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The most widely used task functional magnetic resonance imaging (fMRI) analyses use parametric statistical methods that depend on a variety of assumptions. In this work, we use real resting-state data and a total of 3 million random task group analyses to compute empirical familywise error rates for the fMRI software packages SPM, FSL, and AFNI, as well as a nonparametric permutation method. For a nominal familywise error rate of 5%, the parametric statistical methods are shown to be conservative for voxelwise inference and invalid for clusterwise inference. Our results suggest that the principal cause of the invalid cluster inferences is spatial autocorrelation functions that do not follow the assumed Gaussian shape. By comparison, the nonparametric permutation test is found to produce nominal results for voxelwise as well as clusterwise inference. These findings speak to the need of validating the statistical methods being used in the field of neuroimaging.

High-resolution phase—contrast magnetic resonance imaging can now assess flow in proximal and distal cerebral arteries. The aim of this study was to describe how total cerebral blood flow (tCBF) is distributed into the vascular tree with regard to age, sex and anatomic variations. Forty-nine healthy young (mean 25 years) and 45 elderly (mean 71 years) individuals were included. Blood flow rate (BFR) in 21 intra- and extracerebral arteries was measured. Total cerebral blood flow was defined as BFR in the internal carotid plus vertebral arteries and mean cerebral perfusion as tCBF/brain volume. Carotid/vertebral distribution was 72%/28% and was not related to age, sex, or brain volume. Total cerebral blood flow (717±123 mL/min) was distributed to each side as follows: middle cerebral artery (MCA), 21%; distal MCA, 6%; anterior cerebral artery (ACA), 12%, distal ACA, 4%; ophthalmic artery, 2%; posterior cerebral artery (PCA), 8%; and 20% to basilar artery. Deviating distributions were observed in subjects with ‘fetal’ PCA. Blood flow rate in cerebral arteries decreased with increasing age (P<0.05) but not in extracerebral arteries. Mean cerebral perfusion was higher in women (women: 61±8; men: 55±6 mL/min/100 mL, P<0.001). The study describes a new method to outline the flow profile of the cerebral vascular tree, including reference values, and should be used for grading the collateral flow system.

In the application of deep learning on optical coherence tomography (OCT) data, it is common to train classification networks using 2D images originating from volumetric data. Given the micrometer resolution of OCT systems, consecutive images are often very similar in both visible structures and noise. Thus, an inappropriate data split can result in overlap between the training and testing sets, with a large portion of the literature overlooking this aspect. In this study, the effect of improper dataset splitting on model evaluation is demonstrated for three classification tasks using three OCT open-access datasets extensively used, Kermany’s and Srinivasan’s ophthalmology datasets, and AIIMS breast tissue dataset. Results show that the classification performance is inflated by 0.07 up to 0.43 in terms of Matthews Correlation Coefficient (accuracy: 5% to 30%) for models tested on datasets with improper splitting, highlighting the considerable effect of dataset handling on model evaluation. This study intends to raise awareness on the importance of dataset splitting given the increased research interest in implementing deep learning on OCT data.

A single, arterial-optimized 4D-flow MRI acquisition may enable fast assessment of both cerebral arterial and venous flow. However, arteries and veins require different velocity encoding (VENC) settings for optimal velocity-to-noise ratio (VNR). Consequently, venous measurements using arterial-optimized VENC settings are subject to reduced VNR and require further evaluation. This study compared cerebral venous flow and pulsatility assessments using a high-VENC (110 cm/s, adapted to the arterial system) and a low-VENC (40 cm/s, adapted to the venous system) 4D-flow MRI sequence at 3 Tesla. Flow and pulsatility index (PI) were calculated for cerebral veins, sinuses and internal jugular veins in 36 elderly volunteers (79 ± 5 years). The high-VENC acquisitions allowed visualization of nearly all venous structures. Mean flow differences were small and the correlation, strong, when comparing both acquisitions across sinuses ( R  = 0.90–0.99, difference = -8–7%) and cortical veins ( R  = 0.93, difference = − 6%). Inflow-outflow differences at the confluence of sinuses were similar between acquisitions. PI showed moderate to strong agreement except in the straight sinus. Both the vein of Galen and the jugular veins suffered from aliasing in the venous VENC acquisitions. In summary, this study demonstrated that a VENC setting adapted for the arterial cerebral circulation was feasible for studying cerebral venous flow and pulsatility.

Large annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and data protection legislation. Generative AI models, such as generative adversarial networks (GANs) and diffusion models, can today produce very realistic synthetic images, and can potentially facilitate data sharing. However, in order to share synthetic medical images it must first be demonstrated that they can be used for training different networks with acceptable performance. Here, we therefore comprehensively evaluate four GANs (progressive GAN, StyleGAN 1–3) and a diffusion model for the task of brain tumor segmentation (using two segmentation networks, U-Net and a Swin transformer). Our results show that segmentation networks trained on synthetic images reach Dice scores that are 80%–90% of Dice scores when training with real images, but that memorization of the training images can be a problem for diffusion models if the original dataset is too small. Our conclusion is that sharing synthetic medical images is a viable option to sharing real images, but that further work is required. The trained generative models and the generated synthetic images are shared on AIDA data hub.

Blood–brain barrier (BBB) disruption may contribute to cognitive decline, but questions remain whether this association is more pronounced for certain brain regions, such as the hippocampus, or represents a whole-brain mechanism. Further, whether human BBB leakage is triggered by excessive vascular pulsatility, as suggested by animal studies, remains unknown. In a prospective cohort (N = 50; 68–84 years), we used contrast-enhanced MRI to estimate the permeability-surface area product (PS) and fractional plasma volume ( v p ), and 4D flow MRI to assess cerebral arterial pulsatility. Cognition was assessed by the Montreal Cognitive Assessment (MoCA) score. We hypothesized that high PS would be associated with high arterial pulsatility, and that links to cognition would be specific to hippocampal PS. For 15 brain regions, PS ranged from 0.38 to 0.85 (·10 −3  min −1 ) and v p from 0.79 to 1.78%. Cognition was related to PS (·10 −3  min −1 ) in hippocampus (β = – 2.9; p  = 0.006), basal ganglia (β = – 2.3; p  = 0.04), white matter (β = – 2.6; p  = 0.04), whole-brain (β = – 2.7; p  = 0.04) and borderline-related for cortex (β = – 2.7; p  = 0.076). Pulsatility was unrelated to PS for all regions ( p  > 0.19). Our findings suggest PS–cognition links mainly reflect a whole-brain phenomenon with only slightly more pronounced links for the hippocampus, and provide no evidence of excessive pulsatility as a trigger of BBB disruption.

Background White matter lesions (WML) and dilated perivascular spaces (PVS) are features of small vessel disease (SVD), commonly observed in aging and dementia, with unknown pathophysiology. Human studies have documented contrast accumulation within and in proximity of SVD-lesions. However, whether such observations mainly reflect excessive blood-brain barrier (BBB) leakage, or altered microvascular density in the investigated regions, remains unclear. Methods To evaluate the roles of BBB leakage and vascular density in aging and SVD, dynamic contrast enhanced (DCE) MRI was used to estimate the permeability-surface area product (PS) and fractional plasma volume ( ) in normal-appearing brain tissue and in proximity of and within WML and PVS in a population-based cohort ( N  = 56; 34/22 m/f; age 64 to 84 years). Analysis of variance (ANOVA) was used to assess regional differences in PS and and analysis of covariance (ANCOVA) was used to assess regional differences in PS with and vascular risk as covariates. Results Pronounced increases in PS and were observed from normal-appearing white matter (NAWM) to WML peripheries to WMLs. Similar PS and increases were observed from basal ganglia (BG) to BG-PVS. Further, PS in NAWM and white matter (WM) PVS were found to increase with cortex-to-ventricular depth. However, ANCOVA models with as a covariate showed that variance in PS was mainly explained by v p (η 2 =0.17 to η 2 =0.35; all p  < 10 − 3 ), whereas the effect of region was only borderline-significant when comparing NAWM, WML peripheries and WML ( p  = 0.03) and non-significant for the other comparisons ( p  > 0.29). Conclusions Our findings support the notion that contrast leakage across the BBB accumulates within and in proximity of SVD-related lesions. However, high contrast accumulation may mainly reflect high vascularization, and to a lesser degree than previously recognized BBB dysfunction.

Diffusion models were initially developed for text-to-image generation and are now being utilized to generate high quality synthetic images. Preceded by generative adversarial networks (GANs), diffusion models have shown impressive results using various evaluation metrics. However, commonly used metrics such as Frechet inception distance and inception score are not suitable for determining whether diffusion models are simply reproducing the training images. Here we train StyleGAN and a diffusion model, using BRATS20, BRATS21 and a chest x-ray (CXR) pneumonia dataset, to synthesize brain MRI and CXR images, and measure the correlation between the synthetic images and all training images. Our results show that diffusion models are more likely to memorize the training images, compared to StyleGAN, especially for small datasets and when using 2D slices from 3D volumes. Researchers should be careful when using diffusion models (and to some extent GANs) for medical imaging, if the final goal is to share the synthetic images.

Diffusion magnetic resonance imaging (diffusion MRI) is widely employed to probe the diffusive motion of water molecules within the tissue. Numerous diseases and processes affecting the central nervous system can be detected and monitored via diffusion MRI thanks to its sensitivity to microstructural alterations in tissue. The latter has prompted interest in quantitative mapping of the microstructural parameters, such as the fiber orientation distribution function (fODF), which is instrumental for noninvasively mapping the underlying axonal fiber tracts in white matter through a procedure known as tractography. However, such applications demand repeated acquisitions of MRI volumes with varied experimental parameters demanding long acquisition times and/or limited spatial resolution. In this work, we present a deep-learning-based approach for increasing the spatial resolution of diffusion MRI data in the form of fODFs obtained through constrained spherical deconvolution. The proposed approach is evaluated on high quality data from the Human Connectome Project, and is shown to generate upsampled results with a greater correspondence to ground truth high-resolution data than can be achieved with ordinary spline interpolation methods. Furthermore, we employ a measure based on the earth mover’s distance to assess the accuracy of the upsampled fODFs. At low signal-to-noise ratios, our super-resolution method provides more accurate estimates of the fODF compared to data collected with 8 times smaller voxel volume.

Cerebrovascular resistance (CVR) regulates blood flow in the brain, but little is known about the vascular resistances of the individual cerebral territories. We present a method to calculate these resistances and investigate how CVR varies in the hemodynamically disturbed brain. We included 48 patients with stroke/TIA (29 with symptomatic carotid stenosis). By combining flow rate (4D flow MRI) and structural computed tomography angiography (CTA) data with computational fluid dynamics (CFD) we computed the perfusion pressures out from the circle of Willis, with which CVR of the MCA, ACA, and PCA territories was estimated. 56 controls were included for comparison of total CVR (tCVR). CVR were 33.8 ± 10.5, 59.0 ± 30.6, and 77.8 ± 21.3 mmHg s/ml for the MCA, ACA, and PCA territories. We found no differences in tCVR between patients, 9.3 ± 1.9 mmHg s/ml, and controls, 9.3 ± 2.0 mmHg s/ml (p = 0.88), nor in territorial CVR in the carotid stenosis patients between ipsilateral and contralateral hemispheres. Territorial resistance associated inversely to territorial brain volume (p < 0.001). These resistances may work as reference values when modelling blood flow in the circle of Willis, and the method can be used when there is need for subject-specific analysis.