Factorial designs in fMRI analysis: A comparative exploration of full and flexible factorial approaches

Understanding the intricacies of the human brain demands rigorous analysis of dynamic functional neuroimaging data like functional Magnetic Resonance Imaging (fMRI). This paper investigates the application of two powerful analytical approaches-full and flexible factorial analysis-for exploring brain activity in fMRI studies. First, the main principles of each method are given broadly, by highlighting their strengths and limitations. Then, design structures, adaptability, data complexity, flexibility, and factor effects are handled in this context. Utilizing theoretical and real-world fMRI scenarios, it is shown how full and factorial analyses provide the factor combinations in simple and complex designs. Drawing on these insights, the critical role of aligning the chosen approach with the specific research question and data structure of each fMRI study is emphasized. Researchers can use these statistical analyses to reveal the complex structure of brain activity by diverse experimental designs. By exhibiting the unique strengths and limitations of full and flexible factorial analysis, this paper aims for researchers to choose the right methodology for their research.