The Superior Temporal Sulcus Is Causally Connected to the Amygdala : A Combined TBS-fMRI Study.

Nonhuman primate neuroanatomical studies have identified a cortical pathway from the superior temporal sulcus (STS) projecting into dorsal subregions of the amygdala, but whether this same pathway exists in humans is unknown. Here, we addressed this question by combining theta burst transcranial magnetic stimulation (TBS) with fMRI to test the prediction that the STS and amygdala are functionally connected during face perception. Human participants (N = 17) were scanned, over two sessions, while viewing 3 s video clips of moving faces, bodies, and objects. During these sessions, TBS was delivered over the face-selective right posterior STS (rpSTS) or over the vertex control site. A region-of-interest analysis revealed results consistent with our hypothesis. Namely, TBS delivered over the rpSTS reduced the neural response to faces (but not to bodies or objects) in the rpSTS, right anterior STS (raSTS), and right amygdala, compared with TBS delivered over the vertex. By contrast, TBS delivered over the rpSTS did not significantly reduce the neural response to faces in the right fusiform face area or right occipital face area. This pattern of results is consistent with the existence of a cortico-amygdala pathway in humans for processing face information projecting from the rpSTS, via the raSTS, into the amygdala. This conclusion is consistent with nonhuman primate neuroanatomy and with existing face perception models

A broadly tuned network for affective body language in the macaque brain.

Data and visual stimuli from the publication: *Taubert, J., Japee, S., Patterson, A., Wild, H., Goyal, S., Yu, D., Ungerleider, L. G. (accepted) A broadly tuned network for affective body language in the macaque brain

From visual awareness to consciousness without sensory input: The role of spontaneous brain activity

International audienc

WILD FACES DATABASE: A database of heterogeneous faces for studying naturalistic expressions.

Data and visual stimuli from the publication: Long, H.*, Peluso, N.*, Baker, C.I. et al. A database of heterogeneous faces for studying naturalistic expressions. Sci Rep 13, 5383 (2023). *equally contributing first authors https://doi.org/10.1038/s41598-023-32659-

Equivalent processing of facial expression and identity by macaque visual system and task-optimized neural network

Both the primate visual system and artificial deep neural network (DNN) models show an extraordinary ability to simultaneously classify facial expression and identity. However, the neural computations underlying the two systems are unclear. Here, we developed a multi-task DNN model that optimally classified both monkey facial expressions and identities. By comparing the fMRI neural representations of the macaque visual cortex with the best-performing DNN model, we found that both systems: (1) share initial stages for processing low-level face features which segregate into separate branches at later stages for processing facial expression and identity respectively, and (2) gain more specificity for the processing of either facial expression or identity as one progresses along each branch towards higher stages. Correspondence analysis between the DNN and monkey visual areas revealed that the amygdala and anterior fundus face patch (AF) matched well with later layers of the DNN's facial expression branch, while the anterior medial face patch (AM) matched well with later layers of the DNN's facial identity branch. Our results highlight the anatomical and functional similarities between macaque visual system and DNN model, suggesting a common mechanism between the two systems

SUMA: AN INTERFACE FOR SURFACE-BASED INTRA- AND INTER-SUBJECT ANALYSIS WITH AFNI

Surface-based brain imaging analysis is increasingly being used for detailed analysis of the topology of brain activation patterns and changes in cerebral gray matter. Here we present SUMA, a new interface for visualizing and performing surfacebased brain imaging analysis that is tightly coupled to AFNI – a volume-based brain imaging analysis suite. The interactive part of SUMA is used for rapid and interactive surface and data visualization, access and manipulations with direct link to the volumetric data rendered in AFNI. The batch-mode part of SUMA allows for surface based operations such as geometry and data smoothing [1, 2], surface to volume domain mapping in both directions and node-based statistical and computational tools. We also present methods for mapping low resolution functional data onto the cortical surface while preserving the topological information present in the volumetric data and detail an efficient procedure for performing cross-subject, surfacebased analysis with minimal interpolation of the functional data. 1

The role of inferior frontal junction in controlling the spatially global effect of feature-based attention in human visual areas

<div><p>Feature-based attention has a spatially global effect, i.e., responses to stimuli that share features with an attended stimulus are enhanced not only at the attended location but throughout the visual field. However, how feature-based attention modulates cortical neural responses at unattended locations remains unclear. Here we used functional magnetic resonance imaging (fMRI) to examine this issue as human participants performed motion- (Experiment 1) and color- (Experiment 2) based attention tasks. Results indicated that, in both experiments, the respective visual processing areas (middle temporal area [MT+] for motion and V4 for color) as well as early visual, parietal, and prefrontal areas all showed the classic feature-based attention effect, with neural responses to the unattended stimulus significantly elevated when it shared the same feature with the attended stimulus. Effective connectivity analysis using dynamic causal modeling (DCM) showed that this spatially global effect in the respective visual processing areas (MT+ for motion and V4 for color), intraparietal sulcus (IPS), frontal eye field (FEF), medial frontal gyrus (mFG), and primary visual cortex (V1) was derived by feedback from the inferior frontal junction (IFJ). Complementary effective connectivity analysis using Granger causality modeling (GCM) confirmed that, in both experiments, the node with the highest outflow and netflow degree was IFJ, which was thus considered to be the source of the network. These results indicate a source for the spatially global effect of feature-based attention in the human prefrontal cortex.</p></div