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<p>Publisher's description: Buddha Mind in Contemporary Art documents the growing presence of Buddhist perspectives in contemporary culture. This shift began in the nineteenth century and is now pervasive in many aspects of everyday experience. In the arts especially, the increasing importance of process over product has promoted a profound change in the relationship between artist and audience. But while artists have been among the most perceptive interpreters of Buddhism in the West, art historians and critics have been slow to develop the intellectual tools to analyze the impact of Buddhist concepts. This timely, multi-faceted volume explores the relationships between Buddhist practice and the contemporary arts in lively essays by writers from a range of disciplines and in revealing interviews with some of the most influential artists of our time. Elucidating the common ground between the creative mind, the perceiving mind, and the meditative mind, the contributors tackle essential questions about the relationship of art and life. Among the writers are curators, art critics, educators, and Buddhist commentators in psychology, literature, and cognitive science. They consider the many Western artists today who recognize the Buddhist notion of emptiness, achieved through focused meditation, as a place of great creative potential for the making and experiencing of art. The artists featured in the interviews, all internationally recognized, include Maya Lin, Bill Viola, and Ann Hamilton. Extending earlier twentieth-century aesthetic interests in blurring the boundaries of art and life, the artists view art as a way of life, a daily practice, in ways parallel to that of the Buddhist practitioner. Their works, woven throughout the book, richly convey how Buddhism has been both a source for and a lens through which we now perceive art.</p>

Motion correction of fMRI data is a widely used step prior to data analysis. In this study, a comparison of the motion correction tools provided by several leading fMRI analysis software packages was performed, including AFNI, AIR, BrainVoyager, FSL, and SPM2. Comparisons were performed using data from typical human studies as well as phantom data. The identical reconstruction, preprocessing, and analysis steps were used on every data set, except that motion correction was performed using various configurations from each software package. Each package was studied using default parameters, as well as parameters optimized for speed and accuracy. Forty subjects performed a Go/No-go task (an event-related design that investigates inhibitory motor response) and an N-back task (a block-design paradigm investigating working memory). The human data were analyzed by extracting a set of general linear model (GLM)-derived activation results and comparing the effect of motion correction on thresholded activation cluster size and maximum t value. In addition, a series of simulated phantom data sets were created with known activation locations, magnitudes, and realistic motion. Results from the phantom data indicate that AFNI and SPM2 yield the most accurate motion estimation parameters, while AFNI's interpolation algorithm introduces the least smoothing. AFNI is also the fastest of the packages tested. However, these advantages did not produce noticeably better activation results in motion-corrected data from typical human fMRI experiments. Although differences in performance between packages were apparent in the human data, no single software package produced dramatically better results than the others. The "accurate" parameters showed virtually no improvement in cluster t values compared to the standard parameters. While the "fast" parameters did not result in a substantial increase in speed, they did not degrade the cluster results very much either. The phantom and human data indicate that motion correction can be a valuable step in the data processing chain, yielding improvements of up to 20% in the magnitude and up to 100% in the cluster size of detected activations, but the choice of software package does not substantially affect this improvement.
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This article examines the lay meditation movement occurring in contemporary Buddhism in Sri Lanka. The lay meditation movement represents a different perspective from the nationalistic Sinhala Buddhism that has dominated the discourse in the wake of the intractable ethnic conflict in the country. The lay meditation movement reflects the contemporary ferment in Buddhist discourse among the laity. One of the key themes in this movement is the privileging of experience because it gives the lay groups authority to challenge contemporary orthodoxy and it has empowered a new class of spiritual leaders, the lay gurus. Paraphrasing Stirrat, we can say that these lay gurus are leading the lay meditation movement towards ‘a series of different interpretations of what it means’ to be a Buddhist today. In its overall effect the lay meditation movement not only reconstructs what it means to be a Buddhist today but also points in the direction of establishing new forms of sectarianism that could be considered to be ‘new religious movements’ under the umbrella of Buddhism.

Neurosurgical treatment of psychiatric disorders has been influenced by evolving neurobiological models of symptom generation. The advent of functional neuroimaging and advances in the neurosciences have revolutionized understanding of the functional neuroanatomy of psychiatric disorders. This article reviews neuroimaging studies of depression from the last 3 decades and describes an emerging neurocircuitry model of mood disorders, focusing on critical circuits of cognition and emotion, particularly those networks involved in the regulation of evaluative, expressive and experiential aspects of emotion. The relevance of this model for neurotherapeutics is discussed, as well as the role of functional neuroimaging of psychiatric disorders.
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Recent years have seen an explosion of interest in using neural oscillations to characterize the mechanisms supporting cognition and emotion. Oftentimes, oscillatory activity is indexed by mean power density in predefined frequency bands. Some investigators use broad bands originally defined by prominent surface features of the spectrum. Others rely on narrower bands originally defined by spectral factor analysis (SFA). Presently, the robustness and sensitivity of these competing band definitions remains unclear. Here, a Monte Carlo-based SFA strategy was used to decompose the tonic ("resting" or "spontaneous") electroencephalogram (EEG) into five bands: delta (1-5Hz), alpha-low (6-9Hz), alpha-high (10-11Hz), beta (12-19Hz), and gamma (>21Hz). This pattern was consistent across SFA methods, artifact correction/rejection procedures, scalp regions, and samples. Subsequent analyses revealed that SFA failed to deliver enhanced sensitivity; narrow alpha sub-bands proved no more sensitive than the classical broadband to individual differences in temperament or mean differences in task-induced activation. Other analyses suggested that residual ocular and muscular artifact was the dominant source of activity during quiescence in the delta and gamma bands. This was observed following threshold-based artifact rejection or independent component analysis (ICA)-based artifact correction, indicating that such procedures do not necessarily confer adequate protection. Collectively, these findings highlight the limitations of several commonly used EEG procedures and underscore the necessity of routinely performing exploratory data analyses, particularly data visualization, prior to hypothesis testing. They also suggest the potential benefits of using techniques other than SFA for interrogating high-dimensional EEG datasets in the frequency or time-frequency (event-related spectral perturbation, event-related synchronization/desynchronization) domains.
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The impact of using motion estimates as covariates of no interest was examined in general linear modeling (GLM) of both block design and rapid event-related functional magnetic resonance imaging (fMRI) data. The purpose of motion correction is to identify and eliminate artifacts caused by task-correlated motion while maximizing sensitivity to true activations. To optimize this process, a combination of motion correction approaches was applied to data from 33 subjects performing both a block-design and an event-related fMRI experiment, including analysis: (1) without motion correction; (2) with motion correction alone; (3) with motion-corrected data and motion covariates included in the GLM; and (4) with non-motion-corrected data and motion covariates included in the GLM. Inclusion of covariates was found to be generally useful for increasing the sensitivity of GLM results in the analysis of event-related data. When motion parameters were included in the GLM for event-related data, it made little difference if motion correction was actually applied to the data. For the block design, inclusion of motion covariates had a deleterious impact on GLM sensitivity when even moderate correlation existed between motion and the experimental design. Based on these results, we present a general strategy for block designs, event-related designs, and hybrid designs to identify and eliminate probable motion artifacts while maximizing sensitivity to true activations.
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Right-handed subjects tend to look to the left when answering affective questions. The relative shift in gaze from right to left is accentuated when the questions also involve spatial manipulation and attenuated when the questions require verbal manipulation. The data support the hypothesis that the right hemisphere has a special role in emotion in the intact brain, and that predictable patterning of hemispheric activity can occur when specific combinations of cognitive and affective processes interact.
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