Differences in educational opportunity predict white matter development

Coarse measures of socioeconomic status, such as parental income or parental education, have been linked to differences in white matter development. However, these measures do not provide insight into specific aspects of an individual's environment and how they relate to brain development. On the other hand, educational intervention studies have shown that changes in an individual's educational context can drive measurable changes in their white matter. These studies, however, rarely consider socioeconomic factors in their results. In the present study, we examined the unique relationship between educational opportunity and white matter development, when controlling other known socioeconomic factors. To explore this question, we leveraged the rich demographic and neuroimaging data available in the ABCD study, as well the unique data-crosswalk between ABCD and the Stanford Education Data Archive (SEDA). We find that educational opportunity is related to accelerated white matter development, even when accounting for other socioeconomic factors, and that this relationship is most pronounced in white matter tracts associated with academic skills. These results suggest that the school a child attends has a measurable relationship with brain development for years to come.

The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset

Supplemental material is available for this article.

FEMA: Fast and efficient mixed-effects algorithm for large sample whole-brain imaging data

The linear mixed-effects model (LME) is a versatile approach to account for dependence among observations. Many large-scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole-brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole-brain vertex-wise, voxel-wise, and connectome-wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross-sectional and longitudinal effects of age on region-of-interest level and vertex-wise cortical thickness, as well as connectome-wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive DevelopmentSM Study release 4.0. Our analyses reveal distinct spatial patterns for the annualized changes in vertex-wise cortical thickness and connectome-wide connectivity values in early adolescence, highlighting a critical time of brain maturation. The simulations and application to real data show that FEMA enables advanced investigation of the relationships between large numbers of neuroimaging metrics and variables of interest while considering complex study designs, including repeated measures and family structures, in a fast and efficient manner. The source code for FEMA is available via: https://github.com/cmig-research-group/cmig_tools/.

White matter and literacy: A dynamic system in flux

Cross-sectional studies have linked differences in white matter tissue properties to reading skills. However, past studies have reported a range of, sometimes conflicting, results. Some studies suggest that white matter properties act as individual-level traits predictive of reading skill, whereas others suggest that reading skill and white matter develop as a function of an individual's educational experience. In the present study, we tested two hypotheses: a) that diffusion properties of the white matter reflect stable brain characteristics that relate to stable individual differences in reading ability or b) that white matter is a dynamic system, linked with learning over time. To answer these questions, we examined the relationship between white matter and reading in a five-year longitudinal dataset and a series of large-scale, single-observation, cross-sectional datasets (N = 14,249 total participants). We find that gains in reading skill correspond to longitudinal changes in the white matter. However, in the cross-sectional datasets, we find no evidence for the hypothesis that individual differences in white matter predict reading skill. These findings highlight the link between dynamic processes in the white matter and learning.

3D U-Net for automated detection of multiple sclerosis lesions: utility of transfer learning from other pathologies

Background and purpose

Deep learning algorithms for segmentation of multiple sclerosis (MS) plaques generally require training on large datasets. This manuscript evaluates the effect of transfer learning from segmentation of another pathology to facilitate use of smaller MS-specific training datasets. That is, a model trained for detection of one type of pathology was re-trained to identify MS lesions and active demyelination.

Materials and methods

In this retrospective study using MRI exams from 149 patients spanning 4/18/2014 to 7/8/2021, 3D convolutional neural networks were trained with a variable number of manually-segmented MS studies. Models were trained for FLAIR lesion segmentation at a single timepoint, new FLAIR lesion segmentation comparing two timepoints, and enhancing (actively demyelinating) lesion segmentation on T1 post-contrast imaging. Models were trained either de-novo or fine-tuned with transfer learning applied to a pre-existing model initially trained on non-MS data. Performance was evaluated with lesionwise sensitivity and positive predictive value (PPV).

Results

For single timepoint FLAIR lesion segmentation with 10 training studies, a fine-tuned model demonstrated improved performance [lesionwise sensitivity 0.55 ± 0.02 (mean ± standard error), PPV 0.66 ± 0.02] compared to a de-novo model (sensitivity 0.49 ± 0.02, p = 0.001; PPV 0.32 ± 0.02, p < 0.001). For new lesion segmentation with 30 training studies and their prior comparisons, a fine-tuned model demonstrated similar sensitivity (0.49 ± 0.05) and significantly improved PPV (0.60 ± 0.05) compared to a de-novo model (sensitivity 0.51 ± 0.04, p = 0.437; PPV 0.43 ± 0.04, p = 0.002). For enhancement segmentation with 20 training studies, a fine-tuned model demonstrated significantly improved overall performance (sensitivity 0.74 ± 0.06, PPV 0.69 ± 0.05) compared to a de-novo model (sensitivity 0.44 ± 0.09, p = 0.001; PPV 0.37 ± 0.05, p = 0.001).

Conclusion

By fine-tuning models trained for other disease pathologies with MS-specific data, competitive models identifying existing MS plaques, new MS plaques, and active demyelination can be built with substantially smaller datasets than would otherwise be required to train new models.

The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma.

Development of Gestational Age-Based Fetal Brain and Intracranial Volume Reference Norms Using Deep Learning

BACKGROUND AND PURPOSE

Fetal brain MR imaging interpretations are subjective and require subspecialty expertise. We aimed to develop a deep learning algorithm for automatically measuring intracranial and brain volumes of fetal brain MRIs across gestational ages.

MATERIALS AND METHODS

This retrospective study included 246 patients with singleton pregnancies at 19-38 weeks gestation. A 3D U-Net was trained to segment the intracranial contents of 2D fetal brain MRIs in the axial, coronal, and sagittal planes. An additional 3D U-Net was trained to segment the brain from the output of the first model. Models were tested on MRIs of 10 patients (28 planes) via Dice coefficients and volume comparison with manual reference segmentations. Trained U-Nets were applied to 200 additional MRIs to develop normative reference intracranial and brain volumes across gestational ages and then to 9 pathologic fetal brains.

RESULTS

Fetal intracranial and brain compartments were automatically segmented in a mean of 6.8 (SD, 1.2) seconds with median Dices score of 0.95 and 0.90, respectively (interquartile ranges, 0.91-0.96/0.89-0.91) on the test set. Correlation with manual volume measurements was high (Pearson r = 0.996, P < .001). Normative samples of intracranial and brain volumes across gestational ages were developed. Eight of 9 pathologic fetal intracranial volumes were automatically predicted to be >2 SDs from this age-specific reference mean. There were no effects of fetal sex, maternal diabetes, or maternal age on intracranial or brain volumes across gestational ages.

CONCLUSIONS

Deep learning techniques can quickly and accurately quantify intracranial and brain volumes on clinical fetal brain MRIs and identify abnormal volumes on the basis of a normative reference standard.

An Integrated Analysis of Clinical, Genomic, and Imaging Features Reveals Predictors of Neurocognitive Outcomes in a Longitudinal Cohort of Pediatric Cancer Survivors, Enriched with CNS Tumors (Rad ART Pro)

Background

Neurocognitive deficits in pediatric cancer survivors occur frequently; however, individual outcomes are unpredictable. We investigate clinical, genetic, and imaging predictors of neurocognition in pediatric cancer survivors, with a focus on survivors of central nervous system (CNS) tumors exposed to radiation.

Methods

One hundred eighteen patients with benign or malignant cancers (median diagnosis age: 7; 32% embryonal CNS tumors) were selected from an existing multi-institutional cohort (RadART Pro) if they had: 1) neurocognitive evaluation; 2) available DNA; 3) standard imaging. Utilizing RadART Pro, we collected clinical history, genomic sequencing, CNS imaging, and neurocognitive outcomes. We performed single nucleotide polymorphism (SNP) genotyping for candidate genes associated with neurocognition: COMT, BDNF, KIBRA, APOE, KLOTHO. Longitudinal neurocognitive testing were performed using validated computer-based CogState batteries. The imaging cohort was made of patients with available iron-sensitive (n = 28) and/or T2 FLAIR (n = 41) sequences. Cerebral microbleeds (CMB) were identified using a semi-automated algorithm. Volume of T2 FLAIR white matter lesions (WML) was measured using an automated method based on a convolutional neural network. Summary statistics were performed for patient characteristics, neurocognitive assessments, and imaging. Linear mixed effects and hierarchical models assessed patient characteristics and SNP relationship with neurocognition over time. Nested case-control analysis was performed to compare candidate gene carriers to non-carriers.

Results

CMB presence at baseline correlated with worse performance in 3 of 7 domains, including executive function. Higher baseline WML volumes correlated with worse performance in executive function and verbal learning. No candidate gene reliably predicted neurocognitive outcomes; however, APOE ϵ4 carriers trended toward worse neurocognitive function over time compared to other candidate genes and carried the highest odds of low neurocognitive performance across all domains (odds ratio 2.85, P=0.002). Hydrocephalus and seizures at diagnosis were the clinical characteristics most frequently associated with worse performance in neurocognitive domains (5 of 7 domains). Overall, executive function and verbal learning were the most frequently negatively impacted neurocognitive domains.

Conclusion

Presence of CMB, APOE ϵ4 carrier status, hydrocephalus, and seizures correlate with worse neurocognitive outcomes in pediatric cancer survivors, enriched with CNS tumors exposed to radiation. Ongoing research is underway to verify trends in larger cohorts.

Interinstitutional Portability of a Deep Learning Brain MRI Lesion Segmentation Algorithm

PURPOSE

To assess how well a brain MRI lesion segmentation algorithm trained at one institution performed at another institution, and to assess the effect of multi-institutional training datasets for mitigating performance loss.

MATERIALS AND METHODS

In this retrospective study, a three-dimensional U-Net for brain MRI abnormality segmentation was trained on data from 293 patients from one institution (IN1) (median age, 54 years; 165 women; patients treated between 2008 and 2018) and tested on data from 51 patients from a second institution (IN2) (median age, 46 years; 27 women; patients treated between 2003 and 2019). The model was then trained on additional data from various sources: (a) 285 multi-institution brain tumor segmentations, (b) 198 IN2 brain tumor segmentations, and (c) 34 IN2 lesion segmentations from various brain pathologic conditions. All trained models were tested on IN1 and external IN2 test datasets, assessing segmentation performance using Dice coefficients.

RESULTS

The U-Net accurately segmented brain MRI lesions across various pathologic conditions. Performance was lower when tested at an external institution (median Dice score, 0.70 [IN2] vs 0.76 [IN1]). Addition of 483 training cases of a single pathologic condition, including from IN2, did not raise performance (median Dice score, 0.72; P = .10). Addition of IN2 training data with heterogeneous pathologic features, representing only 10% (34 of 329) of total training data, increased performance to baseline (Dice score, 0.77; P < .001). This final model produced total lesion volumes with a high correlation to the reference standard (Spearman r = 0.98).

CONCLUSION

For brain MRI lesion segmentation, adding a modest amount of relevant training data from an external institution to a previously trained model supported successful application of the model to this external institution.Keywords: Neural Networks, Brain/Brain Stem, Segmentation Supplemental material is available for this article. © RSNA, 2021.

Microstructural development from 9 to 14 years: Evidence from the ABCD Study

During late childhood behavioral changes, such as increased risk-taking and emotional reactivity, have been associated with the maturation of cortico-cortico and cortico-subcortical circuits. Understanding microstructural changes in both white matter and subcortical regions may aid our understanding of how individual differences in these behaviors emerge. Restriction spectrum imaging (RSI) is a framework for modelling diffusion-weighted imaging that decomposes the diffusion signal from a voxel into hindered, restricted, and free compartments. This yields greater specificity than conventional methods of characterizing diffusion. Using RSI, we quantified voxelwise restricted diffusion across the brain and measured age associations in a large sample (n = 8086) from the Adolescent Brain and Cognitive Development (ABCD) study aged 9-14 years. Older participants showed a higher restricted signal fraction across the brain, with the largest associations in subcortical regions, particularly the basal ganglia and ventral diencephalon. Importantly, age associations varied with respect to the cytoarchitecture within white matter fiber tracts and subcortical structures, for example age associations differed across thalamic nuclei. This suggests that age-related changes may map onto specific cell populations or circuits and highlights the utility of voxelwise compared to ROI-wise analyses. Future analyses will aim to understand the relevance of this microstructural developmental for behavioral outcomes.

AEgIS at ELENA: outlook for physics with a pulsed cold antihydrogen beam

The efficient production of cold antihydrogen atoms in particle traps at CERN's Antiproton Decelerator has opened up the possibility of performing direct measurements of the Earth's gravitational acceleration on purely antimatter bodies. The goal of the AEgIS collaboration is to measure the value of g for antimatter using a pulsed source of cold antihydrogen and a Moiré deflectometer/Talbot-Lau interferometer. The same antihydrogen beam is also very well suited to measuring precisely the ground-state hyperfine splitting of the anti-atom. The antihydrogen formation mechanism chosen by AEgIS is resonant charge exchange between cold antiprotons and Rydberg positronium. A series of technical developments regarding positrons and positronium (Ps formation in a dedicated room-temperature target, spectroscopy of the n=1-3 and n=3-15 transitions in Ps, Ps formation in a target at 10 K inside the 1 T magnetic field of the experiment) as well as antiprotons (high-efficiency trapping of [Formula: see text], radial compression to sub-millimetre radii of mixed [Formula: see text] plasmas in 1 T field, high-efficiency transfer of [Formula: see text] to the antihydrogen production trap using an in-flight launch and recapture procedure) were successfully implemented. Two further critical steps that are germane mainly to charge exchange formation of antihydrogen-cooling of antiprotons and formation of a beam of antihydrogen-are being addressed in parallel. The coming of ELENA will allow, in the very near future, the number of trappable antiprotons to be increased by more than a factor of 50. For the antihydrogen production scheme chosen by AEgIS, this will be reflected in a corresponding increase of produced antihydrogen atoms, leading to a significant reduction of measurement times and providing a path towards high-precision measurements.This article is part of the Theo Murphy meeting issue 'Antiproton physics in the ELENA era'.

AEg̅IS latest results

The validity of the Weak Equivalence Principle (WEP) as predicted by General Relativity has been tested up to astounding precision using ordinary matter. The lack hitherto of a stable source of a probe being at the same time electrically neutral, cold and stable enough to be measured has prevented highaccuracy testing of the WEP on anti-matter. The AEg̅IS (Antimatter Experiment: Gravity, Interferometry, Spectroscopy) experiment located at CERN's AD (Antiproton Decelerator) facility aims at producing such a probe in the form of a pulsed beam of cold anti-hydrogen, and at measuring by means of a moiré deflectometer the gravitational force that Earth's mass exerts on it. Low temperature and abundance of the H̅ are paramount to attain a high precision measurement. A technique employing a charge-exchange reaction between antiprotons coming from the AD and excited positronium atoms is being developed at AEg̅IS and will be presented hereafter, alongside an overview of the experimental apparatus and the current status of the experiment

Towards the first measurement of matter-antimatter gravitational interaction

The AEgIS (Antimatter Experiment: Gravity, Interferometry, Spectroscopy) is a CERN based experiment with the central aim to measure directly the gravitational acceleration of antihydrogen. Antihydrogen atoms will be produced via charge exchange reactions which will consist of Rydberg-excited positronium atoms sent to cooled antiprotons within an electromagnetic trap. The resulting Rydberg antihydrogen atoms will then be horizontally accelerated by an electric field gradient (Stark effect), they will then pass through a moiré deflectometer. The vertical deflection caused by the Earth's gravitational field will test for the first time the Weak Equivalence Principle for antimatter. Detection will be undertaken via a position sensitive detector. Around 103 antihydrogen atoms are needed for the gravitational measurement to be completed. The present status, current achievements and results will be presented, with special attention toward the laser excitation of positronium (Ps) to the n=3 state and the production of Ps atoms in the transmission geometry.

Early EUS of the bile duct before endoscopic sphincterotomy for acute biliary pancreatitis

BACKGROUND

Endoscopic sphincterotomy can benefit patients with suspected biliary pancreatitis, although there are procedure-related complications. EUS can be used to select patients for endoscopic sphincterotomy. The results of this strategy were assessed.

METHODS

Information on patients referred for EUS were recorded in a database. One hundred twenty-three patients with suspected biliary pancreatitis (57 men, 66 women; median age 55 years) were included and followed. All underwent EUS followed by endoscopic sphincterotomy during the same procedure if choledocholithiasis was identified. Outcomes were studied in relation to the initial severity of biliary pancreatitis (Ranson and Balthazar scores), presence of stones, and time span between onset of biliary pancreatitis and EUS plus endoscopic sphincterotomy.

RESULTS

Thirty-five patients (28%) had a Ranson score greater than 3 on admission and 38 (31%) were Balthazar D-E. The median time from admission to EUS was 3 days. EUS imaging of the bile duct was complete in all but 3 patients. Thirty-three patients (27%) had choledocholithiasis on EUS and underwent endoscopic sphincterotomy. Stones were more frequent in patients with jaundice (p < 0.005) and when EUS was performed less than 3 days after admission (p < 0.05). One hundred patients (81%) recovered without complication. Two patients (1.6%) died, 1 had recurrent BP develop, 6 (5%) had further biliary symptoms, and 16 (13%) had complications of pancreatitis develop (9 pseudocysts). There were 3 mild endoscopic sphincterotomy-related complications (complication rate 6.5%).

CONCLUSIONS

In this series in which endoscopic sphincterotomy was performed selectively depending on the endosonographic presence or absence of ductal stones early in the course of the pancreatitis, and not according to its predicted severity, mortality and complications of endoscopic sphincterotomy were low and unrelated to the predicted severity of biliary pancreatitis or the presence of choledocholithiasis. Controlled trials are needed to confirm the superiority of this strategy compared with ERCP alone for the management of biliary pancreatitis.

[Fascioliasis of the common bile duct: endoscopic ultrasonographic diagnosis and endoscopic sphincterotomy]

Two cases of biliary fasciolasis are reported. The patients presented with biliary pain and/or acute pancreatitis. Pre-operative ultrasound endoscopy showed main bile duct dilation and linear elongated echogenic structures in the common bile duct lumen. Endoscopic retrograde cholangiography and endoscopic sphincterotomy were performed. Parasites were endoscopically removed resulting in disappearance of symptoms and biological abnormalities. Serological tests and pathological examination confirmed the presence of Fasciola hepatica. During follow-up, stool examination failed to show any Fasciola hepatica eggs, and in one case, serology became negative. This report emphasizes the value of ultrasound endoscopy in the diagnosis of unsuspected biliary fasciolasis. This report also confirms the therapeutic role of endoscopic sphincterotomy in patients with obstructive biliary fasciolasis.