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Treves IN, Marusak HA, Decker A, Kucyi A, Hubbard NA, Bauer CCC, Leonard J, Grotzinger H, Giebler MA, Torres YC, Imhof A, Romeo R, Calhoun VD, Gabrieli JDE. Dynamic Functional Connectivity Correlates of Trait Mindfulness in Early Adolescence. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100367. [PMID: 39286525 PMCID: PMC11402920 DOI: 10.1016/j.bpsgos.2024.100367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 05/02/2024] [Accepted: 07/16/2024] [Indexed: 09/19/2024] Open
Abstract
Background Trait mindfulness-the tendency to attend to present-moment experiences without judgment-is negatively correlated with adolescent anxiety and depression. Understanding the neural mechanisms that underlie trait mindfulness may inform the neural basis of psychiatric disorders. However, few studies have identified brain connectivity states that are correlated with trait mindfulness in adolescence, and they have not assessed the reliability of such states. Methods To address this gap in knowledge, we rigorously assessed the reliability of brain states across 2 functional magnetic resonance imaging scans from 106 adolescents ages 12 to 15 (50% female). We performed both static and dynamic functional connectivity analyses and evaluated the test-retest reliability of how much time adolescents spent in each state. For the reliable states, we assessed associations with self-reported trait mindfulness. Results Higher trait mindfulness correlated with lower anxiety and depression symptoms. Static functional connectivity (intraclass correlation coefficients 0.31-0.53) was unrelated to trait mindfulness. Among the dynamic brains states that we identified, most were unreliable within individuals across scans. However, one state, a hyperconnected state of elevated positive connectivity between networks, showed good reliability (intraclass correlation coefficient = 0.65). We found that the amount of time that adolescents spent in this hyperconnected state positively correlated with trait mindfulness. Conclusions By applying dynamic functional connectivity analysis on over 100 resting-state functional magnetic resonance imaging scans, we identified a highly reliable brain state that correlated with trait mindfulness. This brain state may reflect a state of mindfulness, or awareness and arousal more generally, which may be more pronounced in people who are higher in trait mindfulness.
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Affiliation(s)
- Isaac N Treves
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Hilary A Marusak
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, Michigan
| | - Alexandra Decker
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Aaron Kucyi
- Department of Psychological & Brain Sciences, Drexel University, Philadelphia, Pennsylvania
| | | | - Clemens C C Bauer
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Department of Psychology, Northeastern University, Boston, Massachusetts
| | - Julia Leonard
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Hannah Grotzinger
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, California
| | | | - Yesi Camacho Torres
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Andrea Imhof
- Department of Psychology, University of Oregon, Eugene, Oregon
| | - Rachel Romeo
- Departments of Human Development & Quantitative Methodology and Hearing & Speech Sciences, and Program in Neuroscience & Cognitive Science, University of Maryland College Park, Baltimore, Maryland
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, and Emory, Atlanta, Georgia
| | - John D E Gabrieli
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
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Bhavna K, Ghosh N, Banerjee R, Roy D. Characterization of the temporal stability of ToM and pain functional brain networks carry distinct developmental signatures during naturalistic viewing. Sci Rep 2024; 14:22479. [PMID: 39341890 PMCID: PMC11438989 DOI: 10.1038/s41598-024-72945-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Accepted: 09/11/2024] [Indexed: 10/01/2024] Open
Abstract
A temporally stable functional brain network pattern among coordinated brain regions is fundamental to stimulus selectivity and functional specificity during the critical period of brain development. Brain networks that are recruited in time to process internal states of others' bodies (like hunger and pain) versus internal mental states (like beliefs, desires, and emotions) of others' minds allow us to ask whether a quantitative characterization of the stability of these networks carries meaning during early development and constrain cognition in a specific way. Previous research provides critical insight into the early development of the theory-of-mind (ToM) network and its segregation from the Pain network throughout normal development using functional connectivity. However, a quantitative characterization of the temporal stability of ToM networks from early childhood to adulthood remains unexplored. In this work, reusing a large sample of children (n = 122, 3-12 years) and adults (n = 33) dataset that is available on the OpenfMRI database under the accession number ds000228, we addressed this question based on their fMRI data during a short and engaging naturalistic movie-watching task. The movie highlights the characters' bodily sensations (often pain) and mental states (beliefs, desires, emotions), and is a feasible experiment for young children. Our results tracked the change in temporal stability using an unsupervised characterization of ToM and Pain networks DFC patterns using Angular and Mahalanobis distances between dominant dynamic functional connectivity subspaces. Our findings reveal that both ToM and Pain networks exhibit lower temporal stability as early as 3-years and gradually stabilize by 5-years, which continues till adolescence and late adulthood (often sharing similarity with adult DFC stability patterns). Furthermore, we find that the temporal stability of ToM brain networks is associated with the performance of participants in the false belief task to access mentalization at an early age. Interestingly, higher temporal stability is associated with the pass group, and similarly, moderate and low temporal stability are associated with the inconsistent group and the fail group. Our findings open an avenue for applying the temporal stability of large-scale functional brain networks during cortical development to act as a biomarker for multiple developmental disorders concerning impairment and discontinuity in the neural basis of social cognition.
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Affiliation(s)
- Km Bhavna
- Department of Computer Science and Engineering, Indian Institute of Technology, Jodhpur, 342030, Rajasthan, India
| | - Niniva Ghosh
- School of AIDE, Indian Institute of Technology, Centre for Brain Science Application, Jodhpur, 342030, Rajasthan, India
| | - Romi Banerjee
- Department of Computer Science and Engineering, Indian Institute of Technology, Jodhpur, 342030, Rajasthan, India
- School of AIDE, Indian Institute of Technology, Centre for Brain Science Application, Jodhpur, 342030, Rajasthan, India
| | - Dipanjan Roy
- Department of Computer Science and Engineering, Indian Institute of Technology, Jodhpur, 342030, Rajasthan, India.
- School of AIDE, Indian Institute of Technology, Centre for Brain Science Application, Jodhpur, 342030, Rajasthan, India.
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Zhang H, Yang B, Li Q, Liu L, Fei N, Xian J. Abnormal dynamic features of spontaneous brain activity and their concordance in neuromyelitis optica spectrum disorder related optic neuritis: A resting-state fMRI study. Brain Res 2024; 1846:149228. [PMID: 39251055 DOI: 10.1016/j.brainres.2024.149228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Revised: 08/22/2024] [Accepted: 09/05/2024] [Indexed: 09/11/2024]
Abstract
OBJECTIVE Characterizing the neuropathological features of neuromyelitis optica spectrum disorder-related optic neuritis (NMOSD-ON) is crucial for understanding its mechanisms. Given the important role of dynamic features in the brain's functional architecture, we aim to investigate the dynamic features of spontaneous brain activity and their concordance using resting-state functional magnetic resonance imaging (rs-fMRI) in NMOSD-ON. METHODS Fourteen NMOSD-ON patients and 21 healthy controls (HCs) underwent rs-fMRI and ophthalmological examinations. Five dynamic indices depicting different aspects of functional characteristics were calculated using a sliding window method based on rs-fMRI data. Kendall's coefficient was utilized to measure concordance among these indices at each time point. The differences of dynamic features between two groups were evaluated using two-sample t-tests, with correlations explored between altered dynamics and clinical parameters. RESULTS Compared to HCs, NMOSD-ON patients exhibited significant decreases in dynamic regional homogeneity (dReHo) and dynamic degree centrality (dDC) in visual regions, including bilateral cuneus, lingual gyrus, calcarine sulcus, and occipital gyrus. Conversely, increases were observed in left insula, left thalamus, and bilateral caudate. The concordance of NMOSD-ON patients was significantly lower than HCs. The dReHo of right cuneus negatively correlated with mean deviation of visual field (r = -0.591, p = 0.026) and the dReHo of left cuneus negatively correlated with disease duration (r = -0.588, p = 0.030). CONCLUSION The evidence suggests that regional dynamic functional alterations involving vision, emotional processing, and cognitive control may provide a new understanding of brain changes in the progression of NMOSD-ON.
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Affiliation(s)
- Hanjuan Zhang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Bingbing Yang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Qing Li
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Lei Liu
- Department of Neurology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Nanxi Fei
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
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Cui W, Chen B, He J, Fan G, Wang S. Dynamic functional network connectivity in children with profound bilateral congenital sensorineural hearing loss. Pediatr Radiol 2024; 54:1738-1747. [PMID: 39134864 DOI: 10.1007/s00247-024-06022-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 07/30/2024] [Accepted: 07/31/2024] [Indexed: 09/07/2024]
Abstract
BACKGROUND Functional magnetic resonance imaging (fMRI) studies have revealed extensive functional reorganization in patients with sensorineural hearing loss (SNHL). However, almost no study focuses on the dynamic functional connectivity after hearing loss. OBJECTIVE This study aimed to investigate dynamic functional connectivity changes in children with profound bilateral congenital SNHL under the age of 3 years. MATERIALS AND METHODS Thirty-two children with profound bilateral congenital SNHL and 24 children with normal hearing were recruited for the present study. Independent component analysis identified 18 independent components composing five resting-state networks. A sliding window approach was used to acquire dynamic functional matrices. Three states were identified using the k-means algorithm. Then, the differences in temporal properties and the variance of network efficiency between groups were compared. RESULTS The children with SNHL showed longer mean dwell time and decreased functional connectivity between the auditory network and sensorimotor network in state 3 (P < 0.05), which was characterized by relatively stronger functional connectivity between high-order resting-state networks and motion and perception networks. There was no difference in the variance of network efficiency. CONCLUSIONS These results indicated the functional reorganization due to hearing loss. This study also provided new perspectives for understanding the state-dependent connectivity patterns in children with SNHL.
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Affiliation(s)
- Wenzhuo Cui
- Department of Radiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, Liaoning, PR China
| | - Boyu Chen
- Department of Radiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, Liaoning, PR China
| | - Jiachuan He
- Department of Radiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, Liaoning, PR China
| | - Guoguang Fan
- Department of Radiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, Liaoning, PR China
| | - Shanshan Wang
- Department of Radiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, Liaoning, PR China.
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Feng S, Huang Y, Li H, Zhou S, Ning Y, Han W, Zhang Z, Liu C, Li J, Zhong L, Wu K, Wu F. Dynamic effective connectivity in the cerebellar dorsal dentate nucleus and the cerebrum, cognitive impairment, and clinical correlates in patients with schizophrenia. Schizophr Res 2024; 271:394-401. [PMID: 38729789 DOI: 10.1016/j.schres.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 04/16/2024] [Accepted: 05/03/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND Schizophrenia (SZ) is characterized by disconnected cerebral networks. Recent studies have shown that functional connectivity between the cerebellar dorsal dentate nucleus (dDN) and cerebrum is correlated with psychotic symptoms, and processing speed in SZ patients. Dynamic effective connectivity (dEC) is a reliable indicator of brain functional status. However, the dEC between the dDN and cerebrum in patients with SZ remains largely unknown. METHODS Resting-state functional MRI data, symptom severity, and cognitive performance were collected from 74 SZ patients and 53 healthy controls (HC). Granger causality analysis and sliding time window methods were used to calculate dDN-based dEC maps for all subjects, and k-means clustering was performed to obtain several dEC states. Finally, between-group differences in dynamic effective connectivity variability (dECV) and clinical correlations were obtained using two-sample t-tests and correlation analysis. RESULTS We detected four dEC states from the cerebrum to the right dDN (IN states) and three dEC states from the right dDN to the cerebrum (OUT states), with SZ group having fewer transitions in the OUT states. SZ group had increased dECV from the right dDN to the right middle frontal gyrus (MFG) and left lingual gyrus (LG). Correlations were found between the dECV from the right dDN to the right MFG and symptom severity and between the dECV from the right dDN to the left LG and working memory performance. CONCLUSIONS This study reveals a dynamic causal relationship between cerebellar dDN and the cerebrum in SZ and provides new evidence for the involvement of cerebellar neural circuits in neurocognitive functions in SZ.
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Affiliation(s)
- Shixuan Feng
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yuanyuan Huang
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Hehua Li
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Sumiao Zhou
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yuping Ning
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China; Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China
| | - Wei Han
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Ziyun Zhang
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Chenyu Liu
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Junhao Li
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Liangda Zhong
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Kai Wu
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China; Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China; Guangdong Province Key Laboratory of Biomedical Engineering, South China University of Technology, Guangzhou, China; Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan.
| | - Fengchun Wu
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China; Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China.
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Liu H, Zhong YL, Huang X. Specific static and dynamic functional network connectivity changes in thyroid-associated ophthalmopathy and it predictive values using machine learning. Front Neurosci 2024; 18:1429084. [PMID: 39247050 PMCID: PMC11377277 DOI: 10.3389/fnins.2024.1429084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 08/05/2024] [Indexed: 09/10/2024] Open
Abstract
Background Thyroid-associated ophthalmopathy (TAO) is a prevalent autoimmune disease characterized by ocular symptoms like eyelid retraction and exophthalmos. Prior neuroimaging studies have revealed structural and functional brain abnormalities in TAO patients, along with central nervous system symptoms such as cognitive deficits. Nonetheless, the changes in the static and dynamic functional network connectivity of the brain in TAO patients are currently unknown. This study delved into the modifications in static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) among thyroid-associated ophthalmopathy patients using independent component analysis (ICA). Methods Thirty-two patients diagnosed with thyroid-associated ophthalmopathy and 30 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. ICA method was utilized to extract the sFNC and dFNC changes of both groups. Results In comparison to the HC group, the TAO group exhibited significantly increased intra-network functional connectivity (FC) in the right inferior temporal gyrus of the executive control network (ECN) and the visual network (VN), along with significantly decreased intra-network FC in the dorsal attentional network (DAN), the default mode network (DMN), and the left middle cingulum of the ECN. On the other hand, FNC analysis revealed substantially reduced connectivity intra- VN and inter- cerebellum network (CN) and high-level cognitive networks (DAN, DMN, and ECN) in the TAO group compared to the HC group. Regarding dFNC, TAO patients displayed abnormal connectivity across all five states, characterized by notably reduced intra-VN connectivity and CN connectivity with high-level cognitive networks (DAN, DMN, and ECN), alongside compensatory increased connectivity between DMN and low-level perceptual networks (VN and basal ganglia network). No significant differences were observed between the two groups for the three dynamic temporal metrics. Furthermore, excluding the classification outcomes of FC within VN (with an accuracy of 51.61% and area under the curve of 0.35208), the FC-based support vector machine (SVM) model demonstrated improved performance in distinguishing between TAO and HC, achieving accuracies ranging from 69.35 to 77.42% and areas under the curve from 0.68229 to 0.81667. The FNC-based SVM classification yielded an accuracy of 61.29% and an area under the curve of 0.57292. Conclusion In summary, our study revealed that significant alterations in the visual network and high-level cognitive networks. These discoveries contribute to our understanding of the neural mechanisms in individuals with TAO, offering a valuable target for exploring future central nervous system changes in thyroid-associated eye diseases.
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Affiliation(s)
- Hao Liu
- School of Ophthalmology and Optometry, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - Yu-Lin Zhong
- Department of Ophthalmology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| | - Xin Huang
- Department of Ophthalmology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
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Wang J, Yang Z, Klugah-Brown B, Zhang T, Yang J, Yuan J, Biswal BB. The critical mediating roles of the middle temporal gyrus and ventrolateral prefrontal cortex in the dynamic processing of interpersonal emotion regulation. Neuroimage 2024:120789. [PMID: 39159702 DOI: 10.1016/j.neuroimage.2024.120789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Revised: 07/30/2024] [Accepted: 08/12/2024] [Indexed: 08/21/2024] Open
Abstract
Interpersonal emotion regulation (IER) is a crucial ability for effectively recovering from negative emotions through social interaction. It has been emphasized that the empathy network, cognitive control network, and affective generation network sustain the deployment of IER. However, the temporal dynamics of functional connectivity among these networks of IER remains unclear. This study utilized IER task-fMRI and sliding window approach to examine both the stationary and dynamic functional connectivity (dFC) of IER. Fifty-five healthy participants were recruited for the present study. Through clustering analysis, four distinct brain states were identified in dFC. State 1 demonstrated situation modification stage of IER, with strong connectivity between affective generation and visual networks. State 2 exhibited pronounced connectivity between empathy network and both cognitive control and affective generation networks, reflecting the empathy stage of IER. Next, a 'top-down' pattern is observed between the connectivity of cognitive control and affective generation networks during the cognitive control stage of state 3. The affective response modulation stage of state 4 mainly involved connections between empathy and affective generation networks. Specifically, the degree centrality of the left middle temporal gyrus (MTG) mediated the association between one's IER tendency and the regulatory effects in state 2. The betweenness centrality of the left ventrolateral prefrontal cortex (VLPFC) mediated the association between one's IER efficiency and the regulatory effects in state 3. Altogether, these findings revealed that dynamic connectivity transitions among empathy, cognitive control, and affective generation networks, with the left VLPFC and MTG playing dominant roles, evident across the IER processing.
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Affiliation(s)
- Jiazheng Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhenzhen Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Benjamin Klugah-Brown
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Tao Zhang
- Mental Health Education Center, Xihua University, Chengdu, China, 610039
| | - Jiemin Yang
- Sichuan Key Laboratory of Psychology and Behavior of Discipline Inspection and Supervision, Institute of Brain and Psychological Science, Sichuan Normal University, Chengdu, Sichuan 610041, China
| | - JiaJin Yuan
- Sichuan Key Laboratory of Psychology and Behavior of Discipline Inspection and Supervision, Institute of Brain and Psychological Science, Sichuan Normal University, Chengdu, Sichuan 610041, China.
| | - Bharat B Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, United States.
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Wei X, Zhou R, Zheng S, Zhang Y, Feng X, Lü J. Network-based transcranial direct current stimulation enhances attention function in healthy young adults: a preliminary study. Front Hum Neurosci 2024; 18:1421230. [PMID: 39175659 PMCID: PMC11338793 DOI: 10.3389/fnhum.2024.1421230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 07/30/2024] [Indexed: 08/24/2024] Open
Abstract
Purpose Attention, a complex cognitive process, is linked to the functional activities of the brain's dorsal attention network (DAN) and default network (DN). This study aimed to investigate the feasibility, safety, and blinding efficacy of a transcranial direct current stimulation (tDCS) paradigm designed to increase the excitability of the DAN while inhibiting the DN (DAN+/DN-tDCS) on attention function in healthy young adults. Methods In this randomized controlled experiment, participants were assigned to either the DAN+/DN-tDCS group or the sham group. A single intervention session was conducted at a total intensity of 4 mA for 20 min. Participants completed the Attention Network Test (ANT) immediately before and after stimulation. Blinding efficacy and adverse effects were assessed post-stimulation. Results Forty participants completed the study, with 20 in each group. Paired-sample t-test showed a significant post-stimulation improvement in executive effect performance (t = 2.245; p = 0.037) in the DAN+/DN-tDCS group. The sham group did not exhibit any significant differences in ANT performance. Participants identified the stimulation type with 52.50% accuracy, indicating no difference in blinding efficacy between groups (p = 0.241). Mild-to-moderate adverse effects, such as stinging, itching, and skin reddening, were reported in the DAN+/DN-tDCS group (p < 0.05). Conclusion DAN+/DN-tDCS enhanced attention function in healthy young individuals, particularly in improving executive effect performance. This study presents novel strategies for enhancing attentional performance and encourages further investigation into the mechanisms and outcomes of these interventions across diverse populations.
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Affiliation(s)
- Xiaoyu Wei
- Key Laboratory of Exercise and Health Sciences of Ministry of Education, Shanghai University of Sport, Shanghai, China
- School of Exercise and Health, Shanghai University of Sport, Shanghai, China
| | - Rong Zhou
- Key Laboratory of Exercise and Health Sciences of Ministry of Education, Shanghai University of Sport, Shanghai, China
- School of Exercise and Health, Shanghai University of Sport, Shanghai, China
| | - Suwang Zheng
- Key Laboratory of Exercise and Health Sciences of Ministry of Education, Shanghai University of Sport, Shanghai, China
- School of Exercise and Health, Shanghai University of Sport, Shanghai, China
| | - Yufeng Zhang
- Key Laboratory of Exercise and Health Sciences of Ministry of Education, Shanghai University of Sport, Shanghai, China
- School of Exercise and Health, Shanghai University of Sport, Shanghai, China
| | - Xiaofan Feng
- Key Laboratory of Exercise and Health Sciences of Ministry of Education, Shanghai University of Sport, Shanghai, China
- School of Exercise and Health, Shanghai University of Sport, Shanghai, China
| | - Jiaojiao Lü
- Key Laboratory of Exercise and Health Sciences of Ministry of Education, Shanghai University of Sport, Shanghai, China
- School of Exercise and Health, Shanghai University of Sport, Shanghai, China
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Fu Z, Sui J, Iraji A, Liu J, Calhoun VD. Cognitive and psychiatric relevance of dynamic functional connectivity states in a large (N > 10,000) children population. Mol Psychiatry 2024:10.1038/s41380-024-02683-6. [PMID: 39085394 DOI: 10.1038/s41380-024-02683-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 07/16/2024] [Accepted: 07/24/2024] [Indexed: 08/02/2024]
Abstract
Children's brains dynamically adapt to the stimuli from the internal state and the external environment, allowing for changes in cognitive and mental behavior. In this work, we performed a large-scale analysis of dynamic functional connectivity (DFC) in children aged 9~11 years, investigating how brain dynamics relate to cognitive performance and mental health at an early age. A hybrid independent component analysis framework was applied to the Adolescent Brain Cognitive Development (ABCD) data containing 10,988 children. We combined a sliding-window approach with k-means clustering to identify five brain states with distinct DFC patterns. Interestingly, the occurrence of a strongly connected state with the most within-network synchrony and the anticorrelations between networks, especially between the sensory networks and between the cerebellum and other networks, was negatively correlated with cognitive performance and positively correlated with dimensional psychopathology in children. Meanwhile, opposite relationships were observed for a DFC state showing integration of sensory networks and antagonism between default-mode and sensorimotor networks but weak segregation of the cerebellum. The mediation analysis further showed that attention problems mediated the effect of DFC states on cognitive performance. This investigation unveils the neurological underpinnings of DFC states, which suggests that tracking the transient dynamic connectivity may help to characterize cognitive and mental problems in children and guide people to provide early intervention to buffer adverse influences.
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Affiliation(s)
- Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
- Department of Computer Science, Georgia State University, Atlanta, GA, USA.
| | - Jing Sui
- IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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Treves IN, Marusak HA, Decker A, Kucyi A, Hubbard NA, Bauer CCC, Leonard J, Grotzinger H, Giebler MA, Torres YC, Imhof A, Romeo R, Calhoun VD, Gabrieli JDE. Dynamic functional connectivity correlates of trait mindfulness in early adolescence. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.01.601544. [PMID: 39005413 PMCID: PMC11244904 DOI: 10.1101/2024.07.01.601544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Background Trait mindfulness, the tendency to attend to present-moment experiences without judgement, is negatively correlated with adolescent anxiety and depression. Understanding the neural mechanisms underlying trait mindfulness may inform the neural basis of psychiatric disorders. However, few studies have identified brain connectivity states that correlate with trait mindfulness in adolescence, nor have they assessed the reliability of such states. Methods To address this gap in knowledge, we rigorously assessed the reliability of brain states across 2 functional magnetic resonance imaging (fMRI) scan from 106 adolescents aged 12 to 15 (50% female). We performed both static and dynamic functional connectivity analyses and evaluated the test-retest reliability of how much time adolescents spent in each state. For the reliable states, we assessed associations with self-reported trait mindfulness. Results Higher trait mindfulness correlated with lower anxiety and depression symptoms. Static functional connectivity (ICCs from 0.31-0.53) was unrelated to trait mindfulness. Among the dynamic brains states we identified, most were unreliable within individuals across scans. However, one state, an hyperconnected state of elevated positive connectivity between networks, showed good reliability (ICC=0.65). We found that the amount of time that adolescents spent in this hyperconnected state positively correlated with trait mindfulness. Conclusions By applying dynamic functional connectivity analysis on over 100 resting-state fMRI scans, we identified a highly reliable brain state that correlated with trait mindfulness. The brain state may reflect a state of mindfulness, or awareness and arousal more generally, which may be more pronounced in those who are higher in trait mindfulness.
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Affiliation(s)
- Isaac N Treves
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA
| | - Hilary A Marusak
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI
| | - Alexandra Decker
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
| | - Aaron Kucyi
- Department of Psychological & Brain Sciences, Drexel University, Philadelphia, PA
| | | | - Clemens C C Bauer
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA
- Department of Psychology, Northeastern University, Boston, MA
| | - Julia Leonard
- Department of Psychology, Yale University, New Haven, CT
| | - Hannah Grotzinger
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA
| | | | - Yesi Camacho Torres
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
| | - Andrea Imhof
- Department of Psychology, University of Oregon, Eugene, OR
| | - Rachel Romeo
- Departments of Human Development & Quantitative Methodology and Hearing & Speech Sciences, and Program in Neuroscience & Cognitive Science, University of Maryland College Park, Baltimore, MD
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, and Emory, Atlanta, GA
| | - John D E Gabrieli
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA
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11
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Chen G, Guo Z, Chen P, Yang Z, Yan H, Sun S, Ma W, Zhang Y, Qi Z, Fang W, Jiang L, Tao Q, Wang Y. Bright light therapy-induced improvements of mood, cognitive functions and cerebellar functional connectivity in subthreshold depression: A randomized controlled trial. Int J Clin Health Psychol 2024; 24:100483. [PMID: 39101053 PMCID: PMC11296024 DOI: 10.1016/j.ijchp.2024.100483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 06/25/2024] [Indexed: 08/06/2024] Open
Abstract
Background The efficacy of bright light therapy (BLT) in ameliorating depression has been validated. The present study is to investigate the changes of depressive symptoms, cognitive function and cerebellar functional connectivity (FC) following BLT in individuals with subthreshold depression (StD). Method Participants were randomly assigned to BLT group (N = 47) or placebo (N = 41) in this randomized controlled trial between March 2020 and June 2022. Depression severity and cognitive function were assessed, as well as resting-state functional MRI scan was conducted before and after 8-weeks treatment. Seed-based whole-brain static FC (sFC) and dynamic FC (dFC) analyses of the bilateral cerebellar subfields were conducted. Besides, a multivariate regression model examined whether baseline brain FC was associated with changes of depression severity and cognitive function during BLT treatment. Results After 8-week BLT treatment, individuals with StD showed improved depressive symptoms and attention/vigilance cognitive function. BLT also increased sFC between the right cerebellar lobule IX and left temporal pole, and decreased sFC within the cerebellum, and dFC between the right cerebellar lobule IX and left medial prefrontal cortex. Moreover, the fusion of sFC and dFC at baseline could predict the improvement of attention/vigilance in response to BLT. Conclusions The current study identified that BLT improved depressive symptoms and attention/vigilance, as well as changed cerebellum-DMN connectivity, especially in the cerebellar-frontotemporal and cerebellar internal FC. In addition, the fusion features of sFC and dFC at pre-treatment could serve as an imaging biomarker for the improvement of attention/vigilance cognitive function after BLT in StD.
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Affiliation(s)
- Guanmao Chen
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, 510630, China
| | - Zixuan Guo
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, 510630, China
| | - Pan Chen
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, 510630, China
| | - Zibin Yang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, 510630, China
| | - Hong Yan
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, 510630, China
| | - Shilin Sun
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, 510630, China
| | - Wenhao Ma
- Department of Public Health and Preventive Medicine, School of Basic Medicine, Jinan University, Guangzhou 510632, China
- Division of Medical Psychology and Behavior Science, School of Basic Medicine, Jinan University, Guangzhou 510632, China
| | - Yuan Zhang
- Department of Public Health and Preventive Medicine, School of Basic Medicine, Jinan University, Guangzhou 510632, China
- Division of Medical Psychology and Behavior Science, School of Basic Medicine, Jinan University, Guangzhou 510632, China
| | - Zhangzhang Qi
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, 510630, China
| | - Wenjie Fang
- Department of Public Health and Preventive Medicine, School of Basic Medicine, Jinan University, Guangzhou 510632, China
- Division of Medical Psychology and Behavior Science, School of Basic Medicine, Jinan University, Guangzhou 510632, China
| | - Lijun Jiang
- Department of Public Health and Preventive Medicine, School of Basic Medicine, Jinan University, Guangzhou 510632, China
- Division of Medical Psychology and Behavior Science, School of Basic Medicine, Jinan University, Guangzhou 510632, China
| | - Qian Tao
- Department of Public Health and Preventive Medicine, School of Basic Medicine, Jinan University, Guangzhou 510632, China
- Division of Medical Psychology and Behavior Science, School of Basic Medicine, Jinan University, Guangzhou 510632, China
| | - Ying Wang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, 510630, China
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12
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Maksymchuk N, Bustillo JR, Mathalon DH, Preda A, Miller RL, Calhoun VD. Static and Dynamic Cross-Network Functional Connectivity Shows Elevated Entropy in Schizophrenia Patients. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.15.599084. [PMID: 38948857 PMCID: PMC11212858 DOI: 10.1101/2024.06.15.599084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Schizophrenia (SZ) patients exhibit abnormal static and dynamic functional connectivity across various brain domains. We present a novel approach based on static and dynamic inter-network connectivity entropy (ICE), which represents the entropy of a given network's connectivity to all the other brain networks. This novel approach enables the investigation of how connectivity strength is heterogeneously distributed across available targets in both SZ patients and healthy controls. We analyzed fMRI data from 151 schizophrenia patients and demographically matched 160 healthy controls. Our assessment encompassed both static and dynamic ICE, revealing significant differences in the heterogeneity of connectivity levels across available brain networks between SZ patients and healthy controls (HC). These networks are associated with subcortical (SC), auditory (AUD), sensorimotor (SM), visual (VIS), cognitive control (CC), default mode network (DMN) and cerebellar (CB) functional brain domains. Elevated ICE observed in individuals with SZ suggests that patients exhibit significantly higher randomness in the distribution of time-varying connectivity strength across functional regions from each source network, compared to healthy control group. C-means fuzzy clustering analysis of functional ICE correlation matrices revealed that SZ patients exhibit significantly higher occupancy weights in clusters with weak, low-scale functional entropy correlation, while the control group shows greater occupancy weights in clusters with strong, large-scale functional entropy correlation. k-means clustering analysis on time-indexed ICE vectors revealed that cluster with highest ICE have higher occupancy rates in SZ patients whereas clusters characterized by lowest ICE have larger occupancy rates for control group. Furthermore, our dynamic ICE approach revealed that it appears healthy for a brain to primarily circulate through complex, less structured connectivity patterns, with occasional transitions into more focused patterns. However, individuals with SZ seem to struggle with transiently attaining these more focused and structured connectivity patterns. Proposed ICE measure presents a novel framework for gaining deeper insights into understanding mechanisms of healthy and disease brain states and a substantial step forward in the developing advanced methods of diagnostics of mental health conditions.
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Affiliation(s)
- Natalia Maksymchuk
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Juan R. Bustillo
- Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Daniel H. Mathalon
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, CA, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | - Robyn L. Miller
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
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13
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Wiafe SL, Asante NO, Calhoun VD, Faghiri A. Studying time-resolved functional connectivity via communication theory: on the complementary nature of phase synchronization and sliding window Pearson correlation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.12.598720. [PMID: 38915498 PMCID: PMC11195172 DOI: 10.1101/2024.06.12.598720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Time-resolved functional connectivity (trFC) assesses the time-resolved coupling between brain regions using functional magnetic resonance imaging (fMRI) data. This study aims to compare two techniques used to estimate trFC, to investigate their similarities and differences when applied to fMRI data. These techniques are the sliding window Pearson correlation (SWPC), an amplitude-based approach, and phase synchronization (PS), a phase-based technique. To accomplish our objective, we used resting-state fMRI data from the Human Connectome Project (HCP) with 827 subjects (repetition time: 0.7s) and the Function Biomedical Informatics Research Network (fBIRN) with 311 subjects (repetition time: 2s), which included 151 schizophrenia patients and 160 controls. Our simulations reveal distinct strengths in two connectivity methods: SWPC captures high-magnitude, low-frequency connectivity, while PS detects low-magnitude, high-frequency connectivity. Stronger correlations between SWPC and PS align with pronounced fMRI oscillations. For fMRI data, higher correlations between SWPC and PS occur with matched frequencies and smaller SWPC window sizes (~30s), but larger windows (~88s) sacrifice clinically relevant information. Both methods identify a schizophrenia-associated brain network state but show different patterns: SWPC highlights low anti-correlations between visual, subcortical, auditory, and sensory-motor networks, while PS shows reduced positive synchronization among these networks. In sum, our findings underscore the complementary nature of SWPC and PS, elucidating their respective strengths and limitations without implying the superiority of one over the other.
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Affiliation(s)
- Sir-Lord Wiafe
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Nana O. Asante
- ETH Zürich, Zürich, Rämistrasse 101, Switzerland
- Ashesi University, 1 University Avenue Berekuso, Ghana
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
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14
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Foster M, Scheinost D. Brain states as wave-like motifs. Trends Cogn Sci 2024; 28:492-503. [PMID: 38582654 DOI: 10.1016/j.tics.2024.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 02/29/2024] [Accepted: 03/11/2024] [Indexed: 04/08/2024]
Abstract
There is ample evidence of wave-like activity in the brain at multiple scales and levels. This emerging literature supports the broader adoption of a wave perspective of brain activity. Specifically, a brain state can be described as a set of recurring, sequential patterns of propagating brain activity, namely a wave. We examine a collective body of experimental work investigating wave-like properties. Based on these works, we consider brain states as waves using a scale-agnostic framework across time and space. Emphasis is placed on the sequentiality and periodicity associated with brain activity. We conclude by discussing the implications, prospects, and experimental opportunities of this framework.
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Affiliation(s)
- Maya Foster
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Engineering, Yale School of Medicine, New Haven, CT, USA
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15
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Ye B, Wu Y, Cao M, Xu C, Zhou C, Zhang X. Altered patterns of dynamic functional connectivity of brain networks in deficit and non-deficit schizophrenia. Eur Arch Psychiatry Clin Neurosci 2024:10.1007/s00406-024-01803-1. [PMID: 38662092 DOI: 10.1007/s00406-024-01803-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 03/19/2024] [Indexed: 04/26/2024]
Abstract
This study aims to investigate the altered patterns of dynamic functional network connectivity (dFNC) between deficit schizophrenia (DS) and non-deficit schizophrenia (NDS), and further explore the associations with cognitive impairments. 70 DS, 91 NDS, and 120 matched healthy controls (HCs) were enrolled. The independent component analysis was used to segment the whole brain. The fMRI brain atlas was used to identify functional networks, and the dynamic functional connectivity (FC) of each network was detected. Correlation analysis was used to explore the associations between altered dFNC and cognitive functions. Four dynamic states were identified. Compared to NDS, DS showed increased FC between sensorimotor network and default mode network in state 1 and decreased FC within auditory network in state 4. Additionally, DS had a longer mean dwell time of state 2 and a shorter one in state 3 compared to NDS. Correlation analysis showed that fraction time and mean dwell time of states were correlated with cognitive impairments in DS. This study demonstrates the distinctive altered patterns of dFNC between DS and NDS patients. The associations with impaired cognition provide specific neuroimaging evidence for the pathogenesis of DS.
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Affiliation(s)
- Biying Ye
- Department of Fourth Clinical Medical College, Nanjing Medical University, Nanjing, China
| | - Yiqiao Wu
- Department of Fourth Clinical Medical College, Nanjing Medical University, Nanjing, China
| | - Mingjun Cao
- Department of Fourth Clinical Medical College, Nanjing Medical University, Nanjing, China
| | - Chanhuan Xu
- Department of Fourth Clinical Medical College, Nanjing Medical University, Nanjing, China
| | - Chao Zhou
- Department of Geriatric Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, No.264 Guangzhou Road, Nanjing, 210029, Jiangsu, China.
| | - Xiangrong Zhang
- Department of Geriatric Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, No.264 Guangzhou Road, Nanjing, 210029, Jiangsu, China.
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16
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Luo Y, Xiao M, Chen X, Zeng W, Chen H. Harsh, unpredictable childhood environments are associated with inferior frontal gyrus connectivity and binge eating tendencies in late adolescents. Appetite 2024; 195:107210. [PMID: 38266713 DOI: 10.1016/j.appet.2024.107210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 12/03/2023] [Accepted: 01/09/2024] [Indexed: 01/26/2024]
Abstract
Harsh, unpredictable childhood environments (HUCE) are associated with obesity older in life, but knowledge of how HUCE affect binge eating tendencies is lacking. Five hundred and one late adolescents aged 16-22 were recruited to finish resting state functional magnetic resonance imaging scan, behavioral measures including retrospective recall of childhood environmental harshness and unpredictability, binge eating tendencies and demographics. Three hundred and seventy-six of participants further completed the computerized visual probe task designed to evaluate attentional engagement towards high and low calorie food. As right inferior frontal gyrus (IFG) was the key nodes that related to both early life adversity and binge eating tendencies, it was treated as the interest region in the dynamic functional connectivity analyses. Results found that HUCE are associated with significant but modest decreases in connectivity of right inferior frontal gyrus (IFG)- bilateral medial frontal gyrus, right IFG - bilateral inferior parietal lobule (IPL), and right IFG - left superior frontal gyrus connectivity, as well as attentional engagement to high-calorie food and binge eating tendencies. A machine-learning method named linear support vector regression (SVR) and leave one out cross-validation (LOOCV) procedure used to examine the robustness of the brain-behavior relationship further confirm the findings. Mediation analyses suggested that right IFG - left IPL connectivity mediates the association of HUCE and binge eating tendencies. Findings suggest right IFG - left IPL connectivity may serve as a crucial neurobiological underpinning of HUCE to regulate binge eating behaviors. As such, these results contribute to a novel perspective and hypotheses in elucidating developmental neuro-mechanisms related to binge eating.
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Affiliation(s)
- Yijun Luo
- School of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, 400715, China
| | - Minyue Xiao
- School of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, 400715, China
| | - Ximei Chen
- School of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, 400715, China
| | - Weiyu Zeng
- School of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, 400715, China
| | - Hong Chen
- School of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, 400715, China.
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17
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Sun J, Zhang J, Chen Q, Yang W, Wei D, Qiu J. Psychological resilience-related functional connectomes predict creative personality. Psychophysiology 2024; 61:e14463. [PMID: 37855121 DOI: 10.1111/psyp.14463] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 06/25/2023] [Accepted: 07/27/2023] [Indexed: 10/20/2023]
Abstract
Both psychological resilience and creativity are complex concepts that have positive effects on individual adaptation. Previous studies have shown overlaps between the key brain regions or brain functional networks related to psychological resilience and creativity. However, no direct experimental evidence has been provided to support the assumption that psychological resilience and creativity share a common brain basis. Therefore, the present study investigated the relationship between psychological resilience and creativity using neural imaging method with a machine learning approach. At the behavioral level, we found that psychological resilience was positively related to creative personality. Predictive analysis based on static functional connectivity (FC) and dynamic FC demonstrated that FCs related to psychological resilience could effectively predict an individual's creative personality score. Both the static FC and dynamic FC were mainly located in the default mode network. These results prove that psychological resilience and creativity share a common brain functional basis. These findings also provide insights into the possibility of promoting individual positive adaptation from negative events or situations in a creative way.
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Affiliation(s)
- Jiangzhou Sun
- College of International Studies, Southwest University, Chongqing, China
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Jingyi Zhang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Qunlin Chen
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Wenjing Yang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Beijing, China
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18
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Bravo Balsa L, Abu-Akel A, Mevorach C. Dynamic functional connectivity in the right temporoparietal junction captures variations in male autistic trait expression. Autism Res 2024; 17:702-715. [PMID: 38456581 DOI: 10.1002/aur.3117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 02/21/2024] [Indexed: 03/09/2024]
Abstract
Autistic individuals can experience difficulties with attention reorienting and Theory of Mind (ToM), which are closely associated with anterior and posterior subdivisions of the right temporoparietal junction. While the link between these processes remains unclear, it is likely subserved by a dynamic crosstalk between these two subdivisions. We, therefore, examined the dynamic functional connectivity (dFC) between the anterior and posterior temporoparietal junction, as a biological marker of attention and ToM, to test its contribution to the manifestation of autistic trait expression in Autism Spectrum Condition (ASC). Two studies were conducted, exploratory (14 ASC, 15 TD) and replication (29 ASC, 29 TD), using resting-state fMRI data and the Social Responsiveness Scale (SRS) from the Autism Brain Imaging Data Exchange repository. Dynamic Independent Component Analysis was performed in both datasets using the CONN toolbox. An additional sliding-window analysis was performed in the replication study to explore different connectivity states (from highly negatively to highly positively correlated). Dynamic FC was reduced in ASC compared to TD adults in both the exploratory and replication datasets and was associated with increased SRS scores (especially in ASC). Regression analyses revealed that decreased SRS autistic expression was predicted by engagement of highly negatively correlated states, while engagement of highly positively correlated states predicted increased expression. These findings provided consistent evidence that the difficulties observed in ASC are associated with altered patterns of dFC between brain regions subserving attention reorienting and ToM processes and may serve as a biomarker of autistic trait expression.
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Affiliation(s)
- Laura Bravo Balsa
- Centre for Human Brain Health, University of Birmingham, Edgbaston, UK
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Ahmad Abu-Akel
- School of Psychological Sciences, University of Haifa, Haifa, Israel
- Haifa Brain and Behavior Hub, University of Haifa, Haifa, Israel
| | - Carmel Mevorach
- Centre for Human Brain Health, University of Birmingham, Edgbaston, UK
- School of Psychology, University of Birmingham, Edgbaston, UK
- Centre for Developmental Science, University of Birmingham, Edgbaston, UK
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19
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Han H, Jiang J, Gu L, Gan JQ, Wang H. Brain connectivity patterns derived from aging-related alterations in dynamic brain functional networks and their potential as features for brain age classification. J Neural Eng 2024; 21:026015. [PMID: 38479020 DOI: 10.1088/1741-2552/ad33b1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 03/13/2024] [Indexed: 03/26/2024]
Abstract
Objective.Recent studies have demonstrated that the analysis of brain functional networks (BFNs) is a powerful tool for exploring brain aging and age-related neurodegenerative diseases. However, investigating the mechanism of brain aging associated with dynamic BFN is still limited. The purpose of this study is to develop a novel scheme to explore brain aging patterns by constructing dynamic BFN using resting-state functional magnetic resonance imaging data.Approach.A dynamic sliding-windowed non-negative block-diagonal representation (dNBDR) method is proposed for constructing dynamic BFN, based on which a collection of dynamic BFN measures are suggested for examining age-related differences at the group level and used as features for brain age classification at the individual level.Results.The experimental results reveal that the dNBDR method is superior to the sliding time window with Pearson correlation method in terms of dynamic network structure quality. Additionally, significant alterations in dynamic BFN structures exist across the human lifespan. Specifically, average node flexibility and integration coefficient increase with age, while the recruitment coefficient shows a decreased trend. The proposed feature extraction scheme based on dynamic BFN achieved the highest accuracy of 78.7% in classifying three brain age groups.Significance. These findings suggest that dynamic BFN measures, dynamic community structure metrics in particular, play an important role in quantitatively assessing brain aging.
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Affiliation(s)
- Hongfang Han
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, People's Republic of China
| | - Jiuchuan Jiang
- School of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210003, Jiangsu, People's Republic of China
| | - Lingyun Gu
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, People's Republic of China
| | - John Q Gan
- School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, United Kingdom
| | - Haixian Wang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, People's Republic of China
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20
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Islam S, Khanra P, Nakuci J, Muldoon SF, Watanabe T, Masuda N. State-transition dynamics of resting-state functional magnetic resonance imaging data: model comparison and test-to-retest analysis. BMC Neurosci 2024; 25:14. [PMID: 38438838 PMCID: PMC10913599 DOI: 10.1186/s12868-024-00854-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 02/07/2024] [Indexed: 03/06/2024] Open
Abstract
Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of individuals.
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Affiliation(s)
- Saiful Islam
- Institute for Artificial Intelligence and Data Science, University at Buffalo, State University of New York at Buffalo, 215 Lockwood Hall, Buffalo, 14260, NY, USA
| | - Pitambar Khanra
- Department of Mathematics , University at Buffalo, State University of New York at Buffalo, 244 Mathematics Building , Buffalo, 14260, NY, USA
| | - Johan Nakuci
- School of Psychology, Georgia Institute of Technology, North Avenue, Atlanta, 30332, GA, USA
| | - Sarah F Muldoon
- Department of Mathematics , University at Buffalo, State University of New York at Buffalo, 244 Mathematics Building , Buffalo, 14260, NY, USA
- Institute for Artificial Intelligence and Data Science, University at Buffalo, State University of New York at Buffalo, 215 Lockwood Hall, Buffalo, 14260, NY, USA
- Neuroscience Program, University at Buffalo, State University of New York at Buffalo, 955 Main Street, Buffalo, 14203, NY, USA
| | - Takamitsu Watanabe
- International Research Centre for Neurointelligence, The University of Tokyo Institutes for Advanced Study, 731 Hongo Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Naoki Masuda
- Department of Mathematics , University at Buffalo, State University of New York at Buffalo, 244 Mathematics Building , Buffalo, 14260, NY, USA.
- Institute for Artificial Intelligence and Data Science, University at Buffalo, State University of New York at Buffalo, 215 Lockwood Hall, Buffalo, 14260, NY, USA.
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21
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Zheng W, Zhang Q, Zhao Z, Zhang P, Zhao L, Wang X, Yang S, Zhang J, Yao Z, Hu B. Aberrant dynamic functional connectivity of thalamocortical circuitry in major depressive disorder. J Zhejiang Univ Sci B 2024:1-21. [PMID: 38423537 DOI: 10.1631/jzus.b2300401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 09/24/2023] [Indexed: 03/02/2024]
Abstract
Thalamocortical circuitry has a substantial impact on emotion and cognition. Previous studies have demonstrated alterations in thalamocortical functional connectivity (FC), characterized by region-dependent hypo- or hyper-connectivity, among individuals with major depressive disorder (MDD). However, the dynamical reconfiguration of the thalamocortical system over time and potential abnormalities in dynamic thalamocortical connectivity associated with MDD remain unclear. Hence, we analyzed dynamic FC (dFC) between ten thalamic subregions and seven cortical subnetworks from resting-state functional magnetic resonance images of 48 patients with MDD and 57 healthy controls (HCs) to investigate time-varying changes in thalamocortical FC in patients with MDD. Moreover, dynamic laterality analysis was conducted to examine the changes in functional lateralization of the thalamocortical system over time. Correlations between the dynamic measures of thalamocortical FC and clinical assessment were also calculated. We identified four dynamic states of thalamocortical circuitry wherein patients with MDD exhibited decreased fractional time and reduced transitions within a negative connectivity state that showed strong correlations with primary cortical networks, compared with the HCs. In addition, MDD patients also exhibited increased fluctuations in functional laterality in the thalamocortical system across the scan duration. The thalamo-subnetwork analysis unveiled abnormal dFC variability involving higher-order cortical networks in the MDD cohort. Significant correlations were found between increased dFC variability with dorsal attention and default mode networks and the severity of symptoms. Our study comprehensively investigated the pattern of alteration of the thalamocortical dFC in MDD patients. The heterogeneous alterations of dFC between the thalamus and both primary and higher-order cortical networks may help characterize the deficits of sensory and cognitive processing in MDD.
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Affiliation(s)
- Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Qin Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Ziyang Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Pengfei Zhang
- Second Clinical School, Lanzhou University, Lanzhou 730030, China
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730030, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China
| | - Leilei Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Xiaomin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Songyu Yang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Jing Zhang
- Second Clinical School, Lanzhou University, Lanzhou 730030, China. ,
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730030, China. ,
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China. ,
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China. ,
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
- Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences, Lanzhou 730000, China.
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22
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Seeburger DT, Xu N, Ma M, Larson S, Godwin C, Keilholz SD, Schumacher EH. Time-varying functional connectivity predicts fluctuations in sustained attention in a serial tapping task. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2024; 24:111-125. [PMID: 38253775 PMCID: PMC10979291 DOI: 10.3758/s13415-024-01156-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/08/2024] [Indexed: 01/24/2024]
Abstract
The mechanisms for how large-scale brain networks contribute to sustained attention are unknown. Attention fluctuates from moment to moment, and this continuous change is consistent with dynamic changes in functional connectivity between brain networks involved in the internal and external allocation of attention. In this study, we investigated how brain network activity varied across different levels of attentional focus (i.e., "zones"). Participants performed a finger-tapping task, and guided by previous research, in-the-zone performance or state was identified by low reaction time variability and out-of-the-zone as the inverse. In-the-zone sessions tended to occur earlier in the session than out-of-the-zone blocks. This is unsurprising given the way attention fluctuates over time. Employing a novel method of time-varying functional connectivity, called the quasi-periodic pattern analysis (i.e., reliable, network-level low-frequency fluctuations), we found that the activity between the default mode network (DMN) and task positive network (TPN) is significantly more anti-correlated during in-the-zone states versus out-of-the-zone states. Furthermore, it is the frontoparietal control network (FPCN) switch that differentiates the two zone states. Activity in the dorsal attention network (DAN) and DMN were desynchronized across both zone states. During out-of-the-zone periods, FPCN synchronized with DMN, while during in-the-zone periods, FPCN switched to synchronized with DAN. In contrast, the ventral attention network (VAN) synchronized more closely with DMN during in-the-zone periods compared with out-of-the-zone periods. These findings demonstrate that time-varying functional connectivity of low frequency fluctuations across different brain networks varies with fluctuations in sustained attention or other processes that change over time.
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Affiliation(s)
- Dolly T Seeburger
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA.
| | - Nan Xu
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Marcus Ma
- College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
| | - Sam Larson
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Christine Godwin
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA
| | - Shella D Keilholz
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Eric H Schumacher
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA.
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23
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Lin X, Huo Y, Wang Q, Liu G, Shi J, Fan Y, Lu L, Jing R, Li P. Using normative modeling to assess pharmacological treatment effect on brain state in patients with schizophrenia. Cereb Cortex 2024; 34:bhae003. [PMID: 38252996 DOI: 10.1093/cercor/bhae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/28/2023] [Accepted: 12/30/2023] [Indexed: 01/24/2024] Open
Abstract
Quantifying individual differences in neuroimaging metrics is attracting interest in clinical studies with mental disorders. Schizophrenia is diagnosed exclusively based on symptoms, and the biological heterogeneity makes it difficult to accurately assess pharmacological treatment effects on the brain state. Using the Cambridge Centre for Ageing and Neuroscience data set, we built normative models of brain states and mapped the deviations of the brain characteristics of each patient, to test whether deviations were related to symptoms, and further investigated the pharmacological treatment effect on deviation distributions. Specifically, we found that the patients can be divided into 2 groups: the normalized group had a normalization trend and milder symptoms at baseline, and the other group showed a more severe deviation trend. The baseline severity of the depression as well as the overall symptoms could predict the deviation of the static characteristics for the dorsal and ventral attention networks after treatment. In contrast, the positive symptoms could predict the deviations of the dynamic fluctuations for the default mode and dorsal attention networks after treatment. This work evaluates the effect of pharmacological treatment on static and dynamic brain states using an individualized approach, which may assist in understanding the heterogeneity of the illness pathology as well as the treatment response.
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Affiliation(s)
- Xiao Lin
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), Peking University, Beijing 100191, China
| | - Yanxi Huo
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing 100192, China
| | - Qiandong Wang
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing 100875, China
| | - Guozhong Liu
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing 100192, China
| | - Jie Shi
- National Institute on Drug Dependence and Beijing Key Laboratory on Drug Dependence Research, Peking University, Beijing 100191, China
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, United States
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), Peking University, Beijing 100191, China
| | - Rixing Jing
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing 100192, China
| | - Peng Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), Peking University, Beijing 100191, China
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24
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Ragone E, Tanner J, Jo Y, Zamani Esfahlani F, Faskowitz J, Pope M, Coletta L, Gozzi A, Betzel R. Modular subgraphs in large-scale connectomes underpin spontaneous co-fluctuation events in mouse and human brains. Commun Biol 2024; 7:126. [PMID: 38267534 PMCID: PMC10810083 DOI: 10.1038/s42003-024-05766-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 01/02/2024] [Indexed: 01/26/2024] Open
Abstract
Previous studies have adopted an edge-centric framework to study fine-scale network dynamics in human fMRI. To date, however, no studies have applied this framework to data collected from model organisms. Here, we analyze structural and functional imaging data from lightly anesthetized mice through an edge-centric lens. We find evidence of "bursty" dynamics and events - brief periods of high-amplitude network connectivity. Further, we show that on a per-frame basis events best explain static FC and can be divided into a series of hierarchically-related clusters. The co-fluctuation patterns associated with each cluster centroid link distinct anatomical areas and largely adhere to the boundaries of algorithmically detected functional brain systems. We then investigate the anatomical connectivity undergirding high-amplitude co-fluctuation patterns. We find that events induce modular bipartitions of the anatomical network of inter-areal axonal projections. Finally, we replicate these same findings in a human imaging dataset. In summary, this report recapitulates in a model organism many of the same phenomena observed in previously edge-centric analyses of human imaging data. However, unlike human subjects, the murine nervous system is amenable to invasive experimental perturbations. Thus, this study sets the stage for future investigation into the causal origins of fine-scale brain dynamics and high-amplitude co-fluctuations. Moreover, the cross-species consistency of the reported findings enhances the likelihood of future translation.
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Affiliation(s)
| | - Jacob Tanner
- Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47401, USA
| | - Youngheun Jo
- Department of Psychological and Brain Sciences and Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA
| | - Farnaz Zamani Esfahlani
- Stephenson School of Biomedical Engineering, The University of Oklahoma, Norman, OK, 73019, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences and Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA
| | - Maria Pope
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47401, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, 47401, USA
| | | | - Alessandro Gozzi
- Functional Neuroimaging Lab, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, Rovereto, Italy
| | - Richard Betzel
- Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA.
- Department of Psychological and Brain Sciences and Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA.
- Program in Neuroscience, Indiana University, Bloomington, IN, 47401, USA.
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25
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Xu K, Wang J, Liu G, Yan J, Chang M, Jiang L, Zhang J. Altered dynamic effective connectivity of the default mode network in type 2 diabetes. Front Neurol 2024; 14:1324988. [PMID: 38288329 PMCID: PMC10822894 DOI: 10.3389/fneur.2023.1324988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 12/27/2023] [Indexed: 01/31/2024] Open
Abstract
Introduction Altered functional connectivity of resting-state functional magnetic resonance imaging (rs-fMRI) within default mode network (DMN) regions has been verified to be closely associated with cognitive decline in patients with Type 2 diabetes mellitus (T2DM), but most studies neglected the fluctuations of brain activities-the dynamic effective connectivity (DEC) within DMN of T2DM is still unknown. Methods For the current investigation, 40 healthy controls (HC) and 36 T2DM patients have been recruited as participants. To examine the variation of DEC between T2DM and HC, we utilized the methodologies of independent components analysis (ICA) and multivariate granger causality analysis (mGCA). Results We found altered DEC within DMN only show decrease in state 1. In addition, the causal information flow of diabetic patients major affected areas which are closely associated with food craving and metabolic regulation, and T2DM patients stayed longer in low activity level and exhibited decreased transition rate between states. Moreover, these changes related negatively with the MoCA scores and positively with HbA1C level. Conclusion Our study may offer a fresh perspective on brain dynamic activities to understand the mechanisms underlying T2DM-related cognitive deficits.
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Affiliation(s)
- Kun Xu
- Second Clinical School, Lanzhou University, Lanzhou, China
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
| | - Jun Wang
- Second Clinical School, Lanzhou University, Lanzhou, China
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
| | - Guangyao Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou University Second Hospital, Lanzhou, China
| | - Jiahao Yan
- Second Clinical School, Lanzhou University, Lanzhou, China
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
| | - Miao Chang
- Second Clinical School, Lanzhou University, Lanzhou, China
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
| | - Linzhen Jiang
- Second Clinical School, Lanzhou University, Lanzhou, China
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
| | - Jing Zhang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou University Second Hospital, Lanzhou, China
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26
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Fu Z, Sui J, Iraji A, Liu J, Calhoun V. Cognitive and Psychiatric Relevance of Dynamic Functional Connectivity States in a Large (N>10,000) Children Population. RESEARCH SQUARE 2024:rs.3.rs-3586731. [PMID: 38260417 PMCID: PMC10802706 DOI: 10.21203/rs.3.rs-3586731/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Children's brains dynamically adapt to the stimuli from the internal state and the external environment, allowing for changes in cognitive and mental behavior. In this work, we performed a large-scale analysis of dynamic functional connectivity (DFC) in children aged 9 ~ 11 years, investigating how brain dynamics relate to cognitive performance and mental health at an early age. A hybrid independent component analysis framework was applied to the Adolescent Brain Cognitive Development (ABCD) data containing 10,988 children. We combined a sliding-window approach with k-means clustering to identify five brain states with distinct DFC patterns. Interestingly, the occurrence of a strongly connected state was negatively correlated with cognitive performance and positively correlated with dimensional psychopathology in children. Meanwhile, opposite relationships were observed for a sparsely connected state. The composite cognitive score and the ADHD score were the most significantly correlated with the DFC states. The mediation analysis further showed that attention problems mediated the effect of DFC states on cognitive performance. This investigation unveils the neurological underpinnings of DFC states, which suggests that tracking the transient dynamic connectivity may help to characterize cognitive and mental problems in children and guide people to provide early intervention to buffer adverse influences.
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Affiliation(s)
- Zening Fu
- Georgia Institute of Technology, Emory University and Georgia State University
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27
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Xiang J, Sun Y, Wu X, Guo Y, Xue J, Niu Y, Cui X. Abnormal Spatial and Temporal Overlap of Time-Varying Brain Functional Networks in Patients with Schizophrenia. Brain Sci 2023; 14:40. [PMID: 38248255 PMCID: PMC10813230 DOI: 10.3390/brainsci14010040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 12/25/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024] Open
Abstract
Schizophrenia (SZ) is a complex psychiatric disorder with unclear etiology and pathological features. Neuroscientists are increasingly proposing that schizophrenia is an abnormality in the dynamic organization of brain networks. Previous studies have found that the dynamic brain networks of people with SZ are abnormal in both space and time. However, little is known about the interactions and overlaps between hubs of the brain underlying spatiotemporal dynamics. In this study, we aimed to investigate different patterns of spatial and temporal overlap of hubs between SZ patients and healthy individuals. Specifically, we obtained resting-state functional magnetic resonance imaging data from the public dataset for 43 SZ patients and 49 healthy individuals. We derived a representation of time-varying functional connectivity using the Jackknife Correlation (JC) method. We employed the Betweenness Centrality (BC) method to identify the hubs of the brain's functional connectivity network. We then applied measures of temporal overlap, spatial overlap, and hierarchical clustering to investigate differences in the organization of brain hubs between SZ patients and healthy controls. Our findings suggest significant differences between SZ patients and healthy controls at the whole-brain and subnetwork levels. Furthermore, spatial overlap and hierarchical clustering analysis showed that quasi-periodic patterns were disrupted in SZ patients. Analyses of temporal overlap revealed abnormal pairwise engagement preferences in the hubs of SZ patients. These results provide new insights into the dynamic characteristics of the network organization of the SZ brain.
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Affiliation(s)
- Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
| | - Yumeng Sun
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
| | - Xubin Wu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
| | - Yuxiang Guo
- School of Software, Taiyuan University of Technology, Taiyuan 030024, China;
| | - Jiayue Xue
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
| | - Yan Niu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
| | - Xiaohong Cui
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
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28
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Peterson M, Braga RM, Floris DL, Nielsen JA. Evidence for a Compensatory Relationship between Left- and Right-Lateralized Brain Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.08.570817. [PMID: 38106130 PMCID: PMC10723397 DOI: 10.1101/2023.12.08.570817] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
The two hemispheres of the human brain are functionally asymmetric. At the network level, the language network exhibits left-hemisphere lateralization. While this asymmetry is widely replicated, the extent to which other functional networks demonstrate lateralization remains a subject of Investigation. Additionally, it is unknown how the lateralization of one functional network may affect the lateralization of other networks within individuals. We quantified lateralization for each of 17 networks by computing the relative surface area on the left and right cerebral hemispheres. After examining the ecological, convergent, and external validity and test-retest reliability of this surface area-based measure of lateralization, we addressed two hypotheses across multiple datasets (Human Connectome Project = 553, Human Connectome Project-Development = 343, Natural Scenes Dataset = 8). First, we hypothesized that networks associated with language, visuospatial attention, and executive control would show the greatest lateralization. Second, we hypothesized that relationships between lateralized networks would follow a dependent relationship such that greater left-lateralization of a network would be associated with greater right-lateralization of a different network within individuals, and that this pattern would be systematic across individuals. A language network was among the three networks identified as being significantly left-lateralized, and attention and executive control networks were among the five networks identified as being significantly right-lateralized. Next, correlation matrices, an exploratory factor analysis, and confirmatory factor analyses were used to test the second hypothesis and examine the organization of lateralized networks. We found general support for a dependent relationship between highly left- and right-lateralized networks, meaning that across subjects, greater left lateralization of a given network (such as a language network) was linked to greater right lateralization of another network (such as a ventral attention/salience network) and vice versa. These results further our understanding of brain organization at the macro-scale network level in individuals, carrying specific relevance for neurodevelopmental conditions characterized by disruptions in lateralization such as autism and schizophrenia.
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Affiliation(s)
- Madeline Peterson
- Department of Psychology, Brigham Young University, Provo, UT, 84602, USA
| | - Rodrigo M. Braga
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Dorothea L. Floris
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
| | - Jared A. Nielsen
- Department of Psychology, Brigham Young University, Provo, UT, 84602, USA
- Neuroscience Center, Brigham Young University, Provo, UT, 84604, USA
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29
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Pagani M, Gutierrez-Barragan D, de Guzman AE, Xu T, Gozzi A. Mapping and comparing fMRI connectivity networks across species. Commun Biol 2023; 6:1238. [PMID: 38062107 PMCID: PMC10703935 DOI: 10.1038/s42003-023-05629-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/22/2023] [Indexed: 12/18/2023] Open
Abstract
Technical advances in neuroimaging, notably in fMRI, have allowed distributed patterns of functional connectivity to be mapped in the human brain with increasing spatiotemporal resolution. Recent years have seen a growing interest in extending this approach to rodents and non-human primates to understand the mechanism of fMRI connectivity and complement human investigations of the functional connectome. Here, we discuss current challenges and opportunities of fMRI connectivity mapping across species. We underscore the critical importance of physiologically decoding neuroimaging measures of brain (dys)connectivity via multiscale mechanistic investigations in animals. We next highlight a set of general principles governing the organization of mammalian connectivity networks across species. These include the presence of evolutionarily conserved network systems, a dominant cortical axis of functional connectivity, and a common repertoire of topographically conserved fMRI spatiotemporal modes. We finally describe emerging approaches allowing comparisons and extrapolations of fMRI connectivity findings across species. As neuroscientists gain access to increasingly sophisticated perturbational, computational and recording tools, cross-species fMRI offers novel opportunities to investigate the large-scale organization of the mammalian brain in health and disease.
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Affiliation(s)
- Marco Pagani
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
- Autism Center, Child Mind Institute, New York, NY, USA
- IMT School for Advanced Studies, Lucca, Italy
| | - Daniel Gutierrez-Barragan
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - A Elizabeth de Guzman
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Ting Xu
- Center for the Integrative Developmental Neuroscience, Child Mind Institute, New York, NY, USA
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy.
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30
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Türker B, Belloli L, Owen AM, Naci L, Sitt JD. Processing of the same narrative stimuli elicits common functional connectivity dynamics between individuals. Sci Rep 2023; 13:21260. [PMID: 38040845 PMCID: PMC10692174 DOI: 10.1038/s41598-023-48656-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 11/29/2023] [Indexed: 12/03/2023] Open
Abstract
It has been suggested that conscious experience is linked to the richness of brain state repertories, which change in response to environmental and internal stimuli. High-level sensory stimulation has been shown to alter local brain activity and induce neural synchrony across participants. However, the dynamic interplay of cognitive processes underlying moment-to-moment information processing remains poorly understood. Using naturalistic movies as an ecological laboratory model of the real world, here we investigate how the processing of complex naturalistic stimuli alters the dynamics of brain network interactions and how these in turn support information processing. Participants underwent fMRI recordings during movie watching, scrambled movie watching, and resting. By measuring the phase-synchrony between different brain networks, we analyzed whole-brain connectivity patterns. Our finding revealed distinct connectivity patterns associated with each experimental condition. We found higher synchronization of brain patterns across participants during movie watching compared to rest and scrambled movie conditions. Furthermore, synchronization levels increased during the most engaging parts of the movie. The synchronization dynamics among participants were associated with suspense; scenes with higher levels of suspense induced greater synchronization. These results suggest that processing the same high-level information elicits common neural dynamics across individuals, and that whole-brain functional connectivity tracks variations in processed information and subjective experience.
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Affiliation(s)
- Başak Türker
- Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Sorbonne Université, 75013, Paris, France.
| | - Laouen Belloli
- Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Sorbonne Université, 75013, Paris, France
- Instituto de Ciencias de la Computacion, CONICET-UBA, Buenos Aires, Argentina
| | - Adrian M Owen
- The Western Institute for Neuroscience, Western Interdisciplinary Research Building, University of Western Ontario, London, ON, N6A 5B7, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Lloyd Building, Dublin, Ireland
| | - Jacobo D Sitt
- Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Sorbonne Université, 75013, Paris, France.
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31
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Rahrig H, Ma L, Brown KW, Martelli AM, West SJ, Lasko EN, Chester DS. Inside the mindful moment: The effects of brief mindfulness practice on large-scale network organization and intimate partner aggression. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2023; 23:1581-1597. [PMID: 37880570 PMCID: PMC10842035 DOI: 10.3758/s13415-023-01136-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/06/2023] [Indexed: 10/27/2023]
Abstract
Mindfulness can produce neuroplastic changes that support adaptive cognitive and emotional functioning. Recently interest in single-exercise mindfulness instruction has grown considerably because of the advent of mobile health technology. Accordingly, the current study sought to extend neural models of mindfulness by investigating transient states of mindfulness during single-dose exposure to focused attention meditation. Specifically, we examined the ability of a brief mindfulness induction to attenuate intimate partner aggression via adaptive changes to intrinsic functional brain networks. We employed a dual-regression approach to examine a large-scale functional network organization in 50 intimate partner dyads (total n = 100) while they received either mindfulness (n = 50) or relaxation (n = 50) instruction. Mindfulness instruction reduced coherence within the Default Mode Network and increased functional connectivity within the Frontoparietal Control and Salience Networks. Additionally, mindfulness decoupled primary visual and attention-linked networks. Yet, this induction was unable to elicit changes in subsequent intimate partner aggression, and such aggression was broadly unassociated with any of our network indices. These findings suggest that minimal doses of focused attention-based mindfulness can promote transient changes in large-scale brain networks that have uncertain implications for aggressive behavior.
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Affiliation(s)
- Hadley Rahrig
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA.
| | - Liangsuo Ma
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA
| | - Kirk Warren Brown
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA
- Carnegie Mellon University, Pittsburgh, PA, USA
| | | | | | - Emily N Lasko
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA
| | - David S Chester
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA
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Wang H, Zhu R, Tian S, Shao J, Dai Z, Xue L, Sun Y, Chen Z, Yao Z, Lu Q. Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI. Cogn Neurodyn 2023; 17:1609-1619. [PMID: 37974586 PMCID: PMC10640554 DOI: 10.1007/s11571-022-09907-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 07/19/2022] [Accepted: 10/28/2022] [Indexed: 12/04/2022] Open
Abstract
The diagnosis of bipolar disorders (BD) mainly depends on the clinical history and behavior observation, while only using clinical tools often limits the diagnosis accuracy. The study aimed to create a novel BD diagnosis framework using multilayer modularity in the dynamic minimum spanning tree (MST). We collected 45 un-medicated BD patients and 47 healthy controls (HC). The sliding window approach was utilized to construct dynamic MST via resting-state functional magnetic resonance imaging (fMRI) data. Firstly, we used three null models to explore the effectiveness of multilayer modularity in dynamic MST. Furthermore, the module allegiance exacted from dynamic MST was applied to train a classifier to discriminate BD patients. Finally, we explored the influence of the FC estimator and MST scale on the performance of the model. The findings indicated that multilayer modularity in the dynamic MST was not a random process in the human brain. And the model achieved an accuracy of 83.70% for identifying BD patients. In addition, we found the default mode network, subcortical network (SubC), and attention network played a key role in the classification. These findings suggested that the multilayer modularity in dynamic MST could highlight the difference between HC and BD patients, which opened up a new diagnostic tool for BD patients. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09907-x.
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Affiliation(s)
- Huan Wang
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Rongxin Zhu
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029 China
| | - Shui Tian
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Junneng Shao
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Zhongpeng Dai
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Li Xue
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Yurong Sun
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Zhilu Chen
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029 China
| | - Zhijian Yao
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029 China
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093 China
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
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Zhang T, Zhang Y, Ren J, Zhou H, Yang M, Li L, Lei D, Gong Q, Zhou D, Yang T. Dynamic alterations of striatal-related functional networks in juvenile absence epilepsy. Epilepsy Behav 2023; 149:109506. [PMID: 37925871 DOI: 10.1016/j.yebeh.2023.109506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 10/18/2023] [Accepted: 10/20/2023] [Indexed: 11/07/2023]
Abstract
PURPOSE To explore the features of dynamic functional connectivity (dFC) variability of striatal-cortical/subcortical networks in juvenile absence epilepsy (JAE). METHODS We collected resting-state functional magnetic imaging data from 18 JAE patients and 28 healthy controls. The striatum was divided into six pairs of regions: the inferior-ventral striatum (VSi), superior-ventral striatum (VSs), dorsal-caudal putamen, dorsal-rostral putamen, dorsal-caudate (DC) and ventral-rostral putamen. We assessed the dFC variability of each subdivision in the whole brain using the sliding-window method, and correlated altered circuit with clinical variables in JAE patients. RESULTS We found altered dFC variability of striatal-cortical/subcortical networks in patients with JAE. The VSs exhibited decreased dFC variability with subcortical regions, and dFC variability between VSs and thalamus was negatively correlated with epilepsy duration. For the striatal-cortical networks, the dFC variability was decreased in VSi-affective network but increased in DC-executive network. The altered dynamics of striatal-cortical networks involved crucial nodes of the default mode network (DMN). CONCLUSION JAE patients exhibit excessive stability in the striatal-subcortical networks. For striatal-cortical networks in JAE, the striatal-affective circuit was more stable, while the striatal-executive circuit was more variable. Furthermore, crucial nodes of DMN were changed in striatal-cortical networks in JAE.
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Affiliation(s)
- Tianyu Zhang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yingying Zhang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jiechuan Ren
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Huanyu Zhou
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Menghan Yang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Lei Li
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Du Lei
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Dong Zhou
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Tianhua Yang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
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Soshi T. Neural Coupling between Interhemispheric and Frontoparietal Functional Connectivity during Semantic Processing. Brain Sci 2023; 13:1601. [PMID: 38002560 PMCID: PMC10670303 DOI: 10.3390/brainsci13111601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 11/09/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023] Open
Abstract
Interhemispheric and frontoparietal functional connectivity have been reported to increase during explicit information processing. However, it is unclear how and when interhemispheric and frontoparietal functional connectivity interact during explicit semantic processing. Here, we tested the neural coupling hypothesis that explicit semantic processing promotes neural activity in the nondominant right hemispheric areas, owing to synchronization with enhanced frontoparietal functional connectivity at later processing stages. We analyzed electroencephalogram data obtained using a semantic priming paradigm, which comprised visual priming and target words successively presented under direct or indirect attention to semantic association. Scalp potential analysis demonstrated that the explicit processing of congruent targets reduced negative event-related potentials, as previously reported. Current source density analysis showed that explicit semantic processing activated the right temporal area during later temporal intervals. Subsequent dynamic functional connectivity and neural coupling analyses revealed that explicit semantic processing increased the correlation between right temporal source activities and frontoparietal functional connectivity in later temporal intervals. These findings indicate that explicit semantic processing increases neural coupling between the interhemispheric and frontoparietal functional connectivity during later processing stages.
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Affiliation(s)
- Takahiro Soshi
- Department of English Language Studies, Faculty of Foreign Language Studies, Mejiro University, Shinjyuku, Tokyo 161-8539, Japan
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35
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Zhong S, Chen P, Lai S, Zhang Y, Chen G, He J, Pan Y, Tang G, Wang Y, Jia Y. Hippocampal Dynamic Functional Connectivity, HPA Axis Activity, and Personality Trait in Bipolar Disorder with Suicidal Attempt. Neuroendocrinology 2023; 114:179-191. [PMID: 37729896 DOI: 10.1159/000534033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 09/05/2023] [Indexed: 09/22/2023]
Abstract
INTRODUCTION Suicide in bipolar disorder (BD) is a multifaceted behavior, involving specific neuroendocrine and psychological mechanisms. According to previous studies, we hypothesized that suicidal BD patients may exhibit impaired dynamic functional connectivity (dFC) variability of hippocampal subregions and hypothalamic-pituitary-adrenal (HPA) axis activity, which may be associated with suicide-related personality traits. The objective of our study was to clarify this. METHODS Resting-state functional magnetic resonance imaging data were obtained from 79 patients with BD, 39 with suicidal attempt (SA), and 40 without SA, and 35 healthy controls (HCs). The activity of the HPA axis was assessed by measuring morning plasma adrenocorticotropic hormone (ACTH) and cortisol (CORT) levels. All participants underwent personality assessment using Minnesota Multiphasic Personality Inventory-2 (MMPI-2). RESULTS BD patients with SA exhibited increased dFC variability between the right caudal hippocampus and the left superior temporal gyrus (STG) when compared with non-SA BD patients and HCs. BD with SA also showed significantly lower ACTH levels in comparison with HCs, which was positively correlated with increased dFC variability between the right caudal hippocampus and the left STG. BD with SA had significantly higher scores of Hypochondriasis, Depression, and Schizophrenia than non-SA BD. Additionally, multivariable regression analysis revealed the interaction of ACTH × dFC variability between the right caudal hippocampus and the left STG independently predicted MMPI-2 score (depression evaluation) in suicidal BD patients. CONCLUSION These results suggested that suicidal BD exhibited increased dFC variability of hippocampal-temporal cortex and less HPA axis hyperactivity, which may affect their personality traits.
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Affiliation(s)
- Shuming Zhong
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou, China,
| | - Pan Chen
- Medical Imaging Center, First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Shunkai Lai
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Yiliang Zhang
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Guanmao Chen
- Medical Imaging Center, First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Jiali He
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Youling Pan
- Medical Imaging Center, First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Guixian Tang
- Medical Imaging Center, First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Ying Wang
- Medical Imaging Center, First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Yanbin Jia
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou, China
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36
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Kang J, Xie H, Mao W, Wu J, Li X, Geng X. EEG Connectivity Diversity Differences between Children with Autism and Typically Developing Children: A Comparative Study. Bioengineering (Basel) 2023; 10:1030. [PMID: 37760132 PMCID: PMC10525147 DOI: 10.3390/bioengineering10091030] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/12/2023] [Accepted: 08/24/2023] [Indexed: 09/29/2023] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social interaction and communication, and repetitive or stereotyped behaviors. Previous studies have reported altered brain connectivity in ASD children compared to typically developing children. In this study, we investigated the diversity of connectivity patterns between children with ASD and typically developing children using phase lag entropy (PLE), a measure of the variability of phase differences between two time series. We also developed a novel wavelet-based PLE method for the calculation of PLE at specific scales. Our findings indicated that the diversity of connectivity in ASD children was higher than that in typically developing children at Delta and Alpha frequency bands, both within brain regions and across hemispheric brain regions. These findings provide insight into the underlying neural mechanisms of ASD and suggest that PLE may be a useful tool for investigating brain connectivity in ASD.
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Affiliation(s)
- Jiannan Kang
- Child Rehabilitation Division, Ningbo Rehabilitation Hospital, Ningbo 315040, China
| | - Hongxiang Xie
- Child Rehabilitation Division, Ningbo Rehabilitation Hospital, Ningbo 315040, China
| | - Wenqin Mao
- Child Rehabilitation Division, Ningbo Rehabilitation Hospital, Ningbo 315040, China
| | - Juanmei Wu
- Child Rehabilitation Division, Ningbo Rehabilitation Hospital, Ningbo 315040, China
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Xinling Geng
- School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
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37
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Wang W, Bo T, Zhang G, Li J, Ma J, Ma L, Hu G, Tong H, Lv Q, Araujo DJ, Luo D, Chen Y, Wang M, Wang Z, Wang GZ. Noncoding transcripts are linked to brain resting-state activity in non-human primates. Cell Rep 2023; 42:112652. [PMID: 37335775 DOI: 10.1016/j.celrep.2023.112652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 04/05/2023] [Accepted: 05/30/2023] [Indexed: 06/21/2023] Open
Abstract
Brain-derived transcriptomes are known to correlate with resting-state brain activity in humans. Whether this association holds in nonhuman primates remains uncertain. Here, we search for such molecular correlates by integrating 757 transcriptomes derived from 100 macaque cortical regions with resting-state activity in separate conspecifics. We observe that 150 noncoding genes explain variations in resting-state activity at a comparable level with protein-coding genes. In-depth analysis of these noncoding genes reveals that they are connected to the function of nonneuronal cells such as oligodendrocytes. Co-expression network analysis finds that the modules of noncoding genes are linked to both autism and schizophrenia risk genes. Moreover, genes associated with resting-state noncoding genes are highly enriched in human resting-state functional genes and memory-effect genes, and their links with resting-state functional magnetic resonance imaging (fMRI) signals are altered in the brains of patients with autism. Our results highlight the potential for noncoding RNAs to explain resting-state activity in the nonhuman primate brain.
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Affiliation(s)
- Wei Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Tingting Bo
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Clinical Neuroscience Center, Ruijin Hospital Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ge Zhang
- Department of Medical Imaging, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, No. 7 Weiwu Road, Zhengzhou, Henan, China
| | - Jie Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Junjie Ma
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Liangxiao Ma
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ganlu Hu
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, China
| | - Huige Tong
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Qian Lv
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Daniel J Araujo
- Center for Cancer Immunotherapy, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Dong Luo
- School of Biomedical Engineering, Hainan University, Haikou, Hainan, China
| | - Yuejun Chen
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai 201210, China
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, No. 7 Weiwu Road, Zhengzhou, Henan, China
| | - Zheng Wang
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China; School of Biomedical Engineering, Hainan University, Haikou, Hainan, China.
| | - Guang-Zhong Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
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Fu S, Liang S, Lin C, Wu Y, Xie S, Li M, Lei Q, Li J, Yu K, Yin Y, Hua K, Li W, Wu C, Ma X, Jiang G. Aberrant brain entropy in posttraumatic stress disorder comorbid with major depressive disorder during the coronavirus disease 2019 pandemic. Front Psychiatry 2023; 14:1143780. [PMID: 37333934 PMCID: PMC10272369 DOI: 10.3389/fpsyt.2023.1143780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 05/09/2023] [Indexed: 06/20/2023] Open
Abstract
Aim Previously, neuroimaging studies on comorbid Posttraumatic-Major depression disorder (PTSD-MDD) comorbidity found abnormalities in multiple brain regions among patients. Recent neuroimaging studies have revealed dynamic nature on human brain activity during resting state, and entropy as an indicator of dynamic regularity may provide a new perspective for studying abnormalities of brain function among PTSD-MDD patients. During the COVID-19 pandemic, there has been a significant increase in the number of patients with PTSD-MDD. We have decided to conduct research on resting-state brain functional activity of patients who developed PTSD-MDD during this period using entropy. Methods Thirty three patients with PTSD-MDD and 36 matched TCs were recruited. PTSD and depression symptoms were assessed using multiple clinical scales. All subjects underwent functional magnetic resonance imaging (fMRI) scans. And the brain entropy (BEN) maps were calculated using the BEN mapping toolbox. A two-sample t-test was used to compare the differences in the brain entropy between the PTSD-MDD comorbidity group and TC group. Furthermore, correlation analysis was conducted between the BEN changes in patients with PTSD-MDD and clinical scales. Results Compared to the TCs, PTSD-MDD patients had a reduced BEN in the right middle frontal orbital gyrus (R_MFOG), left putamen, and right inferior frontal gyrus, opercular part (R_IFOG). Furthermore, a higher BEN in the R_MFOG was related to higher CAPS and HAMD-24 scores in the patients with PTSD-MDD. Conclusion The results showed that the R_MFOG is a potential marker for showing the symptom severity of PTSD-MDD comorbidity. Consequently, PTSD-MDD may have reduced BEN in frontal and basal ganglia regions which are related to emotional dysregulation and cognitive deficits.
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Affiliation(s)
- Shishun Fu
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Sipei Liang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Chulan Lin
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Yunfan Wu
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Shuangcong Xie
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Meng Li
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Qiang Lei
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Jianneng Li
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Kanghui Yu
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Yi Yin
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Kelei Hua
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Wuming Li
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Caojun Wu
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Xiaofen Ma
- The Department of Nuclear Medicine, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Guihua Jiang
- The Department of Medical Imaging Guangdong Second Provincial General Hospital, Guangzhou, China
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Rocca MA, Margoni M, Battaglini M, Eshaghi A, Iliff J, Pagani E, Preziosa P, Storelli L, Taoka T, Valsasina P, Filippi M. Emerging Perspectives on MRI Application in Multiple Sclerosis: Moving from Pathophysiology to Clinical Practice. Radiology 2023; 307:e221512. [PMID: 37278626 PMCID: PMC10315528 DOI: 10.1148/radiol.221512] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 11/28/2022] [Accepted: 01/17/2023] [Indexed: 06/07/2023]
Abstract
MRI plays a central role in the diagnosis of multiple sclerosis (MS) and in the monitoring of disease course and treatment response. Advanced MRI techniques have shed light on MS biology and facilitated the search for neuroimaging markers that may be applicable in clinical practice. MRI has led to improvements in the accuracy of MS diagnosis and a deeper understanding of disease progression. This has also resulted in a plethora of potential MRI markers, the importance and validity of which remain to be proven. Here, five recent emerging perspectives arising from the use of MRI in MS, from pathophysiology to clinical application, will be discussed. These are the feasibility of noninvasive MRI-based approaches to measure glymphatic function and its impairment; T1-weighted to T2-weighted intensity ratio to quantify myelin content; classification of MS phenotypes based on their MRI features rather than on their clinical features; clinical relevance of gray matter atrophy versus white matter atrophy; and time-varying versus static resting-state functional connectivity in evaluating brain functional organization. These topics are critically discussed, which may guide future applications in the field.
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Affiliation(s)
- Maria Assunta Rocca
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Monica Margoni
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Marco Battaglini
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Arman Eshaghi
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Jeffrey Iliff
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Elisabetta Pagani
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Paolo Preziosa
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Loredana Storelli
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Toshiaki Taoka
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Paola Valsasina
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Massimo Filippi
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
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40
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Long Y, Ouyang X, Yan C, Wu Z, Huang X, Pu W, Cao H, Liu Z, Palaniyappan L. Evaluating test-retest reliability and sex-/age-related effects on temporal clustering coefficient of dynamic functional brain networks. Hum Brain Mapp 2023; 44:2191-2208. [PMID: 36637216 PMCID: PMC10028647 DOI: 10.1002/hbm.26202] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 11/25/2022] [Accepted: 01/01/2023] [Indexed: 01/14/2023] Open
Abstract
The multilayer dynamic network model has been proposed as an effective method to understand the brain function. In particular, derived from the definition of clustering coefficient in static networks, the temporal clustering coefficient provides a direct measure of the topological stability of dynamic brain networks and shows potential in predicting altered brain functions. However, test-retest reliability and demographic-related effects on this measure remain to be evaluated. Using a data set from the Human Connectome Project (157 male and 180 female healthy adults; 22-37 years old), the present study investigated: (1) the test-retest reliability of temporal clustering coefficient across four repeated resting-state functional magnetic resonance imaging scans as measured by intraclass correlation coefficient (ICC); and (2) sex- and age-related effects on temporal clustering coefficient. The results showed that (1) the temporal clustering coefficient had overall moderate test-retest reliability (ICC > 0.40 over a wide range of densities) at both global and subnetwork levels, (2) female subjects showed significantly higher temporal clustering coefficient than males at both global and subnetwork levels, particularly within the default-mode and subcortical regions, and (3) temporal clustering coefficient of the subcortical subnetwork was positively correlated with age in young adults. The results of sex effects were robustly replicated in an independent REST-meta-MDD data set, while the results of age effects were not. Our findings suggest that the temporal clustering coefficient is a relatively reliable and reproducible approach for identifying individual differences in brain function, and provide evidence for demographically related effects on the human brain dynamic connectomes.
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Affiliation(s)
- Yicheng Long
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Xuan Ouyang
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Chaogan Yan
- CAS Key Laboratory of Behavioral Science, Institute of PsychologyChinese Academy of SciencesBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression Research, Institute of PsychologyChinese Academy of SciencesBeijingChina
| | - Zhipeng Wu
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Xiaojun Huang
- Department of PsychiatryJiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical CollegeNanchangChina
| | - Weidan Pu
- Medical Psychological InstituteThe Second Xiangya Hospital, Central South UniversityChangshaChina
| | - Hengyi Cao
- Center for Psychiatric NeuroscienceFeinstein Institute for Medical ResearchManhassetNew YorkUSA
- Division of Psychiatry ResearchZucker Hillside HospitalGlen OaksNew YorkUSA
| | - Zhening Liu
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Lena Palaniyappan
- Department of PsychiatryUniversity of Western OntarioLondonOntarioCanada
- Robarts Research InstituteUniversity of Western OntarioLondonOntarioCanada
- Lawson Health Research InstituteLondonOntarioCanada
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41
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Lin X, Jing R, Chang S, Liu L, Wang Q, Zhuo C, Shi J, Fan Y, Lu L, Li P. Understanding the heterogeneity of dynamic functional connectivity patterns in first-episode drug naïve depression using normative models. J Affect Disord 2023; 327:217-225. [PMID: 36736793 DOI: 10.1016/j.jad.2023.01.109] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 01/20/2023] [Accepted: 01/30/2023] [Indexed: 02/04/2023]
Abstract
BACKGROUND The heterogeneity of the clinical symptoms and presumptive neural pathologies has stunted progress toward identifying reproducible biomarkers and limited therapeutic interventions' effectiveness for the first episode drug-naïve major depressive disorders (FEDN-MDD). This study combined the dynamic features of fMRI data and normative modeling to quantitative and individualized metrics for delineating the biological heterogeneity of FEDN-MDD. METHOD Two hundred seventy-four adults with FEDN-MDD and 832 healthy controls from International Big-Data Center for Depression Research were included. Subject-specific dynamic brain networks and network fluctuation characteristics were computed for each subject using the group information-guided independent component analysis. Then, we mapped the heterogeneity of the dynamic features (network fluctuation characteristics and dynamic functional connectivity within brain networks) in the patients group via normative modeling. RESULTS The FEDN-MDD whose network fluctuation characteristics deviate from the normative model also showed significant differences within the default mode network, executive control network, and limbic network compared with healthy controls. Furthermore, the network fluctuation characteristics are significantly increased in patients with FEDN-MDD. About 4.74 % of the patients showed a deviation of dynamic functional connectivity, and only 3.35 % of the controls deviated from the normative model in above 100 connectivities. More patients than healthy controls showed extreme dynamic variabilities in above 100 connectivities. CONCLUSIONS This work evaluates the efficacy of an individualized approach based on normative modeling for understanding the heterogeneity of abnormal dynamic functional connectivity patterns in FEDN-MDD, and could be used as complementary to classical case-control comparisons.
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Affiliation(s)
- Xiao Lin
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit (No.2018RU006), Peking University, Beijing 100191, China
| | - Rixing Jing
- School of Instrument Science and Opto-Electronic Engineering, Beijing Information Science and Technology University, Beijing 100192, China
| | - Suhua Chang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit (No.2018RU006), Peking University, Beijing 100191, China
| | - Lin Liu
- National Institute on Drug Dependence and Beijing Key Laboratory on Drug Dependence Research, Peking University, Beijing 100191, China
| | - Qiandong Wang
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing 100875, China
| | - Chuanjun Zhuo
- Key Laboratory of Real-Time Tracing of Brain Circuits of Neurology and Psychiatry (RTBNB_Lab), Tianjin Fourth Centre Hospital, Tianjin Medical University Affiliated Tianjin Fourth Centre Hospital, Nankai University Affiliated Fourth Hospital, Tianjin 300142, China
| | - Jie Shi
- National Institute on Drug Dependence and Beijing Key Laboratory on Drug Dependence Research, Peking University, Beijing 100191, China
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit (No.2018RU006), Peking University, Beijing 100191, China; National Institute on Drug Dependence and Beijing Key Laboratory on Drug Dependence Research, Peking University, Beijing 100191, China; Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Beijing 100191, China
| | - Peng Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit (No.2018RU006), Peking University, Beijing 100191, China.
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42
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Ford A, Kovacs-Balint ZA, Wang A, Feczko E, Earl E, Miranda-Domínguez Ó, Li L, Styner M, Fair D, Jones W, Bachevalier J, Sánchez MM. Functional maturation in visual pathways predicts attention to the eyes in infant rhesus macaques: Effects of social status. Dev Cogn Neurosci 2023; 60:101213. [PMID: 36774827 PMCID: PMC9925610 DOI: 10.1016/j.dcn.2023.101213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 01/31/2023] [Accepted: 02/06/2023] [Indexed: 02/10/2023] Open
Abstract
Differences in looking at the eyes of others are one of the earliest behavioral markers for social difficulties in neurodevelopmental disabilities, including autism. However, it is unknown how early visuo-social experiences relate to the maturation of infant brain networks that process visual social stimuli. We investigated functional connectivity (FC) within the ventral visual object pathway as a contributing neural system. Densely sampled, longitudinal eye-tracking and resting state fMRI (rs-fMRI) data were collected from infant rhesus macaques, an important model of human social development, from birth through 6 months of age. Mean trajectories were fit for both datasets and individual trajectories from subjects with both eye-tracking and rs-fMRI data were used to test for brain-behavior relationships. Exploratory findings showed infants with greater increases in FC between left V1 to V3 visual areas have an earlier increase in eye-looking before 2 months. This relationship was moderated by social status such that infants with low social status had a stronger association between left V1 to V3 connectivity and eye-looking than high status infants. Results indicated that maturation of the visual object pathway may provide an important neural substrate supporting adaptive transitions in social visual attention during infancy.
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Affiliation(s)
- Aiden Ford
- Neuroscience Program, Emory University, Atlanta, GA, USA; Marcus Autism Center, USA.
| | | | - Arick Wang
- Emory Natl. Primate Res. Ctr., Emory Univ., Atlanta, GA, USA; Dept of Psychology, Emory University, Atlanta, GA, USA
| | - Eric Feczko
- Dept. of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Masonic Institute of the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Eric Earl
- Data Science and Sharing Team, National Institute of Mental Health, NIH, DHHS, Bethesda, MD, USA
| | - Óscar Miranda-Domínguez
- Dept. of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Masonic Institute of the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Longchuan Li
- Marcus Autism Center, USA; Children's Healthcare of Atlanta, GA, USA; Dept. of Pediatrics, Emory University, Sch. of Med., Atlanta, GA, USA
| | - Martin Styner
- Dept. of Psychiatry, Univ. of North Carolina, Chapel Hill, NC, USA
| | - Damien Fair
- Dept. of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Masonic Institute of the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Institute of Child Development, University of Minnesota, Minneapolis, MN, USA; Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Warren Jones
- Marcus Autism Center, USA; Children's Healthcare of Atlanta, GA, USA; Dept. of Pediatrics, Emory University, Sch. of Med., Atlanta, GA, USA
| | - Jocelyne Bachevalier
- Emory Natl. Primate Res. Ctr., Emory Univ., Atlanta, GA, USA; Dept of Psychology, Emory University, Atlanta, GA, USA
| | - Mar M Sánchez
- Emory Natl. Primate Res. Ctr., Emory Univ., Atlanta, GA, USA; Dept. Psychiatry & Behavioral Sciences, Emory Univ., Sch. of Med., Atlanta, GA, USA
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Kovacs-Balint ZA, Raper J, Richardson R, Gopakumar A, Kettimuthu KP, Higgins M, Feczko E, Earl E, Ethun KF, Li L, Styner M, Fair D, Bachevalier J, Sanchez MM. The role of puberty on physical and brain development: A longitudinal study in male Rhesus Macaques. Dev Cogn Neurosci 2023; 60:101237. [PMID: 37031512 PMCID: PMC10114189 DOI: 10.1016/j.dcn.2023.101237] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 02/20/2023] [Accepted: 03/21/2023] [Indexed: 04/07/2023] Open
Abstract
This study examined the role of male pubertal maturation on physical growth and development of neurocircuits that regulate stress, emotional and cognitive control using a translational nonhuman primate model. We collected longitudinal data from male macaques between pre- and peri-puberty, including measures of physical growth, pubertal maturation (testicular volume, blood testosterone -T- concentrations) and brain structural and resting-state functional MRI scans to examine developmental changes in amygdala (AMY), hippocampus (HIPPO), prefrontal cortex (PFC), as well as functional connectivity (FC) between those regions. Physical growth and pubertal measures increased from pre- to peri-puberty. The indexes of pubertal maturation -testicular size and T- were correlated at peri-puberty, but not at pre-puberty (23 months). Our findings also showed ICV, AMY, HIPPO and total PFC volumetric growth, but with region-specific changes in PFC. Surprisingly, FC in these neural circuits only showed developmental changes from pre- to peri-puberty for HIPPO-orbitofrontal FC. Finally, testicular size was a better predictor of brain structural maturation than T levels -suggesting gonadal hormones-independent mechanisms-, whereas T was a strong predictor of functional connectivity development. We expect that these neural circuits will show more drastic pubertal-dependent maturation, including stronger associations with pubertal measures later, during and after male puberty.
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Affiliation(s)
- Z A Kovacs-Balint
- Emory National Primate Research Center, Emory University, Atlanta, GA 30329, USA.
| | - J Raper
- Emory National Primate Research Center, Emory University, Atlanta, GA 30329, USA; Dept. of Pediatrics, Emory University, Atlanta, GA 30322, USA
| | - R Richardson
- Emory National Primate Research Center, Emory University, Atlanta, GA 30329, USA
| | - A Gopakumar
- Emory National Primate Research Center, Emory University, Atlanta, GA 30329, USA
| | - K P Kettimuthu
- Emory National Primate Research Center, Emory University, Atlanta, GA 30329, USA
| | - M Higgins
- Office of Nursing Research, Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA 30322, USA
| | - E Feczko
- Dept. of Pediatrics, University of Minnesota, Minneapolis, MN 55414, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, USA
| | - E Earl
- Dept. of Behavioral Neuroscience, Oregon Health & Sciences University, Portland, OR 97239, USA
| | - K F Ethun
- Emory National Primate Research Center, Emory University, Atlanta, GA 30329, USA
| | - L Li
- Dept. of Pediatrics, Emory University, Atlanta, GA 30322, USA; Marcus Autism Center; Children's Healthcare of Atlanta, GA, USA
| | - M Styner
- Dept. of Psychiatry, University of North Carolina, Chapel Hill, NC 27514, USA
| | - D Fair
- Dept. of Pediatrics, University of Minnesota, Minneapolis, MN 55414, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, USA
| | - J Bachevalier
- Emory National Primate Research Center, Emory University, Atlanta, GA 30329, USA
| | - M M Sanchez
- Emory National Primate Research Center, Emory University, Atlanta, GA 30329, USA; Dept. of Psychiatry & Behavioral Sciences, Emory University, Atlanta, GA 30322, USA
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Zong X, Wu K, Li L, Zhang J, Ma S, Kang L, Zhang N, Lv L, Sang D, Weng S, Chen H, Zheng J, Hu M. Striatum-related spontaneous coactivation patterns predict treatment response on positive symptoms of drug-naive first-episode schizophrenia with risperidone monotherapy. Front Psychiatry 2023; 14:1093030. [PMID: 37009110 PMCID: PMC10050338 DOI: 10.3389/fpsyt.2023.1093030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 03/03/2023] [Indexed: 03/17/2023] Open
Abstract
BackgroundEvidence from functional magnetic resonance imaging (fMRI) studies of schizophrenia suggests that interindividual variation in the stationary striatal functional circuit may be correlated with antipsychotic treatment response. However, little is known about the role of the dynamic striatum-related network in predicting patients’ clinical improvement. The spontaneous coactivation pattern (CAP) technique has recently been found to be important for elucidating the non-stationary nature of functional brain networks.MethodsForty-two drug-naive first-episode schizophrenia patients underwent fMRI and T1W imaging before and after 8 weeks of risperidone monotherapy. The striatum was divided into 3 subregions, including the putamen, pallidum, and caudate. Spontaneous CAPs and CAP states were utilized to measure the dynamic characteristics of brain networks. We used DPARSF and Dynamic Brain Connectome software to analyze each subregion-related CAP and CAP state for each group and then compared the between-group differences in the neural network biomarkers. We used Pearson’s correlation analysis to determine the associations between the neuroimaging measurements with between-group differences and the improvement in patients’ psychopathological symptoms.ResultsIn the putamen-related CAPs, patients showed significantly increased intensity in the bilateral thalamus, bilateral supplementary motor areas, bilateral medial, and paracingulate gyrus, left paracentral lobule, left medial superior frontal gyrus, and left anterior cingulate gyrus compared with healthy controls. After treatment, thalamic signals in the putamen-related CAP 1 showed a significant increase, while the signals of the medial and paracingulate gyrus in the putamen-related CAP 3 revealed a significant decrease. The increase in thalamic signal intensity in the putamen-related CAP 1 was significantly and positively correlated with the percentage reduction in PANSS_P.ConclusionThis study is the first to combine striatal CAPs and fMRI to explore treatment response-related biomarkers in the early phase of schizophrenia. Our findings suggest that dynamic changes in CAP states in the putamen-thalamus circuit may be potential biomarkers for predicting patients’ variation in the short-term treatment response of positive symptoms.
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Affiliation(s)
- Xiaofen Zong
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Kai Wu
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Li
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiangbo Zhang
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Simeng Ma
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lijun Kang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Nan Zhang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Deen Sang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Shenhong Weng
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
- Shenhong Weng,
| | - Huafu Chen
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Huafu Chen,
| | - Junjie Zheng
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China
- Junjie Zheng,
| | - Maolin Hu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
- *Correspondence: Maolin Hu,
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Bencivenga F, Tullo MG, Maltempo T, von Gal A, Serra C, Pitzalis S, Galati G. Effector-selective modulation of the effective connectivity within frontoparietal circuits during visuomotor tasks. Cereb Cortex 2023; 33:2517-2538. [PMID: 35709758 PMCID: PMC10016057 DOI: 10.1093/cercor/bhac223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 05/05/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
Despite extensive research, the functional architecture of the subregions of the dorsal posterior parietal cortex (PPC) involved in sensorimotor processing is far from clear. Here, we draw a thorough picture of the large-scale functional organization of the PPC to disentangle the fronto-parietal networks mediating visuomotor functions. To this aim, we reanalyzed available human functional magnetic resonance imaging data collected during the execution of saccades, hand, and foot pointing, and we combined individual surface-based activation, resting-state functional connectivity, and effective connectivity analyses. We described a functional distinction between a more lateral region in the posterior intraparietal sulcus (lpIPS), preferring saccades over pointing and coupled with the frontal eye fields (FEF) at rest, and a more medial portion (mpIPS) intrinsically correlated to the dorsal premotor cortex (PMd). Dynamic causal modeling revealed feedforward-feedback loops linking lpIPS with FEF during saccades and mpIPS with PMd during pointing, with substantial differences between hand and foot. Despite an intrinsic specialization of the action-specific fronto-parietal networks, our study reveals that their functioning is finely regulated according to the effector to be used, being the dynamic interactions within those networks differently modulated when carrying out a similar movement (i.e. pointing) but with distinct effectors (i.e. hand and foot).
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Affiliation(s)
- Federica Bencivenga
- Corresponding author: Department of Psychology, “Sapienza” University of Rome, Via dei Marsi 78, 00185 Rome, Italy.
| | | | - Teresa Maltempo
- Cognitive and Motor Rehabilitation and Neuroimaging Unit, Santa Lucia Foundation (IRCCS Fondazione Santa Lucia), Via Ardeatina 306/354, 00179 Roma, Italy
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza Lauro De Bosis 15, 00135 Roma, Italy
| | - Alessandro von Gal
- Brain Imaging Laboratory, Department of Psychology, Sapienza University, Via dei Marsi 78, 00185 Roma, Italy
- PhD program in Behavioral Neuroscience, Sapienza University of Rome, Via dei Marsi 78, 00185 Roma, Italy
| | - Chiara Serra
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza Lauro De Bosis 15, 00135 Roma, Italy
| | - Sabrina Pitzalis
- Cognitive and Motor Rehabilitation and Neuroimaging Unit, Santa Lucia Foundation (IRCCS Fondazione Santa Lucia), Via Ardeatina 306/354, 00179 Roma, Italy
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza Lauro De Bosis 15, 00135 Roma, Italy
| | - Gaspare Galati
- Brain Imaging Laboratory, Department of Psychology, Sapienza University, Via dei Marsi 78, 00185 Roma, Italy
- Cognitive and Motor Rehabilitation and Neuroimaging Unit, Santa Lucia Foundation (IRCCS Fondazione Santa Lucia), Via Ardeatina 306/354, 00179 Roma, Italy
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Abnormal dynamics of brain functional networks in children with Tourette syndrome. J Psychiatr Res 2023; 159:249-257. [PMID: 36764224 DOI: 10.1016/j.jpsychires.2023.01.046] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 12/30/2022] [Accepted: 01/26/2023] [Indexed: 01/29/2023]
Abstract
Tourette syndrome (TS) is a childhood-onset neurodevelopmental disorder characterized by the presence of multiple motor and vocal tics. Research using resting-state functional magnetic resonance imaging (rfMRI) have found aberrant static functional connectivity (FC) and its topological properties in the brain networks of TS. Our study is the first to investigate the dynamic functional connectivity (dFC) in the whole brain network of TS patients, focusing on the temporal properties of dFC states and the temporal variability of topological organization. The rfMRI data of 36 male children with TS and 27 matched healthy controls were collected and further analyzed by group spatial independent component analysis, sliding windows approach based dFC analysis, k-means clustering analysis, and graph theory analysis. The clustering analysis identified three dFC states. Of these states, state 2, characterized by increased inter-network connections in subcortical network (SCN), sensorimotor network (SMN), and default mode network (DMN), and decreased inter-network connections between salience network (SAN) and executive control network (ECN), was found to have higher fractional window and dwell time in TS, which was also positively correlated with tic severity. TS patients also exhibited higher temporal variability of whole-brain-network global efficiency and local efficiency, and higher temporal variability of nodal efficiency and local efficiency in SCN, DMN, ECN, SAN, and SMN. Additionally, temporal variability of the efficiency and local efficiency in insula was positively correlated with tic severity. Our findings revealed abnormal temporal property of dFC states and temporal variability of topological organization in TS, providing new insights into clinical diagnoses and neuropathology of TS.
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Long Y, Liu X, Liu Z. Temporal Stability of the Dynamic Resting-State Functional Brain Network: Current Measures, Clinical Research Progress, and Future Perspectives. Brain Sci 2023; 13:429. [PMID: 36979239 PMCID: PMC10046056 DOI: 10.3390/brainsci13030429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/20/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023] Open
Abstract
Based on functional magnetic resonance imaging and multilayer dynamic network model, the brain network's quantified temporal stability has shown potential in predicting altered brain functions. This manuscript aims to summarize current knowledge, clinical research progress, and future perspectives on brain network's temporal stability. There are a variety of widely used measures of temporal stability such as the variance/standard deviation of dynamic functional connectivity strengths, the temporal variability, the flexibility (switching rate), and the temporal clustering coefficient, while there is no consensus to date which measure is the best. The temporal stability of brain networks may be associated with several factors such as sex, age, cognitive functions, head motion, circadian rhythm, and data preprocessing/analyzing strategies, which should be considered in clinical studies. Multiple common psychiatric disorders such as schizophrenia, major depressive disorder, and bipolar disorder have been found to be related to altered temporal stability, especially during the resting state; generally, both excessively decreased and increased temporal stabilities were thought to reflect disorder-related brain dysfunctions. However, the measures of temporal stability are still far from applications in clinical diagnoses for neuropsychiatric disorders partly because of the divergent results. Further studies with larger samples and in transdiagnostic (including schizoaffective disorder) subjects are warranted.
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Affiliation(s)
| | | | - Zhening Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha 410011, China
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48
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Gao W, Biswal B, Yang J, Li S, Wang Y, Chen S, Yuan J. Temporal dynamic patterns of the ventromedial prefrontal cortex underlie the association between rumination and depression. Cereb Cortex 2023; 33:969-982. [PMID: 35462398 DOI: 10.1093/cercor/bhac115] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 02/21/2022] [Accepted: 02/22/2022] [Indexed: 11/14/2022] Open
Abstract
As a major contributor to the development of depression, rumination has proven linked with aberrant default-mode network (DMN) activity. However, it remains unclear how the spontaneous spatial and temporal activity of DMN underlie the association between rumination and depression. To illustrate this issue, behavioral measures and resting-state functional magnetic resonance images were connected in 2 independent samples (NSample1 = 100, NSample2 = 95). Fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) were used to assess spatial characteristic patterns, while voxel-wise functional concordance (across time windows) (VC) and Hurst exponent (HE) were used to assess temporal dynamic patterns of brain activity. Results from both samples consistently show that temporal dynamics but not spatial patterns of DMN are associated with rumination. Specifically, rumination is positively correlated with HE and VC (but not fALFF and ReHo) values, reflecting more consistent and regular temporal dynamic patterns in DMN. Moreover, subregion analyses indicate that temporal dynamics of the ventromedial prefrontal cortex (VMPFC) reliably predict rumination scores. Furthermore, mediation analyses show that HE and VC of VMPFC mediate the association between rumination and depression. These findings shed light on neural mechanisms of individual differences in rumination and corresponding risk for depression.
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Affiliation(s)
- Wei Gao
- Institute of Brain and Psychological Science, Sichuan Normal University, Chengdu, Sichuan, China.,Faculty of Psychology, Southwest University, Chongqing, China
| | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Jiemin Yang
- Institute of Brain and Psychological Science, Sichuan Normal University, Chengdu, Sichuan, China
| | - Songlin Li
- School of Educational Science, Sichuan Normal University, Chengdu, Sichuan, China
| | - YanQing Wang
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Shengdong Chen
- School of Psychology, Qufu Normal University, Qufu, Shandong, China
| | - JiaJin Yuan
- Institute of Brain and Psychological Science, Sichuan Normal University, Chengdu, Sichuan, China
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Zheng K, Li B, Lu H, Wang H, Liu J, Yan B, Friston KJ, Wu Y, Liu J, Zhang X, Liu M, Li L, Qin J, Chen B, Hu D, Li L. Aberrant temporal-spatial complexity of intrinsic fluctuations in major depression. Eur Arch Psychiatry Clin Neurosci 2023; 273:169-181. [PMID: 35419632 DOI: 10.1007/s00406-022-01403-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 03/25/2022] [Indexed: 11/28/2022]
Abstract
Accumulating evidence suggests that the brain is highly dynamic; thus, investigation of brain dynamics especially in brain connectivity would provide crucial information that stationary functional connectivity could miss. This study investigated temporal expressions of spatial modes within the default mode network (DMN), salience network (SN) and cognitive control network (CCN) using a reliable data-driven co-activation pattern (CAP) analysis in two independent data sets. We found enhanced CAP-to-CAP transitions of the SN in patients with MDD. Results suggested enhanced flexibility of this network in the patients. By contrast, we also found reduced spatial consistency and persistence of the DMN in the patients, indicating reduced variability and stability in individuals with MDD. In addition, the patients were characterized by prominent activation of mPFC. Moreover, further correlation analysis revealed that persistence and transitions of RCCN were associated with the severity of depression. Our findings suggest that functional connectivity in the patients may not be simply attenuated or potentiated, but just alternating faster or slower among more complex patterns. The aberrant temporal-spatial complexity of intrinsic fluctuations reflects functional diaschisis of resting-state networks as characteristic of patients with MDD.
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Affiliation(s)
- Kaizhong Zheng
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China
| | - Baojuan Li
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Hongbing Lu
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Huaning Wang
- Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Jin Liu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Baoyu Yan
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Karl J Friston
- The Wellcome Department of Imaging Neuroscience, Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3AR, UK
| | - Yuxia Wu
- Department of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an, 710032, China
| | - Jian Liu
- Network Center, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Xi Zhang
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Mengwan Liu
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Liang Li
- Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, 27 Tai-Ping Road, Beijing, 100850, China
| | - Jian Qin
- Department of Intelligence Science and Technology, College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, China
| | - Badong Chen
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.
| | - Dewen Hu
- Department of Intelligence Science and Technology, College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, China.
| | - Lingjiang Li
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China.
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50
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Song I, Lee TH. Considering dynamic nature of the brain: the clinical importance of connectivity variability in machine learning classification and prediction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.26.525765. [PMID: 36747828 PMCID: PMC9901018 DOI: 10.1101/2023.01.26.525765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
The brain connectivity of resting-state fMRI (rs-fMRI) represents an intrinsic state of brain architecture, and it has been used as a useful neural marker for detecting psychiatric conditions as well as for predicting psychosocial characteristics. However, most studies using brain connectivity have focused more on the strength of functional connectivity over time (static-FC) but less attention to temporal characteristics of connectivity changes (FC-variability). The primary goal of the current study was to investigate the effectiveness of using the FC-variability in classifying an individual's pathological characteristics from others and predicting psychosocial characteristics. In addition, the current study aimed to prove that benefits of the FC-variability are reliable across various analysis procedures. To this end, three open public large resting-state fMRI datasets including individuals with Autism Spectrum Disorder (ABIDE; N = 1249), Schizophrenia disorder (COBRE; N = 145), and typical development (NKI; N = 672) were utilized for the machine learning (ML) classification and prediction based on their static-FC and the FC-variability metrics. To confirm the robustness of FC-variability utility, we benchmarked the ML classification and prediction with various brain parcellations and sliding window parameters. As a result, we found that the ML performances were significantly improved when the ML included FC-variability features in classifying pathological populations from controls (e.g., individuals with autism spectrum disorder vs. typical development) and predicting psychiatric severity (e.g., score of autism diagnostic observation schedule), regardless of parcellation selection and sliding window size. Additionally, the ML performance deterioration was significantly prevented with FC-variability features when excessive features were inputted into the ML models, yielding more reliable results. In conclusion, the current finding proved the usefulness of the FC-variability and its reliability.
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Affiliation(s)
- Inuk Song
- Department of Psychology, Virginia Tech
| | - Tae-Ho Lee
- Department of Psychology, Virginia Tech
- School of Neuroscience, Virginia Tech
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