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Functional magnetic resonance imaging (fMRI) using a blood-oxygenation-level-dependent (BOLD) contrast is a common method for studying human brain function noninvasively. Gradient-echo (GRE) BOLD is highly sensitive to the blood oxygenation change in blood vessels; however, the spatial signal specificity can be degraded due to signal leakage from activated lower layers to superficial layers in depth-dependent (also called laminar or layer-specific) fMRI. Alternatively, physiological variables such as cerebral blood volume using the VAscular-Space-Occupancy (VASO) contrast have shown higher spatial specificity compared to BOLD. To better understand the physiological mechanisms such as blood volume and oxygenation changes and to interpret the measured depth-dependent responses, models are needed which reflect vascular properties at this scale. For this purpose, we extended and modified the "cortical vascular model" previously developed to predict layer-specific BOLD signal changes in human primary visual cortex to also predict a layer-specific VASO response. To evaluate the model, we compared the predictions with experimental results of simultaneous VASO and BOLD measurements in a group of healthy participants. Fitting the model to our experimental data provided an estimate of CBV change in different vascular compartments upon neural activity. We found that stimulus-evoked CBV change mainly occurs in small arterioles, capillaries, and intracortical arteries and that the contribution from venules and ICVs is smaller. Our results confirm that VASO is less susceptible to large vessel effects compared to BOLD, as blood volume changes in intracortical arteries did not substantially affect the resulting depth-dependent VASO profiles, whereas depth-dependent BOLD profiles showed a bias towards signal contributions from intracortical veins.

Neuroimaging data analysis often requires purpose-built software, which can be challenging to install and may produce different results across computing environments. Beyond being a roadblock to neuroscientists, these issues of accessibility and portability can hamper the reproducibility of neuroimaging data analysis pipelines. Here, we introduce the Neurodesk platform, which harnesses software containers to support a comprehensive and growing suite of neuroimaging software (https://www.neurodesk.org/). Neurodesk includes a browser-accessible virtual desktop environment and a command line interface, mediating access to containerized neuroimaging software libraries on various computing platforms, including personal and high-performance computers, cloud computing and Jupyter Notebooks. This community-oriented, open-source platform enables a paradigm shift for neuroimaging data analysis, allowing for accessible, flexible, fully reproducible, and portable data analysis pipelines.

Neuroimaging data analysis often requires purpose-built software, which can be challenging to install and may produce different results across computing environments. Beyond being a roadblock to neuroscientists, these issues of accessibility and portability can hamper the reproducibility of neuroimaging data analysis pipelines. Here, we introduce the Neurodesk platform, which harnesses software containers to support a comprehensive and growing suite of neuroimaging software (https://www.neurodesk.org/). Neurodesk includes a browser-accessible virtual desktop environment and a command line interface, mediating access to containerized neuroimaging software libraries on various computing platforms, including personal and high-performance computers, cloud computing and Jupyter Notebooks. This community-oriented, open-source platform enables a paradigm shift for neuroimaging data analysis, allowing for accessible, flexible, fully reproducible, and portable data analysis pipelines.

Of the sources of noise affecting blood oxygen level-dependent functional magnetic resonance imaging (fMRI), respiration and cardiac fluctuations are responsible for the largest part of the variance, particularly at high and ultrahigh field. Existing approaches to removing physiological noise either use external recordings, which can be unwieldy and unreliable, or attempt to identify physiological noise from the magnitude fMRI data. Data-driven approaches are limited by sensitivity, temporal aliasing, and the need for user interaction. In the light of the sensitivity of the phase of the MR signal to local changes in the field stemming from physiological processes, we have developed an unsupervised physiological noise correction method using the information carried in the phase and the magnitude of echo-planar imaging data. Our technique, Physiological Regressor Estimation from Phase and mAgnItude, sub-tR (PREPAIR) derives time series signals sampled at the slice TR from both phase and magnitude images. It allows physiological noise to be captured without aliasing, and efficiently removes other sources of signal fluctuations not related to physiology, prior to regressor estimation. We demonstrate that the physiological signal time courses identified with PREPAIR agree well with those from external devices and retrieve challenging cardiac dynamics. The removal of physiological noise was as effective as that achieved with the most used approach based on external recordings, RETROICOR. In comparison with widely used recording-free physiological noise correction tools-PESTICA and FIX, both performed in unsupervised mode-PREPAIR removed significantly more respiratory and cardiac noise than PESTICA, and achieved a larger increase in temporal signal-to-noise-ratio at both 3 and 7 T.

Of the sources of noise which affect BOLD fMRI, respiration and cardiac fluctuations are responsible for the largest part of the variance, particularly at high and ultra-high field. Existing approaches to removing physiological noise either use external recordings, which can be unwieldy and unreliable, or attempt to identify physiological noise from the magnitude fMRI data. Data-driven approaches are limited by sensitivity, temporal aliasing and the need for user interaction. In the light of the sensitivity of the phase of the MR signal to local changes in the field stemming from physiological processes, we have developed an unsupervised physiological noise correction method which uses the information carried in both the phase and the magnitude of EPI data. Our technique, Physiological Regressor Estimation from Phase and mAgnItude, sub-tR (PREPAIR) derives time series signals which are sampled at the slice TR from both phase and magnitude images. It allows physiological noise to be captured without aliasing, and efficiently removes other sources of signal fluctuations which are not related to physiology, prior to regressor estimation. We demonstrate that the physiological signal time courses identified with PREPAIR not only agree well with those from external devices, but also retrieve challenging cardiac dynamics. The removal of physiological noise was as effective as that achieved with the most commonly used approach based on external recordings, RETROICOR. In comparison with widely used physiological noise correction tools which do not use external signals, PESTICA and FIX, PREPAIR removed more respiratory and cardiac noise and achieved a larger increase in tSNR at both 3 T and 7 T.

Mesoscopic (0.1-0.5 mm) interrogation of the living human brain is critical for a comprehensive understanding of brain structure and function. However, in vivo techniques for mesoscopic imaging have been hampered by the sensitivity challenges of acquiring data at very high resolutions and the lack of analysis tools that can retain fine-scale detail while also accurately positioning measurements relative to the complex folded structure of the cerebral cortex. Here, we present an experimental dataset in which we image the anatomical structure of the visual and auditory cortices of five participants at 0.35 × 0.35 × 0.35 mm3 resolution. To analyze this challenging dataset, we design and implement two sets of novel methodology: a method for mitigating imaging artifacts related to blood motion and a suite of software tools for accurate quantification and visualization of the mesoscopic structure of the cortical surface. Applying these methods, we demonstrate the ability to clearly identify structures that are visible only at the mesoscopic scale, including cortical layers and intracortical blood vessels. We freely share our dataset and tools with the research community, thereby enabling investigations of fine-scale neurobiological structures in both the current and future datasets. Overall, our results demonstrate the viability of mesoscopic imaging as a quantitative tool for studying the living human brain.

As the global health crisis unfolded throughout the world, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical and legal barriers and effectively enabled many individuals who have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g. caregiving responsibilities. Yet, the mere existence of online conferences is unfortunately no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the setup of the conference can provide more visibility to well-established researchers and reduce opportunities for early career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design.Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to: attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.

The pial arterial vasculature of the human brain is the only blood supply to the neocortex, but quantitative data on the morphology and topology of these mesoscopic arteries (diameter 50– 300 µm) remains scarce. Because it is commonly assumed that blood flow velocities in these vessels are prohibitively slow, non-invasive time-of-flight MRI angiography (TOF-MRA)—which is well-suited to high 3D imaging resolutions—has not been applied to imaging the pial arteries. Here, we provide a theoretical framework that outlines how TOF-MRA can visualize small pial arteries in vivo, by employing extremely small voxels at the size of individual vessels. We then provide evidence for this theory by imaging the pial arteries at 140-µm isotropic resolution using a 7T MRI scanner and prospective motion correction, and show that pial arteries one voxel-width in diameter can be detected. We conclude that imaging pial arteries is not limited by slow blood flow, but instead by achievable image resolution. This study represents the first targeted, comprehensive account of imaging pial arteries in vivo in the human brain. This ultra-high-resolution angiography will enable the characterization of pial vascular anatomy across the brain to investigate patterns of blood supply and relationships between vascular and functional architecture.

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops “computational assays” for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use.In this paper, we present theTranslationalAlgorithms forPsychiatry-AdvancingScience (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.

Available online 30 April 2021. Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress. The present manuscript is part of a growing community effort to collate Brainhack-related insights and expertise into a Jupyter Book (http://brainhack.org/brainhack_jupyter_book/) that will serve as a centralized set of resources for the community; we acknowledge all the individuals who contributed and will make ongoing contributions to these resources. A pre-print version of the present manuscript is available as part of the Jupyter Book. Moreover, we would like to acknowledge all Brainhack organizers, supporters, presenters, and participants for their contribution to growing and maintaining this community. The benefits described in this manuscript would not be possible without them. We also thank all institutions, labs, and organizations who have helped this community grow, meet in stimulating environments, and add an excellent educational resource pool and agenda. With an expanding community, Brainhack’s support network keeps growing, and we thank all labs and individual researchers for their dedication and expertise offered to this community (see http://brainhack.org/brainhack_jupyter_book/acknowledgments.html for a full list of individual acknowledgments; an updated list will be maintained in the Jupyter Book). Grants and funding bodies: This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 867458 awarded to Julia M. Huntenburg; from ANR-19-DATA-0025 NeuroWebLab for Katja Heuer, Roberto Toro, and Nicholas Traut; and from NIH K00MH122372 for Stephanie Noble. The Brainhack Community member list and contributions of the different authors are detailed at http://brainhack.org/brainhack_jupyter_book/contributors.html. Our crediting system is described here: http://brainhack.org/brainhack_jupyter_book/neuroview_authorship-agreement.html.

This work reviews recent advances in technologies for functional magnetic resonance imaging (fMRI) of the human brain and highlights the push for higher functional specificity based on increased spatial resolution and specific MR contrasts to reveal previously undetectable functional properties of small-scale cortical structures. We discuss how the combination of MR hardware, advanced acquisition techniques and various MR contrast mechanisms have enabled recent advances in functional neuroimaging. However, these advanced fMRI practices have only been applied to a handful of neuroscience questions to date, with the majority of the neuroscience community still using conventional imaging techniques. We thus discuss upcoming challenges and possibilities for fMRI technology development in human neuroscience. We hope that readers interested in functional brain imaging acquire an understanding of current and novel developments and potential future applications, even if they don't have a background in MR physics or engineering. We summarize the capabilities of standard fMRI acquisition schemes with pointers to relevant literature and comprehensive reviews and introduce more recent developments.

When performing statistical analysis of single-subject fMRI data, serial correlations need to be taken into account to allow for valid inference. Otherwise, the variability in the parameter estimates might be under-estimated resulting in increased false-positive rates. Serial correlations in fMRI data are commonly characterized in terms of a first-order autoregressive (AR) process and then removed via pre-whitening. The required noise model for the pre-whitening depends on a number of parameters, particularly the repetition time (TR). Here we investigate how the sub-second temporal resolution provided by simultaneous multislice (SMS) imaging changes the noise structure in fMRI time series. We fit a higher-order AR model and then estimate the optimal AR model order for a sequence with a TR of less than 600 ms providing whole brain coverage. We show that physiological noise modelling successfully reduces the required AR model order, but remaining serial correlations necessitate an advanced noise model. We conclude that commonly used noise models, such as the AR(1) model, are inadequate for modelling serial correlations in fMRI using sub-second TRs. Rather, physiological noise modelling in combination with advanced pre-whitening schemes enable valid inference in single-subject analysis using fast fMRI sequences.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease defined by the presence of upper and lower motor neuron dysfunction. Clinical presentation and disease progression are variable between patients with ALS, with psychosocial, neuropsychological, nutritional and genetic factors all related to ALS outcome. We present a detailed characterisation of a patient with familial ALS with a novel TBK1 mutation with coexistent brain atrophy, behavioural changes and metabolic dysfunction. This male patient was first seen at age 62 years with a 3-month history of gait unsteadiness, weakness of the right arm and emotional lability. There was a history of depression. His father had died of ALS at age 67 years. At first assessment, there was weakness of the right upper limb, ALS Functional Rating Scale-Revised (ALSFRS-R) was 39 and his weight was 72 kg. After 6 months, there was onset of dysphagia. He had tongue fasciculations, brisk jaw jerk, weakness, wasting, brisk reflexes and fasciculations of upper and lower limbs. The ALSFRS-R was 34 and weight had further declined to 65 kg. Three months later, he had deteriorated further with severe weakness, nausea, anorexia and depression. ALSFRS-R was 25 and weight was 54 kg. His respiratory function declined from FVC of 4.57 L to 1.05 L over 6 months. He died ~16 months after the onset of symptoms. The patient participated in a series of neuropsychological, imaging and metabolic studies of unselected consenting patients with ALS enrolled from our clinic.1 Neuropsychological assessment results for this patient are presented in online supplementary table 1. Comparison of his individual results with those of other patients with ALS is presented in online supplementary tables 2 to 4. Genetic assessment was done by massively parallel sequencing using a custom-designed neuromuscular gene capture panel that tests for 66 genes known to be associated …

Applying a weak electrical current to the cortex has the potential to modulate neural functioning and behaviour. The most common stimulation technique, transcranial direct current stimulation (tDCS), has been used for causal investigations of brain and cognitive functioning, and to treat psychiatric conditions such as depression. However, the efficacy of tDCS in modulating behaviour varies across individuals. Moreover, despite being associated with different neural effects, the two polarities of electrical stimulation – anodal and cathodal – can result in similar behavioural outcomes. Here we employed a previously replicated behavioural paradigm that has been associated with polarity non-specific disruption of training effects in a simple decision-making task. We then used the linear ballistic accumulator model to quantify latent components of the decision-making task. In addition, magnetic resonance imaging measures were acquired prior to tDCS sessions to quantify cortical morphology and local neurochemical concentrations. Both anodal and cathodal stimulation disrupted learning-related task improvement relative to sham (placebo) stimulation, but the two polarities of stimulation had distinct effects on latent task components. Whereas anodal stimulation tended to affect decision thresholds for the behavioural task, cathodal stimulation altered evidence accumulation rates. Moreover, performance variability with anodal stimulation was related to cortical thickness of the inferior frontal gyrus, whereas performance variability with cathodal stimulation was related to cortical thickness in the inferior precentral sulcus, as well as to prefrontal neurochemical excitability. Our findings demonstrate that both cortical morphology and local neurochemical balance are important determinants of individual differences in behavioural responses to electrical brain stimulation.

Somatosensation is fundamental to our ability to sense our body and interact with the world. Our body is continuously sampling the environment using a variety of receptors tuned to different features, and this information is routed up to primary somatosensory cortex. Strikingly, the spatial organization of the peripheral receptors in the body are well maintained, with the resulting representation of the body in the brain being referred to as the somatosensory homunculus. Recent years have seen considerable advancements in the field of high-resolution fMRI, which have enabled an increasingly detailed examination of the organization and properties of this homunculus. Here we combined advanced imaging techniques at ultra-high field (7T) with a recently developed Bayesian population receptive field (pRF) modeling framework to examine pRF properties in primary somatosensory cortex. In each subject, vibrotactile stimulation of the fingertips (i.e., the peripheral mechanoreceptors) modulated the fMRI response along the post-central gyrus and these signals were used to estimate pRFs. We found the pRF center location estimates to be in accord with previous work as well as evidence of other properties in line with the underlying neurobiology. Specifically, as expected from the known properties of cortical magnification, we find a larger representation of the index finger compared to the other stimulated digits (middle, index, little). We also show evidence that the little finger is marked by the largest pRF sizes, and that pRF size increases from anterior to posterior regions of S1. The ability to estimate somatosensory pRFs in humans provides an unprecedented opportunity to examine the neural mechanisms underlying somatosensation and is critical for studying how the brain, body, and environment interact to inform perception and action.

Functional magnetic resonance imaging (fMRI) enables the non-invasive investigation of human brain function. Identifying functionally segregated regions involved in the processing of sensory information and higher cognitive functions has provided detailed insight into its organisational architecture. The acquisition of fMRI data at ultra-high field provides enhanced signal sensitivity and contrast, and new acquisition techniques are continuously developed to increase image resolution and acquisition speed. The ultimate goal is to maximize functional sensitivity, i.e. the ability to detect and localise neurovascular activity using fMRI. Given the overwhelming number of parameters that need to be selected to compile an fMRI acquisition protocol, and their complex influences on image fidelity and contrast, ascertaining functional sensitivity is fundamental for every fMRI experiment. Only then a faithful representation of the underlying neurovascular activity can be obtained, forming the basis for new insights into brain function.In this thesis, I will discuss key constituents of functional sensitivity, provide an overview of the impact of fMRI acquisition parameters, and outline relevant evaluation measures. I will then apply this framework to the functional imaging of the nuclei of the basal ganglia, whose tissue properties cause a reduction in functional sensitivity compared to cortical areas. I will show how multi-echo fMRI – a new class of fMRI data acquisition techniques – can overcome this limitation. By rapidly acquiring sets of images with different signal properties, the extensive signal loss in subcortical regions can be mitigated without sacrificing functional sensitivity in cortical areas.Subsequently, I will investigate the noise properties of fMRI data acquired with a sub-second sampling interval at the single-subject level. Single-subject fMRI is especially relevant for clinical applications, but requires comprehensive noise modelling for valid inference. Accordingly, I will show that modelling of physiological noise sources such as cardiac and respiratory activity, and advanced pre-whitening schemes are necessary for valid inference using fast acquisition techniques.To finish, I will introduce a custom software toolbox – uniQC – which provides a general framework to evaluate functional sensitivity. In particular, uniQC combines various visualisation tools with straightforward access to common mathematical and image operations and essential fMRI preprocessing steps in an object-oriented framework. Importantly, the handling of images with arbitrary dimensionality – such as multi-echo or multi-coil images – is inherently provided, and individual processing steps are generalized accordingly. Thus, flexible, yet straightforward evaluation of fMRI data becomes possible.This thesis provides thorough guidance on the evaluation of functional sensitivity for fMRI, and how acquisition and analysis strategies can be tailored to specific research questions to obtain the best data possible. Utilizing advanced acquisition and analysis techniques to improve functional sensitivity across the whole brain will ultimately facilitate a more comprehensive understanding of human brain function.

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

There is now considerable evidence that applying a small electrical current to the cerebral cortex can have wide ranging effects on cognition and performance, and may provide substantial benefit as a treatment for conditions such as depression. However, there is variability across subjects in the extent to which stimulation modulates behaviour, providing a challenge for the development of applications. Here, we employed an individual differences approach to test if baseline concentrations of the neurochemicals GABA and glutamate are associated with an individual's response to transcranial direct current stimulation (tDCS). Using a previously replicated response selection training paradigm, we applied tDCS to the left prefrontal cortex part-way through the learning of a six-alternative-forced-choice task. Across three sessions, subjects received anodal, cathodal, or sham stimulation. Pre-tDCS baseline measures of GABA and glutamate, acquired using magnetic resonance spectroscopy (MRS), correlated with the extent to which stimulation modulated behaviour. Specifically, relative concentrations of GABA and glutamate (used as an index of neurochemical excitability) in the prefrontal cortex were associated with the degree to which active stimulation disrupted response selection training. This work represents an important step forward in developing models to predict stimulation efficacy, and provides a unique insight into how trait-based properties of the targeted cortex interact with stimulation.

The nuclei of the basal ganglia pose a special problem for functional MRI, especially at ultra-high field, because T2* variations between different regions result in suboptimal BOLD sensitivity when using gradient-echo echo-planar imaging (EPI). Specifically, the iron-rich lentiform nucleus of the basal ganglia, including the putamen and globus pallidus, suffers from substantial signal loss when imaging is performed using conventional single-echo EPI with echo times (TE) optimized for the cortex. Multi-echo EPI acquires several echoes at different echo times for every imaging slice, allowing images to be reconstructed with a weighting of echo times that is optimized individually for each voxel according to the underlying tissue or T2* properties. Here we show that multi-echo simultaneous multi-slice (SMS) EPI can improve functional activation of iron-rich subcortical regions while maintaining sensitivity within cortical areas. Functional imaging during a motor task known to elicit strong activations in the cortex and the subcortex (basal ganglia) was performed to compare the performance of multi-echo SMS EPI to single-echo SMS EPI. Notably within both the caudate nucleus and putamen of the basal ganglia, multi-echo SMS EPI yielded higher tSNR (an average 84% increase) and CNR (an average 58% increase), an approximate 3-fold increase in supra-threshold voxels, and higher t-values (an average 39% increase). The degree of improvement in the group level t-statistics was negatively correlated to the underlying T2* of the voxels, such that the shorter the T2*, as in the iron-rich nuclei of the basal ganglia, the higher the improvement of t-values in the activated region.

Purpose To assess the utility of concurrent magnetic field monitoring for observing and correcting for variations in k-space trajectories and global background fields that occur in single-shot echo planar imaging (EPI) time series as typically used in functional MRI (fMRI). Methods Field monitoring was performed using an array of NMR field probes operated concurrently with series of single-shot EPI acquisitions from a static phantom. The observed fluctuations in field evolution were analyzed in terms of their temporal and spatial behavior at the field level as well as at the level of reconstructed image series. The potential to correct for such fluctuations was assessed by accounting for them upon image reconstruction. An indication of the number and relative magnitude of underlying effects was obtained via principal component analysis. Results Trajectory and global field variations were found to induce substantial image fluctuations. Global field fluctuations induced standard deviations in image intensity up to 31%. Fluctuations in the trajectory induced ghosting artifacts with standard deviations up to 2%. Concurrent magnetic field monitoring reduced the fluctuations in the EPI time series to a maximum of 1.2%. Conclusion Concurrent magnetic field monitoring holds the potential to improve the net sensitivity of fMRI by reducing signal fluctuations unrelated to brain activity. Magn Reson Med 74:396-409, 2015.

Attention to sensory information has been shown to modulate the neuronal processing of that information. For example, visuospatial attention acts by modulating responses at retinotopically appropriate regions of visual cortex (Puckett and DeYoe, 2015; Tootell et al. 1998). Much less, however, is known about the neuronal processing associated with attending to other modalities of sensory information. One reason for this is that visual cortex is relatively large, and therefore easier to access non-invasively in humans using tools such as functional magnetic resonance imaging (fMRI). With high-resolution fMRI, however, it is now possible to access smaller cortical areas such as primary somatosensory cortex (Martuzzi et al., 2014; Sanchez-Panchuelo et al., 2010; Schweisfurth et al. 2014; Schweizer et al. 2008). Here, we combined a novel experimental design and high-resolution fMRI at ultra-high field (7T) to measure the effects of attention to tactile stimulation in primary somatosensory cortex, S1. We find that attention modulates somatotopically appropriate regions of S1, and importantly, that this modulation can be measured at the level of the cortical representation of individual fingertips.

This work investigates the role of magnetic field fluctuations as a confound in fMRI. In standard fMRI experiments with single-shot EPI acquisition at 3 Tesla the uniform and gradient components of the magnetic field were recorded with NMR field sensors. By principal component analysis it is found that differences of field evolution between the EPI readouts are explainable by few components relating to slow and within-shot field dynamics of hardware and physiological origin. The impact of fluctuating field components is studied by selective data correction and assessment of its influence on image fluctuation and SFNR. Physiological field fluctuations, attributed to breathing, were found to be small relative to those of hardware origin. The dominant confounds were hardware-related and attributable to magnet drift and thermal changes. In raw image time series, field fluctuation caused significant SFNR loss, reflected by a 67% gain upon correction. Large part of this correction can be accomplished by traditional image realignment, which addresses slow and spatially uniform field changes. With realignment, explicit field correction increased the SFNR on the order of 6%. In conclusion, field fluctuations are a relevant confound in fMRI and can be addressed effectively by retrospective data correction. Based on the physics involved it is anticipated that the advantage of full field correction increases with field strength, with non-Cartesian readouts, and upon phase-sensitive BOLD analysis.

To assess the utility of concurrent magnetic field monitoring for observing and correcting for variations in k-space trajectories and global background fields that occur in single-shot echo planar imaging (EPI) time series as typically used in functional MRI (fMRI).Field monitoring was performed using an array of NMR field probes operated concurrently with series of single-shot EPI acquisitions from a static phantom. The observed fluctuations in field evolution were analyzed in terms of their temporal and spatial behavior at the field level as well as at the level of reconstructed image series. The potential to correct for such fluctuations was assessed by accounting for them upon image reconstruction. An indication of the number and relative magnitude of underlying effects was obtained via principal component analysis.Trajectory and global field variations were found to induce substantial image fluctuations. Global field fluctuations induced standard deviations in image intensity up to 31%. Fluctuations in the trajectory induced ghosting artifacts with standard deviations up to 2%. Concurrent magnetic field monitoring reduced the fluctuations in the EPI time series to a maximum of 1.2%.Concurrent magnetic field monitoring holds the potential to improve the net sensitivity of fMRI by reducing signal fluctuations unrelated to brain activity.

The pial arterial vasculature of the human brain is the only blood supply to the neocortex, but quantitative data on the morphology and topology of these mesoscopic arteries (diameter 50–300 µm) remains scarce. Because it is commonly assumed that blood flow velocities in these vessels are prohibitively slow, non-invasive time-of-flight magnetic resonance angiography (TOF-MRA)—which is well suited to high 3D imaging resolutions—has not been applied to imaging the pial arteries. Here, we provide a theoretical framework that outlines how TOF-MRA can visualize small pial arteries in vivo, by employing extremely small voxels at the size of individual vessels. We then provide evidence for this theory by imaging the pial arteries at 140 µm isotropic resolution using a 7 Tesla (T) magnetic resonance imaging (MRI) scanner and prospective motion correction, and show that pial arteries one voxel width in diameter can be detected. We conclude that imaging pial arteries is not limited by slow blood flow, but instead by achievable image resolution. This study represents the first targeted, comprehensive account of imaging pial arteries in vivo in the human brain. This ultra-high-resolution angiography will enable the characterization of pial vascular anatomy across the brain to investigate patterns of blood supply and relationships between vascular and functional architecture.