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Unilateral Ear Canal Atresia:A Study ofCortical Morphologyand Functional Connectivity
Karolinska Institutet. (Department of Clinical Sciences, Intervention and Technology)
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems.ORCID iD: 0000-0002-6827-9162
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems.
Karolinska Institutet. (Department of Clinical Sciences, Intervention and Technology)
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Objectives

The objective is to investigate if unilateral conductive hearing loss in ear canal atresia without hearing treatment in childhood leads to cortical reorganization in functional connectivity and gray matter morphology.

Design

A prospective study including 18 patients with unilateral congenital ear canal atresia age- and gender-matched with normal hearing controls were examined with audiometry, T1, T2 FLAIR and resting state functional magnetic resonance imaging. The thickness and volume of 68 cortical regions were computed from the anatomical images, and seed-based correlation analysis was performed on the resting state functional data for 6 auditory cortical regions of interest.

Results

No statistically significant differences were seen when applying correction for multiple comparisons. However, trends were observed (uncorrected p<0.05) showing larger cortical volumes of the right precuneus cortex in the atretic group (p=0.014), as well as increased functional connectivity of this region coupled to the planum polare in the right hemisphere (p<0.02). The right side precuneus cortex volume was also the most important variable for distinguishing between patients and controls. Cortical volumes of right primary motor cortex (p=0.034) and right somatosensory cortex (p=0.043) were also larger in the atretic group. No differences were observed in the primary auditory cortices’ volume or thickness.

Conclusion

No differences were found within the primary auditory cortices in cortical thickness or volume, which might reflect childhood plasticity with increased bilateral cortical representation of the normal ear, or cross-modal plasticity with stimuli from other senses. Morphology and functional connectivity pattern indicate increased integration of visual and auditory input in unilateral atresia, although future studies are required to support these findings.

Keywords [en]
Unilateral ear canal atresia, gray matter morphology, functional brain connectivity, MRI
National Category
Neurosciences
Research subject
Technology and Health
Identifiers
URN: urn:nbn:se:kth:diva-272725OAI: oai:DiVA.org:kth-272725DiVA, id: diva2:1426841
Note

QC 20200506

Available from: 2020-04-27 Created: 2020-04-27 Last updated: 2020-05-06Bibliographically approved
In thesis
1. Development and application of rule- and learning-based approaches within the scope of neuroimaging: Tensor voting, tractography and machine learning
Open this publication in new window or tab >>Development and application of rule- and learning-based approaches within the scope of neuroimaging: Tensor voting, tractography and machine learning
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The opportunity to non-invasively probe the structure and function of different parts of the human body makes medical imaging an indispensable tool in clinical diagnostics and related fields of research. Especially neuroscientists rely on modalities like structural or functional Magnetic Resonance Imaging, Computed Tomography or Positron Emission Tomography to study the human brain in vivo. But also in clinical routine, diagnosis, screening or follow-up of different pathological conditions build upon the use of neuroimaging.

Computational solutions are essential for the analysis of medical images. While in the case of conventional photography the recorded signal comprises the actual image, most medical imaging devices require the reconstruction of an image from the acquired data. However, not only the image formation, but also further processing tasks to assist doctors or researchers in the interpretation of the data and eventually in subsequent decision making, rely more and more on automation. Typical tasks range from locating and measuring objects in a single patient, e.g. a particular organ, a tumour or a specific region in the brain, to comparing such measurements over time between groups consisting of large numbers of subjects. Automated solutions for these scenarios are required to model complex relations of data in the presence of acquisition noise and subject variability while assuring a tractable computational demand.

Traditionally, the development of computational algorithms for medical imaging problems focused on rule-based strategies. Explicitly defined rules that encode the knowledge of the developer are characteristic for such approaches. Within the last decade, this paradigm began to change and learning-based models dramatically gained in popularity. These rely on fitting a complex model to large amounts of data samples, often annotated, which are representative for a particular problem. Instead of manually designing the sought-after solution, it is ‘learned from the data’. While these models have shown enormous potential, they also pose important questions for method developers. How can I get hold of enough data? How much data is enough? How can I obtain proper annotations?

This thesis comprises six studies covering the development and the application of methods along the whole pipeline of medical image analysis. Studies I and II propose different extensions to the method of tensor voting to make it applicable in specific medical imaging problems. Studies III–V address the use of modern machine learning techniques, in particular neural networks, in the field of tractography. Notably, the challenge of obtaining adequately annotated data samples is a topic in Study V. In Study VI, a prospective neuroimaging study of unilateral ear canal atresia in adults is presented, covering the application of methods from data acquisition to group comparison. Overall, the compiled works contributed, in one way or the other, to the non-invasive extraction of knowledge from the human body through automated processing of medical images.

Abstract [sv]

Möjligheten att från utsidan undersöka struktur och funktion hos olika delar av människokroppen har gjort medicinsk avbildning till ett oumbärligt verktyg i klinisk diagnostik och relaterade forskningsområden. Särskilt inom neurovetenskap är forskarna starkt beroende av metoder som strukturell eller funktionell magnetresonanstomografi (MRT), datortomografi (DT) eller positronemissionstomografi (PET) för att studera hjärnan hos den levande människan. Men också i rutinsjukvården bygger diagnostik, hälsokontroll eller uppföljning av olika sjukdomstillstånd på avbildning av hjärnan.

Beräkningsmetoder är oundgängliga för att analysera medicinska bilder. I motsats till konventionell fotografi, där den insamlade signalen innehåller själva bilden, kräver de flesta medicinska avbildningsmetoder att bilden rekonstrueras från insamlade data. Men det är inte bara bildalstringen, utan även den fortsatta behandlingen för att stödja det efterföljande beslutsfattandet, som är mer och mer automatiserade. Typiska uppgifter kan handla om att lokalisera och mäta strukturer i den enskilda patienten – t.ex. ett visst organ, en tumör eller en del av hjärnan – eller att jämföra sådana mätningar över tid mellan grupper bestående av ett stort antal personer. Automatiserade lösningar för dessa uppgifter krävs för att modellera komplexa relationer mellan data som är behäftade med insamlingsbrus och individuell variation utan att beräkningarna blir ohanterligt krävande.

Av tradition har utvecklingen av beräkningslösningar för medicinska avbildningsproblem fokuserat på regelbaserade strategier, ett arbetssätt som kännetecknas av explicit definierade regler som omsätter utvecklarens kunskaper. Under det senaste årtiondet har detta paradigm börjat ändras, och inlärningsbaserade modeller har ökat dramatiskt i popularitet. Dessa bygger på att en komplex modell anpassas till stora datamängder, ofta försedda med något slags anteckningar av erfarna praktiker (annoteringar), som kännetecknar ett specifikt problem. I stället för att manuellt konstruera den eftersökta lösningen, blir den inlärd från data. Samtidigt som dessa modeller har en enorm potential, ställer de utvecklarna inför viktiga frågor: Hur ska jag få tag i tillräckligt mycket data? Hur mycket data är tillräckligt? Hur ska jag få annoteringar av hög kvalitet?

Denna avhandling omfattar sex studier som täcker utveckling och tillämpning av metoder genom hela den medicinska bildbehandlingskedjan. Studie I och II föreslår olika utvidgningar av metoden tensorröstning för att göra den tillämpbar på specifika medicinska avbildningsproblem. Studie III–V behandlar användningen av moderna maskinlärningstekniker, mer specifikt neuronnät, inom området traktografi (avbildning av nervbanor i hjärnan). Utmaningen i att erhålla tillräckliga mängder annoterade data är centralt i Studie V. Studie VI utgör en prospektiv hjärnavbildningsstudie på vuxna med outvecklad inre hörselgång och innefattar tillämpning av metoder från datainsamlingtill gruppjämförelser. Sammantaget har alla de ingående arbetena, på ett eller annat sätt, bidragit till icke-invasiv generering av kunskap om människokroppen genom automatiserad medicinsk bildbehandling.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2020. p. 71
Series
TRITA-CBH-FOU ; 2020:14
Keywords
tensor voting, tractography, deep learning, tractogram filtering, diffusion magnetic resonance imaging, tensorröstning, traktografi, djupinlärning, traktogramfiltrering, diffusions-MRT
National Category
Medical Image Processing
Research subject
Applied Medical Technology
Identifiers
urn:nbn:se:kth:diva-272728 (URN)978-91-7873-531-0 (ISBN)
Public defence
2020-05-18, https://kth-se.zoom.us/j/66671899677, 09:00 (English)
Opponent
Supervisors
Note

QC 2020-04-27

Available from: 2020-04-27 Created: 2020-04-27 Last updated: 2020-05-13Bibliographically approved

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Jörgens, DanielZantop, KarenMoreno, Rodrigo

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