Exploring Intrinsic Functional Differences of Gyri, Sulci and 2-Hinge, 3-Hinge Joints on Cerebral Cortex

Ge, Fangfei; Zhang, Shu; Huang, Heng; Jiang, Xi; Dong, Qinglin; Wang, Xianqiao; Liu, Tianming

doi:10.1109/isbi.2019.8759395

Cited by 6 publications

(8 citation statements)

References 7 publications

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“…The human brain is considered to be a multi-frequency oscillation system (Deco et al ., 2017 ), in which high-frequency activity may reflect local domain cortical processing, and low-frequency activity synchronization across distributed brain regions (von Stein and Sarnthein, 2000 ; Buzsáki and Draguhn, 2004 ; Canolty and Knight, 2010 ; Siegel et al ., 2012 ; Buzsáki et al ., 2013 ). While the conventional temporal resolution of fMRI with a repetition time (TR) of 2 s corresponding to a sampling frequency of 0.5 Hz has warranted the gyro-sulcal functional activity time-frequency analysis without bias (Zhang et al ., 2018a ; Ge et al ., 2019 ), the development of in vivo fMRI techniques has significantly improved fMRI signal temporal resolution and further facilitated the analysis in wider frequency bands of brain activity. For example, the HCP fMRI data (Barch et al ., 2013 ; Smith et al ., 2013 ) has a TR of 0.72 s corresponding to a sampling frequency of 1.39 Hz, which covers multiple frequency bands of brain activity (0.01–0.027, 0.027–0.073, 0.073–0.198, 0.198–0.5, and 0.5–0.69 Hz) based on previous electrophysiological studies (Penttonen and Buzsáki, 2003 ; Buzsáki et al ., 2013 ).…”

Section: Gyro-sulcal Functional Differences From Various Perspectivesmentioning

confidence: 99%

“…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%

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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%

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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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“…Troubled by the formation mechanisms of 3-Hinges, Razavi et al ( 2021 ) constructed a computational model of a growing brain and speculated that axonal wiring may be one of the most important contributors to 3-Hinges formation. The number, location, and shape of gyral hinges were used to quantitatively analyze the folding patterns of cerebral cortex (Nie et al, 2012 ; Ge et al, 2019 ; Huang et al, 2019 ). Gyral hinges receive an increasing attention not only because of their morphology, but also due to their importance in anatomy, axonal wiring diagram and brain functions: (1) they have thicker cortices (Li et al, 2010 ) and stronger axonal fiber connections (Ge et al, 2018 ); (2) they serve as the hubs of the cortico-cortical axonal fiber connective network (Zhang et al, 2020 ); and (3) they are more involved in global functional networks than other gyri (Zhang et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by deep learning methods in many applications, Ge et al ( 2019 ) applied convolutional neural network (CNN) to the cortical folding pattern recognition from functional magnetic resonance images (fMRI) to distinguish gyral hinges from other folding patterns. Although deep learning technique is promising in gyral hinge identification task due to its strength in latent feature exploration and utilization, the method in Ge et al ( 2019 ) needs a precise cross-modality mapping to transfer the volumetric space of the fMRI data to the vertices on the cortical surface in T1-weighted MRI space, so did the method reported in Liu et al ( 2022 ). Benefiting from the rich information of fMRI data, their work was influential on recognition of cortical folding pattern.…”

Section: Introductionmentioning

confidence: 99%

Identification for the cortical 3-Hinges folding pattern based on cortical morphological and structural features

Cao

Li²,

Zhang³

et al. 2023

Front. Neurosci.

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The Cortical 3-Hinges Folding Pattern (i.e., 3-Hinges) is one of the brain's hallmarks, and it is of great reference for predicting human intelligence, diagnosing eurological diseases and understanding the brain functional structure differences among gender. Given the significant morphological variability among individuals, it is challenging to identify 3-Hinges, but current 3-Hinges researches are mainly based on the computationally expensive Gyral-net method. To address this challenge, this paper aims to develop a deep network model to realize the fast identification of 3-Hinges based on cortical morphological and structural features. The main work includes: (1) The morphological and structural features of the cerebral cortex are extracted to relieve the imbalance between the number of 3-Hinges and each brain image's voxels; (2) The feature vector is constructed with the K nearest neighbor algorithm from the extracted scattered features of the morphological and structural features to alleviate over-fitting in training; (3) The squeeze excitation module combined with the deep U-shaped network structure is used to learn the correlation of the channels among the feature vectors; (4) The functional structure roles that 3-Hinges plays between adolescent males and females are discussed in this work. The experimental results on both adolescent and adult MRI datasets show that the proposed model achieves better performance in terms of time consumption. Moreover, this paper reveals that cortical sulcus information plays a critical role in the procedure of identification, and the cortical thickness, cortical surface area, and volume characteristics can supplement valuable information for 3-Hinges identification to some extent. Furthermore, there are significant structural differences on 3-Hinges among adolescent gender.

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

Jiang

Yang

Yan

et al. 2020

Machine Learning in Medical Imaging

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Exploring Intrinsic Functional Differences of Gyri, Sulci and 2-Hinge, 3-Hinge Joints on Cerebral Cortex

Cited by 6 publications

References 7 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

Identification for the cortical 3-Hinges folding pattern based on cortical morphological and structural features

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

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