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Beyond Average Outcomes: A Latent Profile Analysis of Diverse Developmental Trajectories in Preterm and Early Term-Born Children from the Adolescent Brain Cognitive Development Study

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Title: Beyond Average Outcomes: A Latent Profile Analysis of Diverse Developmental Trajectories in Preterm and Early Term-Born Children from the Adolescent Brain Cognitive Development Study
Language: English
Authors: Iris Menu (ORCID 0000-0001-7587-2493), Lanxin Ji (ORCID 0000-0003-4509-0225), Tanya Bhatia, Mark Duffy (ORCID 0009-0001-0243-2267), Cassandra L. Hendrix (ORCID 0000-0002-1462-3941), Moriah E. Thomason (ORCID 0000-0001-9745-1147)
Source: Child Development. 2025 96(1):36-54.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 19
Publication Date: 2025
Sponsoring Agency: National Institutes of Health (NIH) (DHHS)
Contract Number: DA055338
ES03229
K99MH133978
MH122447
MH125870
MH126468
Document Type: Journal Articles
Reports - Research
Descriptors: Child Development, Premature Infants, Cognitive Development, Scores, Academic Achievement, Public Health, Profiles, Behavior Patterns, Brain Hemisphere Functions, Intervention, Clinical Diagnosis, Adolescents, Screening Tests
DOI: 10.1111/cdev.14143
ISSN: 0009-3920
1467-8624
Abstract: Preterm birth poses a major public health challenge, with significant and heterogeneous developmental impacts. Latent profile analysis was applied to the National Institutes of Health Toolbox performance of 1891 healthy prematurely born children from the Adolescent Brain and Cognitive Development study (970 boys, 921 girls; 10.00 ± 0.61 years; 1.3% Asian, 13.7% Black, 17.5% Hispanic, 57.0% White, 10.4% Other). Three distinct neurocognitive profiles emerged: consistently performing above the norm (19.7%), mixed scores (41.0%), and consistently performing below the norm (39.3%). These profiles were associated with lasting cognitive, neural, behavioral, and academic differences. These findings underscore the importance of recognizing diverse developmental trajectories in prematurely born children, advocating for personalized diagnosis and intervention to enhance care strategies and long-term outcomes for this heterogeneous population.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1455437
Database: ERIC
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  Value: <anid>AN0181984181;cdv01jan.25;2025Jan03.02:49;v2.2.500</anid> <title id="AN0181984181-1">Beyond average outcomes: A latent profile analysis of diverse developmental trajectories in preterm and early term‐born children from the Adolescent Brain Cognitive Development study </title> <p>Preterm birth poses a major public health challenge, with significant and heterogeneous developmental impacts. Latent profile analysis was applied to the National Institutes of Health Toolbox performance of 1891 healthy prematurely born children from the Adolescent Brain and Cognitive Development study (970 boys, 921 girls; 10.00 ± 0.61 years; 1.3% Asian, 13.7% Black, 17.5% Hispanic, 57.0% White, 10.4% Other). Three distinct neurocognitive profiles emerged: consistently performing above the norm (19.7%), mixed scores (41.0%), and consistently performing below the norm (39.3%). These profiles were associated with lasting cognitive, neural, behavioral, and academic differences. These findings underscore the importance of recognizing diverse developmental trajectories in prematurely born children, advocating for personalized diagnosis and intervention to enhance care strategies and long‐term outcomes for this heterogeneous population.</p> <p></p> <ulist> <item> Abbreviations</item> <p></p> <item> ABCD Study® Adolescent Brain Cognitive Development study</item> <p></p> <item> ADHD attention‐deficit/hyperactivity disorder</item> <p></p> <item> ANOVA analysis of variance</item> <p></p> <item> ASD autism spectrum disorder</item> <p></p> <item> BIC Bayesian information criterion</item> <p></p> <item> CBCL Child Behavior Checklist</item> <p></p> <item> FC functional connectivity</item> <p></p> <item> LPA latent profile analysis</item> <p></p> <item> NIHTB National Institutes of Health Toolbox</item> <p></p> <item> ROI regions of interest</item> <p></p> <item> T 1 w T 1 ‐weighted</item> <p></p> <item> T 2 w T 2 ‐weighted</item> </ulist> <p>In 2020, it was estimated that 13.4 million babies were born prematurely (i.e., before 37 weeks of gestation) worldwide: his represents nearly 1 in 10 births (Ohuma et al., [<reflink idref="bib45" id="ref1">45</reflink>]) and makes preterm birth a major public health issue (Butler & Behrman, [<reflink idref="bib8" id="ref2">8</reflink>]). The implications of preterm birth are profound, acting as a risk factor for a range of neurodevelopmental disorders including autism spectrum disorder (ASD; Laverty et al., [<reflink idref="bib35" id="ref3">35</reflink>]) and attention‐deficit/hyperactivity disorder (ADHD; Fitzallen et al., [<reflink idref="bib20" id="ref4">20</reflink>]), as well as psychiatric disorders such as psychosis (Vanes et al., [<reflink idref="bib59" id="ref5">59</reflink>]). The risks are not limited to particular pathologies but encompass a broader range of developmental cognitive issues: Almost one in six very preterm infants display cognitive or motor delay by the age of 2 (Pascal et al., [<reflink idref="bib47" id="ref6">47</reflink>]). A comprehensive meta‐analysis found that preterm birth is linked with medium to large deficits in three critical cognitive domains: executive function, processing speed, and intelligence (Brydges et al., [<reflink idref="bib6" id="ref7">6</reflink>]). These deficits can then cascade onto reading and mathematical abilities, resulting in overall lower academic achievement (Rose et al., [<reflink idref="bib51" id="ref8">51</reflink>]; for a systematic review and meta‐analysis of academic outcomes in children born preterm, see McBryde et al., [<reflink idref="bib40" id="ref9">40</reflink>]). Importantly, these outcomes appear not to have improved over time (Cheong et al., [<reflink idref="bib11" id="ref10">11</reflink>]). Ultimately, adults born preterm manifest "higher rates of neurodevelopmental disability, academic difficulties, and mental health problems," along with "delayed maturation; mild motor impairments; slower information processing; specific impairments of memory, visual perception or language processing; executive dysfunction; limitations in mental flexibility; and social difficulties are common" (p. 333, Allen et al., [<reflink idref="bib3" id="ref11">3</reflink>]) although considerable variability in outcomes has been observed in adulthood (Vollmer & Stålnacke, [<reflink idref="bib60" id="ref12">60</reflink>]).</p> <p>However, the conventional symptom‐based approach falls short in capturing the broad heterogeneity of neuropsychological profiles present in preterm‐born children, including those who do not exhibit apparent clinical symptoms. This limitation hinders progress in pinpointing potential risk for long‐term developmental deficits, the implementation of appropriate interventions, and the understanding of underlying mechanisms of these deficits. In the era of precision psychiatry, cluster approaches have enhanced understanding of major psychological disorders, such as schizophrenia, opening avenues for more precise treatment options (Alkan & Evans, [<reflink idref="bib2" id="ref13">2</reflink>]; Habtewold et al., [<reflink idref="bib24" id="ref14">24</reflink>]; Ji et al., [<reflink idref="bib29" id="ref15">29</reflink>]). The necessity for such a targeted, precise, person‐centered approach is clear for prematurely born children (Jois, [<reflink idref="bib32" id="ref16">32</reflink>]), whose neuropsychological profiles exhibit a wide range of diversity (Sansavini et al., [<reflink idref="bib53" id="ref17">53</reflink>]). In line with this, a recent study used latent profile analysis (LPA), a person‐centered analytical approach, to explore the impact of preterm birth on ADHD, ASD, and anxiety domains (Fitzallen et al., [<reflink idref="bib18" id="ref18">18</reflink>]). The authors discovered that preterm‐born children did not fit into one profile, but rather three: low expression, moderate expression, and high expression profiles (Fitzallen et al., [<reflink idref="bib18" id="ref19">18</reflink>]). Males and extremely prematurely born children were more likely to fall into the moderate and high expression profiles (Fitzallen et al., [<reflink idref="bib18" id="ref20">18</reflink>]). Another study identified distinct cognitive subgroups among a relatively small group of 118 children born preterm, with these profiles remaining stable from 5½ to 18 years (Stålnacke et al., [<reflink idref="bib55" id="ref21">55</reflink>]). Through similar clustering methods, other studies have unveiled a broad spectrum of behavioral and neurodevelopmental profiles among preterm born children aged 2–8 years. For example, while some preterm born children exhibit a profile aligning with the "preterm behavioral phenotype" (i.e., difficulties with emotions, attention, and peer relationships behavioral and emotional difficulties typically observed in ADHD, ASD, and anxiety disorders; Fitzallen et al., [<reflink idref="bib20" id="ref22">20</reflink>]), not observed in full‐term born children, others encounter minimal issues or exhibit behaviors akin to their full‐term counterparts (Burnett et al., [<reflink idref="bib7" id="ref23">7</reflink>]; Johnson et al., [<reflink idref="bib31" id="ref24">31</reflink>]; Lean et al., [<reflink idref="bib36" id="ref25">36</reflink>]). These studies highlight the complexity of preterm birth, challenging the binary comparison between preterm and full‐term births typically addressed in research.</p> <p>The Adolescent Brain Cognitive Development study (ABCD Study®; https://abcdstudy.org/) constitutes an ideal candidate to identify subgroups among prematurely born children. With more than 10,000 children, this longitudinal cohort stands as one of the largest in the world. We know of only two prior studies that have utilized the ABCD cohort to investigate preterm birth (Ji, Li, et al., [<reflink idref="bib30" id="ref26">30</reflink>]; Ma et al., [<reflink idref="bib37" id="ref27">37</reflink>]). The first found that preterm‐born children aged 9–10 years were at a significantly higher risk of neurocognitive dysfunction and psychopathological issues compared to a matched control group of full‐term children (Ji, Li, et al., [<reflink idref="bib30" id="ref28">30</reflink>]). This risk was not confined to one or two neurocognitive/behavioral areas, but was associated with most of the domains assessed by the NIH toolbox and the Child Behavior Checklist (CBCL; Ji, Li, et al., [<reflink idref="bib30" id="ref29">30</reflink>]). The second study refrained from considering preterm birth as a dichotomous variable. Instead, it evaluated the impact of gestational age at birth using five distinct categories: full term (≥40 weeks), early term (37–39 weeks), late preterm (36 weeks), moderate preterm (34–35 weeks), and early preterm (≤33 weeks) (Ma et al., [<reflink idref="bib37" id="ref30">37</reflink>]). Of note, these categories do not follow standard preterm categories, that is, moderate‐late preterm 32–37 weeks, very preterm 28–32 weeks, extremely preterm <28 weeks, but were created by the authors to ensure similar sample sizes across categories. This study found that a younger gestational age was correlated with lower cognitive function at ages 9–10 and 2 years later across most of the domains assessed by the NIH toolbox (Ma et al., [<reflink idref="bib37" id="ref31">37</reflink>]). To date, no study has interrogated potential preterm subtypes using the ABCD dataset, nor has any study investigated unique neurocognitive trajectories in conjunction with a diverse range of outcomes (behavioral, academic, neural) with this or any other dataset of this scale.</p> <p>Cognitive developmental impairments in children born preterm are frequently attributed to differences observed in the preterm brain. Preterm‐born children exhibit decreased cerebral volumes, particularly in the frontotemporal and hippocampal regions and in the left hemisphere (Ment & Vohr, [<reflink idref="bib41" id="ref32">41</reflink>]). Within the ABCD cohort, Ji, Li, et al. ([<reflink idref="bib30" id="ref33">30</reflink>]) found an association between the higher neurocognitive and psychopathological risk observed in prematurely born children and structural changes in specific brain regions. Ma et al. ([<reflink idref="bib37" id="ref34">37</reflink>]) further established this connection, demonstrating that regional brain volumes mediated the association between gestational age and cognitive function 9–10 years later. Similar brain–behavior associations have been corroborated in other cohorts of preterm neonates (Ullman et al., [<reflink idref="bib58" id="ref35">58</reflink>]), adolescents (Mullen et al., [<reflink idref="bib42" id="ref36">42</reflink>]), and adults (Nosarti et al., [<reflink idref="bib44" id="ref37">44</reflink>]). Furthermore, these effects extend beyond brain structure: preterm neonates display a unique functional organization (Scheinost et al., [<reflink idref="bib54" id="ref38">54</reflink>]), with increased functional centrality within visual areas and decreased centrality in motor areas, regions which undergo significant development during the perinatal period (Fenn‐Moltu et al., [<reflink idref="bib16" id="ref39">16</reflink>]; Ji, Majbri, et al., [<reflink idref="bib28" id="ref40">28</reflink>]). Most remarkably, these effects are detectable in utero, with a different functional connectivity (FC) pattern between fetuses destined for preterm birth and those who will be born full‐term (Thomason et al., [<reflink idref="bib57" id="ref41">57</reflink>]), signaling intrauterine onset of neurological differences that accompany preterm birth.</p> <p>Therefore, while there is no doubt that preterm birth can lead to a wide range of cognitive, behavioral, and neurodevelopmental consequences, the underlying mechanisms are yet to be fully understood. To shed light on these mechanisms, it is essential to delve into the individual differences among children born preterm. This study addresses this gap, defining variant neurocognitive profiles in 1891 children born early term or preterm, using person‐centered, LPA. Drawing on the ABCD dataset (release 5.0), we used NIH toolbox performance to explore the existence of unique neurocognitive profiles at age 9–10 years. Then, to gain a better understanding of these profile patterns, we evaluated behavioral and academic outcomes as well as MRI results across these profiles. In exploring the enduring impact, we further conducted analyses to examine the persistence of profiles in both cognitive and neural perspectives 2 years later. This targeted approach will guide us in understanding the pathways through which preterm birth can lead to neurodevelopmental deficits, paving the way for more individualized interventions.</p> <hd id="AN0181984181-2">METHODS</hd> <p></p> <hd id="AN0181984181-3">Participants</hd> <p>The neuroimaging data and cognitive assessments leveraged in this study were sourced from the Annual Curated Data Release 5.0 of the ABCD study (https://doi.org/10.15154/8873‐zj65). The ABCD study is an ongoing, nationwide, observational assessment of brain development. Comprising data from 11,875 children aged 9–11 years, recruited from 21 centers across the United States, the ABCD study captures a diverse range in geographic, socioeconomic, ethnic, and health backgrounds (Casey et al., [<reflink idref="bib10" id="ref42">10</reflink>]; Hagler et al., [<reflink idref="bib25" id="ref43">25</reflink>]). All 21 centers have obtained full written informed consent from parents and assent from the children, adhering to research procedures and ethical guidelines in accordance with the institutional review boards. Participants, along with their parents or caregivers, have completed a series of visits encompassing clinical interviews, surveys, neurocognitive tests, and neuroimaging.</p> <p>The ABCD exclusion criteria include any diagnosis of cerebral palsy, brain tumor, stroke, brain aneurysm, brain hemorrhage, subdural hematoma, other medical condition deemed exclusionary, or any abnormal findings following MRI exam. In the present study, we also excluded participants with parent‐endorsed, <emph>Yes</emph>, in response to a question asking whether the child had "<emph>Any other serious medical or neurological conditions</emph>" (<emph>N</emph> = 2), as well as those that completed fewer than seven National Institutes of Health Toolbox (NIHTB) tests (see a description of the tests in the NIH Toolbox subsection) at baseline or that scoring outside of three standard deviations (<55 or >145) on one or more NIHTB tests (<emph>N</emph> = 311). The question devhx_12a_p ("<emph>Was the child born prematurely?</emph>") from the Developmental History questionnaire was used to categorize children as having been born preterm. If parents answered yes, they then indicated "<emph>About how many weeks premature was the child when they were born?</emph>" (1–12; 13 = Greater than 12 weeks). Of note, the way parents interpreted these questions may have resulted in the inclusion of children born early term. We excluded one more participant who had inconsistent answers on preterm status across the baseline and the 4‐year follow‐up.</p> <p>The final sample was constituted of 1891 children (970 boys, 921 girls; 10.00 ± 0.61 years [8.92–11.00]), all of whom were born early term or preterm. The children mostly identified as White (57.0%; 1.3% Asian, 13.7% Black, 17.5% Hispanic, 10.4% Other, 0.0% Missing). Nine hundred and seventy‐six children had siblings in the sample (see Tables S16 and S17 for a replication of the analyses including family id as a covariate). Distribution of gestational age [<28–39 weeks] at birth is available in Figure S1.</p> <hd id="AN0181984181-4">Behavior measures</hd> <p></p> <hd id="AN0181984181-5">NIH toolbox</hd> <p>The NIHTB Cognition Battery is a computerized cognitive function assessment, validated, and normed for participants aged 3–85 years. The toolbox comprises seven tasks, each measuring different domains. Detailed information about these tasks, the domains they measure, and their dependent variables can be found in Table 1.</p> <p>1 TABLE Description of the seven National Institutes of Health Toolbox (NIHTB) tests.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">NIHTB task</th><th align="left">Domain</th><th align="left">Description of the test</th></tr></thead><tbody valign="top"><tr><td align="left">Flanker Test</td><td align="left">Executive functions, attention</td><td align="left">The child must indicate the direction of a central arrow, ignoring surrounding arrows that either point in the same direction (congruent trials) or the opposite direction (incongruent trials)</td></tr><tr><td align="left">Dimensional Change Card Sort Test</td><td align="left">Executive functions</td><td align="left">Children are instructed to match pictures, varying in shape and color, to targets, first by one dimension (e.g., color), then the other (e.g., shape). "Switch" trials require changing the matching dimension, e.g., switching from shape to color, needing cognitive flexibility to choose the correct stimulus quickly</td></tr><tr><td align="left">Picture Sequence Memory Test</td><td align="left">Episodic memory</td><td align="left">Children are asked to recall long series of illustrated objects and activities shown on a screen in a specific order while corresponding audio phrases are played. They have to remember the picture sequence over two trials, points are awarded for each consecutive pair of correctly placed images</td></tr><tr><td align="left">Picture Vocabulary Test</td><td align="left">Language</td><td align="left">The child listens to an audio recording of a word, and sees four photos on the computer screen. They are then asked to choose the picture that best represents the word's meaning</td></tr><tr><td align="left">Reading Recognition Test</td><td align="left">Language</td><td align="left">The child is required to read and pronounce letters and words as accurately as possible, and the test administrator scores each attempt as correct or incorrect</td></tr><tr><td align="left">Pattern Comparison Processing Speed Test</td><td align="left">Processing speed</td><td align="left">Children are asked to discern whether two pictures placed side‐by‐side are the same or not. Each pair of items is presented one at a time on the computer screen. Children are given 90 s to respond to as many items as possible, up to a maximum of 130</td></tr><tr><td align="left">List Sort Working Memory Test</td><td align="left">Working memory</td><td align="left">Images of various foods and animals are shown, each accompanied by an audio recording and written text (e.g., "elephant"). Participants are asked to name the items in order from smallest to largest, first within a single dimension (either animals or foods), and then across the two dimensions (first foods, then animals)</td></tr></tbody></table> </ephtml> </p> <p>For all seven NIHTB tasks, we used the age‐corrected scores (<emph>M</emph> = 100, SD = 15). These are the most useful for assessing performances expected for one's age in contexts like school or work settings, or when comparing against other age‐only adjusted performances on diverse cognitive instruments (Casaletto et al., [<reflink idref="bib9" id="ref44">9</reflink>]). These scores benchmark the test‐taker's performance against those in the NIHTB nationally representative normative sample at the same age. For example, a score of 100 indicates a performance that matches the national average whereas scores of 115 or 85 signify that the participant's performance is 1 standard deviation above or below the national average, respectively, compared to participants of the same age.</p> <hd id="AN0181984181-6">Child Behavior Checklist</hd> <p>The Parent CBCL is a questionnaire used to evaluate the dimensional psychopathology and adaptive functioning of children in the ABCD cohort (Ji, Li, et al., [<reflink idref="bib30" id="ref45">30</reflink>]). The CBCL includes seven scales: anxious/depressed, withdrawn/depressed, somatic complaints, social problems, thought problems, attention problems, rule‐breaking behavior, and aggressive behavior. <emph>T</emph>‐scores are computed for each scale. A <emph>t</emph>‐score of 65 or less indicates non‐clinical symptoms, a <emph>t</emph>‐score between 65 and 70 suggests that the child is at risk for problem behaviors, and a <emph>t</emph>‐score of 70 or more indicates clinical symptoms.</p> <hd id="AN0181984181-7">Grades at school</hd> <p>At the ABCD 2‐year follow‐up, parents were asked, "What grades did your child receive in school last year?" Responses were adapted to accommodate various grading systems used in the United States. The grading variable was coded so that lower values indicated better grades, and higher values indicated poorer grades. The scale ranged from 1 (A+/97–100/4.0 or higher/Exceeding the Standards/Exceeds excellent) to 12 (F/0–65/0.00–0.99/No Evidence/Standards/Fail/Does not meet minimum standards).</p> <hd id="AN0181984181-8">Neuroimaging</hd> <p>Neuroimaging data were acquired and pre‐processed by the ABCD Data Core. Scanning was conducted on General Electric 750, 3‐T Siemens Prisma or Philips machines, following the ABCD imaging protocol standardized across scanners and sites (Casey et al., [<reflink idref="bib10" id="ref46">10</reflink>]). Standard adult‐size coils were used across all ages. Participants with high head motion or poor data quality were excluded from the ABCD data release. Details and rationale on the specific protocol and parameters are described in Casey et al. ([<reflink idref="bib10" id="ref47">10</reflink>]). All data used in the current study were processed by the ABCD Data Core using the standardized ABCD pipeline, described in Hagler et al. ([<reflink idref="bib25" id="ref48">25</reflink>]), and provided within the ABCD data release 5.0. Specific details on processing and management of the curated, processed ABCD MRI dataset used here are provided in the following sections.</p> <hd id="AN0181984181-9">Structural processing</hd> <p>We utilized high resolution T<subs>1</subs>‐weighted (T<subs>1</subs>w) and T<subs>2</subs>‐weighted (T<subs>2</subs>w) 3D structural images curated and processed by the ABCD Data Core. Images were corrected for gradient nonlinearity distortions (Jovicich et al., [<reflink idref="bib33" id="ref49">33</reflink>]). T<subs>2</subs>w images were registered to T<subs>1</subs>w images using mutual information (Wells et al., [<reflink idref="bib63" id="ref50">63</reflink>]). Intensity non‐uniformity correction, tissue segmentation, and spatial smoothing were performed before resampling the images to 1 mm isotropic voxels and aligning them with an atlas brain. Cortical surface reconstruction was done using FreeSurfer v7.1.1 (https://surfer.nmr.mgh.harvard.edu) and involved skull‐stripping, white matter segmentation, mesh creation, topological defect correction, surface optimization, and nonlinear registration to a spherical surface‐based atlas (Hagler et al., [<reflink idref="bib25" id="ref51">25</reflink>]). Cortical volume, thickness, and area (68 regions; Desikan et al., [<reflink idref="bib13" id="ref52">13</reflink>]) and subcortical volumes (22 regions) were evaluated in the present analyses. The ABCD Data Core labeled regions of interest (ROIs) using atlas‐based segmentation for subcortical structures (Fischl et al., [<reflink idref="bib17" id="ref53">17</reflink>]) and performed image quality control. We included only the data recommended for use in our analysis.</p> <hd id="AN0181984181-10">Functional processing</hd> <p>Resting‐state data were normalized and time course detrended. Noninterest signals, such as motion, white matter, ventricles, and whole brain, were removed by generalized linear model regression (Casey et al., [<reflink idref="bib10" id="ref54">10</reflink>]). Subsequently, frames with excessive motion were eliminated (i.e., >0.3‐mm framewise displacement, ≥5 contiguous frames, motion filtered for respiratory signals), and data were bandpass filtered between 0.009 and 0.08 Hz (Hagler et al., [<reflink idref="bib25" id="ref55">25</reflink>]). Functional MRI time courses were then projected onto FreeSurfer's cortical surface. The ABCD Data Core calculated and included in the ABCD data release 5.0 within‐ and between‐network connectivity based on the Gordon parcellation atlas (Gordon et al., [<reflink idref="bib23" id="ref56">23</reflink>]) for 13 predefined resting state networks. As with the structural neuroimaging data, the ABCD Data Core also performed quality control and we used only the recommended data in our analysis. In our study, we assessed Fisher <emph>Z</emph>‐transformed averages of all pairwise correlations within each of the 13 networks and between each of the 13 networks with the other 12 networks, culminating in an analysis of 91 dependent variables (78 between‐network connectivity variables and 13 within‐network connectivity variables).</p> <hd id="AN0181984181-11">Statistical analysis</hd> <p></p> <hd id="AN0181984181-12">Latent profile analysis</hd> <p>We used LPA to identify groups of preterm‐born children categorized based on similar patterns of neurocognitive abilities at baseline as measured by the seven tests from the NIHTB. We conducted the LPA using the package <emph>tidyLPA</emph> version 1.1.0 (Rosenberg et al., [<reflink idref="bib52" id="ref57">52</reflink>]) on R version 4.2.2 (R Core Team, [<reflink idref="bib49" id="ref58">49</reflink>]). The best‐fitting model following was selected based on recommendations from Weller et al. ([<reflink idref="bib62" id="ref59">62</reflink>]): a low Bayesian information criterion (BIC), a high entropy value (indicating low classification error—at least 0.60), at least 5% of the total participant count in a given profile, a probability of at least 0.80 to belong to the cluster, a significant bootstrapped likelihood ratio test, and interpretable results.</p> <hd id="AN0181984181-13">Association analysis between profiles and behavioral and school outcomes</hd> <p>Once we identified the different profiles of preterm‐born children through LPA, we examined differences in behavioral outcomes (11 scales from the CBCL at baseline and at the 2‐year follow‐up) and school outcomes (school grades reported by the parents at the 2‐year follow‐up) among these groups. For each score, we used the Welch's <emph>F</emph>‐test to assess the main effect of the profile, and then applied the Games‐Howell test with a Holm correction to evaluate significant comparisons across profiles.</p> <hd id="AN0181984181-14">Association analysis between profiles and brain structure</hd> <p>We examined differences in brain structure between the different profiles identified by the LPA at baseline and at the 2‐year follow‐up, focusing on three classic structural metrics: cortical and subcortical volumes, cortical surface, and cortical thickness.</p> <p>First, at the whole‐brain level, we used a linear regression model and an analysis of variance (ANOVA) to evaluate the primary effect of the profile, while controlling for sex, age, and site, on total cortical volume, total cortical surface, and mean cortical thickness. Total cortical volume was added as a covariate only for cortical thickness because adjusting for intracranial volume increases the accuracy and generalizability of models based on cortical thickness,  but decreases those based on surface area and gray matter volume (Dhamala et al., [<reflink idref="bib14" id="ref60">14</reflink>]). Post hoc pairwise comparisons were conducted to investigate specific differences in brain structure among the different profiles.</p> <p>Next, at the regional level, we examined the effect of profile and the paired‐comparisons across profiles on the 68 cortical and 22 subcortical regions. Once again, we used linear regression models and ANOVAs to study the profile's effect, using the same covariates as in our previous models, and two‐sample <emph>t</emph>‐tests to investigate specific differences in brain structure among pairs of different profiles. A Bonferroni correction was applied (<emph>p</emph> < .05/68 for cortical volumes, surfaces and thicknesses; <emph>p</emph> < .05/22 for subcortical volumes).</p> <hd id="AN0181984181-15">Association analysis between profiles and brain function</hd> <p>Lastly, we examined the differences in brain FC among the distinct profiles identified by the LPA. We used the 13 networks from Gordon (91 unique correlations) to investigate differences in within‐ and between‐network connectivity, using the same method as in the brain structure differences investigation (linear models, ANOVAs and post hoc pairwise comparisons). The models included sex, age, and site as covariates. A Bonferroni correction was applied (<emph>p</emph> < .05/91).</p> <hd id="AN0181984181-16">RESULTS</hd> <p></p> <hd id="AN0181984181-17">Latent profile outcomes</hd> <p>The three‐profile solution was best based on the criteria set forth by Weller et al. ([<reflink idref="bib62" id="ref61">62</reflink>]) (Akaike information criterion [AIC] = 107,655.37, BIC = 107,821.72, Entropy = 0.64, Minimum probability = 0.81, Smallest <emph>n</emph> = 0.20, Bootstrapped likelihood ratio test <emph>p</emph> value < .05; see Table S1 for fit indices of all tested solutions). This tree‐profile solution was comprised of (see also Figure 1):</p> <p></p> <ulist> <item> ‐ A first profile ("homogeneous high," <emph>N</emph>  = 372, 19.7% of the total sample) where subjects showed in average scores above the norm (scores >100) for all NIHTB tasks.</item> <p></p> <item> ‐ A second profile ("heterogeneous," <emph>N</emph>  = 775, 41.0%) where, in average, subjects showed scores above the norm for four tasks (scores >100 in <emph>List Sort Working Memory Test</emph> , <emph>Picture Sequence Memory Test</emph> , <emph>Picture Vocabulary Test</emph> and <emph>Reading Recognition Test</emph>) and below the norm for the three others (scores <100 in <emph>Dimensional Change Card Sort Test</emph> , <emph>Flanker Test</emph> and <emph>Pattern Comparison Processing Speed Test</emph>).</item> <p></p> <item> ‐ A third profile ("homogeneous low", <emph>N</emph>  = 744, 39.3%) where, in average, subjects showed scores below the norm (scores <100) for all NIHTB tasks.</item> </ulist> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01jan25/cdev14143-fig-0001.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev14143-fig-0001.jpg" title="1 Latent profile analysis for the seven National Institutes of Health Toolbox tests scores. Pink line graph indicating the three‐class solution featuring standardized mean values on the y‐axis and the tests used on the x‐axis. Continuous black line represents national norm (M = 100), dotted lines represent one standard deviation (SD = 15). Profile 1: Homogeneous high neurocognitive profile (19.7%); Profile 2: Heterogeneous neurocognitive profile (41.0%); Profile 3: Homogeneous low neurocognitive profile (39.3%)." /> </p> <p></p> <p>For all NIHTB tasks, the three profiles exhibited significantly different scores (all <emph>p</emph>s < .05, see Table 2). Specifically, as can be seen in Figure 1 and Table 3, children from the third profile group had significantly lower scores than children in profiles one and two in all tasks. Children from the second profile had lower scores than children from the first profile in <emph>Dimensional Change Card Sort Test</emph>, <emph>Flanker Test, Pattern Comparison Processing Speed Test</emph>, and <emph>Picture Sequence Memory Test</emph> but higher scores in <emph>List Sort Working Memory Test</emph>, <emph>Picture Vocabulary Test</emph>, and <emph>Reading Recognition Test</emph>.</p> <p>2 TABLE Participants characteristics per profile.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left"><italic>F</italic> (chi‐square/Welch's one‐way ANOVA)</th><th align="left">Profile 1 (<italic>M</italic> <p><bold>±</bold></p> SD)</th><th align="left">Profile 2 (<italic>M</italic> <p><bold>±</bold></p> SD)</th><th align="left">Profile 3 (<italic>M</italic> <p><bold>±</bold></p> SD)</th><th align="left">Profile 1 versus 2 (Holm‐corrected <italic>p</italic>)</th><th align="left">Profile 1 versus 3 (Holm‐corrected <italic>p</italic>)</th><th align="left">Profile 2 versus 3 (Holm‐corrected <italic>p</italic>)</th></tr></thead><tbody valign="top"><tr><td align="left">Sex</td><td align="left">χ<sup>2</sup>(2) = 2.20, p = .33</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Female</td><td align="left" /><td align="left">N = 193 (51.9%)</td><td align="left">N = 377 (48.6%)</td><td align="left">N = 351 (47.2%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Male</td><td align="left" /><td align="left">N = 179 (48.1%)</td><td align="left">N = 398 (51.4%)</td><td align="left">N = 393 (52.8%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Gestational age at birth (weeks early)</td><td align="left">F(2, 1021.11) = 14.78, p < .001</td><td align="left">4.68 ± 2.08</td><td align="left">4.69 ± 2.19</td><td align="left">5.33 ± 2.68</td><td align="char" char=".">1.00</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">Age at baseline (years)</td><td align="left">F(2, 988.70) = 6.35, p < .001</td><td align="left">10.10 ± 0.62</td><td align="left">9.97 ± 0.61</td><td align="left">9.97 ± 0.61</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td><td align="char" char=".">1.00</td></tr><tr><td align="left">Race/ethnicity</td><td align="left">χ<sup>2</sup>(8) = 192.65, p < .001</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Asian</td><td align="left" /><td align="left">N = 5 (1.3%)</td><td align="left">N = 12 (1.5%)</td><td align="left">N = 8 (1.1%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Black</td><td align="left" /><td align="left">N = 24 (6.5%)</td><td align="left">N = 46 (5.9%)</td><td align="left">N = 189 (25.4%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Hispanic</td><td align="left" /><td align="left">N = 50 (13.4%)</td><td align="left">N = 121 (15.6%)</td><td align="left">N = 160 (21.5%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">White</td><td align="left" /><td align="left">N = 265 (71.2%)</td><td align="left">N = 503 (64.9%)</td><td align="left">N = 310 (41.7%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Other</td><td align="left" /><td align="left">N = 28 (7.5%)</td><td align="left">N = 93 (12.0%)</td><td align="left">N = 76 (10.2%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Missing</td><td align="left" /><td align="left">N = 0 (0.0%)</td><td align="left">N = 0 (0.0%)</td><td align="left">N = 1 (0.1%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Parental income (combined)</td><td align="left">χ<sup>2</sup>(18) = 196.43, p < .001</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><$5000</td><td align="left" /><td align="left">N = 0 (0.0%)</td><td align="left">N = 6 (0.8%)</td><td align="left">N = 37 (5.0%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">$5000–$11,999</td><td align="left" /><td align="left">N = 6 (1.6%)</td><td align="left">N = 16 (2.1%)</td><td align="left">N = 34 (4.6%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">$12,000–$15,999</td><td align="left" /><td align="left">N = 3 (0.8%)</td><td align="left">N = 11 (1.4%)</td><td align="left">N = 21 (2.8%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">$16,000–$24,999</td><td align="left" /><td align="left">N = 12 (3.2%)</td><td align="left">N = 17 (2.2%)</td><td align="left">N = 54 (7.3%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">$25,000–$34,999</td><td align="left" /><td align="left">N = 7 (1.9%)</td><td align="left">N = 21 (2.7%)</td><td align="left">N = 54 (7.3%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">$35,000–$49,999</td><td align="left" /><td align="left">N = 15 (4.0%)</td><td align="left">N = 58 (7.5%)</td><td align="left">N = 73 (9.8%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">$50,000–$74,999</td><td align="left" /><td align="left">N = 51 (13.7%)</td><td align="left">N = 98 (12.6%)</td><td align="left">N = 111 (14.9%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">$75,000–$99,999</td><td align="left" /><td align="left">N = 63 (16.9%)</td><td align="left">N = 119 (15.4%)</td><td align="left">N = 75 (10.1%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">$100,000–$199,999</td><td align="left" /><td align="left">N = 137 (36.8%)</td><td align="left">N = 282 (36.4%)</td><td align="left">N = 155 (20.8%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">>$200,000</td><td align="left" /><td align="left">N = 55 (14.8%)</td><td align="left">N = 107 (13.8%)</td><td align="left">N = 43 (5.8%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Missing</td><td align="left" /><td align="left">N = 23 (6.2%)</td><td align="left">N = 40 (5.2%)</td><td align="left">N = 87 (11.7%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Maternal education</td><td align="left">χ<sup>2</sup>(32) = 196.02, p < .001</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">No high school nor GED diploma</td><td align="left" /><td align="left">N = 6 (1.6%)</td><td align="left">N = 17 (2.2%)</td><td align="left">N = 60 (8.1%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">High school</td><td align="left" /><td align="left">N = 15 (4.0%)</td><td align="left">N = 41 (5.3%)</td><td align="left">N = 97 (13.0%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">GED or equivalent diploma</td><td align="left" /><td align="left">N = 4 (1.1%)</td><td align="left">N = 11 (1.4%)</td><td align="left">N = 22 (3.0%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Some college</td><td align="left" /><td align="left">N = 53 (14.2%)</td><td align="left">N = 124 (16.0%)</td><td align="left">N = 161 (21.6%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Associate degree</td><td align="left" /><td align="left">N = 53 (14.2%)</td><td align="left">N = 95 (12.3%)</td><td align="left">N = 133 (17.9%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Bachelor's degree</td><td align="left" /><td align="left">N = 125 (33.6%)</td><td align="left">N = 271 (35.0%)</td><td align="left">N = 156 (21.0%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Master's degree</td><td align="left" /><td align="left">N = 94 (25.3%)</td><td align="left">N = 166 (21.4%)</td><td align="left">N = 83 (11.2%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Professional School degree</td><td align="left" /><td align="left">N = 14 (3.8%)</td><td align="left">N = 28 (3.6%)</td><td align="left">N = 9 (1.2%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Doctoral degree</td><td align="left" /><td align="left">N = 8 (2.2%)</td><td align="left">N = 21 (2.7%)</td><td align="left">N = 14 (1.9%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Missing</td><td align="left" /><td align="left">N = 0 (0.0%)</td><td align="left">N = 1 (0.1%)</td><td align="left">N = 0 (0.0%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Native language</td><td align="left">χ<sup>2</sup>(28) = 41.87, p < .05</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">English</td><td align="left" /><td align="left">N = 337 (90.6%)</td><td align="left">N = 710 (91.6%)</td><td align="left">N = 629 (84.5%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Spanish</td><td align="left" /><td align="left">N = 14 (3.8%)</td><td align="left">N = 29 (3.7%)</td><td align="left">N = 53 (7.1%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Other</td><td align="left" /><td align="left">N = 5 (1.3%)</td><td align="left">N = 6 (0.8%)</td><td align="left">N = 13 (1.7%)</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Missing</td><td align="left" /><td align="left">N = 16 (4.3%)</td><td align="left">N = 30 (3.9%)</td><td align="left">N = 49 (6.6%)</td><td align="left" /><td align="left" /><td align="left" /></tr></tbody></table> </ephtml> </p> <p>As for the characteristics of the participants, the three profiles showed no significant difference in sex representation (<emph>p</emph> = .33), maintaining a balanced ratio around 50%. But when it comes to gestational age, children from the third profile were born significantly at a younger gestational age than children from the first and second profiles (<emph>p</emph>s < .001; see Table 2).</p> <hd id="AN0181984181-19">Differences in behavioral and school outcomes across profiles</hd> <p></p> <hd id="AN0181984181-20">At baseline</hd> <p>We found that, concurrently with the neurocognitive differences observed at baseline, children in the third profile scored higher on the <emph>Social problems</emph>, <emph>Attention problems</emph>, <emph>Rule‐breaking behavior</emph>, and <emph>Aggressive behavior</emph> scales of the CBCL compared to those in the first or second profiles (all Holm‐adjusted <emph>p</emph> values < .01). They also scored higher on the <emph>Externalizing</emph> and <emph>Total</emph> scales (all Holm‐adjusted <emph>p</emph> values < .001), but not on the <emph>Internalizing</emph> scale (all Holm‐adjusted <emph>p</emph> values = 1.00; see Table 3).</p> <p>When examining the distribution of scores when categorizing them into normal (≤65), borderline ([65–70]), and clinical ranges (≥70), we observed variation among the three profiles for the <emph>Social problems</emph> (<emph>χ</emph><sups>2</sups>(<reflink idref="bib4" id="ref62">4</reflink>) = 20.67, <emph>p</emph> < .001; Profile 1: 99.46% normal, 0.27% borderline, 0.27% clinical; Profile 2: 97.93% normal, 0.78% borderline, 1.29% clinical; Profile 3: 95.03% normal, 1.75% borderline, 3.23% clinical), <emph>Attention problems</emph> (<emph>χ</emph><sups>2</sups>(<reflink idref="bib4" id="ref63">4</reflink>) = 29.49, <emph>p</emph> < .001; Profile 1: 97.58% normal, 1.34% borderline, 1.08% clinical; Profile 2: 95.35% normal, 2.84% borderline, 1.81% clinical; Profile 3: 90.19% normal, 6.59% borderline, 3.23% clinical), and <emph>Aggressive behavior</emph> (<emph>χ</emph><sups>2</sups>(<reflink idref="bib4" id="ref64">4</reflink>) = 17.85, <emph>p</emph> < .001; Profile 1: 96.77% normal, 1.88% borderline, 1.34% clinical; Profile 2: 96.65% normal, 1.68% borderline, 1.68% clinical; Profile 3: 95.30% normal, 1.48% borderline, 3.23% clinical) scales (see also Figure 2). However, we saw no such variation for the <emph>Rule‐breaking behavior</emph> scale (<emph>χ</emph><sups>2</sups>(<reflink idref="bib4" id="ref65">4</reflink>) = 6.05, <emph>p</emph> = .20; Profile 1: 97.04% normal, 2.42% borderline, 0.54% clinical; Profile 2: 98.19% normal, 0.77% borderline, 1.03% clinical; Profile 3: 94.89% normal, 2.28% borderline, 2.82% clinical, see Figure 2). The varying prevalence of normal, borderline, and clinical ranges across different profiles in the <emph>Attention problems</emph>, <emph>Social problems</emph> and <emph>Aggressive behavior</emph> scales suggests that behavioral differences among profiles unfortunately extend beyond the normal range with children from profile 3 being more clinically affected.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01jan25/cdev14143-fig-0002.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev14143-fig-0002.jpg" title="2 Distribution of Child Behavior Checklist scores at baseline when categorizing them into normal, borderline and clinical ranges across profiles." /> </p> <p></p> <hd id="AN0181984181-22">At the 2‐year follow‐up</hd> <p>We found that early term‐ and preterm‐born children from the third profile still scored significantly lower than those from the first and second profiles 2 years later. This was consistent across all of the five NIHTB tasks measured at this time point (<emph>Picture Sequence Memory</emph> [<emph>N</emph><subs>profile 1</subs> = 322, <emph>N</emph><subs>profile 2</subs> = 677, <emph>N</emph><subs>profile 3</subs> = 627], <emph>Picture Vocabulary</emph> [<emph>N</emph><subs>profile 1</subs> = 328, <emph>N</emph><subs>profile 2</subs> = 702, <emph>N</emph><subs>profile 3</subs> = 643], <emph>Reading Recognition</emph> [<emph>N</emph><subs>profile 1</subs> = 321, <emph>N</emph><subs>profile 2</subs> = 682, <emph>N</emph><subs>profile 3</subs> = 640], <emph>Flanker</emph> [<emph>N</emph><subs>profile 1</subs> = 275, <emph>N</emph><subs>profile 2</subs> = 562, <emph>N</emph><subs>profile 3</subs> = 532], and <emph>Pattern Comparison Processing Speed</emph> [<emph>N</emph><subs>profile 1</subs> = 245, <emph>N</emph><subs>profile 2</subs> = 532, <emph>N</emph><subs>profile 3</subs> = 507] tests; all Holm‐adjusted <emph>p</emph>s < .001). Moreover, children from the second profile also scored significantly lower than those from the first profile on the <emph>Picture Sequence Memory</emph> (Holm‐adjusted <emph>p</emph> < .05), <emph>Flanker</emph> (Holm‐adjusted <emph>p</emph> < .001), and <emph>Pattern Comparison Processing Speed</emph> (Holm‐adjusted <emph>p</emph> < .001) tests.</p> <p>In addition, children from profile 3 (<emph>N</emph> = 523) also continued to present higher scores in the <emph>Social</emph> (Holm‐adjusted <emph>p</emph>s < .05) and <emph>Attention</emph> (Holm‐adjusted <emph>p</emph>s < .001) scales compared to children from the first (<emph>N</emph> = 281) and second profiles (<emph>N</emph> = 537). No differences between profiles were observed in the other scales. The distribution of score categories significantly differed between profiles for the <emph>Attention problems</emph> (<emph>χ</emph><sups>2</sups>(<reflink idref="bib4" id="ref66">4</reflink>) = 14.37, <emph>p</emph> < .001; Profile 1: 97.51% normal, 1.78% borderline, 0.71% clinical; Profile 2: 97.21% normal, 1.12% borderline, 1.68% clinical; Profile 3: 93.31% normal, 4.02% borderline, 2.68% clinical) scale, but not for the <emph>Social problems</emph> scale (<emph>χ</emph><sups>2</sups>(<reflink idref="bib4" id="ref67">4</reflink>) = 5.93, <emph>p</emph> = .20; Profile 1: 97.86% normal, 0.71% borderline, 1.42% clinical; Profile 2: 95.90% normal, 2.05% borderline, 2.05% clinical; Profile 3: 94.46% normal, 3.06% borderline, 2.49% clinical). Thus, 2 years later, children from profile 3 are still more clinically affected on the <emph>Attention problems</emph> scale, but not on the <emph>Social problems</emph> scale, although differences in scores were observed in this scale when not categorizing them.</p> <p>We also confirmed that children from profile 3 (<emph>N</emph> = 630) had lower grades compared to those from profiles 1 (<emph>N</emph> = 329) and 2 (<emph>N</emph> = 695; Holm‐corrected <emph>p</emph>s < .001) based on parent report at this 2‐year follow up visit. Additionally, children from profile 2 had lower grades than those from profile 1 (Holm‐corrected <emph>p</emph> < .05; <emph>F</emph>(<reflink idref="bib2" id="ref68">2</reflink>, 927.27) = 100.26, <emph>p</emph> < .001), see Table 3.</p> <p>3 TABLE Profiles differences in National Institutes of Health Toolbox (NIHTB) tests, Child Behavior Checklist (CBCL) scales, and school grades at baseline and 2‐year follow‐up.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left"><italic>F</italic> (chi‐square/Welch's one‐way ANOVA)</th><th align="left">Profile 1 (<italic>M</italic> <p><bold>±</bold></p> SD)</th><th align="left">Profile 2 (<italic>M</italic> <p><bold>±</bold></p> SD)</th><th align="left">Profile 3 (<italic>M</italic> <p><bold>±</bold></p> SD)</th><th align="left">Profile 1 versus 2 (Holm‐corrected <italic>p</italic>)</th><th align="left">Profile 1 versus 3 (Holm‐corrected <italic>p</italic>)</th><th align="left">Profile 2 versus 3 (Holm‐corrected <italic>p</italic>)</th></tr></thead><tbody valign="top"><tr><td align="left">NIHTB at baseline</td></tr><tr><td align="left">Flanker (N = 1891)</td><td align="left">F(2, 961.26) = 307.59, p < .001</td><td align="char" char="±">106.16 ± 12.54</td><td align="char" char="±">96.89 ± 11.11</td><td align="char" char="±">87.69 ± 11.53</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">Dimensional Change Card Sort (N = 1891)</td><td align="left">F(2, 952.94) = 1174.76, p < .001</td><td align="char" char="±">117.51 ± 9.93</td><td align="char" char="±">94.31 ± 8.44</td><td align="char" char="±">88.00 ± 9.07</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">Picture Sequence Memory (N = 1891)</td><td align="left">F(2, 966.15) = 151.67, p < .001</td><td align="char" char="±">106.36 ± 14.64</td><td align="char" char="±">102.77 ± 13.98</td><td align="char" char="±">93.17 ± 12.84</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">Picture Vocabulary (N = 1891)</td><td align="left">F(2, 952.64) = 432.38, p < .001</td><td align="char" char="±">110.06 ± 14.22</td><td align="char" char="±">112.74 ± 13.27</td><td align="char" char="±">94.79 ± 11.69</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">Reading Recognition (N = 1891)</td><td align="left">F(2, 916.71) = 424.16, p < .001</td><td align="char" char="±">103.56 ± 14.98</td><td align="char" char="±">106.38 ± 14.03</td><td align="char" char="±">89.12 ± 10.30</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">Pattern Comparison Processing Speed (N = 1891)</td><td align="left">F(2, 1012.70) = 309.73, p < .001</td><td align="char" char="±">112.57 ± 15.70</td><td align="char" char="±">95.90 ± 16.13</td><td align="char" char="±">86.80 ± 15.70</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">List Sort Working Memory (N = 1891)</td><td align="left">F(2, 951.32) = 375.69, p < .001</td><td align="char" char="±">103.74 ± 13.22</td><td align="char" char="±">105.81 ± 12.06</td><td align="char" char="±">90.39 ± 10.94</td><td align="char" char="."><.05</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">NIHTB at 2‐year follow‐up</td></tr><tr><td align="left">Flanker (N = 1369)</td><td align="left">F(2, 708.15) = 77.98, p < .001</td><td align="char" char="±">101.00 ± 13.34</td><td align="char" char="±">96.77 ± 11.91</td><td align="char" char="±">89.90 ± 12.50</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">Picture Sequence Memory (N = 1626)</td><td align="left">F(2, 844.59) = 84.28, p < .001</td><td align="char" char="±">108.58 ± 14.91</td><td align="char" char="±">105.92 ± 14.16</td><td align="char" char="±">97.42 ± 14.33</td><td align="char" char="."><.05</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">Picture Vocabulary (N = 1673)</td><td align="left">F(2, 856.42) = 224.88, p < .001</td><td align="char" char="±">105.66 ± 13.97</td><td align="char" char="±">107.04 ± 13.30</td><td align="char" char="±">92.79 ± 12.69</td><td align="char" char=".">.30</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">Reading Recognition (N = 1643)</td><td align="left">F(2, 848.81) = 207.56, p < .001</td><td align="char" char="±">102.21 ± 13.27</td><td align="char" char="±">104.20 ± 13.55</td><td align="char" char="±">90.56 ± 12.14</td><td align="char" char=".">.07</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">Pattern Comparison Processing Speed (N = 1284)</td><td align="left">F(2, 670.99) = 52.43, p < .001</td><td align="char" char="±">113.48 ± 16.50</td><td align="char" char="±">106.95 ± 17.28</td><td align="char" char="±">100.24 ± 17.79</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">CBCL at baseline</td></tr><tr><td align="left">Anxious/depressed (N = 1891)</td><td align="left">F(2, 1008.17) = 0.30, p = .74</td><td align="char" char="±">53.21 ± 5.63</td><td align="char" char="±">53.44 ± 5.74</td><td align="char" char="±">53.49 ± 6.11</td><td align="char" char=".">1.00</td><td align="char" char=".">1.00</td><td align="char" char=".">1.00</td></tr><tr><td align="left">Depressed (N = 1891)</td><td align="left">F(2, 1043.95) = 2.72, p = .07</td><td align="char" char="±">52.95 ± 5.02</td><td align="char" char="±">53.34 ± 5.65</td><td align="char" char="±">53.75 ± 6.06</td><td align="char" char=".">.74</td><td align="char" char=".">.16</td><td align="char" char=".">.74</td></tr><tr><td align="left">Somatic complaints (N = 1891)</td><td align="left">F(2, 1020.10) = 0.42, p = .66</td><td align="char" char="±">54.68 ± 5.72</td><td align="char" char="±">54.94 ± 6.08</td><td align="char" char="±">55.02 ± 6.36</td><td align="char" char=".">1.00</td><td align="char" char=".">1.00</td><td align="char" char=".">1.00</td></tr><tr><td align="left">Social problems (N = 1891)</td><td align="left">F(2, 1118.46) = 24.19, p < .001</td><td align="char" char="±">51.88 ± 3.31</td><td align="char" char="±">52.36 ± 4.28</td><td align="char" char="±">53.74 ± 5.75</td><td align="char" char=".">.09</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">Thought problems (N = 1891)</td><td align="left">F(2, 1022.25) = 2.38, p = .09</td><td align="char" char="±">53.22 ± 5.17</td><td align="char" char="±">53.27 ± 5.37</td><td align="char" char="±">53.85 ± 6.15</td><td align="char" char=".">.99</td><td align="char" char=".">.38</td><td align="char" char=".">.38</td></tr><tr><td align="left">Attention problems (N = 1891)</td><td align="left">F(2, 1052.03) = 30.96, p < .001</td><td align="char" char="±">52.58 ± 4.69</td><td align="char" char="±">52.99 ± 5.13</td><td align="char" char="±">55.07 ± 6.74</td><td align="char" char=".">.37</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">Rule‐breaking behavior (N = 1891)</td><td align="left">F(2, 993.55) = 11.97, p < .001</td><td align="char" char="±">52.33 ± 4.11</td><td align="char" char="±">52.05 ± 3.83</td><td align="char" char="±">53.19 ± 5.18</td><td align="char" char=".">.52</td><td align="char" char="."><.05</td><td align="char" char="."><.001</td></tr><tr><td align="left">Aggressive behavior (N = 1891)</td><td align="left">F(2, 1032.64) = 9.23, p < .001</td><td align="char" char="±">52.28 ± 4.58</td><td align="char" char="±">52.27 ± 4.78</td><td align="char" char="±">53.41 ± 6.12</td><td align="char" char=".">1.00</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">Externalizing (N = 1891)</td><td align="left">F(2, 1000.80) = 10.45, p < .001</td><td align="char" char="±">44.75 ± 9.71</td><td align="char" char="±">44.66 ± 9.56</td><td align="char" char="±">46.89 ± 10.61</td><td align="char" char=".">.99</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">Internalizing (N = 1891)</td><td align="left">F(2, 1010.63) = 0.48, p = .62</td><td align="char" char="±">47.97 ± 10.02</td><td align="char" char="±">48.56 ± 10.29</td><td align="char" char="±">48.50 ± 10.99</td><td align="char" char=".">1.00</td><td align="char" char=".">1.00</td><td align="char" char=".">1.00</td></tr><tr><td align="left">Total (N = 1891)</td><td align="left">F(2, 1021.53) = 10.79, p < .001</td><td align="char" char="±">44.49 ± 10.06</td><td align="char" char="±">44.98 ± 10.61</td><td align="char" char="±">47.20 ± 11.48</td><td align="char" char=".">.73</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">CBCL at 2‐year follow‐up</td></tr><tr><td align="left">Anxious/depressed (N = 1341)</td><td align="left">F(2, 748.32) = 0.05, p = .95</td><td align="char" char="±">53.16 ± 5.47</td><td align="char" char="±">53.25 ± 5.49</td><td align="char" char="±">53.29 ± 6.16</td><td align="char" char=".">1.00</td><td align="char" char=".">1.00</td><td align="char" char=".">1.00</td></tr><tr><td align="left">Depressed (N = 1341)</td><td align="left">F(2, 789.47) = 1.66, p = .19</td><td align="char" char="±">53.02 ± 4.84</td><td align="char" char="±">53.70 ± 6.01</td><td align="char" char="±">53.23 ± 5.96</td><td align="char" char=".">.56</td><td align="char" char=".">.85</td><td align="char" char=".">.81</td></tr><tr><td align="left">Somatic complaints (N = 1341)</td><td align="left">F(2, 739.29) = 0.31, p = .74</td><td align="char" char="±">54.15 ± 5.65</td><td align="char" char="±">54.32 ± 5.43</td><td align="char" char="±">54.49 ± 6.18</td><td align="char" char=".">1.00</td><td align="char" char=".">1.00</td><td align="char" char=".">1.00</td></tr><tr><td align="left">Social problems (N = 1341)</td><td align="left">F(2, 771.25) = 5.91, p < .001</td><td align="char" char="±">52.06 ± 4.35</td><td align="char" char="±">52.27 ± 4.70</td><td align="char" char="±">53.19 ± 5.66</td><td align="char" char=".">.79</td><td align="char" char="."><.05</td><td align="char" char="."><.05</td></tr><tr><td align="left">Thought problems (N = 1341)</td><td align="left">F(2, 754.94) = 0.74, p = .48</td><td align="char" char="±">53.08 ± 5.21</td><td align="char" char="±">53.21 ± 5.39</td><td align="char" char="±">53.54 ± 5.99</td><td align="char" char=".">1.00</td><td align="char" char=".">1.00</td><td align="char" char=".">1.00</td></tr><tr><td align="left">Attention problems (N = 1341)</td><td align="left">F(2, 776.30) = 11.15, p < .001</td><td align="char" char="±">52.62 ± 4.44</td><td align="char" char="±">52.75 ± 4.87</td><td align="char" char="±">54.16 ± 6.02</td><td align="char" char=".">.92</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">Rule‐breaking behavior (N = 1341)</td><td align="left">F(2, 741.38) = 2.98, p = .05</td><td align="char" char="±">51.93 ± 3.93</td><td align="char" char="±">51.73 ± 3.78</td><td align="char" char="±">52.35 ± 4.48</td><td align="char" char=".">.77</td><td align="char" char=".">.71</td><td align="char" char=".">.12</td></tr><tr><td align="left">Aggressive behavior (N = 1341)</td><td align="left">F(2, 772.19) = 2.55, p = .08</td><td align="char" char="±">52.10 ± 4.42</td><td align="char" char="±">52.14 ± 4.74</td><td align="char" char="±">52.83 ± 6.09</td><td align="char" char=".">.99</td><td align="char" char=".">.30</td><td align="char" char=".">.30</td></tr><tr><td align="left">Externalizing (N = 1341)</td><td align="left">F(2, 741.96) = 2.15, p = .12</td><td align="char" char="±">43.96 ± 9.52</td><td align="char" char="±">43.96 ± 9.35</td><td align="char" char="±">45.11 ± 10.26</td><td align="char" char=".">1.00</td><td align="char" char=".">.50</td><td align="char" char=".">.40</td></tr><tr><td align="left">Internalizing (N = 1341)</td><td align="left">F(2, 751.93) = 1.72, p = .18</td><td align="char" char="±">47.69 ± 9.79</td><td align="char" char="±">48.23 ± 10.03</td><td align="char" char="±">47.02 ± 11.04</td><td align="char" char=".">1.00</td><td align="char" char=".">1.00</td><td align="char" char=".">.46</td></tr><tr><td align="left">Total (N = 1341)</td><td align="left">F(2, 748.20) = 0.82, p = .44</td><td align="char" char="±">43.88 ± 10.62</td><td align="char" char="±">44.41 ± 10.58</td><td align="char" char="±">44.93 ± 12.14</td><td align="char" char=".">1.00</td><td align="char" char=".">1.00</td><td align="char" char=".">1.00</td></tr><tr><td align="left">School grades at 2‐year follow‐up</td></tr><tr><td align="left">Parents answer (N = 1654)</td><td align="left">F(2, 928.42) = 100.50, p < .001</td><td align="char" char="±">2.96 ± 1.74</td><td align="char" char="±">3.28 ± 1.99</td><td align="char" char="±">4.71 ± 2.36</td><td align="char" char="."><.05</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr></tbody></table> </ephtml> </p> <hd id="AN0181984181-23">Brain structural differences across profiles</hd> <p></p> <hd id="AN0181984181-24">At baseline</hd> <p>At baseline, we found that children from the third profile (<emph>N</emph> = 647) had significantly smaller total cortical volumes and surface areas than those from the first (<emph>N</emph> = 342) and second profiles (<emph>N</emph> = 712; see Table 4). We conducted a whole‐brain analysis to determine if specific brain regions influenced these effects. We examined 68 cortical and an additional 22 subcortical ROIs for volume, applying a Bonferroni correction (<emph>p</emph> < .00074 and <emph>p</emph> < .0023, respectively). The results indicated significant volume differences across profiles for 45 cortical and 22 subcortical ROIs, and significant surface area differences across profiles in 55 ROIs. The <emph>F</emph>‐test (main effect of profile variable) and <emph>t</emph>‐test results (between‐profile comparisons) can be seen in Figure 3 and in Table S2. Finally, we did not find any significant differences in cortical thickness across profiles, neither in mean cortical thickness nor across the 68 cortical ROIs.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01jan25/cdev14143-fig-0003.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev14143-fig-0003.jpg" title="3 Regional differences in cortical volume (top panel), subcortical volume (middle panel), and surface area (bottom panel) among the three profiles at baseline." /> </p> <p></p> <hd id="AN0181984181-26">At 2‐year follow‐up</hd> <p>At the 2‐year follow‐up, we found that the children from the third profile (<emph>N</emph> = 454) still had significantly smaller total cortical volumes and surface areas compared to children from the first (<emph>N</emph> = 236) and second profiles (<emph>N</emph> = 502; see Table 4). We also identified significant volume differences across profiles for 33 cortical and 18 subcortical ROIs, as well as significant surface area differences in 49 ROIs. The results from the <emph>F</emph>‐tests and <emph>t</emph>‐tests are depicted in Figure 4 and Table S3. We still did not observe any significant differences in average cortical thickness. But, applying a whole‐brain approach with 68 ROIs and a Bonferroni correction (<emph>p</emph> < .00074), we found one region—the right posterior cingulate—with significant cortical thickness differences between the second and third profiles (estimate = −0.03, <emph>t</emph> = −4.21, <emph>p</emph> < .001).</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01jan25/cdev14143-fig-0004.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev14143-fig-0004.jpg" title="4 Regional differences in cortical volume (top panel), subcortical volume (middle panel), and surface area (bottom panel) among the three profiles at the 2‐year follow‐up." /> </p> <p></p> <p>4 TABLE Profiles differences in brain structure at baseline and 2‐year follow‐up.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left"><italic>F</italic> (ANOVA)</th><th align="left">Profile 1 (<italic>M</italic> <p><bold>±</bold></p> SD)</th><th align="left">Profile 2 (<italic>M</italic> <p><bold>±</bold></p> SD)</th><th align="left">Profile 3 (<italic>M</italic> <p><bold>±</bold></p> SD)</th><th align="left">Profile 1 versus 2 (Holm‐corrected <italic>p</italic>)</th><th align="left">Profile 1 versus 3 (Holm‐corrected <italic>p</italic>)</th><th align="left">Profile 2 versus 3 (Holm‐corrected <italic>p</italic>)</th></tr></thead><tbody valign="top"><tr><td align="left">Baseline</td></tr><tr><td align="left">Cortical volume (mm<sup>3</sup>)</td><td align="left">F(2, 1675) = 30.96, p < .001</td><td align="char" char="±">597,129.26 ± 49,009.69</td><td align="char" char="±">601,042.94 ± 53,054.64</td><td align="char" char="±">580,414.57 ± 58,754.87</td><td align="char" char=".">.77</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">Cortical surface (mm<sup>2</sup>)</td><td align="left">F(2, 1675) = 34.55, p < .001</td><td align="char" char="±">189,044.94 ± 15,976.95</td><td align="char" char="±">190,829.81 ± 17,354.65</td><td align="char" char="±">183,730.01 ± 18,800.37</td><td align="char" char=".">.57</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">Cortical thickness (mm)</td><td align="left">F(2, 1674) = 2.52, p = .08</td><td align="char" char="±">2.74 ± 0.08</td><td align="char" char="±">2.73 ± 0.08</td><td align="char" char="±">2.73 ± 0.08</td><td align="char" char=".">.71</td><td align="char" char=".">.91</td><td align="char" char=".">.23</td></tr><tr><td align="left">2‐year follow‐up</td></tr><tr><td align="left">Cortical volume (mm<sup>3</sup>)</td><td align="left">F(2, 1167) = 22.35, p < .001</td><td align="char" char="±">590,095.25 ± 48,242.41</td><td align="char" char="±">592,494.74 ± 53,251.30</td><td align="char" char="±">571,944.65 ± 61,560.64</td><td align="char" char=".">.98</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">Cortical surface (mm<sup>2</sup>)</td><td align="left">F(2, 1167) = 24.57, p < .001</td><td align="char" char="±">190,377.09 ± 16,137.28</td><td align="char" char="±">191,929.95 ± 17,557.99</td><td align="char" char="±">184,561.35 ± 19,128.27</td><td align="char" char=".">.97</td><td align="char" char="."><.001</td><td align="char" char="."><.001</td></tr><tr><td align="left">Cortical thickness (mm)</td><td align="left">F(2, 1168) = 2.67, p = .07</td><td align="char" char="±">2.70 ± 0.07</td><td align="char" char="±">2.69 ± 0.08</td><td align="char" char="±">2.69 ± 0.08</td><td align="char" char=".">.67</td><td align="char" char=".">.91</td><td align="char" char=".">.20</td></tr></tbody></table> </ephtml> </p> <hd id="AN0181984181-28">Associations between profiles and brain function</hd> <p></p> <hd id="AN0181984181-29">At baseline</hd> <p>Significant differences were identified in three within‐network connectivities: the cingulo‐opercular (<emph>F</emph> = 12.09, <emph>p</emph> < .001), the cingulo‐parietal (<emph>F</emph> = 8.45, <emph>p</emph> < .001), and the default (<emph>F</emph> = 8.99, <emph>p</emph> < .001) networks. Seven pair networks also significantly differed across profiles, including auditory–salience, auditory–visual, default–dorsal attention, retrosplenial temporal–sensorimotor hand, retrosplenial temporal–visual, and sensorimotor hand–sensorimotor mouth.</p> <p>In comparison to the first profile, the third profile children had lower within‐network connectivity in the cingulo‐opercular, cingulo‐parietal, and dorsal attention networks (DANs). They also showed lower connectivity between the visual and retrosplenial temporal networks but higher connectivity between the sensorimotor hand and sensorimotor mouth networks. When compared to the second profile, children from the third profile exhibited lower within‐network connectivity in the cingulo‐opercular network, lower connectivity between the visual and retrosplenial temporal network, but higher connectivity between the sensorimotor hand–sensorimotor mouth, sensorimotor hand–retrosplenial temporal, visual–auditory, and ventral attention‐default networks.</p> <p>No significant differences were found in any within‐ or between‐networks connectivity between the first and second profiles. The results of the <emph>F</emph>‐tests (main effect of profile variable) and <emph>t</emph>‐tests (between‐profile comparisons) are displayed in Figure 5. Details of the estimates. <emph>F</emph>‐values and <emph>t</emph>‐values are available in Tables S2–S5.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01jan25/cdev14143-fig-0005.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev14143-fig-0005.jpg" title="5 Chord diagrams of the differences in functional connectivity among the three profiles within the 13 networks defined by Gordon et al. ([23]). The top panel displays baseline data, while the bottom panel shows the 2‐year follow‐up. F‐values are represented in light pink, positive t‐values in light purple, and negative t‐values in light blue. A representation of these 13 networks, generated using ggseg, is shown on the right." /> </p> <p></p> <hd id="AN0181984181-31">Two‐year follow‐up</hd> <p>At the 2‐year follow‐up, significant differences were found in two within‐network connectivities: the cingulo‐parietal (<emph>F</emph> = 9.16, <emph>p</emph> < .001) and the dorsal attention (<emph>F</emph> = 9.82, <emph>p</emph> < .001) networks. The connectivity of three pair networks significantly differed across profiles as well, including default–dorsal attention and dorsal attention–ventral attention.</p> <p>Compared to the first profile, children from the third profile had lower within‐network connectivity in the cingulo‐parietal network but higher connectivity between the dorsal attention–default networks. When compared to the second profile, children from the third profile showed lower within‐network connectivity in the DAN but higher connectivity between the dorsal attention–default and dorsal attention–ventral attention networks.</p> <p>No significant differences were observed in any within‐ or between‐networks connectivity between the first and second profiles. The results of the <emph>F</emph>‐tests (main effect of profile variable) and <emph>t</emph>‐tests (between‐profile comparisons) are shown in Figure 5. Details of the estimates, <emph>F</emph>‐values, and <emph>t</emph>‐values are available in Tables S6–S9.</p> <hd id="AN0181984181-32">Associations between profiles and participant and state‐level characteristics</hd> <p>Finally, exploratory analyses examined whether sociocultural or structural factors predicted child profile membership. Compared to the first profile, children from the third profile were more likely to be born earlier, younger at baseline, female, and have mothers with lower reported educational attainment. Children from the third profile were also more likely to be Black race compared to children in the first or second profile. In terms of state‐level characteristics, children in the third profile were less likely to live in communities with access to preventative healthcare and more likely to live in states with high discrimination scores and without the Affordable Care Act Medicaid expansion. All comparisons and associated estimates are available in Tables S18 and S19 and Figure S3.</p> <hd id="AN0181984181-33">DISCUSSION</hd> <p>This study explored the heterogeneity of neuropsychological profiles in 1891 children born early term or preterm within the ABCD dataset. Leveraging the LPA, a person‐centered approach, we uncovered inter‐individual differences and identified three subgroups within this population based on standard neuropsychological assessments at age 9–10. Furthermore, we assessed the far‐reaching impact, up to 2 years later, of these inter‐individual differences on other crucial areas such as behavior, academic performance, and brain structure and function.</p> <p>Our LPA identified three distinct profiles: a group where children consistently exceeded the norm across all tasks (profile 1), a heterogeneous group with scores both above and below the norm (profile 2), and a last group where children consistently scored below the norm across all tasks (profile 3). Regrettably, this final profile, which displayed the most challenging outcomes across every neurocognitive domain, represents an important segment of the early term and preterm population in our sample (39.3%). These effects were not only persistent for at least 2 years but also corresponded to performance on behavioral assessments and in academic settings. Interestingly, even though literature reports associations of preterm birth with both internalizing and externalizing domains (Hornman et al., [<reflink idref="bib27" id="ref69">27</reflink>]; Ji, Li, et al., [<reflink idref="bib30" id="ref70">30</reflink>]), the behavioral differences in this third profile were limited to externalizing subscales. Attention stood out as the most impacted domain, with a noteworthy 9.81% of the children in this third profile falling into the borderline or clinical range for attention problems at baseline. Since preterm‐born children are more likely to exhibit ADHD compared to full‐term children (Crump et al., [<reflink idref="bib12" id="ref71">12</reflink>]; Fitzallen et al., [<reflink idref="bib20" id="ref72">20</reflink>]), our neuropsychological profiling approach has the potential to help identify these at‐risk children, contributing to early targeted interventions. Overall, it is important to note that while the majority of children within this profile exhibited scores below the norm, these scores still fall within the normal range, which is overlooked in diagnostic studies (Astle et al., [<reflink idref="bib4" id="ref73">4</reflink>]). These scores and inter‐individual differences with the other profiles, even within the norm, impact not only their behavioral development but also their academic outcomes, hence warranting our consideration. This result supports the transdiagnostic approach: moving from a diagnosis‐centered to a child‐centered perspective will allow us to focus on the child's most significant characteristics for future support and remediation, rather than characteristics that fit a certain diagnostic category (Astle et al., [<reflink idref="bib4" id="ref74">4</reflink>]).</p> <p>The second profile (41.0% of our sample) also stands out, as these children consistently score below the norm on all executive tasks, yet surpass the norm in language tasks. One hypothesis is that these children manage to overcome their initial language deficits, but not their executive function ones, maybe related to external factors such as a favorable socioeconomic environment, demonstrated to substantially impact language abilities (Akshoomoff et al., [<reflink idref="bib1" id="ref75">1</reflink>]; Noble et al., [<reflink idref="bib43" id="ref76">43</reflink>]). On another hand, executive functions are often compromised in individuals born prematurely due to incomplete perinatal brain development, which disrupts the frontoparietal network, crucial for optimal executive function (Brydges et al., [<reflink idref="bib6" id="ref77">6</reflink>]; Martínez‐Nadal & Bosch, [<reflink idref="bib39" id="ref78">39</reflink>]). Therefore, it is possible that executive functions may be more impacted by preterm birth (profiles 2 and 3), leading to cascading effects on other neurocognitive domains for some children (profile 3) but not all (profile 2). To understand the potential compensatory/protective mechanisms at play in this second profile, future research including a wide range of multilevel measures is needed. Overall, this neurocognitive profile results show greater similarity with profile 1 in behavioral outcomes than profile 3. Despite this, these children do tend to have lower academic grades than children from the first profile. More targeted intervention, for example, addressing executive function and processing speed, could be uniquely beneficial for these children.</p> <p>Lastly, it is important to highlight that nearly 20% of the children in our sample demonstrated positive neurocognitive development, consistently scoring within the norm across all domains (profile 1). This underscores that preterm birth does not necessarily dictate poor outcomes, but can result in positive ones. By identifying and understanding these interindividual differences, the ultimate goal is to expand the proportion of children experiencing these positive outcomes.</p> <p>Examination of brain structure revealed that children from the third profile had a notably smaller gray matter volume and surface area compared to children from the first and second profiles. These results were not isolated to specific regions, but instead almost involved the entire brain. These findings echo those observed when comparing preterm and full‐term children, where smaller brain volume was reported in preterm‐born children (e.g., Ment & Vohr, [<reflink idref="bib41" id="ref79">41</reflink>]). Such findings were also observed in study comparing preterm and full‐term children using the ABCD cohort (Ji, Li, et al., [<reflink idref="bib30" id="ref80">30</reflink>]). Thus, the question whether the preterm/full‐term differences observed previously could be driven by a subset of the preterm population may warrant further investigations. Our findings remained consistent over time and were significantly more pronounced between profiles 2 and 3 than between profiles 1 and 3. While this could potentially be due to power differences, with profile 1 (<emph>N</emph> = 372) encompassing half the number of children than profile 2 (<emph>N</emph> = 775), it might also be indicative of compensatory mechanisms in brain at work in the second profile. Indeed, children from the second profile showed no significant difference in brain structure compared to those from the first profile. This likely mirrors the minimal behavioral differences between these two profiles, despite the substantial differences in neurocognition and academic outcomes. Interestingly, while we observed similar patterns with volume and surface area, this was not replicated for cortical thickness. A more in‐depth investigation is required to clarify this result.</p> <p>It is worth noting that the two components of cortical volume, surface area and thickness, are believed to have independent genetic and environmental etiologies (Panizzon et al., [<reflink idref="bib46" id="ref81">46</reflink>]; Strike et al., [<reflink idref="bib56" id="ref82">56</reflink>]). Notably, genes that influence surface area play a crucial role in the early growth and development of the brain (Panizzon et al., [<reflink idref="bib46" id="ref83">46</reflink>]), potentially making this component more sensitive to premature birth. As our findings indicate that negative outcomes associated with early term and preterm birth (profile 3) are predominantly associated with changes in surface area rather than thickness, future research are needed to explore both thickness and surface area components, examining their individual influencers and impacts, and how they intersect. This could possibly help understand the brain changes through which preterm birth can lead to negative outcomes, in addition to offering a more nuanced understanding of cortical volume and its implications.</p> <p>The FC analysis corroborated evidence of minimal difference between profile 1 and 2, and significant differences when comparing profile 3 with profiles 1 and 2. However, unlike the consistent decreased volumes identified by structural analysis, we observe both increased and decreased FC in profile 3 among multiple key functional networks when compared to profiles 1 and 2. One notable network displaying converging alterations is the DAN, exhibiting lower FC within itself but higher FC with other networks, including the default mode network. The alteration of DAN FC aligns well with existing FC studies comparing preterm and full‐term children (Wehrle et al., [<reflink idref="bib61" id="ref84">61</reflink>]; Wheelock et al., [<reflink idref="bib64" id="ref85">64</reflink>]). Given that DAN plays a pivotal role in selective and sustained attention (Fortenbaugh et al., [<reflink idref="bib21" id="ref86">21</reflink>]; Rohr et al., [<reflink idref="bib50" id="ref87">50</reflink>]), the altered DAN FC in profile 3 resonates with our significant findings of lower attention. In fact, ADHD is also associated with reduced FC of DAN across studies (Friedman‐Hill et al., [<reflink idref="bib22" id="ref88">22</reflink>]; Hart et al., [<reflink idref="bib26" id="ref89">26</reflink>]). Together with the neurocognitive findings, the FC results further affirm the potential of our profiling approach in facilitating early ADHD diagnoses and prevention. Another intriguing aspect of enhanced connectivity in profile 3 involves the FC between the sensorimotor hand area and the sensorimotor mouth area, which are known to develop even before birth. Since the delayed motor development is constantly reported in preterm infancy, with some studies suggesting that they could catch up at later school age (Brown et al., [<reflink idref="bib5" id="ref90">5</reflink>]), the heightened FC between two sensorimotor networks in our 9‐ to 11‐year‐old children may indicate successful functional compensation since infancy. Unfortunately, the current study lacks measurements in different areas, including motor development. To better define the effects of preterm birth on an individual basis, we would need to expand the measures to all developmental domains. Nonetheless, this study sets the stage for further exploration.</p> <p>As expected, our study revealed that children from the third profile were born at a significantly lower gestational age compared to those in the first and second profiles. This finding aligns with numerous studies that demonstrated an association between shorter gestational age at birth and poorer outcomes (Brydges et al., [<reflink idref="bib6" id="ref91">6</reflink>]; Dong et al., [<reflink idref="bib15" id="ref92">15</reflink>]; Fitzallen et al., [<reflink idref="bib18" id="ref93">18</reflink>]; Ma et al., [<reflink idref="bib37" id="ref94">37</reflink>]) in line with the gestational age gradient concept, which suggests that each week decrease in gestational age at birth is associated with a rise in the incidence and severity of morbidities (Fitzallen et al., [<reflink idref="bib19" id="ref95">19</reflink>]). However, it is crucial to note that, within the third profile, there was a notable range in gestational age at birth. Therefore, this variable cannot serve as a definitive criterion for determining profile affiliation. Of note, exploratory analyses showed that associations between profile membership and cognitive, behavioral, and MRI outcomes remain largely unchanged when including gestational age at birth as a covariate (see Tables S14 and S15).</p> <p>Our analyses also highlight the important role of sociocultural factors and local policy in shaping child outcomes following early birth. Specifically, preterm and early‐term children of Black race were nearly four times as likely to be categorized as Profile 3. Black families have the highest rates of preterm birth (13.77% in 2016) across all races and origins in the United States (Martin et al., [<reflink idref="bib38" id="ref96">38</reflink>]). In addition to higher prevalence, our results suggest that Black families are more likely to experience worse child outcomes after preterm birth. Rather than being biologically driven, race‐correlated risk results from systemic barriers that are experienced disproportionately by Black families in the United States. In our analyses, children living in states with higher levels of structural and interpersonal discrimination and where legislators did not adopt the Affordable Care Act's Medicaid expansion were also at higher risk for belonging to Profile 3. Conversely, we identified a small protective effect of access to prevention‐focused healthcare. Children living in communities with greater prevalence of health insurance and higher engagement in preventative healthcare were less likely to belong to Profile 3. It is broadly understood that lack of structural supports and implementation of segregationist policies that interfere with access to education, healthcare, and economic opportunities can interact with experienced discrimination to drive health disparities. Here, we see evidence of race‐correlated health disparities among children who were all born preterm or early term. These results are of major importance and demand immediate action: social and structural intervention is necessary to ensure all preterm‐born children receive equitable and impactful care.</p> <p>Our study has several limitations. In particular, it does not represent the broad spectrum of preterm‐born children. The ABCD criteria exclude extremely preterm births (less than 28 weeks of gestation), diagnoses associated with irregular schooling, and several medical outcomes, including previous diagnoses of cerebral palsy or brain hemorrhage. This may have excluded a significant number of children born prematurely, resulting in a relatively healthy cohort. Therefore, while we anticipate the existence of other preterm profiles, they cannot be represented in the ABCD cohort. A systematic follow‐up of children born preterm would likely reveal higher rates of impairments. Then, the ABCD study's assessment of preterm birth relied on parental reports, not medical records, which resulted in some imprecision. Parents were asked, "<emph>Was the child born prematurely?</emph>" and if affirmative, "<emph>About how many weeks premature was the child when they were born?</emph>". Parents' interpretation of this last question may have differed based on their beliefs about when the child was supposed to be born. Additionally, we observed that some parents reported more than 12 weeks premature (i.e., less than 28 weeks of gestation), despite it being an exclusion criterion. This prompted us to question the validity and accuracy of this measure, viewing it more as an estimate of the true gestational age at birth. As a result, we decided to include all children whose parents responded affirmatively to the first question. Consequently, our focus extends to preterm and early term children. Importantly, multiple studies have shown that late preterm birth (34–37 weeks) and early term birth (37–39 weeks) are associated with poorer outcomes (Crump et al., [<reflink idref="bib12" id="ref97">12</reflink>]; Dong et al., [<reflink idref="bib15" id="ref98">15</reflink>]; Kajantie et al., [<reflink idref="bib34" id="ref99">34</reflink>]; Quigley et al., [<reflink idref="bib48" id="ref100">48</reflink>]). This indicates that the relationship between gestational age and brain development spans the entire gestation period, not just for preterm infants (Dong et al., [<reflink idref="bib15" id="ref101">15</reflink>]).</p> <p>In conclusion, we identified three distinct neurocognitive profiles among a cohort of 1891 children born early term or preterm. These profiles were associated with diverse cognitive, neural, behavioral, and academic outcomes. This confirms that interindividual differences among preterm‐born children are not just significant, but demand further in‐depth investigation. While preterm versus full‐term group comparisons have served their purpose in evaluating the long‐term and wide‐ranging consequences of preterm birth, the time has now come to pivot toward a more person‐centered approach. This shift will open areas for more targeted, efficient, and impactful care for children born prematurely.</p> <hd id="AN0181984181-34">AUTHOR CONTRIBUTIONS</hd> <p>I.M. was involved in conceptualization and methodology; I.M. and M.E.T. were involved in data curation; I.M., C.L.H., and L.J. were involved in formal analysis; M.E.T. involved in funding acquisition and supervision; I.M., L.J., T.B., and M.D. were involved in visualization; I.M. and L.J. were involved in writing—original draft; I.M., L.J., T.B., M.D., C.L.H., and M.E.T. were involved in writing—review and editing.</p> <hd id="AN0181984181-35">ACKNOWLEDGMENTS</hd> <p>We express our gratitude to Luis Martinez Agulleiro and F. Xavier Castellanos for their assistance with ABCD data access, and to Mekhala Mantravadi for her initial contributions to the project. Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development<sups>SM</sups> (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9–10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal‐partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/.</p> <hd id="AN0181984181-36">FUNDING INFORMATION</hd> <p>This project was supported by awards from the National Institutes of Health, MH125870, MH126468, MH122447, DA055338, and ES032294. Author CLH is supported by a grant K99MH133978 from NIMH.</p> <hd id="AN0181984181-37">CONFLICT OF INTEREST STATEMENT</hd> <p>The authors report no biomedical financial interests or potential conflicts of interest.</p> <hd id="AN0181984181-38">DATA AVAILABILITY STATEMENT</hd> <p>Neuroimaging and cognitive data from the ABCD dataset are available from https://nda.nih.gov/abcd with the approval of the ABCD consortium. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. The ABCD data repository grows and changes over time. The ABCD data used in this report came from https://doi.org/10.15154/8873‐zj65. The materials necessary to attempt to replicate the findings presented here are publicly accessible. Materials are available at the following URL: https://doi.org/10.15154/8873‐zj65. The analytic code necessary to reproduce the analyses presented in this paper is publicly accessible. The code associated with this study is freely available on OSF (https://osf.io/fd8gy/). The analyses presented here were not preregistered.</p> <p>GRAPH: Data S1.</p> <ref id="AN0181984181-39"> <title> REFERENCES </title> <blist> <bibl id="bib1" idref="ref75" type="bt">1</bibl> <bibtext> Akshoomoff, N., Newman, E., Thompson, W. K., McCabe, C., Bloss, C. S., Chang, L., Amaral, D. G., Casey, B. J., Ernst, T. M., Frazier, J. A., Gruen, J. 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Thomason</p> <p>Reported by Author; Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib45" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib35" firstref="ref3"></nolink> <nolink nlid="nl3" bibid="bib20" firstref="ref4"></nolink> <nolink nlid="nl4" bibid="bib59" firstref="ref5"></nolink> <nolink nlid="nl5" bibid="bib47" firstref="ref6"></nolink> <nolink nlid="nl6" bibid="bib51" firstref="ref8"></nolink> <nolink nlid="nl7" bibid="bib40" firstref="ref9"></nolink> <nolink nlid="nl8" bibid="bib11" firstref="ref10"></nolink> <nolink nlid="nl9" bibid="bib60" firstref="ref12"></nolink> <nolink nlid="nl10" bibid="bib24" firstref="ref14"></nolink> <nolink nlid="nl11" bibid="bib29" firstref="ref15"></nolink> <nolink nlid="nl12" bibid="bib32" firstref="ref16"></nolink> <nolink nlid="nl13" bibid="bib53" firstref="ref17"></nolink> <nolink nlid="nl14" bibid="bib18" firstref="ref18"></nolink> <nolink nlid="nl15" bibid="bib55" firstref="ref21"></nolink> <nolink nlid="nl16" bibid="bib31" firstref="ref24"></nolink> <nolink nlid="nl17" bibid="bib36" firstref="ref25"></nolink> <nolink nlid="nl18" bibid="bib30" firstref="ref26"></nolink> <nolink nlid="nl19" bibid="bib37" firstref="ref27"></nolink> <nolink nlid="nl20" bibid="bib41" firstref="ref32"></nolink> <nolink nlid="nl21" bibid="bib58" firstref="ref35"></nolink> <nolink nlid="nl22" bibid="bib42" firstref="ref36"></nolink> <nolink nlid="nl23" bibid="bib44" firstref="ref37"></nolink> <nolink nlid="nl24" bibid="bib54" firstref="ref38"></nolink> <nolink nlid="nl25" bibid="bib16" firstref="ref39"></nolink> <nolink nlid="nl26" bibid="bib28" firstref="ref40"></nolink> <nolink nlid="nl27" bibid="bib57" firstref="ref41"></nolink> <nolink nlid="nl28" bibid="bib10" firstref="ref42"></nolink> <nolink nlid="nl29" bibid="bib25" firstref="ref43"></nolink> <nolink nlid="nl30" bibid="bib33" firstref="ref49"></nolink> <nolink nlid="nl31" bibid="bib63" firstref="ref50"></nolink> <nolink nlid="nl32" bibid="bib13" firstref="ref52"></nolink> <nolink nlid="nl33" bibid="bib17" firstref="ref53"></nolink> <nolink nlid="nl34" bibid="bib23" firstref="ref56"></nolink> <nolink nlid="nl35" bibid="bib52" firstref="ref57"></nolink> <nolink nlid="nl36" bibid="bib49" firstref="ref58"></nolink> <nolink nlid="nl37" bibid="bib62" firstref="ref59"></nolink> <nolink nlid="nl38" bibid="bib14" firstref="ref60"></nolink> <nolink nlid="nl39" bibid="bib27" firstref="ref69"></nolink> <nolink nlid="nl40" bibid="bib12" firstref="ref71"></nolink> <nolink nlid="nl41" bibid="bib43" firstref="ref76"></nolink> <nolink nlid="nl42" bibid="bib39" firstref="ref78"></nolink> <nolink nlid="nl43" bibid="bib46" firstref="ref81"></nolink> <nolink nlid="nl44" bibid="bib56" firstref="ref82"></nolink> <nolink nlid="nl45" bibid="bib61" firstref="ref84"></nolink> <nolink nlid="nl46" bibid="bib64" firstref="ref85"></nolink> <nolink nlid="nl47" bibid="bib21" firstref="ref86"></nolink> <nolink nlid="nl48" bibid="bib50" firstref="ref87"></nolink> <nolink nlid="nl49" bibid="bib22" firstref="ref88"></nolink> <nolink nlid="nl50" bibid="bib26" firstref="ref89"></nolink> <nolink nlid="nl51" bibid="bib15" firstref="ref92"></nolink> <nolink nlid="nl52" bibid="bib19" firstref="ref95"></nolink> <nolink nlid="nl53" bibid="bib38" firstref="ref96"></nolink> <nolink nlid="nl54" bibid="bib34" firstref="ref99"></nolink> <nolink nlid="nl55" bibid="bib48" firstref="ref100"></nolink>
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PubType: Academic Journal
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Beyond Average Outcomes: A Latent Profile Analysis of Diverse Developmental Trajectories in Preterm and Early Term-Born Children from the Adolescent Brain Cognitive Development Study
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Iris+Menu%22">Iris Menu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-7587-2493">0000-0001-7587-2493</externalLink>)<br /><searchLink fieldCode="AR" term="%22Lanxin+Ji%22">Lanxin Ji</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4509-0225">0000-0003-4509-0225</externalLink>)<br /><searchLink fieldCode="AR" term="%22Tanya+Bhatia%22">Tanya Bhatia</searchLink><br /><searchLink fieldCode="AR" term="%22Mark+Duffy%22">Mark Duffy</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0001-0243-2267">0009-0001-0243-2267</externalLink>)<br /><searchLink fieldCode="AR" term="%22Cassandra+L%2E+Hendrix%22">Cassandra L. Hendrix</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-1462-3941">0000-0002-1462-3941</externalLink>)<br /><searchLink fieldCode="AR" term="%22Moriah+E%2E+Thomason%22">Moriah E. Thomason</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9745-1147">0000-0001-9745-1147</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Child+Development%22"><i>Child Development</i></searchLink>. 2025 96(1):36-54.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 19
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: SourceSuprt
  Label: Sponsoring Agency
  Group: SrcSuprt
  Data: National Institutes of Health (NIH) (DHHS)
– Name: NumberContract
  Label: Contract Number
  Group: NumCntrct
  Data: DA055338<br />ES03229<br />K99MH133978<br />MH122447<br />MH125870<br />MH126468
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Child+Development%22">Child Development</searchLink><br /><searchLink fieldCode="DE" term="%22Premature+Infants%22">Premature Infants</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Development%22">Cognitive Development</searchLink><br /><searchLink fieldCode="DE" term="%22Scores%22">Scores</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Public+Health%22">Public Health</searchLink><br /><searchLink fieldCode="DE" term="%22Profiles%22">Profiles</searchLink><br /><searchLink fieldCode="DE" term="%22Behavior+Patterns%22">Behavior Patterns</searchLink><br /><searchLink fieldCode="DE" term="%22Brain+Hemisphere+Functions%22">Brain Hemisphere Functions</searchLink><br /><searchLink fieldCode="DE" term="%22Intervention%22">Intervention</searchLink><br /><searchLink fieldCode="DE" term="%22Clinical+Diagnosis%22">Clinical Diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Adolescents%22">Adolescents</searchLink><br /><searchLink fieldCode="DE" term="%22Screening+Tests%22">Screening Tests</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1111/cdev.14143
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0009-3920<br />1467-8624
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Preterm birth poses a major public health challenge, with significant and heterogeneous developmental impacts. Latent profile analysis was applied to the National Institutes of Health Toolbox performance of 1891 healthy prematurely born children from the Adolescent Brain and Cognitive Development study (970 boys, 921 girls; 10.00 ± 0.61 years; 1.3% Asian, 13.7% Black, 17.5% Hispanic, 57.0% White, 10.4% Other). Three distinct neurocognitive profiles emerged: consistently performing above the norm (19.7%), mixed scores (41.0%), and consistently performing below the norm (39.3%). These profiles were associated with lasting cognitive, neural, behavioral, and academic differences. These findings underscore the importance of recognizing diverse developmental trajectories in prematurely born children, advocating for personalized diagnosis and intervention to enhance care strategies and long-term outcomes for this heterogeneous population.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1455437
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1455437
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/cdev.14143
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 19
        StartPage: 36
    Subjects:
      – SubjectFull: Child Development
        Type: general
      – SubjectFull: Premature Infants
        Type: general
      – SubjectFull: Cognitive Development
        Type: general
      – SubjectFull: Scores
        Type: general
      – SubjectFull: Academic Achievement
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      – SubjectFull: Public Health
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      – SubjectFull: Profiles
        Type: general
      – SubjectFull: Behavior Patterns
        Type: general
      – SubjectFull: Brain Hemisphere Functions
        Type: general
      – SubjectFull: Intervention
        Type: general
      – SubjectFull: Clinical Diagnosis
        Type: general
      – SubjectFull: Adolescents
        Type: general
      – SubjectFull: Screening Tests
        Type: general
    Titles:
      – TitleFull: Beyond Average Outcomes: A Latent Profile Analysis of Diverse Developmental Trajectories in Preterm and Early Term-Born Children from the Adolescent Brain Cognitive Development Study
        Type: main
  BibRelationships:
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      – PersonEntity:
          Name:
            NameFull: Iris Menu
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            NameFull: Lanxin Ji
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            NameFull: Tanya Bhatia
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            NameFull: Mark Duffy
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            NameFull: Cassandra L. Hendrix
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            NameFull: Moriah E. Thomason
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            – D: 01
              M: 01
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 0009-3920
            – Type: issn-electronic
              Value: 1467-8624
          Numbering:
            – Type: volume
              Value: 96
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            – TitleFull: Child Development
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