Artifact removal in the context of group ICA: A comparison of single-subject and group approaches.

Independent component analysis (ICA) has been widely applied to identify intrinsic brain networks from fMRI data. Group ICA computes group-level components from all data and subsequently estimates individual-level components to recapture intersubject variability. However, the best approach to handle artifacts, which may vary widely among subjects, is not yet clear. In this work, we study and compare two ICA approaches for artifacts removal. One approach, recommended in recent work by the Human Connectome Project, first performs ICA on individual subject data to remove artifacts, and then applies a group ICA on the cleaned data from all subjects. We refer to this approach as Individual ICA based artifacts Removal Plus Group ICA (IRPG). A second proposed approach, called Group Information Guided ICA (GIG-ICA), performs ICA on group data, then removes the group-level artifact components, and finally performs subject-specific ICAs using the group-level non-artifact components as spatial references. We used simulations to evaluate the two approaches with respect to the effects of data quality, data quantity, variable number of sources among subjects, and spatially unique artifacts. Resting-state test-retest datasets were also employed to investigate the reliability of functional networks. Results from simulations demonstrate GIG-ICA has greater performance compared with IRPG, even in the case when single-subject artifacts removal is perfect and when individual subjects have spatially unique artifacts. Experiments using test-retest data suggest that GIG-ICA provides more reliable functional networks. Based on high estimation accuracy, ease of implementation, and high reliability of functional networks, we find GIG-ICA to be a promising approach.

Reduced error signalling in medication-naive children with ADHD: associations with behavioural variability and post-error adaptations.

BACKGROUND: We examined the blood-oxygen level-dependent (BOLD) activation in brain regions that signal errors and their association with intraindividual behavioural variability and adaptation to errors in children with attention-deficit/hyperactivity disorder (ADHD). METHODS: We acquired functional MRI data during a Flanker task in medication-naive children with ADHD and healthy controls aged 8-12 years and analyzed the data using independent component analysis. For components corresponding to performance monitoring networks, we compared activations across groups and conditions and correlated them with reaction times (RT). Additionally, we analyzed post-error adaptations in behaviour and motor component activations. RESULTS: We included 25 children with ADHD and 29 controls in our analysis. Children with ADHD displayed reduced activation to errors in cingulo-opercular regions and higher RT variability, but no differences of interference control. Larger BOLD amplitude to error trials significantly predicted reduced RT variability across all participants. Neither group showed evidence of post-error response slowing; however, post-error adaptation in motor networks was significantly reduced in children with ADHD. This adaptation was inversely related to activation of the right-lateralized ventral attention network (VAN) on error trials and to task-driven connectivity between the cingulo-opercular system and the VAN. LIMITATIONS: Our study was limited by the modest sample size and imperfect matching across groups. CONCLUSION: Our findings show a deficit in cingulo-opercular activation in children with ADHD that could relate to reduced signalling for errors. Moreover, the reduced orienting of the VAN signal may mediate deficient post-error motor adaptions. Pinpointing general performance monitoring problems to specific brain regions and operations in error processing may help to guide the targets of future treatments for ADHD.

Multidimensional frequency domain analysis of full-volume fMRI reveals significant effects of age, gender, and mental illness on the spatiotemporal organization of resting-state brain activity.

Clinical research employing functional magnetic resonance imaging (fMRI) is often conducted within the connectionist paradigm, focusing on patterns of connectivity between voxels, regions of interest (ROIs) or spatially distributed functional networks. Connectivity-based analyses are concerned with pairwise correlations of the temporal activation associated with restrictions of the whole-brain hemodynamic signal to locations of a priori interest. There is a more abstract question however that such spatially granular correlation-based approaches do not elucidate: Are the broad spatiotemporal organizing principles of brains in certain populations distinguishable from those of others? Global patterns (in space and time) of hemodynamic activation are rarely scrutinized for features that might characterize complex psychiatric conditions, aging effects or gender-among other variables of potential interest to researchers. We introduce a canonical, transparent technique for characterizing the role in overall brain activation of spatially scaled periodic patterns with given temporal recurrence rates. A core feature of our technique is the spatiotemporal spectral profile (STSP), a readily interpretable 2D reduction of the native four-dimensional brain × time frequency domain that is still "big enough" to capture important group differences in globally patterned brain activation. Its power to distinguish populations of interest is demonstrated on a large balanced multi-site resting fMRI dataset with nearly equal numbers of schizophrenia patients and healthy controls. Our analysis reveals striking differences in the spatiotemporal organization of brain activity that correlate with the presence of diagnosed schizophrenia, as well as with gender and age. To the best of our knowledge, this is the first demonstration that a 4D frequency domain analysis of full volume fMRI data exposes clinically or demographically relevant differences in resting-state brain function.

Dynamic coherence analysis of resting fMRI data to jointly capture state-based phase, frequency, and time-domain information.

Many approaches for estimating functional connectivity among brain regions or networks in fMRI have been considered in the literature. More recently, studies have shown that connectivity which is usually estimated by calculating correlation between time series or by estimating coherence as a function of frequency has a dynamic nature, during both task and resting conditions. Sliding-window methods have been commonly used to study these dynamic properties although other approaches such as instantaneous phase synchronization have also been used for similar purposes. Some studies have also suggested that spectral analysis can be used to separate the distinct contributions of motion, respiration and neurophysiological activity from the observed correlation. Several recent studies have merged analysis of coherence with study of temporal dynamics of functional connectivity though these have mostly been limited to a few selected brain regions and frequency bands. Here we propose a novel data-driven framework to estimate time-varying patterns of whole-brain functional network connectivity of resting state fMRI combined with the different frequencies and phase lags at which these patterns are observed. We show that this analysis identifies both broad-band cluster centroids that summarize connectivity patterns observed in many frequency bands, as well as clusters consisting only of functional network connectivity (FNC) from a narrow range of frequencies along with associated phase profiles. The value of this approach is demonstrated by its ability to reveal significant group differences in males versus females regarding occupancy rates of cluster that would not be separable without considering the frequencies and phase lags. The method we introduce provides a novel and informative framework for analyzing time-varying and frequency specific connectivity which can be broadly applied to the study of the healthy and diseased human brain.

Acetylcholine mediates behavioral and neural post-error control.

Humans often commit errors when they are distracted by irrelevant information and no longer focus on what is relevant to the task at hand. Adjustments following errors are essential for optimizing goal achievement. The posterior medial frontal cortex (pMFC), a key area for monitoring errors, has been shown to trigger such post-error adjustments by modulating activity in visual cortical areas. However, the mechanisms by which pMFC controls sensory cortices are unknown. We provide evidence for a mechanism based on pMFC-induced recruitment of cholinergic projections to task-relevant sensory areas. Using fMRI in healthy volunteers, we found that error-related pMFC activity predicted subsequent adjustments in task-relevant visual brain areas. In particular, following an error, activity increased in those visual cortical areas involved in processing task-relevant stimulus features, whereas activity decreased in areas representing irrelevant, distracting features. Following treatment with the muscarinic acetylcholine receptor antagonist biperiden, activity in visual areas was no longer under control of error-related pMFC activity. This was paralleled by abolished post-error behavioral adjustments under biperiden. Our results reveal a prominent role of acetylcholine in cognitive control that has not been recognized thus far. Regaining optimal performance after errors critically depends on top-down control of perception driven by the pMFC and mediated by acetylcholine. This may explain the lack of adaptivity in conditions with reduced availability of cortical acetylcholine, such as Alzheimer's disease.

Transcranial Magnetic Stimulation Changes Response Selectivity of Neurons in the Visual Cortex.

BACKGROUND: Transcranial magnetic stimulation (TMS) is used to selectively alter neuronal activity of specific regions in the cerebral cortex. TMS is reported to induce either transient disruption or enhancement of different neural functions. However, its effects on tuning properties of sensory neurons have not been studied quantitatively. OBJECTIVE/HYPOTHESIS: Here, we use specific TMS application parameters to determine how they may alter tuning characteristics (orientation, spatial frequency, and contrast sensitivity) of single neurons in the cat's visual cortex. METHODS: Single unit spikes were recorded with tungsten microelectrodes from the visual cortex of anesthetized and paralyzed cats (12 males). Repetitive TMS (4 Hz, 4 s) was delivered with a 70 mm figure-8 coil. We quantified basic tuning parameters of individual neurons for each pre- and post-TMS condition. The statistical significance of changes for each tuning parameter between the two conditions was evaluated with a Wilcoxon signed-rank test. RESULTS: We generally find long-lasting suppression which persists well beyond the stimulation period. Pre- and post-TMS orientation tuning curves show constant peak values. However, strong suppression at non-preferred orientations tends to narrow the widths of tuning curves. Spatial frequency tuning exhibits an asymmetric change in overall shape, which results in an emphasis on higher frequencies. Contrast tuning curves show nonlinear changes consistent with a gain control mechanism. CONCLUSIONS: These findings suggest that TMS causes extended interruption of the balance between sub-cortical and intra-cortical inputs.

EEG Spectral Features Discriminate between Alzheimer's and Vascular Dementia.

Alzheimer's disease (AD) and vascular dementia (VaD) present with similar clinical symptoms of cognitive decline, but the underlying pathophysiological mechanisms differ. To determine whether clinical electroencephalography (EEG) can provide information relevant to discriminate between these diagnoses, we used quantitative EEG analysis to compare the spectra between non-medicated patients with AD (n = 77) and VaD (n = 77) and healthy elderly normal controls (NC) (n = 77). We use curve-fitting with a combination of a power loss and Gaussian function to model the averaged resting-state spectra of each EEG channel extracting six parameters. We assessed the performance of our model and tested the extracted parameters for group differentiation. We performed regression analysis in a multivariate analysis of covariance with group, age, gender, and number of epochs as predictors and further explored the topographical group differences with pair-wise contrasts. Significant topographical differences between the groups were found in several of the extracted features. Both AD and VaD groups showed increased delta power when compared to NC, whereas the AD patients showed a decrease in alpha power for occipital and temporal regions when compared with NC. The VaD patients had higher alpha power than NC and AD. The AD and VaD groups showed slowing of the alpha rhythm. Variability of the alpha frequency was wider for both AD and VaD groups. There was a general decrease in beta power for both AD and VaD. The proposed model is useful to parameterize spectra, which allowed extracting relevant clinical EEG key features that move toward simple and interpretable diagnostic criteria.

Static and Dynamic Intrinsic Connectivity following Mild Traumatic Brain Injury.

Mild traumatic brain injury (mTBI) is the most common neurological disorder and is typically characterized by temporally limited cognitive impairment and emotional symptoms. Previous examinations of intrinsic resting state networks in mTBI have primarily focused on abnormalities in static functional connectivity, and deficits in dynamic functional connectivity have yet to be explored in this population. Resting-state data was collected on 48 semi-acute (mean = 14 days post-injury) mTBI patients and 48 matched healthy controls. A high-dimensional independent component analysis (N = 100) was utilized to parcellate intrinsic connectivity networks (ICN), with a priori hypotheses focusing on the default-mode network (DMN) and sub-cortical structures. Dynamic connectivity was characterized using a sliding window approach over 126 temporal epochs, with standard deviation serving as the primary outcome measure. Finally, distribution-corrected z-scores (DisCo-Z) were calculated to investigate changes in connectivity in a spatially invariant manner on a per-subject basis. Following appropriate correction for multiple comparisons, no significant group differences were evident on measures of static or dynamic connectivity within a priori ICN. Reduced (HC > mTBI patients) static connectivity was observed in the DMN at uncorrected (p < 0.005) thresholds. Finally, a trend (p = 0.07) for decreased dynamic connectivity in patients across all ICN was observed during spatially invariant analyses (DisCo-Z). In the semi-acute phase of recovery, mTBI was not reliably associated with abnormalities in static or dynamic functional connectivity within the DMN or sub-cortical structures.

Deep learning for neuroimaging: a validation study.

Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. These methods include deep belief networks and their building block the restricted Boltzmann machine. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.

Restricted Boltzmann machines for neuroimaging: an application in identifying intrinsic networks.

Matrix factorization models are the current dominant approach for resolving meaningful data-driven features in neuroimaging data. Among them, independent component analysis (ICA) is arguably the most widely used for identifying functional networks, and its success has led to a number of versatile extensions to group and multimodal data. However there are indications that ICA may have reached a limit in flexibility and representational capacity, as the majority of such extensions are case-driven, custom-made solutions that are still contained within the class of mixture models. In this work, we seek out a principled and naturally extensible approach and consider a probabilistic model known as a restricted Boltzmann machine (RBM). An RBM separates linear factors from functional brain imaging data by fitting a probability distribution model to the data. Importantly, the solution can be used as a building block for more complex (deep) models, making it naturally suitable for hierarchical and multimodal extensions that are not easily captured when using linear factorizations alone. We investigate the capability of RBMs to identify intrinsic networks and compare its performance to that of well-known linear mixture models, in particular ICA. Using synthetic and real task fMRI data, we show that RBMs can be used to identify networks and their temporal activations with accuracy that is equal or greater than that of factorization models. The demonstrated effectiveness of RBMs supports its use as a building block for deeper models, a significant prospect for future neuroimaging research.

Tracking whole-brain connectivity dynamics in the resting state.

Spontaneous fluctuations are a hallmark of recordings of neural signals, emergent over time scales spanning milliseconds and tens of minutes. However, investigations of intrinsic brain organization based on resting-state functional magnetic resonance imaging have largely not taken into account the presence and potential of temporal variability, as most current approaches to examine functional connectivity (FC) implicitly assume that relationships are constant throughout the length of the recording. In this work, we describe an approach to assess whole-brain FC dynamics based on spatial independent component analysis, sliding time window correlation, and k-means clustering of windowed correlation matrices. The method is applied to resting-state data from a large sample (n = 405) of young adults. Our analysis of FC variability highlights particularly flexible connections between regions in lateral parietal and cingulate cortex, and argues against a labeling scheme where such regions are treated as separate and antagonistic entities. Additionally, clustering analysis reveals unanticipated FC states that in part diverge strongly from stationary connectivity patterns and challenge current descriptions of interactions between large-scale networks. Temporal trends in the occurrence of different FC states motivate theories regarding their functional roles and relationships with vigilance/arousal. Overall, we suggest that the study of time-varying aspects of FC can unveil flexibility in the functional coordination between different neural systems, and that the exploitation of these dynamics in further investigations may improve our understanding of behavioral shifts and adaptive processes.

Brain functional networks extraction based on fMRI artifact removal: Single subject and group approaches.

Independent component analysis (ICA) has been widely applied to identify brain functional networks from multiple-subject fMRI. However, the best approach to handle artifacts is not yet clear. In this work, we study and compare two ICA approaches for artifact removal using simulations and real fMRI data. The first approach, recommended by the human connectome project, performs ICA on individual data to remove artifacts, and then applies group ICA on the cleaned data from all subjects. We refer to this approach as Individual ICA artifact Removal Plus Group ICA (TRPG). A second approach, Group Information Guided ICA (GIG-ICA), performs ICA on group data, and then removes the artifact group independent components (ICs), followed by individual subject ICA using the remaining group ICs as spatial references. Experiments demonstrate that GIG-ICA is more accurate in estimation of sources and time courses, more robust to data quality and quantity, and more reliable for identifying networks than IRPG.

Dynamic functional connectivity: promise, issues, and interpretations.

The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations.

Data visualization in the neurosciences: overcoming the curse of dimensionality.

In publications, presentations, and popular media, scientific results are predominantly communicated through graphs. But are these figures clear and honest or misleading? We examine current practices in data visualization and discuss improvements, advocating design choices which reveal data rather than hide it.

Reliability of the amplitude of low-frequency fluctuations in resting state fMRI in chronic schizophrenia.

The resting state amplitude of low frequency fluctuations (ALFF) in functional magnetic resonance imaging has been shown to be reliable in healthy subjects, and to correlate with antipsychotic treatment response in antipsychotic-naïve schizophrenia patients. We found moderate to high test-retest stability of ALFF in chronically treated schizophrenia patients assessed twice over a median interval of 2.5 months.

Correspondence between structure and function in the human brain at rest.

To further understanding of basic and complex cognitive functions, previous connectome research has identified functional and structural connections of the human brain. Functional connectivity is often measured by using resting-state functional magnetic resonance imaging (rs-fMRI) and is generally interpreted as an indirect measure of neuronal activity. Gray matter (GM) primarily consists of neuronal and glia cell bodies; therefore, it is surprising that the majority of connectome research has excluded GM measures. Therefore, we propose that by exploring where GM corresponds to function would aid in the understanding of both structural and functional connectivity and in turn the human connectome. A cohort of 603 healthy participants underwent structural and functional scanning on the same 3 T scanner at the Mind Research Network. To investigate the spatial correspondence between structure and function, spatial independent component analysis (ICA) was applied separately to both GM density (GMD) maps and to rs-fMRI data. ICA of GM delineates structural components based on the covariation of GMD regions among subjects. For the rs-fMRI data, ICA identified spatial patterns with common temporal features. These decomposed structural and functional components were then compared by spatial correlation. Basal ganglia components exhibited the highest structural to resting-state functional spatial correlation (r = 0.59). Cortical components generally show correspondence between a single structural component and several resting-state functional components. We also studied relationships between the weights of different structural components and identified the precuneus as a hub in GMD structural network correlations. In addition, we analyzed relationships between component weights, age, and gender; concluding that age has a significant effect on structural components.

Decomposing the brain: components and modes, networks and nodes.

Smith and colleagues recently presented a temporal independent component analysis (tICA) decomposition of resting-state functional MRI data. Compared to the widely used spatial ICA (sICA), tICA better allows for a brain region to engage in multiple, independent interactions with other regions and will potentially offer new insights into brain function.

Brain connectivity networks in schizophrenia underlying resting state functional magnetic resonance imaging.

Schizophrenia (SZ) is a severe neuropsychiatric disorder. A leading hypothesis is that SZ is a brain dysconnection syndrome, involving abnormal interactions between widespread brain networks. Resting state functional magnetic resonance imaging (R-fMRI) is a powerful tool to explore the dysconnectivity of brain networks in SZ and other disorders. Seed-based functional connectivity analysis, spatial independent component analysis (ICA), and graph theory-based analysis are popular methods to quantify brain network connectivity in R-fMRI data. Widespread network dysconnectivity in SZ has been observed using both seed-based analysis and ICA, although most seed-based studies report decreased connectivity while ICA studies report both increases and decreases. Importantly, most of the findings from both techniques are also associated with typical symptoms of the illness. Disrupted topological properties and altered modular community structure of brain system in SZ have been shown using graph theory-based analysis. Overall, the resting-state findings regarding brain networks deficits have advanced our understanding of the underlying pathology of SZ. In this article, we review aberrant brain connectivity networks in SZ measured in R-fMRI by the above approaches, and discuss future challenges.

SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability.

We introduce SimTB, a MATLAB toolbox designed to simulate functional magnetic resonance imaging (fMRI) datasets under a model of spatiotemporal separability. The toolbox meets the increasing need of the fMRI community to more comprehensively understand the effects of complex processing strategies by providing a ground truth that estimation methods may be compared against. SimTB captures the fundamental structure of real data, but data generation is fully parameterized and fully controlled by the user, allowing for accurate and precise comparisons. The toolbox offers a wealth of options regarding the number and configuration of spatial sources, implementation of experimental paradigms, inclusion of tissue-specific properties, addition of noise and head movement, and much more. A straightforward data generation method and short computation time (3-10 seconds for each dataset) allow a practitioner to simulate and analyze many datasets to potentially understand a problem from many angles. Beginning MATLAB users can use the SimTB graphical user interface (GUI) to design and execute simulations while experienced users can write batch scripts to automate and customize this process. The toolbox is freely available at http://mialab.mrn.org/software together with sample scripts and tutorials.

Capturing inter-subject variability with group independent component analysis of fMRI data: a simulation study.

A key challenge in functional neuroimaging is the meaningful combination of results across subjects. Even in a sample of healthy participants, brain morphology and functional organization exhibit considerable variability, such that no two individuals have the same neural activation at the same location in response to the same stimulus. This inter-subject variability limits inferences at the group-level as average activation patterns may fail to represent the patterns seen in individuals. A promising approach to multi-subject analysis is group independent component analysis (GICA), which identifies group components and reconstructs activations at the individual level. GICA has gained considerable popularity, particularly in studies where temporal response models cannot be specified. However, a comprehensive understanding of the performance of GICA under realistic conditions of inter-subject variability is lacking. In this study we use simulated functional magnetic resonance imaging (fMRI) data to determine the capabilities and limitations of GICA under conditions of spatial, temporal, and amplitude variability. Simulations, generated with the SimTB toolbox, address questions that commonly arise in GICA studies, such as: (1) How well can individual subject activations be estimated and when will spatial variability preclude estimation? (2) Why does component splitting occur and how is it affected by model order? (3) How should we analyze component features to maximize sensitivity to intersubject differences? Overall, our results indicate an excellent capability of GICA to capture between-subject differences and we make a number of recommendations regarding analytic choices for application to functional imaging data.