Biology subjects

Shahbazi, E.

Publications and source records attributed to Shahbazi, E..

2 recordsLinked to original sources

bioRxiv2024

3D-Printable Non-invasive Head Immobilization System for Non-Human Primates

A critical contribution to understanding the primate brain is our ability to directly record, manipulate, or image the brain in awake, behaving monkeys. A challenge with carrying out these studies is that typically the monkeys head must be fixed in place. While non-invasive head immobilization systems (NHIS) have been demonstrated to be effective, they have not fully replaced the use of surgically applied headposts. Here, we introduce a novel NHIS, with an option for voluntary engagement, for macaques using a widely available resource: the 3D printer. We designed customized 3D-printable head-immobilization masks for monkeys and a docking system using standard CT scans and user-friendly open-source software packages (FLoRIN, Blender, Python). We examined the efficacy and stability of our mask system for collecting eye fixation data by measuring trial-by-trial gaze precision and accuracy in two mask-immobilized monkeys and three headposted monkeys, including a within-subjects comparison. Our results demonstrate that monkeys stabilized by the mask maintain gaze precision and accuracy comparable with their headposted counterparts. While the headpost outperformed the mask, the difference in precision was 0.03{degrees} of visual angle, and the difference in accuracy was less than 0.2{degrees}, well within the acceptable range for most use-cases. Our process allows for the production of 3D-printable masks based on CT or MRI scans with the flexibility to incorporate design modifications for experimental equipment and resizing, potentially serving as a promising and more easily accessible alternative to headposts and other NHISs for primate neuroscience research.

physiology↗

bioRxiv2022

Perceptography: unveiling visual perceptual hallucinations induced by optogenetic stimulation of the inferior temporal cortex.

Neurons in the inferotemporal (IT) cortex respond selectively to complex visual features, implying their role in object perception. However, perception is subjective and cannot be read out from neural responses; thus, bridging the causal gap between neural activity and perception demands independent characterization of perception. Historically though, the complexity of the perceptual alterations induced by artificial stimulation of IT cortex has rendered them impossible to quantify. Here we addressed this old problem by combining machine learning with high-throughput behavioral optogenetics in macaque monkeys. In closed-loop experiments, we generated complex and highly specific images that the animal could not discriminate from the state of being cortically stimulated. These images, named "perceptograms" for the first time, reveal and depict the contents of the complex hallucinatory percepts induced by local neural perturbation in IT cortex. Furthermore, we found that the nature and magnitude of these hallucinations highly depend on concurrent visual input, stimulation location, and intensity. Objective characterization of stimulation-induced perceptual events opens the door to developing a mechanistic theory of visual perception. Further, it enables us to make better visual prosthetic devices and gain a greater understanding of visual hallucinations in mental disorders. One-Sentence SummaryCombining state-of-the-art AI with high-throughput closed-loop brain stimulation experiments, for the first time, we took "pictures" of the complex and subjective visual hallucinations induced by local stimulation in the inferior temporal cortex, a cortical area associated with object recognition.

neuroscience↗