MAGNETIC RESONANCE IMAGING BASED FUNCTIONAL CONNECTIVITY METHODS

Abstract

Functional connectivity analyses based on functional Magnetic Resonance Imaging (fMRI) data have gained an important place in brain research. There are alternative functional connectivity estimation approaches, which, despite the similarity of the overall results, produce significant differences in their details. For effective use of the functional connectivity metrics, the strengths and weaknesses of various approaches need to be well understood. While the seed-based functional connectivity analyses based on the selection of those anatomic regions of interest derived from the literature represent a stronger approach for hypothesis testing, the independent component analysis (ICA) as a data-driven approach provides an unbiased evaluation possibility for exploratory data analysis. Another difference between the methods is related to group analyses in terms of registering individual brains to a common template or implementing anatomical definitions on the spatial coordinates of individual brains. While the latter increases the success in studies on pathologies that lead to large-scale brain deformations, the former may be advantageous for deriving normative results from large data sets. Lastly, volume vs surface-based approaches for the definition of cortical anatomy in the individual space also significantly affect the results of functional connectivity analyses. In this review, functional connectivity estimation methods will be compared by evaluating them using these three perspectives.

Keywords

• Functional neuroimaging,functional connectivity,intrinsic connectivity networks,seed-based correlation analysis,independent component analysis,cortical parcellation

Project Number

42362

MANYETİK REZONANS GÖRÜNTÜLEME TEMELLİ FONKSİYONEL BAĞLANTISALLIK YÖNTEMLERİ

Abstract

Fonksiyonel Manyetik Rezonans Görüntüleme (fMRG) verilerine dayanan fonksiyonel bağlantısallık analizleri beyin araştırmalarında önemli bir yer kazanmıştır. Özellikle dinlenim durumunda beynin büyük ölçekli nörokognitif ağlarının ortaya koyulabilmesi ve bunların hastalıklardaki değişimlerinin gösterilebilmesi bu araştırma yöntemine olan ilgiyi artırmıştır. Öte yandan, genel hatlarıyla birbirine benzer olsa da detayda farklılaşan sonuçlar üreten alternatif fonksiyonel bağlantısallık hesaplama yaklaşımları mevcuttur. Fonksiyonel nörogörüntülemenin etkin kullanımı için bu farklı yaklaşımların, ele alınan problem ve incelenen popülasyona bağlı olarak beynin büyük ölçekli ağlarının yapısal örüntülerini ve işlevlerini ortaya koymaktaki güçlü ve zayıf yönlerinin anlaşılması gereklidir. Bu çerçevede, hipotez testi için literatürden kaynaklı anatomik ilgi alanlarının seçimine dayanan tohum temelli fonksiyonel bağlantısallık analizi daha güçlü bir yaklaşımken, keşifçi araştırmalarda tümüyle veri güdümlü olan bağımsız bileşen analizi (BBA) tüm beyin verisini tarafsız değerlendirme olanağı sunmaktadır. Yöntemler arasındaki diğer önemli ayrım, grup analizleri için incelenen beyinlerin anatomik olarak ortak bir şablon üzerinde çakıştırılması veya anatomik tanımlamaların her beynin kendi mekânsal koordinatlarında gerçekleştirilmesidir. Beyinde büyük ölçekli deformasyonlara yol açan patolojilerde ikinci yolun seçimi başarımı büyük ölçüde arttırırken, büyük ölçekli sağlıklı katılımcı veri kümelerinden normatif sonuçlar çıkartmak için ilk yaklaşım daha avantajlı olabilir. Son olarak, bireysel koordinatlarda kortekse ilişkin anatomik tanımlamaların gerçekleştirilmesinde hacim veya yüzeye dayalı yaklaşımlar da fonksiyonel bağlantısallık çalışmalarının sonuçlarını önemli ölçüde etkilemektedir. Bu derlemede, fonksiyonel bağlantısallık hesaplama yaklaşımları bu üç perspektiften ele alınarak karşılaştırılacaktır.

Keywords

• Fonksiyonel nörogörüntüleme,fonksiyonel bağlantısallık,entrensek bağlantısallık ağları,tohum temelli korelasyon analizi,bağımsız bileşen analizi,kortikal parselasyon

Supporting Institution

İstanbul Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi

Project Number

42362

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