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Wu H, Dai W, Hong Z, Qin Y, Yang M, Wang B, Liao J. Higher-order sensorimotor circuit of the whole-brain functional network involved in pruritus regulation in atopic dermatitis. J Eur Acad Dermatol Venereol 2024; 38:873-882. [PMID: 38069553 DOI: 10.1111/jdv.19691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 11/09/2023] [Indexed: 04/26/2024]
Abstract
BACKGROUND Little is known about the neural mechanisms underlying pruritus regulation in Atopic dermatitis (AD). OBJECTIVE To investigate the functional changes of the resting-state whole brain network of AD participants and the mechanisms by which they were involved in pruritus regulation. METHOD Based on the functional magnetic resonance imaging data from 19 AD participants and 37 healthy controls (HC), a graph-theoretical measure of degree centrality (DC) conjoined with a voxel-level seed-based functional connectivity (FC) method was used to identify abnormal higher-order nodes and the functionally relevant circuit in AD participants compared to healthy controls (HC). RESULTS Of 64 participants screened, 19 AD participants (12M/7F, median [IQR] age, 27 [14] years) and 36 HCs (13M/23F, median [IQR] age, 20 [1] years) were enrolled. DC values of the left superior frontal gyrus (LSFG) increased in AD participants and exhibited a negative correlation with the SCORAD score (r = -0.561, p = 0.012) compared with HC. In the FC analysis with LSFG as the seed, FC values of several sensory and motor regions increased in AD participants, highly overlapping with the anatomical distribution of the inferior fronto-occipital fascicle (IFOF). AD participants with severe pruritus exhibited lower levels of DC (T = -2.316, p = 0.033) and FC between the LSFG and left insula (T = -2.203, p = 0.042) than those with mild-to- moderate pruritus. CONCLUSIONS AND RELEVANCE LSFG was involved in pruritus regulation in AD by forming a high-order sensorimotor circuit through the IFOF, a white matter fascicle that proved to provide multimodal integration in motor control and sensory information processing. These results offer more mechanism-guided treatment targets for severe pruritus in AD.
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Affiliation(s)
- Haishan Wu
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, China National Technology Institute on Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Wenyu Dai
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, China National Technology Institute on Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Zhaoyi Hong
- Department of Dermatology, Second Xiangya Hospital, Hunan Key Laboratory of Medical Epigenomics, Central South University, Changsha, Hunan, China
| | - Yue Qin
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, China National Technology Institute on Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Min Yang
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, China National Technology Institute on Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Bolun Wang
- Scientific Research Program of Hunan Provincial Health Commission, Department of Radiology, Clinical Research Center for Medical Imaging in Hunan Province, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Jieyue Liao
- Department of Dermatology, Second Xiangya Hospital, Hunan Key Laboratory of Medical Epigenomics, Central South University, Changsha, Hunan, China
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Xue Y, Bai MS, Dong HY, Wang TT, Mohamed ZA, Jia FY. Altered intra- and inter-network brain functional connectivity associated with prolonged screen time in pre-school children with autism spectrum disorder. Eur J Pediatr 2024; 183:2391-2399. [PMID: 38448613 DOI: 10.1007/s00431-024-05500-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/27/2024] [Accepted: 02/28/2024] [Indexed: 03/08/2024]
Abstract
Prolonged screen time (ST) has adverse effects on autistic characteristics and language development. However, the mechanisms underlying the effects of prolonged ST on the neurodevelopment of children with autism spectrum disorder (ASD) remain unclear. Neuroimaging technology may help to further explain the role of prolonged ST in individuals with ASD. This study included 164 cases, all cases were divided into low-dose ST exposure (LDE group 108 cases) and high-dose ST exposure (HDE group 56 cases) based on the average ST of all subjects. Spatial independent component analysis (ICA) was used to identify resting state networks (RSNs) and investigate intra- and inter-network alterations in ASD children with prolonged ST. We found that the total Childhood Autism Rating Scale (CARS) scores in the HDE group were significantly higher than those in the LDE group (36.2 ± 3.1 vs. 34.6 ± 3.9, p = 0.008). In addition, the developmental quotient (DQ) of hearing and language in the HDE group were significantly lower than those in the LDE group (31.5 ± 13.1 vs. 42.5 ± 18.5, p < 0.001). A total of 13 independent components (ICs) were identified. Between-group comparison revealed that the HDE group exhibited decreased functional connectivity (FC) in the left precuneus (PCUN) of the default mode network (DMN), the right middle temporal gyrus (MTG) of the executive control network (ECN), and the right median cingulate and paracingulate gyri (MCG) of the attention network (ATN), compared with the LDE group. Additionally, there was an increase in FC in the right orbital part of the middle frontal gyrus (ORBmid) of the salience network (SAN), compared with the LDE group. The inter-network analysis revealed increased FC between the visual network (VN) and basal ganglia (BG) and decreased FC between the sensorimotor network (SMN) and DMN, SMN and ATN, SMN and auditory network (AUN), and DMN and SAN in the HDE group, compared with the LDE group. There was a significant negative correlation between altered FC values in MTG and total CARS scores in subjects (r = - 0.18, p = 0.018). Conclusion: ASD children with prolonged ST often exhibit lower DQ of language development and more severe autistic characteristics. The alteration of intra- and inter-network FC may be a key neuroimaging feature of the effect of prolonged ST on neurodevelopment in ASD children. Clinical trial registration: ChiCTR2100051141. What is Known: • Prolonged ST has adverse effects on autistic characteristics and language development. • Neuroimaging technology may help to further explain the role of prolonged ST in ASD. What is New: • This is the first study to explore the impact of ST on intra- and inter-network FC in children with ASD. • ASD children with prolonged ST have atypical changes in intra- and inter-brain network FC.
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Affiliation(s)
- Yang Xue
- Department of Developmental and Behavioral Pediatrics, Children's Hospital of the First Hospital of Jilin University, The First Hospital of Jilin University, Jilin University, Changchun, China
- The Child Health Clinical Research Center of Jilin Province, Changchun, China
| | - Miao-Shui Bai
- Department of Developmental and Behavioral Pediatrics, Children's Hospital of the First Hospital of Jilin University, The First Hospital of Jilin University, Jilin University, Changchun, China
- The Child Health Clinical Research Center of Jilin Province, Changchun, China
| | - Han-Yu Dong
- Department of Developmental and Behavioral Pediatrics, Children's Hospital of the First Hospital of Jilin University, The First Hospital of Jilin University, Jilin University, Changchun, China
- The Child Health Clinical Research Center of Jilin Province, Changchun, China
| | - Tian-Tian Wang
- Department of Developmental and Behavioral Pediatrics, Children's Hospital of the First Hospital of Jilin University, The First Hospital of Jilin University, Jilin University, Changchun, China
- The Child Health Clinical Research Center of Jilin Province, Changchun, China
| | - Zakaria Ahmed Mohamed
- Department of Developmental and Behavioral Pediatrics, Children's Hospital of the First Hospital of Jilin University, The First Hospital of Jilin University, Jilin University, Changchun, China
- The Child Health Clinical Research Center of Jilin Province, Changchun, China
| | - Fei-Yong Jia
- Department of Developmental and Behavioral Pediatrics, Children's Hospital of the First Hospital of Jilin University, The First Hospital of Jilin University, Jilin University, Changchun, China.
- The Child Health Clinical Research Center of Jilin Province, Changchun, China.
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Murray L, Frederick BB, Janes AC. Data-driven connectivity profiles relate to smoking cessation outcomes. Neuropsychopharmacology 2024; 49:1007-1013. [PMID: 38280945 DOI: 10.1038/s41386-024-01802-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 01/04/2024] [Accepted: 01/08/2024] [Indexed: 01/29/2024]
Abstract
At a group level, nicotine dependence is linked to differences in resting-state functional connectivity (rs-FC) within and between three large-scale brain networks: the salience network (SN), default mode network (DMN), and frontoparietal network (FPN). Yet, individuals may display distinct patterns of rs-FC that impact treatment outcomes. This study used a data-driven approach, Group Iterative Multiple Model Estimation (GIMME), to characterize shared and person-specific rs-FC features linked with clinically-relevant treatment outcomes. 49 nicotine-dependent adults completed a resting-state fMRI scan prior to a two-week smoking cessation attempt. We used GIMME to identify group, subgroup, and individual-level networks of SN, DMN, and FPN connectivity. Regression models assessed whether within- and between-network connectivity of individual rs-FC models was associated with baseline cue-induced craving, and craving and use of regular cigarettes (i.e., "slips") during cessation. As a group, participants displayed shared patterns of connectivity within all three networks, and connectivity between the SN-FPN and DMN-SN. However, there was substantial heterogeneity across individuals. Individuals with greater within-network SN connectivity experienced more slips during treatment, while individuals with greater DMN-FPN connectivity experienced fewer slips. Individuals with more anticorrelated DMN-SN connectivity reported lower craving during treatment, while SN-FPN connectivity was linked to higher craving. In conclusion, in nicotine-dependent adults, GIMME identified substantial heterogeneity within and between the large-scale brain networks. Individuals with greater SN connectivity may be at increased risk for relapse during treatment, while a greater positive DMN-FPN and negative DMN-SN connectivity may be protective for individuals during smoking cessation treatment.
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Affiliation(s)
- Laura Murray
- Cognitive and Pharmacological Neuroimaging Unit, National Institute on Drug Abuse, Biomedical Research Center, 251 Bayview Blvd, Baltimore, MD, 21224, USA.
| | - Blaise B Frederick
- McLean Imaging Center, McLean Hospital, 115 Mill Street, Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, 02215, USA
| | - Amy C Janes
- Cognitive and Pharmacological Neuroimaging Unit, National Institute on Drug Abuse, Biomedical Research Center, 251 Bayview Blvd, Baltimore, MD, 21224, USA
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Long JY, Qin K, Pan N, Fan WL, Li Y. Impaired topology and connectivity of grey matter structural networks in major depressive disorder: evidence from a multi-site neuroimaging data-set. Br J Psychiatry 2024; 224:170-178. [PMID: 38602159 DOI: 10.1192/bjp.2024.41] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) has been increasingly understood as a disruption of brain connectome. Investigating grey matter structural networks with a large sample size can provide valuable insights into the structural basis of network-level neuropathological underpinnings of MDD. AIMS Using a multisite MRI data-set including nearly 2000 individuals, this study aimed to identify robust topology and connectivity abnormalities of grey matter structural network linked to MDD and relevant clinical phenotypes. METHOD A total of 955 MDD patients and 1009 healthy controls were included from 23 sites. Individualised structural covariance networks (SCN) were established based on grey matter volume maps. Following data harmonisation, network topological metrics and focal connectivity were examined for group-level comparisons, individual-level classification performance and association with clinical ratings. Various validation strategies were applied to confirm the reliability of findings. RESULTS Compared with healthy controls, MDD individuals exhibited increased global efficiency, abnormal regional centralities (i.e. thalamus, precentral gyrus, middle cingulate cortex and default mode network) and altered circuit connectivity (i.e. ventral attention network and frontoparietal network). First-episode drug-naive and recurrent patients exhibited different patterns of deficits in network topology and connectivity. In addition, the individual-level classification of topological metrics outperforms that of structural connectivity. The thalamus-insula connectivity was positively associated with the severity of depressive symptoms. CONCLUSIONS Based on this high-powered data-set, we identified reliable patterns of impaired topology and connectivity of individualised SCN in MDD and relevant subtypes, which adds to the current understanding of neuropathology of MDD and might guide future development of diagnostic and therapeutic markers.
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Affiliation(s)
- Jing-Yi Long
- Wuhan Mental Health Center, Wuhan, China; Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China; and Research Center for Psychological and Health Sciences, China University of Geosciences, Wuhan, China
| | - Kun Qin
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Nanfang Pan
- Huaxi Magnetic Resonance Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Wen-Liang Fan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; and Department of Radiology, Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yi Li
- Wuhan Mental Health Center, Wuhan, China; Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China; and Research Center for Psychological and Health Sciences, China University of Geosciences, Wuhan, China
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5
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Koller DP, Schirner M, Ritter P. Human connectome topology directs cortical traveling waves and shapes frequency gradients. Nat Commun 2024; 15:3570. [PMID: 38670965 DOI: 10.1038/s41467-024-47860-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Traveling waves and neural oscillation frequency gradients are pervasive in the human cortex. While the direction of traveling waves has been linked to brain function and dysfunction, the factors that determine this direction remain elusive. We hypothesized that structural connectivity instrength gradients - defined as the gradually varying sum of incoming connection strengths across the cortex - could shape both traveling wave direction and frequency gradients. We confirm the presence of instrength gradients in the human connectome across diverse cohorts and parcellations. Using a cortical network model, we demonstrate how these instrength gradients direct traveling waves and shape frequency gradients. Our model fits resting-state MEG functional connectivity best in a regime where instrength-directed traveling waves and frequency gradients emerge. We further show how structural subnetworks of the human connectome generate opposing wave directions and frequency gradients observed in the alpha and beta bands. Our findings suggest that structural connectivity instrength gradients affect both traveling wave direction and frequency gradients.
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Affiliation(s)
- Dominik P Koller
- Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
- Department of Neurology with Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany.
| | - Michael Schirner
- Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Department of Neurology with Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, 10115, Berlin, Germany
- Einstein Center for Neuroscience Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Einstein Center Digital Future, Wilhelmstraße 67, 10117, Berlin, Germany
| | - Petra Ritter
- Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
- Department of Neurology with Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany.
- Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, 10115, Berlin, Germany.
- Einstein Center for Neuroscience Berlin, Charitéplatz 1, 10117, Berlin, Germany.
- Einstein Center Digital Future, Wilhelmstraße 67, 10117, Berlin, Germany.
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6
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Luo AC, Sydnor VJ, Pines A, Larsen B, Alexander-Bloch AF, Cieslak M, Covitz S, Chen AA, Esper NB, Feczko E, Franco AR, Gur RE, Gur RC, Houghton A, Hu F, Keller AS, Kiar G, Mehta K, Salum GA, Tapera T, Xu T, Zhao C, Salo T, Fair DA, Shinohara RT, Milham MP, Satterthwaite TD. Functional connectivity development along the sensorimotor-association axis enhances the cortical hierarchy. Nat Commun 2024; 15:3511. [PMID: 38664387 DOI: 10.1038/s41467-024-47748-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
Human cortical maturation has been posited to be organized along the sensorimotor-association axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-association axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1126). Across datasets, the development of functional connectivity systematically varied along the sensorimotor-association axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These consistent and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.
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Affiliation(s)
- Audrey C Luo
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Adam Pines
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455, USA
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sydney Covitz
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Andrew A Chen
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA
| | | | - Eric Feczko
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Alexandre R Franco
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, 10962, USA
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Fengling Hu
- Penn Statistics in Imaging and Visualization Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arielle S Keller
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Gregory Kiar
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Kahini Mehta
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Giovanni A Salum
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Tinashe Tapera
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, 02115, USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Chenying Zhao
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Taylor Salo
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455, USA
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
- Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, 10962, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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7
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Nghiem TAE, Lee B, Chao THH, Branigan NK, Mistry PK, Shih YYI, Menon V. Space wandering in the rodent default mode network. Proc Natl Acad Sci U S A 2024; 121:e2315167121. [PMID: 38557177 PMCID: PMC11009630 DOI: 10.1073/pnas.2315167121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 01/17/2024] [Indexed: 04/04/2024] Open
Abstract
The default mode network (DMN) is a large-scale brain network known to be suppressed during a wide range of cognitive tasks. However, our comprehension of its role in naturalistic and unconstrained behaviors has remained elusive because most research on the DMN has been conducted within the restrictive confines of MRI scanners. Here, we use multisite GCaMP (a genetically encoded calcium indicator) fiber photometry with simultaneous videography to probe DMN function in awake, freely exploring rats. We examined neural dynamics in three core DMN nodes-the retrosplenial cortex, cingulate cortex, and prelimbic cortex-as well as the anterior insula node of the salience network, and their association with the rats' spatial exploration behaviors. We found that DMN nodes displayed a hierarchical functional organization during spatial exploration, characterized by stronger coupling with each other than with the anterior insula. Crucially, these DMN nodes encoded the kinematics of spatial exploration, including linear and angular velocity. Additionally, we identified latent brain states that encoded distinct patterns of time-varying exploration behaviors and found that higher linear velocity was associated with enhanced DMN activity, heightened synchronization among DMN nodes, and increased anticorrelation between the DMN and anterior insula. Our findings highlight the involvement of the DMN in collectively and dynamically encoding spatial exploration in a real-world setting. Our findings challenge the notion that the DMN is primarily a "task-negative" network disengaged from the external world. By illuminating the DMN's role in naturalistic behaviors, our study underscores the importance of investigating brain network function in ecologically valid contexts.
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Affiliation(s)
| | - Byeongwook Lee
- Department of Psychiatry & Behavioral Sciences, Stanford University, Palo Alto, CA94304
| | - Tzu-Hao Harry Chao
- Center for Animal MRI, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Nicholas K. Branigan
- Department of Psychiatry & Behavioral Sciences, Stanford University, Palo Alto, CA94304
| | - Percy K. Mistry
- Department of Psychiatry & Behavioral Sciences, Stanford University, Palo Alto, CA94304
| | - Yen-Yu Ian Shih
- Center for Animal MRI, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC27514
| | - Vinod Menon
- Department of Psychiatry & Behavioral Sciences, Stanford University, Palo Alto, CA94304
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, CA94304
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA94305
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8
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Silva Alves A, Rigoni I, Mégevand P, Lagarde S, Picard F, Seeck M, Vulliémoz S, Roehri N. High-density electroencephalographic functional networks in genetic generalized epilepsy: Preserved whole-brain topology hides local reorganization. Epilepsia 2024; 65:961-973. [PMID: 38306118 DOI: 10.1111/epi.17903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 02/03/2024]
Abstract
OBJECTIVE Genetic generalized epilepsy (GGE) accounts for approximately 20% of adult epilepsy cases and is considered a disorder of large brain networks, involving both hemispheres. Most studies have not shown any difference in functional whole-brain network topology when compared to healthy controls. Our objective was to examine whether this preserved global network topology could hide local reorganizations that balance out at the global network level. METHODS We recorded high-density electroencephalograms from 20 patients and 20 controls, and reconstructed the activity of 118 regions. We computed functional connectivity in windows free of interictal epileptiform discharges in broad, delta, theta, alpha, and beta frequency bands, characterized the network topology, and used the Hub Disruption Index (HDI) to quantify the topological reorganization. We examined the generalizability of our results by reproducing a 25-electrode clinical system. RESULTS Our study did not reveal any significant change in whole-brain network topology among GGE patients. However, the HDI was significantly different between patients and controls in all frequency bands except alpha (p < .01, false discovery rate [FDR] corrected, d < -1), and accompanied by an increase in connectivity in the prefrontal regions and default mode network. This reorganization suggests that regions that are important in transferring the information in controls were less so in patients. Inversely, the crucial regions in patients are less so in controls. These findings were also found in delta and theta frequency bands when using 25 electrodes (p < .001, FDR corrected, d < -1). SIGNIFICANCE In GGE patients, the overall network topology is similar to that of healthy controls but presents a balanced local topological reorganization. This reorganization causes the prefrontal areas and default mode network to be more integrated and segregated, which may explain executive impairment associated with GGE. Additionally, the reorganization distinguishes patients from controls even when using 25 electrodes, suggesting its potential use as a diagnostic tool.
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Affiliation(s)
- André Silva Alves
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Isotta Rigoni
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Pierre Mégevand
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Stanislas Lagarde
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Aix Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Fabienne Picard
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Margitta Seeck
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Serge Vulliémoz
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nicolas Roehri
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
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9
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Lee DA, Kim SE, Park KM. Increased Thalamic Connectivity in Juvenile Myoclonic Epilepsy Based on Electroencephalography Source-Level Analysis. Brain Connect 2024; 14:182-188. [PMID: 38343360 DOI: 10.1089/brain.2023.0084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2024] Open
Abstract
Background: This study investigated alterations in the intrinsic thalamic network of patients with juvenile myoclonic epilepsy (JME) based on an electroencephalography (EEG) source-level analysis. Materials and Methods: We enrolled patients newly diagnosed with JME as well as healthy controls. The assessments were conducted in the resting state. We computed sources based on the scalp electrical potentials using a minimum-norm imaging method and a standardized, low-resolution, brain electromagnetic tomography approach. To create a functional connectivity matrix, we used the Talairach atlas to define thalamic nodes and applied the coherence method to measure brain synchronization as edges. We then calculated the intrinsic thalamic network using graph theory. We compared the intrinsic thalamic network of patients with JME with those of healthy controls. Results: This study included 67 patients with JME and 66 healthy controls. EEG source-level analysis revealed significant differences in the intrinsic thalamic networks between patients with JME and healthy controls. The measures of functional connectivity (radius, diameter, and characteristic path length) were significantly lower in patients with JME than in healthy controls (radius: 2.769 vs. 3.544, p = 0.015; diameter: 4.464 vs. 5.443, p = 0.024; and characteristic path length: 2.248 vs. 2.616, p = 0.046). Conclusions: We demonstrated alterations in the intrinsic thalamic network in patients with JME compared with those in healthy controls based on the EEG source-level analysis. These findings indicated increased thalamic connectivity in the JME group. These intrinsic thalamic network changes may be related to the pathophysiology of JME.
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Affiliation(s)
- Dong Ah Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Sung Eun Kim
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
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10
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Yuan H, Li X, Wei B. Modeling default mode network patterns via a universal spatio-temporal brain attention skip network. Neuroimage 2024; 287:120522. [PMID: 38253216 DOI: 10.1016/j.neuroimage.2024.120522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 01/16/2024] [Accepted: 01/18/2024] [Indexed: 01/24/2024] Open
Abstract
Designing a comprehensive four-dimensional resting-state functional magnetic resonance imaging (4D Rs-fMRI) based default mode network (DMN) modeling methodology to reveal the spatio-temporal patterns of individual DMN, is crucial for understanding the cognitive mechanisms of the brain and the pathogenesis of psychiatric disorders. However, there are still two limitations of existing approaches for DMN modeling. The approaches either (1) simply split the spatio-temporal components and ignore the overall character of the spatio-temporal patterns or (2) are biased in the process of feature extraction for DMN modeling, and their spatio-temporal accuracy is thus not warranted. To this end, we propose a novel Spatio-Temporal Brain Attention Skip Network (STBAS-Net) to model the personalized spatio-temporal patterns of the DMN. STBAS-Net consists of spatial and temporal components, where the multi-head attention skip connection block in the spatial component achieves detailed feature extraction and enhancement in the shallow stage. Under the guidance of spatial information, we technically fuse multiple spatio-temporal information in the temporal component, which dexterously exploits the overall spatio-temporal features and achieves mutual constraints of spatio-temporal patterns to characterize the spatio-temporal patterns of the DMN. We verify the proposed STBAS-Net on a publicly released 4D Rs-fMRI dataset and an EMCI dataset. The experimental results show that compared with existing advanced methods, the proposed network can more accurately model the personalized spatio-temporal patterns of the human brain DMN and successfully identify abnormal spatio-temporal patterns in EMCI patients. This study provides a potential tool for revealing the spatio-temporal patterns of the human brain DMN and is expected to provide an effective methodological framework for future exploration of abnormal brain spatio-temporal patterns and modeling of other functional brain networks.
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Affiliation(s)
- Hang Yuan
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, Shandong 266112, PR China; Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, Shandong 266112, PR China
| | - Xiang Li
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, Shandong 266112, PR China; Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, Shandong 266112, PR China
| | - Benzheng Wei
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, Shandong 266112, PR China; Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, Shandong 266112, PR China.
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11
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Benozzo D, Baron G, Coletta L, Chiuso A, Gozzi A, Bertoldo A. Macroscale coupling between structural and effective connectivity in the mouse brain. Sci Rep 2024; 14:3142. [PMID: 38326324 PMCID: PMC10850485 DOI: 10.1038/s41598-024-51613-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 01/07/2024] [Indexed: 02/09/2024] Open
Abstract
Exploring how the emergent functional connectivity (FC) relates to the underlying anatomy (structural connectivity, SC) is one of the major goals of modern neuroscience. At the macroscale level, no one-to-one correspondence between structural and functional links seems to exist. And we posit that to better understand their coupling, two key aspects should be considered: the directionality of the structural connectome and limitations in explaining networks functions through an undirected measure such as FC. Here, we employed an accurate directed SC of the mouse brain acquired through viral tracers and compared it with single-subject effective connectivity (EC) matrices derived from a dynamic causal model (DCM) applied to whole-brain resting-state fMRI data. We analyzed how SC deviates from EC and quantified their respective couplings by conditioning on the strongest SC links and EC links. We found that when conditioning on the strongest EC links, the obtained coupling follows the unimodal-transmodal functional hierarchy. Whereas the reverse is not true, as there are strong SC links within high-order cortical areas with no corresponding strong EC links. This mismatch is even more clear across networks; only within sensory motor networks did we observe connections that align in terms of both effective and structural strength.
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Affiliation(s)
- Danilo Benozzo
- Department of Information Engineering, University of Padova, Padua, Italy.
| | - Giorgia Baron
- Department of Information Engineering, University of Padova, Padua, Italy
| | - Ludovico Coletta
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems @ UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Alessandro Chiuso
- Department of Information Engineering, University of Padova, Padua, Italy
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems @ UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padova, Padua, Italy.
- Padova Neuroscience Center (PNC), Padua, Italy.
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12
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Lurie DJ, Pappas I, D'Esposito M. Cortical timescales and the modular organization of structural and functional brain networks. Hum Brain Mapp 2024; 45:e26587. [PMID: 38339903 PMCID: PMC10823764 DOI: 10.1002/hbm.26587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 12/01/2023] [Accepted: 12/21/2023] [Indexed: 02/12/2024] Open
Abstract
Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the "timescale" over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure-function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks.
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Affiliation(s)
- Daniel J. Lurie
- Department of PsychologyUniversity of CaliforniaBerkeleyCaliforniaUSA
- Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPennsylvaniaUSA
| | - Ioannis Pappas
- Department of Neurology, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Mark D'Esposito
- Department of Psychology and Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyCaliforniaUSA
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13
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Baik K, Kim YJ, Park M, Chung SJ, Sohn YH, Jeong Y, Lee PH. Functional Brain Networks of Minor and Well-Structured Major Hallucinations in Parkinson's Disease. Mov Disord 2024; 39:318-327. [PMID: 38140793 DOI: 10.1002/mds.29681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 10/08/2023] [Accepted: 11/17/2023] [Indexed: 12/24/2023] Open
Abstract
BACKGROUND Minor hallucinations (mHs) and well-structured major hallucinations (MHs) are common symptoms of Parkinson's disease (PD) psychosis. OBJECTIVES To investigate the resting-state networks (RSNs) in patients with PD without hallucinations (PD-nH), with mH (PD-mH), and with MH (PD-MH). METHODS A total of 73 patients with PD were enrolled (27 PD-nH, 23 PD-mH, and 23 PD-MH). Using seed-based functional connectivity analyses, we investigated the RSNs supposedly related to hallucinations in PD: the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN), ventral attention network (VAN), and visual network (VN). We compared the cognitive function and RSN connectivity among the three groups. In addition, we performed a seed-to-seed analysis to examine the inter-network connectivity within each group using the corresponding RSN seeds. RESULTS PD-MH group had lower test scores for attention and visuospatial functions compared with those in the other groups. The connectivity of the right intracalcarine cortex within the DAN was lower in the PD-MH group than in the others. The PD-mH and PD-MH groups showed higher connectivity in the left orbitofrontal cortex within DMN compared with the PD-nH group, whereas the connectivity was lower in the right middle frontal gyrus (MFG) within ECN, precuneus cortex within VAN, right middle temporal gyrus and precuneus cortex within DAN, and left MFG within VN. The PD-mH and PD-MH groups showed different inter-network connectivity between the five RSNs, especially regarding DAN connectivity. CONCLUSIONS DAN dysfunction may be a key factor in the progression from mH to MH in patients with PD. © 2023 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Kyoungwon Baik
- Department of Neurology, Anam Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Yae Ji Kim
- Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
- KI for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Mincheol Park
- Department of Neurology, Chung-Ang University College of Medicine and Graduate School of Medicine, Gwangmyeong Hospital, Gwangmyeong, South Korea
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Seok Jong Chung
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Young H Sohn
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Yong Jeong
- Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
- KI for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Phil Hyu Lee
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
- Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, South Korea
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14
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Assem M, Shashidhara S, Glasser MF, Duncan J. Basis of executive functions in fine-grained architecture of cortical and subcortical human brain networks. Cereb Cortex 2024; 34:bhad537. [PMID: 38244562 PMCID: PMC10839840 DOI: 10.1093/cercor/bhad537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 12/19/2023] [Accepted: 12/20/2023] [Indexed: 01/22/2024] Open
Abstract
Theoretical models suggest that executive functions rely on both domain-general and domain-specific processes. Supporting this view, prior brain imaging studies have revealed that executive activations converge and diverge within broadly characterized brain networks. However, the lack of precise anatomical mappings has impeded our understanding of the interplay between domain-general and domain-specific processes. To address this challenge, we used the high-resolution multimodal magnetic resonance imaging approach of the Human Connectome Project to scan participants performing 3 canonical executive tasks: n-back, rule switching, and stop signal. The results reveal that, at the individual level, different executive activations converge within 9 domain-general territories distributed in frontal, parietal, and temporal cortices. Each task exhibits a unique topography characterized by finely detailed activation gradients within domain-general territory shifted toward adjacent resting-state networks; n-back activations shift toward the default mode, rule switching toward dorsal attention, and stop signal toward cingulo-opercular networks. Importantly, the strongest activations arise at multimodal neurobiological definitions of network borders. Matching results are seen in circumscribed regions of the caudate nucleus, thalamus, and cerebellum. The shifting peaks of local gradients at the intersection of task-specific networks provide a novel mechanistic insight into how partially-specialized networks interact with neighboring domain-general territories to generate distinct executive functions.
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Affiliation(s)
- Moataz Assem
- MRC Cognition and Brain Sciences Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 7EF, United Kingdom
| | - Sneha Shashidhara
- MRC Cognition and Brain Sciences Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 7EF, United Kingdom
- Psychology Department, Ashoka University, Sonipat, 131029, India
| | - Matthew F Glasser
- Department of Radiology, Washington University in St. Louis, Saint Louis, MO, 63110, United States
- Department of Neuroscience, Washington University in St. Louis, Saint Louis, MO, 63110, United States
| | - John Duncan
- MRC Cognition and Brain Sciences Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 7EF, United Kingdom
- Department of Experimental Psychology, University of Oxford, Oxford, OX1 3UD, United Kingdom
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15
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Sun F, Cui D, Jiao Q, Niu J, Zhang X, Shi Y, Liu H, Ouyang Z, Yu G, Dou R, Guo Y, Dong L, Cao W. The co-activation pattern between the DMN and other brain networks affects the cognition of older adults: evidence from naturalistic stimulation fMRI data. Cereb Cortex 2024; 34:bhad466. [PMID: 38044469 DOI: 10.1093/cercor/bhad466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 11/11/2023] [Accepted: 11/13/2023] [Indexed: 12/05/2023] Open
Abstract
Brain function changes affect cognitive functions in older adults, yet the relationship between cognition and the dynamic changes of brain networks during naturalistic stimulation is not clear. Here, we recruited the young, middle-aged and older groups from the Cambridge Center for Aging and Neuroscience to investigate the relationship between dynamic metrics of brain networks and cognition using functional magnetic resonance imaging data during movie-watching. We found six reliable co-activation pattern (CAP) states of brain networks grouped into three pairs with opposite activation patterns in three age groups. Compared with young and middle-aged adults, older adults dwelled shorter time in CAP state 4 with deactivated default mode network (DMN) and activated salience, frontoparietal and dorsal-attention networks (DAN), and longer time in state 6 with deactivated DMN and activated DAN and visual network, suggesting altered dynamic interaction between DMN and other brain networks might contribute to cognitive decline in older adults. Meanwhile, older adults showed easier transfer from state 6 to state 3 (activated DMN and deactivated sensorimotor network), suggesting that the fragile antagonism between DMN and other cognitive networks might contribute to cognitive decline in older adults. Our findings provided novel insights into aberrant brain network dynamics associated with cognitive decline.
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Affiliation(s)
- Fengzhu Sun
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271099, China
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Dong Cui
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271099, China
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Qing Jiao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271099, China
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Jinpeng Niu
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Xiaotong Zhang
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Yajun Shi
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Haiqin Liu
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Zhen Ouyang
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Guanghui Yu
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Ruhai Dou
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Yongxin Guo
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Li Dong
- MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Weifang Cao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271099, China
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an 271016, China
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16
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Zhang Y, Han X, Ge X, Xu T, Wang Y, Mu J, Liu F. Modular brain network in volitional eyes closing: enhanced integration with a marked impact on hubs. Cereb Cortex 2024; 34:bhad464. [PMID: 38044477 DOI: 10.1093/cercor/bhad464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 11/12/2023] [Accepted: 11/13/2023] [Indexed: 12/05/2023] Open
Abstract
Volitional eyes closing would shift brain's information processing modes from the "exteroceptive" to "interoceptive" state. This transition induced by the eyes closing is underpinned by a large-scale reconfiguration of brain network, which is still not fully comprehended. Here, we investigated the eyes-closing-relevant network reconfiguration by examining the functional integration among intrinsic modules. Our investigation utilized a publicly available dataset with 48 subjects being scanned in both eyes closed and eyes open conditions. It was found that the modular integration was significantly enhanced during the eyes closing, including lower modularity index, higher participation coefficient, less provincial hubs, and more connector hubs. Moreover, the eyes-closing-enhanced integration was particularly noticeable in the hubs of network, mainly located in the default-mode network. Finally, the hub-dominant modular enhancement was positively correlated to the eyes-closing-reduced entropy of BOLD signal, suggesting a close connection to the diminished consciousness of individuals. Collectively, our findings strongly suggested that the enhanced modular integration with substantially reorganized hubs characterized the large-scale cortical underpinning of the volitional eyes closing.
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Affiliation(s)
- Yi Zhang
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Xiao Han
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Xuelian Ge
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Tianyong Xu
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Yanjie Wang
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Jiali Mu
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Fan Liu
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China
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17
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Oota SR, Yadav A, Dash A, Bapi RS, Sharma A. Attention-based fusion of multiple graphheat networks for structural to functional brain mapping. Sci Rep 2024; 14:1184. [PMID: 38216636 PMCID: PMC10786906 DOI: 10.1038/s41598-023-50408-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 12/19/2023] [Indexed: 01/14/2024] Open
Abstract
Over the last decade, there has been growing interest in learning the mapping from structural connectivity (SC) to functional connectivity (FC) of the brain. The spontaneous fluctuations of the brain activity during the resting-state as captured by functional MRI (rsfMRI) contain rich non-stationary dynamics over a relatively fixed structural connectome. Among the modeling approaches, graph diffusion-based methods with single and multiple diffusion kernels approximating static or dynamic functional connectivity have shown promise in predicting the FC given the SC. However, these methods are computationally expensive, not scalable, and fail to capture the complex dynamics underlying the whole process. Recently, deep learning methods such as GraphHeat networks and graph diffusion have been shown to handle complex relational structures while preserving global information. In this paper, we propose a novel attention-based fusion of multiple GraphHeat networks (A-GHN) for mapping SC-FC. A-GHN enables us to model multiple heat kernel diffusion over the brain graph for approximating the complex Reaction Diffusion phenomenon. We argue that the proposed deep learning method overcomes the scalability and computational inefficiency issues but can still learn the SC-FC mapping successfully. Training and testing were done using the rsfMRI data of 1058 participants from the human connectome project (HCP), and the results establish the viability of the proposed model. On HCP data, we achieve a high Pearson correlation of 0.788 (Desikan-Killiany atlas with 87 regions) and 0.773 (AAL atlas with 86 regions). Furthermore, experiments demonstrate that A-GHN outperforms the existing methods in learning the complex nature of the structure-function relation of the human brain.
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Affiliation(s)
- Subba Reddy Oota
- Computer Science, Inria Bordeaux, Bordeaux, France.
- Cognitive Science Lab (CSL), International Institute of Information Technology (IIIT), Hyderabad, India.
| | - Archi Yadav
- Cognitive Science Lab (CSL), International Institute of Information Technology (IIIT), Hyderabad, India
| | - Arpita Dash
- Cognitive Science Lab (CSL), International Institute of Information Technology (IIIT), Hyderabad, India
| | - Raju S Bapi
- Cognitive Science Lab (CSL), International Institute of Information Technology (IIIT), Hyderabad, India
| | - Avinash Sharma
- Cognitive Science Lab (CSL), International Institute of Information Technology (IIIT), Hyderabad, India
- CVIT Lab, International Institute of Information Technology (IIIT), Hyderabad, India
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18
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Ramos-Usuga D, Jimenez-Marin A, Cabrera-Zubizarreta A, Benito-Sanchez I, Rivera D, Martínez-Gutiérrez E, Panera E, Boado V, Labayen F, Cortes JM, Arango-Lasprilla JC. Cognitive and brain connectivity trajectories in critically ill COVID-19 patients. NeuroRehabilitation 2024; 54:359-371. [PMID: 38393927 DOI: 10.3233/nre-230216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
Abstract
BACKGROUND Multiple Organ failure (MOF) is one of the main causes of admission to the Intensive Care Unit (ICU) of patients infected with COVID-19 and can cause short- and long-term neurological deficits. OBJECTIVE To compare the cognitive functioning and functional brain connectivity at 6-12 months after discharge in two groups of individuals with MOF, one due to COVID-19 and the other due to another cause (MOF-group), with a group of Healthy Controls (HC). METHODS Thirty-six participants, 12 from each group, underwent a neuropsychological and neuroimaging assessment at both time-points. Functional connectivity of the resting state networks was compared between COVID-19 and HC while controlling for the effect of MOF. The association between functional connectivity and neuropsychological performance was also investigated. RESULTS Compared to the HC, COVID-19 group demonstrated hypoconnectivity between the Default Mode Network and Salience Network. This pattern was associated with worse performance on tests of attention and information processing speed, at both time-points. CONCLUSION The study of the association between cognitive function and brain functional connectivity in COVID-19 allows the understanding of the short- and long-term neurological alterations of this disease and promotes the development of intervention programs to improve the quality of life for this understudied population.
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Affiliation(s)
- Daniela Ramos-Usuga
- Biobizkaia Health Research Institute, Barakaldo, Spain
- Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Antonio Jimenez-Marin
- Biobizkaia Health Research Institute, Barakaldo, Spain
- Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | | | - Itziar Benito-Sanchez
- Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Diego Rivera
- Department of Health Sciences, Public University of Navarre, Pamplona, Spain
- Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Endika Martínez-Gutiérrez
- Biobizkaia Health Research Institute, Barakaldo, Spain
- Dipartamento Interateneo di Fisica, National Institute for Nuclear Physics - Bari, Bari, Italy
| | - Elena Panera
- Intensive Care Unit, Cruces University Hospital, Barakaldo, Spain
| | - Victoria Boado
- Intensive Care Unit, Cruces University Hospital, Barakaldo, Spain
| | - Fermín Labayen
- Intensive Care Unit, Cruces University Hospital, Barakaldo, Spain
| | - Jesus M Cortes
- Biobizkaia Health Research Institute, Barakaldo, Spain
- IKERBASQUE, The Basque Foundation for Science, Bilbao, Spain
- Department of Cell Biology and Histology, University of the Basque Country (UPV/EHU), Leioa, Spain
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19
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Fauchon C, Bastuji H, Peyron R, Garcia-Larrea L. Fractal Similarity of Pain Brain Networks. Adv Neurobiol 2024; 36:639-657. [PMID: 38468056 DOI: 10.1007/978-3-031-47606-8_32] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
The conscious perception of pain is the result of dynamic interactions of neural activities from local brain regions to distributed brain networks. Mapping out the networks of functional connections between brain regions that form and disperse when an experimental participant received nociceptive stimulations allow to characterize the pattern of network connections related to the pain experience.Although the pattern of intra- and inter-areal connections across the brain are incredibly complex, they appear also largely scale free, with "fractal" connectivity properties reproducing at short and long-time scales. Our results combining intracranial recordings and functional imaging in humans during pain indicate striking similarities in the activity and topological representation of networks at different orders of temporality, with reproduction of patterns of activation from the millisecond to the multisecond range. The connectivity analyzed using graph theory on fMRI data was organized in four sets of brain regions matching those identified through iEEG (i.e., sensorimotor, default mode, central executive, and amygdalo-hippocampal).Here, we discuss similarities in brain network organization at different scales or "orders," in participants as they feel pain. Description of this fractal-like organization may provide clues about how our brain regions work together to create the perception of pain and how pain becomes chronic when its organization is altered.
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Affiliation(s)
- Camille Fauchon
- Université Clermont Auvergne, CHU de Clermont-Ferrand, Inserm, Neuro-Dol, Clermont-Ferrand, France.
- Université Jean Monnet, Inserm, CRNL, NeuroPain, Saint-Etienne, France.
| | - Hélène Bastuji
- Université Claude Bernard Lyon 1, UJM, Inserm, CRNL, NeuroPain, Bron, France
| | - Roland Peyron
- Université Jean Monnet, Inserm, CRNL, NeuroPain, Saint-Etienne, France
- CHU, centre de la douleur, Saint-Etienne, France
| | - Luis Garcia-Larrea
- Université Claude Bernard Lyon 1, UJM, Inserm, CRNL, NeuroPain, Bron, France
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20
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van der Horn HJ, Ling JM, Wick TV, Dodd AB, Robertson-Benta CR, McQuaid JR, Zotev V, Vakhtin AA, Ryman SG, Cabral J, Phillips JP, Campbell RA, Sapien RE, Mayer AR. Dynamic Functional Connectivity in Pediatric Mild Traumatic Brain Injury. Neuroimage 2024; 285:120470. [PMID: 38016527 PMCID: PMC10815936 DOI: 10.1016/j.neuroimage.2023.120470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/13/2023] [Accepted: 11/20/2023] [Indexed: 11/30/2023] Open
Abstract
Resting-state fMRI can be used to identify recurrent oscillatory patterns of functional connectivity within the human brain, also known as dynamic brain states. Alterations in dynamic brain states are highly likely to occur following pediatric mild traumatic brain injury (pmTBI) due to the active developmental changes. The current study used resting-state fMRI to investigate dynamic brain states in 200 patients with pmTBI (ages 8-18 years, median = 14 years) at the subacute (∼1-week post-injury) and early chronic (∼ 4 months post-injury) stages, and in 179 age- and sex-matched healthy controls (HC). A k-means clustering analysis was applied to the dominant time-varying phase coherence patterns to obtain dynamic brain states. In addition, correlations between brain signals were computed as measures of static functional connectivity. Dynamic connectivity analyses showed that patients with pmTBI spend less time in a frontotemporal default mode/limbic brain state, with no evidence of change as a function of recovery post-injury. Consistent with models showing traumatic strain convergence in deep grey matter and midline regions, static interhemispheric connectivity was affected between the left and right precuneus and thalamus, and between the right supplementary motor area and contralateral cerebellum. Changes in static or dynamic connectivity were not related to symptom burden or injury severity measures, such as loss of consciousness and post-traumatic amnesia. In aggregate, our study shows that brain dynamics are altered up to 4 months after pmTBI, in brain areas that are known to be vulnerable to TBI. Future longitudinal studies are warranted to examine the significance of our findings in terms of long-term neurodevelopment.
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Affiliation(s)
| | - Josef M Ling
- The Mind Research Network/LBERI, Albuquerque, NM 87106
| | - Tracey V Wick
- The Mind Research Network/LBERI, Albuquerque, NM 87106
| | - Andrew B Dodd
- The Mind Research Network/LBERI, Albuquerque, NM 87106
| | | | | | - Vadim Zotev
- The Mind Research Network/LBERI, Albuquerque, NM 87106
| | | | | | - Joana Cabral
- Life and Health Sciences Research Institute, University of Minho, Braga, Portugal
| | | | - Richard A Campbell
- Department of Psychiatry & Behavioral Sciences, University of New Mexico, Albuquerque, NM 87131
| | - Robert E Sapien
- Department of Emergency Medicine, University of New Mexico, Albuquerque, NM 87131
| | - Andrew R Mayer
- The Mind Research Network/LBERI, Albuquerque, NM 87106; Department of Psychiatry & Behavioral Sciences, University of New Mexico, Albuquerque, NM 87131; Department of Psychology, University of New Mexico, Albuquerque, NM 87131; Department of Neurology, University of New Mexico, Albuquerque, NM 87131
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21
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Huang Z, Zhang X, Yang X, Ding S, Cai J. Aberrant brain intra- and internetwork functional connectivity in children with Prader-Willi syndrome. Neuroradiology 2024; 66:135-144. [PMID: 38001311 PMCID: PMC10761436 DOI: 10.1007/s00234-023-03259-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 11/18/2023] [Indexed: 11/26/2023]
Abstract
PURPOSE Prader-Willi syndrome (PWS) suffers from brain functional reorganization and developmental delays during childhood, but the underlying neurodevelopmental mechanism is unclear. This paper aims to investigate the intra- and internetwork functional connectivity (FC) changes, and their relationships with developmental delays in PWS children. METHODS Resting-state functional magnetic resonance imaging datasets of PWS children and healthy controls (HCs) were acquired. Independent component analysis was used to acquire core resting-state networks (RSNs). The intra- and internetwork FC patterns were then investigated. RESULTS In terms of intranetwork FC, children with PWS had lower FC in the dorsal attention network, the auditory network, the medial visual network (VN) and the sensorimotor network (SMN) than HCs (FWE-corrected, p < 0.05). In terms of internetwork FC, PWS children had decreased FC between the following pairs of regions: posterior default mode network (DMN) and anterior DMN; posterior DMN and SMN; SMN and posterior VN and salience network and medial VN (FDR-corrected, p < 0.05). Partial correlation analyses revealed that the intranetwork FC patterns were positively correlated with developmental quotients in PWS children, while the internetwork FC patterns were completely opposite (p < 0.05). Intranetwork FC patterns showed an area under the receiver operating characteristic curve of 0.947, with a sensitivity of 96.15% and a specificity of 81.25% for differentiating between PWS and HCs. CONCLUSION Impaired intra- and internetwork FC patterns in PWS children are associated with developmental delays, which may result from neural pathway dysfunctions. Intranetwork FC reorganization patterns can discriminate PWS children from HCs. REGISTRATION NUMBER ON THE CHINESE CLINICAL TRAIL REGISTRY ChiCTR2100046551.
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Affiliation(s)
- Zhongxin Huang
- Department of Radiology, Children's Hospital of Chongqing Medical University, No. 136, Zhongshan Second Road 400014, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, 400014, China
- Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Chongqing, 400014, China
| | - Xiangmin Zhang
- Department of Radiology, Children's Hospital of Chongqing Medical University, No. 136, Zhongshan Second Road 400014, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, 400014, China
- Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Chongqing, 400014, China
| | - Xinyi Yang
- Department of Radiology, Children's Hospital of Chongqing Medical University, No. 136, Zhongshan Second Road 400014, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, 400014, China
- Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Chongqing, 400014, China
| | - Shuang Ding
- Department of Radiology, Children's Hospital of Chongqing Medical University, No. 136, Zhongshan Second Road 400014, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, 400014, China
- Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Chongqing, 400014, China
| | - Jinhua Cai
- Department of Radiology, Children's Hospital of Chongqing Medical University, No. 136, Zhongshan Second Road 400014, Chongqing, China.
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, 400014, China.
- Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Chongqing, 400014, China.
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22
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Baruzzi V, Lodi M, Sorrentino F, Storace M. Bridging functional and anatomical neural connectivity through cluster synchronization. Sci Rep 2023; 13:22430. [PMID: 38104227 PMCID: PMC10725511 DOI: 10.1038/s41598-023-49746-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 12/11/2023] [Indexed: 12/19/2023] Open
Abstract
The dynamics of the brain results from the complex interplay of several neural populations and is affected by both the individual dynamics of these areas and their connection structure. Hence, a fundamental challenge is to derive models of the brain that reproduce both structural and functional features measured experimentally. Our work combines neuroimaging data, such as dMRI, which provides information on the structure of the anatomical connectomes, and fMRI, which detects patterns of approximate synchronous activity between brain areas. We employ cluster synchronization as a tool to integrate the imaging data of a subject into a coherent model, which reconciles structural and dynamic information. By using data-driven and model-based approaches, we refine the structural connectivity matrix in agreement with experimentally observed clusters of brain areas that display coherent activity. The proposed approach leverages the assumption of homogeneous brain areas; we show the robustness of this approach when heterogeneity between the brain areas is introduced in the form of noise, parameter mismatches, and connection delays. As a proof of concept, we apply this approach to MRI data of a healthy adult at resting state.
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Affiliation(s)
| | - Matteo Lodi
- DITEN, University of Genoa, Via Opera Pia 11a, 16145, Genova, Italy
| | - Francesco Sorrentino
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Marco Storace
- DITEN, University of Genoa, Via Opera Pia 11a, 16145, Genova, Italy.
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23
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Mummaneni A, Kardan O, Stier AJ, Chamberlain TA, Chao AF, Berman MG, Rosenberg MD. Functional brain connectivity predicts sleep duration in youth and adults. Hum Brain Mapp 2023; 44:6293-6307. [PMID: 37916784 PMCID: PMC10681648 DOI: 10.1002/hbm.26488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 08/22/2023] [Accepted: 09/04/2023] [Indexed: 11/03/2023] Open
Abstract
Sleep is critical to a variety of cognitive functions and insufficient sleep can have negative consequences for mood and behavior across the lifespan. An important open question is how sleep duration is related to functional brain organization which may in turn impact cognition. To characterize the functional brain networks related to sleep across youth and young adulthood, we analyzed data from the publicly available Human Connectome Project (HCP) dataset, which includes n-back task-based and resting-state fMRI data from adults aged 22-35 years (task n = 896; rest n = 898). We applied connectome-based predictive modeling (CPM) to predict participants' mean sleep duration from their functional connectivity patterns. Models trained and tested using 10-fold cross-validation predicted self-reported average sleep duration for the past month from n-back task and resting-state connectivity patterns. We replicated this finding in data from the 2-year follow-up study session of the Adolescent Brain Cognitive Development (ABCD) Study, which also includes n-back task and resting-state fMRI for adolescents aged 11-12 years (task n = 786; rest n = 1274) as well as Fitbit data reflecting average sleep duration per night over an average duration of 23.97 days. CPMs trained and tested with 10-fold cross-validation again predicted sleep duration from n-back task and resting-state functional connectivity patterns. Furthermore, demonstrating that predictive models are robust across independent datasets, CPMs trained on rest data from the HCP sample successfully generalized to predict sleep duration in the ABCD Study sample and vice versa. Thus, common resting-state functional brain connectivity patterns reflect sleep duration in youth and young adults.
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Affiliation(s)
| | - Omid Kardan
- Department of PsychologyThe University of ChicagoChicagoIllinoisUSA
- Department of PsychiatryUniversity of MichiganAnn ArborMichiganUSA
| | - Andrew J. Stier
- Department of PsychologyThe University of ChicagoChicagoIllinoisUSA
| | - Taylor A. Chamberlain
- Department of PsychologyThe University of ChicagoChicagoIllinoisUSA
- Department of PsychologyColumbia UniversityNew YorkNew YorkUSA
| | - Alfred F. Chao
- Department of PsychologyThe University of ChicagoChicagoIllinoisUSA
| | - Marc G. Berman
- Department of PsychologyThe University of ChicagoChicagoIllinoisUSA
- Neuroscience InstituteThe University of ChicagoChicagoIllinoisUSA
| | - Monica D. Rosenberg
- Department of PsychologyThe University of ChicagoChicagoIllinoisUSA
- Neuroscience InstituteThe University of ChicagoChicagoIllinoisUSA
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24
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Chung MK, Ramos CG, De Paiva FB, Mathis J, Prabhakaran V, Nair VA, Meyerand ME, Hermann BP, Binder JR, Struck AF. Unified topological inference for brain networks in temporal lobe epilepsy using the Wasserstein distance. Neuroimage 2023; 284:120436. [PMID: 37931870 DOI: 10.1016/j.neuroimage.2023.120436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 09/14/2023] [Accepted: 10/30/2023] [Indexed: 11/08/2023] Open
Abstract
Persistent homology offers a powerful tool for extracting hidden topological signals from brain networks. It captures the evolution of topological structures across multiple scales, known as filtrations, thereby revealing topological features that persist over these scales. These features are summarized in persistence diagrams, and their dissimilarity is quantified using the Wasserstein distance. However, the Wasserstein distance does not follow a known distribution, posing challenges for the application of existing parametric statistical models. To tackle this issue, we introduce a unified topological inference framework centered on the Wasserstein distance. Our approach has no explicit model and distributional assumptions. The inference is performed in a completely data driven fashion. We apply this method to resting-state functional magnetic resonance images (rs-fMRI) of temporal lobe epilepsy patients collected from two different sites: the University of Wisconsin-Madison and the Medical College of Wisconsin. Importantly, our topological method is robust to variations due to sex and image acquisition, obviating the need to account for these variables as nuisance covariates. We successfully localize the brain regions that contribute the most to topological differences. A MATLAB package used for all analyses in this study is available at https://github.com/laplcebeltrami/PH-STAT.
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Affiliation(s)
- Moo K Chung
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, USA.
| | | | | | | | | | - Veena A Nair
- Department of Radiology, University of Wisconsin-Madison, USA.
| | - Mary E Meyerand
- Departments of Medical Physics & Biomedical Engineering, University of Wisconsin-Madison, USA.
| | - Bruce P Hermann
- Department of Neurology, University of Wisconsin-Madison, USA.
| | | | - Aaron F Struck
- Department of Neurology, University of Wisconsin-Madison, USA.
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25
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Iraji A, Fu Z, Faghiri A, Duda M, Chen J, Rachakonda S, DeRamus T, Kochunov P, Adhikari BM, Belger A, Ford JM, Mathalon DH, Pearlson GD, Potkin SG, Preda A, Turner JA, van Erp TGM, Bustillo JR, Yang K, Ishizuka K, Faria A, Sawa A, Hutchison K, Osuch EA, Theberge J, Abbott C, Mueller BA, Zhi D, Zhuo C, Liu S, Xu Y, Salman M, Liu J, Du Y, Sui J, Adali T, Calhoun VD. Identifying canonical and replicable multi-scale intrinsic connectivity networks in 100k+ resting-state fMRI datasets. Hum Brain Mapp 2023; 44:5729-5748. [PMID: 37787573 PMCID: PMC10619392 DOI: 10.1002/hbm.26472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 04/30/2023] [Accepted: 06/19/2023] [Indexed: 10/04/2023] Open
Abstract
Despite the known benefits of data-driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual variations and inter-subject correspondence limits the clinical utility of rsfMRI and its application to single-subject analyses. Here, using rsfMRI data from over 100k individuals across private and public datasets, we identify replicable multi-spatial-scale canonical intrinsic connectivity network (ICN) templates via the use of multi-model-order independent component analysis (ICA). We also study the feasibility of estimating subject-specific ICNs via spatially constrained ICA. The results show that the subject-level ICN estimations vary as a function of the ICN itself, the data length, and the spatial resolution. In general, large-scale ICNs require less data to achieve specific levels of (within- and between-subject) spatial similarity with their templates. Importantly, increasing data length can reduce an ICN's subject-level specificity, suggesting longer scans may not always be desirable. We also find a positive linear relationship between data length and spatial smoothness (possibly due to averaging over intrinsic dynamics), suggesting studies examining optimized data length should consider spatial smoothness. Finally, consistency in spatial similarity between ICNs estimated using the full data and subsets across different data lengths suggests lower within-subject spatial similarity in shorter data is not wholly defined by lower reliability in ICN estimates, but may be an indication of meaningful brain dynamics which average out as data length increases.
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Affiliation(s)
- A. Iraji
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
| | - Z. Fu
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - A. Faghiri
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - M. Duda
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - J. Chen
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - S. Rachakonda
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - T. DeRamus
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - P. Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of MedicineUniversity of MarylandBaltimoreMarylandUSA
| | - B. M. Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, School of MedicineUniversity of MarylandBaltimoreMarylandUSA
| | - A. Belger
- Department of PsychiatryUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - J. M. Ford
- Department of PsychiatryUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- San Francisco VA Medical CenterSan FranciscoCaliforniaUSA
| | - D. H. Mathalon
- Department of PsychiatryUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- San Francisco VA Medical CenterSan FranciscoCaliforniaUSA
| | - G. D. Pearlson
- Departments of Psychiatry and Neuroscience, School of MedicineYale UniversityNew HavenConnecticutUSA
| | - S. G. Potkin
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - A. Preda
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - J. A. Turner
- Department of Psychiatry and Behavioral HealthOhio State University Medical Center in ColumbusColumbusOhioUSA
| | - T. G. M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - J. R. Bustillo
- Department of Psychiatry and Behavioral SciencesUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - K. Yang
- Department of Psychiatry, School of MedicineJohns Hopkins UniversityBaltimoreMarylandUSA
| | - K. Ishizuka
- Department of Psychiatry, School of MedicineJohns Hopkins UniversityBaltimoreMarylandUSA
| | - A. Faria
- Department of Psychiatry, School of MedicineJohns Hopkins UniversityBaltimoreMarylandUSA
| | - A. Sawa
- Departments of Psychiatry, Neuroscience, Biomedical Engineering, Pharmacology, and Genetic MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- Department of Mental HealthJohns Hopkins University Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - K. Hutchison
- Department of PsychologyUniversity of ColoradoBoulderColoradoUSA
| | - E. A. Osuch
- Department of Psychiatry, Schulich School of Medicine and DentistryLondon Health Sciences Centre, Lawson Health Research InstituteLondonCanada
| | - J. Theberge
- Department of Psychiatry, Schulich School of Medicine and DentistryLondon Health Sciences Centre, Lawson Health Research InstituteLondonCanada
| | - C. Abbott
- Department of Psychiatry (CCA)University of New MexicoAlbuquerqueNew MexicoUSA
| | - B. A. Mueller
- Department of PsychiatryUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - D. Zhi
- The State Key Lab of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - C. Zhuo
- Tianjin Mental Health CenterNankai University Affiliated Anding HospitalTianjinChina
| | - S. Liu
- The Department of PsychiatryFirst Clinical Medical College/First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Y. Xu
- The Department of PsychiatryFirst Clinical Medical College/First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - M. Salman
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- School of Electrical & Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
| | - J. Liu
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
| | - Y. Du
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- School of Computer and Information TechnologyShanxi UniversityTaiyuanChina
| | - J. Sui
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- The State Key Lab of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - T. Adali
- Department of CSEEUniversity of Maryland Baltimore CountyBaltimoreMarylandUSA
| | - V. D. Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
- Department of Psychiatry, School of MedicineJohns Hopkins UniversityBaltimoreMarylandUSA
- School of Electrical & Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
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26
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Hirsch F, Wohlschlaeger A. Subcortical influences on the topology of cortical networks align with functional processing hierarchies. Neuroimage 2023; 283:120417. [PMID: 37866758 DOI: 10.1016/j.neuroimage.2023.120417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 10/18/2023] [Accepted: 10/19/2023] [Indexed: 10/24/2023] Open
Abstract
fMRI of the human brain reveals spatiotemporal patterns of functional connectivity (FC), forming distinct cortical networks. Lately, subcortical contributions to these configurations are receiving renewed interest, but investigations rarely focus explicitly on their effects on cortico-cortical FC. Here, we employ a straightforward multivariable approach and graph-theoretic tools to assess subcortical impact on topological features of cortical networks. Given recent evidence showing that structures like the thalamus and basal ganglia integrate input from multiple networks, we expect increased segregation between cortical networks after removal of subcortical effects on their FC patterns. We analyze resting state data of young and healthy participants (male and female; N = 100) from the human connectome project. We find that overall, the cortical network architecture becomes less segregated, and more integrated, when subcortical influences are accounted for. Underlying these global effects are the following trends: 'Transmodal' systems become more integrated with the rest of the network, while 'unimodal' networks show the opposite effect. For single nodes this hierarchical organization is reflected by a close correspondence with the spatial layout of the principal gradient of FC (Margulies et al., 2016). Lastly, we show that the limbic system is significantly less coherent with subcortical influences removed. The findings are validated in a (split-sample) replication dataset. Our results provide new insight regarding the interplay between subcortex and cortical networks, by putting the integrative impact of subcortex in the context of macroscale patterns of cortical organization.
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Affiliation(s)
- Fabian Hirsch
- Department of Diagnostic and Interventional Neuroradiology, Klinikum R.d.Isar, Technical University Munich, Ismaninger Str. 22, Munich 81675, Germany.
| | - Afra Wohlschlaeger
- Department of Diagnostic and Interventional Neuroradiology, Klinikum R.d.Isar, Technical University Munich, Ismaninger Str. 22, Munich 81675, Germany
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27
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Cai M, Ma J, Wang Z, Zhao Y, Zhang Y, Wang H, Xue H, Chen Y, Zhang Y, Wang C, Zhao Q, Xue K, Liu F. Individual-level brain morphological similarity networks: Current methodologies and applications. CNS Neurosci Ther 2023; 29:3713-3724. [PMID: 37519018 PMCID: PMC10651978 DOI: 10.1111/cns.14384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/12/2023] [Accepted: 07/18/2023] [Indexed: 08/01/2023] Open
Abstract
AIMS The human brain is an extremely complex system in which neurons, clusters of neurons, or regions are connected to form a complex network. With the development of neuroimaging techniques, magnetic resonance imaging (MRI)-based brain networks play a key role in our understanding of the intricate architecture of human brain. Among them, the structural MRI-based brain morphological network approach has attracted increasing attention due to the advantages in data acquisition, image quality, and in revealing the structural organizing principles intrinsic to the brain. This review is to summarize the methodology and related applications of individual-level morphological networks. BACKGROUND There have been a growing number of studies related to brain morphological similarity networks. Conventional morphological networks are intersubject covariance networks constructed using a certain morphological indicator of a group of subjects; individual-level morphological networks, on the other hand, measure the morphological similarity between brain regions for individual brains and can reflect the morphological information of single subjects. In recent years, individual morphological networks have demonstrated significant worth in exploring the topological changes of the human brain under both normal and disease conditions. Such studies provided novel perspectives for understanding human brain development and exploring the pathological mechanisms of neuropsychiatric disorders. CONCLUSION This paper mainly focuses on the studies of brain morphological networks at the individual level, introduces several ways for network construction, reviews representative work in this field, and finally points out current problems and future directions.
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Affiliation(s)
- Mengjing Cai
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Juanwei Ma
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Zirui Wang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Yao Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Yijing Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - He Wang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Hui Xue
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Yayuan Chen
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Yujie Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Chunyang Wang
- Department of Scientific ResearchTianjin Medical University General HospitalTianjinChina
| | - Qiyu Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
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28
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Silchenko AN, Hoffstaedter F, Eickhoff SB. Impact of sample size and regression of tissue-specific signals on effective connectivity within the core default mode network. Hum Brain Mapp 2023; 44:5858-5870. [PMID: 37713540 PMCID: PMC10619387 DOI: 10.1002/hbm.26481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 07/24/2023] [Accepted: 08/28/2023] [Indexed: 09/17/2023] Open
Abstract
Interactions within brain networks are inherently directional, which are inaccessible to classical functional connectivity estimates from resting-state functional magnetic resonance imaging (fMRI) but can be detected using spectral dynamic causal modeling (DCM). The sample size and unavoidable presence of nuisance signals during fMRI measurement are the two important factors influencing the stability of group estimates of connectivity parameters. However, most recent studies exploring effective connectivity (EC) have been conducted with small sample sizes and minimally pre-processed datasets. We explore the impact of these two factors by analyzing clean resting-state fMRI data from 330 unrelated subjects from the Human Connectome Project database. We demonstrate that both the stability of the model selection procedures and the inference of connectivity parameters are highly dependent on the sample size. The minimum sample size required for stable DCM is approximately 50, which may explain the variability of the DCM results reported so far. We reveal a stable pattern of EC within the core default mode network computed for large sample sizes and demonstrate that the use of subject-specific thresholded whole-brain masks for tissue-specific signals regression enhances the detection of weak connections.
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Affiliation(s)
- Alexander N. Silchenko
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7)Research Center JülichJülichGermany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7)Research Center JülichJülichGermany
- Institute of Systems Neuroscience, Medical FacultyHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7)Research Center JülichJülichGermany
- Institute of Systems Neuroscience, Medical FacultyHeinrich Heine University DüsseldorfDüsseldorfGermany
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29
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Seitzman BA, Anandarajah H, Dworetsky A, McMichael A, Coalson RS, Agamah AM, Jiang C, Gu H, Barbour DL, Schlaggar BL, Limbrick DD, Rubin JB, Shimony JS, Perkins SM. Cognitive deficits and altered functional brain network organization in pediatric brain tumor patients. Brain Imaging Behav 2023; 17:689-701. [PMID: 37695507 PMCID: PMC10942739 DOI: 10.1007/s11682-023-00798-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/06/2023] [Indexed: 09/12/2023]
Abstract
Survivors of pediatric brain tumors experience significant cognitive deficits from their diagnosis and treatment. The exact mechanisms of cognitive injury are poorly understood, and validated predictors of long-term cognitive outcome are lacking. Resting state functional magnetic resonance imaging allows for the study of the spontaneous fluctuations in bulk neural activity, providing insight into brain organization and function. Here, we evaluated cognitive performance and functional network architecture in pediatric brain tumor patients. Forty-nine patients (7-18 years old) with a primary brain tumor diagnosis underwent resting state imaging during regularly scheduled clinical visits. All patients were tested with a battery of cognitive assessments. Extant data from 139 typically developing children were used as controls. We found that obtaining high-quality imaging data during routine clinical scanning was feasible. Functional network organization was significantly altered in patients, with the largest disruptions observed in patients who received propofol sedation. Awake patients demonstrated significant decreases in association network segregation compared to controls. Interestingly, there was no difference in the segregation of sensorimotor networks. With a median follow-up of 3.1 years, patients demonstrated cognitive deficits in multiple domains of executive function. Finally, there was a weak correlation between decreased default mode network segregation and poor picture vocabulary score. Future work with longer follow-up, longitudinal analyses, and a larger cohort will provide further insight into this potential predictor.
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Affiliation(s)
- Benjamin A Seitzman
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Hari Anandarajah
- Department of Pediatrics, St. Louis Children's Hospital, Washington University School of Medicine, St. Louis, MO, USA
| | - Ally Dworetsky
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Alana McMichael
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Rebecca S Coalson
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - A Miriam Agamah
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Catherine Jiang
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Hongjie Gu
- Department of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Dennis L Barbour
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
| | - Bradley L Schlaggar
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Kennedy Krieger Institute, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - David D Limbrick
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Joshua B Rubin
- Department of Pediatrics, St. Louis Children's Hospital, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Stephanie M Perkins
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA.
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30
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Filippi M, Cividini C, Basaia S, Spinelli EG, Castelnovo V, Leocadi M, Canu E, Agosta F. Age-related vulnerability of the human brain connectome. Mol Psychiatry 2023; 28:5350-5358. [PMID: 37414925 DOI: 10.1038/s41380-023-02157-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 06/05/2023] [Accepted: 06/22/2023] [Indexed: 07/08/2023]
Abstract
Multifactorial models integrating brain variables at multiple scales are warranted to investigate aging and its relationship with neurodegeneration. Our aim was to evaluate how aging affects functional connectivity of pivotal regions of the human brain connectome (i.e., hubs), which represent potential vulnerability 'stations' to aging, and whether such effects influence the functional and structural changes of the whole brain. We combined the information of the functional connectome vulnerability, studied through an innovative graph-analysis approach (stepwise functional connectivity), with brain cortical thinning in aging. Using data from 128 cognitively normal participants (aged 20-85 years), we firstly investigated the topological functional network organization in the optimal healthy condition (i.e., young adults) and observed that fronto-temporo-parietal hubs showed a highly direct functional connectivity with themselves and among each other, while occipital hubs showed a direct functional connectivity within occipital regions and sensorimotor areas. Subsequently, we modeled cortical thickness changes over lifespan, revealing that fronto-temporo-parietal hubs were among the brain regions that changed the most, whereas occipital hubs showed a quite spared cortical thickness across ages. Finally, we found that cortical regions highly functionally linked to the fronto-temporo-parietal hubs in healthy adults were characterized by the greatest cortical thinning along the lifespan, demonstrating that the topology and geometry of hub functional connectome govern the region-specific structural alterations of the brain regions.
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Affiliation(s)
- Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Vita-Salute San Raffaele University, Milan, Italy.
| | - Camilla Cividini
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Silvia Basaia
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Edoardo G Spinelli
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Veronica Castelnovo
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Michela Leocadi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Elisa Canu
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Federica Agosta
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
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31
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Mawla I, Huang Z, Kaplan CM, Ichesco E, Waller N, Larkin TE, Zöllner HJ, Edden RA, Harte SE, Clauw DJ, Mashour GA, Napadow V, Harris RE. Large-scale momentary brain co-activation patterns are associated with hyperalgesia and mediate focal neurochemistry and cross-network functional connectivity in fibromyalgia. Pain 2023; 164:2737-2748. [PMID: 37751539 PMCID: PMC10652715 DOI: 10.1097/j.pain.0000000000002973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 04/14/2023] [Accepted: 05/02/2023] [Indexed: 09/28/2023]
Abstract
ABSTRACT Fibromyalgia has been characterized by augmented cross-network functional communication between the brain's sensorimotor, default mode, and attentional (salience/ventral and dorsal) networks. However, the underlying mechanisms of these aberrant communication patterns are unknown. In this study, we sought to understand large-scale topographic patterns at instantaneous timepoints, known as co-activation patterns (CAPs). We found that a sustained pressure pain challenge temporally modulated the occurrence of CAPs. Using proton magnetic resonance spectroscopy, we found that greater basal excitatory over inhibitory neurotransmitter levels within the anterior insula orchestrated higher cross-network connectivity between the anterior insula and the default mode network through lower occurrence of a CAP encompassing the attentional networks during sustained pain. Moreover, we found that hyperalgesia in fibromyalgia was mediated through increased occurrence of a CAP encompassing the sensorimotor network during sustained pain. In conclusion, this study elucidates the role of momentary large-scale topographic brain patterns in shaping noxious information in patients with fibromyalgia, while laying the groundwork for using precise spatiotemporal dynamics of the brain for the potential development of therapeutics.
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Affiliation(s)
- Ishtiaq Mawla
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, United States
- Department of Anesthesiology, Chronic Pain and Fatigue Research Center, University of Michigan, Ann Arbor, MI, United States
| | - Zirui Huang
- Department of Anesthesiology, Center for Consciousness Science, University of Michigan, Ann Arbor, MI, United States
| | - Chelsea M. Kaplan
- Department of Anesthesiology, Chronic Pain and Fatigue Research Center, University of Michigan, Ann Arbor, MI, United States
| | - Eric Ichesco
- Department of Anesthesiology, Chronic Pain and Fatigue Research Center, University of Michigan, Ann Arbor, MI, United States
| | - Noah Waller
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, United States
- Department of Anesthesiology, Chronic Pain and Fatigue Research Center, University of Michigan, Ann Arbor, MI, United States
| | - Tony E. Larkin
- Department of Anesthesiology, Chronic Pain and Fatigue Research Center, University of Michigan, Ann Arbor, MI, United States
| | - Helge J. Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Richard A.E. Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Steven E. Harte
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, United States
- Department of Anesthesiology, Chronic Pain and Fatigue Research Center, University of Michigan, Ann Arbor, MI, United States
| | - Daniel J. Clauw
- Department of Anesthesiology, Chronic Pain and Fatigue Research Center, University of Michigan, Ann Arbor, MI, United States
| | - George A. Mashour
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, United States
- Department of Anesthesiology, Center for Consciousness Science, University of Michigan, Ann Arbor, MI, United States
| | - Vitaly Napadow
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Network, Harvard Medical School, Charlestown, MA, United States
| | - Richard E. Harris
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, United States
- Department of Anesthesiology, Chronic Pain and Fatigue Research Center, University of Michigan, Ann Arbor, MI, United States
- Susan Samueli Integrative Health Institute, School of Medicine, University of California at Irvine, Irvine, CA, United States
- Department of Anesthesiology and Perioperative Care, School of Medicine, University of California at Irvine, Irvine, CA, United States
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32
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Zhuang K, Zeitlen DC, Beaty RE, Vatansever D, Chen Q, Qiu J. Diverse functional interaction driven by control-default network hubs supports creative thinking. Cereb Cortex 2023; 33:11206-11224. [PMID: 37823346 DOI: 10.1093/cercor/bhad356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/06/2023] [Accepted: 09/07/2023] [Indexed: 10/13/2023] Open
Abstract
Complex cognitive processes, like creative thinking, rely on interactions among multiple neurocognitive processes to generate effective and innovative behaviors on demand, for which the brain's connector hubs play a crucial role. However, the unique contribution of specific hub sets to creative thinking is unknown. Employing three functional magnetic resonance imaging datasets (total N = 1,911), we demonstrate that connector hub sets are organized in a hierarchical manner based on diversity, with "control-default hubs"-which combine regions from the frontoparietal control and default mode networks-positioned at the apex. Specifically, control-default hubs exhibit the most diverse resting-state connectivity profiles and play the most substantial role in facilitating interactions between regions with dissimilar neurocognitive functions, a phenomenon we refer to as "diverse functional interaction". Critically, we found that the involvement of control-default hubs in facilitating diverse functional interaction robustly relates to creativity, explaining both task-induced functional connectivity changes and individual creative performance. Our findings suggest that control-default hubs drive diverse functional interaction in the brain, enabling complex cognition, including creative thinking. We thus uncover a biologically plausible explanation that further elucidates the widely reported contributions of certain frontoparietal control and default mode network regions in creativity studies.
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Affiliation(s)
- Kaixiang Zhuang
- School of Psychology, Southwest University (SWU), Chongqing 400715, China
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Daniel C Zeitlen
- Department of Psychology, Pennsylvania State University, University Park, Pennsylvania 16801, United States
| | - Roger E Beaty
- Department of Psychology, Pennsylvania State University, University Park, Pennsylvania 16801, United States
| | - Deniz Vatansever
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Qunlin Chen
- School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Jiang Qiu
- School of Psychology, Southwest University (SWU), Chongqing 400715, China
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33
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Tian Y, Xu G, Zhang J, Chen K, Liu S. Nodal properties of the resting-state brain functional network in childhood and adolescence. J Neuroimaging 2023; 33:1015-1023. [PMID: 37735776 DOI: 10.1111/jon.13155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/05/2023] [Accepted: 09/06/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND AND PURPOSE Changes in the topological properties of brain functional network nodes during childhood and adolescence can provide more detailed and intuitive information on the rules of brain development. This study aims to explore the characteristics of nodal attributes in child and adolescent brain functional networks and analyze the correlation between nodal attributes in different brain regions and age. METHODS Forty-two healthy volunteers aged 6-18 years who were right-handed primary and middle school students were recruited, and the subgroup analysis included children (6-12 years, n = 19) and adolescents (13-18 years, n = 23). Resting-state functional magnetic resonance imaging data were collected using a 3.0 Tesla MRI scanner. The topological properties of the functional brain network were analyzed using graph theory. RESULTS Compared with the children group, the degree centrality and nodal efficiency of multiple brain regions in the adolescent group were significantly increased, and the nodal shortest path was reduced (q<0.05, false discovery rate corrected). These brain regions were widely distributed in the whole brain and significantly correlated with age. Compared with the children group, reduced degree centralities were observed in the left dorsolateral fusiform gyrus, left rostral cuneus gyrus, and right medial superior occipital gyrus. CONCLUSION The transmission efficiency of the brain's core network gradually increased, and the subnetwork function gradually improved in children and adolescents with age. The functional development of each brain area in the occipital visual cortex was uneven and there was functional differentiation within the occipital visual cortex.
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Affiliation(s)
- Yu Tian
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Zunyi, China
- Department of Radiology, the Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, China
| | - Gaoqiang Xu
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Jing Zhang
- Department of Radiology, the Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, China
| | - Kuntao Chen
- Department of Radiology, the Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, China
| | - Songjiang Liu
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Zunyi, China
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34
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Hannum A, Lopez MA, Blanco SA, Betzel RF. High-accuracy machine learning techniques for functional connectome fingerprinting and cognitive state decoding. Hum Brain Mapp 2023; 44:5294-5308. [PMID: 37498048 PMCID: PMC10543109 DOI: 10.1002/hbm.26423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 05/26/2023] [Accepted: 06/30/2023] [Indexed: 07/28/2023] Open
Abstract
The human brain is a complex network comprised of functionally and anatomically interconnected brain regions. A growing number of studies have suggested that empirical estimates of brain networks may be useful for discovery of biomarkers of disease and cognitive state. A prerequisite for realizing this aim, however, is that brain networks also serve as reliable markers of an individual. Here, using Human Connectome Project data, we build upon recent studies examining brain-based fingerprints of individual subjects and cognitive states based on cognitively demanding tasks that assess, for example, working memory, theory of mind, and motor function. Our approach achieves accuracy of up to 99% for both identification of the subject of an fMRI scan, and for classification of the cognitive state of a previously unseen subject in a scan. More broadly, we explore the accuracy and reliability of five different machine learning techniques on subject fingerprinting and cognitive state decoding objectives, using functional connectivity data from fMRI scans of a high number of subjects (865) across a number of cognitive states (8). These results represent an advance on existing techniques for functional connectivity-based brain fingerprinting and state decoding. Additionally, 16 different functional connectome (FC) matrix construction pipelines are compared in order to characterize the effects of different aspects of the production of FCs on the accuracy of subject and task classification, and to identify possible confounds.
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Affiliation(s)
- Andrew Hannum
- Department of Computer ScienceUniversity of DenverDenverColoradoUSA
| | - Mario A. Lopez
- Department of Computer ScienceUniversity of DenverDenverColoradoUSA
| | - Saúl A. Blanco
- Department of Computer ScienceIndiana UniversityBloomingtonIndianaUSA
| | - Richard F. Betzel
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonIndianaUSA
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35
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Zheng W, Wang X, Liu T, Hu B, Wu D. Preterm-birth alters the development of nodal clustering and neural connection pattern in brain structural network at term-equivalent age. Hum Brain Mapp 2023; 44:5372-5386. [PMID: 37539754 PMCID: PMC10543115 DOI: 10.1002/hbm.26442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 07/14/2023] [Accepted: 07/18/2023] [Indexed: 08/05/2023] Open
Abstract
Preterm-born neonates are prone to impaired neurodevelopment that may be associated with disrupted whole-brain structural connectivity. The present study aimed to investigate the longitudinal developmental pattern of the structural network from preterm birth to term-equivalent age (TEA), and identify how prematurity influences the network topological organization and properties of local brain regions. Multi-shell diffusion-weighted MRI of 28 preterm-born scanned a short time after birth (PB-AB) and at TEA (PB-TEA), and 28 matched term-born (TB) neonates in the Developing Human Connectome Project (dHCP) were used to construct structural networks through constrained spherical deconvolution tractography. Structural network development from preterm birth to TEA showed reduced shortest path length, clustering coefficient, and modularity, and more "connector" hubs linking disparate communities. Furthermore, compared with TB newborns, premature birth significantly altered the nodal properties (i.e., clustering coefficient, within-module degree, and participation coefficient) in the limbic/paralimbic, default-mode, and subcortical systems but not global topology at TEA, and we were able to distinguish the PB from TB neonates at TEA based on the nodal properties with 96.43% accuracy. Our findings demonstrated a topological reorganization of the structural network occurs during the perinatal period that may prioritize the optimization of global network organization to form a more efficient architecture; and local topology was more vulnerable to premature birth-related factors than global organization of the structural network, which may underlie the impaired cognition and behavior in PB infants.
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Affiliation(s)
- Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and EngineeringLanzhou UniversityLanzhouChina
| | - Xiaomin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and EngineeringLanzhou UniversityLanzhouChina
| | - Tingting Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument ScienceZhejiang UniversityHangzhouChina
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and EngineeringLanzhou UniversityLanzhouChina
- School of Medical TechnologyBeijing Institute of TechnologyBeijingChina
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina
- Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of SemiconductorsChinese Academy of SciencesLanzhouChina
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument ScienceZhejiang UniversityHangzhouChina
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Kurtin DL, Araña‐Oiarbide G, Lorenz R, Violante IR, Hampshire A. Planning ahead: Predictable switching recruits task-active and resting-state networks. Hum Brain Mapp 2023; 44:5030-5046. [PMID: 37471699 PMCID: PMC10502652 DOI: 10.1002/hbm.26430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 06/08/2023] [Accepted: 07/05/2023] [Indexed: 07/22/2023] Open
Abstract
Switching is a difficult cognitive process characterised by costs in task performance; specifically, slowed responses and reduced accuracy. It is associated with the recruitment of a large coalition of task-positive regions including those referred to as the multiple demand cortex (MDC). The neural correlates of switching not only include the MDC, but occasionally the default mode network (DMN), a characteristically task-negative network. To unpick the role of the DMN during switching we collected fMRI data from 24 participants playing a switching paradigm that perturbed predictability (i.e., cognitive load) across three switch dimensions-sequential, perceptual, and spatial predictability. We computed the activity maps unique to switch vs. stay trials and all switch dimensions, then evaluated functional connectivity under these switch conditions by computing the pairwise mutual information functional connectivity (miFC) between regional timeseries. Switch trials exhibited an expected cost in reaction time while sequential predictability produced a significant benefit to task accuracy. Our results showed that switch trials recruited a broader activity map than stay trials, including regions of the DMN, the MDC, and task-positive networks such as visual, somatomotor, dorsal, salience/ventral attention networks. More sequentially predictable trials recruited increased activity in the somatomotor and salience/ventral attention networks. Notably, changes in sequential and perceptual predictability, but not spatial predictability, had significant effects on miFC. Increases in perceptual predictability related to decreased miFC between control, visual, somatomotor, and DMN regions, whereas increases in sequential predictability increased miFC between regions in the same networks, as well as regions within ventral attention/ salience, dorsal attention, limbic, and temporal parietal networks. These results provide novel clues as to how DMN may contribute to executive task performance. Specifically, the improved task performance, unique activity, and increased miFC associated with increased sequential predictability suggest that the DMN may coordinate more strongly with the MDC to generate a temporal schema of upcoming task events, which may attenuate switching costs.
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Affiliation(s)
- Danielle L. Kurtin
- NeuroModulation Lab, Department of Psychology, Faculty of Health and Medical SciencesUniversity of SurreyGuildfordUK
- Department of Brain Sciences, Faculty of MedicineImperial College LondonLondonUK
| | | | - Romy Lorenz
- MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
- The Poldrack LabStanford UniversityStanfordCaliforniaUSA
- Department of NeurophysicsMax‐Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Ines R. Violante
- NeuroModulation Lab, Department of Psychology, Faculty of Health and Medical SciencesUniversity of SurreyGuildfordUK
| | - Adam Hampshire
- Department of Brain Sciences, Faculty of MedicineImperial College LondonLondonUK
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37
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Tang X, Zhang J, Liu L, Yang M, Li S, Chen J, Ma Y, Zhang J, Liu H, Lu C, Ding G. Distinct brain state dynamics of native and second language processing during narrative listening in late bilinguals. Neuroimage 2023; 280:120359. [PMID: 37661079 DOI: 10.1016/j.neuroimage.2023.120359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 07/01/2023] [Accepted: 08/31/2023] [Indexed: 09/05/2023] Open
Abstract
The process of complex cognition, which includes language processing, is dynamic in nature and involves various network modes or cognitive modes. This dynamic process can be manifested by a set of brain states and transitions between them. Previous neuroimaging studies have shed light on how bilingual brains support native language (L1) and second language (L2) through a shared network. However, the mechanism through which this shared brain network enables L1 and L2 processing remains unknown. This study examined this issue by testing the hypothesis that L1 and L2 processing is associated with distinct brain state dynamics in terms of brain state integration and transition flexibility. A group of late Chinese-English bilinguals was scanned using functional magnetic resonance imaging (fMRI) while listening to eight short narratives in Chinese (L1) and English (L2). Brain state dynamics were modeled using the leading eigenvector dynamic analysis framework. The results show that L1 processing involves more integrated states and frequent transitions between integrated and segregated states, while L2 processing involves more segregated states and fewer transitions. Our work provides insight into the dynamic process of narrative listening comprehension in late bilinguals and sheds new light on the neural representation of language processing and related disorders.
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Affiliation(s)
- Xiangrong Tang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Juan Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Beijing Institute of Graphic Communication, Beijing 102600, China
| | - Lanfang Liu
- Department of Psychology, Faculty of Arts and Sciences, Beijing Normal University at Zhuhai, Zhuhai 519087, China; Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University at Zhuhai, Zhuhai 519087, China.
| | - Menghan Yang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Department of Psychological and Brain Sciences, Dartmouth College, Hanover NH 03755, USA
| | - Shijie Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Jie Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Faculty of Psychology, Beijing Normal University, Beijing 100875, China
| | - Yumeng Ma
- Department of Neuroscience, Physiology and Pharmacology, University College London, London WC1E 6BT, UK; Department of Psychology, Emory University, Atlanta GA 30322, USA
| | - Jia Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; School of Psychology, Beijing Language and Culture University, Beijing 100083, China
| | - Haiyi Liu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Chunming Lu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Guosheng Ding
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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Yates TS, Ellis CT, Turk-Browne NB. Functional networks in the infant brain during sleep and wake states. Cereb Cortex 2023; 33:10820-10835. [PMID: 37718160 DOI: 10.1093/cercor/bhad327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 08/18/2023] [Accepted: 08/20/2023] [Indexed: 09/19/2023] Open
Abstract
Functional brain networks are assessed differently earlier versus later in development: infants are almost universally scanned asleep, whereas adults are typically scanned awake. Observed differences between infant and adult functional networks may thus reflect differing states of consciousness rather than or in addition to developmental changes. We explore this question by comparing functional networks in functional magnetic resonance imaging (fMRI) scans of infants during natural sleep and awake movie-watching. As a reference, we also scanned adults during awake rest and movie-watching. Whole-brain functional connectivity was more similar within the same state (sleep and movie in infants; rest and movie in adults) compared with across states. Indeed, a classifier trained on patterns of functional connectivity robustly decoded infant state and even generalized to adults; interestingly, a classifier trained on adult state did not generalize as well to infants. Moreover, overall similarity between infant and adult functional connectivity was modulated by adult state (stronger for movie than rest) but not infant state (same for sleep and movie). Nevertheless, the connections that drove this similarity, particularly in the frontoparietal control network, were modulated by infant state. In sum, infant functional connectivity differs between sleep and movie states, highlighting the value of awake fMRI for studying functional networks over development.
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Affiliation(s)
- Tristan S Yates
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Cameron T Ellis
- Department of Psychology, Stanford University, Stanford, CA, United States
| | - Nicholas B Turk-Browne
- Department of Psychology, Yale University, New Haven, CT, United States
- Wu Tsai Institute, Yale University, New Haven, CT, United States
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Bandyopadhyay A, Ghosh S, Biswas D, Chakravarthy VS, S Bapi R. A phenomenological model of whole brain dynamics using a network of neural oscillators with power-coupling. Sci Rep 2023; 13:16935. [PMID: 37805660 PMCID: PMC10560247 DOI: 10.1038/s41598-023-43547-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 09/25/2023] [Indexed: 10/09/2023] Open
Abstract
We present a general, trainable oscillatory neural network as a large-scale model of brain dynamics. The model has a cascade of two stages - an oscillatory stage and a complex-valued feedforward stage - for modelling the relationship between structural connectivity and functional connectivity from neuroimaging data under resting brain conditions. Earlier works of large-scale brain dynamics that used Hopf oscillators used linear coupling of oscillators. A distinctive feature of the proposed model employs a novel form of coupling known as power coupling. Oscillatory networks based on power coupling can accurately model arbitrary multi-dimensional signals. Training the lateral connections in the oscillator layer is done by a modified form of Hebbian learning, whereas a variation of the complex backpropagation algorithm does training in the second stage. The proposed model can not only model the empirical functional connectivity with remarkable accuracy (correlation coefficient between simulated and empirical functional connectivity- 0.99) but also identify default mode network regions. In addition, we also inspected how structural loss in the brain can cause significant aberration in simulated functional connectivity and functional connectivity dynamics; and how it can be restored with optimized model parameters by an in silico perturbational study.
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Affiliation(s)
| | - Sayan Ghosh
- Indian Institue of Technology Madras, Biotechnology, Chennai, 600036, India
| | - Dipayan Biswas
- Indian Institue of Technology Madras, Biotechnology, Chennai, 600036, India
| | | | - Raju S Bapi
- IIIT Hyderabad, Biotechnology, Hyderabad, 500008, India
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40
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Zhang X, Yang X, Wu B, Pan N, He M, Wang S, Kemp GJ, Gong Q. Large-scale brain functional network abnormalities in social anxiety disorder. Psychol Med 2023; 53:6194-6204. [PMID: 36330833 PMCID: PMC10520603 DOI: 10.1017/s0033291722003439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 09/06/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND Although aberrant brain regional responses are reported in social anxiety disorder (SAD), little is known about resting-state functional connectivity at the macroscale network level. This study aims to identify functional network abnormalities using a multivariate data-driven method in a relatively large and homogenous sample of SAD patients, and assess their potential diagnostic value. METHODS Forty-six SAD patients and 52 demographically-matched healthy controls (HC) were recruited to undergo clinical evaluation and resting-state functional MRI scanning. We used group independent component analysis to characterize the functional architecture of brain resting-state networks (RSNs) and investigate between-group differences in intra-/inter-network functional network connectivity (FNC). Furtherly, we explored the associations of FNC abnormalities with clinical characteristics, and assessed their ability to discriminate SAD from HC using support vector machine analyses. RESULTS SAD patients showed widespread intra-network FNC abnormalities in the default mode network, the subcortical network and the perceptual system (i.e. sensorimotor, auditory and visual networks), and large-scale inter-network FNC abnormalities among those high-order and primary RSNs. Some aberrant FNC signatures were correlated to disease severity and duration, suggesting pathophysiological relevance. Furthermore, intrinsic FNC anomalies allowed individual classification of SAD v. HC with significant accuracy, indicating potential diagnostic efficacy. CONCLUSIONS SAD patients show distinct patterns of functional synchronization abnormalities both within and across large-scale RSNs, reflecting or causing a network imbalance of bottom-up response and top-down regulation in cognitive, emotional and sensory domains. Therefore, this could offer insights into the neurofunctional substrates of SAD.
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Affiliation(s)
- Xun Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China
| | - Xun Yang
- School of Public Affairs, Chongqing University, Chongqing 400044, China
| | - Baolin Wu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China
| | - Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China
| | - Min He
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China
| | - Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China
| | - Graham J. Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L69 3BX, UK
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian 361000, China
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Kristanto D, Hildebrandt A, Sommer W, Zhou C. Cognitive abilities are associated with specific conjunctions of structural and functional neural subnetworks. Neuroimage 2023; 279:120304. [PMID: 37536528 DOI: 10.1016/j.neuroimage.2023.120304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 07/19/2023] [Accepted: 07/31/2023] [Indexed: 08/05/2023] Open
Abstract
Cognitive neuroscience assumes that different mental abilities correspond to at least partly separable brain subnetworks and strives to understand their relationships. However, single-task approaches typically revealed multiple brain subnetworks to be involved in performance. Here, we chose a bottom-up approach of investigating the association between structural and functional brain subnetworks, on the one hand, and domain-specific cognitive abilities, on the other. Structural network was identified using machine-learning graph neural network by clustering anatomical brain properties measured in 838 individuals enroled in the WU-Minn Young Adult Human Connectome Project. Functional network was adapted from seven Resting State Networks (7-RSN). We then analyzed the results of 15 cognitive tasks and estimated five latent abilities: fluid reasoning (Gf), crystallized intelligence (Gc), memory (Mem), executive functions (EF), and processing speed (Gs). In a final step we determined linear associations between these independently identified ability and brain entities. We found no one-to-one mapping between latent abilities and brain subnetworks. Analyses revealed that abilities are associated with properties of particular combinations of brain subnetworks. While some abilities are more strongly associated to within-subnetwork connections, others are related with connections between multiple subnetworks. Importantly, domain-specific abilities commonly rely on node(s) as hub(s) to connect with other subnetworks. To test the robustness of our findings, we ran the analyses through several defensible analytical decisions. Together, the present findings allow a novel perspective on the distinct nature of domain-specific cognitive abilities building upon unique combinations of associated brain subnetworks.
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Affiliation(s)
- Daniel Kristanto
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China; Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany
| | - Andrea Hildebrandt
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany; Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Germany
| | - Werner Sommer
- Department of Psychology, Humboldt University at Berlin, Berlin, Germany; Department of Psychology, Zhejiang Normal University, Jin Hua, China; Department of Physics, Hong Kong Baptist University, Hong Kong, China.
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China; Life Science Imaging Centre, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China; Department of Physics, Zhejiang University, Hangzhou 310000, China.
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Matsuda K, Shirakami A, Nakajima R, Akutsu T, Shimono M. Whole-Brain Evaluation of Cortical Microconnectomes. eNeuro 2023; 10:ENEURO.0094-23.2023. [PMID: 37903612 PMCID: PMC10616907 DOI: 10.1523/eneuro.0094-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 09/08/2023] [Accepted: 09/30/2023] [Indexed: 11/01/2023] Open
Abstract
The brain is an organ that functions as a network of many elements connected in a nonuniform manner. In the brain, the neocortex is evolutionarily newest and is thought to be primarily responsible for the high intelligence of mammals. In the mature mammalian brain, all cortical regions are expected to have some degree of homology, but have some variations of local circuits to achieve specific functions performed by individual regions. However, few cellular-level studies have examined how the networks within different cortical regions differ. This study aimed to find rules for systematic changes of connectivity (microconnectomes) across 16 different cortical region groups. We also observed unknown trends in basic parameters in vitro such as firing rate and layer thickness across brain regions. Results revealed that the frontal group shows unique characteristics such as dense active neurons, thick cortex, and strong connections with deeper layers. This suggests the frontal side of the cortex is inherently capable of driving, even in isolation and that frontal nodes provide the driving force generating a global pattern of spontaneous synchronous activity, such as the default mode network. This finding provides a new hypothesis explaining why disruption in the frontal region causes a large impact on mental health.
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Affiliation(s)
- Kouki Matsuda
- Graduate Schools of Medicine, Kyoto University, 53 Kawaramachi, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan
| | - Arata Shirakami
- Graduate Schools of Medicine, Kyoto University, 53 Kawaramachi, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan
| | - Ryota Nakajima
- Graduate Schools of Medicine, Kyoto University, 53 Kawaramachi, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan
| | - Masanori Shimono
- Graduate Schools of Medicine, Kyoto University, 53 Kawaramachi, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan
- Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita-shi, Osaka 565-0871
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Sun F, Wang Y, Li Y, Li Y, Wang S, Xu F, Wang X. Variation in functional networks between clinical and subclinical discharges in childhood absence epilepsy: A multi-frequency MEG study. Seizure 2023; 111:109-121. [PMID: 37598560 DOI: 10.1016/j.seizure.2023.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 08/06/2023] [Accepted: 08/09/2023] [Indexed: 08/22/2023] Open
Abstract
OBJECTIVE Two types of spike-and-wave discharges (SWDs) exist in childhood absence epilepsy (CAE): clinical discharges are prolonged and manifest primarily as impaired consciousness, whereas subclinical discharges are brief with few objectively visible symptoms. This study aimed to compare neural functional network and default mode network (DMN) activity between clinical and subclinical discharges to better understand the underlying mechanism of CAE. METHODS Using magnetoencephalography (MEG) data from 21 patients, we obtained 25 segments each of clinical discharges and subclinical discharges. Amplitude envelope correlation analysis was used to construct functional networks and graph theory was used to calculate network topological data. We then compared differences in functional connectivity within the DMN between clinical and subclinical discharges. All statistical comparisons were performed using paired-sample tests. RESULTS Compared to subclinical discharges, the functional network of clinical discharges exhibited higher synchronization - particularly in the parahippocampal gyrus - as early as 10 s before the seizure. Additionally, the functional network of clinical SWDs presented an anterior shift of key nodes in the alpha frequency band. Regarding clinical discharge progression, there were gradual increases in the parameter node strengths (S), clustering coefficients (C), and global efficiency (E) of the functional networks, while the path lengths (L) decreased. These changes were most prominent at the onset of discharges and followed by some recovery in the high-frequency bands, but no significant change in the low-frequency bands. Furthermore, connections within the DMN during the discharge period were significantly stronger for clinical discharge compared to subclinical discharges. CONCLUSIONS These findings suggest that a more regular network before abnormal discharges in clinical discharges contributes to SWD explosion and that the parahippocampal gyrus plays an important role in maintaining oscillations. An absence seizure is a gradual process and the emergence of SWDs may be accompanied by initiation of inhibitory mechanisms. Enhanced functional connectivity among DMN brain regions may indicate that patients have entered a state of introspection, and functional abnormalities in the parahippocampal gyrus may be associated with patients' transient memory loss.
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Affiliation(s)
- Fangling Sun
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yingfan Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yihan Li
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yanzhang Li
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Siyi Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Fengyuan Xu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaoshan Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.
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Wilcox RR, Barbey AK. Connectome-based predictive modeling of fluid intelligence: evidence for a global system of functionally integrated brain networks. Cereb Cortex 2023; 33:10322-10331. [PMID: 37526284 DOI: 10.1093/cercor/bhad284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 06/21/2023] [Accepted: 07/16/2023] [Indexed: 08/02/2023] Open
Abstract
Cognitive neuroscience continues to advance our understanding of the neural foundations of human intelligence, with significant progress elucidating the role of the frontoparietal network in cognitive control mechanisms for flexible, intelligent behavior. Recent evidence in network neuroscience further suggests that this finding may represent the tip of the iceberg and that fluid intelligence may depend on the collective interaction of multiple brain networks. However, the global brain mechanisms underlying fluid intelligence and the nature of multi-network interactions remain to be well established. We therefore conducted a large-scale Connectome-based Predictive Modeling study, administering resting-state fMRI to 159 healthy college students and examining the contributions of seven intrinsic connectivity networks to the prediction of fluid intelligence, as measured by a state-of-the-art cognitive task (the Bochum Matrices Test). Specifically, we aimed to: (i) identify whether fluid intelligence relies on a primary brain network or instead engages multiple brain networks; and (ii) elucidate the nature of brain network interactions by assessing network allegiance (within- versus between-network connections) and network topology (strong versus weak connections) in the prediction of fluid intelligence. Our results demonstrate that whole-brain predictive models account for a large and significant proportion of variance in fluid intelligence (18%) and illustrate that the contribution of individual networks is relatively modest by comparison. In addition, we provide novel evidence that the global architecture of fluid intelligence prioritizes between-network connections and flexibility through weak ties. Our findings support a network neuroscience approach to understanding the collective role of brain networks in fluid intelligence and elucidate the system-wide network mechanisms from which flexible, adaptive behavior is constructed.
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Affiliation(s)
- Ramsey R Wilcox
- Decision Neuroscience Laboratory, University of Nebraska-Lincoln, NE 68501, United States
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, NE 68501, United States
- Department of Psychology, University of Nebraska-Lincoln, NE 68501, United States
- Department of Psychology, University of Illinois, Urbana, IL 61801, United States
| | - Aron K Barbey
- Decision Neuroscience Laboratory, University of Nebraska-Lincoln, NE 68501, United States
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, NE 68501, United States
- Department of Psychology, University of Nebraska-Lincoln, NE 68501, United States
- Department of Psychology, University of Illinois, Urbana, IL 61801, United States
- Department of Bioengineering, University of Illinois, Urbana, IL 61801, United States
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Nazari R, Salehi M. Early development of the functional brain network in newborns. Brain Struct Funct 2023; 228:1725-1739. [PMID: 37493690 DOI: 10.1007/s00429-023-02681-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 07/06/2023] [Indexed: 07/27/2023]
Abstract
During the prenatal period and the first postnatal years, the human brain undergoes rapid growth, which establishes a preliminary infrastructure for the subsequent development of cognition and behavior. To understand the underlying processes of brain functioning and identify potential sources of developmental disorders, it is essential to uncover the developmental rules that govern this critical period. In this study, graph theory modeling and network science analysis were employed to investigate the impact of age, gender, weight, and typical and atypical development on brain development. Local and global topologies of functional connectomes obtained from rs-fMRI data were collected from 421 neonates aged between 31 and 45 postmenstrual weeks who were in natural sleep without any sedation. The results showed that global efficiency, local efficiency, clustering coefficient, and small-worldness increased with age, while modularity and characteristic path length decreased with age. The normalized rich-club coefficient displayed a U-shaped pattern during development. The study also examined the global and local impacts of gender, weight, and group differences between typical and atypical cases. The findings presented some new insights into the maturation of functional brain networks and their relationship with cognitive development and neurodevelopmental disorders.
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Affiliation(s)
- Reza Nazari
- Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
| | - Mostafa Salehi
- Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran.
- School of Computer Science, Institute for Research in Fundamental Science (IPM), Tehran, P.O.Box 19395-5746, Iran.
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Abstract
Recent advancements in computational approaches and neuroimaging techniques have refined our understanding of the precuneus. While previously believed to be largely a visual processing region, the importance of the precuneus in complex cognitive functions has been previously less familiar due to a lack of focal lesions in this deeply seated region, but also a poor understanding of its true underlying anatomy. Fortunately, recent studies have revealed significant information on the structural and functional connectivity of this region, and this data has provided a more detailed mechanistic understanding of the importance of the precuneus in healthy and pathologic states. Through improved resting-state functional MRI analyses, it has become clear that the function of the precuneus can be better understood based on its functional association with large scale brain networks. Dual default mode network systems have been well explained in recent years in supporting episodic memory and theory of mind; however, a novel 'para-cingulate' network, which is a subnetwork of the larger central executive network, with likely significant roles in self-referential processes and related psychiatric symptoms is introduced here and requires further clarification. Importantly, detailed anatomic studies on the precuneus structural connectivity inside and beyond the cingulate cortex has demonstrated the presence of large structural white matter connections, which provide an additional layer of meaning to the structural-functional significance of this region and its association with large scale brain networks. Together, the structural-functional connectivity of the precuneus has provided central elements which can model various neurodegenerative diseases and psychiatric disorders, such as Alzheimer's disease and depression.
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Affiliation(s)
- Nicholas B Dadario
- Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ 07102, USA
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Liu ZQ, Shafiei G, Baillet S, Misic B. Spatially heterogeneous structure-function coupling in haemodynamic and electromagnetic brain networks. Neuroimage 2023; 278:120276. [PMID: 37451374 DOI: 10.1016/j.neuroimage.2023.120276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 07/04/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023] Open
Abstract
The relationship between structural and functional connectivity in the brain is a key question in connectomics. Here we quantify patterns of structure-function coupling across the neocortex, by comparing structural connectivity estimated using diffusion MRI with functional connectivity estimated using both neurophysiological (MEG-based) and haemodynamic (fMRI-based) recordings. We find that structure-function coupling is heterogeneous across brain regions and frequency bands. The link between structural and functional connectivity is generally stronger in multiple MEG frequency bands compared to resting state fMRI. Structure-function coupling is greater in slower and intermediate frequency bands compared to faster frequency bands. We also find that structure-function coupling systematically follows the archetypal sensorimotor-association hierarchy, as well as patterns of laminar differentiation, peaking in granular layer IV. Finally, structure-function coupling is better explained using structure-informed inter-regional communication metrics than using structural connectivity alone. Collectively, these results place neurophysiological and haemodynamic structure-function relationships in a common frame of reference and provide a starting point for a multi-modal understanding of structure-function coupling in the brain.
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Affiliation(s)
- Zhen-Qi Liu
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Golia Shafiei
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
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Liu X, Tyler LK, Cam-Can, Davis SW, Rowe JB, Tsvetanov KA. Cognition's dependence on functional network integrity with age is conditional on structural network integrity. Neurobiol Aging 2023; 129:195-208. [PMID: 37392579 DOI: 10.1016/j.neurobiolaging.2023.06.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 05/29/2023] [Accepted: 06/02/2023] [Indexed: 07/03/2023]
Abstract
Maintaining good cognitive function is crucial for well-being across the lifespan. We proposed that the degree of cognitive maintenance is determined by the functional interactions within and between large-scale brain networks. Such connectivity can be represented by the white matter architecture of structural brain networks that shape intrinsic neuronal activity into integrated and distributed functional networks. We explored how the function-structure connectivity convergence, and the divergence of functional connectivity from structural connectivity, contribute to the maintenance of cognitive function across the adult lifespan. Multivariate analyses were used to investigate the relationship between function-structure connectivity convergence and divergence with multivariate cognitive profiles, respectively. Cognitive function was increasingly dependent on function-structure connectivity convergence as age increased. The dependency of cognitive function on connectivity was particularly strong for high-order cortical networks and subcortical networks. The results suggest that brain functional network integrity sustains cognitive functions in old age, as a function of the integrity of the brain's structural connectivity.
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Affiliation(s)
- Xulin Liu
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
| | - Lorraine K Tyler
- The Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge, UK
| | - Cam-Can
- Cambridge Centre for Ageing and Neuroscience (Cam-CAN), MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Simon W Davis
- Department of Neurology, Duke University, School of Medicine, Durham, NC, USA
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Kamen A Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; The Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge, UK
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Mandloi S, Syed M, Shoraka O, Ailes I, Kang KC, Sathe A, Heller J, Thalheimer S, Mohamed FB, Sharan A, Harrop J, Krisa L, Matias C, Alizadeh M. The role of the insula in chronic pain following spinal cord injury: A resting-state fMRI study. J Neuroimaging 2023; 33:781-791. [PMID: 37188633 DOI: 10.1111/jon.13117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 04/23/2023] [Accepted: 05/04/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND AND PURPOSE Spinal cord injury (SCI) results in the loss of motor and sensory function from disconnections between efferent and afferent pathways. Most SCI patients are affected with chronic neuropathic pain, but there is a paucity of data concerning neuroplastic changes following SCI. Chronic pain disrupts default networks and is associated with abnormal insular connectivity. The posterior insula (PI) is associated with the degree of pain and intensity of pain. The anterior insula (AI) is related to signal changes. Comprehension of SCI pain mechanisms is essential to elucidate effective treatment options. METHODS This study examines the insular gyri functional connectivity (FC) of seven (five male, two female) SCI participants with moderate-severe chronic pain compared to 10 (five male, five female) healthy controls (HC). All subjects had 3-Tesla MRI performed and resting-state functional MRI (fMRI) was acquired. FC metrics were obtained from the comparisons of resting-state fMRI among our various groups. A seed-to-voxel analysis was pursued, encompassing six gyri of the insula. For multiple comparisons, a correction was applied with a significance level of p < .05. RESULTS There were significant differences in FC of the insula between SCI participants with chronic pain compared with HC. In the SCI participants, there was hyperconnectivity of the AI and PI to the frontal pole. In addition, there was increased FC noted between the PI and the anterior cingulate cortex. Hyperconnectivity was also observed between the AI and the occipital cortex. CONCLUSIONS These findings illustrate that there is a complex hyperconnectivity and modulation of pain pathways after traumatic SCI.
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Affiliation(s)
- Shreya Mandloi
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Mashaal Syed
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Omid Shoraka
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Isaiah Ailes
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Ki Chang Kang
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Anish Sathe
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Joshua Heller
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Sara Thalheimer
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Feroze B Mohamed
- Thomas Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Ashwini Sharan
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - James Harrop
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Laura Krisa
- Department of Physical Therapy, Jefferson College of Rehabilitation Sciences, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Caio Matias
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Mahdi Alizadeh
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
- Thomas Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
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50
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Deming P, Cook CJ, Meyerand ME, Kiehl KA, Kosson DS, Koenigs M. Impaired salience network switching in psychopathy. Behav Brain Res 2023; 452:114570. [PMID: 37421987 PMCID: PMC10527938 DOI: 10.1016/j.bbr.2023.114570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 06/22/2023] [Accepted: 07/05/2023] [Indexed: 07/10/2023]
Abstract
Growing evidence suggests that psychopathy is related to altered connectivity within and between three large-scale brain networks that support core cognitive functions, including allocation of attention. In healthy individuals, default mode network (DMN) is involved in internally-focused attention and cognition such as self-reference. Frontoparietal network (FPN) is anticorrelated with DMN and is involved in externally-focused attention to cognitively demanding tasks. A third network, salience network (SN), is involved in detecting salient cues and, crucially, appears to play a role in switching between the two anticorrelated networks, DMN and FPN, to efficiently allocate attentional resources. Psychopathy has been related to reduced anticorrelation between DMN and FPN, suggesting SN's role in switching between these two networks may be diminished in the disorder. To test this hypothesis, we used independent component analysis to derive DMN, FPN, and SN activity in resting-state fMRI data in a sample of incarcerated men (N = 148). We entered the activity of the three networks into dynamic causal modeling to test SN's switching role. The previously established switching effect of SN among young, healthy adults was replicated in a group of low psychopathy participants (posterior model probability = 0.38). As predicted, SN's switching role was significantly diminished in high psychopathy participants (t(145) = 26.39, p < .001). These findings corroborate a novel theory of brain function in psychopathy. Future studies may use this model to test whether disrupted SN switching is related to high psychopathy individuals' abnormal allocation of attention.
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Affiliation(s)
- Philip Deming
- Department of Psychology, Northeastern University, 360 Huntington Ave., Boston, MA 02115, USA.
| | - Cole J Cook
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Ave., Rm 1005, Madison, WI 53705, USA
| | - Mary E Meyerand
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Ave., Rm 1005, Madison, WI 53705, USA; Department of Biomedical Engineering, University of Wisconsin-Madison, 1415 Engineering Drive, Madison, WI 53706, USA
| | - Kent A Kiehl
- The Mind Research Network and Lovelace Biomedical, 1101 Yale Blvd. NE, Albuquerque, NM 87106, USA
| | - David S Kosson
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL 60064, USA
| | - Michael Koenigs
- Department of Psychiatry, University of Wisconsin-Madison, 6001 Research Park Blvd., Madison, WI 53719, USA
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