A connectomic approach to the lateral geniculate nucleus

AbstractAlthough the core functions and structure of the lateral geniculate nucleus (LGN) are well understood, this core is surrounded by questions about the integration of feedforward and feedback connections, interactions between different channels of information, and how activity dependent development restructures synaptic networks. Our understanding of the organization of the mouse LGN is particularly limited given how important it has become as a model system. Advances in circuit scale electron microscopy (cellular connectomics) have made it possible to reconstruct the synaptic connectivity of hundreds of neurons within in a circuit the size of the mouse LGN. These circuit reconstructions can reveal cell type-to-cell type canonical wiring diagrams as well as the higher order wiring motifs that are only visible in reconstructions of intact networks. Connectomic analysis of the LGN therefore not only can answer longstanding questions about the organization of the visual thalamus but also presents unique opportunities for investigating fundamental properties of mammalian circuit formation.</jats:p

Connectomes as constitutively epistemic objects: critical perspectives on modeling in current neuroanatomy

in a nervous system of a given species. This chapter provides a critical perspective on the role of connectomes in neuroscientific practice and asks how the connectomic approach fits into a larger context in which network thinking permeates technology, infrastructure, social life, and the economy. In the first part of this chapter, we argue that, seen from the perspective of ongoing research, the notion of connectomes as “complete descriptions” is misguided. Our argument combines Rachel Ankeny’s analysis of neuroanatomical wiring diagrams as “descriptive models” with Hans-Joerg Rheinberger’s notion of “epistemic objects,” i.e., targets of research that are still partially unknown. Combining these aspects we conclude that connectomes are constitutively epistemic objects: there just is no way to turn them into permanent and complete technical standards because the possibilities to map connection properties under different modeling assumptions are potentially inexhaustible. In the second part of the chapter, we use this understanding of connectomes as constitutively epistemic objects in order to critically assess the historical and political dimensions of current neuroscientific research. We argue that connectomics shows how the notion of the “brain as a network” has become the dominant metaphor of contemporary brain research. We further point out that this metaphor shares (potentially problematic) affinities to the form of contemporary “network societies.” We close by pointing out how the relation between connectomes and networks in society could be used in a more fruitful manner

Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy

Neural circuits can be reconstructed from brain images acquired by serial section electron microscopy. Image analysis has been performed by manual labor for half a century, and efforts at automation date back almost as far. Convolutional nets were first applied to neuronal boundary detection a dozen years ago, and have now achieved impressive accuracy on clean images. Robust handling of image defects is a major outstanding challenge. Convolutional nets are also being employed for other tasks in neural circuit reconstruction: finding synapses and identifying synaptic partners, extending or pruning neuronal reconstructions, and aligning serial section images to create a 3D image stack. Computational systems are being engineered to handle petavoxel images of cubic millimeter brain volumes

Local molecular and global connectomic contributions to cross-disorder cortical abnormalities

Contains fulltext : 253170.pdf (Publisher’s version ) (Open Access

Mol Psychiatry

Psychiatry is undergoing a paradigm shift from the acceptance of distinct diagnoses to a representation of psychiatric illness that crosses diagnostic boundaries. How this transition is supported by a shared neurobiology remains largely unknown. In this study, we first identify single nucleotide polymorphisms (SNPs) associated with psychiatric disorders based on 136 genome-wide association studies. We then conduct a joint analysis of these SNPs and brain structural connectomes in 678 healthy children in the PING study. We discovered a strong, robust, and transdiagnostic mode of genome-connectome covariation which is positively and specifically correlated with genetic risk for psychiatric illness at the level of individual SNPs. Similarly, this mode is also significantly positively correlated with polygenic risk scores for schizophrenia, alcohol use disorder, major depressive disorder, a combined bipolar disorder-schizophrenia phenotype, and a broader cross-disorder phenotype, and significantly negatively correlated with a polygenic risk score for educational attainment. The resulting "vulnerability network" is shown to mediate the influence of genetic risks onto behaviors related to psychiatric vulnerability (e.g., marijuana, alcohol, and caffeine misuse, perceived stress, and impulsive behavior). Its anatomy overlaps with the default-mode network, with a network of cognitive control, and with the occipital cortex. These findings suggest that the brain vulnerability network represents an endophenotype funneling genetic risks for various psychiatric illnesses through a common neurobiological root. It may form part of the neural underpinning of the well-recognized but poorly explained overlap and comorbidity between psychiatric disorders.IDDRC U54 HD090255/U.S. Department of Health & Human ServicesNational Institutes of Health (NIH)/S10 OD025111/OD/NIH HHS/United StatesFoundation Fellowship/Royal College of Psychiatrists (RCPsych)/WT_/Wellcome Trust/United KingdomNARSAD Distinguished Investigator award/Brain and Behavior Research Foundation (Brain & Behavior Research Foundation)/R44 MH086984/MH/NIMH NIH HHS/United StatesR01 EB019483/EB/NIBIB NIH HHS/United StatesU54 HD090255/HD/NICHD NIH HHS/United StatesR01 NS079788/NS/NINDS NIH HHS/United StatesFellowship/Foulkes Foundation/S10 OD025111/CD/ODCDC CDC HHS/United States2021-11-06T00:00:00Z32372008PMC76446228645vault:3615

Brain explorer for connectomic analysis

Visualization plays a vital role in the analysis of multimodal neuroimaging data. A major challenge in neuroimaging visualization is how to integrate structural, functional, and connectivity data to form a comprehensive visual context for data exploration, quality control, and hypothesis discovery. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomical structure. In this paper, new surface texture techniques are developed to map non-spatial attributes onto both 3D brain surfaces and a planar volume map which is generated by the proposed volume rendering technique, spherical volume rendering. Two types of non-spatial information are represented: (1) time series data from resting-state functional MRI measuring brain activation; (2) network properties derived from structural connectivity data for different groups of subjects, which may help guide the detection of differentiation features. Through visual exploration, this integrated solution can help identify brain regions with highly correlated functional activations as well as their activation patterns. Visual detection of differentiation features can also potentially discover image-based phenotypic biomarkers for brain diseases