A comprehensive evaluation of increasing temporal resolution with multiband-accelerated protocols and effects on statistical outcome measures in fMRI

Women and men with distressing low sexual desire exhibit sexually dimorphic brain processing

Distressing low sexual desire, termed Hypoactive Sexual Desire Disorder (HSDD), affects approximately 10% of women and 8% of men. In women, the 'top-down' theory of HSDD describes hyperactivity in higher-level cognitive brain regions, suppressing lower-level emotional/sexual brain areas. However, it is unknown how this neurofunctional disturbance compares to HSDD in men. To investigate this, we employed task-based functional MRI in 32 women and 32 men with HSDD to measure sexual-brain processing during sexual versus non-sexual videos, as well as psychometric questionnaires to assess sexual desire/arousal. We demonstrate that women had greater activation in higher-level and lower-level brain regions, compared to men. Indeed, women who had greater hypothalamic activation in response to sexual videos, reported higher psychometric scores in the evaluative (r = 0.55, P = 0.001), motivational (r = 0.56, P = 0.003), and physiological (r = 0.57, P = 0.0006) domains of sexual desire and arousal after watching the sexual videos in the scanner. By contrast, no similar correlations were observed in men. Taken together, this is the first direct comparison of the neural correlates of distressing low sexual desire between women and men. The data supports the 'top-down' theory of HSDD in women, whereas in men HSDD appears to be associated with different neurofunctional processes.

Clinical Applications and Recent Updates of Simultaneous Multi-slice Technique in Accelerated MRI

Simultaneous multi-slice (SMS) is a magnetic resonance imaging (MRI) acceleration technique that utilizes multi-band radio-frequency pulses to simultaneously excite and encode multiple slices. Currently, SMS has been widely studied and applied in the MRI examination to reduce acquisition time, which can significantly improve the examination efficiency and patient throughput. Moreover, SMS technique can improve spatial resolution, which is of great value in disease diagnosis, treatment response monitoring, and prognosis prediction. This review will briefly introduce the technical principles of SMS, and summarize its current clinical applications. More importantly, we will discuss the recent technical progress and future research direction of SMS, hoping to highlight the clinical value and scientific potential of this technique.

Frequency Analysis of EEG Microstate Sequences in Wakefulness and NREM Sleep

The majority of EEG microstate analyses concern wakefulness, and the existing sleep studies have focused on changes in spatial microstate properties and on microstate transitions between adjacent time points, the shortest available time scale. We present a more extensive time series analysis of unsmoothed EEG microstate sequences in wakefulness and non-REM sleep stages across many time scales. Very short time scales are assessed with Markov tests, intermediate time scales by the entropy rate and long time scales by a spectral analysis which identifies characteristic microstate frequencies. During the descent from wakefulness to sleep stage N3, we find that the increasing mean microstate duration is a gradual phenomenon explained by a continuous slowing of microstate dynamics as described by the relaxation time of the transition probability matrix. The finite entropy rate, which considers longer microstate histories, shows that microstate sequences become more predictable (less random) with decreasing vigilance level. Accordingly, the Markov property is absent in wakefulness but in sleep stage N3, 10/19 subjects have microstate sequences compatible with a second-order Markov process. A spectral microstate analysis is performed by comparing the time-lagged mutual information coefficients of microstate sequences with the autocorrelation function of the underlying EEG. We find periodic microstate behavior in all vigilance states, linked to alpha frequencies in wakefulness, theta activity in N1, sleep spindle frequencies in N2, and in the delta frequency band in N3. In summary, we show that EEG microstates are a dynamic phenomenon with oscillatory properties that slow down in sleep and are coupled to specific EEG frequencies across several sleep stages.

Individual differences in the evaluation of ambiguous visual and auditory threat-related expressions

This study investigated the neural correlates of the judgement of auditory and visual ambiguous threat-related information, and the influence of state anxiety on this process. Healthy subjects were scanned using a fast, high-resolution functional magnetic resonance imaging (fMRI) multiband sequence while they performed a two-alternative forced-choice emotion judgement task on faces and vocal utterances conveying explicit anger or fear, as well as ambiguous ones. Critically, the latter was specific to each subject, obtained through a morphing procedure and selected prior to scanning following a perceptual decision-making task. Behavioural results confirmed a greater task-difficulty for subject-specific ambiguous stimuli and also revealed a judgement bias for visual fear, and, to a lesser extent, for auditory anger. Imaging results showed increased activity in regions of the salience and frontoparietal control networks (FPCNs) and deactivation in areas of the default mode network for ambiguous, relative to explicit, expressions. In contrast, the right amygdala (AMG) responded more strongly to explicit stimuli. Interestingly, its response to the same ambiguous stimulus depended on the subjective judgement of the expression. Finally, we found that behavioural and neural differences between ambiguous and explicit expressions decreased as a function of state anxiety scores. Taken together, our results show that behavioural and brain responses to emotional expressions are determined not only by emotional clarity but also modality and the subjects' subjective perception of the emotion expressed, and that some of these responses are modulated by state anxiety levels.

Distinct parallel activation and interaction between dorsal and ventral pathways during phonological and semantic processing: A cTBS-fMRI study

Successful visual word recognition requires the integration of phonological and semantic information, which is supported by the dorsal and ventral pathways in the brain. However, the functional specialization or interaction of these pathways during phonological and semantic processing remains unclear. Previous research has been limited by its dependence on correlational functional magnetic resonance imaging (fMRI) results or causal validation using patient populations, which are susceptible to confounds such as plasticity and lesion characteristics. To address this, the present study employed continuous theta-burst stimulation combined with fMRI in a within-subject design to assess rapid adaptation in regional activity and functional connectivity of the dorsal and ventral pathways during phonological and semantic tasks. This assessment followed the precise inhibition of the left inferior parietal lobule and anterior temporal lobe in the dorsal and ventral pathways, respectively. Our results reveal that both the dorsal and ventral pathways were activated during phonological and semantic processing, while the adaptation activation and interactive network were modulated by the task type and inhibited region. The two pathways exhibited interconnectivity in phonological processing, and disruption of either pathway led to rapid adaptation across both pathways. In contrast, only the ventral pathway exhibited connectivity in semantic processing, and disruption of this pathway alone resulted in adaptive effects primarily in the ventral pathway. These findings provide essential evidence supporting the interactive theory, phonological information processing in particular, potentially providing meaningful implications for clinical populations.

Aberrant Effective Connectivity During Eye Gaze Processing Is Linked to Social Functioning and Symptoms in Schizophrenia

BACKGROUND

Patients with schizophrenia show abnormal gaze processing, which is associated with social dysfunction. These abnormalities are related to aberrant connectivity among brain regions that are associated with visual processing, social cognition, and cognitive control. In this study, we investigated 1) how effective connectivity during gaze processing is disrupted in schizophrenia and 2) how this may contribute to social dysfunction and clinical symptoms.

METHODS

Thirty-nine patients with schizophrenia/schizoaffective disorder (SZ) and 33 healthy control participants completed an eye gaze processing task during functional magnetic resonance imaging. Participants viewed faces with different gaze angles and performed explicit and implicit gaze processing. Four brain regions-the secondary visual cortex, posterior superior temporal sulcus, inferior parietal lobule, and posterior medial frontal cortex-were identified as nodes for dynamic causal modeling analysis.

RESULTS

Both the SZ and healthy control groups showed similar model structures for general gaze processing. Explicit gaze discrimination led to changes in effective connectivity, including stronger excitatory, bottom-up connections from the secondary visual cortex to the posterior superior temporal sulcus and inferior parietal lobule and inhibitory, top-down connections from the posterior medial frontal cortex to the secondary visual cortex. Group differences in top-down modulation from the posterior medial frontal cortex to the posterior superior temporal sulcus and inferior parietal lobule were noted, such that these inhibitory connections were attenuated in the healthy control group but further strengthened in the SZ group. Connectivity was associated with social dysfunction and symptom severity.

CONCLUSIONS

The SZ group showed notably stronger top-down inhibition during explicit gaze discrimination, which was associated with more social dysfunction but less severe symptoms among patients. These findings help pinpoint neural mechanisms of aberrant gaze processing and may serve as future targets for interventions that combine neuromodulation with social cognitive training.

Frontoparietal functional connectivity moderates the link between time spent on social media and subsequent negative affect in daily life

Evidence on the harms and benefits of social media use is mixed, in part because the effects of social media on well-being depend on a variety of individual difference moderators. Here, we explored potential neural moderators of the link between time spent on social media and subsequent negative affect. We specifically focused on the strength of correlation among brain regions within the frontoparietal system, previously associated with the top-down cognitive control of attention and emotion. Participants (N = 54) underwent a resting state functional magnetic resonance imaging scan. Participants then completed 28 days of ecological momentary assessment and answered questions about social media use and negative affect, twice a day. Participants who spent more than their typical amount of time on social media since the previous time point reported feeling more negative at the present moment. This within-person temporal association between social media use and negative affect was mainly driven by individuals with lower resting state functional connectivity within the frontoparietal system. By contrast, time spent on social media did not predict subsequent affect for individuals with higher frontoparietal functional connectivity. Our results highlight the moderating role of individual functional neural connectivity in the relationship between social media and affect.

Neuroimaging in psychedelic drug development: past, present, and future

Psychedelic therapy (PT) is an emerging paradigm with great transdiagnostic potential for treating psychiatric disorders, including depression, addiction, post-traumatic stress disorder, and potentially others. 'Classic' serotonergic psychedelics, such as psilocybin and lysergic acid diethylamide (LSD), which have a key locus of action at the 5-HT2A receptor, form the main focus of this movement, but substances including ketamine, 3,4-Methylenedioxymethamphetamine (MDMA) and ibogaine also hold promise. The modern phase of development of these treatment modalities in the early 21st century has occurred concurrently with the wider use of advanced human neuroscientific research methods; principally neuroimaging. This can potentially enable assessment of drug and therapy brain effects with greater precision and quantification than any previous novel development in psychiatric pharmacology. We outline the major trends in existing data and suggest the modern development of PT has benefitted greatly from the use of neuroimaging. Important gaps in existing knowledge are identified, namely: the relationship between acute drug effects and longer-term (clinically-relevant) effects, the precise characterisation of effects at the 5-HT2A receptor and relationships with functional/clinical effects, and the possible impact of these compounds on neuroplasticity. A road-map for future research is laid out, outlining clinical studies which will directly address these three questions, principally using combined Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) methods, plus other adjunct techniques. Multimodal (PET/MRI) studies using modern PET techniques such as the 5-HT2A-selective ligand [11 C]Cimbi-36 (and other ligands sensitive to neuroplasticity changes) alongside MRI measures of brain function would provide a 'molecular-functional-clinical bridge' in understanding. Such results would help to resolve some of these questions and provide a firmer foundation for the ongoing development of PT.

Associations between regular cannabis use and brain resting-state functional connectivity in adolescents and adults

BACKGROUND/AIM

Cannabis use is highly prevalent in adolescents; however, little is known about its effects on adolescent brain function.

METHOD

Resting-state functional magnetic resonance imaging was used in matched groups of regular cannabis users (N = 70, 35 adolescents: 16-17 years old, 35 adults: 26-29 years old) and non-regular-using controls (N = 70, 35 adolescents/35 adults). Pre-registered analyses examined the connectivity of seven major cortical and sub-cortical brain networks (default mode network, executive control network (ECN), salience network, hippocampal network and three striatal networks) using seed-based analysis methods with cross-sectional comparisons between user groups and age groups.

RESULTS

The regular cannabis use group (across both age groups), relative to controls, showed localised increases in connectivity only in the ECN analysis. All networks showed localised connectivity differences based on age group, with the adolescents generally showing weaker connectivity than adults, consistent with the developmental effects. Mean connectivity across entire network regions of interest (ROIs) was also significantly decreased in the ECN in adolescents. However, there were no significant interactions found between age group and user group in any of the seed-based or ROI analyses. There were also no associations found between cannabis use frequency and any of the derived connectivity measures.

CONCLUSION

Regular cannabis use is associated with changes in connectivity of the ECN, which may reflect allostatic or compensatory changes in response to regular cannabis intoxication. However, these associations were not significantly different in adolescents compared to adults.

Effects of phase encoding direction on test-retest reliability of human functional connectome

The majority of human connectome studies in the literature based on functional magnetic resonance imaging (fMRI) data use either an anterior-to-posterior (AP) or a posterior-to-anterior (PA) phase encoding direction (PED). However, whether and how PED would affect test-retest reliability of functional connectome is unclear. Here, in a sample of healthy subjects with two sessions of fMRI scans separated by 12 weeks (two runs per session, one with AP, the other with PA), we tested the influence of PED on global, nodal, and edge connectivity in the constructed brain networks. All data underwent the state-of-the-art Human Connectome Project (HCP) pipeline to correct for phase-encoding-related distortions before entering analysis. We found that at the global level, the PA scans showed significantly higher intraclass correlation coefficients (ICCs) for global connectivity compared with AP scans, which was particularly prominent when using the Seitzman-300 atlas (versus the CAB-NP-718 atlas). At the nodal level, regions most strongly affected by PED were consistently mapped to the cingulate cortex, temporal lobe, sensorimotor areas, and visual areas, with significantly higher ICCs during PA scans compared with AP scans, regardless of atlas. Better ICCs were also observed during PA scans at the edge level, in particular when global signal regression (GSR) was not performed. Further, we demonstrated that the observed reliability differences between PEDs may relate to a similar effect on the reliability of temporal signal-to-noise ratio (tSNR) in the same regions (that PA scans were associated with higher reliability of tSNR than AP scans). Averaging the connectivity outcome from the AP and PA scans could increase median ICCs, especially at the nodal and edge levels. Similar results at the global and nodal levels were replicated in an independent, public dataset from the HCP-Early Psychosis (HCP-EP) study with a similar design but a much shorter scan session interval. Our findings suggest that PED has significant effects on the reliability of connectomic estimates in fMRI studies. We urge that these effects need to be carefully considered in future neuroimaging designs, especially in longitudinal studies such as those related to neurodevelopment or clinical intervention.

Increased low-frequency brain responses to music after psilocybin therapy for depression

BACKGROUND

Psychedelic-assisted psychotherapy with psilocybin is an emerging therapy with great promise for depression, and modern psychedelic therapy (PT) methods incorporate music as a key element. Music is an effective emotional/hedonic stimulus that could also be useful in assessing changes in emotional responsiveness following PT.

METHODS

Brain responses to music were assessed before and after PT using functional Magnetic Resonance Imaging (fMRI) and ALFF (Amplitude of Low Frequency Fluctuations) analysis methods. Nineteen patients with treatment-resistant depression underwent two treatment sessions involving administration of psilocybin, with MRI data acquired one week prior and the day after completion of psilocybin dosing sessions.

RESULTS

Comparison of music-listening and resting-state scans revealed significantly greater ALFF in bilateral superior temporal cortex for the post-treatment music scan, and in the right ventral occipital lobe for the post-treatment resting-state scan. ROI analyses of these clusters revealed a significant effect of treatment in the superior temporal lobe for the music scan only. Voxelwise comparison of treatment effects showed relative increases for the music scan in the bilateral superior temporal lobes and supramarginal gyrus, and relative decreases in the medial frontal lobes for the resting-state scan. ALFF in these music-related clusters was significantly correlated with intensity of subjective effects felt during the dosing sessions.

LIMITATIONS

Open-label trial. Relatively small sample size.

CONCLUSIONS

These data suggest an effect of PT on the brain's response to music, implying an elevated responsiveness to music after psilocybin therapy that was related to subjective drug effects felt during dosing.

The Effects of Acute Cannabis With and Without Cannabidiol on Neural Reward Anticipation in Adults and Adolescents

BACKGROUND

Adolescents may respond differently to cannabis than adults, yet no previous functional magnetic resonance imaging study has examined acute cannabis effects in this age group. In this study, we investigated the neural correlates of reward anticipation after acute exposure to cannabis in adolescents and adults.

METHODS

This was a double-blind, placebo-controlled, randomized, crossover experiment. Forty-seven adolescents (n = 24, 12 females, ages 16-17 years) and adults (n = 23, 11 females, ages 26-29 years) matched on cannabis use frequency (0.5-3 days/week) completed the Monetary Incentive Delay task during functional magnetic resonance imaging after inhaling cannabis with 0.107 mg/kg Δ⁹-tetrahydrocannabinol ("THC") (8 mg THC for a 75-kg person) or with THC plus 0.320 mg/kg cannabidiol ("THC+CBD") (24 mg CBD for a 75-kg person), or placebo cannabis. We investigated reward anticipation activity with whole-brain analyses and region of interest analyses in the right and left ventral striatum, right and left anterior cingulate cortex, and right insula.

RESULTS

THC reduced anticipation activity compared with placebo in the right (p = .005, d= 0.49) and left (p = .003, d = 0.50) ventral striatum and the right insula (p = .01, d = 0.42). THC+CBD reduced activity compared with placebo in the right ventral striatum (p = .01, d = 0.41) and right insula (p = .002, d = 0.49). There were no differences between "THC" and "THC+CBD" conditions and no significant drug by age group interaction effect, supported by Bayesian analyses. There were no significant effects in the whole-brain analyses.

CONCLUSIONS

In weekly cannabis users, cannabis suppresses the brain's anticipatory reward response to money, and CBD does not modulate this effect. Furthermore, the adolescent reward circuitry is not differentially sensitive to acute effects of cannabis on reward anticipation.

Network organization of resting-state cerebral hemodynamics and their aliasing contributions measured by functional near-infrared spectroscopy

Objective. Spontaneous fluctuations of cerebral hemodynamics measured by functional magnetic resonance imaging (fMRI) are widely used to study the network organization of the brain. The temporal correlations among the ultra-slow, <0.1 Hz fluctuations across the brain regions are interpreted as functional connectivity maps and used for diagnostics of neurological disorders. However, despite the interest narrowed in the ultra-slow fluctuations, hemodynamic activity that exists beyond the ultra-slow frequency range could contribute to the functional connectivity, which remains unclear.Approach. In the present study, we have measured the brain-wide hemodynamics in the human participants with functional near-infrared spectroscopy (fNIRS) in a whole-head, cap-based and high-density montage at a sampling rate of 6.25 Hz. In addition, we have acquired resting state fMRI scans in the same group of participants for cross-modal evaluation of the connectivity maps. Then fNIRS data were deliberately down-sampled to a typical fMRI sampling rate of ∼0.5 Hz and the resulted differential connectivity maps were subject to a k-means clustering.Main results. Our diffuse optical topographical analysis of fNIRS data have revealed a default mode network (DMN) in the spontaneous deoxygenated and oxygenated hemoglobin changes, which remarkably resemble the same fMRI network derived from participants. Moreover, we have shown that the aliased activities in the down-sampled optical signals have altered the connectivity patterns, resulting in a network organization of aliased functional connectivity in the cerebral hemodynamics.Significance.The results have for the first time demonstrated that fNIRS as a broadly accessible modality can image the resting-state functional connectivity in the posterior midline, prefrontal and parietal structures of the DMN in the human brain, in a consistent pattern with fMRI. Further empowered by the fast sampling rate of fNIRS, our findings suggest the presence of aliased connectivity in the current understanding of the human brain organization.

Thalamocortical contributions to working memory processes during the n-back task

Working memory allows individuals to temporally maintain and manipulate information that is no longer accessible from the sensorium. Whereas prior studies have detailed frontoparietal contributions to working memory processes, less emphasis has been placed on subcortical regions, in particular the human thalamus. The thalamus has a complex anatomy that consists of several distinct nuclei, many of which have dense anatomical connectivity with frontoparietal regions, and thus might play an important yet underspecified role for working memory. The goal of our study is to characterize the detailed functional neuroanatomy of the human thalamus and thalamocortical interactions during the n-back task. To that end, we analyzed an n-back fMRI dataset consisting of 395 subjects from the Human Connectome Project (HCP). We found that thalamic nuclei in the anterior, medial, ventral lateral, and posterior medial thalamus showed stronger evoked responses in response to higher working memory load. Activity in most thalamic nuclei were only modulated by working memory load, but not by categorical membership of the memorized stimuli, suggesting that thalamic function supports domain-general processing for working memory. To determine whether thalamocortical interactions contribute to cortical activity for working memory, we employed an activity flow mapping analysis to test whether thalamocortical interactions can predict cortical task activity patterns. In support, this data-driven thalamocortical interaction model explained a significant amount of variance in the observed cortical activity patterns modulated by working memory load. Our results suggest that the anterior, medial, and posterior medial thalamus, and their associated thalamocortical interactions, contribute to the modulations of distributed cortical activity during working memory.

A comparison of multiband and multiband multiecho gradient-echo EPI for task fMRI at 3 T

A multiband (MB) echo-planar imaging (EPI) sequence is compared to a multiband multiecho (MBME) EPI protocol to investigate differences in sensitivity for task functional magnetic resonance imaging (fMRI) at 3 T. Multiecho sampling improves sensitivity in areas where single-echo-EPI suffers from dropouts. However, It requires in-plane acceleration to reduce the echo train length, limiting the slice acceleration factor and the temporal and spatial resolution Data were acquired for both protocols in two sessions 24 h apart using an adapted color-word interference Stroop task. Besides protocol comparison statistically, we performed test-retest reliability across sessions for different protocols and denoising methods. We evaluated the sensitivity of two different echo-combination strategies for MBME-EPI. We examined the performance of three different data denoising approaches: "Standard," "AROMA," and "FIX" for MB and MBME, and assessed whether a specific method is preferable. We consider using an appropriate autoregressive model order within the general linear model framework to correct TR differences between the protocols. The comparison between protocols and denoising methods showed at group level significantly higher mean z-scores and the number of active voxels for MBME in the motor, subcortical and medial frontal cortices. When comparing different echo combinations, our results suggest that a contrast-to-noise ratio weighted echo combination improves sensitivity in MBME compared to simple echo-summation. This study indicates that MBME can be a preferred protocol in task fMRI at spatial resolution (≥2 mm), primarily in medial prefrontal and subcortical areas.

Neural responses to reward anticipation and feedback in adult and adolescent cannabis users and controls

Chronic use of drugs may alter the brain's reward system, though the extant literature concerning long-term cannabis use and neural correlates of reward processing has shown mixed results. Adolescents may be more vulnerable to the adverse effects of cannabis than adults; however, this has not been investigated for reward processing. As part of the 'CannTeen' study, in the largest functional magnetic resonance imaging study of reward processing and cannabis use to date, we investigated reward anticipation and feedback in 125 adult (26-29 years) and adolescent (16-17 years) cannabis users (1-7 days/week cannabis use) and gender- and age-matched controls, using the Monetary Incentive Delay task. Blood-oxygen-level-dependent responses were examined using region of interest (ROI) analyses in the bilateral ventral striatum for reward anticipation and right ventral striatum and left ventromedial prefrontal cortex for feedback, and exploratory whole-brain analyses. Results showed no User-Group or User-Group × Age-Group effects during reward anticipation or feedback in pre-defined ROIs. These null findings were supported by post hoc Bayesian analyses. However, in the whole-brain analysis, cannabis users had greater feedback activity in the prefrontal and inferior parietal cortex compared to controls. In conclusion, cannabis users and controls had similar neural responses during reward anticipation and in hypothesised reward-related regions during reward feedback. The whole-brain analysis revealed tentative evidence of greater fronto-parietal activity in cannabis users during feedback. Adolescents showed no increased vulnerability compared with adults. Overall, reward anticipation and feedback processing appear spared in adolescent and adult cannabis users, but future longitudinal studies are needed to corroborate this.

Lessons learned: A neuroimaging research center's transition to open and reproducible science

Human functional neuroimaging has evolved dramatically in recent years, driven by increased technical complexity and emerging evidence that functional neuroimaging findings are not generally reproducible. In response to these trends, neuroimaging scientists have developed principles, practices, and tools to both manage this complexity as well as to enhance the rigor and reproducibility of neuroimaging science. We group these best practices under four categories: experiment pre-registration, FAIR data principles, reproducible neuroimaging analyses, and open science. While there is growing recognition of the need to implement these best practices there exists little practical guidance of how to accomplish this goal. In this work, we describe lessons learned from efforts to adopt these best practices within the Brain Imaging Research Center at the University of Arkansas for Medical Sciences over 4 years (July 2018-May 2022). We provide a brief summary of the four categories of best practices. We then describe our center's scientific workflow (from hypothesis formulation to result reporting) and detail how each element of this workflow maps onto these four categories. We also provide specific examples of practices or tools that support this mapping process. Finally, we offer a roadmap for the stepwise adoption of these practices, providing recommendations of why and what to do as well as a summary of cost-benefit tradeoffs for each step of the transition.

Performance of Temporal and Spatial Independent Component Analysis in Identifying and Removing Low-Frequency Physiological and Motion Effects in Resting-State fMRI

Effective separation of signal from noise (including physiological processes and head motion) is one of the chief challenges for improving the sensitivity and specificity of resting-state fMRI (rs-fMRI) measurements and has a profound impact when these noise sources vary between populations. Independent component analysis (ICA) is an approach for addressing these challenges. Conventionally, due to the lower amount of temporal than spatial information in rs-fMRI data, spatial ICA (sICA) is the method of choice. However, with recent developments in accelerated fMRI acquisitions, the temporal information is becoming enriched to the point that the temporal ICA (tICA) has become more feasible. This is particularly relevant as physiological processes and motion exhibit very different spatial and temporal characteristics when it comes to rs-fMRI applications, leading us to conduct a comparison of the performance of sICA and tICA in addressing these types of noise. In this study, we embrace the novel practice of using theory (simulations) to guide our interpretation of empirical data. We find empirically that sICA can identify more noise-related signal components than tICA. However, on the merit of functional-connectivity results, we find that while sICA is more adept at reducing whole-brain motion effects, tICA performs better in dealing with physiological effects. These interpretations are corroborated by our simulation results. The overall message of this study is that if ICA denoising is to be used for rs-fMRI, there is merit in considering a hybrid approach in which physiological and motion-related noise are each corrected for using their respective best-suited ICA approach.

Functional MRI in major depressive disorder: A review of findings, limitations, and future prospects

Objective diagnosis and prognosis in major depressive disorder (MDD) remains a challenge due to the absence of biomarkers based on physiological parameters or medical tests. Numerous studies have been conducted to identify functional magnetic resonance imaging‐based biomarkers of depression that either objectively differentiate patients with depression from healthy subjects, predict personalized treatment outcome, or characterize biological subtypes of depression. While there are some findings of consistent functional biomarkers, there is still lack of robust data acquisition and analysis methodology. According to current findings, primarily, the anterior cingulate cortex, prefrontal cortex, and default mode network play a crucial role in MDD. Yet, there are also less consistent results and the involvement of other regions or networks remains ambiguous. We further discuss image acquisition, processing, and analysis limitations that might underlie these inconsistencies. Finally, the current review aims to address and discuss possible remedies and future opportunities that could improve the search for consistent functional imaging biomarkers of depression. Novel acquisition techniques, such as multiband and multiecho imaging, and neural network‐based cleaning approaches can enhance the signal quality in limbic and frontal regions. More comprehensive analyses, such as directed or dynamic functional features or the identification of biological depression subtypes, can improve objective diagnosis or treatment outcome prediction and mitigate the heterogeneity of MDD. Overall, these improvements in functional MRI imaging techniques, processing, and analysis could advance the search for biomarkers and ultimately aid patients with MDD and their treatment course.

Multi-band FMRI compromises detection of mesolimbic reward responses

Recent innovations in Functional Magnetic Resonance Imaging (FMRI) have sped data collection by enabling simultaneous scans of neural activity in multiple brain locations, but have these innovations come at a cost? In a meta-analysis and preregistered direct comparison of original data, we examined whether acquiring FMRI data with multi-band versus single-band scanning protocols might compromise detection of mesolimbic activity during reward processing. Meta-analytic results (n = 44 studies; cumulative n = 5005 subjects) indicated that relative to single-band scans, multi-band scans showed significantly decreased effect sizes for reward anticipation in the Nucleus Accumbens (NAcc) by more than half. Direct within-subject comparison of single-band versus multi-band scanning data (multi-band factors = 4 and 8; n = 12 subjects) acquired during repeated administration of the Monetary Incentive Delay task indicated that reductions in temporal signal-to-noise ratio could account for compromised detection of task-related responses in mesolimbic regions (i.e., the NAcc). Together, these findings imply that researchers should opt for single-band over multi-band scanning protocols when probing mesolimbic responses with FMRI. The findings also have implications for inferring mesolimbic activity during related tasks and rest, for summarizing historical results, and for using neuroimaging data to track individual differences in reward-related brain activity.

Personality network neuroscience: Promises and challenges on the way toward a unifying framework of individual variability

We propose that the application of network theory to established psychological personality conceptions has great potential to advance a biologically plausible model of human personality. Stable behavioral tendencies are conceived as personality “traits.” Such traits demonstrate considerable variability between individuals, and extreme expressions represent risk factors for psychological disorders. Although the psychometric assessment of personality has more than hundred years tradition, it is not yet clear whether traits indeed represent “biophysical entities” with specific and dissociable neural substrates. For instance, it is an open question whether there exists a correspondence between the multilayer structure of psychometrically derived personality factors and the organizational properties of traitlike brain systems. After a short introduction into fundamental personality conceptions, this article will point out how network neuroscience can enhance our understanding about human personality. We will examine the importance of intrinsic (task-independent) brain connectivity networks and show means to link brain features to stable behavioral tendencies. Questions and challenges arising from each discipline itself and their combination are discussed and potential solutions are developed. We close by outlining future trends and by discussing how further developments of network neuroscience can be applied to personality research.

Imaging the temporal dynamics of brain states with highly sampled fMRI

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

Contribution of the multi-echo approach in accelerated functional magnetic resonance imaging multiband acquisition

We wanted to verify the effect of combining multi‐echo (ME) functional magnetic resonance imaging (fMRI) with slice acceleration in simultaneous multi‐slice acquisition. The aim was to shed light on the benefits of multiple echoes for various acquisition settings, especially for levels of slice acceleration and flip angle. Whole‐brain ME fMRI data were obtained from 26 healthy volunteers (using three echoes; seven runs with slice acceleration 1, 4, 6, and 8; and two different flip angles for each of the first three acceleration factors) and processed as single‐echo (SE) data and ME data based on optimal combinations weighted by the contrast‐to‐noise ratio. Global metrics (temporal signal‐to‐noise ratio, signal‐to‐noise separation, number of active voxels, etc.) and local characteristics in regions of interest were used to evaluate SE and ME data. ME results outperformed SE results in all runs; the differences became more apparent for higher acceleration, where a significant decrease in data quality is observed. ME fMRI can improve the observed data quality metrics over SE fMRI for a wide range of accelerated fMRI acquisitions.

Advances in resting state fMRI acquisitions for functional connectomics

Resting state functional magnetic resonance imaging (rs-fMRI) is based on spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal, which occur simultaneously in different brain regions, without the subject performing an explicit task. The low-frequency oscillations of the rs-fMRI signal demonstrate an intrinsic spatiotemporal organization in the brain (brain networks) that may relate to the underlying neural activity. In this review article, we briefly describe the current acquisition techniques for rs-fMRI data, from the most common approaches for resting state acquisition strategies, to more recent investigations with dedicated hardware and ultra-high fields. Specific sequences that allow very fast acquisitions, or multiple echoes, are discussed next. We then consider how acquisition methods weighted towards specific parts of the BOLD signal, like the Cerebral Blood Flow (CBF) or Volume (CBV), can provide more spatially specific network information. These approaches are being developed alongside the commonly used BOLD-weighted acquisitions. Finally, specific applications of rs-fMRI to challenging regions such as the laminae in the neocortex, and the networks within the large areas of subcortical white matter regions are discussed. We finish the review with recommendations for acquisition strategies for a range of typical applications of resting state fMRI.

Statistical power or more precise insights into neuro-temporal dynamics? Assessing the benefits of rapid temporal sampling in fMRI

Functional magnetic resonance imaging (fMRI), a non-invasive and widely used human neuroimaging method, is most known for its spatial precision. However, there is a growing interest in its temporal sensitivity. This is despite the temporal blurring of neuronal events by the blood oxygen level dependent (BOLD) signal, the peak of which lags neuronal firing by 4-6 seconds. Given this, the goal of this review is to answer a seemingly simple question - "What are the benefits of increased temporal sampling for fMRI?". To answer this, we have combined fMRI data collected at multiple temporal scales, from 323 to 1000 milliseconds, with a review of both historical and contemporary temporal literature. After a brief discussion of technological developments that have rekindled interest in temporal research, we next consider the potential statistical and methodological benefits. Most importantly, we explore how fast fMRI can uncover previously unobserved neuro-temporal dynamics - effects that are entirely missed when sampling at conventional 1 to 2 second rates. With the intrinsic link between space and time in fMRI, this temporal renaissance also delivers improvements in spatial precision. Far from producing only statistical gains, the array of benefits suggest that the continued temporal work is worth the effort.

Weight Loss by Low-Calorie Diet Versus Gastric Bypass Surgery in People With Diabetes Results in Divergent Brain Activation Patterns: A Functional MRI Study

OBJECTIVE

Weight loss achieved with very-low-calorie diets (VLCDs) can produce remission of type 2 diabetes (T2D), but weight regain very often occurs with reintroduction of higher calorie intakes. In contrast, bariatric surgery produces clinically significant and durable weight loss, with diabetes remission that translates into reductions in mortality. We hypothesized that in patients living with obesity and prediabetes/T2D, longitudinal changes in brain activity in response to food cues as measured using functional MRI would explain this difference.

RESEARCH DESIGN AND METHODS

Sixteen participants underwent gastric bypass surgery, and 19 matched participants undertook a VLCD (meal replacement) for 4 weeks. Brain responses to food cues and resting-state functional connectivity were assessed with functional MRI pre- and postintervention and compared across groups.

RESULTS

We show that Roux-en-Y gastric bypass surgery (RYGB) results in three divergent brain responses compared with VLCD-induced weight loss: 1) VLCD resulted in increased brain reward center food cue responsiveness, whereas in RYGB, this was reduced; 2) VLCD resulted in higher neural activation of cognitive control regions in response to food cues associated with exercising increased cognitive restraint over eating, whereas RYGB did not; and 3) a homeostatic appetitive system (centered on the hypothalamus) is better engaged following RYGB-induced weight loss than VLCD.

CONCLUSIONS

Taken together, these findings point to divergent brain responses to different methods of weight loss in patients with diabetes, which may explain weight regain after a short-term VLCD in contrast to enduring weight loss after RYGB.

Safety and data quality of EEG recorded simultaneously with multi-band fMRI

PURPOSE

Simultaneously recorded electroencephalography and functional magnetic resonance imaging (EEG-fMRI) is highly informative yet technically challenging. Until recently, there has been little information about EEG data quality and safety when used with newer multi-band (MB) fMRI sequences. Here, we measure the relative heating of a MB protocol compared with a standard single-band (SB) protocol considered to be safe. We also evaluated EEG quality recorded concurrently with the MB protocol on humans.

MATERIALS AND METHODS

We compared radiofrequency (RF)-related heating at multiple electrodes and magnetic field magnitude, B1+RMS, of a MB fMRI sequence with whole-brain coverage (TR = 440 ms, MB factor = 4) against a previously recommended, safe SB sequence using a phantom outfitted with a 64-channel EEG cap. Next, 9 human subjects underwent eyes-closed resting state EEG-fMRI using the MB sequence. Additionally, in three of the subjects resting state EEG was recorded also during the SB sequence and in an fMRI-free condition to directly compare EEG data quality across scanning conditions. EEG data quality was assessed by the ability to remove gradient and cardioballistic artifacts along with a clean spectrogram.

RESULTS

The heating induced by the MB sequence was lower than that of the SB sequence by a factor of 0.73 ± 0.38. This is consistent with an expected heating ratio of 0.64, calculated from the square of the ratio of B1+RMS values of the sequences. In the resting state EEG data, gradient and cardioballistic artifacts were successfully removed using traditional template subtraction. All subjects showed an individual alpha peak in the spectrogram with a posterior topography characteristic of eyes-closed EEG. The success of artifact rejection for the MB sequence was comparable to that in traditional SB sequences.

CONCLUSIONS

Our study shows that B1+RMS is a useful indication of the relative heating of fMRI protocols. This observation indicates that simultaneous EEG-fMRI recordings using this MB sequence can be safe in terms of RF-related heating, and that EEG data recorded using this sequence is of acceptable quality after traditional artifact removal techniques.

Which multiband factor should you choose for your resting-state fMRI study?

Multiband acquisition, also called simultaneous multislice, has become a popular technique in resting-state functional connectivity studies. Multiband (MB) acceleration leads to a higher temporal resolution but also leads to spatially heterogeneous noise amplification, suggesting the costs may be greater in areas such as the subcortex. We evaluate MB factors of 2, 3, 4, 6, 8, 9, and 12 with 2 mm isotropic voxels, and additionally 2 mm and 3.3 mm single-band acquisitions, on a 32-channel head coil. Noise amplification was greater in deeper brain regions, including subcortical regions. Correlations were attenuated by noise amplification, which resulted in spatially varying biases that were more severe at higher MB factors. Temporal filtering decreased spatial biases in correlations due to noise amplification, but also tended to decrease effect sizes. In seed-based correlation maps, left-right putamen connectivity and thalamo-motor connectivity were highest in the single-band 3.3 mm protocol. In correlation matrices, MB 4, 6, and 8 had a greater number of significant correlations than the other acquisitions (both with and without temporal filtering). We recommend single-band 3.3 mm for seed-based subcortical analyses, and MB 4 provides a reasonable balance for studies analyzing both seed-based correlation maps and connectivity matrices. In multiband studies including secondary analyses of large-scale datasets, we recommend reporting effect sizes or test statistics instead of correlations. If correlations are reported, temporal filtering (or another method for thermal noise removal) should be used. The Emory Multiband Dataset is available on OpenNeuro.

The Paternal Brain in Action: A Review of Human Fathers' fMRI Brain Responses to Child-Related Stimuli

As fathers are increasingly involved in childcare, understanding the neurological underpinnings of fathering has become a key research issue in developmental psychobiology research. This systematic review specifically focused on (1) highlighting methodological issues of paternal brain research using functional magnetic resonance imaging (fMRI) and (2) summarizing findings related to paternal brain responses to auditory and visual infant stimuli. Sixteen papers were included from 157 retrieved records. Sample characteristics (e.g., fathers’ and infant’s age, number of kids, and time spent caregiving), neuroimaging information (e.g., technique, task, stimuli, and processing), and main findings were synthesized by two independent authors. Most of the reviewed works used different stimuli and tasks to test fathers’ responses to child visual and/or auditory stimuli. Pre-processing and first-level analyses were performed with standard pipelines. Greater heterogeneity emerged in second-level analyses. Three main cortical networks (mentalization, embodied simulation, and emotion regulation) and a subcortical network emerged linked with fathers’ responses to infants’ stimuli, but additional areas (e.g., frontal gyrus, posterior cingulate cortex) were also responsive to infants’ visual or auditory stimuli. This review suggests that a distributed and complex brain network may be involved in facilitating fathers’ sensitivity and responses to infant-related stimuli. Nonetheless, specific methodological caveats, the exploratory nature of large parts of the literature to date, and the presence of heterogeneous tasks and measures also demonstrate that systematic improvements in study designs are needed to further advance the field.

Intra-session test-retest reliability of functional connectivity in infants

Resting functional MRI studies of the infant brain are increasingly becoming an important tool in developmental neuroscience. Whereas the test-retest reliability of functional connectivity (FC) measures derived from resting fMRI data have been characterized in the adult and child brain, similar assessments have not been conducted in infants. In this study, we examined the intra-session test-retest reliability of FC measures from 119 infant brain MRI scans from four neurodevelopmental studies. We investigated edge-level and subject-level reliability within one MRI session (between and within runs) measured by the Intraclass correlation coefficient (ICC). First, using an atlas-based approach, we examined whole-brain connectivity as well as connectivity within two common resting fMRI networks - the default mode network (DMN) and the sensorimotor network (SMN). Second, we examined the influence of run duration, study site, and scanning manufacturer (e.g., Philips and General Electric) on ICCs. Lastly, we tested spatial similarity using the Jaccard Index from networks derived from independent component analysis (ICA). Consistent with resting fMRI studies from adults, our findings indicated poor edge-level reliability (ICC = 0.14-0.18), but moderate-to-good subject-level intra-session reliability for whole-brain, DMN, and SMN connectivity (ICC = 0.40-0.78). We also found significant effects of run duration, site, and scanning manufacturer on reliability estimates. Some ICA-derived networks showed strong spatial reproducibility (e.g., DMN, SMN, and Visual Network), and were labelled based on their spatial similarity to analogous networks measured in adults. These networks were reproducibly found across different study sites. However, other ICA-networks (e.g. Executive Control Network) did not show strong spatial reproducibility, suggesting that the reliability and/or maturational course of functional connectivity may vary by network. In sum, our findings suggest that developmental scientists may be on safe ground examining the functional organization of some major neural networks (e.g. DMN and SMN), but judicious interpretation of functional connectivity is essential to its ongoing success.

Detecting Task Functional MRI Activation Using the Multiband Multiecho (MBME) Echo-Planar Imaging (EPI) Sequence

BACKGROUND

Blood oxygen level-dependent (BOLD) functional MRI (fMRI) has been widely applied to detect brain activations. Recent advances in multiband (MB) and multiecho (ME) techniques have greatly improved fMRI methods. MB imaging improves temporal and/or spatial resolution, while ME imaging has been shown to improve BOLD sensitivity. This study aimed to evaluate the novel MBME echo planar imaging (EPI) sequence utilizing MB and ME simultaneously to determine if the MBME outperform the MB single echo (MBSE) sequence for task fMRI.

PURPOSE

To compare the performance of MBME with MBSE in a task fMRI study.

STUDY TYPE

Prospective.

POPULATION

A total of 29 healthy volunteers aged 20-46 years (9 male, 20 female).

FIELD STRENGTH/SEQUENCE

MBSE and MBME gradient-echo EPI sequences were applied at 3T. Additional T1 -weighted magnetization-prepared rapid acquisition with gradient echo (MPRAGE) was collected.

ASSESSMENT

A checkerboard visual task was presented during the functional MBSE and MBME scans. The MBME or MBSE signal was evaluated using the temporal signal-to-noise ratio (tSNR). Task activation was evaluated using the z-score, volume, sensitivity, and specificity. Test-retest metrics of task activation were examined with the Dice coefficient (DC) and intraclass correlation coefficient (ICC) on subjects with repeated scans.

STATISTICAL TESTS

A linear mixed-effects model was used to compared MBME and MBSE activation at the voxel base. The paired t-test was used to compare tSNR, activation z-score, and volume, along with sensitivity, specificity, and DC between MBSE and MBME.

RESULTS

While similar task activation was detected in the visual cortex, MBME showed higher activation volume and higher sensitivity compared with MBSE (P < 0.05). ICC was higher for MBME than MBSE, while there was a trend of differences in DC (P = 0.08).

DATA CONCLUSION

MBME resulted in higher task fMRI activation volume and sensitivity without losing specificity. Reliability was also higher for MBME scans compared with MBSE.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY STAGE: 1.

Hybrid auditory fMRI: In pursuit of increasing data acquisition while decreasing the impact of scanner noise

BACKGROUND

Two challenges in auditory fMRI include the loud scanner noise during sound presentation and slow data acquisition. Here, we introduce a new auditory imaging protocol, termed "hybrid", that alleviates these obstacles.

NEW METHOD

We designed a within-subject experiment (N = 14) wherein language-driven activity was measured by hybrid, interleaved silent (ISSS), and continuous multiband acquisition. To determine the advantage of noise attenuation during sound presentation, hybrid was compared to multiband. To identify the benefits of increased temporal resolution, hybrid was compared to ISSS. Data were evaluated by whole-brain univariate general linear modeling (GLM) and multivariate pattern analysis (MVPA).

RESULTS

Comparison with existing methods: CONCLUSIONS: Our data revealed that hybrid imaging restored neural activity in the canonical language network that was absent due to the loud noise or slow sampling in the conventional imaging protocols. With its noise-attenuated sound presentation windows and increased acquisition speed, the hybrid protocol is well-suited for auditory fMRI research tracking neural activity pertaining to fast, time-varying acoustic events.

Exploring communication between the thalamus and cognitive control-related functional networks in the cerebral cortex

It has been suggested by multiple studies (postmortem studies, invasive animal studies, and diffusion tensor imaging in the human brain) that the thalamus is important for communication among cortical regions. Many functional magnetic resonance imaging (fMRI) studies, including noninvasive and whole-brain studies, have reported thalamic co-activation with several cognitive control-related cortical systems. This forms a complex network that may be important for advanced cognitive control-related processes, such as working memory and attention. Nevertheless, how the thalamus communicates with the cognitive control-related network in the intact human brain is an essential question and needs further investigation. To address this question, we conducted a study using dynamic functional connectivity analysis and effective connectivity analysis based on fMRI data from young, healthy adult participants. The results showed that the middle thalamus exhibited both high in- and out-degree regarding the complex network related to cognitive control during both rest and task conditions. Furthermore, intrinsic communication via the middle thalamic regions showed dynamically co-varying patterns, and the thalamic regions showed high flexibility in dynamic community analysis. These results indicated that the mid-thalamic region is an important station for communication between nodes in cognitive control-related networks.

Test-retest reliability of functional MRI food receipt, anticipated receipt, and picture tasks

BACKGROUND

Functional MRI (fMRI) tasks are increasingly being used to advance knowledge of the etiology and maintenance of obesity and eating disorders. Thus, understanding the test-retest reliability of BOLD signal contrasts from these tasks is important.

OBJECTIVES

To evaluate test-retest reliability of responses in reward-related brain regions to food receipt paradigms (palatable tastes, anticipated palatable tastes), food picture paradigms (high-calorie food pictures), a monetary reward paradigm (winning money and anticipating winning money), and a thin female model picture paradigm (thin female model pictures).

METHOD

We conducted secondary univariate contrast-based analyses in data drawn from 4 repeated-measures fMRI studies. Participants (Study 1: N = 60, mean [M] age = 15.2 ± 1.1 y; Study 2: N = 109, M age = 15.1 ± 0.9 y; Study 3: N = 39, M age = 21.2 ± 3.7 y; Study 4: N = 62, M age = 29.7 ± 6.2 y) completed the same tasks over 3-wk to 3-y test-retest intervals. Studies 3 and 4 included participants with eating disorders and obesity, respectively.

RESULTS

Test-retest reliability of the food receipt and food picture paradigms was poor, with average ICC values ranging from 0.07 to 0.20. The monetary reward paradigm and the thin female model picture paradigm also showed poor test-retest reliability: average ICC values 0.21 and 0.12, respectively. Although several regions demonstrated moderate to good test-retest reliability, these results did not replicate across studies using similar paradigms. In Studies 3 and 4, but not Study 1, test-retest reliability in visual processing regions was moderate to good when contrasting single conditions with a low-level baseline.

CONCLUSIONS

Results underscore the importance of examining the temporal reliability of fMRI tasks and call for the development and use of well-validated standardized fMRI tasks in eating- and obesity-related studies that can provide more reliable measures of neural activation. The trials were registered at clinicaltrials.gov as NCT02084836, NCT01949636, NCT03261050, and NCT03375853.

Brain dynamics and structure-function relationships via spectral factorization and the transfer function

It is shown how the brain's linear transfer function provides a means to systematically analyze brain connectivity and dynamics, and to infer connectivity, eigenmodes, and activity measures such as spectra, evoked responses, coherence, and causality, all of which are widely used in brain monitoring. In particular, the Wilson spectral factorization algorithm is outlined and used to efficiently obtain linear transfer functions from experimental two-point correlation functions. The algorithm is tested on a series of brain-like structures of increasing complexity which include time delays, asymmetry, two-dimensionality, and complex network connectivity. These tests are used to verify the algorithm is suitable for application to brain dynamics, specify sampling requirements for experimental time series, and to verify that its runtime is short enough to obtain accurate results for systems of similar size to current experiments. The results can equally well be applied to inference of the transfer function in complex linear systems other than brains.

Recent Advances in Neuroimaging of Epilepsy

Human neuroimaging has had a major impact on the biological understanding of epilepsy and the relationship between pathophysiology, seizure management, and outcomes. This review highlights notable recent advancements in hardware, sequences, methods, analyses, and applications of human neuroimaging techniques utilized to assess epilepsy. These structural, functional, and metabolic assessments include magnetic resonance imaging (MRI), positron emission tomography (PET), and magnetoencephalography (MEG). Advancements that highlight non-invasive neuroimaging techniques used to study the whole brain are emphasized due to the advantages these provide in clinical and research applications. Thus, topics range across presurgical evaluations, understanding of epilepsy as a network disorder, and the interactions between epilepsy and comorbidities. New techniques and approaches are discussed which are expected to emerge into the mainstream within the next decade and impact our understanding of epilepsies. Further, an increasing breadth of investigations includes the interplay between epilepsy, mental health comorbidities, and aberrant brain networks. In the final section of this review, we focus on neuroimaging studies that assess bidirectional relationships between mental health comorbidities and epilepsy as a model for better understanding of the commonalities between both conditions.

An empirical investigation of the benefit of increasing the temporal resolution of task-evoked fMRI data with multi-band imaging

Objective

There is a tendency for reducing TR in MRI experiments with multi-band imaging. We empirically investigate its benefit for the group-level statistical outcome in task-evoked fMRI.

Methods

Three visual fMRI data sets were collected from 17 healthy adult participants. Multi-band acquisition helped vary the TR (2000/1000/410 ms, respectively). Because these data sets capture different temporal aspects of the haemodynamic response (HRF), we tested several HRF models. We computed a composite descriptive statistic, H, from β’s of each first-level model fit and carried it to the group-level analysis. The number of activated voxels and the t value of the group-level analysis as well as a goodness-of-fit measure were used as surrogate markers of data quality for comparison.

Results

Increasing the temporal sampling rate did not provide a universal improvement in the group-level statistical outcome. Rather, both the voxel-wise and ROI-averaged group-level results varied widely with anatomical location, choice of HRF and the setting of the TR. Correspondingly, the goodness-of-fit of HRFs became worse with increasing the sampling frequency.

Conclusion

Rather than universally increasing the temporal sampling rate in cognitive fMRI experiments, these results advocate the performance of a pilot study for the specific ROIs of interest to identify the appropriate temporal sampling rate for the acquisition and the correspondingly suitable HRF for the analysis of the data.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10334-021-00918-z.

The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses

We present the Amsterdam Open MRI Collection (AOMIC): three datasets with multimodal (3 T) MRI data including structural (T1-weighted), diffusion-weighted, and (resting-state and task-based) functional BOLD MRI data, as well as detailed demographics and psychometric variables from a large set of healthy participants (N = 928, N = 226, and N = 216). Notably, task-based fMRI was collected during various robust paradigms (targeting naturalistic vision, emotion perception, working memory, face perception, cognitive conflict and control, and response inhibition) for which extensively annotated event-files are available. For each dataset and data modality, we provide the data in both raw and preprocessed form (both compliant with the Brain Imaging Data Structure), which were subjected to extensive (automated and manual) quality control. All data is publicly available from the OpenNeuro data sharing platform.

A method to adjust a prior distribution in Bayesian second-level fMRI analysis

Previous research has shown the potential value of Bayesian methods in fMRI (functional magnetic resonance imaging) analysis. For instance, the results from Bayes factor-applied second-level fMRI analysis showed a higher hit rate compared with frequentist second-level fMRI analysis, suggesting greater sensitivity. Although the method reported more positives as a result of the higher sensitivity, it was able to maintain a reasonable level of selectivity in term of the false positive rate. Moreover, employment of the multiple comparison correction method to update the default prior distribution significantly improved the performance of Bayesian second-level fMRI analysis. However, previous studies have utilized the default prior distribution and did not consider the nature of each individual study. Thus, in the present study, a method to adjust the Cauchy prior distribution based on a priori information, which can be acquired from the results of relevant previous studies, was proposed and tested. A Cauchy prior distribution was adjusted based on the contrast, noise strength, and proportion of true positives that were estimated from a meta-analysis of relevant previous studies. In the present study, both the simulated images and real contrast images from two previous studies were used to evaluate the performance of the proposed method. The results showed that the employment of the prior adjustment method resulted in improved performance of Bayesian second-level fMRI analysis.

Higher temporal resolution multiband fMRI provides improved presurgical language maps

PURPOSE

We investigated the hypothesis that increasing fMRI temporal resolution using a multiband (MB) gradient echo-echo planar imaging (GRE-EPI) pulse sequence provides fMRI language maps of higher statistical quality than those acquired with a traditional GRE-EPI sequence.

METHODS

This prospective study enrolled 29 consecutive patients receiving language fMRI prior to a potential brain resection for tumor, AVM, or epilepsy. A 4-min rhyming task was performed at 3.0 Tesla with a traditional GRE-EPI pulse sequence (TR = 2000, TE = 30, matrix = 64/100%, slice = 4/0, FOV = 24, slices = 30, time points = 120) and an additional MB GRE-EPI pulse sequence with an acceleration factor of 6 (TR = 333, TE = 30, matrix 64/100%, slice = 4/0, FOV = 24, time points = 720). Spatially filtered t statistical maps were generated. Volumes of interest (VOIs) were drawn around activations at Broca's, dorsolateral prefrontal cortex, Wernicke's, and the visual word form areas. The t value maxima were measured for the overall brain and each of the VOIs. A paired t test was performed for the corresponding traditional and MB GRE-EPI measurements.

RESULTS

The mean age of subjects was 42.6 years old (18-75). Sixty-two percent were male. The average overall brain t statistic maxima for the MB pulse sequence (t = 15.4) was higher than for the traditional pulse sequence (t = 9.3, p = < .0001). This also held true for Broca's area (p < 0.0001), Wernicke's area (p < .0001), dorsolateral prefrontal cortex (p < .0001), and the visual word form area (p < .0001).

CONCLUSION

A MB GRE-EPI fMRI pulse sequence employing high temporal resolution provides clinical fMRI language maps of greater statistical significance than those obtained with a traditional GRE-EPI sequence.

Hemodynamic Response Function in Brain White Matter in a Resting State

The hemodynamic response function (HRF) characterizes temporal variations of blood oxygenation level-dependent (BOLD) signals. Although a variety of HRF models have been proposed for gray matter responses to functional demands, few studies have investigated HRF profiles in white matter particularly under resting conditions. In the present work we quantified the nature of the HRFs that are embedded in resting state BOLD signals in white matter, and which modulate the temporal fluctuations of baseline signals. We demonstrate that resting state HRFs in white matter could be derived by referencing to intrinsic avalanches in gray matter activities, and the derived white matter HRFs had reduced peak amplitudes and delayed peak times as compared with those in gray matter. Distributions of the time delays and correlation profiles in white matter depend on gray matter activities as well as white matter tract distributions, indicating that resting state BOLD signals in white matter encode neural activities associated with those of gray matter. This is the first investigation of derivations and characterizations of resting state HRFs in white matter and their relations to gray matter activities. Findings from this work have important implications for analysis of BOLD signals in the brain.

Altered complexity of resting-state BOLD activity in Alzheimer's disease-related neurodegeneration: a multiscale entropy analysis

Brain complexity, which reflects the ability of the brain to adapt to a changing environment, has been found to be significantly changed with age. However, there is less evidence on the alterations of brain complexity in neurodegenerative disorders such as Alzheimer's disease (AD). Here we investigated the altered complexity of resting-state blood oxygen level-dependent signals in AD-related neurodegeneration using multiscale entropy (MSE) analysis. All participants were recruited from the Alzheimer's Disease Neuroimaging Initiative, including healthy controls (HC, n=62), amnestic mild cognitive impairment (aMCI, n =81) patients, and Alzheimer's disease (AD, n=25) patients. Our results showed time scale-dependent MSE differences across the three groups. In scale=1, significantly changed MSE patterns (HC>aMCI>AD) were found in four brain regions, including the hippocampus, middle frontal gyrus, intraparietal lobe, and superior frontal gyrus. In scale=4, reversed MSE patterns (HC<aMCI<AD) were found in the middle frontal gyrus and middle occipital gyrus. Furthermore, the values of regional entropy were significantly associated with cognitive functions positively on the short time scale, while negatively on the longer time scale. Our findings suggest that MSE could be a reliable measure for characterizing brain deterioration in AD, and may provide insights into the neural mechanism of AD-related neurodegeneration.

Current methods and new directions in resting state fMRI

Resting state functional connectivity magnetic resonance imaging (rsfcMRI) has become a key component of investigations of neurocognitive and psychiatric behaviors. Over the past two decades, several methods and paradigms have been adopted to utilize and interpret data from resting-state fluctuations in the brain. These findings have increased our understanding of changes in many disease states. As the amount of resting state data available for research increases with big datasets and data-sharing projects, it is important to review the established traditional analysis methods and recognize areas where research methodology can be adapted to better accommodate the scale and complexity of rsfcMRI analysis. In this paper, we review established methods of analysis as well as areas that have been receiving increasing attention such as dynamic rsfcMRI, independent vector analysis, multiband rsfcMRI and network of networks.

Temporal Signal-to-Noise Changes in Combined Multislice- and In-Plane-Accelerated Echo-Planar Imaging with a 20- and 64-Channel Coil

Echo-planar imaging (EPI) is the most common method of functional MRI for acquiring the blood oxygenation level-dependent (BOLD) contrast, allowing the acquisition of an entire brain volume within seconds. However, because imaging protocols are limited by hardware (e.g., fast gradient switching), researchers must compromise between spatial resolution, temporal resolution, or whole-brain coverage. Earlier attempts to circumvent this problem included developing protocols in which slices of a volume were acquired faster (i.e., in-plane acceleration (S)) or simultaneously (i.e., multislice acceleration (M)). However, applying acceleration methods can lead to a reduction in the temporal signal-to-noise ratio (tSNR): a critical measure of signal stability over time. Using a 20- and 64-channel receiver coil, we show that enabling S-acceleration consistently yielded a substantial decrease in tSNR, regardless of the receiver coil, whereas M-acceleration yielded less pronounced tSNR decrease. Moreover, tSNR losses tended to occur in temporal, insular, and medial brain regions and were more noticeable with the 20-channel coil, while with the 64-channel coil, the tSNR in lateral frontoparietal regions remained relatively stable up to six-fold M-acceleration producing comparable tSNR to that of no acceleration. Such methodological explorations can guide researchers and clinicians in optimizing imaging protocols depending on the brain regions under investigation.

Does higher sampling rate (multiband + SENSE) improve group statistics - An example from social neuroscience block design at 3T

Multiband (MB) or Simultaneous multi-slice (SMS) acquisition schemes allow the acquisition of MRI signals from more than one spatial coordinate at a time. Commercial availability has brought this technique within the reach of many neuroscientists and psychologists. Most early evaluation of the performance of MB acquisition employed resting state fMRI or the most basic tasks. In this study, we tested whether the advantages of using MB acquisition schemes generalize to group analyses using a cognitive task more representative of typical cognitive neuroscience applications. Twenty-three subjects were scanned on a Philips 3 ​T scanner using five sequences, up to eight-fold acceleration with MB-factors 1 to 4, SENSE factors up to 2 and corresponding TRs of 2.45s down to 0.63s, while they viewed (i) movie blocks showing complex actions with hand object interactions and (ii) control movie blocks without hand object interaction. Data were processed using a widely used analysis pipeline implemented in SPM12 including the unified segmentation and canonical HRF modelling. Using random effects group-level, voxel-wise analysis we found that all sequences were able to detect the basic action observation network known to be recruited by our task. The highest t-values were found for sequences with MB4 acceleration. For the MB1 sequence, a 50% bigger voxel volume was needed to reach comparable t-statistics. The group-level t-values for resting state networks (RSNs) were also highest for MB4 sequences. Here the MB1 sequence with larger voxel size did not perform comparable to the MB4 sequence. Altogether, we can thus recommend the use of MB4 (and SENSE 1.5 or 2) on a Philips scanner when aiming to perform group-level analyses using cognitive block design fMRI tasks and voxel sizes in the range of cortical thickness (e.g. 2.7 ​mm isotropic). While results will not be dramatically changed by the use of multiband, our results suggest that MB will bring a moderate but significant benefit.

On the Quality, Statistical Efficiency, and Safety of Simultaneously Recorded Multiband fMRI/EEG

The recent development of multiband functional magnetic resonance imaging (MB-fMRI) allows for the reduction of sampling period by simultaneously exciting multiple slices-the number of which is referred to as the multiband factor. Simultaneously recorded electroencephalography (EEG)/MB-fMRI has yet to be validated for data quality against conventional single band (SB)-fMRI. Pilot scans were conducted on phantoms twice and on a healthy volunteer to ensure no heating effects. In the main study, two thermometer probes were attached to 16 healthy individuals (ages 20-39, 9 females) whilst they completed two sets of 16-min resting-state and two sets of 9-min n-back task scans-each set consisting of one MB4 and one SB pulse sequence. No heating effects were reported and thermometer data showed mean increases of < 1.0 °C. Minimal differences between the two scan types were found in EEG channel variance and spectra. Expected decreases in MB4-fMRI tSNR were observed. In n-back task scans, little to no differences were detected in both EEG source analyses and fMRI local analyses for mixed effects. Resting-state posterior cingulate cortex seed-based analyses of the default mode network along with EEG-informed fMRI analysis of the occipital alpha anticorrelation effect showed improved statistical and spatial sensitivity at lower scan durations. Using EEG/MB4-fMRI for n-back tasks provided no statistical advantages nor disadvantages. However, for studying the resting-state, MB4-fMRI potentially allows for reduced scanning durations for equivalent statistical significance to be obtained or alternatively, larger effect sizes for the same scanning duration. As such, simultaneous EEG/MB4-fMRI is a viable alternative to EEG/SB-fMRI.

Inter-slice leakage and intra-slice aliasing in simultaneous multi-slice echo-planar images

Simultaneous multi-slice (SMS) imaging is a popular technique for increasing acquisition speed in echo-planar imaging (EPI) fMRI. However, SMS data are prone to motion sensitivity and slice leakage artefacts, which spread signal between simultaneously acquired slices. Relevant to motion sensitivity, artefacts from moving anatomic structures propagate along the phase-encoding (PE) direction. This is particularly relevant for eye movement. As signal from the eye is acquired along with signal from simultaneously excited slices during SMS, there is potential for signal to spread in-plane and between spatially remote slices. After identifying an artefact temporally coinciding with signal fluctuations in the eye and spatially distributed in correspondence with multiband slice acceleration and parallel imaging factors, we conducted a series of small experiments to investigate eye movement artefacts in SMS data and the contribution of PE direction to the invasiveness of these artefacts. Five healthy adult volunteers were scanned during a blinking task using a standard SMS-EPI protocol with posterior-to-anterior (P ≫ A), anterior-to-posterior (A ≫ P) or right-to-left (R ≫ L) PE direction. The intensity of signal fluctuations (artefact severity) was measured at expected artefact positions and control positions. We demonstrated a direct relationship between eye movements and artefact severity across expected artefact regions. Within-brain artefacts were apparent in P ≫ A- and A ≫ P-acquired data but not in R ≫ L data due to the shift in artefact positions. Further research into eye motion artefacts in SMS data is warranted but researchers should exercise caution with SMS protocols. We recommend rigorous piloting of SMS protocols and switching to R ≫ L/L ≫ R PE where feasible.

Comparison of SMS-EPI and 3D-EPI at 7T in an fMRI localizer study with matched spatiotemporal resolution and homogenized excitation profiles

The simultaneous multi-slice EPI (SMS-EPI, a.k.a. MB-EPI) sequence has met immense popularity recently in functional neuroimaging. A still less common alternative is the use of 3D-EPI, which offers similar acceleration capabilities. The aim of this work was to compare the SMS-EPI and the 3D-EPI sequences in terms of sampling strategies for the detection of task-evoked activations at 7T using detection theory. To this end, the spatial and temporal resolutions of the sequences were matched (1.6 mm isotropic resolution, TR = 1200 ms) and their excitation profiles were homogenized by means of calibration-free parallel-transmission (Universal Pulses). We used a fast-event “localizer” paradigm of 5:20 min in order to probe sensorimotor functions (visual, auditory and motor tasks) as well as higher level functions (language comprehension, mental calculation), where results from a previous large-scale study at 3T (N = 81) served as ground-truth reference for the brain areas implicated in each cognitive function. In the current study, ten subjects were scanned while their activation maps were generated for each cognitive function with the GLM analysis. The SMS-EPI and 3D-EPI sequences were compared in terms of raw tSNR, t-score testing for the mean signal, activation strength and accuracy of the robust sensorimotor functions. To this end, the sensitivity and specificity of these contrasts were computed by comparing their activation maps to the reference brain areas obtained in the 3T study. Estimated flip angle distributions in the brain reported a normalized root mean square deviation from the target value below 10% for both sequences. The analysis of the t-score testing for the mean signal revealed temporal noise correlations, suggesting the use of this metric instead of the traditional tSNR for testing fMRI sequences. The SMS-EPI and 3D-EPI thereby yielded similar performance from a detection theory perspective.