Cortical connectivity in speech and sensory networks associated with childrens’ listening and attention disorders

Cortical connectivity in speech and sensory networks associated with childrens’ listening and attention disorders | 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 Cortical connectivity in speech and sensory networks associated with childrens’ listening and attention disorders View ORCID Profile Hannah J. Stewart , Erin K. Cash , Lisa L. Hunter , View ORCID Profile Jonathan E. Peelle , Leanne Tamm , View ORCID Profile Stephen P. Becker , Jennifer Vannest , David R. Moore doi: https://doi.org/10.1101/2025.10.22.25338400 Hannah J. Stewart 1 Communication Sciences Research Centre, Cincinnati Children’s Hospital Medical Center , Cincinnati, Ohio, USA 2 Research in Patient Services, Cincinnati Children’s Hospital Medical Center , Cincinnati, Ohio, USA 3 Department of Psychology, Lancaster University , Lancaster, UK 4 Division of Psychology and Language Sciences, University College London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hannah J. Stewart For correspondence: h.stewart5{at}lancaster.ac.uk Erin K. Cash 1 Communication Sciences Research Centre, Cincinnati Children’s Hospital Medical Center , Cincinnati, Ohio, USA 2 Research in Patient Services, Cincinnati Children’s Hospital Medical Center , Cincinnati, Ohio, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lisa L. Hunter 1 Communication Sciences Research Centre, Cincinnati Children’s Hospital Medical Center , Cincinnati, Ohio, USA 2 Research in Patient Services, Cincinnati Children’s Hospital Medical Center , Cincinnati, Ohio, USA 5 Department of Otolaryngology-Head and Neck Surgery, University of Cincinnati , Ohio, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jonathan E. Peelle 6 Department of Communication Sciences and Disorders and Department of Psychology, Northeastern University , Boston, Massachusetts, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jonathan E. Peelle Leanne Tamm 7 Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center , Cincinnati, OH, USA 8 Department of Pediatrics, College of Medicine, University of Cincinnati , Cincinnati, Ohio, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stephen P. Becker 7 Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center , Cincinnati, OH, USA 8 Department of Pediatrics, College of Medicine, University of Cincinnati , Cincinnati, Ohio, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stephen P. Becker Jennifer Vannest 2 Research in Patient Services, Cincinnati Children’s Hospital Medical Center , Cincinnati, Ohio, USA 9 Department of Communication Sciences and Disorders, University of Cincinnati , Ohio, USA 10 Division of Speech-Language Pathology, Cincinnati Children’s Hospital Medical Centre , Ohio, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site David R. Moore 1 Communication Sciences Research Centre, Cincinnati Children’s Hospital Medical Center , Cincinnati, Ohio, USA 2 Research in Patient Services, Cincinnati Children’s Hospital Medical Center , Cincinnati, Ohio, USA 5 Department of Otolaryngology-Head and Neck Surgery, University of Cincinnati , Ohio, USA 9 Department of Communication Sciences and Disorders, University of Cincinnati , Ohio, USA 11 Manchester Centre for Audiology and Deafness, University of Manchester , Manchester, M13 9PL, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Children with neurodevelopmental disorders have a high rate of listening difficulties, but despite decades of research, the relation between these conditions remains unclear. Using resting state fMRI to image the child’s brain noninvasively, we investigate the distribution of speech, non-speech ‘sound’, and visual processing networks in the forebrain, and examine a cross-section of age differences within these networks in children (6-14 years old) with normal hearing, including typically developing children, children with listening difficulties (LiD), and children with attention-deficit/hyperactivity disorder (ADHD). Relative to typically developing children, a reduction in functional connectivity of the speech network was found in children with LiD. No reduction was found in connections processing non-speech sounds or visual stimuli in the children with LiD, suggesting a specific deficit in speech processing. A second group of children diagnosed with ADHD showed reduced connectivity in both speech and sound networks, but not in the visual network, suggesting a common underlying cause for auditory and speech difficulties in the auditory system of children with ADHD. We conclude that listening difficulties in children are mediated by speech-specific neural mechanisms. The findings strengthen research calls for obligatory speech intelligibility testing under challenging listening conditions (noise, reverberation) as a component of clinical pediatric audiological assessment. Introduction Around 10-20% of children and young adults and 30-50% of older people report hearing or listening difficulties (LiD), where they struggle to perceive target sounds, usually speech, in noisy environments ( Moore et al., 2017 ; Moore, 2014 ; Moore et al., 2020 ; Motlagh-Zadeh et al., 2019 ; Su & Chan, 2017 ). The reasons for LiD, especially for listeners with normal hearing sensitivity, have long been a puzzle for audiologists and hearing researchers. In prior work, we recruited a group of children with caregiver-reported LiD and a group of their typically developing (TD) peers. Both groups had normal hearing sensitivity out to 16 kHz ( Hunter et al., 2021 ). Nevertheless, the children with LiD had auditory perceptual deficits and prevalent, wide-ranging neurodevelopmental disorders ( Moore et al., 2018 , 2020 ; Petley et al., 2021 ). Indeed, less than half of children and young adults with difficulties listening have a clinical hearing loss defined by audiometry (i.e., detection of tones in quiet). These findings place the source of LiD in either the central auditory nervous system, or in cortical structures that integrate auditory with other sources of cognitive function (e.g., attention, language, memory, vision) used for speech decoding and understanding. LiD has high coexistence with both symptoms and diagnosis of other neurodevelopmental disorders ( Figure 1 ; ( D. R. Moore et al., 2018 , 2025 ; M. Sharma et al., 2009 ; Tomlin et al., 2015 ). A close relation between clinical hearing loss and neurodevelopmental disorders has also been established ( Chilosi et al., 2010 ; e.g., language: Moeller & Tomblin, 2015 ; Tomblin et al., 2020 ; ADHD: Soleimani et al., 2020 ; autism: Meinzen-Derr et al., 2014 ). However, hearing loss has been dismissed as a general explanation because most children diagnosed with neurodevelopmental disorders are assumed to be audiometrically normal, but with the added complication that a much higher rate of children with developmental disabilities do not access a gold-standard hearing assessment compared to neurotypical children ( Bonino et al., 2024 ). In addition, many children with clinically diagnosed hearing loss lack a neurodevelopmental disorder ( Norbury et al., 2001 ). Yet, compared to their typically developing peers, greater rates of LiD are seen in children with attention-deficit/hyperactivity disorder (ADHD) ( Lanzetta-Valdo et al., 2017 ; Riccio et al., 1994 ), autism ( Dawes et al., 2008 ), developmental language disorder (DLD) ( Ferguson et al., 2013 , 2014 ), reading difficulties ( Dawes et al., 2008 ) and other neurodevelopmental disorders ( Ahmmed, 2021 ). Recently, it has been found that more mild forms of hearing loss, sometimes called “subclinical” hearing loss ( Mishra et al., 2022 ; Moore, Zobay, et al., 2020; Sharma et al., 2023 ), including “hidden” hearing loss ( Liberman, 2015 ; Schaette & McAlpine, 2011 ), and not yet recognized clinically, are associated with impaired speech perception in noise and cognitive performance. The failure of many neurodevelopmental disorder studies and clinics to test hearing has also likely contributed to lack of reported associations. Download figure Open in new tab Figure 1: Relation between hearing loss, listening difficulties and neurodevelopmental disorders. Children with clinical hearing loss and caregiver-reported LiD commonly have other neurodevelopmental disorders (NDD; e.g. ADHD, DLD, autism and dyslexia). It is currently assumed that most children diagnosed with NDD have clinically normal hearing (thresholds < 15-20 dB HL), but many NDD studies and clinics do not test hearing. Recent evidence of “subclinical” hearing loss (thresholds 10-20 dB HL; very high frequency hearing), including “hidden” hearing loss (suprathreshold response impairment), suggest other possible auditory contributions to NDD. Neural and physiological mechanisms of LiD are largely unknown. Previous resting state fMRI studies ( Alvand et al., 2022 ; Stewart et al., 2022 ) have examined the auditory cortex and the temporal lobe in children with LiD, showing a close link between speech and language problems and reduced functional connectivity. However, the cortical markers of LiD may be more widespread across areas associated with neurodevelopmental disorders. In this study we examine whole-brain Speech, non-speech ‘Sound’, and Visual processing networks in TD children, children with LiD, and children with ADHD. We hypothesized that both clinical groups would show altered connectivity of speech networks compared to their TD peers. Such a finding would support a common etiology for LiD across diagnostic categories. Using a meta-analytic approach (Neurosynth: ( Yarkoni et al., 2011 ), we identified separate networks of regions of interest (ROIs) associated with processing Speech and Vision. We also developed a non-speech ‘Sound’ network of ROIs for which we generated a novel meta-analysis based around commonly used research auditory stimuli, such as tones (pure and complex), noises (white/pink/etc.), music and environmental sounds, to act as a control for the Speech ROIs. We used fMRI to examine resting state functional connectivity to assess temporal correlations of spontaneous fluctuations between ROIs, capturing interactions between brain regions in functionally associated networks ( Biswal, 2012 ; Damoiseaux et al., 2006 ). Results Brain connectivity related to listening ability Resting state functional MR images (rsfMRI) were obtained from the same MRI scanner for 39 TD children, 42 children with LiD, and 15 children diagnosed with ADHD ( Figure 2A and B ). All participants were audiometrically normal (Suppl. Figure 1) with the TD and LiD groups defined using a validated caregiver questionnaire, the Evaluation of Children’s Listening and Processing Skills (ECLiPS; Barry & Moore, 2021 ; Denys et al., 2024 ); and the ADHD group diagnosed using the caregiver-administered Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS; Kaufman et al., 1997 ). Download figure Open in new tab Figure 2: Methodology. In the Listening study 81 children aged 6-14 years completed a large battery of behavioral and brain function tests. They all had “normal hearing” as defined by pure tone audiometry for the children with listening difficulty (LiD) and typical development (TD) (See Supplementary Fig. 1 for more details). Children with LiD were recruited through clinical or family referral. TD and LiD were confirmed though a validated caregiver questionnaire (ECLiPS). Children with LiD performed poorly across a wide range of auditory (e.g. LiSN-S) and cognitive (NIH Toolbox) behavioral tasks ( Petley et al., 2021 , see also for full recruitment details). An additional group of children from the ADHD study were processed for comparison with the children tested for LiD. The children with ADHD had “normal hearing” as defined by parental report and medical records. We defined networks of regions of interest (ROI) across the cortex using the Neurosynth online resource with appropriate search terms for meta-analyses (see Methods for more). This created (C) 49 Speech, (D) 69 non-speech Sound and (E) 22 Visual ROIs, parcellated using the Craddock ADHD 200 atlas. Full ROI details are in Supplementary table X. For visualization, BrainNet software was used to display foci in C (Xia, Wang & He, 2013). Max and mean motion were calculated with an outlier’s threshold of 0.9. “Speech”, “Sound” and “Vision” networks of ROIs, defined using Neurosynth and parcellated with The Neuro Bureau ADHD-200 atlas ( Bellec et al., 2017 ), are shown in Figure 2C . As expected, Speech and Sound networks were highly overlapping, but distinct across the superior temporal, frontal, and inferior parietal cortex. Visual networks, serving as a cortical control, were largely limited to the occipital cortex (Suppl. Figure 2). Within network connectivity: Between groups Among children with LiD, Speech network connectivity was notably sparse relative to TD children ( Figure 3A ). We used two approaches to quantify this, both using FDR-correction. First, a GLM controlling for age compared ROI-to-ROI pair connectivity (temporal correlations of spontaneous fluctuations in the BOLD signal), between all ROI pairs within the Speech network (726 pairs at p < .05 as standard for all reported results; Suppl. Figure 3A). Second, ANOVA (F(2) = 73.35, p < .001, η p 2 = .59; Table 1 ) and post-hoc Holm-corrected (family of 3) t-tests compared the averaged connectivity from across the whole Speech cortical network between groups (LiD vs TD t(79) = -11.94, p < .001, d = .35; Figure 3D ; Suppl. Figure 4A; Suppl. Table 1). Download figure Open in new tab Figure 3: Highly specific differences in speech connectivity between children with and without listening difficulties were observed in direct quantitative comparisons between connectivity in the two groups. ROI-ROI resting state connectivity for each group of children in the (A) Speech, (B) Sound and (C) Visual networks. Thicker, more saturated colour lines represent stronger connections between cortical areas. Anatomical representations of these ROI networks in full brain and brain slices are presented in Supplementary Figure 2. Heat maps further showing the quantitative strength of these connections can be found in Supplementary Figure 3. Large significant group differences were found in the (A) Speech network, especially between TD children and children with LiD. Minor group differences were found in the (B) Sound and (C) Visual networks. Cortical connectivity maps illustrating group differences are in Supp. Fig. 4. The averaged connectivity of each network (D) across all of its ROI-to-ROI pairs, separated by group (TD grey, LiD green, ADHD blue; see also Table 1 ). Boxplots show the groups’ upper quartile, median and lower quartile. Glass brain images are in neurological convention and were created in CONN functional connectivity toolbox ( https://web.conn-toolbox.org ). View this table: View inline View popup Download powerpoint Table 1: Significant group differences in averaged connectivity from across the Speech and Sound networks, but not the Visual network. Table shows group means and SD along with ANOVA results, see also Fig. 3D and Sup. Table 3 for post-hoc results comparing between groups. Children with ADHD had an intermediate level of connectivity in the Speech network, with ROI-to-ROI pair analyses ( Figure 3A ; Suppl. Figure 3A) showing less connectivity than the TD group (136 ROI pairs at p < .05) but more than the LiD (29 ROI pairs at p < .05). This pattern with the ADHD group was also found in the averaged connectivity across the Speech network (ADHD vs TD t(60) = -6.79, p < .001, d = .29; ADHD vs LiD t(63) = 3.40, p < .001, d = .46; Figure 2D ; Suppl. Figure 4A; Suppl. Table 1). The Sound network was, in contrast, almost indistinguishable between the LiD and TD groups. Connectivity between all Sound ROI-to-ROI pairs showed only two with significant group differences ( p = .035 and p = .039; Figure. 3B ; Suppl. Figure 3B). Averaged connectivity across the Sound cortical network (F(2) = 3.68, p < .029, η p 2 = .068, Table 1 ; Figure 3D ; Suppl. Figure 4B; Suppl. Table 1) found no LiD vs TD group difference ( p = .81). The ADHD group had a more sparsely connected sound network compared to the other groups and these sparse connections had weaker connectivity than the other groups, as shown by both GLM, assessing connectivity between all Sound ROI-to-ROI pairs (6 pairs for ADHD vs TD; and 4 pairs for ADHD vs LiD, at p < .05; Figure 3B ; Suppl. Figure 3B), and averaged connectivity in the Sound network (ADHD vs TD t(60) = -2.53, p = .039, d = .28; ADHD vs LiD t(63) = 2.36, p = .040, d = .28; Figure 3D ; Suppl. Figure 4B; Suppl. Table 1). The Visual network had very similar connectivity between the three groups (Groups in Figure 2C ; Group differences in Suppl. Figure 4C), with just a single significantly different ROI-to-ROI pair when comparing the ADHD and LiD groups ( p < .05). This was reflected in the averaged connectivity analysis of the Visual network ( p = .32; Table 1 ; Figure 3D ; Suppl. Figure 4C). To check for possible procedural differences between the listening and ADHD studies, we compared network connectivity between age-matched TD children in each study using a GLM (Suppl. Figure 5A). Minor significant connectivity differences were found in six ROI pairs in the Speech network ( p < .05); and six ROI pairs in the Sound network ( p < .05). No differences were found in the Visual network. We also compared network connectivity between children with LiD with (n = 11) or without (n = 31) a diagnosis of auditory processing disorder (APD; Suppl. Figure 5B). APD is a widely used diagnosis, especially in pediatric audiology, and a hypothesized source of LiD ( Dillon & Cameron, 2021 ; Petley et al., 2021 ). No significant connectivity differences were found in the Speech or the Visual network, but two ROI pairs in the Sound network were significantly stronger in LiD children with an APD diagnosis (p < .001). Finally, we similarly compared children with LiD with (n =11 ) and without (n = 31) referrals to speech, language pathology (Suppl. Figure 5C). Results showed 15 ROI pairs with group differences in the Speech network ( p < .05), two in the Sound network ( p = .048) and none in the Visual network. Processing along the auditory cortical pathway To examine effects of LiD within classically-defined central auditory system components of the Speech network, we measured connectivity between ROI pairs along the auditory forebrain pathway for the TD and LiD groups. Pairs were first examined between the left thalamus and bilateral primary auditory areas (Heschl’s gyrus). Results showed stronger connectivity between primary auditory areas (left thalamus and left Heschl’s gyrus; Figure 4A ) for the TD group compared to the LiD. The right thalamus was not highlighted as a ROI in Neurosynth and so was not included in this analysis. As connections between the medial geniculate body (principal nucleus of the auditory thalamus) and primary auditory cortex are homolateral ( Malmierca & Hackett, 2010 ) it is possible that a similar pattern would be found between the right thalamus and right Heschl’s gyrus. Download figure Open in new tab Figure 4: There was a higher proportion of TD dominance in connectivity strength between ROI pairs within the Speech network as we moved from primary auditory cortex to secondary auditory cortex to frontal-temporal areas. Strength of ROI-to-ROI connectivity (left axis) per group (LiD green, TD grey) and effect sizes (yellow dots, right axis) of the group comparisons corrected for age between: (A) basic auditory processing areas: left thalamus (ROIs 67, 113) to left (ROIs 144, 176) and right (ROIs 7, 185) Heschl’s gyrus; (B) secondary auditory cortex areas: left (ROIs 144, 176) and right (ROIs 7, 185) Heschl’s gyrus to left (ROIs 109, 197) and right (ROIs 22, 70) temporal areas; and frontal-to-temporal areas, moving in a dorsal-posterior direction, between left (ROIs 144, 197, 109) and right (ROIs 22, 70) temporal areas to (C) left frontal (ROIs 168, 48, 124, 157, 66, 179) and (D) right frontal (ROIs 60, 94, 59, 129, 110, 155) areas. Significant group differences in connectivity strength were found in: (A) two out of eight ROI pairs, both within the left hemisphere; (B) 9 out of 16 ROI pairs; (C) 24 out of 30 ROI pairs; and (D) 14 out of 30 ROI pairs. * indicates ROI pairs with a significant group difference, p-values were respectively adjusted for the following number of comparisons: 8, 16, 30 and 30. We next examined connectivity of ROI pairs between the primary and secondary auditory cortical areas bilaterally (Heschl’s gyrus and planum temporale in the posterior, superior temporal gyrus). Here, we found a higher proportion of pairs where the TD group had stronger connectivity ( Figure 4B ) compared to the thalamus and primary AC pairs. Primary-secondary AC pairs that showed significant group differences mainly involved the left hemisphere, with left Heschl’s and planum temporale connecting to the left supramarginal gyrus and bilateral superior temporal gyrus. Significant connections from right Heschl’s were with left supramarginal gyrus and left superior temporal gyrus. Finally, we examined ROI pairs between bilateral secondary auditory temporal areas and bilateral temporo-frontal cortical areas used for phonological processing, language recognition, language production, working memory, and social/emotional understanding (bilateral temporal gyrus; left pars triangularis, frontal orbital cortex, and pars opercularis; and right temporal pole, central opercular cortex, insular cortex and precentral gyrus). The left frontal connections showed the strongest group differences in connectivity strength ( Figure 4C ). TD dominance was found in almost all of the pairs (83% from the left temporal and 75% from the right temporal), with non-significant group differences in connections between left temporal areas and left pars triangularis, frontal orbital cortex and pars opercularis, and right temporal areas and left insular cortex and pars opercularis. Comparatively, the right frontal connections only showed a TD dominance in 56% of connections with the left temporal and 25% with the right temporal areas ( Figure 4D ). Significant group differences in connections were mainly between bilateral temporal areas and right temporal pole, and between left temporal and right frontal areas (precentral gyrus, insular cortex and central opercular cortex). Brain connectivity related to hearing and cognitive skills In the larger listening study for which these LiD and TD groups were recruited, extensive behavioral data were available ( Hunter et al., 2021 , 2023 ; Moore et al., 2025 ; Moore, Hugdahl, et al., 2020; Petley et al., 2021 , 2024 ). Brain-behavior relationships were analyzed for the ROI pairs from the Speech network that had shown significant group differences in the previous auditory pathway analysis (* ROI pairs in Figure 4 ; Supp. Table 4). Significant positive correlations were found between connectivity strength and listening (both subjective, ECLiPS, and objective, SCAN) and cognitive skills, but not for speech-in-noise skills across the TD children and children with LiD. Both the strength and significance of brain-behavior correlations increased as the ROI-to-ROI pairs moved rostrally ( Figure 5 ; Supp. Figure 6). Connectivity z-scores from ROI pairs between the left thalamus and primary auditory cortical areas showed positive correlations with listening skills (both outcome measures) but not for speech-in-noise and cognition (.2 ≤ r ≤ .40, p ≤ .027; Figure 5A ; Supp. Figure 6A). Connectivity z-scores from ROI pairs between primary and secondary auditory cortical areas were significantly correlated with listening skills (consistently for the ECLiPS, and sporadically for the SCAN) and cognition, and more uniformly so for pairs including the left primary auditory cortex (.22 ≤ r ≤ .5, p ≤ .049; Figure 5B ; Supp. Figure 6B). Temporo-frontal cortical connectivity z-scores showed very different patterns of results for left and right frontal areas. The connections between bilateral temporal and left frontal areas showed strong positive correlations with listening skills (both outcome measures) and cognition (.23 ≤ r ≤ .58, p ≤ .047; Figure 5C ; Supp. Figure 6C). However, connections between bilateral temporal and right frontal areas showed weaker correlations and only with subjectively measured listening skills (the ECLiPS) and, sporadically, with cognition (.22 ≤ r ≤ .51, p ≤ .048; Figure 5D ; Supp. Figure 6D). These right frontal area connections did not show any correlations with objectively measured listening skills (the SCAN). Download figure Open in new tab Figure 5: Significant brain-behavior correlations in the Speech network increased as we moved from primary auditory cortical areas to secondary auditory cortex areas to frontal-temporal areas. Brain-behaviour correlations were conducted on ROI-to-ROI pairs that showed significant group differences between TD and LiD groups (see Fig 4 ). Significant positive brain-behaviour correlations were found between (A) primary auditory cortical connectivity and general listening skills, but not for speech in noise and cognition; (B) secondary auditory cortical connectivity and listening skills and cognition; (C) temporo-left frontal cortical connectivity and listening skills (questionnaire and behavioural) and cognition, but not for speech-in-noise; and (D) temporo-right frontal cortical connectivity and listening skills (questionnaire) and cognition, but not for listening skills (behavioural) and speech-in-noise. (E) demonstrates the brain-behavior relationship of one of these fronto-temporal connections (Speech ROI-ROI 48-197). Effect of age on brain connectivity Speech network connectivity in both TD and LiD groups generally increased with age ( Figure 6A ), as confirmed by a GLM with FDR-correction (all groups p < .05), comparing ROI-to-ROI connectivity between all ROI pairs and ages separately for each group (TD and LiD: 6/7, 8/9, 10/11, 12/13/14 y.o.; and ADHD 8/9, and 10/12 y.o.). Planned post-hoc GLMs comparing age groups (Supp. Table 5) highlighted age-related increases in connectivity for the TD children within primary and secondary auditory cortical areas (e.g., bilateral Heschl’s gyrus, left superior temporal gyrus, and right planum temporale) until the age of 10/11. TD children showed right hemisphere dominated changes in connectivity including the primary auditory cortex (superior temporal gyrus), planum temporale, precentral gyrus and inferior frontal gyrus between ages 6/7 to 8/9 y.o. They also showed increases in connectivity between the left thalamus and bilateral motor cortex when comparing the youngest to the oldest age groups, 6/7 to 12/13/14 y.o., with no changes in connectivity after 10/11 y.o. Download figure Open in new tab Figure 6: Age related changes in speech and sound connectivity in all three groups. ROI-ROI resting state connectivity for (A) Speech, (B) Sound and (C) Visual networks broken down into age groups for each participant group. Thicker, more saturated color lines represent stronger connections between cortical areas. TD children show significant increases and decreases in connectivity of the Speech network along with increases in connectivity in the Sound network until age 10/11. The children with LiD show big increases in connectivity in the Speech network in the younger age groups, along with smaller increases in the Sound network in the 8/9 and 10/11 y.o. groups. They then show a decrease in connectivity in both the Speech and Sound networks comparing the 10/11 y.o. to the 12/13 y.o.. The children with ADHD show increases in connectivity in both the Speech and Sound networks comparing the 8/9 y.o. to the 10/12 y.o. Only the TD children showed very minor increases in connectivity in the Visual network with age. LiD children also showed an increase in connectivity associated with the left thalamus but, in this group, with the limbic area (left paracingulate gyrus) from 6/7 to 8/9 y.o.. However, in sharp contrast to TD children, children with LiD had a significant decrease in connectivity strength after 10/11 y.o. between auditory and fronto-temporal areas involved in phonological processing and semantic judgements (left pars triangularis and pars opercularis). The children with ADHD showed yet another pattern of development whereby they did not show significant age-related changes until the ages of 10/12. These later age-related changes in the ADHD children showed significant increases in connectivity between the secondary auditory and fronto-temporal cortical areas. The Sound network showed a very different pattern for development with age ( Figure 6B ; all groups p < .05; planned post-hoc analysis in Supp. Table 5). From 6/7 to 10/11 y.o. the TD children showed decreases in connectivity between the left putamen and right limbic areas (cingulate gyrus), and within the right limbic regions (e.g., cingulate gyrus and paracingulate gyrus). Note that Neurosynth did not identify the left cingulate or paracingulate gyrus as part of the Sound network. In the older age ranges, the TD children showed some similarities to the development of the Speech network, with increases in connectivity between and within the left thalamus, and bilateral primary and secondary auditory cortex areas. This pattern continued through to the oldest age group. In contrast, the children with LiD showed slower and later changes in connectivity; 12/13 year olds showed decreased connectivity compared to the younger age groups within the LiD group, between bilateral primary auditory cortex (superior temporal gyrus), right limbic regions (insular cortex and paracingulate gyrus) and parietal areas (right supramarginal gyrus). Between 8/9 and 10/11 y.o. they also showed increases in connectivity between subcortical left pallidum and right limbic areas (paracingulate gyrus); within limbic areas (right paracingulate gyrus and left insular cortex); and between left secondary auditory cortex (superior temporal gyrus) and right middle frontal gyrus. No age-related changes were seen in the Sound network for the children with LiD after age 10/11. The children with ADHD showed just a few increases in connectivity from ages 8/9 to 10/12 between right limbic areas (cingulate and paracingulate gyrus) and left precentral gyrus; and between left middle frontal gyrus and left insular cortex. They also showed an increase between right motor (precentral gyrus) and superior frontal gyrus, often associated with working memory ( Boisgueheneuc et al., 2006 ). There were no significant changes of connectivity with age within the Visual network for the LiD and ADHD groups ( Figure 6C ; Supp. Table 5). The TD children showed a minor increase in connectivity between left lateral occipital cortex and right occipital fusiform gyrus comparing 6/7 to 8/9 year olds. They also showed decreases in connectivity in occipital cortex areas from age 8; within left lateral occipital cortex areas until 10/11 years olds and then between left and right lateral occipital cortices until 12/13/14 years olds. Discussion In this study we examined whole brain functional connectivity of temporal-correlations within three networks (Speech, Sound, and Visual) defined by meta-analysis. The two clinical groups (LiD and ADHD) showed altered connectivity compared to TD children, but also to each other and across age. Children with LiD showed a dramatic and wide-ranging reduction in Speech cortical connectivity compared to TD children and another neurodevelopmental disorder, ADHD. Reduced connectivity in the LiD group related most closely to the childrens’ listening skills, as reported by their caregivers, but also, more weakly, to objectively measured listening skills and cognition. Notably, speech-in-noise ability was not, in general, related to connectivity. Children with ADHD also showed decreased connectivity in both the Speech and Sound networks compared to TD children. However, compared to children with LiD, this decrease was less pronounced in the Speech network and more significant in the Sound network. Considerable changes across the age range studied were also seen in all groups in both Speech and Sound networks, but not in the Visual network. Children with listening difficulties These data show that children with LiD have a speech-listening cortical deficit. Relative to TD children, children with LiD showed very minor differences in their Sound cortical network and none in their Visual cortical network when compared to TD children. This clearly highlights a specific, cortical speech-listening deficit for children with LiD, rather than a more general auditory processing deficit. This interpretation ties in with previous behavioral results showing that these children with LiD had normal audiometry ( Hunter et al., 2021 ), but impaired performance on auditory tasks involving speech ( Hoyda et al., 2025 ; Kojima et al., 2024 ; Petley et al., 2021 ). In addition to changes in cortical speech processing, there is previous evidence for impaired temporal processing of speech signals in the subcortical central auditory nervous system of children with LiD ( Kraus & Anderson, 2016 ). We previously reported that the children with LiD in the current study had significantly increased acoustic reflex growth and slightly reduced auditory brainstem evoked electrical response latencies that correlated with speech in noise and behavioral temporal processing problems ( Hunter et al., 2023 ; Petley et al., 2024 ). Reduced, delayed or distorted auditory evoked responses have been found in the auditory cortex and related, nearby speech areas of children with neurodevelopmental disorders ( Goswami, 2022 ). These findings have subtle functional changes in brainstem and cortical processing in children with LiD. The findings reported here extend other recent rsfMRI studies that identified more punctate cortical connectivity differences between children with and without LiD ( Alvand et al., 2022 ; Stewart et al., 2022 ). While global structural or functional changes have been observed in profound deafness ( Cui et al., 2022 ; Huang et al., 2015 ; Li et al., 2019 ; Park et al., 2018 ; Shi et al., 2016 ), Alvand et al. (2022 , 2023 ) found multimodal regional differences in specific cortical networks of children with LiD. In our previous work ( Stewart et al., 2022 ), a speech recognition task was used in task-based fMRI. We found that children with LiD had significantly less functional connectivity in networks for processing speech intelligibility and semantics, but not for processing phonology, compared to TD children. Further analysis to examine connectivity between these task-based ROIs was not possible due to use of the Sparse/HUSH paradigm ( Hall et al., 1999 ; Vannest et al., 2009 ) that reduces the effects of scanner-related noise. While these auditory tasks did not distinguish clear differences in activation that related to the children’s other behavioural results, the tasks were all specific to speech recognition rather than non-speech sounds (see Rogalsky et al., 2022 for a review of the differences in the neuroanatomy of speech processing dependent on task type). The study reported here took a different approach, using resting state fMRI and a data-driven methodology to delineate Speech and Sound networks, alongside a control sensory network - Vision. The activation likelihood estimation meta-analysis used ( Laird et al., 2011 ; Yarkoni et al., 2011 ) was powerful because it was not limited to a specific task paradigm (e.g., recognition or oddball). Instead, the identified networks represented the cortical areas used across the whole brain to process auditory (speech and sound) and visual stimuli. The connectivity analysis thus allowed assessment of what areas were working in sync, and whether there was a decoupling between areas. Children with ADHD Compared to the TD children, these data show that children with ADHD have weaker Speech and Sound cortical networks. However, their Speech cortical connectivity is stronger than children with LiD. We propose that the similarities in maturation of both the speech and non-speech auditory networks within the ADHD group may reflect changes in bottom-up processes. Although the age range was limited for the ADHD group, there was a small increase in connectivity of both Speech and Sound networks across the ages 8-12 years. Weakness in the Speech network was anticipated, likely reflecting a life-long difficulty attending to disconnected words and conversations. This is consistent with children who have ADHD often exhibiting poorer-than-typical speech and language skills, proposed to be impacted by delayed development of cognitive skills, such as executive function ( Hawkins et al., 2016 ). However, weakness in the Sound cortical network of children with ADHD was unexpected, since reactions to non-speech sounds might be expected to be more subcortical and not subject to the same non-sensory cortical influences that influence speech processing. It is possible that weaker connectivity in multiple cortical networks contributes to poorer attention. However, reduced functional connectivity was not seen for the ADHD group in the Visual network. Weaker connectivity in the Sound network may instead relate to reported benefits in focus when inattentive children listen to task-irrelevant white noise while completing executive function tasks ( Helps et al., 2014 ). Current theories discussing why task-irrelevant noise (usually non-speech sounds) can help children with ADHD (see Pickens et al., 2019 for review) propose that bottom-up processes may contribute to problems attending and listening by children with ADHD. These results suggest at least a partial dissociation between children with ADHD and LiD in that the groups have different network connectivity and development within both the speech and sound networks. Children with ADHD may have generally under-developed cortical processing of auditory (both speech and sound) information. In contrast, the minimal differences in Sound network connectivity between children with LiD and their TD peers, combined with the increasing strength of brain-behaviour relationships in the Speech network from the thalamus to the temporal cortex and then to the frontal areas suggest that children with LiD have subcortical auditory processing similar to TD children. The distinctiveness between the ADHD and LiD groups appears to be influenced by differences in their cortical language and cognitive machinery. Neurodevelopmental disorders, including listening difficulties ( Moore & Hunter, 2013 ), have often been suggested to have more difficulties in common than they do separately. However, the results presented here are more consistent with a transdiagnostic approach ( Astle et al., 2022 ). Although children with LiD and ADHD have overlapping cognitive difficulties, their difficulties may be underpinned by at least some different neural mechanisms. Typically developing children TD children had rich functional connectivity through all three cortical networks. Several connectivity properties could be predicted from adult literature (e.g. Speech: Hickok, 2012 ; Sounds: Lewis et al., 2011 ; Visual: Bullier, 2001 ) and are consistent with previous regional activation data ( Stewart et al., 2022 ). In the Speech and Sound networks, connectivity was distributed across the left and right temporal, frontal and parietal cortex, and through the corpus callosum, with relatively minor but nevertheless distinctive changes during development from age 6-13. The Visual cortical network was also richly connected by age 6 and showed very minor changes with age. Age-related connectivity changes in the Speech network focused in the right hemisphere between ages 6/7 to 8/9, including the primary auditory cortex areas, motor cortex and inferior frontal gyrus (associated with a wider inhibition network, Hampshire et al., 2010 ). But development of connectivity between the left auditory areas and motor cortex continued through to 12/13/14 y.o., consistent with the development of speech lateralization during childhood through either language learning or maturation ( Holland et al., 2007 ; Kadis et al., 2011 ; Karunanayaka et al., 2007 ; Sroka et al., 2015 ; see Enge et al., 2020 for a meta-analysis). Compared to speech processing, the development of sound processing is much less understood. Data from the current study’s TD children extend our understanding of the development of parietal operculum (PO) connectivity to sound areas, showing stronger connectivity with age between the left PO and right supramarginal gyrus, important for rhythm memory ( Oberhuber et al., 2016 ; Schaal et al., 2017 ). The PO is the largest sensory processing neurocircuit ( Jung et al., 2009 ) with bilateral connections across all five lobes. Direct electrical stimulation in adults has demonstrated clear asymmetries, the left frontal and parietal PO showing associations with language and motor, respectively, and the right temporal PO with auditory function ( Mălîia et al., 2018 ). Previous developmental work has shown that functional connectivity between language areas (left IFG pars triangularis) and right PO are stronger in 16-18 year old typically developing adolescents compared to 4-6 year old children ( Youssofzadeh et al., 2018 ). However, both the TD children and children with LiD showed the opposite specialisations for the left and right PO compared to adult results ( Mălîia et al., 2018 ; Youssofzadeh et al., 2018 ), possibly because of bilateral sound and speech processing becoming more lateralized with age ( Enge et al., 2020 ). Speech network brain-behaviour links A decrease in the connectivity of the default mode network (DMN) has been related to impairment of attention and control ( Xu et al., 2016 ) and may therefore be critical for attention aspects of everyday listening. Average path length and betweenness centrality of white matter within the DMN has been significantly correlated with speech in noise behavioral results ( Alvand et al., 2023 ). Decreased DMN gray matter regional homogeneity has also been found in children with APD and attention deficits ( Pluta et al., 2014 ), with further evidence from children with suspected APD highlighting frontal areas specifically ( Farah et al., 2014 ). However, in both these studies structural deficits were not directly compared with behavioral ability, so further interpretation is limited. This study’s brain-behavior relationships were restricted to the LiD and TD groups, and confirmed the specificity of the speech-listening deficit in children with LiD. The proportion of ROI-to-ROI connections having significant correlations with behavioral outcomes (across both the TD and LiD groups) increased from thalamic to primary auditory cortex, from primary to secondary auditory cortex and, finally, to anterior temporal and frontal cortex. This sequential progression of correlated neural activity mirrored the known route of speech synthesis and listening behavior. The ascending auditory system to primary auditory cortex is thought to process mainly foundational sound properties (e.g. pitch, Tramo et al., 2002 ). Secondary auditory cortex and posterior temporal areas process rhythm ( Penhune et al., 1999 ), integrate across frequency, and perform initial phoneme encoding ( DeWitt & Rauschecker, 2012 ). Higher-level, anterior temporal and frontal areas are involved in interpreting and comprehending speech ( DeWitt & Rauschecker, 2012 , 2013 ). However, more dynamic interplays at each of these different levels has recently been illustrated with mice (e.g. Krall et al., 2024 ; Williamson & Polley, 2019 ). No significant correlations were found across the Speech cortical network with spatial release from masking (SRM), a fundamental mechanism of auditory perception that aids listening in noisy environments (R. Y. Litovsky, 2012 ). The behavioral measure indexing SRM was the Spatial Advantage score of the LiSN-S. This index has been reported to detect children having a specific, clinical Spatial Processing Disorder (SPD; Cameron et al., 2011 ; Cameron & Dillon, 2008 ). We chose the Spatial Advantage in part because it segregates sensory from cognitive aspects of listening (D. R. Moore & Dillon, 2018 ; Petley et al., 2021 ). In the behavioral analysis at this time point in our longitudinal study, we did not find the Spatial Advantage to be a significant predictor of everyday listening skills, measured by the ECLiPS ( Petley et al., 2021 ). However, it proved to be a significant predictor when using the greater power of the full longitudinal assessment, as discussed in ( Kojima et al., 2024 ). As this finding was echoed in our cortical imaging results, it raises the intriguing hypothesis that SPD may be a subcortical, predominantly sensory disorder that does not intersect, neurally or behaviorally, with caregiver reports of LiD. Such an interpretation is consistent with other behavioral data suggesting that SPD may be a distinct, easily treatable disorder of childhood ( Cameron et al., 2012 ). Maturation of Speech and Sound functional connectivity The human sense of hearing responds to auditory stimuli prior to birth. Genetic programing and auditory experience, including language, then contribute to the further maturation of hearing and its associated cortical areas. We found age-related changes in connectivity in both the Speech and Sound networks in each of the groups between 6-14 years of age. Overall, these results fit with a long maturation of the auditory cortex into the teenage years ( Calcus, 2024 ; Leibold, 2011 ; R. Litovsky, 2015 ) and beyond ( Eggermont & Moore, 2011 ). Functional connectivity changes in the Speech network of TD children support the developmental framework of the central auditory nervous system set out by Eggermont and Moore (2011) . Thalamocortical connections increase across the age groups (6/7 to 12/13/14 y.o.) and intracortical connections (between primary and secondary auditory cortices) strengthen until age 10/11 y.o. Similar increases in thalamocortical connectivity were observed in the TD children’s Sound network through to ages 12/13/14 y.o., reinforcing the generality of this developmental pattern across overlapping ROIs of the Speech and Sound network. This timeline aligns with the maturation of auditory spectral and temporal processing thresholds (7-9 years; ( Moore et al., 2010 ), and the prolonged development of auditory stream segregation into adolescence (see Calcus, 2024 and Leibold, 2011 for reviews of non-linguistic behavioural and neurophysiological evidence). Connectivity in later cortical stages of the auditory system differed sharply between the clinical groups and TD children, especially in younger age groups, suggesting the likelihood of differences at ages prior to those studied here. Each group showed distinct developmental patterns across auditory networks, particularly in older children. In contrast, the visual network was fully developed by age 6-7 across all groups and showed no age-related changes. These findings align with reports that auditory cortical maturation extends to age 20 ( Eggermont & Moore, 2011 ), while visual development typically completes by age 7 (Mohammed & Khalil, 2020). An unexpected drop in connectivity between auditory and fronto-temporal areas of the Speech network at age 12-13 in children with LiD may reflect a pre-existing weakness exacerbated by puberty, which has been proposed as an important auditory critical period ( Pinheiro et al., 2024 ; Werker & Hensch, 2015 ). This decline may also be influenced by social shifts during the transition to secondary school ( Norton, 1978 ), leading to individual variability in auditory system development and differential impact across clinical groups, with TD children relatively unaffected. This drop in connectivity may also reflect a bidirectional interaction between brain and behaviour during development, as proposed by Jones & Rauwolf (2025) . Whereby children with LiD have learnt to rely on non-speech strategies for processing the world as a consequence of both prior and ongoing difficulties with everyday listening. Limitations The activation likelihood estimation meta-analysis ( Laird et al., 2011 ; Yarkoni et al., 2011 ) used to create our Speech, Sound and Visual networks (in Neurosynth and GingerALE; Eickhoff et al., 2009 ; Turkeltaub et al., 2002 ; Yarkoni et al., 2011 ) did not distinguish between children and adults in the source studies, so we must assume they were a combination of both. This factor may account for some unexpected aspects of the ROIs used here. For example, in the Speech network, the right thalamus was not included, and while other speech ROIs were bilateral, they were not completely mirrored between hemispheres. Whatever the cause, this limitation is important to note, especially when interpreting lateralization and the group differences in connections between temporal and frontal areas in the Speech network. Unfortunately, we do not have the same behavioural data with the ADHD group as we do for the LiD and TD groups. This would have allowed an extremely thorough examination of the brain-behaviour relationships between the two clinical groups. However, with the data we have available, clear conclusions can still be drawn with many new avenues for future research. Clinical implications - The power of caregiver reports : Here we provide biological evidence for LiD as a distinct entity that is less influenced by objective measures (often speech heavy) than by holistic report. Simply by using a caregiver questionnaire regarding everyday listening difficulties the data show us that the cortical connectivity of a non-speech sound network by children with LiD is similar to that of their TD peers. However, the connectivity of a speech specific network is vastly different. Thus we add to previous calls (e.g., Ertmer, 2011 ; Sanchez et al., 2022 ) for obligatory speech intelligibility testing under challenging listening conditions (e.g., noise, reverberation) as part of clinical pediatric auditory assessments, regardless of the referral route. - LiD is not a developmental delay : As evidenced by splitting across age groups, the differences in speech cortical connectivity continue into the teenage years, with little change, for children with LiD. This adds to our longitudinal behavioural analysis ( Kojima et al., 2024 ) showing both cognitive and everyday listening difficulties persist in children with LiD as we followed them for up to six years. - ADHD auditory profile : Children with ADHD had altered cortical connectivity for both speech and non-speech sounds. Their results were positioned between the TD children and children with LiD, thus adding to the growing transdiagnostic literature. Both these auditory networks continued to develop across age groups in the children with ADHD. Conclusions We did not find a common etiology for LiD across diagnostic categories, but functional neural connectivity profiles were predictive of everyday listening difficulties. Cortical connectivity of children with LiD was characterised by a greatly diminished Speech network relative to both TD children and those diagnosed with ADHD. Children with ADHD had weaker speech and sound auditory connectivity networks compared to TD children. Cortical connectivity was generally similar across the age range examined, at least in the ADHD and TD groups. However, for the Speech and Sound networks, changes were found across age in all groups. The data clearly showed that children with both LiD and ADHD maintained altered neural connectivity across age groups. The findings of this study speak to one of the enduring hypotheses concerning language-based learning disabilities in children, that they are actually a form of delayed development. Current results, together with prior behavioral evidence, suggest that LiD, a higher level functional language deficit, may be an enduring problem of neurodevelopment. Materials and Methods Participants Eighty-one children aged 6-14 took part in a larger listening study and were found to have normal hearing thresholds (< 25 dB HL at standard frequencies, Supp. Figure 1). Of these children 39 were typically developing (TD) with no history or diagnosis of developmental disorders or delays and 42 were classified as having LiD from caregiver reports of everyday listening abilities ( Figure 2 ; ECLiPS, Barry & Moore, 2021 ; Denys et al., 2024 ). Of these LiD children, 21 had a caregiver reported diagnosis of ADHD and 21 did not. A further 23 children took part in a separate ADHD study ( Figure 2 ). Fifteen of these children were diagnosed with ADHD after caregiver-administered semi-structured diagnostic interviews using the K-SADS ( Kaufman et al., 1997 ). The remaining eight were classified as TD and were used to compare between study protocols (Supp. Figure 5A). While it was not possible to conduct audiograms with the children from the ADHD study, each had a caregiver report of normal hearing and their medical histories were checked for audiology assessments, of which there were none. Resting state MRI acquisition 3 T Philips Ingenia scanners with a 65-channel head coil and Avotec audiovisual system were used for both the listening and ADHD studies. Both studies collected high-resolution T1-weighted anatomical scans with TR/TE=8.1/3.7 ms, FOV 25.6 x 25.6 x 16.0 cm, matrix 256 x 256 and slice thickness = 1mm. The listening study used a 5-minute long rs-fMRI acquisition with TR/TE = 2000/30 ms, voxel size = 2.5 × 2.5 × 3.5 mm, 39 axial slices in ascending slice order and 150 volumes. The ADHD study had a longer rs-fMRI protocol (10 minutes) with TR/TE = 2000/30 ms, voxel size = 2.8 × 2.8 × 3.0 mm, 39 axial slices in ascending slice order and 300 volumes. All participants were awake and non-sedated throughout their scanning protocols and were asked to look at a white fixation point in the centre of a black screen. In the ADHD study the children were asked to not take any ADHD relevant medication for 24 hours prior to the scan ( Westbrook et al., 2020 ). All the children were scanned at Cincinnati Children’s Hospital by the same MRI techs. However, different 3 T scanners were used for the two studies (TD/LiD and ADHD). See supplementary text for details comparing TD children from both studies to assess the influence of the different scanners on data collection (Supp. Figure 5A). Resting state pre-processing Pre-processing and analysis was performed in the CONN toolbox using standard spatial and temporal pipelines ( Whitfield-Gabrieli & Nieto-Castanon, 2012 ). The ADHD scans were cut so that the first 150 volumes, matching the length of the listening study scans, were analyzed instead of the full 300. The Artifact Detection Tool (ART) within CONN was used to regress out framewise motion. A one-way ANOVA showed no group effect on the number of frames regressed out ( Table 1 ), F (3, 106) = 2.22, p = .09, η p 2 = .059. Regions of interest selection and parcellation Three networks were synthesized through large-scale activation likelihood estimation meta-analysis of published fMRI cortical activations by Neurosynth ( Yarkoni et al., 2011 ) and GingerALE ( Eickhoff et al., 2009 ; Turkeltaub et al., 2002 ). After applying an intensity threshold of 8 to the resulting activations from the meta-analyses the areas of activation were still too large for ROI-to-ROI rs-fMRI connectivity analysis. To make smaller more appropriate ROIs data-driven spatially constrained parcellation ( Craddock et al., 2012 ) was applied to each network using the pediatric ADHD-200 sample ( Bellec et al., 2017 ). Neurosynth used 528 papers with the keyword ‘speech’ to create the Speech network which was parcellated into 49 ROIs ( Figure 2C ; Supp. Figure 2). Similarly, 603 papers with the keywords ‘vision’ and ‘visual system’ were parcellated into 22 ROIs for the Visual network ( Figure 2C ; Supp. Figure 2). To ensure that the Sound network did not include fMRI activations from speech paradigms we used Neurosynth search terms such as ‘auditory’ and ‘listening’ (see Supp. Table 2 for full list of keywords) producing a list of 1,337 unique papers. We removed studies that used speech (syllables, words, multi-talker babble etc.) in their paradigms, leaving non-verbal stimuli (e.g., music, pure/complex tones, environmental sounds) paradigms. This left 195 papers whose cortical activations were entered into GingerALE and produced 69 parcellated ROIs ( Figure 2B ; Supp. Figure 2). See Supp. Table 3 for ROI details (Brodman’s area, center of parcel coordinates and brain regions). Listening study questionnaires and behavioral tasks Listening skills (ECLiPS; Barry & Moore, 2021 ; Denys et al., 2024 ) The children’s everyday listening and communication abilities were collected via the ECLiPS questionnaire . LiD group inclusion in the listening study was defined as an ECLiPS total scaled score < 7 or a previous diagnosis of APD (n = 11). The ECLiPS is composed of 38 items describing commonly observed behaviors (e.g., the ability to follow conversations) which the caregivers rate how much they agreed to each statement on a five-point Likert scale from strongly disagree to strongly agree. Responses are averaged into five subscales (speech & auditory processing; environmental & auditory sensitivity; language/ literacy/ laterality; memory & attention; and pragmatic & social skills), three composite scores (Language, Listening and Social) or a total composite score. All of the ECLiPS scores are scaled for age with the composite and total scores standardized for a population mean of 10 (SD = 3), based on British data ( Barry et al., 2015 ). Communication skills (CCC-2; Bishop, 1998 ) Caregivers also responded to 70 items in the CCC-2 questionnaire asking about their child’s communication skills. Responses make up 10 subscales (speech, syntax, semantics, coherence, initiation, scripted language, context, nonverbal communication, social relations and interests). These subscales then combine to create two composite scores, a General Communication Composite (GCC, all subscales apart from the social relations and interests) and Social Interaction Differences Index. We used the GCC in our analysis. Listening skills (SCAN-3:C; Keith, 2000 ) This is a standardized test for APD for children aged 5 to 12 years. We administered the SCAN-3:C either in a sound-attenuated booth or a quiet room via a laptop with MAYA USB soundcard and Sennheiser HD 215 headphones. The standardized composite score was used in analysis. The SCAN-3:C is made up of four subtests where participants repeat back: low-pass filtered, monosyllabic words in quiet (low-pass Filtered Words); unfiltered monosyllabic words against multi-talker speech at +8dB SNR (Auditory Figure Ground); monosyllabic words (Competing Words-directed ear) or sentences (Competing Sentences) presented in either the left or right ear while different monosyllabic words or sentences (respectively) are presented simultaneously in the other ear. Subtest scores are age-scaled and are combined to make a standardized composite score. Speech Hearing in Noise (LiSN-S; Cameron & Dillon, 2007 ) The Listening in Spatialized Noise-Sentences (LiSN-S) test measures speech listening in informational masking. We administered the US version of the LISN-S ( Cameron et al., 2009 ) either in a sound-attenuated booth or a quiet room via a laptop with task-specific soundcard and Sennheiser HD 215 headphones. We used the standardized Spatial advantage score in our analysis. In the LISN-S participants are asked to repeat back target sentences (T) while ignoring two sets of distractor sentences (D1, D2) in four different listening conditions. The talkers may have the same or different voices, or come from the same or different directions (0°, ± 90° azimuth). From the four listening conditions, the LISN-S software calculates three difference scores to give isolation between the auditory and cognitive contributions of the task ( Moore & Dillon, 2018 ): Talker advantage (different voices - same voice); Spatial advantage (different directions - same direction); and Total advantage (different voices and directions - same voices and directions). Cognition (National Institutes of Health (NIH) Toolbox Cognition Battery; Weintraub et al., 2013 ) We administered the NIH Toolbox either in a sound-attenuated booth or a quiet room via online or an iPad app following the NIH Toolbox procedures. We used the Early Childhood Composite score in our analysis. This composite is an average of four standardized cognitive task scores: Picture Vocabulary test (PVT); Flanker test (Flanker); Dimensional Change Card Sort test (DCCS); and Picture Sequence Memory test (PSMT). The PVT tests vocabulary knowledge through an adaptive assessment where the participant matches the audio recording of a word with one of four pictures that most closely matches the meaning of the word. The Flanker, tests selective attention and the executive control ability of inhibition. The task asks participants to report the direction of a central target (an arrow or fish dependent on the participant’s age) in a horizontal string of four other distractors (also arrow or fish) that may be congruent (same direction as the target) or incongruent (opposite direction). The DCCS assesses attention switching by asking participants to switch between two rules for sorting cards trial by trial (e.g., color and then by shape). Finally, the PSMT assesses episodic memory by first telling the participant audio-recorded stories with an increasing sequence of cards (6 to 18, depending on age) to illustrate each story, and then asking the participant to recall the story by sorting the now jumbled illustrative cards into the correct order. Analysis The mean time course of all voxels within each ROI was used to calculate individual pairwise Pearson correlations for ROI-to-ROI analysis within each network (Speech, Sound and Visual). The r values were normalized to z values via Fisher’s z-transformation. Statistical thresholds were set to p < .05 (FDR-corrected) at the single voxel level, and the resulting connections were thresholded at seed-level by intensity with FDR correction ( p < .05). Within network connectivity: Between groups Connectivity between ROIs for each network (Speech, Sound and Visual) were first compared between groups using a general linear model (GLM) while controlling for age. Post hoc GLMs were run between groups for networks with significant group level differences. Second, the averaged within-network functional connectivity values for each network were extracted from CONN and compared between groups using ANOVA and post-hoc t-tests with Holm-correction in JASP. GLMs also compared TD children from the listening and ADHD studies as a data quality check; children with LiD with and without an APD diagnosis; and children with LiD with and without a speech, language pathology referral. Relating Speech network connectivity to behavior Corrected t-tests compared the strength of connectivity (z-score) of individual Speech network ROI-to-ROI pairs connectivity (z-score) between groups (TD and LiD) ( Figure 4 ). Only these two groups were used in this analysis due to the depth of behavioural measures and audiological assessments used in the listening study. ROI pairs were grouped into basic auditory processing to primary auditory cortex; primary to secondary auditory cortex; and temporal-to-frontal. Connections with significant group differences were further explored using planned Pearson correlations with Bonferroni correction to assess brain-behavior relationships ( Figure 5 ; Supp. Figure 6). Behavioral outcomes were listening skills (subjective: ECLiPS total score; and objective: SCAN composite) communication (GCC from the CCC-2), speech in noise skills (LiSN-S spatial advantage score) and cognition (NIH ECC). Within network connectivity: Across development GLMs were used to compare age groups within each diagnostic group (TD, LiD and ADHD). Post hoc GLMs were run between age groups for networks with significant overall age group differences. Data Availability ROI files can be found at osf.io/qzkh4 Funding This research was supported by NIH R01DC014078, NIH K23MH108603, and a Cincinnati Children’s Research Foundation Trustee Award. David Moore was part supported by the UK National Institute for Health and Care Research (NIHR) Manchester Biomedical Research Centre (BRC) (NIHR203308). Hannah Stewart was part supported by her UKRI Future Leaders Fellowship (MR/X035999/1). Competing interests All authors report no conflicts of interest. Data and materials availability ROI files can be found at osf.io/qzkh4. Author contributions HJS -meta-analysis design and analysis, MRI acquisition and analysis, figures and tables, manuscript preparation and revisions EKC - meta-analysis analysis, figures, assisted with manuscript preparation and revisions LLH - study design, manuscript preparation and revisions JEP - analysis and presentation, manuscript preparation and revisions LT - ADHD study, manuscript revisions SPB - ADHD study, manuscript revisions JV - study design, analysis and presentation, manuscript preparation DRM - meta-analysis design, study design and presentation, analysis and presentation, manuscript preparation and revisions Acknowledgements Thanks to Audrey Perdew and Nicholette Sloat for recruitment and testing in the listening study; to Dr Kimberly Leikin and Dr Ronan McGarrigle for assisting in the listening study MRI acquisition; to Amanda Withrow, Dana Schindler, and Josalyn Foster for recruitment and testing in the ADHD study; to the CCH MRI techs Matt Lanier, Lacey Haas, Kaley Bridgewater, Kelsey Murphy, Brynne Williams and Marty Jones for MRI acquisitions in both studies; Megan Griffiths for Figure 4 and Supp. Figure 6; and Dr Chelsea Blankenship and Jody Caldwell-Kurtzman for their continued support. Many thanks to Dr Jon Dudley for analysis discussions. Abbreviations ADHD attention-deficit/hyperactivity disorder APD auditory processing disorder BOLD blood oxygenation level dependent DMN default mode network FDR false discovery rate FOV field-of-view IC insular cortex IPS intraparietal sulcus LiD listening difficulties MRI magnetic resonance imaging MTG middle temporal gyrus ROI region of interest rsMRI resting state MRI STG superior temporal gyrus TD typically developing TE time to echo TFC temporal fusiform cortex TL temporal lobe TR repetition time References ↵ Ahmmed , A. U . ( 2021 ). Auditory processing disorder and its comorbidities: A need for consistency in test cutoff scores . American Journal of Audiology , 30 ( 1 ), 128 – 144 . OpenUrl PubMed ↵ Alvand , A. , Kuruvilla-Mathew , A. , Kirk , I. J. , Roberts , R. P. , Pedersen , M. , & Purdy , S. C . ( 2022 ). 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