Faculty Bibliography

The degree to which elderly adults experience cognitive impairments from centrally acting anticholinergic drugs is variable, but the cause of this variability is unknown. The present study examined the epsilon4 allele as a possible modulator of the effects of trihexyphenidyl hydrochloride (Artane( trade mark )), an anticholinergic drug, on memory functioning. Of the 24 cognitively intact, elderly participants (age range 62-76), 12 who possessed the epsilon4 allele, participated in a double-blind, randomized, placebo-controlled, crossover, three-way study. All participants were tested after receiving a single oral dose of trihexyphenidyl (1 or 2 mg) or placebo, with a 7-day washout period between sessions. Memory and psychomotor tests were administered at baseline, and at 1, 2.5, and 5 h post-treatment. Results showed that participants with the epsilon4 allele demonstrated significant impairments in delayed recall after both 1 and 2 mg doses of trihexyphenidyl while the non-epsilon4 group did not. Additionally, while acute administration of the 2 mg dose significantly impaired total recall in both epsilon4 and non-epsilon4 carriers, the epsilon4 carriers showed a more persistent impairment. These findings held when participants with the epsilon2 allele were excluded from the analyses. The epsilon4 groups did not differ with respect to psychomotor performance or plasma drug levels. These results provide evidence suggesting that the epsilon4 allele plays a significant role in increasing cognitive sensitivity to trihexyphenidyl and that a temporal component of memory consolidation may be especially vulnerable. A larger study is warranted to confirm these preliminary findings

In the standard approach to functional imaging studies, brain-behavior relationships are studied by contrasting data obtained during different behavioral states. It is generally assumed that relative change yields meaningful data about relevant brain processes, and that the magnitude of the change reflects the extent of a region's involvement in the behavior being studied. The present study takes a different approach by asking the question, Can functional imaging data predict performance? Regional cerebral blood flow was measured using positron emission tomography in a group of 13 right-handed, normal volunteers during speech production and quiet baseline. A number of methodological assumptions were addressed by examining the relationships between different imaging measures derived from the same raw data and performance on the speech task. The results demonstrate that several common assumptions are not necessarily true. First, although measures based on 'activated' scans alone had predictive value with respect to speech rate, measures based on contrasts between 'baseline' and 'activated' states did not. This was true regardless of whether the contrast was based on subtraction or covariance analyses. Second, while many regions demonstrated large signal increases during speech, speech rate could be predicted by a linear combination of data from two regions, neither of which had the highest 'activation' peak, and one of which had a negative relationship with performance. The results demonstrate that contrasting experimental conditions do not necessarily isolate or enhance brain activity related to performance, and that the current assumptions about activation in functional imaging need to be reconsidered

Much of the recent emphasis on prosody (the melody and rhythm of speech) and its disorders (dysprosody) has been on cognitive-affective functions attributed to cortical areas of the right cerebral hemisphere, with little further behavioral or neuroanatomical specification. This focus is inappropriately narrow both from the perspectives of neuropathogenesis and neurobehavioral phenomenology, and it is based on a limited view of prosody. Current models of brain organization for prosody propose lateralized representation based on functional (affective vs. linguistic) or featural (timing vs. pitch) properties of prosodic material. However, a role for subcortical structures in prosody is being increasingly described, and prosodic functions are now known to span a broad range in communication. In this article we describe normal prosody and present an overview of neurobehavioral disorders associated with acquired adult dysprosody. From these considerations we propose a neurobehavior-based approach to a more effective study of prosodic disturbance, and eventually, to better insight into normal prosody

We introduce a data-analysis framework and performance metrics for evaluating and optimizing the interaction between activation tasks, experimental designs, and the methodological choices and tools for data acquisition, preprocessing, data analysis, and extraction of statistical parametric maps (SPMs). Our NPAIRS (nonparametric prediction, activation, influence, and reproducibility resampling) framework provides an alternative to simulations and ROC curves by using real PET and fMRI data sets to examine the relationship between prediction accuracy and the signal-to-noise ratios (SNRs) associated with reproducible SPMs. Using cross-validation resampling we plot training-test set predictions of the experimental design variables (e.g., brain-state labels) versus reproducibility SNR metrics for the associated SPMs. We demonstrate the utility of this framework across the wide range of performance metrics obtained from [(15)O]water PET studies of 12 age- and sex-matched data sets performing different motor tasks (8 subjects/set). For the 12 data sets we apply NPAIRS with both univariate and multivariate data-analysis approaches to: (1) demonstrate that this framework may be used to obtain reproducible SPMs from any data-analysis approach on a common Z-score scale (rSPM[Z]); (2) demonstrate that the histogram of a rSPM[Z] image may be modeled as the sum of a data-analysis-dependent noise distribution and a task-dependent, Gaussian signal distribution that scales monotonically with our reproducibility performance metric; (3) explore the relation between prediction and reproducibility performance metrics with an emphasis on bias-variance tradeoffs for flexible, multivariate models; and (4) measure the broad range of reproducibility SNRs and the significant influence of individual subjects. A companion paper describes learning curves for four of these 12 data sets, which describe an alternative mutual-information prediction metric and NPAIRS reproducibility as a function of training-set sizes from 2 to 18 subjects. We propose the NPAIRS framework as a validation tool for testing and optimizing methodological choices and tools in functional neuroimaging.

Learning curves are presented as an unbiased means for evaluating the performance of models for neuroimaging data analysis. The learning curve measures the predictive performance in terms of the generalization or prediction error as a function of the number of independent examples (e.g., subjects) used to determine the parameters in the model. Cross-validation resampling is used to obtain unbiased estimates of a generic multivariate Gaussian classifier, for training set sizes from 2 to 16 subjects. We apply the framework to four different activation experiments, in this case [(15)O]water data sets, although the framework is equally valid for multisubject fMRI studies. We demonstrate how the prediction error can be expressed as the mutual information between the scan and the scan label, measured in units of bits. The mutual information learning curve can be used to evaluate the impact of different methodological choices, e.g., classification label schemes, preprocessing choices. Another application for the learning curve is to examine the model performance using bias/variance considerations enabling the researcher to determine if the model performance is limited by statistical bias or variance. We furthermore present the sensitivity map as a general method for extracting activation maps from statistical models within the probabilistic framework and illustrate relationships between mutual information and pattern reproducibility as derived in the NPAIRS framework described in a companion paper.

Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel-time series. The optimal number of clusters was chosen using a cross-validated likelihood method, which highlights the clustering pattern that generalizes best over the subjects. Data were acquired with PET at different time points during practice of a visuomotor task. The results from cluster analysis show practice-related activity in a fronto-parieto-cerebellar network, in agreement with previous studies of motor learning. These voxels were separated from a group of voxels showing an unspecific time-effect and another group of voxels, whose activation was an artifact from smoothing

Based on single-cell recordings in primates, the relationship between neuronal activity and force magnitude is thought to be monotonic, at least for a subset of pyramidal cells in the motor cortex. Functional neuroimaging studies have also suggested a monotonic relationship between cerebral activation and force magnitude. In order to more precisely define this relationship and to characterize the activation pattern(s) associated with the modulation of static force, we studied 40 normal subjects using [(15)O]water PET and a simple visuomotor task-application of static force on a micro force sensor with the thumb and index finger of the right hand. When our experimental design did not produce the expected result (evidence of a relationship between cerebral activation and force magnitude in ten subjects), we made serial changes in the experimental protocol, including the addition of control (baseline) trials, and increased the number of subjects in an effort to increase our sensitivity to variations in force magnitude. We compared univariate and multivariate data-analytic strategies, but we relied on our multivariate results to elucidate the interaction of attentional and motor networks. We found that increasing the number of subjects from 10 to 20 resulted in an increase in statistical power and a more stable (i.e., more replicable) but qualitatively similar result, and that the inclusion of control trials in a 10-subject group did not enhance our ability to discern significant brain-behavior relationships. Our results suggest that sample sizes greater than 20 may be required to detect parametric variation in some instances and that failure to detect such variation may result from unanticipated neurobehavioral effects