In spite of the great progress that has been made towards automating brain extraction in human magnetic resonance imaging (MRI), challenges remain in the automation of this task for mouse models of brain disorders. Researchers often resort to editing brain segmentation results manually when automated methods fail to produce accurate delineations. However, manual corrections can be labor-intensive and introduce interrater variability. This motivated our development of a new deep-learning-based method for brain segmentation of mouse MRI, which we call Mouse Brain Extractor. We adapted the existing SwinUNETR architecture (Hatamizadeh et al., 2021) with the goal of making it more robust to scale variance. Our approach is to supply the network model with supplementary spatial information in the form of absolute positional encoding. We use a new scheme for positional encoding, which we call Global Positional Encoding (GPE). GPE is based on a shared coordinate frame that is relative to the entire input image. This differs from the positional encoding used in SwinUNETR, which solely employs relative pairwise image patch positions. GPE also differs from the conventional absolute positional encoding approach, which encodes position relative to a subimage rather than the entire image. We trained and tested our method on a heterogeneous dataset of N=223 mouse MRI, for which we generated a corresponding set of manually-edited brain masks. These data were acquired previously in other studies using several different scanners and imaging protocols and included in vivo and ex vivo images of mice with heterogeneous brain structure due to different genotypes, strains, diseases, ages, and sexes. We evaluated our method's results against those of seven existing rodent brain extraction methods and two state-of-the art deep-learning approaches, nnU-Net (Isensee et al., 2018) and SwinUNETR. Overall, our proposed method achieved average Dice scores on the order of 0.98 and average HD95 measures on the order of 100 μm when compared to the manually-labeled brain masks. In statistical analyses, our method significantly outperformed the conventional approaches and performed as well as or significantly better than the nnU-Net and SwinUNETR methods. These results suggest that Global Positional Encoding provides additional contextual information that enables our Mouse Brain Extractor to perform competitively on datasets containing multiple resolutions.

There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The Anatomical Pipeline extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The Diffusion Pipeline processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for susceptibility-induced geometric image distortion, and fitting diffusion models to the DWI data. The Functional Pipeline performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. It coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Project’s grayordinate space. The outputs of each pipeline can then be processed during group-level analysis. The outputs of the Anatomical Pipeline and the Diffusion Pipeline are analyzed using the BrainSuite Statistics Toolbox in R (bstr), which provides functionality for hypothesis testing and statistical modeling. The outputs of the Functional Pipeline can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collection’s Population Imaging of Psychology dataset.

Magnetic resonance imaging of mice and other small animals plays an important role in translational medicine, where animal models are essential in the study of diseases and their potential treatments. While a large number of brain imaging studies using mice are conducted every year, there are few tools designed specifically for analyzing mouse MRI. Researchers often resort to adapting tools designed for processing MRI of human brains to work for the different structure, composition, and appearance of the mouse brain. While these methods may provide a reasonable initialization, researchers often have to perform extensive manual editing. In this work, we adapted a patch-based 2-Stage CNN Architecture to segment brain and non-brain in mouse MRI. We trained our model using brain MRI of healthy mice and mice with experimental autoimmune encephalomyelitis. These images had been previously acquired by our research team and processed to extract the brain using detailed manual editing. We compared our method with five existing tools, including BSE,1 rBET,2 AFNI,3 PCNN,4 and nnU-Net,5 using manually delineated mouse MRI. Both our method and nnU-Net achieved mean Dice scores on the order of 0.99 and HD95 measures on the order of one voxel, substantially outperforming the other methods. Our proposed method had slightly better Dice, HD99, and sensitivity measures than nnU-Net, while nnU-Net had slightly better HD95 scores. While these differences were small enough that further evaluation of the methods on a broader set of images would be warranted, they do suggest that our proposed method is competitive with a state-of-the-art deep learning method.

Paired associative stimulation (PAS) is a widely used noninvasive brain stimulation protocol to assess neural plasticity. Its reproducibility, however, has been rarely tested and with mixed results. With two consecutive studies, we aimed to provide further tests and a more systematic assessment of PAS reproducibility. We measured intraclass correlation coefficients (ICCs)—a widely used tool to assess whether groups of measurements resemble each other—in two PAS studies on healthy volunteers. The first study included five PAS sessions recording 10 MEPS every 10 min for an hour post-PAS. The second study included two PAS sessions recording 50 MEPS at 20 and 50 min post-PAS, based on analyses from the first study. In both studies PAS sessions were spaced one week apart. Within sessions ICC was fair to excellent for both studies, yet between sessions ICC was poor for both studies. We suggest that long term meta-plasticity effects (longer than one week) may interfere with between sessions reproducibility.

Recent evidence suggests the aging process is accelerated by HIV. Degradation of white matter (WM) has been independently associated with HIV and healthy aging. Thus, WM may be vulnerable to joint effects of HIV and aging. Diffusion-weighted imaging (DWI) was conducted with HIV-seropositive (n = 72) and HIV-seronegative (n = 34) adults. DWI data underwent tractography, which was parcellated into 18 WM tracts of interest (TOIs). Functional Analysis of Diffusion Tensor Tract Statistics (FADTTS) regression was conducted assessing the joint effect of advanced age and HIV on fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) along TOI fibers. In addition to main effects of age and HIV on WM microstructure, the interactive effect of age and HIV was significantly related to lower FA and higher MD, AD, and RD across all TOIs. The location of findings was consistent with the clinical presentation of HIV-associated neurocognitive disorders. While older age is related to poorer WM microstructure, its detrimental effect on WM is stronger among HIV+ relative to HIV- individuals. Loss of WM integrity in the context of advancing age may place HIV+ individuals at increased risk for brain and cognitive compromise.

Sickle cell disease (SCD) is a chronic blood disorder that is often associated with acute and chronic cerebrovascular complications, including strokes and impaired cognition. Using functional resting state magnetic resonance images, we performed whole-brain analysis of the amplitude of low frequency fluctuations (ALFF), to detect areas of spontaneous blood oxygenation level dependent signal across brain regions. We compared the ALFF of 20 SCD patients to that observed in 19 healthy, age and ethnicity-matched, control subjects. Significant differences were found in several brain regions, including the insula, precuneus, anterior cingulate cortex and medial superior frontal gyrus. To identify the ALFF differences resulting from anemia alone, we also compared the ALFF of SCD patients to that observed in 12 patients having comparable hemoglobin levels but lacking sickle hemoglobin. Increased ALFF in the orbitofrontal cortex and the anterior and posterior cingulate cortex and decreased ALFF in the frontal pole, cerebellum and medial superior frontal gyrus persisted after accounting for the effect of anemia. The presence of white matter hyperintensities was associated with depressed frontal and medial superior frontal gyri activity in the SCD subjects. Decreased ALFF in the frontal lobe was correlated with decreased verbal fluency and cognitive flexibility. These findings may lead to a better understanding of the pathophysiology of SCD.

Objective We assessed the individual level reliability of neural plasticity changes induced by paired associative stimulation (PAS), which combines peripheral nerve stimulation with transcranial magnetic stimulation to induce short-term plastic changes in the brain. Methods For 5 consecutive weeks, motor evoked potentials (MEPs) of 8 healthy subjects were acquired every 10 minutes post-PAS intervention for a period of 60 minutes. The post-PAS MEPs were evaluated against base-line MEPs using permutation and Kolmogorov-Smirnov tests to determine whether the MEP magnitudes changed after PAS. Moreover, various sample sizes of the MEP data were used to deduce the minimum number of MEPs needed to reliably detect individual propensity to neural plasticity. Results Group analysis exhibited significant increase in post-PAS MEPs, confirming previous results. While high between-sessions variability was observed at individual level, data show that between 40 to 50 MEPs can reliably assess each subject’s responsiveness to PAS. Subjects exhibited three different plasticity patterns: in the modulated hemisphere only, both hemispheres, or neither hemisphere. Conclusions PAS can reliably assess individual differences in neural plasticity. Significance A marker of individual plasticity may be useful to predict the effects of a motor rehabilitation, drug or other intervention to increase recovery of function after brain injury. Highlights Paired associative stimulation (PAS) assesses neural plasticity non invasively. The study shows how PAS can reliably determine individual differences in plasticity. PAS may be used to predict intervention outcome or individualize treatment dose.

BackgroundRearrangements involving ETV6 (12p13) are among the most common structural abnormalities in pediatric B-cell acute lymphoblastic leukemia (B-ALL) and involve numerous partner genes. Additionally, the t(8;14)(q11.2;q32), which can result in the placement of CEBPD (8q11.2) near the regulatory regions of IGH@ (14q32) and consequent overexpression of CEPBD, occurs at a higher frequency in individuals with Down syndrome-associated ALL (DS-ALL) compared to both the general and pediatric population. The coexistence of cytogenetically detectable ETV6 abnormalities and t(8;14)(q11.2;q32) is a rare occurrence in B-ALL and has only been reported in a single case in the literature.FindingsHerein, we present a case of B-ALL in a 9-year old male with Down syndrome in which conventional cytogenetic analysis revealed two reciprocal translocations: a t(8;14)(q11.2;q32) and a t(2;12)(p12;p13). Interphase and metaphase fluorescence in situ hybridization (FISH) analysis using break apart probes confirmed the involvement of IGH@ and ETV6 in these translocations, respectively. Additionally, interphase FISH revealed a clonal subpopulation bearing biallelic IGH@ rearrangements not observed by conventional cytogenetic analysis.ConclusionsTo the best of our knowledge, this is the first reported case of B-ALL bearing an ETV6 translocation with a partner gene on the short arm of chromosome 2 confirmed by FISH. Additionally, it is the second reported case of t(8;14)(q11.2;q32)-ALL bearing a concomitant, cytogenetically detectable abnormality involving ETV6. This case provides insight into a novel translocation involving ETV6 as well as potentially unique and understudied mechanisms of clonal evolution in pediatric B-ALL.

Sickle cell disease may result in neurological damage and strokes, leading to morbidity and mortality. Currently, there are no dependable biomarkers to predict impending strokes. In this study, we analyzed neuronal processes at resting state and more particularly how this disease affects the default mode network. The amplitude of low frequency fluctuations was used to reflect areas of spontaneous BOLD signal across brain regions. We compared the activations of sickle cell disease patients to a control group using variance analysis and t-test. Significant regional differences among the two groups were observed, especially in the default mode network areas and cortical regions near large cerebral arteries. These findings suggest that sickle cell disease causes activation modifications near vessels, and these changes could be used as a biomarker of the disease.

Mantle cell lymphoma (MCL) is a mature B-cell neoplasm composed of monomorphic small to medium-sized atypical lymphocytes arising from naïve mantle zone B-cells, with a generally aggressive and incurable clinical course. The t(11;14)(q13;q32) between IGH@ and CCND1 is present in almost all cases of MCL. Secondary cytogenetic abnormalities are common, and have been associated in some cases with clinical progression. Variant and cryptic t(11;14) translocations have been reported as well. Herein, we present the case of an 80-year old woman with classical MCL, and a cryptic t(11;14) translocation detected by fluorescence in situ hybridization (FISH), and not by conventional cytogenetics. FISH on previously G-banded metaphases showed a cryptic CCND1-IGH@ fusion signal on a derivative chromosome 10, and another fusion signal on one of the abnormal copies of chromosome 11. Cases such as this highlight the importance of FISH studies as part of an algorithmic and multidisciplinary approach to diagnosis.