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Open AcceProceedingsComputational neuroanatomy: ontology-based representation of neural components and connectivityDaniel L Rubin*†1,2, Ion-Florin Talos†3, Michael Halle3, Mark A Musen2 and Ron Kikinis3
Address: 1Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA, 2Stanford Medical Informatics, Stanford University School of Medicine, Stanford, CA, USA and 3Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA

Email: Daniel L Rubin* - [email protected]; Ion-Florin Talos - [email protected]; Michael Halle - [email protected]; Mark A Musen - [email protected]; Ron Kikinis - [email protected]

* Corresponding author †Equal contributors

AbstractBackground: A critical challenge in neuroscience is organizing, managing, and accessing theexplosion in neuroscientific knowledge, particularly anatomic knowledge. We believe thatexplicit knowledge-based approaches to make neuroscientific knowledge computationallyaccessible will be helpful in tackling this challenge and will enable a variety of applicationsexploiting this knowledge, such as surgical planning.

Results: We developed ontology-based models of neuroanatomy to enable symboliclookup, logical inference and mathematical modeling of neural systems. We built aprototype model of the motor system that integrates descriptive anatomic and qualitativefunctional neuroanatomical knowledge. In addition to modeling normal neuroanatomy, ourapproach provides an explicit representation of abnormal neural connectivity in diseasestates, such as common movement disorders. The ontology-based representation encodesboth structural and functional aspects of neuroanatomy. The ontology-based models can beevaluated computationally, enabling development of automated computer reasoningapplications.

Conclusion: Neuroanatomical knowledge can be represented in machine-accessibleformat using ontologies. Computational neuroanatomical approaches such as described inthis work could become a key tool in translational informatics, leading to decision supportapplications that inform and guide surgical planning and personalized care for neurologicaldisease in the future.

from The First Summit on Translational Bioinformatics 2008San Francisco, CA, USA. 10–12 March 2008

Published: 5 February 2009

BMC Bioinformatics 2009, 10(Suppl 2):S3 doi:10.1186/1471-2105-10-S2-S3

<supplement> <title> <p>Selected Proceedings of the First Summit on Translational Bioinformatics 2008</p> </title> <editor>Atul J Butte, Indra Neil Sarkar, Marco Ramoni, Yves Lussier and Olga Troyanskaya</editor> <note>Proceedings</note> </supplement>

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BackgroundA goal for translational research in neuroscience is tounderstand and cure crippling neuropsychiatric diseasessuch as Parkinson's disease, to track chronic diseases assymptoms progress and remit; and to guide precise neuro-surgical interventions while sparing the normal tissueunderlying critical cognitive functions and behaviors.There is an unprecedented opportunity to understand andtreat neurological diseases, given the significant progressin the variety and sophistication of neuroimaging tech-niques and the rapid accumulation of neuroscientific datain electronic form. Translational biomedical informaticsmethods are becoming critical to scientific progress byorganizing and disseminating new neuroscientific knowl-edge in this highly complex and rapidly evolving domain.

Imaging is key aspect of the evaluation of neuropsychiat-ric disease. Images provide spatial information about ana-tomic structures in the brain that is critical toneurosurgeons in planning interventional procedures.

However, the images themselves lack the anatomic andfunctional knowledge (excitatory and inhibitory proper-ties of connections) that are critical in surgical planning.Integrating rich anatomic and functional knowledge withthe spatial information in images is thus a critical task forclinical care and surgical planning for neurosurgicalpatients. The explosion in electronically accessible knowl-edge needed to inform these tasks is necessitating devel-opment of computational approaches to help researchersand clinicians manage and use the knowledge effectively.

Image atlases are one promising computational approachfor representing and characterizing neurological disease.Image atlases are representations and databases of ana-tomical and spatial information that capture significantattributes of the brain from imaging studies and thatinform the creation of robust mathematical models [1].They are produced by segmenting images of the brain in asingle idealized subject to produce an image map, label-ling each structure to identify the anatomic structures, and

Image atlas of the brainFigure 1Image atlas of the brain. Image atlases represent spatial information by providing a parcellation of the anatomic structures contained in the brain (left). Each structure is represented as a spatial region of uniform color. Other anatomic knowledge about the structure, such as functional information, is not represented. Image atlases are generally used to infer the anatomic localization of brain structures in individual subjects by registering their images to the atlas. For example, the anatomic identity of areas of activity in fMRI are identified in this manner (right).



registering images from individual patients with the atlasto infer the location of structures (Figure 1).

While image atlases are making important contributionsto neuroscience, they lack complete knowledge abouttheir contents, such as how anatomic structures are con-nected, and the functional significance of abnormalitiesin various structures. Functional information – whether aconnection is excitatory or inhibitory – is not represented.To develop applications that can help physicians to tailoroptimal treatment for neurological disease, such as surgi-cal treatment planning and personalized care, we needcomputational methods to integrate and access both spa-tial anatomic information and functional knowledgeabout the contents of neuroanatomical images.

Ontologies provide a means to make the anatomic andfunctional neuroanatomical knowledge explicit formachine processing and accessible to decision supportapplications. An ontology specifies the entities, theirattributes and relations in a domain, providing anexplicit, human-readable and machine-accessible struc-tured description of the domain. Ontologies are beingembraced in biology to express a common vocabulary,shared understanding, and complex relationships amongdiverse biological data in a way that is useful for bothhuman understanding and automated computer reason-ing; in fact, ontologies have opened entire new avenuesfor organizing, integrating and retrieving biological data[2]. We believe ontologies will be advantageous in repre-senting the knowledge in the neurosurgical domain, andcould provide the computational substrate to enable avariety of intelligent applications, such as surgical plan-ning decision support and computerized training.

Our hypothesis is that it is possible to create an ontology-based representation of anatomic and functional neuro-anatomical knowledge. The current work is an extensionof our recent research [3], focusing on our methodologyfor ontological modeling of the neural components, con-nections, and enablement of computational reasoningusing this model. The ontology can support automatedreasoning and inform practitioners about the functionalconsequences of deranged neural connectivity. This func-tionality could ultimately be useful for automated com-puter reasoning tasks such as surgical planning. Weundertook this work to demonstrate the feasibility of ourapproach in a focused use case.

Our ultimate goal is to integrate ontologically-modeledknowledge of anatomy and function with geometric brainatlas information (label maps and three-dimensionalmodels) derived from high-resolution, multi-modalimaging. Such integrated spatial and anatomic knowledge

will enable image-based reasoning applications and per-sonalized care for individual patients.

MethodsAs we previously described [4], we extracted the relevantfunctional neuroanatomical information needed to repre-sent the functional organization of the motor initiationneural network from authoritative neuroscience text-books [3,5-7] The anatomic knowledge was summarizedas a diagram indicating the major anatomic componentsand their neural connections (Figure 2). Certain neuralconnections have particular anatomic importance, such aswhether they belonged to the direct or indirect pathway;this information was conveyed using labels on the con-nectors in the diagram. Finally, the neural connectionshave dominant functional activity in terms of being pri-marily excitatory or inhibitory on the nuclei to which theyconnect, indicated by the color and shape of the connec-tors in the diagram (Figure 2). The representation of exci-tatory and inhibitory connections in these tracts reflectedthe model commonly used in neurosurgical evaluation:+1 for excitation and -1 for inhibition.

In addition to collecting knowledge about canonical nor-mal anatomy, we acquired knowledge about Parkinson'sdisease, a disorder affecting the motor initiation network.In Parkinson's disease, there is degeneration of neural ele-ments, leading to a decrease in the activity of the directbasal ganglia pathway relative to the indirect pathway

Functional organization of the motor initiation neural net-workFigure 2Functional organization of the motor initiation neu-ral network. This figure is a diagrammatic representation of the major brain structures and connections related to the motor initiation network. Anatomically significant neural pathways are labeled ("DP" = Direct Pathway; "IP" = Indirect Pathway). Functional properties of neural connections (exci-tatory or inhibitory) are indicated by the color of the con-nections (line connectors in the diagram).



activity (Figure 3). This, in turn, results in an increasedinhibitory output from the internal pallidal segment (glo-bus pallidus pars interna, GPi resulting from unbalancedinhibition), ultimately resulting in decreased corticalstimulation and elicitation of the symptoms of the disease– a hypokinetic movement disorder characterized byimpaired initiation of movement, reduced velocity andamplitude of movement, and resting tremor withincreased muscle tone.

We created an ontology of functional neuroanatomy,based on the anatomic knowledge we had acquired fornormal and disease states (Figure 2 and Figure 3). Theontology was built using a disciplined modelingapproach, inspired by that adopted by the FoundationalModel of Anatomy [8]; in fact, where possible, anatomicentities from the FMA were used in our ontology. How-ever, FMA does not describe neural connectivity to formneural pathways, nor does it describe the functionalaspects of neural connections (excitation and inhibition).Thus, we extended our ontology to include this knowl-edge in the form of attributed relations, similar to priorwork in creating symbolic models of cardiovascular phys-iology [9]. The final ontology contains 235 classes, 50slots (attributes), and 394 instances. The top-level classhierarchy of the ontology is organized into major axes ofneuroanatomical information: functional system, nerve,neural network, neural network connection, and neuralnetwork node (Figure 4).

In our ontology, anatomic structures and connections arerepresented by instances of ontology classes of the corre-sponding anatomic entities (Figure 4). In order to visual-ize the neuroanatomical ontology-based model, we usedthe Protégé Diagram Widget,[10] which provides a graph-ical paradigm for creating, linking, and visualizinginstances created using an ontology. We created differentglyphs to represent the different types of components inneuroanatomical model to clarify the distinction amongdifferent types of anatomic structures (Figure 5). Theinstances contain attributes indicating how the anatomicstructures are connected to other components. Other partsof the ontology specify the functional organization of thebrain – which groups of nuclei and connections corre-spond to neural pathways. Functional information aboutneural connections was specified qualitatively in theontology using a NeuralActivity attributed relation withvalues of "excitatory" and "inhibitory" (Figure 5C). Forour representation of normal neural anatomy in theontology, there are nine instances of excitatory neuralconnections and nine instances of inhibitory neural con-nections.

To represent abnormal connections, we altered the nor-mal model, creating abnormal neural connections thathad the appropriate values for the NeuralActivity attributeappropriate to the abnormality. Specifically, if a neuralconnection was impaired, then the value of NeuralActivity

Ontology of functional neuroanatomical knowledgeFigure 4Ontology of functional neuroanatomical knowledge. The ontology (shown in Protege) contains classes represent-ing anatomic components of the nervous system (nerves, nuclei, and connections) and the functional organization of the nervous system (neural networks and functional sys-tems). Each class contains slots – attributes of the classes which provides the anatomic and functional knowledge in this representation. For example, the anatomic entity Globus Pal-lidus is seen to have an input and output, an elementary func-tional operation, and a neural network of which it is a component.

Pathological alterations of neural circuits in diseaseFigure 3Pathological alterations of neural circuits in disease. This figure illustrates the abnormal connections (dashed line connectors) that exist in Parkinson's disease, characterized by impaired activity in the direct basal ganglia pathway rela-tive to the indirect pathway. Note that this results in an imbalance of activation in the neural network.



was set to "no activity". Alternatively, we could representsuch connections by deleting the corresponding arcs in

the model. For example, for Parkinson's disease, there aretwo instances representing impaired connections:impaired excitation from from Substantia Nigra to Puta-men, and impaired inhibition from Putamen to GPi (Fig-ure 3). In the ontology, these instances are created fromthe Impaired_Excitatory_Neural_Connection class, bothhaving "no activity" for the value of the NeuralActivityattribute.

To assess the potential benefits of our approach, we inter-viewed a neurosurgeon with neuroanatomical expertisewho evaluated our models and qualitatively comparedthe benefits of our explicit, structured neuroanatomicalrepresentation with the non-computational alternative(all information processing in the head of the practi-tioner). In addition, we explored the possibility of creat-ing an automated reasoning application by manuallyapplying an algorithm to calculate net activation of partic-ular nuclei in neural network models. Net activation wascalculated by visiting each node in the model and sum-ming all incoming excitatory connections, while subtract-ing the sum of all incoming inhibitory connections. Forthis calculation, excitation and inhibition were equallyweighted (+1 for excitation and -1 for inhibition). The netvalue produced in this manner determined whether eachnucleus was excited or inhibited, and subsequently prop-agated to downstream nuclei in the network (Figure 6).

We applied this algorithm to calculate the net activationof motor cortex (the ultimate target of interest in diseasesof the motor initiation network). This was performed byiterating all neural connections, commencing with nucleireceiving no inputs, and calculating the net activation ateach brain nucleus, propagating the net activation of eachnucleus along the neural pathways until the cortex wasreached. We compared the results in the normal statemodel and in the Parkinson's disease state model.

ResultsWe used our ontology to build a representation of the nor-mal motor initiation network by creating ontologyinstances for the anatomic entities to which they corre-spond. Accordingly, there is a one-to-one mapping fromeach instance in the ontology (each node in the graphicalview of the ontology) to each structure in the brain (Figure5). This correspondence provides the link between spatialimage information and ontology-based anatomic knowl-edge.

The ontology model also represented the connectionsamong brain components (arcs in the graphical view),each having the appropriate attributes to specify the func-tional connectivity (excitatory or inhibitory; Figure 5). Forexample, the Putamen is represented as an instance ofNeuralNetworkNode (Figure 5B). Connections between

Representing neuroanatomical knowledge in ontologyFigure 5Representing neuroanatomical knowledge in ontol-ogy. (A) Anatomic structures in the brain are represented as instances in an ontology of neuroanatomy/connectivity and displayed as a graph, showing anatomic structures (nodes) and connections (arcs) similar to the diagrammatic represen-tation of the same knowledge (Figure 2). (B) Nodes and arcs contain attributes making their inputs and outputs explicit. (C) The functional behavior of each connection (excitatory or inhibitory) is represented as attributes on the connectivity relations.



brain regions were represented by creating instances of theNeuralConnection class. Accordingly, the connectionbetween the Putamen and the primary motor cortex wasestablished by creating the arcLinkPrimaryMotorCortex_Putamen. The arcs (neural con-nections) were assigned the necessary value for the Neura-lActivity attribute (excitatory or inhibitory) as required tomodel the functional neuroanatomic knowledge (Figure5C). The resulting graphical model of brain anatomy inthe ontology representation had a very similar appearanceto the diagrammatic representation from the knowledgesource used to create the model (compare Figure 2 andFigure 5).

Our ontology could also represent abnormalities in dis-ease states with abnormal functional connectivity. Specif-ically, we represented the neural network in Parkinson'sdisease (Figure 3). As with the model for the normal statefor the motor initiation network, the graphical model ofthe ontological representation of functional neuroanat-omy in the disease model had a very similar appearanceto the diagrammatic representation from the knowledgesource used to create this model (compare Figure 3 andFigure 7A).

In addition to providing a structured representation of theinformation needed to create a graphical symbolic displayof the neuroanatomical knowledge, the ontology pro-vided a computational infrastructure to evaluate the func-

tional consequences of connectivity derangements in theneural networks we studied. In the Parkinson's model, wecould evaluate the net activation in different brain nucleias a consequence of the functional derangements in theconnections affected in this disease. For example, by trac-ing the connections from the SubstantiaNigra to the Pri-maryMotorCortex, we could conclude that there is netinhibition of the latter (Figure 7B). While in our particularmodel both excitation and inhibition were equallyweighted according to the neurosurgical perspective, theknowledge representation and processing algorithmcould be altered to reflect alternative functional neuroan-atomic knowledge, such as real-valued excitation andinhibition.

Similarly, we could use this model as a platform to inferthe consequences (in terms of net activation) resultingfrom different surgical interventions that would disruptparticular neural connection pathways. Such inferencecould be useful in guiding surgical planning. In combina-tion with the schematic view of the ontology-basedmodel, users could interrogate particular portions of themodel to study the functional aspects of neural connectiv-ity. The neurosurgical domain expert who had developedthe neural network models by hand believed that ourcomputational approach would be beneficial for simula-tion and surgical planning in complex cases.

DiscussionComputational methods can transform rapidly accumu-lating biomedical data into proactive, predictive, and par-ticipatory health solutions. Ontologies are a key tool inthe translational bioinformatics arsenal, because they pro-vide explicit, machine-processable and human-compre-hensible descriptions of biomedical data elements andentities needed for computers to help people make senseof the wealth of biomedical information. Neuroscience isa complex domain, rich in anatomic and functionalknowledge. Our goal was to develop an ontology-basedsymbolic model of structural and functional neuroanat-omy. The ultimate objective is to use this computationallyaccessible knowledge to drive a decision support systemfor surgical planning in a variety of neurological diseases.

In the current study, we have demonstrated the feasibilityof encoding the knowledge necessary to describe the basicfunctional organization of the motor system. We chosethe motor system because it displays little anatomic varia-bility and its function is better characterized than that ofother, more complex functional systems in the brain. Fur-thermore, the motor system is involved in several impor-tant pathologic processes with high impact on publichealth, such as movement disorders. Although the scopeof our current prototype ontology is limited to a singlefunctional system, we believe that our methods are exten-

Calculating net activation in brain nucleiFigure 6Calculating net activation in brain nuclei. Anatomic structures in the brain are connected to other structures via neural tracts, whose net effect on a particular brain nucleus is either excitatory or inhibitory. The net activation of a partic-ular nucleus is calculated by summing all incoming excitatory connections and subtracting the incoming inhibitory connec-tions, and assuming equal weight for each connection (+1 for excitation and -1 for inhibition). In this example, the tract connecting substantia nigra to putamen is deranged, and its inhibitory input to putamen is lacking. Thus, there is net acti-vation of the putamen ("+1").



sible, and that our modeling approach will be applicableto a richer breadth of neural networks. For example, wehave already demonstrated that a common ontologicalframework can describe both normal as well as patholog-ical neurological states (Figures 2, 3, 5 and 7).

Our ontology encodes two complementary aspects ofneuroanatomy: (a) a structural aspect, concerned withspatially-localized structures and relationships, and (b) afunctional aspect, dealing with physiological aspects ofneural connections between neural structures – excitationor inhibition of one nucleus on another exerted via neuralpathways. The structural knowledge is represented in thetopology of the network of ontological components (Fig-ure 5B), while the functional aspects are represented asattributes on the connections between neural compo-nents (Figure 5C). The ontology provides a facile mecha-nism for neuroscience practitioners to browse and edit theneuroanatomical knowledge, because it can be displayedin a graphical form similar to that which they are accus-tomed (compare Figure 2 and Figure 5).

Our methods are a direct extension of previous endeavorswe undertook for creating explicit and computable repre-sentations of hemodynamic models of the cardiovascularsystem [9]. In that work, we also created an ontology-based model of structural and functional components ofa biomedical system, albeit in the cardiovascular domain.One can view the neuroanatomical models in the currentwork as completely analogous, comprising both structuraland functional components whose attributes are specifiedexplicitly in the ontology.

There are several limitations of our work. Our current rep-resentation assumes a simple ternary-valued activation ofconnections – excitatory, inhibitory, or deranged (noactivity). In reality, such connections are likely real-val-ued, as neural tracts comprise many fibers, some of whichare activated and some not. Our choice was guided by theneurosurgical perspective that makes this simplifyingassumption; certainly our representation could be alteredto accommodate real values for activation in the future.

Another limitation is that we have represented only themotor initiation network of the brain, and we have notrepresented the full spectrum of neural diseases. Webelieve our modelling approach is extensible to other neu-ral systems beyond the motor network, given that suchsystems comprise nuclei and connections. The value ofour methods in modelling other diseases would need tobe studied, however. In addition, we believe computa-tional approaches to neuroanatomy such as described inthis report will be useful mainly in complex conditions –an expert would not likely need assistance in simple orwell-known scenarios. However, as the richness in ana-tomic and functional neural knowledge expands, webelieve our framework will be useful to organizing thisknowledge and making it computationally accessible toapplications.

There are benefits to our approach. First, we have madeneuroanatomic knowledge explicit in the ontology, in aformat that is both human-readable (in the Protege Dia-gram Widget) and machine-accessible. The ontologicalrepresentation can be modified directly in the diagram,and functional consequents can be immediately deducedfrom the ontology.

Another benefit of our approach is that the neuroanatom-ical knowledge is in a machine-accessible format. Compu-ter reasoning applications can be created to process theanatomic knowledge in intelligent ways, such as in surgi-cal planning applications capable of identifying optimumtargets for functional stereotactic surgery. A decision sup-port application informed by the richness of structuraland functional anatomic knowledge could also guide thedecisions about the optimum tissue ablation path in such

Representing neuroanatomical abnormalitiesFigure 7Representing neuroanatomical abnormalities. Abnor-mal anatomic structures are represented by altering the attributes of their connections to other structures. (A) Motor initiation neural network in Parkinson's disease, show-ing impaired connections (decrease in activity) in the direct basal ganglia pathway relative to the indirect pathway activity (dotted arc, labeled "IMPAIRED") as well as impaired putamino-pallidal connections. (B) Net activation of brain structures can be computed in the ontological representa-tion of the neuroanatomical network by propagating net acti-vation at each nucleus. In this representation of the Parkinson's disease, each connection is assumed to have uni-tary activation or inhibition, and there is net inhibition of the cortex.



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surgical interventions. We have already shown here thatthe functional consequences in a disease state can beinferred by traversing the connections in the model andcalculating the net activation of different brain regions(Figure 6 and Figure 7). We are currently creating an appli-cation to derive these inferences automatically, by propa-gating activation across the neural network, informingpractitioners about the functional consequences of neuralconnectivity derangements and interventions.

A potential application for our work is an ontology-aug-mented neuroanatomy atlas, serving as the basis for amultitude of intelligent applications that can combineprocessing of spatial information with analysis of thefunction of the corresponding regions in images. Suchknowledge-enhanced atlases can enable applications forsimulation and neuroanatomy teaching. Symbolic mod-els of functional neuroanatomy, alone or in combinationwith digital brain atlases, could pave the way for futureknowledge-based applications for neuroscientific researchand clinical care.

ConclusionWe have shown that functional neuroanatomical knowl-edge can be represented in a computational format usingan ontology. The ontology provides a means to peruse theknowledge, while making it accessible to computer rea-soning, such as decision support, modeling, and teachingapplications.

Competing interestsThe authors declare that they have no competing interests.

Authors' contributionsDLR and IFT conceived this work, acquired the anatomicknowledge, and produced the computational models inthis work. MH developed code to enable access to the ana-tomic knowledge. MAM and RK participated in its designand coordination of this study. All authors read andapproved the final manuscript.

AcknowledgementsThis work was supported by the National Center for Biomedical Ontology, under roadmap-initiative grant U54 HG004028 from the National Institutes of Health, the Neuroimage Analysis Center (NIH grant P41-RR013218), and the National Center for Image-guided Therapy (NIH grant U41 RR019703).

This article has been published as part of BMC Bioinformatics Volume 10 Sup-plement 2, 2009: Selected Proceedings of the First Summit on Translational Bioinformatics 2008. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/10?issue=S2.

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