Automatic segmentation of Drosophila neural compartments using GAL4 expression data reveals novel visual pathways

We made use of two recent, large-scale Drosophila GAL4 libraries and associated confocal imaging datasets to automatically segment large brain regions into smaller putative functional units such as neuropils and fiber tracts. The method we developed is based on the hypothesis that molecular identity can be used to assign individual voxels to biologically meaningful regions. Our results (available at https://strawlab.org/braincode) are consistent with this hypothesis because regions with well-known anatomy, namely the antennal lobes and central complex, were automatically segmented into familiar compartments. We then applied the algorithm to the central brain regions receiving input from the optic lobes. Based on the automated segmentation and manual validation, we can identify and provide promising driver lines for 10 previously identified and 14 novel types of visual projection neurons and their associated optic glomeruli. The same strategy can be used in other brain regions and likely other species, including vertebrates.

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A mathematical model of ephaptic interactions in neuronal fiber pathways: Could there be more than transmission along the tracts?

Network Neuroscience ◽

10.1162/netn_a_00134 ◽

2020 ◽

Vol 4 (3) ◽

pp. 595-610

Author(s):

Hiba Sheheitli ◽

Viktor K. Jirsa

Keyword(s):

Large Scale ◽

Numerical Solutions ◽

Phase Locking ◽

Finite Size ◽

Network Models ◽

Brain Regions ◽

Brain Network ◽

Fiber Tracts ◽

Proposed Model ◽

Ephaptic Coupling

While numerous studies of ephaptic interactions have focused on either axons of peripheral nerves or on cortical structures, no attention has been given to the possibility of ephaptic interactions in white matter tracts. Inspired by the highly organized, tightly packed geometry of axons in fiber pathways, we aim to investigate the potential effects of ephaptic interactions along these structures that are resilient to experimental probing. We use axonal cable theory to derive a minimal model of a sheet of N ephaptically coupled axons. Numerical solutions of the proposed model are explored as ephaptic coupling is varied. We demonstrate that ephaptic interactions can lead to local phase locking between adjacent traveling impulses and that, as coupling is increased, traveling impulses trigger new impulses along adjacent axons, resulting in finite size traveling fronts. For strong enough coupling, impulses propagate laterally and backwards, resulting in complex spatiotemporal patterns. While common large-scale brain network models often model fiber pathways as simple relays of signals between different brain regions, our work calls for a closer reexamination of the validity of such a view. The results suggest that in the presence of significant ephaptic interactions, the brain fiber tracts can act as a dynamic active medium.

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Topographic organization of single-neuron projectome for mouse prefrontal cortex

10.21203/rs.3.rs-117637/v1 ◽

2020 ◽

Author(s):

Jun Yan ◽

Le Gao ◽

Sang Liu ◽

Lingfeng Gou ◽

Yachuang Hu ◽

...

Keyword(s):

Prefrontal Cortex ◽

Large Scale ◽

Single Neuron ◽

Projection Neurons ◽

Distribution Analysis ◽

Structural Principle ◽

Fiber Tracts ◽

Branching Points ◽

Before And After ◽

High Level

Abstract Neurons in the prefrontal cortex (PFC) are responsible for high-level cognitive functions. Comparing to sensory and motor cortices, however, the connectivity organization of PFC is far more complex and still poorly understood. Here we report that whole-brain reconstruction of complete axonal morphologies of over 6,000 projection neurons in mouse PFC revealed fine-grained topographic relationship between the soma locations in PFC and their projection patterns before and after arriving the cortical and subcortical target regions. We first mapped the long-range projections of intratelencephalic (IT), pyramidal tract (PT), and corticothalamic (CT) neurons, and found that each class of these neurons can be further categorized into target-based subclasses, with somata of each subclass preferentially located at different PFC domains. Furthermore, the distribution of individual axon projections within each target region exhibited subregion preference, with their somata located in specific PFC subdomains. Mapping of single axons revealed a topographic order of primary axons within IT, PT, and CT fiber tracts that preserved the topography of their soma locations within PFC, and a spatial order of collateral branching points that yielded ordered clusters of collaterals aiming at specific targets. Within the target regions, we further observed subregion-specific arbor distribution that depended on the soma location. Such arbor distribution analysis of cortico-cortical PFC axons unveiled asymmetric terminal connectivity in PFC network. Our results demonstrate how large-scale single-neuron projectome analysis can provide new insights into the structural principle within the brain.

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The connectome predicts resting state functional connectivity across the Drosophila brain

10.1101/2020.12.11.422105 ◽

2020 ◽

Cited By ~ 1

Author(s):

Maxwell H Turner ◽

Kevin Mann ◽

Thomas R. Clandinin

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Neural Circuit ◽

Brain Networks ◽

Brain Regions ◽

Central Complex ◽

Resting State Functional Connectivity ◽

Brain Structure And Function ◽

And Function

Connectomic datasets have emerged as invaluable tools for understanding neural circuits in many systems. What constraints does the connectome place on information processing and routing in a large scale neural circuit? For mesoscale brain networks, the relationship between cell and synaptic level connectivity and brain function is not well understood. Here, we use data from the Drosophila connectome in conjunction with whole-brain in vivo imaging to relate structural and functional connectivity in the central brain. We find that functional connectivity is strongly associated with the strength of both direct and indirect anatomical pathways. We also show that some brain regions, including the mushroom body and central complex, show considerably higher functional connectivity to other brain regions than is predicted based on their direct anatomical connections. We find several key topological similarities between mesoscale brain networks in flies and mammals, revealing conserved principles relating brain structure and function.

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Visual projection neurons in the Drosophila lobula link feature detection to distinct behavioral programs

eLife ◽

10.7554/elife.21022 ◽

2016 ◽

Vol 5 ◽

Cited By ~ 94

Author(s):

Ming Wu ◽

Aljoscha Nern ◽

W Ryan Williamson ◽

Mai M Morimoto ◽

Michael B Reiser ◽

...

Keyword(s):

Visual Processing ◽

Feature Detection ◽

Brain Structures ◽

Brain Regions ◽

Projection Neurons ◽

Small Object ◽

Two Photon ◽

Visual Projection ◽

Anatomical Connection

Visual projection neurons (VPNs) provide an anatomical connection between early visual processing and higher brain regions. Here we characterize lobula columnar (LC) cells, a class of Drosophila VPNs that project to distinct central brain structures called optic glomeruli. We anatomically describe 22 different LC types and show that, for several types, optogenetic activation in freely moving flies evokes specific behaviors. The activation phenotypes of two LC types closely resemble natural avoidance behaviors triggered by a visual loom. In vivo two-photon calcium imaging reveals that these LC types respond to looming stimuli, while another type does not, but instead responds to the motion of a small object. Activation of LC neurons on only one side of the brain can result in attractive or aversive turning behaviors depending on the cell type. Our results indicate that LC neurons convey information on the presence and location of visual features relevant for specific behaviors.

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A Large-Scale Structural and Functional Connectome of Social Mentalizing

10.1101/2020.11.27.400929 ◽

2020 ◽

Author(s):

Yin Wang ◽

Athanasia Metoki ◽

Yunman Xia ◽

Yinyin Zang ◽

Yong He ◽

...

Keyword(s):

Default Mode Network ◽

Large Scale ◽

Brain Regions ◽

Distributed Network ◽

Social Brain ◽

Default Mode ◽

Fiber Tracts ◽

Neuroimaging Data ◽

Social Mentalizing ◽

The Mind

AbstractHumans have a remarkable ability to infer the mind of others. This mentalizing skill relies on a distributed network of brain regions but how these regions connect and interact is not well understood. Here we leveraged large-scale multimodal neuroimaging data to elucidate the connectome-level organization and brain-wide mechanisms of mentalizing processing. Key features of the mentalizing connectome have been delineated in exquisite detail and its relationship with the default mode network has been extensively scrutinized. Our study demonstrates that mentalizing processing unfolds across functionally heterogeneous regions with highly structured fiber tracts and unique hierarchical functional architecture, which make it distinguishable from the default mode network and other social brain networks.

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Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states

Communications Biology ◽

10.1038/s42003-021-01700-6 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Arian Ashourvan ◽

Preya Shah ◽

Adam Pines ◽

Shi Gu ◽

Christopher W. Lynn ◽

...

Keyword(s):

White Matter ◽

Maximum Entropy ◽

Large Scale ◽

Structural Connectivity ◽

Quantitative Relationship ◽

Brain Regions ◽

Maximum Entropy Model ◽

Entropy Model ◽

Activation Patterns ◽

Co Activation

AbstractA major challenge in neuroscience is determining a quantitative relationship between the brain’s white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes’ activation patterns’ probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM’s interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions’ distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain’s structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

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On the structural connectivity of large-scale models of brain networks at cellular level

Scientific Reports ◽

10.1038/s41598-021-83759-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Giuseppe Giacopelli ◽

Domenico Tegolo ◽

Emiliano Spera ◽

Michele Migliore

Keyword(s):

Large Scale ◽

Brain Networks ◽

Structural Connectivity ◽

Network Models ◽

Brain Regions ◽

Cellular Level ◽

Scale Model ◽

Mathematical Framework ◽

Underlying Mechanisms ◽

Experimental Findings

AbstractThe brain’s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the corresponding biological system. To solve this problem, we propose to use a new mathematical framework able to use sparse and limited experimental data to quantitatively reproduce the structural connectivity of biological brain networks at cellular level.

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Reconfiguration of human evolving large-scale epileptic brain networks prior to seizures: an evaluation with node centralities

Scientific Reports ◽

10.1038/s41598-020-78899-7 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Rieke Fruengel ◽

Timo Bröhl ◽

Thorsten Rings ◽

Klaus Lehnertz

Keyword(s):

Large Scale ◽

Brain Networks ◽

Brain Regions ◽

Brain Network ◽

Theoretical Concept ◽

Global Network ◽

Time Resolved ◽

Functional Connections ◽

Node Centrality ◽

Network Mechanism

AbstractPrevious research has indicated that temporal changes of centrality of specific nodes in human evolving large-scale epileptic brain networks carry information predictive of impending seizures. Centrality is a fundamental network-theoretical concept that allows one to assess the role a node plays in a network. This concept allows for various interpretations, which is reflected in a number of centrality indices. Here we aim to achieve a more general understanding of local and global network reconfigurations during the pre-seizure period as indicated by changes of different node centrality indices. To this end, we investigate—in a time-resolved manner—evolving large-scale epileptic brain networks that we derived from multi-day, multi-electrode intracranial electroencephalograpic recordings from a large but inhomogeneous group of subjects with pharmacoresistant epilepsies with different anatomical origins. We estimate multiple centrality indices to assess the various roles the nodes play while the networks transit from the seizure-free to the pre-seizure period. Our findings allow us to formulate several major scenarios for the reconfiguration of an evolving epileptic brain network prior to seizures, which indicate that there is likely not a single network mechanism underlying seizure generation. Rather, local and global aspects of the pre-seizure network reconfiguration affect virtually all network constituents, from the various brain regions to the functional connections between them.

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Robust information routing by dorsal subiculum neurons

Science Advances ◽

10.1126/sciadv.abf1913 ◽

2021 ◽

Vol 7 (11) ◽

pp. eabf1913

Author(s):

Takuma Kitanishi ◽

Ryoko Umaba ◽

Kenji Mizuseki

Keyword(s):

Nucleus Accumbens ◽

Large Scale ◽

Dorsal Hippocampus ◽

Retrosplenial Cortex ◽

Projection Neurons ◽

Target Region ◽

Ca1 Neurons ◽

Axonal Projections ◽

Output Structure ◽

Hippocampal Ca1

The dorsal hippocampus conveys various information associated with spatial navigation; however, how the information is distributed to multiple downstream areas remains unknown. We investigated this by identifying axonal projections using optogenetics during large-scale recordings from the rat subiculum, the major hippocampal output structure. Subicular neurons demonstrated a noise-resistant representation of place, speed, and trajectory, which was as accurate as or even more accurate than that of hippocampal CA1 neurons. Speed- and trajectory-dependent firings were most prominent in neurons projecting to the retrosplenial cortex and nucleus accumbens, respectively. Place-related firing was uniformly observed in neurons targeting the retrosplenial cortex, nucleus accumbens, anteroventral thalamus, and medial mammillary body. Theta oscillations and sharp-wave/ripples tightly controlled the firing of projection neurons in a target region–specific manner. In conclusion, the dorsal subiculum robustly routes diverse navigation-associated information to downstream areas.

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Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition

Scientific Reports ◽

10.1038/s41598-020-80293-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Abhishek Uday Patil ◽

Sejal Ghate ◽

Deepa Madathil ◽

Ovid J. L. Tzeng ◽

Hsu-Wen Huang ◽

...

Keyword(s):

Cognitive Processes ◽

Large Scale ◽

Brain Networks ◽

Blood Oxygen Level Dependent ◽

Detection Algorithm ◽

Brain Regions ◽

Creative Cognition ◽

Network Flexibility ◽

Community Detection Algorithm ◽

Window Approach

AbstractCreative cognition is recognized to involve the integration of multiple spontaneous cognitive processes and is manifested as complex networks within and between the distributed brain regions. We propose that the processing of creative cognition involves the static and dynamic re-configuration of brain networks associated with complex cognitive processes. We applied the sliding-window approach followed by a community detection algorithm and novel measures of network flexibility on the blood-oxygen level dependent (BOLD) signal of 8 major functional brain networks to reveal static and dynamic alterations in the network reconfiguration during creative cognition using functional magnetic resonance imaging (fMRI). Our results demonstrate the temporal connectivity of the dynamic large-scale creative networks between default mode network (DMN), salience network, and cerebellar network during creative cognition, and advance our understanding of the network neuroscience of creative cognition.

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