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With recent improvements in human magnetic resonance imaging (MRI) at ultra-high fields, the amount of data collected per subject in a given MRI experiment has increased considerably. Standard image processing packages are often challenged by the size of these data. Dedicated methods are needed to leverage their extraordinary spatial resolution. Here, we introduce a flexible Python toolbox that implements a set of advanced techniques for high-resolution neuroimaging. With these tools, segmentation and laminar analysis of cortical MRI data can be performed at resolutions up to 500 μm in reasonable times. Comprehensive online documentation makes the toolbox easy to use and install. An extensive developer’s guide encourages contributions from other researchers that will help to accelerate progress in the promising field of high-resolution neuroimaging.

Aging is accompanied by vascular and structural changes in the brain, which include decreased grey matter volume (GMV), cerebral blood flow (CBF), and cerebrovascular reactivity (CVR). Enhanced fitness in aging has been related to preservation of GMV and CBF, and in some cases CVR, although there are contradictory relationships reported between CVR and fitness. To gain a better understanding of the complex interplay between fitness and GMV, CBF and CVR, the present study assessed these factors concurrently. Data from 50 participants, aged 55 to 72, were used to derive GMV, CBF, CVR and VO2peak. Results revealed that lower CVR was associated with higher VO2peak throughout large areas of the cerebral cortex. Within these regions lower fitness was associated with higher CBF and a faster hemodynamic response to hypercapnia. Overall, our results indicate that the relationships between age, fitness, cerebral health and cerebral hemodynamics are complex, likely involving changes in chemosensitivity and autoregulation in addition to changes in arterial stiffness. Future studies should collect other physiological outcomes in parallel with quantitative imaging, such as measures of chemosensitivity and autoregulation, to further understand the intricate effects of fitness on the aging brain, and how this may bias quantitative measures of cerebral health.

Purpose of review Ablations and particularly deep brain stimulation (DBS) of a variety of CNS targets are established therapeutic tool for movement disorders. Accurate targeting of the intended structure is crucial for optimal clinical outcomes. However, most targets used in functional neurosurgery are poorly visualized on routine MRI and indirect targeting methods are commonly used. This article reviews recent neuroimaging advancements for targeting in movement disorders. Recent findings Dedicated MRI sequences can often visualise anatomical structures commonly targeted during DBS surgery, including at 1.5T field strengths. Due to recent technological advancements, MR images using ultra-high magnetic field strengths and new acquisition parameters allow for markedly improved visualization of common movement disorder targets. In addition, novel neuroimaging techniques have enabled group-level analysis of DBS patients and delineation of areas associated with clinical benefits. These areas might diverge from the conventionally targeted nuclei and may instead correspond to white matter tracts or hubs of functional networks. Summary Neuroimaging advancements have enabled direct visualization-based targeting as well as optimization and adjustment of conventionally targeted structures.

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The vascular organization of the human brain can determine neurological and neurophysiological functions, yet thus far it has not been comprehensively mapped. Aging and diseases such as dementia are known to be associated with changes to the vasculature and normative data could help detect these vascular changes in neuroimaging studies. Furthermore, given the well-known impact of venous vessels on the blood oxygen level dependent (BOLD) signal, information about the common location of veins could help detect biases in existing datasets. In this work, a quantitative atlas of the venous vasculature using quantitative susceptibility maps (QSM) acquired with a 0.6 mm isotropic resolution is presented. The Venous Neuroanatomy (VENAT) atlas was created from 5 repeated 7 Tesla MRI measurements in young and healthy volunteers (n = 20, 10 females, mean age = 25.1 ± 2.5 years) using a two-step registration method on 3D segmentations of the venous vasculature. This cerebral vein atlas includes the average vessel location, diameter (mean: 0.84 ± 0.33 mm) and curvature (0.11 ± 0.05 mm -1 ) from all participants and provides an in vivo measure of the angio-architectonic organization of the human brain and its variability. This atlas can be used as a basis to understand changes in the vasculature during aging and neurodegeneration, as well as vascular and physiological effects in neuroimaging.

Resting‐state (rs) functional magnetic resonance imaging (fMRI) is used to detect low‐frequency fluctuations in the blood oxygen‐level dependent (BOLD) signal across brain regions. Correlations between temporal BOLD signal fluctuations are commonly used to infer functional connectivity. However, because BOLD is based on the dilution of deoxyhemoglobin, it is sensitive to veins of all sizes, and its amplitude is biased by draining veins. These biases affect local BOLD signal location and amplitude, and may also influence BOLD‐derived connectivity measures, but the magnitude of this venous bias and its relation to vein size and proximity is unknown. Here, veins were identified using high‐resolution quantitative susceptibility maps and utilized in a biophysical model to investigate systematic venous biases on common local rsfMRI‐derived measures. Specifically, we studied the impact of vein diameter and distance to veins on the amplitude of low‐frequency fluctuations (ALFF), fractional ALFF (fALFF), Hurst exponent (HE), regional homogeneity (ReHo), and eigenvector centrality values in the grey matter. Values were higher across all distances in smaller veins, and decreased with increasing vein diameter. Additionally, rsfMRI values associated with larger veins decrease with increasing distance from the veins. ALFF and ReHo were the most biased by veins, while HE and fALFF exhibited the smallest bias. Across all metrics, the amplitude of the bias was limited in voxel‐wise data, confirming that venous structure is not the dominant source of contrast in these rsfMRI metrics. Finally, the models presented can be used to correct this venous bias in rsfMRI metrics.

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