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Hemibrain Neuronal Connectome

Olfactory and Thermo/Hygrosensory Processing

@kaggle.thedevastator_hemibrain_neuronal_connectome

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About this Dataset

Hemibrain Neuronal Connectome

Hemibrain Neuronal Connectome

Olfactory and Thermo/Hygrosensory Processing

By [source]

About this dataset

The dataset contains 11 CSV files providing different aspects of the connectome analysis such as identified antennal lobe receptor neurons (ALRNs), local neurons (ALLNs), projection neurons (ALPNs), third-order olfactory neurons (TOONs), descending/ventral nervous system cells (DNs) from hemibrain space root points, start points for neuron compartments etc., amongst others. Additionally it includes an OBJ file containing 3D triangle mesh for surface plots of 51 glomeruli derived from ALRN pre-synapses and another 3D mesh consisting of 51 glomeruli derived from ALPN pre-synapses representing both olfactory plus seven thermo/hygrosensory glomleruli. With this wealth of data detailing various components at play , researchers can now traverse a deeper level into understanding the complex nature of olfaction & its connections while making possible links between brain structure & behaviour

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How to use the dataset

  • Learn about root points for different neurons – Each neuron can be identified by its root point and start point coordinates which can be found within each CSV file. Use these coordinates along with additional notes associated with NNs or glomeruli for further exploration into specific features for individual neurons or glomeruli’s functionality within their respective tissues such as ALRNs & TPONs studies etc..

  • Create & analyze 3D triangle mesh – Create a 3D triangle mesh using all blanks from OBJ which will render all 51 olfactory + 7 thermo/hygrosensory antennal lobe glomeruli generated from ALRN presynapses, & all 51 olfactory + 7 thermo/hygrosensory antennal lobe glomeruli generated from ALPN presynapses necessary for accurate analysis. Observe through visualization how they interact with other components connected directly or indirectly across different points mapped throughout these meshes like nodes distinctively representing synapses when making connections between particular neurons giving rise to distinct patterns corresponding various pathways thereby enabling a better description of their responses towards stimuli derived via divergent outputs provided across targeted regions eventually leading up patterned behavior studies involving either efferent/afferent transmission upon reception as required then stated under stimulation specifically identifying/tackling emergence of specific predefined behavior(s).

  • Correlate results – By correlating results obtained from creating 3D triangle mesh alongside note values assigned based separate trajectories belonging toward both nodes within those same meshes can provide user more insights on different types activities consistently dependent upon integration degree assisting users better interpret study’s outcome list accurately modifying decisions taken sincerely competing achieve maximum

Research Ideas

  • Using mappings of the connectome, researchers could develop tools and models that allow users to visualize different aspects of olfactory/thermo/hygrosensory processing. Such tools would be useful for educational and research purposes, allowing students to have a better understanding of the complexities behind these senses.
  • Researchers could create a virtual reality model that uses information from the dataset's 3D meshes to simulate how different neurons interact with one another during processing, and how inputs from various parts of the body are integrated in real-time.
  • With much higher resolution data on glomeruli, this dataset can be used to gain insight into how glomeruli respond differently based on composition variations and changes in stimulus intensity. This could potentially lead to improved methods for studying and modulating glomerular responses for targeted or enhanced signaling pathways or detection algorithms

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: S2_hemibrain_olfactory_information.csv

Column name Description
glomerulus The glomerulus of the neuron. (String)
laterality The laterality of the neuron. (String)
expected_cit The expected number of citrate-sensitive neurons. (Integer)
expected_RN_female_1h The expected number of receptor neurons in female 1 hour. (Integer)
expected_RN_female_SD The expected number of receptor neurons in female standard deviation. (Integer)
missing The number of missing receptor neurons. (Integer)
RN_frag The number of fragmented receptor neurons. (Integer)
odour_scenes The odour scenes associated with the glomerulus. (String)
key_ligand The key ligand associated with the glomerulus. (String)
valence The valence associated with the glomerulus. (String)

File: S7_hemibrain_DN_meta.csv

Column name Description
pre The pre-synaptic neuron of the connection. (Integer)
post The post-synaptic neuron of the connection. (Integer)
upstream The upstream neuron of the connection. (Integer)
downstream The downstream neuron of the connection. (Integer)
status The status of the connection. (String)
name The name of the neuron. (String)
voxels The voxel coordinates of the neuron. (Integer)
soma The soma coordinates of the neuron. (Integer)
connectivity.type The type of connectivity of the neuron. (String)
cell.type The type of cell of the neuron. (String)
class The class of the neuron. (String)
cellBodyFiber The cell body fiber of the neuron. (Integer)
layer The layer of the neuron. (Integer)
ct.layer The ct layer of the neuron. (Integer)
total.length The total length of the neuron. (Integer)
notes Any additional notes about the neuron. (String)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

S1 Hemibrain Neuron Layers

@kaggle.thedevastator_hemibrain_neuronal_connectome.s1_hemibrain_neuron_layers
  • 358.21 kB
  • 24,549 rows
  • 4 columns
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CREATE TABLE s1_hemibrain_neuron_layers (
  "bodyid" BIGINT,
  "layer_mean" DOUBLE,
  "layer_olf_mean" DOUBLE,
  "layer_th_mean" DOUBLE
);

S2 Hemibrain Olfactory Information

@kaggle.thedevastator_hemibrain_neuronal_connectome.s2_hemibrain_olfactory_information
  • 10.35 kB
  • 58 rows
  • 11 columns
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CREATE TABLE s2_hemibrain_olfactory_information (
  "glomerulus" VARCHAR,
  "laterality" VARCHAR,
  "expected_cit" VARCHAR,
  "expected_rn_female_1h" DOUBLE,
  "expected_rn_female_sd" DOUBLE,
  "missing" VARCHAR,
  "rn_frag" VARCHAR,
  "receptor" VARCHAR,
  "odour_scenes" VARCHAR,
  "key_ligand" VARCHAR,
  "valence" VARCHAR
);

S3 Hemibrain Alrn Meta

@kaggle.thedevastator_hemibrain_neuronal_connectome.s3_hemibrain_alrn_meta
  • 154.9 kB
  • 2,651 rows
  • 24 columns
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CREATE TABLE s3_hemibrain_alrn_meta (
  "bodyid" BIGINT,
  "pre" BIGINT,
  "post" BIGINT,
  "upstream" BIGINT,
  "downstream" BIGINT,
  "status" VARCHAR,
  "name" VARCHAR,
  "voxels" BIGINT,
  "soma" BOOLEAN,
  "side" VARCHAR,
  "connectivity_type" VARCHAR,
  "cell_type" VARCHAR,
  "class" VARCHAR,
  "cellbodyfiber" VARCHAR,
  "glomerulus" VARCHAR,
  "presyn_glom" DOUBLE,
  "layer" DOUBLE,
  "ct_layer" DOUBLE,
  "axon_outputs" DOUBLE,
  "axon_inputs" DOUBLE,
  "total_length" DOUBLE,
  "cable_length_glom_um" DOUBLE,
  "axon_length" DOUBLE,
  "notes" VARCHAR
);

S4 Hemibrain Alln Meta

@kaggle.thedevastator_hemibrain_neuronal_connectome.s4_hemibrain_alln_meta
  • 45.3 kB
  • 197 rows
  • 28 columns
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CREATE TABLE s4_hemibrain_alln_meta (
  "bodyid" BIGINT,
  "pre" BIGINT,
  "post" BIGINT,
  "upstream" BIGINT,
  "downstream" BIGINT,
  "status" VARCHAR,
  "name" VARCHAR,
  "voxels" BIGINT,
  "soma" BOOLEAN,
  "side" VARCHAR,
  "connectivity_type" VARCHAR,
  "cell_type" VARCHAR,
  "group" VARCHAR,
  "anatomy_group" VARCHAR,
  "class" VARCHAR,
  "cellbodyfiber" VARCHAR,
  "layer" DOUBLE,
  "ct_layer" BIGINT,
  "axon_outputs" BIGINT,
  "dend_outputs" BIGINT,
  "axon_inputs" BIGINT,
  "dend_inputs" BIGINT,
  "total_length" DOUBLE,
  "axon_length" DOUBLE,
  "dend_length" DOUBLE,
  "pd_length" DOUBLE,
  "segregation_index" DOUBLE,
  "notes" VARCHAR
);

S5 Hemibrain Alpn Meta

@kaggle.thedevastator_hemibrain_neuronal_connectome.s5_hemibrain_alpn_meta
  • 61.06 kB
  • 333 rows
  • 29 columns
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CREATE TABLE s5_hemibrain_alpn_meta (
  "bodyid" BIGINT,
  "pre" BIGINT,
  "post" BIGINT,
  "upstream" BIGINT,
  "downstream" BIGINT,
  "status" VARCHAR,
  "name" VARCHAR,
  "voxels" BIGINT,
  "soma" BOOLEAN,
  "side" VARCHAR,
  "connectivity_type" VARCHAR,
  "cell_type" VARCHAR,
  "class" VARCHAR,
  "cellbodyfiber" VARCHAR,
  "glomerulus" VARCHAR,
  "layer" DOUBLE,
  "ct_layer" DOUBLE,
  "axon_outputs" DOUBLE,
  "dend_outputs" DOUBLE,
  "axon_inputs" DOUBLE,
  "dend_inputs" DOUBLE,
  "total_length" DOUBLE,
  "axon_length" DOUBLE,
  "dend_length" DOUBLE,
  "pd_length" DOUBLE,
  "segregation_index" DOUBLE,
  "is_canonical" BOOLEAN,
  "across_dataset_cluster" BIGINT,
  "notes" VARCHAR
);

S6 Hemibrain Toon Meta

@kaggle.thedevastator_hemibrain_neuronal_connectome.s6_hemibrain_toon_meta
  • 288.69 kB
  • 2,383 rows
  • 30 columns
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CREATE TABLE s6_hemibrain_toon_meta (
  "bodyid" BIGINT,
  "pre" BIGINT,
  "post" BIGINT,
  "upstream" BIGINT,
  "downstream" BIGINT,
  "status" VARCHAR,
  "name" VARCHAR,
  "voxels" BIGINT,
  "soma" BOOLEAN,
  "side" VARCHAR,
  "connectivity_type" VARCHAR,
  "cell_type" VARCHAR,
  "class" VARCHAR,
  "cellbodyfiber" VARCHAR,
  "putative_classic_transmitter" VARCHAR,
  "putative_other_transmitter" VARCHAR,
  "fafb_match" VARCHAR,
  "fafb_match_quality" VARCHAR,
  "layer" DOUBLE,
  "ct_layer" DOUBLE,
  "axon_outputs" BIGINT,
  "dend_outputs" BIGINT,
  "axon_inputs" BIGINT,
  "dend_inputs" BIGINT,
  "total_length" DOUBLE,
  "axon_length" DOUBLE,
  "dend_length" DOUBLE,
  "pd_length" DOUBLE,
  "segregation_index" DOUBLE,
  "notes" VARCHAR
);

S7 Hemibrain Dn Meta

@kaggle.thedevastator_hemibrain_neuronal_connectome.s7_hemibrain_dn_meta
  • 34.11 kB
  • 345 rows
  • 17 columns
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CREATE TABLE s7_hemibrain_dn_meta (
  "bodyid" BIGINT,
  "pre" BIGINT,
  "post" BIGINT,
  "upstream" BIGINT,
  "downstream" BIGINT,
  "status" VARCHAR,
  "name" VARCHAR,
  "voxels" BIGINT,
  "soma" BOOLEAN,
  "connectivity_type" VARCHAR,
  "cell_type" VARCHAR,
  "class" VARCHAR,
  "cellbodyfiber" VARCHAR,
  "layer" DOUBLE,
  "ct_layer" DOUBLE,
  "total_length" DOUBLE,
  "notes" VARCHAR
);

S8 Hemibrain Root Points

@kaggle.thedevastator_hemibrain_neuronal_connectome.s8_hemibrain_root_points
  • 503.03 kB
  • 22,560 rows
  • 4 columns
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CREATE TABLE s8_hemibrain_root_points (
  "bodyid" BIGINT,
  "x" DOUBLE,
  "y" DOUBLE,
  "z" DOUBLE
);

S9 Hemibrain Compartment Startpoints

@kaggle.thedevastator_hemibrain_neuronal_connectome.s9_hemibrain_compartment_startpoints
  • 1.45 kB
  • 6 rows
  • 1 column
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CREATE TABLE s9_hemibrain_compartment_startpoints (
  "x" VARCHAR
);

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