Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity

Altered task modulation of global signal topography in the default-mode network of unmedicated major depressive disorder

BACKGROUND

Altered global signal (GS) topography features in the resting-state fMRI of major depressive disorder (MDD), showing abnormally strong global signal representation in the default-mode network (DMN). Whether the abnormal local to global change also shapes activity during task states, and how it relates to psychopathological symptoms, e.g., abnormally slow time speed of motor, cognitive, and affective symptoms, remains unknown.

METHODS

We investigated fMRI-based GS with its topographical representation during task states in unmedicated 51 MDD subjects and 28 healthy subjects. Task-related global signal correlation (GSCORR) was probed by a novel paradigm testing the processing of negative/neutral emotions during different time speeds, i.e., slow and fast.

RESULTS

We observed a significant interaction between time speed and emotion of GSCORR in various DMN regions in healthy subjects. Next, we showed that MDD exhibits reduced task-related GSCORR in various DMN regions during specifically the fast processing of negative emotions. Finally, we demonstrated that GSCORR in DMN and other brain regions (motor-related regions, inferior frontal cortex) correlated with the degree of psychomotor retardation especially during the fast emotional stimuli.

LIMITATIONS

The measurement of interoceptive variables like respiration rate or heart rate were not included in our fMRI acquisition.

CONCLUSION

Together, we demonstrated the functional relevance of GS topography by showing reduced GSCORR in DMN during specifically the fast processing of negative emotions in MDD, suggesting the abnormal slowness, i.e., reduced time speed, to be a key feature of both brain and symptoms in MDD.

State-related neural influences on fMRI connectivity estimation

The spatiotemporal structure of functional magnetic resonance imaging (fMRI) signals has provided a valuable window into the network underpinnings of human brain function and dysfunction. Although some cross-regional temporal correlation patterns (functional connectivity; FC) exhibit a high degree of stability across individuals and species, there is growing acknowledgment that measures of FC can exhibit marked changes over a range of temporal scales. Further, FC can covary with experimental task demands and ongoing neural processes linked to arousal, consciousness and perception, cognitive and affective state, and brain-body interactions. The increased recognition that such interrelated neural processes modulate FC measurements has raised both challenges and new opportunities in using FC to investigate brain function. Here, we review recent advances in the quantification of neural effects that shape fMRI FC and discuss the broad implications of these findings in the design and analysis of fMRI studies. We also discuss how a more complete understanding of the neural factors that shape FC measurements can resolve apparent inconsistencies in the literature and lead to more interpretable conclusions from fMRI studies.

Time-of-Day Effects in Resting-State Functional Magnetic Resonance Imaging: Changes in Effective Connectivity and Blood Oxygenation Level Dependent Signal

Introduction:

In the light of the ongoing replication crisis in the field of neuroimaging, it is necessary to assess the possible exogenous and endogenous factors that may affect functional magnetic resonance imaging (fMRI). The current project investigated time-of-day effects in the spontaneous fluctuations (<0.1 Hz) of the blood oxygenation level dependent (BOLD) signal.

Method:

Using data from the human connectome project release S1200, cross-spectral density dynamic causal modeling (DCM) was used to analyze time-dependent effects on the hemodynamic response and effective connectivity parameters. The DCM analysis covered three networks, namely the default mode network, the central executive network, and the saliency network. Hierarchical group-parametric empirical Bayes (PEB) was used to test varying design-matrices against the time-of-day model.

Results:

Hierarchical group-PEB found no support for changes in effective connectivity, whereas the hemodynamic parameters exhibited a significant time-of-day dependent effect, indicating a diurnal vascular effect that might affect the measured BOLD signal in the absence of any diurnal variations of the underlying neuronal activations and effective connectivity.

Conclusion:

We conclude that these findings urge the need to account for the time of data acquisition in future MRI studies and suggest that time-of-day dependent metabolic variations contribute to reduced reliability in resting-state fMRI studies.

Impact statement

The results from this study suggest that the circadian mechanism influences the blood oxygenation level dependent signal in resting-state functional magnetic resonance imaging (fMRI). The current study urges to record and report the time of fMRI scan acquisition in future research, as it may increase the replicability of findings. Both exploratory and clinical studies would benefit by incorporating this small change in fMRI protocol, which to date has been often overlooked.

Imaging the temporal dynamics of brain states with highly sampled fMRI

The spontaneous dynamics of the brain modulate its function from moment to moment, shaping neural computation and cognition. Functional MRI (fMRI), while classically used as a tool for spatial localization, is increasingly being used to identify the temporal dynamics of brain activity. fMRI analyses focused on the temporal domain have revealed important new information about the dynamics underlying states such as arousal, attention, and sleep. Dense temporal sampling – either by using fast fMRI acquisition, or multiple repeated scan sessions within individuals – can further enrich the information present in these studies. This review focuses on recent developments in using fMRI to identify dynamics across brain states, particularly vigilance and sleep states, and the potential for highly temporally sampled fMRI to answer these questions.

Time in Brain: How Biological Rhythms Impact on EEG Signals and on EEG-Derived Brain Networks

Electroencephalography (EEG) is a widely employed tool for exploring brain dynamics and is used extensively in various domains, ranging from clinical diagnosis via neuroscience, cognitive science, cognitive psychology, psychophysiology, neuromarketing, neurolinguistics, and pharmacology to research on brain computer interfaces. EEG is the only technique that enables the continuous recording of brain dynamics over periods of time that range from a few seconds to hours and days and beyond. When taking long-term recordings, various endogenous and exogenous biological rhythms may impinge on characteristics of EEG signals. While the impact of the circadian rhythm and of ultradian rhythms on spectral characteristics of EEG signals has been investigated for more than half a century, only little is known on how biological rhythms influence characteristics of brain dynamics assessed with modern EEG analysis techniques. At the example of multiday, multichannel non-invasive and invasive EEG recordings, we here discuss the impact of biological rhythms on temporal changes of various characteristics of human brain dynamics: higher-order statistical moments and interaction properties of multichannel EEG signals as well as local and global characteristics of EEG-derived evolving functional brain networks. Our findings emphasize the need to take into account the impact of biological rhythms in order to avoid erroneous statements about brain dynamics and about evolving functional brain networks.

Protocol for magnetic resonance imaging acquisition, quality assurance, and quality check for the Accelerator program for Discovery in Brain disorders using Stem cells

OBJECTIVE

The Accelerator program for Discovery in Brain disorders using Stem cells (ADBS) is a longitudinal study on five cohorts of patients with major psychiatric disorders from genetically high-risk families, their unaffected first-degree relatives, and healthy subjects. We describe the ADBS protocols for acquisition, quality assurance (QA), and quality check (QC) for multimodal magnetic resonance brain imaging studies.

METHODS

We describe the acquisition and QC protocols for structural, functional, and diffusion images. For QA, we acquire proton density and functional images on phantoms, along with repeated scans on human volunteer. We describe the analysis of phantom data and test-retest reliability of volumetric and diffusion measures.

RESULTS

Analysis of acquired phantom data shows linearity of proton density signal with increasing proton fraction, and an overall stability of various spatial and temporal QA measures. Examination of dice coefficient and statistical analyses of coefficient of variation in test-retest data on the human volunteer showed consistency of volumetric and diffusivity measures at whole-brain, regional, and voxel-level.

CONCLUSION

The described acquisition and QA-QC procedures can yield consistent and reliable quantitative measures. It is expected that this longitudinal neuroimaging dataset will, upon its release, serve the scientific community well and pave the way for interesting discoveries.

Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior

Resting-state functional magnetic resonance imaging (rs-fMRI) allows estimation of individual-specific cortical parcellations. We have previously developed a multi-session hierarchical Bayesian model (MS-HBM) for estimating high-quality individual-specific network-level parcellations. Here, we extend the model to estimate individual-specific areal-level parcellations. While network-level parcellations comprise spatially distributed networks spanning the cortex, the consensus is that areal-level parcels should be spatially localized, that is, should not span multiple lobes. There is disagreement about whether areal-level parcels should be strictly contiguous or comprise multiple noncontiguous components; therefore, we considered three areal-level MS-HBM variants spanning these range of possibilities. Individual-specific MS-HBM parcellations estimated using 10 min of data generalized better than other approaches using 150 min of data to out-of-sample rs-fMRI and task-fMRI from the same individuals. Resting-state functional connectivity derived from MS-HBM parcellations also achieved the best behavioral prediction performance. Among the three MS-HBM variants, the strictly contiguous MS-HBM exhibited the best resting-state homogeneity and most uniform within-parcel task activation. In terms of behavioral prediction, the gradient-infused MS-HBM was numerically the best, but differences among MS-HBM variants were not statistically significant. Overall, these results suggest that areal-level MS-HBMs can capture behaviorally meaningful individual-specific parcellation features beyond group-level parcellations. Multi-resolution trained models and parcellations are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Kong2022_ArealMSHBM).

What have we really learned from functional connectivity in clinical populations?

Functional connectivity (FC), or the statistical interdependence of blood-oxygen dependent level (BOLD) signals between brain regions using fMRI, has emerged as a widely used tool for probing functional abnormalities in clinical populations due to the promise of the approach across conceptual, technical, and practical levels. With an already vast and steadily accumulating neuroimaging literature on neurodevelopmental, psychiatric, and neurological diseases and disorders in which FC is a primary measure, we aim here to provide a high-level synthesis of major concepts that have arisen from FC findings in a manner that cuts across different clinical conditions and sheds light on overarching principles. We highlight that FC has allowed us to discover the ubiquity of intrinsic functional networks across virtually all brains and clarify typical patterns of neurodevelopment over the lifespan. This understanding of typical FC maturation with age has provided important benchmarks against which to evaluate divergent maturation in early life and degeneration in late life. This in turn has led to the important insight that many clinical conditions are associated with complex, distributed, network-level changes in the brain, as opposed to solely focal abnormalities. We further emphasize the important role that FC studies have played in supporting a dimensional approach to studying transdiagnostic clinical symptoms and in enhancing the multimodal characterization and prediction of the trajectory of symptom progression across conditions. We highlight the unprecedented opportunity offered by FC to probe functional abnormalities in clinical conditions where brain function could not be easily studied otherwise, such as in disorders of consciousness. Lastly, we suggest high priority areas for future research and acknowledge critical barriers associated with the use of FC methods, particularly those related to artifact removal, data denoising and feasibility in clinical contexts.

Physiological and motion signatures in static and time-varying functional connectivity and their subject identifiability

Human brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity.

Examining the effects of time of day and sleep on generalization

Extracting shared structure across our experiences allows us to generalize our knowledge to novel contexts. How do different brain states influence this ability to generalize? Using a novel category learning paradigm, we assess the effect of both sleep and time of day on generalization that depends on the flexible integration of recent information. Counter to our expectations, we found no evidence that this form of generalization is better after a night of sleep relative to a day awake. Instead, we observed an effect of time of day, with better generalization in the morning than the evening. This effect also manifested as increased false memory for generalized information. In a nap experiment, we found that generalization did not benefit from having slept recently, suggesting a role for time of day apart from sleep. In follow-up experiments, we were unable to replicate the time of day effect for reasons that may relate to changes in category structure and task engagement. Despite this lack of consistency, we found a morning benefit for generalization when analyzing all the data from experiments with matched protocols (n = 136). We suggest that a state of lowered inhibition in the morning may facilitate spreading activation between otherwise separate memories, promoting this form of generalization.

Smartphones and the Neuroscience of Mental Health

Improvements in understanding the neurobiological basis of mental illness have unfortunately not translated into major advances in treatment. At this point, it is clear that psychiatric disorders are exceedingly complex and that, in order to account for and leverage this complexity, we need to collect longitudinal data sets from much larger and more diverse samples than is practical using traditional methods. We discuss how smartphone-based research methods have the potential to dramatically advance our understanding of the neuroscience of mental health. This, we expect, will take the form of complementing lab-based hard neuroscience research with dense sampling of cognitive tests, clinical questionnaires, passive data from smartphone sensors, and experience-sampling data as people go about their daily lives. Theory- and data-driven approaches can help make sense of these rich data sets, and the combination of computational tools and the big data that smartphones make possible has great potential value for researchers wishing to understand how aspects of brain function give rise to, or emerge from, states of mental health and illness.

Global Signal Topography of the Human Brain: A Novel Framework of Functional Connectivity for Psychological and Pathological Investigations

The global signal (GS), which was once regarded as a nuisance of functional magnetic resonance imaging, has been proven to convey valuable neural information. This raised the following question: what is a GS represented in local brain regions? In order to answer this question, the GS topography was developed to measure the correlation between global and local signals. It was observed that the GS topography has an intrinsic structure characterized by higher GS correlation in sensory cortices and lower GS correlation in higher-order cortices. The GS topography could be modulated by individual factors, attention-demanding tasks, and conscious states. Furthermore, abnormal GS topography has been uncovered in patients with schizophrenia, major depressive disorder, bipolar disorder, and epilepsy. These findings provide a novel insight into understanding how the GS and local brain signals coactivate to organize information in the human brain under various brain states. Future directions were further discussed, including the local-global confusion embedded in the GS correlation, the integration of spatial information conveyed by the GS, and temporal information recruited by the connection analysis. Overall, a unified psychopathological framework is needed for understanding the GS topography.

Co-activation patterns across multiple tasks reveal robust anti-correlated functional networks

Whether antagonistic brain states constitute a fundamental principle of human brain organization has been debated over the past decade. Some argue that intrinsically anti-correlated brain networks in resting-state functional connectivity are an artifact of preprocessing. Others argue that anti-correlations are biologically meaningful predictors of how the brain will respond to different stimuli. Here, we investigated the co-activation patterns across the whole brain in various tasks and test whether brain regions demonstrate anti-correlated activity similar to those observed at rest. We examined brain activity in 47 task contrasts from the Human Connectome Project (N = 680) and found robust antagonistic interactions between networks. Regions of the default network exhibited the highest degree of cortex-wide negative connectivity. The negative co-activation patterns across tasks showed good correspondence to that derived from resting-state data processed with global signal regression (GSR). Interestingly, GSR-processed resting-state data was a significantly better predictor of task-induced modulation than data processed without GSR. Finally, in a cohort of 25 patients with depression, we found that task-based anti-correlations between the dorsolateral prefrontal cortex (DLPFC) and subgenual anterior cingulate cortex were associated with clinical efficacy of transcranial magnetic stimulation therapy targeting the DLPFC. Overall, our findings indicate that anti-correlations are a biologically meaningful phenomenon and may reflect an important principle of functional brain organization.

Temporal organisation of the brain's intrinsic motor network: The relationship with circadian phenotype and motor performance

Background

Functional connectivity (FC) of the motor network (MN) is often used to investigate how intrinsic properties of the brain are associated with motor abilities and performance. In addition, the MN is a key feature in clinical work to map the recovery after stroke and aid the understanding of neurodegenerative disorders. Time of day variation and individual differences in circadian timing, however, have not yet been considered collectively when looking at FC.

Methods

A total of 33 healthy, right handed individuals (13 male, 23.1 ± 4.2 years) took part in the study. Actigraphy, sleep diaries and circadian phase markers (dim light melatonin onset and cortisol awakening response) were used to determine early (ECP, n = 13) and late (LCP, n = 20) circadian phenotype groups. Resting state functional MRI testing sessions were conducted at 14:00 h, 20:00 h and 08:00 h and preceded by a maximum voluntary contraction test for isometric grip strength to measure motor performance.

Results

Significant differences in FC of the MN between ECPs and LCPs were found, as well as significant variations between different times of day. A higher amplitude in diurnal variation of FC and performance was observed in LCPs compared to ECPs, with the morning being most significantly affected. Overall, lower FC was significantly associated with poorer motor performance.

Discussion

Our findings uncover intrinsic differences between times of day and circadian phenotype groups. This suggests that central mechanisms contribute to diurnal variation in motor performance and the functional integrity of the MN at rest influences the ability to perform in a motor task.

A hitchhiker's guide to working with large, open-source neuroimaging datasets

Large datasets that enable researchers to perform investigations with unprecedented rigor are growing increasingly common in neuroimaging. Due to the simultaneous increasing popularity of open science, these state-of-the-art datasets are more accessible than ever to researchers around the world. While analysis of these samples has pushed the field forward, they pose a new set of challenges that might cause difficulties for novice users. Here we offer practical tips for working with large datasets from the end-user's perspective. We cover all aspects of the data lifecycle: from what to consider when downloading and storing the data to tips on how to become acquainted with a dataset one did not collect and what to share when communicating results. This manuscript serves as a practical guide one can use when working with large neuroimaging datasets, thus dissolving barriers to scientific discovery.

Organisational and neuromodulatory underpinnings of structural-functional connectivity decoupling in patients with Parkinson's disease

Parkinson's dementia is characterised by changes in perception and thought, and preceded by visual dysfunction, making this a useful surrogate for dementia risk. Structural and functional connectivity changes are seen in humans with Parkinson's disease, but the organisational principles are not known. We used resting-state fMRI and diffusion-weighted imaging to examine changes in structural-functional connectivity coupling in patients with Parkinson's disease, and those at risk of dementia. We identified two organisational gradients to structural-functional connectivity decoupling: anterior-to-posterior and unimodal-to-transmodal, with stronger structural-functional connectivity coupling in anterior, unimodal areas and weakened towards posterior, transmodal regions. Next, we related spatial patterns of decoupling to expression of neurotransmitter receptors. We found that dopaminergic and serotonergic transmission relates to decoupling in Parkinson's overall, but instead, serotonergic, cholinergic and noradrenergic transmission relates to decoupling in patients with visual dysfunction. Our findings provide a framework to explain the specific disorders of consciousness in Parkinson's dementia, and the neurotransmitter systems that underlie these.

Identifying Diurnal Variability of Brain Connectivity Patterns Using Graph Theory

Significant differences exist in human brain functions affected by time of day and by people’s diurnal preferences (chronotypes) that are rarely considered in brain studies. In the current study, using network neuroscience and resting-state functional MRI (rs-fMRI) data, we examined the effect of both time of day and the individual’s chronotype on whole-brain network organization. In this regard, 62 participants (39 women; mean age: 23.97 ± 3.26 years; half morning- versus half evening-type) were scanned about 1 and 10 h after wake-up time for morning and evening sessions, respectively. We found evidence for a time-of-day effect on connectivity profiles but not for the effect of chronotype. Compared with the morning session, we found relatively higher small-worldness (an index that represents more efficient network organization) in the evening session, which suggests the dominance of sleep inertia over the circadian and homeostatic processes in the first hours after waking. Furthermore, local graph measures were changed, predominantly across the left hemisphere, in areas such as the precentral gyrus, putamen, inferior frontal gyrus (orbital part), inferior temporal gyrus, as well as the bilateral cerebellum. These findings show the variability of the functional neural network architecture during the day and improve our understanding of the role of time of day in resting-state functional networks.

Post-learning micro- and macro-structural neuroplasticity changes with time and sleep

Neuroplasticity refers to the fact that our brain can partially modify both structure and function to adequately respond to novel environmental stimulations. Neuroplasticity mechanisms are not only operating during the acquisition of novel information (i.e., online) but also during the offline periods that take place after the end of the actual learning episode. Structural brain changes as a consequence of learning have been consistently demonstrated on the long term using non-invasive neuroimaging methods, but short-term changes remained more elusive. Fortunately, the swift development of advanced MR methods over the last decade now allows tracking fine-grained cerebral changes on short timescales beyond gross volumetric modifications stretching over several days or weeks. Besides a mere effect of time, post-learning sleep mechanisms have been shown to play an important role in memory consolidation and promote long-lasting changes in neural networks. Sleep was shown to contribute to structural modifications over weeks of prolonged training, but studies evidencing more rapid post-training sleep structural effects linked to memory consolidation are still scarce in human. On the other hand, animal studies convincingly show how sleep might modulate synaptic microstructure. We aim here at reviewing the literature establishing a link between different types of training/learning and the resulting structural changes, with an emphasis on the role of post-training sleep and time in tuning these modifications. Open questions are raised such as the role of post-learning sleep in macrostructural changes, the links between different MR structural measurement-related modifications and the underlying microstructural brain processes, and bidirectional influences between structural and functional brain changes.

Time is of the essence: Coupling sleep-wake and circadian neurobiology to the antidepressant effects of ketamine

Several studies have demonstrated the effectiveness of ketamine in rapidly alleviating depression and suicidal ideation. Intense research efforts have been undertaken to expose the precise mechanism underlying the antidepressant action of ketamine; however, the translation of findings into new clinical treatments has been slow. This translational gap is partially explained by a lack of understanding of the function of time and circadian timing in the complex neurobiology around ketamine. Indeed, the acute pharmacological effects of a single ketamine treatment last for only a few hours, whereas the antidepressant effects peak at around 24 hours and are sustained for the following few days. Numerous studies have investigated the acute and long-lasting neurobiological changes induced by ketamine; however, the most dramatic and fundamental change that the brain undergoes each day is rarely taken into consideration. Here, we explore the link between sleep and circadian regulation and rapid-acting antidepressant effects and summarize how diverse phenomena associated with ketamine's antidepressant actions - such as cortical excitation, synaptogenesis, and involved molecular determinants - are intimately connected with the neurobiology of wake, sleep, and circadian rhythms. We review several recently proposed hypotheses about rapid antidepressant actions, which focus on sleep or circadian regulation, and discuss their implications for ongoing research. Considering these aspects may be the last piece of the puzzle necessary to gain a more comprehensive understanding of the effects of rapid-acting antidepressants on the brain.

Cortical gradients of functional connectivity are robust to state-dependent changes following sleep deprivation

Sleep deprivation leads to significant impairments in cognitive performance and changes to the interactions between large scale cortical networks, yet the hierarchical organization of cortical activity across states is still being explored. We used functional magnetic resonance imaging to assess activations and connectivity during cognitive tasks in 20 healthy young adults, during three states: (i) following a normal night of sleep, (ii) following 24hr of total sleep deprivation, and (iii) after a morning recovery nap. Situating cortical activity during cognitive tasks along hierarchical organizing gradients based upon similarity of functional connectivity patterns, we found that regional variations in task-activations were captured by an axis differentiating areas involved in executive control from default mode regions and paralimbic cortex. After global signal regression, the range of functional differentiation along this axis at baseline was significantly related to decline in working memory performance (2-back task) following sleep deprivation, as well as the extent of recovery in performance following a nap. The relative positions of cortical regions within gradients did not significantly change across states, except for a lesser differentiation of the visual system and increased coupling of the posterior cingulate cortex with executive control areas after sleep deprivation. This was despite a widespread increase in the magnitude of functional connectivity across the cortex following sleep deprivation. Cortical gradients of functional differentiation thus appear relatively insensitive to state-dependent changes following sleep deprivation and recovery, suggesting that there are no large-scale changes in cortical functional organization across vigilance states. Certain features of particular gradient axes may be informative for the extent of decline in performance on more complex tasks following sleep deprivation, and could be beneficial over traditional voxel- or parcel-based approaches in identifying realtionships between state-dependent brain activity and behavior.

Task-related activity in human visual cortex

The brain exhibits widespread endogenous responses in the absence of visual stimuli, even at the earliest stages of visual cortical processing. Such responses have been studied in monkeys using optical imaging with a limited field of view over visual cortex. Here, we used functional MRI (fMRI) in human participants to study the link between arousal and endogenous responses in visual cortex. The response that we observed was tightly entrained to task timing, was spatially extensive, and was independent of visual stimulation. We found that this response follows dynamics similar to that of pupil size and heart rate, suggesting that task-related activity is related to arousal. Finally, we found that higher reward increased response amplitude while decreasing its trial-to-trial variability (i.e., the noise). Computational simulations suggest that increased temporal precision underlies both of these observations. Our findings are consistent with optical imaging studies in monkeys and support the notion that arousal increases precision of neural activity.

The brain exhibits widespread endogenous responses in the absence of visual stimuli, even at the earliest stages of visual cortical processing. This fMRI study characterizes a widespread hemodynamic response in early visual cortex that is not related to visual input but instead reflects a participant’s engagement in a task, is modulated by expected monetary reward, and may reflect neural quenching.

Bring the Noise: Reconceptualizing Spontaneous Neural Activity

Definitions of what constitutes the 'signal of interest' in neuroscience can be controversial, due in part to continuously evolving notions regarding the significance of spontaneous neural activity. This review highlights how the challenge of separating brain signal from noise has led to new conceptualizations of brain functional organization at both the micro- and macroscopic level. Recent debates in the functional neuroimaging community surrounding artifact removal processes have revived earlier discussions surrounding how to appropriately isolate and measure neuronal signals against a background of noise from various sources. Insights from electrophysiological studies and computational modeling can inform current theory and data analytic practices in human functional neuroimaging, given that signal and noise may be inextricably linked in the brain.

Rest-task modulation of fMRI-derived global signal topography is mediated by transient coactivation patterns

Recent resting-state functional MRI (fMRI) studies have revealed that the global signal (GS) exhibits a nonuniform spatial distribution across the gray matter. Whether this topography is informative remains largely unknown. We therefore tested rest-task modulation of GS topography by analyzing static GS correlation and dynamic coactivation patterns in a large sample of an fMRI dataset (n = 837) from the Human Connectome Project. The GS topography in the resting state and in seven different tasks was first measured by correlating the GS with the local time series (GSCORR). In the resting state, high GSCORR was observed mainly in the primary sensory and motor regions, whereas low GSCORR was seen in the association brain areas. This pattern changed during the seven tasks, with mainly decreased GSCORR in sensorimotor cortex. Importantly, this rest-task modulation of GSCORR could be traced to transient coactivation patterns at the peak period of GS (GS-peak). By comparing the topography of GSCORR and respiration effects, we observed that the topography of respiration mimicked the topography of GS in the resting state, whereas both differed during the task states; because of such partial dissociation, we assume that GSCORR could not be equated with a respiration effect. Finally, rest-task modulation of GS topography could not be exclusively explained by other sources of physiological noise. Together, we here demonstrate the informative nature of GS topography by showing its rest-task modulation, the underlying dynamic coactivation patterns, and its partial dissociation from respiration effects during task states.

Recent resting-state fMRI studies have shown that the global signal exhibits a nonuniform spatial distribution across gray matter, but is this informative? This neuroimaging study reveals novel insights into the informative nature of global signal by rest-task modulation of the global signal topography.