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Stephen C Strother

Publications and source records attributed to Stephen C Strother.

15 recordsLinked to original sources
PubMed2006

Dysfunction of dorsolateral prefrontal cortex in antipsychotic-naïve schizophreniform psychosis.

Reports of abnormal activation of the dorsolateral prefrontal cortex (dlPFC) are common in functional neuroimaging studies of schizophrenia, although very few have examined brain activity in patients close to the onset of illness. In this H(2)(15)O PET study, eight young male patients with first-episode schizophreniform psychosis and age-matched control subjects performed a version of the Stroop task that we have previously shown to engage the middle-frontal gyrus. At the time of testing, patients were antipsychotic-naïve and were scanned within 1 week of initial contact with our clinical program. All patients received a later diagnosis of schizophrenia 6 months after participating in the study. Whole-brain (within-group) and region-of-interest (between-group) analyses were carried out and data underwent spatial reproducibility testing. Compared with healthy subjects, patients showed significantly greater reaction-time (RT) interference but normal RT accuracy on the Stroop task. This pattern correlated with significant under-activation of the posterior left middle-frontal gyri in the patient versus control group. These findings support an emerging model of impaired cognitive control in schizophrenia and suggest that there is significant dysfunction of the dlPFC close to the onset of illness that may coincide with, or be modulated by, the transition-to-illness phase.

Adolescent↗
PubMed2006

Evaluating brain activity in obsessive-compulsive disorder: preliminary insights from a multivariate analysis.

Obsessive-compulsive disorder (OCD) has been linked to a dysfunction of brain corticostriatal networks, although functional imaging studies of OCD rarely apply network-sensitive analysis methods. In this study, we compared a univariate and a multivariate analysis of PET data in OCD patients and healthy subjects; the latter approach was considered more suitable for characterizing functional networks of brain activity. Although both methods suggested there was abnormal corticostriatal activity in OCD patients, the nature, extent and magnitude of this activity was clearly enhanced by the multivariate approach. Implications for the analysis of such studies are discussed.

Adult↗
PubMed2006

Mapping cerebral blood flow during speech production in hereditary ataxia.

Dysarthria is a significant feature of the dominantly inherited spinocerebellar ataxias (SCA), but little is known about the patterns of brain activity associated with this disorder of motor speech control. Positron emission tomography (PET) was used to study regional cerebral blood flow during speech and rest in a group of 24 subjects with hereditary ataxia with mild-to-moderate dysarthria. These data were compared to the results obtained from a group of 13 age-matched, normal speakers. In the ataxic subjects, speech rates during scanning were significantly slowed compared to normal speakers. Significant reductions in mean regional blood flow were found in the cerebellum but not in supratentorial regions in the ataxic subjects. Multiple linear regression was used to model speech rate from regional blood flow. Four regions were identified as having significant relationships with speech rate in the model: the left inferior frontal and transverse temporal regions, and the right inferior cerebellar region and caudate nucleus. The relationship between flow and rate was positive in the inferior frontal and cerebellar regions and negative in the caudate and the transverse temporal region. The ataxic model represents an elaboration of the relationship previously reported for normal speakers, likely reflecting both the effects of, and compensation for, cerebellar degeneration in motor speech control. Although the mean regional blood flow values presented a pattern of functional organization for motor speech control at odds with lesion data, the performance-based model was in agreement with clinical experience. Incorporating performance data in functional image analysis may be more revealing of system characteristics than simply examining mean blood flow values.

Adult↗
PubMed2006

Motivation and synthesis of the FIAC experiment: Reproducibility of fMRI results across expert analyses.

The Functional Imaging Analysis Contest (FIAC) culminated in the FIAC Workshop held at the 11th Annual Meeting of the Organization for Human Brain Mapping in Toronto in 2005. This special issue summarizes various analyses used by contestants with a single functional magnetic resonance imaging (fMRI) study, a cortical-language study using sentence repetition. The results from the cognitive neuroscientists who developed the test-base language study, and report their data analysis, are complemented by expert analyses of the same test-base data by most of the major groups actively developing fMRI software packages. Analyses include many variants of the general linear model (GLM), cutting-edge spatial- and temporal-wavelets, permutation-based, and ICA approaches. A number of authors also include surface-based approaches. Several articles describe the important emerging areas of diagnostics for GLM analysis, multivariate predictive modeling, and functional connectivity analysis. While the FIAC did not achieve all of its goals, it helped identify new activation regions in the test-base data, and more important, through this special issue it illustrates the significant methods-driven variability that potentially exists in the literature. Variable results from different methods reported here should provide a cautionary note and motivate the Human Brain Mapping community to explore more thoroughly the methodologies they use for analyzing fMRI data.

Brain Mapping↗
PubMed2005

Functional connectivity during Stroop task performance.

Using covariance-based multivariate analysis, we examined patterns of functional connectivity in rCBF on a practice-extended version of the Stroop color-word paradigm. Color-word congruent and incongruent conditions were presented in six AB trials to healthy subjects during 12 H2(15)O PET scans. Analyses identified two reproducible canonical eigenimages (CE) from the PET data, which were converted to a standard Z score scale after cross-validation resampling and correction for random subject effects. The first CE corresponded to practice-dependent changes in covarying rCBF that occurred over early task repetitions and correlated with improved behavioral performance. This included many regions previously implicated by PET and fMRI studies of this task, which we suggest may represent two "parallel" networks: (i) a cingulo-frontal system that was initially engaged in selecting and mapping a task-relevant response (color naming) when the attentional demands of the task were greatest; and (ii) a ventral visual processing stream whose concurrent decrease in activity represented the task-irrelevant inhibition of word reading. The second CE corresponded to a consistent paradigmatic effect of Stroop interference on covarying rCBF. Coactivations were located in dorsal and ventral prefrontal regions as well as frontopolar cortex. This pattern supports existing evidence that prefrontal regions are involved in maintaining attentional control over conflicting response systems. Taken together, these findings may be more in line with theoretical models that emphasize a role for practice in the emergence of Stroop phenomena. These findings may also provide some additional insight into the nature of anterior cingulate- and prefrontal cortical contributions to implementing cognitive control in the brain.

Arousal↗
PubMed2004

The effect of set on the resting state in functional imaging: a role for the striatum?

The effects of experimental set on brain activity during the resting state were identified in four positron emission tomography (PET) studies that employed different tasks, identical resting states, and the same subjects. A group of 11, right-handed, normal volunteers participated in four PET studies that shared the same block design in which four resting scans were alternated with four performance scans. The tasks were finger opposition, syllable repetition, sustained phonation, and repetitive lip closure. Each study used only one task and different studies were done on different days. Repeated measures analysis of variance revealed that resting regional cerebral blood flow was significantly affected by the task being studied. This influence varied with task and region. A series of analyses examining left/right regional pairs revealed significant effects of state (rest versus performance) and task and their interaction on most brain regions. The caudate was the only region in which state was not significant either as a main effect or in an interaction with task. Additional analyses revealed that resting and performance blood flow values from a single study were more highly correlated than were resting values across studies. Set may be associated with brain changes that fall into a class of recently described pre- and post-performance effects and may reflect the striatum's role in learning.

Adult↗
PubMed2003

Evaluating subject specific preprocessing choices in multisubject fMRI data sets using data-driven performance metrics.

This study investigated the possible benefit of subject specific optimization of preprocessing strategies in functional magnetic resonance imaging (fMRI) experiments. The optimization was performed using the data-driven performance metrics developed recently [Neuroimage 15 (2002), 747]. We applied numerous preprocessing strategies and a multivariate statistical analysis to each of the 20 subjects in our two example fMRI data sets. We found that the optimal preprocessing strategy varied, in general, from subject to subject. For example, in one data set, optimum smoothing levels varied from 16 mm (4 subjects), 10 mm (5 subjects), to no smoothing at all (1 subject). This strongly suggests that group-specific preprocessing schemes may not give optimum results. For both studies, optimizing the preprocessing for each subject resulted in an increased number of suprathresholded voxels in within-subject analyses. Furthermore, we demonstrated that we were able to aggregate the optimized data with a random effects group analysis, resulting in improved sensitivity in one study and the detection of interesting, previously undetected results in the other.

Adult↗
PubMed2003

Predicting performance from functional imaging data: methods matter.

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.

Adult↗
PubMed2002

Cluster analysis of activity-time series in motor learning.

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.

Adult↗
PubMed2002

Abnormal functional connectivity in posttraumatic stress disorder.

This study investigated the efficacy of a combined multivariate/resampling procedure for the analysis of PET activation studies. The covariance-based multivariate analysis was used to investigate distributed brain systems in posttraumatic stress disorder (PTSD) patients and matched controls during performance of a working memory task. The results were compared to univariate results obtained in an earlier study. We also examined whether the PTSD patients demonstrated a breakdown in functional connectivity that may be associated with working memory difficulties often experienced by these patients. A resampling procedure was used specifically to test the reliability of measured between-group effects, to avoid mistaken inference on the basis of random intersubject differences. Significant and reproducible differences in network connectivity were obtained for the two groups. The functional connectivity pattern of the patient group was characterized by relatively more activation in the bilateral inferior parietal lobes and the left precentral gyrus than the control group, and less activation in the inferior medial frontal lobe, bilateral middle frontal gyri and right inferior temporal gyrus. The resampling procedure provided direct evidence that working memory updating was abnormal in PTSD patients relative to matched controls. This work focuses on the need to identify extended brain networks (in addition to regionally specific changes) for the full characterization of brain responses in neuroimaging experiments. Our multivariate analysis explicitly measures the reliability of the patterns of functional connectivity we obtain and demonstrates the potential of such analyses for the study of brain network dysfunction in psychopathology.

Adult↗
PubMed2002

The quantitative evaluation of functional neuroimaging experiments: the NPAIRS data analysis framework.

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.

Adult↗
PubMed2002

An evaluation of methods for detecting brain activations from functional neuroimages.

Brain activation studies based on PET or fMRI seek to explore neuroscience questions by statistically analyzing the acquired images to produce statistical parametric images (SPIs). An increasingly wide range of univariate and multivariate analysis techniques are used to generate SPIs in order to detect mean-signal activations and/or long-range spatial interactions. However, little is known about the comparative detection performance of even simple techniques in finite data sets. Our aims are (1) to empirically compare the detection performance of a range of techniques using simulations of a simple image phantom and receiver operating characteristics (ROC) analysis, and (2) to construct two near-optimal detectors, both generalized likelihood ratio tests as upper performance bounds. We found that for finite samples of (10-100) images, even when the t-test with single-voxel variance estimates (single-voxel t-test) is the "correct" (i.e. unbiased) model for simple local additive signals, better detection performance is obtained using pooled variance estimates or adaptive, multivariate covariance-based detectors. Normalization by voxel-based variance estimates causes significantly decreased detection performance using either single-voxel t-tests or correlation-coefficient thresholding compared to pooled-variance t-tests or covariance thresholding, respectively. Moreover, we found that SVD by itself, or followed by an adaptive Fisher linear discriminant, provides a detector that is (1) more sensitive to mean differences than a single-voxel t-test, (2) insensitive to the large local signal variances detected by covariance thresholding, and (3) much more sensitive to signal correlations than correlation-coefficient thresholding. Adaptive, multivariate covariance-based approaches and pooled-variance t-tests represent promising directions for obtaining optimal signal detection in functional neuroimaging studies.

Brain↗
PubMed2002

Production and dosimetry of [14O]water for PET activation studies.

[14O]water was produced by the 14N(p,n)14O reaction in quantities greater than 80mCi (EOB). The carbon-11 impurity in the final product was approximately 0.003 +/- 0.002% of 14O activity at EOB. A factor of 0.5 +/- 0.02 was obtained to correct the dose calibrator for the additional 2.313 MeV gamma-ray. The effective dose equivalent was calculated for 14O and 15O using MIRDOSE, and the 14O dose was found to be approximately 70% of that for 15O. For scanner performance based on noise equivalent count measurements, the peak activity concentration for 14O was 30% lower than that for 15O. At their peak activity concentrations the noise equivalent count rate for 14O was found to be about 13% less than that for 15O.

Blood Circulation↗