Fonksiyonel Nöronal Görüntülerde Aktivasyonların Yerini Belirlemek için Değişim Noktası Algılama Yöntemleri

Year 2022, , 541 - 554, 30.06.2022

Cemre Candemir , Kaya Oğuz

https://doi.org/10.35193/bseufbd.1091035

Abstract

fMRI görüntüleri için en yaygın analizde amaç, deneyde görevler olarak verilen ilgili uyaranlara karşılık beyinde görsel işleme veya motor işlevler gibi belirli işlevlere yanıt veren konumları bulan aktivasyon tespitidir. Öte yandan, aktivasyonun tetiklendiği anın tespit edilmesi de önemlidir. Herhangi bir verinin anormal davranışını analiz edebilen güçlü tekniklerden biri de değişim noktası (DN) analizidir. CP algılama algoritmalarının, fonksiyonel manyetik rezonans görüntüleme (fMRI) dizilerindeki aktivasyonları bulmak için de kullanılabileceğini öneriyoruz. Makalemiz bu açıdan iki yönlü yenilikçi bir çalışma sunmaktadır. İlk olarak, son teknoloji bir konu olarak fMRI sinyallerindeki aktivasyonları bulmak için CP algılama algoritmalarını kullanmayı öneriyoruz. Ayrıca, bu tür noktaları bulmak için bir dizi değişim noktası analiz yöntemi, regresyon tabanlı yöntem, istatistiksel yöntem ve kayan çift pencere yöntemi öneriyor ve karşılaştırıyoruz. İkinci olarak, bu yöntemleri, fMRI görevlerini gerçekleştirirken gerçek deneklerden elde edilen fMRI sinyallerine uyguluyoruz. Önerilen yöntemler, bir motor görev, bir görsel görev ve bir dilsel görev olmak üzere üç farklı fMRI deneyine uygulandı. Analiz, yöntemlerin, istatistiksel parametrik haritalar (SPM) gibi yerleşik yöntemlere uygun aktivasyonlar bulduğunu göstermektedir. Elde edilen %94'e varan sonuçlar, aynı zamanda önerilen yöntemlerin fMRI zaman serilerinde aktivasyon anlarını bulmak için etkin bir şekilde kullanılabileceğini göstermektedir.

Keywords

Aktivasyon Tespiti, Aktivasyon Anları, Değişim Noktası Problemi, fMRI, Regresyon

Change Point Detection Methods for Locating Activations in Functional Neuronal Images

Abstract

The most common analysis for fMRI images is activation detection, in which the purpose is to find the locations in the brain that respond to specific functions, such as visual processing or motor functions by providing related stimuli as tasks in the experiment. On the other hand, it is also important to detect the instance the activation is triggered. One of the powerful techniques that can analyze the abnormal behavior of any data is change point (CP) analysis. We suggest that CP detection algorithms also can be used to locate the activations in functional magnetic resonance imaging (fMRI) sequences, as well. Our paper presents a two-fold innovative study in that respect. First, we propose to use CP detection algorithms to locate the activations in fMRI signals as a state-of-art topic. Furthermore, we propose and compare a set of change point analysis methods, a regression-based method (RBM), a statistical method (SM), and a mean difference of double sliding windows method (MDSW)) to locate such points. Second, we apply these methods to the fMRI signals, which are acquired from the real subjects, while they were performing fMRI tasks. Proposed methods were applied to three different fMRI experiments with a motor task, a visual task, and a linguistic task. The analysis shows that the methods find activations in accordance with established methods such as statistical parametric maps (SPM). The acquired up to 94 % results also show that the proposed methods can be used effectively to locate the activation times on fMRI time series.

Keywords

Activation Detection, Activation Instance, Change Point Problem, fMRI, Regression

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