Ictal Involvement of the Pulvinar and the Anterior Nucleus of the Thalamus in Patients With Refractory Epilepsy.

Background And Objectives: Deep brain stimulation (DBS) targeting the anterior nucleus of the thalamus (ANT) has been shown to be effective in treating some patients with medically refractory epilepsy. However, it remains unknown how seizures spread through the ANT relative to other thalamic nuclei. This study aimed to investigate, through simultaneous recordings from both ANT and pulvinar (PLV) nucleus, their roles in seizure propagation.

Predictive modeling of biomedical temporal data in healthcare applications: review and future directions.

Predictive modeling of clinical time series data is challenging due to various factors. One such difficulty is the existence of missing values, which leads to irregular data. Another challenge is capturing correlations across multiple dimensions in order to achieve accurate predictions.

A Transfer Learning-Based Framework for Classifying Lymph Node Metastasis in Prostate Cancer Patients.

Prostate cancer is the second most common new cancer diagnosis in the United States. It is usually slow-growing, and when it is low-grade and confined to the prostate gland, it can be treated either conservatively (through active surveillance) or with surgery. However, if the cancer has spread beyond the prostate, such as to the lymph nodes, then that indicates a more aggressive cancer, and surgery may not be adequate.

Early Prediction of Progression to Alzheimer's Disease using Multi-Modality Neuroimages by a Novel Ordinal Learning Model ADPacer.

Machine learning has shown great promise for integrating multi-modality neuroimaging datasets to predict the risk of progression/conversion to Alzheimer's Disease (AD) for individuals with Mild Cognitive Impairment (MCI). Most existing work aims to classify MCI patients into converters versus non-converters using a pre-defined timeframe. The limitation is a lack of granularity in differentiating MCI patients who convert at different paces.

E-BDL: Enhanced Band-Dependent Learning Framework for Augmented Radar Sensing.

Radar sensors, leveraging the Doppler effect, enable the nonintrusive capture of kinetic and physiological motions while preserving privacy. Deep learning (DL) facilitates radar sensing for healthcare applications such as gait recognition and vital-sign measurement. However, band-dependent patterns, indicating variations in patterns and power scales associated with frequencies in time-frequency representation (TFR), challenge radar sensing applications using DL.

Exploring the Cost-Effectiveness of Newborn Screening for Metachromatic Leukodystrophy (MLD) in the UK.

Metachromatic leukodystrophy (MLD) is a fatal inherited lysosomal storage disease that can be detected through newborn bloodspot screening. The feasibility of the screening assay and the clinical rationale for screening for MLD have been previously demonstrated, so the aim of this study is to determine whether the addition of screening for MLD to the routine newborn screening program in the UK is a cost-effective use of National Health Service (NHS) resources. A health economic analysis from the perspective of the NHS and Personal Social Services was developed based on a decision-tree framework for each MLD subtype using long-term outcomes derived from a previously presented partitioned survival and Markov economic model.

Eigen-entropy based time series signatures to support multivariate time series classification.

Most current algorithms for multivariate time series classification tend to overlook the correlations between time series of different variables. In this research, we propose a framework that leverages Eigen-entropy along with a cumulative moving window to derive time series signatures to support the classification task. These signatures are enumerations of correlations among different time series considering the temporal nature of the dataset.

Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images.

Harnessing the power of deep neural networks in the medical imaging domain is challenging due to the difficulties in acquiring large annotated datasets, especially for rare diseases, which involve high costs, time, and effort for annotation. Unsupervised disease detection methods, such as anomaly detection, can significantly reduce human effort in these scenarios. While anomaly detection typically focuses on learning from images of healthy subjects only, real-world situations often present unannotated datasets with a mixture of healthy and diseased subjects.

Ordinal Classification with Distance Regularization for Robust Brain Age Prediction.

Age is one of the major known risk factors for Alzheimer's Disease (AD). Detecting AD early is crucial for effective treatment and preventing irreversible brain damage. Brain age, a measure derived from brain imaging reflecting structural changes due to aging, may have the potential to identify AD onset, assess disease risk, and plan targeted interventions.

Brain Structural and Functional Abnormalities Associated with Acute Post-Traumatic Headache: Iron Deposition and Functional Connectivity.

Background: The purpose of this study was to interrogate brain iron accumulation in participants with acute post-traumatic headache (PTH) due to mild traumatic brain injury (mTBI), and to determine if functional connectivity is affected in areas with iron accumulation. We aimed to examine the correlations between iron accumulation and headache frequency, post-concussion symptom severity, number of mTBIs and time since most recent TBI.

Methods: Sixty participants with acute PTH and 60 age-matched healthy controls (HC) underwent 3T magnetic resonance imaging including quantitative T* maps and resting-state functional connectivity imaging.

Higher precision, first tier newborn screening for metachromatic leukodystrophy using 16:1-OH-sulfatide.

Newborn screening (NBS) for metachromatic leukodystrophy (MLD) is based on first-tier measurement of sulfatides in dried blood spots (DBS) followed by second-tier measurement of arylsulfatase A in the same DBS. This approach is very precise with 0-1 false positives per ∼30,000 newborns tested. Recent data reported here shows that the sulfatide molecular species with an α-hydroxyl, 16‑carbon, mono-unsaturated fatty acyl group (16:1-OH-sulfatide) is superior to the original biomarker 16:0-sulfatide in reducing the number of first-tier false positives.

Retrospective Review of Positive Newborn Screening Results for Isovaleric Acidemia and Development of a Strategy to Improve the Efficacy of Newborn Screening in the UK.

Since the UK commenced newborn screening for isovaleric acidemia in 2015, changes in prescribing have increased the incidence of false positive (FP) results due to pivaloylcarnitine. A review of screening results between 2015 and 2022 identified 24 true positive (TP) and 84 FP cases, with pivalate interference confirmed in 76/84. Initial C5 carnitine (C5C) did not discriminate between FP and TP with median (range) C5C of 2.

Improving newborn screening test performance for metachromatic leukodystrophy: Recommendation from a pre-pilot study that identified a late-infantile case for treatment.

Metachromatic leukodystrophy (MLD) is a devastating rare neurodegenerative disease. Typically, loss of motor and cognitive skills precedes early death. The disease is characterised by deficient lysosomal arylsulphatase A (ARSA) activity and an accumulation of undegraded sulphatide due to pathogenic variants in the ARSA gene.

Correlation enhanced distribution adaptation for prediction of fall risk.

With technological advancements in diagnostic imaging, smart sensing, and wearables, a multitude of heterogeneous sources or modalities are available to proactively monitor the health of the elderly. Due to the increasing risks of falls among older adults, an early diagnosis tool is crucial to prevent future falls. However, during the early stage of diagnosis, there is often limited or no labeled data (expert-confirmed diagnostic information) available in the target domain (new cohort) to determine the proper treatment for older adults.

Harmonizing florbetapir and PiB PET measurements of cortical Aβ plaque burden using multiple regions-of-interest and machine learning techniques: An alternative to the Centiloid approach.

Introduction: Machine learning (ML) can optimize amyloid (Aβ) comparability among positron emission tomography (PET) radiotracers. Using multi-regional florbetapir (FBP) measures and ML, we report better Pittsburgh compound-B (PiB)/FBP harmonization of mean-cortical Aβ (mcAβ) than Centiloid.

Methods: PiB-FBP pairs from 92 subjects in www.

BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging.

Medical imaging-based biomarkers derived from small objects (e.g., cell nuclei) play a crucial role in medical applications.

Self-Supervised Contrastive Learning to Predict the Progression of Alzheimer's Disease with 3D Amyloid-PET.

Early diagnosis of Alzheimer's disease (AD) is an important task that facilitates the development of treatment and prevention strategies, and may potentially improve patient outcomes. Neuroimaging has shown great promise, including the amyloid-PET, which measures the accumulation of amyloid plaques in the brain-a hallmark of AD. It is desirable to train end-to-end deep learning models to predict the progression of AD for individuals at early stages based on 3D amyloid-PET.

Corrections to "STRIDE: Systematic Radar Intelligence Analysis for ADRD Risk Evaluation with Gait Signature Simulation and Deep Learning".

[This corrects the article PMC10399976.].

STRIDE: Systematic Radar Intelligence Analysis for ADRD Risk Evaluation with Gait Signature Simulation and Deep Learning.

Abnormal gait is a significant non-cognitive biomarker for Alzheimer's disease (AD) and AD-related dementia (ADRD). Micro-Doppler radar, a non-wearable technology, can capture human gait movements for potential early ADRD risk assessment. In this research, we propose to design STRIDE integrating micro-Doppler radar sensors with advanced artificial intelligence (AI) technologies.

Harmonizing Healthy Cohorts to Support Multicenter Studies on Migraine Classification using Brain MRI Data.

Multicenter and multi-scanner imaging studies might be needed to provide sample sizes large enough for developing accurate predictive models. However, multicenter studies, which likely include confounding factors due to subtle differences in research participant characteristics, MRI scanners, and imaging acquisition protocols, might not yield generalizable machine learning models, that is, models developed using one dataset may not be applicable to a different dataset. The generalizability of classification models is key for multi-scanner and multicenter studies, and for providing reproducible results.

Use of novel structural features to identify urinary biomarkers during acute kidney injury that predict progression to chronic kidney disease.

Background: A significant barrier to biomarker development in the field of acute kidney injury (AKI) is the use of kidney function to identify candidates. Progress in imaging technology makes it possible to detect early structural changes prior to a decline in kidney function. Early identification of those who will advance to chronic kidney disease (CKD) would allow for the initiation of interventions to halt progression.

Doppler radar remote sensing of respiratory function.

Doppler radar remote sensing of torso kinematics can provide an indirect measure of cardiopulmonary function. Motion at the human body surface due to heart and lung activity has been successfully used to characterize such measures as respiratory rate and depth, obstructive sleep apnea, and even the identity of an individual subject. For a sedentary subject, Doppler radar can track the periodic motion of the portion of the body moving as a result of the respiratory cycle as distinct from other extraneous motions that may occur, to provide a spatial temporal displacement pattern that can be combined with a mathematical model to indirectly assess quantities such as tidal volume, and paradoxical breathing.