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Toffoli L, Zdorovtsova N, Epihova G, Duma GM, Cristaldi FDP, Pastore M, Astle DE, Mento G. Dynamic transient brain states in preschoolers mirror parental report of behavior and emotion regulation. Hum Brain Mapp 2024; 45:e70011. [PMID: 39327923 PMCID: PMC11427750 DOI: 10.1002/hbm.70011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 08/01/2024] [Accepted: 08/13/2024] [Indexed: 09/28/2024] Open
Abstract
The temporal dynamics of resting-state networks may represent an intrinsic functional repertoire supporting cognitive control performance across the lifespan. However, little is known about brain dynamics during the preschool period, which is a sensitive time window for cognitive control development. The fast timescale of synchronization and switching characterizing cortical network functional organization gives rise to quasi-stable patterns (i.e., brain states) that recur over time. These can be inferred at the whole-brain level using hidden Markov models (HMMs), an unsupervised machine learning technique that allows the identification of rapid oscillatory patterns at the macroscale of cortical networks. The present study used an HMM technique to investigate dynamic neural reconfigurations and their associations with behavioral (i.e., parental questionnaires) and cognitive (i.e., neuropsychological tests) measures in typically developing preschoolers (4-6 years old). We used high-density EEG to better capture the fast reconfiguration patterns of the HMM-derived metrics (i.e., switching rates, entropy rates, transition probabilities and fractional occupancies). Our results revealed that the HMM-derived metrics were reliable indices of individual neural variability and differed between boys and girls. However, only brain state transition patterns toward prefrontal and default-mode brain states, predicted differences on parental-report questionnaire scores. Overall, these findings support the importance of resting-state brain dynamics as functional scaffolds for behavior and cognition. Brain state transitions may be crucial markers of individual differences in cognitive control development in preschoolers.
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Affiliation(s)
- Lisa Toffoli
- NeuroDev Lab, Department of General PsychologyUniversity of PaduaPaduaItaly
| | | | - Gabriela Epihova
- MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
| | - Gian Marco Duma
- Scientific Institute, IRCCS E. Medea, ConeglianoTrevisoItaly
| | | | - Massimiliano Pastore
- Department of Developmental Psychology and SocialisationUniversity of PaduaPaduaItaly
| | - Duncan E. Astle
- MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
- Department of PsychiatryUniversity of CambridgeCambridgeUK
| | - Giovanni Mento
- NeuroDev Lab, Department of General PsychologyUniversity of PaduaPaduaItaly
- Scientific Institute, IRCCS E. Medea, ConeglianoTrevisoItaly
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Fenske SJ, Liu J, Chen H, Diniz MA, Stephens RL, Cornea E, Gilmore JH, Gao W. Sex differences in resting state functional connectivity across the first two years of life. Dev Cogn Neurosci 2023; 60:101235. [PMID: 36966646 PMCID: PMC10066534 DOI: 10.1016/j.dcn.2023.101235] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 02/17/2023] [Accepted: 03/19/2023] [Indexed: 03/29/2023] Open
Abstract
Sex differences in behavior have been reported from infancy through adulthood, but little is known about sex effects on functional circuitry in early infancy. Moreover, the relationship between early sex effects on the functional architecture of the brain and later behavioral performance remains to be elucidated. In this study, we used resting-state fMRI and a novel heatmap analysis to examine sex differences in functional connectivity with cross-sectional and longitudinal mixed models in a large cohort of infants (n = 319 neonates, 1-, and 2-year-olds). An adult dataset (n = 92) was also included for comparison. We investigated the relationship between sex differences in functional circuitry and later measures of language (collected in 1- and 2-year-olds) as well as indices of anxiety, executive function, and intelligence (collected in 4-year-olds). Brain areas showing the most significant sex differences were age-specific across infancy, with two temporal regions demonstrating consistent differences. Measures of functional connectivity showing sex differences in infancy were significantly associated with subsequent behavioral scores of language, executive function, and intelligence. Our findings provide insights into the effects of sex on dynamic neurodevelopmental trajectories during infancy and lay an important foundation for understanding the mechanisms underlying sex differences in health and disease.
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Zhu Z, Wang H, Bi H, Lv J, Zhang X, Wang S, Zou L. Dynamic functional connectivity changes of resting-state brain network in attention-deficit/hyperactivity disorder. Behav Brain Res 2023; 437:114121. [PMID: 36162641 DOI: 10.1016/j.bbr.2022.114121] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/16/2022] [Accepted: 09/20/2022] [Indexed: 11/26/2022]
Abstract
Patients with attention-deficit/hyperactivity disorder (ADHD) have shown abnormal functional connectivity and network disruptions at the whole-brain static level. However, the changes in brain networks in ADHD patients from dynamic functional connectivity (DFC) perspective have not been fully understood. Accordingly, we executed DFC analysis on resting-state fMRI data of 25 ADHD patients and 27 typically developing (TD) children. A sliding window and Pearson correlation were used to construct the dynamic brain network of all subjects. The k-means+ + clustering method was used to recognize three recurring DFC states, and finally, the mean dwell time, the fraction of time spent for each state, and graph theory metrics were quantified for further analysis. Our results showed that ADHD patients had abnormally increased mean dwell time and the fraction of time spent in state 2, which reached a significant level (p < 0.05). In addition, a weak correlation between the default mode network was associated in three states, and the positive correlations between visual network and attention network were smaller than TD in three states. Finally, the integration of each network node of ADHD in state 2 is more potent than that of TD, and the degree of node segregation is smaller than that of TD. These findings provide new evidence for the DFC study of ADHD; dynamic changes may better explain the developmental delay of ADHD and have particular significance for studying neurological mechanisms and adjuvant therapy of ADHD.
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Affiliation(s)
- Zhihao Zhu
- The School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, China
| | - Hongwei Wang
- The School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou University, Changzhou, Jiangsu 213164, China
| | - Hui Bi
- The School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou University, Changzhou, Jiangsu 213164, China
| | - Jidong Lv
- The School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, China
| | - Xiaotong Zhang
- The College of Electrical Engineering, Zhejiang University, 38 Zheda Road, Hangzhou 310000, China
| | - Suhong Wang
- Clinical Psychology, The Third Affiliated Hospital of Soochow University, Juqian Road No. 185, Changzhou, Jiangsu 213164, China.
| | - Ling Zou
- The School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, China; The Key Laboratory of Brain Machine Collaborative Intelligence Foundation of Zhejiang Province, Hangzhou, Zhejiang 310018, China.
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4
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Kerr-German A, White SF, Santosa H, Buss AT, Doucet GE. Assessing the relationship between maternal risk for attention deficit hyperactivity disorder and functional connectivity in their biological toddlers. Eur Psychiatry 2022; 65:e66. [PMID: 36226356 PMCID: PMC9641653 DOI: 10.1192/j.eurpsy.2022.2325] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/26/2022] [Accepted: 09/27/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder associated with increased risk for poor educational attainment and compromised social integration. Currently, clinical diagnosis rarely occurs before school-age, despite behavioral signs of ADHD in very early childhood. There is no known brain biomarker for ADHD risk in children ages 2-3 years-old. METHODS The current study aimed to investigate the functional connectivity (FC) associated with ADHD risk in 70 children aged 2.5 and 3.5 years via functional near-infrared spectroscopy (fNIRS) in bilateral frontal and parietal cortices; regions involved in attentional and goal-directed cognition. Children were instructed to passively watch videos for approximately 5 min. Risk for ADHD in each child was assessed via maternal symptoms of ADHD, and brain data was evaluated for FC. RESULTS Higher risk for maternal ADHD was associated with lower FC in a left-sided parieto-frontal network. Further, the interaction between sex and risk for ADHD was significant, where FC reduction in a widespread bilateral parieto-frontal network was associated with higher risk in male, but not female, participants. CONCLUSIONS These findings suggest functional organization differences in the parietal-frontal network in toddlers at risk for ADHD; potentially advancing the understanding of the neural mechanisms underlying the development of ADHD.
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Affiliation(s)
- Anastasia Kerr-German
- Boys Town National Research Hospital, Center for Childhood Deafness, Language and Learning, Omaha, Nebraska68131, USA
| | - Stuart F. White
- Boys Town National Research Hospital, Institute for Human Neuroscience, Boys Town, Nebraska68010, USA
- Department of Pharmacology and Neuroscience, Creighton School of Medicine, Omaha, Nebraska68124, USA
| | - Hendrik Santosa
- Department of Radiology, University of Pittsburg, Pittsburg, Pennsylvania15260, USA
| | - Aaron T. Buss
- Department of Psychology, University of Tennessee, Knoxville, Tennessee37996, USA
| | - Gaelle E. Doucet
- Boys Town National Research Hospital, Institute for Human Neuroscience, Boys Town, Nebraska68010, USA
- Department of Pharmacology and Neuroscience, Creighton School of Medicine, Omaha, Nebraska68124, USA
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Shanmugan S, Seidlitz J, Cui Z, Adebimpe A, Bassett DS, Bertolero MA, Davatzikos C, Fair DA, Gur RE, Gur RC, Larsen B, Li H, Pines A, Raznahan A, Roalf DR, Shinohara RT, Vogel J, Wolf DH, Fan Y, Alexander-Bloch A, Satterthwaite TD. Sex differences in the functional topography of association networks in youth. Proc Natl Acad Sci U S A 2022; 119:e2110416119. [PMID: 35939696 PMCID: PMC9388107 DOI: 10.1073/pnas.2110416119] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 07/15/2022] [Indexed: 01/16/2023] Open
Abstract
Prior work has shown that there is substantial interindividual variation in the spatial distribution of functional networks across the cerebral cortex, or functional topography. However, it remains unknown whether there are sex differences in the topography of individualized networks in youth. Here, we leveraged an advanced machine learning method (sparsity-regularized non-negative matrix factorization) to define individualized functional networks in 693 youth (ages 8 to 23 y) who underwent functional MRI as part of the Philadelphia Neurodevelopmental Cohort. Multivariate pattern analysis using support vector machines classified participant sex based on functional topography with 82.9% accuracy (P < 0.0001). Brain regions most effective in classifying participant sex belonged to association networks, including the ventral attention, default mode, and frontoparietal networks. Mass univariate analyses using generalized additive models with penalized splines provided convergent results. Furthermore, transcriptomic data from the Allen Human Brain Atlas revealed that sex differences in multivariate patterns of functional topography were spatially correlated with the expression of genes on the X chromosome. These results highlight the role of sex as a biological variable in shaping functional topography.
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Affiliation(s)
- Sheila Shanmugan
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
| | - Jakob Seidlitz
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
| | - Zaixu Cui
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
- Chinese Institute for Brain Research, Beijing,102206, China
| | - Azeez Adebimpe
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
| | - Danielle S. Bassett
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104
- Santa Fe Institute, Santa Fe, NM 87501
| | - Maxwell A. Bertolero
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
| | - Christos Davatzikos
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104
| | - Damien A. Fair
- Department of Behavioral Neuroscience, Department of Psychiatry, Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR 97239
| | - Raquel E. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104
| | - Ruben C. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
| | - Hongming Li
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104
| | - Adam Pines
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
| | - Armin Raznahan
- Section on Developmental Neurogenomics Unit, Intramural Research Program, National Institutes of Mental Health, Bethesda, MD 20892
| | - David R. Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
| | - Russell T. Shinohara
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104
| | - Jacob Vogel
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
| | - Daniel H. Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104
| | - Yong Fan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104
| | - Aaron Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
| | - Theodore D. Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104
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Jeong SO, Kang J, Pae C, Eo J, Park SM, Son J, Park HJ. Empirical Bayes estimation of pairwise maximum entropy model for nonlinear brain state dynamics. Neuroimage 2021; 244:118618. [PMID: 34571159 DOI: 10.1016/j.neuroimage.2021.118618] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 11/16/2022] Open
Abstract
The pairwise maximum entropy model (pMEM) has recently gained widespread attention to exploring the nonlinear characteristics of brain state dynamics observed in resting-state functional magnetic resonance imaging (rsfMRI). Despite its unique advantageous features, the practical application of pMEM for individuals is limited as it requires a much larger sample than conventional rsfMRI scans. Thus, this study proposes an empirical Bayes estimation of individual pMEM using the variational expectation-maximization algorithm (VEM-MEM). The performance of the VEM-MEM is evaluated for several simulation setups with various sample sizes and network sizes. Unlike conventional maximum likelihood estimation procedures, the VEM-MEM can reliably estimate the individual model parameters, even with small samples, by effectively incorporating the group information as the prior. As a test case, the individual rsfMRI of children with attention deficit hyperactivity disorder (ADHD) is analyzed compared to that of typically developed children using the default mode network, executive control network, and salient network, obtained from the Healthy Brain Network database. We found that the nonlinear dynamic properties uniquely established on the pMEM differ for each group. Furthermore, pMEM parameters are more sensitive to group differences and are better associated with the behavior scores of ADHD compared to the Pearson correlation-based functional connectivity. The simulation and experimental results suggest that the proposed method can reliably estimate the individual pMEM and characterize the dynamic properties of individuals by utilizing empirical information of the group brain state dynamics.
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Affiliation(s)
- Seok-Oh Jeong
- Department of Statistics, Hankuk University of Foreign Studies, Yong-In, Republic of Korea
| | - Jiyoung Kang
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Chongwon Pae
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jinseok Eo
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Graduate School of Medical Science, Yonsei University College of Medicine, Brain Korea 21 Project, Seoul, Republic of Korea
| | - Sung Min Park
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
| | - Junho Son
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Graduate School of Medical Science, Yonsei University College of Medicine, Brain Korea 21 Project, Seoul, Republic of Korea
| | - Hae-Jeong Park
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Graduate School of Medical Science, Yonsei University College of Medicine, Brain Korea 21 Project, Seoul, Republic of Korea; Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea.
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7
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Warthen KG, Welsh RC, Sanford B, Koppelmans V, Burmeister M, Mickey BJ. Neuropeptide Y Variation Is Associated With Altered Static and Dynamic Functional Connectivity of the Salience Network. Front Syst Neurosci 2021; 15:629488. [PMID: 34867217 PMCID: PMC8636673 DOI: 10.3389/fnsys.2021.629488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Accepted: 10/25/2021] [Indexed: 11/24/2022] Open
Abstract
Neuropeptide Y (NPY) is a neurotransmitter that has been implicated in the development of anxiety and mood disorders. Low levels of NPY have been associated with risk for these disorders, and high levels with resilience. Anxiety and depression are associated with altered intrinsic functional connectivity of brain networks, but the effect of NPY on functional connectivity is not known. Here, we test the hypothesis that individual differences in NPY expression affect resting functional connectivity of the default mode and salience networks. We evaluated static connectivity using graph theoretical techniques and dynamic connectivity with Leading Eigenvector Dynamics Analysis (LEiDA). To increase our power of detecting NPY effects, we genotyped 221 individuals and identified 29 healthy subjects at the extremes of genetically predicted NPY expression (12 high, 17 low). Static connectivity analysis revealed that lower levels of NPY were associated with shorter path lengths, higher global efficiency, higher clustering, higher small-worldness, and average higher node strength within the salience network, whereas subjects with high NPY expression displayed higher modularity and node eccentricity within the salience network. Dynamic connectivity analysis showed that the salience network of low-NPY subjects spent more time in a highly coordinated state relative to high-NPY subjects, and the salience network of high-NPY subjects switched between states more frequently. No group differences were found for static or dynamic connectivity of the default mode network. These findings suggest that genetically driven individual differences in NPY expression influence risk of mood and anxiety disorders by altering the intrinsic functional connectivity of the salience network.
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Affiliation(s)
- Katherine G. Warthen
- Department of Biomedical Engineering, The University of Utah, Salt Lake City, UT, United States
| | - Robert C. Welsh
- Department of Psychiatry, The University of Utah, Salt Lake City, UT, United States
| | - Benjamin Sanford
- Department of Psychiatry, University of Michigan, Michigan, MI, United States
| | - Vincent Koppelmans
- Department of Psychiatry, The University of Utah, Salt Lake City, UT, United States
| | - Margit Burmeister
- Michigan Neuroscience Institute and Departments of Computational Medicine & Bioinformatics, Human Genetics and Psychiatry, The University of Michigan, Michigan, MI, United States
| | - Brian J. Mickey
- Department of Psychiatry, The University of Utah, Salt Lake City, UT, United States
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8
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Ezaki T, Himeno Y, Watanabe T, Masuda N. Modelling state-transition dynamics in resting-state brain signals by the hidden Markov and Gaussian mixture models. Eur J Neurosci 2021; 54:5404-5416. [PMID: 34250639 PMCID: PMC9291560 DOI: 10.1111/ejn.15386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 07/05/2021] [Accepted: 07/06/2021] [Indexed: 11/29/2022]
Abstract
Recent studies have proposed that one can summarize brain activity into dynamics among a relatively small number of hidden states and that such an approach is a promising tool for revealing brain function. Hidden Markov models (HMMs) are a prevalent approach to inferring such neural dynamics among discrete brain states. However, the impact of assuming Markovian structure in neural time series data has not been sufficiently examined. Here, to address this situation and examine the performance of the HMM, we compare the model with the Gaussian mixture model (GMM), which is with no temporal regularization and thus a statistically simpler model than the HMM, by applying both models to synthetic time series generated from empirical resting‐state functional magnetic resonance imaging (fMRI) data. We compared the GMM and HMM for various sampling frequencies, lengths of recording per participant, numbers of participants and numbers of independent component signals. We find that the HMM attains a better accuracy of estimating the hidden state than the GMM in a majority of cases. However, we also find that the accuracy of the GMM is comparable to that of the HMM under the condition that the sampling frequency is reasonably low (e.g., TR = 2.88 or 3.60 s) or the data are relatively short. These results suggest that the GMM can be a viable alternative to the HMM for investigating hidden‐state dynamics under this condition.
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Affiliation(s)
- Takahiro Ezaki
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.,PRESTO, Japan Science and Technology Agency, Saitama, Japan
| | - Yu Himeno
- Department of Aeronautics and Astronautics, The University of Tokyo, Tokyo, Japan
| | - Takamitsu Watanabe
- Laboratory for Cognition Circuit Dynamics, RIKEN Centre for Brain Science, Saitama, Japan.,International Research Center for Neurointelligence, The University of Tokyo, Tokyo, Japan
| | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, USA.,Computational and Data-Enabled Science and Engineering Program, State University of New York at Buffalo, Buffalo, New York, USA
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9
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Integration of brain and behavior measures for identification of data-driven groups cutting across children with ASD, ADHD, or OCD. Neuropsychopharmacology 2021; 46:643-653. [PMID: 33168947 PMCID: PMC8027842 DOI: 10.1038/s41386-020-00902-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 09/25/2020] [Accepted: 10/26/2020] [Indexed: 11/08/2022]
Abstract
Autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD) and attention-deficit/hyperactivity disorder (ADHD) are clinically and biologically heterogeneous neurodevelopmental disorders (NDDs). The objective of the present study was to integrate brain imaging and behavioral measures to identify new brain-behavior subgroups cutting across these disorders. A subset of the data from the Province of Ontario Neurodevelopmental Disorder (POND) Network was used including participants with different NDDs (aged 6-16 years) that underwent cross-sectional T1-weighted and diffusion-weighted magnetic resonance imaging (MRI) scanning on the same 3T scanner, and behavioral/cognitive assessments. Similarity Network Fusion was applied to integrate cortical thickness, subcortical volume, white matter fractional anisotropy (FA), and behavioral measures in 176 children with ASD, ADHD or OCD with complete data that passed quality control. Normalized mutual information was used to determine top contributing model features. Bootstrapping, out-of-model outcome measures and supervised machine learning were each used to examine stability and evaluate the new groups. Cortical thickness in socio-emotional and attention/executive networks and inattention symptoms comprised the top ten features driving participant similarity and differences between four transdiagnostic groups. Subcortical volumes (pallidum, nucleus accumbens, thalamus) were also different among groups, although white matter FA showed limited differences. Features driving participant similarity remained stable across resampling, and the new groups showed significantly different scores on everyday adaptive functioning. Our findings open the possibility of studying new data-driven groups that represent children with NDDs more similar to each other than others within their own diagnostic group. Future work is needed to build on this early attempt through replication of the current findings in independent samples and testing longitudinally for prognostic value.
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10
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Pereira-Sanchez V, Franco AR, Vieira D, de Castro-Manglano P, Soutullo C, Milham MP, Castellanos FX. Systematic Review: Medication Effects on Brain Intrinsic Functional Connectivity in Patients With Attention-Deficit/Hyperactivity Disorder. J Am Acad Child Adolesc Psychiatry 2021; 60:222-235. [PMID: 33137412 DOI: 10.1016/j.jaac.2020.10.013] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 10/02/2020] [Accepted: 10/24/2020] [Indexed: 12/11/2022]
Abstract
OBJECTIVE Resting-state functional magnetic resonance imaging (R-fMRI) studies of the neural correlates of medication treatment in attention-deficit/hyperactivity disorder (ADHD) have not been systematically reviewed. Our objective was to systematically identify, assess and summarize within-subject R-fMRI studies of pharmacological-induced changes in patients with ADHD. We critically appraised strengths and limitations, and provide recommendations for future research. METHOD Systematic review of published original reports in English meeting criteria in pediatric and adult patients with ADHD up to July 1, 2020. A thorough search preceded selection of studies matching prespecified criteria. Strengths and limitations of selected studies, regarding design and reporting, were identified based on current best practices. RESULTS We identified and reviewed 9 studies (5 pediatric and 4 adult studies). Sample sizes were small-medium (16-38 patients), and included few female participants. Medications were methylphenidate, amphetamines, and atomoxetine. Wide heterogeneity was observed in designs, analyses and results, which could not be combined quantitatively. Qualitatively, the multiplicity of brain regions and networks identified, some of which correlated with clinical improvements, do not support a coherent mechanistic hypothesis of medication effects. Overall, reports did not meet current standards to ensure reproducibility. CONCLUSION In this emerging field, the few studies using R-fMRI to analyze the neural correlates of medications in patients with ADHD suggest a potential modulatory effect of stimulants and atomoxetine on several intrinsic brain activity metrics. However, methodological heterogeneity and reporting issues need to be addressed in future research to validate findings which may contribute to clinical care. Such a goal is not yet at hand.
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Affiliation(s)
- Victor Pereira-Sanchez
- NYU Grossman School of Medicine, New York, New York; Clinica Universidad de Navarra, Pamplona, Navarra, Spain.
| | - Alexandre R Franco
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York; Child Mind Institute, New York, New York
| | | | | | | | - Michael P Milham
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York; Child Mind Institute, New York, New York
| | - Francisco X Castellanos
- NYU Grossman School of Medicine, New York, New York; Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York
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Chen MH, Chen YL, Bai YM, Huang KL, Wu HJ, Hsu JW, Su TP, Tsai SJ, Tu PC, Li CT, Lin WC, Wu YT. Functional connectivity of specific brain networks related to social and communication dysfunction in adolescents with attention-deficit hyperactivity disorder. Psychiatry Res 2020; 284:112785. [PMID: 31982661 DOI: 10.1016/j.psychres.2020.112785] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 01/11/2020] [Accepted: 01/12/2020] [Indexed: 01/21/2023]
Abstract
BACKGROUND Adolescents with attention-deficit hyperactivity disorder (ADHD) may have impaired social cognition and communication. However, the functioning of the brain networks involved in the social cognition and communication impairment in ADHD patients remains unclear. METHODS In total, 18 adolescents with ADHD and 16 age- and sex-matched typically developing adolescents (controls)-all of whom underwent a brain magnetic resonance imaging examination-were enrolled. Their parents filled out Swanson, Nolan, and Pelham IV (SNAP-IV) and Social Responsiveness Scale (SRS) questionnaires. Functional connectivity analyses based on the default mode network, frontoparietal network, and cinguloopercular network were performed. RESULTS Compared with controls, adolescents with ADHD exhibited higher total and subscale scores on SNAP-IV and SRS. Higher SNAP-IV and SRS scores were associated with higher functional connectivity between the default mode network (ventromedial prefrontal cortex) and cinguloopercular network (anterior insula) and between the FPN (dorsolateral and prefrontal cortex) and cinguloopercular network, but with lower functional connectivity between the default mode network (posterior cingulate cortex) and frontoparietal network (inferior parietal lobule) and between the default mode network (precuneus) and cinguloopercular network (temporoparietal junction). DISCUSSION Social cognition and communication impairment and ADHD may commonly share the aberrant functional connectivity in the default mode network, frontoparietal network, and cinguloopercular network.
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Affiliation(s)
- Mu-Hong Chen
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Yen-Ling Chen
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Biophotonics, National Yang-Ming University, Taipei, Taiwan; Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Ya-Mei Bai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan; Brain Research Center, National Yang-Ming University, Taipei, Taiwan
| | - Kai-Lin Huang
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Hui-Ju Wu
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Ju-Wei Hsu
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan.
| | - Tung-Ping Su
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan; Division of Psychiatry, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan; Department of Psychiatry, Cheng Hsin General Hospital, Taipei, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan; Brain Research Center, National Yang-Ming University, Taipei, Taiwan
| | - Pei-Chi Tu
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Philosophy of Mind and Cognition, National Yang-Ming University, Taipei, Taiwan; Division of Psychiatry, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Cheng-Ta Li
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan; Brain Research Center, National Yang-Ming University, Taipei, Taiwan
| | - Wei-Chen Lin
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang-Ming University, Taipei, Taiwan; Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan.
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