This overview describes the goals and objectives of the third conference conducted as part of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) initiative. This third conference was focused on selecting specific paradigms from cognitive neuroscience that measured the constructs identified in the first CNTRICS meeting, with the goal of facilitating the translation of these paradigms into use in clinical trials contexts. To identify such paradigms, we had an open nomination process in which the field was asked to nominate potentially relevant paradigms and to provide information on several domains relevant to selecting the most promising tasks for each construct (eg, construct validity, neural bases, psychometrics, availability of animal models). Our goal was to identify 1-2 promising tasks for each of the 11 constructs identified at the first CNTRICS meeting. In this overview article, we describe the on-line survey used to generate nominations for promising tasks, the criteria that were used to select the tasks, the rationale behind the criteria, and the ways in which breakout groups worked together to identify the most promising tasks from among those nominated. This article serves as an introduction to the set of 6 articles included in this special issue that provide information about the specific tasks discussed and selected for the constructs from each of 6 broad domains (working memory, executive control, attention, long-term memory, perception, and social cognition).
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http://dx.doi.org/10.1093/schbul/sbn163 | DOI Listing |
J Vis
January 2025
Department of Psychology, New York University, New York, NY, USA.
Active object recognition, fundamental to tasks like reading and driving, relies on the ability to make time-sensitive decisions. People exhibit a flexible tradeoff between speed and accuracy, a crucial human skill. However, current computational models struggle to incorporate time.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Background: This study responds to the urgent need for automated and reliable methods to detect cognitive impairments on a large scale. It leverages natural language processing (NLP) techniques to predict dementia and mild cognitive impairment (MCI) using clinical notes from electronic health records (EHR).
Method: Our study used an EHR dataset from Massachusetts General Brigham, which included clinical notes from a 2-year period (2017-2018) covering 12 types of patient encounters.
Background: Objective and sensitive measures of everyday function are needed for accurate clinical diagnosis and evaluation of outcomes in clinical trials for dementia. However, most objective everyday function measures are difficult to administer and have not been validated against biomarkers of Alzheimer's disease (AD) neuropathology. This study evaluated the neuroimaging correlates of a highly sensitive, ecologically valid, and easily implementable performance-based test of function called the Virtual Kitchen Challenge (VKC).
View Article and Find Full Text PDFBackground: Alzheimer's Disease (AD) is characterized by progressive impairment of cognition and memory, including the loss of episodic memory. The use of non-invasive brain stimulation therapies to modulate memory encoding processes is a promising avenue for potential treatment. Previous studies have shown that the use of Transcranial Magnetic Stimulation (TMS) applied to lateral parietal cortex can improve memory in older adults who have received a diagnosis of Mild Cognitive Impairment.
View Article and Find Full Text PDFBackground: Changes in the structure and use of language are well established clinical characteristics of Alzheimer's disease. In recent years, there has been a concerted effort to objectively quantify these changes using the latest advances in Natural Language Processing (NLP) tools. Much academic research has been conducted to evaluate how these speech characteristics change with the course of illness, but they have yet to be elevated beyond exploratory endpoints in trials.
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