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Abstract <p>The analysis of task-modulated functional connectivity in functional magnetic resonance imaging (fMRI) data is a key tool in the modern field of human brain connectome research. However, the reliability of the resulting estimates depends significantly on the methods for suppressing nonneuronal sources of signal variability, primarily head movements and physiological cardiorespiratory oscillations. Despite the widespread use of various noise reduction strategies, their impact on the results of psychophysiological interaction analysis remains poorly understood. In this study, 31 noise reduction strategies were compared in the analysis of fMRI data in 85 subjects performing a motor inhibitory control task. Task-modulated functional connectivity was assessed using a psychophysiological interaction model with the primary motor cortex as the source region. Processing schemes differing in their methods of accounting for head-motion parameters and physiological noise components were compared. The effectiveness of the strategies was assessed on the basis of the size of statistically significant clusters, their correspondence with the known organization of motor networks, and the degree of association of head-motion parameters with the variability of the global fMRI signal, reflecting the residual influence of movement on the data. It was shown that overly aggressive noise suppression, despite reducing the correlation of head-motion with signal variability, can lead to a decrease in the sensitivity of the analysis and a reduction in the size of the detected effects. At the same time, balanced strategies allow for an increased detection of neurobiologically expected connections compared to no specialized processing and provide a better correspondence between the obtained results and the functional architecture of the motor system. The obtained data emphasize the need for a balanced choice of noise reduction methods in the analysis of fMRI data and demonstrate that overly strict correction of nonneuronal noise can lead to the loss of informative neural signal. The work formulates practical recommendations for optimizing procedures for suppressing physiological and motion-related noise when analyzing task-modulated functional connections.</p>

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Optimization of Physiological and Motion-Related Noise Suppression in Task-Modulated Functional Connectivity Analysis Using fMRI Data

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • R. S. Masharipov,
  • D. V. Cherednichenko,
  • M. D. Didur,
  • M. V. Kireev

摘要

Abstract

The analysis of task-modulated functional connectivity in functional magnetic resonance imaging (fMRI) data is a key tool in the modern field of human brain connectome research. However, the reliability of the resulting estimates depends significantly on the methods for suppressing nonneuronal sources of signal variability, primarily head movements and physiological cardiorespiratory oscillations. Despite the widespread use of various noise reduction strategies, their impact on the results of psychophysiological interaction analysis remains poorly understood. In this study, 31 noise reduction strategies were compared in the analysis of fMRI data in 85 subjects performing a motor inhibitory control task. Task-modulated functional connectivity was assessed using a psychophysiological interaction model with the primary motor cortex as the source region. Processing schemes differing in their methods of accounting for head-motion parameters and physiological noise components were compared. The effectiveness of the strategies was assessed on the basis of the size of statistically significant clusters, their correspondence with the known organization of motor networks, and the degree of association of head-motion parameters with the variability of the global fMRI signal, reflecting the residual influence of movement on the data. It was shown that overly aggressive noise suppression, despite reducing the correlation of head-motion with signal variability, can lead to a decrease in the sensitivity of the analysis and a reduction in the size of the detected effects. At the same time, balanced strategies allow for an increased detection of neurobiologically expected connections compared to no specialized processing and provide a better correspondence between the obtained results and the functional architecture of the motor system. The obtained data emphasize the need for a balanced choice of noise reduction methods in the analysis of fMRI data and demonstrate that overly strict correction of nonneuronal noise can lead to the loss of informative neural signal. The work formulates practical recommendations for optimizing procedures for suppressing physiological and motion-related noise when analyzing task-modulated functional connections.