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Predicting spatial navigation abilities in humans using shape analysis of the hippocampus

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  1. Tez No: 799144
  2. Yazar: ARDA DOĞAN
  3. Danışmanlar: DR. VİCTOR SCHİNAZİ, PROF. DR. CHRİSTOPH H¨OLSCHER, PROF. DR. KLAAS PR¨USSMANN
  4. Tez Türü: Yüksek Lisans
  5. Konular: Genetik, Zooloji, Genetics, Zoology
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2018
  8. Dil: İngilizce
  9. Üniversite: Eidgenössische Technische Hochschule Zürich (ETH)
  10. Enstitü: Yurtdışı Enstitü
  11. Ana Bilim Dalı: Belirtilmemiş.
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: 76

Özet

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Özet (Çeviri)

Humans and animals can use place or response learning to navigate. Place learning is associated with storing a spatial representation of the environment and is governed by the hippocampus. In contrast, response learning is manifested by acquiring reliable sequence of responses and is mediated by the caudate. However, the relationship between the hippocampus and the caudate is not fully understood. Hartley and Burgess (2005) proposed three possible ways of interaction between these two mediators. Competition occurs when they drive conflicting responses in a navigation task. Cooperation occurs when either of these structures is recruited according to the given task. Compensation occurs when one of the two structures is functionally impaired, resulting in the recruitment of the other. The goal of this Masters work is to investigate the relationship between the hippocampus and the caudate using local shape analysis and machine learning methods. In this work we report the results of an experiment that involved two Virtual Reality (VR) tasks. In one task twenty participants were trained with an arrow whereas a map was used on the other task. Subjects were also tested without a navigation aid and the time scores in training and test trials in both tasks were recorded. Then, the participants' hippocampi and the caudates were bilaterally segmented from T1-weighted Magnetic Resonance (MR) images. Next, Spherical Harmonics (SPHARM) coefficients were extracted as local descriptors and used as features in support vector machines (SVM) classification to assess the subcortical structures' ability of predicting the behavioral scores. Furthermore, statistically significant regions were investigated with global statistical analysis using the Voxel Based Morphometry (VBM) and local statistical shape analysis using the Multivariate Analysis of Covariance (MANCOVA). Classification results showed that the left hippocampus predicted the participants' ability to learn during map task training trials with 74.9% accuracy and the right caudate predicted the map task test trial scores with 73.5% accuracy. The VBM analysis revealed a positive correlation in the posterior and negative correlation in the anterior regions of the left hippocampus with the map task learning scores. Negative correlation in the head of the right caudate with the map task test scores was also observed. Local statistical analysis results agreed with the VBM results but also provided exact locations of the volume changes. This Masters work showed a competitive relation between the hippocampus and caudate and proposed a novel way to observe localized shape changes in these structures.

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