Mercy College

About: Mercy College is a education organization based out in Dobbs Ferry, New York, United States. It is known for research contribution in the topics: Occupational therapy & Early childhood education. The organization has 369 authors who have published 622 publications receiving 8010 citations. The organization is also known as: Mercy NY.

Understanding awareness deficits following brain injury

Abstract: The critical role that awareness deficits play in contributing to rehabilitation outcome and independent functioning of brain injured adults is readily acknowledged by rehabilitation professionals. However, there are inconsistencies in the scope of what is included within the concept of awareness and the way in which it is measured within the literature. A comprehensive model of awareness is needed to guide the development of measurement tools and interventions. This paper expands upon concepts originally proposed by Crosson and colleagues [12] and proposes a comprehensive model of awareness that integrates parallel themes in cognitive psychology, social psychology and neuropsychology. It argues that a hierarchical view of awareness does not capture the complexities and subtleties of awareness symptoms and proposes a dynamic relationship between knowledge, beliefs, task demands and context of a situation. A distinction between knowledge and awareness, that one has prior to a task, and that, which is activated during task performance, is emphasized. Suggestions for expanding assessment and intervention methodology are derived from the perspective of research within the areas of metacognition and self-efficacy.

An open resource for transdiagnostic research in pediatric mental health and learning disorders

Abstract: Technological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5–21). The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing fMRI, diffusion MRI, morphometric MRI), electroencephalography, eye-tracking, voice and video recordings, genetics and actigraphy. Here, we present the rationale, design and implementation of HBN protocols. We describe the first data release (n=664) and the potential of the biobank to advance related areas (e.g., biophysical modeling, voice analysis). Machine-accessible metadata file describing the reported data (ISA-Tab format)

An international study of human handedness: the data.

Abstract: Human handedness has been the subject of systematic study since 1646, but there is no agreement among researchers as to who can be considered a left-hander, what is the etiology of left-handedness, or what the proportion of left-handedness is in the world's population. This article reports the results of a handedness survey administered to 12,000 subjects in 17 countries, the largest handedness survey attemped. The paper discusses methods for determining handedness, the probability of a genetic component for handedness, and the relationship of sex, birth order, multiple birth, and first-degree relative's handedness on subject's handedness. A hypothesis for the etiology of left-handedness is presented.

A Noise-Filtered Under-Sampling Scheme for Imbalanced Classification

Abstract: Under-sampling is a popular data preprocessing method in dealing with class imbalance problems, with the purposes of balancing datasets to achieve a high classification rate and avoiding the bias toward majority class examples. It always uses full minority data in a training dataset. However, some noisy minority examples may reduce the performance of classifiers. In this paper, a new under-sampling scheme is proposed by incorporating a noise filter before executing resampling. In order to verify the efficiency, this scheme is implemented based on four popular under-sampling methods, i.e., Undersampling + Adaboost, RUSBoost, UnderBagging, and EasyEnsemble through benchmarks and significance analysis. Furthermore, this paper also summarizes the relationship between algorithm performance and imbalanced ratio. Experimental results indicate that the proposed scheme can improve the original undersampling-based methods with significance in terms of three popular metrics for imbalanced classification, i.e., the area under the curve, ${F}$ -measure, and ${G}$ -mean.