Daniel E. Glaser

Classical and Bayesian inference in neuroimaging: Applications

In Friston et al. ((2002) Neuroimage 16: 465-483) we introduced empirical Bayes as a potentially useful way to estimate and make inferences about effects in hierarchical models. In this paper we present a series of models that exemplify the diversity of problems that can be addressed within this framework. In hierarchical linear observation models, both classical and empirical Bayesian approaches can be framed in terms of covariance component estimation (e.g., variance partitioning). To illustrate the use of the expectation-maximization (EM) algorithm in covariance component estimation we focus first on two important problems in fMRI: nonsphericity induced by (i) serial or temporal correlations among errors and (ii) variance components caused by the hierarchical nature of multisubject studies. In hierarchical observation models, variance components at higher levels can be used as constraints on the parameter estimates of lower levels. This enables the use of parametric empirical Bayesian (PEB) estimators, as distinct from classical maximum likelihood (ML) estimates. We develop this distinction to address: (i) The difference between response estimates based on ML and the conditional means from a Bayesian approach and the implications for estimates of intersubject variability. (ii) The relationship between fixed- and random-effect analyses. (iii) The specificity and sensitivity of Bayesian inference and, finally, (iv) the relative importance of the number of scans and subjects. The forgoing is concerned with within- and between-subject variability in multisubject hierarchical fMRI studies. In the second half of this paper we turn to Bayesian inference at the first (within-voxel) level, using PET data to show how priors can be derived from the (between-voxel) distribution of activations over the brain. This application uses exactly the same ideas and formalism but, in this instance, the second level is provided by observations over voxels as opposed to subjects. The ensuing posterior probability maps (PPMs) have enhanced anatomical precision and greater face validity, in relation to underlying anatomy. Furthermore, in comparison to conventional SPMs they are not confounded by the multiple comparison problem that, in a classical context, dictates high thresholds and low sensitivity. We conclude with some general comments on Bayesian approaches to image analysis and on some unresolved issues.

Anterior cingulate activity during error and autonomic response

The contribution of anterior cingulate cortex (ACC) to human cognition remains unclear. The rostral (rACC) and dorsal (dACC) ACC cortex are implicated in tasks that require increased response control due to emotional and cognitive interference, respectively. However, both rACC and dACC are activated by conditions that induce changes in visceral arousal, suggesting that ACC supports a generation of integrated bodily responses. To clarify the relationship between purely cognitive and psychophysiological accounts of ACC function, we scanned 15 subjects using functional magnetic resonance imaging while they performed numerical versions of the Stroop task. To index autonomic arousal, we simultaneously measured pupil diameter. Performance errors accounted for most of the variance in a pupil-derived measure of evoked autonomic arousal. In analysis of the functional imaging data, activity within a region spanning rACC and dACC predicted trial-by-trial variation in autonomic response magnitude and was enhanced during error trials, shown using conjunction analyses. Activity within other loci within rACC predicted evoked autonomic arousal and showed sensitivity to errors but did not meet criteria for both. These data highlight the role of ACC in psychophysiological aspects of error processing and suggest that an interface exists within ACC between cognitive and biobehavioral systems in the service of response adaptation.

Towards a sensorimotor aesthetics of performing art

The field of neuroaesthetics attempts to identify the brain processes underlying aesthetic experience, including but not limited to beauty. Previous neuroaesthetic studies have focussed largely on paintings and music, while performing arts such as dance have been less studied. Nevertheless, increasing knowledge of the neural mechanisms that represent the bodies and actions of others, and which contribute to empathy, make a neuroaesthetics of dance timely. Here, we present the first neuroscientific study of aesthetic perception in the context of the performing arts. We investigated brain areas whose activity during passive viewing of dance stimuli was related to later, independent aesthetic evaluation of the same stimuli. Brain activity of six naïve male subjects was measured using fMRI, while they watched 24 dance movements, and performed an irrelevant task. In a later session, participants rated each movement along a set of established aesthetic dimensions. The ratings were used to identify brain regions that were more active when viewing moves that received high average ratings than moves that received low average ratings. This contrast revealed bilateral activity in the occipital cortices and in right premotor cortex. Our results suggest a possible role of visual and sensorimotor brain areas in an automatic aesthetic response to dance. This sensorimotor response may explain why dance is widely appreciated in so many human cultures.

Functional optical signal analysis: a software tool for near-infrared spectroscopy data processing incorporating statistical parametric mapping

Optical topography (OT) relies on the near infrared spectroscopy (NIRS) technique to provide noninvasively a spatial map of functional brain activity. OT has advantages over conventional fMRI in terms of its simple approach to measuring the hemodynamic response, its ability to distinguish between changes in oxy- and deoxy-hemoglobin and the range of human participants that can be readily investigated. We offer a new software tool, functional optical signal analysis (fOSA), for analyzing the spatially resolved optical signals that provides statistical inference capabilities about the distribution of brain activity in space and time and by experimental condition. It does this by mapping the signal into a standard functional neuroimaging analysis software, statistical parametric mapping (SPM), and forms, in effect, a new SPM toolbox specifically designed for NIRS in an OT configuration. The validity of the program has been tested using synthetic data, and its applicability is demonstrated with experimental data.

Imaging Informational Conflict: A Functional Magnetic Resonance Imaging Study of Numerical Stroop

We employed a parametric version of the comparison Stroop paradigm to investigate the processing of numerical magnitude and physical size under task-relevant and -irrelevant conditions to investigate two theoretical issues: (1) What is the neural fate of task-irrelevant information? (2) What is the neural basis of the resolution of the conflict between task-relevant and -irrelevant information? We show in 18 healthy adults that numerical magnitudes of numbers call for higher processing requirements than physical sizes. The enhanced activation elicited by numerical magnitudes is not modulated by task relevance, indicating autonomous processing. Moreover, the normal behavioral distance effect when the numerical dimension is task relevant and reversed distance effect when it is not show that autonomous processing fully encodes numerical magnitudes. Conflict trials elicited greater activation in bilateral inferior frontal gyri, right middle frontal gyri, and right superior frontal gyri. We postulate two sources to the conflict, namely, at cognitive and response levels.

A heuristic for the degrees of freedom of statistics based on multiple variance parameters

In neuroimaging, data are often modeled using general linear models. Here, we focus on GLMs with error covariances which are modeled as a linear combination of multiple variance/covariance components. Each of these components is weighted by one variance parameter. In many analyses variance parameters are estimated using restricted maximum likelihood (ReML). Most classical approaches assume the error covariance matrix can be factorized into a single variance parameter and a nonspherical correlation matrix. In this context, the F test based on a single variance parameter, with a suitable correction to the degrees of freedom, is the standard inference tool. This correction can also be adapted to models with multiple variance parameters. However, this extension overlooks the uncertainty about the variance parameter estimates and P values tend to be underestimated. Here, we show how one can overcome this problem to render the F test more exact. This issue is important, because serial correlations in fMRI time series are generally modeled using multiple variance parameters. Another application is to hierarchical linear models, which are used for modeling multisubject data. To illustrate our approach, we apply it to some typical modeling scenarios in fMRI data analysis.

Creation: Life and How to Make It [Review Article]

Working mostly alone, almost single-handedly writing 250,000 lines of computer code, Steve Grand produced Creatures , a revolutionary computer game that allowed players to create living beings complete with brains,...

Creation: Life and How to Make It

Working mostly alone, almost single-handedly writing 250,000 lines of computer code, Steve Grand produced Creatures , a revolutionary computer game that allowed players to create living beings complete with brains,...

Review of Creation: Life and how to make it by Steve Grand. 2000

Working mostly alone, almost single-handedly writing 250,000 lines of computer code, Steve Grand produced Creatures , a revolutionary computer game that allowed players to create living beings complete with brains,...

Creation: Life and How to Make It. Steve Grand. (2000, Weidenfeld; 2001, Harvard University Press).

Working mostly alone, almost single-handedly writing 250,000 lines of computer code, Steve Grand produced Creatures , a revolutionary computer game that allowed players to create living beings complete with brains,...