Behavioral Heterogeneity in Relation with Brain Functional Networks in Young Children

Wee, Chong-Yaw; Poh, Joann S.; Wang, Qiang; Broekman, Birit F.P.; Chong, Yap‐Seng; Kwek, Kenneth; Saw, Seang‐Mei; Gluckman, Peter D.; Fortier, Marielle V.; Meaney, Michael J.; Qiu, Anqi

doi:10.1093/cercor/bhx205

Cited by 9 publications

(3 citation statements)

References 44 publications

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“…25 Previous RSFC studies either focused on a limited set of psychopathology dimensions (e.g. internalizing and externalizing) or on diagnostic categories, [26][27][28] and did not examine hierarchy of psychopathology dimensions consistent with the HiTOP model. Among children and adolescents, greater p-factor scores derived from the CBCL were associated with reduced maturation of the default mode network (DMN), although this was driven primary by neurodevelopmental symptoms.…”

mentioning

confidence: 99%

Associations Between Resting-State Functional Connectivity and a Hierarchical Dimensional Structure of Psychopathology in Middle Childhood

Karcher

Michelini

Kotov

et al. 2021

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

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Background: Previous research from the Adolescent Brain Cognitive Development (ABCD) study delineated and validated a hierarchical 5-factor structure with a general psychopathology ('p') factor at the apex and five specific factors (internalizing, somatoform, detachment, neurodevelopmental, externalizing) using parent-reported child symptoms. The current study is the first examining associations between dimensions from a hierarchical structure and resting state functional connectivity (RSFC) networks.Methods: Using 9-11-year-old children from the ABCD baseline sample, we compared the variance explained by each hierarchy level (p-factor, 2-factor, 3-factor, 4-factor, and 5-factor models) in RSFC. Analyses were first conducted in a discovery dataset (n=3790) with significant associations examined in a replication dataset (n=3791). Results:The current study found associations between p-factor and lower connectivity within default mode network (DMN), although stronger effects emerged for the neurodevelopmental factor. Neurodevelopmental impairments were related to variation in RSFC networks associated with attention to internal states and external stimuli. These networks included within DMN, DMN with cingulo-opercular (CON) and 'Other' (Unassigned) networks, CON with ventral attention and 'Other' network, and dorsal attention with 'Other' network. Results held when accounting for parental psychopathology. Conclusion:The hierarchical structure of psychopathology showed replicable links to RSFC alterations in middle childhood. The p-factor had minimal association with altered connectivity, while the specific neurodevelopmental dimension showed robust

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confidence: 99%

Associations Between Resting-State Functional Connectivity and a Hierarchical Dimensional Structure of Psychopathology in Middle Childhood

Karcher

Michelini

Kotov

et al. 2021

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

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“…This study included four datasets obtained from the Growing Up in Singapore Towards healthy Outcomes (GUSTO) longitudinal birth cohort (Qiu et al, 2017; Wee et al, 2018), the Cognition and Brain Development in Children (CBDC) study (Zhong et al, 2014), the Pediatric Imaging, Neurocognition, and Genetics (PING) database (Jernigan et al, 2016, http://ping.chd.ucsd.edu/), and the Brain and Cognition Aging Study (BCAS) (Lee, Archer, Wong, Chen, & Qiu, 2013; Lee, Ratnarajah, Tuan, Chen, & Qiu, 2015). Figure S1 displays a flow chart of the subject selection.…”

Section: Methodsmentioning

confidence: 99%

Do intrinsic brain functional networks predict working memory from childhood to adulthood?

Zhang

Hao

Lee

et al. 2020

Human Brain Mapping

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Working memory (WM) is defined as the ability to maintain a representation online to guide goal-directed behavior. Its capacity in early childhood predicts academic achievements in late childhood and its deficits are found in various neurodevelopmental disorders. We employed resting-state fMRI (rs-fMRI) of 468 participants aged from 4 to 55 years and connectome-based predictive modeling (CPM) to explore the potential predictive power of intrinsic functional networks to WM in preschoolers, early and late school-age children, adolescents, and adults. We defined intrinsic functional networks among brain regions identified by activation likelihood estimation (ALE) meta-analysis on existing WM functional studies (ALE-based intrinsic functional networks) and intrinsic functional networks generated based on the whole brain (whole-brain intrinsic functional networks). We employed the CPM on these networks to predict WM in each age group. The CPM using the ALE-based and whole-brain intrinsic functional networks predicted WM of individual adults, while the prediction power of the ALE-based intrinsic functional networks was superior to that of the whole-brain intrinsic functional networks. Nevertheless, the CPM using the whole-brain but not the ALE-based intrinsic functional networks predicted WM in adolescents. And, the CPM using neither the ALE-based nor whole-brain networks predicted WM in any of the children groups. Our findings showed the trend of the prediction power of the intrinsic functional networks to cognition in individuals from early childhood to adulthood.

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“…Although many infant neuroimaging studies have revealed the brain growth patterns during 0 and 2 years old (Gilmore et al, 2010;Li et al, 2015Li et al, , 2016, they are largely performed at the population level. Hence, we still lack critical knowledge on the individualized brain organization and development during infancy, which is essential for mapping individual brain characteristics to individual behavior phenotypes (Kanai and Rees, 2011;Mueller et al, 2013;Wee et al, 2018;Girault et al, 2019) and exploring personalized diagnosis and treatment of mental disorders (Orru et al, 2012;Wolfers et al, 2015). As a key solution, infant brain fingerprinting is used to discover reliable and robust individualized brain organization patterns that are capable of accurately distinguishing one individual from others, like fingerprints.…”

Section: Introductionmentioning

confidence: 99%

Existence of Functional Connectome Fingerprint during Infancy and Its Stability over Months

Wang

Zhang

et al. 2021

J. Neurosci.

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The functional connectome fingerprint is a cluster of individualized brain functional connectivity patterns that are capable of distinguishing one individual from others. Although its existence has been demonstrated in adolescents and adults, whether such individualized patterns exist during infancy is barely investigated despite its importance in identifying the origin of the intrinsic connectome patterns that potentially mirror distinct behavioral phenotypes. To fill this knowledge gap, capitalizing on a longitudinal high-resolution structural and resting-state functional MRI dataset with 104 human infants (53 females) with 806 longitudinal scans (age, 16-876 d) and infant-specific functional parcellation maps, we observe that the brain functional connectome fingerprint may exist since infancy and keeps stable over months during early brain development. Specifically, we achieve an ;78% individual identification rate by using ;5% selected functional connections, compared with the best identification rate of 60% without connection selection. The frontoparietal networks recognized as the most contributive networks in adult functional connectome fingerprinting retain their superiority in infants despite being widely acknowledged as rapidly developing systems during childhood. The existence and stability of the functional connectome fingerprint are further validated on adjacent age groups. Moreover, we show that the infant frontoparietal networks can reach similar accuracy in predicting individual early learning composite scores as the whole-brain connectome, again resembling the observations in adults and highlighting the relevance of functional connectome fingerprint to cognitive performance. For the first time, these results suggest that each individual may retain a unique and stable marker of functional connectome during early brain development.

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Behavioral Heterogeneity in Relation with Brain Functional Networks in Young Children

Cited by 9 publications

References 44 publications

Associations Between Resting-State Functional Connectivity and a Hierarchical Dimensional Structure of Psychopathology in Middle Childhood

Associations Between Resting-State Functional Connectivity and a Hierarchical Dimensional Structure of Psychopathology in Middle Childhood

Do intrinsic brain functional networks predict working memory from childhood to adulthood?

Existence of Functional Connectome Fingerprint during Infancy and Its Stability over Months

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