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An important step in the analysis of magnetic resonance imaging (MRI) data for neuroimaging is the automated segmentation of white matter hyperintensities (WMHs). Fluid Attenuated Inversion Recovery (FLAIR-weighted) is an MRI contrast that is particularly useful to visualize and quantify WMHs, a hallmark of cerebral small vessel disease and Alzheimer's disease (AD). In order to achieve high spatial resolution in each of the three voxel dimensions, clinical MRI protocols are evolving to a three-dimensional (3D) FLAIR-weighted acquisition. The current study details the deployment of deep learning tools to enable automated WMH segmentation and characterization from 3D FLAIR-weighted images acquired as part of a national AD imaging initiative. Based on data from the ongoing Norwegian Disease Dementia Initiation (DDI) multicenter study, two 3D models-one off-the-shelf from the NVIDIA nnU-Net framework and the other internally developed-were trained, validated, and tested. A third cutting-edge Deep Bayesian network model (HyperMapp3r) was implemented without any de-novo tuning to serve as a comparison architecture. The 2.5D in-house developed and 3D nnU-Net models were trained and validated in-house across five national collection sites among 441 participants from the DDI study, of whom 194 were men and whose average age was (64.91 +/- 9.32) years. Both an external dataset with 29 cases from a global collaborator and a held-out subset of the internal data from the 441 participants were used to test all three models. These test sets were evaluated independently. The ground truth human-in-the-loop segmentation was compared against five established WMH performance metrics. The 3D nnU-Net had the highest performance out of the three tested networks, outperforming both the internally developed 2.5D model and the SOTA Deep Bayesian network with an average dice similarity coefficient score of 0.76 +/- 0.16. Our findings demonstrate that WMH segmentation models can achieve high performance when trained exclusively on FLAIR input volumes that are 3D volumetric acquisitions. Single image input models are desirable for ease of deployment, as reflected in the current embedded clinical research project. The 3D nnU-Net had the highest performance, which suggests a way forward for our need to automate WMH segmentation while also evaluating performance metrics during on-going data collection and model retraining.

To meet the need for Parkinson's disease biomarkers and evidence for amount and distribution of pathological changes, MRI diffusion tensor imaging (DTI) has been explored in a number of previous studies. However, conflicting results warrant further investigations. As tissue microstructure, particularly of the grey matter, is heterogeneous, a more precise diffusion model may benefit tissue characterization. The purpose of this study was to analyze the diffusion-based imaging technique restriction spectrum imaging (RSI) and DTI, and their ability to detect microstructural changes within brain regions associated with motor function in Parkinson's disease. Diffusion weighted (DW) MR images of a total of 100 individuals, (46 Parkinson's disease patients and 54 healthy controls) were collected using b-values of 0-4000s/mm2. Output diffusion-based maps were estimated based on the RSI-model combining the full set of DW-images (Cellular Index (CI), Neurite Density (ND)) and DTI-model combining b = 0 and b = 1000 s/mm2 (fractional anisotropy (FA), Axial-, Mean- and Radial diffusivity (AD, MD, RD)). All parametric maps were analyzed in a voxel-wise group analysis, with focus on typical brain regions associated with Parkinson's disease pathology. CI, ND and DTI diffusivity metrics (AD, MD, RD) demonstrated the ability to differentiate between groups, with strongest performance within the thalamus, prone to pathology in Parkinson's disease. Our results indicate that RSI may improve the predictive power of diffusion-based MRI, and provide additional information when combined with the standard diffusivity measurements. In the absence of major atrophy, diffusion techniques may reveal microstructural pathology. Our results suggest that protocols for MRI diffusion imaging may be adapted to more sensitive detection of pathology at different sites of the central nervous system.

Working memory capacity is pivotal for a broad specter of cognitive tasks and develops throughout childhood. This must in part rely on development of neural connections and white matter microstructure maturation, but there is scarce knowledge of specific relations between this and different aspects of working memory. Diffusion tensor imaging (DTI) enables us to study development of brain white matter microstructure. In a longitudinal DTI study of 148 healthy children between 4 and 11 years scanned twice with an on average 1.6 years interval, we characterized change in fractional anisotropy (FA), mean (MD), radial (RD) and axial diffusivity (AD) in 10 major white matter tracts hypothesized to be of importance for working memory. The results showed relationships between change in several tracts and change in visuospatial working memory. Specifically, improvement in visuospatial working memory capacity was significantly associated with decreased MD, RD and AD in inferior longitudinal fasciculus (ILF), inferior fronto-occipital fasciculus (IFOF) and uncinate fasciculus (UF) in the right hemisphere, as well as forceps major (FMaj). No significant relationships were found between change in DTI metrics and change in verbal working memory capacity. These findings yield new knowledge about brain development and corresponding working memory improvements in childhood.

Heart rate variability (HRV) has become an increasingly popular index of cardiac autonomic control in the biobehavioral sciences due to its relationship with mental illness and cognitive traits. However, the intraindividual stability of HRV in response to sleep and diurnal disturbances, which are commonly reported in mental illness, and its relationship with executive function are not well understood. Here, in 40 healthy adult males we calculated high frequency HRV-an index of parasympathetic nervous system (PNS) activity-using pulse oximetry during brain imaging, and assessed attentional and executive function performance in a subsequent behavioral test session at three time points: morning, evening, and the following morning. Twenty participants were randomly selected for total sleep deprivation whereas the other 20 participants slept as normal. Sleep deprivation and morning-to-night variation did not influence high frequency HRV at either a group or individual level; however, sleep deprivation abolished the relationship between orienting attention performance and HRV. We conclude that a day of wake and a night of laboratory-induced sleep deprivation do not alter supine high frequency HRV in young healthy male adults.

To establish whether reliable voxel-wise assessment of perfusion in cerebral white matter (WM) is possible using arterial spin labeling (ASL) at 3T in a cohort of healthy subjects.Pseudo-continuous ASL (PCASL) with background suppression (BS) optimized for WM measurements was performed at 3T in eight healthy male volunteers aged 25-41. Four different labeling schemes were evaluated by varying the labeling duration (LD) and post-labeling delay (PLD). Eight slices with voxel dimension 3.75x3.75x5 mm3 were acquired from the anterosuperior aspect of the brain, and 400 image/control pairs were collected for each run. Rigid head immobilization was applied using individually fitted thermoplastic masks. For each voxel in the resulting ASL time series, the time needed to reach a 95% significance level for the ASL signal to be higher than zero (paired t-test), was estimated.The four protocols detected between 88% and 95% (after Bonferroni correction: 75% and 88%) of WM voxels at 95% significance level. In the most efficient sequence, 80% was reached after 5 min and 95% after 53 min (after Bonferroni correction 40% and 88% respectively). For all protocols, the fraction of significant WM voxels increased in an asymptotic fashion with increasing scan time. A small subgroup of voxels was shown to not benefit at all from prolonged measurement.Acquisition of a significant ASL signal from a majority of WM voxels is possible within clinically acceptable scan times, whereas full coverage needs prohibitively long scan times, as a result of the asymptotic trajectory.

Elucidating the neurobiological effects of sleep and waking remains an important goal of the neurosciences. Recently, animal studies indicated that sleep is important for cell membrane and myelin maintenance in the brain and that these structures are particularly susceptible to insufficient sleep. Here, we tested the hypothesis that a day of waking and sleep deprivation would be associated with changes in diffusion tensor imaging (DTI) indices of white matter microstructure sensitive to axonal membrane and myelin alterations.Twenty-one healthy adult males underwent DTI in the morning [7:30AM; time point (TP)1], after 14 hours of waking (TP2), and then after another 9 hours of waking (TP3). Whole brain voxel-wise analysis was performed with tract based spatial statistics.A day of waking was associated with widespread increases in white matter fractional anisotropy, which were mainly driven by radial diffusivity reductions, and sleep deprivation was associated with widespread fractional anisotropy decreases, which were mainly explained by reductions in axial diffusivity. In addition, larger decreases in axial diffusivity after sleep deprivation were associated with greater sleepiness. All DTI changes remained significant after adjusting for hydration measures.This is the first DTI study of sleep deprivation in humans. Although previous studies have observed localized changes in DTI indices of cerebral microstructure over the course of a few hours, further studies are needed to confirm widespread DTI changes within hours of waking and to clarify whether such changes in white matter microstructure serve as neurobiological substrates of sleepiness.