A Non-Parametric Approach for the Activation Detection of Block Design fMRI Simulated Data Using Self-Organizing Maps and Support Vector Machine

Abstract

Functional magnetic resonance imaging (fMRI) is a popular method to probe the functional organization of the brain using hemodynamic responses. In this method, volume images of the entire brain are obtained with a very good spatial resolution and low temporal resolution. However, they always suffer from high dimensionality in the face of classification algorithms. In this work, we combine a support vector machine (SVM) with a self-organizing map (SOM) for having a feature-based classification by using SVM. Then, a linear kernel SVM is used for detecting the active areas. Here, we use SOM for feature extracting and labeling the datasets. SOM has two major advances: (i) it reduces dimension of data sets for having less computational complexity and (ii) it is useful for identifying brain regions with small onset differences in hemodynamic responses. Our non-parametric model is compared with parametric and non-parametric methods. We use simulated fMRI data sets and block design inputs in this paper and consider the contrast to noise ratio (CNR) value equal to 0.6 for simulated datasets. fMRI simulated dataset has contrast 1–4% in active areas. The accuracy of our proposed method is 93.63% and the error rate is 6.37%.

A hybrid model for data visualization using linear algebra methods and machine learning algorithm

“…In recent years, there has been an exponential increase in the amount of digital information being generated across various fields, which has led to a significant surge in the size, complexity, diversity, and dimensions of data [1], which has given rise to a new type of data known as high dimensional data (HDD) [2], [3] HDD has been widely utilized across various industries, including healthcare, the Internet, education, commerce, and social networking [4], to name a few. The ever-increasing availability of new high-dimensional data can take on various formats, such as text [5], digital images [6], speech signals [7], and videos [8], among others.…”

Transformer-based dimensionality reduction

“…In this information age, a large amount of data has been generated in various fields, including education, medical care, Internet, social media and business Transformer-based dimensionality reduction [1]. These data are usually high-dimensional, heterogeneous, complex and massive [2], and they have different forms, such as text, digital image, voice signal and video.…”

Transformer-based dimensionality reduction

“…In this information age, a large amount of data has been generated in various fields, including education, medical care, Internet, social media and business [1]. These data are usually highdimensional, heterogeneous, complex and massive [2], and they have different forms, such as text, digital image, voice signal and video.…”