2 CE Credits. Special Issue on Prediction (JINS 22:10, 2016): CE Bundle 3

apa-logo_white_screenThe International Neuropsychological Society is approved by the American Psychological Association to sponsor continuing education for psychologists. The International Neuropsychological Society maintains responsibility for this program and its content.
Educational Objectives
  1. Be able to describe cognitive, academic, and psychosocial outcomes after an incident demyelinating event in childhood.
  2. Describe the role of white matter integrity in the maintenance of episodic memory.
  3. List biomarkers associated with prodromal Parkinson’s Disease.

Course Information
Target Audience:Intermediate
Availability:Date Available: 2016-11-30
You may obtain CE for this JINS package at any time.
Offered for CEYes
CostMembers $20
Non-Members $30
Refund PolicyThis JINS package is not eligible for refunds
CE Credits2.0

John L. Woodard, Larry J. Seidman, Julie C. Stout


Prediction of behavior and related outcomes has long been a principal goal of psychology. The development of intelligence tests in the early 20th century led to new strategies for predicting academic and occupational performance (Sternberg, 1996). Similarly, in the mid-19th century, Fechner’s use of mathematical models for prediction of behavior (Wertheimer, 1987) initiated a long tradition that has continued to the present day, which can be seen in the mathematical models of learning developed by Clark Hull (Hull, 1943), dynamic models of the development of spatial memory abilities (Spencer, Smith, & Thelen, 2001), and others. These ideas also led to information processing theories of memory (Atkinson & Shiffrin, 1968; Raaijmakers & Shiffrin, 1980), which have been used to predict specific stages of memory processing that are impaired in patient groups (Brown et al., 1995; Brown, Woodard, & Rich, 1994). Recently, use of prediction strategies in neuropsychology has turned toward development of new approaches for identification and characterization of the development and disease course of neurocognitive and neuropsychiatric disorders.

Prediction involves making a statement about an event that is uncertain, typically based on some type of known information. Although the term “prediction” brings to mind the forecasting of some future outcome or event, the term prediction can also be used for concurrent diagnostic purposes. Since approximately 2000, the number of research studies focused on prediction of diagnosis and clinical trajectories has increased dramatically. Figure 1 shows the number of publications per year returned from a simple search of the terms “preclinical prediction” in the National Library of Medicine (PubMed) database from 1950 to October 1, 2016. Studies of preclinical prediction in neuropsychology have typically focused on identification of persons at the highest risk for specific cognitive conditions. Early identification of risk, or of subtle signs of incipient disease, opens the possibility for treatments to prevent disease development, or to delay onset or slow progression of clinically significant symptoms. Prediction studies after brain injury have also been important for prognosticating the trajectory of recovery and for planning resource allocation for treatment.

State-of-the-art prediction strategies have been facilitated by at least two significant developments over the past 25 years. First, the availability of high-speed computers and software capable of performing complex statistical analyses has supported the development and validation of complex theoretical predictive models. For instance, machine learning approaches to prediction would not be possible without powerful computing resources (Hey, 2010). Machine learning has been successfully used in the context of diagnosis (Bigler, 2013; Mundt, Freed, & Greist, 2000; Teipel, Meindl, Grinberg, Heinsen, & Hampel, 2008) and prognosis (Gutman et al., 2015; Koutsouleris et al., 2009; Moradi, Pepe, Gaser, Huttunen, & Tohka, 2015; Schmidt-Richberg et al., 2016). Second, the identification of biomarkers of a variety of neurological and psychiatric conditions has provided a set of predictors that are highly sensitive to risk factors and pathological changes leading to these conditions (Chong, Lim, & Sahadevan, 2006; Craig-Schapiro, Fagan, & Holtzman, 2009; Mayeux, 2004; Sharma & Laskowitz, 2012; Shaw, Korecka, Clark, Lee, & Trojanowski, 2007). Such biomarkers can be used to test predictions regarding possible etiologies associated with neuropsychological abnormalities (Ivanoiu et al., 2015; Miller et al., 2008; Wirth et al., 2013). Improved genetic testing has also contributed to more accurate predictions of functional changes when combined with neuropsychological and imaging data (O’Hara et al., 1998; Reiman et al., 2004; Small et al., 1996). Some researchers have argued, based on the literature, that “neuromarkers often provide better predictions (neuroprognosis), alone or in combination with other measures, than traditional behavioral measures” (page 11, Gabrieli, Ghosh, & Whitfield-Gabrieli, 2015). As data increasingly emerge, our field will be enriched by identifying the best combinations of measures to predict disease.

Many neurocognitive disorders are known to evolve from a presymptomatic to mildly clinical state to a fully clinical disorder. In essence, they go through different biological and clinical “stages” (McGorry et al., 2007). Two disorders receiving a great deal of research over the past two decades are Alzheimer’s disease (AD) and schizophrenia. For example, AD is preceded by mild cognitive impairment (MCI), and in many cases schizophrenia is preceded by a clinical high-risk (CHR) state identified by attenuated positive psychotic symptoms (i.e., mild delusions and hallucinations with some degree of intact reality testing; Tsuang et al., 2013; Yung & McGorry, 1996). At least in principle, identifying predictors and mechanisms of transition to AD or to psychosis among individuals showing signs of incipient neurocognitive disorders are critical steps in the search for preventive or early intervention strategies (Woodberry, Shapiro, Bryant, & Seidman, 2016). Interest in early detection and prevention of schizophrenia and other psychotic disorders has led to more than a decade of work studying young people who may be at risk of developing a psychotic illness, and advances have been made in prediction of transition to psychosis from a CHR stage (Cannon et al., 20082016; Carrion et al., 2016), including the usage of neuropsychological measures (Giuliano et al., 2012; Seidman, Giuliano, & Walker, 2010; Seidman et al., 2016).

Studies investigating early detection of neurological and psychiatric conditions have improved understanding of etiology and diagnosis significantly, and they have opened new avenues for management. Presymptomatic detection is also essential to the development of effective intervention strategies, as it provides a window for preventing/delaying onset or reducing severity. This special issue of the Journal of the International Neuropsychological Society includes nine papers describing cutting-edge empirical findings that exemplify key methodological advances for preclinical detection of a variety of neurological, neurodevelopmental, and neuropsychiatric conditions. Methodological approaches taken include assessment of familial and genetic risk analyses, phenotypic characterization using cognitive and/or imaging methods, and evaluation of biomarker effectiveness. These papers provide substantive integrative and synthetic summaries of the current status of preclinical detection methodologies and future directions for the field.

As novel biomarkers of early, disease-related changes are identified, strategies for making optimal use of this information are becoming increasingly important. In this special issue, several papers focus on description of novel methodologies for combining biomarker data with other clinical information for diagnosis or prognosis. In a thorough literature review, Cooper and colleagues describe state-of-the-art objective biomarkers in prodromal Parkinson’s disease (PD), and they discuss several strategies for combining these biomarkers with clinical and genetic data for improving sensitivity and specificity for identification of persons with prodromal PD. Soldan and colleagues demonstrate that beta-amyloid and phosphorylated tau measured in cerebrospinal fluid can predict cognitive functioning as long as 10 years later. Using data from the Alzheimer’s Disease Neuroimaging Initiative, Edmonds and colleagues demonstrate how a novel method for staging preclinical AD using amyloid positron emission tomography (PET) imaging can be combined with detailed cognitive assessment to better characterize preclinical AD. Notably, this study found that considerable amyloid accumulation had already occurred before clinical diagnosis. Finally, in a cross-sectional study, Quenon and colleagues demonstrate the relationships between imaging measures of extent of early AD neuropathology, as indexed by in vivo neuroimaging biomarkers (amyloid PET, hippocampal volume, and measures of cortical thickness) and level of memory performance on the Free and Cued Selective Reminding Test. Although biomarkers of early AD neuropathology predicted overall memory performance, cueing efficiency, which is frequently impaired in AD, demonstrated particularly strong relationships with cortical thickness of regions that are commonly atrophic in early AD.

Assessment of the influence of familial and genetic risk factors has also become an important tool for forecasting diagnostic status. The family high-risk approach allows a defined selection process for ascertaining non-ill subjects in a family in which there is an identified proband with the illness. An advantage of such an approach is that it is not dependent on symptom expression, but rather genetic risk, and thus an unaffected individual could be studied at any age, enabling developmentally guided probes of risk (Agnew-Blais & Seidman, 2013). The “unaffected relatives” are typically offspring or siblings who are considered to be at higher risk for the illness or for phenotypes associated with the illness, because they carry approximately 50% of the genes for the illness. This approach has been used for over half a century, and has been one of the most fruitful ways of identifying components of the vulnerability to various illnesses, particularly schizophrenia. The most typical outcome used in many of these studies originally was “developing the illness” (e.g., schizophrenia, AD, etc.). However, outcomes can also be expressed in a range of phenotypes reflecting the underlying disorder, and outcomes such as functional disability are also very important. A wide range of phenotypes (e.g., working memory or attention problems, smaller hippocampi) can be studied at different ages to evaluate developmental effects, and in different sub-populations (e.g., those with higher vs. lower genetic loading) to study the specific subgroup expression of the phenotypes.

In this issue, Lancaster and colleagues demonstrate that baseline diffusion tensor imaging of the white matter microstructure in the medial temporal lobe can predict longitudinal changes in episodic memory functioning over 3 years in a sample of cognitively healthy older adults with an enriched familial and genetic risk for AD. Koscik and colleagues compare sensitivity for predicting subsequent cognitive impairment using either variability in performance across cognitive tasks or combinations of outcomes from particular tasks (e.g., memory and executive tasks) taken at baseline several years earlier. In an investigation of neuropsychological endophenotypes of familial risk for schizophrenia and affective psychosis, Seidman and colleagues found that working memory impairment was more robust than vigilance for characterizing the cognitive impairment associated with familial risk for schizophrenia. Although persons with familial risk for affective psychosis showed more impaired vigilance relative to other groups, this effect was eliminated after adjustment for several psychopathological symptoms. This work was part of an agenda to identify the most sensitive and specific neuropsychological predictors of risk for different forms of psychosis (see also Seidman et al., 2016). Each of these studies demonstrates novel methodologies for studying the influence of familial and genetic risk for possible diagnosis and prognosis.

Finally, two articles in this issue focus on the use of prediction strategies for prognosticating outcome after brain injury has already occurred in pediatric samples. Ransom and colleagues use evidence-based assessment (EBA) to identify teenage students who are at-risk for post-concussive academic difficulty. Self-reported post-injury symptoms and executive functioning difficulty, rather than parent-reported sequelae, showed the strongest relationships with perceived post-injury academic difficulties. This study demonstrates the utility of the EBA framework within the context of neuropsychological assessment. Till and colleagues studied cognitive, academic, and psychosocial difficulties experienced by children diagnosed with an acquired demyelinating syndrome (ADS), one-third of whom were later diagnosed with multiple sclerosis (MS), over a 6-month follow-up period. Children with ADS were shown to demonstrate a favorable neurocognitive outcome in the short-term, including children diagnosed with MS.

In summary, the papers in this Special Issue present several novel approaches toward developing methodologies for prediction in neuropsychology. Research on optimizing the information obtained from biomarkers will undoubtedly continue to be stimulated by the identification of new biomarkers in the future. Introduction of new frameworks for assessment, such as EBA, and other strategies for evaluating longitudinal cognitive, clinical, and neuroimaging changes in outcome, as presented by several studies in this special issue, will also be helpful for moving the field forward. Capitalization on new developments in genetic analyses and assessment of familial risk factors will also be important tools for improving predictive accuracy.

Nevertheless, we also face challenges with respect to definition of appropriate statistical models used for assessment of change, growth, or decline (Cronbach & Furby, 1970; Francis, Fletcher, Stuebing, Davidson, & Thompson, 1991; Gottman & Rushe, 1993; Harrell, 2015; Singer & Willett, 2003; Steyerberg, 2009; Steyerberg & Harrell, 2016; Steyerberg et al., 2010; Temkin, Heaton, Grant, & Dikmen, 1999). While these issues are certainly not new, continued focus on improving definitions of the change that we are predicting, and on models for assessing the effectiveness of variables predicting this change, are certainly warranted. Despite these challenges, research on preclinical prediction continues to grow, 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.

Individual Titles, Authors, and Articles:

Diffusion Tensor Imaging Predictors of Episodic Memory Decline in Healthy Elders at Genetic Risk for Alzheimer’s Disease
  • Melissa A. Lancaster | Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, Illinois
  • Michael Seidenberg | Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, Illinois
  • J. Carson Smith | Department of Kinesiology, University of Maryland, College Park, Maryland
  • Kristy A. Nielson | Department of Psychology, Marquette University, Milwaukee, Wisconsin, Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
  • John L. Woodard | Department of Psychology, Wayne State University, Detroit, Wisconsin
  • Sally Durgerian | Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
  • Stephen M. Rao | Neurological Institute, Cleveland Clinic, Cleveland, Ohio

E-mail address | raos2@ccf.org

The authors declare no conflict of interest.


White matter (WM) integrity within the mesial temporal lobe (MTL) is important for episodic memory (EM) functioning. The current study investigated the ability of diffusion tensor imaging (DTI) in MTL WM tracts to predict 3-year changes in EM performance in healthy elders at disproportionately higher genetic risk for Alzheimer’s disease (AD).


Fifty-one cognitively intact elders (52% with family history (FH) of dementia and 33% possessing an Apolipoprotein E ε4 allelle) were administered the Rey Auditory Verbal Learning Test (RAVLT) at study entry and at 3-year follow-up. DTI scanning, conducted at study entry, examined fractional anisotropy and mean, radial and axial diffusion within three MTL WM tracts: uncinate fasciculus (UNC), cingulate-hippocampal (CHG), and fornix-stria terminalis (FxS). Correlations were performed between residualized change scores computed from RAVLT trials 1–5, immediate recall, and delayed recall scores and baseline DTI measures; MTL gray matter (GM) and WM volumes; demographics; and AD genetic and metabolic risk factors.


Higher MTL mean and axial diffusivity at baseline significantly predicted 3-year changes in EM, whereas baseline MTL GM and WM volumes, FH, and metabolic risk factors did not. Both ε4 status and DTI correlated with change in immediate recall.


Longitudinal EM changes in cognitively intact, healthy elders can be predicted by disruption of the MTL WM microstructure. These results are derived from a sample with a disproportionately higher genetic risk for AD, suggesting that the observed WM disruption in MTL pathways may be related to early neuropathological changes associated with the preclinical stage of AD. (JINS, 2016,22, 1005–1015)

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Cognitive and Behavioral Functioning in Childhood Acquired Demyelinating Syndromes
  • Christine Till | Neurosciences and Mental Health Program, The Hospital for Sick Children, Toronto, Canada, Department of Psychology, York University, Toronto, Canada
  • Austin Noguera | Neurosciences and Mental Health Program, The Hospital for Sick Children, Toronto, Canada
  • Leonard H. Verhey | Neurosciences and Mental Health Program, The Hospital for Sick Children, Toronto, Canada
  • Julia O’Mahony | Neurosciences and Mental Health Program, The Hospital for Sick Children, Toronto, Canada, Institute of Healthy Policy, Management, and Evaluation, University of Toronto, Toronto, Canada
  • E. Ann Yeh | Neurosciences and Mental Health Program, The Hospital for Sick Children, Toronto, Canada, Division of Neurology, The Hospital for Sick Children, Toronto, Canada
  • Jean K. Mah | Division of Neurology, Alberta Children’s Hospital, Calgary, Alberta
  • Katia J. Sinopoli | Neurosciences and Mental Health Program, The Hospital for Sick Children, Toronto, Canada
  • Brian L. Brooks | Neurosciences program, Alberta Children’s Hospital; Departments of Pediatrics, Clinical Neurosciences, and Psychology, University of Calgary; and Alberta Children’s Hospital Research Institute, Calgary, Alberta, Canada
  • Berengere Aubert-Broche | McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
  • D. Louis Collins | McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
  • Sridar Narayanan | McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
  • Douglas L. Arnold | McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
  • Brenda L. Banwell | Neurosciences and Mental Health Program, The Hospital for Sick Children, Toronto, Canada, Children’s Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania

E-mail address | ctill@yorku.ca

None to report related to the current study. Dr. Banwell serves as a centralized MRI reviewer for Novartis, and as an unpaid advisor regarding clinical trials for Novartis, Teva Neuroscience, Biogen Idec, and Sanofi. Dr. Banwell is a senior editor for Multiple Sclerosis and Related Disorders. Dr. Brooks declares the following potential conflicts of interest: he receives royalties for sales of a pediatric memory battery [Sherman, E.M.S. and Brooks, B.L. (2015). Child and Adolescent Memory Profile. Lutz, FL; Psychological Assessment Resources Inc.], a pediatric performance validity test [Sherman, E.M.S. and Brooks, B.L. (in press); Memory Validity Profile. Lutz, FL; Psychological Assessment Resources, Inc.], and a pediatric textbook [Sherman, E.M.S. and Brooks, B.L.; Eds. (2012), Pediatric Forensic Neuropsychology. Oxford University Press]. Dr. Collins receives consulting from NeuroRx.


The aim of this study was to describe cognitive, academic, and psychosocial outcomes after an incident demyelinating event (acquired demyelinating syndromes, ADS) in childhood and to investigate the contribution of brain lesions and confirmed MS diagnosis on outcome.


Thirty-six patients with ADS (mean age=12.2 years,SD=2.7, range: 7–16 years) underwent brain MRI scans at presentation and at 6-months follow-up. T2-weighted lesions on MRI were assessed using a binary classification. At 6-months follow-up, patients underwent neuropsychological evaluation and were compared with 42 healthy controls.


Cognitive, academic, and behavioral outcomes did not differ between the patients with ADS and controls. Three of 36 patients (8.3%) were identified with cognitive impairment, as determined by performance falling ≤1.5SDbelow normative values on more than four independent tests in the battery. Poor performance on a visuomotor integration task was most common, observed among 6/32 patients, but this did not differ significantly from controls. Twelve of 36 patients received a diagnosis of MS within 3 years post-ADS. Patients with MS did not differ from children with monophasic ADS in terms of cognitive performance at the 6-months follow-up. Fatigue symptoms were reported in 50% of patients, irrespective of MS diagnosis. Presence of brain lesions at onset and 6 months post-incident demyelinating event did not associate with cognitive outcome.


Children with ADS experience a favorable short-term neurocognitive outcome, even those confirmed to have MS. Longitudinal evaluations of children with monophasic ADS and MS are required to determine the possibility of late-emerging sequelae and their time course. (JINS, 2016,22, 1050–1060)

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Biomarkers in Prodromal Parkinson Disease: a Qualitative Review
  • Christine A. Cooper | Parkinson’s Disease and Movement Disorders Center, Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania
  • Lama M. Chahine | Parkinson’s Disease and Movement Disorders Center, Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania


This research received no specific grant from any funding agency, commercial or not-for-profit sectors. Dr. Cooper has no conflicts of interest and no disclosures. Dr. Chahine has no conflicts of interest. Dr. Chahine (i) receives support from the NIH (P50 NS053488); (ii) receives support from the Michael J. Fox Foundation as site Principal Investigator of the Parkinson’s Progression Marker’s Initiative and (iii) receives royalties from Wolters Kluwel (for book authorship).


Over the past several years, the concept of prodromal Parkinson disease (PD) has been increasingly recognized. This term refers to individuals who do not fulfill motor diagnostic criteria for PD, but who have clinical, genetic, or biomarker characteristics suggesting risk of developing PD in the future. Clinical diagnosis of prodromal PD has low specificity, prompting the need for objective biomarkers with higher specificity. In this qualitative review, we discuss objectively defined putative biomarkers for PD and prodromal PD.


We searched Pubmed and Embase for articles pertaining to objective biomarkers for PD and their application in prodromal cohorts. Articles were selected based on relevance and methodology.

Key Findings:

Objective biomarkers of demonstrated utility in prodromal PD include ligand-based imaging and transcranial sonography. Development of serum, cerebrospinal fluid, and tissue-based biomarkers is underway, but their application in prodromal PD has yet to meaningfully occur. Combining objective biomarkers with clinical or genetic prodromal features increases the sensitivity and specificity for identifying prodromal PD.


Several objective biomarkers for prodromal PD show promise but require further study, including their application to and validation in prodromal cohorts followed longitudinally. Accurate identification of prodromal PD will likely require a multimodal approach. (JINS, 2016,22, 956–967)

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