Gray Matter Volumes of the Superior Temporal Gyrus Link Preterm Birth and Developmentally Disordered Eye Gazing Patterns in Toddlers at Eighteen Months

Gray Matter Volumes of the Superior Temporal Gyrus Link Preterm Birth and Developmentally Disordered Eye Gazing Patterns in Toddlers at Eighteen Months | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Gray Matter Volumes of the Superior Temporal Gyrus Link Preterm Birth and Developmentally Disordered Eye Gazing Patterns in Toddlers at Eighteen Months Yanan Su , Guangfei Li , Shanmei Wang , Dongmei Hao , Clara S. Li , Yiyao Ye-Lin , Xiaolin Wang , Ruolin Zhang , Lin Yang , Chiang-Shan R. Li doi: https://doi.org/10.1101/2025.06.18.25329884 Yanan Su a Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation , Beijing, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Guangfei Li a Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation , Beijing, China b BJUT-UPV Joint Research Laboratory in Biomedical Engineering , Beijing, China , and Valencia, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: guangfei.li{at}bjut.edu.cn Shanmei Wang a Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation , Beijing, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dongmei Hao a Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation , Beijing, China b BJUT-UPV Joint Research Laboratory in Biomedical Engineering , Beijing, China , and Valencia, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Clara S. Li c University of Connecticut School of Medicine , Farmington, CT, USA d Department of Psychiatry, Yale University School of Medicine , New Haven, CT, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yiyao Ye-Lin b BJUT-UPV Joint Research Laboratory in Biomedical Engineering , Beijing, China , and Valencia, Spain e Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xiaolin Wang f College of Mathematical Medicine, Zhejiang Normal University , Jinhua, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ruolin Zhang g School of Medical Technology, Beijing Institute of Technology , Beijing, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lin Yang a Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation , Beijing, China b BJUT-UPV Joint Research Laboratory in Biomedical Engineering , Beijing, China , and Valencia, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chiang-Shan R. Li d Department of Psychiatry, Yale University School of Medicine , New Haven, CT, USA h Department of Neuroscience, Yale University School of Medicine , New Haven, CT, USA i Wu Tsai Institute, Yale University , New Haven, CT, USA (Running title: preterm birth, STG, and autism) Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Data/Code Preview PDF ABSTRACT Background Preterm birth involves structural brain changes and increases the risk of neurodevelopmental disorders, including social cognitive dysfunction as implicated in autism spectrum disorder (ASD). However, it remains unclear whether or how volumetric brain changes may impact the risk of social cognitive dysfunction in toddlers of preterm birth. Methods We curated data of 569 toddlers approximately 18 months of age, including 76 with preterm (PB) and 493 with term (TB) birth, from the developing Human Connectome Project. We processed the imaging data, collected at birth, and investigated group differences in gray matter volume (GMV) of the brain and eye-tracking data collected at 18 months as well as the interrelationships amongst birth age, GMVs, and eye-tracking markers of ASD. Results In a covariance analysis with age at scan, total intracranial volume, sex, and number of embryos at gestation as covariates, PB demonstrated higher GMV in bilateral superior temporal gyri (STG). Right STG GMV’s were negatively correlated with birth age and positively with the proportion of looking at faces and mouths in PB, but not in TB. Further, path analyses suggested right STG GMV at birth as a marker of preferential face and mouth viewing in PB at 18 months. Conclusions The findings associate earlier birth age with atypical volumetrics of the right STG and eye gazing patterns in preterm children at 18 months. Longitudinal studies are needed to examine whether these neural and behavioral markers may reflect risks of social cognitive dysfunction in children with neurodevelopmental disorders, including ASD. 1. Introduction 1.1. Preterm birth, brain insults, and developmental disorders Preterm birth is a leading causes of infant mortality and morbidity, including adverse developmental outcomes, especially for those born with a very short gestational age ( Platt, 2014 ). These include cerebral palsy, epilepsy, developmental coordination disorders, cognitive deficits, as well as socio-emotional and learning difficulties in childhood ( Johnson et al., 2015 ; Moreira et al., 2014 ; Orchinik et al., 2011 ; Saigal and Doyle, 2008 ). Mid and late gestational stages are critical to fetal development, with brain growth increasing exponentially through the second half of pregnancy ( Andescavage et al., 2017 ; Hinton et al., 2024 ; Li et al., 2025 ; Yang et al., 2024 ). Structural and microstructural brain changes ( Gonzalez-Perez et al., 2024 ; Inder et al., 2005 ) and the associated functional abnormalities ( Disselhoff et al., 2024 ; França et al., 2024 ; Rogers et al., 2018 ) resulting from preterm birth are likely key causal factors of behavioral and psychiatric problems that develop through adulthood ( Johnson and Marlow, 2011 ). Neurodevelopmental disorders and cognitive deficits are frequent outcomes of preterm birth ( Bhutta et al., 2002 ). Preterm relative to full-term infants have two-fold the risk of neurodevelopmental disorders at age 2 years( Johnson et al., 2015 ). Preterm birth has been associated with risks of depression ( Nomura et al., 2007 ; Patton et al., 2004 ), attention deficit or hyperactivity disorder ( Linnet et al., 2006 ), and autism spectrum disorders (ASD) ( Bokobza et al., 2019 ; Crump et al., 2021 ; D’Onofrio et al., 2013 ; Harel-Gadassi et al., 2018 ; Johnson et al., 2010 ; Johnson and Marlow, 2011 ; Khachadourian et al., 2023 ). The risks are inversely proportional to gestational age ( Marlow et al., 2005 ; Marret et al., 2013 ; Schendel and Bhasin, 2008 ), with children of extremely (gestational age < 28 weeks) and very (28 to 32 weeks) preterm birth ( Delobel-Ayoub et al., 2009 ) particularly vulnerable. For instance, earlier studies reported a nearly 30% higher risk of ASD in preterm children ( Wang et al., 2017a ) and four times the risk in those born at less than 27 weeks ( Kuzniewicz et al., 2014 ), as compared to full-term children. 1.2. Behavioral markers of neurodevelopmental disorders Early behavioral markers of neurodevelopmental disorders (NDD) include delays or atypicalities in sensory processing, motor skills, language development, social engagement, as well as repetitive behaviors or difficulties with attention and object use ( Micai et al., 2020 ). Although no behavioral makers are diagnostic of a specific disorder, the hypothesis of social brain dysfunction has drawn significant attention in the research of the etiologies of ASD ( Flores et al., 2025 ; Sato et al., 2023 ). Notably, the manifestation of social brain dysfunction may depend on age, and it does not rule out the possibility of brain-wide pathology and broader behavioral disarray in ASD ( Gliga et al., 2014 ). Further, social cognitive dysfunction is observed in NDD other than ASD, including epilepsy, attention deficit hyperactivity and specific learning disorders ( Novak et al., 2024 ; Pastorino et al., 2021 ). Indeed, studies have suggested shared and converging molecular and functional pathways for many NDD ( Parenti et al., 2020 ). Nonetheless, social brain dysfunction has most been investigated for ASD, and we focused on studies of a critical behavioral marker – eye movement during exposure to social scenes – of social brain dysfunction to highlight the scientific premise of the current work. As with many other NDD, ASD is characterized by persistent social and communication difficulties ( First, 2013 ), with age-different symptoms, for instance, lack of eye contact and gaze-following in children (Reynolds and Culican, 2023; Stuart et al., 2023 ) and of social interaction in adolescents and adults ( Au et al., 2021 ; Corbett et al., 2014 ; Simon et al., 2021 ). Abnormal gaze patterns suggest a fundamental problem with attention to socially relevant versus irrelevant information ( Frazier et al., 2017 ). An eye-tracking study showed that the percentage of time fixated on the eyes was significantly decreased in preschool children with ASD, relative to controls ( Kong et al., 2022 ). Infants and children later diagnosed with ASD looked less at faces and showed less eye contact and gaze-following behavior ( Stuart et al., 2023 ). Many other studies reported that young children with ASD look significantly less at people’s eyes and more at people’s mouths ( Jones et al., 2008b ; Tanaka and Sung, 2016 ; Zwaigenbaum et al., 2005 ). Adolescents and adults with ASD had similar gaze patterns in the face recognition task, including reduced attention to the face and to the eyes ( Mastergeorge et al., 2021 ; Setien-Ramos et al., 2023 ). Together, these findings suggest altered eye gaze patterns during exposure to human faces and social scenes and the utility of eye tracking in revealing social cognitive dysfunction in NDD, including ASD. 1.3. Sex differences in NDD NDD show sex differences in clinical presentations ( Nowak and Jacquemont, 2020 ) and underlying biological mechanisms ( Breach and Lenz, 2023 ; Dan, 2021 ; May et al., 2019 ). NDD occur more often in boys than girls ( Nowak and Jacquemont, 2020 ); for instance, males as compared to females are more likely to be diagnosed with ASD ( Elsabbagh et al., 2012 ; Halladay et al., 2015 ), with a ratio of approximately 3:1 ( Loomes et al., 2017 ) and boys also diagnosed at an earlier age than girls ( Giarelli et al., 2010 ; Rutherford et al., 2016 ). Other studies documented sex differences in the clinical manifestations of ASD ( Eckerd, 2020 ; Evans et al., 2019 ; Hull et al., 2017 ; Hull et al., 2020 ; Lawson et al., 2018 ; Ros-Demarize et al., 2020 ). Eye-tracking studies have also reported sex differences in the behavioral markers of NDD. For instance, an experimental study of children 8 years of age showed that, though both deficient in social attention relative to neurotypical counterparts, females appeared to be less impaired than males with ASD ( Harrop et al., 2019 ). Males but not females around 19 years old with high autistic-like traits showed worse eye gaze patterns than those with lower traits ( Whyte and Scherf, 2018 ). Male infants with risk of ASD paid more attention to the mouth, whereas females with the risk paid little attention to the mouth but more to the eyes, as compared to their control counterparts ( Kleberg et al., 2019 ), when viewing human faces. Thus, it would seem important to investigate sex differences in these behavioral markers of NDD. 1.4. Gray matter volumetric markers of ASD Magnetic resonance imaging has been widely used to study brain volumetric markers of NDD, including ASD ( Liloia et al., 2024 ). Understanding brain development in NDD requires characterization of the changes in gray matter volume (GMV) across ages. A meta-analysis of more than 100 MRI studies showed that GMVs increase significantly from the second trimester, peak in growth rate at about five months, and start to decline at about 6 years of age ( Bethlehem et al., 2022 ). Many studies have reported atypical GMV in individuals with ASD, with regional changes related to the core symptoms ( Xu et al., 2024 ). For instance, children/adolescents with ASD were more likely than adults to have higher GMV in bilateral fusiform gyrus, right cingulate gyrus, and insula, relative to their controls ( Duerden et al., 2012 ). Adults with ASD have higher gray matter density in the precentral gyrus ( Winter, 2016 ) and lower GMV in the left parahippocampal gyrus ( Yaxu et al., 2020 ). Children with ASD showed excessive brain volume growth as compared with typically developing children ( Courchesne et al., 2007 ; de Jong et al., 2021 ; Hazlett et al., 2011 ), especially in the temporal and frontal lobes ( Mostofsky et al., 2007 ; Radua et al., 2011 ; Wang et al., 2017b ). This overgrowth is not present at birth but appears late in the first year of life ( Hazlett et al., 2005 ; Shen et al., 2013 ). Thus, whereas the findings of regional structural abnormalities may vary with age, enlarged GMV appeared to common feature in ASD ( Wang et al., 2022 ). 1.5. The present study Preterm birth may cause behavioral and structural brain changes that reflect the risk of NDD, including ASD. Here, we examined regional volumetric differences in preterm as compared to full-term infants at term-equivalent age, and eye gaze patterns during exposure to social scenes, a maker of social cognitive dysfunction, at 18 months of age, as well as sex differences in the abnormalities. We also explored whether the volumetric abnormalities support the impact of pre-term birth on eye gaze indicators of ASD risk. We broadly hypothesized volumetric differences in the social brain circuits, including the superior temporal cortex, in preterm vs. full-term infants and sex differences in how these volumetric abnormalities relate eye gaze behaviors. 2. Methods 2.1. Participants and assessments We employed data collected from the Developing Human Connectome Project (dHCP, www.developingconnectome.org/ ). This project has received ethical approval (14/LO/1169, IRAS 138070), with written informed consent obtained from the parents. The dHCP included a total of 887 MR images from 783 subjects, with 101 subjects scanned twice, once at 26.7 to 42.7 (33.8 ± 2.8) weeks and a second time at 29.3 to 44.9 (41.1 ± 2.1) weeks, five subjects scanned three times, each at 28.6 to 34.9 (31.9 ± 3.2) weeks, 29.3 to 35.6 (32.4 ± 4.4) weeks, and 39.9 to 42.7 (41.7 ± 1.2) weeks, and one subject scanned four times, first at 33.3 weeks and last at 42.3 weeks. We excluded 1) those without a radiology score or with a score > 2, indicating gross structural abnormality, punctate lesions or other significant radiological findings (n = 218), 2) those scanned using sedation (n = 5), 3) those scanned before 37 weeks of postmenstrual age (PMA, i.e., gestational age + chronological age, n = 91), and 4) those scanned twice (n=4; the one with scan time closer to the 38 weeks of PMA was included in the analyses). Thus, a total of 569 subjects were included in this study and categorized according to birth age: < 37 weeks as the preterm birth (PB, n = 76) and ≥ 37 weeks as term birth (TB, n = 493) group. We also distinguished males and females with four subgroups: PB males (n = 46, PBM), PB females (n = 30, PBF), TB males (n = 264, TBM), and TB females (n = 229, TBF). The Quantitative Checklist for ASD in Toddlers (Q-CHAT), a 25-item screening questionnaire, was used to assess autistic traits ( Allison et al., 2008 ). A higher score on the Q-CHAT indicates more behaviors and traits in line with those of ASD. However, not all of the participants had Q-CHAT scores. Of the entire cohort, 34 PBM, 199 TBM, 21 PB, 195 TBF were evaluated with the Q-CHAT ( Table 1 , lower panel). Demographic and clinical information of the four groups is summarized in Table 1 . As expected, PB relative to TB showed lower birth age, smaller TIV, higher number cases of non-singlet birth and with a radiological score of 2 rather than 1. View this table: View inline View popup Table 1. Demographic and clinical measures of the participants. 2.2 Image data acquisition, preprocessing, and analyses Images were acquired on a 3T Phillips 3-Tesla Achieva system in a natural sleep state, using a dedicated neonatal brain imaging system, including a neonatal 32-channel receive coil and a customized setup ( Hughes et al., 2017 ). All images were acquired in Evelina Newborn Imaging Centre, Evelina London Children’s Hospital. T2-weight images were obtained using a TSE sequence with parameters of TR = 12000ms, TE = 156ms, SENSE factor 2.11 (axial) and 2.58 (sagittal), and resolution (mm) of 0.8 × 0.8 × 1.6. Note that T2 rather than T1 images were acquired because of their better contrasts of brain tissues in fetal or newborn brain ( Makropoulos et al., 2018a ). All anatomical images were reviewed by an expert perinatal neuroradiologist, and scores of 1 and 2 were considered normal or having lesions unimportant for the analysis, and scores of 3 to 5 or Q indicated that the anatomical data were of poor quality and/or had lesions that would impact the analysis. Bias-corrected T2-weight images were selected which were processed through the dHCP minimal preprocessing pipeline for Neonatal Cortical Surface Reconstruction ( Makropoulos et al., 2018b ). Additional preprocessing procedures for the images included extracting the brain using BET in the FMRIB software library (FSL) ( Jenkinson et al., 2012 ), strictly recording the brain volume into the standard space of the CRL 38-week template ( Gholipour et al., 2023 ) by ANTs ( Avants et al., 2008 ), followed by segmentation using the atlas ( Serag et al., 2012a ; Serag et al., 2012b ) used in Draw-EM ( Makropoulos et al., 2014 ; Makropoulos et al., 2018b ), and smoothing the gray matter, white matter, and cerebrospinal fluid obtained from segmentation to compute the volume for statistical analysis. TIV was calculated as the sum of the volumes of gray matter, white matter, and cerebrospinal fluid. For the entire cohort of 569 subjects (PBM=46, TBM=264, PBF=30, TBF=229), we performed a two-sample t test of PB vs. TB, PBM vs. TBM, and PBF vs. TBF with age at scan, TIV, and number of embryos at gestation as covariates. We also performed a group X sex ANOVA with the same covariates to examine the interaction term for sex differences. For all analyses, we assessed the results at voxel p < 0.001, uncorrected in combination with a cluster p < 0.05 FWE-corrected, based on Gaussian random field theory as implemented in SPM. 2.3 Eye-Tracking data acquisition and analyses Spatial characteristics of gaze were collected from a total of 609 subjects (78% of the sample) at a median age of 18 months and 12 days (range 17 months, 8 days to 34 months, 15 days) during the “50 faces” task, derived from the “50 People, One Question” project ( Krolak-Salmon, 2011 ), where the participants watched a 41-second video of a street interview. Eye-Tracking data were acquired using a Tobii TX-300 (Tobii AB, Sweden) gaze tracking system, with the subject sitting approximately 60 cm from the screen. The dHCP employed a customized stimulus series of animated video clips to evaluate visual attention. The video covered primarily the interviewee’s face and body parts above the chest and the framing focused on the interviewee. The figures in the background were relatively ambiguous with non-social stimulation ( Braithwaite et al., 2023 ). The sound of the original video was removed and replaced by classical music to remove the potential influences of language ( Figure 1b ). Download figure Open in new tab Figure 1. (a) Histogram of age of birth and age at scan in PB and TB: mean ± SD. *p<0.05; ***p<0.001. (b) Eye-tracking experiment of the “50 faces” task. (c) Histogram of the proportion of looking at faces, mouths* (faces - eyes), and eyes* (faces - mouths) in PB and TB: mean ± SD. (d) Regression of age at birth vs. proportion of looking at faces in PB vs. TB. (e) Regressions of age at birth vs. proportion of looking at mouths* in PB vs. TB. Y coordinates represent the residuals after regressing out age at scan, sex, and number of embryos at gestation. Participants’ eye positions were tracked throughout the viewing period, and eye-tracking data were provided by the dHCP ( Braithwaite et al., 2023 ). Specifically, areas-of-interest (AOIs) were manually drawn frame by frame around face, eyes, mouth, people in the background, using Motion software (Apple Inc, USA), with the key variables of mean proportion, duration, and peak duration of looking at each AOI recorded ( Braithwaite et al., 2023 ). Face AOI included eyes and mouth. Here, we focused on proportion of gaze time spent on faces as well as proportion of “faces minus eyes” and “faces minus mouths” for evaluation of eye-tracking performance and for correlation with neural and clinical metrics ( Table 1 ). We considered “faces – eyes” and “faces – mouths” each as an indicator of mouths- and eyes- gazing because participants may demonstrate transitional fixations on their gaze shift to the mouths/eyes; thus, we included these transitional fixations as part of their effort in looking at mouths and eyes, respectively. We used faces, eyes* (faces – mouths), and mouths* (faces – eyes) proportion in evaluating the group differences in eye gazing metrics and the correlation of these metrics with clinical and neural measures. We also reported AccuracyDeg or PrecisionDeg, each defined as the spatial displacement of recorded gaze from the point fixated and variability in consecutive samples on the same fixation point, respectively, which reflected the general quality of eye tracking data ( Braithwaite et al., 2023 ). Supplementary Table S1 show all other recorded eye-tracking measures. For the subgroup of toddlers evaluated with the eye-tracking task (PBM=28, TBM=128, PBF=17, TBF=138), we performed correlation analysis of eye-tracking metrics and the GMVs of brain regions identified of whole-brain group analyses, with age at scan, TIV, sex, and number of embryos at the time of gestation as covariates. We evaluated the results of correlation with a p value corrected for the number of tests in a specific set of analysis. 2.4 Path analyses We employed path analysis to evaluate how severity of preterm birth, eye-tracking metrics and the neural correlates were inter-related ( Chen et al., 2008 ; Hu and Bentler, 1995 ; Ji et al., 2024 ; Li et al., 2024 ; Li et al., 2021a ; Li et al., 2021b ; Li et al., 2023 ). Specifically, we tested whether the volumetric markers may inter-link birth age and eye-tracking measures of significance (see “Results”) in the subgroup of toddlers evaluated with the eye-tracking task (PBM=28, TBM=128, PBF=17, TBF=138). Model fit was assessed with standard fit indices which included the Root Mean Square Estimation ofApproximation (RMSEA, < 0.08 for an acceptable fit), Chi-square (χ 2 /df, 0.9), and Standardized Root Mean Square Residual (SRMR, < 0.06). We considered all possible path models for the sake of completeness, although, with imaging and eye-tracking data each collected at birth and at around 18 months of age, only models birth age ➔ volumetric markers ➔ eye-tracking metrics are conceptually valid. 3. Results 3.1. PB vs. TB in clinical and eye-tracking metrics Clinical characteristics as well as the statistics of group x sex ANOVA are summarized in Table 1 , with a narrative description provided in the Supplementary Results . Here, we described on the main findings. The Q-CHAT score showed no significant group (F = 0.39, p = 0.530) or sex (F = 1.94, p = 0.164) main effect, nor interaction effect (F = 0.22, p = 0.883). For eye-tracking metrics, AccuracyDeg or PrecisionDeg showed no significant group (all p’s > 0.182) or sex (all p’s > 0.672) main effect or group by sex interaction effect (all p’s > 0.559), suggesting no significant differences in the quality of eye-tracking data. Among the three primary metrics – proportion of faces, eyes*, and mouths*, none showed a significant group or sex main or group by sex interaction effect at a corrected threshold of p < 0.05/3 = 0.017. At an uncorrected threshold, only the proportion of looking at eyes* showed a significant group main effect (F = 3.90, p = 0.049, uncorrected), but not sex main (F = 0.00, p = 0.976) or interaction (F = 0.55, p = 0.460) effect ( Table 2 ). Specifically, PB relative to TB showed a higher proportion of looking at eyes* (0.39 ± 0.12 vs. 0.37 ± 0.14; F = 3.90, p = 0.049). View this table: View inline View popup Table 2. Eye-tracking measures of the participants. 3.2 Correlation amongst age at birth, Q-CHAT score, and eye-tracking metrics As shown in Supplementary Table S2 (and Supplementary Figure S1 in matrix format), age at birth was not significantly correlated with Q-CHAT score in any group at a corrected threshold (all p’s > 0.044). None of the three eye-tracking measures showed a significant correlation with Q-CHAT score in any group (all p’s > 0.128). The statistics of correlation of Q-CHAT score with other eye-tracking metrics were summarized in the Supplementary Table S3. In the correlation of birth age and eye-tracking metrics, the proportion of looking at mouths* was significantly and negatively correlated with age at birth in PBF (r = -0.67, p = 0.009) but not in PBM (r = 0.12, p = 0.567), at a relaxed threshold of p < 0.05/3 = 0.017. The slope test showed a significant sex difference (PBF vs. PBM) in the slope of the linear regressions (Z = -2.78, p = 0.005). None of the three primary eye-tracking measures showed a significant correlation with age at birth in any other group (all p’s > 0.071). The statistics of correlation of age at birth with other eye-tracking metrics were summarized in the Supplementary Table S4. 3.3 Gray matter volumes For the entire cohort of 569 subjects (PBM=46, TBM=264, PBF=30, TBF=229), we performed a two-sample t test for the whole brain of PB vs. TB, PBM vs. TBM, and PBF vs. TBF with age at scan, TIV, and number of embryos at gestation as covariates. Evaluated at voxel p < 0.001, uncorrected in combination with a cluster p < 0.05 FWE-corrected, the findings are shown for all PB vs. TB in Figure 2a , for PBM vs. TBM in Figure 2b , and for PBF vs. TBF in Figure 2c . The clusters are summarized in Table 3 . At the same threshold, we did not observe any clusters showing significant interaction [i.e., (PBF – TBF) – (PBM – TBM)] or sex main effect in a group by sex ANOVA for the whole brain. Download figure Open in new tab Figure 2. Regional GMV’s with group differences. (a) PB vs. TB. Color bars show voxel T values: warm: PB > TB, cool: PB TBM, cool: PBM TBF, cool: PBF < TBF. All comparisons were with age at scan, TIV, and number of embryos at gestation as covariates, evaluated at voxel p < 0.001, uncorrected, cluster p < 0.05 FWE-corrected. Clusters are overlaid on the T2 structural image in neurological orientation: right = right. L: left; R: right. STG: superior temporal gyrus; HIP: Hippocampus; REC: Gyrus rectus; ROL: Rolandic operculum; INS: Insula. MFG: middle frontal gyrus; PoCG: Postcentral gyrus; MTG: Middle temporal gyrus. View this table: View inline View popup Download powerpoint Table 3. GMV differences in PB vs. TB, PBM vs. TBM, and PBF vs. TBF. 3.4 Correlation between age at birth, GMV’s, and eye-tracking metrics 3.4.1 Regions showing higher GMV in PB vs. TB PB relative to TB showed higher GMVs in bilateral superior temporal gyrus (STG). With age at scan, TIV, and number of embryos at gestation as covariates, age at birth was significantly and negatively correlated with right STG GMV for the entire cohort (r = -0.47, p < 0.001) and in PB (r = -0.37, p = 0.017) but not in TB alone (r = -0.03, p = 0.660), with the slope test confirming the difference in slope of the regression for PB vs. TB (Z = -2.18, p = 0.029) ( Figure 3a ). Age at birth was significantly and negatively correlated with left STG GMV only for the entire cohort (r = -0.33, p < 0.001) but not in PB (r = -0.16, p = 0.306) or in TB (r = -0.07, p = 0.257) alone. Download figure Open in new tab Figure 3. (a) Correlation of age at birth and STG_R GMV (as identified from whole-brain analysis; Figure 2a ). (b) Correlation of STG_R GMV and proportion of looking at faces. (c) Correlation of STG_R GMV and proportion of looking at mouths*. (d) Two path analyses in PB of age at birth, STG_R GMV, and proportion of looking at faces or proportion of looking at mouths*. Purple and green arrow each indicates positive and negative effect. Y coordinates are the unstandardized residuals after regressing covariates: age at scan, sex, TIV and number of embryos at gestation. **p<0.01; ***p<0.001. CR: critical ratio = path coefficient/ standard error. We next examined whether the right and left STG GMVs were related to eye-tracking metrics at a corrected p 0.037). Left STG GMV was significantly correlated with proportion of looking at faces (r = 0.18, p = 0.002) and eyes* (r = 0.22, p < 0.001), but not mouths* (r = 0.04, p = 0.529). In PB, right STG GMV showed a significant and positive correlation with the proportion of looking at faces (r = 0.50, p = 0.001) but not mouths* (r = 0.39, p = 0.012) or eyes* (r = 0.18, p = 0.273). Left STG GMV showed a significant and positive correlation with proportion of looking at faces (r = 0.41, p = 0.007), but not with proportion of looking at mouths* or eyes* (all p’s > 0.094). In TB, right STG GMV was not correlated with any of the three metrics (all p’s > 0.266). Left STG GMV was significantly and positively correlated with proportion of looking at eyes* (r = 0.20, p = 0.001), but not mouths* (r = 0.04, p = 0.474) or faces (r = 0.15, p = 0.014). These correlations as well as slope tests of PB vs. TB are shown in Supplementary Figure S2. Notably, the regression of right STG GMV with faces and mouths* looking differed significantly in slope between PB and TB ( Figure 3b and 3c ). The r and p values of all regressions are summarized in Supplementary Table S5 . 3.4.2 Regions showing higher GMV in TB vs. PB TB relative to PB showed higher GMVs in bilateral hippocampus, left gyrus rectus, and bilateral rolandic operculum. With the same covariates, age at birth was significantly and positively correlated with GMVs of bilateral hippocampus and bilateral rolandic operculum across all subjects (all p’s < 0.001) and in TB (all p’s < 0.001). These correlations were also all positive for PB, although the r values were smaller likely due to the small sample size of this subgroup. In contrast, none of these regional GMVs were significantly correlated with the any of the three eye-tracking metrics at a corrected threshold. The r and p values of these correlations are summarized in Supplementary Table S6a to S6e. 3.5 Mediation analyses of age at birth, GMV’s, and eye-tracking metrics In PB alone, right STG GMV showed both negative correlation with age ( Figure 3a ) at birth and positive correlation with proportion of looking at faces and mouths* ( Figure 3b and 3c ). Thus, we performed path analyses to examine the inter-relationship between age at birth, right STG GMV and proportion of looking at mouths*. For the sake of completeness, we evaluated all six models, although the models with eye-tracking metrics as independent variables were chronologically unlikely. The results of path analyses showed the models age at birth → right STG GMV → proportion of looking at faces and the model age at birth → right STG GMV → proportion of looking at mouths* with a significant and the best fit ( Figure 3d ). Other models did not show any significant model fit ( Supplementary Table S7 and S8 ; Supplementary Figure S3 and S4 ). 3.6 Sex differences: exploratory analyses As shown in Figure 4 , slope tests showed sex difference in the relationship between age at birth and right STG GMV, and that between age at birth and proportion of looking at mouths*, both predominated by PBF. Download figure Open in new tab Figure 4. Sex differences in correlation of age at birth with STG_R GMV (as identified from whole-brain analysis; Figure 2a ), and with proportion of looking at mouths*, using age at scan, TIV and the number of embryos at gestation as covariates. (a) PBF vs. TBF in correlation of age at birth with STG_R GMV. (b) PBF vs. PBM in correlation of age at birth with STG_R GMV. (c) PBF vs. TBF in correlation of age at birth with proportion of looking at mouths*. (d) PBF vs. PBM in correlation of age at birth with proportion of looking at mouths*. Y coordinates of (a) and (b) are the unstandardized residuals after regressing out: age at scan, TIV and number of embryos at gestation. Y coordinates of (c) and (d) are the unstandardized residuals after regressing two covariates: age at scan and number of embryos at gestation. 4. Discussion In this study, we explored the brain volumetrics at birth and eye fixations while viewing a video of human interaction in PB and TB toddlers at 18 months of age. The results showed (1) larger GMV in right STG in PB vs. TB; (2) a positive correlation between the degree of prematurity and looking at faces and mouths* in PB; and (3) a path model with right STG GMV inter-linking birth age and preferential face- and mouth-looking in PB. PB relative to TB also showed smaller bilateral hippocampal GMVs but the volumetrics were not significantly associated with eye gazing measures. These results suggest that premature birth leads to atypical development of superior temporal cortex and gazing patterns that are indicative of social cognitive dysfunction and risks of NDD, including the ASD. We discussed the main findings in the below. 4.1 Preterm birth and higher STG GMV Earlier birth age has been associated with larger brain volumes, likely reflecting accelerated brain growth in premature birth (Meredith Weiss et al., 2024 ). Here, preterm as compared to term newborns exhibited greater GMV in bilateral STG, consistent with an earlier report ( Karolis et al., 2017 ) and studies suggesting vulnerability of temporal cortical and hippocampal regions to structural alterations in very preterm birth ( Erdei et al., 2023 ; Ream and Lehwald, 2018 ). These structural markers of preterm birth may persist in adolescence and adulthood, with long-lasting effects on cognitive and affective function ( Bjuland et al., 2014 ; Kelly et al., 2023 ). For instance, adolescents of preterm birth had higher cortical thickness in temporal gyrus ( Ji et al., 2024 ; Nath et al., 2023 ). Adults of preterm birth had higher GMVs of the STG, prefrontal cortex, and occipital regions ( Shang et al., 2019 ). In particular, structural changes in the posterior STG, which harbors the planum temporale or Wernicke area ( Lindell and Hudry, 2013 ), the primary site of auditory and sensory speech processing ( Hullett et al., 2016 ; Leff et al., 2009 ; Yi et al., 2019 ), may lead to deficits in speech and language development ( Bhaya-Grossman and Chang, 2022 ; Eyler et al., 2012 ). Further, the STG is involved in processing social cues, including people’s face expressions, voices, gestures, and other contextual information ( Adolphs, 2003 ; Paulus et al., 2005 ; Singer et al., 2004 ; Xu et al., 2009 ). Thus, insults to the STG as can occur during preterm birth may lead to social cognitive dysfunction, a hallmark of ASD and other NDD ( Bigler et al., 2007 ; Pelphrey et al., 2004 ). In the current sample, PB and TB did not differ in total Q-CHAT score, which we speculated may result from the low validity of Q-CHAT ( Thabtah and Peebles, 2019 ) and the broader challenge of evaluating clinical features “diagnostic” of NDD in very young children, and/or the relatively small sample size of PB. With this caveat, we discussed briefly the literature of ASD. Relative to typically developing children, those with ASD showed higher temporal lobe GMV ( Foster et al., 2015 ; Lukito et al., 2020 ; Piven et al., 1995 ; Retico et al., 2016 ; Wang et al., 2017b ), with some studies implicating specifically the right temporal cortex across age groups ( Carper and Courchesne, 2005 ; Carper et al., 2002 ; Wang et al., 2017b ; Xiao et al., 2014 ). Notably, a study of children 2 to 7 years showed higher GMV in the right STG in those with ASD, as compared to controls ( Guo et al., 2021 ). The current findings add to this literature by showing this volumetric change in preterm newborns. On the other hand, while these findings suggest STG GMV as a risk marker ( Mueller et al., 2013 ; Pierce, 2011 ), other brain regions, including those in the frontal and occipital lobes, have also been implicated in the pathophysiology of ASD ( Courchesne et al., 2007 ; Guo et al., 2021 ; Liu et al., 2017 ; Sparks et al., 2002 ). The increase in regional GMV’s in preterm infants may reflect premature exposure to the external environment in utero and deficient synaptic pruning and/or compensatory overdevelopment of the brain. Notably, although characterized by early brain overgrowth ( Bryńska, 2012 ; Courchesne et al., 2011 ; Ecker et al., 2015 ; Hazlett et al., 2017 ; Schumann et al., 2010 ), ASD may manifest stagnation in development later during puberty and early adulthood ( Carper et al., 2002 ; Lange et al., 2015 ). Thus, age needs to be carefully considered in the characterization of structural brain abnormalities in ASD and likely other NDD. 4.2 Attention to facial features as reflected in eye-tracking metrics Likewise, with the caveat, we discussed the current findings along with eye-tracking studies of ASD, because the bulk of this literature focused on ASD. Individuals with NDD exhibit attentional biases in social behaviors, as could be revealed in experimental studies of visual exploration of faces and social scenes with people engaged in conversation ( Sadria et al., 2019 ; Shic et al., 2020 ). We observed more frequent of looking at the non-eyes in preterm, as compared to full-term toddlers, although the difference did not reach significance. To the extent that preterm birth represents a risk of ASD, this finding is consistent with previous reports of shorter and longer duration of gaze directed to eyes and mouths, respectively, in individuals with ASD viewing human faces. For instance, compared to the control group, two-year-olds with autism looked at others’ eyes significantly less ( Jones and Klin, 2013 ) but at their mouths more ( Jones et al., 2008b ). At two years of age, children with ASD showed excessive mouths and reduced eyes gazing when observing social interactions that involved “dynamic” stimuli, such as child-oriented speech, or those that demanded shared attention ( Campbell et al., 2014 ; Chawarska et al., 2012 ). In children around 5 years old shown 44 consecutive photographs of faces, those with ASD spent more time looking at mouths and less time looking at eyes, as compared to neurotypical children ( Sadria et al., 2019 ). The current finding suggests that preferential looking at the mouths can be observed in toddlers, a very early stage of neurodevelopment, and that pre-term toddlers may acquire social information largely from mouth shape and movement ( Jones et al., 2008a ; Pelphrey et al., 2002 ; Wang et al., 2020 ). Notably, earlier research pointed to the opposite conclusion in studies of adults ( Corden et al., 2008 ; Falck-Ytter and von Hofsten, 2011 ; Klin et al., 2002 ). Thus, as with the imaging literature, age should be considered in understanding eye gazing pattern as a behavioral marker of ASD, and the current findings should be considered as specific to a sample 18 months of age. Possibly due to the accelerated brain development in the early years and delayed development later on, individuals with ASD have significantly higher and lower brain ages in early childhood and adolescence, respectively ( He et al., 2020 ), and preterm infants share many of these features in their early years ( Mendez et al., 2023 ), with similar facial recognition behaviors, such as looking more at mouths and less at eyes ( Telford et al., 2016 ). 4.3 Potential sex differences Previous studies documented higher frequency and severity of ASD in males than in females, as discussed earlier. Here, however, we observed indistinguishable Q-CHAT score between sexes, likely due to the small sample of PB. Earlier studies have also reported sex differences in attention and eye movement patterns during viewing of social scenes. For instance, females demonstrated more eye shifts amongst the facial areas, suggesting perhaps more effort in visual exploration, in neurotypical infants at 9 to 10 months ( Rennels and Cummings, 2013 ). Chawarska and colleagues reported greater attention to social stimuli such as faces among girls at high risk for ASD ( Chawarska et al., 2016 ). Throughout development, women pay more attention to faces than men ( Gluckman and Johnson, 2013 ). Studies have also shown that girls relative to boys with ASD 6 to10 years looked at faces more ( Harrop et al., 2018 ). Around age 1, males with ASD paid more attention to mouth emotions than male controls and females with ASD. In contrast, females with ASD had fewer gazes on mouth compared to female controls ( Kleberg et al., 2019 ). Along with these earlier studies, we found sex differences in the current study; the earlier they were born, the more likely females but not males looked at non-eyes parts of the faces, which included largely the mouths. Kleberg et.al showed the opposite results in adults, with autistic men and women each looking more and less at the mouths relative to their neurotypical counterparts, again suggesting neurodevelopmental differences through the course of ASD ( Kleberg et al., 2019 ). 4.4 Limitations and Conclusions We considered a number of limitations of the study. First, the total Q-CHAT score did not show a significant difference between PB and RB or a correlation with any of the clinical or eye-tracking metrics, which we speculated may result from its low validity (<50%) ( Thabtah and Peebles, 2019 ) or the relatively small sample size of PB. The latter also limits the possibility of a more thorough and powered investigation of sex differences. The subgroup sample of female PB in the database is small, and a larger dataset is needed to confirm these sex-different findings. Second, the dHCP removed speech from the eye-tracking task, which could have a significant influence on the behavioral outcomes. The current results should be considered as specific to this video stimulus. Third, mediation analysis suggests rSTG GMV as a mediator of greater frequency of face and mouth looking, seemingly antithetical in terms of the risk of social cognitive dysfunction. Thus, whether and to what extent elevated rSTG GMV reflects a risk phenotype or compensatory mechanism remains to be clarified ( Hadad and Yashar, 2022 ). Fourth, the imaging data were collected within the first few weeks of life, and the eye-tracking data around eighteen months. Thus, many environmental factors, including parenting, can influence the findings beyond premature birth. A longitudinal study with imaging data collected at two or more time points would provide opportunities to charactering volumetric changes that may be crucial to social cognitive functioning during this early developmental stage. Finally, this research considered gray matter changes only; more work is needed to study white matter and functional changes ( Dimond et al., 2019 ). To conclude, preterm birth alters the gray matter volumes of right STG in newborns, with higher right STG GMV associated with eye-tracking marker of social cognitive function at 18 months, especially in females. Higher right STG GMV may represent a neural phenotype linking preterm birth to behavioral markers of social cognitive dysfunction in NDD, including ASD. Longitudinal studies will determine whether the volumetrics at birth and eye-tracking markers at 18 months would predict the onset and severity of ASD and other NDD. Data Availability All data produced are available online at https://www.developingconnectome.org/ https://www.developingconnectome.org/ Competing interests The authors declare that they have no competing interests. Author contributions Conception and design: Guangfei Li and Chiang-Shan R. Li Administrative support: Dongmei Hao and Chiang-Shan R. Li Provision of study materials or patients: Yanan Su, Yiyao Ye-Lin, Xiaolin Wang, Ruolin Zhang, Lin Yang and Chiang-Shan R. Li Collection and assembly of data: Yanan Su, Shanmei Wang and Guangfei Li Data analysis and interpretation: Yanan Su, Guangfei Li, Clara S. Li and Chiang-Shan R. Li. Manuscript writing: Yanan Su, Guangfei Li and Chiang-Shan R. Li. Final approval of manuscript: All authors. Ethics Statement The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and approved by United Kingdom Health Research Authority (Research Ethics Committee reference number: 14/LO/1169). Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin. Data Availability Statemen t The developing HCP data are available to the public at http://www.developingconnectome.org/data-release/third-data-release/ , or https://nda.nih.gov/edit_collection.html?id=3955 Supplement A. Behavioral markers of ASD Children with ASD are usually identified around 3 to 4 years of age, despite earlier signs of atypical development (Lai et al., 2014). Assessment of very young children for risks of ASD is challenging. Many children later diagnosed with ASD do not differ from typically developing children in social smiling and facial gaze at 6 and 12 months of age (Landa and Garrett-Mayer, 2006). In addition to clinical assessments, such as Denver Developmental Screening Test (De-Andrés-Beltrán et al., 2015), Bayley Infant Neurodevelopmental Screener (Albers and Grieve, 2007), Quantitative Checklist for Autism in Toddlers ( Allison et al., 2008 ), and Modified Checklist for Autism in Toddlers (Dereu, 2021), tracking of eye movements during viewing of social scenes can help in identifying behavioral markers of ASD in very young children (Murias et al., 2018; Shan et al., 2023). B. Supplementary Tables and Results View this table: View inline View popup Supplementary Table S1. All Eye-tracking measures of the participants. PB vs. TB in clinical and eye-tracking metrics Clinical characteristics as well as the statistics of group x sex ANOVA are summarized in Table 1 , age at birth showed significant group (F = 1417, p < 0.001) and sex (F = 10.17, p = 0.002) main as well as interaction (F = 12.19, p = 0.001) effect. In planned comparisons, PB showed a younger age at birth than TB (PB: 32.24 ± 3.36; TB: 40.05 ± 1.26), as expected. Female were born later than male (Female: 39.07 ± 3.23; Male: 38.95 ± 3.09). PBF showed an older age at birth than PBM (PBF: 31.38 ± 3.23; PBM: 32.80 ± 3.36) and showed a younger age at birth than TBF (PBF: 31.38 ± 3.23; TBF: 40.08 ± 1.30). TBM were born later than PBM (PBM: 32.80 ± 3.36; TBM: 40.02 ± 1.24) and were born earlier than TBF (TBF: 40.27 ± 1.23; TBM: 40.05 ± 1.26). Age at scan showed a significant sex (F = 4.60, p = 0.032) main effect with females scanned later than males (Female: 41.56 ± 1.80; Male: 41.19 ± 1.72) but not group main (F = 2.90, p = 0.089) or interaction (F = 0.53, p = 0.465) effect. Age at eye-tracking experiment showed a significant group (F = 29.96, p < 0.001) but not sex (F = 0.52, p =0.472) main or interaction (F = 0.38, p = 0.538) effect. PB performed eye-tracking experiments at a later age than TB (PB: 21.27 ± 2.70; TB: 19.01 ± 1.97). Mo_edu_age showed no group (F < 0.01, p =0.986) or sex (F = 1.00, p = 0.317) main or interaction (F = 0.86, p =0.355) effect. Fa_edu_age showed significant sex (F = 6.84, p = 0.009) but not group (F = 3.34, p = 0.068) main or interaction effect (F = 2.34, p = 0.127). Fathers of PB (PB: 21.84 ± 3.14; TB: 23.46 ± 4.86) had a younger age when they were last in continuous full-time education. TIV and “singleton” had significant group main (TIV: F = 49.26, p < 0.001; Singleton: F = 94.34, p < 0.001), but not sex main (TIV: F = 0.33, p = 0.564; Singleton: F = 0.23, p = 0.634) or interaction (TIV: F = 0.35, p = 0.553; Singleton: F = 0.43, p = 0.512) effect. TB had a greater TIV than PB (PB: 385.66 ± 8.84; TB: 392.12 ± 7.02), and PB were more likely to be born multiple births (PB: 27/76; TB: 20/493). Female showed a smaller TIV than male (Female: 390.87 ± 7.66; Male: 391.59 ± 7.55), and male were more likely to be born multiple births (Female: 20/259; Male: 27/310). Radiology score (RS) showed significant group main effect (F = 5.79, p = 0.016) and interaction effect (F = 3.93, p = 0.048), but not sex main effect (F = 0.04, p = 0.842). More children in PB had a RS of 2 than those in TB (PB: 37/76; TB: 177/493). PBF showed a worse RS than PBM (PBF: 17/30; PBM: 20/46) and showed also a worse RS than TBF (PBF: 17/30; TBF: 69/229). TBM had a better RS than PBM (PBM: 20/46; TBM: 108/264) and had a worse RS than TBF (TBF: 69/229; TBM: 108/264). View this table: View inline View popup Supplementary Table S2. Correlation of Q-CHAT score and age at birth with eye-tracking measures. View this table: View inline View popup Supplementary Table S3. Correlation of Q-CHAT score with age at birth and eye-tracking measures. View this table: View inline View popup Supplementary Table S4. Correlation of age at birth and eye-tracking measures. View this table: View inline View popup Supplementary Table S5. Correlation of right and left STG GMV with birth_age and three eye-tracking metrics. View this table: View inline View popup Download powerpoint Supplementary Table S6a. Correlation of HIP_L with birth_age and three eye-tracking metrics. View this table: View inline View popup Download powerpoint Supplementary Table S6b. Correlation of HIP_R with birth_age and three eye-tracking metrics. View this table: View inline View popup Download powerpoint Supplementary Table S6c. Correlation of REC_L with birth_age and three eye-tracking metrics. View this table: View inline View popup Download powerpoint Supplementary Table S6d . Correlation of ROL_L with birth_age and three eye-tracking metrics. View this table: View inline View popup Download powerpoint Supplementary Table S6e. Correlation of ROL_R with birth_age and three eye-tracking metrics. View this table: View inline View popup Supplementary Table S7. Statistics of path analyses of age at birth, GMV in right STG and proportion of looking at faces, with age at scan, sex, TIV, and number of embryos at gestation as covariates. View this table: View inline View popup Download powerpoint Supplementary Tables S8. Statistics of path analyses of age at birth, GMV in right STG and proportion of looking at mouths*, with age at scan, sex, TIV, and number of embryos at gestation as covariates. C. Supplementary Figures Download figure Open in new tab Supplementary Figure S1. Correlation matrices based on Table 3 . PBM: male in PB; TBM: male in TB; PBF: female in PB; TBF: female in TB; birth_age: gestational age at birth; faces_Prop: proportion of looking at faces; mouths*_Prop: proportion of looking at faces minus eyes; eyes*_Prop: proportion of looking at faces minus mouths. With age at scan, sex and number of embryos at gestation as covariates in All, PB and TB; with age at scan and number of embryos at gestation as covariates in other groups. *p<0.05; **p<0.01. Download figure Open in new tab Supplementary Figure S2. Regression of right STG (left column) and left STG (right column) with the three primary eye-tracking metrics. PB in red and TB in blue. The Z and p values of slope tests are shown for those regressions that differed significantly in slope between PB and TB. Download figure Open in new tab Supplementary Figure S3. Path analysis in PB of age at birth, right STG GMV, and proportion of looking at faces. Solid line and dotted line each indicates a significant and non-significant path effect. Purple and green arrow each indicates positive and negative effect. The modeling included age at scan, TIV, sex, and the number of embryos at gestation as covariates. **p<0.01, ***p<0.001. CR: critical ratio. Note: For the sake of completeness, we evaluated all six models, although the models with proportion of looking at faces as independent variables were chronologically unlikely. Download figure Open in new tab Supplementary Figure S4. Path analysis in PB of age at birth, right STG GMV, and proportion of looking at mouths*. Solid line and dotted line each indicates a significant and non-significant path effect. Purple and green arrow each indicates positive and negative effect. The modeling included age at scan, TIV, sex, and the number of embryos at gestation as covariates. **p<0.01, CR: critical ratio. Note: For the sake of completeness, we evaluated all six models, although the models with proportion of looking at mouths* as independent variables were chronologically unlikely. Funding Support and Acknowledgement The current study is supported by National Natural Science Foundation of China (12402350, U20A20388) and National Foreign Experts Program (H20240220). The developing Human Connectome Project was funded by the European Research Council under the European Union Seventh Framework Programme (FP/20072013)/ERC Grant Agreement no. 319456. This work was supported by the NIHR Biomedical Research Centres at Guys and St Thomas NHS Trust and the South London and Maudsley NHS Trust; the ESPRC/Wellcome Centre for Medical Engineering; and the MRC Centre for Neurodevelopmental Disorders. TA was supported by MRC Clinician Scientist Fellowship (MR/P008712/1) and MRC translation support award (MR/V036874/1). JO’M was supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 206675/Z/17/Z). References 1. ↵ Adolphs , R ., 2003 . Cognitive neuroscience of human social behaviour . Nat Rev Neurosci 4 , 165 – 178 . OpenUrl CrossRef PubMed Web of Science 2. ↵ Allison , C. , Baron-Cohen , S. , Wheelwright , S. , Charman , T. , Richler , J. , Pasco , G. , Brayne , C ., 2008 . The Q-CHAT (Quantitative CHecklist for Autism in Toddlers): A Normally Distributed Quantitative Measure of Autistic Traits at 18–24 Months of Age: Preliminary Report . J Autism Dev Disord 38 , 1414 – 1425 . OpenUrl CrossRef PubMed Web of Science 3. ↵ Andescavage , N.N. , du Plessis , A. , McCarter , R. , Serag , A. , Evangelou , I. , Vezina , G. , Robertson , R. , Limperopoulos , C. , 2017 . Complex Trajectories of Brain Development in the Healthy Human Fetus . Cereb Cortex 27 , 5274 – 5283 . OpenUrl CrossRef PubMed 4. ↵ Au , A.H. , Shum , K.K. , Cheng , Y. , Tse , H.M. , Wong , R.M. , Li , J. , Au , T.K ., 2021 . Autism spectrum disorder screening in preschools . Autism 25 , 516 – 528 . OpenUrl PubMed 5. ↵ Avants , B.B. , Tustison , N. , Song , G.J.O.I ., 2008 . Advanced normalization tools (ANTS ). 1 – 35 . 6. ↵ Bethlehem , R.A.I. , Seidlitz , J. , White , S.R. , Vogel , J.W. , Anderson , K.M. , Adamson , C. , Adler , S. , Alexopoulos , G.S. , Anagnostou , E. , Areces-Gonzalez , A. , Astle , D.E. , Auyeung , B. , Ayub , M. , Bae , J. , Ball , G. , Baron-Cohen , S. , Beare , R. , Bedford , S.A. , Benegal , V. , Beyer , F. , Blangero , J. , Blesa Cábez , M. , Boardman , J.P. , Borzage , M. , Bosch-Bayard , J.F. , Bourke , N. , Calhoun , V.D. , Chakravarty , M.M. , Chen , C. , Chertavian , C. , Chetelat , G. , Chong , Y.S. , Cole , J.H. , Corvin , A. , Costantino , M. , Courchesne , E. , Crivello , F. , Cropley , V.L. , Crosbie , J. , Crossley , N. , Delarue , M. , Delorme , R. , Desrivieres , S. , Devenyi , G.A. , Di Biase , M.A. , Dolan , R. , Donald , K.A. , Donohoe , G. , Dunlop , K. , Edwards , A.D. , Elison , J.T. , Ellis , C.T. , Elman , J.A. , Eyler , L. , Fair , D.A. , Feczko , E. , Fletcher , P.C. , Fonagy , P. , Franz , C.E. , Galan-Garcia , L. , Gholipour , A. , Giedd , J. , Gilmore , J.H. , Glahn , D.C. , Goodyer , I.M. , Grant , P.E. , Groenewold , N.A. , Gunning , F.M. , Gur , R.E. , Gur , R.C. , Hammill , C.F. , Hansson , O. , Hedden , T. , Heinz , A. , Henson , R.N. , Heuer , K. , Hoare , J. , Holla , B. , Holmes , A.J. , Holt , R. , Huang , H. , Im , K. , Ipser , J. , Jack , C.R. , Jackowski , A.P. , Jia , T. , Johnson , K.A. , Jones , P.B. , Jones , D.T. , Kahn , R.S. , Karlsson , H. , Karlsson , L. , Kawashima , R. , Kelley , E.A. , Kern , S. , Kim , K.W. , Kitzbichler , M.G. , Kremen , W.S. , Lalonde , F. , Landeau , B. , Lee , S. , Lerch , J. , Lewis , J.D. , Li , J. , Liao , W. , Liston , C. , Lombardo , M.V. , Lv , J. , Lynch , C. , Mallard , T.T. , Marcelis , M. , Markello , R.D. , Mathias , S.R. , Mazoyer , B. , McGuire , P. , Meaney , M.J. , Mechelli , A. , Medic , N. , Misic , B. , Morgan , S.E. , Mothersill , D. , Nigg , J. , Ong , M.Q.W. , Ortinau , C. , Ossenkoppele , R. , Ouyang , M. , Palaniyappan , L. , Paly , L. , Pan , P.M. , Pantelis , C. , Park , M.M. , Paus , T. , Pausova , Z. , Paz-Linares , D. , Pichet Binette , A. , Pierce , K. , Qian , X. , Qiu , J. , Qiu , A. , Raznahan , A. , Rittman , T. , Rodrigue , A. , Rollins , C.K. , Romero-Garcia , R. , Ronan , L. , Rosenberg , M.D. , Rowitch , D.H. , Salum , G.A. , Satterthwaite , T.D. , Schaare , H.L. , Schachar , R.J. , Schultz , A.P. , Schumann , G. , Schöll , M. , Sharp , D. , Shinohara , R.T. , Skoog , I. , Smyser , C.D. , Sperling , R.A. , Stein , D.J. , Stolicyn , A. , Suckling , J. , Sullivan , G. , Taki , Y. , Thyreau , B. , Toro , R. , Traut , N. , Tsvetanov , K.A. , Turk-Browne , N.B. , Tuulari , J.J. , Tzourio , C. , Vachon-Presseau , É. , Valdes-Sosa , M.J. , Valdes-Sosa , P.A. , Valk , S.L. , van Amelsvoort , T. , Vandekar , S.N. , Vasung , L. , Victoria , L.W. , Villeneuve , S. , Villringer , A. , Vértes , P.E. , Wagstyl , K. , Wang , Y.S. , Warfield , S.K. , Warrier , V. , Westman , E. , Westwater , M.L. , Whalley , H.C. , Witte , A.V. , Yang , N. , Yeo , B. , Yun , H. , Zalesky , A. , Zar , H.J. , Zettergren , A. , Zhou , J.H. , Ziauddeen , H. , Zugman , A. , Zuo , X.N. , Rowe , C. , Frisoni , G.B. , Binette , A.P. , Bullmore , E.T. , Alexander-Bloch , A.F. , R, B., Aibl, Alzheimer’s Disease Neuroimaging , I. , Alzheimer’s Disease Repository Without Borders, I., Team, C., Cam, C.A.N., Ccnp, Cobre, cVeda, Group, E.D.B.A.W., Developing Human Connectome, P., FinnBrain, Harvard Aging Brain, S., Imagen, Kne, The Mayo Clinic Study of, A., Nspn, Pond, The, P.-A.D.R.G., Vetsa , 2022 . Brain charts for the human lifespan. Nature 604 , 525 – 533 . OpenUrl CrossRef PubMed 7. ↵ Bhaya-Grossman , I. , Chang , E.F ., 2022 . Speech Computations of the Human Superior Temporal Gyrus . Annu Rev Psychol 73 , 79 – 102 . OpenUrl CrossRef PubMed 8. ↵ Bhutta , A.T. , Cleves , M.A. , Casey , P.H. , Cradock , M.M. , Anand , K.J ., 2002 . Cognitive and behavioral outcomes of school-aged children who were born preterm: a meta-analysis . Jama 288 , 728 – 737 . OpenUrl CrossRef PubMed Web of Science 9. ↵ Bigler , E.D. , Mortensen , S. , Neeley , E.S. , Ozonoff , S. , Krasny , L. , Johnson , M. , Lu , J. , Provencal , S.L. , McMahon , W. , Lainhart , J.E ., 2007 . Superior temporal gyrus, language function, and autism . Dev Neuropsychol 31 , 217 – 238 . OpenUrl CrossRef PubMed Web of Science 10. ↵ Bjuland , K.J. , Rimol , L.M. , Løhaugen , G.C. , Skranes , J ., 2014 . Brain volumes and cognitive function in very-low-birth-weight (VLBW) young adults . Eur J Paediatr Neurol 18 , 578 – 590 . OpenUrl CrossRef PubMed 11. ↵ Bokobza , C. , Van Steenwinckel , J. , Mani , S. , Mezger , V. , Fleiss , B. , Gressens , P. , 2019 . Neuroinflammation in preterm babies and autism spectrum disorders . Pediatr Res 85 , 155 – 165 . OpenUrl CrossRef PubMed 12. ↵ Braithwaite , E. , Kyriakopoulou , V. , Mason , L. , Davidson , A. , Tusor , N. , Harper , N. , Earl , M. , Datoo-Partridge , S. , Young , A. , Chew , A. , Falconer , S. , Hajnal , J.V. , Johnson , M.H. , Nosarti , C. , Edwards , A.D. , Jones , E.J.H ., 2023 . Objective assessment of visual attention in toddlerhood . Cold Spring Harbor Laboratory . 13. ↵ Breach , M.R. , Lenz , K.M ., 2023 . Sex Differences in Neurodevelopmental Disorders: A Key Role for the Immune System . Curr Top Behav Neurosci 62 , 165 – 206 . OpenUrl CrossRef PubMed 14. ↵ Bryńska , A ., 2012 . [Seeking the aetiology of autistic spectrum disorder. Part 1: Structural neuroimaging] . Psychiatr Pol 46 , 1053 – 1060 . OpenUrl PubMed 15. ↵ Campbell , D.J. , Shic , F. , Macari , S. , Chawarska , K ., 2014 . Gaze response to dyadic bids at 2 years related to outcomes at 3 years in autism spectrum disorders: A subtyping analysis . J Autism Dev Disord 44 , 431 – 442 . OpenUrl CrossRef PubMed 16. ↵ Carper , R.A. , Courchesne , E ., 2005 . Localized enlargement of the frontal cortex in early autism . Biol Psychiatry 57 , 126 – 133 . OpenUrl CrossRef PubMed Web of Science 17. ↵ Carper , R.A. , Moses , P. , Tigue , Z.D. , Courchesne , E ., 2002 . Cerebral Lobes in Autism: Early Hyperplasia and Abnormal Age Effects . Neuroimage 16 , 1038 – 1051 . OpenUrl CrossRef PubMed Web of Science 18. ↵ Chawarska , K. , Macari , S. , Powell , K. , DiNicola , L. , Shic , F ., 2016 . Enhanced Social Attention in Female Infant Siblings at Risk for Autism . J Am Acad Child Adolesc Psychiatry 55 , 188 – 195 .e181. OpenUrl CrossRef PubMed 19. ↵ Chawarska , K. , MacAri , S. , Shic , F ., 2012 . Context modulates attention to social scenes in toddlers with autism . Journal of Child Psychology and Psychiatry and Allied Disciplines 53 , 903 – 913 . OpenUrl CrossRef PubMed Web of Science 20. ↵ Chen , F. , Curran , P.J. , Bollen , K.A. , Kirby , J. , Paxton , P ., 2008 . An Empirical Evaluation of the Use of Fixed Cutoff Points in RMSEA Test Statistic in Structural Equation Models . Sociological methods & research 36 , 462 – 494 . OpenUrl CrossRef PubMed Web of Science 21. ↵ Corbett , B.A. , Newsom , C. , Key , A.P. , Qualls , L.R. , Edmiston , E.K ., 2014 . Examining the relationship between face processing and social interaction behavior in children with and without autism spectrum disorder . J Neurodev Disord 6 , 35 . OpenUrl PubMed 22. ↵ Corden , B. , Chilvers , R. , Skuse , D ., 2008 . Avoidance of emotionally arousing stimuli predicts social–perceptual impairment in Asperger’s syndrome . Neuropsychologia 46 , 137 – 147 . OpenUrl CrossRef PubMed Web of Science 23. ↵ Courchesne , E. , Campbell , K. , Solso , S ., 2011 . Brain growth across the life span in autism: age-specific changes in anatomical pathology . Brain Res 1380 , 138 – 145 . OpenUrl CrossRef PubMed Web of Science 24. ↵ Courchesne , E. , Pierce , K. , Schumann , C.M. , Redcay , E. , Buckwalter , J.A. , Kennedy , D.P. , Morgan , J ., 2007 . Mapping Early Brain Development in Autism . Neuron 56 , 399 – 413 . OpenUrl CrossRef PubMed Web of Science 25. ↵ Crump , C. , Sundquist , J. , Sundquist , K ., 2021 . Preterm or Early Term Birth and Risk of Autism . Pediatrics 148 . 26. ↵ D’Onofrio , B.M. , Class , Q.A. , Rickert , M.E. , Larsson , H. , Långström , N. , Lichtenstein , P ., 2013 . Preterm birth and mortality and morbidity: a population-based quasi-experimental study . JAMA Psychiatry 70 , 1231 – 1240 . OpenUrl PubMed 27. ↵ Dan , B ., 2021 . Sex differences in neurodevelopmental disorders . Developmental Medicine & Child Neurology 63 , 492 – 492 . OpenUrl PubMed 28. ↵ de Jong , J.O. , Llapashtica , C. , Genestine , M. , Strauss , K. , Provenzano , F. , Sun , Y. , Zhu , H. , Cortese , G.P. , Brundu , F. , Brigatti , K.W. , Corneo , B. , Migliori , B. , Tomer , R. , Kushner , S.A. , Kellendonk , C. , Javitch , J.A. , Xu , B. , Markx , S. , 2021 . Cortical overgrowth in a preclinical forebrain organoid model of CNTNAP2-associated autism spectrum disorder . Nat Commun 12 , 4087 . OpenUrl CrossRef PubMed 29. ↵ Delobel-Ayoub , M. , Arnaud , C. , White-Koning , M. , Casper , C. , Pierrat , V. , Garel , M. , Burguet , A. , Roze , J.C. , Matis , J. , Picaud , J.C. , Kaminski , M. , Larroque , B ., 2009 . Behavioral problems and cognitive performance at 5 years of age after very preterm birth: the EPIPAGE Study . Pediatrics 123 , 1485 – 1492 . OpenUrl CrossRef PubMed Web of Science 30. ↵ Dimond , D. , Schuetze , M. , Smith , R.E. , Dhollander , T. , Cho , I. , Vinette , S. , Ten Eycke , K. , Lebel , C. , McCrimmon , A. , Dewey , D. , Connelly , A. , Bray , S ., 2019 . Reduced White Matter Fiber Density in Autism Spectrum Disorder . Cereb Cortex 29 , 1778 – 1788 . OpenUrl CrossRef PubMed 31. ↵ Disselhoff , V. , Jakab , A. , Latal , B. , Schnider , B. , Wehrle , F.M. , Hagmann , C.F. , Held , U. , O’Gorman , R.T. , Fauchère , J.-C. , Hüppi , P ., the EpoKids Research, G., 2024 . Inhibition abilities and functional brain connectivity in school-aged term-born and preterm-born children . Pediatric Research . 32. ↵ Duerden , E.G. , Mak-Fan , K.M. , Taylor , M.J. , Roberts , S.W ., 2012 . Regional differences in grey and white matter in children and adults with autism spectrum disorders: an activation likelihood estimate (ALE) meta-analysis . Autism Res 5 , 49 – 66 . OpenUrl CrossRef PubMed Web of Science 33. ↵ Ecker , C. , Bookheimer , S.Y. , Murphy , D.G ., 2015 . Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan . Lancet Neurol 14 , 1121 – 1134 . OpenUrl CrossRef PubMed 34. ↵ Eckerd , M ., 2020 . Detection and Diagnosis of ASD in Females . Journal of Health Service Psychology 46 , 37 – 47 . OpenUrl 35. ↵ Elsabbagh , M. , Divan , G. , Koh , Y.J. , Kim , Y.S. , Kauchali , S. , Marcín , C. , Montiel-Nava , C. , Patel , V. , Paula , C.S. , Wang , C. , Yasamy , M.T. , Fombonne , E ., 2012 . Global prevalence of autism and other pervasive developmental disorders . Autism Res 5 , 160 – 179 . OpenUrl CrossRef PubMed Web of Science 36. ↵ Erdei , C. , Bell , K.A. , Garvey , A.A. , Blaschke , C. , Belfort , M.B. , Inder , T.E ., 2023 . Novel metrics to characterize temporal lobe of very preterm infants on term-equivalent brain MRI . Pediatric Research 94 , 979 – 986 . OpenUrl PubMed 37. ↵ Evans , S.C. , Boan , A.D. , Bradley , C. , Carpenter , L.A ., 2019 . Sex/Gender Differences in Screening for Autism Spectrum Disorder: Implications for Evidence-Based Assessment . J Clin Child Adolesc Psychol 48 , 840 – 854 . OpenUrl PubMed 38. ↵ Eyler , L.T. , Pierce , K. , Courchesne , E ., 2012 . A failure of left temporal cortex to specialize for language is an early emerging and fundamental property of autism . Brain 135 , 949 – 960 . OpenUrl CrossRef PubMed Web of Science 39. ↵ Braddick , O. , Atkinson , J. , Innocenti , G.M Falck-Ytter , T. , von Hofsten , C ., 2011 . Chapter 12 - How special is social looking in ASD: A review . In: Braddick , O. , Atkinson , J. , Innocenti , G.M . (Eds.), Progress in Brain Research. Elsevier , pp. 209 – 222 . 40. ↵ First , M.B ., 2013 . Diagnostic and statistical manual of mental disorders , 5th edition, and clinical utility. J Nerv Ment Dis 201, 727-729. 41. ↵ Flores , G. , Reyes-Lizaola , S. , Aguilar-Hernández , L. , Gil-Velazco , A. , Tendilla-Beltrán , H. , Morales-Medina , J.C ., 2025 . Role of the amygdala and possible biomarkers in psychiatric disorders . NeuroMarkers 2 , 100032 . OpenUrl 42. ↵ Foster , N.E.V. , Doyle-Thomas , K.A.R. , Tryfon , A. , Ouimet , T. , Anagnostou , E. , Evans , A.C. , Zwaigenbaum , L. , Lerch , J.P. , Lewis , J.D. , Hyde , K.L ., 2015 . Structural Gray Matter Differences During Childhood Development in Autism Spectrum Disorder: A Multimetric Approach . Pediatric Neurology 53 , 350 – 359 . OpenUrl CrossRef PubMed 43. ↵ França , L.G.S. , Ciarrusta , J. , Gale-Grant , O. , Fenn-Moltu , S. , Fitzgibbon , S. , Chew , A. , Falconer , S. , Dimitrova , R. , Cordero-Grande , L. , Price , A.N. , Hughes , E. , O’Muircheartaigh , J. , Duff , E. , Tuulari , J.J. , Deco , G. , Counsell , S.J. , Hajnal , J.V. , Nosarti , C. , Arichi , T. , Edwards , A.D. , McAlonan , G. , Batalle , D ., 2024 . Neonatal brain dynamic functional connectivity in term and preterm infants and its association with early childhood neurodevelopment . Nature Communications 15 , 16 . OpenUrl PubMed 44. ↵ Frazier , T.W. , Strauss , M. , Klingemier , E.W. , Zetzer , E.E. , Hardan , A.Y. , Eng , C. , Youngstrom , E.A ., 2017 . A Meta-Analysis of Gaze Differences to Social and Nonsocial Information Between Individuals With and Without Autism . J Am Acad Child Adolesc Psychiatry 56 , 546 – 555 . OpenUrl CrossRef PubMed 45. Gholipour , A. , Rollins , C.K. , Velasco-Annis , C. , Ouaalam , A. , Akhondi-Asl , A. , Afacan , O. , Ortinau , C.M. , Clancy , S. , Limperopoulos , C. , Yang , E. , Estroff , J.A. , Warfield , S.K ., 2017 . A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth . Sci Rep 7 , 476 . OpenUrl CrossRef PubMed 46. ↵ Gholipour , A. , Velasco-Annis , C. , Rollins , C.K. , Vasung , L. , Ouaalam , A. , Ortinau , C. , Akhondi-Asl , A. , Clancy , S. , Yang , E. , Estroff , J. , Warfield , S.K. , 2023 . IMAGINE Fetal T2-weighted MRI Atlas. Harvard Dataverse . 47. ↵ Giarelli , E. , Wiggins , L.D. , Rice , C.E. , Levy , S.E. , Kirby , R.S. , Pinto-Martin , J. , Mandell , D ., 2010 . Sex differences in the evaluation and diagnosis of autism spectrum disorders among children . Disabil Health J 3 , 107 – 116 . OpenUrl CrossRef PubMed Web of Science 48. ↵ Gliga , T. , Jones , E.J.H. , Bedford , R. , Charman , T. , Johnson , M.H ., 2014 . From early markers to neuro-developmental mechanisms of autism . Developmental Review 34 , 189 – 207 . OpenUrl CrossRef PubMed 49. ↵ Gluckman , M. , Johnson , S.P ., 2013 . Attentional capture by social stimuli in young infants . Front Psychol 4 , 527 . OpenUrl PubMed 50. ↵ Gonzalez-Perez , O. , Zavalza-Galvez , Y. , Galvez-Contreras , A.Y. , Gonzalez-Castaneda , R.E. , 2024 . The potential role of phenytoin in myelin restoration in the postnatal brain: a narrative review. Regenerative Medicine Reports 1 . 51. ↵ Guo , X. , Duan , X. , Suckling , J. , Wang , J. , Kang , X. , Chen , H. , Biswal , B.B. , Cao , J. , He , C. , Xiao , J. , Huang , X. , Wang , R. , Han , S. , Fan , Y.S. , Guo , J. , Zhao , J. , Wu , L. , Chen , H ., 2021 . Mapping Progressive Gray Matter Alterations in Early Childhood Autistic Brain . Cereb Cortex 31 , 1500 – 1510 . OpenUrl CrossRef PubMed 52. ↵ Hadad , B.S. , Yashar , A ., 2022 . Sensory Perception in Autism: What Can We Learn? Annu Rev Vis Sci 8 , 239 – 264 . OpenUrl PubMed 53. ↵ Halladay , A.K. , Bishop , S. , Constantino , J.N. , Daniels , A.M. , Koenig , K. , Palmer , K. , Messinger , D. , Pelphrey , K. , Sanders , S.J. , Singer , A.T. , Taylor , J.L. , Szatmari , P ., 2015 . Sex and gender differences in autism spectrum disorder: summarizing evidence gaps and identifying emerging areas of priority . Mol Autism 6 , 36 . OpenUrl CrossRef PubMed 54. ↵ Harel-Gadassi , A. , Friedlander , E. , Yaari , M. , Bar-Oz , B. , Eventov-Friedman , S. , Mankuta , D. , Yirmiya , N ., 2018 . Risk for ASD in Preterm Infants: A Three-Year Follow-Up Study . Autism Res Treat 2018 , 8316212 . OpenUrl PubMed 55. ↵ Harrop , C. , Jones , D. , Zheng , S. , Nowell , S. , Schultz , R. , Parish-Morris , J ., 2019 . Visual attention to faces in children with autism spectrum disorder: are there sex differences? Mol Autism 10 , 28 . OpenUrl PubMed 56. ↵ Harrop , C. , Jones , D. , Zheng , S. , Nowell , S.W. , Boyd , B.A. , Sasson , N ., 2018 . Sex differences in social attention in autism spectrum disorder . Autism Res 11 , 1264 – 1275 . OpenUrl PubMed 57. ↵ Hazlett , H.C. , Gu , H. , Munsell , B.C. , Kim , S.H. , Styner , M. , Wolff , J.J. , Elison , J.T. , Swanson , M.R. , Zhu , H. , Botteron , K.N. , Collins , D.L. , Constantino , J.N. , Dager , S.R. , Estes , A.M. , Evans , A.C. , Fonov , V.S. , Gerig , G. , Kostopoulos , P. , McKinstry , R.C. , Pandey , J. , Paterson , S. , Pruett , J.R. , Schultz , R.T. , Shaw , D.W. , Zwaigenbaum , L. , Piven , J ., 2017 . Early brain development in infants at high risk for autism spectrum disorder . Nature 542 , 348 – 351 . OpenUrl CrossRef PubMed 58. ↵ Hazlett , H.C. , Poe , M. , Gerig , G. , Smith , R.G. , Provenzale , J. , Ross , A. , Gilmore , J. , Piven , J ., 2005 . Magnetic resonance imaging and head circumference study of brain size in autism: birth through age 2 years . Arch Gen Psychiatry 62 , 1366 – 1376 . OpenUrl CrossRef PubMed Web of Science 59. ↵ Hazlett , H.C. , Poe , M.D. , Gerig , G. , Styner , M. , Chappell , C. , Smith , R.G. , Vachet , C. , Piven , J ., 2011 . Early brain overgrowth in autism associated with an increase in cortical surface area before age 2 years . Arch Gen Psychiatry 68 , 467 – 476 . OpenUrl CrossRef PubMed Web of Science 60. ↵ He , C. , Chen , H. , Uddin , L.Q. , Erramuzpe , A. , Bonifazi , P. , Guo , X. , Xiao , J. , Chen , H. , Huang , X. , Li , L. , Sheng , W. , Liao , W. , Cortes , J.M. , Duan , X ., 2020 . Structure-Function Connectomics Reveals Aberrant Developmental Trajectory Occurring at Preadolescence in the Autistic Brain . Cereb Cortex 30 , 5028 – 5037 . OpenUrl CrossRef PubMed 61. ↵ Hinton , A. , Jr. , Neikirk , K. , Le , H. , Harris , C. , Oliver , A. , Martin , P. , Gaye , A ., 2024 . Estrogen receptors in mitochondrial metabolism: age-related changes and implications for pregnancy complications . Aging Adv 1 , 154 – 171 . OpenUrl PubMed 62. ↵ Hu , L.-T. , Bentler , P.M ., 1995 . Evaluating model fit. Structural equation modeling: Concepts, issues, and applications. Sage Publications, Inc, Thousand Oaks, CA , US , pp. 76 – 99 . 63. ↵ Hughes , E.J. , Winchman , T. , Padormo , F. , Teixeira , R. , Wurie , J. , Sharma , M. , Fox , M. , Hutter , J. , Cordero-Grande , L. , Price , A.N. , Allsop , J. , Bueno-Conde , J. , Tusor , N. , Arichi , T. , Edwards , A.D. , Rutherford , M.A. , Counsell , S.J. , Hajnal , J.V ., 2017 . A dedicated neonatal brain imaging system . Magn Reson Med 78 , 794 – 804 . OpenUrl CrossRef PubMed 64. ↵ Hull , L. , Petrides , K.V. , Allison , C. , Smith , P. , Baron-Cohen , S. , Lai , M.C. , Mandy , W ., 2017 . "Putting on My Best Normal": Social Camouflaging in Adults with Autism Spectrum Conditions . J Autism Dev Disord 47 , 2519 – 2534 . OpenUrl CrossRef PubMed 65. ↵ Hull , L. , Petrides , K.V. , Mandy , W ., 2020 . The Female Autism Phenotype and Camouflaging: a Narrative Review . Review Journal of Autism and Developmental Disorders 7 , 306 – 317 . OpenUrl CrossRef 66. ↵ Hullett , P.W. , Hamilton , L.S. , Mesgarani , N. , Schreiner , C.E. , Chang , E.F ., 2016 . Human Superior Temporal Gyrus Organization of Spectrotemporal Modulation Tuning Derived from Speech Stimuli . J Neurosci 36 , 2014 – 2026 . OpenUrl Abstract / FREE Full Text 67. ↵ Inder , T.E. , Warfield , S.K. , Wang , H. , Hüppi , P.S. , Volpe , J.J ., 2005 . Abnormal cerebral structure is present at term in premature infants . Pediatrics 115 , 286 – 294 . OpenUrl CrossRef PubMed Web of Science 68. ↵ Jenkinson , M. , Beckmann , C.F. , Behrens , T.E. , Woolrich , M.W. , Smith , S.M ., 2012 . FSL . Neuroimage 62 , 782 – 790 . OpenUrl CrossRef PubMed Web of Science 69. ↵ Ji , W. , Li , G. , Jiang , F. , Zhang , Y. , Wu , F. , Zhang , W. , Hu , Y. , Wang , J. , Wei , X. , Li , Y. , Manza , P. , Tomasi , D. , Gao , X. , Wang , G.-J. , Zhang , Y. , Volkow , N.D ., 2024 . Preterm birth associated alterations in brain structure, cognitive functioning and behavior in children from the ABCD dataset . Psychol Med 54 , 409 – 418 . OpenUrl CrossRef PubMed 70. ↵ Johnson , S. , Evans , T.A. , Draper , E.S. , Field , D.J. , Manktelow , B.N. , Marlow , N. , Matthews , R. , Petrou , S. , Seaton , S.E. , Smith , L.K. , Boyle , E.M. , 2015 . Neurodevelopmental outcomes following late and moderate prematurity: A population-based cohort study . Archives of Disease in Childhood: Fetal and Neonatal Edition 100 , F301 – F308 . OpenUrl Abstract / FREE Full Text 71. ↵ Johnson , S. , Hollis , C. , Kochhar , P. , Hennessy , E. , Wolke , D. , Marlow , N ., 2010 . Autism spectrum disorders in extremely preterm children . J Pediatr 156 , 525 – 531 .e522. OpenUrl CrossRef PubMed Web of Science 72. ↵ Johnson , S. , Marlow , N ., 2011 . Preterm Birth and Childhood Psychiatric Disorders . Pediatric Research 69 , 11 – 18 . OpenUrl CrossRef 73. ↵ Jones , W. , Carr , K. , Klin , A ., 2008a . Absence of Preferential Looking to the Eyes of Approaching Adults Predicts Level of Social Disability in 2-Year-Old Toddlers With Autism Spectrum Disorder . Archives of General Psychiatry 65 , 946 – 954 . OpenUrl CrossRef PubMed Web of Science 74. ↵ Jones , W. , Carr , K. , Klin , A ., 2008b . Absence of preferential looking to the eyes of approaching adults predicts level of social disability in 2-year-old toddlers with autism spectrum disorder . Arch Gen Psychiatry 65 , 946 – 954 . OpenUrl CrossRef PubMed Web of Science 75. ↵ Jones , W. , Klin , A ., 2013 . Attention to eyes is present but in decline in 2-6-month-old infants later diagnosed with autism . Nature 504 , 427 – 431 . OpenUrl CrossRef PubMed Web of Science 76. ↵ Karolis , V.R. , Froudist-Walsh , S. , Kroll , J. , Brittain , P.J. , Tseng , C.J. , Nam , K.W. , Reinders , A. , Murray , R.M. , Williams , S.C.R. , Thompson , P.M. , Nosarti , C ., 2017 . Volumetric grey matter alterations in adolescents and adults born very preterm suggest accelerated brain maturation . Neuroimage 163 , 379 – 389 . OpenUrl CrossRef PubMed 77. ↵ Kelly , C.E. , Shaul , M. , Thompson , D.K. , Mainzer , R.M. , Yang , J.Y. , Dhollander , T. , Cheong , J.L. , Inder , T.E. , Doyle , L.W. , Anderson , P.J ., 2023 . Long-lasting effects of very preterm birth on brain structure in adulthood: A systematic review and meta-analysis . Neurosci Biobehav Rev 147 , 105082 . OpenUrl CrossRef PubMed 78. ↵ Khachadourian , V. , Mahjani , B. , Sandin , S. , Kolevzon , A. , Buxbaum , J.D. , Reichenberg , A. , Janecka , M ., 2023 . Comorbidities in autism spectrum disorder and their etiologies . Transl Psychiatry 13 , 71 . OpenUrl PubMed 79. ↵ Kleberg , J.L. , Nyström , P. , Bölte , S. , Falck-Ytter , T ., 2019 . Sex Differences in Social Attention in Infants at Risk for Autism . J Autism Dev Disord 49 , 1342 – 1351 . OpenUrl PubMed 80. ↵ Klin , A. , Jones , W. , Schultz , R. , Volkmar , F. , Cohen , D ., 2002 . Visual Fixation Patterns During Viewing of Naturalistic Social Situations as Predictors of Social Competence in Individuals With Autism . Arch Gen Psychiatry 59 , 809 – 816 . OpenUrl CrossRef PubMed Web of Science 81. ↵ Kong , X.J. , Wei , Z. , Sun , B. , Tu , Y. , Huang , Y. , Cheng , M. , Yu , S. , Wilson , G. , Park , J. , Feng , Z. , Vangel , M. , Kong , J. , Wan , G ., 2022 . Different Eye Tracking Patterns in Autism Spectrum Disorder in Toddler and Preschool Children . Front Psychiatry 13 , 899521 . OpenUrl CrossRef PubMed 82. ↵ Krolak-Salmon , P ., 2011 . [Emotion perception in neurodegenerative disorders] . Rev Med Interne 32 , 721 – 723 . OpenUrl PubMed 83. ↵ Kuzniewicz , M.W. , Wi , S. , Qian , Y. , Walsh , E.M. , Armstrong , M.A. , Croen , L.A ., 2014 . Prevalence and Neonatal Factors Associated with Autism Spectrum Disorders in Preterm Infants . J Pediatr 164 , 20 – 25 . OpenUrl CrossRef PubMed Web of Science 84. ↵ Lange , N. , Travers , B.G. , Bigler , E.D. , Prigge , M.B. , Froehlich , A.L. , Nielsen , J.A. , Cariello , A.N. , Zielinski , B.A. , Anderson , J.S. , Fletcher , P.T. , Alexander , A.A. , Lainhart , J.E ., 2015 . Longitudinal volumetric brain changes in autism spectrum disorder ages 6-35 years . Autism Res 8 , 82 – 93 . OpenUrl CrossRef PubMed 85. ↵ Lawson , L.P. , Joshi , R. , Barbaro , J. , Dissanayake , C ., 2018 . Gender Differences During Toddlerhood in Autism Spectrum Disorder: A Prospective Community-Based Longitudinal Follow-Up Study . J Autism Dev Disord 48 , 2619 – 2628 . OpenUrl PubMed 86. ↵ Leff , A.P. , Schofield , T.M. , Crinion , J.T. , Seghier , M.L. , Grogan , A. , Green , D.W. , Price , C.J ., 2009 . The left superior temporal gyrus is a shared substrate for auditory short-term memory and speech comprehension: evidence from 210 patients with stroke . Brain 132 , 3401 – 3410 . OpenUrl CrossRef PubMed Web of Science 87. ↵ Li , G. , Cao , Y. , Yang , C. , Li , X. , Yang , Y. , Yang , L. , Hao , D. , Li , C.R ., 2024 . Sex differences in dorsolateral prefrontal cortical and superior colliculus activities support the impact of alcohol use severity and sleep deficiency on two-back memory . Quant Imaging Med Surg 14 , 4972 – 4986 . OpenUrl PubMed 88. ↵ Li , G. , Chen , Y. , Le , T.M. , Wang , W. , Tang , X. , Li , C.-S.R. , 2021a . Neural correlates of individual variation in two-back working memory and the relationship with fluid intelligence . Scientific Reports 11 , 9980 . OpenUrl PubMed 89. ↵ Li , G. , Chen , Y. , Tang , X. , Li , C.-S.R ., 2021b . Alcohol use severity and the neural correlates of the effects of sleep disturbance on sustained visual attention . Journal of Psychiatric Research 142 , 302 – 311 . OpenUrl CrossRef PubMed 90. ↵ Li , G. , Zhang , Z. , Zhang , Y. , Tang , X. , Li , C.R ., 2023 . The effects of cocaine use severity and abstinence on behavioral performance and neural processes of response inhibition . Psychiatry Res Neuroimaging 336 , 111734 . OpenUrl 91. ↵ Li , W. , Yang , Y. , Li , G. , Nieto-Del-Amor , F. , Prats-Boluda , G. , Garcia-Casado , J. , Ye-Lin , Y. , Hao , D ., 2025 . Synchronization study of electrohysterography for discrimination of imminent delivery in pregnant women with threatened preterm labor . Comput Biol Med 184 , 109417 . OpenUrl PubMed 92. ↵ Liloia , D. , Zamfira , D.A. , Tanaka , M. , Manuello , J. , Crocetta , A. , Keller , R. , Cozzolino , M. , Duca , S. , Cauda , F. , Costa , T ., 2024 . Disentangling the role of gray matter volume and concentration in autism spectrum disorder: A meta-analytic investigation of 25 years of voxel-based morphometry research . Neuroscience & Biobehavioral Reviews 164 , 105791 . OpenUrl PubMed 93. ↵ Lindell , A.K. , Hudry , K ., 2013 . Atypicalities in cortical structure, handedness, and functional lateralization for language in autism spectrum disorders . Neuropsychol Rev 23 , 257 – 270 . OpenUrl CrossRef PubMed 94. ↵ Linnet , K.M. , Wisborg , K. , Agerbo , E. , Secher , N.J. , Thomsen , P.H. , Henriksen , T.B ., 2006 . Gestational age, birth weight, and the risk of hyperkinetic disorder . Arch Dis Child 91 , 655 – 660 . OpenUrl Abstract / FREE Full Text 95. ↵ Liu , J. , Yao , L. , Zhang , W. , Xiao , Y. , Liu , L. , Gao , X. , Shah , C. , Li , S. , Tao , B. , Gong , Q. , Lui , S. , 2017 . Gray matter abnormalities in pediatric autism spectrum disorder: a meta-analysis with signed differential mapping . Eur Child Adolesc Psychiatry 26 , 933 – 945 . OpenUrl CrossRef PubMed 96. ↵ Loomes , R. , Hull , L. , Mandy , W.P.L ., 2017 . What Is the Male-to-Female Ratio in Autism Spectrum Disorder? A Systematic Review and Meta-Analysis . J Am Acad Child Adolesc Psychiatry 56 , 466 – 474 . OpenUrl CrossRef PubMed 97. ↵ Lukito , S. , Norman , L. , Carlisi , C. , Radua , J. , Hart , H. , Simonoff , E. , Rubia , K ., 2020 . Comparative meta-analyses of brain structural and functional abnormalities during cognitive control in attention-deficit/hyperactivity disorder and autism spectrum disorder . Psychol Med 50 , 894 – 919 . OpenUrl CrossRef PubMed 98. ↵ Makropoulos , A. , Counsell , S.J. , Rueckert , D ., 2018a . A review on automatic fetal and neonatal brain MRI segmentation . Neuroimage 170 , 231 – 248 . OpenUrl CrossRef PubMed 99. ↵ Makropoulos , A. , Gousias , I.S. , Ledig , C. , Aljabar , P. , Serag , A. , Hajnal , J.V. , Edwards , A.D. , Counsell , S.J. , Rueckert , D ., 2014 . Automatic whole brain MRI segmentation of the developing neonatal brain . IEEE Trans Med Imaging 33 , 1818 – 1831 . OpenUrl CrossRef PubMed 100. ↵ Makropoulos , A. , Robinson , E.C. , Schuh , A. , Wright , R. , Fitzgibbon , S. , Bozek , J. , Counsell , S.J. , Steinweg , J. , Vecchiato , K. , Passerat-Palmbach , J. , Lenz , G. , Mortari , F. , Tenev , T. , Duff , E.P. , Bastiani , M. , Cordero-Grande , L. , Hughes , E. , Tusor , N. , Tournier , J.D. , Hutter , J. , Price , A.N. , Teixeira , R. , Murgasova , M. , Victor , S. , Kelly , C. , Rutherford , M.A. , Smith , S.M. , Edwards , A.D. , Hajnal , J.V. , Jenkinson , M. , Rueckert , D ., 2018b . The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction . Neuroimage 173 , 88 – 112 . OpenUrl CrossRef PubMed 101. ↵ Marlow , N. , Wolke , D. , Bracewell , M.A. , Samara , M ., 2005 . Neurologic and developmental disability at six years of age after extremely preterm birth . N Engl J Med 352 , 9 – 19 . OpenUrl CrossRef PubMed Web of Science 102. ↵ Marret , S. , Marchand-Martin , L. , Picaud , J.C. , Hascoët , J.M. , Arnaud , C. , Rozé , J.C. , Truffert , P. , Larroque , B. , Kaminski , M. , Ancel , P.Y ., 2013 . Brain injury in very preterm children and neurosensory and cognitive disabilities during childhood: the EPIPAGE cohort study . PLoS One 8 , e62683 . OpenUrl CrossRef PubMed 103. ↵ Mastergeorge , A.M. , Kahathuduwa , C. , Blume , J ., 2021 . Eye-Tracking in Infants and Young Children at Risk for Autism Spectrum Disorder: A Systematic Review of Visual Stimuli in Experimental Paradigms . J Autism Dev Disord 51 , 2578 – 2599 . OpenUrl CrossRef PubMed 104. ↵ May , T. , Adesina , I. , McGillivray , J. , Rinehart , N.J ., 2019 . Sex differences in neurodevelopmental disorders . Curr Opin Neurol 32 , 622 – 626 . OpenUrl CrossRef PubMed 105. ↵ Mendez , A.I. , Tokish , H. , McQueen , E. , Chawla , S. , Klin , A. , Maitre , N.L. , Klaiman , C ., 2023 . A Comparison of the Clinical Presentation of Preterm Birth and Autism Spectrum Disorder: Commonalities and Distinctions in Children Under 3 . Clin Perinatol 50 , 81 – 101 . OpenUrl PubMed 106. ↵ Meredith Weiss , S. , Aydin , E. , Lloyd-Fox , S. , Johnson , M.H., 2024 . Trajectories of brain and behaviour development in the womb, at birth and through infancy . Nat Hum Behav 8 , 1251 – 1262 . OpenUrl PubMed 107. ↵ Micai , M. , Fulceri , F. , Caruso , A. , Guzzetta , A. , Gila , L. , Scattoni , M.L ., 2020 . Early behavioral markers for neurodevelopmental disorders in the first 3 years of life: An overview of systematic reviews . Neurosci Biobehav Rev 116 , 183 – 201 . OpenUrl PubMed 108. ↵ Moreira , R.S. , Magalhães , L.C. , Alves , C.R.L ., 2014 . Effect of preterm birth on motor development, behavior, and school performance of schoola-age children: A systematic review . Jornal de Pediatria 90 , 119 – 134 . OpenUrl PubMed 109. ↵ Mostofsky , S.H. , Burgess , M.P. , Gidley Larson , J.C ., 2007 . Increased motor cortex white matter volume predicts motor impairment in autism . Brain 130 , 2117 – 2122 . OpenUrl CrossRef PubMed Web of Science 110. ↵ Mueller , S. , Keeser , D. , Samson , A.C. , Kirsch , V. , Blautzik , J. , Grothe , M. , Erat , O. , Hegenloh , M. , Coates , U. , Reiser , M.F. , Hennig-Fast , K. , Meindl , T ., 2013 . Convergent Findings of Altered Functional and Structural Brain Connectivity in Individuals with High Functioning Autism: A Multimodal MRI Study . PLoS One 8 , e67329 . OpenUrl CrossRef PubMed 111. ↵ Nath , N. , Beltrano , W. , Haynes , L. , Dewey , D. , Bray , S ., 2023 . Long-Term Effects of Preterm Birth on Children’s Brain Structure: An Analysis of the Adolescent Brain Cognitive Development (ABCD) Study . eNeuro 10 . 112. ↵ Nomura , Y. , Brooks-Gunn , J. , Davey , C. , Ham , J. , Fifer , W.P ., 2007 . The role of perinatal problems in risk of co-morbid psychiatric and medical disorders in adulthood . Psychol Med 37 , 1323 – 1334 . OpenUrl CrossRef PubMed Web of Science 113. ↵ Novak , L. , Malinakova , K. , Trnka , R. , Mikoska , P. , Sverak , T. , Kiiski , H. , Tavel , P. , van Dijk , J.P. , 2024 . Neural bases of social deficits in ADHD: A systematic review. Does the Theory of Mind matter? Brain Research Bulletin 215 , 111011 . OpenUrl PubMed 114. ↵ Gallagher , A. , Bulteau , C. , Cohen , D. , Michaud , J.L Nowak , S. , Jacquemont , S ., 2020 . Chapter 24 - The effects of sex on prevalence and mechanisms underlying neurodevelopmental disorders . In: Gallagher , A. , Bulteau , C. , Cohen , D. , Michaud , J.L . (Eds.), Handbook of Clinical Neurology. Elsevier , pp. 327 – 339 . 115. ↵ Orchinik , L.J. , Taylor , H.G. , Espy , K.A. , Minich , N. , Klein , N. , Sheffield , T. , Hack , M ., 2011 . Cognitive outcomes for extremely preterm/extremely low birth weight children in kindergarten . Journal of the International Neuropsychological Society 17 , 1067 – 1079 . OpenUrl CrossRef PubMed 116. ↵ Parenti , I. , Rabaneda , L.G. , Schoen , H. , Novarino , G ., 2020 . Neurodevelopmental Disorders: From Genetics to Functional Pathways . Trends in Neurosciences 43 , 608 – 621 . OpenUrl CrossRef PubMed 117. ↵ Pastorino , G.M.G. , Operto , F.F. , Padovano , C. , Vivenzio , V. , Scuoppo , C. , Pastorino , N. , Roccella , M. , Vetri , L. , Carotenuto , M. , Coppola , G ., 2021 . Social Cognition in Neurodevelopmental Disorders and Epilepsy . Front Neurol 12 , 658823 . OpenUrl PubMed 118. ↵ Patton , G.C. , Coffey , C. , Carlin , J.B. , Olsson , C.A. , Morley , R ., 2004 . Prematurity at birth and adolescent depressive disorder . Br J Psychiatry 184 , 446 – 447 . OpenUrl Abstract / FREE Full Text 119. ↵ Paulus , M.P. , Feinstein , J.S. , Leland , D. , Simmons , A.N ., 2005 . Superior temporal gyrus and insula provide response and outcome-dependent information during assessment and action selection in a decision-making situation . Neuroimage 25 , 607 – 615 . OpenUrl CrossRef PubMed Web of Science 120. ↵ Pelphrey , K.A. , Sasson , N.J. , Reznick , J.S. , Paul , G. , Goldman , B.D. , Piven , J ., 2002 . Visual scanning of faces in autism . J Autism Dev Disord 32 , 249 – 261 . OpenUrl CrossRef PubMed Web of Science 121. ↵ Pelphrey , K.A. , Viola , R.J. , McCarthy , G ., 2004 . When strangers pass: processing of mutual and averted social gaze in the superior temporal sulcus . Psychol Sci 15 , 598 – 603 . OpenUrl CrossRef PubMed Web of Science 122. ↵ Pierce , K ., 2011 . Early functional brain development in autism and the promise of sleep fMRI . Brain Res 1380 , 162 – 174 . OpenUrl CrossRef PubMed Web of Science 123. ↵ Piven , J. , Arndt , S. , Bailey , J. , Havercamp , S. , Andreasen , N.C. , Palmer , P ., 1995 . An MRI study of brain size in autism . Am J Psychiatry 152 , 1145 – 1149 . OpenUrl CrossRef PubMed Web of Science 124. ↵ Platt , M.J ., 2014 . Outcomes in preterm infants . Public Health 128 , 399 – 403 . OpenUrl CrossRef PubMed 125. ↵ Radua , J. , Via , E. , Catani , M. , Mataix-Cols , D ., 2011 . Voxel-based meta-analysis of regional white-matter volume differences in autism spectrum disorder versus healthy controls . Psychol Med 41 , 1539 – 1550 . OpenUrl CrossRef PubMed 126. ↵ Ream , M.A. , Lehwald , L ., 2018 . Neurologic Consequences of Preterm Birth . Curr Neurol Neurosci Rep 18 , 48 . OpenUrl CrossRef PubMed 127. ↵ Rennels , J.L. , Cummings , A.J ., 2013 . Sex Differences in Facial Scanning: Similarities and Dissimilarities Between Infants and Adults . Int J Behav Dev 37 , 111 – 117 . OpenUrl PubMed 128. ↵ Retico , A. , Giuliano , A. , Tancredi , R. , Cosenza , A. , Apicella , F. , Narzisi , A. , Biagi , L. , Tosetti , M. , Muratori , F. , Calderoni , S ., 2016 . The effect of gender on the neuroanatomy of children with autism spectrum disorders: a support vector machine case-control study. Mol Autism 7, 5. Reynolds, M., Culican, S.M., 2023 . Visual Autism. 10 , 606 . OpenUrl 129. ↵ Rogers , C.E. , Lean , R.E. , Wheelock , M.D. , Smyser , C.D ., 2018 . Aberrant structural and functional connectivity and neurodevelopmental impairment in preterm children . J Neurodev Disord 10 , 38 . OpenUrl CrossRef PubMed 130. ↵ Ros-Demarize , R. , Bradley , C. , Kanne , S.M. , Warren , Z. , Boan , A. , Lajonchere , C. , Park , J. , Carpenter , L.A ., 2020 . ASD symptoms in toddlers and preschoolers: An examination of sex differences . Autism Res 13 , 157 – 166 . OpenUrl PubMed 131. ↵ Rutherford , M. , McKenzie , K. , Johnson , T. , Catchpole , C. , O’Hare , A. , McClure , I. , Forsyth , K. , McCartney , D. , Murray , A ., 2016 . Gender ratio in a clinical population sample, age of diagnosis and duration of assessment in children and adults with autism spectrum disorder . Autism 20 , 628 – 634 . OpenUrl CrossRef PubMed 132. ↵ Sadria , M. , Karimi , S. , Layton , A.T ., 2019 . Network centrality analysis of eye-gaze data in autism spectrum disorder . Computers in Biology and Medicine 111 , 103332 . OpenUrl PubMed 133. ↵ Saigal , S. , Doyle , L.W ., 2008 . An overview of mortality and sequelae of preterm birth from infancy to adulthood . The Lancet 371 , 261 – 269 . OpenUrl 134. ↵ Sato , M. , Nakai , N. , Fujima , S. , Choe , K.Y. , Takumi , T ., 2023 . Social circuits and their dysfunction in autism spectrum disorder . Molecular Psychiatry 28 , 3194 – 3206 . OpenUrl CrossRef PubMed 135. ↵ Schendel , D. , Bhasin , T.K ., 2008 . Birth weight and gestational age characteristics of children with autism, including a comparison with other developmental disabilities . Pediatrics 121 , 1155 – 1164 . OpenUrl CrossRef PubMed Web of Science 136. ↵ Schumann , C.M. , Bloss , C.S. , Barnes , C.C. , Wideman , G.M. , Carper , R.A. , Akshoomoff , N. , Pierce , K. , Hagler , D. , Schork , N. , Lord , C. , Courchesne , E ., 2010 . Longitudinal magnetic resonance imaging study of cortical development through early childhood in autism . J Neurosci 30 , 4419 – 4427 . OpenUrl Abstract / FREE Full Text 137. ↵ Serag , A. , Aljabar , P. , Ball , G. , Counsell , S.J. , Boardman , J.P. , Rutherford , M.A. , Edwards , A.D. , Hajnal , J.V. , Rueckert , D ., 2012a . Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression . Neuroimage 59 , 2255 – 2265 . OpenUrl CrossRef PubMed Web of Science 138. ↵ Serag , A.M. , Kyriakopoulou , V. , Rutherford , M.A. , Edwards , A.D. , Hajnal , J.V. , Aljabar , P. , Counsell , S.J. , Boardman , J.P. , Rueckert , D. , 2012b . A Multi-channel 4D Probabilistic Atlas of the Developing Brain: Application to Fetuses and Neonates . 139. ↵ Setien-Ramos , I. , Lugo-Marín , J. , Gisbert-Gustemps , L. , Díez-Villoria , E. , Magán-Maganto , M. , Canal-Bedia , R. , Ramos-Quiroga , J.A ., 2023 . Eye-Tracking Studies in Adults with Autism Spectrum Disorder: A Systematic Review and Meta-analysis . J Autism Dev Disord 53 , 2430 – 2443 . OpenUrl PubMed 140. ↵ Shang , J. , Fisher , P. , Bäuml , J.G. , Daamen , M. , Baumann , N. , Zimmer , C. , Bartmann , P. , Boecker , H. , Wolke , D. , Sorg , C. , Koutsouleris , N. , Dwyer , D.B ., 2019 . A machine learning investigation of volumetric and functional MRI abnormalities in adults born preterm . Hum Brain Mapp 40 , 4239 – 4252 . OpenUrl CrossRef PubMed 141. ↵ Shen , M.D. , Nordahl , C.W. , Young , G.S. , Wootton-Gorges , S.L. , Lee , A. , Liston , S.E. , Harrington , K.R. , Ozonoff , S. , Amaral , D.G ., 2013 . Early brain enlargement and elevated extra-axial fluid in infants who develop autism spectrum disorder . Brain 136 , 2825 – 2835 . OpenUrl CrossRef PubMed Web of Science 142. ↵ Shic , F. , Wang , Q. , Macari , S.L. , Chawarska , K ., 2020 . The role of limited salience of speech in selective attention to faces in toddlers with autism spectrum disorders . J Child Psychol Psychiatry 61 , 459 – 469 . OpenUrl CrossRef PubMed 143. ↵ Simon , A.E. , Keller , P. , Cattapan , K ., 2021 . Commentary about social avoidance and its significance in adolescents and young adults . Psychiatry Research 297 , 113718 . OpenUrl PubMed 144. ↵ Singer , T. , Kiebel , S.J. , Winston , J.S. , Dolan , R.J. , Frith , C.D ., 2004 . Brain responses to the acquired moral status of faces . Neuron 41 , 653 – 662 . OpenUrl CrossRef PubMed Web of Science 145. ↵ Sparks , B.F. , Friedman , S.D. , Shaw , D.W. , Aylward , E.H. , Echelard , D. , Artru , A.A. , Maravilla , K.R. , Giedd , J.N. , Munson , J. , Dawson , G. , Dager , S.R ., 2002 . Brain structural abnormalities in young children with autism spectrum disorder . Neurology 59 , 184 – 192 . OpenUrl CrossRef PubMed 146. ↵ Stuart , N. , Whitehouse , A. , Palermo , R. , Bothe , E. , Badcock , N ., 2023 . Eye Gaze in Autism Spectrum Disorder: A Review of Neural Evidence for the Eye Avoidance Hypothesis . J Autism Dev Disord 53 , 1884 – 1905 . OpenUrl CrossRef PubMed 147. ↵ Tanaka , J.W. , Sung , A ., 2016 . The "Eye Avoidance" Hypothesis of Autism Face Processing . J Autism Dev Disord 46 , 1538 – 1552 . OpenUrl CrossRef PubMed 148. ↵ Telford , E.J. , Fletcher-Watson , S. , Gillespie-Smith , K. , Pataky , R. , Sparrow , S. , Murray , I.C. , O’Hare , A. , Boardman , J.P ., 2016 . Preterm birth is associated with atypical social orienting in infancy detected using eye tracking . J Child Psychol Psychiatry 57 , 861 – 868 . OpenUrl PubMed 149. ↵ Thabtah , F. , Peebles , D ., 2019 . Early Autism Screening: A Comprehensive Review . Int J Environ Res Public Health 16 . 150. ↵ Wang , C. , Geng , H. , Liu , W. , Zhang , G ., 2017a . Prenatal, perinatal, and postnatal factors associated with autism: A meta-analysis . Medicine (Baltimore ) 96 , e6696 . OpenUrl CrossRef PubMed 151. ↵ Wang , H. , Ma , Z.H. , Xu , L.Z. , Yang , L. , Ji , Z.Z. , Tang , X.Z. , Liu , J.R. , Li , X. , Cao , Q.J. , Liu , J ., 2022 . Developmental brain structural atypicalities in autism: a voxel-based morphometry analysis . Child Adolesc Psychiatry Ment Health 16 , 7 . OpenUrl CrossRef PubMed 152. ↵ Wang , J. , Fu , K. , Chen , L. , Duan , X. , Guo , X. , Chen , H. , Wu , Q. , Xia , W. , Wu , L. , Chen , H ., 2017b . Increased Gray Matter Volume and Resting-State Functional Connectivity in Somatosensory Cortex and their Relationship with Autistic Symptoms in Young Boys with Autism Spectrum Disorder . Front Physiol 8 , 588 . OpenUrl PubMed 153. ↵ Wang , Q. , Hoi , S.P. , Wang , Y. , Song , C. , Li , T. , Lam , C.M. , Fang , F. , Yi , L ., 2020 . Out of mind, out of sight? Investigating abnormal face scanning in autism spectrum disorder using gaze-contingent paradigm . Dev Sci 23 , e12856 . OpenUrl PubMed 154. ↵ Whyte , E.M. , Scherf , K.S ., 2018 . Gaze Following Is Related to the Broader Autism Phenotype in a Sex-Specific Way: Building the Case for Distinct Male and Female Autism Phenotypes . 6 , 280 – 287 . OpenUrl 155. ↵ Winter , T ., 2016 . Neuromechanisms of bimanual coordination in the autistic brain: A joint behavioural and neuroimaging study . 156. ↵ Xiao , Z. , Qiu , T. , Ke , X. , Xiao , X. , Xiao , T. , Liang , F. , Zou , B. , Huang , H. , Fang , H. , Chu , K. , Zhang , J. , Liu , Y ., 2014 . Autism spectrum disorder as early neurodevelopmental disorder: evidence from the brain imaging abnormalities in 2-3 years old toddlers . J Autism Dev Disord 44 , 1633 – 1640 . OpenUrl PubMed 157. ↵ Xu , J. , Gannon , P.J. , Emmorey , K. , Smith , J.F. , Braun , A.R ., 2009 . Symbolic gestures and spoken language are processed by a common neural system . Proc Natl Acad Sci U S A 106 , 20664 – 20669 . OpenUrl Abstract / FREE Full Text 158. ↵ Xu , K. , Sun , Z. , Qiao , Z. , Chen , A ., 2024 . Diagnosing autism severity associated with physical fitness and gray matter volume in children with autism spectrum disorder: Explainable machine learning method . Complementary Therapies in Clinical Practice 54 , 101825 . OpenUrl PubMed 159. ↵ Yang , Y. , Bradley , C. , Li , G. , Monfort-Ortiz , R. , Nieto-Del-Amor , F. , Hao , D. , Ye-Lin , Y ., 2024 . A computationally efficient anisotropic electrophysiological multiscale uterus model: From cell to organ and myometrium to abdominal surface . Comput Methods Programs Biomed 257 , 108487 . OpenUrl PubMed 160. ↵ Yaxu , Y. , Ren , Z. , Ward , J. , Jiang , Q ., 2020 . Atypical Brain Structures as a Function of Gray Matter Volume (GMV) and Gray Matter Density (GMD) in Young Adults Relating to Autism Spectrum Traits . Front Psychol 11 , 523 . OpenUrl PubMed 161. ↵ Yi , H.G. , Leonard , M.K. , Chang , E.F ., 2019 . The Encoding of Speech Sounds in the Superior Temporal Gyrus . Neuron 102 , 1096 – 1110 . OpenUrl CrossRef PubMed 162. ↵ Zwaigenbaum , L. , Bryson , S. , Rogers , T. , Roberts , W. , Brian , J. , Szatmari , P ., 2005 . Behavioral manifestations of autism in the first year of life . Int J Dev Neurosci 23 , 143 – 152 . 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Share Gray Matter Volumes of the Superior Temporal Gyrus Link Preterm Birth and Developmentally Disordered Eye Gazing Patterns in Toddlers at Eighteen Months Yanan Su , Guangfei Li , Shanmei Wang , Dongmei Hao , Clara S. Li , Yiyao Ye-Lin , Xiaolin Wang , Ruolin Zhang , Lin Yang , Chiang-Shan R. Li medRxiv 2025.06.18.25329884; doi: https://doi.org/10.1101/2025.06.18.25329884 Share This Article: Copy Citation Tools Gray Matter Volumes of the Superior Temporal Gyrus Link Preterm Birth and Developmentally Disordered Eye Gazing Patterns in Toddlers at Eighteen Months Yanan Su , Guangfei Li , Shanmei Wang , Dongmei Hao , Clara S. Li , Yiyao Ye-Lin , Xiaolin Wang , Ruolin Zhang , Lin Yang , Chiang-Shan R. 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