Functional Connectivity Uniqueness and Stability? A Signature of Cognitive and Psychiatric Problems in Children

Functional Connectivity Uniqueness and Stability? 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A Signature of Cognitive and Psychiatric Problems in Children Zening Fu, Jingyu Liu, Mustafa Salman, Jing Sui, Vince Calhoun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1742626/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Brain functional connectivity (FC) derived from functional magnetic resonance imaging (fMRI) has been serving as a potential “fingerprint” for adults. However, intra-subject variation of FC can be substantial and carry biologically meaningful information, especially during adolescence, a time of intense brain changes. Here, for the first time, we performed a large-scale analysis on cross-scan FC stability and its association with a diverse range of health measures in children from the Adolescent Brain Cognitive Development (ABCD) data. Functional network connectivity (FNC), a network analog of FC, was extracted via an automated independent component analysis framework on 9071 subjects (age: 9 ~ 11 years) and compared across 4 scans. We found that the FNC profile can identify a given child from a large group with high accuracy (> 80%), though intra-subject variations exist cross-scan . The robustness of this finding is verified by replicating the child identification using the second-year scans and between longitudinal scans with a two-year interval. Moreover, cross-scan FNC stability was predictive of sleep condition, cognitive performance, and psychiatric problems in children, with higher stability correlated with better cognitive performance, longer sleep duration, and less psychotic expression. Meanwhile, parental psychopathology and prenatal exposure were associated with the FNC stability in their children. Overall, our findings show that a child’s connectivity profile is not only intrinsic but also exhibits reliable variability cross-scan , regardless of brain growth and development. The intra-subject connectivity stability may serve as a valuable biomarker to draw inferences on early cognitive and psychiatric behaviors in children. children cross-scan functional connectivity stability cognition psychopathology sleep Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Functional connectivity (FC) derived from functional magnetic resonance imaging (fMRI) data has shifted neuroimaging focus away from exploring localized brain activity toward characterizing co-activation patterns across distributed brain regions 1 , 2 . FC has been associated with cognition 3 and various neurological 4 and mental 5 , 6 disorders. Although the population-based studies have revealed a great deal of knowledge on the FC organization profile, brain FC is assumed to be unique to individuals, regardless of how the brain is engaged during scanning 7 . FC heterogeneity has long been appreciated in fMRI studies, even within the same population 8 – 10 . By using a multi-condition fMRI dataset from the Human Connectome Project, studies have shown that FC profile can distinguish adult subjects across scan sessions and even between distinct task conditions 7 , 11 , 12 , acting as a “fingerprint”. A recent study has captured five different functional clusters in the pulvinar with specific connectivity fingerprints, which are associated with distinct components of cognition 13 . However, an individual’s FC is not constant but continuously changes with remarkable variations at different scales, adapting to internal and external demands 14 – 16 . That is to say, besides the intrinsic patterns, FC can also exhibit prominent intra-subject variability, which might underly important biological mechanisms 17 . Many existing works have only examined the FC variation within a single scan 18 – 20 , and an individual’s cross-scan FC variability has received relatively little attention from the neuroimaging field. A comprehensive understanding of cross-scan FC is necessary for developing robust FC-based biomarkers. Unlike adults, children show considerable growth and development in the brain 21 and might exhibit more temporal variability in brain FC 22 , 23 . The temporal variation in resting-state FC declines with aging, especially for those FC involved in frontal-parietal and occipital networks 22 . Age is negatively associated with the variability of dynamic brain connectivity states captured by a clustering strategy on dynamic FC estimates 23 . The prominent variation in youths’ FC might be due to more neuroplasticity in the adolescent’s brain 24 , 25 or be induced by larger motion artifacts 26 of the adolescent subjects, though this is still far from understood. On the other hand, some other studies assume that FC is unique to an adolescent and show that the FC profile can identify an adolescent from a group of subjects 25 , 27 . Despite such progress, we argue that the exploration of cross-scan FC in youths has been limited, as most studies have used relatively small numbers of participants (e.g., sample size < 1000). In addition, although previous studies have investigated the robustness of FC results across scans and between longitudinal sessions 28 , 29 , their datasets are across a wide age range, where the age-induced FC differences might potentially influence the cross-scan FC, especially during the adolescence 30 . More importantly, most of the existing work has only focused on the fingerprint property of FC across scans, ignoring the intra-subject FC variation cross-scan and its potential relationship with individual behavior in children. The stability of FC across scans may be linked to neural mechanisms, reflected by its relevance to adolescents’ neurocognitive development, adverse mental problem outcomes, and other healthy backgrounds. Therefore, there is a need for a reliable large-scale study as the present one can include more than 9000 subjects with multiple scans collected from longitudinal sessions to comprehensively examine the relevance of the cross-scan FC stability to individual differences in behavior and mental conditions. Here, we investigate the cross-scan FC stability in children using a multimodal database called Adolescent Brain Cognitive Development (ABCD). The ABCD database includes more than 11,800 subjects, with multiple scans collected from two longitudinal sessions. This dataset has collected a comprehensive range of measures related to mental problems, cognitions, and other health backgrounds 31 that have been shown useful for the investigation of the relationship between adolescent behaviors and brain functions 32 . Our hypothesis is twofold. First, the FC profile of children shows intrinsic patterns across scans, and the intrinsic patterns can identify a given child from a large group of subjects. We also expected this identification would be successful between longitudinal sessions, regardless of the developments and changes in the children’s brains. Our second hypothesis is that children’s FC also shows intra-subject variations across scans, which will be associated with distinct behavioral phenotypes in children, such as cognitive performance, psychiatric problems, and sleep conditions. Results Flowchart of the Cross-scan Functional Connectivity Analysis Fig. 1 displays the flowchart of the cross-scan functional network connectivity (FNC) analysis. We first applied a Neurmark framework to extract robust intrinsic connectivity networks (ICNs) that are comparable across subjects, scans, and sessions. FNC was estimated using the time-courses (TCs) of ICNs from each scan. After obtaining the FNC matrix of each scan, cross-scan FNC similarity was measured by the correlation between FNC from different scans. Then individual identification was performed based on the cross-scan FNC similarity. Finally, we examined the associations between intra-subject FNC similarity and individuals' behaviors via a linear mixed-effect model (LMM). Functional Networks 53 ICNs were extracted by the Neuromark framework, with activation peaks falling on the cortical and subcortical gray matter areas across the whole brain. The ICNs were assigned to seven different functional domains according to their anatomical locations and functional information 14 , including subcortical (SC), auditory (AUD), visual (VS), sensorimotor (SM), cognitive-control (CC), default-mode (DM), and cerebellar (CB) domains. Details of the spatial maps and coordinates of ICNs are provided in the supplementary materials. Intra-subject FNC Shows High Similarity across Scans Fig. 2a displays the FNC of subjects with maximum and minimum intra-subject FNC similarities between scans. Children show different levels of cross-scan FNC similarity than each other. For example subject 1, the FNC of scan 1 and the FNC of scan 2 share the highest intra-subject similarity (r = 0.9448). In contrast, for example subject 2, the FNC show less stability between scan 1 and scan 2, where the intra-subject FNC similarity is only r = 0.1914. Fig. 2b displays the percentage of children with intra-subject FNC similarity larger than a given percentage of inter-subject FNC similarity, from 60% to 99%. Our results show that intra-subject FNC similarity is larger than a majority of inter-subject FNC similarity, though intra-subject FNC variability exists. The FNC shows the highest intra-subject similarity between scan 1 and scan 2. More than 90% of subjects have intra-subject FNC similarity larger than 60% of inter-subject FNC similarity and more than 65% of subjects have intra-subject FNC similarity larger than 99% of inter-subject FNC similarity. The intra-subject FNC shows the lowest similarity between scan 1 and scan 4. Still, more than 80% of subjects have intra-subject FNC similarity larger than 60% of inter-subject FNC similarity, and about 40% of subjects have intra-subject FNC similarity larger than 99% of inter-subject FNC similarity. The results are replicated by examining the scans from the second-year session. Similarly, subjects can have different levels of cross-scan FNC similarity. FNC shows the highest intra-subject similarity between scan 1 and scan 2 and the lowest intra-subject similarity between scan 1 and scan 4. FNC also shows intra-subject similarities between longitudinal scans. Although a two-year time interval between scans incurred a significant decrease in intra-subject similarity, the intra-subject similarity is still larger than the majority of inter-subject FNC similarity, especially when the FNC was averaged within the session. Individual Identification using Whole-brain FNC Fig. 3 shows the identification results of each pair of identification. At the baseline session, the identification accuracy was 93.99%, 84.78%, 81.87%, and 93.10% based on the database-target of scan 1-scan 2, the target-database of scan 1-scan 3, the target-database of scan 1-scan 4, and the target-database of scan 1-scan mean respectively. The identification was replicated by using the FNC of the second-year scans. Similar to the results from the baseline, the highest identification accuracy 95.16% was achieved based on the database-target of scan 1-scan 2, while the lowest identification accuracy 82.80% was achieved based on the database-target of scan 1-scan 4. The individual identification was further performed using the FNC from longitudinal scans. Scans from the baseline session were the database and scans from the second-year session were the target. Although more intra-subject FNC variations were introduced, the FNC of a child from the baseline session can still be used to identify his/her FNC from the second-year follow-up session. The highest accuracy was 91.43%, which was achieved by averaging the FNC across all four scans within each session before identification. The nonparametric permutation testing shows that the average identification accuracy was 50% if the identity was shuffled for each scan. The real identification accuracy was significantly higher than the accuracy obtained by the permutation tests. FNC Stability Correlates with Cognitive Performance We noted that besides the intrinsic patterns, FNC also shows significant variability across scans and the mechanisms underlying the cross-scan FNC stability are still unknown. In this study, we focused on children’s cognitive performance, mental problems, sleep conditions, and screen usage. These scores have been linked to a wide range of brain functions and structures in previous studies 33–36 . The investigation of the associations between these scores and FNC stability might advance our understanding of the neural underpinnings of children’s behaviors. The cognitive measures were positively correlated with the intra-subject FNC stability (False discovery rate [FDR] corrected, q < 0.05). Specifically, 10 out of 10 of the cognitive summary scores were positively correlated with FNC stability, with correlation r values ranging from 0.0376 to 0.1070. The Total Composite Score was the score most significantly positively correlated with the FNC stability (r = 0.1070, Cohen’s d = 0.2152, p = 4.82×10 -24 ). For the neurocognitive battery in the subdomain, TPVT was the score most significantly positively correlated with the FNC stability (r = 0.0841, Cohen’s d = 0.1688, p = 1.54×10 -15 ) while TFT was the score least significantly positively correlated with intra-subject FNC stability (r = 0.0376, Cohen’s d = 0.0753, p = 3.68×10 -4 ). To better visualize the associations, we divided the children into four groups from low cognitive performance to high cognitive performance according to each cognitive score (group 1: 0%~25%, group 2: 25%~50%; group 3: 50%~75%, and group 4: 75%~100%) and the averaged cross-scan FNC stability within each group is displayed using bar plots in Fig. 4a. Clear increasing trends can be observed along group 1 to group 4. Along with the scatter plots in Fig. 4a, our results indicate that children with good cognitive performance tended to have higher FNC stability. FNC Stability Correlates with Psychiatric Problems The psychopathological measures of children were negatively correlated with the intra-subject FNC stability. 12 out of 20 psychiatric problem scores show significantly negative correlations with FNC stability, with r values ranging from -0.0257 to -0.0496 (FDR corrected, q < 0.05). The social problem score was the score most significantly negatively correlated with the FNC stability (r = -0.0496, Cohen’s d = -0.0992, p = 2.38×10-6). Similarly, we divided the children into four groups according to each psychopathological measure. The mean and the standard error of the mean for the cross-scan FNC stability of each group were displayed in Fig. 4b. The FNC stability show decreasing trends along group 1 to group 4, indicating that children with high psychiatric problem scores tended to have lower FNC stability. FNC Stability Correlates with Sleep Conditions and Screen Usage We further found significant associations between FNC stability and the sleep conditions of children. The cross-scan FNC stability was negatively correlated with the sleep duration score (r = -0.0752, Cohen’s d = -0.1508, p = 7.74×10 -13 ). In the ABCD measurement system, high sleep duration score indicates short sleep duration (1 = 9-11 hours; 2 = 8-9 hours; 3 = 7-8 hours; 4 = 5-7 hours; 5 = Less than 5 hours). The FNC stability was also negatively correlated with the score that evaluates how long an adolescent falls asleep (sleepdisturb2_p). A higher score in sleepdisturb2_p indicates a longer time to fall asleep. The FNC stability was negatively correlated with other sleep behaviors of adolescents, such as sleepdisturb24_p (evaluates a child feels unable to move when waking up in the morning) and sleepdisturb26_p (evaluates a child falls asleep suddenly in inappropriate situations). Higher scores in these measurements indicate more frequently that the event happens (1 = Never; 2 = Occasionally (once or twice per month or less); 3 = Sometimes (once or twice per week); 4 = Often (3 or 5 times per week); 5 = Always). The overall results indicate that children with worse sleep conditions (e.g., shorter sleep duration or longer time to fall asleep) tended to have lower FNC stability (Fig. 4d). Children’s screen usage is also negatively correlated with cross-scan FNC stability. 14 out of 14 youth screen time utilization scores, including the use of television, internet, cell phone, and video games, show negative correlations with individuals’ FNC stability (FDR corrected, q<0.05). Children with more screen usage tended to have lower FNC stability. Details of the correlations statistics can be found in the supplementary materials (Fig. 4c). FNC Stability Correlates with Parent Psychopathology and Prenatal Exposure Moreover, parental dimensional psychopathology showed significant correlations with their children’s FNC stability (Fig. 5a and b). Specifically, the positive questions in the parents’ psychopathology assessment, including asr_q15_p (I am pretty honest), asr_q73_p (I meet my responsibilities to my family), asr_q88_p (I enjoy being with people), asr_q98_p (I like to help others), asr_q106_p (I try to be fair to others), asr_q123_p (I am a happy person), were positively correlated with the FNC stability of children with r values ranging from 0.0315 to 0.0583 (FDR corrected, q < 0.05). In contrast, the negative questions in the parents’ psychopathology assessment were negatively correlated with the FNC stability of children with r values ranging from -0.0287 to -0.0482 (FDR corrected, q < 0.05). These results indicate that parents with positive behaviors will result in higher FNC stability in children while parents with negative behaviors will result in lower FNC stability in children. Our analysis also showed that prenatal exposure before and during pregnancy was associated with FNC stability in children. Parents with prenatal exposure to tobacco and marijuana will result in lower FNC stability in children (Fig. 5d). Also, a planned pregnancy will result in higher FNC stability in children. The age of the parents during the pregnancy showed significant correlations with FNC stability as well. While older mothers will result in higher FNC stability in children, fathers’ ages between 30~40 years old (when the child was born) result in the highest FNC stability in adolescents (Fig. 5c). Discussion The Fingerprint Property of Children’s FNC Brain FC and its network analog, FNC, are believed to provide a window into brain function and intrinsic brain organization 2,37−39 . Neuroimaging studies have successfully established that adults’ FC profile shows substantial inter-subject variability, and such variability can distinguish individuals from another scan 7 , 12 . Unlike adults, children might show more intra-subject variability in FC due to the developments in the brain 40 . More heterogeneous brain states and confounding effects in youths (e.g., head motions) 41 , 42 might also influence the individual identifications. Recent research has found that the identification of youths is not significantly different from that of adults 25 . However, this study relied on adolescents with a large age range (12 ~ 18 years). Brain developments at different ages can be different, potentially introducing unpredictable variability in the identification. Also, the results based on such a small sample size (N = 140) can be biased by the sampling variability, further resulting in replication failures 43 . Our study used large data from the ABCD project with almost the same age to show that the cross-scan FNC similarity can be robustly observed in children. We noted that the identification accuracy decreased and the intra-subject FNC variability increased as the time interval of scans increased. These results indicate that the assumption of the FNC uniqueness might oversimplify the interrelationships between brain regions. Accumulated evidence has shown that brain connectivity is highly dynamic with dramatic variations across scans, tasks, and time 44 – 46 . An individual’s FC will show both similarity and variability across scans, depending on the brain state in which the FC is measured 17 . Previous work has captured reliable single-subject variation of the functional connectome, which might be potentially associated with the mental health in the formative years 30 , social skills 47 , and memory performance in aging 48 . Via a large-scale analysis of more than 9000 children, our result provides further evidence that FC exhibits cross-scan variability at an early age. Our result additionally suggests that cross-scan FC variability might be indicative of brain state during imaging. In other words, as a subject stayed in the scanner for a longer time, their brain state might be far away from the initial “resting-state”, resulting in a larger difference between the brain FNC. However, future studies with simultaneously in-scanner state measures are clearly needed to elucidate the mechanistic link between cross-scan variability and transient brain conditions. We successfully performed the individual identification between longitudinal scans with a two-year interval, although with reduced overall accuracy. This finding is in line with a previous result based on a relatively small sample size, which suggested that a larger time interval can incur a significant decrease in identification accuracy 25 . We further found that averaging FNC across scans within each session can increase the accuracy. Growth and development inside the children’s brain will introduce FC variation intra-subject, which is associated with children’s neurodevelopment and behaviors 49 , 50 . Averaging FNC within the same session can mitigate the heterogeneity induced by transient brain states, but not the variability induced by brain development. Our result suggests that the decreased identification accuracy between longitudinal scans can be due to both brain development and the difference in the temporal brain conditions. The successfully longitudinal identification further supports that the FC profile contains fundamental properties that are unique to each child, regardless of the FC developments during adolescence 25 . Individual FNC Stability and Children’s Behaviors Besides the intra-subject similarity, children’s FNC also showed intra-subject FNC variability across scans. The balance between cross-scan FNC similarity and variability does not appear to be driven by random noise but might convey underlying physiological and psychological information in children. The cross-scan FNC stability was positively correlated with cognitive performance, including reading recognition, pattern comparison, memory, etc. Previously, neuroimaging studies have been typically focusing on the FC strength and suggesting that its inter-subject variability is relevant to individual differences in behaviors 3 , 7 , 51 . However, brain FC is not static, but with considerable variation between tasks and rest, across scans, and even within a single scan 17 , 46 , 52 . Spontaneous FC variations can predict the performance of different cognitive tasks 53 , 54 . Literature also showed that individuals with stable FC across-time show advanced cognitive performance, reflected by the increased accuracy and the more stable response time 55 , 56 . Our finding has extended the investigation of FC variability within a single scan to the investigation of FC stability across scans and showed robust relationships between cross-scan FC stability and cognitions. A possible explanation of this finding is that the resting-state is in a “relaxed” brain condition that ameliorates adaptive reconfiguration of brain networks in the context of cognitive tasks. A stable FC during the execution of cognitive tasks is associated with successful cognition and more difficult task conditions require increased stability of FC 55 , 56 . The stable FC during the resting-state might facilitate the brain switching from a relaxed condition to a task demand condition that purportedly requires sustained cognition, consequently resulting in better cognitive performance 57 . In addition to the correlations with cognitions, we found negative correlations between FNC stability and the dimensional psychopathology in children. Children with less cross-scan FNC stability will have more dimensional psychopathological problems and more frequent mania episodes. This result is in line with a previous finding showing that the stability of FC is related to psychiatric disorders 30 . Although they did not identify different FC distinctiveness between groups in children (< 14 years), they found that adolescents with increased psychiatric symptom scores show less FC distinctiveness compared to controls 30 . Our result extends their finding by showing that the associations between FC stability and dimensional psychopathology exist during a very early age (9 ~ 11 years). One interpretation of these associations is that decreased FC stability underlies the dysregulated brain rhythms that characterize psychiatric problems. Of note, increased rumination is associated with higher medial prefrontal cortex to insula FC variability, suggesting that the intra-subject FC heterogeneity might trigger rumination by enhancing sensitivity to self-referential information 58 . It is also suggested that the unstable FC may be associated with deficits in executive functioning and reflect weaknesses in brain circuits responsible for cognitive control 59 . A growing body of literature has linked dynamic FC patterns to psychiatric problems. Individuals with autism spectrum disorder have larger FC variability in time, associated with the increase depending on autism symptom severity 60 . By using magnetoencephalography, researchers have shown that schizophrenia patients exhibit more trials-to-trials network topology variability during a 2-back working memory task 61 . Increased FC variability has also been observed in both patients with depressed bipolar disorder and major depressive disorder, who shared overlapping symptoms that typically confound the diagnosis 62 . Our results complement the prior work by showing that an individual’s FNC exhibits reliable variability across scans, which might signify underlying biological mechanisms in mental health. The stability of cross-scan FNC can add information to the connectivity strength and will be a potential brain biomarker that predicts early psychiatric problems in children. Interestingly, our analysis further showed that the FNC stability of children is also relevant to parental conditions. The development history of children can be an important indicator of later mental and psychical behaviors in youths 31 . The increase in drug use among pregnant mothers has become a social problem in the US 63 . Prenatal cannabis exposure is associated with a greater risk for psychopathology in adolescents 64 . Here in this study, we found that prenatal tobacco and marijuana exposure is associated with lower cross-scan FNC stability during middle childhood. Considering the associations between FNC stability and cognition and dimensional psychopathology, this result underscores the potential to use FNC stability to advance our understanding of the relationships between prenatal drug usage and cognitive developments and mental health among offspring. Another interesting finding of our present study is that the parents’ psychopathology was correlated with the FNC stability of children, where the positive behaviors in parents were associated with higher FNC stability in their children while the negative behaviors in parents were associated with lower FNC stability in their children. We speculate that the family environment might influence the stability of FC in children. This speculation is supported by a further analysis showing a positive correlation between children’s FNC stability and neighborhood safety, an important living environmental factor. The inherited characteristic can be another cause of these relationships. Analysis including genetic data is needed in future studies for validating this hypothesis. In sum, our study revealed that children’s FNC shows certain similarities and substantial variability across scans from the same day and longitudinal sessions. The intrinsic patterns of FNC successfully identify each child from a large cohort of 9071 subjects. Besides, cross-scan FNC stability carries important psychological and physiological information underlying distinct behavioral phenotypes in children. To the best of our knowledge, this is the first attempt to investigate the cross-scan FNC on children on a large-scale. Given this foundation, future neuroimaging studies should move beyond the focus of the connectivity uniqueness to an individual, but to investigate the cross-scan connectivity variability, probing how intra-subject stability and variability of an individual’s functional brain organization provide a window into neuropsychological mechanisms. Materials And Methods Participants and behavioral assessments The present study used a longitudinal dataset shared by the ABCD, the largest long-term study of brain development and child health in the United States ( https://abcdstudy.org/ ). We used the release 2.01 of the ABCD dataset, containing over 11,800 children aged 9–11 years, with two imaging sessions (baseline and the second-year) and multiple scans within each session. The ABCD study incorporated a comprehensive range of measures, including neurocognitive battery, physical and mental health assessments, socioeconomic, ethnic, and other health backgrounds, to assess predictors and outcomes related to different domains 31 , 32 . The parent’s full written informed consent and the child’s assent were obtained under protocols approved by the Institutional Review Board. We performed data quality control (QC) on the preprocessed fMRI data via the Neuromark framework 65 . The Neuromark QC yields 9071 subjects for the baseline analysis, 2918 subjects for the second-year analysis, and 2290 subjects for the cross-sessional analysis; each analysis has at least four good resting-state fMRI scans within each session. The basic demographics can be found in Table I. Table I. Basic Demographics of Subjects Basic Demographics Baseline Second-year Total Subject 9071 2918 Age (month) 119.06 ± 7.52 142.86 ± 7.51 Gender (F/M) 4365/4706 1333/1585 Height (inch) 55.28 ± 3.35 60.04 ± 3.53 Weight (lbs) 82.97 ± 23.52 107.57 ± 31.99 Race (W/B/H/A/O) 4771/1325/1863/181/929 1631/317/629/57/284 Cognition (nihtbx_totalcomp) 86.44 ± 8.99 89.08 ± 10.19 Psychiatric problem (cbcl_scr_syn_totprob) 45.73 ± 11.35 44.78 ± 11.27 Sleep disturbance (sleepdisturb1_p) 1.72 ± 0.81 1.99 ± 0.86 Neuromark Framework To capture reliable intrinsic connectivity networks (ICNs) and their corresponding TCs for each subject and each scan, a robust independent component analysis (ICA)-based framework called Neuromark 65 was applied to the ABCD data. Unlike atlas-based methods that typically assume fixed brain regions across subjects, the Neuromark can identify brain networks comparable across subjects while adapting single-subject variability with the networks. Two healthy controls datasets, the human connectome project (HCP, 823 subjects after the subject selection) and the genomics superstruct project (GSP, 1005 subjects after the subject selection) were used for the construction of the network templates. The reproducible templates were used as the spatial network priors to back-reconstruct spatial maps and TCs for the ABCD data. The effectiveness of Neuromark has been demonstrated in previous work, with a wide range of brain markers and abnormalities identified in different populations 66 – 71 . More details of the Neuromark framework are provided in 65 and the supplementary materials. Functional Network Connectivity Children’s data can be noisy with more confounding effects, such as larger head motions. We therefore performed four additional post-processing steps to carefully regress out the remaining noise in the TCs of ICNs: 1) detrending linear, quadratic, and cubic trends, 2) removal of detected outliers, 3) multiple regression of the head motions parameters (3 rotations and 3 translations) and their derivatives, 4) band-pass filtering with a cutoff frequency of 0.01 Hz-0.15 Hz. Pearson correlation coefficients between post-processed TCs were calculated to measure the FNC for each scan. FNC Similarity and Subject Identification We calculated the correlation between whole-brain FNC from different scans to measure the FNC similarity. Specifically, the correlation between FNC of scans from the same subject was defined as the intra-subject FNC similarity while the correlation between FNC of scans from different subjects was defined as the inter-subject FNC similarity. For each subject, there are one intra-subject similarity and 9070 inter-subject similarities. Then we calculated the percentage of children who have intra-subject FNC similarities larger than a given percentage of inter-subject FNC similarities (60%, 70%, 80%, 90%, 95%, and 99%). We performed individual identification using the FNC similarity. For each subject, we compared his/her intra-subject FNC similarity with a randomly picked inter-subject FNC similarity. The predicted identity was that with the larger correlation value. We performed this step for every subject to obtain an identification vector, which can be used to calculate the overall identification accuracy. The whole procedure was repeated 1000 times to estimate the distribution of the identification accuracy. The identification was also performed using domain-based FNC, and the results are provided in the supplementary materials. We further performed nonparametric permutation testing to assess the statistical significance of identification accuracy. We permuted subject identity for the FNC of scans to shuffle the intra-subject and inter-subject FNC similarity. The same identification was performed 1000 times on the permuted data to have the identification accuracy for the permuted data. Test-retest Reliability and Longitudinal Identification We further calculated the FNC similarity between other scans (between scan 1 and scan 3, between scan 1 and scan 4, and between scan 1 and scan mean [mean FNC across scans 2 ~ 4]) from the baseline session. We also calculated the FNC similarity between scans from the second-year session. The same identification was performed using the FNC similarity between scans. To investigate whether the FNC profile can identify an individual from a longitudinal scan while there are developmental changes in the brain, we measured the FNC similarity between a scan from the baseline session and a scan from the second-year session and then performed the identification based on the FNC similarity between longitudinal scans. Cognitive Measures The cross-scan FNC stability was measured by the intra-subject similarity between FNC of scan 1 and mean FNC across scans 2 ~ 4. To show the test-retest reliability of the associations, we also replicated the results 1) using different FNC stability measures, 2) controlling for head motion in the LMM, 3) only using subjects with little head motion, and 4) using the second-year data. Details of the replication can be found in the supplementary materials. We first investigate the associations between FNC stability and cognitive assessments. The cognitive performance of each adolescent was measured via the NIH Cognition Battery Toolbox (abcd_tbss01) 32 . Higher scores indicate better cognitive performance. The NIH neurocognitive battery contains 7 distributional characteristics, including the Toolbox Picture Vocabulary Task (TPVT), the Toolbox Oral Reading Recognition Task (TORRT), the Toolbox Pattern Comparison Processing Speed Test (TPCPST), the Toolbox List Sorting Working Memory Test (TLSWMT), the Toolbox Picture Sequence Memory Test (TPSMT), the Toolbox Flanker Task (TFT), and the Toolbox Dimensional Change Card Sort Task (TDCCS). There are also 3 composite scores, including a Crystalized Intelligence Composite and a Fluid Intelligence Composite, and a Total Score Composite. In total 10 cognitive scores were used in the analysis. The detailed information on each score can be found in 32 . Mental Problem Measures The associations between cross-scan FNC stability and children’s mental health conditions were also investigated. The mental health conditions of children were measured by the Parent Child Behavior Checklist Scores (abcd_cbcls01). These checklist scores contain 11 syndrome scales related to psychiatric problems and 1 total Syndrome Scale, 6 DSM-Oriented scales, and 3 CBCL Scale2007 Scales. In total 20 scores from the Parent Child Behavior Checklist Scores were used for the investigation. The ABCD Parent General Behavior Inventory-Mania (abcd_pgbi01) was also used to assess the subsyndromal mania. It contains 10 scores that evaluate the children’s behaviors of mania. Higher scores on the mental health measures indicate dimensional psychopathology. Sleep Conditions and Screen Usage The assessments of sleep conditions and sleep disorders of children were measured by ABCD Parent Sleep Disturbance Scale for Children (abcd_sds01). It includes 26 questionnaires to evaluate the sleep disturbance of each child. For example, question 1 is “How many hours of sleep does your child get on most nights?” and question 2 is “How long after going to bed does your child usually fall asleep?”. The scores will be between 1 to 5, with a higher score indicating a worse sleep condition (e.g., fewer hours of sleep and longer time to fall asleep). The screen time utilization of youth, which is measured by the ABCD Youth Screen Time Survey (abcd_stq01) was also used to investigate its relationships with cross-scan FNC stability. It contains 14 scores that evaluate the screen usage of a child during the weekdays and weekends, with higher scores indicating longer screen usage. Parental Behaviors and Prenatal Exposure We were also interested in the potential relationships between parental factors and the children’s FNC stability. The prenatal exposure before and during pregnancy, measured by ABCD Developmental History Questionnaire (dhx01) was used for the investigation. We focused on prenatal exposure to tobacco, alcohol, and marijuana, and the parents’ age when the child was born. The parent dimensional psychopathology, measured by ABCD Parent Adult Self Report Raw Scores Aseba (pasr01), was also used in the analysis. These scores evaluate parent dimensional psychopathology from either a positive question (e.g., question q15: I am pretty honest) or a negative question (e.g., question q12: I feel lonely). The higher scores in the positive question indicate a better condition of parents while the higher scores in the negative question indicate a worse dimensional psychopathological condition. Association between Cross-scan FNC Stability and Behaviors An LMM was adopted to investigate the associations between intra-subject cross-scan FNC stability and the behavioral assessments. The LMM was also used to examine the associations between the children’s FNC stability and their parents’ conditions and neighborhood safety. The ABCD data contain related data at sites and within families due to twins and siblings. The LMM can model families nested within the site to take account of this effect. It has been successfully applied in previous ABCD studies and identified meaningful brain-wide associations with a wide range of individual behaviors 33 , 72 . In this work, cross-scan FNC stability was modeled as the dependent variable, while each score/behavior was modeled as a fixed effect. Age, gender, race, height, and weight that were considered as confounding effects were modeled as other fixed effects. The family structures and sites were modeled as random effects 33 . The correlation r-value, t-statistic, and effect size Cohen’s d was obtained for each association analysis to reflect the relationship between FNC stability and a behavioral score. The results are corrected by false discovery rate (FDR) correction 73 . Confounding Effect of the Head Motion in Children Head motion during fMRI collections can significantly influence the data quality, which may further impact the estimation of functional connectivity and its stability. Children have reduced inhibitory control, which may result in significantly more head motion than adults 41 , 74 . In this study, we did not exclude those subjects with larger head motions because we want to retain more subjects to have larger statistical power in the analysis. Although we did not use head motion for subject selection, we have carefully minimized the impacts of head motion in the estimation of FNC. First, we performed head motion correction in the preprocessing using the FSL toolbox. Second, the Neuromark is an ICA-based framework that is capable of extracting networks which are independent of the noise components, such as the head motion. The ICA-based strategy has been widely used for removing motion artifacts in previous studies 75 , 76 . Third, after extracting the TCs of ICNs, additional postprocessing steps such as dispiking and multiple regression of the head motions parameters were performed to further minimize the influence of head motion on the TCs as well as the estimation of FNC. To further show the robustness of our findings, we calculated the mean framewise displacement (FD) 77 , 78 for each scan and repeated our analysis by adding mean FD as one of the fixed effects in the LMM. The overall findings are consistent with the results we have in the main text. Additionally, we also replicated the majority of the findings after excluding those subjects with large head motions (subjects were excluded if one of the scans with head motion > 0.3 mean FD). Details of these analyses and results are provided in the supplementary materials. Declarations Data Availability The code of Neuromark framework and the Neuromark template have been released and integrated in the group ICA Toolbox (GIFT, https://trendscenter.org/software/gift/), which can be downloaded and used directly by users worldwide. The ABCD data used in the present study can be accessed upon application from NDA (https://nda.nih.gov/). Other MATLAB codes of this study can be obtained from the corresponding author. Acknowledgements This work was supported by National Institutes of Health (No. R01DA049238 and R01MH118695, R01MH117107), National Science Foundation (No. 2112455). Use of the ABCD (https://abcdstudy.org/) dataset is acknowledged. A full list of supporters of ABCD project is available at https://abcdstudy.org/nih-collaborators. Author Contributions Zening Fu and Vince Calhoun designed the study; Zening Fu performed the data analysis; Zening Fu, Jingyu Liu, Jing Sui, and Vince Calhoun wrote the paper. Mustafa Salman helped with data preprocessing. All authors contributed to the results interpretation and discussion. Competing Interests The authors declare no competing interests. References Biswal B, Zerrin Yetkin F, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magn Reson Med. 1995;34(4):537–541. doi: 10.1002/mrm.1910340409 Biswal BB, Mennes M, Zuo XN, et al. Toward discovery science of human brain function. Proc Natl Acad Sci U S A. 2010;107(10):4734–4739. doi: 10.1073/pnas.0911855107 Park HJ, Friston K. Structural and functional brain networks: From connections to cognition. Science (80-). 2013;342(6158). doi: 10.1126/science.1238411 Greicius M. Resting-State Functional Connectivity in Neuropsychiatric Disorders . Vol 21.; 2008. doi: 10.1001/jamapsychiatry.2015.0484 Arbabshirani MR, Plis S, Sui J, Calhoun VD. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage. 2017;145:137–165. doi: 10.1016/j.neuroimage.2016.02.079 Arbabshirani MR, Kiehl KA, Pearlson GD, Calhoun VD. Classification of schizophrenia patients based on resting-state functional network connectivity. Front Neurosci. 2013;(7 JUL). doi: 10.3389/fnins.2013.00133 Finn ES, Shen X, Scheinost D, et al. Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nat Neurosci. 2015;18(11):1664–1671. doi: 10.1038/nn.4135 Cribben I, Wager TD, Lindquist MA. Detecting functional connectivity change points for single-subject fMRI data. Front Comput Neurosci . 2013;(OCT). doi: 10.3389/fncom.2013.00143 Gordon EM, Nelson SM. Three types of individual variation in brain networks revealed by single-subject functional connectivity analyses. Curr Opin Behav Sci. 2021;40:79–86. doi: 10.1016/j.cobeha.2021.02.014 Anderson JS, Ferguson MA, Lopez-Larson M, Yurgelun-Todd D. Reproducibility of single-subject functional connectivity measurements. Am J Neuroradiol. 2011;32(3):548–555. doi: 10.3174/ajnr.A2330 Li K, Wisner K, Atluri G. Feature selection framework for functional connectome fingerprinting. Hum Brain Mapp. 2021;42(12):3717–3732. doi: 10.1002/hbm.25379 Sarar G, Rao B, Liu T. Functional connectome fingerprinting using shallow feedforward neural networks. Proc Natl Acad Sci U S A. 2021;118(15):2021. doi: 10.1073/pnas.2021852118 Guedj C, Vuilleumier P. Functional connectivity fingerprints of the human pulvinar: Decoding its role in cognition. Neuroimage. 2020;221:117162. doi: 10.1016/j.neuroimage.2020.117162 Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD. Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex. 2014;24(3):663–676. doi: 10.1093/cercor/bhs352 Buzsáki G. Rhythms of the Brain .; 2009. doi: 10.1093/acprof:oso/9780195301069.001.0001 van den Heuvel MP, Hulshoff Pol HE. Exploring the brain network: A review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol. 2010;20(8):519–534. doi: 10.1016/j.euroneuro.2010.03.008 Finn ES, Scheinost D, Finn DM, Shen X, Papademetris X, Constable RT. Can brain state be manipulated to emphasize individual differences in functional connectivity? Neuroimage. 2017;160:140–151. doi: 10.1016/j.neuroimage.2017.03.064 Chang C, Glover GH. Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage. 2010;50(1):81–98. doi: 10.1016/j.neuroimage.2009.12.011 Zalesky A, Fornito A, Cocchi L, Gollo LL, Breakspear M. Time-resolved resting-state brain networks. Proc Natl Acad Sci U S A. 2014;111(28):10341–10346. doi: 10.1073/pnas.1400181111 Fu Z, Du Y, Calhoun VD. The Dynamic Functional Network Connectivity Analysis Framework. Engineering. 2019;5(2):190–193. doi: 10.1016/j.eng.2018.10.001 Johnson SB, Blum RW, Giedd JN. Adolescent Maturity and the Brain: The Promise and Pitfalls of Neuroscience Research in Adolescent Health Policy. J Adolesc Heal. 2009;45(3):216–221. doi: 10.1016/j.jadohealth.2009.05.016 Chen Y, Wang W, Zhao X, et al. Age-related decline in the variation of dynamic functional connectivity: A resting state analysis. Front Aging Neurosci. 2017;9(JUN):203. doi: 10.3389/fnagi.2017.00203 Xia Y, Chen Q, Shi L, et al. Tracking the dynamic functional connectivity structure of the human brain across the adult lifespan. Hum Brain Mapp. 2019;40(3):717–728. doi: 10.1002/hbm.24385 Jalbrzikowski M, Larsen B, Hallquist MN, Foran W, Calabro F, Luna B. Development of White Matter Microstructure and Intrinsic Functional Connectivity Between the Amygdala and Ventromedial Prefrontal Cortex: Associations With Anxiety and Depression. Biol Psychiatry. 2017;82(7):511–521. doi: 10.1016/j.biopsych.2017.01.008 Jalbrzikowski M, Liu F, Foran W, et al. Functional connectome fingerprinting accuracy in youths and adults is similar when examined on the same day and 1.5-years apart. Hum Brain Mapp. 2020;41(15):4187–4199. doi: 10.1002/hbm.25118 Power JD, Cohen AL, Nelson SM, et al. Functional Network Organization of the Human Brain. Neuron. 2011;72(4):665–678. doi: 10.1016/j.neuron.2011.09.006 Demeter D V., Engelhardt LE, Mallett R, et al. Functional Connectivity Fingerprints at Rest Are Similar across Youths and Adults and Vary with Genetic Similarity. iScience. 2020;23(1):100801. doi: 10.1016/j.isci.2019.100801 Sato JR, Biazoli CE, Zugman A, et al. Long-term stability of the cortical volumetric profile and the functional human connectome throughout childhood and adolescence. Eur J Neurosci. 2021;54(6):6187–6201. doi: 10.1111/ejn.15435 Finn ES, Rosenberg MD. Beyond fingerprinting: Choosing predictive connectomes over reliable connectomes. Neuroimage. 2021;239:118254. doi: 10.1016/j.neuroimage.2021.118254 Kaufmann T, Alnæs D, Doan NT, Brandt CL, Andreassen OA, Westlye LT. Delayed stabilization and individualization in connectome development are related to psychiatric disorders. Nat Neurosci. 2017;20(4):513–515. doi: 10.1038/nn.4511 Barch DM, Albaugh MD, Avenevoli S, et al. Demographic, physical and mental health assessments in the adolescent brain and cognitive development study: Rationale and description. Dev Cogn Neurosci. 2018;32:55–66. doi: 10.1016/j.dcn.2017.10.010 Luciana M, Bjork JM, Nagel BJ, et al. Adolescent neurocognitive development and impacts of substance use: Overview of the adolescent brain cognitive development (ABCD) baseline neurocognition battery. Dev Cogn Neurosci. 2018;32:67–79. doi: 10.1016/j.dcn.2018.02.006 Cheng W, Rolls E, Gong W, et al. Sleep duration, brain structure, and psychiatric and cognitive problems in children. Mol Psychiatry. 2021;26(8):3992–4003. doi: 10.1038/s41380-020-0663-2 Gorham LS, Jernigan T, Hudziak J, Barch DM. Involvement in Sports, Hippocampal Volume, and Depressive Symptoms in Children. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019;4(5):484–492. doi: 10.1016/j.bpsc.2019.01.011 Dutil C, Walsh JJ, Featherstone RB, et al. Influence of sleep on developing brain functions and structures in children and adolescents: A systematic review. Sleep Med Rev. 2018;42:184–201. doi: 10.1016/j.smrv.2018.08.003 Paulus MP, Squeglia LM, Bagot K, et al. Screen media activity and brain structure in youth: Evidence for diverse structural correlation networks from the ABCD study. Neuroimage. 2019;185:140–153. doi: 10.1016/j.neuroimage.2018.10.040 Biswal B, Zerrin Yetkin F, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magn Reson Med. 1995;34(4):537–541. doi: 10.1002/mrm.1910340409 Londei A, D’Ausilio A, Basso D, Belardinelli MO. A new method for detecting causality in fMRI data of cognitive processing. Cogn Process. 2006;7(1):42–52. doi: 10.1007/s10339-005-0019-5 Jafri MJ, Pearlson GD, Stevens M, Calhoun VD. A method for functional network connectivity among spatially independent resting-state components in schizophrenia. Neuroimage. 2008;39(4):1666–1681. doi: 10.1016/j.neuroimage.2007.11.001 Váša F, Romero-Garcia R, Kitzbichler MG, et al. Conservative and disruptive modes of adolescent change in human brain functional connectivity. Proc Natl Acad Sci U S A. 2020;117(6):3248–3253. doi: 10.1073/pnas.1906144117 Satterthwaite TD, Wolf DH, Loughead J, et al. Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth. Neuroimage. 2012;60(1):623–632. doi: 10.1016/j.neuroimage.2011.12.063 Satterthwaite TD, Wolf DH, Ruparel K, et al. Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth. Neuroimage. 2013;83:45–57. doi: 10.1016/j.neuroimage.2013.06.045 Marek S, Tervo-Clemmens B, Calabro FJ, et al. Towards Reproducible Brain-Wide Association Studies Affiliations. Johnny Uriarte. 2020;11:15–18. doi: 10.1101/2020.08.21.257758 Shine JM, Koyejo O, Poldrack RA. Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attention. Proc Natl Acad Sci U S A. 2016;113(35):9888–9891. doi: 10.1073/pnas.1604898113 Cole MW, Reynolds JR, Power JD, Repovs G, Anticevic A, Braver TS. Multi-task connectivity reveals flexible hubs for adaptive task control. Nat Neurosci. 2013;16(9):1348–1355. doi: 10.1038/nn.3470 Hutchison RM, Womelsdorf T, Allen EA, et al. Dynamic functional connectivity: Promise, issues, and interpretations. Neuroimage. 2013;80:360–378. doi: 10.1016/j.neuroimage.2013.05.079 Vanderwal T, Eilbott J, Kelly C, et al. Stability and similarity of the pediatric connectome as developmental measures. Neuroimage. 2021;226:117537. doi: 10.1016/j.neuroimage.2020.117537 Ousdal OT, Kaufmann T, Kolskår K, et al. Longitudinal stability of the brain functional connectome is associated with episodic memory performance in aging. Hum Brain Mapp. 2020;41(3):697–709. doi: 10.1002/hbm.24833 Wang H, Fan L, Song M, et al. Functional Connectivity Predicts Individual Development of Inhibitory Control during Adolescence. Cereb Cortex. 2021;31(5):2686–2700. doi: 10.1093/cercor/bhaa383 Ernst M, Torrisi S, Balderston N, Grillon C, Hale EA. FMRI functional connectivity applied to adolescent neurodevelopment. Annu Rev Clin Psychol. 2015;11:361–377. doi: 10.1146/annurev-clinpsy-032814-112753 Ferreira LK, Busatto GF. Resting-state functional connectivity in normal brain aging. Neurosci Biobehav Rev. 2013;37(3):384–400. doi: 10.1016/j.neubiorev.2013.01.017 Allen EA, Damaraju E, Eichele T, Wu L, Calhoun VD. EEG Signatures of Dynamic Functional Network Connectivity States. Brain Topogr. 2018;31(1):101–116. doi: 10.1007/s10548-017-0546-2 Chen T, Cai W, Ryali S, Supekar K, Menon V. Distinct Global Brain Dynamics and Spatiotemporal Organization of the Salience Network. PLoS Biol. 2016;14(6). doi: 10.1371/journal.pbio.1002469 Douw L, Wakeman DG, Tanaka N, Liu H, Stufflebeam SM. State-dependent variability of dynamic functional connectivity between frontoparietal and default networks relates to cognitive flexibility. Neuroscience. 2016;339:12–21. doi: 10.1016/j.neuroscience.2016.09.034 Elton A, Gao W. Task-related modulation of functional connectivity variability and its behavioral correlations. Hum Brain Mapp. 2015;36(8):3260–3272. doi: 10.1002/hbm.22847 Hutchison RM, Morton JB. Tracking the brain’s functional coupling dynamics over development. J Neurosci. 2015;35(17):6849–6859. doi: 10.1523/JNEUROSCI.4638-14.2015 Cohen JR. The behavioral and cognitive relevance of time-varying, dynamic changes in functional connectivity. Neuroimage. 2018;180:515–525. doi: 10.1016/j.neuroimage.2017.09.036 Kaiser RH, Whitfield-Gabrieli S, Dillon DG, et al. Dynamic Resting-State Functional Connectivity in Major Depression. Neuropsychopharmacology. 2016;41(7):1822–1830. doi: 10.1038/npp.2015.352 Banich MT, Milham MP, Atchley RA, et al. Prefrontal regions play a predominant role in imposing an attentional “set”: Evidence from fMRI. Cogn Brain Res. 2000;10(1–2):1–9. doi: 10.1016/S0926-6410(00)00015-X Li Y, Zhu Y, Nguchu BA, et al. Dynamic Functional Connectivity Reveals Abnormal Variability and Hyper-connected Pattern in Autism Spectrum Disorder. Autism Res. 2020;13(2):230–243. doi: 10.1002/aur.2212 Siebenhühner F, Weiss SA, Coppola R, Weinberger DR, Bassett DS. Intra- and Inter-Frequency Brain Network Structure in Health and Schizophrenia. PLoS One. 2013;8(8). doi: 10.1371/journal.pone.0072351 Chen G, Chen P, Gong JY, et al. Shared and specific patterns of dynamic functional connectivity variability of striato-cortical circuitry in unmedicated bipolar and major depressive disorders. Psychol Med. Published online 2020:1–10. doi: 10.1017/S0033291720002378 Young-Wolff KC, Sarovar V, Tucker LY, et al. Self-reported Daily, Weekly, and Monthly Cannabis Use among Women before and during Pregnancy. JAMA Netw Open. Published online 2019. doi: 10.1001/jamanetworkopen.2019.6471 Paul SE, Hatoum AS, Fine JD, et al. Associations between Prenatal Cannabis Exposure and Childhood Outcomes: Results from the ABCD Study. JAMA Psychiatry. 2021;78(1):64–76. doi: 10.1001/jamapsychiatry.2020.2902 Du Y, Fu Z, Sui J, et al. NeuroMark: an automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders. NeuroImage Clin. Published online August 11, 2020:102375. doi: https://doi.org/10.1016/j.nicl.2020.102375 Fu Z, Tu Y, Calhoun VD, et al. Dynamic functional network connectivity associated with post-traumatic stress symptoms in COVID-19 survivors. Neurobiol Stress. 2021;15. doi: 10.1016/j.ynstr.2021.100377 Tu Y, Fu Z, Mao C, et al. Distinct thalamocortical network dynamics are associated with the pathophysiology of chronic low back pain. Nat Commun. 2020;11(1). doi: 10.1038/s41467-020-17788-z Fu Z, Iraji A, Turner JA, et al. Dynamic state with covarying brain activity-connectivity: On the pathophysiology of schizophrenia. Neuroimage. 2021;224:117385. doi: 10.1016/j.neuroimage.2020.117385 LI kaicheng, Fu Z, Luo X, et al. The influence of cerebral small vessel disease on static and dynamic functional network connectivity in subjects along Alzheimer’s disease continuum. Brain Connect. Published online November 16, 2020. doi: 10.1089/brain.2020.0819 Fu Z, Sui J, Espinoza R, et al. Whole-brain Functional Connectivity Dynamics associated with Electroconvulsive Therapy Treatment Response. Biol Psychiatry Cogn Neurosci Neuroimaging . Published online July 23, 2021. doi: 10.1016/j.bpsc.2021.07.004 Fu Z, Sui J, Turner JA, et al. Dynamic functional network reconfiguration underlying the pathophysiology of schizophrenia and autism spectrum disorder. Hum Brain Mapp . Published online September 23, 2020:hbm.25205. doi: 10.1002/hbm.25205 Dick AS, Garcia NL, Pruden SM, et al. No evidence for a bilingual executive function advantage in the nationally representative ABCD study. Nat Hum Behav. 2019;3(7):692–701. doi: 10.1038/s41562-019-0609-3 Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc Ser B. 1995;57(1):289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x Bedard AC, Nichols S, Barbosa JA, Schachar R, Logan GD, Tannock R. The development of selective inhibitory control across the life span. Dev Neuropsychol. 2002;21(1):93–111. doi: 10.1207/S15326942DN2101_5 Pruim RHR, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage. 2015;112:267–277. doi: 10.1016/j.neuroimage.2015.02.064 Parkes L, Fulcher B, Yücel M, Fornito A. An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. Neuroimage. 2018;171:415–436. doi: 10.1016/j.neuroimage.2017.12.073 Patel AX, Kundu P, Rubinov M, et al. A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series. Neuroimage. 2014;95:287–304. doi: 10.1016/j.neuroimage.2014.03.012 Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage. 2012;59(3):2142–2154. doi: 10.1016/j.neuroimage.2011.10.018 Additional Declarations There is NO Competing Interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1742626","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":115540134,"identity":"9771b885-6c25-473b-af5b-b84a4a0d752f","order_by":0,"name":"Zening Fu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnElEQVRIiWNgGAWjYDAC5gOMDyCsBGK1sCUwG5CshU2CNC3ybTxmlV/+HGbgZ88xIE4LI1DLbdm2wwySPW+I1MIs32N2W7LhMIPBDWJtYWPjMSuWADrMnmgtPEAtjB/YgLZIEKtFgo2tWJqxLZ1H4syzAuK0yLcxb/z444+1HH978gbitIAAMw/QgcQrBwHGH6SpHwWjYBSMgpEGABhyJKjCpk2vAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-1591-4900","institution":"Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zening","middleName":"","lastName":"Fu","suffix":""},{"id":115540135,"identity":"cd9bcdf2-ac53-4b79-9ec4-08fd0cca8142","order_by":1,"name":"Jingyu Liu","email":"","orcid":"","institution":"TheTReNDS center, Neuroscience Institute Georgia State University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jingyu","middleName":"","lastName":"Liu","suffix":""},{"id":115540136,"identity":"22067ee9-3466-4cf3-8d30-41f6563050a3","order_by":2,"name":"Mustafa Salman","email":"","orcid":"","institution":"Georgia Institute of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mustafa","middleName":"","lastName":"Salman","suffix":""},{"id":115540137,"identity":"97f0b035-1ccb-4d74-aef8-420e95c81c54","order_by":3,"name":"Jing Sui","email":"","orcid":"","institution":"Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Sui","suffix":""},{"id":115540138,"identity":"ff19a0ec-a7cf-4973-9a36-748d18179165","order_by":4,"name":"Vince Calhoun","email":"","orcid":"https://orcid.org/0000-0001-9058-0747","institution":"The Mind Research Network/The Univ. of New Mexico","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Vince","middleName":"","lastName":"Calhoun","suffix":""}],"badges":[],"createdAt":"2022-06-09 16:41:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1742626/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1742626/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":23147688,"identity":"09a787f0-d0d6-47dd-9e0b-eb12ca60ede3","added_by":"auto","created_at":"2022-06-27 21:14:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2107134,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe flowchart of the FNC analysis to investigate cross-scan FNC stability.\u003c/strong\u003e \u003cstrong\u003ea)\u003c/strong\u003e Neuromark framework extracts robust functional components from the ABCD data. \u003cstrong\u003eb)\u003c/strong\u003e FNC is estimated using the TCs of components from each scan. \u003cstrong\u003ec)\u003c/strong\u003e \u003cem\u003eCross-scan\u003c/em\u003e FNC similarity is measured by the correlation between FNC from different scans. \u003cstrong\u003ed)\u003c/strong\u003e Individual identification is performed based on the cross-scan FNC similarity. \u003cstrong\u003ee)\u003c/strong\u003e Association analysis between intra-subject FNC stability and individuals' behaviors via the LMM.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-1742626/v1/8c92c88af82b49ae415a2c0e.png"},{"id":23147685,"identity":"77090dab-adb0-4fcd-8382-87667199e3c5","added_by":"auto","created_at":"2022-06-27 21:14:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3149519,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCross-scan\u003c/em\u003e FNC similarity within the same session and between longitudinal sessions.\u003c/strong\u003e FNC similarity is calculated using scans 1) within the baseline session; 2) within the second-year session; and 3) from longitudinal sessions. \u003cstrong\u003ea)\u003c/strong\u003e Subjects with the maximum and minimum FNC similarity within the baseline, the second-year follow-up and from cross-sessional.\u0026nbsp;\u003cstrong\u003eb)\u003c/strong\u003e Percentage of subjects with intra-subject FNC similarity larger than a given percentage of inter-subject FNC similarity. Higher intra-subject FNC similarity is observed between scans from the same session. Intra-subject FNC similarity between longitudinal sessions is higher if FNC is averaged across scans.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-1742626/v1/4dc050523e5dfeacb06b5700.png"},{"id":23147773,"identity":"463392a3-247c-4274-8354-db67d45d2513","added_by":"auto","created_at":"2022-06-27 21:19:43","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2255268,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIndividual identification using FNC across scans. a)\u003c/strong\u003e Identification accuracy based on the FNC similarity between the scans within the baseline session. Scan 1 is the database while other scans are the target. Individual identification is replicated using FNC of scans within the second-year session. \u003cstrong\u003eb)\u003c/strong\u003e individual identification is performed between scans from longitudinal sessions. Scans from the baseline session are the database and scans from the second-year session are the target. Identification accuracy drops when developmental brain changes are introduced. Averaging FNC across scans within each session can improve the performance of longitudinal identification.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1742626/v1/ec37805214726f0977a8172a.jpeg"},{"id":23147689,"identity":"151cb952-9734-4596-96bd-d1f7110931f1","added_by":"auto","created_at":"2022-06-27 21:14:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1790998,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCross-scan\u003c/em\u003e FNC stability is associated with children’s behaviors. \u003c/strong\u003eThe bar is the mean of FNC stability, and the error bar is the standard error of the mean (SEM) of FNC stability.\u003cstrong\u003e a)\u003c/strong\u003e Positive relationships between FNC stability and cognitive measures (FDR corrected, q \u0026lt; 0.05). Children with good cognitive performance tended to have higher \u003cem\u003ecross-scan\u003c/em\u003e FNC stability. \u003cstrong\u003eb)\u003c/strong\u003e Negative relationships between FNC stability and psychiatric problem scores (FDR corrected, q \u0026lt; 0.05). Children with high psychiatric problem scores tended to have lower \u003cem\u003ecross-scan\u003c/em\u003e FNC stability. \u003cstrong\u003ec)\u003c/strong\u003e Negative relationships between FNC stability and the children screen usage (FDR corrected, q \u0026lt; 0.05). Children with more screen usage (e.g., longer time watching TV and video) tended to have lower \u003cem\u003ecross-scan\u003c/em\u003e FNC stability. \u003cstrong\u003ed)\u003c/strong\u003e Negative relationships between FNC stability and the children’s sleep condition. (FDR corrected, q \u0026lt; 0.05). Children with bad sleep conditions (e.g., shorter sleep duration and longer time to fall asleep) tended to have lower \u003cem\u003ecross-scan\u003c/em\u003e FNC stability.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-1742626/v1/8b627482b87dcf2dfda8b91e.png"},{"id":23147686,"identity":"b71b7f6e-6571-476e-aa03-1e79dadb04f6","added_by":"auto","created_at":"2022-06-27 21:14:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1025051,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChildren’s FNC stability is associated with developmental history and their parents’ dimensional psychopathology. a) \u003c/strong\u003eNegative relationships between children’s FNC stability and their parents’ negative behaviors (e.g., question q12: I feel lonely). Children with parents having negative psychopathology tend to have lower \u003cem\u003ecross-scan\u003c/em\u003e FNC stability. \u003cstrong\u003eb) \u003c/strong\u003ePositive relationships between children’s FNC stability and their parents’ positive behaviors (e.g., question q15: I am pretty honest). Children with parents having positive behaviors tend to have higher \u003cem\u003ecross-scan\u003c/em\u003e FNC stability. \u003cstrong\u003ec)\u003c/strong\u003e Relationships between FNC stability and parents’ age at birth. The parents’ age is positively correlated with children’s FNC stability. \u003cstrong\u003ed)\u003c/strong\u003e Relationships between FNC and prenatal exposure. Prenatal exposure to tobacco and marijuana is associated with lower \u003cem\u003ecross-scan\u003c/em\u003e FNC stability in children.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-1742626/v1/94c6ab310e4c5020e477f95a.png"},{"id":24450340,"identity":"70dd37e4-f6f2-475b-b60a-51a488600a25","added_by":"auto","created_at":"2022-07-28 13:37:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2842093,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1742626/v1/16c1288e-200c-4535-ad51-279be844c116.pdf"},{"id":23147774,"identity":"e1efc679-4e39-40c5-b18d-4c8a694c8f86","added_by":"auto","created_at":"2022-06-27 21:19:44","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10257920,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary materials\u003c/p\u003e","description":"","filename":"Supplementarymaterial.doc","url":"https://assets-eu.researchsquare.com/files/rs-1742626/v1/b7af5ab4f9b65a11179fbd31.doc"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Functional Connectivity Uniqueness and Stability? A Signature of Cognitive and Psychiatric Problems in Children","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFunctional connectivity (FC) derived from functional magnetic resonance imaging (fMRI) data has shifted neuroimaging focus away from exploring localized brain activity toward characterizing co-activation patterns across distributed brain regions \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. FC has been associated with cognition \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e and various neurological \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e and mental \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e disorders. Although the population-based studies have revealed a great deal of knowledge on the FC organization profile, brain FC is assumed to be unique to individuals, regardless of how the brain is engaged during scanning \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. FC heterogeneity has long been appreciated in fMRI studies, even within the same population \u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. By using a multi-condition fMRI dataset from the Human Connectome Project, studies have shown that FC profile can distinguish adult subjects across scan sessions and even between distinct task conditions \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, acting as a \u0026ldquo;fingerprint\u0026rdquo;. A recent study has captured five different functional clusters in the pulvinar with specific connectivity fingerprints, which are associated with distinct components of cognition \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, an individual\u0026rsquo;s FC is not constant but continuously changes with remarkable variations at different scales, adapting to internal and external demands \u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. That is to say, besides the intrinsic patterns, FC can also exhibit prominent intra-subject variability, which might underly important biological mechanisms \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Many existing works have only examined the FC variation within a single scan \u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, and an individual\u0026rsquo;s \u003cem\u003ecross-scan\u003c/em\u003e FC variability has received relatively little attention from the neuroimaging field. A comprehensive understanding of \u003cem\u003ecross-scan\u003c/em\u003e FC is necessary for developing robust FC-based biomarkers.\u003c/p\u003e \u003cp\u003eUnlike adults, children show considerable growth and development in the brain \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and might exhibit more temporal variability in brain FC \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The temporal variation in resting-state FC declines with aging, especially for those FC involved in frontal-parietal and occipital networks \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Age is negatively associated with the variability of dynamic brain connectivity states captured by a clustering strategy on dynamic FC estimates \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The prominent variation in youths\u0026rsquo; FC might be due to more neuroplasticity in the adolescent\u0026rsquo;s brain \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e or be induced by larger motion artifacts \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e of the adolescent subjects, though this is still far from understood. On the other hand, some other studies assume that FC is unique to an adolescent and show that the FC profile can identify an adolescent from a group of subjects \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite such progress, we argue that the exploration of \u003cem\u003ecross-scan\u003c/em\u003e FC in youths has been limited, as most studies have used relatively small numbers of participants (e.g., sample size\u0026thinsp;\u0026lt;\u0026thinsp;1000). In addition, although previous studies have investigated the robustness of FC results across scans and between longitudinal sessions \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, their datasets are across a wide age range, where the age-induced FC differences might potentially influence the \u003cem\u003ecross-scan\u003c/em\u003e FC, especially during the adolescence \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. More importantly, most of the existing work has only focused on the fingerprint property of FC across scans, ignoring the intra-subject FC variation \u003cem\u003ecross-scan\u003c/em\u003e and its potential relationship with individual behavior in children. The stability of FC across scans may be linked to neural mechanisms, reflected by its relevance to adolescents\u0026rsquo; neurocognitive development, adverse mental problem outcomes, and other healthy backgrounds. Therefore, there is a need for a reliable large-scale study as the present one can include more than 9000 subjects with multiple scans collected from longitudinal sessions to comprehensively examine the relevance of the \u003cem\u003ecross-scan\u003c/em\u003e FC stability to individual differences in behavior and mental conditions.\u003c/p\u003e \u003cp\u003eHere, we investigate the \u003cem\u003ecross-scan\u003c/em\u003e FC stability in children using a multimodal database called Adolescent Brain Cognitive Development (ABCD). The ABCD database includes more than 11,800 subjects, with multiple scans collected from two longitudinal sessions. This dataset has collected a comprehensive range of measures related to mental problems, cognitions, and other health backgrounds \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e that have been shown useful for the investigation of the relationship between adolescent behaviors and brain functions \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Our hypothesis is twofold. First, the FC profile of children shows intrinsic patterns across scans, and the intrinsic patterns can identify a given child from a large group of subjects. We also expected this identification would be successful between longitudinal sessions, regardless of the developments and changes in the children\u0026rsquo;s brains. Our second hypothesis is that children\u0026rsquo;s FC also shows intra-subject variations across scans, which will be associated with distinct behavioral phenotypes in children, such as cognitive performance, psychiatric problems, and sleep conditions.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eFlowchart of the \u003cem\u003eCross-scan\u003c/em\u003e Functional Connectivity Analysis\u003c/h2\u003e\n\u003cp\u003eFig. 1 displays the flowchart of the cross-scan functional network connectivity (FNC) analysis. We first applied a Neurmark framework to extract robust intrinsic connectivity networks (ICNs) that are comparable across subjects, scans, and sessions. FNC was estimated using the time-courses (TCs) of ICNs from each scan. After obtaining the FNC matrix of each scan, \u003cem\u003ecross-scan\u003c/em\u003e FNC similarity was measured by the correlation between FNC from different scans. Then individual identification was performed based on the cross-scan FNC similarity. Finally, we examined the associations between intra-subject FNC similarity and individuals\u0026apos; behaviors via a linear mixed-effect model (LMM).\u003c/p\u003e\n\u003ch2\u003eFunctional Networks\u003c/h2\u003e\n\u003cp\u003e53 ICNs were extracted by the Neuromark framework, with activation peaks falling on the cortical and subcortical gray matter areas across the whole brain. The ICNs were assigned to seven different functional domains according to their anatomical locations and functional information \u003csup\u003e14\u003c/sup\u003e, including subcortical (SC), auditory (AUD), visual (VS), sensorimotor (SM), cognitive-control (CC), default-mode (DM), and cerebellar (CB) domains. Details of the spatial maps and coordinates of ICNs are provided in the supplementary materials.\u003c/p\u003e\n\u003ch2\u003eIntra-subject FNC Shows High Similarity across Scans\u003c/h2\u003e\n\u003cp\u003eFig. 2a displays the FNC of subjects with maximum and minimum intra-subject FNC similarities between scans. Children show different levels of cross-scan FNC similarity than each other. For example subject 1, the FNC of scan 1 and the FNC of scan 2 share the highest intra-subject similarity (r = 0.9448). In contrast, for example subject 2, the FNC show less stability between scan 1 and scan 2, where the intra-subject FNC similarity is only r = 0.1914. Fig. 2b displays the percentage of children with intra-subject FNC similarity larger than a given percentage of inter-subject FNC similarity, from 60% to 99%. Our results show that intra-subject FNC similarity is larger than a majority of inter-subject FNC similarity, though intra-subject FNC variability exists. The FNC shows the highest intra-subject similarity between scan 1 and scan 2. More than 90% of subjects have intra-subject FNC similarity larger than 60% of inter-subject FNC similarity and more than 65% of subjects have intra-subject FNC similarity larger than 99% of inter-subject FNC similarity. The intra-subject FNC shows the lowest similarity between scan 1 and scan 4. Still, more than 80% of subjects have intra-subject FNC similarity larger than 60% of inter-subject FNC similarity, and about 40% of subjects have intra-subject FNC similarity larger than 99% of inter-subject FNC similarity.\u003c/p\u003e\n\u003cp\u003eThe results are replicated by examining the scans from the second-year session. Similarly, subjects can have different levels of cross-scan FNC similarity. FNC shows the highest intra-subject similarity between scan 1 and scan 2 and the lowest intra-subject similarity between scan 1 and scan 4. FNC also shows intra-subject similarities between longitudinal scans. Although a two-year time interval between scans incurred a significant decrease in intra-subject similarity, the intra-subject similarity is still larger than the majority of inter-subject FNC similarity, especially when the FNC was averaged within the session.\u003c/p\u003e\n\u003ch2\u003eIndividual Identification using Whole-brain FNC\u003c/h2\u003e\n\u003cp\u003eFig. 3 shows the identification results of each pair of identification. At the baseline session, the identification accuracy was 93.99%, 84.78%, 81.87%, and 93.10% based on the database-target of scan 1-scan 2, the target-database of scan 1-scan 3, the target-database of scan 1-scan 4, and the target-database of scan 1-scan mean respectively. The identification was replicated by using the FNC of the second-year scans. Similar to the results from the baseline, the highest identification accuracy 95.16% was achieved based on the database-target of scan 1-scan 2, while the lowest identification accuracy 82.80% was achieved based on the database-target of scan 1-scan 4.\u003c/p\u003e\n\u003cp\u003eThe individual identification was further performed using the FNC from longitudinal scans. Scans from the baseline session were the database and scans from the second-year session were the target. Although more intra-subject FNC variations were introduced, the FNC of a child from the baseline session can still be used to identify his/her FNC from the second-year follow-up session. The highest accuracy was 91.43%, which was achieved by averaging the FNC across all four scans within each session before identification.\u003c/p\u003e\n\u003cp\u003eThe nonparametric permutation testing shows that the average identification accuracy was 50% if the identity was shuffled for each scan. The real identification accuracy was significantly higher than the accuracy obtained by the permutation tests.\u003c/p\u003e\n\u003ch2\u003eFNC Stability Correlates with Cognitive Performance\u003c/h2\u003e\n\u003cp\u003eWe noted that besides the intrinsic patterns, FNC also shows significant variability across scans and the mechanisms underlying the \u003cem\u003ecross-scan\u003c/em\u003e FNC stability are still unknown. In this study, we focused on children\u0026rsquo;s cognitive performance, mental problems, sleep conditions, and screen usage. These scores have been linked to a wide range of brain functions and structures in previous studies \u003csup\u003e33\u0026ndash;36\u003c/sup\u003e. The investigation of the associations between these scores and FNC stability might advance our understanding of the neural underpinnings of children\u0026rsquo;s behaviors. The cognitive measures were positively correlated with the intra-subject FNC stability (False discovery rate [FDR] corrected, q \u0026lt; 0.05). Specifically, 10 out of 10 of the cognitive summary scores were positively correlated with FNC stability, with correlation r values ranging from 0.0376 to 0.1070. The Total Composite Score was the score most significantly positively correlated with the FNC stability (r = 0.1070, Cohen\u0026rsquo;s d = 0.2152, p = 4.82\u0026times;10\u003csup\u003e-24\u003c/sup\u003e). For the neurocognitive battery in the subdomain, TPVT was the score most significantly positively correlated with the FNC stability (r = 0.0841, Cohen\u0026rsquo;s d = 0.1688, p = 1.54\u0026times;10\u003csup\u003e-15\u003c/sup\u003e) while TFT was the score least significantly positively correlated with intra-subject FNC stability (r = 0.0376, Cohen\u0026rsquo;s d = 0.0753, p = 3.68\u0026times;10\u003csup\u003e-4\u003c/sup\u003e). To better visualize the associations, we divided the children into four groups from low cognitive performance to high cognitive performance according to each cognitive score (group 1: 0%~25%, group 2: 25%~50%; group 3: 50%~75%, and group 4: 75%~100%) and the averaged \u003cem\u003ecross-scan\u003c/em\u003e FNC stability within each group is displayed using bar plots in Fig. 4a. Clear increasing trends can be observed along group 1 to group 4. Along with the scatter plots in Fig. 4a, our results indicate that children with good cognitive performance tended to have higher FNC stability.\u003c/p\u003e\n\u003ch2\u003eFNC Stability Correlates with Psychiatric Problems\u003c/h2\u003e\n\u003cp\u003eThe psychopathological measures of children were negatively correlated with the intra-subject FNC stability. 12 out of 20 psychiatric problem scores show significantly negative correlations with FNC stability, with r values ranging from -0.0257 to -0.0496 (FDR corrected, q \u0026lt; 0.05). The social problem score was the score most significantly negatively correlated with the FNC stability (r = -0.0496, Cohen\u0026rsquo;s d = -0.0992, p = 2.38\u0026times;10-6). Similarly, we divided the children into four groups according to each psychopathological measure. The mean and the standard error of the mean for the \u003cem\u003ecross-scan\u003c/em\u003e FNC stability of each group were displayed in Fig. 4b. The FNC stability show decreasing trends along group 1 to group 4, indicating that children with high psychiatric problem scores tended to have lower FNC stability.\u003c/p\u003e\n\u003ch2\u003eFNC Stability Correlates with Sleep Conditions and Screen Usage\u003c/h2\u003e\n\u003cp\u003eWe further found significant associations between FNC stability and the sleep conditions of children. The \u003cem\u003ecross-scan\u003c/em\u003e FNC stability was negatively correlated with the sleep duration score (r = -0.0752, Cohen\u0026rsquo;s d = -0.1508, p = 7.74\u0026times;10\u003csup\u003e-13\u003c/sup\u003e). In the ABCD measurement system, high sleep duration score indicates short sleep duration (1 = 9-11 hours; 2 = 8-9 hours; 3 = 7-8 hours; 4 = 5-7 hours; 5 = Less than 5 hours). The FNC stability was also negatively correlated with the score that evaluates how long an adolescent falls asleep (sleepdisturb2_p). A higher score in sleepdisturb2_p indicates a longer time to fall asleep. The FNC stability was negatively correlated with other sleep behaviors of adolescents, such as sleepdisturb24_p (evaluates a child feels unable to move when waking up in the morning) and sleepdisturb26_p (evaluates a child falls asleep suddenly in inappropriate situations). Higher scores in these measurements indicate more frequently that the event happens (1 = Never; 2 = Occasionally (once or twice per month or less); 3 = Sometimes (once or twice per week); 4 = Often (3 or 5 times per week); 5 = Always). The overall results indicate that children with worse sleep conditions (e.g., shorter sleep duration or longer time to fall asleep) tended to have lower FNC stability (Fig. 4d).\u003c/p\u003e\n\u003cp\u003eChildren\u0026rsquo;s screen usage is also negatively correlated with \u003cem\u003ecross-scan\u003c/em\u003e FNC stability. 14 out of 14 youth screen time utilization scores, including the use of television, internet, cell phone, and video games, show negative correlations with individuals\u0026rsquo; FNC stability (FDR corrected, q\u0026lt;0.05). Children with more screen usage tended to have lower FNC stability. Details of the correlations statistics can be found in the supplementary materials (Fig. 4c).\u003c/p\u003e\n\u003ch2\u003eFNC Stability Correlates with Parent Psychopathology and Prenatal Exposure\u003c/h2\u003e\n\u003cp\u003eMoreover, parental dimensional psychopathology showed significant correlations with their children\u0026rsquo;s FNC stability (Fig. 5a and b). Specifically, the positive questions in the parents\u0026rsquo; psychopathology assessment, including asr_q15_p (I am pretty honest), asr_q73_p (I meet my responsibilities to my family), asr_q88_p (I enjoy being with people), asr_q98_p (I like to help others), asr_q106_p (I try to be fair to others), asr_q123_p (I am a happy person), were positively correlated with the FNC stability of children with r values ranging from 0.0315 to 0.0583 (FDR corrected, q \u0026lt; 0.05). In contrast, the negative questions in the parents\u0026rsquo; psychopathology assessment were negatively correlated with the FNC stability of children with r values ranging from -0.0287 to -0.0482 (FDR corrected, q \u0026lt; 0.05). These results indicate that parents with positive behaviors will result in higher FNC stability in children while parents with negative behaviors will result in lower FNC stability in children.\u003c/p\u003e\n\u003cp\u003eOur analysis also showed that prenatal exposure before and during pregnancy was associated with FNC stability in children. Parents with prenatal exposure to tobacco and marijuana will result in lower FNC stability in children (Fig. 5d). Also, a planned pregnancy will result in higher FNC stability in children. The age of the parents during the pregnancy showed significant correlations with FNC stability as well. While older mothers will result in higher FNC stability in children, fathers\u0026rsquo; ages between 30~40 years old (when the child was born) result in the highest FNC stability in adolescents (Fig. 5c).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe Fingerprint Property of Children\u0026rsquo;s FNC\u003c/h2\u003e \u003cp\u003eBrain FC and its network analog, FNC, are believed to provide a window into brain function and intrinsic brain organization \u003csup\u003e2,37\u0026minus;39\u003c/sup\u003e. Neuroimaging studies have successfully established that adults\u0026rsquo; FC profile shows substantial inter-subject variability, and such variability can distinguish individuals from another scan \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Unlike adults, children might show more intra-subject variability in FC due to the developments in the brain \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. More heterogeneous brain states and confounding effects in youths (e.g., head motions) \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e might also influence the individual identifications. Recent research has found that the identification of youths is not significantly different from that of adults \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. However, this study relied on adolescents with a large age range (12\u0026thinsp;~\u0026thinsp;18 years). Brain developments at different ages can be different, potentially introducing unpredictable variability in the identification. Also, the results based on such a small sample size (N\u0026thinsp;=\u0026thinsp;140) can be biased by the sampling variability, further resulting in replication failures \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Our study used large data from the ABCD project with almost the same age to show that the \u003cem\u003ecross-scan\u003c/em\u003e FNC similarity can be robustly observed in children. We noted that the identification accuracy decreased and the intra-subject FNC variability increased as the time interval of scans increased. These results indicate that the assumption of the FNC uniqueness might oversimplify the interrelationships between brain regions. Accumulated evidence has shown that brain connectivity is highly dynamic with dramatic variations across scans, tasks, and time \u003csup\u003e\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. An individual\u0026rsquo;s FC will show both similarity and variability across scans, depending on the brain state in which the FC is measured \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Previous work has captured reliable single-subject variation of the functional connectome, which might be potentially associated with the mental health in the formative years \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, social skills \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, and memory performance in aging \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Via a large-scale analysis of more than 9000 children, our result provides further evidence that FC exhibits \u003cem\u003ecross-scan\u003c/em\u003e variability at an early age. Our result additionally suggests that \u003cem\u003ecross-scan\u003c/em\u003e FC variability might be indicative of brain state during imaging. In other words, as a subject stayed in the scanner for a longer time, their brain state might be far away from the initial \u0026ldquo;resting-state\u0026rdquo;, resulting in a larger difference between the brain FNC. However, future studies with simultaneously in-scanner state measures are clearly needed to elucidate the mechanistic link between \u003cem\u003ecross-scan\u003c/em\u003e variability and transient brain conditions.\u003c/p\u003e \u003cp\u003eWe successfully performed the individual identification between longitudinal scans with a two-year interval, although with reduced overall accuracy. This finding is in line with a previous result based on a relatively small sample size, which suggested that a larger time interval can incur a significant decrease in identification accuracy \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. We further found that averaging FNC across scans within each session can increase the accuracy. Growth and development inside the children\u0026rsquo;s brain will introduce FC variation intra-subject, which is associated with children\u0026rsquo;s neurodevelopment and behaviors \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Averaging FNC within the same session can mitigate the heterogeneity induced by transient brain states, but not the variability induced by brain development. Our result suggests that the decreased identification accuracy between longitudinal scans can be due to both brain development and the difference in the temporal brain conditions. The successfully longitudinal identification further supports that the FC profile contains fundamental properties that are unique to each child, regardless of the FC developments during adolescence \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIndividual FNC Stability and Children\u0026rsquo;s Behaviors\u003c/h2\u003e \u003cp\u003eBesides the intra-subject similarity, children\u0026rsquo;s FNC also showed intra-subject FNC variability across scans. The balance between \u003cem\u003ecross-scan\u003c/em\u003e FNC similarity and variability does not appear to be driven by random noise but might convey underlying physiological and psychological information in children. The \u003cem\u003ecross-scan\u003c/em\u003e FNC stability was positively correlated with cognitive performance, including reading recognition, pattern comparison, memory, etc. Previously, neuroimaging studies have been typically focusing on the FC strength and suggesting that its inter-subject variability is relevant to individual differences in behaviors \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. However, brain FC is not static, but with considerable variation between tasks and rest, across scans, and even within a single scan \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Spontaneous FC variations can predict the performance of different cognitive tasks \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Literature also showed that individuals with stable FC across-time show advanced cognitive performance, reflected by the increased accuracy and the more stable response time \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Our finding has extended the investigation of FC variability within a single scan to the investigation of FC stability across scans and showed robust relationships between \u003cem\u003ecross-scan\u003c/em\u003e FC stability and cognitions. A possible explanation of this finding is that the resting-state is in a \u0026ldquo;relaxed\u0026rdquo; brain condition that ameliorates adaptive reconfiguration of brain networks in the context of cognitive tasks. A stable FC during the execution of cognitive tasks is associated with successful cognition and more difficult task conditions require increased stability of FC \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. The stable FC during the resting-state might facilitate the brain switching from a relaxed condition to a task demand condition that purportedly requires sustained cognition, consequently resulting in better cognitive performance \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition to the correlations with cognitions, we found negative correlations between FNC stability and the dimensional psychopathology in children. Children with less \u003cem\u003ecross-scan\u003c/em\u003e FNC stability will have more dimensional psychopathological problems and more frequent mania episodes. This result is in line with a previous finding showing that the stability of FC is related to psychiatric disorders \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Although they did not identify different FC distinctiveness between groups in children (\u0026lt;\u0026thinsp;14 years), they found that adolescents with increased psychiatric symptom scores show less FC distinctiveness compared to controls \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Our result extends their finding by showing that the associations between FC stability and dimensional psychopathology exist during a very early age (9\u0026thinsp;~\u0026thinsp;11 years). One interpretation of these associations is that decreased FC stability underlies the dysregulated brain rhythms that characterize psychiatric problems. Of note, increased rumination is associated with higher medial prefrontal cortex to insula FC variability, suggesting that the intra-subject FC heterogeneity might trigger rumination by enhancing sensitivity to self-referential information \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. It is also suggested that the unstable FC may be associated with deficits in executive functioning and reflect weaknesses in brain circuits responsible for cognitive control \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. A growing body of literature has linked dynamic FC patterns to psychiatric problems. Individuals with autism spectrum disorder have larger FC variability in time, associated with the increase depending on autism symptom severity \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. By using magnetoencephalography, researchers have shown that schizophrenia patients exhibit more trials-to-trials network topology variability during a 2-back working memory task \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Increased FC variability has also been observed in both patients with depressed bipolar disorder and major depressive disorder, who shared overlapping symptoms that typically confound the diagnosis \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Our results complement the prior work by showing that an individual\u0026rsquo;s FNC exhibits reliable variability across scans, which might signify underlying biological mechanisms in mental health. The stability of \u003cem\u003ecross-scan\u003c/em\u003e FNC can add information to the connectivity strength and will be a potential brain biomarker that predicts early psychiatric problems in children.\u003c/p\u003e \u003cp\u003eInterestingly, our analysis further showed that the FNC stability of children is also relevant to parental conditions. The development history of children can be an important indicator of later mental and psychical behaviors in youths \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The increase in drug use among pregnant mothers has become a social problem in the US \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Prenatal cannabis exposure is associated with a greater risk for psychopathology in adolescents \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Here in this study, we found that prenatal tobacco and marijuana exposure is associated with lower \u003cem\u003ecross-scan\u003c/em\u003e FNC stability during middle childhood. Considering the associations between FNC stability and cognition and dimensional psychopathology, this result underscores the potential to use FNC stability to advance our understanding of the relationships between prenatal drug usage and cognitive developments and mental health among offspring. Another interesting finding of our present study is that the parents\u0026rsquo; psychopathology was correlated with the FNC stability of children, where the positive behaviors in parents were associated with higher FNC stability in their children while the negative behaviors in parents were associated with lower FNC stability in their children. We speculate that the family environment might influence the stability of FC in children. This speculation is supported by a further analysis showing a positive correlation between children\u0026rsquo;s FNC stability and neighborhood safety, an important living environmental factor. The inherited characteristic can be another cause of these relationships. Analysis including genetic data is needed in future studies for validating this hypothesis.\u003c/p\u003e \u003cp\u003eIn sum, our study revealed that children\u0026rsquo;s FNC shows certain similarities and substantial variability across scans from the same day and longitudinal sessions. The intrinsic patterns of FNC successfully identify each child from a large cohort of 9071 subjects. Besides, \u003cem\u003ecross-scan\u003c/em\u003e FNC stability carries important psychological and physiological information underlying distinct behavioral phenotypes in children. To the best of our knowledge, this is the first attempt to investigate the \u003cem\u003ecross-scan\u003c/em\u003e FNC on children on a large-scale. Given this foundation, future neuroimaging studies should move beyond the focus of the connectivity uniqueness to an individual, but to investigate the \u003cem\u003ecross-scan\u003c/em\u003e connectivity variability, probing how intra-subject stability and variability of an individual\u0026rsquo;s functional brain organization provide a window into neuropsychological mechanisms.\u003c/p\u003e \u003c/div\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and behavioral assessments\u003c/h2\u003e \u003cp\u003eThe present study used a longitudinal dataset shared by the ABCD, the largest long-term study of brain development and child health in the United States (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://abcdstudy.org/\u003c/span\u003e\u003cspan address=\"https://abcdstudy.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We used the release 2.01 of the ABCD dataset, containing over 11,800 children aged 9\u0026ndash;11 years, with two imaging sessions (baseline and the second-year) and multiple scans within each session. The ABCD study incorporated a comprehensive range of measures, including neurocognitive battery, physical and mental health assessments, socioeconomic, ethnic, and other health backgrounds, to assess predictors and outcomes related to different domains \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The parent\u0026rsquo;s full written informed consent and the child\u0026rsquo;s assent were obtained under protocols approved by the Institutional Review Board. We performed data quality control (QC) on the preprocessed fMRI data via the Neuromark framework \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. The Neuromark QC yields 9071 subjects for the baseline analysis, 2918 subjects for the second-year analysis, and 2290 subjects for the cross-sessional analysis; each analysis has at least four good resting-state fMRI scans within each session. The basic demographics can be found in Table I.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eTable I. Basic Demographics of Subjects\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic Demographics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecond-year\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Subject\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e (month)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119.06\u0026thinsp;\u0026plusmn;\u0026thinsp;7.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142.86\u0026thinsp;\u0026plusmn;\u0026thinsp;7.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e (F/M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4365/4706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1333/1585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeight\u003c/b\u003e (inch)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.28\u0026thinsp;\u0026plusmn;\u0026thinsp;3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.04\u0026thinsp;\u0026plusmn;\u0026thinsp;3.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeight\u003c/b\u003e (lbs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.97\u0026thinsp;\u0026plusmn;\u0026thinsp;23.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107.57\u0026thinsp;\u0026plusmn;\u0026thinsp;31.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e (W/B/H/A/O)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4771/1325/1863/181/929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1631/317/629/57/284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCognition\u003c/b\u003e (nihtbx_totalcomp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.44\u0026thinsp;\u0026plusmn;\u0026thinsp;8.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.08\u0026thinsp;\u0026plusmn;\u0026thinsp;10.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePsychiatric problem\u003c/b\u003e (cbcl_scr_syn_totprob)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.73\u0026thinsp;\u0026plusmn;\u0026thinsp;11.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.78\u0026thinsp;\u0026plusmn;\u0026thinsp;11.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep disturbance\u003c/b\u003e (sleepdisturb1_p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eNeuromark Framework\u003c/h2\u003e \u003cp\u003eTo capture reliable intrinsic connectivity networks (ICNs) and their corresponding TCs for each subject and each scan, a robust independent component analysis (ICA)-based framework called Neuromark \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e was applied to the ABCD data. Unlike atlas-based methods that typically assume fixed brain regions across subjects, the Neuromark can identify brain networks comparable across subjects while adapting single-subject variability with the networks. Two healthy controls datasets, the human connectome project (HCP, 823 subjects after the subject selection) and the genomics superstruct project (GSP, 1005 subjects after the subject selection) were used for the construction of the network templates. The reproducible templates were used as the spatial network priors to back-reconstruct spatial maps and TCs for the ABCD data. The effectiveness of Neuromark has been demonstrated in previous work, with a wide range of brain markers and abnormalities identified in different populations \u003csup\u003e\u003cspan additionalcitationids=\"CR67 CR68 CR69 CR70\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. More details of the Neuromark framework are provided in \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e and the supplementary materials.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Network Connectivity\u003c/h2\u003e \u003cp\u003eChildren\u0026rsquo;s data can be noisy with more confounding effects, such as larger head motions. We therefore performed four additional post-processing steps to carefully regress out the remaining noise in the TCs of ICNs: 1) detrending linear, quadratic, and cubic trends, 2) removal of detected outliers, 3) multiple regression of the head motions parameters (3 rotations and 3 translations) and their derivatives, 4) band-pass filtering with a cutoff frequency of 0.01 Hz-0.15 Hz. Pearson correlation coefficients between post-processed TCs were calculated to measure the FNC for each scan.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFNC Similarity and Subject Identification\u003c/h2\u003e \u003cp\u003eWe calculated the correlation between whole-brain FNC from different scans to measure the FNC similarity. Specifically, the correlation between FNC of scans from the same subject was defined as the intra-subject FNC similarity while the correlation between FNC of scans from different subjects was defined as the inter-subject FNC similarity. For each subject, there are one intra-subject similarity and 9070 inter-subject similarities. Then we calculated the percentage of children who have intra-subject FNC similarities larger than a given percentage of inter-subject FNC similarities (60%, 70%, 80%, 90%, 95%, and 99%).\u003c/p\u003e \u003cp\u003eWe performed individual identification using the FNC similarity. For each subject, we compared his/her intra-subject FNC similarity with a randomly picked inter-subject FNC similarity. The predicted identity was that with the larger correlation value. We performed this step for every subject to obtain an identification vector, which can be used to calculate the overall identification accuracy. The whole procedure was repeated 1000 times to estimate the distribution of the identification accuracy. The identification was also performed using domain-based FNC, and the results are provided in the supplementary materials.\u003c/p\u003e \u003cp\u003eWe further performed nonparametric permutation testing to assess the statistical significance of identification accuracy. We permuted subject identity for the FNC of scans to shuffle the intra-subject and inter-subject FNC similarity. The same identification was performed 1000 times on the permuted data to have the identification accuracy for the permuted data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eTest-retest Reliability and Longitudinal Identification\u003c/h2\u003e \u003cp\u003eWe further calculated the FNC similarity between other scans (between scan 1 and scan 3, between scan 1 and scan 4, and between scan 1 and scan mean [mean FNC across scans 2\u0026thinsp;~\u0026thinsp;4]) from the baseline session. We also calculated the FNC similarity between scans from the second-year session. The same identification was performed using the FNC similarity between scans. To investigate whether the FNC profile can identify an individual from a longitudinal scan while there are developmental changes in the brain, we measured the FNC similarity between a scan from the baseline session and a scan from the second-year session and then performed the identification based on the FNC similarity between longitudinal scans.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCognitive Measures\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003ecross-scan\u003c/em\u003e FNC stability was measured by the intra-subject similarity between FNC of scan 1 and mean FNC across scans 2\u0026thinsp;~\u0026thinsp;4. To show the test-retest reliability of the associations, we also replicated the results 1) using different FNC stability measures, 2) controlling for head motion in the LMM, 3) only using subjects with little head motion, and 4) using the second-year data. Details of the replication can be found in the supplementary materials.\u003c/p\u003e \u003cp\u003eWe first investigate the associations between FNC stability and cognitive assessments. The cognitive performance of each adolescent was measured via the NIH Cognition Battery Toolbox (abcd_tbss01)\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Higher scores indicate better cognitive performance. The NIH neurocognitive battery contains 7 distributional characteristics, including the Toolbox Picture Vocabulary Task (TPVT), the Toolbox Oral Reading Recognition Task (TORRT), the Toolbox Pattern Comparison Processing Speed Test (TPCPST), the Toolbox List Sorting Working Memory Test (TLSWMT), the Toolbox Picture Sequence Memory Test (TPSMT), the Toolbox Flanker Task (TFT), and the Toolbox Dimensional Change Card Sort Task (TDCCS). There are also 3 composite scores, including a Crystalized Intelligence Composite and a Fluid Intelligence Composite, and a Total Score Composite. In total 10 cognitive scores were used in the analysis. The detailed information on each score can be found in \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eMental Problem Measures\u003c/h2\u003e \u003cp\u003eThe associations between \u003cem\u003ecross-scan\u003c/em\u003e FNC stability and children\u0026rsquo;s mental health conditions were also investigated. The mental health conditions of children were measured by the Parent Child Behavior Checklist Scores (abcd_cbcls01). These checklist scores contain 11 syndrome scales related to psychiatric problems and 1 total Syndrome Scale, 6 DSM-Oriented scales, and 3 CBCL Scale2007 Scales. In total 20 scores from the Parent Child Behavior Checklist Scores were used for the investigation. The ABCD Parent General Behavior Inventory-Mania (abcd_pgbi01) was also used to assess the subsyndromal mania. It contains 10 scores that evaluate the children\u0026rsquo;s behaviors of mania. Higher scores on the mental health measures indicate dimensional psychopathology.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eSleep Conditions and Screen Usage\u003c/h2\u003e \u003cp\u003eThe assessments of sleep conditions and sleep disorders of children were measured by ABCD Parent Sleep Disturbance Scale for Children (abcd_sds01). It includes 26 questionnaires to evaluate the sleep disturbance of each child. For example, question 1 is \u0026ldquo;How many hours of sleep does your child get on most nights?\u0026rdquo; and question 2 is \u0026ldquo;How long after going to bed does your child usually fall asleep?\u0026rdquo;. The scores will be between 1 to 5, with a higher score indicating a worse sleep condition (e.g., fewer hours of sleep and longer time to fall asleep). The screen time utilization of youth, which is measured by the ABCD Youth Screen Time Survey (abcd_stq01) was also used to investigate its relationships with \u003cem\u003ecross-scan\u003c/em\u003e FNC stability. It contains 14 scores that evaluate the screen usage of a child during the weekdays and weekends, with higher scores indicating longer screen usage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eParental Behaviors and Prenatal Exposure\u003c/h2\u003e \u003cp\u003eWe were also interested in the potential relationships between parental factors and the children\u0026rsquo;s FNC stability. The prenatal exposure before and during pregnancy, measured by ABCD Developmental History Questionnaire (dhx01) was used for the investigation. We focused on prenatal exposure to tobacco, alcohol, and marijuana, and the parents\u0026rsquo; age when the child was born. The parent dimensional psychopathology, measured by ABCD Parent Adult Self Report Raw Scores Aseba (pasr01), was also used in the analysis. These scores evaluate parent dimensional psychopathology from either a positive question (e.g., question q15: I am pretty honest) or a negative question (e.g., question q12: I feel lonely). The higher scores in the positive question indicate a better condition of parents while the higher scores in the negative question indicate a worse dimensional psychopathological condition.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociation between\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eCross-scan\u003c/span\u003e \u003cb\u003eFNC Stability and Behaviors\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAn LMM was adopted to investigate the associations between intra-subject \u003cem\u003ecross-scan\u003c/em\u003e FNC stability and the behavioral assessments. The LMM was also used to examine the associations between the children\u0026rsquo;s FNC stability and their parents\u0026rsquo; conditions and neighborhood safety. The ABCD data contain related data at sites and within families due to twins and siblings. The LMM can model families nested within the site to take account of this effect. It has been successfully applied in previous ABCD studies and identified meaningful brain-wide associations with a wide range of individual behaviors \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. In this work, \u003cem\u003ecross-scan\u003c/em\u003e FNC stability was modeled as the dependent variable, while each score/behavior was modeled as a fixed effect. Age, gender, race, height, and weight that were considered as confounding effects were modeled as other fixed effects. The family structures and sites were modeled as random effects \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The correlation r-value, t-statistic, and effect size Cohen\u0026rsquo;s d was obtained for each association analysis to reflect the relationship between FNC stability and a behavioral score. The results are corrected by false discovery rate (FDR) correction \u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eConfounding Effect of the Head Motion in Children\u003c/h2\u003e \u003cp\u003eHead motion during fMRI collections can significantly influence the data quality, which may further impact the estimation of functional connectivity and its stability. Children have reduced inhibitory control, which may result in significantly more head motion than adults \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. In this study, we did not exclude those subjects with larger head motions because we want to retain more subjects to have larger statistical power in the analysis.\u003c/p\u003e \u003cp\u003eAlthough we did not use head motion for subject selection, we have carefully minimized the impacts of head motion in the estimation of FNC. First, we performed head motion correction in the preprocessing using the FSL toolbox. Second, the Neuromark is an ICA-based framework that is capable of extracting networks which are independent of the noise components, such as the head motion. The ICA-based strategy has been widely used for removing motion artifacts in previous studies \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. Third, after extracting the TCs of ICNs, additional postprocessing steps such as dispiking and multiple regression of the head motions parameters were performed to further minimize the influence of head motion on the TCs as well as the estimation of FNC.\u003c/p\u003e \u003cp\u003eTo further show the robustness of our findings, we calculated the mean framewise displacement (FD) \u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e,\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e for each scan and repeated our analysis by adding mean FD as one of the fixed effects in the LMM. The overall findings are consistent with the results we have in the main text. Additionally, we also replicated the majority of the findings after excluding those subjects with large head motions (subjects were excluded if one of the scans with head motion\u0026thinsp;\u0026gt;\u0026thinsp;0.3 mean FD). Details of these analyses and results are provided in the supplementary materials.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch1\u003eData Availability\u003c/h1\u003e\n\u003cp\u003eThe code of Neuromark framework and the Neuromark template have been released and integrated in the group ICA Toolbox (GIFT, https://trendscenter.org/software/gift/), which can be downloaded and used directly by users worldwide. The ABCD data used in the present study can be accessed upon application from NDA (https://nda.nih.gov/). Other MATLAB codes of this study can be obtained from the corresponding author.\u003c/p\u003e\n\u003ch1\u003eAcknowledgements\u003c/h1\u003e\n\u003cp\u003eThis work was supported by National Institutes of Health (No. R01DA049238 and R01MH118695, R01MH117107), National Science Foundation (No. 2112455). Use of the ABCD (https://abcdstudy.org/) dataset is acknowledged. A full list of supporters of ABCD project is available at https://abcdstudy.org/nih-collaborators.\u003c/p\u003e\n\u003ch1\u003eAuthor Contributions\u003c/h1\u003e\n\u003cp\u003eZening Fu and Vince Calhoun designed the study; Zening Fu performed the data analysis; Zening Fu, Jingyu Liu, Jing Sui, and Vince Calhoun wrote the paper. Mustafa Salman helped with data preprocessing. All authors contributed to the results interpretation and discussion.\u003c/p\u003e\n\u003ch1\u003eCompeting Interests\u003c/h1\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBiswal B, Zerrin Yetkin F, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magn Reson Med. 1995;34(4):537\u0026ndash;541. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/mrm.1910340409\u003c/span\u003e\u003cspan address=\"10.1002/mrm.1910340409\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiswal BB, Mennes M, Zuo XN, et al. Toward discovery science of human brain function. Proc Natl Acad Sci U S A. 2010;107(10):4734\u0026ndash;4739. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.0911855107\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0911855107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark HJ, Friston K. Structural and functional brain networks: From connections to cognition. Science (80-). 2013;342(6158). doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.1238411\u003c/span\u003e\u003cspan address=\"10.1126/science.1238411\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreicius M. \u003cem\u003eResting-State Functional Connectivity in Neuropsychiatric Disorders\u003c/em\u003e. Vol\u0026nbsp;21.; 2008. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamapsychiatry.2015.0484\u003c/span\u003e\u003cspan address=\"10.1001/jamapsychiatry.2015.0484\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArbabshirani MR, Plis S, Sui J, Calhoun VD. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage. 2017;145:137\u0026ndash;165. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2016.02.079\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2016.02.079\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArbabshirani MR, Kiehl KA, Pearlson GD, Calhoun VD. Classification of schizophrenia patients based on resting-state functional network connectivity. Front Neurosci. 2013;(7 JUL). doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnins.2013.00133\u003c/span\u003e\u003cspan address=\"10.3389/fnins.2013.00133\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinn ES, Shen X, Scheinost D, et al. Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nat Neurosci. 2015;18(11):1664\u0026ndash;1671. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nn.4135\u003c/span\u003e\u003cspan address=\"10.1038/nn.4135\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCribben I, Wager TD, Lindquist MA. Detecting functional connectivity change points for single-subject fMRI data. \u003cem\u003eFront Comput Neurosci\u003c/em\u003e. 2013;(OCT). doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fncom.2013.00143\u003c/span\u003e\u003cspan address=\"10.3389/fncom.2013.00143\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGordon EM, Nelson SM. Three types of individual variation in brain networks revealed by single-subject functional connectivity analyses. Curr Opin Behav Sci. 2021;40:79\u0026ndash;86. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cobeha.2021.02.014\u003c/span\u003e\u003cspan address=\"10.1016/j.cobeha.2021.02.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderson JS, Ferguson MA, Lopez-Larson M, Yurgelun-Todd D. Reproducibility of single-subject functional connectivity measurements. Am J Neuroradiol. 2011;32(3):548\u0026ndash;555. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3174/ajnr.A2330\u003c/span\u003e\u003cspan address=\"10.3174/ajnr.A2330\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi K, Wisner K, Atluri G. Feature selection framework for functional connectome fingerprinting. Hum Brain Mapp. 2021;42(12):3717\u0026ndash;3732. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hbm.25379\u003c/span\u003e\u003cspan address=\"10.1002/hbm.25379\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarar G, Rao B, Liu T. Functional connectome fingerprinting using shallow feedforward neural networks. Proc Natl Acad Sci U S A. 2021;118(15):2021. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.2021852118\u003c/span\u003e\u003cspan address=\"10.1073/pnas.2021852118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuedj C, Vuilleumier P. Functional connectivity fingerprints of the human pulvinar: Decoding its role in cognition. Neuroimage. 2020;221:117162. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2020.117162\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2020.117162\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD. Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex. 2014;24(3):663\u0026ndash;676. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/cercor/bhs352\u003c/span\u003e\u003cspan address=\"10.1093/cercor/bhs352\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuzs\u0026aacute;ki G. \u003cem\u003eRhythms of the Brain\u003c/em\u003e.; 2009. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/acprof:oso/9780195301069.001.0001\u003c/span\u003e\u003cspan address=\"10.1093/acprof:oso/9780195301069.001.0001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan den Heuvel MP, Hulshoff Pol HE. Exploring the brain network: A review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol. 2010;20(8):519\u0026ndash;534. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.euroneuro.2010.03.008\u003c/span\u003e\u003cspan address=\"10.1016/j.euroneuro.2010.03.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinn ES, Scheinost D, Finn DM, Shen X, Papademetris X, Constable RT. Can brain state be manipulated to emphasize individual differences in functional connectivity? Neuroimage. 2017;160:140\u0026ndash;151. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2017.03.064\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2017.03.064\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang C, Glover GH. Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage. 2010;50(1):81\u0026ndash;98. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2009.12.011\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2009.12.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZalesky A, Fornito A, Cocchi L, Gollo LL, Breakspear M. Time-resolved resting-state brain networks. Proc Natl Acad Sci U S A. 2014;111(28):10341\u0026ndash;10346. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.1400181111\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1400181111\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu Z, Du Y, Calhoun VD. The Dynamic Functional Network Connectivity Analysis Framework. Engineering. 2019;5(2):190\u0026ndash;193. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.eng.2018.10.001\u003c/span\u003e\u003cspan address=\"10.1016/j.eng.2018.10.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson SB, Blum RW, Giedd JN. Adolescent Maturity and the Brain: The Promise and Pitfalls of Neuroscience Research in Adolescent Health Policy. J Adolesc Heal. 2009;45(3):216\u0026ndash;221. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jadohealth.2009.05.016\u003c/span\u003e\u003cspan address=\"10.1016/j.jadohealth.2009.05.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, Wang W, Zhao X, et al. Age-related decline in the variation of dynamic functional connectivity: A resting state analysis. Front Aging Neurosci. 2017;9(JUN):203. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnagi.2017.00203\u003c/span\u003e\u003cspan address=\"10.3389/fnagi.2017.00203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia Y, Chen Q, Shi L, et al. Tracking the dynamic functional connectivity structure of the human brain across the adult lifespan. Hum Brain Mapp. 2019;40(3):717\u0026ndash;728. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hbm.24385\u003c/span\u003e\u003cspan address=\"10.1002/hbm.24385\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJalbrzikowski M, Larsen B, Hallquist MN, Foran W, Calabro F, Luna B. Development of White Matter Microstructure and Intrinsic Functional Connectivity Between the Amygdala and Ventromedial Prefrontal Cortex: Associations With Anxiety and Depression. Biol Psychiatry. 2017;82(7):511\u0026ndash;521. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.biopsych.2017.01.008\u003c/span\u003e\u003cspan address=\"10.1016/j.biopsych.2017.01.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJalbrzikowski M, Liu F, Foran W, et al. Functional connectome fingerprinting accuracy in youths and adults is similar when examined on the same day and 1.5-years apart. Hum Brain Mapp. 2020;41(15):4187\u0026ndash;4199. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hbm.25118\u003c/span\u003e\u003cspan address=\"10.1002/hbm.25118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePower JD, Cohen AL, Nelson SM, et al. Functional Network Organization of the Human Brain. Neuron. 2011;72(4):665\u0026ndash;678. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuron.2011.09.006\u003c/span\u003e\u003cspan address=\"10.1016/j.neuron.2011.09.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemeter D V., Engelhardt LE, Mallett R, et al. Functional Connectivity Fingerprints at Rest Are Similar across Youths and Adults and Vary with Genetic Similarity. iScience. 2020;23(1):100801. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.isci.2019.100801\u003c/span\u003e\u003cspan address=\"10.1016/j.isci.2019.100801\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSato JR, Biazoli CE, Zugman A, et al. Long-term stability of the cortical volumetric profile and the functional human connectome throughout childhood and adolescence. Eur J Neurosci. 2021;54(6):6187\u0026ndash;6201. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/ejn.15435\u003c/span\u003e\u003cspan address=\"10.1111/ejn.15435\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinn ES, Rosenberg MD. Beyond fingerprinting: Choosing predictive connectomes over reliable connectomes. Neuroimage. 2021;239:118254. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2021.118254\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2021.118254\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaufmann T, Aln\u0026aelig;s D, Doan NT, Brandt CL, Andreassen OA, Westlye LT. Delayed stabilization and individualization in connectome development are related to psychiatric disorders. Nat Neurosci. 2017;20(4):513\u0026ndash;515. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nn.4511\u003c/span\u003e\u003cspan address=\"10.1038/nn.4511\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarch DM, Albaugh MD, Avenevoli S, et al. Demographic, physical and mental health assessments in the adolescent brain and cognitive development study: Rationale and description. Dev Cogn Neurosci. 2018;32:55\u0026ndash;66. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.dcn.2017.10.010\u003c/span\u003e\u003cspan address=\"10.1016/j.dcn.2017.10.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuciana M, Bjork JM, Nagel BJ, et al. Adolescent neurocognitive development and impacts of substance use: Overview of the adolescent brain cognitive development (ABCD) baseline neurocognition battery. Dev Cogn Neurosci. 2018;32:67\u0026ndash;79. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.dcn.2018.02.006\u003c/span\u003e\u003cspan address=\"10.1016/j.dcn.2018.02.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng W, Rolls E, Gong W, et al. Sleep duration, brain structure, and psychiatric and cognitive problems in children. Mol Psychiatry. 2021;26(8):3992\u0026ndash;4003. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41380-020-0663-2\u003c/span\u003e\u003cspan address=\"10.1038/s41380-020-0663-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGorham LS, Jernigan T, Hudziak J, Barch DM. Involvement in Sports, Hippocampal Volume, and Depressive Symptoms in Children. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019;4(5):484\u0026ndash;492. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bpsc.2019.01.011\u003c/span\u003e\u003cspan address=\"10.1016/j.bpsc.2019.01.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDutil C, Walsh JJ, Featherstone RB, et al. Influence of sleep on developing brain functions and structures in children and adolescents: A systematic review. Sleep Med Rev. 2018;42:184\u0026ndash;201. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.smrv.2018.08.003\u003c/span\u003e\u003cspan address=\"10.1016/j.smrv.2018.08.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaulus MP, Squeglia LM, Bagot K, et al. Screen media activity and brain structure in youth: Evidence for diverse structural correlation networks from the ABCD study. Neuroimage. 2019;185:140\u0026ndash;153. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2018.10.040\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2018.10.040\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiswal B, Zerrin Yetkin F, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magn Reson Med. 1995;34(4):537\u0026ndash;541. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/mrm.1910340409\u003c/span\u003e\u003cspan address=\"10.1002/mrm.1910340409\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLondei A, D\u0026rsquo;Ausilio A, Basso D, Belardinelli MO. A new method for detecting causality in fMRI data of cognitive processing. Cogn Process. 2006;7(1):42\u0026ndash;52. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10339-005-0019-5\u003c/span\u003e\u003cspan address=\"10.1007/s10339-005-0019-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJafri MJ, Pearlson GD, Stevens M, Calhoun VD. A method for functional network connectivity among spatially independent resting-state components in schizophrenia. Neuroimage. 2008;39(4):1666\u0026ndash;1681. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2007.11.001\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2007.11.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV\u0026aacute;ša F, Romero-Garcia R, Kitzbichler MG, et al. Conservative and disruptive modes of adolescent change in human brain functional connectivity. Proc Natl Acad Sci U S A. 2020;117(6):3248\u0026ndash;3253. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.1906144117\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1906144117\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSatterthwaite TD, Wolf DH, Loughead J, et al. Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth. Neuroimage. 2012;60(1):623\u0026ndash;632. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2011.12.063\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2011.12.063\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSatterthwaite TD, Wolf DH, Ruparel K, et al. Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth. Neuroimage. 2013;83:45\u0026ndash;57. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2013.06.045\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2013.06.045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarek S, Tervo-Clemmens B, Calabro FJ, et al. Towards Reproducible Brain-Wide Association Studies Affiliations. Johnny Uriarte. 2020;11:15\u0026ndash;18. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2020.08.21.257758\u003c/span\u003e\u003cspan address=\"10.1101/2020.08.21.257758\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShine JM, Koyejo O, Poldrack RA. Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attention. Proc Natl Acad Sci U S A. 2016;113(35):9888\u0026ndash;9891. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.1604898113\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1604898113\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCole MW, Reynolds JR, Power JD, Repovs G, Anticevic A, Braver TS. Multi-task connectivity reveals flexible hubs for adaptive task control. Nat Neurosci. 2013;16(9):1348\u0026ndash;1355. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nn.3470\u003c/span\u003e\u003cspan address=\"10.1038/nn.3470\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHutchison RM, Womelsdorf T, Allen EA, et al. Dynamic functional connectivity: Promise, issues, and interpretations. Neuroimage. 2013;80:360\u0026ndash;378. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2013.05.079\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2013.05.079\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVanderwal T, Eilbott J, Kelly C, et al. Stability and similarity of the pediatric connectome as developmental measures. Neuroimage. 2021;226:117537. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2020.117537\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2020.117537\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOusdal OT, Kaufmann T, Kolsk\u0026aring;r K, et al. Longitudinal stability of the brain functional connectome is associated with episodic memory performance in aging. Hum Brain Mapp. 2020;41(3):697\u0026ndash;709. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hbm.24833\u003c/span\u003e\u003cspan address=\"10.1002/hbm.24833\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, Fan L, Song M, et al. Functional Connectivity Predicts Individual Development of Inhibitory Control during Adolescence. Cereb Cortex. 2021;31(5):2686\u0026ndash;2700. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/cercor/bhaa383\u003c/span\u003e\u003cspan address=\"10.1093/cercor/bhaa383\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErnst M, Torrisi S, Balderston N, Grillon C, Hale EA. FMRI functional connectivity applied to adolescent neurodevelopment. Annu Rev Clin Psychol. 2015;11:361\u0026ndash;377. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1146/annurev-clinpsy-032814-112753\u003c/span\u003e\u003cspan address=\"10.1146/annurev-clinpsy-032814-112753\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerreira LK, Busatto GF. Resting-state functional connectivity in normal brain aging. Neurosci Biobehav Rev. 2013;37(3):384\u0026ndash;400. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neubiorev.2013.01.017\u003c/span\u003e\u003cspan address=\"10.1016/j.neubiorev.2013.01.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllen EA, Damaraju E, Eichele T, Wu L, Calhoun VD. EEG Signatures of Dynamic Functional Network Connectivity States. Brain Topogr. 2018;31(1):101\u0026ndash;116. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10548-017-0546-2\u003c/span\u003e\u003cspan address=\"10.1007/s10548-017-0546-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen T, Cai W, Ryali S, Supekar K, Menon V. Distinct Global Brain Dynamics and Spatiotemporal Organization of the Salience Network. PLoS Biol. 2016;14(6). doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pbio.1002469\u003c/span\u003e\u003cspan address=\"10.1371/journal.pbio.1002469\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDouw L, Wakeman DG, Tanaka N, Liu H, Stufflebeam SM. State-dependent variability of dynamic functional connectivity between frontoparietal and default networks relates to cognitive flexibility. Neuroscience. 2016;339:12\u0026ndash;21. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroscience.2016.09.034\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroscience.2016.09.034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElton A, Gao W. Task-related modulation of functional connectivity variability and its behavioral correlations. Hum Brain Mapp. 2015;36(8):3260\u0026ndash;3272. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hbm.22847\u003c/span\u003e\u003cspan address=\"10.1002/hbm.22847\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHutchison RM, Morton JB. Tracking the brain\u0026rsquo;s functional coupling dynamics over development. J Neurosci. 2015;35(17):6849\u0026ndash;6859. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1523/JNEUROSCI.4638-14.2015\u003c/span\u003e\u003cspan address=\"10.1523/JNEUROSCI.4638-14.2015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen JR. The behavioral and cognitive relevance of time-varying, dynamic changes in functional connectivity. Neuroimage. 2018;180:515\u0026ndash;525. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2017.09.036\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2017.09.036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaiser RH, Whitfield-Gabrieli S, Dillon DG, et al. Dynamic Resting-State Functional Connectivity in Major Depression. Neuropsychopharmacology. 2016;41(7):1822\u0026ndash;1830. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/npp.2015.352\u003c/span\u003e\u003cspan address=\"10.1038/npp.2015.352\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanich MT, Milham MP, Atchley RA, et al. Prefrontal regions play a predominant role in imposing an attentional \u0026ldquo;set\u0026rdquo;: Evidence from fMRI. Cogn Brain Res. 2000;10(1\u0026ndash;2):1\u0026ndash;9. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0926-6410(00)00015-X\u003c/span\u003e\u003cspan address=\"10.1016/S0926-6410(00)00015-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Zhu Y, Nguchu BA, et al. Dynamic Functional Connectivity Reveals Abnormal Variability and Hyper-connected Pattern in Autism Spectrum Disorder. Autism Res. 2020;13(2):230\u0026ndash;243. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/aur.2212\u003c/span\u003e\u003cspan address=\"10.1002/aur.2212\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiebenh\u0026uuml;hner F, Weiss SA, Coppola R, Weinberger DR, Bassett DS. Intra- and Inter-Frequency Brain Network Structure in Health and Schizophrenia. PLoS One. 2013;8(8). doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0072351\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0072351\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen G, Chen P, Gong JY, et al. Shared and specific patterns of dynamic functional connectivity variability of striato-cortical circuitry in unmedicated bipolar and major depressive disorders. Psychol Med. Published online 2020:1\u0026ndash;10. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/S0033291720002378\u003c/span\u003e\u003cspan address=\"10.1017/S0033291720002378\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoung-Wolff KC, Sarovar V, Tucker LY, et al. Self-reported Daily, Weekly, and Monthly Cannabis Use among Women before and during Pregnancy. JAMA Netw Open. Published online 2019. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamanetworkopen.2019.6471\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2019.6471\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaul SE, Hatoum AS, Fine JD, et al. Associations between Prenatal Cannabis Exposure and Childhood Outcomes: Results from the ABCD Study. JAMA Psychiatry. 2021;78(1):64\u0026ndash;76. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamapsychiatry.2020.2902\u003c/span\u003e\u003cspan address=\"10.1001/jamapsychiatry.2020.2902\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu Y, Fu Z, Sui J, et al. NeuroMark: an automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders. NeuroImage Clin. Published online August 11, 2020:102375. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.nicl.2020.102375\u003c/span\u003e\u003cspan address=\"10.1016/j.nicl.2020.102375\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu Z, Tu Y, Calhoun VD, et al. Dynamic functional network connectivity associated with post-traumatic stress symptoms in COVID-19 survivors. Neurobiol Stress. 2021;15. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ynstr.2021.100377\u003c/span\u003e\u003cspan address=\"10.1016/j.ynstr.2021.100377\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTu Y, Fu Z, Mao C, et al. Distinct thalamocortical network dynamics are associated with the pathophysiology of chronic low back pain. Nat Commun. 2020;11(1). doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-020-17788-z\u003c/span\u003e\u003cspan address=\"10.1038/s41467-020-17788-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu Z, Iraji A, Turner JA, et al. Dynamic state with covarying brain activity-connectivity: On the pathophysiology of schizophrenia. Neuroimage. 2021;224:117385. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2020.117385\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2020.117385\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLI kaicheng, Fu Z, Luo X, et al. The influence of cerebral small vessel disease on static and dynamic functional network connectivity in subjects along Alzheimer\u0026rsquo;s disease continuum. Brain Connect. Published online November 16, 2020. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/brain.2020.0819\u003c/span\u003e\u003cspan address=\"10.1089/brain.2020.0819\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu Z, Sui J, Espinoza R, et al. Whole-brain Functional Connectivity Dynamics associated with Electroconvulsive Therapy Treatment Response. \u003cem\u003eBiol Psychiatry Cogn Neurosci Neuroimaging\u003c/em\u003e. Published online July 23, 2021. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bpsc.2021.07.004\u003c/span\u003e\u003cspan address=\"10.1016/j.bpsc.2021.07.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu Z, Sui J, Turner JA, et al. Dynamic functional network reconfiguration underlying the pathophysiology of schizophrenia and autism spectrum disorder. \u003cem\u003eHum Brain Mapp\u003c/em\u003e. Published online September 23, 2020:hbm.25205. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hbm.25205\u003c/span\u003e\u003cspan address=\"10.1002/hbm.25205\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDick AS, Garcia NL, Pruden SM, et al. No evidence for a bilingual executive function advantage in the nationally representative ABCD study. Nat Hum Behav. 2019;3(7):692\u0026ndash;701. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41562-019-0609-3\u003c/span\u003e\u003cspan address=\"10.1038/s41562-019-0609-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc Ser B. 1995;57(1):289\u0026ndash;300. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.2517-6161.1995.tb02031.x\u003c/span\u003e\u003cspan address=\"10.1111/j.2517-6161.1995.tb02031.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBedard AC, Nichols S, Barbosa JA, Schachar R, Logan GD, Tannock R. The development of selective inhibitory control across the life span. Dev Neuropsychol. 2002;21(1):93\u0026ndash;111. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1207/S15326942DN2101_5\u003c/span\u003e\u003cspan address=\"10.1207/S15326942DN2101_5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePruim RHR, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage. 2015;112:267\u0026ndash;277. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2015.02.064\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2015.02.064\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParkes L, Fulcher B, Y\u0026uuml;cel M, Fornito A. An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. Neuroimage. 2018;171:415\u0026ndash;436. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2017.12.073\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2017.12.073\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel AX, Kundu P, Rubinov M, et al. A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series. Neuroimage. 2014;95:287\u0026ndash;304. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2014.03.012\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2014.03.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePower JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage. 2012;59(3):2142\u0026ndash;2154. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2011.10.018\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2011.10.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"children, cross-scan functional connectivity stability, cognition, psychopathology, sleep","lastPublishedDoi":"10.21203/rs.3.rs-1742626/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1742626/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBrain functional connectivity (FC) derived from functional magnetic resonance imaging (fMRI) has been serving as a potential \u0026ldquo;fingerprint\u0026rdquo; for adults. However, intra-subject variation of FC can be substantial and carry biologically meaningful information, especially during adolescence, a time of intense brain changes. Here, for the first time, we performed a large-scale analysis on \u003cem\u003ecross-scan\u003c/em\u003e FC stability and its association with a diverse range of health measures in children from the Adolescent Brain Cognitive Development (ABCD) data. Functional network connectivity (FNC), a network analog of FC, was extracted via an automated independent component analysis framework on 9071 subjects (age: 9\u0026thinsp;~\u0026thinsp;11 years) and compared across 4 scans. We found that the FNC profile can identify a given child from a large group with high accuracy (\u0026gt;\u0026thinsp;80%), though intra-subject variations exist \u003cem\u003ecross-scan\u003c/em\u003e. The robustness of this finding is verified by replicating the child identification using the second-year scans and between longitudinal scans with a two-year interval. Moreover, \u003cem\u003ecross-scan\u003c/em\u003e FNC stability was predictive of sleep condition, cognitive performance, and psychiatric problems in children, with higher stability correlated with better cognitive performance, longer sleep duration, and less psychotic expression. Meanwhile, parental psychopathology and prenatal exposure were associated with the FNC stability in their children. Overall, our findings show that a child\u0026rsquo;s connectivity profile is not only intrinsic but also exhibits reliable variability \u003cem\u003ecross-scan\u003c/em\u003e, regardless of brain growth and development. The intra-subject connectivity stability may serve as a valuable biomarker to draw inferences on early cognitive and psychiatric behaviors in children.\u003c/p\u003e","manuscriptTitle":"Functional Connectivity Uniqueness and Stability? A Signature of Cognitive and Psychiatric Problems in Children","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-06-27 21:14:41","doi":"10.21203/rs.3.rs-1742626/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7827919a-bcfa-44a3-98b9-a98416f07fa7","owner":[],"postedDate":"June 27th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-01-30T11:52:16+00:00","versionOfRecord":[],"versionCreatedAt":"2022-06-27 21:14:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1742626","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1742626","identity":"rs-1742626","version":["v1"]},"buildId":"-D5TCW68w8eVRRLyjaTIo","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}