Biology subjects

Posner, J.

Publications and source records attributed to Posner, J..

6 recordsLinked to original sources

bioRxiv2026

Semi-quantitative Classification of HIV-1 Nucleic Acids Using ResNet Image Analysis of Discretized Isothermal Amplification Reactions in a Microfluidic Chip

Isothermal nucleic acid amplification tests enable rapid and decentralized molecular diagnostics but often lack robust quantitative readouts compared to quantitative PCR. Here, we present a semi-quantitative nucleic acid measurement approach using machine learning to extract spatiotemporal features from real-time fluorescence imaging of rapid isothermal amplification reactions in microfluidic chips. A convolutional neural network was trained on multiple images sampled throughout a chip-based recombinase polymerase amplification reaction to classify samples into clinically relevant or logarithmically spaced concentration ranges spanning five orders of magnitude. The clinical classification model achieved 94.6% accuracy, and the logarithmic model achieved 92.7% accuracy, with most errors occurring between adjacent concentration categories. By learning spatiotemporal patterns of fluorescence development rather than relying on explicit feature extraction, the model remained accurate at both high and low nucleic acid concentration regimes where other quantitative isothermal molecular tests struggle. This approach enables automated interpretation of amplification reactions and extends the usable dynamic range of the assay. These results demonstrate that integrating machine learning with image-based amplification methods can support rapid semi-quantitative molecular testing and may facilitate broader deployment of nucleic acid diagnostics outside centralized laboratory settings. Author summaryMany rapid nucleic acid testing methods for infectious diseases are simple to run but struggle to measure how much genetic material is present, which limits their usefulness in clinical decision-making. In our work, we study a technique that produces visible fluorescent patterns during nucleic acid amplification reactions. Traditionally, the amount of nucleic acids present are measured by counting individual bright spots, but this becomes difficult when the target nucleic acid concentration is high and the spots merge together. We developed a machine learning approach that models how the fluorescence pattern changes over time. By analyzing a sequence of images from each reaction, our model can assign samples to concentration ranges across a wide span. This allows us to extract meaningful information even when traditional analysis methods break down. Because this approach works with simple imaging systems and does not require complex equipment, it could help support more informative and accessible diagnostic testing in point-of-care and low-resource settings.

bioengineering↗

bioRxiv2026

sf-pediatric: A robust and age-adaptable end-to-end pipeline for pediatric diffusion MRI

Diffusion MRI (dMRI) provides a powerful, non-invasive window into white matter (WM) development. Yet, most existing processing pipelines are not well-suited to the rapidly evolving neurophysiology of the pediatric brain. Here, we introduce sf-pediatric, a scalable, end-to-end, age-adaptable dMRI pipeline that integrates normative models of brain diffusivities to enable optimal subject-specific analysis from birth through 18 years old. Leveraging normative trajectories derived from nearly 2,000 participants from six cohorts, sf-pediatric dynamically calibrates diffusion priors, template selection, segmentation, and WM atlases based on the subjects age. By incorporating automatic quality control into a portable, tested, containerized, open-access, and press-button framework across computing environments, sf-pediatric provides a robust pipeline for large-scale pediatric dMRI studies. We validated this approach by showing improved local modeling and cortical fanning while preserving reproducibility and the ability to derive brain-behavior relationships. Additionally, we demonstrated robust recovery of known developmental trajectories of WM microstructure and connectome-derived network organization.

neuroscience↗

bioRxiv2021

Large-scale exploration of whole-brain structural connectivity in anorexia nervosa: alterations in the connectivity of frontal and subcortical networks

BackgroundAnorexia nervosa (AN) is characterized by disturbances in cognition and behavior surrounding eating and weight. The severity of AN combined with the absence of localized brain abnormalities suggests distributed, systemic underpinnings that may be identified using diffusion-weighted MRI (dMRI) and tractography to reconstruct white matter pathways. MethodsdMRI data acquired from female patients with AN (n = 147) and female healthy controls (HC; n = 119), aged 12-40 years, were combined across five studies. Probabilistic tractography was completed, and full cortex connectomes describing streamline counts between 84 brain regions generated and harmonized. Graph theory methods were used to describe alterations in network organization in AN. The network-based statistic tested between-group differences in brain subnetwork connectivity. The metrics strength and efficiency indexed the connectivity of brain regions (network nodes), and were compared between groups using multiple linear regression. ResultsIndividuals with AN, relative to HC, had reduced connectivity in a network comprising subcortical regions and greater connectivity between frontal cortical regions (p < 0.05, FWE corrected). Node-based analyses indicated reduced connectivity of the left hippocampus in patients relative to HC (p < 0.05, permutation corrected). Severity of illness, assessed by BMI, was associated with subcortical connectivity (p < 0.05, uncorrected). ConclusionsAnalyses identified reduced structural connectivity of subcortical networks and regions, and stronger cortical network connectivity, amongst individuals with AN relative to HC. These findings are consistent with alterations in feeding, emotion and executive control circuits in AN, and may direct hypothesis-driven research into mechanisms of persistent restrictive eating behavior.

neuroscience↗

bioRxiv2021

ID-Seg: An Accurate and Reliable Infant Deep learning Segmentation Framework for Limbic Structures

Early postnatal period brain magnetic resonance imaging (MRI) is becoming an important approach to measure the impact of prenatal exposures on neurodevelopment and to investigate early biomarkers for risk. Among brain structures, Limbic structures are particular of interest in psychiatric disorder-related research. However, despite the promise of infant neuroimaging and the success of initial infant MRI studies, assessing limbic regions structure and function remains a significant challenge due to low inter-regional intensity contrast and high curvature (e.g., hippocampus). In addition, the agreement between existing automatic techniques and manual segmentation remains either untested or insufficient, particularly for the amygdala and hippocampus. In this work, we developed an accurate (based on three segmentation evaluation metrics), reliable and efficient infant deep learning segmentation framework (ID-Seg) to address the aforementioned challenges. Specifically, we leveraged a large dataset of 473 infant MRI scans to train ID-Seg and rigorously evaluated ID-Segs performance on internal and external datasets with manual segmentations. Compared with a state-of-the-art segmentation pipeline, we demonstrated that ID-Seg significantly improved the segmentation accuracy of limbic structures (hippocampus and amygdala) in newborn infants. Moreover, in a medium-size dataset, we found that ID-Seg-derived morphometric measures yield strong brain-behavior associations. As such, our ID-Seg may improve our capacity and efficiency to measure MRI-based brain features relevant to neuropsychological development and ultimately advance the success of quantitative analyses on large-scale datasets.

bioengineering↗

bioRxiv2020

Intra-session test-retest reliability of functional connectivity in infants

Resting functional MRI studies of the infant brain are increasingly becoming an important tool in developmental neuroscience. Whereas the test-retest reliability of functional connectivity (FC) measures derived from resting fMRI data have been characterized in the adult and child brain, similar assessments have not been conducted in infants. In this study, we examined the intra-session test-retest reliability of FC measures from 119 infant brain MRI scans from four neurodevelopmental studies. We investigated edge-level and subject-level reliability within one MRI session (between and within runs) measured by the Intraclass correlation coefficient (ICC). First, using an atlas-based approach, we examined whole-brain connectivity as well as connectivity within two common resting fMRI networks - the default mode network (DMN) and the sensorimotor network (SMN). Second, we examined the influence of run duration, study site, and scanning manufacturer (e.g., Philips and General Electric) on ICCs. Lastly, we tested spatial similarity using the Jaccard Index from networks derived from independent component analysis (ICA). Consistent with resting fMRI studies from adults, our findings indicated poor edge-level reliability (ICC = 0.14 - 0.18), but moderate-to-good subject-level intra-session reliability for whole-brain, DMN, and SMN connectivity (ICC = 0.40 - 0.78). We also found significant effects of run duration, site, and scanning manufacturer on reliability estimates. Some ICA-derived networks showed strong spatial reproducibility (e.g., DMN, SMN, and Visual Network), and were labelled based on their spatial similarity to analogous networks measured in adults. These networks were reproducibly found across different study studies. However, other ICA-networks (e.g. Executive Control Network) did not show strong spatial reproducibility, suggesting that the reliability and/or maturational course of functional connectivity may vary by network. In sum, our findings suggest that developmental scientist may be on safe ground examining the functional organization of some major neural networks (e.g. DMN and SMN), but judicious interpretation of functional connectivity is essential to its ongoing success. HighlightsO_LIInfant functional connectivity (FC) shows poor edge-level reliability (ICCs) C_LIO_LIHowever, subject-level infant FC estimates show good-to-excellent ICCs C_LIO_LISpatial reproducibility is better for some resting networks (DMN, SMN) than others (ECN) C_LIO_LIReliability estimates differ across study site and MRI scanner C_LIO_LIConclusion - Infant FC can be a reliable measurement, but judicious use is needed C_LI

bioinformatics↗

bioRxiv2020

A transfer-learning approach for first-year developmental infant brain segmentation using deep neural networks

The months between birth and age 2 are increasingly recognized as a period critical for neuro-development, with potentially life-long implications for cognitive functioning. However, little is known about the growth trajectories of brain structure and function across this time period. This is in large part because of insufficient approaches to analyze infant MRI scans at different months, especially brain segmentation. Addressing technical gaps in infant brain segmentation would significantly improve our capacity to efficiently measure and identify relevant infant brain structures and connectivity, and their role in long-term development. In this paper, we propose a transfer-learning approach based on convolutional neural network (CNN)-based image segmentation architecture, QuickNAT, to segment brain structures for newborns and 6-month infants separately. We pre-trained QuickNAT on auxiliary labels from a large-scale dataset, fine-tuned on manual labels, and then cross-validated the models performance on two separate datasets. Compared to other commonly used methods, our transfer-learning approach showed superior segmentation performance on both newborns and 6-month infants. Moreover, we demonstrated improved hippocampus segmentation performance via our approach in preterm infants.

neuroscience↗