Receptor architecture of macaque and human early visual areas: not equal, but comparable

Dysconnectivity of the Nucleus Accumbens and Amygdala in Youths with Thought Problems: A Dimensional Approach

Background: Youths with thought problems (TP) are at risk to develop psychosis and obsessive-compulsive disorder (OCD). Yet, the pathophysiological mechanisms underpinning TP are still unclear. Functional magnetic resonance imaging (fMRI) studies have shown that striatal and limbic alterations are associated with psychosis-like and obsessive-like symptoms in individuals at clinical risk for psychosis, schizophrenia, and OCD. More specifically, nucleus accumbens (NAcc) and amygdala are mainly involved in these associations. The current study aims to investigate the neural correlates of TP in youth populations using a dimensional approach and explore potential cognitive functions and neurotransmitters associated with it. Methods: Seed-to-voxels functional connectivity analyses using NAcc and amygdala as regions-of-interest were conducted with resting-state fMRI data obtained from 1360 young individuals, and potential confounders related to TP such as anxiety and cognitive functions were included as covariates in multiple regression analyses. Replicability was tested in using an adult cohort. In addition, functional decoding and neurochemical correlation analyses were performed to identify the associated cognitive functions and neurotransmitters. Results: The altered functional connectivities between the right NAcc and posterior parahippocampal gyrus, between the right amygdala and lateral prefrontal cortex, and between the left amygdala and the secondary visual area were the best predictors of TP in multiple regression model. These functional connections are mainly involved in social cognition and reward processing. Conclusions: The results show that alterations in the functional connectivity of the NAcc and the amygdala in neural pathways involved in social cognition and reward processing are associated with severity of TP in youths.

InSpectro-Gadget: A Tool for Estimating Neurotransmitter and Neuromodulator Receptor Distributions for MRS Voxels

Magnetic resonance spectroscopy (MRS) is widely used to estimate concentrations of glutamate and γ -aminobutyric acid (GABA) in specific regions of the living human brain. As cytoarchitectural properties differ across the brain, interpreting these measurements can be assisted by having knowledge of such properties for the MRS region(s) studied. In particular, some knowledge of likely local neurotransmitter receptor patterns can potentially give insights into the mechanistic environment GABA- and glutamatergic neurons are functioning in. This may be of particular utility when comparing two or more regions, given that the receptor populations may differ substantially across them. At the same time, when studying MRS data from multiple participants or timepoints, the homogeneity of the sample becomes relevant, as measurements taken from areas with different cytoarchitecture may be difficult to compare. To provide insights into the likely cytoarchitectural environment of user-defined regions-of-interest, we produced an easy to use tool - InSpectro-Gadget - that interfaces with receptor mRNA expression information from the Allen Human Brain Atlas. This Python tool allows users to input masks and automatically obtain a graphical overview of the receptor population likely to be found within. This includes comparison between multiple masks or participants where relevant. The receptors and receptor subunit genes featured include GABA- and glutamatergic classes, along with a wide range of neuromodulators. The functionality of the tool is explained here and its use is demonstrated through a set of example analyses. The tool is available at https://github.com/lizmcmanus/Inspectro-Gadget .

Cytoarchitectonic, receptor distribution and functional connectivity analyses of the macaque frontal lobe

Based on quantitative cyto- and receptor architectonic analyses, we identified 35 prefrontal areas, including novel subdivisions of Walker's areas 10, 9, 8B, and 46. Statistical analysis of receptor densities revealed regional differences in lateral and ventrolateral prefrontal cortex. Indeed, structural and functional organization of subdivisions encompassing areas 46 and 12 demonstrated significant differences in the interareal levels of α2 receptors. Furthermore, multivariate analysis included receptor fingerprints of previously identified 16 motor areas in the same macaque brains and revealed 5 clusters encompassing frontal lobe areas. We used the MRI datasets from the non-human primate data sharing consortium PRIME-DE to perform functional connectivity analyses using the resulting frontal maps as seed regions. In general, rostrally located frontal areas were characterized by bigger fingerprints, that is, higher receptor densities, and stronger regional interconnections. Whereas more caudal areas had smaller fingerprints, but showed a widespread connectivity pattern with distant cortical regions. Taken together, this study provides a comprehensive insight into the molecular structure underlying the functional organization of the cortex and, thus, reconcile the discrepancies between the structural and functional hierarchical organization of the primate frontal lobe. Finally, our data are publicly available via the EBRAINS and BALSA repositories for the entire scientific community.

Connectivity reveals homology between the visual systems of the human and macaque brains

The visual systems of humans and nonhuman primates share many similarities in both anatomical and functional organization. Understanding the homology and differences between the two systems can provide important insights into the neural basis of visual perception and cognition. This research aims to investigate the homology between human and macaque visual systems based on connectivity, using diffusion tensor imaging and resting-state functional magnetic resonance imaging to construct structural and functional connectivity fingerprints of the visual systems in humans and macaques, and quantitatively analyze the connectivity patterns. By integrating multimodal magnetic resonance imaging, this research explored the homology and differences between the two systems. The results showed that 9 brain regions in the macaque visual system formed highly homologous mapping relationships with 11 brain regions in the human visual system, and the related brain regions between the two species showed highly structure homologous, with their functional organization being essentially conserved across species. Finally, this research generated a homology information map of the visual system for humans and macaques, providing a new perspective for subsequent cross-species analysis.

Gradients of neurotransmitter receptor expression in the macaque cortex

Dynamics and functions of neural circuits depend on interactions mediated by receptors. Therefore, a comprehensive map of receptor organization across cortical regions is needed. In this study, we used in vitro receptor autoradiography to measure the density of 14 neurotransmitter receptor types in 109 areas of macaque cortex. We integrated the receptor data with anatomical, genetic and functional connectivity data into a common cortical space. We uncovered a principal gradient of receptor expression per neuron. This aligns with the cortical hierarchy from sensory cortex to higher cognitive areas. A second gradient, driven by serotonin 5-HT1A receptors, peaks in the anterior cingulate, default mode and salience networks. We found a similar pattern of 5-HT1A expression in the human brain. Thus, the macaque may be a promising translational model of serotonergic processing and disorders. The receptor gradients may enable rapid, reliable information processing in sensory cortical areas and slow, flexible integration in higher cognitive areas.

Assessment of brain imaging and cognitive function in a modified rhesus monkey model of depression

Depression incurs a huge personal and societal burden, impairing cognitive and social functioning and affecting millions of people worldwide. A better understanding of the biological basis of depression could facilitate the development of new and improved therapies. Rodent models have limitations and do not fully recapitulate human disease, hampering clinical translation. Primate models of depression help to bridge this translational gap and facilitate research into the pathophysiology of depression. Here we optimized a protocol for administering unpredictable chronic mild stress (UCMS) to non-human primates and evaluated the influence of UCMS on cognition using the classical Wisconsin General Test Apparatus (WGTA) method. We used resting-state functional MRI to explore changes in amplitude of low-frequency fluctuations and regional homogeneity in rhesus monkeys. Our work highlights that the UCMS paradigm effectively induces behavioral and neurophysiological (functional MRI) changes in monkeys but without significantly impacting cognition. The UCMS protocol requires further optimization in non-human primates to authentically simulate changes in cognition associated with depression.

The pale spear-nosed bat: A neuromolecular and transgenic model for vocal learning

Vocal learning, the ability to produce modified vocalizations via learning from acoustic signals, is a key trait in the evolution of speech. While extensively studied in songbirds, mammalian models for vocal learning are rare. Bats present a promising study system given their gregarious natures, small size, and the ability of some species to be maintained in captive colonies. We utilize the pale spear-nosed bat (Phyllostomus discolor) and report advances in establishing this species as a tractable model for understanding vocal learning. We have taken an interdisciplinary approach, aiming to provide an integrated understanding across genomics (Part I), neurobiology (Part II), and transgenics (Part III). In Part I, we generated new, high-quality genome annotations of coding genes and noncoding microRNAs to facilitate functional and evolutionary studies. In Part II, we traced connections between auditory-related brain regions and reported neuroimaging to explore the structure of the brain and gene expression patterns to highlight brain regions. In Part III, we created the first successful transgenic bats by manipulating the expression of FoxP2, a speech-related gene. These interdisciplinary approaches are facilitating a mechanistic and evolutionary understanding of mammalian vocal learning and can also contribute to other areas of investigation that utilize P. discolor or bats as study species.

Mutual influence between language and perception in multi-agent communication games

Language interfaces with many other cognitive domains. This paper explores how interactions at these interfaces can be studied with deep learning methods, focusing on the relation between language emergence and visual perception. To model the emergence of language, a sender and a receiver agent are trained on a reference game. The agents are implemented as deep neural networks, with dedicated vision and language modules. Motivated by the mutual influence between language and perception in cognition, we apply systematic manipulations to the agents’ (i) visual representations, to analyze the effects on emergent communication, and (ii) communication protocols, to analyze the effects on visual representations. Our analyses show that perceptual biases shape semantic categorization and communicative content. Conversely, if the communication protocol partitions object space along certain attributes, agents learn to represent visual information about these attributes more accurately, and the representations of communication partners align. Finally, an evolutionary analysis suggests that visual representations may be shaped in part to facilitate the communication of environmentally relevant distinctions. Aside from accounting for co-adaptation effects between language and perception, our results point out ways to modulate and improve visual representation learning and emergent communication in artificial agents.

Language is grounded in the world and used to coordinate and achieve common objectives. We simulate grounded, interactive language use with a communication game. A sender refers to an object in the environment and if the receiver selects the correct object both agents are rewarded. By practicing the game, the agents develop their own communication protocol. We use this setup to study interactions between emerging language and visual perception. Agents are implemented as neural networks with dedicated vision modules to process images of objects. By manipulating their visual representations we can show how variations in perception are reflected in linguistic variations. Conversely, we demonstrate that differences in language are reflected in the agents’ visual representations. Our simulations mirror several empirically observed phenomena: labels for concrete objects and properties (e.g., “striped”, “bowl”) group together visually similar objects, object representations adapt to the categories imposed by language, and representational spaces between communication partners align. In addition, an evolutionary analysis suggests that visual representations may be shaped, in part, to facilitate communication about environmentally relevant information. In sum, we use communication games with neural network agents to model co-adaptation effects between language and visual perception. Future work could apply this computational framework to other interfaces between language and cognition.