Biology subjects

Heine, J.

Publications and source records attributed to Heine, J..

11 recordsLinked to original sources

bioRxiv2026

Comparison of Total X-Ray Energy Absorbed by Fibroglandular Tissue and its Mass Derived from Full Field Digital Mammography and Digital Breast Tomosynthesis

This study investigated whether total x-ray energy absorbed by fibroglandular tissue and fibroglandular tissue mass derived from raw and clinical full field digital mammography (FFDM) images corresponded with the respective quantities derived from digital breast tomosynthesis (DBT). A total of 840 paired FFDM and DBT observations were analyzed separately for the craniocaudal (CC) and mediolateral oblique (MLO) orientations. Total absorbed x-ray energy and fibroglandular tissue mass were calculated using an automated breast density detection method in conjunction with the recorded average glandular dose. Relationships between FFDM and DBT quantities were evaluated in the log-log domain using linear and quadratic models, with model performance assessed by R2, adjusted R2, mean squared error (MSE), chi-square goodness-of-fit tests, and residual analysis. Strong associations were observed between FFDM and respective DBT quantities. For raw FFDM, linear energy models produced R2 values of 0.734 and 0.901 for the CC and MLO views, respectively, while the corresponding mass models produced R2 values of 0.634 and 0.870. Inclusion of a quadratic term improved model performance modestly. For clinical FFDM, linear energy models produced R2 values of 0.854 and 0.919, while the corresponding mass models produced R2 values of 0.721 and 0.812. The first-order coefficients for the clinical-image energy models were approximately unity (0.99-1.05), and inclusion of the quadratic term produced negligible changes in model performance and residual distributions. The goodness-of-fit tests did not reject adequate fit for any model. Clinical FFDM images can therefore provide estimates of total absorbed x-ray energy and fibroglandular tissue mass that are strongly associated with corresponding quantities derived from DBT. The approximately unit first-order coefficients and minimal benefit from quadratic modeling support a simple linear log-log calibration for clinical FFDM. This approach may facilitate combining FFDM and DBT based data in larger retrospective and longitudinal studies of cumulative mammographic x-ray exposure. These findings demonstrate proof-of-concept internal consistency but do not establish absolute dosimetric accuracy.

bioinformatics↗

bioRxiv2026

Synthetic Data Generation and Nonparametric Techniques for Assessing Multivariate Similarity to Address Small-Sample Size Challenges

Data modeling in biomedical research often operates in the small-sample regime, where the number of observations is small relative to the data dimensionality; the detrimental effects of limited sample sizes are well documented in cancer studies. Synthetic data offers a potential solution to data shortfalls provided that the data generated is an adequate facsimile of the underlying distribution; the adequacy of such synthetic data remains an open-ended problem. In this work, we evaluate a synthetic generator proposed previously. The generator applies a series of transformations to the observed data to accommodate the small-sample size resulting in an uncoupled representation, where uncorrelated marginal distributions are modeled with optimized univariate kernel density estimation. In this report, (1) we develop a nonparametric method for assessing multivariate similarity based on the Cramer-Wold theorem and random projection testing, (2) investigate when the absence of bivariate correlation approximates independence in a non-normal setting, and (3) evaluate artifacts induced by data compression. The presentation is primarily methodological; low-dimensional data were used so each stage of the generation process could be analyzed explicitly. A formal testing framework was developed by comparing random projection level outcomes with a two-sample test, modeling these outcomes as Bernoulli trials, aggregating replicate outcomes within each projection direction, and pooling outcomes across many directions, yielding a scalable standardized normal test-statistic. The key innovation was decoupling the two-sample test significance level from that governing finalized normal inference. We showed the same projection framework also evaluates the full multivariate covariance structure. The generator produced high-fidelity multivariate synthetic data when the bivariate correlation approximates independence in the non-normal setting; in highly compressed data, residual modes were best modeled as normally distributed regardless of their intrinsic distributional form. Ongoing work includes applying these methods to higher-dimensional, diverse data.

bioinformatics↗

bioRxiv2025

Advancements in an Automated Breast Density Detection Technique for Breast Cancer Risk Prediction: a Synthetic Signal-dependent Noise Construct

Breast density is an important breast cancer risk factor estimated from mammograms and useful for breast cancer risk prediction. We describe a new formulation built on the backbone of an established automated percentage of breast density detection method. This framework relies on signal-dependent noise (SDN), characterized by a statistical dependence between the variance and mean signal. Variations in this dependency due to different image data representations cause degradation in the algorithms performance; the current work addresses this problem by synthesizing a stochastic process conditioned on a given mammogram instead of analyzing the mammogram directly. Image data used in the analysis was derived from three breast cancer case-control studies employing different mammographic technologies including full field digital mammography (both raw and clinical images) and digital breast tomosynthesis. The new formulation produced significant odds ratios across all image data representations due to these methodological advancements: (1) synthesis of SDN to an optimal quadratic structure given an arbitrary image; and (2) ensemble averaging over a given image, boosting the signal. We also demonstrate methods to standardize and combine measurements from different technologies using a probability density transformation technique. This automated technique can be applied to images from different technologies with minimal adjustment, thereby making it suitable for both research and clinical applications.

bioengineering↗

bioRxiv2024

Development of AI-assisted microscopy frameworks through realistic simulation in pySTED

The integration of artificial intelligence (AI) into microscopy systems significantly enhances performance, optimizing both the image acquisition and analysis phases. Development of AI-assisted super-resolution microscopy is often limited by the access to large biological datasets, as well as by the difficulties to benchmark and compare approaches on heterogeneous samples. We demonstrate the benefits of a realistic STED simulation platform, pySTED, for the development and deployment of AI-strategies for super-resolution microscopy. The simulation environment provided by pySTED allows the augmentation of data for the training of deep neural networks, the development of online optimization strategies, and the training of reinforcement learning models, that can be deployed successfully on a real microscope.

bioinformatics↗

bioRxiv2023

Breast Density Analysis of Digital Breast Tomosynthesis

We evaluated an automated percentage of breast density (BD) technique (PDa) with digital breast tomosynthesis (DBT) data. The approach is based on the wavelet expansion followed by analyzing signal dependent noise. Several measures were investigated as risk factors: normalized volumetric; total dense volume; average of the DBT slices (slice-mean); a two-dimensional (2D) metric applied to the synthetic images; and the mean and standard deviations of the pixel values. Volumetric measures were derived theoretically, and PDa was modeled as a function of compressed breast thickness. An alternative method for constructing synthetic 2D mammograms was investigated using the volume results. A matched case-control study (n = 426 pairs) was analyzed. Conditional logistic regression modeling, controlling body mass index and ethnicity, was used to estimate odds ratios (ORs) for each measure with 95% confidence intervals provided parenthetically. There were several significant findings: volumetric measure [OR = 1.43 (1.18, 1.72)], which produced an identical OR as the slice-mean measure as predicted; [OR =1.44 (1.18, 1.75)] when applied to the synthetic images; and mean of the pixel values (volume or 2D synthetic) [ORs [~] 1.31 (1.09, 1.57)]. PDa was modeled as 2nd degree polynomial (concave-down): its maximum value occurred at 0.41x(compressed breast thickness), which was similar across case-control groups, and was significant from this position [OR = 1.47 (1.21, 1.78)]. A standardized 2D synthetic image was produced, where each pixel value represents the percentage of BD above its location. The significant findings indicate the validity of the technique. Derivations supported by empirical analyses produced a new synthetic 2D standardized image technique. Ancillary to the objectives, the results provide evidence for understanding the percentage of BD measure applied to 2D mammograms. Notwithstanding the findings, the study design provides a template for investigating other measures such as texture.

biophysics↗

bioRxiv2022

Reduced resilience of functional state transitions in patients with anti-NMDA receptor encephalitis

ObjectivePatients with anti-NMDA receptor encephalitis suffer from a severe neuropsychiatric syndrome, yet most patients show no abnormalities in routine magnetic resonance imaging. In contrast, advanced neuroimaging studies have consistently identified disrupted functional connectivity in these patients, with recent work suggesting increased volatility of functional state dynamics. Here, we investigate these network dynamics through the spatiotemporal trajectory of meta-state transitions, yielding a time-resolved account of brain state exploration in anti-NMDA receptor encephalitis. MethodsResting-state functional magnetic resonance imaging data were acquired in 73 patients with NMDAR encephalitis and 73 age- and sex-matched healthy controls. Time-resolved functional connectivity was clustered into brain meta-states, giving rise to a time-resolved transition network graph with states as nodes and transitions between brain meta-states as weighted, directed edges. Network topology, robustness, and transition cost of these transition networks were compared between groups. ResultsTransition networks of patients showed significantly lower local efficiency (t = -2.54, pFDR = 0.026), lower robustness (t = -2.01, pFDR = 0.048) and higher leap size (t = 2.33, pFDR = 0.026) compared to controls. Furthermore, the ratio of within-to-between module transitions and state similarity was significantly lower in patients. Importantly, alterations of brain state transitions correlated with disease severity. InterpretationThese findings reveal systematic alterations of transition networks in patients, suggesting that anti-NMDA receptor encephalitis is characterized by reduced stability of brain state transitions and that this reduced resilience of transition networks plays a clinically relevant role in the manifestation of the disease.

neuroscience↗

bioRxiv2021

Techniques to Produce and Evaluate Realistic Multivariate Synthetic Data

BackgroundData modeling in biomedical-healthcare research requires a sufficient sample size for exploration and reproducibility purposes. A small sample size can inhibit model performance evaluations (i.e., the small sample problem). ObjectiveA synthetic data generation technique addressing the small sample size problem is evaluated. We show: (1) from the space of arbitrarily distributed samples, a subgroup (class) has a latent multivariate normal characteristic; (2) synthetic populations (SPs) of unlimited size can be generated from this class with univariate kernel density estimation (uKDE) followed by standard normal random variable generation techniques; and (3) samples drawn from these SPs are statistically like their respective samples. MethodsThree samples (n = 667), selected pseudo-randomly, were investigated each with 10 input variables (i.e., X). uKDE (optimized with differential evolution) was used to augment the sample size in X (i.e., the input variables). The enhanced sample size was used to construct maps that produced univariate normally distributed variables in Y (mapped input variables). Principal component analysis in Y produced uncorrelated variables in T, where the univariate probability density functions (pdfs) were approximated as normal with specific variances; a given SP in T was generated with normally distributed independent random variables with these specified variances. Reversing each step produced the respective SPs in Y and X. Synthetic samples of the same size were drawn from these SPs for comparisons with their respective samples. Multiple tests were deployed: to assess univariate and multivariate normality; to compare univariate and multivariate pdfs; and to compare covariance matrices. ResultsOne sample was approximately multivariate normal in X and all samples were approximately multivariate normal in Y, permitting the generation of unlimited sized SPs. Uni/multivariate pdf and covariance comparisons (in X, Y and T) showed similarity between samples and synthetic samples. ConclusionsThe work shows that a class of multivariate samples has a latent normal characteristic; for such samples, our technique is a simplifying mechanism that offers an approximate solution to the small sample problem by generating similar synthetic data. Further studies are required to understand this latent normal class, as two samples exhibited this characteristic in the study.

bioinformatics↗

bioRxiv2020

State-dependent signatures of Anti-NMDA-Receptor Encephalitis: a dynamic functional connectivity study

ObjectiveTraditional static functional connectivity (FC) analyses have shown functional network alterations in patients with anti-NMDA receptor encephalitis (NMDARE). Here, we use a dynamic FC approach that increases the temporal resolution of connectivity analyses from minutes to seconds. We hereby explore the spatiotemporal variability of large-scale brain network activity in NMDARE and assess the discriminatory power of functional brain states in a supervised classification approach. MethodsWe included resting-state fMRI data from 57 patients and 61 controls to extract four discrete connectivity states and assess state-wise group differences in FC, dwell time, transition frequency, fraction time and occurrence rate. Additionally, for each state, logistic regression models with embedded feature selection were trained to predict group status in a leave-one-out cross-validation scheme. ResultsCompared to controls, patients exhibited diverging dynamic FC patterns in three out of four states mainly encompassing the default-mode network and frontal areas. This was accompanied by a characteristic shift in the dwell time pattern and higher volatility of state transitions in patients. Moreover, dynamic FC measures were associated with disease severity, disease duration and positive and negative schizophrenia-like symptoms. Predictive power was highest in dynamic FC models and outperformed static analyses, reaching up to 78.6% classification accuracy. ConclusionsBy applying time-resolved analyses, we disentangle state-specific FC impairments and characteristic changes in temporal dynamics not detected in static analyses, offering new perspectives on functional reorganization underlying NMDARE. Correlation of dynamic FC measures with disease symptoms and severity indicates their clinical relevance and potential as prognostic biomarkers in NMDARE.

neuroscience↗

bioRxiv2019

Spatial Correlation and Breast Cancer Risk

We present a novel method for evaluating the spatial correlation structure in two-dimensional (2D) mammograms and evaluate its merits for risk prediction. Two matched case-control studies were analyzed. Study 1 included women (N = 588 pairs) with mammograms acquired with either Hologic Selenia full field digital mammography (FFDM) units or Hologic Dimensions digital breast tomosynthesis units. Study 2 included women (N =180 pairs) with mammograms acquired with a General Electric Senographe 2000D FFDM unit. Matching variables included age, HRT usage/duration, screening history, and mammography unit. The local autocorrelation function was determined with Fourier analysis and compared with template defined as 2D double-sided exponential function with one spatial extent parameter: n = 4, 12, 24, 50, 74, 100, and 124 defined in pixel widths. The difference between local correlation and template was gauged within a kernel with an adjustable parameter and summarized, producing two measures: the mean (mn+1), and standard (sn+1). Both adjustable parameters were varied in Study 1. Select measures that produced significant associations with breast cancer were translated to Study 2. Breast cancer associations were evaluated with conditional logistic regression, adjusted for body mass index and ethnicity. Odds ratios (ORs) were estimated as per standard increment with 95% confidence intervals (CIs). Two measures were selected for breast cancer association analysis in Study 1: m75 and s25. Both measures revealed significant associations with breast cancer: OR = 1.45 (1.23, 1.66) for m75 and OR = 1.30 (1.14, 1.49) for s25. When translating to Study 2, these measures also revealed significant associations: OR = 1.49 (1.12, 1.96) for m75 and OR = 1.34 (1.06, 1.69) for s25. Novel correlation metrics presented in this work revealed significant associations with breast cancer risk. This approach is general and may have applications beyond mammography.

bioengineering↗

bioRxiv2018

Empirically-Derived Synthetic Populations to Overcome Small Sample Sizes

Limited sample sizes can hinder biomedical research and lead to spurious findings. The objective of this work is to present a new method to generate synthetic populations (SPs) from sparse data samples to aid in modeling developments. Matched case-control data (n=180 pairs) defined the limited samples. Cases and controls were considered as two separate limited samples. Synthetic populations were generated for these observed samples using multivariate unconstrained bandwidth kernel density estimations. We included four continuous variables and one categorical variable for each individual. Bandwidth matrices were determined with Differential Evolution (DE) optimization driven by covariance comparisons. Four synthetic samples (n=180) were constructed from their respective SP for comparison purposes. Similarity between the observed samples with equally sized synthetic-samples was compared under the hypothesis that their sample distributions were the same. Distributions were compared with the maximum mean discrepancy (MMD) test statistic based on a Kernel Two-Sample Test. To evaluate similarity within a modeling context, Principal Component Analysis (PCA) score distributions and residuals were summarized with the distance to the model in X-space (DModX) as additional comparisons.\n\nFour SPs were generated with the optimization procedure. The probability of selecting a replicate when randomly constructing synthetic samples with n=180 was infinitesimally small. The MMD tests indicated that the observed sample distributions were similar to the respective synthetic distributions. For both case and control samples, PCA scores and residuals did not deviate significantly when compared with their respective synthetic samples.\n\nThe reasonableness of this SP generation approach was demonstrated. This approach produced synthetic data at the patient level statistically similar to the observed samples, and thus could be used to generate larger-sized simulated data. The methodology coupled kernel density estimation with DE optimization and deployed novel similarity metrics derived from PCA. The use of large-sized synthetic samples may be a way to overcome sparse datasets. To further develop this approach into a research tool for model building purposes, additional evaluation with increased dimensionality is required; moreover, comparisons with other techniques such as bootstrapping and cross-validation will be required for a complete evaluation.

bioinformatics↗

bioRxiv2018

Imaging beyond the super-resolution limits using ultrastructure expansion microscopy (UltraExM)

For decades, electron microscopy (EM) was the only method able to reveal the ultrastructure of cellular organelles and molecular complexes because of the diffraction limit of optical microscopy. In recent past, the emergence of superresolution fluorescence microscopy enabled the visualization of cellular structures with so far unmatched spatial resolution approaching virtually molecular dimensions. Despite these technological advances, currently super-resolution microscopy does not permit the same resolution level as provided by electron microscopy, impeding the attribution of a protein to an ultrastructural element. Here, we report a novel method of near-native expansion microscopy (UltraExM), enabling the visualization of preserved ultrastructures of macromolecular assemblies with subdiffraction-resolution by standard optical microscopy. UltraExM revealed for the first time the ultrastructural localization of tubulin glutamylation in centrioles. Combined with super-resolution microscopy, UltraExM unveiled the centriolar chirality, an ultrastructural signature, which was only visualizable by electron microscopy.

cell biology↗