- Visual Analysis of Neuronal Connectivity Motifs
Jakob Troidl1 , Simon Warchol1 , Jinhan Choi5 , Jordan Matelsky3,4 , Nagaraju Dhanyasi2 , Xueying Wang2 ,Brock Wester3 , Donglai Wei5 , Jeff W. Lichtman2 , Hanspeter Pfister1 , and Johanna Beyer1

Fig. 1: Visual Motif Analysis. (a) In Vimo, neuroscientists search large brain networks (view online: [29]) by (b) sketching neuronalconnectivity motifs. (c) Neurons forming a motif instance (MI) are visualized in 3D. Vimo emphasizes the relationship between thesketched motif and the neurons’ connectivity using a continuous focus&context approach (d, e). First, Vimo prunes neuron branches
unrelated to the motif (d). Next, users explore the connectivity between neurons in an exploded view that untangles complex neuron
morphologies and uses hierarchical synapse clustering and bundling to highlight connections (e). Data: FlyEM Hemibrain [44].
Abstract—Recent advances in high-resolution connectomics provide researchers with access to accurate petascale reconstructions ofneuronal circuits and brain networks for the first time. Neuroscientists are analyzing these networks to better understand information
processing in the brain. In particular, scientists are interested in identifying specific small network motifs, i.e., repeating subgraphs ofthe larger brain network that are believed to be neuronal building blocks. Although such motifs are typically small (e.g., 2 - 6 neurons),
the vast data sizes and intricate data complexity present significant challenges to the search and analysis process. To analyze thesemotifs, it is crucial to review instances of a motif in the brain network and then map the graph structure to detailed 3D reconstructions of
the involved neurons and synapses. We present Vimo, an interactive visual approach to analyze neuronal motifs and motif chains inlarge brain networks. Experts can sketch network motifs intuitively in a visual interface and specify structural properties of the involved
neurons and synapses to query large connectomics datasets. Motif instances (MIs) can be explored in high-resolution 3D renderings.
To simplify the analysis of MIs, we designed a continuous focus&context metaphor inspired by visual abstractions. This allows users
to transition from a highly-detailed rendering of the anatomical structure to views that emphasize the underlying motif structure andsynaptic connectivity. Furthermore, Vimo supports the identification of motif chains where a motif is used repeatedly (e.g., 2 - 4 times)
to form a larger network structure. We evaluate Vimo in a user study and an in-depth case study with seven domain experts on motifs ina large connectome of the fruit fly, including more than 21,000 neurons and 20 million synapses. We find that Vimo enables hypothesis
generation and confirmation through fast analysis iterations and connectivity highlighting.
Index Terms—Visual motif analysis, Focus&Context, Scientific visualization, Neuroscience, Connectomics.

1

I NTRODUCTION

Recent developments in high-throughput electron microscopy (EM)have allowed large-scale brain mapping at the level of individual
synapses, i.e., connectomics. These new brain tissue datasets, alongwith advances in automated segmentation, enable scientists to accurately reconstruct 3D wiring diagrams of the biological neural networks.
For example, the new H01 dataset [50] captures a cubic millimeter of human brain tissue containing about 57, 000 segmented neurons, and their
150 million synapses, automatically reconstructed from 1.4 petabytes
of imaging data. Once the data is fully proofread, studying neuronalconnectivity is the key to understanding how the brain computes. Since
analyzing an entire brain network is infeasible, neuroscientists use
motif analysis as a divide-and-conquer approach to extract small, andcomprehensible subgraphs from the brain network, which represent neu• 1 School of Engineering & Applied Sciences, Harvard University• 2 Department of Cellular & Molecular Biology, Harvard University• 3 Applied Physics Laboratory, Johns Hopkins University• 4 Department of Bioengineering, University of Pennsylvania• 5 Department of Computer Science, Boston College
Manuscript received xx xxx. 201x; accepted xx xxx. 201x. Date of Publication
xx xxx. 201x; date of current version xx xxx. 201x. For information on
obtaining reprints of this article, please send e-mail to: reprints@ieee.org.
Digital Object Identifier: xx.xxxx/TVCG.201x.xxxxxxx

ronal building blocks [21, 24, 28, 47, 62]. However, synapse-level motif
analysis of connectomes is still underexplored. The reason for that isthree-fold: First, few datasets exist that are large enough to containthousands of complete and proofread neurons at an image resolutionthat resolves individual synapses [12, 34, 44, 50, 54]. Thus, previous approaches mainly focused on small volumes and truncated networks [1]or used heuristics to estimate synaptic connections in lower-resolution
data [51]. Second, large graphs are difficult to analyze, especially for
non-graph experts. Finding motifs in a large graph is computationallyexpensive, and most previous motif analysis tools require programming
experience. Third, neurons and synapses have a dual representation in
the graph compared to the original imaging data. In a graph, neurons
are nodes, and synapses are edges. In brain tissue, however, neurons
are long, tree-like structures, while synapses are small surfaces. These
conceptually different representations make it difficult to visualize how
the motif structure relates to a set of connected neurons.
A graph motif is a recurring small subgraph of a larger graph, suchas a feedback loop between neurons. For example, researchers haverecently identified network motifs in the brain of a fruit fly of three
to six neurons that are responsible for context-dependent action selection [24]. However, neuroscientists typically need to combine motif
search with an in-depth analysis of motif instances (MIs), which are
specific neurons that form a motif. We find that neuroscientists preferanalyzing the 3D anatomy of MIs over abstract connectivity graphs or
2D abstractions since oversimplified 2D layouts can not depict complex spatial arrangments like entangled neurons. Also, 2D layouts can

Fig. 2: Different motif query approaches. Our visual sketchingapproach (left), high-level domain-specific language DotMotif [33]
(middle), and graph query language Cypher [17] (right).
distort neuronal anatomy leading to inaccurate estimates of information
flow. Thus, only 3D views provide accurate spatial context for neuroscientists. However, the structure of neurons is so intricate and complex
that even for small motifs, a 3D view of several neurons quickly suffersfrom occlusion and visual clutter. Ideally, a spatial view allows for
identifying parts of a neuron actively involved in a motif. Yet, no visual
tools exist to explore the 3D nature of neuronal motifs.
In this paper, we present Vimo, a novel visualization and analysis
tool to explore neuronal motifs and motif chains in large brain networks. Our work makes three main contributions. First, we identify
domain-specific goals for neuronal motif analysis based on interviews
with neuroscience domain experts. Thus, we derive a set of analysis
tasks that are the basis for the design of our web-based visualization
tool Vimo. Second, we propose a scalable motif analysis workflow,
including the design of a flexible motif searching approach based oninteractive sketching. Scientists draw nodes and edges in a sketching panel to query for their desired motif without having to program.
Our queries leverage biological constraints such as neuron types toreduce computational complexity, scale to large brain networks, andgive interactive feedback on the number of motif occurrences in thebrain network. Additionally, we integrate a focus&context scheme foranalyzing motif instances and motif-based synaptic chains to facilitate
the user’s understanding of how the neuron’s morphology relates tomotif connectivity. We do so by de-emphasizing non-motif-relevant
parts of the neurons (context) to focus on the motif-relevant aspects. Importantly, we do not change neuron morphology but gradually shift thefocus using neuron pruning, exploded views, and hierarchical synapse
clustering (see Fig. 1c-e). Third, we demonstrate the integration of our
motif analysis workflow into Vimo, an open-source [56] and web-based
visualization tool. Vimo’s novelty lies in combining state-of-the-art
methods from scientific visualization, graph theory, and neuroscience
into a useful, interactive tool based on a detailed goal and task analysis
with domain scientists. We also integrated Vimo’s motif sketching interface into the Neuprint connectome analysis platform [7] and released
a domain-agnostic UI component for motif sketching [9] to facilitate
motif analysis in disciplines other than connectomics. We evaluate
Vimo with a qualitative user study and a case study of visual input
neurons in the fly brain.
2

R ELATED W ORK

Connectomics and Motif Analysis. Motif analysis [39] plays a central
role in connectome analysis [18, 24, 46, 59]. Hence, numerous computational methods have emerged for examining connectivity motifs withinconnectomes. Matejek et al. [32] present an optimized and parallelized
subgraph enumeration algorithm to count the occurrences of motifs
in large brain networks. DotMotif [33] is a domain-specific language
to write motif queries and compiles to common python tools and theCypher graph query language [17]. Vimo builds on DotMotif, but offers
a visual non-programming interface to create motif queries (see Fig. 2).
Previous Motif Analysis Workflow. In the past, the motif analysis
workflow of our collaborators was considerably more laborious due
to complex and inaccessible technology. They often cited time constraints and difficult-to-use tools as barriers that prevented them fromperforming motif analysis. For instance, writing programmatic motif
queries is challenging as users must be well-versed with graph query
programming languages [17, 40]. Additionally, existing visualization
tools have not integrated 3D analysis of MIs into their workflows. Previously, users had to copy neuron IDs obtained from programmatic graphqueries into tools like Neuroglancer [30]. Furthermore, none of theFig. 3: Motifs in brain networks. (a) Large connectomes like the
FlyEM hemibrain dataset [44] can be interpreted as (b) directed graphs,
where neurons represent nodes and synapses are represented as edges.
(c) Network motifs are recurrent subgraphs in the larger brain network.
For example, the graph shown in (d) contains two instances (MI-1,
MI-2) of a feedforward motif (c).
available tools offer motif simplification strategies for a more in-depth
examination of motif connectivity. Therefore, our collaborators mostly
gave up on motif analysis or looked at just very selected motifs in
Neuroglancer [30] but did not refine or extend their analyses.
Visualization for Connectomics. Visual analysis approaches havebeen used in many subareas of connectomics, including interactive
proofreading [13, 20], volume exploration [2], and neighborhood- [55]and morphology analysis [8, 36]. Beyer et al. [3] provide a comprehensive survey of visualization methods in connectomics. Most neuralconnectivity visualizations use node-link diagrams [2, 41], and therefore do not support a detailed analysis of MIs. Recently, Ganglberger et
al. [19] proposed a 2D node-link layout that preserves the spatial context of 3D brain networks. However, their work focuses on macroscale
brain parcellations and thus does not extend to nanoscale connectivity.
Alternative approaches have used matrix views [22] or visual abstractions in 2D that retain some morphological features, such as neuron
branches [1] or arborizations [51]. These methods offer compact visual
encodings that allow scientists to get a quick overview of the data. However, the 3D morphology of neurons and their spatial relations in thedata is lost. Furthermore, most prior works focus on either very small
volumes and, thereby, truncated brain networks [1, 2], hypotheticalneuronal circuits [60], or use lower resolution datasets and heuristics
to generate synapse positions [51]. Most recently, Plaza et al. [38]developed neuPrint, a tool to query neuronal connectivity quickly in thebrowser. neuPrint uses the Cypher language [17] to query for motifs
but does not focus on visually highlighting the connectivity in MIs.
Visual Graph Queries. Visual query interfaces have been used inmany application areas and allow users to specify queries in an intuitive
way [6, 16, 31]. Cuenca et al. [11] propose Vertigo, an approach to construct and suggest graph queries and explore their results in multi-layer
networks. Vertigo visualizes all detected subgraphs as an overlayed
heatmap to a graph drawing. This approach, however, is not feasible forlarge connectomic graphs, as all information on the 3D morphology ofneurons would be lost. Vigor [37] focuses on effectively summarizing
subgraph queries by grouping results based on node features and structural result similarity. In Vimo, users can define structural similarity
constraints like neuron types directly in the query interface.
Visualization of Network Motifs. Visualization of motifs has been
proposed in different domains. MAVisto [48] supports visual motif
analysis in biological networks. However, MIs are only visualized as
node-link diagrams, and 3D shapes of biological objects are hidden.
Dunne [15] simplifies node-link diagrams of motifs using glyphs. Those
simplification techniques do not extend to complex spatial data likeneuronal structures. For dynamic networks, Cakmak et al. [5] compare
motif significance profiles over time by plotting them as a time series.
Visual Abstraction of Tree-Like Structures. Different visual abstractions have been proposed to analyze neurons and tree-like structures.
Hu et al. [23] project a complex 3D tree-like structure onto a 2D plane
while avoiding overlaps. However, their method does not scale togroups of treelike structures, as needed for neuronal motif analysis.
Mohammed et al. [36] use a continuous 2D abstraction space to analyze astrocytes and neurons. Neurolines [1] uses a 2D subway map
metaphor to display neurons. However, both approaches do not support
motif analysis, focus on relatively small subvolumes, and would notvisually scale to large connectivity graphs.

Fig. 4: Data Overview. (a) The FlyEM hemibrain [44] dataset contains more than 21,000 neurons, forming a vast connectivity network. Here, weshow 1% of those neurons as a Neuroglancer [30] rendering. An interactive version is accessible online [29]. (b) Small subsets of the dataset
form specific network motifs. Each set of neurons forming a specific motif is called a motif instance (MI). (c) However, understanding motif
connectivity in the complex 3D morphology of neurons is not trivial. Consequently, domain experts require more advanced tools to better
understand how connectivity motifs are manifested in a set of neurons.
3

B IOLOGICAL BACKGROUND

Brain anatomy. Brain tissue primarily consists of long tubular tree-likeneurons, which transmit signals to each other via synapses. A single
neuron can connect to hundreds or thousands of other neurons. The highinterconnectivity of neurons combined with their complex morphology
results in tangled 3D brain networks that are difficult to analyze (seeFig. 4a). Different neuron types have unique morphological properties
and connectivity preferences. Most brains of model organisms, such as
the fruit fly (Drosophila Melanogaster), are further divided into spatial
regions with distinct anatomical and functional properties.
Brain networks and motifs. Connectomics datasets with tens or
hundreds of thousands of neurons can be interpreted as large directed
graphs, with neurons as nodes and synapses forming edges between thenodes. Recent advances in microscopy and computational neuroscience
have revealed characteristic non-random patterns in neural networks ofinvertebrate and mammalian brains, especially in the cerebral cortex.
These small recurring wiring patterns [35], termed network motifs,
suggest the hypothesis that neuronal connections are arranged using
some basic building blocks with hierarchical order [52] (see Fig. 3).
Connectomics Data. Analyzing neuronal tissue at the level of synapses
requires ultra-high-resolution imaging techniques such as serial-section
scanning electron microscopy (SEM), which can produce more than
1 petabyte of data for each imaged cubic millimeter [50] of tissue.
In this paper, we demonstrate our visual motif query and analysis
approach on the FlyEM hemibrain, one of the largest open-source
connectomic datasets. It contains the entire central region of the fruit
fly brain, including 21, 000 proofread neurons classified into more than
5, 000 types, over 20 million synapses, 13 main brain regions, and over
200 sub-brain regions [44]. This dataset is a major resource in the
Drosophila neuroscience community and has led to exciting scientificdiscoveries [24, 27, 46]. Vimo uses the connectivity graph, 3D neuronalskeletons, and synapse locations of the hemibrain data (see Sec. 10).
4

G OAL & TASK A NALYSISThe idea of Vimo originated from meetings with neuroscience collaborators, who were excited about new EM datasets, but needed the means toanalyze synapse-level connectivity. They want to identify and explore
the morphological properties of motifs and synaptic chains without
being overwhelmed by the 3D structure of intertwined neurons. At
the same time, they need to see motifs in the original 3D space tounderstand the spatial relation of neurons and synapses.
Following the design study approach by Sedlmair et al. [49], we
identified a set of domain goals and tasks in semi-structured interviews
with five experts from the Harvard Center for Brain Science, HHMI
Janelia, and Columbia University. All scientists are internationally recognized experts in analyzing neuronal circuits reconstructed from EM
images. Three are experts in Drosophila connectomics. We also held
unstructured interviews at an international connectomics conference,
where we presented an early prototype to refine our goals and tasks.

4.1

Domain Goals

The neuroscientists’ main objective is finding small biologically relevant motifs in large neuronal networks. Further, they want to analyze
interesting motif instances visually in more detail to fully understandhow the motif’s connectivity relates to each neuron’s morphology.
G1 - Motif identification in large brain connectivity data. Our
collaborators want to search large brain networks for instances of a wide
range of small motifs (≤ 8 neurons) based on neuronal connectivity
and additional biological constraints on the involved neurons andsynapses. Neuroscientists want to specify details such as the brain
region a neuron trajects, the neuron type, or the number of synapses a
neuron makes in a specific brain region. For example, a specific type ofring neuron in the fly brain is involved in action selection tasks [4], andscientists want to analyze motifs involving this specific neuron type.
Biological constraints ensure the expressiveness of motif queries whenstudying such behaviors. Furthermore, neuroscientists are interested
if a specific motif is exceptionally over or underrepresented in theconnectome network.
G2 - Analysis of motif instances. Since a motif search in a large brain
network might result in dozens to thousands of hits, our collaboratorsneed to be able to easily identify a small set of interesting motif instances (i.e., MIs) and then analyze them in high-resolution. Neurons
form complex 3D shapes, span long distances, and connect with up to
thousands of other neurons. Therefore, our collaborators previously
struggled to identify how a set of neurons forms a motif and how
the abstract connectivity motif relates to the actual 3D anatomy of theMI (see Fig. 4). However, this understanding is crucial for following
information flow along neurons. In particular, our collaborators havequestions such as “Which branches and subbranches of the neuron
make up the motif?”, “Are several spatially distinct parts of a neuron
involved in the same motif?” (see Fig. 4bc), or “What is the anatomical context of involved neurons (e.g., in which brain regions are they
located)?” (see Fig. 5c).
G3 - Identification and analysis of motif chains. In addition to
exploring single MIs, our collaborators are interested in exploring
motif chains. Starting from a motif of interest and a seed neuron, theywant to explore if the neuron is involved in different instances ofthe same motif. This allows scientists to investigate if a single neuron
computes similar functions, even in multiple MIs. Furthermore, ourcollaborators are interested in motif-based synaptic chains. Startingfrom a seed MI, they want to follow chains of computation to see if thesame motif is formed consecutively over several connected neurons (seeFig. 10a). This analysis can give more holistic insights into information
flow (e.g., from visual input neurons to the central brain of Drosophila).
Scalability Considerations. To analyze recently published large connectomes [44, 50, 62] with the aforementioned goals, scalability is themain requirement of our application. Those datasets contain tens of
thousands of neurons and millions of synapses. Therefore, the entire
analysis pipeline, including data access, algorithms, and interaction
metaphors, need to support large data and scale to future datasets.

Fig. 5: Vimo workflow. (a) Users query for motifs based on a sketch and set domain-specific biological constraints, like connectivity strengths
and anatomical regions of interest, for targeted interrogation of the brain network. (b) After querying the connectome for a motif, users selectfound MIs for further investigation. (c) While exploring motif neurons and synapses (red dots), (d) users can adjust the visualization to graduallyhighlight the connectivity of neurons using our focus&context technique. (d) Next, users can continue to explore multiple connected MIs.
4.2

Tasks

Based on the above domain goals, we derived a set of analysis tasks
Vimo needs to support:
T1 - Query for connectivity motifs. Scientists need a fast way to
specify motifs and biological constraints for large graph queries (G1)without prior programming experience.
T2 - Identify biologically-relevant motif instances. Scientists need to
be able to explore query results to identify interesting MIs (G2).
T3 - Explore the 3D structure of MIs. For an interesting MI, scientists
want to explore the anatomy of its neurons and synapses in the context
of the surrounding brain region (G2).
T4 - Visually identify the relation between neuron morphology andmotif connectivity. After their initial exploration of an MI, scientists
need to identify the parts of the neurons that make up the motif andanalyze the detailed connectivity of the motif instance (G2).
T5 - Computationally and visually identify connected motif instances. Starting from the neurons of a selected motif instance, scientists want to explore whether the same neurons are involved in otherinstances of the same underlying motif (G3).
T6 - Trace synaptic chains starting from seed MI. Starting from aseed MI, scientists want to identify and visually follow synaptic chains
made up of repeating specific motifs (G3).
5Vimo D ESIGN AND W ORKFLOW

Our main goal for Vimo is to provide neuroscientists with the means
to easily explore the connectivity of connectomes and allow them tounderstand the network layout of neurons with their complex anatomical structure. Thus, we designed a workflow that offers intuitive userinteractions and visualizations that highlight the connectivity of thedata while keeping the anatomical data undistorted (see Fig. 5).
Vimo Workflow. In collaboration with domain experts, we designed
an interactive visual query interface based on sketching to search for
network motifs in the data. The interface is aimed at domain experts
with no programming experience and allows users to specify motif
connectivity and biological constraints (G1). The main difficulty ofconnectivity analysis in high-resolution connectomic data is the complexity of both neuron morphology and connectivity. Neurons aredensely packed in brain tissue and often highly intertwined. Synapses
between two neurons might be clustered or spread out along the lengthof the neurons. Therefore, Vimo offers a 3D view to display neurons andsynapses of a selected MI in detail. This allows scientists to understandneuron shape and how motif neurons are intertwined. To support neuroscientists in the understanding of both morphology and connectivity,
we designed a method for gradually highlighting neuronal connectivity,
similar to continuous visual abstractions. Users can progressively focus
on the underlying connectivity of the data while still retaining accessto the morphology of neurons (G2). Vimo allows users to identify andfollow interesting synaptic chains created by the repeated appearanceof a motif. To reduce the search space for synaptic chains, we let users
start with a seed motif or neuron, supporting user-driven data exploration (G3). Finally, throughout the design of Vimo, we focused on
scalability by using datasets hosted in the cloud and only downloading
small subsets to the user’s machine during runtime (see Sec. 10).

Fig. 6: Motif sketching interface. (a) Users draw nodes and edges
of a motif and define biological constraints on the nodes and edges.
Additionally, Vimo gives real-time feedback on the absolute count ofa sketched motif in the dataset and indicates whether a motif is overor under-represented in the dataset (red label). (b) When searching thebrain network for large motifs, Vimo warns users of potentially longruntimes using a red exclamation mark icon (b - top).
6

I NTERACTIVE M OTIF Q UERIESIn contrast to previous efforts in motif analysis that rely on programming languages [33, 38], Vimo provides a visual sketching interface tocreate queries. This gives scientists an intuitive approach to search for
motifs. Additionally, Vimo provides real-time feedback on the numberof occurrences of a sketched motif in the brain network to guide theuser during the analysis process (see Fig. 6a).
6.1

Motif Sketching

In Vimo, users search for motif instances by sketching an exemplar.
They draw a set of nodes and edges to define the motif (i.e., neurons
and the connections between them) (see Fig. 6), specify additional
biological constraints, and inspect the results of their motif query.
Defining Constraints. To create biologically meaningful queries, users
can interactively set constraints on the nodes and edges of the motif
sketch. Our query interface supports constraints on the neuron’s type,the spatial location and brain region of neurons and synapses, thestrength of a connection, and the neuron’s ID (T1). For instance,
when analyzing the hemibrain dataset, users can define one of over
5, 000 neuron types or trajectories through over 200 brain regions in
the hemibrain dataset. We also introduce wildcard constraints thatsummarize certain families of neuron types to make queries more
flexible. Scheffer et al. summarize all brain region constraints [43] andavailable neuron types [44]. Vimo offers an intuitive and expressive
query builder interface to define one or multiple constraints per nodeand edge (see Fig. 6). Autocompletion of neuron types and brain
regions further helps users select from thousands of available options.
Guided Sketching. Deciding which motifs to study in greater detailis not always obvious to neuroscientists. During formative interviews,
experts expressed interest in analyzing particularly common or rare
motifs in the network. Therefore, as the user is sketching a new motif,
we provide real-time feedback about the significance of the sketchedmotif. In particular, Vimo shows the number of occurrences of the motif

Fig. 7: Overview of the Vimo user interface. (a) A motif sketch of a participant in the user study (see motif-p2-pilot.json at [57]) and (b)list view of resulting MIs. (c) The central view shows a non-simplified, full morphology 3D rendering of two MIs. (d) The multi-motif summary
view visualizes the relationship between the selected MIs when following motif chains. (e) Users control the focus&context abstraction level
using the interactive slider. Continuously simplifying the MI ensures preserving context between successive simplification steps.
in the brain network and whether the motif is estimated to be over- or
under-expressed (T2). Counting the number of occurrences of a motif
in a large graph in real-time is computationally infeasible. Therefore,
we use precomputed counts of motifs with up to five nodes using a
subgraph enumeration technique [32] that leverages a parallel versionof the Kavosh algorithm [26] on a high-performance compute cluster.
At run-time, we perform a simple look-up to display the number of occurrences of a sketched motif (see Fig. 6). Enumerating all occurrencesof motifs with more than five nodes is computationally infeasible, as
the number of possible motif configurations grows exponentially with
the node count [32]. Nonetheless, Vimo supports finding representativeMIs for motifs up to the size of 8 by limiting the number of MIs thatare returned to the user (see Fig. 7). We also indicate the over- or underexpression of a motif in the brain network using a heuristic approach.
We estimate a motif as over-expressed if its occurrence in the brain
network is higher than in a random network. It is under-expressed if
it occurs less frequently in the brain than in the random network. To
analytically approximate the motif count in a random network, we build
on a method [25] using Erdös models, a simple class of random graphs
where each vertex-pair has a fixed probability of being connected by anedge. We indicate over- or under-expression with a red or blue badge
in the sketching panel, respectively (see Fig. 6a).
Running Motif Queries. Vimo automatically compiles the visualsketch into a DotMotif [33] string in real-time. Specifically, we convert
a JSON representation of the motif sketch into a DotMotif query. Once
users are satisfied with their sketch, we send the query string to a graphdatabase to get a list of motif instances. Note that any graph databasesupporting DotMotif queries can be used. The graph database thenreturns a list of MIs fulfilling the properties of the sketch. This list is
sorted based on the order in which the subgraph isomorphism searchalgorithm detects MIs. See Sec. 6.2 for the scalability of motif queries.
Inspecting Motif Query Results. Vimo displays the results as a setof MIs in a list view (see Fig. 7b). To guide users in selecting a motif
instance for further analysis, we show summarizing features for eachneuron in an MI. In particular, we display the neuron’s type, ID, andproofreading status (T2). Clicking on an MI shows 3D models of theneurons and their synapses in the main view of Vimo (see Fig. 7c).
Reproducing and Sharing Sketches. Users can import and export motif sketches as JSON files. This helps researchers reproduce their motif
queries at later times and facilitates sharing of interesting discoveries
with colleagues. A set of interesting motif sketches from the case study
is available online [57] and in the supplementary material.

6.2

Large Motif Queries

Vimo’s motif sketching and interactive motif simplification scale tolarge motif instances (see Fig. 6b). However, subgraph isomorphism
searches in large graphs are computationally expensive [32, 39], whichbecomes a factor as the number of motif nodes increases. For example,
verifying the existence of a motif in a larger network is an NP-complete
problem [10]. We use two strategies to limit the computational complexity of queries to maintain runtimes suitable for interactive systems.
First, users can set targeted biological constraints on the nodes andedges, reducing the search space significantly. For example, neuroscientists are interested in motifs involving external ring neuron (ExR1)
types. The hemibrain dataset [44] contains four neurons of type ExR1,
which drastically reduces the search space by only querying the neighbors of those four ExR1 neurons. Second, users can limit the numberof MIs returned by a query (see Fig. 7b). Keeping this number low
allows the search algorithm to terminate early without finding all MIs
in the network. Visually inspecting a small number of MIs is often
satisfactory for exploratory or initial analysis. We use a heuristic on thenumber of nodes in the motif (e.g., ≥ 5 nodes) to estimate if a motif
query will run longer than 20 seconds, in which case we warn the user
with an exclamation mark icon in the sketching interface (see Fig. 6b).
7

M OTIF V ISUALIZATIONS

Most connectome analyses require a detailed understanding of neuron
wiring. However, due to the complex morphology of neurons, it isdifficult to relate the motif connectivity to a 3D visualization of theneurons (see Fig. 4bc). Neither node-link diagrams nor planar visual
encodings can accurately show spatial relationships between neurons(T3). 2D projections of neurons abstract their spatial entanglement,
which is relevant to understand connectivity. Thus, we designed afocus&context method that gradually emphasizes motif connectivity
in the 3D representation by pruning unimportant neuron branches anddrawing visual links between connected neurons (T4) (see Fig. 8).
7.1

Spatial Exploration

First, Vimo supports interactive inspection of the 3D spatial morphologyof all neurons of a motif instance (T3). We render neurons based on
their skeletons to ensure scalability. Skeletons are simplified stickfigure representations of neurons and are faster to download duringruntime than meshes, as they require less data while retaining essential
morphological details. For each skeleton node, Vimo uses the neuron’s
diameter to adjust the thickness of the rendered tube, leading to moreFig. 8: Gradual Motif Highlighting. (a) In Vimo, users preview a simplified version of the MI and maintain spatial context, as branches set
for pruning are colored in grey. (b) Once neurons have been fully pruned, (c) users explode the view using a slider to decrease the visualcomplexity caused by tangled neurons. (d) In the exploded view, Vimo progressively groups synapses and creates bundled links between pre- andpost-synaptic sites, minimizing visual clutter. Distinct link bundles signify separate clusters of synapses between the purple and yellow neurons.
accurate visual representations. To allow users to quickly identify aneuron’s role in the motif, we color each neuron based on its color
in the motif sketch (see Fig. 7). For context and to enhance a user’s
spatial orientation, the 3D view in Vimo can show the outlines of 200
Drosophila brain regions, which are stored in Neuprint datasets [38].
7.2 Gradual Motif Highlighting and Abstraction
Our visual abstraction approach contains three main steps, all aimed
at gradually highlighting motif connectivity, to help users understandhow neurons form a motif (T4). Users can gradually move between thesteps by dragging a slider (see Fig. 7e), which results in a continuous
animation from one highlighting-and-abstraction level to the next.
Neuron pruning. First, we highlight motif connectivity by visually
removing parts of a neuron that are not involved in the connectivity
motif. Users can gradually peel away all parts of the neuron that are
not in the motif, which reduces visual clutter and helps the user focus
on the important parts of a neuron in relation to the motif (see Fig. 9).
For each neuron in a motif instance, we start with a 3D skeleton
representation (see Fig. 8a and Fig. 9a), which is interpreted as an
undirected graph N = (V, E) with vertices V and edges E. First, we
identify the set of skeleton vertices P ⊂ V which will remain evenafter the neuron is fully pruned (see Fig. 9c). The set S ⊂ V represents
the spatial locations of motif synapses and is computed by mapping
each synapse location to its closest vertex in V . Synapse locations are
assumed to be part of the dataset. P is formally defined as
P = {p ∈ V | ∃a, b ∈ S : p ∈ path(a, b)}(1)

In other words, a vertex p ∈ V is in P if there exists a path between
any two motif synapses a and b that contains p. P represents neuron
branches with synapses involved in the motif (T4). Next, we compute a
distance value d for each vertex v ∈ V to P, by calculating the geodesic
distance g to its closest neighbor in P:
d(v) = min(g(v, p))
p∈P

(2)

This allows us to gradually prune non-motif branches based on d bymoving a slider until only branches with motif synapses remain (seeFig. 9bc). Vertices with higher distance values d are pruned earlier thanvertices with smaller values. After an MI is fully pruned, the user sees
the essence of all neurons making a motif (T4) (see Fig. 9c).
Exploded view. In the second step, we further reduce the visual complexity resulting from entangled neurons. We spatially pull all neurons
apart into an exploded view after all non-motif branches are pruned (seeFig. 8c). This allows scientists to study the morphology and branching patterns of individual neurons and better visualize where synapses
involved in a motif are located. To evenly distribute the neurons inspace, we use the Saff and Kuijlaars algorithm [42] to compute thedirections of the explosion. The algorithm evenly distributes n points
on a unit sphere. We then compute an explosion direction for eachneuron by calculating a vector from the sphere’s center to one of then sampled points. In the exploded view, all pre- and post-synaptic
sites between motif neurons are marked with spheres colored like theirsynaptic partner neuron (see Fig. 8c). This helps the user quickly grasp
the neurons’ connectivity, even as the neurons are spatially apart (T4).
Connectivity Visualization. In the third step, we highlight connectivity
in the exploded view inspired by context-preserving visual links [53].
We initially draw lines between the pre- and post-synaptic sites of theneurons (see Fig. 1e and Fig. 8d). We use two strategies to highlightimportant connectivity features and avoid visual clutter. First, we use
hierarchical clustering of synapses and 3D bundling to aggregate lines.
We chose hierarchical bundling as it can visualize different levels ofsynapse clusters and how they are distributed along the neurons. Basedon feedback from our collaborators, we cluster synapses based on their
spatial proximity to the neuron and which neuron they connect to. The
user can set the bundling strength via the focus&context slider. As analternative to the clustering approach, the user can also decide to only
view connecting lines for user-selected synapses.
Design Alternatives. In an initial version of Vimo, we gradually moved
from the pruned neuron view all the way to a node-link view by abstracting neurons into nodes and collapsing synapses to links. We hoped
this would help scientists better visualize the relationship between the
motif and the involved neurons. However, we ultimately abandoned
this idea since scientists could already see the abstract motif in thesketching interface and had no use for such a simplified view. Additionally, based on feedback from visualization experts, we implemented adepth of field (DOF) effect to improve depth perception. However, during user testing, we found that DOF decreased users’ ability to follow
long traces of neurons, as branches outside the focal plane are blurred.
Therefore, users frequently adjusted the focused region to analyze anentire motif. Thus, we did not use the DOF effect during the evaluation.

8
Fig. 9: Motif pruning. Vimo gradually emphasizes neuron connectivity in MIs. We first show the full 3D morphology (a) and subsequently prune all branches unrelated to the motif (b) until only essential
branches remain (c). In the process, neuron connectivity becomes visually more prominent (colored circles).

M OTIF - BASED S YNAPTIC C HAINS

Examining an individual motif instance (MI) offers a limited perspective on the connectivity of the associated neurons. To investigate how
an MI is integrated within a broader network, researchers must visualize
multiple interconnected MIs (T5, T6) (see Fig. 10). For example, when
exploring pathways from visual input neurons to the central complex in
Drosophila, conducting a multi-motif analysis proves to be pertinent.

Fig. 10: Visualizing multi-motifs. (a - c) Vimo supports exploring connections between multiple motif instances. For instance, the set of neuronsin (a) form a circular connectivity motif three times. Vimo supports tracing such synaptic chains by repeatedly searching for instances of the same
motif. (d) Vimo only highlights one MI at a time and defocuses all other MIs in grey. (e) Our focus&context technique scales to multiple MIs. By
dragging the interactive slider (see Fig. 7e), we peel unrelated branches of all MIs to emphasize neuron connectivity (see blue circle).
8.1 Multi-Motif Analysis
Vimo supports the analysis of synaptic chains in two ways:
Neuron-centric analysis. First, domain experts are interested if aneuron forms the same motif with multiple partners (T5). For instance,in Fig. 10a, neuron A participates in two motif instances (MI-1, MI-2).
Users can specify a seed neuron in the motif sketching interface bysetting a neuron ID as a constraint to ensure that only motif instances
involving that particular neuron are returned.
Synaptic pathway analysis. Second, neuroscientists are interested infollowing synaptic pathways that repeatedly include a sketched motif.
For example, MI-1, MI-2, and MI-3 in Fig. 10 form such a synaptic
pathway. To query for those, users start from a seed motif and set the
ID of a sink neuron in the motif instance to a source neuron in the motif
sketch through a context menu (T6). This approach facilitates the continuous construction of motif-based synaptic pathways downstream or
upstream from a seed motif instance. Additionally, multi-motif analysis
serves as a proxy for examining large motifs comprising numerous neurons. When it is computationally infeasible to search a brain network
for instances of a large motif, users can alternatively construct the large
motif iteratively by using smaller motifs as building blocks.
8.2 Multi-Motif Visualization
Vimo uses three strategies to visualize multiple MIs.
Connectivity Summary. Vimo provides an abstract overview of theconnectivity of all selected MIs in a small node-link diagram (seeFig. 7d). The nodes and edges of the focused MI are colored, while allother elements are grayed out. This overview can help the user to stayoriented in the 3D view (T5). Selecting an edge in the summary viewalso highlights all corresponding synapses in red in the 3D view andthe corresponding edge in the sketch panel (see Fig. 10).
Highlighting motif instances. Inspecting even one MI in 3D canbe overwhelming due to the complex morphology of neurons. Vimo
always focuses on a single MI, while all other unrelated neurons aregrayed out to highlight the neurons of the MI in focus (T6). Users canquickly switch focus by clicking on a different MI in the list view.
Multi motif abstraction. We designed the continuous focus&context
approach (see Sec. 7.2) to scale to multiple MIs. For each MI that aneuron participates in {MI1 , ..., MIn }, our pruning technique computes
a set of distance values D(v) = {d1 (v), ..., dn (v)} for each vertex v ∈ Vusing Equation 2 (see Sec. 7.2). Thus, each vertex is assigned theminimum computed distance to avoid over-pruning an MI.
d(v) = min(D(v))(3)

Pruning and exploding multiple MIs simultaneously helps experts tobetter understand the wiring of a synaptic chain (T5, T6) (see Fig. 10e).
9 U SER I NTERACTIONS
We offer several interactions for visual motif analysis.
Focus&context slider. An interactive slider controls the continuous focus&context approach for motif connectivity highlighting (see Fig. 7e)
to maintain spatial context between consecutive views. First, dragging
the slider starts pruning the MI continuously. After the MI is fully

pruned, moving the slider further explodes the neurons in the 3D view.
Finally, in the exploded view, users can control the bundling strengthof the connectivity links using the slider (see Fig. 1e). For more details
about our focus&context approach, see Sec. 7.2.
Synapse highlighting. In Vimo, synapses are drawn as small spheres
between the pre- and post-synaptic sites of the connected neurons.
Visually highlighting all synapses between two neurons provides asimple yet effective visual clue to study where neurons connect (T4).
A set of synapses connecting a pair of neurons is highlighted by either
clicking on a single synapse sphere in the 3D view or by selecting anedge in the sketching panel or summary view (see Fig. 7d).
Showing brain regions. Interactively rendering the outlines of anatomical brain regions (e.g., Central Complex in Drosophila) provides orientation and allows users to verify if a selected MI adheres to theconstraints specified in the sketch (T3). We show semi-transparent
surfaces of the brain regions overlayed to the 3D renderings of theneurons. Users can interactively select which brain regions to show andquickly enable and disable the renderings.
Graying out non-motif branches. While neuron pruning (see Sec. 7.2)
helps remove unrelated branches to an MI, it removes context about
the motif neurons that might be necessary during analysis. Therefore,
Vimo allows graying out all non-motif branches of an MI (see Fig. 8a)
to highlight important parts of the neurons and show the context of allother neuron branches by toggling a checkbox in the UI (T3, T4).
Further Analysis. Once scientists identify interesting motif characteristics in their exploratory analysis, they need to perform an in-depth
analysis on a set of MIs. This involves statistical analyses, such as studying the distribution of all synapses on a neuron (not just motif synapses).
Vimo supports this step by integrating tightly with neuPrint [38], which
provides a set of tools for general neuron analysis. Users can access
neuPrint data for all neurons of an MI by using the context menu.
10 DATA AND I MPLEMENTATION
Data. Vimo requires a connectivity network and proofread reconstructions of neurons in the form of 3D skeletons and synapses, including
their spatial locations and pre- and post-synaptic partners. Meta-data,like neuron types and anatomical brain regions, are used to increase theexpressivity of motif queries but are not required. Precomputed motif
counts can guide motif sketching (see Sec. 6.1). All other computations
can be performed during runtime. Vimo expects data in the neuPrint
format [38]. neuPrint currently hosts five datasets (three hemibrain versions [44], the fib19:v1.0, and the MANC dataset [54]). New datasets
are expected on the same platform soon. For the development andevaluation of this project, we used the hemibrain v1.2.1 dataset, which
includes 21, 000 proofread neurons and 20 million synapses.
Scalability. Interactively exploring tera- and petascale connectome
datasets requires scalable tool architectures. We leverage different data
representations of neuronal circuits with varying memory requirements
to achieve scalability. For instance, the electron microscopy imaging
data of the hemibrain dataset is 26 TB in size [44]. In contrast, the skeleton representations of the reconstructed neurons require only ∼10 GB,
and the pure connectivity graph compresses the data to ∼25 MB [44].
As a result, the data size required for certain analyses is reduced by aFig. 11: Vimo evaluation examples and tasks. (a) In the exploratory
case study, a participant explored a circular connectivity motif between
visual input neurons. By simplifying the motif using the focus&context
technique, they could identify a synapse cluster; See the colored circle
in (a) and (b). (c) In Task 1 of the qualitative user study, participants
were asked to sketch this known biological motif between ExR neurons
and other neurons in the ellipsoidal body (EB) of Drosophila [24].
factor of a million by transforming the imaging data into a connectivity
network. We exploit the different memory requirements by carefully
choosing the data representation for each step of the analysis workflow.
For instance, Vimo queries the compact connectivity network for motifs.
Based on the detected and selected MIs, Vimo downloads a small setof the neuronal skeletons and synapses from the neuPrint server [38].
Vimo then uses an optimized web-based renderer to display those neuronal skeletons interactively. We only send skeleton vertices and edges
to the GPU and use image-based rendering to generate visual primitives,
like spheres and cones, in the fragment shader. Thus, we avoid costly
data transfers between the CPU and the GPU. Hence, Vimo can be usedon consumer-level hardware without RAM and GPU requirements.
Implementation. Vimo is implemented as a web application using a
React-based client and a Python-based server. Vimo builds on software
libraries like NaVis [45] for data processing and a modified version of
the WebGL-based SharkViewer [61] for 3D rendering. The hemibrain
dataset [44] is hosted remotely in the neuPrint ecosytem [38]. Motifsketching is implemented using the Paper.js library. Vimo translates thevisual sketch to an optimized Cypher query [17] using the Dotmotif [33]language as an intermediate step. This query is sent to a remote graphdatabase to determine a list of MIs. Vimo is open-source [57], and weoffer instructions for users in our tutorials. We also have released a
domain-agnostic React component [9] for motif sketching to lower theentry threshold for motif analysis across disciplines.
11 E VALUATION
We report on an in-depth case study and qualitative user study to evaluate the usability and usefulness of Vimo.
Participants. We evaluated Vimo with 7 experts (P1 - P7, 3 male, 4female) from the Harvard Center for Brain Sciences and HHMI Janelia.
Two participants are also co-authors. To limit participants’ time commitment, three participants performed the case study, and the remaining
four participants completed the user study. All participants are experts
in analyzing neuronal circuits reconstructed from EM image data, andfour are experts in Drosophila connectomics (6 postdoctoral researchersand 1 Ph.D. student in neuroscience). None of the participants currently use interactive tools for motif analysis, even though 6 out of 7
participants rated motif analysis as important for their research.
Setup. We met with each participant for 90 minutes in person or on a
Zoom video call. After a short introduction to the tool, all users, who
had no hands-on experience with Vimo, steered the tool themselves. We
asked all participants to think out loud to capture their thoughts.
11.1 Case Study: Exploratory Analysis
We report on an exploratory case study with three domain experts
specializing in analyzing neuronal circuits in the Drosophila brain. We
provided no specific tasks, as we wanted to test what types of analyses
an experienced neuroscientist would conduct. We describe two motifs
in detail that were analyzed by the experts during the session.
Feedforward outputs of ExR neurons. First, the expert searched
for a familiar connectivity motif to verify the tool’s reliability. They

started by sketching a feed-forward motif involving an external ring
(ExR) neuron, an ellipsoidal body (EB) neuron, and a neuron without
any constraints (T1). The motif sketch can be reproduced by loading
case-study-motif-1.json, available on our GitHub repository [57].
The expert iteratively refined their sketch by adding more constraints
until they found an MI that matched the desired characteristics. Next,
they selected an MI that included one ExR neuron and two EPG neurons(Ellipsoid body - Protocerebral bridge - Gall) (T2). After inspecting
the 3D renderings, they used the summary view to highlight synapses
between the ExR neuron and both EPG neurons (see Fig. 7d). The
expert indicated this helped confirm that the ExR neuron forms synapses
with each EPG neuron on different arbors (T3). They decided to
study this observation in more detail using the focus&context slider
and first pruned all unrelated motif branches of the neurons. Next,
they iteratively moved back and forth between the non-exploded andexploded views to understand how the neurons entangle at areas ofstrong synaptic connectivity. Next, they studied the bundled lines
between the pre-synaptic sites of the ExR neuron and one of the EPG
neurons. They stated that neuron pruning and the exploded view helped
them find a previously unknown connection and quickly identify which
synapses might be considered biological noise (T4). Finally, the expert
was interested in performing a neuron-centric multi-motif analysis(Sec. 8.1) of the currently selected MI and other neurons expressing thesame pattern. Specifically, they wanted to study if there are other MIsof the sketched feed-forward network that also involve the currently
selected ExR neuron and one of the selected EPG neurons. Hence, they
searched the query results for a second MI including these neurons,
and added it to the 3D view (T5). They frequently switched the focus
between both MIs to better understand their spatial relationships andused the multi-motif abstraction (Sec. 8.2) to compare the connectivityof both MIs.
Circular connections in visual input neurons. Next, the expert
sketched a circular motif of three visual input neurons, specificallytuberculo-bulbar (TuBu) neurons and ellipsoid body ring (ER) neurons (T1). The motif sketch and the studied motif instance areavailable for reproducible results (case-study-motif-2.json andcase-study-motif-2-instance.json at [57]). Based on the motif
counts in the sketch panel, the expert found that circular connections areunderrepresented in the dataset, making it interesting for detailed analysis (T2). After analyzing an MI in 3D using Vimo’s focus& context
technique, the expert identified that all synapses are clustered tightly ata specific location (T3, T4) (see Fig. 11ab). Based on this observation,
the expert was interested if other neurons of the same type formed
the same motif but expressed stronger connection strengths. Hence,
they increased the synapse strength constraints in the sketch and foundanother MI with an even stronger synapse cluster close to the previously
observed cluster (T1, T2). As a final step, the expert used the exploded
view to study the internal structure of the cluster and inspected thebundled lines to learn how the circular motif is expressed within thecluster (T4). Overall, the expert stated that neuron pruning and thebundled connectivity links helped them quickly judge if synapses are
clustered tightly or distributed randomly on motif neurons.
11.2

Qualitative User Study

To evaluate the usability and usefulness of Vimo in a qualitative user
study, we asked participants to perform two tasks: motif sketching andanalyzing motif connectivity with our focus&context approach.
11.3

Task 1: Motif Sketching and Querying

We asked participants to sketch and query for a connectivity motif
recently discovered (T1) [24]. The motif describes ExR neurons andother EB neurons that form connections inside and outside the EB. This
motif contributes to the context-dependent action selection behavior
of Drosophila. A correct motif sketch and related motif instance areavailable online to reproduce results [57]. We provided a schematic
illustration of the motif and its constraints (see Fig. 11c) to all participants. Sketching the connectivity between the motif neurons wasstraightforward for all participants while defining node and edge constraints involved a learning curve. Two participants needed help froma neuron to a group of types. For instance, the ExR* type requires aneuron to be in any of the ExR1 - ExR8 types.
Exploded views improve spatial motif understanding. The gradualtransition of neurons into the exploded view helped users understandhow neurons entangle. Understanding the complex entanglement is
crucial for analysis but also an obstacle for mentally mapping the motif
graph structure to the 3D visualizations of neurons.
Vimo improves the state of the art. All study participants agreed
that Vimo improves their motif analysis workflow. Users particularly
liked the interactive motif sketching, neuron pruning, and synapse
highlighting for their visual analysis in 3D (see Fig. 12, Q2, Q3, Q4).
12

Fig. 12: User ratings. We show neutral responses in gray, positive
responses in green, and the percentage of users who (strongly) agreed.
the study conductor to specify appropriate constraints. We observedthat the performance of defining constraints depends on the participant’s familiarity with fly brain anatomy. Participants familiar with thedataset could easily identify how to translate the instruction ’outside
EB pathway’ into an edge constraint. Our survey results show that
all four study participants found the motif sketching interface useful(see Fig. 12, Q2). However, for querying large motifs involving moreneurons (e.g., n ≥ 6), only 3 out of 4 participants found motif sketchinguseful because of visual clutter in the sketching interface. During ourpilot study, we found that neuron-type wildcards can improve the utility for defining node constraints (Sec. 6.1). Therefore, we added thisfunctionality that was then widely used for the remainder of the user
study. Additionally, participants suggested changing synapse strength
constraints to a default of 10 synapses to avoid repetitive interactions.
11.4 Task 2: Analyzing Motif Connectivity in 3D Data
In the second task, we provided users with a motif and the 3D viewof a motif instance and asked them to identify the main parts of the
motif neurons connected in the 3D data (T3, T4). We tested two
conditions: In the first condition, participants had access to the fullfunctionality of Vimo, including our focus&context technique. In thesecond condition, participants could not use our focus&context method.
We used two MIs describing feedback loops of CX neurons [57]. We
counterbalanced between both conditions. Every participant had toperform the task with both conditions. To test their understanding of the
motif connectivity in 3D, we asked participants to draw an illustration of
the neuron branches involved in the motif and their connections for bothconditions. We observed that participants frequently used the interactive
slider (see Fig. 7e) to prune MIs maximally and to toggle between the
non-exploded view and the exploded view to get a better view of neuron
entanglement. Therefore, using our focus&context technique led to lesscluttered illustrations by the participants, indicating a clearer and better
understanding of motif connectivity due to interactive simplification.
We show an example user illustration in the supplemental material.
Additionally, we collected survey responses from all participants about
our interactive motif abstraction approach (see Fig. 12), demonstratingthat neuron pruning (Q3, Q5), synapse highlighting (Q4), and the
exploded views (Q6) are among the most effective interactions of Vimo.
11.5 Key Findings
Vimo leads to fast, iterative analysis. We observed that users often
queried a specific motif but forgot to specify certain constraints inthe motif sketch. Vimo allows users to quickly iterate on their motif
sketches and queries, leading to more relevant motif analyses.
Biological constraints are essential for motif analysis. We found biological constraints to be essential for expressive motif queries. Specifically, wildcards for neuron types were perceived well, constraining

D ISCUSSION

User expertise for high-resolution connectivity analysis. It was challenging to find qualified experts for the user study. Many neurosciencelabs are interested in connectivity analysis at the nanometer scale, however, few have actually done it. Datasets have only recently become
available, and with a lack of scalable computational tools, many scientists have not yet started on this endeavor. We hope that Vimo can
reduce the barrier of entry for this type of research and attract morelabs and researchers to analyze high-resolution connectivity.
Limitations. Vimo focuses on the interactive analysis of motifs andquick, iterative refinement of motif queries. Therefore, the strength of
our tool is in analyzing relatively small motifs with a limited numberof nodes (n ≤ 8). We determined this threshold through iterative testing. While our visualization approach scales to larger motifs, runninglarge motif queries becomes computationally expensive. Such large
queries potentially require hours of computation time, which would
hinder the interactivity of our tool. Furthermore, while Vimo supports
the visual analysis of multiple connected motif instances, large-scalecomparisons of motif instances distributed across the entire dataset are
not yet supported. Finally, in Vimo’s current design, researchers needa prior hypothesis about relevant motifs. In other types of analyses,
researchers start from a fixed set of neurons and are interested in the setof expressed connectivity motifs. This exploratory approach is inverseto the workflow in Vimo and not yet supported.
Tradeoff between accuracy and visual abstractions. A main design
decision of our approach is not to distort neuron anatomy but rather
enhance the user’s perception of the motif connectivity in the originaldata. Many previous approaches have focused on visual abstractions
that simplify neuron anatomy [1, 36]. We take a complementary approach and focus on highlighting connectivity in the original data. This
comes at the cost of a higher cognitive load than using simplified 2D
representations. However, it allows scientists to better understand thedetailed spatial make-up of an MI.
13

C ONCLUSION AND F UTURE W ORK

Vimo takes a first step towards visual motif analysis for nanoscale brain
data. The core idea of our approach is to enable quick and intuitive
sketching of interesting motifs and to allow specifying biological constraints. We support a detailed motif analysis in the original 3D space,using a focus&context approach. We improve understanding of the data
by gradually highlighting motif connectivity relative to the 3D structure
and arrangement of neurons and synapses. Vimo is a key stepping stone
towards neuronal pathway analysis at scale, where structural and functional data are combined for a better understanding of the brain. While
Vimo is targeted to a specific audience, our motif sketching and novel
simplification method for spatial graphs are domain agnostic. Specifically, the focus&context approach can, in principle, be adapted to any
spatial graph data where nodes exhibit complex spatial morphology.
In the future, as more large-scale and proofread datasets become
available [12–14,34,44,50,58], we want to extend Vimo to support datafrom other organisms, such as mice and humans. Vimo is currently being used at HHMI Janelia to analyze the next generation of connectome
datasets. With these new datasets, inter-specimen comparative motif
analysis will be within reach, posing new challenges for visualization.
Thus, future tools should support analyses into whether similar neurons
across specimens form similar motifs.

14

ACKNOWLEDGMENTS

This work was supported by NSF awards IIS-1901030, NSF-IIS2239688, NCS-FO-2124179, and R24MH114785. We gratefully acknowledge internal financial support from the Johns Hopkins University
Applied Physics Laboratory’s Independent Research & Development(IR&D) Program for funding portions of this work. We also thank theparticipants of our user study and the anonymous reviewers for theirvaluable feedback.
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