A multimodal cell census and atlas of the mammalian primary motor cortex

ABSTRACT We report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties, and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties

A Parametric Empirical Bayesian framework for the EEG/MEG inverse problem: generative models for multisubject and multimodal integration

We review recent methodological developments within a Parametric Empirical Bayesian (PEB) framework for reconstructing intracranial sources of extracranial electroencephalographic (EEG) and magnetoencephalographic (MEG) data under linear Gaussian assumptions. The PEB framework offers a natural way to integrate multiple constraints (spatial priors) on this inverse problem, such as those derived from different modalities (e.g., from functional magnetic resonance imaging, fMRI) or from multiple replications (e.g., subjects). Using variations of the same basic generative model, we illustrate the application of PEB to three cases: 1) symmetric integration (fusion) of MEG and EEG; 2) asymmetric integration of MEG or EEG with fMRI, and 3) group-optimisation of spatial priors across subjects. We evaluate these applications on multimodal data acquired from 18 subjects, focusing on energy induced by face perception within a time-frequency window of 100-220ms, 8-18Hz. We show the benefits of multi-modal, multi-subject integration in terms of the model evidence and the reproducibility (over subjects) of cortical responses to faces

State-dependent variability of dynamic functional connectivity between frontoparietal and default networks relates to cognitive flexibility

The brain is a dynamic, flexible network that continuously reconfigures. However, the neural underpinnings of how state-dependent variability of dynamic functional connectivity (vdFC) relates to cognitive flexibility are unclear. We therefore investigated flexible functional connectivity during resting-state and task-state functional magnetic resonance imaging (rs-fMRI and t-fMRI, resp.) and performed separate, out-of-scanner neuropsychological testing. We hypothesize that state-dependent vdFC between the frontoparietal network (FPN) and the default mode network (DMN) relates to cognitive flexibility. Seventeen healthy subjects performed the Stroop color word test and underwent t-fMRI (Stroop computerized version) and rs-fMRI. Time series were extracted from a cortical atlas, and a sliding window approach was used to obtain a number of correlation matrices per subject. vdFC was defined as the standard deviation of connectivity strengths over these windows. Higher task-state FPN–DMN vdFC was associated with greater out-of-scanner cognitive flexibility, while the opposite relationship was present for resting-state FPN–DMN vdFC. Moreover, greater contrast between task-state and resting-state vdFC related to better cognitive performance. In conclusion, our results suggest that not only the dynamics of connectivity between these networks is seminal for optimal functioning, but also that the contrast between dynamics across states reflects cognitive performance

Comparison of noise-normalized minimum norm estimates for MEG analysis using multiple resolution metrics

Noise-normalization has been shown to partly compensate for the localization bias towards superficial sources in minimum norm estimation. However, it has been argued that in order to make inferences for the case of multiple sources, localization properties alone are insufficient. Instead, multiple measures of resolution should be applied to both point-spread and cross-talk functions (PSFs and CTFs). Here, we demonstrate that noise-normalization affects the shapes of PSFs, but not of CTFs. We evaluated PSFs and CTFs for the MNE, dSPM and sLORETA inverse operators, on the metrics dipole localization error (DLE), spatial dispersion (SD) and overall amplitude (OA). We used 306-channel MEG configurations obtained from 17 subjects in a real experiment, including individual noise covariance matrices and head geometries. We confirmed that for PSFs DLE improved after noise normalization, and is zero for sLORETA. However, SD was generally lower for the unnormalized MNE. OA distributions were similar for all three methods, indicating that all three methods may greatly underestimate some sources relative to others. The reliability of differences between methods across subjects was demonstrated using distributions of standard deviations and p-values from paired t-tests. As predicted, the shapes of CTFs were the same for all methods, reflecting the general resolution limits of the inverse problem. This means that noise-normalization is of no consequence where linear estimation procedures are used as “spatial filters.” While low DLE is advantageous for the localization of a single source, or possibly a few spatially distinct sources, the benefit for the case of complex source distributions is not obvious. We suggest that software packages for source estimation should include comprehensive tools for evaluating the performance of different methods