Article
Neurosciences
Marie-Stephanie Cahart, Owen O'Daly, Vincent Giampietro, Maarten Timmers, Johannes Streffer, Steven Einstein, Fernando Zelaya, Flavio Dell'Acqua, Steven C. R. Williams
Summary: This study compared the reliability of conventional single-band fMRI and different multiband (MB) fMRI acquisitions with and without in-plane acceleration across multiple scanning sessions. It found that for cortical areas, MB factor 4 without in-plane acceleration had the highest reliability, while for subcortical areas, conventional single-band fMRI was more reliable.
HUMAN BRAIN MAPPING
(2023)
Article
Neurosciences
Nicholas A. Hubbard, Monroe P. Turner, Kevin R. Sitek, Kathryn L. West, Jakub R. Kaczmarzyk, Lyndahl Himes, Binu P. Thomas, Hanzhang Lu, Bart Rypma
Summary: By using calibrated functional magnetic resonance imaging, this study found that low-frequency fluctuations of cerebral metabolic rate of oxygen (CMRO2) during resting state exhibited organizational properties similar to previous functional and anatomical connectivity studies. Furthermore, voxel-wise CMRO2 connectivity showed spatial patterns consistent with four specific resting-state subnetworks.
HUMAN BRAIN MAPPING
(2021)
Article
Neurosciences
Anees Abrol, Zening Fu, Yuhui Du, Tony W. W. Wilson, Yu-Ping Wang, Julia M. M. Stephen, Vince D. D. Calhoun
Summary: The brain's functional architecture and organization undergo continuous development and modification throughout adolescence. This study systematically evaluated over 47,000 youth and adult brains to examine time-resolved functional connectivity patterns and found distinct differences between the two life stages, indicating an overall inverted U-shaped trajectory in the strengthening and modularization of functional coupling. These findings suggest greater synchrony and integration of the brain's functional connections beyond adolescence, with a gradual decline during healthy aging.
HUMAN BRAIN MAPPING
(2023)
Article
Neuroimaging
Anna J. E. Combes, Baxter P. Rogers, Kurt G. Schilling, Richard D. Lawless, Mereze Visagie, Delaney Houston, Logan Prock, Shekinah Malone, Sanjana Satish, Atlee A. Witt, Colin D. McKnight, Francesca Bagnato, John C. Gore, Seth A. Smith
Summary: Focal lesions can impact the functional connectivity of the ventral and dorsal networks in the cervical spinal cord of individuals with RRMS. This study examines the relationship between alterations in connectivity and damage in normal-appearing tissue. The results suggest that increased connectivity in response to structural damage may play a compensatory role in preserving sensory function in RRMS.
NEUROIMAGE-CLINICAL
(2022)
Article
Clinical Neurology
Dana DeMaster, Beata R. Godlewska, Mingrui Liang, Marina Vannucci, Taya Bockmann, Bo Cao, Sudhakar Selvaraj
Summary: This study aimed to investigate the influence of brain regions on each other in patients with depression and explore the relationship with treatment response. The results showed widespread dysfunction of rsEC in patients with depression, and the connectivity strength was related to baseline depression severity and treatment response. This suggests that functional rsEC may be useful for predicting the effectiveness of antidepressant treatment.
JOURNAL OF AFFECTIVE DISORDERS
(2022)
Article
Immunology
Johnna R. Swartz, Angelica F. Carranza, Laura M. Tully, Annchen R. Knodt, Janina Jiang, Michael R. Irwin, Camelia E. Hostinar
Summary: The study found associations between peripheral inflammation and adolescent brain connectivity, with higher TNF-α levels linked to changes in neural network connections. Associations with IL-6 and CRP were not significant, suggesting that inflammation may have unique effects on brain connectivity during adolescence.
BRAIN BEHAVIOR AND IMMUNITY
(2021)
Article
Geriatrics & Gerontology
Xiuli Zhang, Ruohan Li, Yingying Xia, Houliang Zhao, Lulu Cai, Jingyun Sha, Qihua Xiao, Jie Xiang, Chao Zhang, Kai Xu
Summary: In this study, differences in graph-theoretical properties of whole-brain networks were compared between patients with left-onset Parkinson's disease (LPD), right-onset Parkinson's disease (RPD), and healthy controls. The results showed significantly decreased global and local efficiency in LPD patients when using the rsSC network, with abnormal network metrics and significant correlations with disease characteristics. These findings suggest that motor-related brain networks can differentiate LPD and RPD, and nodal metrics may be important structural features for PD diagnosis and monitoring disease progression.
FRONTIERS IN AGING NEUROSCIENCE
(2022)
Article
Psychiatry
Mireia Masias Bruns, Juan Pablo Ramirez-Mahaluf, Isabel Valli, Maria Ortuno, Daniel Ilzarbe, Elena de la Serna, Olga Puig Navarro, Nicolas A. Crossley, Miguel Angel Gonzalez Ballester, Inmaculada Baeza, Gemma Piella, Josefina Castro-Fornieles, Gisela Sugranyes
Summary: The study aimed to investigate whether patients with first episode, adolescent-onset psychosis (AOP) exhibit dynamic functional connectivity (dFC) alterations similar to those seen in adult-onset and chronic psychosis patients. The results showed that AOP patients had similar dFC alterations to adult-onset and chronic psychosis patients, indicating that these abnormalities are not influenced by chronicity or prolonged antipsychotic treatment exposure. This study provides insight into the neurodevelopmental changes in brain functional connections during adolescence and suggests the potential for using dFC measures as biomarkers for characterizing adolescent-onset psychosis.
SCHIZOPHRENIA BULLETIN
(2023)
Article
Neurosciences
Suyu Bi, Yun Guan, Lixia Tian
Summary: Both movie and resting-state functional MRI are effective and promising techniques for predicting brain age, but there are some differences in connectivity properties, particularly involving components of the default mode network.
Article
Neurosciences
Nicola Bertolino, Daniele Procissi, John F. Disterhoft, Craig Weiss
Summary: Animal imaging studies using rs-fMRI on rabbits undergoing eyeblink conditioning revealed functional brain connectivity changes associated with learning and differences in cognitive performance. The cohort of rabbits exhibited increased functional connectivity in the cingulate cortex, retrosplenial cortex, and thalamus during the learning task, as well as distinct differences in certain brain regions between rabbits with different levels of cognitive performance.
JOURNAL OF COMPARATIVE NEUROLOGY
(2021)
Article
Neurosciences
Peishan Dai, Xiaoyan Zhou, Yilin Ou, Tong Xiong, Jinlong Zhang, Zailiang Chen, Beiji Zou, Xin Wei, Ying Wu, Manyi Xiao
Summary: The study investigated the altered effective connectivity (EC) in children and young adults with amblyopia, showing significant impairments in the EC network of amblyopia patients, which may have a stronger correlation with feedback pathways.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Neurosciences
Lu Zhang, Jiajia Zhao, Qunjie Zhou, Zhaowen Liu, Yi Zhang, Wei Cheng, Weikang Gong, Xiaoping Hu, Wenlian Lu, Edward T. Bullmore, Chun-Yi Zac Lo, Jianfeng Feng
Summary: This study analyzed large-scale resting-state functional magnetic resonance imaging data and identified transitions between sensory, somatomotor, and internal mentation networks in the resting brain. With increasing age, the internal mentation network becomes more prevalent, while sensory and somatomotor networks are more frequently expressed in younger individuals. The findings demonstrate the dynamic patterns of transition between functionally specialized brain states associated with age.
Article
Neurosciences
Limin Peng, Zhiguo Luo, Ling-Li Zeng, Chenping Hou, Hui Shen, Zongtan Zhou, Dewen Hu
Summary: This study developed a brain parcellation method based on dynamic functional connectivity and created a new functional brain atlas. The atlas can reveal finer functional boundaries that static methods may overlook, and shows good agreement with cytoarchitectonic areas and task activation maps.
Article
Neurosciences
Zhihong Lan, Shoujun Xu, Xiangrong Yu, Zhenjie Yu, Meng Li, Feng Chen, Yu Liu, Tianyue Wang, Yunfan Wu, Yungen Gan, Guihua Jiang
Summary: This study investigates the functional connectivity in single-sex children with autism spectrum disorders (ASDs) through resting-state functional magnetic resonance imaging (rs-fMRI), revealing enhanced connectivity in specific brain regions. The study suggests a possible relationship between atypical visual attention and poor learning ability in subjects with ASD.
FRONTIERS IN NEUROSCIENCE
(2022)
Review
Neurosciences
Fatemeh S. N. Mahani, Aref Kalantari, Gereon R. Fink, Mathias Hoehn, Markus Aswendt
Summary: Recent developments in rodent brain imaging have facilitated the analysis of functional and structural connections at the whole brain level. However, the relationship between structural and functional networks remains uncertain. This review systematically investigates existing experimental studies in rodents that explore both structural and functional network measures using in vivo imaging techniques. The results suggest a spatial and temporal disconnection between structural and functional networks during brain diseases, although more research is needed to draw definitive conclusions.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Neurosciences
Jason Langley, Sana Hussain, Daniel E. Huddleston, Ilana J. Bennett, Xiaoping P. Hu
Summary: This study examined the differences in structural connectivity between the locus coeruleus (LC) and the thalamus in younger and older adults using high-resolution diffusion tensor imaging-based tractography. The results showed that LC projections degrade with age, and the extent of degradation is associated with cognitive abilities in older adults.
BRAIN CONNECTIVITY
(2022)
Article
Anatomy & Morphology
Xiao Li, Songyao Zhang, Xi Jiang, Shu Zhang, Junwei Han, Lei Guo, Tuo Zhang
Summary: Postnatal development of the cerebral cortex is crucial for brain function and cognition. This study investigated the longitudinal changes of cortical morphology and topology during early development using a macaque neuroimaging dataset. The results showed that there are four classes of regions based on the changes in surface area and sulcal depth: slowA_slowD, slowA_fastD, fastA_slowD, and fastA_fastD. The study also found correlations between cortical metrics, structural connections, and brain functional sites.
BRAIN STRUCTURE & FUNCTION
(2022)
Article
Neurosciences
Zhibin He, Lei Du, Ying Huang, Xi Jiang, Jinglei Lv, Lei Guo, Shu Zhang, Tuo Zhang
Summary: Previous studies have reported the small-world and rich-world attributes of the global structure of brain networks, but the relationship between these structural and functional characteristics and cortical morphology has not been explicitly studied. By introducing a new folding pattern called the gyral hinge (GH), which combines ordinary gyri from multiple directions, this study found that GHs possess the highest length and cost in the white matter fiber connective network, and that the shortest paths in the network tend to pass through GHs in their middle part. Based on these findings, the authors hypothesize that GHs could be located at the centers of a network core, thus accounting for the highest cost and communication capacity in a corticocortical network.
Article
Mathematics, Interdisciplinary Applications
Shu Zhang, Ruoyang Wang, Zhen Han, Sigang Yu, Huan Gao, Xi Jiang, Tuo Zhang
Summary: The 3-hinge gyral folding pattern is an important brain architecture in brain imaging analysis. This paper proposes a method based on the DICCCOL system to identify common and consistent 3-hinge gyral folding landmarks. It successfully identifies 79 such landmarks and compares them with inconsistent landmarks, showing differences in their functional associations. The proposed DICCCOL-based method has advantages over the widely used brain landmark atlas.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Neurosciences
Wenju Cui, Caiying Yan, Zhuangzhi Yan, Yunsong Peng, Yilin Leng, Chenlu Liu, Shuangqing Chen, Xi Jiang, Jian Zheng, Xiaodong Yang
Summary: This paper proposes a novel method for extracting features from FDG-PET images and distinguishing hard-to-classify samples. The experiments demonstrate that the proposed method significantly improves the classification accuracy.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Neurosciences
Songyao Zhang, Poorya Chavoshnejad, Xiao Li, Lei Guo, Xi Jiang, Junwei Han, Li Wang, Gang Li, Xianqiao Wang, Tianming Liu, Mir Jalil Razavi, Shu Zhang, Tuo Zhang
Summary: The development of the cerebral cortex plays a crucial role in understanding the mechanism of cortical folding and its relationship to brain structure and function. Recent studies have identified typical cortical landmarks that maintain their invariant features across individuals and ages. In this study, the researchers aimed to define and identify novel cortical landmarks called gyral peaks, which are the highest foci on gyri. By analyzing MRI scans of macaque monkeys, the researchers found that gyral peaks exhibited spatial consistency across individuals and remained relatively stable within a certain age range. These gyral peaks had distinct features compared to other gyri, such as a thicker cortex and a closer proximity to structural connectivity-based cortical parcellations. The spatial distribution of gyral peaks also correlated with other cortical landmarks. This study provides insights into the spatial arrangement and temporal development of gyral peaks and their association with brain structure and function.
HUMAN BRAIN MAPPING
(2022)
Article
Computer Science, Artificial Intelligence
Xi Jiang, Jiadong Yan, Yu Zhao, Mingxin Jiang, Yuzhong Chen, Jingchao Zhou, Zhenxiang Xiao, Zifan Wang, Rong Zhang, Benjamin Becker, Dajiang Zhu, Keith M. Kendrick, Tianming Liu
Summary: In this study, a novel Spatio-Temporal Attention 4D Convolutional Neural Network (STA-4DCNN) model is introduced to characterize individualized spatio-temporal patterns of functional brain networks (FBNs). The experimental results show that STA-4DCNN has superior ability in characterizing FBN patterns and can effectively distinguish abnormal patterns in brain disorders. This study provides a powerful tool for FBN characterization and clinical applications.
Article
Neurosciences
Sana Hussain, Jason Langley, Aaron R. R. Seitz, Xiaoping P. P. Hu, Megan A. K. Peters
Summary: Hidden Markov Models (HMMs) are commonly used to analyze neuroimaging data and identify recurring patterns. However, most existing HMMs define states based on activity levels rather than functional connectivity patterns. In this study, we introduced a new HMM that defines states based on full functional connectivity profiles, which outperformed previous methods in accurately identifying connectivity states.
BRAIN CONNECTIVITY
(2023)
Article
Neurosciences
Kaiqing Chen, Xiaoping Hu
Summary: Intranasal administration of creatine increases brain creatine levels and improves cognitive performance in rats, while oral administration and control groups did not show the same effect.
BRAIN RESEARCH BULLETIN
(2023)
Article
Computer Science, Interdisciplinary Applications
Chong Ma, Lin Zhao, Yuzhong Chen, Sheng Wang, Lei Guo, Tuo Zhang, Dinggang Shen, Xi Jiang, Tianming Liu
Summary: This paper introduces a novel eye-gaze-guided vision transformer (EG-ViT) model to rectify harmful shortcuts in medical image analysis and improve model interpretability. Experimental results demonstrate the effectiveness of the proposed model and the performance improvement achieved by infusing domain knowledge.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Biology
Zhenxiang Xiao, Liang He, Boyu Zhao, Mingxin Jiang, Wei Mao, Yuzhong Chen, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
Summary: This study systematically investigates the functional connectivity characteristics between gyri and sulci in the human brain under naturalistic stimulus, and identifies unique features in these connections. This research provides novel insights into the functional brain mechanism under naturalistic stimulus and lays a solid foundation for accurately mapping the brain anatomy-function relationship.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Computer Science, Artificial Intelligence
Shengjie Zhang, Xiang Chen, Xin Shen, Bohan Ren, Ziqi Yu, Haibo Yang, Xi Jiang, Dinggang Shen, Yuan Zhou, Xiao-Yong Zhang
Summary: This study proposes an adversarial self-supervised graph neural network (A-GCL) for diagnosing neurodevelopmental disorders using fMRI data. The A-GCL model shows superior performance and generalizability compared to other GNN-based models, and reveals key functional connections and brain regions associated with neurodevelopmental disorders.
MEDICAL IMAGE ANALYSIS
(2023)
Proceedings Paper
Acoustics
Jerome Charton, Hui Ren, Jay Khambhati, Jeena DeFrancesco, Justin Cheng, Anam A. Waheed, Sylwia Marciniak, Filipe Moura, Rhanderson Cardoso, Bruno B. Lima, Erik Steen, Eigil Samset, Michael H. Picard, Xiang Li, Quanzheng Li
Summary: This study developed a general framework for view classification of Doppler echocardiography using deep learning techniques. By automatically aligning CDI and B-mode videos, it achieved analysis and diagnosis of heart images.
SIMPLIFYING MEDICAL ULTRASOUND, ASMUS 2022
(2022)
Article
Computer Science, Artificial Intelligence
Mingxin Jiang, Yuzhong Chen, Jiadong Yan, Zhenxiang Xiao, Wei Mao, Boyu Zhao, Shimin Yang, Zhongbo Zhao, Tuo Zhang, Lei Guo, Benjamin Becker, Dezhong Yao, Keith M. Kendrick, Xi Jiang
Summary: This study developed new anatomy-guided spatio-temporal graph convolutional networks (AG-STGCNs) to investigate the regularity and variability of functional connectivity differences between gyri and sulci across multiple task domains. Based on fMRI datasets from the Human Connectome Project, the study found significant differences in functional connectivity between gyral and sulcal regions within task domains compared to resting state. The study also found considerable variability in functional connectivity and information flow between gyri and sulci across different task domains, which are correlated with individual cognitive behaviors.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yuzhong Chen, Jiadong Yan, Mingxin Jiang, Tuo Zhang, Zhongbo Zhao, Weihua Zhao, Jian Zheng, Dezhong Yao, Rong Zhang, Keith M. Kendrick, Xi Jiang
Summary: This study proposes an adversarial learning-based node-edge graph attention network (AL-NEGAT) for the identification of autism spectrum disorder (ASD) using multimodal MRI data. The AL-NEGAT model leverages both node and edge features to improve classification accuracy and generalizability. Experimental results demonstrate the effectiveness of the proposed framework in ASD classification.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)