data, and propose an application of a generalized negative-binomial model (GNBM) with structured dispersion in the study. The effects of inappropriate statistical models that ignore the nature of data are addressed through Monte Carlo simulations. Based on the GNBM, significant activation differences are observed in a number of cortical regions for stroke versus control and as a result of treatment; notably, these differences are not detected when the data are analyzed using a conventional linear regression model. Our findings provide an improved functional neuroimaging data analysis protocol, specifically for pixel/voxel counts.     

Improved decoding of neural activity from fMRI signals using non-separable spatiotemporal deconvolutions

The goal of most functional Magnetic Resonance Imaging (fMRI) analyses is to investigate neural activity. Many fMRI analysis methods assume that the temporal dynamics of the hemodynamic response function (HRF) to neural activation is separable from its spatial dynamics. Although there is empirical evidence that the HRF is more complex than suggested by space-time separable canonical HRF models, it is difficult to assess how much information about neural activity is lost when assuming space-time separability. In this study we directly test whether spatiotemporal variability in the HRF that is not captured by separable models contains information about neural signals. We predict intracranially measured neural activity from simultaneously recorded fMRI data using separable and non-separable spatiotemporal deconvolutions of voxel time series around the recording electrode. Our results show that abandoning the spatiotemporal separability assumption consistently improves the decoding accuracy of neural signals from fMRI data. We compare our findings with results from optical imaging and fMRI studies and discuss potential implications for classical fMRI analyses without invasive electrophysiological recordings.     

Real-time independent component analysis of fMRI time-series

Real-time functional magnetic resonance imaging (fMRI) enables one to monitor a subject's brain activity during an ongoing session. The availability of online information about brain activity is essential for developing and refining interactive fMRI paradigms in research and clinical trials and for neurofeedback applications. Data analysis for real-time fMRI has traditionally been based on hypothesis-driven processing methods. Off-line data analysis, conversely, may be usefully complemented by data-driven approaches, such as independent component analysis (ICA), which can identify brain activity without a priori temporal assumptions on brain activity. However, ICA is commonly considered a time-consuming procedure and thus unsuitable to process the high flux of fMRI data while they are acquired. Here, by specific choices regarding the implementation, we exported the ICA framework and implemented it into real-time fMRI data analysis. We show that, reducing the ICA input to a few points within a time-series in a sliding-window approach, computational times become compatible with real-time settings. Our technique produced accurate dynamic readouts of brain activity as well as a precise spatiotemporal history of quasistationary patterns in the form of cumulative activation maps and time courses. Results from real and simulated motor activation data show comparable performances for the proposed ICA implementation and standard linear regression analysis applied either in a sliding-window or in a cumulative mode. Furthermore, we demonstrate the possibility of monitoring transient or unexpected neural activities and suggest that real-time ICA may provide the fMRI researcher with a better understanding and control of subjects' behaviors and performances.     

Motor learning-induced changes in functional brain connectivity as revealed by means of graph-theoretical network analysis

Complex bimanual motor learning causes specific changes in activation across brain regions. However, there is little information on how motor learning changes the functional connectivity between these regions, and whether this is influenced by different sensory feedback modalities. We applied graph-theoretical network analysis (GTNA) to examine functional networks based on motor-task-related fMRI activations. Two groups learned a complex 90° out-of-phase bimanual coordination pattern, receiving either visual or auditory feedback. 3T fMRI scanning occurred before (day 0) and after (day 5) training. In both groups, improved motor performance coincided with increased functional network connectivity (increased clustering coefficients, higher number of network connections and increased connection strength, and shorter communication distances). Day×feedback interactions were absent but, when examining network metrics across all examined brain regions, the visual group had a marginally better connectivity, higher connection strength, and more direct communication pathways. Removal of feedback had no acute effect on the functional connectivity of the trained networks. Hub analyses showed an importance of specific brain regions not apparent in the standard fMRI analyses. These findings indicate that GTNA can make unique contributions to the examination of functional brain connectivity in motor learning.     

An integrative MEG-fMRI study of the primary somatosensory cortex using cross-modal correspondence analysis

We develop a novel approach of cross-modal correspondence analysis (CMCA) to address whether brain activities observed in magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) represent a common neuronal subpopulation, and if so, which frequency band obtained by MEG best fits the common brain areas. Fourteen adults were investigated by whole-head MEG using a single equivalent current dipole (ECD) and synthetic aperture magnetometry (SAM) approaches and by fMRI at 1.5 T using linear time-invariant modeling to generate statistical maps. The same somatosensory stimulus sequences consisting of tactile impulses to the right sided: digit 1, digit 4 and lower lip were used in both neuroimaging modalities. To evaluate the reproducibility of MEG and fMRI results, one subject was measured repeatedly. Despite different MEG dipole locations and locations of maximum activation in SAM and fMRI, CMCA revealed a common subpopulation of the primary somatosensory cortex, which displays a clear homuncular organization. MEG activity in the frequency range between 30 and 60 Hz, followed by the ranges of 20-30 and 60-100 Hz, explained best the defined subrepresentation given by both MEG and fMRI. These findings have important implications for improving and understanding of the biophysics underlying both neuroimaging techniques, and for determining the best strategy to combine MEG and fMRI data to study the spatiotemporal nature of brain activity.     

Defining functional SMA and pre-SMA subregions in human MFC using resting state fMRI: Functional connectivity-based parcellation method

Noninvasive parcellation of the human cerebral cortex is an important goal for understanding and examining brain functions. Recently, the patterns of anatomical connections using diffusion tensor imaging (DTI) have been used to parcellate brain regions. Here, we present a noninvasive parcellation approach that uses “functional fingerprints” obtained by correlation measures on resting state functional magnetic resonance imaging (fMRI) data to parcellate brain regions. In other terms, brain regions are parcellated based on the similarity of their connection – as reflected by correlation during resting state – to the whole brain. The proposed method was used to parcellate the medial frontal cortex (MFC) into supplementary motor areas (SMA) and pre-SMA subregions. In agreement with anatomical landmark-based parcellation, we find that functional fingerprint clustering of the MFC results in anterior and posterior clusters. The probabilistic maps from 12 subjects showed that the anterior cluster is mainly located rostral to the vertical commissure anterior (VCA) line, whereas the posterior cluster is mainly located caudal to VCA line, suggesting the homologues of pre-SMA and SMA. The functional connections from the putative pre-SMA cluster were connected to brain regions which are responsible for complex/cognitive motor control, whereas those from the putative SMA cluster were connected to brain regions which are related to the simple motor control. These findings demonstrate the feasibility of the functional connectivity-based parcellation of the human cerebral cortex using resting state fMRI.