Publications

Markers of downstream events are a key component of clinical trials of disease-modifying therapies for Alzheimer’s disease. Morphological metrics like cortical thickness are established measures of atrophy but are not sensitive enough to detect Aβ-related changes that occur before overt atrophy become visible. We aimed to investigate to what extent diffusion-MRI can provide sensitive markers of cortical microstructural changes and to test their associations with multiple aspects of the Alzheimer’s disease pathological cascade, including both Aβ and tau accumulation, astrocytic activation and cognitive deficits. We applied the mean apparent diffusion propagator model to diffusion-MRI data from 492 cognitively unimpaired elderly and patients with mild cognitive impairment from the Swedish BioFINDER-2 cohort. Participants were stratified in Aβ-negative/tau-negative, Aβ-positive/tau-negative, and Aβ-positive/tau-positive based on Aβ- and tau-PET uptake. Cortical regional values of diffusion-MRI metrics and cortical thickness were compared across groups. Associations between regional values of diffusion-MRI metrics and both Aβ- and tau-PET uptake were also investigated along with the association with plasma level of glial fibrillary acidic protein (GFAP), a marker of astrocytes activation (available in 292 participants). Mean squared displacement revealed widespread microstructural differences already between Aβ-negative/tau-negative and Aβ-positive/tau-negative participants with a spatial distribution that closely resembled the pattern of Aβ accumulation. In contrast, differences in cortical thickness were clearly more limited. Mean squared displacement was also correlated with both Aβ- and tau-PET uptake even independently from one another and from cortical thickness. Further, the same metric exhibited significantly stronger correlations with PET uptake than cortical thickness (p < 0.05). Mean squared displacement was also positively correlated with GFAP with a pattern that resemble Aβ accumulation, and GFAP partially mediated the association between Aβ accumulation and mean squared displacement. Further, impairments in executive functions were significantly more associated with mean squared displacement values extracted from a meta-ROI encompassing regions accumulating Aβ early in the disease process, than with cortical thickness (p < 0.05). Similarly, impairments in memory functions were significantly more associated with mean squared displacement values extracted from a temporal meta-ROI, than with cortical thickness (p < 0.05). Metrics of cortical microstructural alteration derived from diffusion-MRI are highly sensitive to multiple aspects of the Alzheimer’s disease pathological cascade. Of particular interest is the link with both Aβ-PET and GFAP suggesting diffusion-MRI might reflects microstructural changes related to the astrocytic response to Aβ aggregation. Therefore, metrics of cortical diffusion might be important outcome measures in anti-Aβ treatments clinical trials for detecting drug-induced changes in cortical microstructure.

Loss of small pulmonary arteries measured as the ratio of blood vessel volume in arteries <5 mm2 in cross-section to total arterial blood vessel volume (BV5a/TBVa), with lower values indicating more pruning, was associated with 5-yr progressing CT-derived bronchiectasis in smokers (Odds Ratio (OR) [95% Confidence interval], 1.28 [1.07-1.53] per 5% lower BV5a/TBVa, P = 0.007). Corresponding results in smokers with COPD were: OR 1.45 [1.11-1.89] per 5% lower BV5a/TBVa, P = 0.007. The results support a vascular factor for structural progression of bronchiectasis.

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.

Neurological symptoms are common in Covid-19 and cerebral edema has been shown post-mortem. The mechanism behind this is unclear. Elevated intracranial pressure (ICP) has not been extensively studied in Covid-19. ICP can be estimated noninvasively with measurements of the optic nerve sheath diameter (ONSD). We performed a cohort study with ONSD ultrasound measurements in severe cases of Covid-19 at an intensive care unit (ICU). We measured ONSD with ultrasound in adults with severe Covid-19 in the ICU at Karolinska University Hospital in Sweden. Patients were classified as either having normal or elevated ONSD. We compared ICU length of stay (ICU-LOS) and 90 day mortality between the groups. 54 patients were included. 11 of these (20.4%) had elevated ONSD. Patients with elevated ONSD had 12 days longer ICU-LOS (95% CI 2 to 23 p = 0.03) and a risk ratio of 2.3 for ICU-LOS >= 30 days. There were no significant differences in baseline data or 90 day mortality between the groups. Elevated ONSD is common in severe Covid-19 and is associated with adverse outcome. This may be caused by elevated ICP. This is a clinically important finding that needs to be considered when deciding upon various treatment strategies.

Importance: Military service members returning from theaters of war are at increased risk for mental illness, but despite high prevalence and substantial individual and societal burden, the underlying pathomechanisms remain largely unknown. Exposure to high levels of emotional stress in theaters of war and mild traumatic brain injury (mTBI) are presumed factors associated with risk for the development of mental disorders. Objective: To investigate (1) whether war zone-related stress is associated with microstructural alterations in limbic gray matter (GM) independent of mental disorders common in this population, (2) whether associations between war zone-related stress and limbic GM microstructure are modulated by a history of mTBI, and (3) whether alterations in limbic GM microstructure are associated with neuropsychological functioning. Design, Setting, and Participants: This cohort study was part of the TRACTS (Translational Research Center for TBI and Stress Disorders) study, which took place in 2010 to 2014 at the Veterans Affair Rehabilitation Research and Development TBI National Network Research Center. Participants included male veterans (aged 18-65 years) with available diffusion tensor imaging data enrolled in the TRACTS study. Data analysis was performed between December 2017 to September 2021. Exposures: The Deployment Risk and Resilience Inventory (DRRI) was used to measure exposure to war zone-related stress. The Boston Assessment of TBI-Lifetime was used to assess history of mTBI. Stroop Inhibition (Stroop-IN) and Inhibition/Switching (Stroop-IS) Total Error Scaled Scores were used to assess executive or attentional control functions. Main Outcomes and Measures: Diffusion characteristics (fractional anisotropy of tissue [FAT]) of 16 limbic and paralimbic GM regions and measures of functional outcome.

This paper provides a brief introduction to the Arctic atmospheric radioactivity monitoring network. A decade of monitoring results have shown the 137Cs background levels in Arctic air range from 0.05 to 1.50 μBq/m3. The monitoring stations have sufficient sensitivity to detect 137Cs brought to the atmosphere due to resuspension in local soil and reemissions from biomass burning in a daily temporal resolution. These observations can be used as tracers for atmospheric processes. The 133Xe measurements obtained at Yellowknife, Resolute and Spitsbergen could support other research into how air pollution problems arise across intercontinental distances. It will help develop and improve models capable of predicting the long-distance transport and deposition of trace gases in the Arctic. Rainwater monitoring data collected in Finnish Lapland since the 1960’s indicate that 3H radioactivity concentrations reached natural background levels in early 2000s, typically around 1-2 Bq/L monthly, with an annual seasonal variation cycle consistent with the observed of other cosmogenic radionuclides.

Brain iron is central to dopaminergic neurotransmission, a key component in schizophrenia pathology. Iron can also generate oxidative stress, which is one proposed mechanism for gray matter volume reduction in schizophrenia. The role of brain iron in schizophrenia and its potential link to oxidative stress has not been previously examined. In this study, we used 7-Tesla MRI quantitative susceptibility mapping (QSM), magnetic resonance spectroscopy (MRS), and structural T1 imaging in 12 individuals with chronic schizophrenia and 14 healthy age-matched controls. In schizophrenia, there were higher QSM values in bilateral putamen and higher concentrations of phosphocreatine and lactate in caudal anterior cingulate cortex (caCC). Network-based correlation analysis of QSM across corticostriatal pathways as well as the correlation between QSM, MRS, and volume, showed distinct patterns between groups. This study introduces increased iron in the putamen in schizophrenia in addition to network-wide disturbances of iron and metabolic status.

Magnetic resonance imaging (MRI)-based brain segmentation has recently been revolutionized by deep learning methods. These methods use large numbers of annotated segmentations to train algorithms that have the potential to perform brain segmentations reliably and quickly. However, training data for these algorithms are frequently obtained from automated brain segmentation systems, which may contain inaccurate neuroanatomy. Thus, the neuroimaging community would benefit from an open source database of high quality, neuroanatomically curated and manually edited MRI brain images, as well as the publicly available tools and detailed procedures for generating these curated data. Manual segmentation approaches are regarded as the gold standard for brain segmentation and parcellation. These approaches underpin the construction of neuroanatomically accurate human brain atlases. In addition, neuroanatomically precise definitions of MRI-based regions of interest (ROIs) derived from manual brain segmentation are essential for accuracy in structural connectivity studies and in surgical planning for procedures such as deep brain stimulation. However, manual segmentation procedures are time and labor intensive, and not practical in studies utilizing very large datasets, large cohorts, or multimodal imaging. Automated segmentation methods were developed to overcome these issues, and provide high data throughput, increased reliability, and multimodal imaging capability. These methods utilize manually labeled brain atlases to automatically parcellate the brain into different ROIs, but do not have the anatomical accuracy of skilled manual segmentation approaches. In the present study, we developed a custom software module for manual editing of brain structures in the freely available 3D Slicer software platform that employs principles and tools based on pioneering work from the Center for Morphometric Analysis (CMA) at Massachusetts General Hospital. We used these novel 3D Slicer segmentation tools and techniques in conjunction with well-established neuroanatomical definitions of subcortical brain structures to manually segment 50 high resolution T1w MRI brains from the Human Connectome Project (HCP) Young Adult database. The structural definitions used herein are associated with specific neuroanatomical ontologies to systematically interrelate histological and MRI-based morphometric definitions. The resulting brain datasets are publicly available and will provide the basis for a larger database of anatomically curated brains as an open science resource.

PURPOSE: Identifying optimal machine learning pipelines for computer-aided diagnosis is key for the development of robust, reproducible, and clinically relevant imaging biomarkers for endometrial carcinoma. The purpose of this study was to introduce the mathematical development of image descriptors computed from spherical harmonics (SPHARM) decompositions as well as the associated machine learning pipeline, and to evaluate their performance in predicting deep myometrial invasion (MI) and histopathological high-grade in preoperative multiparametric magnetic resonance imaging (MRI). PATIENTS AND METHODS: This retrospective study included 128 women with histopathology-confirmed endometrial carcinomas who underwent 1.5-T MRI before hysterectomy between January 2011 and July 2015. SPHARM descriptors of each tumor were computed on multiparametric MRI images (T2-weighted, diffusion-weighted, dynamic contrast-enhanced-MRI and apparent diffusion coefficient maps). Tensor-based logistic regression was used to classify two-dimensional SPHARM rotationally-invariant descriptors. Head-to-head comparisons with radiomics analyses were performed with DeLong tests with Bonferroni-Holm correction to compare diagnostic performances. RESULTS: With all MRI contrasts, SPHARM analysis resulted in area under the curve, sensitivity, specificity, and balanced accuracy values of 0.94 (95% confidence interval [CI]: 0.85, 1.00), 100% (95% CI: 100, 100), 74% (95% CI: 51, 92), 87% (95% CI: 78, 98), respectively, for predicting deep MI. For predicting high-grade tumor histology, the corresponding values for the same diagnostic metrics were 0.81 (95% CI: 0.64, 0.90), 93% (95% CI: 67, 100), 63% (95% CI: 45, 79) and 78% (95% CI: 64, 86). The corresponding values achieved via radiomics were 0.92 (95% CI: 0.82, 0.95), 82% (95% CI: 65, 93), 80% (95% CI: 51, 94), 81% (95% CI: 70, 91) for deep MI and 0.72 (95% CI: 0.58, 0.83), 93% (95% CI: 65, 100), 55% (95% CI: 41, 69), 74% (95% CI: 52, 88) for high-grade histology. The diagnostic performance of the SPHARM analysis was not significantly different (P = 0.62) from that of radiomics for predicting deep MI but was significantly higher (P = 0.044) for predicting high-grade histology. CONCLUSION: The proposed SPHARM analysis yields similar or higher diagnostic performance than radiomics in identifying deep MI and high-grade status in histology-proven endometrial carcinoma.

Prodigiosin (PG), a member of a family of natural red pigments produced by a variety of bacteria, was first discovered in Serratia marcescens. PG has been reported to have an apoptosis-inducing effect in many cancers, such as lymphoma, colon cancer and nasopharyngeal carcinoma. For this study, we used three glioblastoma (GBM) cell lines (LN229, U251 and A172) to explore the effect of prodigiosin on GBM cells. A CCK8 assay was used to evaluate cell viability. We determinedthe cell cycle distribution by flow cytometry and measured proliferation by an EdU incorporation assay. The expression of different molecules was investigated by western blotting and RT-PCR. We further confirmed our results by plasmid transfection and lentiviral transduction. The LN229 xenograft model was used to study the effect of prodigiosin in vivo. We confirmed that prodigiosin played an anticancer role in several GBM cell lines through the KIAA1524/PP2A/Akt signalling pathway. Prodigiosin inhibited the protein expression of KIAA1524 by suppressing its transcription, which led to activation of PP2A. Afterward, PP2A inhibited the phosphorylation of Akt, thereby inducing increased expression of p53/p21. Furthermore, it was verified that prodigiosin inhibited the KIAA1524/PP2A/Akt axis in vivo in the LN229 xenograft model. These data improve the understanding of the anticancer effects of prodigiosin and further highlight the potential of prodigiosin for the development of anti-glioma drugs.