Longitudinal characterization of clinical, developmental, and behavioral phenotypes in 101 children and adults with FOXG1 syndrome

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With the advent of molecular therapies, establishing the natural history of FOXG1 syndrome is critical to enable clinical trial readiness. However, traditional study designs are challenging to implement for rare disorders without significant burden to participants. Methods The study population included 101 children and adults with (likely) pathogenic variants in or involving FOXG1 (ages 0.4 - 34.8 years). Participant medical records underwent systematic annotation and harmonization of recorded clinical phenotypes, interventions, and outcomes through use of a patient-centric real-world data (RWD) platform. Retrospective medical record data were paired with prospective administration of validated measures of development and behavior, including the Vineland-3, the Aberrant Behavior Checklist, and the Children’s Sleep Habits Questionnaire. Descriptive and inferential statistics were employed to characterize longitudinal phenotypes and to explore genotype-phenotype correlations. Results Through systematic evaluation of 101 people with FOXG1 syndrome, we generated a robust dataset encompassing >40,000 annotated clinical terminology concepts that represent >770 cumulative patient data years. Core clinical phenotypes include DD/ID, gastrointestinal disorders, strabismus, epilepsy, movement disorders, and sleep problems. The FOXG1 syndrome behavioral phenotype is characterized by irritability, including aggressive behaviors, stereotypies, social withdrawal, and lethargy; in those with missense variants, features of autism spectrum disorders are also reported. Data derived from both medical records and validated measures confirm and expand upon previously described genotype-phenotype correlations, whereby truncating variants are associated with greater limitations across motor and communication domains, as well as increased frequency of core FOXG1 syndrome phenotypes. Further, individuals with truncating variants had higher scores on a composite measure of FOXG1 syndrome severity, which persists when modeled longitudinally. Employing the same composite measure, we demonstrate that FOXG1 syndrome is a static encephalopathy without evidence of neurodegeneration. Conclusions By combining retrospective RWD with prospective survey administration in a large sample population, we establish the natural history of FOXG1 syndrome and highlight candidate clinical endpoints for use in clinical trials, including quantitative evaluations of communication and movement disorders. FOXG1 syndrome Neurodevelopmental disorders Natural history studies Genotype-phenotype correlations Clinical trial readiness Rare disorders Real-world data Real-world evidence Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction FOXG1 syndrome (OMIM 613454) is a severe neurodevelopmental disorder caused by pathogenic variants in or involving FOXG1 , which plays critical roles in the specification and development of the forebrain (1). The core clinical phenotypes described for FOXG1 syndrome include universal developmental and intellectual disabilities (DD/ID), postnatal microcephaly, epilepsy, and a hyperkinetic-dyskinetic movement disorder (2–5). Characteristic structural brain anomalies are observed, including abnormalities of the corpus callosum; simplified gyri, most commonly affecting the frontal lobe; and delayed myelination (6). Of note, genotype-phenotype correlations have been reported, whereby missense variants are associated with an attenuated clinical phenotype (3,5,7). Management of FOXG1 syndrome is symptomatic, but recent pre-clinical evidence supports gene replacement as a potential therapeutic strategy (8). With potentially disease-modifying therapies in development, establishing a natural history profile for FOXG1 syndrome is critical in preparing for clinical trials. Further, defining the complete spectrum and clinical trajectory of the disorder can aid in the selection and characterization of candidate clinical endpoints. However, the traditional site-based design can be challenging for rare disorders, where small heterogeneous populations are geographically distributed, and the likelihood of attrition is high when participants have significant medical complexities. To address these limitations, we combined retrospective annotation of patient medical records with quarterly prospective administration of validated measures of development and behavior in 101 children and adults with FOXG1 syndrome. The resulting dataset encompassed >40,000 annotated clinical terminology concepts representing >770 cumulative patient data years. This is the largest longitudinal characterization of FOXG1 syndrome, and provides evidence of a static encephalopathy without apparent neurodegeneration. Methods Study participants The study population was drawn from individuals enrolled in the FOXG1 syndrome natural history study via their joint participation in the FOXG1 syndrome patient registry and Citizen Health (Citizen). Participants had documentation of a likely pathogenic or pathogenic FOXG1 variant in accordance with the American College of Medical Genetics and Genomics (9); individuals with whole gene deletions that encompassed additional genes were excluded. The FOXG1 syndrome natural history study received determinations of exemption through a central IRB via exemption categories 2, 7, and 8 of the revised Common Rule. Generation of a longitudinal real-world dataset Citizen Health (Citizen) is a patient-centric real-world data (RWD) platform that harmonizes unstructured content within medical records to generate longitudinal clinical datasets. Medical records were collected from healthcare organizations within the United States using the ‘right to access’ granted by the Health Insurance Portability and Accountability Act (HIPAA). Participant data were included for analysis where collected documentation exceeded predefined thresholds. Citizen was used to systematically interrogate participant medical records for documentation of clinical phenotypes, interventions, and outcomes, including developmental milestones and seizure burden. All Citizen-generated data were independently reviewed by at least two clinicians with relevant experience and training, including nurses, nurse practitioners, physician assistants, and genetic counselors, who confirmed accurate annotation and performed source document verification. Resulting data were stored in a HIPAA-compliant, controlled access, indexed database (10,11). Measures of development, sleep, and behavior Several measures were administered to study participants at quarterly intervals via the FOXG1 syndrome patient registry. To quantify developmental capabilities, the gross motor function classification system (GMFCS) (12), manual abilities classification system (MACS) (13), and communication function classification system (CFCS) (14) were used. These represent brief validated measures of gross motor, fine motor, and communication function that have been widely used in populations with cerebral palsy. Across all three measures, scores range from I to V, with higher scores corresponding to greater functional impairment. Whereas the GMFCS and MACS provide insight into the level of assistance required to ambulate or manipulate objects, respectively, the CFCS evaluates an individual's ability to send and receive information with both familiar and unfamiliar communication partners. In addition, the Vineland Adaptive Behavior Scales, Third Edition (Vineland-3) was used to evaluate adaptive behavior at annual intervals. Four measures of the Vineland-3 were included in this analysis: (1) v-Scale Scores ( v S), which are norm-referenced standard scores; (2) age equivalents (AEs), which represent the reference age a participant’s raw scores map to for a given domain; (3) developmental quotients (DQs), which are a computed ratio between a participant’s AE and their chronologic age; and (4) growth scale values (GSVs), which represent a participant’s absolute level of performance that is not relative to the reference dataset. The Children’s Sleep Habits Questionnaire (CSHQ) was administered to caregivers in quarterly intervals to measure the frequency of behaviors associated with common pediatric sleep difficulties across eight domains: bedtime resistance, sleep onset delay, sleep duration, sleep anxiety, night waking, parasomnias, sleep disordered breathing, and daytime sleepiness (15). A total sleep disturbance index is calculated, where higher scores are associated with greater frequency of abnormal sleep behaviors. To quantify and characterize behavioral challenges, the Aberrant Behavior Checklist (ABC) was administered at quarterly intervals (16). This scale has been widely used to measure psychiatric symptoms and behavioral disturbances in individuals with DD/ID across five domains: irritability, agitation, and crying (irritability); lethargy and social withdrawal (social withdrawal); stereotypic behavior; hyperactivity and noncompliance; and inappropriate speech. Higher scores across these domains represent more frequent observation of associated behaviors. Computation of a longitudinal FOXG1 syndrome severity score A FOXG1 syndrome severity score has been described previously that encompasses 20 clinical phenotypes distributed across five domains: somatic growth (4 items), motor and speech development (4 items, age-limited), behavior (3 items), neurological features (6 items), and MRI abnormalities (3 items) (3). To quantify FOXG1 syndrome severity longitudinally, items were adapted to accommodate contemporaneous evaluations, as opposed to retrospective or lifetime observations. The presence or absence of phenotypic contributors to the five domains defined by Mitter et al. were evaluated at each year of age: somatic growth (abnormal head circumference, weight, height); motor and speech development (ability to sit unassisted, walk unassisted, perform hand functions, use expressive speech); behavior (abnormal sleep, autism spectrum disorder and/or autistic features); neurological features (epilepsy, spasticity, stereotypies, dyskinesia, feeding difficulties, scoliosis); and MRI features (abnormality of the corpus callosum, delayed myelination, cortical abnormalities), resulting in an 18-item scale, for which higher scores correspond to increased frequency of FOXG1 syndrome-related phenotypes. Statistical analyses Data collection for the described analyses spanned March 2021 through November 2023. Computations were performed using Graphpad Prism 9 and the R statistical framework. When plotting central tendency measures, 95% confidence intervals are presented. For subgroup comparisons, nonparametric statistical tests were performed at a significance level of 0.05 following correction for multiple comparisons, including Fisher's exact test with Bonferroni correction for categorical variables and Kruskall-Wallis test with Dunn’s correction for continuous variables. When evaluating clinical contributors to FOXG1 syndrome severity scores, a multiple regression model was generated using the following variables as inputs: age, head circumference at 1 year, epilepsy age of onset, and variant class (e.g. missense). Results Sample population The sample population consisted of 101 individuals with a likely pathogenic or pathogenic variant in FOXG1 ( Table 1, Supplementary Table 1 ). Most had a truncating variant (n=49, 48.5% frameshift; n=24, 23.8% nonsense), with approximately half occurring N-terminal to the forkhead DNA binding domain (n=40, 54.8%). Ten (9.9%) had documentation of a second genetic disorder evidenced by a pathogenic or likely pathogenic variant(s) in one of the following: FLG , LDLR , MT-RNR1 , MYBPC3 , PKD2 , PKP2 , SCN5A , SLC26A4 ; one individual had trisomy X. The associated clinical features do not overlap significantly with FOXG1 syndrome and are not expected to modify associated phenotypes; therefore, data for these participants were included in all subsequent analyses. The median age of participants at record end was 7.3 years (range 0.4 - 34.8) and median age at FOXG1 syndrome diagnosis was 1.6 years (range 0.0 - 34.3); there was no significant difference in age or age at diagnosis between genotypes. Most participants were white (n=42, 67.7%) and lived in primarily metropolitan areas across the United States. The median duration of follow-up was 6.0 years (range 0.4 - 34.3), representing approximately 773 cumulative patient data years. Earliest medical record documentation precedes FOXG1 syndrome diagnosis for all participants. Identification of core FOXG1 syndrome phenotypes The most common presenting diagnoses were microcephaly (n=23, 22.8%), developmental delay (n=19, 18.8%), hypotonia (n=11, 10.9%), and strabismus (n=10, 9.9%) at a median age of 0.2 years (range 0.0 - 4.9). Brain magnetic resonance imaging (MRI, n=37, 36.6%) and electroencephalogram (EEG, n=20, 19.8%) were the most common diagnostic procedures first recorded for participants, at a median age of 0.5 years (range 0.0 - 16.4). Core FOXG1 syndrome phenotypes, representing those present in ≥50% of the population, and their associated ages of onset are shown in Figure 1A. DD/ID were universally documented for participants; other phenotypes described for >75% of individuals included: hypotonia (n=92, 91.1%), feeding problem (n=85, 84.2%), constipation (n=85, 84.2%), movement disorder (n=82, 81.2%), and strabismus (n=76, 75.2%). To complement annotated phenotypes and characterize the need for medical management, interventions were explored. The most commonly documented medications include: laxatives (n=88, 87.1%), sleep aids (n=65, 64.4%), histamine H 2 receptor antagonists (n=61, 60.4%), and proton pump inhibitors (n=61, 60.4%). Gastrostomy tube insertions (n=41, 40.6%) and strabismus surgeries (n=36, 35.6%) were frequent. Other related therapeutic procedures were fundoplication (n=7, 6.9%), implantation of a baclofen pump (n=1, 1.0%), and ligation of the parotid duct (n=1, 1.0%). Genotype-phenotype analyses were performed for frameshift, nonsense, and missense variants given their relative frequency in the sample population. With the exception of DD/ID, individuals with missense variants demonstrated lower frequencies of core phenotypes ( Figure 1B ). This pattern was significant for gastroesophageal reflux ( P< 0.05 for frameshift vs. missense), strabismus ( P <0.05 for both frameshift and nonsense vs. missense), and feeding problem ( P <0.05 for nonsense vs. missense). Given its relative frequency and candidacy as a clinical endpoint, movement disorder features were characterized. A total of 1,323 neurology exams were annotated, representing a mean of 13.1 exams per participant (range 0 - 77). Approximately 75% (n=79) of individuals had an abnormal movement disorder finding on neurological exam; the frequency across genotypes ranged from 59.1% (missense variants) to 87.5% (nonsense variants). Features of chorea and/or athetosis (n=43, 42.6%), spasticity (n=27, 26.7%), involuntary movements (n=25, 24.8%), and coordination problem (n=22, 21.8%) were most commonly reported. Other findings included: stereotypy (n=18, 17.8%), dystonia (n=17, 16.8%), hyperkinesis (n=16, 15.8%), abnormal eye movement (n=14, 13.9%), and orofacial dyskinesia (n=10, 9.9%). Although coordination problem was documented at consistent frequencies across genotypes, this was the most common movement disorder phenotype in individuals with missense variants (n=6, 27.2%) as opposed to chorea and/or athetosis in those with truncating variants. With respect to growth parameters, the frequency at which individuals had any documentation of a measurement greater than two standard deviations below the mean for head circumference, height, and weight were as follows: 84.2% (n=85), 56.4% (n=57), 47.5% (n=48); all annotated measurements plotted against the Centers for Disease Control and Prevention growth centiles are depicted in Supplementary Figure 1 . While individuals with missense variants were less likely to have growth parameters outside the normal range, this only achieved significance for head circumference (frameshift = 93.9%, nonsense = 91.7%, missense = 54.5%, P< 0.05). Abnormalities were documented on brain MRI reports for 79.2% (n=80) of participants; individuals with missense variants (45.5%) were significantly less likely to have an abnormal brain MRI report compared to both frameshift (91.8%) and nonsense (87.5%) variants ( P <0.05). The most common findings included: abnormalities of the corpus callosum (n=55, 54.5%), delayed myelination (n=35, 34.7%), and abnormal gyrification (n=21, 20.8%). Abnormalities of the corpus callosum; spanning agenesis, partial agenesis, hypoplasia, and dysplasia; were significantly less common in individuals with missense variants (13.6%) compared to those with frameshift (65.3%) and nonsense (66.7%) variants ( P <0.05). With respect to abnormal gyrification, simplified gyri were most frequent (n=19, 18.8%) with pachygyria described more rarely (n=4, 4.0%). Given the dependency of this study on interpretation from local centers, the reported frequencies represent conservative estimates due to varied experience reading pediatric brain MRIs. Characterization of seizure phenotype and management An epilepsy diagnosis was documented in 70.3% (n=71) and ranged from 50.0% in those with missense variants to 77.6% (frameshift variants, Figure 1B ). The median age at seizure onset was 1.3 years (range DOL4 - 10.0 years) and did not vary significantly between genotypes. The median survival time for seizure onset was 1.7 years, which represents the age at which the probability of an epilepsy diagnosis is 50% ( Figure 2A ). Thirty-one percent (n=22) of individuals had seizure-onset before one year of age; the majority of those with an early-onset epilepsy had frameshift variants (n=13, 59.1%). Infantile spasms, epileptic spasms, or West syndrome were documented in 22.5% (n=16) and Lennox-Gastaut syndrome (LGS) in 19.7% (n=14). Of those with an LGS diagnosis, 28.6% (n=4) had a preceding diagnosis of infantile spasms. A specific seizure type was documented for 97.2% (n=69) of individuals. The most commonly reported seizure types were generalized tonic-clonic (n=35, 50.7%), focal-onset (n=28, 40.58%), tonic (n=15, 21.7%), myoclonic (n=12, 17.4%), and generalized-onset (n=11, 15.9%). Notably, the proportion of individuals with history of a prolonged seizure (>5 minutes) was 97.1% (n=67), with status epilepticus described in 30.4% (n=21). Overall, seizure phenotypes were variable, with many participants experiencing both focal- and generalized-onset seizures. A total of 400 EEGs were annotated from the medical records of participants with epilepsy. The median age at first EEG was 0.6 years (range DOL4 - 16.0 years) and the median number of EEGs documented per participant was 4.0 (range 1 - 24). The majority of EEGs were performed in the first three years of life (n=278, 69.5%). Consistent with the observed heterogeneity in seizure semiology, EEG findings were variable, and included focal (n=46, 64.8%), multifocal (n=16, 22.5%), and generalized (n=13, 18.3%) epileptiform discharges. Generalized or focal background slowing was common, and documented in 76.1% (n=54) and 33.8% (n=24) of participants, respectively. To characterize the longitudinal epilepsy phenotype in FOXG1 syndrome, reported seizure frequencies by caregivers at neurology visits or by EEG were annotated from participant medical records. Seizure frequencies were converted to a numeric score, where 0 is equivalent to seizure-freedom and a maximum score of 5 corresponds to multiple seizures per day. At all ages assessed, the majority of participants were observed to have a seizure frequency score of 0, indicating good seizure control ( Figure 2B ). However, a subset of individuals continued to experience high seizure burden, representing those with a seizure frequency score of ≥3 (at least weekly seizures) for ≥50% of epochs (one year). These individuals (n=15, 24.2%) were younger (median 3.8 years vs 8.6 years, P <0.05) and had an earlier age of epilepsy onset (median 0.9 years vs 1.3 years, P <0.05), but did not differ significantly by genotype. We also evaluated the number of unique seizure medications used within the same epoch ( Figure 2C ). To quantify the use of polypharmacy, a similar scoring system was employed where 0 is equivalent to no documentation of anti-seizure medications (ASMs) for the corresponding epoch and 5 corresponds to ≥5 unique ASMs used, excluding rescue medications. Most participants had documented use of at least one ASM, and for many epochs (1, 6-10, 15-16 years), more than half of participants used at least two unique ASMs. The most commonly used ASMs by the study population were levetiracetam (n=59, 83.1%), clobazam (n=35, 49.3%), valproic acid (n=23, 32.4%), oxcarbazepine (n=21, 29.6%), and topiramate (n=20, 28.2%). Throughout the duration of follow-up, participants with epilepsy used a median of 4 unique ASMs (range 0 - 16). Given the frequency of polypharmacy in seizure management for FOXG1 syndrome, we computed lines of therapy (LOT) for ASMs used for a minimum duration of 3 months, excluding rescue medications. These ASM LOTs represent unique combinations or single ASMs and the sequencing in which they are used. Significant heterogeneity was observed both in the number and content of ASM LOTs documented for participants with epilepsy ( Figure 2D ). The median number of LOTs was 4 (range 1 - 12), indicating that more than half of participants received ≥4 lines. The most common first-line regimen was levetiracetam monotherapy (n=33, 48.5%), followed by clonazepam monotherapy (n=7, 10.3%). Second-line therapies were documented for 52 participants and were highly variable; only the combination of levetiracetam and clonazepam was observed in >10% of participants (n=8, 15.4%). For those individuals that went on to receive subsequent ASM LOTs, many received complex polypharmacy, at times including up to 7 unique ASMs used concurrently. The pronounced heterogeneity in prescribing practices for epilepsy management in FOXG1 syndrome precludes evaluations of efficacy for a single drug or drug combinations. For a subset, seizure management also included surgical intervention; 12.7% (n=9) had documented insertion of a vagal nerve stimulator, and 1.4% (n=1) underwent corpus callosotomy. Developmental trajectories in FOXG1 syndrome To characterize developmental outcomes in FOXG1 syndrome, we administered the GMFCS, the MACS, and the CFCS. The median GMFCS score for the cohort was IV (range I - V) and ranged from a median score of II (range I - V) in missense variants to V (range I - V) in frameshift and nonsense variants ( Figure 3A ). Individuals with missense variants had significantly lower scores compared to those with frameshift or nonsense variants ( P <0.001). The GMFCS scores documented for participants largely spanned those who could ambulate with limitations or by using a hand-held mobility device (levels II and III, respectively) to those who use a wheelchair (level V). Gross motor milestone attainment and age at attainment were annotated from the medical record. Approximately 71% of individuals were able to roll (median age of attainment 12.2 months, range 4.0 - 183.4), 43.2% were able to sit without support (median age of attainment 17.5 months, range 6.0 - 198.2), and 25.0% were able to walk independently (median age of attainment 31.6 months, range 7.1 - 263.1, Figure 3B ). Individuals with missense variants sat without support and walked independently more often than those with frameshift or nonsense variants ( P< 0.05). Although participants with missense variants (sitting: median 14.0 months, range 6.3 - 198.2; and walking: median 27.4 months, range 7.1 - 60.3) were younger at age of attainment for sitting and walking compared to frameshift (sitting: 24.9 months, range 6.3 - 198.2; and walking: median 84,9 months, range 21.1 - 263.1) and nonsense (sitting: median 32.9 months, range 9.0 - 85.1; and walking: median 40.6 months, range 33.1 - 48.1) variants, this was not statistically significant. The median MACS score for the cohort was IV (range II - V) and ranged from III (range II - V) in missense variants to IV (range II - V) in nonsense and frameshift variants ( Figure 3C ). Individuals with missense variants had significantly lower MACS scores compared to those with nonsense variants ( P <0.05). The MACS scores generated for participants ranged from those who can perform select activities with sufficient time and supervision (level III) to those who require continuous support to participate in portions of a given activity (level IV). Approximately 83% of individuals were able to grasp (median age of attainment 15.4 months, range 3.8 - 119.1) and 55.2% were able to perform hand functions (median age of attainment 24.7 months, range 6.1 - 111.7, Figure 3D ). Individuals with missense variants were able to perform hand functions more often than those with frameshift or nonsense variants ( P <0.05). As demonstrated through the CFCS, communication capabilities were more consistently impacted across genotypes ( Figure 3E ). The median CFCS score across all genotypes was IV (range III - V) and ranged from IV (range III - V) in missense and frameshift variants to V (range III - V) in nonsense variants. These scores represent individuals who do not communicate consistently or only rarely with familiar partners (levels IV/V). With respect to milestone attainment, all participants were able to vocalize (median age of attainment 8.9 months, range 2.0 - 114.7) and 50.7% were able to use at least one word (median age of attainment 20.0 months, range 7.0 - 165.6). Approximately 88% were reported to use non-verbal communication, including the use of augmentative and alternative communication (AAC) strategies, and 6.6% were reported to use verbal communication ( Figure 3F ). There were no significant genotype-phenotype correlations observed in the attainment or age at attainment for language milestones. To further characterize developmental outcomes in FOXG1 syndrome, the Vineland-3, an established measure of adaptive behaviors, was administered annually at two timepoints; 44 participants completed the first administration and 20 completed the second ( Supplementary Figure 2 ). Results from the Vineland-3 for gross motor, fine motor, receptive language, and expressive language subdomains are presented in Table 2 . We observed a floor effect using scaled scores, such as v S and developmental quotients (DQs) across all subdomains. As with the results from the GMFCS and medical record annotation, individuals with missense variants achieved significantly higher GSVs and DQs for the gross motor subdomain in the first year of administration. We also observed that GSVs and DQs for the fine motor subdomain were significantly higher in missense variants compared to nonsense variants, reflecting findings from the MACS and medical record annotation. Individuals with missense variants also had higher GSVs and DQs for both receptive and expressive language subdomains; this was statistically significant for DQs in the first year of administration for both subdomains when compared to frameshift variants ( P <0.01 and P <0.05, respectively) and in the receptive language subdomain only for nonsense variants ( P <0.05). Similar trends were observed for the second year of administration, but these did not achieve statistical significance, likely due to the reduced cohort size. As GSVs represent absolute, rather than relative performance, they are well-suited to evaluating change in performance over time within an individual or cohort. To establish baseline variability in the absence of intervention, we calculated the difference in GSVs for each participant who underwent two annual administrations of the Vineland-3. The median percent change for gross motor, fine motor, receptive language, and expressive language subdomains were as follows: 0.0%, 0.0%, 0.0%, and 1.0% respectively. Sleep and behavioral phenotypes in FOXG1 syndrome Sleep and behavioral problems contribute to overall quality of life for individuals with rare disorders and their caregivers. Sleep problems were documented in the medical records of 69.3% (n=70) of participants, and the incidence did not differ significantly between genotypes. Approximately 61% (n=62) were documented as using a sleep medication, with melatonin and clonidine most commonly prescribed. To better quantify sleep in FOXG1 syndrome, caregivers were administered the CSHQ at quarterly intervals for two years ( Table 3, Supplementary Figure 3 ). Mean and median scores for all genotypes exceed 41, which has been previously used as a threshold indicative of clinically meaningful sleep problems (17). Across timepoints and genotypes, scores remained stable. Behavior problems were reported in the medical records for 30.7% (n=31) of participants, and included: self-injurious behavior, problem behavior, and aggressive behavior. The frequency of behavioral problems ranged from 12.5% (n=3) in nonsense variants to 54.5% (n=12) in missense variants; individuals with missense variants were more likely to have behavioral problems than those with nonsense variants ( P <0.05). Two individuals received medication specifically to manage challenging behaviors; these included: aripiprazole, clonidine, midazolam, olanzapine, risperidone, and sertraline. To supplement medical record data, the ABC was administered to caregivers at quarterly timepoints for two years ( Table 3, Supplementary Figure 4 ). Individuals with missense variants were found to have significantly higher scores when compared to frameshift and nonsense variants for irritability ( P <0.05 and P <0.0001, respectively), hyperactivity/noncompliance ( P <0.05 and P <0.01, respectively), and inappropriate speech ( P <0.05 and P < 0.05, respectively ) . Across administrations and genotypes, scores remained relatively stable throughout the course of the study. While documentation of autism spectrum disorder (ASD) and/or autistic features remained relatively uncommon in the study population (n=19, 18.9%), genotype had a significant effect, whereby frequency was notably higher in those with missense variants (n=12, 54.5%) compared to frameshift variants (n=6, 12.2%, P <0.05) and nonsense variants (n=1, 4.3%, P <0.05). Longitudinal quantification of FOXG1 syndrome severity using a composite measure Mitter et al. have previously published a composite measure of FOXG1 syndrome severity that includes the following domains: somatic growth, motor and speech development, behavioral phenotypes, neurological phenotypes, and abnormalities on brain imaging (3). Using data derived from medical records as inputs, we replicated maximum and longitudinal FOXG1 syndrome severity scores by documenting the presence or absence of contributing phenotypes at each year of age. When comparing maximum scores for participants, individuals with missense variants had significantly lower severity scores compared to those with frameshift ( P< 0.001) and nonsense ( P <0.001) variants ( Figure 4A ). The median maximum score across all participants was 18 (range 4 - 26); median maximum scores by genotype were 18 (range 8 - 26) for frameshift, 20 (range 4 - 24) for nonsense, and 12 (range 4 - 22) for missense variants. The sample population included ten individuals who were ≥16 years of age at records end (range 16 - 34 years) and who predominantly harbored truncating variants; the median severity score for these individuals was 22. Although the FOXG1 syndrome severity scores for these older individuals are higher ( P <0.05), it is important to note that independent of the observed genotype effect, a subset of evaluated domains are age-dependent and/or reflect long term sequelae of neuronal dysfunction (e.g. neuromuscular scoliosis, spasticity). When evaluating FOXG1 syndrome severity scores longitudinally, a similar genotype-phenotype correlation was observed; at each annual time point between two and eight years of age, severity scores were significantly lower in those with missense variants compared to those with nonsense variants ( Figure 4B ). When compared to individuals with frameshift variants, scores were significantly lower at the 4-year time point. At the 6-year time point, severity scores in individuals with frameshift variants were significantly lower than those with nonsense variants. In order to evaluate predictive contributors to FOXG1 syndrome severity score, we fit a multiple regression model assigning genotype, age, maximum head circumference by one year of age, and epilepsy age of onset as independent variables. Presence of a missense variant was significantly associated with lower FOXG1 syndrome severity scores (t-value = -2.1, P <0.05). Although not significant when all other variables were held constant, a larger head circumference by one year and a later age of epilepsy onset were associated with lower FOXG1 syndrome severity scores (t-value = -0.9, P =0.36 and t-value = -0.4, P = 0.46, respectively). Age did not explain FOXG1 syndrome severity score variability (t-value = 0.1, P =0.92). Overall, the regression model was trending towards significance (F-statistic = 2.1, P =0.06). Discussion In this study, we combined comprehensive and systematic annotation of patient medical records with prospective administration of validated measures to describe the natural history of FOXG1 syndrome in 101 children and adults. To date, this represents one of the largest populations of FOXG1 syndrome described in the literature and is the first to perform a systematic longitudinal phenotypic characterization. We demonstrate the feasibility of a study design that leverages RWD to perform detailed clinical characterization and reduce the burden of participation for individuals and their caregivers. We replicate the core FOXG1 syndrome phenotype, which encompasses DD/ID, hypotonia, feeding problems, constipation, movement disorder, and strabismus, with high frequencies of gastroesophageal reflux, epilepsy, sleep problems, and sialorrhea. Across all evaluations of development, including milestone attainment documented in medical records and the results from validated measures, participants exhibited severe-to-profound functional limitations across motor and language domains. Taken together, the medical and caregiving needs of children and adults with FOXG1 syndrome remain significant, and substantiate the need for coordinated clinical care and supportive resourcing for families. This work expands upon a previously recognized genotype-phenotype correlation, whereby the presentation of FOXG1 syndrome associated with missense variants is distinct from that observed in individuals with truncating variants or gene deletions (3,5,7). Across nearly all explored domains, missense variants were associated with an attenuated disease course characterized by lower frequencies of core FOXG1 syndrome phenotypes, including microcephaly, callosal abnormalities, gastroesophageal reflux, strabismus, and feeding problems. Although movement disorder in FOXG1 syndrome is classically characterized by hyperkinetic involuntary movements (18), in those with missense variants, coordination problems and/or stereotypies may be more commonly described. With respect to developmental outcomes, genotype-phenotype correlations were most pronounced for gross motor capabilities, where individuals with missense variants scored lower on the GMFCS, achieved higher GSVs and v S scores on the Vineland-3 gross motor subdomain, and were more likely to achieve motor milestones, including sitting and walking independently. With greater mobility and fewer medical needs, individuals with missense variants exhibit a behavioral profile characterized by significantly higher scores across the irritability, hyperactivity and noncompliance, and inappropriate speech subdomains on the ABC. Notably, >50% of individuals with missense variants had documentation of autistic features and/or ASD; however, caution should be used given the difficulty in accurately administering developmental testing in those with DD/ID and the use of ASD to increase access to developmental therapies. In sum, individuals with missense variants may not present with the classically described FOXG1 syndrome phenotype and as such, FOXG1 syndrome should be considered even in those who lack characteristic features like microcephaly or significant gross motor limitations. Further, there may be unique needs and caregiver priorities, particularly in the recognition and management of behavioral challenges. To contextualize the FOXG1 syndrome behavioral phenotype, as characterized by use of the ABC, we compared scores across subdomains to those published in other relevant populations. Compared to both a large normative sample population of individuals with DD/ID (19) and individuals with Angelman syndrome (20), participants in this study with FOXG1 syndrome were more likely to demonstrate behaviors associated with social withdrawal and lethargy, as well as stereotypies. Further, individuals with FOXG1 syndrome report more irritability and less hyperactivity when compared to Angelman syndrome. In sum, the FOXG1 syndrome behavioral profile is characterized by stereotypies, as well as behaviors associated with social withdrawal and irritability, which can include tantrums, self-injurious and aggressive behaviors. Clinical trial readiness remains a principal motivation for the design and implementation of this study, particularly with respect to the identification of candidate clinical endpoints. Although this work and others continue to support a consistent and recognizable FOXG1 syndrome clinical profile (3–5), notable heterogeneity remains within and amongst genotypes. For example, while an established primary endpoint for clinical trials in other developmental and epileptic encephalopathies, seizure frequency is unlikely to be appropriate for FOXG1 syndrome given variable prevalence and presentation of epilepsy with sustained seizure control for most individuals. Apart from DD/ID, no core FOXG1 syndrome phenotype was documented for all participants, and significant reductions in frequency for missense variants presents a challenge for designing an effective and equitable clinical trial. Increasingly, composite measures that evaluate change across multiple relevant domains are being used given their ability to better tolerate heterogeneity within populations (21,22). With respect to developmental outcomes, the Vineland-3 has been widely used as a measure of adaptive behavior outcomes and GSVs can alleviate challenges related to floor effects (23). However, throughout the course of this study, participants shared that the Vineland-3 was burdensome to complete and did not provide an accurate view of the subject’s capabilities. With respect to other developmental assessments, these remain difficult to perform due to challenges with administration in populations with severe functional impairments, as well as the reduced likelihood to see significant changes during the duration of a clinical trial. When surveyed, caregivers of individuals with FOXG1 syndrome consistently rank absence of effective communication as a primary concern (5,24); novel communication measures validated in populations with limited verbal speech may be a strong candidate endpoint (25,26). In FOXG1 syndrome, identifying effective communication strategies remains challenging. A combination of fine motor impairments, movement disorder, and cortical visual impairment interfere with the successful application of AAC strategies. There are several limitations associated with this study, primarily resulting from the opportunistic annotation of medical records that document routine clinical care. In contrast to prospectively designed site-based clinical studies, the use of a retrospective data source precludes the ability to perform systematic clinical assessments at a defined frequency, particularly those that are not standard of care. Further, medical records may introduce and perpetuate inaccuracies that are challenging to verify. Citizen has implemented several technical and operational procedures to mitigate the limitations associated with RWD derived from medical records. First, medical record collection was agnostic to institution or electronic medical record vendor, which enables longitudinal follow-up across sites. As records were received, they were measured against internal definitions of medical record type and frequency designed to identify gaps in care or collection that may contribute to an incomplete dataset; participants whose medical records did not meet or exceed minimum completeness thresholds were not included in this study. Data annotation conformed to a mature data model that both defines the scope of data capture and supports redundancy across variables. Further, the use of internationally recognized terminologies allowed for computational approaches to address variability in medical record documentation. Lastly, to limit inaccurate data capture, all annotated data underwent source document verification by experienced clinicians, and resulting datasets were subject to rule-based validations. In support of this approach, the results derived from RWD in this study were consistent with those from the prospectively administered validated measures, including the Vineland-3, and with previously published characterizations (3–5). Conclusions By pairing prospective administration of validated measures with systematic annotation of participant medical records, we present a novel approach to describe the natural history of FOXG1 syndrome using a methodology that can be broadly applied across rare disorders. The employed study design expands access to research participation by minimizing burden on participants and their caregivers, and enables the selection and characterization of candidate clinical endpoints. While this work confirms and expands upon the classically defined core clinical phenotype in detail, notable heterogeneity was observed in study participants, largely driven by genotype-phenotype correlations whereby missense variants are associated with a distinct presentation. In sum, data presented here support the candidacy of communication as a primary endpoint for clinical trials in FOXG1 syndrome, as well as features that impact successful use of alternative communication strategies. Declarations Ethics approval and consent to participate Caregivers and/or legal guardians of study participants provided broad consent to share de-identified data for research. This study received determinations of exemption through a central institutional review board via exemption categories 2, 7, and 8 of the revised Common Rule. Consent for publication Not applicable Availability of data and materials The data that support the analyses presented here contain sensitive and protected health information for participants and is therefore not openly available. Requests for data access can be made to [email protected] , where we can confirm the proposed research scope is consistent with platform consent language and is reviewed and/or approved by an institutional review board. Competing interests Elise Brimble, Elizabeth Blomenberg, and Kelsey Frahlich are current employees of Citizen Health with vested and unvested stock options. Kopika Kuhathaas and Gai Ayalon are current employees of FOXG1 Research Foundation. Funding This work was funded by FOXG1 Research Foundation and the Chan-Zuckerberg Initiative through the Rare As One program. Elise Brimble, Elizabeth Blomenberg, and Kelsey Frahlich are current employees of Citizen Health. Authors' contributions E.Brimble: Study conception/design, data acquisition and analysis, data interpretation, manuscript development and review. P.V., K.K., C.E.H., N.B.B., H.E.O., E.D.M., G.A.: Study conception/design, data interpretation, manuscript development and review. E.Blomenberg, K.F.: Data acquisition and analysis, data interpretation, manuscript development and review. All authors reviewed the manuscript. Acknowledgements We extend our gratitude to the participants and their families. References Danesin C, Peres JN, Johansson M, Snowden V, Cording A, Papalopulu N, et al. Integration of Telencephalic Wnt and Hedgehog Signaling Center Activities by Foxg1. Dev Cell. 2009 Apr 21;16(4):576–87. Kortüm F, Das S, Flindt M, Morris-Rosendahl DJ, Stefanova I, Goldstein A, et al. The core FOXG1 syndrome phenotype consists of postnatal microcephaly, severe mental retardation, absent language, dyskinesia, and corpus callosum hypogenesis. J Med Genet. 2011 Jun 1;48(6):396–406. 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Demographic characteristics of study population FOXG1 Variant Type Frameshift Nonsense Missense All n 49 24 22 101 Demographic Characteristics Median age at record end (range, y) 7.3 (0.9 - 34.8) 8.2 (1.8 - 23.1) 6.6 (2.6 - 16.2) 7.3 (0.4 - 34.8) Median age at diagnosis (range, y) 1.6 (0 - 34.3) 1.3 (0.4 - 16.6) 2.2 (0.5 - 15.4) 1.6 (0.0 - 34.3) Median duration of follow-up (range, y) 6.0 (0.8 - 34.3) 6.5 (1.0 - 21.8) 5.1 (0.7 - 15.6) 6.0 (0.4 - 34.3) Female n (%) 12 (24.5%) 11 (45.8%) 13 (59.1%) 41 (40.6%) Male n (%) 37 (75.5%) 13 (54.2%) 9 (40.9%) 60 (59.4%) Geographic Regions n (%) Midwest 14 (28.6%) 4 (16.7%) 3 (13.6%) 21 (20.8%) Northeast 10 (20.4%) 6 (25.0%) 6 (27.3%) 24 (23.8%) South 14 (28.6%) 10 (41.7%) 8 (36.4%) 33 (32.7%) West 11 (22.4%) 4 (16.7%) 5 (22.7%) 23 (22.8%) Rural-Urban Commuting Area (RUCA) Codes n (%) Metropolitan 45 (91.8%) 21 (87.5%) 19 (86.4%) 91 (90.1%) Micropolitan 2 (4.1%) 2 (8.3%) 1 (4.5%) 5 (5.0%) Small towns 0 (0.0%) 0 (0.0%) 2 (9.1%) 2 (2.0%) Rural areas 2 (4.1%) 1 (4.2%) 0 (0.0%) 3 (3.0%) Self-Reported Race, Ethnicity, or Ancestry n (%) White 19 (79.2%) 16 (80.0%) 4 (33.3%) 42 (67.7%) Mixed Race 2 (8.3%) 3 (15.0%) 3 (25.0%) 8 (12.9%) Latino or Hispanic 2 (8.3%) 1 (5.0%) 2 (16.7%) 7 (11.3%) Asian or Asian-American 0 (0.0%) 0 (0.0%) 4 (25.0%) 3 (4.8%) Black or African-American 1 (4.2%) 0 (0.0%) 0 (0.0%) 1 (1.6%) Data Collection Median # of annotated concepts (range) 398 (91 - 1477) 355.5 (65 - 1309) 253 (49 - 852) 375 (45 - 1477) Self-reported race was available for n=62 participants. Table 2. Prospective administration of the Vineland-3 in FOXG1 syndrome Frameshift Nonsense Missense All Vineland-3 v S DQ v S DQ v S DQ v S DQ Gross Motor 12 mo 1 (1-11) 10.6 (0-45.2)* 1 (1-9) 4.8 (0.5-28.8)** 3 (3-9) 17.8 (3.5-47.5) 1 (1-11) 9.9 (0.0-47.5) 24 mo 1 (1-10) 6.7 (0.0-37.7) 1 (1-1) 3.7 (0.4-4.0) 3 (1-5) 14.4 (0.0-21.4) 1 (1-10) 4.4 (0.0-37.7) Fine Motor 12 mo 4 (3-6) 12.0 (0.0-24.7) 1 (1-7) 3.3 (0.0-30.0)* 5 (1-10) 27.5 (0.0-47.5) 4 (1-10) 9.3 (0.0-47.5) 24 mo 2 (1-4) 8.9 (0.3-20.8) 1 (1-1) 2.4 (0.4-5.1) 3 (1-6) 10.8 (0.0-26.2) 2 (1-6) 5.8 (0.0-26.2) Receptive Language 12 mo 1 (1-5) 5.1 (0.0-21.7)** 1 (1-9) 7.9 (0.0-31.3) 2 (1-7) 18.4 (0.0-50.3) 1 (1-12) 8.5 (0.0-50.3) 24 mo 1 (1-9) 2.8 (0.0-28.3) 1 (1-1) 3.3 (0.0-8.1) 2 (1-11) 19.0 (0.0-33.1) 1 (1-11) 5.7 (0.0-33.1) Expressive Language 12 mo 2 (1-6) 0.0 (0.0-22.6)** 1 (1-6) 0.6 (0.0-25.0)* 1 (1-5) 13.5 (0.0-31.8) 1 (1-10) 2.0 (0.0-31.8) 24 mo 1 (1-6) 0.8 (0.0-23.4) 1 (1-5) 3.0 (0.0-10.3) 3 (1-7) 10.7 (0.0-28.4) 1 (1-7) 1.8 (0.0-28.4) Values are shown as median (range). Bolded cells contain values found to be significantly different from the corresponding value for individuals with missense variants. Table 3. Scored results of the Aberrant Behavior Checklist and Children's Sleep Habits Questionnaire in FOXG1 syndrome FOXG1 Variant Type Frameshift Nonsense Missense All Aberrant Behavior Checklist Irritability 12 mo 6 (0 - 22)* 4 (0 - 11)**** 12 (2 - 27) 6 (0 - 27) 24 mo 5.5 (0 - 23) 4 (2 - 7)* 9.5 (2 - 20) 5.5 (1 - 23) Social Withdrawal 12 mo 9.5 (0 - 28) 5 (1 - 22) 10 (2 - 27) 8 (0 - 28) 24 mo 9.5 (0 - 33) 7 (1 - 13) 9.5 (1 - 18) 8.5 (0 - 33) Stereotypic Behavior 12 mo 7 (0 - 20) 6 (2 - 14) 7 (1 - 14) 7 (0 - 20) 24 mo 7 (0 - 16) 6 (2 - 8) 7.5 (1 - 12) 6 (0 - 16) Hyperactivity/ Noncompliance 12 mo 7 (0 - 26)* 4 (1 - 20)** 11 (2 - 27) 7.5 (0 - 27) 24 mo 7 (0 - 24) 6 (1 - 12) 11 (1 - 22) 7 (0 - 24) Inappropriate Speech 12 mo 1 (0 - 4)* 0 (0 - 5)* 1.5 (0 - 10) 1 (0 - 10) 24 mo 1 (0 - 5) 1 (0 - 4)* 3.5 (0 - 7) 2 (0 - 7) Children’s Sleep Habits Questionnaire 12 mo 52.0 (30.3 - 71.7) 45.0 (24.5 - 61.0) 53.4 (24.0 - 70.0) 50.3 (30.3 - 71.7) 24 mo 50.0 (35.0 - 72.3) 47.8 (36.0 - 64.0) 51.7 (34.0 - 75.3) 50.0 (34.0 - 75.3) Results presented as median (range) of scores averaged across 4 administrations per year. *, P <0.05; **, P <0.001; ****, P <0.0001 compared to missense variants. Additional Declarations Competing interest reported. Elise Brimble, Elizabeth Blomenberg, and Kelsey Frahlich are current employees of Citizen Health with vested and unvested stock options. Kopika Kuhathaas and Gai Ayalon are current employees of FOXG1 Research Foundation. Supplementary Files foxg1SuppFig1.jpg Supplementary Figure 1. Growth charts Measures of head circumference (A), weight (B), and height (C) are shown for study participants compared to the 3rd, 25th, 50th, 75th, and 97th normative percentiles defined by the Centers for Disease Control and Prevention. foxg1SuppFig2.jpg Supplementary Figure 2. Prospective administration of the Vineland-3 in FOXG1 syndrome Scaled v S, growth scale values (GSVs), and age equivalents (AEs) are shown for gross motor (A), fine motor (B), receptive language (C), and expressive language (D) by year of age at administration. foxg1SuppFig3.jpg Supplementary Figure 3. Prospective administration of the Children’s Sleep Habits Questionnaire in FOXG1 syndrome The maximum and longitudinal scores for the CSHQ are shown where scores for frameshift, nonsense, and missense variants are illustrated. The dotted line is plotted at y=41, a value that has been previously used as a threshold for clinically meaningful sleep problems. foxg1Suppfig4.jpg Supplementary Figure 4. Prospective administration of the Aberrant Behavior Checklist in FOXG1 syndrome The maximum and longitudinal scores are shown for the domains of the ABC: irritability (A), social withdrawal (B), stereotypic behavior (C), hyperactivity/noncompliance (D), and inappropriate speech (E). Genotype-phenotype correlations were evaluated using the Kruskall-Wallis test with Dunn’s correction for multiple comparisons (*, P <0.05; **, P <0.01; ****, P <0.0001). SupplementaryTable1.docx Cite Share Download PDF Status: Published Journal Publication published 24 Oct, 2025 Read the published version in Journal of Neurodevelopmental Disorders → Version 1 posted Editorial decision: Revision requested 28 May, 2025 Reviews received at journal 11 Mar, 2025 Reviewers agreed at journal 10 Mar, 2025 Reviewers invited by journal 22 Feb, 2025 Editor assigned by journal 05 Dec, 2024 Submission checks completed at journal 05 Dec, 2024 First submitted to journal 04 Dec, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5582753","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":387273688,"identity":"c677d739-e3f3-47ea-9392-4e02c2421de0","order_by":0,"name":"Elise Brimble","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYDACduaGAx9sgAxmmIgEIS3MjA0HZ6SB9JKihZkHpIWfWC38zIyNh20SbBLnO/M+fPijwi6ff3YD4+OKX7i1SDYzNhzOSUgzNjzMbmzMcybZcsadA8yGZ/twazE4DNSS++OwnGEzG5s0YxuzgYFEAptkYw9uLfYgLRYJh3mAWth//vxXT1iLAdD7hxkSDsvJM7OxMfA2HIZoafiBW4sE0JaDPUC/GDCzMUvzHDtuIHEjsdmwsQG3Fv725sMffoBCrP8Y48cfNdUG/DOSDz5s+INbC8KFB+BMxgYGxjYitMijOoUYW0bBKBgFo2CkAACWkE2EAOJ9cwAAAABJRU5ErkJggg==","orcid":"","institution":"FOXG1 Research Foundation","correspondingAuthor":true,"prefix":"","firstName":"Elise","middleName":"","lastName":"Brimble","suffix":""},{"id":387273690,"identity":"44ade5d5-b445-4a7e-b586-181df7441fe5","order_by":1,"name":"Pam Ventola","email":"","orcid":"","institution":"Cogstate (United States)","correspondingAuthor":false,"prefix":"","firstName":"Pam","middleName":"","lastName":"Ventola","suffix":""},{"id":387273691,"identity":"8a862fce-f225-4acf-86de-acd5734d699b","order_by":2,"name":"Elizabeth Blomenberg","email":"","orcid":"","institution":"Citizen Health","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Blomenberg","suffix":""},{"id":387273694,"identity":"0ae90e1a-ec96-488e-b922-2230ad5c1ad1","order_by":3,"name":"Kelsey Frahlich","email":"","orcid":"","institution":"Citizen Health","correspondingAuthor":false,"prefix":"","firstName":"Kelsey","middleName":"","lastName":"Frahlich","suffix":""},{"id":387273696,"identity":"950e0e3f-45b3-4224-976c-3e31e485020b","order_by":4,"name":"Kopika Kuhathaas","email":"","orcid":"","institution":"FOXG1 Research Foundation","correspondingAuthor":false,"prefix":"","firstName":"Kopika","middleName":"","lastName":"Kuhathaas","suffix":""},{"id":387273698,"identity":"7410e046-e054-4852-871a-7573eac56844","order_by":5,"name":"Christopher E Hart","email":"","orcid":"","institution":"Independent Consultant","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"E","lastName":"Hart","suffix":""},{"id":387273700,"identity":"91500f7e-070f-4941-a7dc-9d75a23769ab","order_by":6,"name":"Nadia Bahi-Buisson","email":"","orcid":"","institution":"Hôpital Necker-Enfants Malades","correspondingAuthor":false,"prefix":"","firstName":"Nadia","middleName":"","lastName":"Bahi-Buisson","suffix":""},{"id":387273703,"identity":"635f0e9f-11bb-4a8d-b415-8c3e0375b1a3","order_by":7,"name":"Heather E Olson","email":"","orcid":"","institution":"Boston Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Heather","middleName":"E","lastName":"Olson","suffix":""},{"id":387273704,"identity":"31730914-fd72-420e-a9d3-dce74ef5cfef","order_by":8,"name":"Eric D Marsh","email":"","orcid":"","institution":"Children's Hospital of Philadelphia","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"D","lastName":"Marsh","suffix":""},{"id":387273705,"identity":"2619e2e9-1848-43e2-a047-d1043679cd34","order_by":9,"name":"Gai Ayalon","email":"","orcid":"","institution":"FOXG1 Research Foundation","correspondingAuthor":false,"prefix":"","firstName":"Gai","middleName":"","lastName":"Ayalon","suffix":""}],"badges":[],"createdAt":"2024-12-05 00:38:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5582753/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5582753/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s11689-025-09653-1","type":"published","date":"2025-10-24T16:16:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":74418662,"identity":"30f636bf-ebe4-4b7e-ad1c-89542ebd6003","added_by":"auto","created_at":"2025-01-22 07:05:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":545384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of common clinical phenotypes in FOXG1 syndrome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor core FOXG1 syndrome phenotypes documented in at least 50% of the study cohort, the distribution of age of phenotype onset \u003cstrong\u003e(A)\u003c/strong\u003e and relative frequency \u003cstrong\u003e(B)\u003c/strong\u003e are shown. Genotype phenotype correlations were evaluated using the Fisher’s Exact Test with Bonferroni correction for multiple comparisons (*, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"foxg1Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5582753/v1/ae81ac49d4d979e89c0c52eb.jpg"},{"id":74418659,"identity":"01cbc8b8-de88-44ed-9019-e23ce15f074a","added_by":"auto","created_at":"2025-01-22 07:05:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1062360,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of epilepsy phenotype and management in FOXG1 syndrome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe probability of seizure onset by year of age, where each tick represents a censored individual due to epilepsy onset or end of records; blue dotted lines are 95% confidence intervals and the median survival time (1.7 years) is shown with black dotted lines \u003cstrong\u003e(A)\u003c/strong\u003e. Distribution of seizure frequency scores by year of age is shown \u003cstrong\u003e(B)\u003c/strong\u003e, where seizure scores represent the following: 0 = seizure freedom, 1 = yearly seizures, 2 = monthly seizures, 3 = weekly seizures, 4 = daily seizures, 5 = multiple daily seizures. Cells with increased shading are those with a greater proportion (%) of individuals with the corresponding score at a given year of age. Distribution of ASM scores by year of age is shown \u003cstrong\u003e(C)\u003c/strong\u003e, where ASM scores represent the following: 0 = no ASMs, 1 = 1 ASM, 2 = 2 concurrent ASMs, 3 = 3 concurrent ASMs, 4 = 4 concurrent ASMs, 5 = 5+ concurrent ASMs documented within the given age epoch. Cells with increased shading are those with a greater proportion (%) of individuals with the corresponding score at a given year of age. The frequency for unique ASM lines of therapy is shown for first- and second-lines using a Sankey plot \u003cstrong\u003e(D)\u003c/strong\u003e. Abbreviations: cannabidiol (CBD) , carbamazepine (CBZ), clobazam (CLB), clonazepam (CLZ), ketogenic diet (KETO), lacosamide (LCM), levetiracetam (LEV), lamotrigine (LTG), oxcarbazepine (OXC), phenobarbital (PHB), phenytoin (PHT), topiramate (TPM), vigabatrin (VGB), valproic acid (VPA), zonisamide (ZNS).\u003c/p\u003e","description":"","filename":"foxg1Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5582753/v1/613e1c07700579feb2c1ed63.jpg"},{"id":74418676,"identity":"9ad887db-b3db-4d04-bbd5-473f65dd5ae6","added_by":"auto","created_at":"2025-01-22 07:05:14","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":487447,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of gross motor, fine motor, and language milestones in FOXG1 syndrome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe maximum and longitudinal scores are shown for the GMFCS \u003cstrong\u003e(A)\u003c/strong\u003e, the MACS \u003cstrong\u003e(C)\u003c/strong\u003e, and the CFCS \u003cstrong\u003e(E)\u003c/strong\u003e. The proportion and age of attainment for gross motor \u003cstrong\u003e(B)\u003c/strong\u003e, fine motor \u003cstrong\u003e(D)\u003c/strong\u003e, and language \u003cstrong\u003e(F)\u003c/strong\u003e milestones annotated from participant medical records are shown by year of age. Genotype-phenotype correlations were evaluated using the Kruskall-Wallis test with Dunn’s correction for multiple comparisons (*, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05; ***, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e","description":"","filename":"foxg1Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5582753/v1/654e24d18da8439a58ee41bf.jpg"},{"id":74418686,"identity":"5eb10bee-b2de-46b5-96bd-7d3ef758e16c","added_by":"auto","created_at":"2025-01-22 07:05:15","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":110879,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLongitudinal characterization of FOXG1 syndrome severity using a composite measure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe maximum and longitudinal scores are shown for the FOXG1 syndrome severity score, where scores for frameshift, nonsense, and missense variants by year of age are shown \u003cstrong\u003e(A)\u003c/strong\u003e. The contributions of independent continuous variables evaluated in a multiple regression model are presented, when all other variables are held constant \u003cstrong\u003e(B)\u003c/strong\u003e. Genotype-phenotype correlations were evaluated using the Kruskall-Wallis test with Dunn’s correction for multiple comparisons (for comparisons between nonsense and missense variants: *, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05; **, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05;\u0026nbsp; ***, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001; for comparisons between frameshift and missense variants: \u003csup\u003e#\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05; for comparisons between frameshift and nonsense variants: \u003csup\u003e$\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"foxg1Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5582753/v1/768dc49a02761f00eb1bce2d.jpg"},{"id":94490814,"identity":"2643adac-6432-4b2a-bc7b-65f0b2927901","added_by":"auto","created_at":"2025-10-27 17:15:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3719196,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5582753/v1/9416fae0-46bf-447b-9eb6-4573e0addf5e.pdf"},{"id":74418674,"identity":"3007510d-2490-4cd0-99ce-5b354005eeae","added_by":"auto","created_at":"2025-01-22 07:05:14","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":887861,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1. Growth charts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMeasures of head circumference \u003cstrong\u003e(A)\u003c/strong\u003e, weight \u003cstrong\u003e(B)\u003c/strong\u003e, and height \u003cstrong\u003e(C)\u003c/strong\u003e are shown for study participants compared to the 3rd, 25th, 50th, 75th, and 97th normative percentiles defined by the Centers for Disease Control and Prevention.\u003c/p\u003e","description":"","filename":"foxg1SuppFig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5582753/v1/079d210a432f72f2a2c7f2e6.jpg"},{"id":74418669,"identity":"e8d848f0-9f52-430c-a424-414525488404","added_by":"auto","created_at":"2025-01-22 07:05:14","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":539604,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2. Prospective administration of the Vineland-3 in FOXG1 syndrome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eScaled \u003cem\u003ev\u003c/em\u003eS, growth scale values (GSVs), and age equivalents (AEs) are shown for gross motor \u003cstrong\u003e(A)\u003c/strong\u003e, fine motor \u003cstrong\u003e(B)\u003c/strong\u003e, receptive language \u003cstrong\u003e(C)\u003c/strong\u003e, and expressive language \u003cstrong\u003e(D) \u003c/strong\u003eby year of age at administration.\u003c/p\u003e","description":"","filename":"foxg1SuppFig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5582753/v1/005cc66fa9e970425a07de32.jpg"},{"id":74418666,"identity":"6ce197a2-861d-43b5-b1fd-f3380d7b8953","added_by":"auto","created_at":"2025-01-22 07:05:14","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":124617,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 3. Prospective administration of the Children’s Sleep Habits Questionnaire in FOXG1 syndrome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe maximum and longitudinal scores for the CSHQ are shown where scores for frameshift, nonsense, and missense variants are illustrated. The dotted line is plotted at y=41, a value that has been previously used as a threshold for clinically meaningful sleep problems.\u003c/p\u003e","description":"","filename":"foxg1SuppFig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5582753/v1/2a58625b5df7f53293353e01.jpg"},{"id":74418688,"identity":"4ec7a989-f098-4793-be90-96cc1f1d17ae","added_by":"auto","created_at":"2025-01-22 07:05:15","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":632800,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 4. Prospective administration of the Aberrant Behavior Checklist in FOXG1 syndrome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe maximum and longitudinal scores are shown for the domains of the ABC: irritability \u003cstrong\u003e(A)\u003c/strong\u003e, social withdrawal \u003cstrong\u003e(B)\u003c/strong\u003e, stereotypic behavior \u003cstrong\u003e(C)\u003c/strong\u003e, hyperactivity/noncompliance \u003cstrong\u003e(D)\u003c/strong\u003e, and inappropriate speech \u003cstrong\u003e(E)\u003c/strong\u003e. Genotype-phenotype correlations were evaluated using the Kruskall-Wallis test with Dunn’s correction for multiple comparisons (*, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05; **, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01; ****, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001).\u003c/p\u003e","description":"","filename":"foxg1Suppfig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5582753/v1/5396df11d015f47ddf909d43.jpg"},{"id":74418679,"identity":"9a12003a-aeb9-4cb6-a83c-2a75f37210c0","added_by":"auto","created_at":"2025-01-22 07:05:14","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":21389,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5582753/v1/cec356635f1c55a7be9d6ffb.docx"}],"financialInterests":"Competing interest reported. Elise Brimble, Elizabeth Blomenberg, and Kelsey Frahlich are current employees of Citizen Health with vested and unvested stock options. Kopika Kuhathaas and Gai Ayalon are current employees of FOXG1 Research Foundation.","formattedTitle":"Longitudinal characterization of clinical, developmental, and behavioral phenotypes in 101 children and adults with FOXG1 syndrome","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFOXG1 syndrome (OMIM 613454) is a severe neurodevelopmental disorder caused by pathogenic variants in or involving \u003cem\u003eFOXG1\u003c/em\u003e, which plays critical roles in the specification and development of the forebrain (1). The core clinical phenotypes described for FOXG1 syndrome include universal developmental and intellectual disabilities (DD/ID), postnatal microcephaly, epilepsy, and a hyperkinetic-dyskinetic movement disorder (2\u0026ndash;5). Characteristic structural brain anomalies are observed, including abnormalities of the corpus callosum; simplified gyri, most commonly affecting the frontal lobe; and delayed myelination (6). Of note, genotype-phenotype correlations have been reported, whereby missense variants are associated with an attenuated clinical phenotype (3,5,7). \u003c/p\u003e\n\u003cp\u003eManagement of FOXG1 syndrome is symptomatic, but recent pre-clinical evidence supports gene replacement as a potential therapeutic strategy (8). With potentially disease-modifying therapies in development, establishing a natural history profile for FOXG1 syndrome is critical in preparing for clinical trials. Further, defining the complete spectrum and clinical trajectory of the disorder can aid in the selection and characterization of candidate clinical endpoints. However, the traditional site-based design can be challenging for rare disorders, where small heterogeneous populations are geographically distributed, and the likelihood of attrition is high when participants have significant medical complexities. To address these limitations, we combined retrospective annotation of patient medical records with quarterly prospective administration of validated measures of development and behavior in 101 children and adults with FOXG1 syndrome. The resulting dataset encompassed \u0026gt;40,000 annotated clinical terminology concepts representing \u0026gt;770 cumulative patient data years. This is the largest longitudinal characterization of FOXG1 syndrome, and provides evidence of a static encephalopathy without apparent neurodegeneration. \u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003eStudy participants\u003c/h3\u003e\n\u003cp\u003eThe study population was drawn from individuals enrolled in the FOXG1 syndrome natural history study via their joint participation in the FOXG1 syndrome patient registry and Citizen Health (Citizen). Participants had documentation of a likely pathogenic or pathogenic \u003cem\u003eFOXG1\u003c/em\u003e variant in accordance with the American College of Medical Genetics and Genomics (9); individuals with whole gene deletions that encompassed additional genes were excluded. The FOXG1 syndrome natural history study received determinations of exemption through a central IRB via exemption categories 2, 7, and 8 of the revised Common Rule. \u003c/p\u003e\n\u003ch3\u003eGeneration of a longitudinal real-world dataset\u003c/h3\u003e\n\u003cp\u003eCitizen Health (Citizen) is a patient-centric real-world data (RWD) platform that harmonizes unstructured content within medical records to generate longitudinal clinical datasets. Medical records were collected from healthcare organizations within the United States using the \u0026lsquo;right to access\u0026rsquo; granted by the Health Insurance Portability and Accountability Act (HIPAA). Participant data were included for analysis where collected documentation exceeded predefined thresholds. Citizen was used to systematically interrogate participant medical records for documentation of clinical phenotypes, interventions, and outcomes, including developmental milestones and seizure burden. All Citizen-generated data were independently reviewed by at least two clinicians with relevant experience and training, including nurses, nurse practitioners, physician assistants, and genetic counselors, who confirmed accurate annotation and performed source document verification. Resulting data were stored in a HIPAA-compliant, controlled access, indexed database (10,11).\u003c/p\u003e\n\n\u003ch3\u003eMeasures of development, sleep, and behavior\u003c/h3\u003e\n\u003cp\u003eSeveral measures were administered to study participants at quarterly intervals via the FOXG1 syndrome patient registry. To quantify developmental capabilities, the gross motor function classification system (GMFCS) (12), manual abilities classification system (MACS) (13), and communication function classification system (CFCS) (14) were used. These represent brief validated measures of gross motor, fine motor, and communication function that have been widely used in populations with cerebral palsy. Across all three measures, scores range from I to V, with higher scores corresponding to greater functional impairment. Whereas the GMFCS and MACS provide insight into the level of assistance required to ambulate or manipulate objects, respectively, the CFCS evaluates an individual\u0026apos;s ability to send and receive information with both familiar and unfamiliar communication partners. \u003c/p\u003e\n\n\u003cp\u003eIn addition, the Vineland Adaptive Behavior Scales, Third Edition (Vineland-3) was used to evaluate adaptive behavior at annual intervals. Four measures of the Vineland-3 were included in this analysis: (1) v-Scale Scores (\u003cem\u003ev\u003c/em\u003eS), which are norm-referenced standard scores; (2) age equivalents (AEs), which represent the reference age a participant\u0026rsquo;s raw scores map to for a given domain; (3) developmental quotients (DQs), which are a computed ratio between a participant\u0026rsquo;s AE and their chronologic age; and (4) growth scale values (GSVs), which represent a participant\u0026rsquo;s absolute level of performance that is not relative to the reference dataset. \u003c/p\u003e\n\n\u003cp\u003eThe Children\u0026rsquo;s Sleep Habits Questionnaire (CSHQ) was administered to caregivers in quarterly intervals to measure the frequency of behaviors associated with common pediatric sleep difficulties across eight domains: bedtime resistance, sleep onset delay, sleep duration, sleep anxiety, night waking, parasomnias, sleep disordered breathing, and daytime sleepiness (15). A total sleep disturbance index is calculated, where higher scores are associated with greater frequency of abnormal sleep behaviors. To quantify and characterize behavioral challenges, the Aberrant Behavior Checklist (ABC) was administered at quarterly intervals (16). This scale has been widely used to measure psychiatric symptoms and behavioral disturbances in individuals with DD/ID across five domains: irritability, agitation, and crying (irritability); lethargy and social withdrawal (social withdrawal); stereotypic behavior; hyperactivity and noncompliance; and inappropriate speech. Higher scores across these domains represent more frequent observation of associated behaviors. \u003c/p\u003e\n\u003ch3\u003eComputation of a longitudinal FOXG1 syndrome severity score\u003c/h3\u003e\n\u003cp\u003eA FOXG1 syndrome severity score has been described previously that encompasses 20 clinical phenotypes distributed across five domains: somatic growth (4 items), motor and speech development (4 items, age-limited), behavior (3 items), neurological features (6 items), and MRI abnormalities (3 items) (3). To quantify FOXG1 syndrome severity longitudinally, items were adapted to accommodate contemporaneous evaluations, as opposed to retrospective or lifetime observations. The presence or absence of phenotypic contributors to the five domains defined by Mitter \u003cem\u003eet al.\u003c/em\u003e were evaluated at each year of age: somatic growth (abnormal head circumference, weight, height); motor and speech development (ability to sit unassisted, walk unassisted, perform hand functions, use expressive speech); behavior (abnormal sleep, autism spectrum disorder and/or autistic features); neurological features (epilepsy, spasticity, stereotypies, dyskinesia, feeding difficulties, scoliosis); and MRI features (abnormality of the corpus callosum, delayed myelination, cortical abnormalities), resulting in an 18-item scale, for which higher scores correspond to increased frequency of FOXG1 syndrome-related phenotypes. \u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eData collection for the described analyses spanned March 2021 through November 2023. Computations were performed using Graphpad Prism 9 and the R statistical framework. When plotting central tendency measures, 95% confidence intervals are presented. For subgroup comparisons, nonparametric statistical tests were performed at a significance level of 0.05 following correction for multiple comparisons, including Fisher\u0026apos;s exact test with Bonferroni correction for categorical variables and Kruskall-Wallis test with Dunn\u0026rsquo;s correction for continuous variables. When evaluating clinical contributors to FOXG1 syndrome severity scores, a multiple regression model was generated using the following variables as inputs: age, head circumference at 1 year, epilepsy age of onset, and variant class (e.g. missense). \u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003eSample population\u003c/h3\u003e\n\u003cp\u003eThe sample population consisted of 101 individuals with a likely pathogenic or pathogenic variant in \u003cem\u003eFOXG1\u003c/em\u003e (\u003cstrong\u003eTable 1, Supplementary Table 1\u003c/strong\u003e). Most had a truncating variant (n=49, 48.5% frameshift; n=24, 23.8% nonsense), with approximately half occurring N-terminal to the forkhead DNA binding domain (n=40, 54.8%). Ten (9.9%) had documentation of a second genetic disorder evidenced by a pathogenic or likely pathogenic variant(s) in one of the following: \u003cem\u003eFLG\u003c/em\u003e, \u003cem\u003eLDLR\u003c/em\u003e, \u003cem\u003eMT-RNR1\u003c/em\u003e, \u003cem\u003eMYBPC3\u003c/em\u003e, \u003cem\u003ePKD2\u003c/em\u003e, \u003cem\u003ePKP2\u003c/em\u003e, \u003cem\u003eSCN5A\u003c/em\u003e, \u003cem\u003eSLC26A4\u003c/em\u003e; one individual had trisomy X. The associated clinical features do not overlap significantly with FOXG1 syndrome and are not expected to modify associated phenotypes; therefore, data for these participants were included in all subsequent analyses.\u003c/p\u003e\n\u003cp\u003eThe median age of participants at record end was 7.3 years (range 0.4 - 34.8) and median age at FOXG1 syndrome diagnosis was 1.6 years (range 0.0 - 34.3); there was no significant difference in age or age at diagnosis between genotypes. Most participants were white (n=42, 67.7%) and lived in primarily metropolitan areas across the United States. The median duration of follow-up was 6.0 years (range 0.4 - 34.3), representing approximately 773 cumulative patient data years. Earliest medical record documentation precedes FOXG1 syndrome diagnosis for all participants.\u003c/p\u003e\n\u003ch3\u003eIdentification of core FOXG1 syndrome phenotypes\u003c/h3\u003e\n\u003cp\u003eThe most common presenting diagnoses were microcephaly (n=23, 22.8%), developmental delay (n=19, 18.8%), hypotonia (n=11, 10.9%), and strabismus (n=10, 9.9%) at a median age of 0.2 years (range 0.0 - 4.9). Brain magnetic resonance imaging (MRI, n=37, 36.6%) and electroencephalogram (EEG, n=20, 19.8%) were the most common diagnostic procedures first recorded for participants, at a median age of 0.5 years (range 0.0 - 16.4). Core FOXG1 syndrome phenotypes, representing those present in \u0026ge;50% of the population, and their associated ages of onset are shown in \u003cstrong\u003eFigure 1A.\u0026nbsp;\u003c/strong\u003eDD/ID were universally documented for participants; other phenotypes described for \u0026gt;75% of individuals included: hypotonia (n=92, 91.1%), feeding problem (n=85, 84.2%), constipation (n=85, 84.2%), movement disorder (n=82, 81.2%), and strabismus (n=76, 75.2%). To complement annotated phenotypes and characterize the need for medical management, interventions were explored. The most commonly documented medications include: laxatives (n=88, 87.1%), sleep aids (n=65, 64.4%), histamine H\u003csub\u003e2\u003c/sub\u003e receptor antagonists (n=61, 60.4%), and proton pump inhibitors (n=61, 60.4%). Gastrostomy tube insertions (n=41, 40.6%) and strabismus surgeries (n=36, 35.6%) were frequent. Other related therapeutic procedures were fundoplication (n=7, 6.9%), implantation of a baclofen pump (n=1, 1.0%), and ligation of the parotid duct (n=1, 1.0%).\u003c/p\u003e\n\u003cp\u003eGenotype-phenotype analyses were performed for frameshift, nonsense, and missense variants given their relative frequency in the sample population. With the exception of DD/ID, individuals with missense variants demonstrated lower frequencies of core phenotypes (\u003cstrong\u003eFigure 1B\u003c/strong\u003e). This pattern was significant for gastroesophageal reflux (\u003cem\u003eP\u0026lt;\u003c/em\u003e0.05 for\u003cem\u003e\u0026nbsp;\u003c/em\u003eframeshift vs. missense), strabismus (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 for both frameshift and nonsense vs. missense), and feeding problem (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 for nonsense vs. missense).\u003c/p\u003e\n\u003cp\u003eGiven its relative frequency and candidacy as a clinical endpoint, movement disorder features were characterized. A total of 1,323 neurology exams were annotated, representing a mean of 13.1 exams per participant (range 0 - 77). Approximately 75% (n=79) of individuals had an abnormal movement disorder finding on neurological exam; the frequency across genotypes ranged from 59.1% (missense variants) to 87.5% (nonsense variants). Features of chorea and/or athetosis (n=43, 42.6%), spasticity (n=27, 26.7%), involuntary movements (n=25, 24.8%), and coordination problem (n=22, 21.8%) were most commonly reported. Other findings included: stereotypy (n=18, 17.8%), dystonia (n=17, 16.8%), hyperkinesis (n=16, 15.8%), abnormal eye movement (n=14, 13.9%), and orofacial dyskinesia (n=10, 9.9%). Although coordination problem was documented at consistent frequencies across genotypes, this was the most common movement disorder phenotype in individuals with missense variants (n=6, 27.2%) as opposed to chorea and/or athetosis in those with truncating variants.\u003c/p\u003e\n\u003cp\u003eWith respect to growth parameters, the frequency at which individuals had any documentation of a measurement greater than two standard deviations below the mean for head circumference, height, and weight were as follows: 84.2% (n=85), 56.4% (n=57), 47.5% (n=48); all annotated measurements plotted against the Centers for Disease Control and Prevention growth centiles are depicted in \u003cstrong\u003eSupplementary Figure 1\u003c/strong\u003e. While individuals with missense variants were less likely to have growth parameters outside the normal range, this only achieved significance for head circumference (frameshift = 93.9%, nonsense = 91.7%, missense = 54.5%, \u003cem\u003eP\u0026lt;\u003c/em\u003e0.05).\u003c/p\u003e\n\u003cp\u003eAbnormalities were documented on brain MRI reports for 79.2% (n=80) of participants; individuals with missense variants (45.5%) were significantly less likely to have an abnormal brain MRI report compared to both frameshift (91.8%) and nonsense (87.5%) variants (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). The most common findings included: abnormalities of the corpus callosum (n=55, 54.5%), delayed myelination (n=35, 34.7%), and abnormal gyrification (n=21, 20.8%). Abnormalities of the corpus callosum; spanning agenesis, partial agenesis, hypoplasia, and dysplasia; were significantly less common in individuals with missense variants (13.6%) compared to those with frameshift (65.3%) and nonsense (66.7%) variants (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). With respect to abnormal gyrification, simplified gyri were most frequent (n=19, 18.8%) with pachygyria described more rarely (n=4, 4.0%). Given the dependency of this study on interpretation from local centers, the reported frequencies represent conservative estimates due to varied experience reading pediatric brain MRIs.\u003c/p\u003e\n\u003ch3\u003eCharacterization of seizure phenotype and management\u003c/h3\u003e\n\u003cp\u003eAn epilepsy diagnosis was documented in 70.3% (n=71) and ranged from 50.0% in those with missense variants to 77.6% (frameshift variants, \u003cstrong\u003eFigure 1B\u003c/strong\u003e). The median age at seizure onset was 1.3 years (range DOL4 - 10.0 years) and did not vary significantly between genotypes. The median survival time for seizure onset was 1.7 years, which represents the age at which the probability of an epilepsy diagnosis is 50% (\u003cstrong\u003eFigure 2A\u003c/strong\u003e). Thirty-one percent (n=22) of individuals had seizure-onset before one year of age; the majority of those with an early-onset epilepsy had frameshift variants (n=13, 59.1%). Infantile spasms, epileptic spasms, or West syndrome were documented in 22.5% (n=16) and Lennox-Gastaut syndrome (LGS) in 19.7% (n=14). Of those with an LGS diagnosis, 28.6% (n=4) had a preceding diagnosis of infantile spasms.\u003c/p\u003e\n\u003cp\u003eA specific seizure type was documented for 97.2% (n=69) of individuals. The most commonly reported seizure types were generalized tonic-clonic (n=35, 50.7%), focal-onset (n=28, 40.58%), tonic (n=15, 21.7%), myoclonic (n=12, 17.4%), and generalized-onset (n=11, 15.9%). Notably, the proportion of individuals with history of a prolonged seizure (\u0026gt;5 minutes) was 97.1% (n=67), with status epilepticus described in 30.4% (n=21). Overall, seizure phenotypes were variable, with many participants experiencing both focal- and generalized-onset seizures.\u003c/p\u003e\n\u003cp\u003eA total of 400 EEGs were annotated from the medical records of participants with epilepsy. The median age at first EEG was 0.6 years (range DOL4 - 16.0 years) and the median number of EEGs documented per participant was 4.0 (range 1 - 24). The majority of EEGs were performed in the first three years of life (n=278, 69.5%). Consistent with the observed heterogeneity in seizure semiology, EEG findings were variable, and included focal (n=46, 64.8%), multifocal (n=16, 22.5%), and generalized (n=13, 18.3%) epileptiform discharges. Generalized or focal background slowing was common, and documented in 76.1% (n=54) and 33.8% (n=24) of participants, respectively.\u003c/p\u003e\n\u003cp\u003eTo characterize the longitudinal epilepsy phenotype in FOXG1 syndrome, reported seizure frequencies by caregivers at neurology visits or by EEG were annotated from participant medical records. Seizure frequencies were converted to a numeric score, where 0 is equivalent to seizure-freedom and a maximum score of 5 corresponds to multiple seizures per day. At all ages assessed, the majority of participants were observed to have a seizure frequency score of 0, indicating good seizure control (\u003cstrong\u003eFigure 2B\u003c/strong\u003e). However, a subset of individuals continued to experience high seizure burden, representing those with a seizure frequency score of \u0026ge;3 (at least weekly seizures) for \u0026ge;50% of epochs (one year). These individuals (n=15, 24.2%) were younger (median 3.8 years vs 8.6 years, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) and had an earlier age of epilepsy onset (median 0.9 years vs 1.3 years, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), but did not differ significantly by genotype.\u003c/p\u003e\n\u003cp\u003eWe also evaluated the number of unique seizure medications used within the same epoch (\u003cstrong\u003eFigure 2C\u003c/strong\u003e). To quantify the use of polypharmacy, a similar scoring system was employed where 0 is equivalent to no documentation of anti-seizure medications (ASMs) for the corresponding epoch and 5 corresponds to \u0026ge;5 unique ASMs used, excluding rescue medications. Most participants had documented use of at least one ASM, and for many epochs (1, 6-10, 15-16 years), more than half of participants used at least two unique ASMs.\u003c/p\u003e\n\u003cp\u003eThe most commonly used ASMs by the study population were levetiracetam (n=59, 83.1%), clobazam (n=35, 49.3%), valproic acid (n=23, 32.4%), oxcarbazepine (n=21, 29.6%), and topiramate (n=20, 28.2%). Throughout the duration of follow-up, participants with epilepsy used a median of 4 unique ASMs (range 0 - 16). Given the frequency of polypharmacy in seizure management for FOXG1 syndrome, we computed lines of therapy (LOT) for ASMs used for a minimum duration of 3 months, excluding rescue medications. These ASM LOTs represent unique combinations or single ASMs and the sequencing in which they are used. Significant heterogeneity was observed both in the number and content of ASM LOTs documented for participants with epilepsy (\u003cstrong\u003eFigure 2D\u003c/strong\u003e). The median number of LOTs was 4 (range 1 - 12), indicating that more than half of participants received \u0026ge;4 lines. The most common first-line regimen was levetiracetam monotherapy (n=33, 48.5%), followed by clonazepam monotherapy (n=7, 10.3%). Second-line therapies were documented for 52 participants and were highly variable; only the combination of levetiracetam and clonazepam was observed in \u0026gt;10% of participants (n=8, 15.4%). For those individuals that went on to receive subsequent ASM LOTs, many received complex polypharmacy, at times including up to 7 unique ASMs used concurrently. The pronounced heterogeneity in prescribing practices for epilepsy management in FOXG1 syndrome precludes evaluations of efficacy for a single drug or drug combinations.\u003c/p\u003e\n\u003cp\u003eFor a subset, seizure management also included surgical intervention; 12.7% (n=9) had documented insertion of a vagal nerve stimulator, and 1.4% (n=1) underwent corpus callosotomy.\u003c/p\u003e\n\u003ch3\u003eDevelopmental trajectories in FOXG1 syndrome\u003c/h3\u003e\n\u003cp\u003eTo characterize developmental outcomes in FOXG1 syndrome, we administered the GMFCS, the MACS, and the CFCS. The median GMFCS score for the cohort was IV (range I - V) and ranged from a median score of II (range I - V) in missense variants to V (range I - V) in frameshift and nonsense variants (\u003cstrong\u003eFigure 3A\u003c/strong\u003e). Individuals with missense variants had significantly lower scores compared to those with frameshift or nonsense variants (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). The GMFCS scores documented for participants largely spanned those who could ambulate with limitations or by using a hand-held mobility device (levels II and III, respectively) to those who use a wheelchair (level V). Gross motor milestone attainment and age at attainment were annotated from the medical record. Approximately 71% of individuals were able to roll (median age of attainment 12.2 months, range 4.0 - 183.4), 43.2% were able to sit without support (median age of attainment 17.5 months, range 6.0 - 198.2), and 25.0% were able to walk independently (median age of attainment 31.6 months, range 7.1 - 263.1, \u003cstrong\u003eFigure 3B\u003c/strong\u003e). Individuals with missense variants sat without support and walked independently more often than those with frameshift or nonsense variants (\u003cem\u003eP\u0026lt;\u003c/em\u003e0.05). Although participants with missense variants (sitting: median 14.0 months, range 6.3 - 198.2; and walking: median 27.4 months, range 7.1 - 60.3) were younger at age of attainment for sitting and walking compared to frameshift (sitting: 24.9 months, range 6.3 - 198.2; and walking: median 84,9 months, range 21.1 - 263.1) and nonsense (sitting: median 32.9 months, range 9.0 - 85.1; and walking: median 40.6 months, range 33.1 - 48.1) variants, this was not statistically significant.\u003c/p\u003e\n\u003cp\u003eThe median MACS score for the cohort was IV (range II - V) and ranged from III (range II - V) in missense variants to IV (range II - V) in nonsense and frameshift variants (\u003cstrong\u003eFigure 3C\u003c/strong\u003e). Individuals with missense variants had significantly lower MACS scores compared to those with nonsense variants (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). The MACS scores generated for participants ranged from those who can perform select activities with sufficient time and supervision (level III) to those who require continuous support to participate in portions of a given activity (level IV). Approximately 83% of individuals were able to grasp (median age of attainment 15.4 months, range 3.8 - 119.1) and 55.2% were able to perform hand functions (median age of attainment 24.7 months, range 6.1 - 111.7, \u003cstrong\u003eFigure 3D\u003c/strong\u003e). Individuals with missense variants were able to perform hand functions more often than those with frameshift or nonsense variants (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003eAs demonstrated through the CFCS, communication capabilities were more consistently impacted across genotypes (\u003cstrong\u003eFigure 3E\u003c/strong\u003e). The median CFCS score across all genotypes was IV (range III - V) and ranged from IV (range III - V) in missense and frameshift variants to V (range III - V) in nonsense variants. These scores represent individuals who do not communicate consistently or only rarely with familiar partners (levels IV/V). With respect to milestone attainment, all participants were able to vocalize (median age of attainment 8.9 months, range 2.0 - 114.7) and 50.7% were able to use at least one word (median age of attainment 20.0 months, range 7.0 - 165.6). Approximately 88% were reported to use non-verbal communication, including the use of augmentative and alternative communication (AAC) strategies, and 6.6% were reported to use verbal communication (\u003cstrong\u003eFigure 3F\u003c/strong\u003e). There were no significant genotype-phenotype correlations observed in the attainment or age at attainment for language milestones.\u003c/p\u003e\n\u003cp\u003eTo further characterize developmental outcomes in FOXG1 syndrome, the Vineland-3, an established measure of adaptive behaviors, was administered annually at two timepoints; 44 participants completed the first administration and 20 completed the second (\u003cstrong\u003eSupplementary Figure 2\u003c/strong\u003e). Results from the Vineland-3 for gross motor, fine motor, receptive language, and expressive language subdomains are presented in \u003cstrong\u003eTable 2\u003c/strong\u003e. We observed a floor effect using scaled scores, such as \u003cem\u003ev\u003c/em\u003eS and developmental quotients (DQs) across all subdomains. As with the results from the GMFCS and medical record annotation, individuals with missense variants achieved significantly higher GSVs and DQs for the gross motor subdomain in the first year of administration. We also observed that GSVs and DQs for the fine motor subdomain were significantly higher in missense variants compared to nonsense variants, reflecting findings from the MACS and medical record annotation. Individuals with missense variants also had higher GSVs and DQs for both receptive and expressive language subdomains; this was statistically significant for DQs in the first year of administration for both subdomains when compared to frameshift variants (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01 and \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, respectively) and in the receptive language subdomain only for nonsense variants (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Similar trends were observed for the second year of administration, but these did not achieve statistical significance, likely due to the reduced cohort size. As GSVs represent absolute, rather than relative performance, they are well-suited to evaluating change in performance over time within an individual or cohort. To establish baseline variability in the absence of intervention, we calculated the difference in GSVs for each participant who underwent two annual administrations of the Vineland-3. The median percent change for gross motor, fine motor, receptive language, and expressive language subdomains were as follows: 0.0%, 0.0%, 0.0%, and 1.0% respectively.\u003c/p\u003e\n\u003ch3\u003eSleep and behavioral phenotypes in FOXG1 syndrome\u003c/h3\u003e\n\u003cp\u003eSleep and behavioral problems contribute to overall quality of life for individuals with rare disorders and their caregivers. Sleep problems were documented in the medical records of 69.3% (n=70) of participants, and the incidence did not differ significantly between genotypes. Approximately 61% (n=62) were documented as using a sleep medication, with melatonin and clonidine most commonly prescribed. To better quantify sleep in FOXG1 syndrome, caregivers were administered the CSHQ at quarterly intervals for two years (\u003cstrong\u003eTable 3, Supplementary Figure 3\u003c/strong\u003e). Mean and median scores for all genotypes exceed 41, which has been previously used as a threshold indicative of clinically meaningful sleep problems (17). Across timepoints and genotypes, scores remained stable.\u003c/p\u003e\n\u003cp\u003eBehavior problems were reported in the medical records for 30.7% (n=31) of participants, and included: self-injurious behavior, problem behavior, and aggressive behavior. The frequency of behavioral problems ranged from 12.5% (n=3) in nonsense variants to 54.5% (n=12) in missense variants; individuals with missense variants were more likely to have behavioral problems than those with nonsense variants (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Two individuals received medication specifically to manage challenging behaviors; these included: aripiprazole, clonidine, midazolam, olanzapine, risperidone, and sertraline. To supplement medical record data, the ABC was administered to caregivers at quarterly timepoints for two years (\u003cstrong\u003eTable 3, Supplementary Figure 4\u003c/strong\u003e). Individuals with missense variants were found to have significantly higher scores when compared to frameshift and nonsense variants for irritability (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05\u003cem\u003e\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;P\u003c/em\u003e\u0026lt;0.0001, respectively), hyperactivity/noncompliance (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 and \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, respectively), and inappropriate speech (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 and\u003cem\u003e\u0026nbsp;\u003c/em\u003eP\u003cem\u003e\u0026lt;\u003c/em\u003e0.05, respectively\u003cem\u003e)\u003c/em\u003e. Across administrations and genotypes, scores remained relatively stable throughout the course of the study.\u003c/p\u003e\n\u003cp\u003eWhile documentation of autism spectrum disorder (ASD) and/or autistic features remained relatively uncommon in the study population (n=19, 18.9%), genotype had a significant effect, whereby frequency was notably higher in those with missense variants (n=12, 54.5%) compared to frameshift variants (n=6, 12.2%, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) and nonsense variants (n=1, 4.3%, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e\n\u003ch3\u003eLongitudinal quantification of FOXG1 syndrome severity using a composite measure\u003c/h3\u003e\n\u003cp\u003eMitter \u003cem\u003eet al.\u003c/em\u003e have previously published a composite measure of FOXG1 syndrome severity that includes the following domains: somatic growth, motor and speech development, behavioral phenotypes, neurological phenotypes, and abnormalities on brain imaging (3). Using data derived from medical records as inputs, we replicated maximum and longitudinal FOXG1 syndrome severity scores by documenting the presence or absence of contributing phenotypes at each year of age. When comparing maximum scores for participants, individuals with missense variants had significantly lower severity scores compared to those with frameshift (\u003cem\u003eP\u0026lt;\u003c/em\u003e0.001) and nonsense (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) variants (\u003cstrong\u003eFigure 4A\u003c/strong\u003e). The median maximum score across all participants was 18 (range 4 - 26); median maximum scores by genotype were 18 (range 8 - 26) for frameshift, 20 (range 4 - 24) for nonsense, and 12 (range 4 - 22) for missense variants. The sample population included ten individuals who were \u0026ge;16 years of age at records end (range 16 - 34 years) and who predominantly harbored truncating variants; the median severity score for these individuals was 22. Although the FOXG1 syndrome severity scores for these older individuals are higher (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), it is important to note that independent of the observed genotype effect, a subset of evaluated domains are age-dependent and/or reflect long term sequelae of neuronal dysfunction (e.g. neuromuscular scoliosis, spasticity).\u003c/p\u003e\n\u003cp\u003eWhen evaluating FOXG1 syndrome severity scores longitudinally, a similar genotype-phenotype correlation was observed; at each annual time point between two and eight years of age, severity scores were significantly lower in those with missense variants compared to those with nonsense variants (\u003cstrong\u003eFigure 4B\u003c/strong\u003e). When compared to individuals with frameshift variants, scores were significantly lower at the 4-year time point. At the 6-year time point, severity scores in individuals with frameshift variants were significantly lower than those with nonsense variants.\u003c/p\u003e\n\u003cp\u003eIn order to evaluate predictive contributors to FOXG1 syndrome severity score, we fit a multiple regression model assigning genotype, age, maximum head circumference by one year of age, and epilepsy age of onset as independent variables. Presence of a missense variant was significantly associated with lower FOXG1 syndrome severity scores (t-value = -2.1, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Although not significant when all other variables were held constant, a larger head circumference by one year and a later age of epilepsy onset were associated with lower FOXG1 syndrome severity scores (t-value = -0.9, \u003cem\u003eP\u003c/em\u003e=0.36 and t-value = -0.4, \u003cem\u003eP\u003c/em\u003e = 0.46, respectively). Age did not explain FOXG1 syndrome severity score variability (t-value = 0.1, \u003cem\u003eP\u003c/em\u003e=0.92). Overall, the regression model was trending towards significance (F-statistic = 2.1, \u003cem\u003eP\u003c/em\u003e=0.06).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we combined comprehensive and systematic annotation of patient medical records with prospective administration of validated measures to describe the natural history of FOXG1 syndrome in 101 children and adults. To date, this represents one of the largest populations of FOXG1 syndrome described in the literature and is the first to perform a systematic longitudinal phenotypic characterization. We demonstrate the feasibility of a study design that leverages RWD to perform detailed clinical characterization and reduce the burden of participation for individuals and their caregivers.\u003c/p\u003e\n\n\u003cp\u003eWe replicate the core FOXG1 syndrome phenotype, which encompasses DD/ID, hypotonia, feeding problems, constipation, movement disorder, and strabismus, with high frequencies of gastroesophageal reflux, epilepsy, sleep problems, and sialorrhea. Across all evaluations of development, including milestone attainment documented in medical records and the results from validated measures, participants exhibited severe-to-profound functional limitations across motor and language domains. Taken together, the medical and caregiving needs of children and adults with FOXG1 syndrome remain significant, and substantiate the need for coordinated clinical care and supportive resourcing for families.\u003c/p\u003e\n\n\u003cp\u003eThis work expands upon a previously recognized genotype-phenotype correlation, whereby the presentation of FOXG1 syndrome associated with missense variants is distinct from that observed in individuals with truncating variants or gene deletions (3,5,7). Across nearly all explored domains, missense variants were associated with an attenuated disease course characterized by lower frequencies of core FOXG1 syndrome phenotypes, including microcephaly, callosal abnormalities, gastroesophageal reflux, strabismus, and feeding problems. Although movement disorder in FOXG1 syndrome is classically characterized by hyperkinetic involuntary movements (18), in those with missense variants, coordination problems and/or stereotypies may be more commonly described. With respect to developmental outcomes, genotype-phenotype correlations were most pronounced for gross motor capabilities, where individuals with missense variants scored lower on the GMFCS, achieved higher GSVs and v\u003cem\u003eS\u003c/em\u003e scores on the Vineland-3 gross motor subdomain, and were more likely to achieve motor milestones, including sitting and walking independently. With greater mobility and fewer medical needs, individuals with missense variants exhibit a behavioral profile characterized by significantly higher scores across the irritability, hyperactivity and noncompliance, and inappropriate speech subdomains on the ABC. Notably, \u0026gt;50% of individuals with missense variants had documentation of autistic features and/or ASD; however, caution should be used given the difficulty in accurately administering developmental testing in those with DD/ID and the use of ASD to increase access to developmental therapies. In sum, individuals with missense variants may not present with the classically described FOXG1 syndrome phenotype and as such, FOXG1 syndrome should be considered even in those who lack characteristic features like microcephaly or significant gross motor limitations. Further, there may be unique needs and caregiver priorities, particularly in the recognition and management of behavioral challenges. \u003c/p\u003e\n\n\u003cp\u003eTo contextualize the FOXG1 syndrome behavioral phenotype, as characterized by use of the ABC, we compared scores across subdomains to those published in other relevant populations. Compared to both a large normative sample population of individuals with DD/ID (19) and individuals with Angelman syndrome (20), participants in this study with FOXG1 syndrome were more likely to demonstrate behaviors associated with social withdrawal and lethargy, as well as stereotypies. Further, individuals with FOXG1 syndrome report more irritability and less hyperactivity when compared to Angelman syndrome. In sum, the FOXG1 syndrome behavioral profile is characterized by stereotypies, as well as behaviors associated with social withdrawal and irritability, which can include tantrums, self-injurious and aggressive behaviors. \u003c/p\u003e\n\n\u003cp\u003eClinical trial readiness remains a principal motivation for the design and implementation of this study, particularly with respect to the identification of candidate clinical endpoints. Although this work and others continue to support a consistent and recognizable FOXG1 syndrome clinical profile (3\u0026ndash;5), notable heterogeneity remains within and amongst genotypes. For example, while an established primary endpoint for clinical trials in other developmental and epileptic encephalopathies, seizure frequency is unlikely to be appropriate for FOXG1 syndrome given variable prevalence and presentation of epilepsy with sustained seizure control for most individuals. Apart from DD/ID, no core FOXG1 syndrome phenotype was documented for all participants, and significant reductions in frequency for missense variants presents a challenge for designing an effective and equitable clinical trial. Increasingly, composite measures that evaluate change across multiple relevant domains are being used given their ability to better tolerate heterogeneity within populations (21,22). \u003c/p\u003e\n\n\u003cp\u003eWith respect to developmental outcomes, the Vineland-3 has been widely used as a measure of adaptive behavior outcomes and GSVs can alleviate challenges related to floor effects (23). However, throughout the course of this study, participants shared that the Vineland-3 was burdensome to complete and did not provide an accurate view of the subject\u0026rsquo;s capabilities. With respect to other developmental assessments, these remain difficult to perform due to challenges with administration in populations with severe functional impairments, as well as the reduced likelihood to see significant changes during the duration of a clinical trial. When surveyed, caregivers of individuals with FOXG1 syndrome consistently rank absence of effective communication as a primary concern (5,24); novel communication measures validated in populations with limited verbal speech may be a strong candidate endpoint (25,26). In FOXG1 syndrome, identifying effective communication strategies remains challenging. A combination of fine motor impairments, movement disorder, and cortical visual impairment interfere with the successful application of AAC strategies. \u003c/p\u003e\n\n\u003cp\u003eThere are several limitations associated with this study, primarily resulting from the opportunistic annotation of medical records that document routine clinical care. In contrast to prospectively designed site-based clinical studies, the use of a retrospective data source precludes the ability to perform systematic clinical assessments at a defined frequency, particularly those that are not standard of care. Further, medical records may introduce and perpetuate inaccuracies that are challenging to verify. Citizen has implemented several technical and operational procedures to mitigate the limitations associated with RWD derived from medical records. First, medical record collection was agnostic to institution or electronic medical record vendor, which enables longitudinal follow-up across sites. As records were received, they were measured against internal definitions of medical record type and frequency designed to identify gaps in care or collection that may contribute to an incomplete dataset; participants whose medical records did not meet or exceed minimum completeness thresholds were not included in this study. Data annotation conformed to a mature data model that both defines the scope of data capture and supports redundancy across variables. Further, the use of internationally recognized terminologies allowed for computational approaches to address variability in medical record documentation. Lastly, to limit inaccurate data capture, all annotated data underwent source document verification by experienced clinicians, and resulting datasets were subject to rule-based validations. In support of this approach, the results derived from RWD in this study were consistent with those from the prospectively administered validated measures, including the Vineland-3, and with previously published characterizations (3\u0026ndash;5). \u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBy pairing prospective administration of validated measures with systematic annotation of participant medical records, we present a novel approach to describe the natural history of FOXG1 syndrome using a methodology that can be broadly applied across rare disorders. The employed study design expands access to research participation by minimizing burden on participants and their caregivers, and enables the selection and characterization of candidate clinical endpoints. While this work confirms and expands upon the classically defined core clinical phenotype in detail, notable heterogeneity was observed in study participants, largely driven by genotype-phenotype correlations whereby missense variants are associated with a distinct presentation. In sum, data presented here support the candidacy of communication as a primary endpoint for clinical trials in FOXG1 syndrome, as well as features that impact successful use of alternative communication strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eCaregivers and/or legal guardians of study participants provided broad consent to share de-identified data for research. This study received determinations of exemption through a central institutional review board via exemption categories 2, 7, and 8 of the revised Common Rule.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch3\u003eAvailability of data and materials\u003c/h3\u003e\n\u003cp\u003eThe data that support the analyses presented here contain sensitive and protected health information for participants and is therefore not openly available. Requests for data access can be made to [email protected], where we can confirm the proposed research scope is consistent with platform consent language and is reviewed and/or approved by an institutional review board.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eElise Brimble, Elizabeth Blomenberg, and Kelsey Frahlich are current employees of Citizen Health with vested and unvested stock options. Kopika Kuhathaas and Gai Ayalon are current employees of FOXG1 Research Foundation.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis work was funded by FOXG1 Research Foundation and the Chan-Zuckerberg Initiative through the Rare As One program. Elise Brimble, Elizabeth Blomenberg, and Kelsey Frahlich are current employees of Citizen Health.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eAuthors\u0026apos; contributions\u003c/h3\u003e\n\u003cp\u003eE.Brimble: Study conception/design, data acquisition and analysis, data interpretation, manuscript development and review. P.V., K.K., C.E.H., N.B.B., H.E.O., E.D.M., G.A.: Study conception/design, data interpretation, manuscript development and review. E.Blomenberg, K.F.: Data acquisition and analysis, data interpretation, manuscript development and review. All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eWe extend our gratitude to the participants and their families.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDanesin C, Peres JN, Johansson M, Snowden V, Cording A, Papalopulu N, et al. Integration of Telencephalic Wnt and Hedgehog Signaling Center Activities by Foxg1. Dev Cell. 2009 Apr 21;16(4):576\u0026ndash;87.\u003c/li\u003e\n\u003cli\u003eKort\u0026uuml;m F, Das S, Flindt M, Morris-Rosendahl DJ, Stefanova I, Goldstein A, et al. The core FOXG1 syndrome phenotype consists of postnatal microcephaly, severe mental retardation, absent language, dyskinesia, and corpus callosum hypogenesis. J Med Genet. 2011 Jun 1;48(6):396\u0026ndash;406.\u003c/li\u003e\n\u003cli\u003eMitter D, Pringsheim M, Kaulisch M, Pl\u0026uuml;macher KS, Schr\u0026ouml;der S, Warthemann R, et al. FOXG1 syndrome: genotype\u0026ndash;phenotype association in 83 patients with FOXG1 variants. Genet Med. 2018 Jan 1;20(1):98\u0026ndash;108.\u003c/li\u003e\n\u003cli\u003eVegas N, Cavallin M, Maillard C, Boddaert N, Toulouse J, Schaefer E, et al. Delineating FOXG1 syndrome: From congenital microcephaly to hyperkinetic encephalopathy. Neurol Genet. 2018 Dec;4(6):e281.\u003c/li\u003e\n\u003cli\u003eBrimble E, Reyes KG, Kuhathaas K, Devinsky O, Ruzhnikov MRZ, Ortiz-Gonzalez XR, et al. Expanding genotype\u0026ndash;phenotype correlations in FOXG1 syndrome: results from a patient registry. Orphanet J Rare Dis. 2023 Jun 12;18(1):149.\u003c/li\u003e\n\u003cli\u003ePringsheim M, Mitter D, Schr\u0026ouml;der S, Warthemann R, Pl\u0026uuml;macher K, Kluger G, et al. Structural brain anomalies in patients with FOXG1 syndrome and in Foxg1+/\u0026minus; mice. Ann Clin Transl Neurol. 2019 Mar 3;6(4):655\u0026ndash;68.\u003c/li\u003e\n\u003cli\u003eMazel B, Delanne J, Garde A, Racine C, Bruel AL, Duffourd Y, et al. FOXG1 variants can be associated with milder phenotypes than congenital Rett syndrome with unassisted walking and language development. Am J Med Genet B Neuropsychiatr Genet. 2024;n/a(n/a):e32970.\u003c/li\u003e\n\u003cli\u003eJeon S, Park J, Likhite S, Moon JH, Shin D, Li L, et al. The postnatal injection of AAV9-FOXG1 rescues corpus callosum agenesis and other brain deficits in the mouse model of FOXG1 syndrome. Mol Ther Methods Clin Dev [Internet]. 2024 Sep 12 [cited 2024 Aug 7];32(3). Available from: https://www.cell.com/molecular-therapy-family/methods/abstract/S2329-0501(24)00091-3\u003c/li\u003e\n\u003cli\u003eRichards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015 May 1;17(5):405\u0026ndash;24.\u003c/li\u003e\n\u003cli\u003eSpelbrink EM, Brown TL, Brimble E, Blanco KA, Nye KL, Porter BE. Characterizing a rare neurogenetic disease, SLC13A5 citrate transporter disorder, utilizing clinical data in a cloud-based medical record collection system. Front Genet. 2023 Mar 21;14:1109547.\u003c/li\u003e\n\u003cli\u003eBrimble E, Kim J, Martin RL, McKnight D, Lacoste AMB. Computation of longitudinal phenotypes in 466 individuals with a developmental and epileptic encephalopathy enables clinical trial readiness [Internet]. medRxiv; 2023 [cited 2024 Aug 7]. p. 2023.03.02.23286645. Available from: https://www.medrxiv.org/content/10.1101/2023.03.02.23286645v2\u003c/li\u003e\n\u003cli\u003ePalisano R, Rosenbaum P, Walter S, Russell D, Wood E, Galuppi B. Development and reliability of a system to classify gross motor function in children with cerebral palsy. Dev Med Child Neurol. 1997;39(4):214\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eEliasson AC, Krumlinde-Sundholm L, R\u0026ouml;sblad B, Beckung E, Arner M, \u0026Ouml;hrvall AM, et al. The Manual Ability Classification System (MACS) for children with cerebral palsy: scale development and evidence of validity and reliability. Dev Med Child Neurol. 2006;48(7):549\u0026ndash;54.\u003c/li\u003e\n\u003cli\u003eHidecker MJC, Paneth N, Rosenbaum PL, Kent RD, Lillie J, Eulenberg JB, et al. Developing and validating the Communication Function Classification System for individuals with cerebral palsy. Dev Med Child Neurol. 2011;53(8):704\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eOwens JA, Spirito A, McGuinn M. The Children\u0026rsquo;s Sleep Habits Questionnaire (CSHQ): psychometric properties of a survey instrument for school-aged children. Sleep. 2000 Dec 15;23(8):1043\u0026ndash;51.\u003c/li\u003e\n\u003cli\u003eAman MG, Singh NN, Stewart AW, Field CJ. Psychometric characteristics of the aberrant behavior checklist. Am J Ment Defic. 1985 Mar;89(5):492\u0026ndash;502.\u003c/li\u003e\n\u003cli\u003eChawla JK, Bernard A, Heussler H, Burgess S. Sleep, Function, Behaviour and Cognition in a Cohort of Children with Down Syndrome. Brain Sci. 2021 Oct;11(10):1317.\u003c/li\u003e\n\u003cli\u003ePapandreou A, Schneider RB, Augustine EF, Ng J, Mankad K, Meyer E, et al. Delineation of the movement disorders associated with FOXG1 mutations. Neurology. 2016 May 10;86(19):1794\u0026ndash;800.\u003c/li\u003e\n\u003cli\u003eMarshburn EC, Aman MG. Factor validity and norms for the aberrant behavior checklist in a community sample of children with mental retardation. J Autism Dev Disord. 1992 Sep;22(3):357\u0026ndash;73.\u003c/li\u003e\n\u003cli\u003eSadhwani A, Willen JM, LaVallee N, Stepanians M, Miller H, Peters SU, et al. Maladaptive behaviors in individuals with Angelman syndrome. Am J Med Genet A. 2019;179(6):983\u0026ndash;92.\u003c/li\u003e\n\u003cli\u003ePercy AK, Neul JL, Benke TA, Marsh ED, Glaze DG. A review of the Rett Syndrome Behaviour Questionnaire and its utilization in the assessment of symptoms associated with Rett syndrome. Front Pediatr [Internet]. 2023 Jul 28 [cited 2024 Aug 7];11. Available from: https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2023.1229553/full\u003c/li\u003e\n\u003cli\u003eTandon PK, Kakkis ED. The multi-domain responder index: a novel analysis tool to capture a broader assessment of clinical benefit in heterogeneous complex rare diseases. Orphanet J Rare Dis. 2021 Apr 19;16(1):183.\u003c/li\u003e\n\u003cli\u003eEisengart JB, Daniel MH, Adams HR, Williams P, Kuca B, Shapiro E. Increasing precision in the measurement of change in pediatric neurodegenerative disease. Mol Genet Metab. 2022 Sep 1;137(1):201\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eNeul JL, Benke TA, Marsh ED, Suter B, Silveira L, Fu C, et al. Top caregiver concerns in Rett syndrome and related disorders: data from the US natural history study. J Neurodev Disord. 2023 Oct 13;15(1):33.\u003c/li\u003e\n\u003cli\u003eReeve BB, Lucas N, Chen D, McFatrich M, Jones HN, Gordon KL, et al. Validation of the Observer-Reported Communication Ability (ORCA) measure for individuals with Rett syndrome. Eur J Paediatr Neurol. 2023 Sep 1;46:74\u0026ndash;81.\u003c/li\u003e\n\u003cli\u003eZigler CK, Lin L, McFatrich M, Lucas N, Gordon KL, Jones HN, et al. Validation of the Observer-Reported Communication Ability (ORCA) Measure for Individuals With Angelman Syndrome. Am J Intellect Dev Disabil. 2023 May 1;128(3):204\u0026ndash;18.\u003c/li\u003e\n\u003cli\u003eWong LC, Huang CH, Chou WY, Hsu CJ, Tsai WC, Lee WT. The clinical and sleep manifestations in children with FOXG1 syndrome. Autism Res. 2023;16(5):953\u0026ndash;66.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Demographic characteristics of study population\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"635\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 408px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFOXG1\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Variant Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrameshift\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonsense\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMissense\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic Characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eMedian age at record end (range, y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e7.3 (0.9 - 34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e8.2 (1.8 - 23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e6.6 (2.6 - 16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e7.3 (0.4 - 34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eMedian age at diagnosis (range, y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1.6 (0 - 34.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1.3 (0.4 - 16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2.2 (0.5 - 15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1.6 (0.0 - 34.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eMedian duration of follow-up (range, y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e6.0 (0.8 - 34.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e6.5 (1.0 - 21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e5.1 (0.7 - 15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e6.0 (0.4 - 34.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eFemale n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e12 (24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e11 (45.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e13 (59.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e41 (40.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eMale n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e37 (75.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e13 (54.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e9 (40.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e60 (59.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eGeographic Regions n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Midwest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e14 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e4 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3 (13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e21 (20.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Northeast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e10 (20.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e6 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e6 (27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e24 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;South\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e14 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e10 (41.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e8 (36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e33 (32.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;West\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e11 (22.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e4 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e5 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e23 (22.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 329px;\"\u003e\n \u003cp\u003eRural-Urban Commuting Area (RUCA) Codes n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Metropolitan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e45 (91.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e21 (87.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e19 (86.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e91 (90.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Micropolitan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e5 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Small towns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2 (9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Rural areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 329px;\"\u003e\n \u003cp\u003eSelf-Reported Race, Ethnicity, or Ancestry n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e19 (79.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e16 (80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e4 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e42 (67.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Mixed Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3 (15.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e8 (12.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Latino or Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e7 (11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Asian or Asian-American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e4 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black or African-American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eData Collection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eMedian # of annotated concepts (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e398 (91 - 1477)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e355.5 (65 - 1309)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e253 (49 - 852)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e375 (45 - 1477)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSelf-reported race was available for n=62 participants.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Prospective administration of the Vineland-3 in FOXG1 syndrome\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"679\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrameshift\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonsense\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMissense\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVineland-3\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ev\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ev\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ev\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ev\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eGross Motor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e12 mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1 (1-11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.6 (0-45.2)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1 (1-9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.8 (0.5-28.8)**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e3 (3-9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e17.8 (3.5-47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1 (1-11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e9.9 (0.0-47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e24 mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1 (1-10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e6.7 (0.0-37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1 (1-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.7 (0.4-4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e3 (1-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e14.4 (0.0-21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1 (1-10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4.4 (0.0-37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eFine Motor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e12 mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e4 (3-6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e12.0 (0.0-24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1 (1-7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.3 (0.0-30.0)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e5 (1-10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e27.5 (0.0-47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e4 (1-10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e9.3 (0.0-47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e24 mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e2 (1-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e8.9 (0.3-20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1 (1-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.4 (0.4-5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e3 (1-6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e10.8 (0.0-26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e2 (1-6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5.8 (0.0-26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eReceptive Language\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e12 mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1 (1-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.1 (0.0-21.7)**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1 (1-9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e7.9 (0.0-31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e2 (1-7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e18.4 (0.0-50.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1 (1-12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e8.5 (0.0-50.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e24 mo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1 (1-9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.8 (0.0-28.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1 (1-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.3 (0.0-8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e2 (1-11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e19.0 (0.0-33.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1 (1-11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5.7 (0.0-33.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eExpressive Language\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e12 mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e2 (1-6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0 (0.0-22.6)**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1 (1-6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.6 (0.0-25.0)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1 (1-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e13.5 (0.0-31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1 (1-10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.0 (0.0-31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e24 mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1 (1-6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.8 (0.0-23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1 (1-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.0 (0.0-10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e3 (1-7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e10.7 (0.0-28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1 (1-7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1.8 (0.0-28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are shown as median (range). Bolded cells contain values found to be significantly different from the corresponding value for individuals with missense variants.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Scored results of the Aberrant Behavior Checklist and Children\u0026apos;s Sleep Habits Questionnaire in FOXG1 syndrome\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 428px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFOXG1 Variant Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrameshift\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonsense\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMissense\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 624px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAberrant Behavior Checklist\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eIrritability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e12 mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6 (0 - 22)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4 (0 - 11)****\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e12 (2 - 27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e6 (0 - 27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e24 mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e5.5 (0 - 23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4 (2 - 7)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e9.5 (2 - 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e5.5 (1 - 23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eSocial Withdrawal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e12 mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e9.5 (0 - 28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e5 (1 - 22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e10 (2 - 27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e8 (0 - 28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e24 mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e9.5 (0 - 33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e7 (1 - 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e9.5 (1 - 18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e8.5 (0 - 33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eStereotypic Behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e12 mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e7 (0 - 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e6 (2 - 14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e7 (1 - 14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e7 (0 - 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e24 mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e7 (0 - 16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e6 (2 - 8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e7.5 (1 - 12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e6 (0 - 16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eHyperactivity/ Noncompliance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e12 mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7 (0 - 26)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4 (1 - 20)**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e11 (2 - 27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e7.5 (0 - 27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e24 mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e7 (0 - 24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e6 (1 - 12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e11 (1 - 22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e7 (0 - 24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eInappropriate Speech\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e12 mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1 (0 - 4)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0 (0 - 5)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.5 (0 - 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1 (0 - 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e24 mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1 (0 - 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1 (0 - 4)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e3.5 (0 - 7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e2 (0 - 7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 624px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChildren\u0026rsquo;s Sleep Habits Questionnaire\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e12 mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e52.0 (30.3 - 71.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e45.0 (24.5 - 61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e53.4 (24.0 - 70.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e50.3 (30.3 - 71.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e24 mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e50.0 (35.0 - 72.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e47.8 (36.0 - 64.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e51.7 (34.0 - 75.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e50.0 (34.0 - 75.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eResults presented as median (range) of scores averaged across 4 administrations per year.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e*, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05; **, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001; ****, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001 compared to missense variants.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neurodevelopmental-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jndd","sideBox":"Learn more about [Journal of Neurodevelopmental Disorders](http://jneurodevdisorders.biomedcentral.com/)","snPcode":"11689","submissionUrl":"https://submission.nature.com/new-submission/11689/3","title":"Journal of Neurodevelopmental Disorders","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"FOXG1 syndrome, Neurodevelopmental disorders, Natural history studies, Genotype-phenotype correlations, Clinical trial readiness, Rare disorders, Real-world data, Real-world evidence","lastPublishedDoi":"10.21203/rs.3.rs-5582753/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5582753/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\n\u003cp\u003eFOXG1 syndrome is a severe genetic neurodevelopmental disorder characterized by developmental and intellectual disabilities (DD/ID), postnatal microcephaly, epilepsy, and movement disorder. With the advent of molecular therapies, establishing the natural history of FOXG1 syndrome is critical to enable clinical trial readiness. However, traditional study designs are challenging to implement for rare disorders without significant burden to participants.\u003c/p\u003e\n\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\n\u003cp\u003eThe study population included 101 children and adults with (likely) pathogenic variants in or involving \u003cem\u003eFOXG1 \u003c/em\u003e(ages 0.4 - 34.8 years). Participant medical records underwent systematic annotation and harmonization of recorded clinical phenotypes, interventions, and outcomes through use of a patient-centric real-world data (RWD) platform. Retrospective medical record data were paired with prospective administration of validated measures of development and behavior, including the Vineland-3, the Aberrant Behavior Checklist, and the Children’s Sleep Habits Questionnaire. Descriptive and inferential statistics were employed to characterize longitudinal phenotypes and to explore genotype-phenotype correlations.\u003c/p\u003e\n\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\n\u003cp\u003eThrough systematic evaluation of 101 people with FOXG1 syndrome, we generated a robust dataset encompassing \u0026gt;40,000 annotated clinical terminology concepts that represent \u0026gt;770 cumulative patient data years. Core clinical phenotypes include DD/ID, gastrointestinal disorders, strabismus, epilepsy, movement disorders, and sleep problems. The FOXG1 syndrome behavioral phenotype is characterized by irritability, including aggressive behaviors, stereotypies, social withdrawal, and lethargy; in those with missense variants, features of autism spectrum disorders are also reported. Data derived from both medical records and validated measures confirm and expand upon previously described genotype-phenotype correlations, whereby truncating variants are associated with greater limitations across motor and communication domains, as well as increased frequency of core FOXG1 syndrome phenotypes. Further, individuals with truncating variants had higher scores on a composite measure of FOXG1 syndrome severity, which persists when modeled longitudinally. Employing the same composite measure, we demonstrate that FOXG1 syndrome is a static encephalopathy without evidence of neurodegeneration.\u003c/p\u003e\n\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\n\u003cp\u003eBy combining retrospective RWD with prospective survey administration in a large sample population, we establish the natural history of FOXG1 syndrome and highlight candidate clinical endpoints for use in clinical trials, including quantitative evaluations of communication and movement disorders.\u003c/p\u003e","manuscriptTitle":"Longitudinal characterization of clinical, developmental, and behavioral phenotypes in 101 children and adults with FOXG1 syndrome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-22 07:05:08","doi":"10.21203/rs.3.rs-5582753/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-28T05:38:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-11T14:00:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142720436922997752082079994778045291885","date":"2025-03-10T19:29:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-22T07:07:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-06T03:11:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-12-06T03:10:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Neurodevelopmental Disorders","date":"2024-12-05T00:25:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neurodevelopmental-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jndd","sideBox":"Learn more about [Journal of Neurodevelopmental Disorders](http://jneurodevdisorders.biomedcentral.com/)","snPcode":"11689","submissionUrl":"https://submission.nature.com/new-submission/11689/3","title":"Journal of Neurodevelopmental Disorders","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"78386c54-9885-4bef-a8a1-e195e8f2c52c","owner":[],"postedDate":"January 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T16:45:43+00:00","versionOfRecord":{"articleIdentity":"rs-5582753","link":"https://doi.org/10.1186/s11689-025-09653-1","journal":{"identity":"journal-of-neurodevelopmental-disorders","isVorOnly":false,"title":"Journal of Neurodevelopmental Disorders"},"publishedOn":"2025-10-24 16:16:42","publishedOnDateReadable":"October 24th, 2025"},"versionCreatedAt":"2025-01-22 07:05:08","video":"","vorDoi":"10.1186/s11689-025-09653-1","vorDoiUrl":"https://doi.org/10.1186/s11689-025-09653-1","workflowStages":[]},"version":"v1","identity":"rs-5582753","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5582753","identity":"rs-5582753","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}