Elucidating microstructural alterations in neurodevelopmental disorders: application of advanced diffusion-weighted imaging in children with Rasopathies

Elucidating microstructural alterations in neurodevelopmental disorders: application of advanced diffusion-weighted imaging in children with Rasopathies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Elucidating microstructural alterations in neurodevelopmental disorders: application of advanced diffusion-weighted imaging in children with Rasopathies Julia Plank, Elveda Gozdas, Erpeng Dai, Chloe McGhee, Mira Raman, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4415218/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Neurodevelopmental disorders (NDDs) can severely impact functioning yet effective treatments are limited. Greater insight into the neurobiology underlying NDDs is critical to the development of successful treatments. Using a genetics-first approach, we investigated the potential of advanced diffusion-weighted imaging (DWI) techniques to characterize the neural microstructure unique to neurofibromatosis type 1 (NF1) and Noonan syndrome (NS). In this prospective study, children with NF1, NS, and typical developing (TD) were scanned using a multi-shell DWI sequence optimized for neurite orientation density and dispersion imaging (NODDI) and diffusion kurtosis imaging (DKI). Region-of-interest and tract-based analysis were conducted on subcortical regions and white matter tracts. Analysis of covariance, principal components, and linear discriminant analysis compared between groups. 88 participants were included: 31 NS, 25 NF1, and 32 TD. Subcortical regions differed between NF1 and NS, particularly in the thalamus where the neurite density index (NDI), orientation dispersion index (ODI), and mean kurtosis (MK) were lower in NF1 compared to NS ( p < .001). The middle cerebellar peduncle showed lower NDI and MK in NF1 compared to NS (both p < .001). Multivariate analyses distinguished between groups using NDI, ODI, and MK measures. Differences in neural microstructure were detected between neurofibromatosis type 1 and Noonan syndrome, particularly in subcortical regions and the middle cerebellar peduncle, in line with pre-clinical evidence. Advanced DWI techniques detected subtle alterations not found in prior work using conventional diffusion tensor imaging. Biological sciences/Neuroscience Health sciences/Biomarkers/Diagnostic markers Health sciences/Diseases/Psychiatric disorders/ADHD Health sciences/Diseases/Psychiatric disorders/Autism spectrum disorders Biological sciences/Genetics Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Neurodevelopmental disorders (NDDs) can have severe impacts on functioning, yet treatment options are limited ( 1 ). Development of appropriate treatments is hindered by the heterogeneity of NDDs and the prevalence of comorbidities ( 2 ). Elucidating the neurobiological mechanisms underlying NDDs is crucial for identification of targets for treatment. Many NDDs have a genetic basis, therefore neuroimaging studies with a ‘genetics-first’ approach may provide insight into the neural structure while limiting the heterogeneity that hinders conclusions. While extensive animal research supports this approach ( 1 ), research on human clinical populations is lacking. Rasopathies are NDDs caused by genetic mutations in the Ras-extracellular signal-regulated kinase mitogen-activated protein kinase signaling pathway ( 3 ). Neurofibromatosis type 1 (NF1) and Noonan syndrome (NS) are common Rasopathies with well-established genetic origins. Mutations in PTPN11, LZTR1, KRAS, SOS1 , or RAF1 genes cause NS, whereas a mutation in nf1 causes NF1 ( 3 ). Autism spectrum disorder, attention-deficit hyperactivity disorder (ADHD), and oppositional defiant disorder are frequent comorbidities ( 4 , 5 ); with several genes implicated in NS also associated with autism spectrum disorder ( 6 ). Pre-clinical studies of NF1 and NS indicate pronounced neural alterations unique to each condition ( 7 ), however, clinical neuroimaging studies in affected individuals suggest similar aberrations ( 8 ). Neuroimaging methods with greater specificity are needed to elucidate differences, should they exist, between the conditions in clinical populations with implications for treatment and translation. Recent advances in MRI techniques may allow a more specific revealing of underlying neural microstructure in NF1 and NS. To our knowledge, advanced diffusion-weighted imaging (DWI) techniques such as neurite orientation density and dispersion imaging (NODDI) and diffusion kurtosis imaging (DKI) have not yet been applied across the brain to clinical populations with NF1 and NS. A previous study of 17 participants with NF1 examined T2-hyperintensities using NODDI and DKI, however they did not compare to controls nor to another clinical syndrome ( 9 ). NODDI and DKI were applied previously to NDDs such as autism spectrum disorder and ADHD ( 10 , 11 ). The results, however, do not indicate a clear pattern across studies. Our study aims to explore the potential of a genetics-first approach in conjunction with advanced diffusion techniques to identify more consistent patterns. We investigated the potential of NODDI and DKI to elucidate microstructural alterations in NF1 and NS. We hypothesized that alterations to NODDI and DKI would yield disparate findings in NF1 and NS consistent with pre-clinical models, e.g., results would reflect promotion of gliogenesis and decreased neurogenesis in NF1, and the opposite in NS ( 12 – 15 ). Identification of differences between NF1 and NS would suggest a genetics-first approach in conjunction with advanced DWI may assist in understanding the neurobiology of NDDs and development of new treatments. 2 Materials and Methods Participants For this prospective study, participants with NF1 and NS were recruited from May 2021 through December 2023 across the United States and Canada. TD participants were recruited using local networks and social media advertising limited to the San Francisco Bay area. 64 members of the participant population were reported previously (NF1 n = 20, NS n = 20, TD n = 26) ( 8 ). The Stanford University School of Medicine Institutional Review Board approved all procedures in this work involving human subjects. Eligible participants in this study included 32 children with NF1, 35 with NS, and 36 TD. Further information is provided in the Supplement . Imaging Protocol The MRI scan was completed on a GE Premier 3.0 Tesla whole-body system using a standard 48-channel head coil (GE Healthcare, Milwaukee, WI). Structural data were collected using a whole-brain high-resolution T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) sequence. DWI data were collected using a multi-shell acquisition with b = 500s/mm 2 (6 directions), b = 1000s/mm 2 (15 directions), b = 2000s/mm 2 (15 directions) and b = 3000s/mm 2 (60 directions). Further parameters are detailed in the Supplement. DWI data were preprocessed (author MR) using FSL 6.0.5 (FMRIB Analysis Group, Oxford, UK), with topup susceptibility-induced distortion correction and eddy for correction of eddy currents-induced distortions and subject movements ( 16 , 17 ). Image Analysis The data analysis pipeline is visualized in Supplementary Fig. 1. NODDI is a multi-compartment biophysical model which models the diffusion signal as a combination of three compartments: cerebrospinal fluid, extracellular, and intracellular, represented by the free water volume fraction, ODI, and NDI, respectively ( 18 , 19 ). NDI estimates the density of axons and dendrites whereas ODI represents their orientational coherence ( 18 ). NODDI can be applied to both grey and white matter. In white matter, higher values of NDI represent greater density of axons, whereas higher values of ODI indicate greater axon fanning and bending. In grey matter, elevated NDI indicates decreased density of dendrites, whereas higher ODI suggests more dispersion of dendrites. The NODDI maps were generated using the NODDI Matlab Toolbox v. 1.05 (UCL, UK) through fitting to the preprocessed diffusion data in MatLab R2023b (Mathworks, Natick, MA). DKI models the diffusion-weighted signal in a similar manner to the diffusion tensor, but with an additional term K to account for the kurtosis of the diffusion distribution ( 20 ). DKI sequences utilize high b -values to probe the complex neural microstructure and quantify the degree of diffusion restriction. The DKI maps for MK were generated using the dtifit command in FSL by including the kurtdir argument for multi-shell data ( 21 ). MK is the average kurtosis in all directions; a higher value indicates greater barriers to diffusion. Similar kurtosis values have been found in white matter and subcortical regions, possibly because both contain myelinated neurons ( 22 ). The maps for each participant were quality checked prior to analysis (JP) ( Supplementary Fig. 2). Average values of NDI, ODI, and MK were extracted from each of the following six regions-of-interest (ROIs): amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. These ROIs comprise grey and white matter, whereas the tracts are predominantly white matter. ROIs and white matter tracts were selected based on prior diffusion tensor imaging studies where significant alterations to parameters such as fractional anisotropy and mean diffusivity were found in NF1 and NS ( 8 , 23 – 25 ). TRActs Constrained by UnderLying Anatomy (TRACULA) delineated 42 major white matter tracts using probabilistic tractography ( 26 ). Further details are in the Supplement . Statistical Analysis For each ROI and white matter tract, an analysis of variance was used to assess between-group differences including age and sex as covariates. The presence of T2-hyperintensities was included as an additional covariate in analysis of subcortical ROIs. Post-hoc analysis was conducted using independent samples pairwise t- tests corrected using the Tukey-Kramer test for unequal groups. Data were visually examined for approximate normality. Tukey-Kramer corrected p -values < .05 were considered statistically significant. At least 18 subjects were required in each group to meet a minimum detectable effect size of d = 0.88, as calculated based on prior diffusion tensor imaging studies on NS and NF1 ( 23 , 27 ). Further details are available in the Supplement. Statistical analysis was completed by JP in R 4.3.1 (R Core Team, 2023). Multivariate approaches were used to complement our univariate results. For tract-based data, we used principal components analysis to extract features and reduce dimensionality using the prcomp package. Principal components analysis is a data-driven technique designed to capture the most significant sources of variability in the data. Univariate results suggested subcortical regions may distinguish NS and NF1, therefore subcortical diffusion parameters were entered into a linear discriminant analysis. This technique was used to predict the probability of belonging to a group based on continuous predictor variables using the MASS package. Leave-one-out cross-validation was used to test the accuracy and specificity of the linear discriminant functions. 3 Results Participant Characteristics A total of 88 subjects (M age =9.36, SD age =2.61; 44 male) were included in analysis (Table 1 ). The flow of participants through the study are shown in Fig. 1 . There were 25 participants in the NF1 group (M age =9.20, SD age =2.27; 15 male); 31 in the NS group (M age =10.0, SD age =3.22; 12 male); and 32 in the TD group (M age =8.88, SD age =2.13; 15 male). Details on participant mutations are in the Supplement . The groups did not differ significantly by age ( p = .090), sex ( p = .259), or pubertal stage ( p > .05) ( Supplementary Table 1 ). Significantly lower scores were found for clinical groups compared to TD on all IQ measures ( p < .010). Results of the sensitivity analysis for T2-hyperintensities are shown in Supplementary Table 2. Table 1 Demographics and descriptive statistics of included participants. All (n = 88) TD (n = 32) NF1 (n = 25) NS (n = 31) Age 9.36 (2.61) 8.88 (2.13) 9.20 (2.27) 10.0 (3.22) Sex (M/F) 44/44 17/15 15/10 12/19 FSIQ 104 (15.4) 115 (12.9) 96.5 (13.3) 98.5 (13.2) VIQ 106 (16.5) 116 (13.1) 101 (17.5) 102 (15.0) PIQ 101 (18.8) 110 (17.0) 92.5 (11.5) 98.7 (21.6) Tanner 1- Stage 1/2/3/4 43/19/14/6 15/11/4/1 9/6/5/3 19/2/5/2 Tanner 2- Stage 1/2/3/4 58/10/11/4 20/7/3/1 15/3/5/0 23/0/3/3 Stimulant 15 0 9 6 Growth hormone 8 0 0 8 Data presented as counts or mean (SD). Tanner 1 refers to Tanner Pubic Hair Scale. Tanner 2 refers to Female Breast Development/Male External Genitalia Scale. Tanner Scales exclude 1 TD, 2 NF1, 2 NS. ANOVA = analysis of variance; FSIQ = Full-Scale Intelligence Quotient; NF1 = neurofibromatosis type 1; NS = Noonan syndrome; PIQ = Performance Intelligence Quotient; TD = typical developing; VIQ = Verbal Intelligence Quotient. ROI analysis of NDI, ODI, and MK In subcortical regions, the NDI results showed a trend of NF1 < NS < TD (Table 2 ). Lower NDI values were found in NF1 and in NS compared to TD (Fig. 2 , Supplementary Fig. 3 ). NDI was lower in NF1 compared to NS in the amygdala, hippocampus, pallidum, and thalamus (all p < .001), with the largest effect observed in the thalamus (estimate − 0.044 [95% CI: -0.053, -0.034], d =-2.36). Overall, NDI detected the greatest number of differences between groups and therefore showed the most sensitivity of the 3 diffusion measures investigated. Table 2 Confidence intervals from analysis of subcortical regions. TD – NF1 TD – NS NF1 – NS ROI Estimate (95% CI) p d Estimate (95% CI) p d Estimate (95% CI) p d NDI Amygdala 0.040 (0.028, 0.051) < .001 1.97 0.009 (-0.001, 0.020) .105 0.45 -0.030 (-0.042, -0.019) < .001 -1.66 Caudate 0.025 (0.009, 0.040) .001 1.00 0.012 (-0.003, 0.026) .134 0.22 -0.013 (-0.028, 0.003) .134 -0.61 Hippocampus 0.061 (0.050, 0.072) < .001 3.34 0.012 (0.002, 0.023) .015 0.59 -0.048 (-0.060, -0.037) < .001 -2.34 Pallidum 0.098 (0.064, 0.132) < .001 1.41 0.034 (0.002, 0.066) .035 0.27 -0.064 (-0.098, -0.030) < .001 -0.98 Putamen 0.007 (-0.004, 0.018) .315 0.37 0.014 (0.003, 0.025) .007 0.50 0.007 (-0.005, 0.018) .322 0.08 Thalamus 0.060 (0.050, 0.070) < .001 3.38 0.016 (0.007, 0.025) < .001 1.07 -0.044 (-0.053, -0.034) < .001 -2.36 ODI Amygdala -0.000 (-0.021, 0.021) .999 0.51 0.005 (-0.014, 0.024) .812 0.15 0.005 (-0.016, 0.026) .825 0.09 Caudate 0.010 (-0.009, 0.029) .409 0.49 -0.010 (-0.028, 0.007) .345 -0.45 -0.021 (-0.040, -0.002) .030 -0.77 Hippocampus 0.012 (-0.007, 0.031) .287 0.66 0.001 (-0.017, 0.018) .997 0.03 -0.011 (-0.031, 0.008) .326 -0.43 Pallidum 0.004 (-0.022, 0.031) .917 0.15 0.008 (-0.017, 0.033) .746 0.14 0.003 (-0.024, 0.030) .955 0.02 Putamen 0.001 (-0.015, 0.016) .996 0.30 -0.015 (-0.029, -0.000) . 043 -0.51 -0.015 (-0.031, 0.000) .051 -0.68 Thalamus 0.012 (0.004, 0.021) .002 1.07 -0.006 (-0.014, 0.002) .193 -0.42 -0.018 (-0.026, -0.010) < .001 -1.39 MK Amygdala 0.014 (-0.012, 0.041) .400 0.45 -0.008 (-0.033, 0.017) .703 -0.30 -0.023 (-0.050, 0.004) .108 -0.71 Caudate -0.008 (-0.061 0.046) .938 -0.05 0.005 (-0.045, 0.055) .971 0.09 0.012 (-0.041, 0.066) .845 0.13 Hippocampus 0.065 (0.044, 0.086) < .001 1.87 0.013 (-0.006, 0.033) .244 0.22 -0.051 (-0.072, -0.030) < .001 -1.37 Pallidum 0.136 (0.075, 0.198) < .001 1.07 0.041 (-0.017, 0.100) .209 0.05 -0.095 (-0.157, -0.033) .001 -0.84 Putamen 0.037 (-0.002, 0.076) .064 0.57 -0.003 (-0.040, 0.033) .971 -0.21 -0.041 (-0.080, -0.017) .039 -0.80 Thalamus 0.075 (0.051, 0.098) < .001 1.86 0.026 (0.004, 0.047) .016 0.53 -0.049 (-0.072, -0.025) < .001 -1.39 CI = Tukey-Kramer corrected and least squares means adjusted confidence intervals; d = Cohen’s d effect size; L = left; MK = mean kurtosis; NDI = neurite density index; ODI = orientation dispersion index; p = Tukey-Kramer corrected p-value from pairwise comparison between two groups; R = right; ROI = region-of-interest; SE = standard error. ODI demonstrated the least sensitivity to changes of the three measures investigated, and the results did not show a clear pattern across the groups. We found evidence of decreased ODI in NF1 compared to NS in the caudate (estimate − 0.021 [95% CI: -0.040, -0.002], p = .030) and thalamus (estimate − 0.018 [95% CI: -0.026, -0.010], d =-1.39) p < .001). The pattern of MK results was like NDI where NF1 < NS < TD. Lower MK was found in NF1 compared to TD in the hippocampus, pallidum, and thalamus (all p < .001). When comparing TD and NS, lower MK was found in NS compared to TD in the thalamus only ( p = .016). MK was lower in NF1 compared to NS in the hippocampus ( p < .001), pallidum ( p = .001), putamen ( p = .039), and thalamus ( p < .001). Tract-based analysis of NDI, ODI, and MK The number of participants included in analysis for each tract are shown in Supplementary Table 3 . In each of the 39 white matter tracts, we found lower NDI values in NF1 and NS compared to TD (all p < .001) ( Supplementary Figs. 4 and 5 ). However, only two tracts showed evidence of differences between the clinical groups. Lower NDI was found in NF1 compared to NS in the parietal body of the corpus callosum (estimate − 0.018 [95% CI: -0.036, -0.000], p = .046) and in the middle cerebellar peduncle (estimate − 0.038 [95% CI: -0.056, -0.021], p < .001). Lower ODI was found in NF1 compared to TD in nine of the tracts; in TD compared to NS in three tracts; and in NF1 compared to NS in 18 tracts. Like NDI, MK was lower in NS compared to TD in all 39 tracts ( p < .01). MK was also lower in NF1 compared to TD in all tracts except the rostrum of the corpus callosum and the left extreme capsule. The only difference between the clinical groups was found in the middle cerebellar peduncle where MK was lower in NF1 compared to NS (estimate − 0.057 [95% CI: -0.089, -0.026], p < .001). Given the similar findings in NDI and MK, we tested the associations between these parameters using Pearson correlations. The results suggest the parameters are highly positively correlated, particularly in the white matter tracts. For example, MK and NDI were positively correlated in the middle cerebellar peduncle ( R = 0.81, p < .001) ( Supplementary Fig. 6) . Multivariate analysis To complement the univariate results, values for each white matter tract were entered into a principal components analysis. The first component alone represented 49.0% of the variance with similar contributions from each tract (Fig. 3 A-D). The second component represented 11.6% of the variance (cumulative variance of 60.6%) again with similar contributions from each tract. The eigenvalues for the first 10 principal components are detailed in Supplementary Table 4. A biplot representation of the loadings in PC1 and PC2 suggest some discrimination between TD and the clinical groups (NF1, NS). Subjects in the clinical groups generally have lower values on PC1 compared to subjects in the TD group. Given that NDI and MK values contribute the most to PC1, we can infer that lower values of NDI and MK are generally found in clinical groups compared to TD. Upon visual inspection of the biplot with respect to PC2, subjects in the clinical and TD groups appear to have similar loadings. ODI values contributed the most to PC2, suggesting the groups may have similar ODI values. These inferences consolidate our pattern of univariate results. As the univariate results showed several differences between clinical groups in the subcortical regions, we tested the ability of the subcortical diffusion values to classify the subjects into groups using linear discriminant analysis. The discriminant functions classified participants with NF1, TD, and NS with 98%, 85% and 84% accuracy respectively ( Supplementary Table 5 ). Two linear discriminant functions were generated, LD1 and LD2 ( Supplementary Table 6 ). For LD1, NDI in the thalamus contributed the most with a weighting of -1.92. Following the leave-one-out cross-validation, the accuracy of the model in classifying NF1 remained high at 97.2% though there were drops in accuracy for TD and NS classifications to 71% and 70% respectively. Figure 4 A-D shows that the NF1 observations can be separated from TD and NS observations, however TD and NS observations show overlap. 4 Discussion Development of effective treatments for NDDs is hindered by the heterogeneity of the disorders and limited understanding of the underlying neurobiology. This study utilized a genetics-first approach to investigate the potential of advanced DWI techniques to elucidate the microstructural abnormalities existent in the brains of children with NF1 and NS. Analysis of white matter tracts found widespread differences between TD and the clinical groups. The middle cerebellar peduncle showed differences between NF1 and NS in both NDI and MK (p < .001). Principal components analysis confirmed that the clinical groups differ most from TD in white matter tract-based NDI and MK, whereas ODI values appear similar across the groups. The subcortical regions showed several differences between NF1 and NS, to the extent that a linear discriminant analysis could classify participants with NF1 an accuracy rate of 97%. These results have important implications for future neuroimaging research in clinical populations with NDDs. Prior diffusion tensor imaging studies on children with NF1 and NS found widespread reduced fractional anisotropy and increased mean diffusivity in both conditions ( 8 , 23 , 25 ). Fractional anisotropy and mean diffusivity are sensitive to disruptions in microstructural integrity; however, they are also non-specific measures and difficult to interpret in voxels with complex microstructural properties (e.g., crossing fibers) ( 28 , 29 ). Pre-clinical studies suggest microstructural differences between NF1 and NS ( 14 , 30 ), however, previous neuroimaging studies in the clinical populations yielded largely identical results in both conditions ( 8 , 23 , 25 ). Our results, particularly in the subcortical regions and the middle cerebellar peduncle, suggest microstructural abnormalities that are unique to each condition. Use of diffusion models with greater specificity, NODDI and DKI, enabled improved elucidation of the brain structure in clinical populations with NF1 and NS. Our observations in NF1 and NS are in alignment with pre-clinical evidence. In NF1, proliferation of astrocytes may disturb the spacing between axons and drive a decrease in observed NDI and MK ( 15 ). In NS, observed reductions in NDI and MK are likely due to defects in the oligodendrocyte lineage and the consequent reduction in myelinated axons ( 13 ). Diffusion parameters in the subcortical regions seemed to distinguish NF1 and NS more so than in the white matter tracts, suggesting the grey matter may be the major source of differences between the two conditions. At a cellular level, it is not yet clear why grey matter may be responsible for between-group differences in NF1 and NS. A previous pre-clinical study of NF1 found astrocyte proliferation restricted to the grey matter ( 31 ), though more recent work indicates astrocyte proliferation in both white and grey matter ( 32 ). The thalamus, a primarily grey matter region, showed lower values of NDI, MK, and ODI in NF1 compared to NS. NDI and MK in the thalamus also contributed the strongest weighting to the linear discriminant function. The thalamus has previously been highlighted in NF1 due to the presence of T2-hyperintensities ( 33 ). However, our sensitivity analysis showed that even when all participants with T2-hyperintensities were excluded, the significant results in the thalamus remain. Other studies have also demonstrated that alterations to DWI parameters persist regardless of the presence of T2-hyperintensities ( 34 ). The presence of T2-hyperintensities therefore is not the sole influence on differences between NF1 and NS in the thalamus. Further research is needed to understand the effects of NF1 and NS on grey matter. Of the white matter tracts, the middle cerebellar peduncle emerged as the only tract where NDI and MK were both lower in NF1 compared to NS. The middle cerebellar peduncle is the major afferent pathway to the cerebellum, a region often implicated in NF1 due to the presence of T2-hyperintensities ( 9 ). This tract is vulnerable to abnormalities, for example myelin abnormalities and intra-myelinic edema – both of which have been implicated in NF1 ( 34 , 35 ). Future research may further examine the middle cerebellar peduncle to understand the importance of its role in NDDs. The strengths of this study lie in the genetics-first approach and application of advanced DWI techniques to NDDs. By focusing on NDDs with specific genetic mutations, we reduced heterogeneity that typically hinders NDD studies. NF1 and NS have similar behavioral profiles and previous DWI of these clinical populations suggested comparable microstructural abnormalities ( 8 ). However, the specificity conferred by NODDI and DKI models enabled elucidation of microstructural differences between the conditions. To our knowledge, this is the first study to apply NODDI and DKI across the whole brain to clinical populations with NF1 and NS. Given that DWI indirectly assesses alterations to brain microstructure, we are not able to derive with certainty the primary drivers of the differences in NDI and MK between the clinical groups. Further research on pre-clinical models coupled with neuroimaging is needed to assess the major influences on NDI and MK in these populations. This study provides evidence of the ability of NODDI and DKI to enhance understanding of NDDs in clinical populations particularly when used in conjunction with a genetics-first methodology. Future research should consider the use of advanced diffusion MRI techniques to elucidate the microstructure underlying NDDs in clinical populations. Use of a genetics-first approach may assist in identifying the distinct neurobiology in NDDs with a genetic basis, such as in autism spectrum disorder and ADHD. These approaches enable greater insight into the underlying neurobiology in NDDs and ultimately may assist in the development of more effective treatments for NDDs. Declarations Conflict of Interest: The authors report no conflicts of interest. Acknowledgments: We thank the families who participated in this research. The authors would also like to thank the Noonan Syndrome Foundation and the RASopathies Network which made this work possible. We would like to thank Stanford University and the Stanford Research Computing Center for providing computational resources and support that contributed to these research results, some of the computing for this project was performed on the Sherlock cluster. We gratefully acknowledge the support of The Lucas Service Center at Stanford. References Homberg JR, Kyzar EJ, Stewart AM, Nguyen M, Poudel MK, Echevarria DJ, et al. Improving treatment of neurodevelopmental disorders: recommendations based on preclinical studies. 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Fattah M, Raman MM, Reiss AL, Green T. PTPN11 Mutations in the Ras-MAPK Signaling Pathway Affect Human White Matter Microstructure. Cereb Cortex. 2021;31(3):1489–99. Karlsgodt KH, Rosser T, Lutkenhoff ES, Cannon TD, Silva A, Bearden CE. Alterations in white matter microstructure in neurofibromatosis-1. PLoS One. 2012;7(10):e47854. Bruckert L, Travis KE, Tam LT, Yeom KW, Campen CJ. Age-related white matter differences in children with neurofibromatosis type 1 compared to controls [Internet]. bioRxiv. 2023. Available from: https://www.medrxiv.org/content/ 10.1101/2023.09.29.23295837.abstract Yendiki A, Panneck P, Srinivasan P, Stevens A, Zöllei L, Augustinack J, et al. Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy. Front Neuroinform. 2011;5:23. Tam LT, Ng NN, McKenna ES, Bruckert L, Yeom KW, Campen CJ. Effects of Age on White Matter Microstructure in Children With Neurofibromatosis Type 1. J Child Neurol. 2021;36(10):894–900. Wheeler-Kingshott CAM, Cercignani M. About “axial” and “radial” diffusivities. Magn Reson Med. 2009;61(5):1255–60. Figley CR, Uddin MN, Wong K, Kornelsen J, Puig J, Figley TD. Potential Pitfalls of Using Fractional Anisotropy, Axial Diffusivity, and Radial Diffusivity as Biomarkers of Cerebral White Matter Microstructure. Front Neurosci. 2021;15:799576. Lee DY, Yeh T-H, Emnett RJ, White CR, Gutmann DH. Neurofibromatosis-1 regulates neuroglial progenitor proliferation and glial differentiation in a brain region-specific manner. Genes Dev. 2010;24(20):2317–29. Zhu Y, Romero MI, Ghosh P, Ye Z, Charnay P, Rushing EJ, et al. Ablation of NF1 function in neurons induces abnormal development of cerebral cortex and reactive gliosis in the brain. Genes Dev. 2001;15(7):859–76. Zhu Y, Harada T, Liu L, Lush ME, Guignard F, Harada C, et al. Inactivation of NF1 in CNS causes increased glial progenitor proliferation and optic glioma formation. Development. 2005;132(24):5577–88. Calvez S, Levy R, Calvez R, Roux C-J, Grévent D, Purcell Y, et al. Focal Areas of High Signal Intensity in Children with Neurofibromatosis Type 1: Expected Evolution on MRI. AJNR Am J Neuroradiol. 2020;41(9):1733–9. Baudou E, Nemmi F, Biotteau M, Maziero S, Assaiante C, Cignetti F, et al. Are morphological and structural MRI characteristics related to specific cognitive impairments in neurofibromatosis type 1 (NF1) children? Eur J Paediatr Neurol. 2020;28:89–100. Morales H, Tomsick T. Middle cerebellar peduncles: Magnetic resonance imaging and pathophysiologic correlate. World J Radiol. 2015;7(12):438–47. Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files MPSupplementaryMaterial20240513.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4415218","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":304598304,"identity":"a858be41-0cd7-424b-8f2d-1f4c750d9402","order_by":0,"name":"Julia Plank","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYBACxgbmhgMMDBZyYF4FSICBwYCAFkaQFgljBjYg7wwxWiBqGCQSG4jWwjwjsfHAzx0S6fPnNz97cKDinmwDe/M2Cbx2zEhsONh7RiJ3wzE2c4MDZ4qNG3iOlRHUcoC3DaiFjcFM+mNbQmKDRI4ZYVv+tkmky7exf5M4CNIi/4awlsNAWxIYjvGYQbRI8BDQ0vOw4bDsGQnDDcdyyiQOnEkwbuNJK7bAp8WwPfnwx7c7bOTlm49vkzhQkSDbz3544w28WhoYoFEDA2z4lIOAPAO6llEwCkbBKBgF6AAAFxtQhR8hWGkAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0008-5929-0651","institution":"Stanford University","correspondingAuthor":true,"prefix":"","firstName":"Julia","middleName":"","lastName":"Plank","suffix":""},{"id":304598305,"identity":"ab22f03f-47d5-4570-9d58-d717ead37b76","order_by":1,"name":"Elveda Gozdas","email":"","orcid":"https://orcid.org/0000-0002-9726-9211","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Elveda","middleName":"","lastName":"Gozdas","suffix":""},{"id":304598306,"identity":"4d081b1c-6d49-42d3-a45c-d75afb01c9e7","order_by":2,"name":"Erpeng Dai","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Erpeng","middleName":"","lastName":"Dai","suffix":""},{"id":304598307,"identity":"ff25ab75-9334-49ce-8868-ecddfd573b8b","order_by":3,"name":"Chloe McGhee","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Chloe","middleName":"","lastName":"McGhee","suffix":""},{"id":304598308,"identity":"a4be1423-d04e-47fa-a722-c03d96a42990","order_by":4,"name":"Mira Raman","email":"","orcid":"https://orcid.org/0000-0002-9876-5364","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mira","middleName":"","lastName":"Raman","suffix":""},{"id":304598309,"identity":"7310c3dd-ead2-4312-a676-8e38dcaa07df","order_by":5,"name":"Tamar Green","email":"","orcid":"https://orcid.org/0000-0001-5661-8297","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Tamar","middleName":"","lastName":"Green","suffix":""}],"badges":[],"createdAt":"2024-05-13 20:35:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4415218/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4415218/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57712393,"identity":"492be5c1-5c33-45d1-b694-e58021c1ac89","added_by":"auto","created_at":"2024-06-04 16:12:59","extension":"tif","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60083,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of participants through the study.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNF1 = neurofibromatosis type 1; NS = Noonan syndrome; TD = typical developing.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4415218/v1/c9a18c5362e76b8f632d2885.tif"},{"id":57712394,"identity":"4c77ae9b-8e42-41ca-aeae-0298a6f23fec","added_by":"auto","created_at":"2024-06-04 16:13:00","extension":"tif","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":188342,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect sizes in subcortical ROIs for NDI, ODI, and MK between groups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROIs with a large effect size (Cohen’s \u003cem\u003ed\u003c/em\u003e ³0.80) are labelled. Effect sizes were converted to absolute values for purpose of visualization. Visualization created using \u003cem\u003eggseg3d \u003c/em\u003epackage in R 4.3.1 (R Core Team, 2023). \u003cem\u003eNDI = neurite density index; NF1 = neurofibromatosis type 1; NS = Noonan syndrome; MK = mean kurtosis; ODI = orientation dispersion index; TD = typical developing.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-4415218/v1/b3b414b52d4c70fb868c6e2a.tif"},{"id":57712396,"identity":"73438775-a3be-41c2-a05f-dc1c98984df1","added_by":"auto","created_at":"2024-06-04 16:13:00","extension":"tif","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":58456,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrincipal components analysis of tract data.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA) \u003c/strong\u003eScree plot of explained variances by each dimension. The first dimension (PC1) explains 49% of the variance in the dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB) \u003c/strong\u003eBiplot of first and second principal components. The values of the datapoints represent loadings on each principal component while colours indicate group membership (blue for TD, pink for NF1, and orange for NS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC) \u003c/strong\u003eBar plot of the top 10 contributions to the first dimension (PC1). The contributions are roughly equal. Dark blue indicates NDI data and light blue indicates MK data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD) \u003c/strong\u003eBar plot of the top 10 contributions to the second dimension (PC2). The left arcuate fasciculus contributes the most to PC2. All contributions shown are from ODI data.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAF = arcuate fasciculus; ATR = anterior thalamic radiation; CBD = cingulum bundle-dorsal; CC = corpus callosum; CST = corticospinal tract; FAT = frontal aslant tract; ILF = inferior longitudinal fasciculus; L = left; MLF = middle longitudinal fasciculus; MK = mean kurtosis; NDI = neurite density index; ODI = orientation dispersion index; PCA = principal components analysis; R = right; SLF = superior longitudinal fasciculus; TD = typical developing\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure3.tif","url":"https://assets-eu.researchsquare.com/files/rs-4415218/v1/58673b97d8a1f336c783c332.tif"},{"id":57713016,"identity":"bbac9f85-2eff-4273-a168-51d6fd8fb5c8","added_by":"auto","created_at":"2024-06-04 16:21:00","extension":"tif","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":98676,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLinear discriminant analysis of subcortical ROI data.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA) \u003c/strong\u003eScatterplot showing separation between groups TD (blue), NF1 (pink), and NS (orange).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB) \u003c/strong\u003eHistogram showing some overlap between TD and NS distribution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC) \u003c/strong\u003eHistogram showing complete separation between TD and NF1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD) \u003c/strong\u003eHistogram showing almost complete separation between NF1 and NS.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNDI = neurite density index; NS = Noonan Syndrome; ROI = region-of-interest; TD = typical developing.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure4.tif","url":"https://assets-eu.researchsquare.com/files/rs-4415218/v1/3b54cfdf3fa284d7a2e17614.tif"},{"id":57713818,"identity":"924c76a4-38d2-4337-9740-1ecea59b04cc","added_by":"auto","created_at":"2024-06-04 16:29:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1202256,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4415218/v1/76b4503b-4d35-4f37-8875-93376a2f827e.pdf"},{"id":57712398,"identity":"6389c382-d17b-4098-a139-12b6d57047ba","added_by":"auto","created_at":"2024-06-04 16:13:00","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":7524618,"visible":true,"origin":"","legend":"","description":"","filename":"MPSupplementaryMaterial20240513.docx","url":"https://assets-eu.researchsquare.com/files/rs-4415218/v1/2e81ceaa1734b40923dd5512.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Elucidating microstructural alterations in neurodevelopmental disorders: application of advanced diffusion-weighted imaging in children with Rasopathies","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eNeurodevelopmental disorders (NDDs) can have severe impacts on functioning, yet treatment options are limited (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Development of appropriate treatments is hindered by the heterogeneity of NDDs and the prevalence of comorbidities (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Elucidating the neurobiological mechanisms underlying NDDs is crucial for identification of targets for treatment. Many NDDs have a genetic basis, therefore neuroimaging studies with a \u0026lsquo;genetics-first\u0026rsquo; approach may provide insight into the neural structure while limiting the heterogeneity that hinders conclusions. While extensive animal research supports this approach (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), research on human clinical populations is lacking.\u003c/p\u003e \u003cp\u003eRasopathies are NDDs caused by genetic mutations in the Ras-extracellular signal-regulated kinase mitogen-activated protein kinase signaling pathway (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Neurofibromatosis type 1 (NF1) and Noonan syndrome (NS) are common Rasopathies with well-established genetic origins. Mutations in \u003cem\u003ePTPN11, LZTR1, KRAS, SOS1\u003c/em\u003e, or \u003cem\u003eRAF1\u003c/em\u003e genes cause NS, whereas a mutation in \u003cem\u003enf1\u003c/em\u003e causes NF1 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Autism spectrum disorder, attention-deficit hyperactivity disorder (ADHD), and oppositional defiant disorder are frequent comorbidities (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e); with several genes implicated in NS also associated with autism spectrum disorder (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Pre-clinical studies of NF1 and NS indicate pronounced neural alterations unique to each condition (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), however, clinical neuroimaging studies in affected individuals suggest similar aberrations (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Neuroimaging methods with greater specificity are needed to elucidate differences, should they exist, between the conditions in clinical populations with implications for treatment and translation.\u003c/p\u003e \u003cp\u003eRecent advances in MRI techniques may allow a more specific revealing of underlying neural microstructure in NF1 and NS. To our knowledge, advanced diffusion-weighted imaging (DWI) techniques such as neurite orientation density and dispersion imaging (NODDI) and diffusion kurtosis imaging (DKI) have not yet been applied across the brain to clinical populations with NF1 and NS. A previous study of 17 participants with NF1 examined T2-hyperintensities using NODDI and DKI, however they did not compare to controls nor to another clinical syndrome (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). NODDI and DKI were applied previously to NDDs such as autism spectrum disorder and ADHD (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The results, however, do not indicate a clear pattern across studies. Our study aims to explore the potential of a genetics-first approach in conjunction with advanced diffusion techniques to identify more consistent patterns.\u003c/p\u003e \u003cp\u003eWe investigated the potential of NODDI and DKI to elucidate microstructural alterations in NF1 and NS. We hypothesized that alterations to NODDI and DKI would yield disparate findings in NF1 and NS consistent with pre-clinical models, e.g., results would reflect promotion of gliogenesis and decreased neurogenesis in NF1, and the opposite in NS (\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Identification of differences between NF1 and NS would suggest a genetics-first approach in conjunction with advanced DWI may assist in understanding the neurobiology of NDDs and development of new treatments.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cp\u003eParticipants\u003c/p\u003e \u003cp\u003eFor this prospective study, participants with NF1 and NS were recruited from May 2021 through December 2023 across the United States and Canada. TD participants were recruited using local networks and social media advertising limited to the San Francisco Bay area. 64 members of the participant population were reported previously (NF1 n\u0026thinsp;=\u0026thinsp;20, NS n\u0026thinsp;=\u0026thinsp;20, TD n\u0026thinsp;=\u0026thinsp;26) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The Stanford University School of Medicine Institutional Review Board approved all procedures in this work involving human subjects. Eligible participants in this study included 32 children with NF1, 35 with NS, and 36 TD. Further information is provided in the \u003cb\u003eSupplement\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eImaging Protocol\u003c/p\u003e \u003cp\u003eThe MRI scan was completed on a GE Premier 3.0 Tesla whole-body system using a standard 48-channel head coil (GE Healthcare, Milwaukee, WI). Structural data were collected using a whole-brain high-resolution T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) sequence. DWI data were collected using a multi-shell acquisition with \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;500s/mm\u003csup\u003e2\u003c/sup\u003e (6 directions), \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1000s/mm\u003csup\u003e2\u003c/sup\u003e (15 directions), \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2000s/mm\u003csup\u003e2\u003c/sup\u003e (15 directions) and \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3000s/mm\u003csup\u003e2\u003c/sup\u003e (60 directions). Further parameters are detailed in the \u003cb\u003eSupplement.\u003c/b\u003e DWI data were preprocessed (author MR) using FSL 6.0.5 (FMRIB Analysis Group, Oxford, UK), with \u003cem\u003etopup\u003c/em\u003e susceptibility-induced distortion correction and \u003cem\u003eeddy\u003c/em\u003e for correction of eddy currents-induced distortions and subject movements (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImage Analysis\u003c/p\u003e \u003cp\u003eThe data analysis pipeline is visualized in \u003cb\u003eSupplementary Fig.\u0026nbsp;1.\u003c/b\u003e NODDI is a multi-compartment biophysical model which models the diffusion signal as a combination of three compartments: cerebrospinal fluid, extracellular, and intracellular, represented by the free water volume fraction, ODI, and NDI, respectively (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). NDI estimates the density of axons and dendrites whereas ODI represents their orientational coherence (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). NODDI can be applied to both grey and white matter. In white matter, higher values of NDI represent greater density of axons, whereas higher values of ODI indicate greater axon fanning and bending. In grey matter, elevated NDI indicates decreased density of dendrites, whereas higher ODI suggests more dispersion of dendrites. The NODDI maps were generated using the NODDI Matlab Toolbox v. 1.05 (UCL, UK) through fitting to the preprocessed diffusion data in MatLab R2023b (Mathworks, Natick, MA).\u003c/p\u003e \u003cp\u003eDKI models the diffusion-weighted signal in a similar manner to the diffusion tensor, but with an additional term \u003cem\u003eK\u003c/em\u003e to account for the kurtosis of the diffusion distribution (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). DKI sequences utilize high \u003cem\u003eb\u003c/em\u003e-values to probe the complex neural microstructure and quantify the degree of diffusion restriction. The DKI maps for MK were generated using the \u003cem\u003edtifit\u003c/em\u003e command in FSL by including the \u003cem\u003ekurtdir\u003c/em\u003e argument for multi-shell data (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). MK is the average kurtosis in all directions; a higher value indicates greater barriers to diffusion. Similar kurtosis values have been found in white matter and subcortical regions, possibly because both contain myelinated neurons (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The maps for each participant were quality checked prior to analysis (JP) (\u003cb\u003eSupplementary Fig.\u0026nbsp;2).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAverage values of NDI, ODI, and MK were extracted from each of the following six regions-of-interest (ROIs): amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. These ROIs comprise grey and white matter, whereas the tracts are predominantly white matter. ROIs and white matter tracts were selected based on prior diffusion tensor imaging studies where significant alterations to parameters such as fractional anisotropy and mean diffusivity were found in NF1 and NS (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). TRActs Constrained by UnderLying Anatomy (TRACULA) delineated 42 major white matter tracts using probabilistic tractography (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Further details are in the \u003cb\u003eSupplement\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eStatistical Analysis\u003c/p\u003e \u003cp\u003eFor each ROI and white matter tract, an analysis of variance was used to assess between-group differences including age and sex as covariates. The presence of T2-hyperintensities was included as an additional covariate in analysis of subcortical ROIs. Post-hoc analysis was conducted using independent samples pairwise \u003cem\u003et-\u003c/em\u003etests corrected using the Tukey-Kramer test for unequal groups. Data were visually examined for approximate normality. Tukey-Kramer corrected \u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;.05 were considered statistically significant. At least 18 subjects were required in each group to meet a minimum detectable effect size of \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.88, as calculated based on prior diffusion tensor imaging studies on NS and NF1 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Further details are available in the \u003cb\u003eSupplement.\u003c/b\u003e Statistical analysis was completed by JP in R 4.3.1 (R Core Team, 2023).\u003c/p\u003e \u003cp\u003eMultivariate approaches were used to complement our univariate results. For tract-based data, we used principal components analysis to extract features and reduce dimensionality using the \u003cem\u003eprcomp\u003c/em\u003e package. Principal components analysis is a data-driven technique designed to capture the most significant sources of variability in the data. Univariate results suggested subcortical regions may distinguish NS and NF1, therefore subcortical diffusion parameters were entered into a linear discriminant analysis. This technique was used to predict the probability of belonging to a group based on continuous predictor variables using the \u003cem\u003eMASS\u003c/em\u003e package. Leave-one-out cross-validation was used to test the accuracy and specificity of the linear discriminant functions.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003eParticipant Characteristics\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eA total of 88 subjects (M\u003csub\u003eage\u003c/sub\u003e=9.36, SD\u003csub\u003eage\u003c/sub\u003e=2.61; 44 male) were included in analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The flow of participants through the study are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There were 25 participants in the NF1 group (M\u003csub\u003eage\u003c/sub\u003e=9.20, SD\u003csub\u003eage\u003c/sub\u003e=2.27; 15 male); 31 in the NS group (M\u003csub\u003eage\u003c/sub\u003e=10.0, SD\u003csub\u003eage\u003c/sub\u003e=3.22; 12 male); and 32 in the TD group (M\u003csub\u003eage\u003c/sub\u003e=8.88, SD\u003csub\u003eage\u003c/sub\u003e=2.13; 15 male). Details on participant mutations are in the \u003cb\u003eSupplement\u003c/b\u003e. The groups did not differ significantly by age (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.090), sex (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.259), or pubertal stage (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05) (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). Significantly lower scores were found for clinical groups compared to TD on all IQ measures (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.010). Results of the sensitivity analysis for T2-hyperintensities are shown in \u003cb\u003eSupplementary Table\u0026nbsp;2.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics and descriptive statistics of included participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll (n\u0026thinsp;=\u0026thinsp;88)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTD (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNF1 (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNS (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.36 (2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.88 (2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.20 (2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.0 (3.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex (M/F)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44/44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17/15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12/19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFSIQ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.5 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.5 (13.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVIQ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106 (16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102 (15.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePIQ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.5 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.7 (21.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTanner 1- Stage 1/2/3/4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43/19/14/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15/11/4/1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9/6/5/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19/2/5/2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTanner 2- Stage 1/2/3/4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58/10/11/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20/7/3/1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15/3/5/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23/0/3/3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStimulant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrowth hormone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData presented as counts or mean (SD). Tanner 1 refers to Tanner Pubic Hair Scale. Tanner 2 refers to Female Breast Development/Male External Genitalia Scale. Tanner Scales exclude 1 TD, 2 NF1, 2 NS.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eANOVA\u0026thinsp;=\u0026thinsp;analysis of variance; FSIQ\u0026thinsp;=\u0026thinsp;Full-Scale Intelligence Quotient; NF1\u0026thinsp;=\u0026thinsp;neurofibromatosis type 1; NS\u0026thinsp;=\u0026thinsp;Noonan syndrome; PIQ\u0026thinsp;=\u0026thinsp;Performance Intelligence Quotient; TD\u0026thinsp;=\u0026thinsp;typical developing; VIQ\u0026thinsp;=\u0026thinsp;Verbal Intelligence Quotient.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eROI analysis of NDI, ODI, and MK\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn subcortical regions, the NDI results showed a trend of NF1\u0026thinsp;\u0026lt;\u0026thinsp;NS\u0026thinsp;\u0026lt;\u0026thinsp;TD (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Lower NDI values were found in NF1 and in NS compared to TD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e). NDI was lower in NF1 compared to NS in the amygdala, hippocampus, pallidum, and thalamus (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), with the largest effect observed in the thalamus (estimate \u0026minus;\u0026thinsp;0.044 [95% CI: -0.053, -0.034], \u003cem\u003ed\u003c/em\u003e=-2.36). Overall, NDI detected the greatest number of differences between groups and therefore showed the most sensitivity of the 3 diffusion measures investigated.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConfidence intervals from analysis of subcortical regions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTD \u0026ndash; NF1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eTD \u0026ndash; NS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eNF1 \u0026ndash; NS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eROI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ed\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEstimate (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ed\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEstimate (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003ed\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eNDI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAmygdala\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.040 (0.028, 0.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009 (-0.001, 0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-0.030 (-0.042, -0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-1.66\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCaudate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.025 (0.009, 0.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012 (-0.003, 0.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-0.013 (-0.028, 0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHippocampus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.061 (0.050, 0.072)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012 (0.002, 0.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-0.048 (-0.060, -0.037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-2.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePallidum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.098 (0.064, 0.132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.034 (0.002, 0.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-0.064 (-0.098, -0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-0.98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePutamen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007 (-0.004, 0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014 (0.003, 0.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e0.007 (-0.005, 0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eThalamus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.060 (0.050, 0.070)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016 (0.007, 0.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-0.044 (-0.053, -0.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-2.36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eODI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAmygdala\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.000 (-0.021, 0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005 (-0.014, 0.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e0.005 (-0.016, 0.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCaudate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010 (-0.009, 0.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.010 (-0.028, 0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-0.021 (-0.040, -0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-0.77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHippocampus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012 (-0.007, 0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001 (-0.017, 0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-0.011 (-0.031, 0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePallidum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004 (-0.022, 0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.008 (-0.017, 0.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e0.003 (-0.024, 0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePutamen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001 (-0.015, 0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.015 (-0.029, -0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.\u003cb\u003e043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-0.015 (-0.031, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eThalamus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012 (0.004, 0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.006 (-0.014, 0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-0.018 (-0.026, -0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-1.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eMK\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAmygdala\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014 (-0.012, 0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.008 (-0.033, 0.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-0.023 (-0.050, 0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCaudate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.008 (-0.061 0.046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005 (-0.045, 0.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e0.012 (-0.041, 0.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHippocampus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.065 (0.044, 0.086)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013 (-0.006, 0.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-0.051 (-0.072, -0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-1.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePallidum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.136 (0.075, 0.198)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.041 (-0.017, 0.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-0.095 (-0.157, -0.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-0.84\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePutamen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.037 (-0.002, 0.076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.003 (-0.040, 0.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-0.041 (-0.080, -0.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e.039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-0.80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eThalamus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.075 (0.051, 0.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.026 (0.004, 0.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-0.049 (-0.072, -0.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-1.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cem\u003eCI\u0026thinsp;=\u0026thinsp;Tukey-Kramer corrected and least squares means adjusted confidence intervals; d\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e effect size; L\u0026thinsp;\u003cem\u003e=\u0026thinsp;left; MK\u0026thinsp;=\u0026thinsp;mean kurtosis; NDI\u0026thinsp;=\u0026thinsp;neurite density index; ODI\u0026thinsp;=\u0026thinsp;orientation dispersion index; p\u0026thinsp;=\u0026thinsp;Tukey-Kramer corrected p-value from pairwise comparison between two groups; R\u0026thinsp;=\u0026thinsp;right; ROI\u0026thinsp;=\u0026thinsp;region-of-interest; SE\u0026thinsp;=\u0026thinsp;standard error.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eODI demonstrated the least sensitivity to changes of the three measures investigated, and the results did not show a clear pattern across the groups. We found evidence of decreased ODI in NF1 compared to NS in the caudate (estimate \u0026minus;\u0026thinsp;0.021 [95% CI: -0.040, -0.002], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.030) and thalamus (estimate \u0026minus;\u0026thinsp;0.018 [95% CI: -0.026, -0.010], \u003cem\u003ed\u003c/em\u003e=-1.39) \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003eThe pattern of MK results was like NDI where NF1\u0026thinsp;\u0026lt;\u0026thinsp;NS\u0026thinsp;\u0026lt;\u0026thinsp;TD. Lower MK was found in NF1 compared to TD in the hippocampus, pallidum, and thalamus (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). When comparing TD and NS, lower MK was found in NS compared to TD in the thalamus only (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.016). MK was lower in NF1 compared to NS in the hippocampus (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), pallidum (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001), putamen (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.039), and thalamus (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTract-based analysis of NDI, ODI, and MK\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe number of participants included in analysis for each tract are shown in \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e. In each of the 39 white matter tracts, we found lower NDI values in NF1 and NS compared to TD (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) (\u003cb\u003eSupplementary Figs.\u0026nbsp;4 and 5\u003c/b\u003e). However, only two tracts showed evidence of differences between the clinical groups. Lower NDI was found in NF1 compared to NS in the parietal body of the corpus callosum (estimate \u0026minus;\u0026thinsp;0.018 [95% CI: -0.036, -0.000], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.046) and in the middle cerebellar peduncle (estimate \u0026minus;\u0026thinsp;0.038 [95% CI: -0.056, -0.021], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Lower ODI was found in NF1 compared to TD in nine of the tracts; in TD compared to NS in three tracts; and in NF1 compared to NS in 18 tracts. Like NDI, MK was lower in NS compared to TD in all 39 tracts (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01). MK was also lower in NF1 compared to TD in all tracts except the rostrum of the corpus callosum and the left extreme capsule. The only difference between the clinical groups was found in the middle cerebellar peduncle where MK was lower in NF1 compared to NS (estimate \u0026minus;\u0026thinsp;0.057 [95% CI: -0.089, -0.026], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003eGiven the similar findings in NDI and MK, we tested the associations between these parameters using Pearson correlations. The results suggest the parameters are highly positively correlated, particularly in the white matter tracts. For example, MK and NDI were positively correlated in the middle cerebellar peduncle (\u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.81, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) (\u003cb\u003eSupplementary Fig.\u0026nbsp;6)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMultivariate analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo complement the univariate results, values for each white matter tract were entered into a principal components analysis. The first component alone represented 49.0% of the variance with similar contributions from each tract (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-D). The second component represented 11.6% of the variance (cumulative variance of 60.6%) again with similar contributions from each tract. The eigenvalues for the first 10 principal components are detailed in \u003cb\u003eSupplementary Table\u0026nbsp;4.\u003c/b\u003e A biplot representation of the loadings in PC1 and PC2 suggest some discrimination between TD and the clinical groups (NF1, NS). Subjects in the clinical groups generally have lower values on PC1 compared to subjects in the TD group. Given that NDI and MK values contribute the most to PC1, we can infer that lower values of NDI and MK are generally found in clinical groups compared to TD. Upon visual inspection of the biplot with respect to PC2, subjects in the clinical and TD groups appear to have similar loadings. ODI values contributed the most to PC2, suggesting the groups may have similar ODI values. These inferences consolidate our pattern of univariate results.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAs the univariate results showed several differences between clinical groups in the subcortical regions, we tested the ability of the subcortical diffusion values to classify the subjects into groups using linear discriminant analysis. The discriminant functions classified participants with NF1, TD, and NS with 98%, 85% and 84% accuracy respectively (\u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e). Two linear discriminant functions were generated, LD1 and LD2 (\u003cb\u003eSupplementary Table\u0026nbsp;6\u003c/b\u003e). For LD1, NDI in the thalamus contributed the most with a weighting of -1.92. Following the leave-one-out cross-validation, the accuracy of the model in classifying NF1 remained high at 97.2% though there were drops in accuracy for TD and NS classifications to 71% and 70% respectively. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-D shows that the NF1 observations can be separated from TD and NS observations, however TD and NS observations show overlap.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDevelopment of effective treatments for NDDs is hindered by the heterogeneity of the disorders and limited understanding of the underlying neurobiology. This study utilized a genetics-first approach to investigate the potential of advanced DWI techniques to elucidate the microstructural abnormalities existent in the brains of children with NF1 and NS. Analysis of white matter tracts found widespread differences between TD and the clinical groups. The middle cerebellar peduncle showed differences between NF1 and NS in both NDI and MK (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Principal components analysis confirmed that the clinical groups differ most from TD in white matter tract-based NDI and MK, whereas ODI values appear similar across the groups. The subcortical regions showed several differences between NF1 and NS, to the extent that a linear discriminant analysis could classify participants with NF1 an accuracy rate of 97%. These results have important implications for future neuroimaging research in clinical populations with NDDs.\u003c/p\u003e \u003cp\u003ePrior diffusion tensor imaging studies on children with NF1 and NS found widespread reduced fractional anisotropy and increased mean diffusivity in both conditions (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Fractional anisotropy and mean diffusivity are sensitive to disruptions in microstructural integrity; however, they are also non-specific measures and difficult to interpret in voxels with complex microstructural properties (e.g., crossing fibers) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Pre-clinical studies suggest microstructural differences between NF1 and NS (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), however, previous neuroimaging studies in the clinical populations yielded largely identical results in both conditions (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Our results, particularly in the subcortical regions and the middle cerebellar peduncle, suggest microstructural abnormalities that are unique to each condition. Use of diffusion models with greater specificity, NODDI and DKI, enabled improved elucidation of the brain structure in clinical populations with NF1 and NS.\u003c/p\u003e \u003cp\u003eOur observations in NF1 and NS are in alignment with pre-clinical evidence. In NF1, proliferation of astrocytes may disturb the spacing between axons and drive a decrease in observed NDI and MK (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In NS, observed reductions in NDI and MK are likely due to defects in the oligodendrocyte lineage and the consequent reduction in myelinated axons (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Diffusion parameters in the subcortical regions seemed to distinguish NF1 and NS more so than in the white matter tracts, suggesting the grey matter may be the major source of differences between the two conditions. At a cellular level, it is not yet clear why grey matter may be responsible for between-group differences in NF1 and NS. A previous pre-clinical study of NF1 found astrocyte proliferation restricted to the grey matter (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), though more recent work indicates astrocyte proliferation in both white and grey matter (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe thalamus, a primarily grey matter region, showed lower values of NDI, MK, and ODI in NF1 compared to NS. NDI and MK in the thalamus also contributed the strongest weighting to the linear discriminant function. The thalamus has previously been highlighted in NF1 due to the presence of T2-hyperintensities (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). However, our sensitivity analysis showed that even when all participants with T2-hyperintensities were excluded, the significant results in the thalamus remain. Other studies have also demonstrated that alterations to DWI parameters persist regardless of the presence of T2-hyperintensities (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). The presence of T2-hyperintensities therefore is not the sole influence on differences between NF1 and NS in the thalamus. Further research is needed to understand the effects of NF1 and NS on grey matter.\u003c/p\u003e \u003cp\u003eOf the white matter tracts, the middle cerebellar peduncle emerged as the only tract where NDI and MK were both lower in NF1 compared to NS. The middle cerebellar peduncle is the major afferent pathway to the cerebellum, a region often implicated in NF1 due to the presence of T2-hyperintensities (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This tract is vulnerable to abnormalities, for example myelin abnormalities and intra-myelinic edema \u0026ndash; both of which have been implicated in NF1 (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Future research may further examine the middle cerebellar peduncle to understand the importance of its role in NDDs.\u003c/p\u003e \u003cp\u003eThe strengths of this study lie in the genetics-first approach and application of advanced DWI techniques to NDDs. By focusing on NDDs with specific genetic mutations, we reduced heterogeneity that typically hinders NDD studies. NF1 and NS have similar behavioral profiles and previous DWI of these clinical populations suggested comparable microstructural abnormalities (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). However, the specificity conferred by NODDI and DKI models enabled elucidation of microstructural differences between the conditions. To our knowledge, this is the first study to apply NODDI and DKI across the whole brain to clinical populations with NF1 and NS. Given that DWI indirectly assesses alterations to brain microstructure, we are not able to derive with certainty the primary drivers of the differences in NDI and MK between the clinical groups. Further research on pre-clinical models coupled with neuroimaging is needed to assess the major influences on NDI and MK in these populations.\u003c/p\u003e \u003cp\u003eThis study provides evidence of the ability of NODDI and DKI to enhance understanding of NDDs in clinical populations particularly when used in conjunction with a genetics-first methodology. Future research should consider the use of advanced diffusion MRI techniques to elucidate the microstructure underlying NDDs in clinical populations. Use of a genetics-first approach may assist in identifying the distinct neurobiology in NDDs with a genetic basis, such as in autism spectrum disorder and ADHD. These approaches enable greater insight into the underlying neurobiology in NDDs and ultimately may assist in the development of more effective treatments for NDDs.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest:\u003c/h2\u003e \u003cp\u003eThe authors report no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eWe thank the families who participated in this research. The authors would also like to thank the Noonan Syndrome Foundation and the RASopathies Network which made this work possible. We would like to thank Stanford University and the Stanford Research Computing Center for providing computational resources and support that contributed to these research results, some of the computing for this project was performed on the Sherlock cluster. We gratefully acknowledge the support of The Lucas Service Center at Stanford.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHomberg JR, Kyzar EJ, Stewart AM, Nguyen M, Poudel MK, Echevarria DJ, et al. Improving treatment of neurodevelopmental disorders: recommendations based on preclinical studies. Expert Opin Drug Discov. 2016;11(1):11\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParenti I, Rabaneda LG, Schoen H, Novarino G. Neurodevelopmental Disorders: From Genetics to Functional Pathways. Trends Neurosci. 2020;43(8):608\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTidyman WE, Rauen KA. Expansion of the RASopathies. 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AJNR Am J Neuroradiol. 2020;41(9):1733\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaudou E, Nemmi F, Biotteau M, Maziero S, Assaiante C, Cignetti F, et al. Are morphological and structural MRI characteristics related to specific cognitive impairments in neurofibromatosis type 1 (NF1) children? Eur J Paediatr Neurol. 2020;28:89\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorales H, Tomsick T. Middle cerebellar peduncles: Magnetic resonance imaging and pathophysiologic correlate. World J Radiol. 2015;7(12):438\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4415218/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4415218/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNeurodevelopmental disorders (NDDs) can severely impact functioning yet effective treatments are limited. Greater insight into the neurobiology underlying NDDs is critical to the development of successful treatments. Using a genetics-first approach, we investigated the potential of advanced diffusion-weighted imaging (DWI) techniques to characterize the neural microstructure unique to neurofibromatosis type 1 (NF1) and Noonan syndrome (NS). In this prospective study, children with NF1, NS, and typical developing (TD) were scanned using a multi-shell DWI sequence optimized for neurite orientation density and dispersion imaging (NODDI) and diffusion kurtosis imaging (DKI). Region-of-interest and tract-based analysis were conducted on subcortical regions and white matter tracts. Analysis of covariance, principal components, and linear discriminant analysis compared between groups. 88 participants were included: 31 NS, 25 NF1, and 32 TD. Subcortical regions differed between NF1 and NS, particularly in the thalamus where the neurite density index (NDI), orientation dispersion index (ODI), and mean kurtosis (MK) were lower in NF1 compared to NS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The middle cerebellar peduncle showed lower NDI and MK in NF1 compared to NS (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Multivariate analyses distinguished between groups using NDI, ODI, and MK measures. Differences in neural microstructure were detected between neurofibromatosis type 1 and Noonan syndrome, particularly in subcortical regions and the middle cerebellar peduncle, in line with pre-clinical evidence. Advanced DWI techniques detected subtle alterations not found in prior work using conventional diffusion tensor imaging.\u003c/p\u003e","manuscriptTitle":"Elucidating microstructural alterations in neurodevelopmental disorders: application of advanced diffusion-weighted imaging in children with Rasopathies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-04 16:12:55","doi":"10.21203/rs.3.rs-4415218/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"37c5df20-b1af-4ff9-96ce-848c719c6f93","owner":[],"postedDate":"June 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32157174,"name":"Biological sciences/Neuroscience"},{"id":32157175,"name":"Health sciences/Biomarkers/Diagnostic markers"},{"id":32157176,"name":"Health sciences/Diseases/Psychiatric disorders/ADHD"},{"id":32157177,"name":"Health sciences/Diseases/Psychiatric disorders/Autism spectrum disorders"},{"id":32157178,"name":"Biological sciences/Genetics"}],"tags":[],"updatedAt":"2024-06-04T16:12:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-04 16:12:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4415218","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4415218","identity":"rs-4415218","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}