DeepN4: Learning N4ITK Bias Field Correction for T1-weighted Images

T1-weighted (T1w) MRI has low frequency intensity artifacts due to magnetic field inhomogeneities. Removal of these biases in T1w MRI images is a critical preprocessing step to ensure spatially consistent image interpretation. N4ITK bias field correction, the current state-of-the-art, is implemented in such a way that makes it difficult to port between different pipelines and workflows, thus making it hard to reimplement and reproduce results across local, cloud, and edge platforms. Moreover, N4ITK is opaque to optimization before and after its application, meaning that methodological development must work around the inhomogeneity correction step. Given the importance of bias fields correction in structural preprocessing and flexible implementation, we pursue a deep learning approximation / reinterpretation of the N4ITK bias fields correction to create a method which is portable, flexible, and fully differentiable. In this paper, we trained a deep learning network "DeepN4" on eight independent cohorts from 72 different scanners and age ranges with N4ITK-corrected T1w MRI and bias field for supervision in log space. We found that we can closely approximate N4ITK bias fields correction with naïve networks. We evaluate the peak signal to noise ratio (PSNR) in test dataset against the N4ITK corrected images. The median PSNR of corrected images between N4ITK and DeepN4 was 47.96 dB. In addition, we assess the DeepN4 model on eight additional external datasets and show the generalizability of the approach. This study establishes that incompatible N4ITK preprocessing steps can be closely approximated by naïve deep neural networks, facilitating more flexibility. All code and models are released at https://github.com/MASILab/DeepN4 .

MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal

Data harmonization is necessary for removing confounding effects in multi-site diffusion image analysis. One such harmonization method, LinearRISH, scales rotationally invariant spherical harmonic (RISH) features from one site ("target") to the second ("reference") to reduce confounding scanner effects. However, reference and target site designations are not arbitrary and resultant diffusion metrics (fractional anisotropy, mean diffusivity) are biased by this choice. In this work we propose MidRISH: rather than scaling reference RISH features to target RISH features, we project both sites to a mid-space. We validate MidRISH with the following experiments: harmonizing scanner differences from 37 matched patients free of cognitive impairment, and harmonizing acquisition and study differences on 117 matched patients free of cognitive impairment. We find that MidRISH reduces bias of reference selection while preserving harmonization efficacy of LinearRISH. Users should be cautious when performing LinearRISH harmonization. To select a reference site is to choose diffusion metric effect-size. Our proposed method eliminates the bias-inducing site selection step.

SpecReFlow: an algorithm for specular reflection restoration using flow-guided video completion

Purpose

Specular reflections (SRs) are highlight artifacts commonly found in endoscopy videos that can severely disrupt a surgeon's observation and judgment. Despite numerous attempts to restore SR, existing methods are inefficient and time consuming and can lead to false clinical interpretations. Therefore, we propose the first complete deep-learning solution, SpecReFlow, to detect and restore SR regions from endoscopy video with spatial and temporal coherence.

Approach

SpecReFlow consists of three stages: (1) an image preprocessing stage to enhance contrast, (2) a detection stage to indicate where the SR region is present, and (3) a restoration stage in which we replace SR pixels with an accurate underlying tissue structure. Our restoration approach uses optical flow to seamlessly propagate color and structure from other frames of the endoscopy video.

Results

Comprehensive quantitative and qualitative tests for each stage reveal that our SpecReFlow solution performs better than previous detection and restoration methods. Our detection stage achieves a Dice score of 82.8% and a sensitivity of 94.6%, and our restoration stage successfully incorporates temporal information with spatial information for more accurate restorations than existing techniques.

Conclusions

SpecReFlow is a first-of-its-kind solution that combines temporal and spatial information for effective detection and restoration of SR regions, surpassing previous methods relying on single-frame spatial information. Future work will look to optimizing SpecReFlow for real-time applications. SpecReFlow is a software-only solution for restoring image content lost due to SR, making it readily deployable in existing clinical settings to improve endoscopy video quality for accurate diagnosis and treatment.

Empirical assessment of the assumptions of ComBat with diffusion tensor imaging

Purpose

Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides unique information about white matter microstructure in the brain but is susceptible to confounding effects introduced by scanner or acquisition differences. ComBat is a leading approach for addressing these site biases. However, despite its frequent use for harmonization, ComBat's robustness toward site dissimilarities and overall cohort size have not yet been evaluated in terms of DTI.

Approach

As a baseline, we match N = 358 participants from two sites to create a "silver standard" that simulates a cohort for multi-site harmonization. Across sites, we harmonize mean fractional anisotropy and mean diffusivity, calculated using participant DTI data, for the regions of interest defined by the JHU EVE-Type III atlas. We bootstrap 10 iterations at 19 levels of total sample size, 10 levels of sample size imbalance between sites, and 6 levels of mean age difference between sites to quantify (i) β AGE , the linear regression coefficient of the relationship between FA and age; (ii) γ ^ s f * , the ComBat-estimated site-shift; and (iii) δ ^ s f * , the ComBat-estimated site-scaling. We characterize the reliability of ComBat by evaluating the root mean squared error in these three metrics and examine if there is a correlation between the reliability of ComBat and a violation of assumptions.

Results

ComBat remains well behaved for β AGE when N > 162 and when the mean age difference is less than 4 years. The assumptions of the ComBat model regarding the normality of residual distributions are not violated as the model becomes unstable.

Conclusion

Prior to harmonization of DTI data with ComBat, the input cohort should be examined for size and covariate distributions of each site. Direct assessment of residual distributions is less informative on stability than bootstrap analysis. We caution use ComBat of in situations that do not conform to the above thresholds.

Evaluation of Mean Shift, ComBat, and CycleGAN for Harmonizing Brain Connectivity Matrices Across Sites

Connectivity matrices derived from diffusion MRI (dMRI) provide an interpretable and generalizable way of understanding the human brain connectome. However, dMRI suffers from inter-site and between-scanner variation, which impedes analysis across datasets to improve robustness and reproducibility of results. To evaluate different harmonization approaches on connectivity matrices, we compared graph measures derived from these matrices before and after applying three harmonization techniques: mean shift, ComBat, and CycleGAN. The sample comprises 168 age-matched, sex-matched normal subjects from two studies: the Vanderbilt Memory and Aging Project (VMAP) and the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD). First, we plotted the graph measures and used coefficient of variation (CoV) and the Mann-Whitney U test to evaluate different methods' effectiveness in removing site effects on the matrices and the derived graph measures. ComBat effectively eliminated site effects for global efficiency and modularity and outperformed the other two methods. However, all methods exhibited poor performance when harmonizing average betweenness centrality. Second, we tested whether our harmonization methods preserved correlations between age and graph measures. All methods except for CycleGAN in one direction improved correlations between age and global efficiency and between age and modularity from insignificant to significant with p-values less than 0.05.

Characterizing patterns of DTI variance in aging brains

Background

As large analyses merge data across sites, a deeper understanding of variance in statistical assessment across the sources of data becomes critical for valid analyses. Diffusion tensor imaging (DTI) exhibits spatially varying and correlated noise, so care must be taken with distributional assumptions.

Purpose

We characterize the role of physiology, subject compliance, and the interaction of subject with the scanner in the understanding of DTI variability, as modeled in spatial variance of derived metrics in homogeneous regions.

Methods

We analyze DTI data from 1035 subjects in the Baltimore Longitudinal Study of Aging (BLSA), with ages ranging from 22.4 to 103 years old. For each subject, up to 12 longitudinal sessions were conducted. We assess variance of DTI scalars within regions of interest (ROIs) defined by four segmentation methods and investigate the relationships between the variance and covariates, including baseline age, time from the baseline (referred to as "interval"), motion, sex, and whether it is the first scan or the second scan in the session.

Results

Covariate effects are heterogeneous and bilaterally symmetric across ROIs. Inter-session interval is positively related (p ≪ 0.001) to FA variance in the cuneus and occipital gyrus, but negatively (p ≪ 0.001) in the caudate nucleus. Males show significantly (p ≪ 0.001) higher FA variance in the right putamen, thalamus, body of the corpus callosum, and cingulate gyrus. In 62 out of 176 ROIs defined by the Eve type-1 atlas, an increase in motion is associated (p < 0.05) with a decrease in FA variance. Head motion increases during the rescan of DTI (Δμ = 0.045 millimeters per volume).

Conclusions

The effects of each covariate on DTI variance, and their relationships across ROIs are complex. Ultimately, we encourage researchers to include estimates of variance when sharing data and consider models of heteroscedasticity in analysis. This work provides a foundation for study planning to account for regional variations in metric variance.

Predicting Age from White Matter Diffusivity with Residual Learning

Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI's advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are non-linearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.

NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics

Machine learning provides a valuable tool for analyzing high-dimensional functional neuroimaging data, and is proving effective in predicting various neurological conditions, psychiatric disorders, and cognitive patterns. In functional magnetic resonance imaging (MRI) research, interactions between brain regions are commonly modeled using graph-based representations. The potency of graph machine learning methods has been established across myriad domains, marking a transformative step in data interpretation and predictive modeling. Yet, despite their promise, the transposition of these techniques to the neuroimaging domain has been challenging due to the expansive number of potential preprocessing pipelines and the large parameter search space for graph-based dataset construction. In this paper, we introduce NeuroGraph, a collection of graph-based neuroimaging datasets, and demonstrated its utility for predicting multiple categories of behavioral and cognitive traits. We delve deeply into the dataset generation search space by crafting 35 datasets that encompass static and dynamic brain connectivity, running in excess of 15 baseline methods for benchmarking. Additionally, we provide generic frameworks for learning on both static and dynamic graphs. Our extensive experiments lead to several key observations. Notably, using correlation vectors as node features, incorporating larger number of regions of interest, and employing sparser graphs lead to improved performance. To foster further advancements in graph-based data driven neuroimaging analysis, we offer a comprehensive open-source Python package that includes the benchmark datasets, baseline implementations, model training, and standard evaluation.

DeepN4: Learning N4ITK Bias Field Correction for T1-weighted Images

T1-weighted (T1w) MRI has low frequency intensity artifacts due to magnetic field inhomogeneities. Removal of these biases in T1w MRI images is a critical preprocessing step to ensure spatially consistent image interpretation. N4ITK bias field correction, the current state-of-the-art, is implemented in such a way that makes it difficult to port between different pipelines and workflows, thus making it hard to reimplement and reproduce results across local, cloud, and edge platforms. Moreover, N4ITK is opaque to optimization before and after its application, meaning that methodological development must work around the inhomogeneity correction step. Given the importance of bias fields correction in structural preprocessing and flexible implementation, we pursue a deep learning approximation / reinterpretation of the N4ITK bias fields correction to create a method which is portable, flexible, and fully differentiable. In this paper, we trained a deep learning network "DeepN4" on eight independent cohorts from 72 different scanners and age ranges with N4ITK-corrected T1w MRI and bias field for supervision in log space. We found that we can closely approximate N4ITK bias fields correction with naïve networks. We evaluate the peak signal to noise ratio (PSNR) in test dataset against the N4ITK corrected images. The median PSNR of corrected images between N4ITK and DeepN4 was 47.96 dB. In addition, we assess the DeepN4 model on eight additional external datasets and show the generalizability of the approach. This study establishes that incompatible N4ITK preprocessing steps can be closely approximated by naïve deep neural networks, facilitating more flexibility. All code and models are released at https://github.com/MASILab/DeepN4.

White matter tract microstructure, macrostructure, and associated cortical gray matter morphology across the lifespan

Characterizing how, when and where the human brain changes across the lifespan is fundamental to our understanding of developmental processes of childhood and adolescence, degenerative processes of aging, and divergence from normal patterns in disease and disorders. We aimed to provide detailed descriptions of white matter pathways across the lifespan by thoroughly characterizing white matter microstructure, white matter macrostructure, and morphology of the cortex associated with white matter pathways. We analyzed 4 large, high-quality, publicly-available datasets comprising 2789 total imaging sessions, and participants ranging from 0 to 100 years old, using advanced tractography and diffusion modeling. We first find that all microstructural, macrostructural, and cortical features of white matter bundles show unique lifespan trajectories, with rates and timing of development and degradation that vary across pathways - describing differences between types of pathways and locations in the brain, and developmental milestones of maturation of each feature. Second, we show cross-sectional relationships between different features that may help elucidate biological changes occurring during different stages of the lifespan. Third, we show unique trajectories of age-associations across features. Finally, we find that age associations during development are strongly related to those during aging. Overall, this study reports normative data for several features of white matter pathways of the human brain that will be useful for studying normal and abnormal white matter development and degeneration.

MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal

Objective

Data harmonization is necessary for removing confounding effects in multi-site diffusion image analysis. One such harmonization method, LinearRISH, scales rotationally invariant spherical harmonic (RISH) features from one site ("target") to the second ("reference") to reduce confounding scanner effects. However, reference and target site designations are not arbitrary and resultant diffusion metrics (fractional anisotropy, mean diffusivity) are biased by this choice. In this work we propose MidRISH: rather than scaling reference RISH features to target RISH features, we project both sites to a mid-space.

Methods

We validate MidRISH with the following experiments: harmonizing scanner differences from 37 matched patients free of cognitive impairment, and harmonizing acquisition and study differences on 117 matched patients free of cognitive impairment.

Conclusion

MidRISH reduces bias of reference selection while preserving harmonization efficacy of LinearRISH.

Significance

Users should be cautious when performing LinearRISH harmonization. To select a reference site is to choose diffusion metric effect-size. Our proposed method eliminates the bias-inducing site selection step.

NeSVoR: Implicit Neural Representation for Slice-to-Volume Reconstruction in MRI

Reconstructing 3D MR volumes from multiple motion-corrupted stacks of 2D slices has shown promise in imaging of moving subjects, e. g., fetal MRI. However, existing slice-to-volume reconstruction methods are time-consuming, especially when a high-resolution volume is desired. Moreover, they are still vulnerable to severe subject motion and when image artifacts are present in acquired slices. In this work, we present NeSVoR, a resolution-agnostic slice-to-volume reconstruction method, which models the underlying volume as a continuous function of spatial coordinates with implicit neural representation. To improve robustness to subject motion and other image artifacts, we adopt a continuous and comprehensive slice acquisition model that takes into account rigid inter-slice motion, point spread function, and bias fields. NeSVoR also estimates pixel-wise and slice-wise variances of image noise and enables removal of outliers during reconstruction and visualization of uncertainty. Extensive experiments are performed on both simulated and in vivo data to evaluate the proposed method. Results show that NeSVoR achieves state-of-the-art reconstruction quality while providing two to ten-fold acceleration in reconstruction times over the state-of-the-art algorithms.

Implementation considerations for deep learning with diffusion MRI streamline tractography

One area of medical imaging that has recently experienced innovative deep learning advances is diffusion MRI (dMRI) streamline tractography with recurrent neural networks (RNNs). Unlike traditional imaging studies which utilize voxel-based learning, these studies model dMRI features at points in continuous space off the voxel grid in order to propagate streamlines, or virtual estimates of axons. However, implementing such models is non-trivial, and an open-source implementation is not yet widely available. Here, we describe a series of considerations for implementing tractography with RNNs and demonstrate they allow one to approximate a deterministic streamline propagator with comparable performance to existing algorithms. We release this trained model and the associated implementations leveraging popular deep learning libraries. We hope the availability of these resources will lower the barrier of entry into this field, spurring further innovation.

Convolutional-recurrent neural networks approximate diffusion tractography from T1-weighted MRI and associated anatomical context

Diffusion MRI (dMRI) streamline tractography is the gold-standard for in vivo estimation of white matter (WM) pathways in the brain. However, the high angular resolution dMRI acquisitions capable of fitting the microstructural models needed for tractography are often time-consuming and not routinely collected clinically, restricting the scope of tractography analyses. To address this limitation, we build on recent advances in deep learning which have demonstrated that streamline propagation can be learned from dMRI directly without traditional model fitting. Specifically, we propose learning the streamline propagator from T1w MRI to facilitate arbitrary tractography analyses when dMRI is unavailable. To do so, we present a novel convolutional-recurrent neural network (CoRNN) trained in a teacher-student framework that leverages T1w MRI, associated anatomical context, and streamline memory from data acquired for the Human Connectome Project. We characterize our approach under two common tractography paradigms, WM bundle analysis and structural connectomics, and find approximately a 5-15% difference between measures computed from streamlines generated with our approach and those generated using traditional dMRI tractography. When placed in the literature, these results suggest that the accuracy of WM measures computed from T1w MRI with our method is on the level of scan-rescan dMRI variability and raise an important question: is tractography truly a microstructural phenomenon, or has dMRI merely facilitated its discovery and implementation?

SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI

Volumetric reconstruction of fetal brains from multiple stacks of MR slices, acquired in the presence of almost unpredictable and often severe subject motion, is a challenging task that is highly sensitive to the initialization of slice-to-volume transformations. We propose a novel slice-to-volume registration method using Transformers trained on synthetically transformed data, which model multiple stacks of MR slices as a sequence. With the attention mechanism, our model automatically detects the relevance between slices and predicts the transformation of one slice using information from other slices. We also estimate the underlying 3D volume to assist slice-to-volume registration and update the volume and transformations alternately to improve accuracy. Results on synthetic data show that our method achieves lower registration error and better reconstruction quality compared with existing state-of-the-art methods. Experiments with real-world MRI data are also performed to demonstrate the ability of the proposed model to improve the quality of 3D reconstruction under severe fetal motion.

Efficacy of second CAR-T (CART2) infusion limited by poor CART expansion and antigen modulation

Chimeric antigen receptor T-cells (CART) are active in relapsed/refractory (r/r) B-cell acute lymphoblastic leukemia (B-ALL), but relapse remains a substantial challenge. Reinfusion with the same CART product (CART2) in patients with suboptimal response or antigen positive relapse following first infusion (CART1) represents a potential treatment strategy, though early experiences suggest limited efficacy of CART2 with CD19 targeting. We report on our experience with CART2 across a host of novel CAR T-cell trials. This was a retrospective review of children and young adults with B-ALL who received reinfusion with an anti-CD19, anti-CD22, or anti-CD19/22 CART construct on one of 3 CAR T-cells trials at the National Cancer Institute (NCT01593696, NCT02315612, NCT0344839) between July 2012 and January 2021. All patients received lymphodepletion (LD) pre-CART (standard LD: 75 mg/m² fludarabine, 900 mg/m² cyclophosphamide; or intensified LD: 120 mg/m² fludarabine, 1200 mg/m² cyclophosphamide). Primary objectives were to describe response to and toxicity of CART2. Indication for CART2, impact of LD intensity, and CAR T-cell expansion and leukemia antigen expression between CART infusions was additionally evaluated. Eighteen patients proceeded to CART2 due to persistent (n=7) or relapsed antigen positive disease (n=11) following CART1. Seven of 18 (38.9%) demonstrated objective response (responders) to CART2: 5 achieved a minimal residual disease (MRD) negative CR, 1 had persistent MRD level disease, and 1 showed a partial remission, the latter with eradication of antigen positive disease and emergence of antigen negative B-ALL. Responders included four patients who had not achieved a CR with CART1. Limited cytokine release syndrome was seen following CART2. Peripheral blood CART1 expansion was higher than CART2 expansion (p=0.03). Emergence of antigen negative/dim B-ALL in 6 (33.3%) patients following CART2 contributed to lack of CR. Five of seven (71.4%) responders received intensified LD pre-CART2, which corresponded with higher CART2 expansion than in those receiving standard LD (p=0.029). Diminished CAR T-cell expansion and antigen downregulation/loss impeded robust responses to CART2. A subset of patients, however, may derive benefit from CART2 despite suboptimal response to CART1. Intensified LD may be one strategy to augment CART2 responses, though further study of factors associated with CART2 response, including serial monitoring of antigen expression, is warranted.

Multimodal Representation Learning via Maximization of Local Mutual Information

We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text. The goal of this approach is to learn useful image representations by taking advantage of the rich information contained in the free text that describes the findings in the image. Our method trains image and text encoders by encouraging the resulting representations to exhibit high local mutual information. We make use of recent advances in mutual information estimation with neural network discriminators. We argue that the sum of local mutual information is typically a lower bound on the global mutual information. Our experimental results in the downstream image classification tasks demonstrate the advantages of using local features for image-text representation learning. Our code is available at: https://github.com/RayRuizhiLiao/mutual_info_img_txt.

Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: Algorithms and results

Cross-scanner and cross-protocol variability of diffusion magnetic resonance imaging (dMRI) data are known to be major obstacles in multi-site clinical studies since they limit the ability to aggregate dMRI data and derived measures. Computational algorithms that harmonize the data and minimize such variability are critical to reliably combine datasets acquired from different scanners and/or protocols, thus improving the statistical power and sensitivity of multi-site studies. Different computational approaches have been proposed to harmonize diffusion MRI data or remove scanner-specific differences. To date, these methods have mostly been developed for or evaluated on single b-value diffusion MRI data. In this work, we present the evaluation results of 19 algorithms that are developed to harmonize the cross-scanner and cross-protocol variability of multi-shell diffusion MRI using a benchmark database. The proposed algorithms rely on various signal representation approaches and computational tools, such as rotational invariant spherical harmonics, deep neural networks and hybrid biophysical and statistical approaches. The benchmark database consists of data acquired from the same subjects on two scanners with different maximum gradient strength (80 and 300 ​mT/m) and with two protocols. We evaluated the performance of these algorithms for mapping multi-shell diffusion MRI data across scanners and across protocols using several state-of-the-art imaging measures. The results show that data harmonization algorithms can reduce the cross-scanner and cross-protocol variabilities to a similar level as scan-rescan variability using the same scanner and protocol. In particular, the LinearRISH algorithm based on adaptive linear mapping of rotational invariant spherical harmonics features yields the lowest variability for our data in predicting the fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK) and the rotationally invariant spherical harmonic (RISH) features. But other algorithms, such as DIAMOND, SHResNet, DIQT, CMResNet show further improvement in harmonizing the return-to-origin probability (RTOP). The performance of different approaches provides useful guidelines on data harmonization in future multi-site studies.

Optimizing Connectivity-Driven Brain Parcellation Using Ensemble Clustering

This work addresses the problem of constructing a unified, topologically optimal connectivity-based brain atlas. The proposed approach aggregates an ensemble partition from individual parcellations without label agreement, providing a balance between sufficiently flexible individual parcellations and intuitive representation of the average topological structure of the connectome. The methods exploit a previously proposed dense connectivity representation, first performing graph-based hierarchical parcellation of individual brains, and subsequently aggregating the individual parcellations into a consensus parcellation. The search for consensus—based on the hard ensemble (HE) algorithm—approximately minimizes the sum of cluster membership distances, effectively estimating a pseudo-Karcher mean of individual parcellations. Computational stability, graph structure preservation, and biological relevance of the simplified representation resulting from the proposed parcellation are assessed on the Human Connectome Project data set. These aspects are assessed using (1) edge weight distribution divergence with respect to the dense connectome representation, (2) interhemispheric symmetry, (3) network characteristics' stability and agreement with respect to individually and anatomically parcellated networks, and (4) performance of the simplified connectome in a biological sex classification task. Ensemble parcellation was found to be highly stable with respect to subject sampling, outperforming anatomical atlases and other connectome-based parcellations in classification as well as preserving global connectome properties. The HE-based parcellation also showed a degree of symmetry comparable with anatomical atlases and a high degree of spatial contiguity without using explicit priors.

Scanner invariant representations for diffusion MRI harmonization

Purpose

In the present work, we describe the correction of diffusion‐weighted MRI for site and scanner biases using a novel method based on invariant representation.

Theory and Methods

Pooled imaging data from multiple sources are subject to variation between the sources. Correcting for these biases has become very important as imaging studies increase in size and multi‐site cases become more common. We propose learning an intermediate representation invariant to site/protocol variables, a technique adapted from information theory‐based algorithmic fairness; by leveraging the data processing inequality, such a representation can then be used to create an image reconstruction that is uninformative of its original source, yet still faithful to underlying structures. To implement this, we use a deep learning method based on variational auto‐encoders (VAE) to construct scanner invariant encodings of the imaging data.

Results

To evaluate our method, we use training data from the 2018 MICCAI Computational Diffusion MRI (CDMRI) Challenge Harmonization dataset. Our proposed method shows improvements on independent test data relative to a recently published baseline method on each subtask, mapping data from three different scanning contexts to and from one separate target scanning context.

Conclusions

As imaging studies continue to grow, the use of pooled multi‐site imaging will similarly increase. Invariant representation presents a strong candidate for the harmonization of these data.

A Cold Atmospheric Pressure Plasma Discharge Device Exerts Antimicrobial Effects

A cold atmospheric pressure plasma device was developed using two parallel plates of Low Temperature Co-fired Ceramic with embedded electrodes. The 2.4 cm wide by 1 mm deep plasma discharge operates at 20 kHz with a 2-5 kV AC drive signal across a 0.25 mm gap. Mixed Argon/oxygen plasmas were directed between the plates to flow toward a bacterial biofilm sample for treatment. Results showed that at 4-5 kV the plasma etched away a bacterial biofilm on glass in 10 minutes. In addition, we showed that short plasma treatments rapidly killed biofilm resident bacteria with ED₉₀ values of <15 s.

Continuous representations of brain connectivity using spatial point processes

We present a continuous model for structural brain connectivity based on the Poisson point process. The model treats each streamline curve in a tractography as an observed event in connectome space, here the product space of the gray matter/white matter interfaces. We approximate the model parameter via kernel density estimation. To deal with the heavy computational burden, we develop a fast parameter estimation method by pre-computing associated Legendre products of the data, leveraging properties of the spherical heat kernel. We show how our approach can be used to assess the quality of cortical parcellations with respect to connectivity. We further present empirical results that suggest that "discrete" connectomes derived from our model have substantially higher test-retest reliability compared to standard methods. In this, the expanded form of this paper for journal publication, we also explore parcellation free analysis techniques that avoid the use of explicit partitions of the cortical surface altogether. We provide an analysis of sex effects on our proposed continuous representation, demonstrating the utility of this approach.

A Continuous Model of Cortical Connectivity

We present a continuous model for structural brain connectivity based on the Poisson point process. The model treats each stream-line curve in a tractography as an observed event in connectome space, here a product space of cortical white matter boundaries. We approximate the model parameter via kernel density estimation. To deal with the heavy computational burden, we develop a fast parameter estimation method by pre-computing associated Legendre products of the data, leveraging properties of the spherical heat kernel. We show how our approach can be used to assess the quality of cortical parcellations with respect to connectivty. We further present empirical results that suggest the "discrete" connectomes derived from our model have substantially higher test-retest reliability compared to standard methods.

Single trial decoding of belief decision making from EEG and fMRI data using independent components features

The complex task of assessing the veracity of a statement is thought to activate uniquely distributed brain regions based on whether a subject believes or disbelieves a given assertion. In the current work, we present parallel machine learning methods for predicting a subject's decision response to a given propositional statement based on independent component (IC) features derived from EEG and fMRI data. Our results demonstrate that IC features outperformed features derived from event related spectral perturbations derived from any single spectral band, yet were similar to accuracy across all spectral bands combined. We compared our diagnostic IC spatial maps with our conventional general linear model (GLM) results, and found that informative ICs had significant spatial overlap with our GLM results, yet also revealed unique regions like amygdala that were not statistically significant in GLM analyses. Overall, these results suggest that ICs may yield a parsimonious feature set that can be used along with a decision tree structure for interpretation of features used in classifying complex cognitive processes such as belief and disbelief across both fMRI and EEG neuroimaging modalities.

Attenuation of histamine-induced endothelial permeability responses after pacing-induced heart failure: role for endogenous catecholamines

OBJECTIVE

After congestive heart failure (CHF), lung endothelial permeability responses to a number of perturbations, including acute barotrauma, angiotensin II, and thapsigargin are blunted. Our hypothesis was that similar attenuation of permeability responses occurs in peripheral vascular beds after CHF. We compared peripheral microvascular permeability responses to the autacoid histamine in control dogs and in dogs paced to heart failure (245 bpm for approximately 36 days). Since catecholamines attenuate autacoid-induced increases in microvascular permeability in skin and muscle in normal animals, we also tested whether the known elevation in catecholamines in CHF was involved in any downregulation of permeability responses in this group.

METHODS

Control and paced dogs were anesthetized, intubated, and ventilated, and a hindpaw lymphatic cannulated. The reflection coefficient for total proteins (sigma) was measured at baseline and during one-hour, local intra-arterial histamine infusion.

RESULTS

In controls, sigma fell from 0.83 +/- 0.02 to 0.73 +/- 0.04 after histamine (p < 0.05), while in the paced group sigma was no different from that at baseline (0.77 +/- 0.02). To test whether this difference was due to endogenous catecholamines, dogs were pretreated with propranolol (controls only) or the specific beta 2-antagonist ICI 118,551 prior to histamine infusion. After beta-blockade, histamine significantly reduced sigma in both control (0.83 +/- 0.01 to 0.55 +/- 0.05) and paced (0.83 +/- 0.01 to 0.57 +/- 0.07) groups (p < 0.05).

CONCLUSION

We conclude that endogenous catecholamines, acting via beta 2-adrenergic receptors, attenuate the permeability response to histamine in pacing-induced heart failure.

The significant change for the Timed 25-foot Walk in the multiple sclerosis functional composite

The Timed 25-Foot Walk is under evaluation as a clinical tool to follow patients with MS. Several approaches have been taken to define a clinically significant change in this measurement. This study was undertaken to define the range of values expected for the Timed 25-Foot Walk and to correlate prospectively the change in walk-time that occurs during an exacerbation of MS associated with subjective difficulty walking. Five results from this study are emphasized. (1) Patients who were minimally affected by MS frequently walked 25 feet between three and five seconds. (2) the walk-time variability, defined as the ratio of the longest to the shortest walk-time, minus 1, times 100%, for three consecutive trials was generally 20% or less; (3) if the two fastest walk-times obtained in three trials were compared, the walk-time variability was almost always 10% or less; (4) for clinically stable individuals, the walk-times of single trials separated by 12 months or less generally varied less than 20%; and (5) patients who complained of difficulty walking, but who did not have changes otherwise detectable by examination, generally had a prolongation of walk-time. These results suggest that an increase of more than 20% in the Timed 25-Foot Walk may indicate a significant change in gait. Multiple Sclerosis (2000) 6 286 - 290

Potential cost savings in residential care for Alzheimer's disease patients

Data from a 1996 cross-sectional study examining the costs of care for Alzheimer's Disease patients are used to estimate the potential cost savings that could result by substituting assisted living for nursing home care for AD residents with health profiles that appear to be manageable within assisted living facilities that specialize in dementia care. Results indicate that up to 13.9% of nursing home costs could be saved, making such a service substitution an attractive alternative in the provision of residential care for certain categories of AD patients.

Varicella-zoster virus infection. The complex prevention-treatment picture

The highly prevalent and contagious varicella-zoster virus is usually benign in healthy persons but may cause substantial morbidity in immunocompromised patients and some adults. New developments in prevention and treatment, as discussed in this article, offer attractive options but also present difficult management decisions for primary care physicians.

Solitary thyroid nodules. Separating benign from malignant conditions

Although features found on history taking, physical examination, thyroid function tests, and imaging studies help categorize solitary thyroid nodules as benign or malignant, fine-needle aspiration biopsy is the diagnostic test of choice. Nodules found to be malignant on cytologic examination should be treated with surgery. Benign nodules may be followed clinically or treated with levothyroxine to suppress their growth. Intermediate nodules should be excised if there is clinical suspicion of malignancy. In suspect nodules, levothyroxine therapy with follow-up ultrasound assessment for size is appropriate. Nodules that do not shrink significantly within 6 months should be excised.

Intravenous gestational cocaine in rats: effects on offspring development and weanling behavior

Pregnant rats were injected with cocaine (CN; 6 mg/kg) or an equal volume of saline (SAL), via the tail vein, on gestation days 8-20. A third group was untreated (UT). Maternal weight gain was not affected by dam treatment despite slight differences in food intake. Litter characteristics (e.g., litter size, pup weight) did not differ among groups. Indices of fetal mortality were not affected by the treatments. Developmental tests, initiated on postnatal day (PND) 2, indicated slight delays in the negative geotaxic response and eye opening in cocaine-exposed pups. Open-field and tail-flick tests were performed on PND 21. Pups were acutely injected with cocaine (10 mg/kg, IP), saline, or received no treatment before placement in a novel open field; morphine (1.5 mg/kg, SC) or saline was injected prior to the tail flick test. Pups from CN dams exhibited a significant decrease in spontaneous exploratory behavior compared to both controls, and a time-dependent increase in rearing compared to pups from UT dams. The acute cocaine injection prior to placement in the open field did not alter locomotion or rearing among dam treatment groups. However, the acute cocaine injection did increase stereotypy ratings for female pups from CN dams compared to similarly treated males, and females from SAL and UT dams. No differences were observed among groups in the tail-flick test. These data suggest that the IV route of administration provides a viable method of cocaine delivery in pregnant rats, and provides further evidence of the developmental and behavioral teratogenicity of prenatal cocaine exposure.

Evaluating the effect of age on endometrial responsiveness to hormone replacement therapy: a histologic ultrasonographic, and tissue receptor analysis

OBJECTIVE

Our objective was to characterize the endometria of women of various ages placed on similar estrogen/progesterone replacement regimens prior to attempted donor embryo transfer using histologic, ultrasonographic, and steroid receptor markers in order to determine if advancing age has a detrimental effect on uterine responsiveness to pharmacologic sex steroid replacement therapy.

STUDY DESIGN

This was a prospective open clinical trial. Functionally agonadal women aged 25 to 60 years receiving hormone replacement therapy underwent transvaginal ultrasound examination of the uterus followed by a timed endometrial biopsy on artificial cycle day 21. Endometrial histology and estrogen and progesterone receptors were analyzed from biopsy material. Subjects were assigned to three groups according to age: Group I, aged 25 to 39 years (n = 48); Group II, aged 40 to 49 years (n = 61); and Group III, aged 50 to 60 years (n = 13). Endometrial preparation was accomplished in all patients using the same sequential regimen consisting of oral micronized estradiol and intramuscular progesterone.

RESULTS

Similar histologic, ultrasonographic, and steroid receptor characteristics were noted in all groups of patients regardless of age. A normal appearing midluteal secretory endometrium was demonstrated histologically in 85% of biopsies. However, 15% of biopsies exhibited intraluminal papillary excrescences within the glands and/or increase in the normal gland-to-stroma ratio. Three patients, one from each group, did not initially respond to replacement therapy and required further treatment.

CONCLUSION

Functionally agonadal women exhibit normal or near-normal endometrial responses to sex steroid replacement therapy designed to imitate the natural cycle through the sixth decade of life.

Managing common suturing problems

The closure of surgical wounds can be improved by applying some basic techniques for suturing and for correcting suture errors. These techniques include 1) the locking stitch, 2) the pulley stitch for closing large gaping defects, and 3) a means of welding polyfilament synthetic polyamide suture and polypropylene suture.

Needs assessment. Process and application

This article describes the process of developing a learning needs assessment. The process includes developing a tool, collecting data, and analyzing results. Understanding the needs assessment process and the pitfalls to avoid may assist educators in implementing their own learning needs assessments.

Kluyveromyces as a Host for Heterologous Gene Expression: Expression and Secretion of Prochymosin

We have developed the yeast Kluyveromyces lactis as a host organism for the production of the milk-clotting enzyme chymosin. In contrast to Saccharomyces cerevisiae, we found that this yeast is capable of the synthesis and secretion of fully active prochymosin. Various signal sequences could be used to efficiently direct the secretion of prochymosin in Kluyveromyces, but not in S. cerevisiae. We conclude that the efficient synthetic and secretory capacity of this heterologous protein is a property of the yeast Kluyveromyces. These results have led to the development of a large scale production process for chymosin.

The use of hyaluronic acid for sperm selection--evaluation of sperm head ultrastructure

The percentage of normal spermatozoal plasma membranes and acrosomes was evaluated by electron microscopy before and after a two-step semen washing and swim-up into culture medium containing either bovine serum albumin or hyaluronic acid. Both techniques of sperm selection increased the concentration of motile spermatozoa when compared with the fresh ejaculate (P less than .001). The percentage of sperm with ultrastructurally intact plasma membranes and acrosomes was increased after both separation procedures in comparison with the original ejaculate (P less than .01). In addition, both techniques of sperm selection significantly reduced the content of sample debris, granulocytes, and nonviable spermatozoa (P less than .01).

Correlation of endometrial maturation with four methods of estimating day of ovulation

Dating of maturity of the endometrium by histologic examination was correlated with four methods of ovulation detection in 13 cycling parous women. Histologic dating was assessed independently by two pathologists and correlated with the postovulatory duration as determined by daily transvaginal ultrasound scanning, serum LH measurements, basal body temperature (BBT), and subtraction of 14 days from the onset of menses. In addition, progesterone and estradiol (E2) were measured in daily serum samples. Dating of the endometrial biopsy was highly correlated (P less than .002) with the day of ovulation as determined by ultrasound, and was found to be within 2 days of the correct postovulatory day on evaluation of 25 of 26 (96.1%) of the interpretations. The accuracy of dating using the LH surge was 84.6% (22 of 26 interpretations), and with the BBT thermogenic shift was 76.9% (20 of 26 interpretations). However, dating of the endometrium was within 2 days of the correct day in only 17 of the 26 interpretations as determined by subtracting 14 days from the onset of the subsequent menses. The accuracy of dating was significantly better correlated (P less than .025) with days from ovulation as determined by ultrasound than as calculated from the onset of menses. There was a significant correlation between endometrial dating and the amount of progesterone (P less than .01) and E2 (P less than .01) secreted from the day of ovulation, as determined by transvaginal ultrasound, to the day of biopsy. These data confirm a strong correlation between endometrial dating and ovarian hormone secretion during the postovulatory phase.(ABSTRACT TRUNCATED AT 250 WORDS)

Cryopreservation of human spermatozoa: correlations of ultrastructural sperm head configuration with sperm motility and ability to penetrate zona-free hamster ova

Freezing and storage of human male gametes is associated with a reduction in the overall semen quality and establishment of pregnancy. This study was done to evaluate the integrity of sperm head ultrastructure (SHU) with computerized and vapor freezing. Comparisons were made between the effect of cryopreservatives glycerol (G) and dimethylsulfoxide (DMSO) on SHU. Twelve ejaculates from five proven fertile donors were studied with the use of routine semen analysis, zona-free hamster ova, and SHU. Both cooling processes, regardless of the preservative used, significantly reduced sperm function and the number of SH with intact plasma membranes. The staged cooling technique was substantially superior to vapor freezing in all parameters analyzed (P less than 0.01). G was less detrimental to the postthaw SHU than 1 M DMSO. A significant positive correlation (r = 0.98; P less than 0.01) was noted between the total number of intact SH and motile sperm. Computerized freezing in a G-diluted semen rendered a sperm environment that allowed the highest number of forms with intact SH membranes and with the best chances to penetrate zona-free hamster ova.

The detection of estrogen receptors in gynecologic tumors using immunoperoxidase and the dextran-coated charcoal assay

The dextran-coated charcoal receptor assay for demonstrating functional estrogen and progesterone receptors was used to evaluate the receptor content of gynecological tumors. An immunocytochemical method, the immunoperoxidase antiperoxidase method, that may detect estrogen receptors, was also employed on the same specimens utilizing sections from formalin-fixed paraffin embedded blocks. The results by the two methods were compared and were correlated with the state of differentiation of the tumors. According to the dextran-coated charcoal method, two cases of well differentiated endometrial adenocarcinoma were strongly positive for both estradiol and progesterone receptors, and three moderately differentiated cases contained lesser amounts of both types of receptors. Six cases of undifferentiated adenocarcinoma ranged from no detectable receptors to very high values, while of five cases of squamous cell carcinoma of the cervix, only two were positive for estradiol receptors by the dextran-coated charcoal method. Staining of tissue sections from these same cases using the immunoperoxidase method, demonstrated a positive correlation to the dextran-coated charcoal assay for estrogen receptors. The Dextran-coated charcoal method is presently being used clinically as a screening measure for statistical probability of a patient's response to hormone therapy. The degree of positive correlation shown here suggests that use of the immunoperoxidase method may have further potential for diagnostic and clinical use, and merits further investigation.