Finding the neural correlates of collaboration using a three-person fMRI hyperscanning paradigm.

Humans have an extraordinary ability to interact and cooperate with others. Despite the social and evolutionary significance of collaboration, research on finding its neural correlates has been limited partly due to restrictions on the simultaneous neuroimaging of more than one participant (also known as hyperscanning). Several studies have used dyadic fMRI hyperscanning to examine the interaction between two participants. However, to our knowledge, no study to date has aimed at revealing the neural correlates of social interactions using a three-person (or triadic) fMRI hyperscanning paradigm. Here, we simultaneously measured the blood-oxygenation level-dependent signal from 12 triads (n = 36 participants), while they engaged in a collaborative drawing task based on the social game of Pictionary General linear model analysis revealed increased activation in the brain regions previously linked with the theory of mind during the collaborative phase compared to the independent phase of the task. Furthermore, using intersubject correlation analysis, we revealed increased synchronization of the right temporo-parietal junction (R TPJ) during the collaborative phase. The increased synchrony in the R TPJ was observed to be positively associated with the overall team performance on the task. In sum, our paradigm revealed a vital role of the R TPJ among other theory-of-mind regions during a triadic collaborative drawing task.

Effect of wearing a face mask on fMRI BOLD contrast.

International spread of the coronavirus SARS-CoV-2 has prompted many MRI scanning facilities to require scan subjects to wear a facial covering ("mask") during scanning as a precaution against transmission of the virus. Because wearing a mask mixes expired air with the subject's inspired air stream, the concentration of inspired carbon dioxide [CO2] is elevated, resulting in mild hypercapnia. Changes in the inspired gas mixture have been demonstrated to alter R2*-weighted Blood Oxygen Dependent (BOLD) contrast. In this study, we investigate a potential for face masking to alter BOLD contrast during a sensory-motor task designed to activate visual, auditory, and sensorimotor cortices in 8 subjects. We utilize a nasal cannula to supply air to the subject wearing a surgical mask in on-off blocks of 90s to displace expired CO2, while the subject performs the sensory-motor task. While only a small fraction (2.5%) of the sensory-motor task activation is related to nasal air modulation, a 30.0% change in gray matter BOLD signal baseline is found due to air modulation. Repeating the scan with mask removed produces a small subject-specific bias in BOLD baseline signal from nasal air supply, which may be due to cognitive influence of airflow or cannula-induced hypoxia. Measurements with capnography demonstrate wearing a mask induces an average increase in ETCO2 of 7.4%. Altogether, these results demonstrate that wearing a face mask during gradient-echo fMRI can alter BOLD baseline signal but minimally affects task activation.

Investigating mechanisms of fast BOLD responses: The effects of stimulus intensity and of spatial heterogeneity of hemodynamics.

Recent studies have demonstrated that fast fMRI can track neural activity well above the temporal limit predicted by the canonical hemodynamic response model. While these findings are promising, the biophysical mechanisms underlying these fast fMRI phenomena remain underexplored. In this study, we discuss two aspects of the hemodynamic response, complementary to several existing hypotheses, that can accommodate faster fMRI dynamics beyond those predicted by the canonical model. First, we demonstrate, using both visual and somatosensory paradigms, that the timing and shape of hemodynamic response functions (HRFs) vary across graded levels of stimulus intensity-with lower-intensity stimulation eliciting faster and narrower HRFs. Second, we show that as the spatial resolution of fMRI increases, voxel-wise HRFs begin to deviate from the canonical model, with a considerable portion of voxels exhibiting faster temporal dynamics than predicted by the canonical HRF. Collectively, both stimulus/task intensity and image resolution can affect the sensitivity of fMRI to fast brain activity, which may partly explain recent observations of fast fMRI signals. It is further noteworthy that, while the present investigations focus on fast neural responses, our findings suggest that a revised hemodynamic model may benefit the many fMRI studies using paradigms with wide ranges of contrast levels (e.g., resting or naturalistic conditions) or with modern, high-resolution MR acquisitions.

Imaging brain function with simultaneous BOLD and viscoelasticity contrast: fMRI/fMRE.

Magnetic resonance elastography (MRE) is emerging as a new tool for studying viscoelastic changes in the brain resulting from functional processes. Here, we demonstrate a novel time series method to generate robust functional magnetic resonance elastography (fMRE) activation maps in response to a visual task with a flashing checkerboard stimulus. Using a single-shot spin-echo (SS-SE) pulse sequence, the underlying raw images inherently contain blood-oxygen-level dependent (BOLD) contrast, allowing simultaneous generation of functional magnetic resonance imaging (fMRI) activation maps from the magnitude and functional magnetic resonance elastography (fMRE) maps from the phase. This allows an accurate comparison of the spatially localized stiffness (fMRE) and BOLD (fMRI) changes within a single scan, eliminating confounds inherent in separately acquired scans. Results indicate that tissue stiffness within the visual cortex increases 6-11% with visual stimuli, whereas the BOLD signal change was 1-2%. Furthermore, the fMRE and fMRI activation maps have strong spatial overlap within the visual cortex, providing convincing evidence that fMRE is possible in the brain. However, the fMRE temporal SNR (tSNRfMRE) maps are heterogeneous across the brain. Using a dictionary matching approach to characterize the time series, the viscoelastic changes are consistent with a viscoelastic response function (VRF) time constant of 12.1 â€‹s ± 3.0 â€‹s for a first-order exponential decay, or a shape parameter of 8.1 â€‹s ± 1.4 â€‹s for a gamma-variate.

On the analysis of rapidly sampled fMRI data.

Recent advances in parallel imaging and simultaneous multi-slice techniques have permitted whole-brain fMRI acquisitions at sub-second sampling intervals, without significantly sacrificing the spatial coverage and resolution. Apart from probing brain function at finer temporal scales, faster sampling rates may potentially lead to enhanced functional sensitivity, owing possibly to both cleaner neural representations (due to less aliased physiological noise) and additional statistical benefits (due to more degrees of freedom for a fixed scan duration). Accompanying these intriguing aspects of fast acquisitions, however, confusion has also arisen regarding (1) how to preprocess/analyze these fast fMRI data, and (2) what exactly is the extent of benefits with fast acquisitions, i.e., how fast is fast enough for a specific research aim? The first question is motivated by the altered spectral distribution and noise characteristics at short sampling intervals, while the second question seeks to reconcile the complicated trade-offs between the functional contrast-to-noise ratio and the effective degrees of freedom. Although there have been recent efforts to empirically approach different aspects of these two questions, in this work we discuss, from a theoretical perspective accompanied by some illustrative, proof-of-concept experimental in vivo human fMRI data, a few considerations that are rarely mentioned, yet are important for both preprocessing and optimizing statistical inferences for studies that employ acquisitions with sub-second sampling intervals. Several summary recommendations include concerns regarding advisability of relying on low-pass filtering to de-noise physiological contributions, employment of statistical models with sufficient complexity to account for the substantially increased serial correlation, and cautions regarding using rapid sampling to enhance functional sensitivity given that different analysis models may associate with distinct trade-offs between contrast-to-noise ratios and the effective degrees of freedom. As an example, we demonstrate that as TR shortens, the intrinsic differences in how noise is accommodated in general linear models and Pearson correlation analyses (assuming Gaussian distributed stochastic signals and noise) can result in quite different outcomes, either gaining or losing statistical power.

Dynamic per slice shimming for simultaneous brain and spinal cord fMRI.

PURPOSE: Simultaneous brain and spinal cord functional MRI is emerging as a new tool to study the central nervous system but is challenging. Poor B0 homogeneity and small size of the spinal cord are principal obstacles to this nascent technology. Here we extend a dynamic shimming approach, first posed by Finsterbusch, by shimming per slice for both the brain and spinal cord. METHODS: We shim dynamically by a simple and fast optimization of linear field gradients and frequency offset separately for each slice in order to minimize off-resonance for both the brain and spinal cord. Simultaneous acquisition of brain and spinal cord fMRI is achieved with high spatial resolution in the spinal cord by means of an echo-planar RF pulse for reduced FOV. Brain slice acquisition is full FOV. RESULTS: T2*-weighted images of brain and spinal cord are acquired with high clarity and minimal observable image artifacts. Fist-clenching fMRI experiments reveal task-consistent activation in motor cortices, cerebellum, and C6-T1 spinal segments. CONCLUSIONS: High quality functional results are obtained for a sensory-motor task. Consistent activation in both the brain and spinal cord is observed at individual levels, not only at group level. Because reduced FOV excitation is applicable to any spinal cord section, future continuation of these methods holds great potential.

Dissociated patterns of anti-correlations with dorsal and ventral default-mode networks at rest.

Previous studies of resting state functional connectivity have demonstrated that the default-mode network (DMN) is negatively correlated with a set of brain regions commonly activated during goal-directed tasks. However, the location and extent of anti-correlations are inconsistent across different studies, which has been posited to result largely from differences in whether or not global signal regression (GSR) was applied as a pre-processing step. Notably, coordinates of seed regions-of-interest defined within the posterior cingulate cortex (PCC)/precuneus, an area often employed to study functional connectivity of the DMN, have been inconsistent across studies. Taken together with recent observations that the DMN contains functionally heterogeneous subdivisions, it is presently unclear whether these seeds map to different DMN subnetworks, whose patterns of anti-correlation may differ. If so, then seed location may be a non-negligible factor that, in addition to differences in preprocessing steps, contributes to the inconsistencies reported among published studies regarding DMN correlations/anti-correlations. In this study, they examined anti-correlations of different subnetworks within the DMN during rest using both seed-based and point process analyses, and discovered that: (1) the ventral branch of the DMN (vDMN) yielded significantly weaker anti-correlations than that associated with the dorsal branch of the DMN (dDMN); (2) vDMN anti-correlations introduced by GSR were distinct from dDMN anti-correlations; (3) PCC/precuneus seeds employed by earlier studies mapped to different DMN subnetworks, which may explain some of the inconsistency (in addition to preprocessing steps) in the reported DMN anti-correlations. Hum Brain Mapp 38:2454-2465, 2017. © 2017 Wiley Periodicals, Inc.

The Function Biomedical Informatics Research Network Data Repository.

The Function Biomedical Informatics Research Network (FBIRN) developed methods and tools for conducting multi-scanner functional magnetic resonance imaging (fMRI) studies. Method and tool development were based on two major goals: 1) to assess the major sources of variation in fMRI studies conducted across scanners, including instrumentation, acquisition protocols, challenge tasks, and analysis methods, and 2) to provide a distributed network infrastructure and an associated federated database to host and query large, multi-site, fMRI and clinical data sets. In the process of achieving these goals the FBIRN test bed generated several multi-scanner brain imaging data sets to be shared with the wider scientific community via the BIRN Data Repository (BDR). The FBIRN Phase 1 data set consists of a traveling subject study of 5 healthy subjects, each scanned on 10 different 1.5 to 4 T scanners. The FBIRN Phase 2 and Phase 3 data sets consist of subjects with schizophrenia or schizoaffective disorder along with healthy comparison subjects scanned at multiple sites. In this paper, we provide concise descriptions of FBIRN's multi-scanner brain imaging data sets and details about the BIRN Data Repository instance of the Human Imaging Database (HID) used to publicly share the data.

Reduced field of view imaging using a static second-order gradient for functional MRI applications.

PURPOSE: Imaging using reduced FOV excitation allows higher resolution or signal-to-noise ratio (SNR) per scan time but often requires long radiofrequency pulses. The goal of this study was to improve a recent reduced field of view (FOV) method that uses a second-order shim gradient to decrease pulse length and evaluate its use in functional MRI (fMRI) applications. THEORY AND METHODS: The method, which was initially limited to excite thin disc-shaped regions at the isocenter, was extended to excite thicker regions off the isocenter and produced accurate excitation profiles on a grid phantom. Visual stimulation fMRI scans were performed with full and reduced FOV. The resolution of the time series images and functional activation maps were assessed using the full-width half-maxima of the autocorrelation functions (FACFs) of the noise images and the activation map values, respectively. RESULTS: The resolution was higher in the reduced FOV time series images (4.1% ± 3.7% FACF reduction, P < 0.02) and functional activation maps (3.1% ± 3.4% FACF reduction, P < 0.01), but the SNR was lower (by 26.5% ± 16.9%). However, for a few subjects, the targeted region could not be localized to the reduced FOV due to the low Z2 gradient strength. CONCLUSION: The results of this study suggest that the proposed method is feasible, though it would benefit from a stronger gradient coil.

Causal interactions between fronto-parietal central executive and default-mode networks in humans.

Information processing during human cognitive and emotional operations is thought to involve the dynamic interplay of several large-scale neural networks, including the fronto-parietal central executive network (CEN), cingulo-opercular salience network (SN), and the medial prefrontal-medial parietal default mode networks (DMN). It has been theorized that there is a causal neural mechanism by which the CEN/SN negatively regulate the DMN. Support for this idea has come from correlational neuroimaging studies; however, direct evidence for this neural mechanism is lacking. Here we undertook a direct test of this mechanism by combining transcranial magnetic stimulation (TMS) with functional MRI to causally excite or inhibit TMS-accessible prefrontal nodes within the CEN or SN and determine consequent effects on the DMN. Single-pulse excitatory stimulations delivered to only the CEN node induced negative DMN connectivity with the CEN and SN, consistent with the CEN/SN's hypothesized negative regulation of the DMN. Conversely, low-frequency inhibitory repetitive TMS to the CEN node resulted in a shift of DMN signal from its normally low-frequency range to a higher frequency, suggesting disinhibition of DMN activity. Moreover, the CEN node exhibited this causal regulatory relationship primarily with the medial prefrontal portion of the DMN. These findings significantly advance our understanding of the causal mechanisms by which major brain networks normally coordinate information processing. Given that poorly regulated information processing is a hallmark of most neuropsychiatric disorders, these findings provide a foundation for ways to study network dysregulation and develop brain stimulation treatments for these disorders.

BOLD fractional contribution to resting-state functional connectivity above 0.1 Hz.

Blood oxygen level dependent (BOLD) spontaneous signals from resting-state (RS) brains have typically been characterized by low-pass filtered timeseries at frequencies ≤ 0.1 Hz, and studies of these low-frequency fluctuations have contributed exceptional understanding of the baseline functions of our brain. Very recently, emerging evidence has demonstrated that spontaneous activities may persist in higher frequency bands (even up to 0.8 Hz), while presenting less variable network patterns across the scan duration. However, as an indirect measure of neuronal activity, BOLD signal results from an inherently slow hemodynamic process, which in fact might be too slow to accommodate the observed high-frequency functional connectivity (FC). To examine whether the observed high-frequency spontaneous FC originates from BOLD contrast, we collected RS data as a function of echo time (TE). Here we focus on two specific resting state networks - the default-mode network (DMN) and executive control network (ECN), and the major findings are fourfold: (1) we observed BOLD-like linear TE-dependence in the spontaneous activity at frequency bands up to 0.5 Hz (the maximum frequency that can be resolved with TR=1s), supporting neural relevance of the RSFC at a higher frequency range; (2) conventional models of hemodynamic response functions must be modified to support resting state BOLD contrast, especially at higher frequencies; (3) there are increased fractions of non-BOLD-like contributions to the RSFC above the conventional 0.1 Hz (non-BOLD/BOLD contrast at 0.4-0.5 Hz is ~4 times that at <0.1 Hz); and (4) the spatial patterns of RSFC are frequency-dependent. Possible mechanisms underlying the present findings and technical concerns regarding RSFC above 0.1 Hz are discussed.

Introducing co-activation pattern metrics to quantify spontaneous brain network dynamics.

Recently, fMRI researchers have begun to realize that the brain's intrinsic network patterns may undergo substantial changes during a single resting state (RS) scan. However, despite the growing interest in brain dynamics, metrics that can quantify the variability of network patterns are still quite limited. Here, we first introduce various quantification metrics based on the extension of co-activation pattern (CAP) analysis, a recently proposed point-process analysis that tracks state alternations at each individual time frame and relies on very few assumptions; then apply these proposed metrics to quantify changes of brain dynamics during a sustained 2-back working memory (WM) task compared to rest. We focus on the functional connectivity of two prominent RS networks, the default-mode network (DMN) and executive control network (ECN). We first demonstrate less variability of global Pearson correlations with respect to the two chosen networks using a sliding-window approach during WM task compared to rest; then we show that the macroscopic decrease in variations in correlations during a WM task is also well characterized by the combined effect of a reduced number of dominant CAPs, increased spatial consistency across CAPs, and increased fractional contributions of a few dominant CAPs. These CAP metrics may provide alternative and more straightforward quantitative means of characterizing brain network dynamics than time-windowed correlation analyses.

Functional Magnetic Resonance Imaging Methods.

Since its inception in 1992, Functional Magnetic Resonance Imaging (fMRI) has become an indispensible tool for studying cognition in both the healthy and dysfunctional brain. FMRI monitors changes in the oxygenation of brain tissue resulting from altered metabolism consequent to a task-based evoked neural response or from spontaneous fluctuations in neural activity in the absence of conscious mentation (the "resting state"). Task-based studies have revealed neural correlates of a large number of important cognitive processes, while fMRI studies performed in the resting state have demonstrated brain-wide networks that result from brain regions with synchronized, apparently spontaneous activity. In this article, we review the methods used to acquire and analyze fMRI signals.

Control of nucleus accumbens activity with neurofeedback.

The nucleus accumbens (NAcc) plays critical roles in healthy motivation and learning, as well as in psychiatric disorders (including schizophrenia and attention deficit hyperactivity disorder). Thus, techniques that confer control of NAcc activity might inspire new therapeutic interventions. By providing second-to-second temporal resolution of activity in small subcortical regions, functional magnetic resonance imaging (fMRI) can resolve online changes in NAcc activity, which can then be presented as "neurofeedback." In an fMRI-based neurofeedback experiment designed to elicit NAcc activity, we found that subjects could increase their own NAcc activity, and that display of neurofeedback significantly enhanced their ability to do so. Subjects were not as capable of decreasing their NAcc activity, however, and enhanced control did not persist after subsequent removal of neurofeedback. Further analyses suggested that individuals who recruited positive aroused affect were better able to increase NAcc activity in response to neurofeedback, and that NAcc neurofeedback also elicited functionally correlated activity in the medial prefrontal cortex. Together, these findings suggest that humans can modulate their own NAcc activity and that fMRI-based neurofeedback may augment their efforts. The observed association between positive arousal and effective NAcc control further supports an anticipatory affect account of NAcc function.

Neural systems predicting long-term outcome in dyslexia.

Individuals with developmental dyslexia vary in their ability to improve reading skills, but the brain basis for improvement remains largely unknown. We performed a prospective, longitudinal study over 2.5 y in children with dyslexia (n = 25) or without dyslexia (n = 20) to discover whether initial behavioral or brain measures, including functional MRI (fMRI) and diffusion tensor imaging (DTI), can predict future long-term reading gains in dyslexia. No behavioral measure, including widely used and standardized reading and language tests, reliably predicted future reading gains in dyslexia. Greater right prefrontal activation during a reading task that demanded phonological awareness and right superior longitudinal fasciculus (including arcuate fasciculus) white-matter organization significantly predicted future reading gains in dyslexia. Multivariate pattern analysis (MVPA) of these two brain measures, using linear support vector machine (SVM) and cross-validation, predicted significantly above chance (72% accuracy) which particular child would or would not improve reading skills (behavioral measures were at chance). MVPA of whole-brain activation pattern during phonological processing predicted which children with dyslexia would improve reading skills 2.5 y later with >90% accuracy. These findings identify right prefrontal brain mechanisms that may be critical for reading improvement in dyslexia and that may differ from typical reading development. Brain measures that predict future behavioral outcomes (neuroprognosis) may be more accurate, in some cases, than available behavioral measures.

Recent developments and future avenues for human corticospinal neuroimaging.

Non-invasive neuroimaging serves as a valuable tool for investigating the mechanisms within the central nervous system (CNS) related to somatosensory and motor processing, emotions, memory, cognition, and other functions. Despite the extensive use of brain imaging, spinal cord imaging has received relatively less attention, regardless of its potential to study peripheral communications with the brain and the descending corticospinal systems. To comprehensively understand the neural mechanisms underlying human sensory and motor functions, particularly in pathological conditions, simultaneous examination of neuronal activity in both the brain and spinal cord becomes imperative. Although technically demanding in terms of data acquisition and analysis, a growing but limited number of studies have successfully utilized specialized acquisition protocols for corticospinal imaging. These studies have effectively assessed sensorimotor, autonomic, and interneuronal signaling within the spinal cord, revealing interactions with cortical processes in the brain. In this mini-review, we aim to examine the expanding body of literature that employs cutting-edge corticospinal imaging to investigate the flow of sensorimotor information between the brain and spinal cord. Additionally, we will provide a concise overview of recent advancements in functional magnetic resonance imaging (fMRI) techniques. Furthermore, we will discuss potential future perspectives aimed at enhancing our comprehension of large-scale neuronal networks in the CNS and their disruptions in clinical disorders. This collective knowledge will aid in refining combined corticospinal fMRI methodologies, leading to the development of clinically relevant biomarkers for conditions affecting sensorimotor processing in the CNS.

Association between heart rate variability and fluctuations in resting-state functional connectivity.

Functional connectivity has been observed to fluctuate across the course of a resting state scan, though the origins and functional relevance of this phenomenon remain to be shown. The present study explores the link between endogenous dynamics of functional connectivity and autonomic state in an eyes-closed resting condition. Using a sliding window analysis on resting state fMRI data from 35 young, healthy male subjects, we examined how heart rate variability (HRV) covaries with temporal changes in whole-brain functional connectivity with seed regions previously described to mediate effects of vigilance and arousal (amygdala and dorsal anterior cingulate cortex; dACC). We identified a set of regions, including brainstem, thalamus, putamen, and dorsolateral prefrontal cortex, that became more strongly coupled with the dACC and amygdala seeds during states of elevated HRV. Effects differed between high and low frequency components of HRV, suggesting specific contributions of parasympathetic and sympathetic tone on individual connections. Furthermore, dynamics of functional connectivity could be separated from those primarily related to BOLD signal fluctuations. The present results contribute novel information about the neural basis of transient changes of autonomic nervous system states, and suggest physiological and psychological components of the recently observed non-stationarity in resting state functional connectivity.

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.

Efficient Bloch-Siegert B1 (+) mapping using spiral and echo-planar readouts.

The Bloch-Siegert (B-S) B1 (+) mapping technique is a fast, phase-based method that is highly SAR limited especially at 7T, necessitating the use of long repetition times. Spiral and echo-planar readouts were incorporated in a gradient-echo based B-S sequence to reduce specific absoprtion rate (SAR) and improve its scan efficiency. A novel, numerically optimized 4 ms B-S off-resonant pulse at + 1960 Hz was used to increase sensitivity and further reduce SAR compared with the conventional 6 ms Fermi B-S pulse. Using echo-planar and spiral readouts, scan time reductions of 8-16 were achieved. By reducing the B-S pulse width by a factor of 1.5, SAR was reduced by a factor of 1.5 and overall sensitivity was increased by a factor of 1.33 due to the nearly halved resonance offset of the new B-S pulse. This was validated on phantoms and volunteers at 7 T.