Advantages of short repetition time resting-state functional MRI enabled by simultaneous multi-slice imaging

Utilizing fMRI to Guide TMS Targets: the Reliability and Sensitivity of fMRI Metrics at 3 T and 1.5 T

US Food and Drug Administration (FDA) cleared a Transcranial Magnetic Stimulation (TMS) system with functional Magnetic Resonance Imaging-guided (fMRI) individualized treatment protocol for major depressive disorder, which employs resting state-fMRI (RS-fMRI) functional connectivity (FC) to pinpoint the target individually to increase the accuracy and effeteness of the stimulation. Furthermore, task activation-guided TMS, as well as the use of RS-fMRI local metrics for targeted the specific abnormal brain regions, are considered a precise scheme for TMS targeting. Since 1.5 T MRI is more available in hospitals, systematic evaluation of the test-retest reliability and sensitivity of fMRI metrics on 1.5 T and 3 T MRI may provide reference for the application of fMRI-guided individualized-precise TMS stimulation. Twenty participants underwent three RS-fMRI scans and one scan of finger-tapping task fMRI with self-initiated (SI) and visual-guided (VG) conditions at both 3 T and 1.5 T. Then the location reliability derived by FC (with three seed regions) and peak activation were assessed by intra-individual distance. The test-retest reliability and sensitivity of five RS-fMRI local metrics were evaluated using intra-class correlation and effect size, separately. The intra-individual distance of peak activation location between 1.5 T and 3 T was 15.8 mm and 19 mm for two conditions, respectively. The intra-individual distance for the FC derived targets at 1.5 T was 9.6-31.2 mm, compared to that of 3 T (7.6-31.1 mm). The test-retest reliability and sensitivity of RS-fMRI local metrics showed similar trends on 1.5 T and 3 T. These findings hasten the application of fMRI-guided individualized TMS treatment in clinical practice.

Non-Cartesian 3D-SPARKLING vs Cartesian 3D-EPI encoding schemes for functional Magnetic Resonance Imaging at 7 Tesla

The quest for higher spatial and/or temporal resolution in functional MRI (fMRI) while preserving a sufficient temporal signal-to-noise ratio (tSNR) has generated a tremendous amount of methodological contributions in the last decade ranging from Cartesian vs. non-Cartesian readouts, 2D vs. 3D acquisition strategies, parallel imaging and/or compressed sensing (CS) accelerations and simultaneous multi-slice acquisitions to cite a few. In this paper, we investigate the use of a finely tuned version of 3D-SPARKLING. This is a non-Cartesian CS-based acquisition technique for high spatial resolution whole-brain fMRI. We compare it to state-of-the-art Cartesian 3D-EPI during both a retinotopic mapping paradigm and resting-state acquisitions at 1mm3 (isotropic spatial resolution). This study involves six healthy volunteers and both acquisition sequences were run on each individual in a randomly-balanced order across subjects. The performances of both acquisition techniques are compared to each other in regards to tSNR, sensitivity to the BOLD effect and spatial specificity. Our findings reveal that 3D-SPARKLING has a higher tSNR than 3D-EPI, an improved sensitivity to detect the BOLD contrast in the gray matter, and an improved spatial specificity. Compared to 3D-EPI, 3D-SPARKLING yields, on average, 7% more activated voxels in the gray matter relative to the total number of activated voxels.

ENIGMA's simple seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury

Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.

Functional Connectivity of the Nucleus Accumbens across Variants of Callous-Unemotional Traits: A Resting-State fMRI Study in Children and Adolescents

A large body of literature suggests that the primary (high callousness-unemotional traits [CU] and low anxiety) and secondary (high CU traits and anxiety) variants of psychopathy significantly differ in terms of their clinical profiles. However, little is known about their neurobiological differences. While few studies showed that variants differ in brain activity during fear processing, it remains unknown whether they also show atypical functioning in motivational and reward system. Latent Profile Analysis (LPA) was conducted on a large sample of adolescents (n = 1416) to identify variants based on their levels of callousness and anxiety. Seed-to-voxel connectivity analysis was subsequently performed on resting-state fMRI data to compare connectivity patterns of the nucleus accumbens across subgroups. LPA failed to identify the primary variant when using total score of CU traits. Using a family-wise cluster correction, groups did not differ on functional connectivity. However, at an uncorrected threshold the secondary variant showed distinct functional connectivity between the nucleus accumbens and posterior insula, lateral orbitofrontal cortex, supplementary motor area, and parietal regions. Secondary LPA analysis using only the callousness subscale successfully distinguish both variants. Group differences replicated results of deficits in functional connectivity between the nucleus accumbens and posterior insula and supplementary motor area, but additionally showed effect in the superior temporal gyrus which was specific to the primary variant. The current study supports the importance of examining the neurobiological markers across subgroups of adolescents at risk for conduct problems to precise our understanding of this heterogeneous population.

Functional Magnetic Resonance Imaging of the Amygdala and Subregions at 3 Tesla: A Scoping Review

The amygdalae are a pair of small brain structures, each of which is composed of three main subregions and whose function is implicated in neuropsychiatric conditions. Functional Magnetic Resonance Imaging (fMRI) has been utilized extensively in investigation of amygdala activation and functional connectivity (FC) with most clinical research sites now utilizing 3 Tesla (3T) MR systems. However, accurate imaging and analysis remains challenging not just due to the small size of the amygdala, but also its location deep in the temporal lobe. Selection of imaging parameters can significantly impact data quality with implications for the accuracy of study results and validity of conclusions. Wide variation exists in acquisition protocols with spatial resolution of some protocols suboptimal for accurate assessment of the amygdala as a whole, and for measuring activation and FC of the three main subregions, each of which contains multiple nuclei with specialized roles. The primary objective of this scoping review is to provide a broad overview of 3T fMRI protocols in use to image the activation and FC of the amygdala with particular reference to spatial resolution. The secondary objective is to provide context for a discussion culminating in recommendations for a standardized protocol for imaging activation of the amygdala and its subregions. As the advantages of big data and protocol harmonization in imaging become more apparent so, too, do the disadvantages of data heterogeneity. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Brain-wide mapping of resting-state networks in mice using high-frame rate functional ultrasound

Functional ultrasound (fUS) imaging is a method for visualizing deep brain activity based on cerebral blood volume changes coupled with neural activity, while functional MRI (fMRI) relies on the blood-oxygenation-level-dependent signal coupled with neural activity. Low-frequency fluctuations (LFF) of fMRI signals during resting-state can be measured by resting-state fMRI (rsfMRI), which allows functional imaging of the whole brain, and the distributions of resting-state network (RSN) can then be estimated from these fluctuations using independent component analysis (ICA). This procedure provides an important method for studying cognitive and psychophysiological diseases affecting specific brain networks. The distributions of RSNs in the brain-wide area has been reported primarily by rsfMRI. RSNs using rsfMRI are generally computed from the time-course of fMRI signals for more than 5 min. However, a recent dynamic functional connectivity study revealed that RSNs are still not perfectly stable even after 10 min. Importantly, fUS has a higher temporal resolution and stronger correlation with neural activity compared with fMRI. Therefore, we hypothesized that fUS applied during the resting-state for a shorter than 5 min would provide similar RSNs compared to fMRI. High temporal resolution rsfUS data were acquired at 10 Hz in awake mice. The quality of the default mode network (DMN), a well-known RSN, was evaluated using signal-noise separation (SNS) applied to different measurement durations of rsfUS. The results showed that the SNS did not change when the measurement duration was increased to more than 210 s. Next, we measured short-duration rsfUS multi-slice measurements in the brain-wide area. The results showed that rsfUS with the short duration succeeded in detecting RSNs distributed in the brain-wide area consistent with RSNs detected by 11.7-T MRI under awake conditions (medial prefrontal cortex and cingulate cortex in the anterior DMN, retrosplenial cortex and visual cortex in the posterior DMN, somatosensory and motor cortexes in the lateral cortical network, thalamus, dorsal hippocampus, and medial cerebellum), confirming the reliability of the RSNs detected by rsfUS. However, bilateral RSNs located in the secondary somatosensory cortex, ventral hippocampus, auditory cortex, and lateral cerebellum extracted from rsfUS were different from the unilateral RSNs extracted from rsfMRI. These findings indicate the potential of rsfUS as a method for analyzing functional brain networks and should encourage future research to elucidate functional brain networks and their relationships with disease model mice.

Multiscale functional connectivity patterns of the aging brain learned from harmonized rsfMRI data of the multi-cohort iSTAGING study

To learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI dataset, consisting of resting-state fMRI scans of 4186 individuals with a wide age range (22 to 97 years, with an average of 63) from five cohorts. We computed multiscale functional connectivity measures of individual subjects using a personalized functional network computational method, harmonized the functional connectivity measures of subjects from multiple datasets in order to build a functional brain age model, and finally evaluated how functional brain age gap correlated with cognitive measures of individual subjects. Our study has revealed that functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction, the data harmonization significantly improved the brain age prediction performance, and the data harmonization in the functional connectivity measures' tangent space worked better than in their original space. Moreover, brain age gap scores of individual subjects derived from the brain age prediction model were significantly correlated with clinical and cognitive measures. Overall, these results demonstrated that multiscale functional connectivity patterns learned from a large-scale multi-site rsfMRI dataset were informative for characterizing the aging brain and the derived brain age gap was associated with cognitive and clinical measures.

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.

Associations between corpus callosum damage, clinical disability, and surface-based homologous inter-hemispheric connectivity in multiple sclerosis

Axonal damage in the corpus callosum is prevalent in multiple sclerosis (MS). Although callosal damage is associated with disrupted functional connectivity between hemispheres, it is unclear how this relates to cognitive and physical disability. We investigated this phenomenon using advanced measures of microstructural integrity in the corpus callosum and surface-based homologous inter-hemispheric connectivity (sHIC) in the cortex. We found that sHIC was significantly decreased in primary motor, somatosensory, visual, and temporal cortical areas in a group of 36 participants with MS (29 relapsing-remitting, 4 secondary progressive MS, and 3 primary-progressive MS) compared with 42 healthy controls (cluster level, p < 0.05). In participants with MS, global sHIC correlated with fractional anisotropy and restricted volume fraction in the posterior segment of the corpus callosum (r = 0.426, p = 0.013; r = 0.399, p = 0.020, respectively). Lower sHIC, particularly in somatomotor and posterior cortical areas, was associated with cognitive impairment and higher disability scores on the Expanded Disability Status Scale (EDSS). We demonstrated that higher levels of sHIC attenuated the effects of posterior callosal damage on physical disability and cognitive dysfunction, as measured by the EDSS and Brief Visuospatial Memory Test-Revised (interaction effect, p < 0.05). We also observed a positive association between global sHIC and years of education (r = 0.402, p = 0.018), supporting the phenomenon of "brain reserve" in MS. Our data suggest that preserved sHIC helps prevent cognitive and physical decline in MS.

Measuring brain beats: Cardiac-aligned fast functional magnetic resonance imaging signals

Blood and cerebrospinal fluid (CSF) pulse and flow throughout the brain, driven by the cardiac cycle. These fluid dynamics, which are essential to healthy brain function, are characterized by several noninvasive magnetic resonance imaging (MRI) methods. Recent developments in fast MRI, specifically simultaneous multislice acquisition methods, provide a new opportunity to rapidly and broadly assess cardiac-driven flow, including CSF spaces, surface vessels and parenchymal vessels. We use these techniques to assess blood and CSF flow dynamics in brief (3.5 min) scans on a conventional 3 T MRI scanner in five subjects. Cardiac pulses are measured with a photoplethysmography (PPG) on the index finger, along with functional MRI (fMRI) signals in the brain. We, retrospectively, align the fMRI signals to the heartbeat. Highly reliable cardiac-gated fMRI temporal signals are observed in CSF and blood on the timescale of one heartbeat (test-retest reliability within subjects R²  > 50%). In blood vessels, a local minimum is observed following systole. In CSF spaces, the ventricles and subarachnoid spaces have a local maximum following systole instead. Slower resting-state scans with slice timing, retrospectively, aligned to the cardiac pulse, reveal similar cardiac-gated responses. The cardiac-gated measurements estimate the amplitude and phase of fMRI pulsations in the CSF relative to those in the arteries, an estimate of the local intracranial impedance. Cardiac aligned fMRI signals can provide new insights about fluid dynamics or diagnostics for diseases where these dynamics are important.

WHOCARES: WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions

Objective. To spatio-temporally resolve cardiac signals in functional magnetic resonance imaging (fMRI) time-series of the human brain using neither external physiological measurements nor ad hoc modelling assumptions.Approach. Cardiac pulsation is a physiological confound of fMRI time-series that introduces spurious signal fluctuations in proximity to blood vessels. fMRI alone is not sufficiently fast to resolve cardiac pulsation. Depending on the ratio between the instantaneous heart-rate and the acquisition sampling frequency (1/TR, with TR being the repetition time), the cardiac signal may alias into the frequency band of neural activation so that its removal through spectral filtering techniques is generally not possible. In this paper, we show that it is feasible to temporally and spatially resolve cardiac signals throughout the brain even when cardiac aliasing occurs by combining fMRI hyper-sampling with simultaneous multislice (SMS) imaging. The technique, which we name WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions (WHOCARES), was developed on 695 healthy subjects selected from the Human Connectome Project and its performance validated against the RETROICOR, HAPPY and the pulse oxymeter signal regression methods.Main results.WHOCARES is capable of retrieving voxel-wise cardiac signal regressors. This is achieved without employing external physiological recordings nor through ad hoc modelling assumptions. The performance of WHOCARES was, on average, superior to RETROICOR, HAPPY and the pulse oxymeter regression methods.Significance.WHOCARES holds basis for the reliable mapping of cardiac activity in fMRI time-series. WHOCARES can be employed for the retrospective removal of cardiac noise in publicly available fMRI datasets where physiological recordings are not available. WHOCARES is freely available athttps://github.com/gferrazzi/WHOCARES.

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.

MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies

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Sharing data from cooperative studies is essential to develop new biomarkers in MS.

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Differences in MRI acquisition, analysis, storage represent a substantial constraint.

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We review the state of the art and developments in the harmonization of MRI.

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We provide recommendations to harmonize large MRI datasets in the MS field.

There is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resources.

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.

Multivariate semi-blind deconvolution of fMRI time series

Whole brain estimation of the haemodynamic response function (HRF) in functional magnetic resonance imaging (fMRI) is critical to get insight on the global status of the neurovascular coupling of an individual in healthy or pathological condition. Most of existing approaches in the literature works on task-fMRI data and relies on the experimental paradigm as a surrogate of neural activity, hence remaining inoperative on resting-stage fMRI (rs-fMRI) data. To cope with this issue, recent works have performed either a two-step analysis to detect large neural events and then characterize the HRF shape or a joint estimation of both the neural and haemodynamic components in an univariate fashion. In this work, we express the neural activity signals as a combination of piece-wise constant temporal atoms associated with sparse spatial maps and introduce an haemodynamic parcellation of the brain featuring a temporally dilated version of a given HRF model in each parcel with unknown dilation parameters. We formulate the joint estimation of the HRF shapes and spatio-temporal neural representations as a multivariate semi-blind deconvolution problem in a paradigm-free setting and introduce constraints inspired from the dictionary learning literature to ease its identifiability. A fast alternating minimization algorithm, along with its efficient implementation, is proposed and validated on both synthetic and real rs-fMRI data at the subject level. To demonstrate its significance at the population level, we apply this new framework to the UK Biobank data set, first for the discrimination of haemodynamic territories between balanced groups (n=24 individuals in each) patients with an history of stroke and healthy controls and second, for the analysis of normal aging on the neurovascular coupling. Overall, we statistically demonstrate that a pathology like stroke or a condition like normal brain aging induce longer haemodynamic delays in certain brain areas (e.g. Willis polygon, occipital, temporal and frontal cortices) and that this haemodynamic feature may be predictive with an accuracy of 74 % of the individual's age in a supervised classification task performed on n=459 subjects.

Resting-State Functional Connectivity of the Ageing Female Brain-Differences Between Young and Elderly Female Adults on Multislice Short TR rs-fMRI

Introduction: Age-related brain changes are one of the most important world health problems due to the rising lifespan and size of the elderly populations. The aim of the study was to assess the effect of ageing in women on coordinated brain activity between eight resting-state networks.

Material and Methods: The study group comprised 60 healthy female volunteers who were divided into two age groups: younger women (aged 20–30 n = 30) and older women (aged 55–80 n = 30). Resting-state data were collected during a 15 min scan in the eyes-closed condition using a 3T MR scanner. Data were preprocessed and analysed using the CONN toolbox version 19.c. The large-scale network analysis included a priori selected regions of interest of the default mode, the sensorimotor, the visual, the salience, the dorsal attention, the fronto-parietal, the language, and the cerebellar network.

Results: Within the visual, the default mode, the salience, and the sensorimotor network, the intra-network resting-state functional connectivity (RSFC) was significantly higher with increasing age. There was also a significant increase in the inter-network RSFC in older females compared to young females found in the following networks: sensorimotor lateral and salience, salience and language, salience and fronto-parietal, cerebellar anterior and default mode, cerebellar posterior and default mode, visual and sensorimotor lateral, visual and sensorimotor, visual lateral and default mode, language and cerebellar anterior, language and cerebellar posterior, fronto-parietal and cerebellar anterior, dorsal attention and sensorimotor, dorsal attention and default mode, sensorimotor superior, and salience. Compared to young females, elderly women presented bilaterally significantly lower inter-network RSFC of the salience supramarginal gyrus and cerebellar posterior, sensorimotor lateral, and cerebellar anterior network, and sensorimotor lateral and cerebellar posterior as well as sensorimotor superior and cerebellar posterior network.

Conclusion: Increased RSFC between some brain networks including the visual, the default mode, the salience, the sensorimotor, the language, the fronto-parietal, the dorsal attention, and the cerebellar networks in elderly females may function as a compensation mechanism during the ageing process of the brain. To the best of our knowledge, this study is the first to report the importance of increase of cerebellar networks RSFC during healthy female ageing.

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.

Disentangling the effects of age and mild traumatic brain injury on brain network connectivity: A resting state fMRI study

INTRODUCTION

Cognitive complaints are common shortly after mild traumatic brain injury (mTBI) but may persist up to years. Age-related cognitive decline can worsen these symptoms. However, effects of age on mTBI sequelae have scarcely been investigated.

METHODS

Fifty-four mTBI patients (median age: 35 years, range 19-64 years, 67% male) and twenty age- and sex-matched healthy controls were studied using resting state functional magnetic resonance imaging in the sub-acute phase. Independent component analysis was used to identify intrinsic connectivity networks (ICNs). A multivariate approach was adopted to evaluate the effects of age and group on the ICNs in terms of (static) functional network connectivity (FNC), intensities of spatial maps (SMs) and time-course spectral power (TC).

RESULTS

We observed significant age-related changes for a) FNC: changes between 10 pairs of ICNs, mostly involving the default mode (DM) and/or the cognitive-control (CC) domains; b) SMs: intensity decrease in clusters across three domains and intensity increase in clusters across two domains, including the CC but not the DM and c) TC: spectral power decrease within the 0-0.15 Hz range and increase within the 0.20-0.25 Hz range for increasing age within networks located in frontal areas, including the anterior DM. Groups only differed for TC within the 0.065-0.10 Hz range in the cerebellar ICN and no age × group interaction effect was found.

CONCLUSIONS

We showed robust effects of age on connectivity between and within ICNs that are associated with cognitive functioning. Differences between mTBI patients and controls were only found for activity in the cerebellar network, increasingly recognized to participate in cognition. Our results suggest that to allow for capturing the true effects related to mTBI and its effects on cognitive functioning, age should be included as a covariate in mTBI studies, in addition to age-matching groups.

The benefit of high-performance gradients on echo planar imaging for BOLD-based resting-state functional MRI

Improved gradient performance in an MRI system reduces distortion in echo planar imaging (EPI), which has been a key imaging method for functional studies. A lightweight, low-cryogen compact 3T MRI scanner (C3T) is capable of achieving 80 mT m^-1 gradient amplitude with 700 T m^-1 s^-1 slew rate, in comparison with a conventional whole-body 3T MRI scanner (WB3T, 50 mT m^-1 with 200 T m^-1 s^-1). We investigated benefits of the high-performance gradients in a high-spatial-resolution (1.5 mm isotropic) functional MRI study. Reduced echo spacing in the EPI pulse sequence inherently leads to less severe geometric distortion, which provided higher accuracy than with WB3T for registration between EPI and anatomical images. The cortical coverage of C3T datasets was improved by more accurate signal depiction (i.e. less dropout or pile-up). Resting-state functional analysis results showed that greater magnitude and extent in functional connectivity (FC) for the C3T than the WB3T when the selected seed region is susceptible to distortions, while the FC matrix for well-known brain networks showed little difference between the two scanners. This shows that the improved quality in EPI is particularly valuable for studying certain brain regions typically obscured by severe distortion.

Dynamic Properties of Human Default Mode Network in Eyes-Closed and Eyes-Open

The default mode network (DMN) reflects spontaneous activity in the resting human brain. Previous studies examined the difference in static functional connectivity (sFC) of the DMN between eyes-closed (EC) and eyes-open (EO) using the resting-state functional magnetic resonance imaging (rs-fMRI) data. However, it remains unclear about the difference in dynamic FC (dFC) of the DMN between EC and EO. To this end, we acquired rs-fMRI data from 19 subjects in two different statues (EC and EO) and selected a seed region-of-interest (ROI) at the posterior cingulate cortex (PCC) to generate the sFC map. We identified the DMN consisting of ten clusters that were significantly correlated with the PCC. By using a sliding-window approach, we analyzed the dFC of the DMN. Then, the Newman's modularity algorithm was applied to identify dFC states based on nodal total connectivity strength in each sliding-window. In addition, graph-theory based network analysis was applied to detect dynamic topological properties of the DMN. We identified three group-level dFC states (State1, 2 and 3) that reflects the strength of dFC within the DMN between EC and EO in different time. The following results were reached: (1) no significant difference in sFC between EC and EO, (2) dFC was lower in State2 but higher in State3 in EC than in EO, (3) lower clustering coefficient, local efficiency, and global efficiency, but higher characteristic path length in State2 in EC than in EO, and (4) lower nodal strength in the precuneus (PCUN), PCC, angular gyrus (ANG), middle temporal gyrus (MTG) and medial prefrontal cortex (MPFC) in State3 in EC. These results suggested different resting statuses, EC and EO, may induce different time-varying neural activity in the DMN.

Dynamic-flip-angle ECG-gating with nuisance signal regression improves resting-state BOLD functional connectivity mapping by reducing cardiogenic noise

PURPOSE

To investigate an ECG-gated dynamic-flip-angle BOLD sequence with improved robustness against cardiogenic noise in resting-state fMRI.

METHODS

ECG-gating minimizes the cardiogenic noise but introduces T₁ -dependent signal variation, which is minimized by combination of a dynamic-flip-angle technique and retrospective nuisance signal regression (NSR) using signals of white matter, CSF, and global average. The technique was studied with simulations in a wide range of T₁ and B₁ fields and phantom imaging with pre-programmed TR variations. Resting-state fMRI of 20 healthy subjects was acquired with non-gated BOLD (NG), ECG-gated constant-flip-angle BOLD (GCFA), ECG-gated BOLD with retrospective T₁ -correction (GRC), and ECG-gated dynamic-flip-angle BOLD (GDFA), all processed by the same NSR method. GDFA was compared to alternative methods over temporal SNR (tSNR), seed-based connectivity, and whole-brain voxelwise connectivity based on intrinsic connectivity distribution (ICD). A previous large-cohort data set (N = 100) was used as a connectivity gold standard.

RESULTS

Simulations and phantom imaging show substantial reduction of the T₁ -dependent signal variation with GDFA alone, and further reduction with NSR. The resting-state study shows improved tSNR in the basal brain, comparing GDFA to NG, after both processed with NSR. Furthermore, GDFA significantly improved subcortical-subcortical and cortical-subcortical connectivity for several representative seeds and significantly improved ICD in the brainstem, thalamus, striatum, and prefrontal cortex, compared to the other 3 approaches.

CONCLUSION

GDFA with NSR improves mapping of the resting-state functional connectivity of the basal-brain regions by reducing cardiogenic noise.

Ultrafast fMRI of the rodent brain using simultaneous multi-slice EPI

Increasing spatial and temporal resolutions of functional MRI (fMRI) measurement has been shown to benefit the study of neural dynamics and functional interaction. However, acceleration of rodent brain fMRI using parallel and simultaneous multi-slice imaging techniques is hampered by the lack of high-density phased-array coils for the small brain. To overcome this limitation, we adapted phase-offset multiplanar and blipped-controlled aliasing echo planar imaging (EPI) to enable simultaneous multi-slice fMRI of the mouse brain using a single loop coil on a 9.4T scanner. Four slice bands of 0.3 × 0.3 × 0.5 mm³ resolution can be simultaneously acquired to cover the whole brain at a temporal resolution of 300 ms or the whole cerebrum in 150 ms. Instead of losing signal-to-noise ratio (SNR), both spatial and temporal SNR can be increased due to the increased k-space sampling compared to a standard single-band EPI. Task fMRI using a visual stimulation shows close to 80% increase of z-score and 4 times increase of activated area in the visual cortex using the multiband EPI due to the highly increased temporal samples. Resting-state fMRI shows reliable detection of bilateral connectivity by both single-band and multiband EPI, but no significant difference was found. Without the need of a dedicated hardware, we have demonstrated a practical method that can enable unparallelly fast whole-brain fMRI for preclinical studies. This technique can be used to increase sensitivity, distinguish transient response or acquire high spatiotemporal resolution fMRI.