A clearing in the objectivity of aesthetics?

As subjective experiences go, beauty matters. Although aesthetics has long been a topic of study, research in this area has not resulted in a level of interest and progress commensurate with its import. Here, we briefly discuss two recent advances, one computational and one neuroscientific, and their pertinence to aesthetic processing. First, we hypothesize that deep neural networks provide the capacity to model representations essential to aesthetic experiences. Second, we highlight the principal gradient as an axis of information processing that is potentially key to examining where and how aesthetic processing takes place in the brain. In concert with established neuroimaging tools, we suggest that these advances may cultivate a new frontier in the understanding of our aesthetic experiences

Counterfactual Explanation of Brain Activity Classifiers Using Image-To-Image Transfer by Generative Adversarial Network

Deep neural networks (DNNs) can accurately decode task-related information from brain activations. However, because of the non-linearity of DNNs, it is generally difficult to explain how and why they assign certain behavioral tasks to given brain activations, either correctly or incorrectly. One of the promising approaches for explaining such a black-box system is counterfactual explanation. In this framework, the behavior of a black-box system is explained by comparing real data and realistic synthetic data that are specifically generated such that the black-box system outputs an unreal outcome. The explanation of the system's decision can be explained by directly comparing the real and synthetic data. Recently, by taking advantage of advances in DNN-based image-to-image translation, several studies successfully applied counterfactual explanation to image domains. In principle, the same approach could be used in functional magnetic resonance imaging (fMRI) data. Because fMRI datasets often contain multiple classes (e.g., multiple behavioral tasks), the image-to-image transformation applicable to counterfactual explanation needs to learn mapping among multiple classes simultaneously. Recently, a new generative neural network (StarGAN) that enables image-to-image transformation among multiple classes has been developed. By adapting StarGAN with some modifications, here, we introduce a novel generative DNN (counterfactual activation generator, CAG) that can provide counterfactual explanations for DNN-based classifiers of brain activations. Importantly, CAG can simultaneously handle image transformation among all the seven classes in a publicly available fMRI dataset. Thus, CAG could provide a counterfactual explanation of DNN-based multiclass classifiers of brain activations. Furthermore, iterative applications of CAG were able to enhance and extract subtle spatial brain activity patterns that affected the classifier's decisions. Together, these results demonstrate that the counterfactual explanation based on image-to-image transformation would be a promising approach to understand and extend the current application of DNNs in fMRI analyses

On co-activation pattern analysis and non-stationarity of resting brain activity

The non-stationarity of resting-state brain activity has received increasing attention in recent years. Functional connectivity (FC) analysis with short sliding windows and coactivation pattern (CAP) analysis are two widely used methods for assessing the dynamic characteristics of brain activity observed with functional magnetic resonance imaging (fMRI). However, the statistical nature of the dynamics captured by these techniques needs to be verified. In this study, we found that the results of CAP analysis were similar for real fMRI data and simulated stationary data with matching covariance structures and spectral contents. We also found that, for both the real and simulated data, CAPs were clustered into spatially heterogeneous modules. Moreover, for each of the modules in the real data, a spatially similar module was found in the simulated data. The present results suggest that care needs to be taken when interpreting observations drawn from CAP analysis as it does not necessarily reflect non-stationarity or a mixture of states in resting brain activity

FMRI activity in the macaque cerebellum evoked by intracortical microstimulation of the primary somatosensory cortex: evidence for polysynaptic propagation.

Simultaneous electrical microstimulation (EM) and functional magnetic resonance imaging (fMRI) is a useful tool for probing connectivity across brain areas in vivo. However, it is not clear whether intracortical EM can evoke blood-oxygenation-level-dependent (BOLD) signal in areas connected polysynaptically to the stimulated site. To test for the presence of the BOLD activity evoked by polysynaptic propagation of the EM signal, we conducted simultaneous fMRI and EM in the primary somatosensory cortex (S1) of macaque monkeys. We in fact observed BOLD activations in the contralateral cerebellum which is connected to the stimulation site (i.e. S1) only through polysynaptic pathways. Furthermore, the magnitude of cerebellar activations was dependent on the current amplitude of the EM, confirming the EM is the cause of the cerebellar activations. These results suggest the importance of considering polysynaptic signal propagation, particularly via pathways including subcortical structures, for correctly interpreting 'functional connectivity' as assessed by simultaneous EM and fMRI

Current amplitude dependence of the response magnitude of the cerebellar activation.

<p>(<b>a</b>) Average time course of the cerebellar activation in response to an individual EM block. The shaded region indicates the EM block. For each time course, the baseline signal [mean of 2 frames (5 sec) before the onset of EM block] was subtracted before averaging. Error bars indicate standard error (SE). Red, 750 µA (240 blocks). Green, 500 µA (264 blocks). Blue, 250 µA (240 blocks). (<b>b</b>) Response magnitude for each current amplitude. Colored lines indicate data for individual monkeys (gray solid line, Monkey 1; gray dotted line, Monkey 2). Error bars indicate SE. *, <i>P</i><0.02. **, <i>P</i><0.0002. ***, <i>P</i><10⁻⁸.</p

List of BOLD activations in the cerebellum.

<p>List of the coordinates of the peaks of EM-evoked BOLD activations in the cerebellum for two monkeys (Monkey 1, 250 µA, 30 runs; Monkey 2, 500 µA, 9 runs). Significance level was set at <i>P</i><0.05 (corrected). Activated regions with volumes ≥6 mm³ (2.1 original voxels) are included. Cb4, cerebellar lobule 4. Cb5, cerebellar lobule 5. Cb6, cerebellar lobule 6. Cop, copula pyramidis. Crus2, crus2 of ansiform lobule. DPFl, dorsal paraflocculus. PM, paramedian lobule. Anatomical areas are labeled by referring to the Paxinos et al. brain atlas <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047515#pone.0047515-Paxinos1" target="_blank">[22]</a>.</p

BOLD activations in the cerebellum induced by electrical stimulation of S1.

<p>(<b>a</b>)–(<b>b</b>) A representative <i>t</i>-score map of BOLD activation in Monkey 1 in one session (250 µA, 30 runs). (a) BOLD activation at the site of EM. In Monkey 1, right S1 was stimulated (arrow). (b) BOLD activations in the cerebellum. Cb5, cerebellar lobule V. Cop, copula myramidis. (<b>c</b>)–(<b>d</b>) A representative <i>t</i>-score map of BOLD activation in Monkey 2 in one session (500 µA, 9 runs). Conventions are the same as in (a) and (b). (c) BOLD activation at the site of EM. In Monkey 2, left S1 was stimulated (arrow). (d) BOLD activations in the cerebellum.</p