Task Dependence, Tissue Specificity, and Spatial Distribution of Widespread Activations in Large Single-Subject Functional MRI Datasets at 7T

Whole-brain, gray, and white matter time-locked functional signal changes with simple tasks and model-free analysis

Recent studies have revealed the production of time-locked blood oxygenation level-dependent (BOLD) functional MRI (fMRI) signals throughout the entire brain in response to tasks, challenging the existence of sparse and localized brain functions and highlighting the pervasiveness of potential false negative fMRI findings. "Whole-brain" actually refers to gray matter, the only tissue traditionally studied with fMRI. However, several reports have demonstrated reliable detection of BOLD signals in white matter, which have previously been largely ignored. Using simple tasks and analyses, we demonstrate BOLD signal changes across the whole brain, in both white and gray matters, in similar manner to previous reports of whole brain studies. We investigated whether white matter displays time-locked BOLD signals across multiple structural pathways in response to a stimulus in a similar manner to the cortex. We find that both white and gray matter show time-locked activations across the whole brain, with a majority of both tissue types showing statistically significant signal changes for all task stimuli investigated. We observed a wide range of signal responses to tasks, with different regions showing different BOLD signal changes to the same task. Moreover, we find that each region may display different BOLD responses to different stimuli. Overall, we present compelling evidence that, just like all gray matter, essentially all white matter in the brain shows time-locked BOLD signal changes in response to multiple stimuli, challenging the idea of sparse functional localization and the prevailing wisdom of treating white matter BOLD signals as artifacts to be removed.

Highlight results, don't hide them: Enhance interpretation, reduce biases and improve reproducibility

Most neuroimaging studies display results that represent only a tiny fraction of the collected data. While it is conventional to present "only the significant results" to the reader, here we suggest that this practice has several negative consequences for both reproducibility and understanding. This practice hides away most of the results of the dataset and leads to problems of selection bias and irreproducibility, both of which have been recognized as major issues in neuroimaging studies recently. Opaque, all-or-nothing thresholding, even if well-intentioned, places undue influence on arbitrary filter values, hinders clear communication of scientific results, wastes data, is antithetical to good scientific practice, and leads to conceptual inconsistencies. It is also inconsistent with the properties of the acquired data and the underlying biology being studied. Instead of presenting only a few statistically significant locations and hiding away the remaining results, studies should "highlight" the former while also showing as much as possible of the rest. This is distinct from but complementary to utilizing data sharing repositories: the initial presentation of results has an enormous impact on the interpretation of a study. We present practical examples and extensions of this approach for voxelwise, regionwise and cross-study analyses using publicly available data that was analyzed previously by 70 teams (NARPS; Botvinik-Nezer, et al., 2020), showing that it is possible to balance the goals of displaying a full set of results with providing the reader reasonably concise and "digestible" findings. In particular, the highlighting approach sheds useful light on the kind of variability present among the NARPS teams' results, which is primarily a varied strength of agreement rather than disagreement. Using a meta-analysis built on the informative "highlighting" approach shows this relative agreement, while one using the standard "hiding" approach does not. We describe how this simple but powerful change in practice-focusing on highlighting results, rather than hiding all but the strongest ones-can help address many large concerns within the field, or at least to provide more complete information about them. We include a list of practical suggestions for results reporting to improve reproducibility, cross-study comparisons and meta-analyses.

Improving the study of brain-behavior relationships by revisiting basic assumptions

Neuroimaging research has been at the forefront of concerns regarding the failure of experimental findings to replicate. In the study of brain-behavior relationships, past failures to find replicable and robust effects have been attributed to methodological shortcomings. Methodological rigor is important, but there are other overlooked possibilities: most published studies share three foundational assumptions, often implicitly, that may be faulty. In this paper, we consider the empirical evidence from human brain imaging and the study of non-human animals that calls each foundational assumption into question. We then consider the opportunities for a robust science of brain-behavior relationships that await if scientists ground their research efforts in revised assumptions supported by current empirical evidence.

Stability of sensorimotor network sculpts the dynamic repertoire of resting state over lifespan

Temporally stable patterns of neural coordination among distributed brain regions are crucial for survival. Recently, many studies highlight association between healthy aging and modifications in organization of functional brain networks, across various time-scales. Nonetheless, quantitative characterization of temporal stability of functional brain networks across healthy aging remains unexplored. This study introduces a data-driven unsupervised approach to capture high-dimensional dynamic functional connectivity (dFC) via low-dimensional patterns and subsequent estimation of temporal stability using quantitative metrics. Healthy aging related changes in temporal stability of dFC were characterized across resting-state, movie-viewing, and sensorimotor tasks (SMT) on a large (n = 645) healthy aging dataset (18-88 years). Prominent results reveal that (1) whole-brain temporal dynamics of dFC movie-watching task is closer to resting-state than to SMT with an overall trend of highest temporal stability observed during SMT followed by movie-watching and resting-state, invariant across lifespan aging, (2) in both tasks conditions stability of neurocognitive networks in young adults is higher than older adults, and (3) temporal stability of whole brain resting-state follows a U-shaped curve along lifespan-a pattern shared by sensorimotor network stability indicating their deeper relationship. Overall, the results can be applied generally for studying cohorts of neurological disorders using neuroimaging tools.

Allostasis as a core feature of hierarchical gradients in the human brain

This paper integrates emerging evidence from two broad streams of scientific literature into one common framework: (a) hierarchical gradients of functional connectivity that reflect the brain's large-scale structural architecture (e.g., a lamination gradient in the cerebral cortex); and (b) approaches to predictive processing and one of its specific instantiations called allostasis (i.e., the predictive regulation of energetic resources in the service of coordinating the body's internal systems). This synthesis begins to sketch a coherent, neurobiologically inspired framework suggesting that predictive energy regulation is at the core of human brain function, and by extension, psychological and behavioral phenomena, providing a shared vocabulary for theory building and knowledge accumulation.

Temporal stability of the hemodynamic response function across the majority of human cerebral cortex

The hemodynamic response function (HRF) measured with functional magnetic resonance imaging is generated by vascular and metabolic responses evoked by brief (<4 s) stimuli. It is known that the human HRF varies across cortex, between subjects, with stimulus paradigms, and even between different measurements in the same cortical location. However, our results demonstrate that strong HRFs are remarkably repeatable across sessions separated by time intervals up to 3 months. In this study, a multisensory stimulus was used to activate and measure the HRF across the majority of cortex (>70%, with lesser reliability observed in some areas of prefrontal cortex). HRFs were measured with high spatial resolution (2‐mm voxels) in central gray matter to minimize variations caused by partial‐volume effects. HRF amplitudes and temporal dynamics were highly repeatable across four sessions in 20 subjects. Positive and negative HRFs were consistently observed across sessions and subjects. Negative HRFs were generally weaker and, thus, more variable than positive HRFs. Statistical measurements showed that across‐session variability is highly correlated to the variability across events within a session; these measurements also indicated a normal distribution of variability across cortex. The overall repeatability of the HRFs over long time scales generally supports the long‐term use of event‐related functional magnetic resonance imaging protocols.

The OpenNeuro resource for sharing of neuroscience data

The sharing of research data is essential to ensure reproducibility and maximize the impact of public investments in scientific research. Here, we describe OpenNeuro, a BRAIN Initiative data archive that provides the ability to openly share data from a broad range of brain imaging data types following the FAIR principles for data sharing. We highlight the importance of the Brain Imaging Data Structure standard for enabling effective curation, sharing, and reuse of data. The archive presently shares more than 600 datasets including data from more than 20,000 participants, comprising multiple species and measurement modalities and a broad range of phenotypes. The impact of the shared data is evident in a growing number of published reuses, currently totalling more than 150 publications. We conclude by describing plans for future development and integration with other ongoing open science efforts.

Imaging faster neural dynamics with fast fMRI: A need for updated models of the hemodynamic response

Fast fMRI enables the detection of neural dynamics over timescales of hundreds of milliseconds, suggesting it may provide a new avenue for studying subsecond neural processes in the human brain. The magnitudes of these fast fMRI dynamics are far greater than predicted by canonical models of the hemodynamic response. Several studies have established nonlinear properties of the hemodynamic response that have significant implications for fast fMRI. We first review nonlinear properties of the hemodynamic response function that may underlie fast fMRI signals. We then illustrate the breakdown of canonical hemodynamic response models in the context of fast neural dynamics. We will then argue that the canonical hemodynamic response function is not likely to reflect the BOLD response to neuronal activity driven by sparse or naturalistic stimuli or perhaps to spontaneous neuronal fluctuations in the resting state. These properties suggest that fast fMRI is capable of tracking surprisingly fast neuronal dynamics, and we discuss the neuroscientific questions that could be addressed using this approach.

Individualized cognitive neuroscience needs 7T: Comparing numerosity maps at 3T and 7T MRI

The field of cognitive neuroscience is weighing evidence about whether to move from the current standard field strength of 3 Tesla (3T) to ultra-high field (UHF) of 7T and above. The present study contributes to the evidence by comparing a computational cognitive neuroscience paradigm at 3T and 7T. The goal was to evaluate the practical effects, i.e. model predictive power, of field strength on a numerosity task using accessible pre-processing and analysis tools. Previously, using 7T functional magnetic resonance imaging and biologically-inspired analyses, i.e. population receptive field modelling, we discovered topographical organization of numerosity-selective neural populations in human parietal cortex. Here we show that these topographic maps are also detectable at 3T. However, averaging of many more functional runs was required at 3T to reliably reconstruct numerosity maps. On average, one 7T run had about four times the model predictive power of one 3T run. We believe that this amount of scanning would have made the initial discovery of the numerosity maps on 3T highly infeasible in practice. Therefore, we suggest that the higher signal-to-noise ratio and signal sensitivity of UHF MRI is necessary to build mechanistic models of the organization and function of our cognitive abilities in individual participants.

Quantitative relations between BOLD responses, cortical energetics and impulse firing across cortical depth

The blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signal arises as a consequence of changes in cerebral blood flow (CBF) and cerebral metabolic rate of oxygen ( CMR O 2 ) that in turn are modulated by changes in neural activity. Recent advances in imaging have achieved sub-millimetre resolution and allowed investigation of the BOLD response as a function of cortical depth. Here, we adapt our previous theory relating the BOLD signal to neural activity to produce a quantitative model that incorporates venous blood draining between cortical layers. The adjustable inputs to the model are the neural activity and a parameter governing this blood draining. A three-layer version for transient neural inputs and a multi-layer version for constant or tonic neural inputs are able to account for a variety of experimental results, including negative BOLD signals.

High-resolution fMRI at 7 Tesla: challenges, promises and recent developments for individual-focused fMRI studies

Limited detection power has been a bottleneck for subject-specific functional MRI (fMRI) studies, however the higher signal-to-noise ratio afforded by ultra-high magnetic fields (≥ 7 Tesla) provides levels of sensitivity and resolution needed to study individual subjects. What may be surprising is that higher imaging resolution may provide both higher specificity and sensitivity due to reductions in partial volume effects and reduced physiological noise. However, challenges remain to ensure high data quality and to reduce variability in ultra-high field fMRI. We discuss session-specific biases including those caused by factors related to instrumentation, anatomy, and physiology-which can translate into variability across sessions-and how to minimize these to help ultra-high field fMRI reach its full potential for individual-focused studies.

A Complex Systems Perspective on Neuroimaging Studies of Behavior and Its Disorders

The study of complex systems deals with emergent behavior that arises as a result of nonlinear spatiotemporal interactions between a large number of components both within the system, as well as between the system and its environment. There is a strong case to be made that neural systems as well as their emergent behavior and disorders can be studied within the framework of complexity science. In particular, the field of neuroimaging has begun to apply both theoretical and experimental procedures originating in complexity science—usually in parallel with traditional methodologies. Here, we illustrate the basic properties that characterize complex systems and evaluate how they relate to what we have learned about brain structure and function from neuroimaging experiments. We then argue in favor of adopting a complex systems-based methodology in the study of neuroimaging, alongside appropriate experimental paradigms, and with minimal influences from noncomplex system approaches. Our exposition includes a review of the fundamental mathematical concepts, combined with practical examples and a compilation of results from the literature.

Anxiety and the Neurobiology of Temporally Uncertain Threat Anticipation

When extreme, anxiety-a state of distress and arousal prototypically evoked by uncertain danger-can be debilitating. Uncertain anticipation is a shared feature of situations that elicit signs and symptoms of anxiety across psychiatric disorders, species, and assays. Despite the profound significance of anxiety for human health and wellbeing, the neurobiology of uncertain-threat anticipation remains unsettled. Leveraging a paradigm adapted from animal research and optimized for fMRI signal decomposition, we examined the neural circuits engaged during the anticipation of temporally uncertain and certain threat in 99 men and women. Results revealed that the neural systems recruited by uncertain and certain threat anticipation are anatomically colocalized in frontocortical regions, extended amygdala, and periaqueductal gray. Comparison of the threat conditions demonstrated that this circuitry can be fractionated, with frontocortical regions showing relatively stronger engagement during the anticipation of uncertain threat, and the extended amygdala showing the reverse pattern. Although there is widespread agreement that the bed nucleus of the stria terminalis and dorsal amygdala-the two major subdivisions of the extended amygdala-play a critical role in orchestrating adaptive responses to potential danger, their precise contributions to human anxiety have remained contentious. Follow-up analyses demonstrated that these regions show statistically indistinguishable responses to temporally uncertain and certain threat anticipation. These observations provide a framework for conceptualizing anxiety and fear, for understanding the functional neuroanatomy of threat anticipation in humans, and for accelerating the development of more effective intervention strategies for pathological anxiety.SIGNIFICANCE STATEMENT Anxiety-an emotion prototypically associated with the anticipation of uncertain harm-has profound significance for public health, yet the underlying neurobiology remains unclear. Leveraging a novel neuroimaging paradigm in a relatively large sample, we identify a core circuit responsive to both uncertain and certain threat anticipation, and show that this circuitry can be fractionated into subdivisions with a bias for one kind of threat or the other. The extended amygdala occupies center stage in neuropsychiatric models of anxiety, but its functional architecture has remained contentious. Here we demonstrate that its major subdivisions show statistically indistinguishable responses to temporally uncertain and certain threat. Collectively, these observations indicate the need to revise how we think about the neurobiology of anxiety and fear.

Quantitative relations between transient BOLD responses, cortical energetics, and impulse firing in different cortical regions

The blood oxygen level-dependent (BOLD) functional magnetic resonance imaging signal arises as a consequence of changes in blood flow (cerebral blood flow) and oxygen usage (cerebral metabolic rate of oxygen) that in turn are modulated by changes in neuronal activity. Much attention has been given to both theoretical and experimental aspects of the energetics but not to the neuronal activity. Here we use our previous theory relating the steady-state BOLD signal to neuronal activity and amalgamate it with the standard dynamic causal model (DCM, Friston) theory to produce a quantitative model relating the time-dependent BOLD signal to the underlying neuronal activity. Unlike existing treatments, this new theory incorporates a nonzero baseline activity in a completely consistent way and is thus able to account for both positive and negative BOLD signals. It can reproduce a wide variety of experimental BOLD signals reported in the literature solely by adjusting the neuronal input activity. In this way it provides support for the claim that the main features of the signals, including poststimulus undershoot and overshoot, are principally a result of changes in neuronal activity.NEW & NOTEWORTHY A previous model relating the steady-state blood oxygen level-dependent (BOLD) signal to neuronal activity, both above and below baseline, is extended to account for transient BOLD signals. This allows for a detailed investigation of the role neuronal activity can play in such signals and also encompasses poststimulus undershoot and overshoot. A wide variety of experimental BOLD signals are reproduced solely by adjusting the neuronal input activity, including recent results regarding the BOLD signal in patients with schizophrenia.

Scale-invariant rearrangement of resting state networks in the human brain under sustained stimulation

Brain activity at rest is characterized by widely distributed and spatially specific patterns of synchronized low-frequency blood-oxygenation level-dependent (BOLD) fluctuations, which correspond to physiologically relevant brain networks. This network behaviour is known to persist also during task execution, yet the details underlying task-associated modulations of within- and between-network connectivity are largely unknown. In this study we exploited a multi-parametric and multi-scale approach to investigate how low-frequency fluctuations adapt to a sustained n-back working memory task. We found that the transition from the resting state to the task state involves a behaviourally relevant and scale-invariant modulation of synchronization patterns within both task-positive and default mode networks. Specifically, decreases of connectivity within networks are accompanied by increases of connectivity between networks. In spite of large and widespread changes of connectivity strength, the overall topology of brain networks is remarkably preserved. We show that these findings are strongly influenced by connectivity at rest, suggesting that the absolute change of connectivity (i.e., disregarding the baseline) may not be the most suitable metric to study dynamic modulations of functional connectivity. Our results indicate that a task can evoke scale-invariant, distributed changes of BOLD fluctuations, further confirming that low frequency BOLD oscillations show a specialized response and are tightly bound to task-evoked activation.

Characterization of the hemodynamic response function across the majority of human cerebral cortex

A brief (<4 s) period of neural activation evokes a stereotypical sequence of vascular and metabolic events to create the hemodynamic response function (HRF) measured using functional magnetic resonance imaging (fMRI). Linear analysis of fMRI data requires that the HRF be treated as an impulse response, so the character and temporal stability of the HRF are critical issues. Here, a simple audiovisual stimulus combined with a fast-paced task was used to evoke a strong HRF across a majority, ∼77%, of cortex during a single scanning session. High spatiotemporal resolution (2-mm voxels, 1.25-s acquisition time) was used to focus HRF measurements specifically on the gray matter for whole brain. The majority of activated cortex responds with positive HRFs, while ∼27% responds with negative (inverted) HRFs. Spatial patterns of the HRF response amplitudes were found to be similar across subjects. Timing of the initial positive lobe of the HRF was relatively stable across the cortical surface with a mean of 6.1 ± 0.6 s across subjects, yet small but significant timing variations were also evident in specific regions of cortex. The results provide guidance for linear analysis of fMRI data. More importantly, this method provides a means to quantify neurovascular function across most of the brain, with potential clinical utility for the diagnosis of brain pathologies such as traumatic brain injury.

Mapping and characterization of positive and negative BOLD responses to visual stimulation in multiple brain regions at 7T

External stimuli and tasks often elicit negative BOLD responses in various brain regions, and growing experimental evidence supports that these phenomena are functionally meaningful. In this work, the high sensitivity available at 7T was explored to map and characterize both positive (PBRs) and negative BOLD responses (NBRs) to visual checkerboard stimulation, occurring in various brain regions within and beyond the visual cortex. Recently-proposed accelerated fMRI techniques were employed for data acquisition, and procedures for exclusion of large draining vein contributions, together with ICA-assisted denoising, were included in the analysis to improve response estimation. Besides the visual cortex, significant PBRs were found in the lateral geniculate nucleus and superior colliculus, as well as the pre-central sulcus; in these regions, response durations increased monotonically with stimulus duration, in tight covariation with the visual PBR duration. Significant NBRs were found in the visual cortex, auditory cortex, default-mode network (DMN) and superior parietal lobule; NBR durations also tended to increase with stimulus duration, but were significantly less sustained than the visual PBR, especially for the DMN and superior parietal lobule. Responses in visual and auditory cortex were further studied for checkerboard contrast dependence, and their amplitudes were found to increase monotonically with contrast, linearly correlated with the visual PBR amplitude. Overall, these findings suggest the presence of dynamic neuronal interactions across multiple brain regions, sensitive to stimulus intensity and duration, and demonstrate the richness of information obtainable when jointly mapping positive and negative BOLD responses at a whole-brain scale, with ultra-high field fMRI.

Task-based dynamic functional connectivity: Recent findings and open questions

The temporal evolution of functional connectivity (FC) within the confines of individual scans is nowadays often explored with functional neuroimaging. This is particularly true for resting-state; yet, FC-dynamics have also been investigated as subjects engage on numerous tasks. It is these research efforts that constitute the core of this survey. First, empirical observations on how FC differs between task and rest-independent of temporal scale-are reviewed, as they underscore how, despite overall preservation of network topography, the brain's FC does reconfigure in systematic ways to accommodate task demands. Next, reports on the relationships between instantaneous FC and perception/performance in subsequent trials are discussed. Similarly, research where different aspects of task-concurrent FC-dynamics are explored or utilized to predict ongoing mental states are also examined. The manuscript finishes with an incomplete list of challenges that hopefully fuels future work in this vibrant area of neuroscientific research. Overall, this review concludes that task-concurrent FC-dynamics, when properly characterized, are relevant to behavior, and that their translational value holds considerable promise.

A study of the electro-haemodynamic coupling using simultaneously acquired intracranial EEG and fMRI data in humans

In current fMRI studies designed to map BOLD changes related to interictal epileptiform discharges (IED), which are recorded on simultaneous EEG, the information contained in the morphology and field extent of the EEG events is exclusively used for their classification. Usually, a BOLD predictor based on IED onset times alone is constructed, effectively treating all events as identical. We used intracranial EEG (icEEG)-fMRI data simultaneously recorded in humans to investigate the effect of including any of the features: amplitude, width (duration), slope of the rising phase, energy (area under the curve), or spatial field extent (number of contacts over which the sharp wave was observed) of the fast wave of the IED (the sharp wave), into the BOLD model, to better understand the neurophysiological origin of sharp wave-related BOLD changes, in the immediate vicinity of the recording contacts. Among the features considered, the width was the only one found to explain a significant amount of additional variance, suggesting that the amplitude of the BOLD signal depends more on the duration of the underlying field potential (reflected in the sharp wave width) than on the degree of neuronal activity synchrony (reflected in the sharp wave amplitude), and, consequently, that including inter-event variations of the sharp wave width in the BOLD signal model may increase the sensitivity of forthcoming EEG-fMRI studies of epileptic activity.

Variance decomposition for single-subject task-based fMRI activity estimates across many sessions

Here we report an exploratory within-subject variance decomposition analysis conducted on a task-based fMRI dataset with an unusually large number of repeated measures (i.e., 500 trials in each of three different subjects) distributed across 100 functional scans and 9 to 10 different sessions. Within-subject variance was segregated into four primary components: variance across-sessions, variance across-runs within a session, variance across-blocks within a run, and residual measurement/modeling error. Our results reveal inhomogeneous and distinct spatial distributions of these variance components across significantly active voxels in grey matter. Measurement error is dominant across the whole brain. Detailed evaluation of the remaining three components shows that across-session variance is the second largest contributor to total variance in occipital cortex, while across-runs variance is the second dominant source for the rest of the brain. Network-specific analysis revealed that across-block variance contributes more to total variance in higher-order cognitive networks than in somatosensory cortex. Moreover, in some higher-order cognitive networks across-block variance can exceed across-session variance. These results help us better understand the temporal (i.e., across blocks, runs and sessions) and spatial distributions (i.e., across different networks) of within-subject natural variability in estimates of task responses in fMRI. They also suggest that different brain regions will show different natural levels of test-retest reliability even in the absence of residual artifacts and sufficiently high contrast-to-noise measurements. Further confirmation with a larger sample of subjects and other tasks is necessary to ensure generality of these results.

What Cascade Spreading Models Can Teach Us about the Brain

The precise relationship between functional and structural connectivity in the brain is not well understood. Research in this area has, so far, mostly remained descriptive. In this issue of Neuron, Mišić et al. (2015) forge a promising new direction by modeling the propagation of information as it relates to spatially constrained network properties. From these preliminary results a glimmer of hope in uncovering deep principles of brain organization begins to emerge.