Surface mesh
Vertices, faces and labels describe a boundary. Excellent for shape and spatial relationships; it does not contain MRI intensity.
OPEN KNOWLEDGE / HUMAN BRAIN / VOL. 01
Inside the
human brain.
Explore the anatomy. Compare the evidence.
Build an intuition for the models.
Named atlas regions.
Six chapters. Three ways in.
An open learning publication by EXEPERT AI
What would it mean to make the brain easier to understand?
A single picture can reveal a shape. An interactive atlas lets you turn it, take it apart and ask what each representation actually tells you.
Move from anatomy to MRI, from connectivity to computational models. Each chapter offers a first explanation, a neuroscience perspective and an AI/ML perspective. You choose the depth.
01 / EXPLORE & COMPAREDifferent maps.
Different maps.
Shared questions.
The Yale surface and BodyParts3D internal structures are independent references. Their cameras are controlled separately; this is not a registered comparison of one person.
MRI volume
Voxels, sequence and affine metadata describe sampled contrast. The prepared adult research reference and glioma case use different contrasts, retain their own metadata and are never treated as one person.
Connectivity graph
Nodes and edges describe relationships. Their meaning depends on measurements or authored assumptions, not the visual layout.
02 / GUIDED LEARNING
Follow your curiosity.
Beginner / orientation
01 / Find your bearings
A brain can be viewed from many directions. Start by learning what the camera can show and what a representation leaves out.
The two cerebral hemispheres sit on either side of the midline. Left and right refer to the person represented, not your screen.
Rotate the atlas, then return to its front preset. An exploded view moves parts apart to make relationships easier to see; those gaps do not exist in the assembled anatomy.
Try it yourself
Open the 3D atlas. Select a named left parcel, rotate the camera, then use Reset. Observe that its identifier stays unchanged.
Check your understanding
Chapter sources: [yale] [bodyparts3d]
Ask about this chapter
Authored explanations are always available. Optional AI answers require a signed-in account and available free quota.
Read all chapters at every depth
01 / Find your bearings
A brain can be viewed from many directions. Start by learning what the camera can show and what a representation leaves out.
Beginner
The two cerebral hemispheres sit on either side of the midline. Left and right refer to the person represented, not your screen.
Rotate the atlas, then return to its front preset. An exploded view moves parts apart to make relationships easier to see; those gaps do not exist in the assembled anatomy.
Neuroscience
Anterior and posterior describe the front and back; superior and inferior describe above and below. Sagittal, coronal and axial planes describe cuts through the anatomy.
This viewer uses the existing Yale orientation conventions. Independent reference meshes are centered for display. Their camera alignment must not be interpreted as registration between subjects.
AI / ML
A mesh stores vertices and faces. An anatomical atlas adds a mapping between geometry and names. A camera transform changes the displayed coordinates without changing anatomical identity.
Keep source coordinates and display transforms separate. A seemingly aligned pair of meshes does not establish voxel correspondence or justify pointwise measurement.
Activity: Open the 3D atlas. Select a named left parcel, rotate the camera, then use Reset. Observe that its identifier stays unchanged.
Whose left does an anatomical label describe? Anatomical left belongs to the person represented, even when the camera turns.
What does exploding a mesh do? Explosion is a display transform; it is not a biological measurement.
Do centered reference models describe one person? Different references retain their own provenance. Display centering is not anatomical registration.
02 / Cortex & hemispheres
The folded outer surface is a useful starting point, but a colored region is a map choice rather than a complete account of brain function.
Beginner
The cerebral cortex is a folded sheet of tissue. Ridges are called gyri and grooves are called sulci. The folds let a large surface fit inside the skull.
Atlas colors help distinguish regions. A task such as speaking or remembering involves interacting regions; it does not live in one isolated colored parcel.
Neuroscience
Parcellation divides a reference anatomy into named regions at a chosen scale. Boundaries can follow different anatomical or functional criteria, and individual anatomy varies.
The Yale mesh has 696 named material primitives. These identifiers are retained when regions move in the viewer. Parcel selection reveals the atlas label without claiming a specific functional localization.
AI / ML
A parcel ID is a stable categorical label within an atlas revision. Do not treat atlas colors as learned embeddings or parcel indices as ordinal numerical measurements.
Resolution affects aggregation: a signal averaged over a large region can differ from the signal in its smaller subdivisions. Comparing datasets requires an explicit mapping and an account of information lost.
Activity: Select two cortical parcels and compare their names. Separate the hemispheres, then restore the assembled view. Explain why color alone does not identify a function.
What is a gyrus? Gyri are ridges; sulci are grooves in the folded cortical surface.
What does a parcel color establish? Colors distinguish labels in this display. They are not activity measurements.
Can an atlas boundary be assumed identical in every individual? Reference atlases summarize anatomy; individual geometry and boundaries vary.
03 / Beneath the surface
Move outer structures aside to inspect internal anatomy. Keep cavities, tissue and illustrative connections distinct.
Beginner
The corpus callosum is a major bundle of fibers connecting the hemispheres. The thalamus, brainstem and cerebellum are other important structures beneath or behind the cerebral surface.
Ventricles are fluid-filled spaces. Their mesh represents the shape of a cavity, not a separate solid organ. Transparent surfaces can help reveal these spatial relationships.
Neuroscience
The brainstem includes the midbrain, pons and medulla. The cerebellum and deep nuclei have distinct organization that is not captured by a cortex-only parcellation.
The BodyParts3D subset preserves structure identifiers. Its inherited cardiac classification of ventricular cavities is corrected locally and recorded in the asset manifest; geometry is not relabeled as cardiac tissue.
AI / ML
Semantic segmentation and mesh grouping are different operations. Correcting a display category does not validate every vertex or establish a new segmentation.
Represent a cavity with an explicit semantic type. Otherwise a downstream model may confuse the volume of a fluid space with a volume of neural tissue.
Activity: Switch to the internal-structures reference. Select a ventricle, then the corpus callosum. Compare their representation descriptions and return opacity to full.
A ventricle mesh represents… The ventricular system contains cerebrospinal fluid; a cavity mesh describes its shape.
Which structure belongs to the brainstem? The pons is a component of the brainstem.
Does correcting a category validate the geometry? Metadata correction and anatomical geometry validation are separate steps.
04 / Surfaces & MRI
A surface model and an MRI volume answer different questions. Compare their information content before comparing their appearance.
Beginner
An MRI volume is a grid of small samples called voxels. Slices reveal internal patterns that a surface mesh may hide. Different MRI sequences produce different tissue contrast.
A glioma case is a clinical example with a tumor. It should not stand in for a typical reference brain. This release includes small prepared teaching volumes with source links and recorded transformations.
Neuroscience
Interpretation depends on acquisition sequence, spatial resolution, preprocessing and pathology. Intensity is not a universal tissue label, and a segmentation mask is a separate derived representation.
NIfTI orientation is encoded through image-to-world transforms such as qform and sform. Verify axis codes, handedness and affine consistency before displaying a slice or comparing volumes.
AI / ML
A pipeline must record exact source revision, subject/file identifiers, licensing, affine transforms and resampling operations. Use stable source-ID order for case selection, then require documented adulthood and permitted redistribution.
Never interpret a mask’s integer label values as continuous signal intensity. Resampling labels generally requires a label-preserving method; interpolation of MRI intensities follows a different rule.
Activity: Use the comparison cards to contrast mesh vertices with MRI voxels. Identify the additional metadata needed before an MRI can be placed beside the atlas.
What is a voxel? Voxels sample a three-dimensional volume; vertices and faces describe a surface.
What must be checked before aligning MRI slices? Orientation is a property of image metadata and transforms, not screen appearance.
Should a glioma case be presented as typical reference anatomy? Pathology and provenance must remain clearly labeled.
05 / Connections & evidence
A connection diagram is a model of relationships. Its meaning depends on how the connections were obtained.
Beginner
A connection can describe a physical pathway or a statistical relationship between recorded signals. These are different kinds of evidence.
Our simulator uses a small graph written for this lesson. Watching activity spread through it illustrates a rule; it does not reveal your own brain or a measured human connectome.
Neuroscience
Diffusion MRI supports estimates of tissue orientation and tractography pathways. Functional connectivity describes statistical dependencies in time series; it does not by itself prove direct anatomical wiring or causation.
The Human Connectome Project provides methods and research datasets with specific data-use terms. This publication links those resources without redistributing unverified participant data.
AI / ML
An adjacency matrix can encode a curated graph, tractography-derived estimates or statistical associations. Identical matrix shapes do not make these quantities interchangeable.
Graph direction, sign, normalization and measurement uncertainty are modeling choices. A propagation model needs declared update equations and units before its behavior can be interpreted.
Activity: Run the simulator twice. Raise coupling while keeping damping and stimulus fixed. Describe the difference without calling it measured human connectivity.
Functional connectivity necessarily proves a direct anatomical connection. Statistical association alone does not establish direct wiring or causation.
Where do this simulator’s edges come from? The graph is deliberately authored and is distributed with the simulator.
What helps interpret a connectivity matrix? The matrix’s provenance and construction determine what its values represent.
06 / Models, parameters & weights
Experiment with a reproducible model, then inspect exactly what its downloadable parameters mean.
Beginner
A model is a simplified description that lets us ask a question. In this experiment, coupling controls how strongly regions influence one another, damping reduces activity, and stimulus supplies an input.
You can download the rules, settings and results. These settings are illustrative simulation parameters. They are not a digital copy of a human brain.
Neuroscience
The update is a bounded, fixed-timestep regional propagation rule with a saturating interaction. It omits spiking neurons, cell types, neurotransmitters, detailed delays and individual anatomy.
Changing parameters probes this rule’s behavior. No fitted observations or validation dataset are supplied, so the experiment cannot support quantitative claims about neural dynamics.
AI / ML
Trained weights are parameters estimated through an optimization process using data and an objective. This kit contains authored parameters and deterministic source code; there is no training run or checkpoint claim.
Generic image-to-3D systems such as TRELLIS and Hunyuan can generate plausible-looking geometry. Visual plausibility does not validate anatomy. Their model licenses and execution requirements also differ from those of this atlas.
Activity: Export a modified run. Restore its configuration and verify that the same seed and parameters reproduce the same rows. Compare the baseline curve with the modified curve.
Are this release’s settings trained human-brain weights? They are authored illustrative simulation parameters, with no training claim.
What is needed to reproduce a run? The update rule, fixed timestep, seed and full configuration determine the result.
Does realistic generated geometry establish anatomical accuracy? Generated appearance must be independently validated before anatomical claims are made.
EXPERIMENT / 01Change a rule.
Change a rule.
Observe the difference.
Four illustrative regions. Authored connections. Fixed timesteps. Every exported run contains the rules and settings needed to reproduce it.
Dimensionless activation · arbitrary simulation time · no measured human connectivity
Output region: baseline dashed / modified solid.
Open the atlas.
Inspect the assumptions.
The EXEPERT Open Brain Atlas and Simulation Kit packages selected geometry, labels, simulation source, configurations, example outputs, checksums and source-specific licenses.
This first release contains illustrative simulation parameters. It contains no trained checkpoints or biological human-brain weights.
Version 0.1.0 · public kit candidate · 4.02 MB. Shared account services are still under compatibility review.
Inspect geometry manifest ↗SHA-256: c91ec2cce27d9b759020b5617b42cf9b530d00afe0c35f643449062a7959f542
04 / SOURCES & LIMITATIONS
Keep the evidence visible.
Included pinned geometry and source identifiers. A reference parcellation, not an individual brain measurement.
Brain-only subset of the existing adult male reference mesh. Ventricles are grouped as cavities, correcting the local cardiac classification.
Prepared T1-weighted ds000001/sub-01 example. Age 26 and original CC0 terms verified against pinned OpenNeuro metadata. Foreground-masked, cropped and downsampled; no disease-free status is claimed.
Original collection for the separately labeled UCSF-PDGM-0004 glioma example. Exact input hashes and orientation transforms accompany the teaching volume.
Prepared UCSF-PDGM-0004 FLAIR example, selected first in stable archive order. It is a glioma case, uses a different contrast from the adult T1 reference, and is not registered to it.
Citation only. Restricted files are excluded from downloads.
Methods and links only; no unverified redistribution of participant data.
Example of generative 3D representations. Generated anatomy is not validated anatomical evidence.
Reference only. Model license and territory restrictions must be reviewed before use.
Authored graph and dimensionless parameters. Not trained checkpoints, measured synapses or biological human-brain weights.