Nonlinear PCA: characterizing interactions between modes of brain activity

The Causal Interaction between Complex Subsystems

Information flow provides a natural measure for the causal interaction between dynamical events. This study extends our previous rigorous formalism of componentwise information flow to the bulk information flow between two complex subsystems of a large-dimensional parental system. Analytical formulas have been obtained in a closed form. Under a Gaussian assumption, their maximum likelihood estimators have also been obtained. These formulas have been validated using different subsystems with preset relations, and they yield causalities just as expected. On the contrary, the commonly used proxies for the characterization of subsystems, such as averages and principal components, generally do not work correctly. This study can help diagnose the emergence of patterns in complex systems and is expected to have applications in many real world problems in different disciplines such as climate science, fluid dynamics, neuroscience, financial economics, etc.

Multitask fMRI and machine learning approach improve prediction of differential brain activity pattern in patients with insomnia disorder

We investigated the differential spatial covariance pattern of blood oxygen level-dependent (BOLD) responses to single-task and multitask functional magnetic resonance imaging (fMRI) between patients with psychophysiological insomnia (PI) and healthy controls (HCs), and evaluated features generated by principal component analysis (PCA) for discrimination of PI from HC, compared to features generated from BOLD responses to single-task fMRI using machine learning methods. In 19 patients with PI and 21 HCs, the mean beta value for each region of interest (ROIbval) was calculated with three contrast images (i.e., sleep-related picture, sleep-related sound, and Stroop stimuli). We performed discrimination analysis and compared with features generated from BOLD responses to single-task fMRI. We applied support vector machine analysis with a least absolute shrinkage and selection operator to evaluate five performance metrics: accuracy, recall, precision, specificity, and F2. Principal component features showed the best classification performance in all aspects of metrics compared to BOLD response to single-task fMRI. Bilateral inferior frontal gyrus (orbital), right calcarine cortex, right lingual gyrus, left inferior occipital gyrus, and left inferior temporal gyrus were identified as the most salient areas by feature selection. Our approach showed better performance in discriminating patients with PI from HCs, compared to single-task fMRI.

Label-free detection of multiple genitourinary cancers from urine by surface-enhanced Raman spectroscopy

Detecting cancers through testing biological fluids, namely, "liquid biopsy", is noninvasive and shows great promise in cancer diagnosis, surveillance and screening. Many metabolites that may reflect cancer specificity are concentrated in and excreted through urine. In this study, urine samples were collected from healthy subjects and patients with bladder or prostate cancer. By using surface-enhanced Raman spectroscopy (SERS) with silver nanoparticles, urine sample spectra from 500-1800 cm^-1 were obtained. The spectra were classified by principal component analysis and linear discriminant analysis (PCA-LDA). The results showed that the classification accuracy of the model for healthy individuals, bladder cancer patients and prostate cancer patients was 91.9%, and the classification accuracy of the test set was 89%, which indicated that SERS combined with the PCA-LDA diagnostic algorithm could be used as a classification and diagnostic tool to detect and distinguish bladder cancer and prostate cancer through testing urine.

Anatomic and Functional Connectivity Relationship in Autistic Children During Three Different Experimental Conditions

A group of 21 autistic children were studied for determining the relationship between the anatomic (AC) versus functional (FC) connectivity, considering short-range and long-range brain networks. AC was assessed by the DW-MRI technique and FC by EEG coherence calculation, in three experimental conditions: basal, watching a popular cartoon with audio (V-A), and with muted audio track (VwA). For short-range connections, basal records, statistical significant correlations were found for all EEG bands in the left hemisphere, but no significant correlations were found for fast EEG frequencies in the right hemisphere. For the V-A condition, significant correlations were mainly diminished for the left hemisphere; for the right hemisphere, no significant correlations were found for the fast EEG frequency bands. For the VwA condition, significant correlations for the rapid EEG frequencies mainly disappeared for the right hemisphere. For long-range connections, basal records showed similar correlations for both hemispheres. For the right hemisphere, significant correlations incremented to all EEG bands for the V-A condition, but these significant correlations disappeared for the fast EEG frequencies in the VwA condition. It appears that in a resting-state condition, AC is better associated with functional connectivity for short-range connections in the left hemisphere. The V-A experimental condition enriches the AC and FC association for long-range connections in the right hemisphere. This might be related to an effective connectivity improvement due to full video stimulation (visual and auditory). An impaired audiovisual interaction in the right hemisphere might explain why significant correlations disappeared for the fast EEG frequencies in the VwA experimental condition.

Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition

We investigated a combination of three classification algorithms, namely the modified maximum uncertainty linear discriminant analysis (mMLDA), the centroid method, and the average linkage, with three types of features extracted from three-dimensional T1-weighted magnetic resonance (MR) brain images, specifically MR intensities, grey matter densities, and local deformations for distinguishing 49 first episode schizophrenia male patients from 49 healthy male subjects. The feature sets were reduced using intersubject principal component analysis before classification. By combining the classifiers, we were able to obtain slightly improved results when compared with single classifiers. The best classification performance (81.6% accuracy, 75.5% sensitivity, and 87.8% specificity) was significantly better than classification by chance. We also showed that classifiers based on features calculated using more computation-intensive image preprocessing perform better; mMLDA with classification boundary calculated as weighted mean discriminative scores of the groups had improved sensitivity but similar accuracy compared to the original MLDA; reducing a number of eigenvectors during data reduction did not always lead to higher classification accuracy, since noise as well as the signal important for classification were removed. Our findings provide important information for schizophrenia research and may improve accuracy of computer-aided diagnostics of neuropsychiatric diseases.

Using fMRI non-local means denoising to uncover activation in sub-cortical structures at 1.5 T for guided HARDI tractography

In recent years, there has been ever-increasing interest in combining functional magnetic resonance imaging (fMRI) and diffusion magnetic resonance imaging (dMRI) for better understanding the link between cortical activity and connectivity, respectively. However, it is challenging to detect and validate fMRI activity in key sub-cortical areas such as the thalamus, given that they are prone to susceptibility artifacts due to the partial volume effects (PVE) of surrounding tissues (GM/WM interface). This is especially true on relatively low-field clinical MR systems (e.g., 1.5 T). We propose to overcome this limitation by using a spatial denoising technique used in structural MRI and more recently in diffusion MRI called non-local means (NLM) denoising, which uses a patch-based approach to suppress the noise locally. To test this, we measured fMRI in 20 healthy subjects performing three block-based tasks : eyes-open closed (EOC) and left/right finger tapping (FTL, FTR). Overall, we found that NLM yielded more thalamic activity compared to traditional denoising methods. In order to validate our pipeline, we also investigated known structural connectivity going through the thalamus using HARDI tractography: the optic radiations, related to the EOC task, and the cortico-spinal tract (CST) for FTL and FTR. To do so, we reconstructed the tracts using functionally based thalamic and cortical ROIs to initiates seeds of tractography in a two-level coarse-to-fine fashion. We applied this method at the single subject level, which allowed us to see the structural connections underlying fMRI thalamic activity. In summary, we propose a new fMRI processing pipeline which uses a recent spatial denoising technique (NLM) to successfully detect sub-cortical activity which was validated using an advanced dMRI seeding strategy in single subjects at 1.5 T.

A SVM-based quantitative fMRI method for resting-state functional network detection

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies.

A kernel machine-based fMRI physiological noise removal method

Functional magnetic resonance imaging (fMRI) technique with blood oxygenation level dependent (BOLD) contrast is a powerful tool for noninvasive mapping of brain function under task and resting states. The removal of cardiac- and respiration-induced physiological noise in fMRI data has been a significant challenge as fMRI studies seek to achieve higher spatial resolutions and characterize more subtle neuronal changes. The low temporal sampling rate of most multi-slice fMRI experiments often causes aliasing of physiological noise into the frequency range of BOLD activation signal. In addition, changes of heartbeat and respiration patterns also generate physiological fluctuations that have similar frequencies with BOLD activation. Most existing physiological noise-removal methods either place restrictive limitations on image acquisition or utilize filtering or regression based post-processing algorithms, which cannot distinguish the frequency-overlapping BOLD activation and the physiological noise. In this work, we address the challenge of physiological noise removal via the kernel machine technique, where a nonlinear kernel machine technique, kernel principal component analysis, is used with a specifically identified kernel function to differentiate BOLD signal from the physiological noise of the frequency. The proposed method was evaluated in human fMRI data acquired from multiple task-related and resting state fMRI experiments. A comparison study was also performed with an existing adaptive filtering method. The results indicate that the proposed method can effectively identify and reduce the physiological noise in fMRI data. The comparison study shows that the proposed method can provide comparable or better noise removal performance than the adaptive filtering approach.

Separating 4D multi-task fMRI data of multiple subjects by independent component analysis with projection

Independent component analysis (ICA) is a widely accepted method to extract brain networks underlying cognitive processes from functional magnetic resonance imaging (fMRI) data. However, the application of ICA to multi-task fMRI data is limited due to the potential non-independency between task-related components. The ICA with projection (ICAp) method proposed by our group (Hum Brain Mapp 2009;30:417-31) is demonstrated to be able to solve the interactions among task-related components for single subject fMRI data. However, it still must be determined if ICAp is capable of processing multi-task fMRI data over a group of subjects. Moreover, it is unclear whether ICAp can be reliably applied to event-related (ER) fMRI data. In this study, we combined the projection method with the temporal concatenation method reported by Calhoun (Hum Brain Mapp 2008;29:828-38), referred to as group ICAp, to perform the group analysis of multi-task fMRI data. Both a human fMRI rest data-based simulation and real fMRI experiments, of block design and ER design, verified the feasibility and reliability of group ICAp, as well as demonstrated that ICAp had the strength to separate 4D multi-task fMRI data into multiple brain networks engaged in each cognitive task and to adequately find the commonalities and differences among multiple tasks.

Application of principal component analysis to study topography of hypoxic-ischemic brain injury

The regions at risk of ischemia following cardio-respiratory arrest have not been systematically analysed. This knowledge may be of use in determining the mechanism of ischemic injury at vulnerable sites. The aim of this study is to evaluate the use of principal component analysis to analyse the covariance patterns of hypoxic ischemic injury. The inclusion criteria were: age ≥ 17 years, cardio-respiratory arrest and coma on admission (2003-2011). Regions of ischemic injury were manually segmented on fluid attenuated inversion recovery (FLAIR) and diffusion weighted (DWI) sequences and linearly registered into common stereotaxic coordinate space. Topography of ischemic injury was assessed using principal component analysis (covariance data) and compared qualitatively against current method of topography analysis, the probabilistic method (frequency data). For the probabilistic data, subgroup analyses were performed using t-statistics while for the covariance data, subgroup analyses were performed by calculating the angle between the principle components. To account for bias due to a higher frequency of coma survivors in the studied group, we performed sensitivity analysis by sequentially removing coma survivors such that the final data set contained higher rate of death. Quantitative analysis between these methods could not be performed as they have different units of measurement. Forty one patients were included in this series (mean age ± SD=51.5 ± 18.9 years). In our probabilistic map, the highest frequency of ischemic injury on the DWI and FLAIR sequences was putamen (0.250), caudate (0.225), temporal lobes (0.175), occipital (0.150) and hippocampus (0.125). The first 6 principal components contained 77.7% of the variance of the data. The first component showed covariance between the deep grey matter nuclei and posterior cortical structures (contains 50.2% of the variance of the data). There was similarity in the findings of the subgroup analyses by the downtime whether it was assessed by t-statistics for probabilistic data or angle between the principal components for the covariance data. The sensitivity analysis showed that the pattern of ischemic injury did not change when the analysis was restricted to patients who died. In conclusion, PCA method has many advantages over probabilistic method. In the context of this dataset, PCA showed covariance between deep grey matter nuclei and the posterior cortical structures whereas the probabilistic map provided complementary information on the frequency of occurrence at these locations.

Data-driven analyses of an fMRI study of a subject experiencing phosphenes

PURPOSE

To compare two data-driven methods of statistical image analysis, principal and independent component analysis (PCA, ICA), in identifying neural networks related to the transient occurrence of phosphenes experienced by a female patient subsequent to a brain infarct.

MATERIALS AND METHODS

An initial functional magnetic resonance imaging (fMRI) session consisted of two acquisitions: one of the patient experiencing phosphenes and a second responding to a well-defined visual stimulation paradigm. A second fMRI session 6 months later, when the patient no longer experienced phosphenes, consisted of an acquisition in which no stimulation was presented. Analysis of correlations between the temporal expression coefficients and models of the hemodynamic response identified salient components. Spectral analysis confirmed the identification. The phosphene model was based solely on the subjective report of the patient.

RESULTS

Both methods revealed occipital cortical and subcortical areas known to be sites for visual information-processing during stimulation, as did SPM. In addition, higher-order visual areas such as the precuneus and the lateral parietal cortex were implicated in the PCA of the phosphenes.

CONCLUSION

The analyses suggest the capability of data-driven approaches to identify the brain structures involved in these transient, spontaneous visual events.

Dynamical principles of emotion-cognition interaction: mathematical images of mental disorders

The key contribution of this work is to introduce a mathematical framework to understand self-organized dynamics in the brain that can explain certain aspects of itinerant behavior. Specifically, we introduce a model based upon the coupling of generalized Lotka-Volterra systems. This coupling is based upon competition for common resources. The system can be regarded as a normal or canonical form for any distributed system that shows self-organized dynamics that entail winnerless competition. Crucially, we will show that some of the fundamental instabilities that arise in these coupled systems are remarkably similar to endogenous activity seen in the brain (using EEG and fMRI). Furthermore, by changing a small subset of the system's parameters we can produce bifurcations and metastable sequential dynamics changing, which bear a remarkable similarity to pathological brain states seen in psychiatry. In what follows, we will consider the coupling of two macroscopic modes of brain activity, which, in a purely descriptive fashion, we will label as cognitive and emotional modes. Our aim is to examine the dynamical structures that emerge when coupling these two modes and relate them tentatively to brain activity in normal and non-normal states.

Dimensionality reduction of fMRI time series data using locally linear embedding

OBJECTIVE

Data-driven methods for fMRI analysis are useful, for example, when an a priori model of signal variations is unavailable. However, activation sources are typically assumed to be linearly mixed, although non-linear properties of fMRI data, including resting-state data, have been observed. In this work, the non-linear locally linear embedding (LLE) algorithm is introduced for dimensionality reduction of fMRI time series data.

MATERIALS AND METHODS

LLE performance was optimised and tested using simulated and volunteer data for task-evoked responses. LLE was compared with principal component analysis (PCA) as a preprocessing step to independent component analysis (ICA). Using an example data set with known non-linear properties, LLE-ICA was compared with PCA-ICA and non-linear PCA-ICA. A resting-state data set was analysed to compare LLE-ICA and PCA-ICA with respect to identifying resting-state networks.

RESULTS

LLE consistently found task-related components as well as known resting-state networks, and the algorithm compared well to PCA. The non-linear example data set demonstrated that LLE, unlike PCA, can separate non-linearly modulated sources in a low-dimensional subspace. Given the same target dimensionality, LLE also performed better than non-linear PCA.

CONCLUSION

LLE is promising for fMRI data analysis and has potential advantages compared with PCA in terms of its ability to find non-linear relationships.

Mapping rabbit whisker barrels using discriminant analysis of high field fMRI data

High field (>4T) functional magnetic resonance imaging (fMRI) techniques provide increased spatial resolution that enables the noninvasive, repeatable study of the sensory cortices at the level of their basic functional units. The examination of these units is important for studies of sensory information processing, learning- or experience-related brain plasticity, or the fundamental relationship between hemodynamic and neuronal activity. However functional units cannot always be distinguished from their surrounding areas by conventional activation mapping methods such as correlation or hypothesis tests, which only consider temporal variation within each individual voxel. We report a novel method to detect individual whisker barrels by using discriminant analysis to jointly characterize high order dependency among multiple voxels. Our results in the whisker barrel cortex of the awake rabbit indicate that the proposed method can differentiate reliably small clusters of activated voxels corresponding to individual whisker barrels within larger areas of functional activation, even in the case of adjacent whiskers in unanesthetized subjects. This method is computationally efficient, requires no specific experimental design for fMRI acquisition, and should be applicable to studies of other sensory systems.

Longitudinal fMRI analysis: A review of methods

Functional magnetic resonance imaging (fMRI) investigations of a longitudinal nature, where participants are scanned repeatedly over time and imaging data are obtained at more than one time-point, are essential to understanding functional changes and development in healthy and pathological brains. The main objective of this paper is to provide a brief summary of common longitudinal analysis approaches, develop an overview of fMRI by introducing how such data manifest, and explore the statistical challenges that arise at the intersection of these two techniques.

Longitudinal fMRI analysis: A review of methods

Functional magnetic resonance imaging (fMRI) investigations of a longitudinal nature, where participants are scanned repeatedly over time and imaging data are obtained at more than one time-point, are essential to understanding functional changes and development in healthy and pathological brains. The main objective of this paper is to provide a brief summary of common longitudinal analysis approaches, develop an overview of fMRI by introducing how such data manifest, and explore the statistical challenges that arise at the intersection of these two techniques.

Unsupervised spatiotemporal fMRI data analysis using support vector machines

In this work we present a new support vector machine (SVM)-based method for fMRI data analysis. SVM has been shown to be a powerful, efficient data-driven tool in pattern recognition, and has been applied to the supervised classification of brain cognitive states in fMRI experiments. We examine the unsupervised mapping of activated brain regions using SVM. Specifically, the mapping process is formulated as an outlier detection problem of one-class SVM (OCSVM) that provides initial mapping results. These results are further refined by applying prototype selection and SVM reclassification. Multiple spatial and temporal features are extracted and selected to facilitate SVM learning. The proposed method was compared with correlation analysis (CA), t-test (TT), and spatial independent component analysis (SICA) methods using synthetic and experimental data. Our results show that the proposed method can provide more accurate and robust activation mapping than CA, TT and SICA, and is computationally more efficient than SICA. Besides its applicability to typical fMRI experiments, the proposed method is also a powerful tool in fMRI studies where a reliable quantification of activated brain regions is required.

Database-managed grid-enabled analysis of neuroimaging data: the CNARI framework

Functional magnetic resonance imaging (fMRI) has led to an enormous growth in the study of cognitive neuroanatomy, and combined with advances in high-field electrophysiology (and other methods), has led to a fast-growing field of human neuroscience. Technological advances in both hardware and software will lead to an ever more promising future for fMRI. We have developed a new computational framework that facilitates fMRI experimentation and analysis, and which has led to some rethinking of the nature of experimental design and analysis. The Computational Neuroscience Applications Research Infrastructure (CNARI) incorporates novel methods for maintaining, serving, and analyzing massive amounts of fMRI data. By using CNARI, it is possible to perform naturalistic, network-based, statistically valid experiments in systems neuroscience on a very large scale, with ease of data manipulation and analysis, within reasonable computational time scales. In this article, we describe this infrastructure and then illustrate its use on a number of actual examples in both cognitive neuroscience and neurological research. We believe that these advanced computational approaches will fundamentally change the future shape of cognitive brain imaging with fMRI.

Identifying neural drivers with functional MRI: an electrophysiological validation

Whether functional magnetic resonance imaging (fMRI) allows the identification of neural drivers remains an open question of particular importance to refine physiological and neuropsychological models of the brain, and/or to understand neurophysiopathology. Here, in a rat model of absence epilepsy showing spontaneous spike-and-wave discharges originating from the first somatosensory cortex (S1BF), we performed simultaneous electroencephalographic (EEG) and fMRI measurements, and subsequent intracerebral EEG (iEEG) recordings in regions strongly activated in fMRI (S1BF, thalamus, and striatum). fMRI connectivity was determined from fMRI time series directly and from hidden state variables using a measure of Granger causality and Dynamic Causal Modelling that relates synaptic activity to fMRI. fMRI connectivity was compared to directed functional coupling estimated from iEEG using asymmetry in generalised synchronisation metrics. The neural driver of spike-and-wave discharges was estimated in S1BF from iEEG, and from fMRI only when hemodynamic effects were explicitly removed. Functional connectivity analysis applied directly on fMRI signals failed because hemodynamics varied between regions, rendering temporal precedence irrelevant. This paper provides the first experimental substantiation of the theoretical possibility to improve interregional coupling estimation from hidden neural states of fMRI. As such, it has important implications for future studies on brain connectivity using functional neuroimaging.

Our understanding of how the brain works relies on the development of neuropsychological models, which describe how brain activity is coordinated among different regions during the execution of a given task. Knowing the directionality of information transfer between connected regions, and in particular distinguishing neural drivers, or the source of forward connections in the brain, from other brain regions, is critical to refine models of the brain. However, whether functional magnetic resonance imaging (fMRI), the most common technique for imaging brain function, allows one to identify neural drivers remains an open question. Here, we used a rat model of absence epilepsy, a form of nonconvulsive epilepsy that occurs during childhood in humans, showing spontaneous spike-and-wave discharges (nonconvulsive seizures) originating from the first somatosensory cortex, to validate several functional connectivity measures derived from fMRI. Standard techniques estimating interactions directly from fMRI data failed because blood flow dynamics varied between regions. However, we were able to identify the neural driver of spike-and-wave discharges when hemodynamic effects were explicitly removed using appropriate modelling. This study thus provides the first experimental substantiation of the theoretical possibility to improve interregional coupling estimation from hidden neural states of fMRI. As such, it has important implications for future studies on connectivity in the functional neuroimaging literature.

Neural long-range interactions can be distinguished from hemodynamic confounds in functional magnetic resonance imaging using new data analysis techniques that will allow experimental validation of models of brain function.

Hierarchical models in the brain

This paper describes a general model that subsumes many parametric models for continuous data. The model comprises hidden layers of state-space or dynamic causal models, arranged so that the output of one provides input to another. The ensuing hierarchy furnishes a model for many types of data, of arbitrary complexity. Special cases range from the general linear model for static data to generalised convolution models, with system noise, for nonlinear time-series analysis. Crucially, all of these models can be inverted using exactly the same scheme, namely, dynamic expectation maximization. This means that a single model and optimisation scheme can be used to invert a wide range of models. We present the model and a brief review of its inversion to disclose the relationships among, apparently, diverse generative models of empirical data. We then show that this inversion can be formulated as a simple neural network and may provide a useful metaphor for inference and learning in the brain.

Models are essential to make sense of scientific data, but they may also play a central role in how we assimilate sensory information. In this paper, we introduce a general model that generates or predicts diverse sorts of data. As such, it subsumes many common models used in data analysis and statistical testing. We show that this model can be fitted to data using a single and generic procedure, which means we can place a large array of data analysis procedures within the same unifying framework. Critically, we then show that the brain has, in principle, the machinery to implement this scheme. This suggests that the brain has the capacity to analyse sensory input using the most sophisticated algorithms currently employed by scientists and possibly models that are even more elaborate. The implications of this work are that we can understand the structure and function of the brain as an inference machine. Furthermore, we can ascribe various aspects of brain anatomy and physiology to specific computational quantities, which may help understand both normal brain function and how aberrant inferences result from pathological processes associated with psychiatric disorders.

Evaluation and optimization of fMRI single-subject processing pipelines with NPAIRS and second-level CVA

In functional magnetic resonance imaging (fMRI) analysis, although the univariate general linear model (GLM) is currently the dominant approach to brain activation detection, there is growing interest in multivariate approaches such as principal component analysis, canonical variate analysis (CVA), independent component analysis and cluster analysis, which have the potential to reveal neural networks and functional connectivity in the brain. To understand the effect of processing options on performance of multivariate model-based fMRI processing pipelines with real fMRI data, we investigated the impact of commonly used fMRI preprocessing steps and optimized the associated multivariate CVA-based, single-subject processing pipelines with the NPAIRS (nonparametric prediction, activation, influence and reproducibility resampling) performance metrics [prediction accuracy and statistical parametric image (SPI) reproducibility] on the Fiswidgets platform. We also compared the single-subject SPIs of univariate GLM with multivariate CVA-based processing pipelines from SPM, FSL.FEAT, NPAIRS.GLM and NPAIRS.CVA software packages (or modules) using a novel second-level CVA. We found that for the block-design data, (a) slice timing correction and global intensity normalization have little consistent impact on the fMRI processing pipeline, but spatial smoothing, temporal detrending or high-pass filtering, and motion correction significantly improved pipeline performance across all subjects; (b) the combined optimization of spatial smoothing, temporal detrending and CVA model parameters on average improved between-subject reproducibility; and (c) the most important pipeline choices include univariate or multivariate statistical models and spatial smoothing. This study suggests that considering options other than simply using GLM with a fixed spatial filter may be of critical importance in determining activation patterns in BOLD fMRI studies.

Neurophysiology and neuroanatomy of reflexive and volitional saccades: evidence from studies of humans

This review provides a summary of the contributions made by human functional neuroimaging studies to the understanding of neural correlates of saccadic control. The generation of simple visually guided saccades (redirections of gaze to a visual stimulus or pro-saccades) and more complex volitional saccades require similar basic neural circuitry with additional neural regions supporting requisite higher level processes. The saccadic system has been studied extensively in non-human (e.g., single-unit recordings) and human (e.g., lesions and neuroimaging) primates. Considerable knowledge of this system's functional neuroanatomy makes it useful for investigating models of cognitive control. The network involved in pro-saccade generation (by definition largely exogenously-driven) includes subcortical (striatum, thalamus, superior colliculus, and cerebellar vermis) and cortical (primary visual, extrastriate, and parietal cortices, and frontal and supplementary eye fields) structures. Activation in these regions is also observed during endogenously-driven voluntary saccades (e.g., anti-saccades, ocular motor delayed response or memory saccades, predictive tracking tasks and anticipatory saccades, and saccade sequencing), all of which require complex cognitive processes like inhibition and working memory. These additional requirements are supported by changes in neural activity in basic saccade circuitry and by recruitment of additional neural regions (such as prefrontal and anterior cingulate cortices). Activity in visual cortex is modulated as a function of task demands and may predict the type of saccade to be generated, perhaps via top-down control mechanisms. Neuroimaging studies suggest two foci of activation within FEF - medial and lateral - which may correspond to volitional and reflexive demands, respectively. Future research on saccade control could usefully (i) delineate important anatomical subdivisions that underlie functional differences, (ii) evaluate functional connectivity of anatomical regions supporting saccade generation using methods such as ICA and structural equation modeling, (iii) investigate how context affects behavior and brain activity, and (iv) use multi-modal neuroimaging to maximize spatial and temporal resolution.

Evaluation and comparison of GLM- and CVA-based fMRI processing pipelines with Java-based fMRI processing pipeline evaluation system

Activation patterns identified by fMRI processing pipelines or fMRI software packages are usually determined by the preprocessing options, parameters, and statistical models used. Previous studies that evaluated options of GLM (general linear model)--based fMRI processing pipelines are mainly based on simulated data with receiver operating characteristics (ROC) analysis, but evaluation of such fMRI processing pipelines on real fMRI data is rare. To understand the effect of processing options on performance of GLM-based fMRI processing pipelines with real fMRI data, we investigated the impact of commonly-used fMRI preprocessing steps; optimized the associated GLM-based single-subject processing pipelines; and quantitatively compared univariate GLM (in FSL.FEAT and NPAIRS.GLM) and multivariate CVA (canonical variates analysis) (in NPAIRS.CVA)-based analytic models in single-subject analysis with a recently developed fMRI processing pipeline evaluation system based on prediction accuracy (classification accuracy) and reproducibility performance metrics. For block-design data, we found that with GLM analysis (1) slice timing correction and global intensity normalization have little consistent impact on fMRI processing pipelines, spatial smoothing and high-pass filtering or temporal detrending significantly increases pipeline performance and thus are essential for robust fMRI statistical analysis; (2) combined optimization of spatial smoothing and temporal detrending improves pipeline performance; and (3) in general, the prediction performance of multivariate CVA is higher than that of the univariate GLM, while univariate GLM is more reproducible than multivariate CVA. Because of the different bias-variance trade-offs of univariate and multivariate models, it may be necessary to consider a consensus approach to obtain more accurate activation patterns in fMRI data.

Functional interactions between prefrontal and visual association cortex contribute to top-down modulation of visual processing

Attention-dependent modulation of neural activity in visual association cortex (VAC) is thought to depend on top-down modulatory control signals emanating from the prefrontal cortex (PFC). In a previous functional magnetic resonance imaging study utilizing a working memory task, we demonstrated that activity levels in scene-selective VAC (ssVAC) regions can be enhanced above or suppressed below a passive viewing baseline level depending on whether scene stimuli were attended or ignored (Gazzaley, Cooney, McEvoy, et al. 2005). Here, we use functional connectivity analysis to identify possible sources of these modulatory influences by examining how network interactions with VAC are influenced by attentional goals at the time of encoding. Our findings reveal a network of regions that exhibit strong positive correlations with a ssVAC seed during all task conditions, including foci in the left middle frontal gyrus (MFG). This PFC region is more correlated with the VAC seed when scenes were remembered and less correlated when scenes were ignored, relative to passive viewing. Moreover, the strength of MFG-VAC coupling correlates with the magnitude of attentional enhancement and suppression of VAC activity. Although our correlation analyses do not permit assessment of directionality, these findings suggest that PFC biases activity levels in VAC by adjusting the strength of functional coupling in accordance with stimulus relevance.

Top-Down Modulation and Normal Aging

Normal aging is characterized by cognitive deficits that cross multiple domains and impair the ability of some older individuals to lead productive, high-quality lives. One of the primary goals of research in our laboratories is to study age-related alterations in neural mechanisms that underlie a wide range of cognitive processes so that we may generate a unifying principle of cognitive aging. Top-down modulation is the mechanism by which we enhance neural activity associated with relevant information and suppress activity for irrelevant information, thus establishing a foundation for both attention and memory processes. We use three converging technologies of human neurophysiology to study top-down modulation in aging: functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and transcranial magnetic stimulation (TMS). Using these tools we have discovered that healthy older adults exhibit a selective inability to effectively suppress neural activity associated with distracting information and that this top-down suppression deficit is correlated with their memory impairment. We are now further characterizing the basis of these age-related alterations in top-down modulation and investigating interventions to remedy them.

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.

Models of functional neuroimaging data

Inferences about brain function, using functional neuroimaging data, require models of how the data were caused. A variety of models are used in practice that range from conceptual models of functional anatomy to nonlinear mathematical models of hemodynamic responses (e.g. as measured by functional magnetic resonance imaging, fMRI) and neuronal responses. In this review, we discuss the most important models used to analyse functional imaging data and demonstrate how they are interrelated. Initially, we briefly review the anatomical foundations of current theories of brain function on which all mathematical models rest. We then introduce some basic statistical models (e.g. the general linear model) used for making classical (i.e. frequentist) and Bayesian inferences about where neuronal responses are expressed. The more challenging question, how these responses are caused, is addressed by models that incorporate biophysical constraints (e.g. forward models from the neural to the hemodynamic level) and/or consider causal interactions between several regions, i.e. models of effective connectivity. Some of the most refined models to date are neuronal mass models of electroencephalographic (EEG) responses. These models enable mechanistic inferences about how evoked responses are caused, at the level of neuronal subpopulations and the coupling among them.

The nonlinear theory of schizophrenia

OBJECTIVE

Nonlinear properties exist within the brain across a hierarchy of scales and within a variety of critical neural processes. Only a few studies of brain activity in schizophrenia, however, have used nonlinear methods. This review paper evaluates the contribution of the nonlinear sciences towards understanding schizophrenia.

METHOD

Applications of nonlinear methods to the study of schizophrenia symptoms and to healthy and schizophrenia functional neuroscience data are reviewed. The main flaws of nonlinear algorithms and recent methods to correct these are also appraised.

RESULTS

Initial research methods utilized in the study of nonlinearity in schizophrenia have fundamental methodological limitations. In the last decade, many of these problems have been addressed, facilitating future progress. Research incorporating these improvements has been applied to normal electroencephalogram (EEG) data and to the symptoms of schizophrenia, but not systematically to brain imaging data collected from patients with schizophrenia.

CONCLUSION

There is strong statistical evidence for weak nonlinearity in normal EEG and in the fluctuations of the symptoms of schizophrenia. However, the contribution of nonlinear processes to brain dysfunction in schizophrenia is yet to be properly established or accurately quantified. Despite this, recent methodological advances suggest that a 'nonlinear theory' of schizophrenia may be helpful in understanding this disorder.

On the neurobiological investigation of language understanding in context

There are two significant problems in using functional neuroimaging methods to study language. Improving the state of functional brain imaging will depend on understanding how the dependent measure of brain imaging differs from behavioral dependent measures (the "dependent measure problem") and how the activation of the motor system may be confounded with non-motor aspects of processing in certain experimental designs (the "motor output problem"). To address these problems, it may be necessary to shift the focus of language research from the study of linguistic competence to the understanding of language use. This will require investigations of language processing in full multi-modal and environmental context, monitoring of natural behaviors, novel experimental design, and network-based analysis. Such a combined naturalistic approach could lead to tremendous new insights into language and the brain.

Learning and inference in the brain

This article is about how the brain data mines its sensory inputs. There are several architectural principles of functional brain anatomy that have emerged from careful anatomic and physiologic studies over the past century. These principles are considered in the light of representational learning to see if they could have been predicted a priori on the basis of purely theoretical considerations. We first review the organisation of hierarchical sensory cortices, paying special attention to the distinction between forward and backward connections. We then review various approaches to representational learning as special cases of generative models, starting with supervised learning and ending with learning based upon empirical Bayes. The latter predicts many features, such as a hierarchical cortical system, prevalent top-down backward influences and functional asymmetries between forward and backward connections that are seen in the real brain. The key points made in article are: (i). hierarchical generative models enable the learning of empirical priors and eschew prior assumptions about the causes of sensory input that are inherent in non-hierarchical models. These assumptions are necessary for learning schemes based on information theory and efficient or sparse coding, but are not necessary in a hierarchical context. Critically, the anatomical infrastructure that may implement generative models in the brain is hierarchical. Furthermore, learning based on empirical Bayes can proceed in a biologically plausible way. (ii). The second point is that backward connections are essential if the processes generating inputs cannot be inverted, or the inversion cannot be parameterised. Because these processes involve many-to-one mappings, are non-linear and dynamic in nature, they are generally non-invertible. This enforces an explicit parameterisation of generative models (i.e. backward connections) to afford recognition and suggests that forward architectures, on their own, are not sufficient for perception. (iii). Finally, non-linearities in generative models, mediated by backward connections, require these connections to be modulatory, so that representations in higher cortical levels can interact to predict responses in lower levels. This is important in relation to functional asymmetries in forward and backward connections that have been demonstrated empirically.

A multitrial analysis for revealing significant corticocortical networks in magnetoencephalography and electroencephalography

We present an MEG/EEG framework to reveal statistically significant brain areas engaged in the same cognitive process across trials without resort to averaging procedures. The variability of neuronal responses is assumed to take place only in the reconstructed time series of cortical sources and not in their positions. This hypothesis allows the use of the surrogate data method to detect recurrently active brain areas across trials adjusted with any cortically constrained focal MEEG inverse solution. Results obtained from synthetic data show that considering several trials enhances the accuracy of the source localisation. We apply this approach on MEG data recorded during a simple visual stimulation. The considered stimulus is frequency tagged in order to reveal the neural network correlated to its perception using phase synchronisation analysis. The results show consistent patterns of distributed synchronous networks centred on occipital areas.

Studying spontaneous EEG activity with fMRI

The multifaceted technological challenge of acquiring simultaneous EEG-correlated fMRI data has now been met and the potential exists for mapping electrophysiological activity with unprecedented spatio-temporal resolution. Work has already begun on studying a host of spontaneous EEG phenomena ranging from alpha rhythm and sleep patterns to epileptiform discharges and seizures, with far reaching clinical implications. However, the transformation of EEG data into linear models suitable for voxel-based statistical hypothesis testing is central to the endeavour. This in turn is predicated upon a number of assumptions regarding the manner in which the generators of EEG phenomena may engender changes in the blood oxygen level dependent (BOLD) signal. Furthermore, important limitations are posed by a set of considerations quite unique to 'paradigmless fMRI'. Here, these issues are assembled and explored to provide an overview of progress made and unresolved questions, with an emphasis on applications in epilepsy.

Dynamical components analysis of fMRI data through kernel PCA

In parallel with standard model-based methods for the analysis of fMRI data, exploratory methods--such as PCA, ICA, and clustering--have been developed to give an account of the dataset with minimal priors: no assumption is made on the data content itself, but the data structure is assumed to show some properties (decorrelation, independence) that allow for the detection of structures of interest. In this paper, we present an alternative that tries to take into account some relevant knowledge for the analysis of the dataset, e.g., the experimental paradigm, while keeping the flexibility of exploratory methods: we use a prior temporal modeling of the data that characterizes each voxel time course. Two implementations are proposed: one based on the General Linear Model, the other one on more flexible short-term predictors, whose complexity is controlled by a Minimum Description Length approach. However, our main concern here is the construction of a multivariate model; the latter is performed with the help of a kernel PCA method that builds a redundant representation of the data through the nonlinearity of the kernel. This allows for a refinement in the description of the (temporal) patterns of interest. In particular, this helps in the characterization of subtle variations in the response to different experimental conditions. We illustrate the usefulness of nonlinearity through the analysis of a synthetic dataset and show on a real dataset how it helps to interpret the experimental results.