Methods for cleaning the BOLD fMRI signal.

No prior work has fully synthesized the diverse strategies available for isolating neural signals from complex imaging data. Researchers frequently struggle to distinguish genuine brain activity from various non-neuronal artifacts. This uncertainty drove the need for a systematic evaluation of current denoising practices. Prior research has shown that blood oxygen-level-dependent signals are inherently indirect measures of brain function. These signals often contain significant interference from vascular and metabolic sources. Such corruption complicates the interpretation of functional brain maps across different subject populations. That gap motivated this comprehensive examination of existing signal cleaning frameworks. Investigators require clear guidance on selecting appropriate tools to mitigate these pervasive data quality issues.

The aim of this review is to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. Researchers face a specific problem where the blood oxygen-level-dependent signal is severely corrupted by non-neuronal fluctuations. These fluctuations originate from instrumental, physiological, or subject-specific sources. This motivation stems from the need to improve the accuracy of brain function investigations in healthy individuals and patients. The authors focus on the methodological operation of different techniques to assist practitioners in their selection. They specifically address the advantages and limitations of various approaches to ensure informed application. By summarizing these strategies, the study seeks to clarify the best practices for signal denoising. This work provides a necessary foundation for researchers aiming to enhance the quality of their functional imaging data.

Review approach involves a systematic categorization of signal cleaning techniques based on their underlying operational principles. The authors evaluate strategies by focusing on their specific advantages and inherent limitations in practical applications. This analysis covers both data-driven frameworks and methods that rely on external physiological recordings. The investigators examine how decomposition tools like principal component analysis function to isolate noise. They also assess the utility of independent component analysis for separating mixed signal sources. The review approach includes a critical look at how phase-based information and multi-echo acquisitions contribute to data refinement. Furthermore, the authors investigate the role of global signal regression within standard preprocessing pipelines. This structured evaluation provides a methodological roadmap for researchers seeking to optimize their imaging workflows.

Key findings from the literature indicate that blood oxygen-level-dependent signals are heavily influenced by a mixture of vascular, metabolic, and instrumental processes. The authors report that motion-related fluctuations and physiological noise represent the two most significant components requiring removal. Data-driven approaches demonstrate high utility because they do not rely on rigid assumptions about the noise model. Principal and independent component analysis emerge as effective tools for simultaneously reducing multiple types of signal interference. The literature suggests that incorporating phase information or multi-echo data significantly improves the ability to distinguish noise from neural activity. Global signal regression is identified as a common but complex strategy for denoising that warrants careful application. The authors find that optimizing the preprocessing pipeline is essential for both task-based and resting-state studies. Finally, the review confirms that signal cleaning is a critical step for ensuring the validity of functional brain investigations.

The authors suggest that effective noise removal remains a prerequisite for reliable functional imaging analysis. They propose that researchers must carefully balance the benefits and limitations of various denoising strategies. Synthesis and implications indicate that data-driven techniques offer flexibility when specific noise models are unavailable. The review highlights that incorporating phase information or multi-echo data can significantly enhance signal quality. Authors note that global signal regression remains a debated but frequently utilized tool for artifact reduction. They emphasize that optimizing preprocessing pipelines is necessary for both task-based and resting-state investigations. Future efforts should focus on refining these methods to better isolate neuronal responses from physiological fluctuations. The researchers conclude that standardized denoising workflows will improve the reproducibility of findings across the neuroimaging field.