Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging.

Neuroimaging research requires purpose-built analysis software, which is challenging to install and may produce different results across computing environments. The community-oriented, open-source Neurodesk platform ( https://www.neurodesk.org/ ) harnesses a comprehensive and growing suite of neuroimaging software containers. Neurodesk includes a browser-accessible virtual desktop, command-line interface and computational notebook compatibility, allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers and the cloud.

HumanBrainAtlas: an in vivo MRI dataset for detailed segmentations.

We introduce HumanBrainAtlas, an initiative to construct a highly detailed, open-access atlas of the living human brain that combines high-resolution in vivo MR imaging and detailed segmentations previously possible only in histological preparations. Here, we present and evaluate the first step of this initiative: a comprehensive dataset of two healthy male volunteers reconstructed to a 0.25 mm isotropic resolution for T1w, T2w, and DWI contrasts. Multiple high-resolution acquisitions were collected for each contrast and each participant, followed by averaging using symmetric group-wise normalisation (Advanced Normalisation Tools). The resulting image quality permits structural parcellations rivalling histology-based atlases, while maintaining the advantages of in vivo MRI. For example, components of the thalamus, hypothalamus, and hippocampus are often impossible to identify using standard MRI protocols-can be identified within the present data. Our data are virtually distortion free, fully 3D, and compatible with the existing in vivo Neuroimaging analysis tools. The dataset is suitable for teaching and is publicly available via our website (hba.neura.edu.au), which also provides data processing scripts. Instead of focusing on coordinates in an averaged brain space, our approach focuses on providing an example segmentation at great detail in the high-quality individual brain. This serves as an illustration on what features contrasts and relations can be used to interpret MRI datasets, in research, clinical, and education settings.

Cannabidiol as a Treatment for Neurobiological, Behavioral, and Psychological Symptoms in Early-Stage Dementia: A Double-Blind, Placebo-Controlled Clinical Trial Protocol.

Rationale: The slowing of disease progression in dementia in the early stages of diagnosis is paramount to improving the quality of life for those diagnosed and their support networks. Accumulating evidence suggests that CBD, a constituent of Cannabis sativa, is associated with neuroprotective, neuroendocrine, and psychotherapeutic effects, suggesting that it may be beneficial to dementia treatment. However, no published human study to date has examined this possibility. This trial aims to determine whether daily treatment with CBD over a 12-week period is associated with improved neurobiological, behavioral, and psychological outcomes in individuals living with early-stage dementia. Methods: Sixty participants with early-stage dementia will be recruited for a randomized, double-blind, placebo-controlled clinical trial. Participants will be randomized into either 99.9% pure CBD or placebo treatment conditions and administered two capsules per day for 12 weeks. Participants will commence a 200 mg/day dose for 2 weeks before escalating to 300 mg/day for the remaining 10 weeks. Neuroimaging and blood-based neuroendocrine profiles will be assessed at baseline and post-treatment. Psychological and behavioral symptoms will be assessed at baseline, 6 weeks, and post-treatment. Monitoring of health and side-effects will be conducted through weekly home visits. Discussion: This study is among the first to investigate the effects of isolated CBD in improving neuroanatomical and neuroendocrine changes, alongside psychological symptoms, during the early stages of dementia diagnosis. The outcomes of this trial have the capacity to inform a potential novel and accessible treatment approach for individuals living with early-stage dementia, and in turn, improve quality of life, prognoses, and treatment outcomes. Trial Registration: This trial has been registered with the Therapeutic Goods Administration (CT-2020-CTN-03849-1v2) and the Australian and New Zealand Clinical Trials Registry (ACTRN12621001364864).

Nature in motion: The tuning of the visual system to the spatiotemporal properties of natural scenes.

Natural scenes contain several statistical regularities despite their superficially diverse appearances (e.g., mountains, rainforests, deserts). First, they exhibit a unique distribution of luminance intensities decreasing across spatial frequency, known as the 1/fα amplitude spectrum (α ≈ 1). Additionally, natural scenes share consistent geometric properties, comprising similar densities of structure across multiple scales-a property classifying them as fractal (e.g., how the branching patterns of rivers and trees appear similar irrespective of scale). These two properties are intimately related and correlate strongly in natural scenes. However, research using thresholded noise images suggests that spatially, the human visual system is preferentially tuned to natural scene structure more so than 1/fα spectra. It is currently unclear whether this dependency on natural geometry extends to the temporal domain. We used a psychophysics task to measure discrimination sensitivity toward two types of synthetic noise movies: gray scale and thresholded (N = 60). Each movie type shared the same geometric properties (measured fractal D), but substantially differing spectral properties (measured α). In both space and time, we observe a characteristic dependency on stimulus structure across movie types, with sensitivity peaking for stimuli with natural geometry despite having altered 1/fα spectra. Although only measured behaviorally, our findings may imply that the neural processes underlying this tuning have developed to be sensitive to the most stable signal in our natural environment-structure (e.g., the structural properties of a tree are consistent from morning to night despite illumination changes across time points).

Correcting Susceptibility Artifacts of MRI Sensors in Brain Scanning: A 3D Anatomy-Guided Deep Learning Approach.

Echo planar imaging (EPI), a fast magnetic resonance imaging technique, is a powerful tool in functional neuroimaging studies. However, susceptibility artifacts, which cause misinterpretations of brain functions, are unavoidable distortions in EPI. This paper proposes an end-to-end deep learning framework, named TS-Net, for susceptibility artifact correction (SAC) in a pair of 3D EPI images with reversed phase-encoding directions. The proposed TS-Net comprises a deep convolutional network to predict a displacement field in three dimensions to overcome the limitation of existing methods, which only estimate the displacement field along the dominant-distortion direction. In the training phase, anatomical T1-weighted images are leveraged to regularize the correction, but they are not required during the inference phase to make TS-Net more flexible for general use. The experimental results show that TS-Net achieves favorable accuracy and speed trade-off when compared with the state-of-the-art SAC methods, i.e., TOPUP, TISAC, and S-Net. The fast inference speed (less than a second) of TS-Net makes real-time SAC during EPI image acquisition feasible and accelerates the medical image-processing pipelines.

Nice and slow: Measuring sensitivity and visual preference toward naturalistic stimuli varying in their amplitude spectra in space and time.

The 1/fα amplitude spectrum is a statistical property of natural scenes characterising a specific distribution of spatial and temporal frequencies and their associated luminance intensities. This property has been studied extensively in the spatial domain whereby sensitivity and visual preference overlap and peak for slopes within the natural range (α ≈ 1), but remains relatively less studied in the temporal domain. Here, we used a 4AFC task to measure sensitivity and a 2AFC task to measure visual preference and across a wide range of spatial (α = 0.25, 1.25, 2.25) and temporal (α = 0.25 to 2.50, step size: 0.25) slope conditions. Stimuli with a shallow temporal slope modulate rapidly (e.g. 0.25), whereas stimuli with a steep slope modulate slowly (e.g. 2.25). Interestingly, sensitivity and visual preference did not closely overlap. While the sensitivity of the visual system is highest for our stimulus with an intermediate modulation rate (1.25), which is most abundant in nature, the stimulus with the slowest modulation rate (2.25) was most preferred. It seems sensible for the visual system to be sensitive to spatiotemporal spectra that most commonly exist in nature (α ≈ 1). However, it is possible that preference might be related to what these properties signal in the natural world. Consider the cases of waves slowly vs. rapidly crashing on a beach or fast vs. slow animals. In both instances the slowest option is often the safest and preferential, suggesting that the temporal 1/fα amplitude spectrum provides additional information that may indicate preferred environmental conditions.

Manipulating the structure of natural scenes using wavelets to study the functional architecture of perceptual hierarchies in the brain.

Functional neuroimaging experiments that employ naturalistic stimuli (natural scenes, films, spoken narratives) provide insights into cognitive function "in the wild". Natural stimuli typically possess crowded, spectrally dense, dynamic, and multimodal properties within a rich multiscale structure. However, when using natural stimuli, various challenges exist for creating parametric manipulations with tight experimental control. Here, we revisit the typical spectral composition and statistical dependences of natural scenes, which distinguish them from abstract stimuli. We then demonstrate how to selectively degrade subtle statistical dependences within specific spatial scales using the wavelet transform. Such manipulations leave basic features of the stimuli, such as luminance and contrast, intact. Using functional neuroimaging of human participants viewing degraded natural images, we demonstrate that cortical responses at different levels of the visual hierarchy are differentially sensitive to subtle statistical dependences in natural images. This demonstration supports the notion that perceptual systems in the brain are optimally tuned to the complex statistical properties of the natural world. The code to undertake these stimulus manipulations, and their natural extension to dynamic natural scenes (films), is freely available.

The impact of self-control training on neural responses following anger provocation.

Self-control training (SCT) is one way to enhance self-controlled behavior. We conducted a novel and exploratory functional magnetic resonance imaging experiment to examine how SCT affects neural responses in a situation that elicits a self-control response: anger provocation. Forty-five healthy young men and women completed two-weeks of SCT or a behavioral monitoring task and were then insulted during scanning. We found significant changes in functional activation and connectivity using a lenient error threshold, which were not observed using a stricter threshold. Activation in the posterior insula was greater for the control compared to the SCT group at post-provocation, trait aggression correlated with neural responses to SCT, and SCT was associated with specific amygdala-cortical connections. Neural changes occurred even though SCT did not affect participants' performance on an inhibition task, reports of trying to control their anger, or their experience of anger. This dissociation prevented clear interpretation about whether the neural changes were indicative of specific anger or anger control processes. Although replication with high-powered studies is needed, we provide evidence that SCT affects neural responses in the context of anger provocation.

An unsupervised deep learning technique for susceptibility artifact correction in reversed phase-encoding EPI images.

Echo planar imaging (EPI) is a fast and non-invasive magnetic resonance imaging technique that supports data acquisition at high spatial and temporal resolutions. However, susceptibility artifacts, which cause the misalignment to the underlying structural image, are unavoidable distortions in EPI. Traditional susceptibility artifact correction (SAC) methods estimate the displacement field by optimizing an objective function that involves one or more pairs of reversed phase-encoding (PE) images. The estimated displacement field is then used to unwarp the distorted images and produce the corrected images. Since this conventional approach is time-consuming, we propose an end-to-end deep learning technique, named S-Net, to correct the susceptibility artifacts the reversed-PE image pair. The proposed S-Net consists of two components: (i) a convolutional neural network to map a reversed-PE image pair to the displacement field; and (ii) a spatial transform unit to unwarp the input images and produce the corrected images. The S-Net is trained using a set of reversed-PE image pairs and an unsupervised loss function, without ground-truth data. For a new image pair of reversed-PE images, the displacement field and corrected images are obtained simultaneously by evaluating the trained S-Net directly. Evaluations on three different datasets demonstrate that S-Net can correct the susceptibility artifacts in the reversed-PE images. Compared with two state-of-the-art SAC methods (TOPUP and TISAC), the proposed S-Net runs significantly faster: 20 times faster than TISAC and 369 times faster than TOPUP, while achieving a similar correction accuracy. Consequently, S-Net accelerates the medical image processing pipelines and makes the real-time correction for MRI scanners feasible. Our proposed technique also opens up a new direction in learning-based SAC.

Feasibility of functional magnetic resonance imaging of ocular dominance and orientation preference in primary visual cortex.

A recent hemodynamic model is extended and applied to simulate and explore the feasibility of detecting ocular dominance (OD) and orientation preference (OP) columns in primary visual cortex by means of functional magnetic resonance imaging (fMRI). The stimulation entails a short oriented bar stimulus being presented to one eye and mapped to cortical neurons with corresponding OD and OP selectivity. Activated neurons project via patchy connectivity to excite other neurons with similar OP in nearby visual fields located preferentially along the direction of stimulus orientation. The resulting blood oxygen level dependent (BOLD) response is estimated numerically via the model's spatiotemporal hemodynamic response function. The results are then used to explore the feasibility of detecting spatial OD-OP modulation, either directly measuring BOLD or by using Wiener deconvolution to filter the image and estimate the underlying neural activity. The effect of noise is also considered and it is estimated that direct detection can be robust for fMRI resolution of around 0.5 mm, whereas detection with Wiener deconvolution is possible at a broader range from 0.125 mm to 1 mm resolution. The detection of OD-OP features is strongly dependent on hemodynamic parameters, such as low velocity and high damping reduce response spreads and result in less blurring. The short-bar stimulus that gives the most detectable response is found to occur when neural projections are at 45 relative to the edge of local OD boundaries, which provides a constraint on the OD-OP architecture even when it is not fully resolved.