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Neuroimaging predictors of cognitive performance across a standardized neurocognitive battery.

ABSTRACT: 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.

SUBMITTER: Roalf DR

PROVIDER: S-EPMC4065574 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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Neuroimaging predictors of cognitive performance across a standardized neurocognitive battery.

Roalf David R DR Ruparel Kosha K Gur Raquel E RE Bilker Warren W Gerraty Raphael R Elliott Mark A MA Gallagher R Sean RS Almasy Laura L Pogue-Geile Michael F MF Prasad Konasale K Wood Joel J Nimgaonkar Vishwajit L VL Gur Ruben C RC

Neuropsychology 20131223 2

<h4>Objective</h4>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.<h4>Method</h4>We demonstrate the feasibility of concurrently measuring fMRI activation patterns and performance on a computerized neurocognitive battery (CNB) in 212 healthy individuals at 2 site ...[more]

PMID: 24364396

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Project description:OBJECTIVE:To identify a neuroimaging signature predictive of brain amyloidosis as a screening tool to identify individuals with mild cognitive impairment (MCI) that are most likely to have high levels of brain amyloidosis or to be amyloid-free. METHODS:The prediction model cohort included 62 MCI subjects screened with structural magnetic resonance imaging (MRI) and (11) C-labeled Pittsburgh compound B positron emission tomography (PET). We identified an anatomical shape variation-based neuroimaging predictor of brain amyloidosis and defined a structural MRI-based brain amyloidosis score (sMRI-BAS). Amyloid beta positivity (A?(+) ) predictive power of sMRI-BAS was validated on an independent cohort of 153 MCI patients with cerebrospinal fluid A?1-42 biomarker data but no amyloid PET scans. We compared the A?(+) predictive power of sMRI-BAS to those of apolipoprotein E (ApoE) genotype and hippocampal volume, the 2 most relevant candidate biomarkers for the prediction of brain amyloidosis. RESULTS:Anatomical shape variations predictive of brain amyloidosis in MCI embraced a characteristic spatial pattern known for high vulnerability to Alzheimer disease pathology, including the medial temporal lobe, temporal-parietal association cortices, posterior cingulate, precuneus, hippocampus, amygdala, caudate, and fornix/stria terminals. A?(+) prediction performance of sMRI-BAS and ApoE genotype jointly was significantly better than the performance of each predictor separately (area under the curve [AUC] = 0.88 vs AUC = 0.70 and AUC = 0.81, respectively) with >90% sensitivity and specificity at 20% false-positive rate and false-negative rate thresholds. Performance of hippocampal volume as an independent predictor of brain amyloidosis in MCI was only marginally better than random chance (AUC = 0.56). INTERPRETATION:As one of the first attempts to use an imaging technique that does not require amyloid-specific radioligands for identification of individuals with brain amyloidosis, our findings could lead to development of multidisciplinary/multimodality brain amyloidosis biomarkers that are reliable, minimally invasive, and widely available.

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Neuroimaging predictors of cognitive performance across a standardized neurocognitive battery.

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Neuroimaging predictors of cognitive performance across a standardized neurocognitive battery.

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