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Special issue of JINS focuses on preclinical predictions in psychiatric and neurological conditions
Psychology has long been driven by the urge to understand how to accurately predict behaviors, and since the turn of the twenty-first century, the numbers of research studies focused on prediction of diagnosis and clinical trajectories has accelerated substantially indicating the extent of the academic and medical interest in this field.
Clinicians are now making ever-greater steps towards building complex diagnostic models for preclinical prediction across a range of psychiatric and neurological conditions, many of which are outlined in a new special issue of the Journal of the International Neuropsychological Society (JINS).
This special issue comprises nine papers describing cutting-edge findings and key methodological advances for preclinical detection. These outline a variety of models and strategies for improving preclinical prediction, with focuses including multiple sclerosis (MS), Alzheimer's disease, and schizophrenia.
In the issue, Lancaster and colleagues demonstrate how longitudinal changes in episodic memory functioning can be predicted, by using baseline diffusion tensor imaging of white matter micro-structure in the medial temporal lobe. Koscik and colleagues compare sensitivity for predicting subsequent cognitive impairment, using either variability in performance of cognitive tasks, or combinations of outcomes from particular tasks.
Seidman and colleagues have developed methods to ascertain the predictability of schizophrenia, finding that working memory impairment was more robust than vigilance for characterizing the cognitive impairment associated with familial risk for schizophrenia.
Finally, two articles in the issue focus on the use of prediction strategies for prognosticating outcome after brain injury has already occurred in pediatric samples; Ransom and colleagues used evidence-based assessment to identify teenage students who are at-risk for post-concussive academic difficulty, while Till and colleagues developed prediction methods for multiple sclerosis (MS) in children by studying the cognitive, academic and psychosocial difficulties experienced by children who had previously received a diagnosis for acquired demyelinating syndrome (ADS).
These papers add to a growing field of research on preclinical prediction, and future studies promise to contribute to improvement in preventative treatments before cognitive decline occurs, as well as to more effective treatments and allocation of resources following brain injury.
The entire special issue is available for free to all until January 31, 2017 at cambridge.org/INSpress.
Notes to editors
For further information, please contact Joon Won Moon via email@example.com.
About the Journal of the International Neuropsychological Society (JINS)
JINS is the official journal of the International Neuropsychological Society, an organization of over 4,700 international members from a variety of disciplines. Our editorial board is comprised of internationally known experts with a broad range of interests. JINS publishes empirically-based articles covering all areas of neuropsychology and the interface of neuropsychology with other areas, such as cognitive neuroscience. Theoretically driven work that has clinical implications is of particular interest.
For further information, go to cambridge.org/ins.
About Cambridge University Press
Cambridge University Press is part of the University of Cambridge. It furthers the University's mission by disseminating knowledge in the pursuit of education, learning and research at the highest international levels of excellence.
Its extensive peer-reviewed publishing lists comprise 50,000 titles covering academic research and professional development, as well as school-level education and English language teaching.
Playing a leading role in today's international marketplace, Cambridge University Press has more than 50 offices around the globe, and it distributes its products to nearly every country in the world.
For further information, go to www.cambridge.org.