Exploring Functional Difference Between Gyri and Sulci via Region-Specific 1D Convolutional Neural Networks

Jiang, Mingxin; Yang, Suo; Yan, Jiadong; Shu, Zhang; Liu, Huan; Zhao, Lin; Dai, Haixing; Lv, Jinglei; Zhang, Tuo; Liu, Tianming; Kendrick, Keith M.; Jiang, Xi

doi:10.1007/978-3-030-59861-7_26

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…Fast Fourier transformation has commonly been used in previous studies for calculating the power spectrum of gyral/sulcal signals (Zhang et al ., 2018a ; Ge et al ., 2019 ; Liu et al ., 2019 ; Jiang et al ., 2020 ). The power spectrum distribution characteristics across different frequency bands, as well as other measures (e.g.…”

Section: Gyro-sulcal Functional Differences From Various Perspectivesmentioning

confidence: 99%

“…The power spectrum distribution characteristics across different frequency bands, as well as other measures (e.g. "1/ f " characteristic of the power spectral density plot) have been analyzed and compared between gyro-sulcal signals (Zhang et al ., 2018a ; Ge et al ., 2019 ; Liu et al ., 2019 ; Jiang et al ., 2020 ). Wavelet entropy (Mallat, 1989 ; Coifman and Wickerhauser, 1992 ; Daubechies, 1992 ; Sang et al ., 2011 ) analysis has also been used (Liu et al ., 2019 ), which quantifies the degree of temporal signal complexity, with higher entropy indicating a more complex signal temporal pattern (Sang et al ., 2011 ).…”

Section: Gyro-sulcal Functional Differences From Various Perspectivesmentioning

confidence: 99%

“…In a recent study (Ge et al ., 2019 ), the CNN-based model was adopted on both healthy people and autism spectrum disorder (ASD) patients. Finally, a more recent study (Jiang et al ., 2020 ) adopted a region-specific one-dimensional CNN model to investigate the characteristics of such gyro-sulcal functional activity differences within each brain region instead of in the whole brain.…”

Section: Gyro-sulcal Functional Differences From Various Perspectivesmentioning

confidence: 99%

“…The gyral and sulcal t-fMRI/rs-fMRI signals (TR ranges from 0.72 to 2 s) can be effectively differentiated based on the proposed time-frequency-based analysis as well as deep-learning-based approaches (Zhang et al ., 2018a ; Ge et al ., 2019 ; Liu et al ., 2019 ; Jiang et al ., 2020 ). Moreover, as shown in Fig.…”

Section: Gyro-sulcal Functional Differences From Various Perspectivesmentioning

confidence: 99%

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Fundamental functional differences between gyri and sulci: implications for brain function, cognition, and behavior

Jiang

Zhang

et al. 2021

Psychoradiology

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Folding of the cerebral cortex is a prominent characteristic of mammalian brains. Alterations or deficits in cortical folding are strongly correlated with abnormal brain function, cognition, and behavior. Therefore, a precise mapping between the anatomy and function of the brain is critical to our understanding of the mechanisms of brain structural architecture in both health and diseases. Gyri and sulci, the standard nomenclature for cortical anatomy, serve as building blocks to make up complex folding patterns, providing a window to decipher cortical anatomy and its relation with brain functions. Huge efforts have been devoted to this research topic from a variety of disciplines including genetics, cell biology, anatomy, neuroimaging, and neurology, as well as involving computational approaches based on machine learning and artificial intelligence algorithms. However, despite increasing progress, our understanding of the functional anatomy of gyro-sulcal patterns is still in its infancy. In this review, we present the current state of this field and provide our perspectives of the methodologies and conclusions concerning functional differentiation between gyri and sulci, as well as the supporting information from genetic, cell biology, and brain structure research. In particular, we will further present a proposed framework for attempting to interpret the dynamic mechanisms of the functional interplay between gyri and sulci. Hopefully, this review will provide a comprehensive summary of anatomo-functional relationships in the cortical gyro-sulcal system together with a consideration of how these contribute to brain function, cognition, and behavior, as well as to mental disorders.

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Section: Gyro-sulcal Functional Differences From Various Perspectivesmentioning

confidence: 99%

Section: Gyro-sulcal Functional Differences From Various Perspectivesmentioning

confidence: 99%

Section: Gyro-sulcal Functional Differences From Various Perspectivesmentioning

confidence: 99%

Section: Gyro-sulcal Functional Differences From Various Perspectivesmentioning

confidence: 99%

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Fundamental functional differences between gyri and sulci: implications for brain function, cognition, and behavior

Jiang

Zhang

et al. 2021

Psychoradiology

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“…To identify and interpret the difference in temporal features of fMRI, the learned convolution kernels could be transferred into the frequency domain to explore the frequency characteristics of fMRI (Liu et al, 2019;Zhang S. et al, 2019;Jiang et al, 2020). Since the size of the convolution kernels at the temporal domain is 7, the frequency domain includes three points, i.e., about half size of the kernel, as shown in Figure 3.…”

Section: Exploration Of Relationship Between High-level Semantic Feat...mentioning

confidence: 99%

A hybrid learning framework for fine-grained interpretation of brain spatiotemporal patterns during naturalistic functional magnetic resonance imaging

Shi²,

Wang³

et al. 2022

Front. Hum. Neurosci.

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Naturalistic stimuli, including movie, music, and speech, have been increasingly applied in the research of neuroimaging. Relative to a resting-state or single-task state, naturalistic stimuli can evoke more intense brain activities and have been proved to possess higher test–retest reliability, suggesting greater potential to study adaptive human brain function. In the current research, naturalistic functional magnetic resonance imaging (N-fMRI) has been a powerful tool to record brain states under naturalistic stimuli, and many efforts have been devoted to study the high-level semantic features from spatial or temporal representations via N-fMRI. However, integrating both spatial and temporal characteristics of brain activities for better interpreting the patterns under naturalistic stimuli is still underexplored. In this work, a novel hybrid learning framework that comprehensively investigates both the spatial (via Predictive Model) and the temporal [via convolutional neural network (CNN) model] characteristics of the brain is proposed. Specifically, to focus on certain relevant regions from the whole brain, regions of significance (ROS), which contain common spatial activation characteristics across individuals, are selected via the Predictive Model. Further, voxels of significance (VOS), whose signals contain significant temporal characteristics under naturalistic stimuli, are interpreted via one-dimensional CNN (1D-CNN) model. In this article, our proposed framework is applied onto the N-fMRI data during naturalistic classical/pop/speech audios stimuli. The promising performance is achieved via the Predictive Model to differentiate the different audio categories. Especially for distinguishing the classic and speech audios, the accuracy of classification is up to 92%. Moreover, spatial ROS and VOS are effectively obtained. Besides, temporal characteristics of the high-level semantic features are investigated on the frequency domain via convolution kernels of 1D-CNN model, and we effectively bridge the “semantic gap” between high-level semantic features of N-fMRI and low-level acoustic features of naturalistic audios in the frequency domain. Our results provide novel insights on characterizing spatiotemporal patterns of brain activities via N-fMRI and effectively explore the high-level semantic features under naturalistic stimuli, which will further benefit the understanding of the brain working mechanism and the advance of naturalistic stimuli clinical application.

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Exploring Gyro-Sulcal Functional Connectivity Differences Across Task Domains via Anatomy-Guided Spatio-Temporal Graph Convolutional Networks

Jiang

Yang

Zhao

et al. 2021

Machine Learning in Medical Imaging

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Exploring Functional Difference Between Gyri and Sulci via Region-Specific 1D Convolutional Neural Networks

Cited by 7 publications

References 20 publications

Fundamental functional differences between gyri and sulci: implications for brain function, cognition, and behavior

Fundamental functional differences between gyri and sulci: implications for brain function, cognition, and behavior

A hybrid learning framework for fine-grained interpretation of brain spatiotemporal patterns during naturalistic functional magnetic resonance imaging

Exploring Gyro-Sulcal Functional Connectivity Differences Across Task Domains via Anatomy-Guided Spatio-Temporal Graph Convolutional Networks

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