Generative models of MRI-derived neuroimaging features and associated dataset of 18,000 samples.

Availability of large and diverse medical datasets is often challenged by privacy and data sharing restrictions. For successful application of machine learning techniques for disease diagnosis, prognosis, and precision medicine, large amounts of data are necessary for model building and optimization. To help overcome such limitations in the context of brain MRI, we present GenMIND: a collection of generative models of normative regional volumetric features derived from structural brain imaging.

Accelerated Brain Aging Mediates the Association Between Psychological Profiles and Clinical Pain in Knee Osteoarthritis.

Chronic pain is driven by factors across the biopsychosocial spectrum. Previously, we demonstrated that magnetic resonance images (MRI)-based brain-predicted age differences (brain-PAD: brain-predicted age minus chronological age) were significantly associated with pain severity in individuals with chronic knee pain. We also previously identified four distinct, replicable, multidimensional psychological profiles significantly associated with clinical pain.

Brain-predicted age difference estimated using DeepBrainNet is significantly associated with pain and function-a multi-institutional and multiscanner study.

Brain age predicted differences (brain-PAD: predicted brain age minus chronological age) have been reported to be significantly larger for individuals with chronic pain compared with those without. However, a debate remains after one article showed no significant differences. Using Gaussian Process Regression, an article provides evidence that these negative results might owe to the use of mixed samples by reporting a differential effect of chronic pain on brain-PAD across pain types.

Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization.

Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.

Applications of generative adversarial networks in neuroimaging and clinical neuroscience.

Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to the broader family of generative methods, which learn to generate realistic data with a probabilistic model by learning distributions from real samples. In the clinical context, GANs have shown enhanced capabilities in capturing spatially complex, nonlinear, and potentially subtle disease effects compared to traditional generative methods.

Benchmarking the generalizability of brain age models: Challenges posed by scanner variance and prediction bias.

Machine learning has been increasingly applied to neuroimaging data to predict age, deriving a personalized biomarker with potential clinical applications. The scientific and clinical value of these models depends on their applicability to independently acquired scans from diverse sources. Accordingly, we evaluated the generalizability of two brain age models that were trained across the lifespan by applying them to three distinct early-life samples with participants aged 8-22 years.

Deep Generative Medical Image Harmonization for Improving Cross-Site Generalization in Deep Learning Predictors.

Background: In the medical imaging domain, deep learning-based methods have yet to see widespread clinical adoption, in part due to limited generalization performance across different imaging devices and acquisition protocols. The deviation between estimated brain age and biological age is an established biomarker of brain health and such models may benefit from increased cross-site generalizability.

Purpose: To develop and evaluate a deep learning-based image harmonization method to improve cross-site generalizability of deep learning age prediction.

MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide.

Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer's disease, have also been identified using machine learning.

A Multidimensional Neural Maturation Index Reveals Reproducible Developmental Patterns in Children and Adolescents.

Adolescence is a time of extensive neural restructuring, leaving one susceptible to atypical development. Although neural maturation in humans can be measured using functional and structural MRI, the subtle patterns associated with the initial stages of abnormal change may be difficult to identify, particularly at an individual level. Brain age prediction models may have utility in assessing brain development in an individualized manner, as deviations between chronological age and predicted brain age could reflect one's divergence from typical development.

Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan.

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3-96 years old).

Problem-centric organization and visualization of patient imaging and clinical data.

A patient's electronic medical record contains a large amount of unstructured textual information. As patient records become increasingly dense owing to an aging population and increased occurrence of chronic diseases, a tool is needed to help organize and navigate patient data in a way that facilitates a clinician's ability to understand this information and that improves efficiency. A system has been developed for physicians that summarizes clinical information from a patient record.

A tool for improving the longitudinal imaging characterization for neuro-oncology cases.

We describe the development of a prototype tool for the construction of longitudinal cases studies that can be used for teaching files, construction of clinical databases, and for patient education. The test domain is neuro-oncology. The features of the tool include: 1) natural language processing tools to assist structuring report information; 2) integration of imaging data; 3) integration of drug information; 4) target data model that includes the dimensions of space, time, existence, and causality; 5) user interface that provides three levels of information including overview, filtered summarization, and details on demand.

A normalized lexical lookup approach to identifying UMLS concepts in free text.

The National Library of Medicine has developed a tool to identify medical concepts from the Unified Medical Language System in free text. This tool - MetaMap (and its java version MMTx) has been used extensively for biomedical text mining applications. We have developed a module for MetaMap which has a high performance in terms of processing speed.

A field theoretical approach to medical natural language processing.

A parser for medical free text reports has been developed that is based on a chemistry/physics inspired "field theory" for word-word sentence-level dependencies. The transition from the linguistic world to the world of interacting particles with potential energies is guided by a psycholinguistics thought experiment related to the amount of "work" required to bring a reference word into an anchored configuration of words. Calibration experiments involving four and five grams were conducted.

Identifying relevant medical reports from an assorted report collection using the multinomial naïve Bayes classifier and the UMLS.

A patient's electronic medical record contains a large number of medical reports and imaging studies. Identifying the relevant information in order to make a diagnosis can be a time consuming process that can easily overwhelm the physician. Summarizing key clinical information for physicians evaluating brain tumor patients is an ongoing research project at our institution.

A study of lexical behaviour of sentences in chest radiology reports.

Statistical natural language processors have been the focus of much research during the past decade. The UCLA Medical Imaging Informatics Group (MII) has developed a statistical NLP for the domain of radiology. We report a study of syntactic and semantic behaviour of sentences in the domain of chest radiology.

Indexing anatomical phrases in neuro-radiology reports to the UMLS 2005AA.

This work describes a methodology to index anatomical phrases to the 2005AA release of the Unified Medical Language System (UMLS). A phrase chunking tool based on Natural Language Processing (NLP) was developed to identify semantically coherent phrases within medical reports. Using this phrase chunker, a set of 2,551 unique anatomical phrases was extracted from brain radiology reports.

A Study of Substance non-use.

120 persons belonging to the four different groups namely, students, unskilled workers, skilled workers and professionals were interviewed using a semi-structured interview schedule on subsunce non-use. Results were analysed using SPSS 7.5 version.

Risperidone in Indian patients with schizophrenia.

Conventional antipsychotic agents are not effective against negative symptoms of schizophrenia and are also noted for their extrapyramidal side effects. Risperidone is a noval antipsychotic agent whose dual antagonism of dopamine and serotonin receptors is believed to underlie its efficacy against negative symptoms and the low incidence of extrapyramidal side effects. An open, non-comparative study of seven weeks duration was performed to evaluate risperidone in the treatment of schizophrenia in Indian patients.

Multivariate analysis of drinking behaviour in a rural population.

This study was carried out to find out the drinking pattern in a rural population, using multivariate techniques. 386 current users identified in a community were assessed with regard to their drinking behaviours using a structured interview. For purposes of the study the questions were condensed into 46 meaningful variables.

The nonspecific symptom screening method a replication study.

Methods have been developed to screen non psychotic disorders in general health clinics based only on non-specific and somatic symptoms. One such method developed in India was applied in a heterogenous group of patients in a different clinical setting. The validity of nonspecific symptom screening method for non psychotic illness was replicated in this study.