Sex-specific relationships between gray matter volume and executive function in young children with and without prenatal alcohol exposure.

Sex differences in brain volume are well established across ages however, limited research has investigated if sex differences in brain structure associate with early cognitive outcomes. Moreover, associations among sex, brain structure, and cognition in individuals with prenatal alcohol exposure (PAE), the most common known cause of developmental delay in North America, are unclear. Here, we investigated associations between executive function (measured by the BRIEF or BRIEF-P Global Executive Composite (GEC) and the Statue subtest of the NEPSY-II) and volumes of 36 gray matter regions in a longitudinal MRI sample of 169 young children (N=37; 534 total scans) aged 2-8 years.

A Novel Foundation Model-Based Framework for Multimodal Retinal Age Prediction.

Unlabelled: The retinal age gap (RAG; the difference between the retina's biological and chronological age) has recently gained increased attention as a potential image-based, non-invasive, and accessible biomarker for a broad spectrum of ocular and non-ocular diseases. Traditionally, machine learning predictions of biological retinal age utilize convolutional neural network (CNN) architectures and data from color fundus photography (CFP). Despite being previously unexplored, the multimodal fusion of two-dimensional CFP with three-dimensional optical coherence tomography (OCT) data has significant potential to enhance retinal age prediction accuracy and the diagnostic utility of the RAG biomarker.

Biological heart and brain ageing in subjects with cardiovascular diseases.

Introduction: The heart-brain axis hypothesis suggests a bidirectional connection between the brain and the heart with relevant implications in health and disease. Cardiovascular diseases have been empirically linked to an increased risk of neurological diseases. However, it remains unclear to what extent different cardiovascular diseases affect brain health quantitatively across subjects and if that is associated with the extent the heart is affected by a disease.

Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge.

Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse multicentric patient data. To this end, the Federated Tumor Segmentation (FeTS) Challenge represents the paradigm for real-world algorithmic performance evaluation. The FeTS challenge is a competition to benchmark (i) federated learning aggregation algorithms and (ii) state-of-the-art segmentation algorithms, across multiple international sites.

Accounting for population structure in deep learning models for genomic analysis.

Background: Deep learning methods are becoming increasingly popular for genotype analyses in recent years. In conventional genomic analyses, it is important to account for confounders to avoid biasing the results. Genetic relatedness is one of the most common confounders in conventional genomic analyses and there is a general consensus that it should be considered in the analysis to prevent distant levels of common ancestry from affecting the identification of causal variants.

Brain Aging in Patients With Cardiovascular Disease From the UK Biobank.

The brain undergoes complex but normal structural changes during the aging process in healthy adults, whereas deviations from the normal aging patterns of the brain can be indicative of various conditions as well as an increased risk for the development of diseases. The brain age gap (BAG), which is defined as the difference between the chronological age and the machine learning-predicted biological age of an individual, is a promising biomarker for determining whether an individual deviates from normal brain aging patterns. While the BAG has shown promise for various neurological diseases and cardiovascular risk factors, its utility to quantify brain changes associated with diagnosed cardiovascular diseases has not been investigated to date, which is the aim of this study.

Highly efficient homomorphic encryption-based federated learning for diabetic retinopathy classification.

Purpose: Diabetic retinopathy (DR) is the leading cause of blindness among working-age adults globally. Although machine learning (ML) has shown promise for DR diagnosis, ensuring model generalizability requires training on data from diverse populations. Federated learning (FL) offers a potential solution by enabling model training on decentralized datasets.

Mild behavioural impairment-apathy and core Alzheimer's disease cerebrospinal fluid biomarkers.

Apathy is a common neuropsychiatric symptom (NPS) in Alzheimer's disease (AD) but can emerge earlier in prodromal and even preclinical stages as part of mild behavioural impairment (MBI-apathy), a syndrome defined by emergent and persistent NPS. In dementia, apathy is associated with higher morbidity, mortality, and caregiver distress. However, the significance of MBI-apathy in dementia-free persons, including its associations with AD biomarkers, remains unclear.

The retinal age gap: an affordable and highly accessible biomarker for population-wide disease screening across the globe.

Traditional biomarkers, such as those obtained from blood tests, are essential for early disease detection, improving health outcomes and reducing healthcare costs. However, they often involve invasive procedures, specialized laboratory equipment or special handling of biospecimens. The retinal age gap (RAG) has emerged as a promising new biomarker that can overcome these limitations, making it particularly suitable for disease screening in low- and middle-income countries.

Dimensionality reduction in 3D causal deep learning for neuroimage generation: an evaluation study.

Purpose: Causal deep learning (DL) using normalizing flows allows the generation of true counterfactual images, which is relevant for many medical applications such as explainability of decisions, image harmonization, and in-silico studies. However, such models are computationally expensive when applied directly to high-resolution 3D images and, therefore, require image dimensionality reduction (DR) to efficiently process the data. The goal of this work was to compare how different DR methods affect counterfactual neuroimage generation.

Using machine learning to investigate the influence of the prenatal chemical exposome on neurodevelopment of young children.

Research investigating the prenatal chemical exposome and child neurodevelopment has typically focused on a limited number of chemical exposures and controlled for sociodemographic factors and maternal mental health. Emerging machine learning approaches may facilitate more comprehensive examinations of the contributions of chemical exposures, sociodemographic factors, and maternal mental health to child neurodevelopment. A machine learning pipeline that utilized feature selection and ranking was applied to investigate which common prenatal chemical exposures and sociodemographic factors best predict neurodevelopmental outcomes in young children.

Structural network measures reveal the emergence of heavy-tailed degree distributions in lottery ticket multilayer perceptrons.

Artificial neural networks (ANNs) were originally modeled after their biological counterparts, but have since conceptually diverged in many ways. The resulting network architectures are not well understood, and furthermore, we lack the quantitative tools to characterize their structures. Network science provides an ideal mathematical framework with which to characterize systems of interacting components, and has transformed our understanding across many domains, including the mammalian brain.

Quantifying the relationship between cell proliferation and morphology during development of the face.

Morphogenesis requires highly coordinated, complex interactions between cellular processes: proliferation, migration and apoptosis, along with physical tissue interactions. How these cellular and tissue dynamics drive morphogenesis remains elusive. Three dimensional (3D) microscopic imaging holds great promise, and generates elegant images, but generating even moderate throughput for quantified images is challenging for many reasons.

Towards realistic simulation of disease progression in the visual cortex with CNNs.

Convolutional neural networks (CNNs) and mammalian visual systems share architectural and information processing similarities. We leverage these parallels to develop an in-silico CNN model simulating diseases affecting the visual system. This model aims to replicate neural complexities in an experimentally controlled environment.

Self-supervised identification and elimination of harmful datasets in distributed machine learning for medical image analysis.

Distributed learning enables collaborative machine learning model training without requiring cross-institutional data sharing, thereby addressing privacy concerns. However, local quality control variability can negatively impact model performance while systematic human visual inspection is time-consuming and may violate the goal of keeping data inaccessible outside acquisition centers. This work proposes a novel self-supervised method to identify and eliminate harmful data during distributed learning model training fully-automatically.

Csec-net: a novel deep features fusion and entropy-controlled firefly feature selection framework for leukemia classification.

Leukemia, a life-threatening form of cancer, poses a significant global health challenge affecting individuals of all age groups, including both children and adults. Currently, the diagnostic process relies on manual analysis of microscopic images of blood samples. In recent years, machine learning employing deep learning approaches has emerged as cutting-edge solutions for image classification problems.

Application of a localized morphometrics approach to imaging-derived brain phenotypes for genotype-phenotype associations in pediatric mental health and neurodevelopmental disorders.

Introduction: Quantitative global or regional brain imaging measurements, known as imaging-specific or -derived phenotypes (IDPs), are commonly used in genotype-phenotype association studies to explore the genomic architecture of the brain and how it may be affected by neurological diseases (e.g., Alzheimer's disease), mental health (e.

Enhanced Reader Confidence and Differentiation of Calcification from Cerebral Microbleed Diagnosis Using QSM Relative to SWI.

Purpose: Accurate detection of cerebral microbleeds (CMBs) is important for detection of multiple conditions. However, CMBs can be challenging to identify on MR images, especially for distinguishing CMBs from the mimic of calcification. We performed a comparative reader study to assess the diagnostic performance of two primary MR sequences for differentiating CMBs from calcification.

Where, why, and how is bias learned in medical image analysis models? A study of bias encoding within convolutional networks using synthetic data.

Background: Understanding the mechanisms of algorithmic bias is highly challenging due to the complexity and uncertainty of how various unknown sources of bias impact deep learning models trained with medical images. This study aims to bridge this knowledge gap by studying where, why, and how biases from medical images are encoded in these models.

Methods: We systematically studied layer-wise bias encoding in a convolutional neural network for disease classification using synthetic brain magnetic resonance imaging data with known disease and bias effects.

A cross-attention-based deep learning approach for predicting functional stroke outcomes using 4D CTP imaging and clinical metadata.

Acute ischemic stroke (AIS) remains a global health challenge, leading to long-term functional disabilities without timely intervention. Spatio-temporal (4D) Computed Tomography Perfusion (CTP) imaging is crucial for diagnosing and treating AIS due to its ability to rapidly assess the ischemic core and penumbra. Although traditionally used to assess acute tissue status in clinical settings, 4D CTP has also been explored in research for predicting stroke tissue outcomes.

Analysis and visualization of the effect of multiple sclerosis on biological brain age.

Introduction: The rate of neurodegeneration in multiple sclerosis (MS) is an important biomarker for disease progression but can be challenging to quantify. The brain age gap, which quantifies the difference between a patient's chronological and their estimated biological brain age, might be a valuable biomarker of neurodegeneration in patients with MS. Thus, the aim of this study was to investigate the value of an image-based prediction of the brain age gap using a deep learning model and compare brain age gap values between healthy individuals and patients with MS.

HarmonyTM: multi-center data harmonization applied to distributed learning for Parkinson's disease classification.

Purpose: Distributed learning is widely used to comply with data-sharing regulations and access diverse datasets for training machine learning (ML) models. The traveling model (TM) is a distributed learning approach that sequentially trains with data from one center at a time, which is especially advantageous when dealing with limited local datasets. However, a critical concern emerges when centers utilize different scanners for data acquisition, which could potentially lead models to exploit these differences as shortcuts.

Foundation model-driven distributed learning for enhanced retinal age prediction.

Objectives: The retinal age gap (RAG) is emerging as a potential biomarker for various diseases of the human body, yet its utility depends on machine learning models capable of accurately predicting biological retinal age from fundus images. However, training generalizable models is hindered by potential shortages of diverse training data. To overcome these obstacles, this work develops a novel and computationally efficient distributed learning framework for retinal age prediction.

Cerebroventricular deformation and vector mapping, a topographic visualizer for surgical interventions in pediatric hydrocephalus.

Objective: Hydrocephalus is a challenging neurosurgical condition due to nonspecific symptoms and complex brain-fluid pressure dynamics. Typically, the assessment of hydrocephalus in children requires radiographic or invasive pressure monitoring. There is usually a qualitative focus on the ventricular spaces even though stress and shear forces extend across the brain.