Mapping the MRI voxel volume in which thermal noise matches physiological noise--implications for fMRI

Neural Networks and Chemical Messengers: Insights into Tobacco Addiction

This study investigates changes in resting-state networks (RSNs) associated with tobacco addiction (TA) and whether these changes reflect alterations in neurotransmitter systems. A total of 90 patients with TA and 46 healthy controls (HCs) matched for age, education, and body mass index undergo functional magnetic resonance imaging (fMRI) scans. Independent component analysis (ICA) is employed to extract RSNs based on a customized network template using the HCP ICA MATCHING toolbox. Additionally, a correlation study is conducted to examine the relationship between changes in functional connectivity (FC) within RSNs and positron emission tomography and single photon emission computed tomography-derived maps, aiming to identify specific neurotransmitter system changes underlying abnormal FC in TA. Compared to HCs, the TA group exhibits decreased FC values in the left precentral gyrus of the sensorimotor network B and in the right calcarine of the visual network B. Furthermore, changes in FC within the visual network B are associated with the 5-hydroxytryptamine system (1a) and opioid receptor system (Kappa) maps. Post-hoc power analysis confirms the adequacy of the sample size, with effect sizes (d) all greater than 0.9, supporting the robustness of the findings. Patients with TA show reduced intranetwork connectivity in the sensorimotor network B and visual network B, which may reflect underlying molecular changes. These findings improve understanding of the neurobiological aspects of TA.

Pattern Separation and Pattern Completion Within the Hippocampal Circuit During Naturalistic Stimuli

Pattern separation and pattern completion in the hippocampus play a critical role in episodic learning and memory. However, there is limited empirical evidence supporting the role of the hippocampal circuit in these processes during complex continuous experiences. In this study, we analyzed high-resolution fMRI data from the "Forrest Gump" open-access dataset (16 participants) using a sliding-window temporal autocorrelation approach to investigate whether the canonical hippocampal circuit (DG-CA3-CA1-SUB) shows evidence consistent with the occurrence of pattern separation or pattern completion during a naturalistic audio movie task. Our results revealed that when processing continuous naturalistic stimuli, the DG-CA3 pair exhibited evidence consistent with the occurrence of the pattern separation process, whereas both the CA3-CA1 and CA1-SUB pairs showed evidence consistent with pattern completion. Moreover, during the latter half of the audio movie, we observed evidence consistent with a reduction in pattern completion in the CA3-CA1 pair and an increase in pattern completion in the CA1-SUB pair. Overall, these findings improve our understanding of the evidence related to the occurrence of pattern separation and pattern completion processes during natural experiences.

Functional connectivity changes in males with nicotine addiction: A triple network model study

BACKGROUND

Nicotine addiction (NA) is recognized as a significant neurobehavioral disorder that affects both individuals and society. It is suggested that alterations in functional network connectivity (FNC) within specific brain networks underlie its neurobiological basis.

METHODS

The default mode network (DMN), executive control network (ECN), and salience network (SN) are identified using data from the Human Connectome Project. The study includes 47 individuals with NA and 35 normal controls (NC), all of whom undergo resting-state fMRI alongside smoking-related clinical assessments. A sliding window analysis is employed to assess connectivity metrics, including static functional network connectivity (FNC), standard deviation (SD), and coefficient of variation (CV), to compare information integration between the groups. Participants with NA are classified based on longitudinal changes in Fagerström Test for Nicotine Dependence (FTND) scores over six years into three categories: addiction tendency (AT), withdrawal tendency (WT), and stable tendency (ST). Correlation analyses are conducted to explore relationships between FNC abnormalities and clinical assessments.

RESULTS

Individuals with NA exhibit reduced static FNC (p_FDR = 0.029) between the dorsal DMN and the right ECN, accompanied by increased SD (p_FDR = 0.029) and CV (p_FDR = 0.029). A significant increase in SD (p_FDR = 0.049) is also observed in the dorsal DMN and left ECN. Correlations indicate that the SD of the dorsal DMN and right ECN relates to the pharmacological dimension of the Russell Smoking Reasons Questionnaire (RRSQ) scale (r = 0.416, p_FDR = 0.044), while CV correlates with changes in the FTND over six years (r = -0.391, p_FDR = 0.044) and the pharmacological dimension of the RRSQ scale (r = 0.402, p_FDR = 0.044). Post-hoc subgroup analyses reveal that these FNC intensity changes are present among WT patients (p_FDR < 0.05).

CONCLUSIONS

Alterations in brain network function within the DMN and ECN are suggested to precede behavioral changes in NA. These findings are interpreted as potential neurobiological markers of nicotine addiction.

An automated pipeline for obtaining labeled ICA-templates corresponding to functional brain systems

The complexity of our actions and thinking is likely reflected in functional brain networks. Independent component analysis (ICA) is a popular data-driven method to compute group differences between such networks. A common way to investigate network differences is based on ICA maps which are generated from study-specific samples. However, this approach limits the generalizability and reproducibility of the results. Alternatively, network ICA templates can be used, but up to date, few such templates exist and are limited in terms of the functional systems they cover. Here, we propose a simple two-step procedure to obtain ICA-templates corresponding to functional brain systems of the researcher's choice: In step 1, the functional system of interest needs to be defined by means of a statistical parameter map (input), which one can generate with open-source software such as NeuroSynth or BrainMap. In step 2, that map is correlated to group-ICA maps provided by the Human Connectome Project (HCP), which is based on a large sample size and uses high quality and standardized acquisition procedures. The HCP-provided ICA-map with the highest correlation to the input map is then used as an ICA template representing the functional system of interest, for example, for subsequent analyses such as dual regression. We provide a toolbox to complete step 2 of the suggested procedure and demonstrate the usage of our pipeline by producing an ICA templates that corresponds to "motor function" and nine additional brain functional systems resulting in an ICA maps with excellent alignment with the gray matter/white matter boundaries of the brain. Our toolbox generates data in two different file formats: volumetric-based (NIFTI) and combined surface/volumetric files (CIFTI). Compared to 10 existing templates, our procedure output component maps with systematically stronger contribution of gray matter to the ICA z-values compared to white matter voxels in 9/10 cases by at least a factor of 2. The toolbox allows users to investigate functional networks of interest, which will enhance interpretability, reproducibility, and standardization of research investigating functional brain networks.

Statistical inference on representational geometries

Neuroscience has recently made much progress, expanding the complexity of both neural activity measurements and brain-computational models. However, we lack robust methods for connecting theory and experiment by evaluating our new big models with our new big data. Here, we introduce new inference methods enabling researchers to evaluate and compare models based on the accuracy of their predictions of representational geometries: A good model should accurately predict the distances among the neural population representations (e.g. of a set of stimuli). Our inference methods combine novel 2-factor extensions of crossvalidation (to prevent overfitting to either subjects or conditions from inflating our estimates of model accuracy) and bootstrapping (to enable inferential model comparison with simultaneous generalization to both new subjects and new conditions). We validate the inference methods on data where the ground-truth model is known, by simulating data with deep neural networks and by resampling of calcium-imaging and functional MRI data. Results demonstrate that the methods are valid and conclusions generalize correctly. These data analysis methods are available in an open-source Python toolbox (rsatoolbox.readthedocs.io).

Prefrontal, parietal, and limbic condition-dependent differences in bipolar disorder: a large-scale meta-analysis of functional neuroimaging studies

BACKGROUND

Over the past few decades, neuroimaging research in Bipolar Disorder (BD) has identified neural differences underlying cognitive and emotional processing. However, substantial clinical and methodological heterogeneity present across neuroimaging experiments potentially hinders the identification of consistent neural biomarkers of BD. This meta-analysis aims to comprehensively reassess brain activation and connectivity in BD in order to identify replicable differences that converge across and within resting-state, cognitive, and emotional neuroimaging experiments.

METHODS

Neuroimaging experiments (using fMRI, PET, or arterial spin labeling) reporting whole-brain results in adults with BD and controls published from December 1999-June 18, 2019 were identified via PubMed search. Coordinates showing significant activation and/or connectivity differences between BD participants and controls during resting-state, emotional, or cognitive tasks were extracted. Four parallel, independent meta-analyses were calculated using the revised activation likelihood estimation algorithm: all experiment types, all resting-state experiments, all cognitive experiments, and all emotional experiments. To confirm reliability of identified clusters, two different meta-analytic significance tests were employed.

RESULTS

205 published studies yielding 506 individual neuroimaging experiments (150 resting-state, 134 cognitive, 222 emotional) comprising 5745 BD and 8023 control participants were included. Five regions survived both significance tests. Individuals with BD showed functional differences in the right posterior cingulate cortex during resting-state experiments, the left amygdala during emotional experiments, including those using a mixed (positive/negative) valence manipulation, and the left superior and right inferior parietal lobules during cognitive experiments, while hyperactivating the left medial orbitofrontal cortex during cognitive experiments. Across all experiments, there was convergence in the right caudate extending to the ventral striatum, surviving only one significance test.

CONCLUSIONS

Our findings indicate reproducible localization of prefrontal, parietal, and limbic differences distinguishing BD from control participants that are condition-dependent, despite heterogeneity, and point towards a framework for identifying reproducible differences in BD that may guide diagnosis and treatment.

Review: The use of functional magnetic resonance imaging (fMRI) in clinical trials and experimental research studies for depression

Functional magnetic resonance imaging (fMRI) is a non-invasive technique that can be used to examine neural responses with and without the use of a functional task. Indeed, fMRI has been used in clinical trials and pharmacological research studies. In mental health, it has been used to identify brain areas linked to specific symptoms but also has the potential to help identify possible treatment targets. Despite fMRI's many advantages, such findings are rarely the primary outcome measure in clinical trials or research studies. This article reviews fMRI studies in depression that sought to assess the efficacy and mechanism of action of compounds with antidepressant effects. Our search results focused on selective serotonin reuptake inhibitors (SSRIs), the most commonly prescribed treatments for depression and ketamine, a fast-acting antidepressant treatment. Normalization of amygdala hyperactivity in response to negative emotional stimuli was found to underlie successful treatment response to SSRIs as well as ketamine, indicating a potential common pathway for both conventional and fast-acting antidepressants. Ketamine's rapid antidepressant effects make it a particularly useful compound for studying depression with fMRI; its effects on brain activity and connectivity trended toward normalizing the increases and decreases in brain activity and connectivity associated with depression. These findings highlight the considerable promise of fMRI as a tool for identifying treatment targets in depression. However, additional studies with improved methodology and study design are needed before fMRI findings can be translated into meaningful clinical trial outcomes.

White matter BOLD signals at 7 Tesla reveal visual field maps in optic radiation and vertical occipital fasciculus

There is growing evidence that blood-oxygen-level-dependent (BOLD) activity in the white matter (WM) can be detected by functional magnetic resonance imaging (fMRI). However, the functional relevance and significance of WM BOLD signals remain controversial. Here we investigated whether 7T BOLD fMRI can reveal fine-scale functional organizations of a WM bundle. Population receptive field (pRF) analyses of the 7T retinotopy dataset from the Human Connectome Project revealed clear contralateral retinotopic organizations of two visual WM bundles: the optic radiation (OR) and the vertical occipital fasciculus (VOF). The retinotopic maps of OR are highly consistent with post-mortem dissections and diffusion tractographies, while the VOF maps are compatible with the dorsal and ventral visual areas connected by the WM. Similar to the grey matter (GM) visual areas, both WM bundles show over-representations of the central visual field and increasing pRF size with eccentricity. Hemodynamic response functions of visual WM were slower and wider compared with those of GM areas. These findings clearly demonstrate that WM BOLD at 7 Tesla is closely coupled with neural activity related to axons, encoding highly specific information that can be used to characterize fine-scale functional organizations of a WM bundle.

Static and dynamic BOLD fMRI components along white matter fibre tracts and their dependence on the orientation of the local diffusion tensor axis relative to the B0-field

Recent studies have reported functional MRI (fMRI) activation within cerebral white matter (WM) using blood-oxygenation-level-dependent (BOLD) contrast. Many blood vessels in WM run parallel to the fibre bundles, and other studies observed dependence of susceptibility contrast-based measures of blood volume on the local orientation of the fibre bundles relative to the magnetic field or B₀ axis. Motivated by this, we characterized the dependence of gradient-echo BOLD fMRI on fibre orientation (estimated by the local diffusion tensor) relative to the B₀ axis to test whether the alignment between bundles and vessels imparts an orientation dependence on resting-state BOLD fluctuations in the WM. We found that the baseline signal level of the T₂*-weighted data is 11% higher in voxels containing fibres parallel to B₀ than those containing perpendicular fibres, consistent with a static influence of either fibre or vessel orientation on local T₂* values. We also found that BOLD fluctuations in most bundles exhibit orientation effects expected from oxygenation changes, with larger amplitudes from voxels containing perpendicular fibres. Different magnitudes of this orientation effect were observed across the major WM bundles, with inferior fasciculus, corpus callosum and optic radiation exhibiting 14-19% higher fluctuations in voxels containing perpendicular compared to parallel fibres.

Self-regulation of the posterior cingulate cortex with real-time fMRI neurofeedback augmented mindfulness training in healthy adolescents: A nonrandomized feasibility study

Mindfulness training (MT) promotes the development of one's ability to observe and attend to internal and external experiences with objectivity and nonjudgment with evidence to improve psychological well-being. Real-time functional MRI neurofeedback (rtfMRI-nf) is a noninvasive method of modulating activity of a brain region or circuit. The posterior cingulate cortex (PCC) has been hypothesized to be an important hub instantiating a mindful state. This nonrandomized, single-arm study examined the feasibility and tolerability of training typically developing adolescents to self-regulate the posterior cingulate cortex (PCC) using rtfMRI-nf during MT. Thirty-four adolescents (mean age: 15 years; 14 females) completed the neurofeedback augmented mindfulness training task, including Focus-on-Breath (MT), Describe (self-referential thinking), and Rest conditions, across three neurofeedback and two non-neurofeedback runs (Observe, Transfer). Self-report assessments demonstrated the feasibility and tolerability of the task. Neurofeedback runs differed significantly from non-neurofeedback runs for the Focus-on-Breath versus Describe contrast, characterized by decreased activity in the PCC during the Focus-on-Breath condition (z = -2.38 to -6.27). MT neurofeedback neural representation further involved the medial prefrontal cortex, anterior cingulate cortex, dorsolateral prefrontal cortex, posterior insula, hippocampus, and amygdala. State awareness of physical sensations increased following rtfMRI-nf and was maintained at 1-week follow-up (Cohens' d = 0.69). Findings demonstrate feasibility and tolerability of rtfMRI-nf in healthy adolescents, replicates the role of PCC in MT, and demonstrate a potential neuromodulatory mechanism to leverage and streamline the learning of mindfulness practice. ( ClinicalTrials.gov identifier #NCT04053582; August 12, 2019).

A Geometric View of Signal Sensitivity Metrics in multi-echo fMRI

In multi-echo fMRI (ME-fMRI), two metrics have been widely used to measure the performance of various acquisition and analysis approaches. These are temporal SNR (tSNR) and differential contrast-to-noise ratio (dCNR). A key step in ME-fMRI is the weighted combination of the data from multiple echoes, and prior work has examined the dependence of tSNR and dCNR on the choice of weights. However, most studies have focused on only one of these two metrics, and the relationship between the two metrics has not been examined. In this work, we present a geometric view that offers greater insight into the relation between the two metrics and their weight dependence. We identify three major regimes: (1) a tSNR robust regime in which tSNR is robust to the weight selection with most weight variants achieving close to optimal performance, whereas dCNR shows a pronounced dependence on the weights with most variants achieving suboptimal performance; (2) a dCNR robust regime in which dCNR is robust to the weight selection with most weight variants achieving close to optimal performance, while tSNR exhibits a strong dependence on the weights with most variants achieving significantly lower than optimal performance; and (3) a within-type robust regime in which both tSNR and dCNR achieve nearly optimal performance when the form of the weights are variants of their respective optimal weights and exhibit a moderate decrease in performance for other weight variants. Insight into the behavior observed in the different regimes is gained by considering spherical representations of the weight dependence of the components used to form each metric. For multi-echo acquisitions, dCNR is shown to be more directly related than tSNR to measures of CNR and signal-to-noise ratio (SNR) for both task-based and resting-state fMRI scans, respectively.

Neurofeedback-Augmented Mindfulness Training Elicits Distinct Responses in the Subregions of the Insular Cortex in Healthy Adolescents

Mindfulness training (MT) reduces self-referential processing and promotes interoception, the perception of sensations from inside the body, by increasing one’s awareness of and regulating responses to them. The posterior cingulate cortex (PCC) and the insular cortex (INS) are considered hubs for self-referential processing and interoception, respectively. Although MT has been consistently found to decrease PCC, little is known about how MT relates to INS activity. Understanding links between mindfulness and interoception may be particularly important for informing mental health in adolescence, when neuroplasticity and emergence of psychopathology are heightened. We examined INS activity during real-time functional magnetic resonance imaging neurofeedback-augmented mindfulness training (NAMT) targeting the PCC. Healthy adolescents (N = 37; 16 female) completed the NAMT task, including Focus-on-Breath (MT), Describe (self-referential processing), and Rest conditions, across three neurofeedback runs and two non-neurofeedback runs (Observe, Transfer). Regression coefficients estimated from the generalized linear model were extracted from three INS subregions: anterior (aINS), mid (mINS), and posterior (pINS). Mixed model analyses revealed the main effect of run for Focus-on-Breath vs. Describe contrast in aINS [R2 = 0.39] and pINS [R2 = 0.33], but not mINS [R2 = 0.34]. Post hoc analyses revealed greater aINS activity and reduced pINS activity during neurofeedback runs, and such activities were related to lower self-reported life satisfaction and less pain behavior, respectively. These findings revealed the specific involvement of insula subregions in rtfMRI-nf MT.

Neural Correlates of Listening to Varying Synchrony Between Beats in Samba Percussion and Relations to Feeling the Groove

Listening to samba percussion often elicits feelings of pleasure and the desire to move with the beat-an experience sometimes referred to as "feeling the groove"- as well as social connectedness. Here we investigated the effects of performance timing in a Brazilian samba percussion ensemble on listeners' experienced pleasantness and the desire to move/dance in a behavioral experiment, as well as on neural processing as assessed via functional magnetic resonance imaging (fMRI). Participants listened to different excerpts of samba percussion produced by multiple instruments that either were "in sync", with no additional asynchrony between instrumental parts other than what is usual in naturalistic recordings, or were presented "out of sync" by delaying the snare drums (by 28, 55, or 83 ms). Results of the behavioral experiment showed increasing pleasantness and desire to move/dance with increasing synchrony between instruments. Analysis of hemodynamic responses revealed stronger bilateral brain activity in the supplementary motor area, the left premotor area, and the left middle frontal gyrus with increasing synchrony between instruments. Listening to "in sync" percussion thus strengthens audio-motor interactions by recruiting motor-related brain areas involved in rhythm processing and beat perception to a higher degree. Such motor related activity may form the basis for "feeling the groove" and the associated desire to move to music. Furthermore, in an exploratory analysis we found that participants who reported stronger emotional responses to samba percussion in everyday life showed higher activity in the subgenual cingulate cortex, an area involved in prosocial emotions, social group identification and social bonding.

The cortical connectome of primate lateral prefrontal cortex

The lateral prefrontal cortex (LPFC) of primates plays an important role in executive control, but how it interacts with the rest of the cortex remains unclear. To address this, we densely mapped the cortical connectome of LPFC, using electrical microstimulation combined with functional MRI (EM-fMRI). We found isomorphic mappings between LPFC and five major processing domains composing most of the cerebral cortex except early sensory and motor areas. An LPFC grid of ∼200 stimulation sites topographically mapped to separate grids of activation sites in the five domains, coarsely resembling how the visual cortex maps the retina. The temporal and parietal maps largely overlapped in LPFC, suggesting topographically organized convergence of the ventral and dorsal streams, and the other maps overlapped at least partially. Thus, the LPFC contains overlapping, millimeter-scale maps that mirror the organization of major cortical processing domains, supporting LPFC's role in coordinating activity within and across these domains.

Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) has become an indispensable tool for investigating the human brain. However, the inherently poor signal-to-noise-ratio (SNR) of the fMRI measurement represents a major barrier to expanding its spatiotemporal scale as well as its utility and ultimate impact. Here we introduce a denoising technique that selectively suppresses the thermal noise contribution to the fMRI experiment. Using 7-Tesla, high-resolution human brain data, we demonstrate improvements in key metrics of functional mapping (temporal-SNR, the detection and reproducibility of stimulus-induced signal changes, and accuracy of functional maps) while leaving the amplitude of the stimulus-induced signal changes, spatial precision, and functional point-spread-function unaltered. We demonstrate that the method enables the acquisition of ultrahigh resolution (0.5 mm isotropic) functional maps but is also equally beneficial for a large variety of fMRI applications, including supra-millimeter resolution 3- and 7-Tesla data obtained over different cortical regions with different stimulation/task paradigms and acquisition strategies.

The signal-to-noise ratio is a key consideration when selecting a magnetic resonance imaging protocol. Thermal noise is major issue, especially in high resolution functional images. Here the authors introduce a method to suppress thermal noise in functional images without losses in spatial precision, increasing the signal-to-noise ratio.

Striatal and septo-hypothalamic responses to anticipation and outcome of affiliative rewards

Humans are intrinsically motivated to bond with others. The ability to experience affiliative emotions (such as affection/tenderness, sexual attraction, and admiration/awe) may incentivize and promote these affiliative bonds. Here, we interrogate the role of the critical reward circuitry, especially the Nucleus Accumbens (NAcc) and the septo-hypothalamic region, in the anticipation of and response to affiliative rewards using a novel incentive delay task. During Functional Magnetic Resonance Imaging (FMRI), participants (n = 23 healthy humans; 14 female) anticipated and watched videos involving affiliative (tenderness, erotic desire, and awe) and nonaffiliative (i.e., food) rewards, as well as neutral scenes. On the one hand, anticipation of both affiliative and nonaffiliative rewards increased activity in the NAcc, anterior insula, and supplementary motor cortex, but activity in the amygdala and the ventromedial prefrontal cortex (vmPFC) increased in response to reward outcomes. On the other hand, affiliative rewards more specifically increased activity in the septo-hypothalamic area. Moreover, NAcc activity during anticipation correlated with positive arousal for all rewards, whereas septo-hypothalamic activity during the outcome correlated with positive arousal and motivation for subsequent re-exposure only for affiliative rewards. Together, these findings implicate a general appetitive response in the NAcc to different types of rewards but suggests a more specific response in the septo-hypothalamic region in response to affiliative rewards outcomes. This work also presents a new task for distinguishing between neural responses to affiliative and non-affiliative rewards.

Altered large-scale functional brain networks in neurological Wilson's disease

Wilson's disease patients with neurological symptoms have motor symptoms and cognitive deficits, including frontal executive, visuospatial processing, and memory impairments. Although the brain structural abnormalities associated with Wilson's disease have been documented, it remains largely unknown how Wilson's disease affects large-scale functional brain networks. In this study, we investigated functional brain networks in Wilson's disease. Particularly, we analyzed resting state functional magnetic resonance images of 30 Wilson's disease patients and 26 healthy controls. First, functional brain networks for each participant were extracted using an independent component analysis method. Then, a computationally efficient pattern classification method was developed to identify discriminative brain functional networks associated with Wilson's disease. Experimental results indicated that Wilson's disease patients, compared with healthy controls, had altered large-scale functional brain networks, including the dorsal anterior cingulate cortex and basal ganglia network, the middle frontal gyrus, the dorsal striatum, the inferior parietal lobule, the precuneus, the temporal pole, and the posterior lobe of cerebellum. Classification models built upon these networks distinguished between neurological WD patients and HCs with accuracy up to 86.9% (specificity: 86.7%, sensitivity: 89.7%). The classification scores were correlated with the United Wilson's Disease Rating Scale measures and durations of disease of the patients. These results suggest that Wilson's disease patients have multiple aberrant brain functional networks, and classification scores derived from these networks are associated with severity of clinical symptoms.

An in-vivo study of BOLD laminar responses as a function of echo time and static magnetic field strength

Layer specific functional MRI requires high spatial resolution data. To compensate the associated poor signal to noise ratio it is common to integrate the signal from voxels at a given cortical depth. If the region is sufficiently large then physiological noise will be the dominant noise source. In this work, activation profiles in response to the same visual stimulus are compared at 1.5 T, 3 T and 7 T using a multi-echo, gradient echo (GE) FLASH sequence, with a 0.75 mm isotropic voxel size and the cortical integration approach. The results show that after integrating over a cortical volume of 40, 60 and 100 mm³ (at 7 T, 3 T, and 1.5 T, respectively), the signal is in the physiological noise dominated regime. The activation profiles obtained are similar for equivalent echo times. BOLD-like noise is found to be the dominant source of physiological noise. Consequently, the functional contrast to noise ratio is not strongly echo-time or field-strength dependent. We conclude that laminar GE-BOLD fMRI at lower field strengths is feasible but that larger patches of cortex will need to be examined, and that the acquisition efficiency is reduced.

Forging a path to mesoscopic imaging success with ultra-high field functional magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) studies with ultra-high field (UHF, 7+ Tesla) technology enable the acquisition of high-resolution images. In this work, we discuss recent achievements in UHF fMRI at the mesoscopic scale, on the order of cortical columns and layers, and examine approaches to addressing common challenges. As researchers push to smaller and smaller voxel sizes, acquisition and analysis decisions have greater potential to degrade spatial accuracy, and UHF fMRI data must be carefully interpreted. We consider the impact of acquisition decisions on the spatial specificity of the MR signal with a representative dataset with 0.8 mm isotropic resolution. We illustrate the trade-offs in contrast with noise ratio and spatial specificity of different acquisition techniques and show that acquisition blurring can increase the effective voxel size by as much as 50% in some dimensions. We further describe how different sources of degradations to spatial resolution in functional data may be characterized. Finally, we emphasize that progress in UHF fMRI depends not only on scientific discovery and technical advancement, but also on informal discussions and documentation of challenges researchers face and overcome in pursuit of their goals. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.

Typicality of functional connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases, and preprocessing pipelines

Functional connectivity analysis of resting-state fMRI data has recently become one of the most common approaches to characterizing individual brain function. It has been widely suggested that the functional connectivity matrix is a useful approximate representation of the brain's connectivity, potentially providing behaviorally or clinically relevant markers. However, functional connectivity estimates are known to be detrimentally affected by various artifacts, including those due to in-scanner head motion. Moreover, as individual functional connections generally covary only very weakly with head motion estimates, motion influence is difficult to quantify robustly, and prone to be neglected in practice. Although the use of individual estimates of head motion, or group-level correlation of motion and functional connectivity has been suggested, a sufficiently sensitive measure of individual functional connectivity quality has not yet been established. We propose a new intuitive summary index, Typicality of Functional Connectivity, to capture deviations from standard brain functional connectivity patterns. In a resting-state fMRI dataset of 245 healthy subjects, this measure was significantly correlated with individual head motion metrics. The results were further robustly reproduced across atlas granularity, preprocessing options, and other datasets, including 1,081 subjects from the Human Connectome Project. In principle, Typicality of Functional Connectivity should be sensitive also to other types of artifacts, processing errors, and possibly also brain pathology, allowing extensive use in data quality screening and quantification in functional connectivity studies as well as methodological investigations.

Facilitating open-science with realistic fMRI simulation: validation and application

With advances in methods for collecting and analyzing fMRI data, there is a concurrent need to understand how to reliably evaluate and optimally use these methods. Simulations of fMRI data can aid in both the evaluation of complex designs and the analysis of data. We present fmrisim, a new Python package for standardized, realistic simulation of fMRI data. This package is part of BrainIAK: a recently released open-source Python toolbox for advanced neuroimaging analyses. We describe how to use fmrisim to extract noise properties from real fMRI data and then create a synthetic dataset with matched noise properties and a user-specified signal. We validate the noise generated by fmrisim to show that it can approximate the noise properties of real data. We further show how fmrisim can help researchers find the optimal design in terms of power. The fmrisim package holds promise for improving the design of fMRI experiments, which may facilitate both the pre-registration of such experiments as well as the analysis of fMRI data.

Improved autoregressive model for correction of noise serial correlation in fast fMRI

PURPOSE

In rapidly acquired functional MRI (fast fMRI) data, the noise serial correlations (SC) can produce problematically overestimated T-statistics which lead to invalid statistical inferences. This study aims to evaluate and improve the accuracy of high-order autoregressive model (AR(p), where p is the model order) based prewhitening method in the SC correction.

METHODS

Fast fMRI images were acquired at rest (null data) using a multiband simultaneous multi-slice echo planar imaging pulse sequence with repetition time (TR) = 300 and 500 ms. The SC effect in the fast fMRI data was corrected using the prewhitening method based on two AR(p) models: (1) the conventional model (fixed AR(p)) which preselects a constant p for all the image voxels; (2) an improved model (AR_AICc ) that employs the corrected Akaike information criterion voxel-wise to automatically select the model orders for each voxel. To evaluate accuracy of SC correction, false positive characteristics were measured by assuming the presence of block and event-related tasks in the null data without image smoothing. The performance of prewhitening was also examined in smoothed images by adding pseudo task fMRI signals into the null data and comparing the detected to simulated activations (ground truth).

RESULTS

The measured false positive characteristics agreed well with the theoretical curve when using the AR_AICc , and the activation maps in the smoothed data matched the ground truth. The AR_AICc showed improved performance than the fixed AR(p) method.

CONCLUSION

The AR_AICc can effectively remove noise SC, and accurate statistical analysis results can be obtained with the AR_AICc correction in fast fMRI.

Topographic Mapping as a Basic Principle of Functional Organization for Visual and Prefrontal Functional Connectivity

The organization of region-to-region functional connectivity has major implications for understanding information transfer and transformation between brain regions. We extended connective field mapping methodology to 3-D anatomic space to derive estimates of corticocortical functional organization. Using multiple publicly available human (both male and female) resting-state fMRI data samples for model testing and replication analysis, we have three main findings. First, we found that the functional connectivity between early visual regions maintained a topographic relationship along the anterior-posterior dimension, which corroborates previous research. Higher order visual regions showed a pattern of connectivity that supports convergence and biased sampling, which has implications for their receptive field properties. Second, we demonstrated that topographic organization is a fundamental aspect of functional connectivity across the entire cortex, with higher topographic connectivity between regions within a functional network than across networks. The principle gradient of topographic connectivity across the cortex resembled whole-brain gradients found in previous work. Last but not least, we showed that the organization of higher order regions such as the lateral prefrontal cortex demonstrate functional gradients of topographic connectivity and convergence. These organizational features of the lateral prefrontal cortex predict task-based activation patterns, particularly visual specialization and higher order rules. In sum, these findings suggest that topographic input is a fundamental motif of functional connectivity between cortical regions for information processing and transfer, with maintenance of topography potentially important for preserving the integrity of information from one region to another.

A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol

Large-scale consortium efforts such as Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and other collaborative efforts show that combining statistical data from multiple independent studies can boost statistical power and achieve more accurate estimates of effect sizes, contributing to more reliable and reproducible research. A meta- analysis would pool effects from studies conducted in a similar manner, yet to date, no such harmonized protocol exists for resting state fMRI (rsfMRI) data. Here, we propose an initial pipeline for multi-site rsfMRI analysis to allow research groups around the world to analyze scans in a harmonized way, and to perform coordinated statistical tests. The challenge lies in the fact that resting state fMRI measurements collected by researchers over the last decade vary widely, with variable quality and differing spatial or temporal signal-to-noise ratio (tSNR). An effective harmonization must provide optimal measures for all quality data. Here we used rsfMRI data from twenty-two independent studies with approximately fifty corresponding T1-weighted and rsfMRI datasets each, to (A) review and aggregate the state of existing rsfMRI data, (B) demonstrate utility of principal component analysis (PCA)-based denoising and (C) develop a deformable ENIGMA EPI template based on the representative anatomy that incorporates spatial distortion patterns from various protocols and populations.

Brainstem neuroimaging of nociception and pain circuitries

The brainstem is known to be an important brain area for nociception and pain processing, and both relaying and coordinating signaling between the cerebrum, cerebellum, and spinal cord. Although preclinical models of pain have characterized the many roles that brainstem nuclei play in nociceptive processing, the degree to which these circuitries extend to humans is not as well known. Unfortunately, the brainstem is also a very challenging region to evaluate in humans with neuroimaging. The challenges for human brainstem imaging arise from the location of this elongated brain structure, proximity to cardiorespiratory noise sources, and the size of its constituent nuclei. These challenges can require dedicated approaches to brainstem imaging, which should be adopted when study hypotheses are focused on brainstem processing of nociception or modulation of pain perception. In fact, our review will highlight many pain neuroimaging studies that have reported some brainstem involvement in nociceptive processing and chronic pain pathology. However, we note that with recent advances in neuroimaging leading to improved spatial and temporal resolution, more studies are needed that take advantage of data collection and analysis methods focused on the challenges of brainstem neuroimaging.

Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates

Methodological research rarely generates a broad interest, yet our work on the validity of cluster inference methods for functional magnetic resonance imaging (fMRI) created intense discussion on both the minutia of our approach and its implications for the discipline. In the present work, we take on various critiques of our work and further explore the limitations of our original work. We address issues about the particular event-related designs we used, considering multiple event types and randomization of events between subjects. We consider the lack of validity found with one-sample permutation (sign flipping) tests, investigating a number of approaches to improve the false positive control of this widely used procedure. We found that the combination of a two-sided test and cleaning the data using ICA FIX resulted in nominal false positive rates for all data sets, meaning that data cleaning is not only important for resting state fMRI, but also for task fMRI. Finally, we discuss the implications of our work on the fMRI literature as a whole, estimating that at least 10% of the fMRI studies have used the most problematic cluster inference method (p = .01 cluster defining threshold), and how individual studies can be interpreted in light of our findings. These additional results underscore our original conclusions, on the importance of data sharing and thorough evaluation of statistical methods on realistic null data.

Intracortical smoothing of small-voxel fMRI data can provide increased detection power without spatial resolution losses compared to conventional large-voxel fMRI data

Continued improvement in MRI acquisition technology has made functional MRI (fMRI) with small isotropic voxel sizes down to 1 mm and below more commonly available. Although many conventional fMRI studies seek to investigate regional patterns of cortical activation for which conventional voxel sizes of 3 mm and larger provide sufficient spatial resolution, smaller voxels can help avoid contamination from adjacent white matter (WM) and cerebrospinal fluid (CSF), and thereby increase the specificity of fMRI to signal changes within the gray matter. Unfortunately, temporal signal-to-noise ratio (tSNR), a metric of fMRI sensitivity, is reduced in high-resolution acquisitions, which offsets the benefits of small voxels. Here we introduce a framework that combines small, isotropic fMRI voxels acquired at 7 T field strength with a novel anatomically-informed, surface mesh-navigated spatial smoothing that can provide both higher detection power and higher resolution than conventional voxel sizes. Our smoothing approach uses a family of intracortical surface meshes and allows for kernels of various shapes and sizes, including curved 3D kernels that adapt to and track the cortical folding pattern. Our goal is to restrict smoothing to the cortical gray matter ribbon and avoid noise contamination from CSF and signal dilution from WM via partial volume effects. We found that the intracortical kernel that maximizes tSNR does not maximize percent signal change (ΔS/S), and therefore the kernel configuration that optimizes detection power cannot be determined from tSNR considerations alone. However, several kernel configurations provided a favorable balance between boosting tSNR and ΔS/S, and allowed a 1.1-mm isotropic fMRI acquisition to have higher performance after smoothing (in terms of both detection power and spatial resolution) compared to an unsmoothed 3.0-mm isotropic fMRI acquisition. Overall, the results of this study support the strategy of acquiring voxels smaller than the cortical thickness, even for studies not requiring high spatial resolution, and smoothing them down within the cortical ribbon with a kernel of an appropriate shape to achieve the best performance-thus decoupling the choice of fMRI voxel size from the spatial resolution requirements of the particular study. The improvement of this new intracortical smoothing approach over conventional surface-based smoothing is expected to be modest for conventional resolutions, however the improvement is expected to increase with higher resolutions. This framework can also be applied to anatomically-informed intracortical smoothing of higher-resolution data (e.g. along columns and layers) in studies with prior information about the spatial structure of activation.

Gradient-echo and spin-echo blood oxygenation level-dependent functional MRI at ultrahigh fields of 9.4 and 15.2 Tesla

Purpose

Sensitivity and specificity of blood oxygenation level–dependent (BOLD) functional MRI (fMRI) is sensitive to magnetic field strength and acquisition methods. We have investigated gradient‐echo (GE)‐ and spin‐echo (SE)‐BOLD fMRI at ultrahigh fields of 9.4 and 15.2  Tesla.

Methods

BOLD fMRI experiments responding to forepaw stimulation were performed with 3 echo times (TE) at each echo type and B₀ in α‐chloralose–anesthetized rats. The contralateral forelimb somatosensory region was selected for quantitative analyses.

Results

At 9.4 T and 15.2 T, average baseline T₂^* (n = 9) was 26.6 and 17.1 msec, whereas baseline T₂ value (n = 9) was 35.7 and 24.5 msec, respectively. Averaged stimulation‐induced ΔR₂^* was –1.72 s^–1 at 9.4 T and –3.09 s^–1 at 15.2 T, whereas ΔR₂ was –1.19 s^–1 at 9.4 T and –1.97 s^–1 at 15.2 T. At the optimal TE of tissue T₂^* or T₂, BOLD percent changes were slightly higher at 15.2 T than at 9.4 T (GE: 7.4% versus 6.4% and SE: 5.7% versus 5.4%). The ΔR₂^* and ΔR₂ ratio of 15.2 T to 9.4 T was 1.8 and 1.66, respectively. The ratio of the macrovessel‐containing superficial to microvessel‐dominant parenchymal BOLD signal was 1.73 to 1.76 for GE‐BOLD versus 1.13 to 1.19 for SE‐BOLD, indicating that the SE‐BOLD contrast is less sensitive to macrovessels than GE‐BOLD.

Conclusion

SE‐BOLD fMRI improves spatial specificity to microvessels compared to GE‐BOLD at both fields. BOLD sensitivity is similar at the both fields and can be improved at ultrahigh fields only for thermal‐noise–dominant ultrahigh‐resolution fMRI.

Differences in BOLD responses in brain reward network reflect the tendency to assimilate a surprising flavor stimulus to an expected stimulus

External information can modify the subjective value of a tasted stimulus, but little is known about neural mechanisms underlying these behavioral modifications. This study used flavored drinks to produce variable degrees of discrepancy between expected and received flavor. During a learning session, 43 healthy young men learned 4 symbol-flavor associations. In a separate session, associations were presented again during an fMRI scan, but half of the trials introduced discrepancy with previously learned associations. Liking ratings of drinks were collected and were analyzed using a linear model to define the degree to which discrepant symbols affected liking ratings of the subjects during the fMRI session. Based on these results, a GLM analysis of fMRI data was conducted to determine neural correlates of observed behavior. Groups of subjects were composed based on their behavior in response to discrepant symbols, and comparison of brain activity between groups showed that activation in the PCC and the caudate nucleus was more potent in those subjects in which liking was not affected by discrepant symbols. These activations were not found in subjects who assimilated unexpected flavors to flavors preceeded by discrepant symbols. Instead, these subjects showed differences in the activity in the parietal operculum. The activity of reward network appears to be related to assimilation of received flavor to expected flavor in response to symbol-flavor discrepancy.

Emotion Regulation Using Virtual Environments and Real-Time fMRI Neurofeedback

Neurofeedback (NFB) enables the voluntary regulation of brain activity, with promising applications to enhance and recover emotion and cognitive processes, and their underlying neurobiology. It remains unclear whether NFB can be used to aid and sustain complex emotions, with ecological validity implications. We provide a technical proof of concept of a novel real-time functional magnetic resonance imaging (rtfMRI) NFB procedure. Using rtfMRI-NFB, we enabled participants to voluntarily enhance their own neural activity while they experienced complex emotions. The rtfMRI-NFB software (FRIEND Engine) was adapted to provide a virtual environment as brain computer interface (BCI) and musical excerpts to induce two emotions (tenderness and anguish), aided by participants' preferred personalized strategies to maximize the intensity of these emotions. Eight participants from two experimental sites performed rtfMRI-NFB on two consecutive days in a counterbalanced design. On one day, rtfMRI-NFB was delivered to participants using a region of interest (ROI) method, while on the other day using a support vector machine (SVM) classifier. Our multimodal VR/NFB approach was technically feasible and robust as a method for real-time measurement of the neural correlates of complex emotional states and their voluntary modulation. Guided by the color changes of the virtual environment BCI during rtfMRI-NFB, participants successfully increased in real time, the activity of the septo-hypothalamic area and the amygdala during the ROI based rtfMRI-NFB, and successfully evoked distributed patterns of brain activity classified as tenderness and anguish during SVM-based rtfMRI-NFB. Offline fMRI analyses confirmed that during tenderness rtfMRI-NFB conditions, participants recruited the septo-hypothalamic area and other regions ascribed to social affiliative emotions (medial frontal / temporal pole and precuneus). During anguish rtfMRI-NFB conditions, participants recruited the amygdala and other dorsolateral prefrontal and additional regions associated with negative affect. These findings were robust and were demonstrable at the individual subject level, and were reflected in self-reported emotion intensity during rtfMRI-NFB, being observed with both ROI and SVM methods and across the two sites. Our multimodal VR/rtfMRI-NFB protocol provides an engaging tool for brain-based interventions to enhance emotional states in healthy subjects and may find applications in clinical conditions associated with anxiety, stress and impaired empathy among others.

Reprint of 'Noise contributions to the fMRI signal: An Overview'

The ability to discriminate signal from noise plays a key role in the analysis and interpretation of functional magnetic resonance imaging (fMRI) measures of brain activity. Over the past two decades, a number of major sources of noise have been identified, including system-related instabilities, subject motion, and physiological fluctuations. This article reviews the characteristics of the various noise sources as well as the mechanisms through which they affect the fMRI signal. Approaches for distinguishing signal from noise and the associated challenges are also reviewed. These challenges reflect the fact that some noise sources, such as respiratory activity, are generated by the same underlying brain networks that give rise to functional signals that are of interest.

Neural bases of ingroup altruistic motivation in soccer fans

Humans have a strong need to belong to social groups and a natural inclination to benefit ingroup members. Although the psychological mechanisms behind human prosociality have extensively been studied, the specific neural systems bridging group belongingness and altruistic motivation remain to be identified. Here, we used soccer fandom as an ecological framing of group membership to investigate the neural mechanisms underlying ingroup altruistic behaviour in male fans using event-related functional magnetic resonance. We designed an effort measure based on handgrip strength to assess the motivation to earn money (i) for oneself, (ii) for anonymous ingroup fans, or (iii) for a neutral group of anonymous non-fans. While overlapping valuation signals in the medial orbitofrontal cortex (mOFC) were observed for the three conditions, the subgenual cingulate cortex (SCC) exhibited increased functional connectivity with the mOFC as well as stronger hemodynamic responses for ingroup versus outgroup decisions. These findings indicate a key role for the SCC, a region previously implicated in altruistic decisions and group affiliation, in dovetailing altruistic motivations with neural valuation systems in real-life ingroup behaviour.

High spatio-temporal resolution in functional MRI with 3D echo planar imaging using cylindrical excitation and a CAIPIRINHA undersampling pattern

PURPOSE

The combination of 3D echo planar imaging (3D-EPI) with a 2D-CAIPIRINHA undersampling scheme provides high flexibility in the optimization for spatial or temporal resolution. This flexibility can be increased further with the addition of a cylindrical excitation pulse, which exclusively excites the brain regions of interest. Here, 3D-EPI was combined with a 2D radiofrequency pulse to reduce the brain area from which signal is generated, and hence, allowing either reduction of the field of view or reduction of parallel imaging noise amplification.

METHODS

3D-EPI with cylindrical excitation and 4 × 3-fold undersampling in a 2D-CAIPIRINHA sampling scheme was used to generate functional MRI (fMRI) data with either 2-mm or 0.9-mm in-plane resolution and 1.1-s temporal resolution over a 5-cm diameter cylinder placed over both temporal lobes for an auditory fMRI experiment.

RESULTS

Significant increases in image signal-to-noise ratio (SNR) and temporal SNR (tSNR) were found for both 2-mm isotropic data and the high-resolution protocol when using the cylindrical excitation pulse. Both protocols yielded highly significant blood oxygenation level-dependent responses for the presentation of natural sounds.

CONCLUSION

The higher tSNR of the cylindrical excitation 3D-EPI data makes this sequence an ideal choice for high spatiotemporal resolution fMRI acquisitions. Magn Reson Med 79:2589-2596, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Methods for cleaning the BOLD fMRI signal

Blood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies.

Optimization of functional MRI for detection, decoding and high-resolution imaging of the response patterns of cortical columns

The capacity of functional MRI (fMRI) to resolve cortical columns depends on several factors. These include the spatial scale of the columnar pattern, the point-spread of the fMRI response, the voxel size, and the signal-to-noise ratio (SNR) considering thermal and physiological noise. However, it remains unknown how these factors combine, and what is the voxel size that optimizes fMRI of cortical columns. Here we combine current knowledge into a quantitative model of fMRI of realistic patterns of cortical columns with different spatial scales and degrees of irregularity. We compare different approaches for identifying patterns of cortical columns, including univariate and multivariate based detection, multi-voxel pattern analysis (MVPA) based decoding, and high-resolution imaging and reconstruction of the pattern of cortical columns. We present the dependence of the performance of each approach on the parameters of the imaged pattern as well as those of the data acquisition. In addition, we predict voxel sizes that optimize fMRI of cortical columns under various scenarios. We found that all measures associated with multivariate detection and decoding could be approximately calculated from a measure we termed "multivariate contrast-to-noise ratio" (mv-CNR), which is a function of the contrast-to-noise ratio (CNR) and number of voxels. Furthermore, mv-CNR implied that the optimal voxel width for detection and decoding is independent of changes in response amplitude, SNR and imaged volume that are not caused by changes in voxel size. For regular patterns, optimal voxel widths for detection, decoding and imaging/reconstructing the pattern of cortical columns were approximately half the main cycle length of the organization. Optimal voxel widths for irregular patterns were less dependent on the main cycle length, and differed between univariate detection, multivariate detection and decoding, and reconstruction. We compared the effects of different factors of Gradient Echo fMRI at 3 Tesla (T), Gradient Echo fMRI at 7T, and Spin-Echo fMRI at 7T on the detection, decoding, and reconstruction measures considered and found that in all cases the width of the fMRI point-spread had the most significant effect. In contrast, different response amplitudes and noise characteristics played a relatively minor role. We recommend specific voxel widths for optimal univariate detection, for multivariate detection and decoding, and for high-resolution imaging of cortical columns under these three data-acquisition scenarios. Our study supports the planning, optimization, and interpretation of high-resolution fMRI of cortical columns and the decoding of information conveyed by these columns.

Improved 7 Tesla resting-state fMRI connectivity measurements by cluster-based modeling of respiratory volume and heart rate effects

Several strategies have been proposed to model and remove physiological noise from resting-state fMRI (rs-fMRI) data, particularly at ultrahigh fields (7 T), including contributions from respiratory volume (RV) and heart rate (HR) signal fluctuations. Recent studies suggest that these contributions are highly variable across subjects and that physiological noise correction may thus benefit from optimization at the subject or even voxel level. Here, we systematically investigated the impact of the degree of spatial specificity (group, subject, newly proposed cluster, and voxel levels) on the optimization of RV and HR models. For each degree of spatial specificity, we measured the fMRI signal variance explained (VE) by each model, as well as the functional connectivity underlying three well-known resting-state networks (RSNs) obtained from the fMRI data after removal of RV+HR contributions. Whole-brain, high-resolution rs-fMRI data were acquired from twelve healthy volunteers at 7 T, while simultaneously recording their cardiac and respiratory signals. Although VE increased with spatial specificity up to the voxel level, the accuracy of functional connectivity measurements improved only up to the cluster level, and subsequently decreased at the voxel level. This suggests that voxelwise modeling over-fits to local fluctuations with no physiological meaning. In conclusion, our results indicate that 7 T rs-fMRI connectivity measurements improve if a cluster-based physiological noise correction approach is employed in order to take into account the individual spatial variability in the HR and RV contributions.

Functional connectivity and activity of white matter in somatosensory pathways under tactile stimulations

Functional MRI has proven to be effective in detecting neural activity in brain cortices on the basis of blood oxygenation level dependent (BOLD) contrast, but has relatively poor sensitivity for detecting neural activity in white matter. To demonstrate that BOLD signals in white matter are detectable and contain information on neural activity, we stimulated the somatosensory system and examined distributions of BOLD signals in related white matter pathways. The temporal correlation profiles and frequency contents of BOLD signals were compared between stimulation and resting conditions, and between relevant white matter fibers and background regions, as well as between left and right side stimulations. Quantitative analyses show that, overall, MR signals from white matter fiber bundles in the somatosensory system exhibited significantly greater temporal correlations with the primary sensory cortex and greater signal power during tactile stimulations than in a resting state, and were stronger than corresponding measurements for background white matter both during stimulations and in a resting state. The temporal correlation and signal power under stimulation were found to be twice those observed from the same bundle in a resting state, and bore clear relations with the side of stimuli. These indicate that BOLD signals in white matter fibers encode neural activity related to their functional roles connecting cortical volumes, which are detectable with appropriate methods.

Challenges and opportunities for brainstem neuroimaging with ultrahigh field MRI

The human brainstem plays a central role in connecting the cerebrum, the cerebellum and the spinal cord to one another, hosting relay nuclei for afferent and efferent signaling, and providing source nuclei for several neuromodulatory systems that impact central nervous system function. While the investigation of the brainstem with functional or structural magnetic resonance imaging has been hampered for years due to this brain structure's physiological and anatomical characteristics, the field has seen significant advances in recent years thanks to the broader adoption of ultrahigh-field (UHF) MRI scanning. In the present review, we focus on the advantages offered by UHF in the context of brainstem imaging, as well as the challenges posed by the investigation of this complex brain structure in terms of data acquisition and analysis. We also illustrate how UHF MRI can shed new light on the neuroanatomy and neurophysiology underlying different brainstem-based circuitries, such as the central autonomic network and neurotransmitter/neuromodulator systems, discuss existing and foreseeable clinical applications to better understand diseases such as chronic pain and Parkinson's disease, and explore promising future directions for further improvements in brainstem imaging using UHF MRI techniques.

Reduction of across-run variability of temporal SNR in accelerated EPI time-series data through FLEET-based robust autocalibration

Temporal signal-to-noise ratio (tSNR) is a key metric for assessing the ability to detect brain activation in fMRI data. A recent study has shown substantial variation of tSNR between multiple runs of accelerated EPI acquisitions reconstructed with the GRAPPA method using protocols commonly used for fMRI experiments. Across-run changes in the location of high-tSNR regions could lead to misinterpretation of the observed brain activation patterns, reduced sensitivity of the fMRI studies, and biased results. We compared conventional EPI autocalibration (ACS) methods with the recently-introduced FLEET ACS method, measuring their tSNR variability, as well as spatial overlap and displacement of high-tSNR clusters across runs in datasets acquired from human subjects at 7T and 3T. FLEET ACS reconstructed data had higher tSNR levels, as previously reported, as well as better temporal consistency and larger overlap of the high-tSNR clusters across runs compared with reconstructions using conventional multi-shot (ms) EPI ACS data. tSNR variability across two different runs of the same protocol using ms-EPI ACS data was about two times larger than for the protocol using FLEET ACS for acceleration factors (R) 2 and 3, and one and half times larger for R=4. The level of across-run tSNR consistency for data reconstructed with FLEET ACS was similar to within-run tSNR consistency. The displacement of high-tSNR clusters across two runs (inter-cluster distance) decreased from ∼8mm in the time-series reconstructed using conventional ms-EPI ACS data to ∼4mm for images reconstructed using FLEET ACS. However, the performance gap between conventional ms-EPI ACS and FLEET ACS narrowed with increasing parallel imaging acceleration factor. Overall, the FLEET ACS method provides a simple solution to the problem of varying tSNR across runs, and therefore helps ensure that an assumption of fMRI analysis-that tSNR is largely consistent across runs-is met for accelerated acquisitions.

The Effect of Gray Matter ICA and Coefficient of Variation Mapping of BOLD Data on the Detection of Functional Connectivity Changes in Alzheimer's Disease and bvFTD

Resting-state fMRI results in neurodegenerative diseases have been somewhat conflicting. This may be due to complex partial volume effects of CSF in BOLD signal in patients with brain atrophy. To encounter this problem, we used a coefficient of variation (CV) map to highlight artifacts in the data, followed by analysis of gray matter voxels in order to minimize brain volume effects between groups. The effects of these measures were compared to whole brain ICA dual regression results in Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD). 23 AD patients, 21 bvFTD patients and 25 healthy controls were included. The quality of the data was controlled by CV mapping. For detecting functional connectivity (FC) differences whole brain ICA (wbICA) and also segmented gray matter ICA (gmICA) followed by dual regression were conducted, both of which were performed both before and after data quality control. Decreased FC was detected in posterior DMN in the AD group and in the Salience network in the bvFTD group after combining CV quality control with gmICA. Before CV quality control, the decreased connectivity finding was not detectable in gmICA in neither of the groups. Same finding recurred when exclusion was based on randomization. The subjects excluded due to artifacts noticed in the CV maps had significantly lower temporal signal-to-noise ratio than the included subjects. Data quality measure CV is an effective tool in detecting artifacts from resting state analysis. CV reflects temporal dispersion of the BOLD signal stability and may thus be most helpful for spatial ICA, which has a blind spot in spatially correlating widespread artifacts. CV mapping in conjunction with gmICA yields results suiting previous findings both in AD and bvFTD.

Impacting the effect of fMRI noise through hardware and acquisition choices - Implications for controlling false positive rates

We review the components of time-series noise in fMRI experiments and the effect of image acquisition parameters on the noise. In addition to helping determine the total amount of signal and noise (and thus temporal SNR), the acquisition parameters have been shown to be critical in determining the ratio of thermal to physiological induced noise components in the time series. Although limited attention has been given to this latter metric, we show that it determines the degree of spatial correlations seen in the time-series noise. The spatially correlations of the physiological noise component are well known, but recent studies have shown that they can lead to a higher than expected false-positive rate in cluster-wise inference based on parametric statistical methods used by many researchers. Based on understanding the effect of acquisition parameters on the noise mixture, we propose several acquisition strategies that might be helpful reducing this elevated false-positive rate, such as moving to high spatial resolution or using highly-accelerated acquisitions where thermal sources dominate. We suggest that the spatial noise correlations at the root of the inflated false-positive rate problem can be limited with these strategies, and the well-behaved spatial auto-correlation functions (ACFs) assumed by the conventional statistical methods are retained if the high resolution data is smoothed to conventional resolutions.

Noise contributions to the fMRI signal: An overview

The ability to discriminate signal from noise plays a key role in the analysis and interpretation of functional magnetic resonance imaging (fMRI) measures of brain activity. Over the past two decades, a number of major sources of noise have been identified, including system-related instabilities, subject motion, and physiological fluctuations. This article reviews the characteristics of the various noise sources as well as the mechanisms through which they affect the fMRI signal. Approaches for distinguishing signal from noise and the associated challenges are also reviewed. These challenges reflect the fact that some noise sources, such as respiratory activity, are generated by the same underlying brain networks that give rise to functional signals that are of interest.

Effects of anesthesia on resting state BOLD signals in white matter of non-human primates

Resting state functional magnetic resonance imaging (rsfMRI) has been widely used to measure functional connectivity between cortical regions of the brain. However, there have been minimal reports of bold oxygenation level dependent (BOLD) signals in white matter, and even fewer attempts to detect resting state connectivity. Recently, there has been growing evidence that suggests that reliable detection of white matter BOLD signals may be possible. We have previously shown that nearest neighbor inter-voxel correlations of resting state BOLD signal fluctuations in white matter are anisotropic and can be represented by a functional correlation tensor, but the biophysical origins of these signal variations are not clear. We aimed to assess whether MRI signal fluctuations in white matter vary for different baseline levels of neural activity. We performed imaging studies on live squirrel monkeys under different levels of isoflurane anesthesia at 9.4T. We found 1) the fractional power (0.01-0.08Hz) in white matter was between 60 to 75% of the level in gray matter; 2) the power in both gray and white matter low frequencies decreased monotonically in similar manner with increasing levels of anesthesia; 3) the distribution of fractional anisotropy values of the functional tensors in white matter were significantly higher than those in gray matter; and 4) the functional tensor eigenvalues decreased with increasing level of anesthesia. Our results suggest that as anesthesia level changes baseline neural activity, white matter signal fluctuations behave similarly to those in gray matter, and functional tensors in white matter are affected in parallel.

Neuroimaging brainstem circuitry supporting cardiovagal response to pain: a combined heart rate variability/ultrahigh-field (7 T) functional magnetic resonance imaging study

Central autonomic control nuclei in the brainstem have been difficult to evaluate non-invasively in humans. We applied ultrahigh-field (7 T) functional magnetic resonance imaging (fMRI), and the improved spatial resolution it affords (1.2 mm isotropic), to evaluate putative brainstem nuclei that control and/or sense pain-evoked cardiovagal modulation (high-frequency heart rate variability (HF-HRV) instantaneously estimated through a point-process approach). The time-variant HF-HRV signal was used to guide the general linear model analysis of neuroimaging data. Sustained (6 min) pain stimulation reduced cardiovagal modulation, with the most prominent reduction evident in the first 2 min. Brainstem nuclei associated with pain-evoked HF-HRV reduction were previously implicated in both autonomic regulation and pain processing. Specifically, clusters consistent with the rostral ventromedial medulla, ventral nucleus reticularis (Rt)/nucleus ambiguus (NAmb) and pontine nuclei (Pn) were found when contrasting sustained pain versus rest. Analysis of the initial 2-min period identified Rt/NAmb and Pn, in addition to clusters consistent with the dorsal motor nucleus of the vagus/nucleus of the solitary tract and locus coeruleus. Combining high spatial resolution fMRI and high temporal resolution HF-HRV allowed for a non-invasive characterization of brainstem nuclei, suggesting that nociceptive afference induces pain-processing brainstem nuclei to function in concert with known premotor autonomic nuclei in order to affect the cardiovagal response to pain.

Neuroimaging Evidence of a Bilateral Representation for Visually Presented Numbers

The clustered architecture of the brain for different visual stimulus categories is one of the most fascinating topics in the cognitive neurosciences. Interestingly, recent research suggests the existence of additional regions for newly acquired stimuli such as letters (letter form area; LFA; Thesen et al., 2012) and numbers (visual number form area; NFA; Shum et al., 2013). However, neuroimaging methods thus far have failed to visualize the NFA in healthy participants, likely due to fMRI signal dropout caused by the air/bone interface of the petrous bone (Shum et al., 2013). In the current study, we combined a 64-channel head coil with high spatial resolution, localized shimming, and liberal smoothing, thereby decreasing the signal dropout and increasing the temporal signal-to-noise ratio in the neighborhood of the NFA. We presented subjects with numbers, letters, false numbers, false letters, objects and their Fourier randomized versions. A group analysis showed significant activations in the inferior temporal gyrus at the previously proposed location of the NFA. Crucially, we found the NFA to be present in both hemispheres. Further, we could identify the NFA on the single-subject level in most of our participants. A detailed analysis of the response profile of the NFA in two separate experiments confirmed the whole-brain results since responses to numbers were significantly higher than to any other presented stimulus in both hemispheres. Our results show for the first time the existence and stimulus selectivity of the NFA in the healthy human brain.

SIGNIFICANCE STATEMENT

This fMRI study shows for the first time a cluster of neurons selective for visually presented numbers in healthy human adults. This visual number form area (NFA) was found in both hemispheres. Crucially, numbers have gained importance for humans too recently for neuronal specialization to be established by evolution. Therefore, investigations of this region will greatly advance our understanding of learning and plasticity in the brain. In addition, these results will aid our knowledge regarding related neurological illnesses (e.g., dyscalculia). To overcome the fMRI signal dropout in the neighborhood of the NFA, we combined high spatial resolution with liberal smoothing. We believe that this approach will be useful to the broad neuroimaging community.