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To investigate potential correlations between the susceptibility values of certain brain regions and the severity of disease or neurodevelopmental status in children with autism spectrum disorder (ASD), 18 ASD children and 15 healthy controls (HCs) were recruited. The neurodevelopmental status was assessed by the Gesell Developmental Schedules (GDS) and the severity of the disease was evaluated by the Autism Behavior Checklist (ABC). Eleven brain regions were selected as regions of interest and the susceptibility values were measured by quantitative susceptibility mapping. To evaluate the diagnostic capacity of susceptibility values in distinguishing ASD and HC, the receiver operating characteristic (ROC) curve was computed. Pearson and Spearman partial correlation analysis were used to depict the correlations between the susceptibility values, the ABC scores, and the GDS scores in the ASD group. ROC curves showed that the susceptibility values of the left and right frontal white matter had a larger area under the curve in the ASD group. The susceptibility value of the right globus pallidus was positively correlated with the GDS-fine motor scale score. These findings indicated that the susceptibility value of the right globus pallidus might be a viable imaging biomarker for evaluating the neurodevelopmental status of ASD children., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Studies that seek to predict the brain microstructure based on MRI require precise alignment of processed brain histology slices to the corresponding 3D MRI data. However, achieving such alignment is a challenging problem, due to tissue distortions and the different contrasts seen in MRI and the processed tissue. Here we present a pipeline for aligning a histology volume to the MRI data of the tissue and to a stereotaxic brain atlas. To this end, we segment the volume of the brain from ex-vivo MRI data, align the MRI data obtained in the native space to an MRI stereotaxic template and create a 3D printed model (a mold or cradle) that precisely fits the brain. The pipeline then makes it possible to create grooves in the 3D model, for guiding blades for cutting slabs of tissue. Placing the brain in the brain-specific 3D printed model aligns the tissue to the MRI data by default. Aligning the MRI data to an MRI stereotaxic template makes it possible to section histology slices parallel to the standard stereotaxic axes of the atlas. This facilitates comparisons to other MRI contrasts and to images of processed tissue aligned to the standard space, while maintaining the high-resolution of the tissue images along the standard stereotaxic plane. Guiding the positioning of the grooves according to species-specific anatomical information from the co-registered atlas facilitates region-specific histology. The pipeline we introduce can be used to create brain-specific sectioning models for a variety of species, including humans, primates, and rodents. To demonstrate the generalizability of the pipeline across species, we show models generated for macaques and rats.

Previous attempts at characterizing the spatial specificity of the blood oxygenation level dependent functional MRI (BOLD fMRI) response by estimating its point-spread function (PSF) have conventionally relied on retinotopic spatial representations of visual stimuli in area V1. Consequently, their estimates were confounded by the width and scatter of receptive fields of V1 neurons. Here, we circumvent these limits by instead using the inherent cortical spatial organization of ocular dominance columns (ODCs) to determine the PSF for both Gradient Echo (GE) and Spin Echo (SE) BOLD imaging at 7 Tesla. By applying Markov chain Monte Carlo sampling on a probabilistic generative model of imaging ODCs, we quantified the PSFs that best predict the spatial structure and magnitude of differential ODCs' responses. Prior distributions for the ODC model parameters were determined by analyzing published data of cytochrome oxidase patterns from post-mortem histology of human V1 and of neurophysiological ocular dominance indices. The average PSF full-widths at half-maximum obtained from differential ODCs' responses following the removal of voxels influenced by contributions from macroscopic blood vessels were 0.86 mm (SE) and 0.99 mm (GE). Our results provide a quantitative basis for the spatial specificity of BOLD fMRI at ultra-high fields, which can be used for planning and interpretation of high-resolution differential fMRI of fine-scale cortical organizations., (Copyright © 2017. Published by Elsevier Inc.)

The capacity of functional MRI (fMRI) to resolve cortical columns depends on several factors. These include the spatial scale of the columnar pattern, the point-spread of the fMRI response, the voxel size, and the signal-to-noise ratio (SNR) considering thermal and physiological noise. However, it remains unknown how these factors combine, and what is the voxel size that optimizes fMRI of cortical columns. Here we combine current knowledge into a quantitative model of fMRI of realistic patterns of cortical columns with different spatial scales and degrees of irregularity. We compare different approaches for identifying patterns of cortical columns, including univariate and multivariate based detection, multi-voxel pattern analysis (MVPA) based decoding, and high-resolution imaging and reconstruction of the pattern of cortical columns. We present the dependence of the performance of each approach on the parameters of the imaged pattern as well as those of the data acquisition. In addition, we predict voxel sizes that optimize fMRI of cortical columns under various scenarios. We found that all measures associated with multivariate detection and decoding could be approximately calculated from a measure we termed "multivariate contrast-to-noise ratio" (mv-CNR), which is a function of the contrast-to-noise ratio (CNR) and number of voxels. Furthermore, mv-CNR implied that the optimal voxel width for detection and decoding is independent of changes in response amplitude, SNR and imaged volume that are not caused by changes in voxel size. For regular patterns, optimal voxel widths for detection, decoding and imaging/reconstructing the pattern of cortical columns were approximately half the main cycle length of the organization. Optimal voxel widths for irregular patterns were less dependent on the main cycle length, and differed between univariate detection, multivariate detection and decoding, and reconstruction. We compared the effects of different factors of Gradient Echo fMRI at 3 Tesla (T), Gradient Echo fMRI at 7T, and Spin-Echo fMRI at 7T on the detection, decoding, and reconstruction measures considered and found that in all cases the width of the fMRI point-spread had the most significant effect. In contrast, different response amplitudes and noise characteristics played a relatively minor role. We recommend specific voxel widths for optimal univariate detection, for multivariate detection and decoding, and for high-resolution imaging of cortical columns under these three data-acquisition scenarios. Our study supports the planning, optimization, and interpretation of high-resolution fMRI of cortical columns and the decoding of information conveyed by these columns., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Numerous studies have demonstrated functional magnetic resonance imaging (fMRI)-based resting-state functional connectivity (RSFC) between cortical areas. Recent evidence suggests that synchronous fluctuations in blood oxygenation level-dependent fMRI reflect functional organization at a scale finer than that of visual areas. In this study, we investigated whether RSFCs within and between lower visual areas are retinotopically organized and whether retinotopically organized RSFC merely reflects cortical distance. Subjects underwent retinotopic mapping and separately resting-state fMRI. Visual areas V1, V2, and V3, were subdivided into regions of interest (ROIs) according to quadrants and visual field eccentricity. Functional connectivity (FC) was computed based on Pearson's linear correlation (correlation), and Pearson's linear partial correlation (correlation between two time courses after the time courses from all other regions in the network are regressed out). Within a quadrant, within visual areas, all correlation and nearly all partial correlation FC measures showed statistical significance. Consistently in V1, V2, and to a lesser extent in V3, correlation decreased with increasing eccentricity separation. Consistent with previously reported monkey anatomical connectivity, correlation/partial correlation values between regions from adjacent areas (V1-V2 and V2-V3) were higher than those between nonadjacent areas (V1-V3). Within a quadrant, partial correlation showed consistent significance between regions from two different areas with the same or adjacent eccentricities. Pairs of ROIs with similar eccentricity showed higher correlation/partial correlation than pairs distant in eccentricity. Between dorsal and ventral quadrants, partial correlation between common and adjacent eccentricity regions within a visual area showed statistical significance; this extended to more distant eccentricity regions in V1. Within and between quadrants, correlation decreased approximately linearly with increasing distances separating the tested ROIs. Partial correlation showed a more complex dependence on cortical distance: it decreased exponentially with increasing distance within a quadrant, but was best fit by a quadratic function between quadrants. We conclude that RSFCs within and between lower visual areas are retinotopically organized. Correlation-based FC is nonselectively high across lower visual areas, even between regions that do not share direct anatomical connections. The mechanisms likely involve network effects caused by the dense anatomical connectivity within this network and projections from higher visual areas. FC based on partial correlation, which minimizes network effects, follows expectations based on direct anatomical connections in the monkey visual cortex better than correlation. Last, partial correlation-based retinotopically organized RSFC reflects more than cortical distance effects.

Multivariate machine learning algorithms applied to human functional MRI (fMRI) data can decode information conveyed by cortical columns, despite the voxel-size being large relative to the width of columns. Several mechanisms have been proposed to underlie decoding of stimulus orientation or the stimulated eye. These include: (I) aliasing of high spatial-frequency components, including the main frequency component of the columnar organization, (II) contributions from local irregularities in the columnar organization, (III) contributions from large-scale non-columnar organizations, (IV) functionally selective veins with biased draining regions, and (V) complex spatio-temporal filtering of neuronal activity by fMRI voxels. Here we sought to assess the plausibility of two of the suggested mechanisms: (I) aliasing and (II) local irregularities, using a naive model of BOLD as blurring and MRI voxel sampling. To this end, we formulated a mathematical model that encompasses both the processes of imaging ocular dominance (OD) columns and the subsequent linear classification analysis. Through numerical simulations of the model, we evaluated the distribution of functional differential contrasts that can be expected when considering the pattern of cortical columns, the hemodynamic point spread function, the voxel size, and the noise. We found that with data acquisition parameters used at 3 Tesla, sub-voxel supra-Nyquist frequencies, including frequencies near the main frequency of the OD organization (0.5 cycles per mm), cannot contribute to the differential contrast. The differential functional contrast of local origin is dominated by low-amplitude contributions from low frequencies, associated with irregularities of the cortical pattern. Realizations of the model with parameters that reflected best-case scenario and the reported BOLD point-spread at 3 Tesla (3.5mm) predicted decoding performances lower than those that have been previously obtained at this magnetic field strength. We conclude that low frequency components that underlie local irregularities in the columnar organization are likely to play a role in decoding. We further expect that fMRI-based decoding relies, in part, on signal contributions from large-scale, non-columnar functional organizations, and from complex spatio-temporal filtering of neuronal activity by fMRI voxels, involving biased venous responses. Our model can potentially be used for evaluating and optimizing data-acquisition parameters for decoding information conveyed by cortical columns., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Functional magnetic resonance imaging (fMRI) at high magnetic fields has made it possible to investigate the columnar organization of the human brain in vivo with high degrees of accuracy and sensitivity. Until now, these results have been limited to the organization principles of early visual cortex (V1). While the middle temporal area (MT) has been the first identified extra-striate visual area shown to exhibit a columnar organization in monkeys, evidence of MT's columnar response properties and topographic layout in humans has remained elusive. Research using various approaches suggests similar response properties as in monkeys but failed to provide direct evidence for direction or axis of motion selectivity in human area MT. By combining state of the art pulse sequence design, high spatial resolution in all three dimensions (0.8 mm isotropic), optimized coil design, ultrahigh field magnets (7 Tesla) and novel high resolution cortical grid sampling analysis tools, we provide the first direct evidence for large-scale axis of motion selective feature organization in human area MT closely matching predictions from topographic columnar-level simulations.

The influence of the global average signal (GAS) on functional-magnetic resonance imaging (fMRI)-based resting-state functional connectivity is a matter of ongoing debate. The global average fluctuations increase the correlation between functional systems beyond the correlation that reflects their specific functional connectivity. Hence, removal of the GAS is a common practice for facilitating the observation of network-specific functional connectivity. This strategy relies on the implicit assumption of a linear-additive model according to which global fluctuations, irrespective of their origin, and network-specific fluctuations are super-positioned. However, removal of the GAS introduces spurious negative correlations between functional systems, bringing into question the validity of previous findings of negative correlations between fluctuations in the default-mode and the task-positive networks. Here we present an alternative method for estimating global fluctuations, immune to the complications associated with the GAS. Principal components analysis was applied to resting-state fMRI time-series. A global-signal effect estimator was defined as the principal component (PC) that correlated best with the GAS. The mean correlation coefficient between our proposed PC-based global effect estimator and the GAS was 0.97±0.05, demonstrating that our estimator successfully approximated the GAS. In 66 out of 68 runs, the PC that showed the highest correlation with the GAS was the first PC. Since PCs are orthogonal, our method provides an estimator of the global fluctuations, which is uncorrelated to the remaining, network-specific fluctuations. Moreover, unlike the regression of the GAS, the regression of the PC-based global effect estimator does not introduce spurious anti-correlations beyond the decrease in seed-based correlation values allowed by the assumed additive model. After regressing this PC-based estimator out of the original time-series, we observed robust anti-correlations between resting-state fluctuations in the default-mode and the task-positive networks. We conclude that resting-state global fluctuations and network-specific fluctuations are uncorrelated, supporting a Resting-State Linear-Additive Model. In addition, we conclude that the network-specific resting-state fluctuations of the default-mode and task-positive networks show artifact-free anti-correlations.

Cells in the mammalian brain tend to be grouped together according to their afferent and efferent connectivity, as well as their physiological properties. The columnar structures of neocortex are prominent examples of such modular organization, and have been studied extensively in anatomical and physiological experiments in rats, cats and monkeys. The importance of noninvasive study of such structures, in particular in human subjects, cannot be overemphasized. Not surprisingly, therefore, many attempts were made to map cortical columns using functional magnetic resonance imaging (fMRI). Yet, the robustness, repeatability, and generality of the hitherto used fMRI methodologies have been a subject of intensive debate. Using differential mapping in a high magnetic field magnet (7 T), we demonstrate here the ability of Hahn Spin-Echo (HSE) BOLD to map the ocular dominance columns (ODCs) of the human visual cortex reproducibly over several days with a high degree of accuracy, relative to expected spatial patterns from post-mortem data. On the other hand, the conventional Gradient-Echo (GE) blood oxygen level dependent (BOLD) signal in some cases failed to resolve ODCs uniformly across the selected gray matter region, due to the presence of non-specific signals. HSE signals uniformly resolved the ODC patterns, providing a more generalized mapping methodology (i.e. one that does not require adjusting experimental approaches based on prior knowledge or assumptions about functional organization and vascular structure in order to avoid confounding large vessel effects) to map unknown columnar systems in the human brain, potentially paving the way both for the study of the functional architecture of human sensory cortices, and of brain modules underlying specific cognitive processes.

Functional MR imaging of the alert, behaving monkey is being used more and more often to detect activation patterns and guide electrophysiological research investigating the neural basis of behavior. Several labs have reported fMRI data from the awake monkey, but none of them has studied and systematically corrected the effects of monkeys' motion on fMRI time series. In this study, a significant refinement of acquisition and correction strategies is reported that can be used to minimize magnetic susceptibility artifacts induced by respiration and by jaw and body movement. Real-time acquisition of sensor signals (e.g., signals induced by jaw and body movement) and MR navigator data were combined to optimize fMRI signal-correction strategies. Within trials, the artifact-induced off-resonance changes were small and mainly reflected the effects of respiration; between trials, movements caused major changes of global frequency and shim (>20 Hz/cm). Several methods were used to assess the stability of the fMRI series: k-space analysis ('dynamic intensity and off-resonance changes in k-space', dubbed DICK and DORK) and image analysis using a Laplace operator and a center-of-mass metric. The variability between trials made it essential to correct for inter-trial variations. On the other hand, images were sufficiently stable with our approach to perform fMRI evaluations on single trials before averaging of trials. Different motion correction strategies were compared: DORK, McFLIRT (rigid body model with three translations and three rotations) and 2D image alignment based on a center-of-mass detection (in-plane translation). The latter yielded the best results and proved to be fast and robust for intra- and inter-trial alignment. Finally, fMRI in the behaving monkey was tested for spatial and temporal reproducibility on a trial-to-trial basis. Highly activated voxels also displayed good reproducibility between trials. On average, the BOLD amplitude response to a short 3-s visual stimulus was close to 2%.

This study investigated the spatio-temporal properties of blood-oxygenation level-dependent (BOLD) functional MRI (fMRI) signals in gray matter, excluding the confounding, inaccurate contributions of large blood vessels. We quantified the spatial specificity of the BOLD response, and we investigated whether this specificity varies as a function of time from stimulus onset. fMRI was performed at 7 Tesla (T), where mapping signals of parenchymal origin are easily detected. Two abutting visual stimuli were adjusted to elicit responses centered on a flat gray matter region in V1. fMRI signals were sampled at high-resolution orthogonal to the retinotopic boundary between the representations of the stimuli. Signals from macro-vessels were masked out. Principal component analysis revealed that the first component in space accounted for 96.2+/-1.6% of the variance over time. The spatial profile of this time-invariant response was fitted with a model consisting of the convolution of a step function and a Gaussian point-spread-function (PSF). The mean full-width at half-maximal-height of the fitted PSF was 2.34+/-0.20 mm. Based on simulations of confounding effects, we estimate that BOLD PSF in human gray matter is smaller than 2 mm. A time-point to time-point analysis revealed that the PSF obtained during the 3rd (1.52 mm) and 4th (1.99 mm) seconds of stimulation were narrower than the mean PSF obtained from the 5th second on (2.42+/-0.15 mm). The position of the edge of the responding region was offset (1.72+/-0.07 mm) from the boundary of the stimulated region, indicating a spatial non-linearity. Simulations showed that the effective contrast between active and non-active columns is reduced 25-fold when imaged using a PSF whose width is equal to the cycle of the imaged columnar organization. Thus, the PSF of the hyper-oxygenated BOLD response in human gray matter is narrower than that reported at 1.5 T, where macro-vessels dominate the mapping signals. The initial phase of this response is more spatially specific than later phases. Data acquisition methods that suppress macro-vascular signals should increase the spatial specificity of BOLD fMRI. The choice of optimal stimulus duration represents a trade-off between the spatial specificity and the overhead associated with short stimulus duration.

Most functional brain imaging studies use task-induced hemodynamic responses to infer underlying changes in neuronal activity. In addition to increases in cerebral blood flow and blood oxygenation level-dependent (BOLD) signals, sustained negative responses are pervasive in functional imaging. The origin of negative responses and their relationship to neural activity remain poorly understood. Through simultaneous functional magnetic resonance imaging and electrophysiological recording, we demonstrate a negative BOLD response (NBR) beyond the stimulated regions of visual cortex, associated with local decreases in neuronal activity below spontaneous activity, detected 7.15 +/- 3.14 mm away from the closest positively responding region in V1. Trial-by-trial amplitude fluctuations revealed tight coupling between the NBR and neuronal activity decreases. The NBR was associated with comparable decreases in local field potentials and multiunit activity. Our findings indicate that a significant component of the NBR originates in neuronal activity decreases.

At very high magnetic fields, GE BOLD fMRI is expected to contain nonspecific contributions and behave differently than HSE fMRI data. Similarly, the two approaches can conceivably suffer from different contributions to temporal instabilities in a times series that ultimately determine the contrast-to-noise ratio (CNR). We investigate the signal and signal fluctuation characteristics in GE and HSE fMRI data with the imaging parameters separately optimized for each contrast at 7 T. In HSE fMRI, activation-induced fractional signal change (DeltaS/S) decreased rapidly, and the ratio of standard deviations of image-to-image fluctuations due to physiological processes (sigmaPhys) to thermal noise (sigmaTherm) remained constant with increasing voxel volume. In contrast, DeltaS/S as well as volume of activated voxels was virtually independent of voxel size for GE BOLD, and sigma(Phys)/sigmaTherm increased with increasing voxel size. The ratio of BOLD signal changes (GE/HSE) was much closer to 1 in tissue areas compared to vessel areas. These observations led to the conclusions that the spatial extent of the activation-induced DeltaS/S was much broader in the GE data, and that the physiological processes that give rise to the temporal fluctuations lost coherence over millimeter distances in HSE compared to GE fMRI data. While further studies are needed to characterize it fully, sigmaPhys in HSE data was clearly different than in GE data. It was concluded that HSE imaging yields a significantly reduced amount of nonspecific signals compared to GE imaging, and, would be the method of choice (over GE) for high-resolution applications in humans.

Functional neuroimaging in the human brain using noninvasive magnetic resonance methods has the potential of providing highly resolved maps of neuronal activation. Decreasing the voxel size and obtaining simultaneously high temporal resolution is a major challenge and is mainly limited by sensitivity. Here, signal-to-noise gains at high magnetic fields (7 Tesla) and an optimized surface coil setup are combined with a novel approach for zoomed functional imaging in the visual cortex. For echoplanar imaging, the acquisition time and segmentation was shortened fourfold by using a reduced field-of-view. An adiabatic outer-volume suppression method, BISTRO, was used to obliterate signal outside the area-of-interest achieving effective suppression even for inhomogeneous B1-fields. A single-shot acquisition was performed at submillimeter resolution in the human brain, while simultaneously maintaining a high temporal resolution of 125 ms. Functional studies with and without field-of-view reduction were performed. Activation and percent change maps were compared with respect to spatial extent, t values and percentage changes of the BOLD contrast. The detection of functional activation was found to be equal within the inter-series variability for the two acquisition schemes. Thus, single-trial BOLD responses were detected for the first time robustly at a 500 x 500 microm2 in plane and 250 ms temporal resolution, significantly expanding the possibilities of event-related functional imaging in the human brain. The magnetization transfer effect induced by the outer-volume suppression pulses was investigated and found to be increased during neuronal activity.

Perfusion-based MRI measures cerebral blood flow (CBF) at the capillary level and can be used for functional studies based on the tight spatial coupling between brain activity and blood flow. Obtaining functional CBF maps with high spatial resolution is a major challenge because the CBF signal is intrinsically low and the SNR is critical. In the present work, CBF-based functional imaging was performed at a considerably smaller voxel size than previously reported in humans. High-resolution CBF maps were obtained with voxel sizes as small as 0.9 x 0.9 x 1.5 mm(3) in the human brain. High sensitivity was made possible by signal-to-noise gains at the high magnetic field of 7 T and by using a novel RF combination coil design. In addition, a reduction of the field-of-view was critical to achieve 0.9-mm in-plane resolution with gradient-echo echo-planar imaging in a single shot. Functional CBF data were compared with functional BOLD data to reveal that, for CBF, large contrast- to-noise gains were obtained at high spatial resolution, indicating that the functional CBF response was more localized. High-resolution functional CBF imaging is significant for neuroscience research because it provides better localization and more specific information than BOLD for monitoring brain function., (Copyright 2002 Wiley-Liss, Inc.)

Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience. Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMatE Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design, and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 25 independent data collections aggregated across 22 sites (total = 217 non-human primates). We also outline the unique pitfalls and challenges that should be considered in the analysis of non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets.

The lack of “gold standards” in Diffusion Weighted Imaging (DWI) makes validation cumbersome. To tackle this task, studies use translational analysis where results in humans are benchmarked against findings in other species. Non-Human Primates (NHP) are particularly interesting for this, as their cytoarchitecture is closely related to humans. However, tools used for processing and analysis must be adapted and finely tuned to work well on NHP images. Here, we propose versaFlow, a modular pipeline implemented in Nextflow, designed for robustness and scalability. The pipeline is tailored to in vivo NHP DWI at any spatial resolution; it allows for maintainability and customization. Processes and workflows are implemented using cutting-edge and state-of-the-art Magnetic Resonance Imaging (MRI) processing technologies and diffusion modeling algorithms, namely Diffusion Tensor Imaging (DTI), Constrained Spherical Deconvolution (CSD), and DIstribution of Anisotropic MicrOstructural eNvironments in Diffusion- compartment imaging (DIAMOND). Using versaFlow, we provide an in-depth study of the variability of diffusion metrics computed on 32 subjects from 3 sites of the Primate Data Exchange (PRIME-DE), which contains anatomical T1-weighted (T1w) and T2-weighted (T2w) images, functional MRI (fMRI), and DWI of NHP brains. This dataset includes images acquired over a range of resolutions, using single and multi-shell gradient samplings, on multiple scanner vendors. We perform a reproducibility study of the processing of versaFlow using the Aix-Marseilles site’s data, to ensure that our implementation has minimal impact on the variability observed in subsequent analyses. We report very high reproducibility for the majority of metrics; only gamma distribution parameters of DIAMOND display less reproducible behaviors, due to the absence of a mechanism to enforce a random number seed in the software we used. This should be taken into consideration when future applications are performed. We show that the PRIME- DE diffusion data exhibits a great level of variability, similar or greater than results obtained in human studies. Its usage should be done carefully to prevent instilling uncertainty in statistical analyses. This hints at a need for sufficient harmonization in acquisition protocols and for the development of robust algorithms capable of managing the variability induced in imaging due to differences in scanner models and/or vendors. [ABSTRACT FROM AUTHOR]

Introduction: Pseudocontinuous Arterial Spin Labeling (pCASL) perfusion imaging allows non-invasive quantification of regional cerebral blood flow (CBF) as part of a multimodal magnetic resonance imaging (MRI) protocol. This study aimed to compare regional CBF in autism spectrum disorders (ASD) individuals with their age-matched typically developing (TD) children using pCASL perfusion imaging. Materials and methods: This cross-sectional study enrolled 17 individuals with ASD and 13 TD children. All participants underwent pCASL examination on a 3.0 T MRI scanner. Children in two groups were assessed for clinical characteristics and developmental profiles using Autism Behavior Checklist (ABC) and Gesell development diagnosis scale (GDDS), respectively. We compared CBF in different cerebral regions of ASD and TD children. We also assessed the association between CBF and clinical characteristics/developmental profile. Results: Compared with TD children, individuals with ASD demonstrated a reduction in CBF in the left frontal lobe, the bilateral parietal lobes, and the bilateral temporal lobes. Within the ASD group, CBF was significantly higher in the right parietal lobe than in the left side. Correlation analysis of behavior characteristics and CBF in different regions showed a positive correlation between body and object domain scores on the ABC and CBF of the bilateral occipital lobes, and separately, between language domain scores and CBF of the left frontal lobe. The score of the social and self-help domain was negatively correlated with the CBF of the left frontal lobe, the left parietal lobe, and the left temporal lobe. Conclusion: Cerebral blood flow was found to be negatively correlated with scores in the social and self-help domain, and positively correlated with those in the body and object domain, indicating that CBF values are a potential MRIbased biomarker of disease severity in ASD patients. The findings may provide novel insight into the pathophysiological mechanisms of ASD. [ABSTRACT FROM AUTHOR]

Abstract: The use of functional magnetic resonance imaging (fMRI) in alert non-human primates is of great potential for research in systems neuroscience. It can be combined with invasive techniques and afford better understanding of non-invasively acquired brain imaging signals in humans. However, the difficulties in optimal application of alert monkey fMRI are multi-faceted, especially at high magnetic fields where the effects of motion and of changes in B 0 are greatly amplified. To overcome these difficulties, strict behavioral controls and elaborate animal-training are needed. Here, we introduce a number of hardware developments, quantify the effect of movements on fMRI data, and present procedures for animal training and scanning for well-controlled and artifact-reduced alert monkey fMRI at high magnetic field. In particular, we describe systems for monitoring jaw and body movements, and for accurately tracking eye movements. A link between body and jaw movement and MRI image artifacts is established, showing that relying on the immobilization of an animal’s head is not sufficient for high-quality imaging. Quantitative analysis showed that body and jaw movement events caused large instabilities in fMRI time series. On average, body movement events caused larger instabilities than jaw movement events. Residual baseline brain image position and signal amplitude shifts were observed after the jaw and body movement events ended. Based on these findings, we introduce a novel behavioral paradigm that relies on training the monkeys to stay still during long trials. A corresponding analysis method discards all data that were not obtained during the movement-free periods. The baseline position and amplitude shifts are overcome by motion correction and trial-by-trial signal normalization. The advantages of the presented method over conventional scanning and analysis are demonstrated with data obtained at 7 T. It is anticipated that the techniques presented here will prove useful for alert monkey fMRI at any magnetic field. [Copyright &y& Elsevier]

Magnetic resonance imaging (MRI) is increasingly being used to delineate morphological changes underlying neurological disorders. Successfully detecting these changes depends on the MRI data quality. Unfortunately, image artifacts frequently compromise the MRI utility, making it critical to screen the data. Currently, quality assessment requires visual inspection, a time-consuming process that suffers from inter-rater variability. Automated methods to detect MRI artifacts could improve the efficiency of the process. Such automated methods have achieved high accuracy using small datasets, with balanced proportions of MRI data with and without artifacts. With the current trend towards big data in neuroimaging, there is a need for automated methods that achieve accurate detection in large and imbalanced datasets. Deep learning (DL) is the ideal MRI artifact detection algorithm for large neuroimaging databases. However, the inference generated by DL does not commonly include a measure of uncertainty. Here, we present the first stochastic DL algorithm to generate automated, high-performing MRI artifact detection implemented on a large and imbalanced neuroimaging database. We implemented Monte Carlo dropout in a 3D AlexNet to generate probabilities and epistemic uncertainties. We then developed a method to handle class imbalance, namely data-ramping to transfer the learning by extending the dataset size and the proportion of the artifact-free data instances. We used a 34,800 scans (98% clean) dataset. At baseline, we obtained 89.3% testing accuracy (F1 = 0.230). Following the transfer learning (with data-ramping), we obtained 94.9% testing accuracy (F1 = 0.357) outperforming focal cross-entropy (92.9% testing accuracy, F1 = 0.304) incorporated for comparison at handling class imbalance. By implementing epistemic uncertainties, we improved the testing accuracy to 99.5% (F1 = 0.834), outperforming the results obtained in previous comparable studies. In addition, we estimated aleatoric uncertainties by incorporating random flips to the MRI volumes, and demonstrated that aleatoric uncertainty can be implemented as part of the pipeline. The methods we introduce enhance the efficiency of managing large databases and the exclusion of artifact images from big data analyses. [Display omitted] • We address difficulties in automatically detecting artifacts in a large, imbalanced image database. • We reproduced high accuracy in detecting artifacts in small balanced datasets. • We implemented Monte Carlo (MC) dropout in 3D AlexNet to generate uncertainty metrics. • We transfer the learning from a small balanced dataset to a large imbalanced dataset, outperforming focal loss. • We outperformed detection in previous studies using comparable large imbalanced datasets. • Users can select an operating point to consider manual inspection vs. artifact detection error. [ABSTRACT FROM AUTHOR]