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.

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.

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.

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.

Impact of autocorrelation on functional connectivity.

Although the impact of serial correlation (autocorrelation) in residuals of general linear models for fMRI time-series has been studied extensively, the effect of autocorrelation on functional connectivity studies has been largely neglected until recently. Some recent studies based on results from economics have questioned the conventional estimation of functional connectivity and argue that not correcting for autocorrelation in fMRI time-series results in "spurious" correlation coefficients. In this paper, first we assess the effect of autocorrelation on Pearson correlation coefficient through theoretical approximation and simulation. Then we present this effect on real fMRI data. To our knowledge this is the first work comprehensively investigating the effect of autocorrelation on functional connectivity estimates. Our results show that although FC values are altered, even following correction for autocorrelation, results of hypothesis testing on FC values remain very similar to those before correction. In real data we show this is true for main effects and also for group difference testing between healthy controls and schizophrenia patients. We further discuss model order selection in the context of autoregressive processes, effects of frequency filtering and propose a preprocessing pipeline for connectivity studies.

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.

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.

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.

Comparison of multi-subject ICA methods for analysis of fMRI data.

Spatial independent component analysis (ICA) applied to functional magnetic resonance imaging (fMRI) data identifies functionally connected networks by estimating spatially independent patterns from their linearly mixed fMRI signals. Several multi-subject ICA approaches estimating subject-specific time courses (TCs) and spatial maps (SMs) have been developed, however, there has not yet been a full comparison of the implications of their use. Here, we provide extensive comparisons of four multi-subject ICA approaches in combination with data reduction methods for simulated and fMRI task data. For multi-subject ICA, the data first undergo reduction at the subject and group levels using principal component analysis (PCA). Comparisons of subject-specific, spatial concatenation, and group data mean subject-level reduction strategies using PCA and probabilistic PCA (PPCA) show that computationally intensive PPCA is equivalent to PCA, and that subject-specific and group data mean subject-level PCA are preferred because of well-estimated TCs and SMs. Second, aggregate independent components are estimated using either noise-free ICA or probabilistic ICA (PICA). Third, subject-specific SMs and TCs are estimated using back-reconstruction. We compare several direct group ICA (GICA) back-reconstruction approaches (GICA1-GICA3) and an indirect back-reconstruction approach, spatio-temporal regression (STR, or dual regression). Results show the earlier group ICA (GICA1) approximates STR, however STR has contradictory assumptions and may show mixed-component artifacts in estimated SMs. Our evidence-based recommendation is to use GICA3, introduced here, with subject-specific PCA and noise-free ICA, providing the most robust and accurate estimated SMs and TCs in addition to offering an intuitive interpretation.

Telemedicine in Radiation Oncology: Is It Here to Stay? Impacts on Patient Care and Resident Education.

PURPOSE: Telemedicine was rapidly and ubiquitously adopted during the COVID-19 pandemic. However, there are growing discussions as to its role postpandemic. METHODS AND MATERIALS: We surveyed patients, radiation oncology (RO) attendings, and RO residents to assess their experience with telemedicine. Surveys addressed quality of patient care and utility of telemedicine for teaching and learning core competencies. Satisfaction was rated on a 6-point Likert-type scale. The quality of teaching and learning was graded on a 5-point Likert-type scale, with overall scores calculated by the average rating of each core competency required by the Accreditation Council for Graduate Medical Education (range, 1-5). RESULTS: Responses were collected from 56 patients, 12 RO attendings, and 13 RO residents. Patient feedback was collected at 17 new-patient, 22 on-treatment, and 17 follow-up video visits. Overall, 88% of patients were satisfied with virtual visits. A lower proportion of on-treatment patients rated their virtual visit as "very satisfactory" (68.2% vs 76.5% for new patients and 82.4% for follow-ups). Only 5.9% of the new patients and none of the follow-up patients were dissatisfied, and 27% of on-treatment patients were dissatisfied. The large majority of patients (88%) indicated that they would continue to use virtual visits as long as a physical examination was not needed. Overall scores for medical training were 4.1 out of 5 (range, 2.8-5.0) by RO residents and 3.2 (range, 2.0-4.0) by RO attendings. All residents and 92% of attendings indicated they would use telemedicine again; however, most indicated that telemedicine is best for follow-up visits. CONCLUSIONS: Telemedicine is a convenient means of delivering care to patients, with some limitations demonstrated for on-treatment patients. The majority of both patients and providers are interested in using telemedicine again, and it will likely continue to supplement patient care.

A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data.

Dynamic functional connectivity, i.e., the study of how interactions among brain regions change dynamically over the course of an fMRI experiment, has recently received wide interest in the neuroimaging literature. Current approaches for studying dynamic connectivity often rely on ad-hoc approaches for inference, with the fMRI time courses segmented by a sequence of sliding windows. We propose a principled Bayesian approach to dynamic functional connectivity, which is based on the estimation of time varying networks. Our method utilizes a hidden Markov model for classification of latent cognitive states, achieving estimation of the networks in an integrated framework that borrows strength over the entire time course of the experiment. Furthermore, we assume that the graph structures, which define the connectivity states at each time point, are related within a super-graph, to encourage the selection of the same edges among related graphs. We apply our method to simulated task-based fMRI data, where we show how our approach allows the decoupling of the task-related activations and the functional connectivity states. We also analyze data from an fMRI sensorimotor task experiment on an individual healthy subject and obtain results that support the role of particular anatomical regions in modulating interaction between executive control and attention networks.

Dynamic spatial processing originates in early visual pathways.

A variety of studies in the visual system demonstrate that coarse spatial features are processed before those of fine detail. This aspect of visual processing is assumed to originate in striate cortex, where single cells exhibit a refinement of spatial frequency tuning over the duration of their response. However, in early visual pathways, well known temporal differences are present between center and surround components of receptive fields. Specifically, response latency of the receptive field center is relatively shorter than that of the surround. This spatiotemporal inseparability could provide the basis of coarse-to-fine dynamics in early and subsequent visual areas. We have investigated this possibility with three separate approaches. First, we predict spatial-frequency tuning dynamics from the spatiotemporal receptive fields of 118 cells in the lateral geniculate nucleus (LGN). Second, we compare these linear predictions to measurements of tuning dynamics obtained with a subspace reverse correlation technique. We find that tuning evolves dramatically in thalamic cells, and that tuning changes are generally consistent with the temporal differences between spatiotemporal receptive field components. Third, we use a model to examine how different sources of dynamic input from early visual pathways can affect tuning in cortical cells. We identify two mechanisms capable of producing substantial dynamics at the cortical level: (1) the center-surround delay in individual LGN neurons, and (2) convergent input from multiple cells with different receptive field sizes and response latencies. Overall, our simulations suggest that coarse-to-fine tuning in the visual cortex can be generated completely by a feedforward process.

Presupplementary Motor Area Contributes to Altered Error Monitoring in Obsessive-Compulsive Disorder.

BACKGROUND: Hyperactive performance monitoring, as measured by the error-related negativity (ERN) in the event-related potential, is a reliable finding in obsessive-compulsive disorder (OCD) research and may be an endophenotype of the disorder. Imaging studies revealed inconsistent results as to which brain regions are involved in altered performance monitoring in OCD. We investigated performance monitoring in OCD with simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) signals to determine the neural source of the enhanced ERN. METHODS: Concurrent EEG and fMRI data were collected from 20 patients with OCD and 22 healthy control subjects during a flanker task. Independent component analysis was used separately on EEG and fMRI to segment the data functionally and focus on processes of interest. The ERN, hemodynamic responses following errors, and intraindividual correlation of the ERN and blood oxygen level-dependent activity were compared between groups. RESULTS: Patients with OCD showed significantly increased ERN amplitudes. Blood oxygen level-dependent activity in midcingulate cortex was not significantly different between groups. Increased activation of the right amygdala and the subgenual anterior cingulate cortex following errors was observed in patients with OCD. Increased intraindividual correlation of the ERN and activity of the presupplementary motor area was found in patients with OCD compared with healthy controls. CONCLUSIONS: Higher error-related activity was found in the amygdala and subgenual anterior cingulate cortex, suggesting a stronger affective response toward errors in patients with OCD. Additionally, increased correlation of the ERN and presupplementary motor area may indicate stronger recruitment of proactive control in OCD.

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.

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.

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.

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.