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Exploring the TMD algorithm: A topological analysis of human and rodent neurons
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science.
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science.
2022 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Utforskande av TMD-algoritmen : En topologisk analys av neuroner från människor och gnagare (Swedish)
Abstract [en]

In this report, we study the topological differences and similarities between human and rodent neuron types. Specifically, we are exploring the Topological Morphology Descriptor (TMD) and what result we can achieve with this relatively new method. Digital reconstructions of neurons from a set of neuron types are downloaded and sent to the TMD algorithm, the output barcodes from the TMD are then compared to each other and analyzed with a hierarchical clustering technique. The questions we are trying to answer are if equivalent cell types of rodent and human neurons are different enough to be categorized into different clusters by the algorithm, and secondly, if different cell types will be categorized into different clusters. Our results show that human and rodent pyramidal cells (L2/L3) are too similar to be distinguished simply by their topological differences with this technique. However, granule and pyramidal cells from both mice and rats were split up into two different clusters with an accuracy of 77% which shows promise that the TMD can be used as an automatic classifier of neuron types in the future.

Abstract [sv]

I denna rapport studerar vi topologiska skillnader och likheter i neurontyper mellan människor, råttor och möss. Specifikt undersöker vi Topological Morphology Descriptor (TMD) och vilka resultat vi kan uppnå med denna relativt nya metod. Digitala rekonstruktioner av neuroner från en uppsättning neurontyper laddas ner och skickas till TMD - algoritmen, streckkoder som genereras av TMD jämförs sedan med varandra och analyseras med en hierarkisk klustring teknik. Frågorna vi försöker svara på är om likvärdiga celltyper av råttor/möss och mänskliga neuroner är tillräckligt olika för att kunna kategoriseras i olika kluster av algoritmen; och för det andra om olika celltyper kommer att kategoriseras i olika kluster. Våra resultat visar att människors och gnagarnas pyramidala celler (L2 / L3) är för lika för att kunna särskiljas endast genom deras topologiska skillnader med denna teknik. Däremot delades granulära och pyramidala celler från både möss och råttor upp i två olika kluster med en noggrannhet på 77%, vilket tyder på att TMD skulle kunna användas som en automatisk klassificerare av neurontyper i framtiden.

Place, publisher, year, edition, pages
2022. , p. 34
Series
TRITA-EECS-EX ; 2022:440
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-320197OAI: oai:DiVA.org:kth-320197DiVA, id: diva2:1703917
Subject / course
Computer Science
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2022-10-17 Created: 2022-10-15 Last updated: 2022-10-17Bibliographically approved

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