Neuroimaging predictors of cognitive performance across a standardized neurocognitive battery.

OBJECTIVE The advent of functional MRI (fMRI) enables the identification of brain regions recruited for specific behavioral tasks. Most fMRI studies focus on group effects in single tasks, which limits applicability where assessment of individual differences and multiple brain systems is needed. METHOD We demonstrate the feasibility of concurrently measuring fMRI activation patterns and performance on a computerized neurocognitive battery (CNB) in 212 healthy individuals at 2 sites. Cross-validated sparse regression of regional brain amplitude and extent of activation were used to predict concurrent performance on 6 neurocognitive tasks: abstraction/mental flexibility, attention, emotion processing, and verbal, face, and spatial memory. RESULTS Brain activation was task responsive and domain specific, as reported in previous single-task studies. Prediction of performance was robust for most tasks, particularly for abstraction/mental flexibility and visuospatial memory. CONCLUSIONS The feasibility of administering a comprehensive neuropsychological battery in the scanner was established, and task-specific brain activation patterns improved prediction beyond demographic information. This benchmark index of performance-associated brain activation can be applied to link brain activation with neurocognitive performance during standardized testing. This first step in standardizing a neurocognitive battery for use in fMRI may enable quantitative assessment of patients with brain disorders across multiple cognitive domains. Such data may facilitate identification of neural dysfunction associated with poor performance, allow for identification of individuals at risk for brain disorders, and help guide early intervention and rehabilitation of neurocognitive deficits.

[1]  Janet B W Williams,et al.  Diagnostic and Statistical Manual of Mental Disorders , 2013 .

[2]  A. Dale,et al.  Hierarchical Genetic Organization of Human Cortical Surface Area , 2012, Science.

[3]  Raquel E Gur,et al.  Age group and sex differences in performance on a computerized neurocognitive battery in children age 8-21. , 2012, Neuropsychology.

[4]  Karin E. Borgmann-Winter,et al.  Computerized neurocognitive profile in young people with 22q11.2 deletion syndrome compared to youths with schizophrenia and At‐Risk for psychosis , 2012, American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics.

[5]  R. Gur,et al.  Computerized neurocognitive test performance in schizophrenia: a lifespan analysis. , 2012, The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry.

[6]  M. Greicius,et al.  Decoding subject-driven cognitive states with whole-brain connectivity patterns. , 2012, Cerebral cortex.

[7]  R. Gur,et al.  Abnormal modulation of amygdala activity in schizophrenia in response to direct- and averted-gaze threat-related facial expressions. , 2011, The American journal of psychiatry.

[8]  Jessica A. Turner,et al.  Multisite reliability of cognitive BOLD data , 2011, NeuroImage.

[9]  B. Turetsky,et al.  Ventrolateral prefrontal cortex and the effects of task demand context on facial affect appraisal in schizophrenia. , 2011, Social cognitive and affective neuroscience.

[10]  R. Gur,et al.  ‘Executive’ Functions and Normal Aging: Selective Impairment in Conditional Exclusion Compared to Abstraction and Inhibition , 2010, Dementia and Geriatric Cognitive Disorders.

[11]  R. Kahn,et al.  Aberrant Frontal and Temporal Complex Network Structure in Schizophrenia: A Graph Theoretical Analysis , 2010, The Journal of Neuroscience.

[12]  D. Lanska Adams and Victor’s Principles of Neurology , 2010 .

[13]  R. Gur,et al.  Association of enhanced limbic response to threat with decreased cortical facial recognition memory response in schizophrenia. , 2010, The American journal of psychiatry.

[14]  Raquel E Gur,et al.  Project among African-Americans to explore risks for schizophrenia (PAARTNERS): evidence for impairment and heritability of neurocognitive functioning in families of schizophrenia patients. , 2010, The American journal of psychiatry.

[15]  R. Gur,et al.  A cognitive neuroscience-based computerized battery for efficient measurement of individual differences: Standardization and initial construct validation , 2010, Journal of Neuroscience Methods.

[16]  A. Toga,et al.  Multisite neuroimaging trials , 2009, Current opinion in neurology.

[17]  Steven P Reise,et al.  Item response theory and clinical measurement. , 2009, Annual review of clinical psychology.

[18]  M. D’Esposito,et al.  fMRI: Applications in Cognitive Neuroscience , 2009 .

[19]  M. Allen,et al.  Clinical Application of Standardized Cognitive Assessment Using fMRI. II. Verbal Fluency , 2009, Behavioural neurology.

[20]  M. Allen,et al.  Clinical Application of Standardized Cognitive Assessment Using fMRI. I. Matrix Reasoning , 2009, Behavioural neurology.

[21]  Juan Manuel Peralta,et al.  A genome screen for quantitative trait loci influencing schizophrenia and neurocognitive phenotypes. , 2008, The American journal of psychiatry.

[22]  Ewald Moser,et al.  Facial emotion recognition and amygdala activation are associated with menstrual cycle phase , 2008, Psychoneuroendocrinology.

[23]  V. Calhoun,et al.  A Review of Challenges in the Use of fMRI for Disease Classification / Characterization and A Projection Pursuit Application from A Multi-site fMRI Schizophrenia Study , 2008, Brain Imaging and Behavior.

[24]  M. Fox,et al.  Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging , 2007, Nature Reviews Neuroscience.

[25]  Functional Magnetic Resonance Imaging in Clinical Practice: Look Before You Leap , 2007, Neuropsychology Review.

[26]  Ewald Moser,et al.  Amygdala activation at 3T in response to human and avatar facial expressions of emotions , 2007, Journal of Neuroscience Methods.

[27]  U. Habel,et al.  Neural correlates of working memory dysfunction in first-episode schizophrenia patients: An fMRI multi-center study , 2007, Schizophrenia Research.

[28]  Michael F. Green,et al.  The Consortium on the Genetics of Endophenotypes in Schizophrenia: model recruitment, assessment, and endophenotyping methods for a multisite collaboration. , 2006, Schizophrenia bulletin.

[29]  John Blangero,et al.  Neurocognitive endophenotypes in a multiplex multigenerational family study of schizophrenia. , 2007, The American journal of psychiatry.

[30]  W. Sturm,et al.  Neuropsychological assessment , 2007, Journal of Neurology.

[31]  Gregory G. Brown,et al.  Reproducibility of functional MR imaging: preliminary results of prospective multi-institutional study performed by Biomedical Informatics Research Network. , 2005, Radiology.

[32]  Jörn Diedrichsen,et al.  Detecting and adjusting for artifacts in fMRI time series data , 2005, NeuroImage.

[33]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[34]  R. Gur,et al.  The Penn Conditional Exclusion Test: a new measure of executive-function with alternate forms of repeat administration. , 2004, Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists.

[35]  Daniel L Schacter,et al.  Encoding activity in anterior medial temporal lobe supports subsequent associative recognition , 2004, NeuroImage.

[36]  R. Gur,et al.  Working memory deficit as a core neuropsychological dysfunction in schizophrenia. , 2003, The American journal of psychiatry.

[37]  Ziad S Saad,et al.  The spatial extent of the BOLD response , 2003, NeuroImage.

[38]  J. Lewin Functional MRI: An introduction to methods , 2003 .

[39]  Robin M. Chan,et al.  Age-related differences in brain activation during emotional face processing , 2003, Neurobiology of Aging.

[40]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[41]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[42]  R. Bajcsy,et al.  A method for obtaining 3-dimensional facial expressions and its standardization for use in neurocognitive studies , 2002, Journal of Neuroscience Methods.

[43]  V. Schmithorst,et al.  Changes in neuronal activation with increasing attention demand in healthy volunteers: An fMRI study , 2001, Synapse.

[44]  Stephen M. Smith,et al.  Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data , 2001, NeuroImage.

[45]  Keith J. Worsley,et al.  Statistical analysis of activation images , 2001 .

[46]  R. Gur,et al.  Computerized Neurocognitive Scanning: I. Methodology and Validation in Healthy People , 2001, Neuropsychopharmacology.

[47]  R. Gur,et al.  Computerized Neurocognitive Scanning: II. The Profile of Schizophrenia , 2001, Neuropsychopharmacology.

[48]  R. Kajiwara,et al.  Voltage-sensitive dye versus intrinsic signal optical imaging: comparison of optically determined functional maps from rat barrel cortex , 2001, Neuroreport.

[49]  G McCarthy,et al.  The effects of single-trial averaging upon the spatial extent of fMRI activation , 2001, Neuroreport.

[50]  N. Logothetis,et al.  Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.

[51]  M I Posner,et al.  Cognitive neuroscience: origins and promise. , 2000, Psychological bulletin.

[52]  Anthony Randal McIntosh,et al.  Towards a network theory of cognition , 2000, Neural Networks.

[53]  N. Kalin,et al.  Emotion, plasticity, context, and regulation: perspectives from affective neuroscience. , 2000, Psychological bulletin.

[54]  R. Cabeza,et al.  Imaging Cognition II: An Empirical Review of 275 PET and fMRI Studies , 2000, Journal of Cognitive Neuroscience.

[55]  S. Strother,et al.  Reproducibility of BOLD‐based functional MRI obtained at 4 T , 1999, Human brain mapping.

[56]  Jonathan D. Cohen,et al.  Reproducibility of fMRI Results across Four Institutions Using a Spatial Working Memory Task , 1998, NeuroImage.

[57]  P. Murtaugh,et al.  METHODS OF VARIABLE SELECTION IN REGRESSION MODELING , 1998 .

[58]  J. Panksepp Affective Neuroscience: The Foundations of Human and Animal Emotions , 1998 .

[59]  R. Gur,et al.  Reliability, performance characteristics, construct validity, and an initial clinical application of a visual object learning test (VOLT). , 1997, Neuropsychology.

[60]  Ruben C. Gur,et al.  Lateralized Changes in Regional Cerebral Blood Flow during Performance of Verbal and Facial Recognition Tasks: Correlations with Performance and “Effort” , 1997, Brain and Cognition.

[61]  J. Lewin,et al.  Inadequacy of motion correction algorithms in functional MRI: Role of susceptibility‐induced artifacts , 1997, Journal of magnetic resonance imaging : JMRI.

[62]  Karl J. Friston,et al.  Functional topography: multidimensional scaling and functional connectivity in the brain. , 1996, Cerebral cortex.

[63]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[64]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[65]  C. Gilbert,et al.  Receptive field expansion in adult visual cortex is linked to dynamic changes in strength of cortical connections. , 1995, Journal of neurophysiology.

[66]  I. Whishaw,et al.  Fundamentals of Human Neuropsychology , 1995 .

[67]  G. Engel,et al.  Neuropsychology , 1994, Schizophrenia Research.

[68]  Richard Coppola,et al.  Regional cerebral blood flow during the wisconsin card sorting test in normal subjects studied by xenon-133 dynamic SPECT: Comparison of absolute values, percent distribution values and covariance analysis , 1993, Psychiatry Research: Neuroimaging.

[69]  J. Rabe-Jabłońska,et al.  [Affective disorders in the fourth edition of the classification of mental disorders prepared by the American Psychiatric Association -- diagnostic and statistical manual of mental disorders]. , 1993, Psychiatria polska.

[70]  H. Keselman,et al.  Backward, forward and stepwise automated subset selection algorithms: Frequency of obtaining authentic and noise variables , 1992 .

[71]  R. Gur,et al.  Neurobehavioral probes for physiologic neuroimaging studies. , 1992, Archives of general psychiatry.

[72]  E. Kaplan,et al.  The process approach to neuropsychological assessment of psychiatric patients. , 1990, The Journal of neuropsychiatry and clinical neurosciences.

[73]  V. Flack,et al.  Frequency of Selecting Noise Variables in Subset Regression Analysis: A Simulation Study , 1987 .

[74]  Ruben C. Gur,et al.  Cognitive task effects on hemispheric blood flow in humans: Evidence for individual differences in hemispheric activation , 1980, Brain and Language.

[75]  Jr. William Rush Dunton,et al.  THE AMERICAN JOURNAL OF PSYCHIATRY , 1944 .