Biology subjects

Ungerleider, L. G.

Publications and source records attributed to Ungerleider, L. G..

6 recordsLinked to original sources

bioRxiv2022

Inability to make facial expressions dampens emotion perception.

Humans rely heavily on facial expressions for social communication to convey their thoughts and emotions and to understand them in others. One prominent but controversial view is that humans learn to recognize the significance of facial expressions by mimicking the expressions of others. This view predicts that an inability to make facial expressions (e.g., facial paralysis) would result in reduced perceptual sensitivity to others facial expressions. To test this hypothesis, we developed a diverse battery of sensitive emotion recognition tasks to characterize emotion perception in individuals with Moebius Syndrome (MBS), a congenital neurological disorder that causes facial palsy. Using computer-based emotion detection tasks we systematically assessed emotion perception thresholds for static and dynamic face and body expressions. We found that while MBS individuals were able to perform challenging perceptual control tasks, they were less efficient at extracting emotion from facial expressions, compared to matched controls. Exploratory analyses of fMRI data from a small group of MBS participants suggested potentially reduced engagement of the amygdala in MBS participants during expression processing relative to matched controls. Collectively, these results support the role of facial mimicry and consequent facial feedback and motor experience in the perception of others expressions.

neuroscience↗

bioRxiv2022

Layer-specific, retinotopically-diffuse modulation in human visual cortex by emotional faces

Emotionally expressive faces evoke enhanced neural responses in multiple brain regions, a phenomenon thought to depend critically on the amygdala. This emotion-related modulation is evident even in primary visual cortex (V1), providing a potential neural substrate by which emotionally salient stimuli can affect perception. How does emotional valence information, computed in the amygdala, reach V1? Here we use high-resolution functional MRI to investigate the layer profile and retinotopic distribution of neural activity specific to emotional facial expressions. Across three experiments, human participants viewed centrally presented face stimuli varying in emotional expression and performed a gender judgment task. We found that facial valence sensitivity was evident only in superficial cortical layers and was not restricted to the retinotopic location of the stimuli, consistent with diffuse feedback-like projections from the amygdala. Together, our results provide a feedback mechanism by which the amygdala directly modulates activity at the earliest stage of visual processing.

neuroscience↗

bioRxiv2022

Disentangling object category representations driven by dynamic and static visual input

Humans can label and categorize objects in a visual scene with high accuracy and speed--a capacity well-characterized with neuroimaging studies using static images. However, motion is another cue that could be used by the visual system to classify objects. To determine how motion-defined object category information is processed in the brain, we created a novel stimulus set to isolate motion-defined signals from other sources of information. We extracted movement information from videos of 6 object categories and applied the motion to random dot patterns. Using these stimuli, we investigated whether fMRI responses elicited by motion cues could be decoded at the object category level in functionally defined regions of occipitotemporal and parietal cortex. Participants performed a one-back repetition detection task as they viewed motion-defined stimuli or static images from the original videos. Linear classifiers could decode object category for both stimulus formats in all higher order regions of interest. More posterior occipitotemporal and ventral regions showed higher accuracy in the static condition and more anterior occipitotemporal and dorsal regions showed higher accuracy in the dynamic condition. Significantly above chance classification accuracies were also observed in all regions when training and testing the SVM classifier across stimulus formats. These results demonstrate that motion-defined cues can elicit widespread robust category responses on par with those elicited by luminance cues in regions of object-selective visual cortex. The informational content of these responses overlapped with, but also demonstrated interesting distinctions from, those elicited by static cues. Significance StatementMuch research on visual object recognition has focused on recognizing objects in static images. However, motion cues are a rich source of information that humans might also use to categorize objects. Here, we present the first study to compare neural representations of several animate and inanimate objects when category information is presented in two formats: static cues or isolated dynamic cues. Our study shows that while higher order brain regions differentially process object categories depending on format, they also contain robust, abstract category representations that generalize across format. These results expand our previous understanding of motion-derived animate and inanimate object category processing and provide useful tools for future research on object category processing driven by multiple sources of visual information.

neuroscience↗

bioRxiv2020

Curvilinear features are important for animate/inaimate categorization in macaques

The current experiment investigated the extent to which perceptual categorization of animacy, i.e. the ability to discriminate animate and inanimate objects, is facilitated by image-based features that distinguish the two object categories. We show that, with nominal training, naive macaques could classify a trial-unique set of 1000 novel images with high accuracy. To test whether image-based features that naturally differ between animate and inanimate objects, such as curvilinear and rectilinear information, contribute to the monkeys accuracy, we created synthetic images using an algorithm that distorted the global shape of the original animate/inanimate images while maintaining their intermediate features (Portilla and Simoncelli, 2000). Performance on the synthesized images was significantly above chance and was predicted by the amount of curvilinear information in the images. Our results demonstrate that, without training, macaques can use an intermediate image feature, curvilinearity, to facilitate their categorization of animate and inanimate objects.

neuroscience↗

bioRxiv2020

A comprehensive macaque fMRI pipeline and hierarchical atlas

Functional neuroimaging research in the non-human primate (NHP) has been advancing at a remarkable rate. The increase in available data establishes a need for robust analysis pipelines designed for NHP neuroimaging and accompanying template spaces to standardize the localization of neuroimaging results. Our group recently developed the NIMH Macaque Template (NMT), a high-resolution population average anatomical template and associated neuroimaging resources, providing researchers with a standard space for macaque neuroimaging (Seidlitz, Sponheim et al., 2018). Here, we release NMT v2, which includes both symmetric and asymmetric templates in stereotaxic orientation, with improvements in spatial contrast, processing efficiency, and segmentation. We also introduce the Cortical Hierarchy Atlas of the Rhesus Macaque (CHARM), a hierarchical parcellation of the macaque cerebral cortex with varying degrees of detail. These tools have been integrated into the neuroimaging analysis software AFNI (Cox, 1996) to provide a comprehensive and robust pipeline for fMRI processing, visualization and analysis of NHP data. AFNIs new @animal_warper program can be used to efficiently align anatomical scans to the NMT v2 space, and afni_proc.py integrates these results with full fMRI processing using macaque-specific parameters: from motion correction through regression modeling. Taken together, the NMT v2 and AFNI represent an all-in-one package for macaque functional neuroimaging analysis, as demonstrated with available demos for both task and resting state fMRI. HighlightsO_LIThe NMT v2, a stereotaxically aligned symmetric macaque template, is introduced. C_LIO_LIA new atlas (CHARM), defined on NMT v2, parcellates the cortex at six spatial scales. C_LIO_LIAFNIs @animal_warper aligns and maps data between monkey anatomicals and templates. C_LIO_LIAFNIs afni_proc.py facilitates monkey fMRI analysis with automated scripting and QC. C_LIO_LIDemos of macaque task and resting state fMRI analysis with these tools are provided. C_LI

neuroscience↗

bioRxiv2019

Thetaburst TMS to the posterior superior temporal sulcus decreases resting-state fMRI connectivity across the face processing network

Humans process faces using a network of face-selective regions distributed across the brain. Neuropsychological patient studies demonstrate that focal damage to nodes in this network can impair face recognition, but such patients are rare. We approximated the effects of damage to the face network in neurologically normal human participants using thetaburst transcranial magnetic stimulation (TBS). Multi-echo functional magnetic resonance imaging (fMRI) resting-state data were collected pre- and post-TBS delivery over the face-selective right superior temporal sulcus (rpSTS), or a control site in the right motor cortex. Results showed that TBS delivered over the rpSTS reduced resting-state connectivity across the extended face-processing network. This connectivity reduction was observed not only between the rpSTS and other face-selective areas, but also between non-stimulated face-selective areas across the ventral, medial and lateral brain surfaces (e.g. between the right amygdala and bilateral fusiform face areas and occipital face areas). TBS delivered over the motor cortex did not produce significant changes in resting-state connectivity across the face-processing network. These results demonstrate that, even without task-induced fMRI signal changes, disrupting a single node in a brain network can decrease the functional connectivity between nodes in that network that have not been directly stimulated. Author SummaryHuman behavior is dependent on brain networks that perform different cognitive functions. We combined thetaburst transcranial magnetic stimulation (TBS) with resting-state fMRI to study the face processing network. Disruption of the face-selective right posterior superior temporal sulcus (rpSTS) reduced fMRI connectivity across the face network. This impairment in connectivity was observed not only between the rpSTS and other face-selective areas, but also between non-stimulated face-selective areas on the ventral and medial brain surfaces (e.g. between the right amygdala and bilateral fusiform face areas and occipital face areas). Thus, combined TBS/fMRI can be used to approximate and measure the effects of focal brain damage on brain networks, and suggests such an approach may be useful for mapping intrinsic network organization. Technical TermsO_ST_ABSTBS vs TMSC_ST_ABSTranscranial magnetic stimulation (TMS) is a method that induces current in neural tissue by using a rapidly changing magnetic field. The pattern of magnetic field changes can vary. Thetaburst TMS (TBS) is a type of TMS where the same stimulation pattern fluctuates at around a 5Hz cycle. Multi-echo fMRIDuring typical fMRI, protons are excited and there is a delay, the echo time, before data are collected. That delay is typically designed to result in a high contrast for blood oxygenation differences. In multi-echo fMRI, data are collected at several echo times each time protons are excited. This results in data that have different levels of contrast for blood oxygenation differences. This added information can be used to empirically decrease noise. Face networkA group of brain regions that show significant activity changes in response to visual face stimuli. While these regions have been defined using univariate analyses with task-based fMRI, they often significantly correlate with each other at rest. In this manuscript, the following regions were a priori defined as part of the face network: posterior superior temporal sulcus (pSTS), amygdala, fusiform face area (FFA), and occipital face area (OFA). Matrix based analysis (MBA)A recent approach that uses a Bayesian multilevel modeling framework to identify pairs of ROIs where a decrease in correlation magnitude was larger than expected along with a measure of statistical evidence. With this approach, correlations between all pairs of ROIs are assessed as part of a single model rather than many independent statistical tests.

neuroscience↗