The natural axis of transmitter receptor distribution in the human cerebral cortex

Transmitter receptors constitute a key component of the molecular machinery for intercellular communication in the brain. Recent efforts have mapped the density of diverse transmitter receptors across the human cerebral cortex with an unprecedented level of detail. Here, we distill these observations into key organizational principles. We demonstrate that receptor densities form a natural axis in the human cerebral cortex, reflecting decreases in differentiation at the level of laminar organization and a sensory-to-association axis at the functional level. Along this natural axis, key organizational principles are discerned: progressive molecular diversity (increase of the diversity of receptor density); excitation/inhibition (increase of the ratio of excitatory-to-inhibitory receptor density); and mirrored, orderly changes of the density of ionotropic and metabotropic receptors. The uncovered natural axis formed by the distribution of receptors aligns with the axis that is formed by other dimensions of cortical organization, such as the myelo- and cytoarchitectonic levels. Therefore, the uncovered natural axis constitutes a unifying organizational feature linking multiple dimensions of the cerebral cortex, thus bringing order to the heterogeneity of cortical organization

Bringing Anatomical Information into Neuronal Network Models

For constructing neuronal network models computational neuroscientists have access to wide-ranging anatomical data that nevertheless tend to cover only a fraction of the parameters to be determined. Finding and interpreting the most relevant data, estimating missing values, and combining the data and estimates from various sources into a coherent whole is a daunting task. With this chapter we aim to provide guidance to modelers by describing the main types of anatomical data that may be useful for informing neuronal network models. We further discuss aspects of the underlying experimental techniques relevant to the interpretation of the data, list particularly comprehensive data sets, and describe methods for filling in the gaps in the experimental data. Such methods of `predictive connectomics' estimate connectivity where the data are lacking based on statistical relationships with known quantities. It is instructive, and in certain cases necessary, to use organizational principles that link the plethora of data within a unifying framework where regularities of brain structure can be exploited to inform computational models. In addition, we touch upon the most prominent features of brain organization that are likely to influence predicted neuronal network dynamics, with a focus on the mammalian cerebral cortex. Given the still existing need for modelers to navigate a complex data landscape full of holes and stumbling blocks, it is vital that the field of neuroanatomy is moving toward increasingly systematic data collection, representation, and publication

Situating the default-mode network along a principal gradient of macroscale cortical organization

Understanding how the structure of cognition arises from the topographical organization of the cortex is a primary goal in neuroscience. Previous work has described local functional gradients extending from perceptual and motor regions to cortical areas representing more abstract functions, but an overarching framework for the association between structure and function is still lacking. Here, we show that the principal gradient revealed by the decomposition of connectivity data in humans and the macaque monkey is anchored by, at one end, regions serving primary sensory/motor functions and at the other end, transmodal regions that, in humans, are known as the default-mode network (DMN). These DMN regions exhibit the greatest geodesic distance along the cortical surface-and are precisely equidistant-from primary sensory/motor morphological landmarks. The principal gradient also provides an organizing spatial framework for multiple large-scale networks and characterizes a spectrum from unimodal to heteromodal activity in a functional metaanalysis. Together, these observations provide a characterization of the topographical organization of cortex and indicate that the role of the DMN in cognition might arise from its position at one extreme of a hierarchy, allowing it to process transmodal information that is unrelated to immediate sensory input

Functional connectivity of task context representations in prefrontal nodes of the multiple demand network

A subset of regions in the lateral and medial prefrontal cortex and the anterior insula increase their activity level whenever a cognitive task becomes more demanding, regardless of the specific nature of this demand. During execution of a task, these areas and the surrounding cortex temporally encode aspects of the task context in spatially distributed patterns of activity. It is not clear whether these patterns reflect underlying anatomical subnetworks that still exist when task execution has finished. We use fMRI in 12 participants performing alternating blocks of three cognitive tasks to address this question. A first data set is used to define multiple demand regions in each participant. A second dataset from the same participants is used to determine multiple demand voxel assemblies with a preference for one task over the others. We then show that these voxels remain functionally coupled during execution of non-preferred tasks and that they exhibit stronger functional connectivity during rest. This indicates that the assemblies of task preference sharing voxels reflect patterns of underlying anatomical connections. Moreover, we show that voxels preferring the same task have more similar whole brain functional connectivity profiles that are consistent across participants. This suggests that voxel assemblies differ in patterns of input-output connections, most likely reflecting task demand-specific information exchange

Task-specific subnetworks extend from prefrontal cortex to striatum

Functional magnetic resonance imaging (fMRI) studies on the dynamic representation of task content focus preferentially on the cerebral cortex. However, neurophysiological studies report coding of task-relevant features also by neurons in the striatum, suggesting basal ganglia involvement in cognitive decision-making. Here we use fMRI data to show that also in humans the striatum is an integrated part of the cognitive brain network. Twelve participants performed 3 cognitive tasks in the scanner, i.e., the Eriksen flanker task, a 2-back matching spatial working memory task, and a response scheme switching task. First, we use region of interest-based multivariate pattern classification to demonstrate that each task reliably induces a unique activity pattern in the striatum and in the lateral prefrontal cortex. We show that the three tasks can also be distinguished in putamen, caudate nucleus and ventral striatum alone. We additionally establish that the contribution of striatum to cognition is not sensitive to habituation or learning. Secondly, we use voxel-to-voxel functional connectivity to establish that voxels in the lateral prefrontal cortex and in the striatum that prefer the same task show significantly stronger functional coupling than voxel pairs in these remote structures that prefer different tasks. These results suggest that striatal neurons form subnetworks with cognition-related regions of the prefrontal cortex. These remote neuron populations are interconnected via functional couplings that exceed the time of execution of the specific tasks

Task-specific subnetworks extend from prefrontal cortex to striatum

Functional magnetic resonance imaging (fMRI) studies on the dynamic representation of task content focus preferentially on the cerebral cortex. However, neurophysiological studies report coding of task-relevant features also by neurons in the striatum, suggesting basal ganglia involvement in cognitive decision-making. Here we use fMRI data to show that also in humans the striatum is an integrated part of the cognitive brain network. Twelve participants performed 3 cognitive tasks in the scanner, i.e., the Eriksen flanker task, a 2-back matching spatial working memory task, and a response scheme switching task. First, we use region of interest-based multivariate pattern classification to demonstrate that each task reliably induces a unique activity pattern in the striatum and in the lateral prefrontal cortex. We show that the three tasks can also be distinguished in putamen, caudate nucleus and ventral striatum alone. We additionally establish that the contribution of striatum to cognition is not sensitive to habituation or learning. Secondly, we use voxel-to-voxel functional connectivity to establish that voxels in the lateral prefrontal cortex and in the striatum that prefer the same task show significantly stronger functional coupling than voxel pairs in these remote structures that prefer different tasks. These results suggest that striatal neurons form subnetworks with cognition-related regions of the prefrontal cortex. These remote neuron populations are interconnected via functional couplings that exceed the time of execution of the specific tasks