Evidence for widespread alterations in cortical microstructure after 32 h of sleep deprivation

Cortical microstructure is influenced by circadian rhythm and sleep deprivation, yet the precise underpinnings of these effects remain unclear. The ratio between T1-weighted and T2-weighted magnetic resonance images (T1w/T2w ratio) has been linked to myelin levels and dendrite density and may offer novel insight into the intracortical microstructure of the sleep deprived brain. Here, we examined intracortical T1w/T2w ratio in 41 healthy young adults (26 women) before and after 32 h of either sleep deprivation (n = 18) or a normal sleep-wake cycle (n = 23). Linear models revealed significant group differences in T1w/T2w ratio change after 32 h in four clusters, including bilateral effects in the insular, cingulate, and superior temporal cortices, comprising regions involved in attentional, auditory and pain processing. Across clusters, the sleep deprived group showed an increased T1w/T2w ratio, while the normal sleep-wake group exhibited a reduced ratio. These changes were not explained by in-scanner head movement, and 95% of the effects across clusters remained significant after adjusting for cortical thickness and hydration. Compared with a normal sleep-wake cycle, 32 h of sleep deprivation yields intracortical T1w/T2w ratio increases. While the intracortical changes detected by this study could reflect alterations in myelin or dendritic density, or both, histological analyses are needed to clarify the precise underlying cortical processes.publishedVersio

Archeologische opgraving van een meerperiodensite in de "Bergenmeersen" in het kader van het Sigmaplan (Gem. Wichelen Prov. Oost-Vlaanderen)

Het archeologisch onderzoek leverde gegevens op uit een groot aantal archeologische periodes, gaande van het middenpaleolithicum tot de nieuwe tijden, met enkele belangrijke inzichten: een bijna continue aanwezigheid in het opgravingsareaal in de steentijden, met enkele duidelijke concentraties uit het finaalpaleolithicum/vroegmesolithicum, en helaas minder grijpbare en meer diffuse gegevens voor de andere periodes. Wellicht was het gebied in met name het middenneolithicum eerder gelegen in de periferie van sites dichter tegen de loop van de vroegere Schelde, bv. op de plaats van de Paardenweide. Ook uit de vroege ijzertijd en Romeinse periode werden aanwijzingen voor bewoning aangetroffen, in beide gevallen werd wellicht een gedeelte van een erf aangesneden in zone B. Uit de vroege en volle middeleeuwen zijn de archeologische gegevens heel schaars, maar in de laatmiddeleeuwse periode werd in zone A een feodale motteversterking opgericht, die samen met de vroegere kerk, ongeveer 300 m ten zuidoosten, ongetwijfeld de kern vormde van het laatmiddeleeuwse Wichelen. Met name het aardewerk duidt op een bewoning van deze site in de 13de en 14de eeuw. Ten slotte wijzen de gegevens op verschillende fasen van inrichting van zone B van de 15de tot de 18de eeuw, vanaf de 17de eeuw, wellicht gerelateerd aan het historisch gekende Hof te Zijpen. Zoals diverse andere onderzoeken in de Sigma-gebieden wijzen deze opgravingsgegevens opnieuw op het belang van de alluviale gebieden voor de archeologie van de diverse periodes, vanaf de steentijd

Het archeologisch onderzoek in Raversijde (Oostende) in de periode 1992-2005

Raversijde - sinds 1970 deel van de stad Oostende, voordien Middelkerke - gaat terug tot een laatmiddeleeuwse vissersnederzetting met de naam Walraversijde. Deze vissersnederzetting was gesitueerd nabij de huidige grens Middelkerke/Oostende in een zone die zich grotendeels binnen het huidige provinciedomein Raversijde bevindt, maar zich ook nog in belangrijke mate uitstrekt tot op het strand ter hoogte van dit domein.In deze publicatie over archeologisch onderzoek in Raversijde komen de opgravingscampagnes op het grondgebied van het provinciedomein Raversijde uit de periode 1992-1998 uitvoerig aan bod. Daarnaast worden een aantal markante opgravingsresultaten van na 1998 belicht: het muntdepot dat op het einde van 1999 werd aangetroffen, de in 2003 aangesneden zone met begravingen en de in 2005 geïdentificeerde Romeinse dijk.Dit 8ste deel van de Relicta Monografieën behandelt chronologisch de resten en sporen uit de prehistorie, de Romeinse periode, de late middeleeuwen en de vroeg-moderne tijden. Deze publicatie is in de eerste plaats een opgravingsverslag: ze beschrijft, analyseert en interpreteert de belangrijkste sporen samen met een selectie van de aangetroffen mobiele resten en de resultaten van natuurwetenschappelijk onderzoek

ExploreASL: an image processing pipeline for multi-center ASL perfusion MRI studies

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners.The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. The toolbox adheres to previously defined international standards for data structure, provenance, and best analysis practice.ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow.ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts to increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice

ExploreASL: an image processing pipeline for multi-center ASL perfusion MRI studies

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners. The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. To facilitate collaboration and data-exchange, the toolbox follows several standards and recommendations for data structure, provenance, and best analysis practice. ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow. ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts which may increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice

Voxel-wise perfusion assessment in cerebral white matter with PCASL at 3T; Is it possible and how long does it take?

Purpose 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. Materials and Methods 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. Results 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. Conclusion 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

A Densely Interconnected Network for Deep Learning Accelerated MRI

Objective: To improve accelerated MRI reconstruction through a densely connected cascading deep learning reconstruction framework. Materials and Methods: A cascading deep learning reconstruction framework (baseline model) was modified by applying three architectural modifications: Input-level dense connections between cascade inputs and outputs, an improved deep learning sub-network, and long-range skip-connections between subsequent deep learning networks. An ablation study was performed, where five model configurations were trained on the NYU fastMRI neuro dataset with an end-to-end scheme conjunct on four- and eight-fold acceleration. The trained models were evaluated by comparing their respective structural similarity index measure (SSIM), normalized mean square error (NMSE) and peak signal to noise ratio (PSNR). Results: The proposed densely interconnected residual cascading network (DIRCN), utilizing all three suggested modifications, achieved a SSIM improvement of 8% and 11% for four- and eight-fold acceleration, respectively. For eight-fold acceleration, the model achieved a 23% decrease in the NMSE when compared to the baseline model. In an ablation study, the individual architectural modifications all contributed to this improvement, by reducing the SSIM and NMSE with approximately 3% and 5% for four-fold acceleration, respectively. Conclusion: The proposed architectural modifications allow for simple adjustments on an already existing cascading framework to further improve the resulting reconstructions

A densely interconnected network for deep learning accelerated MRI

Objective: To improve accelerated MRI reconstruction through a densely connected cascading deep learning reconstruction framework. Materials and methods: A cascading deep learning reconstruction framework (reference model) was modified by applying three architectural modifications: input-level dense connections between cascade inputs and outputs, an improved deep learning sub-network, and long-range skip-connections between subsequent deep learning networks. An ablation study was performed, where five model configurations were trained on the NYU fastMRI neuro dataset with an end-to-end scheme conjunct on four- and eightfold acceleration. The trained models were evaluated by comparing their respective structural similarity index measure (SSIM), normalized mean square error (NMSE), and peak signal to noise ratio (PSNR). Results: The proposed densely interconnected residual cascading network (DIRCN), utilizing all three suggested modifications achieved a SSIM improvement of 8% and 11%, a NMSE improvement of 14% and 23%, and a PSNR improvement of 2% and 3% for four- and eightfold acceleration, respectively. In an ablation study, the individual architectural modifications all contributed to this improvement for both acceleration factors, by improving the SSIM, NMSE, and PSNR with approximately 2–4%, 4–9%, and 0.5–1%, respectively. Conclusion: The proposed architectural modifications allow for simple adjustments on an already existing cascading framework to further improve the resulting reconstructions