Altered cerebrospinal fluid-based clearance mechanisms in aging autistic adults

Altered cerebrospinal fluid-based clearance mechanisms in aging autistic adults | 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 Research Article Altered cerebrospinal fluid-based clearance mechanisms in aging autistic adults Danielle Christensen, Giuseppe Barisano, Bradley J. Wilkes, Young Seon Shin, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9450979/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 Background. Autistic adults demonstrate a 4–6-fold increased risk of unspecified dementia compared with the general population; however, the neurobiological substrates underlying this elevated risk remain unexplored. Alterations in cerebrospinal fluid–based mechanisms involved in brain metabolic waste clearance may represent a shared neuropathological pathway between autism spectrum disorder and dementia. Specifically, developmental deviations in cerebrospinal fluid-related imaging markers have been consistently reported in autistic infants, children, and adolescents, and brain amyloid and other metabolic waste accumulation is a hallmark of Alzheimer’s disease and related dementias. Despite this overlap, cerebrospinal fluid-based regulatory mechanisms have not been systematically examined in ageing autistic adults. Here, we used a multimodal magnetic resonance imaging approach to quantify structural and diffusion-based markers of cerebrospinal fluid regulation in middle-aged and older autistic adults compared with matched controls. Methods. Forty-nine autistic adults aged 30–73 years and 61 age-, sex-, and intelligence quotient–matched controls underwent T1-, T2-, and diffusion-weighted imaging. Measures included white matter perivascular space volume fraction, count fraction, and mean diameter; diffusion-based indices of fluid movement along perivascular pathways; and volumes of the lateral ventricles and choroid plexus. Results. With increasing age, autistic adults exhibited significantly greater increases in white matter perivascular volume fraction within the left inferior parietal lobule compared with controls. Autistic adults also showed significantly reduced diffusion indices and larger bilateral lateral ventricle and choroid plexus volumes relative to controls. Across both groups, increasing age was associated with higher white matter perivascular volume fraction in the right pars triangularis, reduced diffusion indices, and enlargement of the bilateral lateral ventricles and left choroid plexus. Limitations. First, the cross-sectional design limited our ability to quantify intra-individual variability and capture longitudinal trajectories. Second, the sample primarily comprised cognitively unimpaired autistic adults. Third, participants were predominantly of average or above-average intelligence; thus, findings may not generalize to autistic adults with ID. Finally, health factors including sleep disturbance, cardiovascular and metabolic disease, polypharmacy, and lifelong medication exposure, may have influenced these findings. Future large-scale studies should systematically evaluate their potential confounding and moderating effects. Conclusions. These findings demonstrate that ageing autistic adults exhibit convergent alterations in cerebrospinal fluid regulatory mechanisms, reflected in perivascular space morphology, diffusion-based fluid dynamics, and ventricular and choroid plexus enlargement. Together, the results link early developmental deviations to later-life vulnerability and highlight cerebrospinal fluid dysregulation as a potential candidate neurobiological substrate contributing to the increased prevalence of dementia in autistic adults. Figures Figure 1 Figure 2 Background Autistic adults exhibit a 4–6-fold increased risk of unspecified dementia relative to the general population. 1 – 5 This elevated risk persists after adjustment for intellectual disability (ID), psychiatric comorbidities, and lifestyle-related factors, suggesting that autism spectrum disorder (ASD) independently confers a heightened neurobiological vulnerability to pathological brain ageing. While the precise mechanisms underlying this heightened risk have not yet been identified, multiple dementia subtypes—including Alzheimer’s disease, vascular dementia, and frontotemporal dementia—exhibit broad alterations in CSF-based mechanisms critical for brain metabolic waste clearance. 6 – 9 These alterations include enlarged white matter perivascular spaces (WM-PVS), 10–14 reduced indices of fluid diffusion along perivascular spaces, 15–17 and larger volumes of the lateral ventricles 18 – 20 and choroid plexus 21 – 23 . Evidence of dysregulation in CSF-based mechanisms has been reported in middle-aged and older autistic adults, including increased extracellular free-water in frontal lobe transcallosal white matter 24 and enlargement of the cerebral ventricles compared to age-matched neurotypical controls. 25 However, these findings have been derived from single imaging markers rather than from a comprehensive multimodal approach to examine CSF-based mechanisms. Given that ageing neurobiology in ASD remains critically under-researched, the elevated prevalence of dementia in autistic adults underscores the need for systematic investigation of pathological brain ageing processes across middle and later life. Recent work has recognized the pathophysiological overlaps between ASD and Alzheimer’s disease, emphasizing shared alterations among perivascular pathways and brain waste clearance mechanisms. 26 Together, these findings suggest that alterations in CSF-based mechanisms involved in brain metabolic waste clearance may represent an overlapping substrate linking ASD and dementia-related processes, offering insight into shared neurobiological pathways driving this increased prevalence. Proper functioning of CSF-based mechanisms is necessary for the efficient removal of neurotoxic proteins that may otherwise accumulate within the parenchyma over time. 7 , 27 , 28 Brain metabolic waste clearance relies on the bulk fluid exchange between CSF and interstitial fluid along the perivascular spaces (PVS). 8 , 27 , 29 CSF is produced by the choroid plexus, a specialized structure lining the brain’s ventricular system, 30,31 and is derived from arterial blood plasma. 31 Beyond CSF production, the choroid plexus also regulates nutrient transport, metabolic exchange between blood and CSF, and triggering neuroimmune responses in the central nervous system. 31 – 34 The lateral ventricles are the primary compartments that support continuous CSF circulation and maintain homeostasis. 35 , 36 CSF circulates through the ventricular system and exits into the subarachnoid space as extra-axial CSF (EA-CSF). 37 From here, EA-CSF flows along perivascular pathways that surround penetrating brain arterioles 38 , 39 and contributes to CSF-interstitial fluid exchange, 27,40,41 facilitating metabolic waste removal throughout the parenchyma. 7 , 27 , 28 Structurally, PVS are the fluid-filled compartments bounded internally by the abluminal surface of arterioles and externally by a sheath of astrocytic endfeet, 27,42,43 which are specialized glial processes extending from astrocytes. 27 Cardiac-driven arterial pulsatility offers the mechanical force that propels CSF movement along PVS. 40 , 44 This rhythmic contraction and relaxation of the arteriole wall promotes CSF-interstitial fluid exchange and enables metabolic waste removal from the surrounding tissue, 27,44 directing collected byproducts toward perivenous drainage pathways. 45 This fluid-based mechanism, commonly referred to as the glymphatic system, is estimated to remove over half of extracellular amyloid-β from the brain, underscoring its critical role in minimizing the accumulation of neurodegeneration-promoting products. 8 , 46 Developmental alterations in CSF-based mechanisms are well-documented in ASD, 47–56 yet remain largely unexplored in autistic adults. In infants at high risk for ASD, elevated extra-axial CSF 49 and widespread PVS enlargement 52 have been shown to predict later ASD diagnosis. In autistic children aged 1–9 years, increased WM-PVS count and volume fraction have been associated with greater clinical severity of ASD. 47 Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) of deep medullary veins has further revealed lower indices in autistic children aged 2–5 years compared with neurotypical controls, suggesting reduced fluid transport along these pathways. 53 Converging structural MRI studies have also demonstrated enlarged lateral ventricles and choroid plexus in autistic children and adolescents relative to age-matched peers, 54,55,57 suggesting that altered CSF-based mechanisms in ASD are reflected not only in downstream PVS morphology and fluid transport, but also in upstream CSF-handling. Thus, enlargement of the lateral ventricles and choroid plexus implicates abnormalities in structures involved in both CSF handling as well as solute filtration and immune signaling between blood and CSF. 31 – 34 Despite ample evidence of early-life alterations, little is known about how CSF-based features manifest in ASD after childhood, particularly in middle-aged and older autistic adults. Given the critical role of CSF regulation in brain metabolic waste clearance, 7,27 this knowledge gap limits the development of a mechanistic framework for understanding the heightened vulnerability to dementia in this understudied clinical population. 47 In this multimodal imaging study, we used structural and diffusion-based MRI approaches to compare WM-PVS count (the total number of PVS lesions), volume (total lesion volume), and diameter (mean diameter of detected PVS) across the frontal, parietal, temporal, and occipital lobes, as well as DTI-ALPS indices and volumes of the lateral ventricles and choroid plexus, between middle-aged and older autistic adults and matched neurotypical controls. To our knowledge, no prior study has jointly examined WM-PVS morphology, DTI-ALPS, and volumes of the lateral ventricles and choroid plexus in ageing autistic adults. Consistent with findings in autistic infants and children, 47,53–55 we predicted that CSF dysregulation would persist beyond early development and hypothesized that autistic adults would exhibit 1) greater WM-PVS volume, count, and diameter; 2) lower DTI-ALPS indices; and 3) increased lateral ventricle and choroid plexus volumes relative to controls. We further hypothesized that age-associated changes in these measures would be more pronounced in autistic adults compared with neurotypical controls, reflecting the life-course neurobiological vulnerability associated with ASD. Materials Participants A total of 49 autistic adults and 61 neurotypical controls (NT) participated in this cross-sectional study. The two groups were matched on age, sex, and full-scale IQ (FSIQ), performance IQ (pIQ), and verbal IQ (vIQ) (Table 1 ). Autistic participants were recruited through the Center for Autism and Related Disabilities (CARD) at the University of Florida, the University of Central Florida, and the University of South Florida, as well as the SPARK Research Match program ( https://www.sfari.org/resource/research-match ). Controls were primarily recruited through local flyers and word of mouth referrals. All study procedures were approved by the Institutional Review Boards (IRB) at the University of Florida (IRB202100659, approved September 23, 2021). In accordance with the Declaration of Helsinki, written informed consent was obtained from all participants after they received a complete description of the study and its procedures. Table 1 Demographic characteristics between autistic participants (ASD) and neurotypical controls (NT) ASD NT t/χ 2 p Sample size (n) 49 61 − − Age (years) a 46 ± 11 48 ± 12 0.65 0.52 Range 30 − 73 30 − 70 − − Sex (M/F) 28/21 29/32 1.00 0.32 FSIQ score a 107 ± 14 108 ± 12 0.58 0.56 Range 77 − 143 82 − 126 − − vIQ score a 107 ± 14 106 ± 11 -0.27 0.79 Range 71 − 136 83 − 130 − − pIQ score a 105 ± 14 108 ± 15 1.25 0.21 Range 72 − 142 79 − 142 eTIV a 1545 ± 149 1512 ± 157 -1.15 0.25 Relative head motion (mm) 0.46 ± 1.08 0.30 ± 0.67 0.09 0.38 ADOS-2 SA score b 9 ± 3 − − − Range 5 − 18 − − − ADOS-2 RRB b 2 ± 1 − − − Range 0 − 6 − − − a Age, FSIQ, pIQ, vIQ and eTIV shown as mean and standard deviation b ADOS-2 Social Affect (SA) and Restricted and Repetitive Behavior (RRB) are derived from the Module 4 revised algorithm and shown as mean and standard deviation [Insert Table 1 about here] Screening criteria for participants were consistent with those used in previous studies from our lab and others 58 , 59 . Autistic adults with a clinical diagnosis of ASD were required to score > 32 on the Autism Spectrum Quotient for Adults (AQ-50) 60 and \(\:\ge\:\) 65 on the Social Responsiveness Scale Adult Self-Report (SRS-2) 61 to be invited for a comprehensive in-person diagnostic evaluation using the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) 62 . Diagnosis of ASD was confirmed through an integrative review of AQ-50, SRS-2, and ADOS-2 scores, in conjunction with expert clinical judgement by a licensed clinician, following the DSM-5 criteria. 63 Neurotypical controls were recruited if they scored \(\:\le\:\) 22 on the AQ-50 and < 60 on the SRS-2. Exclusion criteria for controls included a family history of ASD or related neurodevelopmental conditions among first- and second-degree relatives. Exclusion criteria for both groups included: (1) a diagnosis of intellectual disability (including non-specific developmental delay), mild cognitive impairment, or dementia; (2) a current or past major psychiatric condition (e.g., schizophrenia, bipolar disorder, or post-traumatic stress disorder); (3) a current or past illness involving the central nervous system (e.g., brain tumor, thyroid disease, Cushing’s disease, or HIV infection); (4) a diagnosed neurological disorder (e.g., Parkinson’s disease, cerebellar ataxia, seizure, dystonia, or stroke); (5) a family history of heritable neurological disease (e.g., Huntington’s disease, Wilson’s disease, or amyotrophic lateral sclerosis); (6) implanted medical devices incompatible with MRI (e.g., cardiac pacemakers, infusion pumps, cochlear implants); (7) current pregnancy; (8) FSIQ < 75 as measured by the Wechsler Abbreviated Scales of Intelligence, Second Edition 64 , or (9) non-English-speaking status. Medication use and polypharmacy are prevalent in ASD 65 . To allow an ecologically representative sample of autistic adults, routine medication use was not an exclusion criterion in this study. Participants taking psychotropic medication included those prescribed antidepressants (ASD = 25, NT = 4), antipsychotics/neuroleptics (ASD = 4), sedatives/hypnotics/anxiolytics (ASD = 7, NT = 2), stimulants (ASD = 9), and anticonvulsants (ASD = 6, NT = 1). MRI data acquisition MRI scans were acquired using a 3T Siemens Prisma scanner equipped with a 64-channel head coil at the University of Florida McKnight Brain Institute. Participants were screened before scanning, and hearing protection was provided prior to entering the scanner room. Foam padding was securely placed around the head to minimize motion during image acquisition. For T1-weighted imaging, the MPRAGE sequence was acquired with the following parameters: repetition time (TR) = 2000 ms, echo time (TE) = 2.99 ms, flip angle = 8°, field of view (FOV) = 256 × 256 mm, matrix size = 320 × 320, 208 sagittal slices, and isotropic voxel size = 0.8 × 0.8 × 0.8 mm 3 . For T2-weighted imaging, a three-dimensional SPACE sequence was collected with the following parameters: TR = 2500 ms, TE = 370 ms, variable refocusing flip angle, FOV = 256 × 256 mm, matrix size = 320 × 320, 208 sagittal slices, and isotropic voxel size = 0.8 × 0.8 × 0.8 mm 3 . Diffusion weighted scans were acquired with an echo-planar imaging sequence with the following parameters: TR = 6400 ms, TE = 58 ms, b-values: 5 × 0 s/mm 2 and 64 × 1000 s/mm 2 , bandwidth = 2442 Hz/pixel, FOV = 256 × 256, matrix size = 128 × 128, 69 axial slices, and isotropic voxel size = 2.0 × 2.0 × 2.0 mm 3 . [Insert Fig. 1 about here] White matter perivascular spaces T2-weighted images were visually assessed by the team’s physician-scientist (GB) for PVS segmentation. Cases with prominent imaging artifacts that significantly compromised accurate PVS quantification were excluded from further analysis (ASD = 2). T2-weighted images were rigidly registered to the processed T1-weighted images using a six-degree-of-freedom transformation and boundary-based cost function, as implemented by the bbregister command using FreeSurfer version 8.0.0. We segmented MRI-visible WM-PVS using a robust, fully automated pipeline that has been previously validated across multiple independent datasets of healthy aging and neurodegeneration 10 (Fig. 1 A). Briefly, we first generated enhanced perivascular contrast (EPC) images for each participant by computing the voxel-wise ratio of T1-weighted to T2-weighted images 66 . Prior work has demonstrated that this approach enhances PVS contrast and increases the sensitivity of both visual detection and automated PVS segmentation 66 . We then applied a multiscale Frangi vesselness filter 67 to the white matter mask applied on the EPC images to enhance tubular structures consistent with PVS morphology. We set the filter parameters to α = 0.5 and β = 0.5, with c defined as half the maximum Hessian norm, in accordance with established recommendations 66 . The Frangi filter assigns a vesselness value to each voxel based on the eigenvalues of the Hessian matrix, thereby preferentially improving elongated, vessel-like structures while suppressing non-tubular signal. We applied the Frangi filter within 32 bilateral white matter regions generated using FreeSurfer according to the Desikan-Killiany atlas. To minimize contamination from non-PVS pathology, we first segmented white matter lesions unrelated to PVS using a fully automated approach 68 and excluded these lesions from the white matter masks prior to vesselness filtering, consistent with prior work 10 . We then automatically derived PVS masks from the resulting vesselness maps using a percentile-based thresholding approach that has been previously validated 69 . For each participant, we quantified PVS metrics including total count, total volume, and mean diameter. Total count and total volume refer to the occurrence and spatial occupancy that visible PVS exhibit in a mask. Mean diameter refers to the average cross-sectional width of detected PVS 69 . We derived these measures using MATLAB’s regionprops3 function applied to the binarized PVS masks. Because both PVS count and volume scale with the volume of the underlying white matter region, we normalized these measures by the corresponding regional white matter mask volume to derive PVS count fraction and PVS volume fraction, respectively 69 . We grouped white matter masks from the left and right hemisphere by anatomical lobe, consistent with prior studies in autistic children 70 . The frontal lobe comprised 11 masks, including the superior frontal gyrus, rostral middle frontal gyrus, caudal middle frontal gyrus, pars opercularis, pars orbitalis, pars triangularis, lateral orbitofrontal gyrus, medial orbitofrontal gyrus, precentral gyrus, paracentral lobule, and frontal pole. The parietal lobe consisted of 5 masks encompassing the postcentral gyrus, supramarginal gyrus, superior parietal lobule, inferior parietal lobule, and precuneus regions. The temporal lobe included 8 masks incorporating the entorhinal cortex, parahippocampal gyrus, fusiform gyrus, superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, transverse temporal gyrus, and temporal pole. Finally, the occipital lobe consisted of 4 masks encompassing the lingual gyrus, pericalcarine cortex, cuneus, and lateral occipital cortex. Across all bilateral regions of interest (ROIs) and PVS metrics, each participant yielded 168 PVS variables (56 ROIs \(\:\times\:\) 3 PVS metrics). DTI-ALPS index We calculated the DTI-ALPS index using an MNI-space approach 15 (Fig. 1 B) and processed diffusion weighted MRI data following pipelines used in previous published work 24 . Specifically, we performed denoising and Gibbs artefact removal using MRtrix3 71 . We then generated brain masks directly from the diffusion weighted images using the dwi2mask function in MRtrix3 71 . Next, we corrected for eddy current induced image distortions and head motion, with corresponding adjustment to gradient directions (i.e. b-vectors) using Eddy, a fully automated quality control framework in FSL 72 . We subsequently reconstructed diffusion tensors using FSL's dtifit function to generate fractional anisotropy (FA) images in native subject space. We registered the resulting FA images to the HCP1065 FA template 73 using the symmetric normalization (SyN) algorithm implemented in Advanced Normalization Tools (ANTs) 74 , which applies both an affine transformation and a nonlinear diffeomorphic warp. These registered FA images in MNI space were visually inspected to confirm appropriate data quality, as in our prior work 24 . We then applied the resulting transformation matrices to each of the six unique tensor elements (Dxx, Dxy, Dxz, Dyy, Dyz, and Dzz) to transform the diffusion tensor into MNI space. To compute the index, we defined four bilateral spherical regions of interest (ROIs; 5mm-diameter) at the level of the lateral ventricles to sample projection and association white matter fibers. We centered projection fiber ROIs at MNI coordinates (± 25, − 20, 28) and association fiber ROIs at (± 39, − 20, 28), based on the HCP1065 1mm FA template and corresponding tensor maps overlaid in RGB to confirm proper orientation. We then calculated the DTI-ALPS index as the ratio of diffusivity along the x-axis to diffusivity perpendicular to the primary fiber direction, defined as: $$\:DTI-ALPS=\:\frac{\text{m}\text{e}\text{a}\text{n}\:(\text{D}\text{x}\text{x}\_\text{p}\text{r}\text{o}\text{j},\:\text{D}\text{x}\text{x}\_\text{a}\text{s}\text{s}\text{o}\text{c})\:}{\text{m}\text{e}\text{a}\text{n}\:(\text{D}\text{y}\text{y}\_\text{p}\text{r}\text{o}\text{j},\:\text{D}\text{z}\text{z}\_\text{a}\text{s}\text{s}\text{o}\text{c})}$$ where Dxx represents diffusivity along projection and association fibers, Dyy represents perpendicular diffusivity in projection fibers, and Dzz represents perpendicular diffusivity in association fibers. We averaged left and right hemisphere values to obtain a single bilateral ALPS index for each participant. Due to the timing in which individuals were recruited in this study, five participants in our sample were unable to receive the diffusion MRI necessary for DTI-ALPS analysis (ASD = 4; NT = 1). Lateral ventricle and choroid plexus volume We performed quantification of lateral ventricle and choroid plexus volume using FreeSurfer versions 7.2.0 75 and FSL version 6.0 72 on a Linux-based computing system. First, T1-weighted images underwent slice-by-slice visual inspection across axial, coronal, and sagittal planes by multiple trained raters. Images that exhibited significant segmentation inaccuracies were corrected using the Recon Edit tool in FreeSurfer and subsequently cross-validated by an independent rater to ensure consistency. T1-weighted images that did not meet quality control criteria were excluded from further analysis (ASD = 1). Images that passed quality control were processed for volumetric segmentation and cortical surface reconstruction following the standard recon-all pipeline, which includes motion correction, intensity normalization, transformation to Talairach space, skull stripping and brain extraction, white matter segmentation, and cortical surface parcellation. We derived volumes of the left and right lateral ventricles and choroid plexus (Fig. 1 C) from T1-weighted MRI scans using automated segmentation outputs generated by FreeSurfer. To account for inter-individual differences in brain size, we normalized these ROIs by the estimated total intracranial volume (eTIV) provided by FreeSurfer. Participant sample size for each MRI measure was 49 ASD/61 NT for WM-PVS and T1-weighted volume, and 46 ASD/59 NT for DTI-ALPS. Statistical analysis Statistical analysis was conducted in RStudio version 4.4.1. Demographic characteristics between autistic participants and neurotypical controls were compared using independent-sample t -tests for continuous variables, including age, FSIQ, pIQ, vIQ, and eTIV. Group differences in sex were assessed using a Chi-square test. Normality of outcome variables was assessed using the Shapiro-Wilk test, which indicated non-normal distributions for several measures, including lateral ventricle and choroid plexus volumes, and approximately 40% of PVS variables. Given the non-normal distribution properties, linear regression models with 5000 permutations were implemented to examine main effects of group (ASD vs. NT), age, and their interaction (group \(\:\times\:\) age). Each MRI metric served as the dependent variable in separate models. Age was mean-centered to improve interpretability of group and group-by-age effects and reduce multicollinearity among predictors. Bonferroni correction was applied to adjust for multiple comparisons 76 . For WM-PVS, Bonferroni thresholds were set within lobes (frontal, parietal, temporal, and occipital) to control the family-wise error rate while preserving anatomical specificity. Specifically, significance was defined as p < 0.00227 for the frontal lobe (22 masks), p < 0.00500 for the parietal lobe (10 masks), p < 0.00313 for the temporal lobe (16 masks), and p < 0.00625 for the occipital lobe (8 masks). Significance was defined as p < 0.025 for the lateral ventricles and choroid plexus volume, as each consisted of two ROIs (left and right), and p < 0.05 for the DTI-ALPS index. Results Participant demographics Table 1 summarizes the demographic characteristics of the groups. Autistic adults and neurotypical controls were matched on age, sex, all IQ scores, and eTIV. Relative head motion also did not differ between groups. [Insert Table 1 about here] White matter perivascular spaces Autistic adults exhibited significantly steeper age-associated increases in WM-PVS volume fraction within the left inferior parietal lobule compared to controls (Fig. 2 ). Furthermore, both groups demonstrated significant age-associated increases in WM-PVS volume fraction within the right pars triangularis (Table 2 ). Multiple lobar patterns in WM-PVS were demonstrated at the uncorrected level ( p-perm < 0.05). For example, 1) group effects were most frequent in the temporal lobe (50%), followed by the parietal (25%) and occipital lobes (25%), with no group effect observed in the frontal lobe; 2) group × age interaction effects were most frequent in the parietal lobe (62.5%), followed by the temporal (25%) and frontal lobes (12.5%), and no interaction effects observed in the occipital lobe; and 3) age effects were predominantly found in the frontal lobe (55.6%), followed by the parietal (22.2%), temporal (18.5%), and occipital lobes (3.7%). Finally, hemispheric distribution showed that age and group main effects were bilaterally distributed, whereas all group × age interaction effects were left-lateralized. No other significant Bonferroni-corrected group, age, or group \(\:\times\:\) age effects were observed (Supplementary Tables 1–4). Table 2 Linear regression results of p-perm < 0.05 WM-PVS metrics Metric Lobe Mask Term β SE t p-perm DM Frontal L-parsopercularis Group⋅Age 0.0066 0.0030 2.21 0.029 DM L-caudalmiddlefrontal Age 0.0041 0.0016 2.62 0.010 VF L-caudalmiddlefrontal Age 0.0001 0.0001 2.48 0.017 VF L-parsopercularis Age 0.0002 0.0001 3.01 0.002 DM L-parstriangularis Age 0.0041 0.0015 2.74 0.009 VF L-precentral Age 0.0001 0.0000 2.30 0.025 DM L-rostralmiddlefrontal Age 0.0039 0.0012 3.15 0.003 VF L-superiorfrontal Age 0.0001 0.0001 1.96 0.047 DM R-parsopercularis Age 0.0045 0.0020 2.22 0.029 VF R-parsopercularis Age 0.0001 0.0001 2.45 0.015 CF R-parstriangularis Age 0.0000 0.0000 2.64 0.011 DM R-parstriangularis Age 0.0054 0.0018 2.98 0.005 VF R-parstriangularis Age 0.0002 0.0000 3.75 0.000 VF R-precentral Age 0.0001 0.0000 2.08 0.039 DM R-rostralmiddlefrontal Age 0.0030 0.0010 2.97 0.004 VF R-superiorfrontal Age 0.0001 0.0000 2.27 0.026 CF Parietal R-inferiorparietal Group 0.0002 0.0001 1.98 0.048 CF R-supramarginal Group 0.0003 0.0001 2.41 0.019 CF L-inferiorparietal Group⋅Age 0.0000 0.0000 2.68 0.010 DM L-inferiorparietal Group⋅Age 0.0069 0.0032 2.19 0.032 VF L-inferiorparietal Group⋅Age 0.0003 0.0001 3.14 0.002 DM L-precuneus Group⋅Age 0.0061 0.0023 2.68 0.008 VF L-precuneus Group⋅Age 0.0001 0.0001 2.07 0.046 CF L-precuneus Age 0.0000 0.0000 -2.01 0.045 DM L-supramarginal Age 0.0040 0.0018 2.19 0.028 DM R-inferiorparietal Age 0.0048 0.0020 2.37 0.023 VF R-postcentral Age 0.0001 0.0000 2.28 0.026 DM R-precuneus Age 0.0029 0.0015 1.96 0.048 VF R-supramarginal Age 0.0001 0.0000 2.46 0.019 CF Temporal L-entorhinal Group -0.0004 0.0002 -2.13 0.037 DM L-parahippocampal Group -0.4256 0.1644 -2.59 0.011 CF R-transversetemporal Group 0.0009 0.0004 2.48 0.014 VF R-transversetemporal Group 0.0034 0.0015 2.32 0.021 DM L-fusiform Group⋅Age 0.0050 0.0024 2.11 0.038 VF L-inferiortemporal Group⋅Age 0.0001 0.0001 2.39 0.021 DM L-inferiortemporal Age 0.0027 0.0012 2.27 0.024 CF L-middletemporal Age 0.0000 0.0000 2.07 0.041 VF L-middletemporal Age 0.0001 0.0000 2.23 0.028 VF R-inferiortemporal Age 0.0001 0.0000 2.06 0.040 VF R-middletemporal Age 0.0001 0.0000 2.36 0.021 DM Occipital L-lingual Group -0.3454 0.1623 -2.13 0.036 VF L-pericalcarine Group -0.0016 0.0008 -1.98 0.048 DM L-lingual Age -0.0198 0.0093 -2.13 0.037 Bold indicates significance after Bonferroni correction DM = Diameter mean; VF = volume fraction; CF = count fraction; L = Left; R = Right [Insert Table 2 ] DTI-ALPS index Autistic adults showed significantly lower DTI-ALPS indices compared to controls (Fig. 2 ). Both groups showed significant age-associated decreases in DTI-ALPS indices, although no group \(\:\times\:\) age interaction was observed (Table 3 ). Table 3 Linear regression results of DTI-ALPS index Metric Term β SE t p-perm DTI-ALPS index Group -0.0624 0.0282 -2.21 0.029 Group⋅Age 0.0025 0.0025 1.01 0.322 Age -0.0043 0.0016 -2.63 0.011 Bold indicates significance after Bonferroni correction [Insert Table 3 ] Lateral ventricle and choroid plexus volumes Autistic adults showed significantly larger lateral ventricle volumes in both the left and right hemispheres relative to controls (Fig. 2 ). Significant age-associated increases in left and right lateral ventricle volumes were observed in both groups; however, no significant group \(\:\times\:\) age interactions were identified (Table 4 ). Autistic adults also demonstrated significantly larger left and right choroid plexus volume compared to controls, and both groups showed age-associated volume increases in the left choroid plexus. No significant group \(\:\times\:\) age effect was observed in either the left or right choroid plexus. Table 4 Linear regression results of lateral ventricles and choroid plexus volumes Metric Term β SE t p-perm L-Lateral ventricle Group 0.0018 0.0006 2.76 0.010 Group*Age 0.0000 0.0001 0.33 0.751 Age 0.0001 0.0000 2.97 0.003 R-Lateral ventricle Group 0.0018 0.0006 3.09 0.001 Group*Age 0.0001 0.0001 1.48 0.142 Age 0.0001 0.0000 2.35 0.018 L-Choroid plexus Group 0.0000 0.0000 2.38 0.021 Group*Age 0.0000 0.0000 -0.08 0.939 Age 0.0000 0.0000 2.37 0.018 R-Choroid plexus Group 0.0000 0.0000 2.73 0.008 Group*Age 0.0000 0.0000 1.18 0.242 Age 0.0000 0.0000 0.97 0.341 Bold indicates significance after Bonferroni correction L = Left; R = Right [Insert Table 4 ] Discussion Our study examined CSF-based mechanisms critical for brain metabolic waste clearance using multimodal imaging in ageing autistic adults and neurotypical controls. We report several novel findings. First, autistic adults exhibited an age-associated increase in WM-PVS volume fraction in the left inferior parietal lobule that was not observed in controls. Second, autistic adults demonstrated lower DTI-ALPS indices compared to controls. Third, autistic adults showed larger left and right lateral ventricles compared to controls. Fourth, autistic adults showed larger left and right choroid plexus volumes relative to controls. Fifth, both groups demonstrated greater WM-PVS volume fraction in the right pars triangularis, reduced DTI-ALPS indices, and increased bilateral lateral ventricle and left choroid plexus volumes with increasing age. Collectively, these findings provide evidence for a mechanistic overlap between CSF-based alterations observed in ageing autistic adults and those reported across multiple subtypes of dementia. Therefore, CSF-based alterations evident from early development through middle and older adulthood may support the framework of a lifelong neurobiological vulnerability contributing to increased dementia risk in ASD. Compared to controls, autistic adults demonstrated an age-associated increase in WM-PVS volume fraction localized to the left inferior parietal lobule. The inferior parietal lobule is a densely vascularized region within the default mode network and serves as a major hub for sensory integration 77 , language 78 , and motor processes 78 through long-range connectivity across multiple cortical networks 79 . Lateralized alterations of the left inferior parietal lobule have been reported in autistic children and adolescents in MRI studies of brain structure and functional connectivity, and have been strongly associated with clinical severity 80 , 81 . Our finding extends this regional atypicality into middle and older adulthood, suggesting increased susceptibility of this integrative association cortex to perivascular burden with aging in ASD. As a metabolically demanding region, the inferior parietal lobule is particularly sensitive to vascular alterations that influence perivascular fluid dynamics, processes that are strongly modulated by arterial stiffness and pulsatility 82 . Notably, the inferior parietal lobule is also recognized as an early site of vascular and metabolic dysfunction in Alzheimer’s disease, as evidenced by hypoperfusion, hypometabolism, and volume atrophy 83 – 85 . Volumetric reductions of the left inferior parietal lobule have been shown to differentiate Alzheimer's disease from normal cognition, with left-lateralized cortical thinning in this region identified as a prominent marker of early neurodegeneration 85 . Although region-specific assessments of WM-PVS in Alzheimer’s disease remain limited, increased WM-PVS burden has been consistently documented 13 , 69 . Taken together, these findings highlight a shared neurobiological substrate involving the left inferior parietal lobule and underscore its heightened vulnerability to pathological brain aging in autistic adults, warranting further investigation to clarify the mechanistic linkage between perivascular dysfunction and neurodegenerative risk in ASD. Autistic adults exhibited lower DTI-ALPS indices relative to controls. The deep medullary veins, medullary arteries, and their associated perivascular spaces run predominantly in the x-direction (left–right orientation) at the level of the lateral ventricles. DTI-ALPS indices serve as a proxy measure of CSF-mediated fluid diffusivity along the perivascular pathways, supporting bulk CSF transport into and out of the brain parenchyma, capturing fluid movement toward both the brain surface and the ventricular system 15 , 86 . Our findings indicate that reduced perivascular fluid diffusivity in ASD persists beyond childhood 70 and extends into middle and older adulthood. Lower DTI-ALPS indices have been reported across the dementia spectrum, including Alzheimer’s disease 15 , 86 , vascular dementia 87 , and Lewy body disease 88 . In middle-aged adults, reduced DTI-ALPS indices predict future cognitive impairment and increased risk of dementia 89 , 90 , highlighting the downstream consequences of chronically impaired fluid diffusivity. In Alzheimer’s disease, lower DTI-ALPS indices have been associated with increased cerebral amyloid-β deposition and worsened clinical outcomes, consistent with a disrupted capacity to regulate the accumulation of neurotoxic species 91 . As a diffusion-based metric, DTI-ALPS indices may also reflect contributions from white matter microstructure alterations and vascular changes that are not specific to perivascular clearance 15 . Nevertheless, reduced perivascular fluid diffusivity in ASD from early life 50 through older adulthood reflect enduring alterations in perivascular fluid regulation that may present lifelong altered clearance of neurotoxic byproducts along major medullary vessels within the brain parenchyma. This impairment aligns with converging evidence of central neuroimmune activation observed in the CSF 92 and brain tissue 93 of autistic children and adults. These findings emphasize functionally relevant alterations in perivascular fluid dynamics in aging autistic adults that may overlap with established biological vulnerabilities associated with dementia progression and coincide with observed differences in WM-PVS. Autistic adults also demonstrated larger left and right lateral ventricle volumes relative to controls. This finding is consistent with prior studies reporting enlarged lateral ventricles in autistic children 55 , adolescents 54 , and adults 58 , indicating that ventricular enlargement represents a persistent neurodevelopmental vulnerability in ASD across the lifespan. Expansion of the cerebral ventricles—particularly in cohorts in which ventriculomegaly is evident early in development—has been interpreted as reflecting compromised CSF homeostasis due to altered circulation, distribution, or resorption dynamics 31 . This interpretation is particularly plausible in ASD, as ventricular enlargement has been observed very early in life, well before the typical onset of aging-related neurodegenerative processes. In parallel, ventricular enlargement has also been linked to broader structural alterations within surrounding brain regions 58 and has been observed in Alzheimer’s disease 18 , frontotemporal dementia 94 , and Lewy body disease 95 . Prior work from our group identified significant associations between enlarged ventricle volumes and reduced volumes in adjacent subcortical hippocampus and amygdala beginning in middle adulthood in ASD, but not during early development, highlighting accelerated structural deviations involving ventricular expansion and neighboring tissue volume reduction later in life 58 . Notably, none of the autistic adults in the current study had a clinical diagnosis of mild cognitive impairment or dementia. Ventricular expansion observed in this cohort may reflect a persistent vulnerability in brain morphogenesis that may confer increased susceptibility to aging-related neurodegeneration rather than dementia-specific pathology in ASD 54 , 55 . Future longitudinal studies are needed to disentangle the combined effects of early developmental vulnerability and aging-associated structural dynamics on ventricular enlargement in ASD. Finally, autistic adults showed larger left and right choroid plexus compared to controls. This finding of choroid plexus enlargement is consistent with early childhood and adolescent studies of ASD 55 , 56 , in which early brain overgrowth 55 and neuroimmune dysregulation 56 have been proposed as potential underlying mechanisms. Enlargement of the choroid plexus has also been commonly reported in aging-related neurodegenerative disorders, including Alzheimer’s disease 21 , vascular dementia 96 , and frontotemporal dementia 97 , implicating compromised CSF production, nutrient transport, metabolic exchange, neuroimmune signaling, and clearance of neurotoxic peptides between the blood and CSF compartments. Our finding in autistic adults suggests that choroid plexus enlargement is observable across middle and older adulthood, and may align with longstanding disruptions in CSF production, homeostasis and neuroimmune signaling contributing to increased vulnerability to aging-related neurodegeneration in autistic adults. Age-associated patterns were observed in both groups across multiple neuroimaging markers. The age-associated increase in WM-PVS volume fraction in the right pars triangularis across both groups suggests that perivascular morphology within this region is particularly sensitive to aging independent of diagnosis. Furthermore, the presence of age-associated reductions in DTI-ALPS demonstrates that decreased fluid diffusivity along perivascular pathways represents a general feature of aging for both groups. The age-associated increases in left and right lateral ventricle volumes we observed in both groups is consistent with studies of neurobiological aging in the general population showing bilateral ventricular enlargement as a common feature of advancing age 98 . Finally, both groups demonstrated a lateralized age-associated volume increase in the left choroid plexus, suggesting an asymmetric susceptibility to enlargement in this CSF-producing and immune-signaling region with increasing age. 31 Limitations Our findings should be interpreted alongside several considerations. First, the cross-sectional design limited our ability to quantify intra-individual variability and capture longitudinal trajectories 99 . Therefore, age- and group-by-age–associated CSF-based alterations should be interpreted cautiously. Longitudinal studies spanning the lifespan are needed to clarify the role of CSF-based mechanisms in neurobiological vulnerability contributing to increased dementia risk in ASD. Second, the sample primarily comprised cognitively unimpaired autistic adults. While this allowed characterization of predisposed CSF-related vulnerability in middle and older adulthood, it limited our ability to disentangle neurodevelopmental from neurodegenerative processes contributing to pathological aging in ASD. Studies including autistic adults across the dementia spectrum will be essential to address this gap. Third, participants were predominantly of average or above-average intelligence; thus, findings may not generalize to autistic adults with ID. Although ID modifies dementia risk in ASD 2 , our results suggest that ASD itself—independent of ID—may be associated with altered CSF-related neurobiology linked to pathological brain aging. Finally, health factors including sleep disturbance, cardiovascular and metabolic disease, polypharmacy, and lifelong medication exposure, may have influenced these findings. Future large-scale studies should systematically evaluate their potential confounding and moderating effects. Conclusions This study represents the first multimodal MRI investigation of CSF-based markers in ageing autistic adults. We identified convergent impairments of CSF–based clearance mechanisms in ASD, including region-specific alterations in white matter perivascular spaces, reduced perivascular fluid diffusivity, and lateral ventricular enlargement. Considered alongside prior findings in autistic children, 54,57 these results support a lifelong pattern of altered CSF regulation that extends into the pathologically vulnerable periods of middle and older adulthood. Collectively, these findings establish a conceptual foundation for future research aimed at disentangling lifelong neurobiological vulnerabilities that may contribute to increased dementia risk in ASD. Declarations Ethics approval The study protocols were approved by the Institutional Review Boards (IRB) at UF (IRB201801378; approval date: July 26, 2022). Consent for publication All authors have read and approved the submission. Acknowledgements We are deeply grateful to all individuals who participated in this study. We thank CARD directors Ann-Marie Orlando, Ph.D., at the University of Florida, Teresa Daly, Ph.D., at the University of Central Florida, Beth Boone, Ph.D., at the University of South Florida, as well as the SPARK Research Match team, for their endless efforts in supporting participant identification and recruitment. We also thank the undergraduate trainees in the Department of Applied Physiology and Kinesiology and the Department of Psychology, as well as professional graduate trainees in the Department of Occupational Therapy, at the University of Florida for their valuable assistance with MRI data inspection and post-processing. Funding sources Dr. Zheng Wang receives funding from the National Institute on Aging (R21AG065621 and R01AG086493), the National Institute of Neurological Disorders and Stroke (R01NS121120), and the University of Florida APK Research Investment Grants Award. Dr. Giuseppe Barisano receives funding from the Alzheimer’s Drug Discovery Foundation (RC-202405-2026586). The REDCap application used in this study was supported by funding from the National Center for Advancing Translational Sciences (UL1T001427). Conflicts of interest All authors declare that they have no competing interests. Consent statement All authors have read and consented to the submission of this manuscript. References Vivanti G, Tao S, Lyall K, Robins DL, Shea LL (2021) The prevalence and incidence of early-onset dementia among adults with autism spectrum disorder. Autism Res 14:2189–2199. https://doi.org/10.1002/aur.2590 Vivanti G et al (2025) Prevalence of Dementia Among US Adults With Autism Spectrum Disorder. JAMA Netw Open 8:e2453691. https://doi.org/10.1001/jamanetworkopen.2024.53691 Chang Z et al (2025) Association between autism and dementia across generations: evidence from a family study of the Swedish population. 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J Neuroimmunol 207:111–116. https://doi.org/10.1016/j.jneuroim.2008.12.002 Manera AL, Dadar M, Collins DL, Ducharme S (2022) Ventricular features as reliable differentiators between bvFTD and other dementias. NeuroImage: Clin 33:102947. https://doi.org/https://doi.org/10.1016/j.nicl.2022.102947 Barber R, Ballard C, McKeith IG, Gholkar A, O'Brien (2000) J. T. MRI volumetric study of dementia with Lewy bodies: a comparison with AD and vascular dementia. Neurology 54:1304–1309. https://doi.org/10.1212/wnl.54.6.1304 Lu W et al (2025) An exploratory study of the diffusion tensor imaging analysis along perivascular spaces (DTI-ALPS) index combined with quantitative analysis of choroid plexus volume and perivascular spaces in different cognitive stages of cerebral small vessel disease. Quant Imaging Med Surg 15:8173–8188. https://doi.org/10.21037/qims-2025-733 Assogna M et al (2023) Association of Choroid Plexus Volume With Serum Biomarkers, Clinical Features, and Disease Severity in Patients With Frontotemporal Lobar Degeneration Spectrum. Neurology 101:e1218–e1230. https://doi.org/doi:10.1212/WNL.0000000000207600 Missori P et al (2016) In normal aging ventricular system never attains pathological values of Evans' index. Oncotarget 7:11860–11863. https://doi.org/10.18632/oncotarget.7644 Wang J, Christensen D, Coombes SA, Wang Z (2024) Cognitive and brain morphological deviations in middle-to-old aged autistic adults: A systematic review and meta-analysis. Neurosci Biobehav Rev 105782. https://doi.org/10.1016/j.neubiorev.2024.105782 Additional Declarations The authors declare potential competing interests as follows: No conflict of interest. Supplementary Files PVS.Supplementary.Tables.02222026.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-9450979","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625102233,"identity":"ca274a32-cea9-4244-bd19-c7f8e8e31a10","order_by":0,"name":"Danielle Christensen","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Danielle","middleName":"","lastName":"Christensen","suffix":""},{"id":625102234,"identity":"a9616f2e-7348-4d5b-a096-cac164f8adc7","order_by":1,"name":"Giuseppe Barisano","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Giuseppe","middleName":"","lastName":"Barisano","suffix":""},{"id":625102235,"identity":"3fd3a58b-d5c4-476e-b76b-0d36b6baac9f","order_by":2,"name":"Bradley J. Wilkes","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Bradley","middleName":"J.","lastName":"Wilkes","suffix":""},{"id":625102236,"identity":"31e28fbf-5f15-4538-ba5f-c2c1a6695b41","order_by":3,"name":"Young Seon Shin","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Young","middleName":"Seon","lastName":"Shin","suffix":""},{"id":625102237,"identity":"b0295f48-58b4-41a2-bfbd-eb7abf5d5e4e","order_by":4,"name":"Jingying Wang","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Jingying","middleName":"","lastName":"Wang","suffix":""},{"id":625102238,"identity":"4be88d56-9cf9-4723-8124-c2d5d47a5a4a","order_by":5,"name":"Ellen Parks","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Ellen","middleName":"","lastName":"Parks","suffix":""},{"id":625102239,"identity":"450cf158-3a2d-4dfc-a84b-d674b89df845","order_by":6,"name":"Ann-Marie Orlando","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Ann-Marie","middleName":"","lastName":"Orlando","suffix":""},{"id":625102240,"identity":"9f91a28f-5584-4754-9218-762da7bc691c","order_by":7,"name":"Bikram Karmakar","email":"","orcid":"","institution":"University of Wisconsin–Madison","correspondingAuthor":false,"prefix":"","firstName":"Bikram","middleName":"","lastName":"Karmakar","suffix":""},{"id":625102241,"identity":"704cd405-4362-4028-b8fc-c1cfd611ce77","order_by":8,"name":"Stephen A. Coombes","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"A.","lastName":"Coombes","suffix":""},{"id":625102242,"identity":"ba68e75b-860e-444a-8eac-ffb3b2d45b23","order_by":9,"name":"Stefano Sotgiu","email":"","orcid":"","institution":"University of Sassari","correspondingAuthor":false,"prefix":"","firstName":"Stefano","middleName":"","lastName":"Sotgiu","suffix":""},{"id":625102243,"identity":"c9ba2c7f-a1b9-48f6-8598-d7125527c2ad","order_by":10,"name":"Zheng Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYDACCTiL+QBDRQGQPkC8FrYEhjMGpGnhMSBOi/zs5mMPvzAcljfnX/NN4oABgxzfjQT8WhjnHEs3lmE4bLhzxtttIC3GkoS0MEvkmElLMNxm3HDj7DbpDwYMiRsIaWGTyP8G0mK/4caZZyBb6glq4ZHIYZP8wHA7ccP5HjaQlgQDQlokJNLMpBkM/idvuMFmbHHAQMJw5pkH+LXIz0h+JvmjIs12w/nDD28cqLCR5ztOwBYQYOYBRYcEWKUEfqUwwPgDRPIfIE71KBgFo2AUjDwAAFsCR2/cQ8cnAAAAAElFTkSuQmCC","orcid":"","institution":"University of Florida","correspondingAuthor":true,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-04-17 15:52:25","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":true,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9450979/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9450979/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107303945,"identity":"0c574abf-b921-4a7d-91c2-7d99d0a57596","added_by":"auto","created_at":"2026-04-20 08:05:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2971028,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCoronal and axial MRI depictions of multimodal methods used in this study to examine CSF-based mechanisms. (A)\u003c/strong\u003eAutomated segmentation of white matter perivascular spaces (WM-PVS). \u003cstrong\u003e(B)\u003c/strong\u003eDiffusion tensor imaging analysis along the perivascular space (DTI-ALPS). \u003cstrong\u003e(C)\u003c/strong\u003eVolumetric segmentation of the lateral ventricles (LV) and choroid plexus (CP).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9450979/v1/58bf7e3b339b23f266d88f5e.png"},{"id":107303946,"identity":"84df757d-8a65-4eda-bdcc-3fbe534e8cf8","added_by":"auto","created_at":"2026-04-20 08:05:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":280415,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatterplots of significant group and group-by-age interactions across CSF-based mechanisms.\u003c/strong\u003e Scatterplot of white matter perivascular space volume fraction in the left inferior parietal lobule with age \u003cstrong\u003e(A)\u003c/strong\u003e. Autistic adults exhibited significantly steeper age-associated increases in WM-PVS volume fraction in the left inferior parietal lobule compared with neurotypical controls. Scatterplot of diffusion tensor imaging analysis along the perivascular space with age \u003cstrong\u003e(B)\u003c/strong\u003e. Autistic adults exhibited significantly lower DTI-ALPS indices relative to neurotypical controls across middle and older adulthood. Scatterplots show normalized volumes of the left lateral ventricle \u003cstrong\u003e(C)\u003c/strong\u003e, right lateral ventricle\u003cstrong\u003e (D)\u003c/strong\u003e, left choroid plexus \u003cstrong\u003e(E)\u003c/strong\u003e, and right choroid plexus\u003cstrong\u003e(F) \u003c/strong\u003ewith age. Autistic adults exhibited significantly larger left and right lateral ventricle and choroid plexus volumes compared to neurotypical controls.\u003c/p\u003e","description":"","filename":"Layout10.png","url":"https://assets-eu.researchsquare.com/files/rs-9450979/v1/f84ed8289eb62c06eff849f9.png"},{"id":107487015,"identity":"619abe5d-08a2-4f6f-83ca-24575b941269","added_by":"auto","created_at":"2026-04-22 02:39:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4059812,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9450979/v1/e9468d81-7af3-48e0-980c-37979c1d6ad5.pdf"},{"id":107484539,"identity":"55c0e7a1-5f5d-48f4-a155-26da77116cb5","added_by":"auto","created_at":"2026-04-22 02:32:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":88159,"visible":true,"origin":"","legend":"","description":"","filename":"PVS.Supplementary.Tables.02222026.docx","url":"https://assets-eu.researchsquare.com/files/rs-9450979/v1/02427e6c87cdf47e940b320c.docx"}],"financialInterests":"The authors declare potential competing interests as follows: No conflict of interest. ","formattedTitle":"\u003cp\u003eAltered cerebrospinal fluid-based clearance mechanisms in aging autistic adults\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eAutistic adults exhibit a 4\u0026ndash;6-fold increased risk of unspecified dementia relative to the general population.\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e This elevated risk persists after adjustment for intellectual disability (ID), psychiatric comorbidities, and lifestyle-related factors, suggesting that autism spectrum disorder (ASD) independently confers a heightened neurobiological vulnerability to pathological brain ageing. While the precise mechanisms underlying this heightened risk have not yet been identified, multiple dementia subtypes\u0026mdash;including Alzheimer\u0026rsquo;s disease, vascular dementia, and frontotemporal dementia\u0026mdash;exhibit broad alterations in CSF-based mechanisms critical for brain metabolic waste clearance.\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e These alterations include enlarged white matter perivascular spaces (WM-PVS),\u003csup\u003e10\u0026ndash;14\u003c/sup\u003e reduced indices of fluid diffusion along perivascular spaces,\u003csup\u003e15\u0026ndash;17\u003c/sup\u003e and larger volumes of the lateral ventricles\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e and choroid plexus\u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Evidence of dysregulation in CSF-based mechanisms has been reported in middle-aged and older autistic adults, including increased extracellular free-water in frontal lobe transcallosal white matter\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e and enlargement of the cerebral ventricles compared to age-matched neurotypical controls.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e However, these findings have been derived from single imaging markers rather than from a comprehensive multimodal approach to examine CSF-based mechanisms. Given that ageing neurobiology in ASD remains critically under-researched, the elevated prevalence of dementia in autistic adults underscores the need for systematic investigation of pathological brain ageing processes across middle and later life. Recent work has recognized the pathophysiological overlaps between ASD and Alzheimer\u0026rsquo;s disease, emphasizing shared alterations among perivascular pathways and brain waste clearance mechanisms.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Together, these findings suggest that alterations in CSF-based mechanisms involved in brain metabolic waste clearance may represent an overlapping substrate linking ASD and dementia-related processes, offering insight into shared neurobiological pathways driving this increased prevalence.\u003c/p\u003e \u003cp\u003eProper functioning of CSF-based mechanisms is necessary for the efficient removal of neurotoxic proteins that may otherwise accumulate within the parenchyma over time.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Brain metabolic waste clearance relies on the bulk fluid exchange between CSF and interstitial fluid along the perivascular spaces (PVS).\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e CSF is produced by the choroid plexus, a specialized structure lining the brain\u0026rsquo;s ventricular system,\u003csup\u003e30,31\u003c/sup\u003e and is derived from arterial blood plasma.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Beyond CSF production, the choroid plexus also regulates nutrient transport, metabolic exchange between blood and CSF, and triggering neuroimmune responses in the central nervous system.\u003csup\u003e\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e The lateral ventricles are the primary compartments that support continuous CSF circulation and maintain homeostasis.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e CSF circulates through the ventricular system and exits into the subarachnoid space as extra-axial CSF (EA-CSF).\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e From here, EA-CSF flows along perivascular pathways that surround penetrating brain arterioles\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e and contributes to CSF-interstitial fluid exchange,\u003csup\u003e27,40,41\u003c/sup\u003e facilitating metabolic waste removal throughout the parenchyma.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Structurally, PVS are the fluid-filled compartments bounded internally by the abluminal surface of arterioles and externally by a sheath of astrocytic endfeet,\u003csup\u003e27,42,43\u003c/sup\u003e which are specialized glial processes extending from astrocytes.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Cardiac-driven arterial pulsatility offers the mechanical force that propels CSF movement along PVS.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e This rhythmic contraction and relaxation of the arteriole wall promotes CSF-interstitial fluid exchange and enables metabolic waste removal from the surrounding tissue,\u003csup\u003e27,44\u003c/sup\u003e directing collected byproducts toward perivenous drainage pathways.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e This fluid-based mechanism, commonly referred to as the glymphatic system, is estimated to remove over half of extracellular amyloid-β from the brain, underscoring its critical role in minimizing the accumulation of neurodegeneration-promoting products.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDevelopmental alterations in CSF-based mechanisms are well-documented in ASD,\u003csup\u003e47\u0026ndash;56\u003c/sup\u003e yet remain largely unexplored in autistic adults. In infants at high risk for ASD, elevated extra-axial CSF\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and widespread PVS enlargement\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e have been shown to predict later ASD diagnosis. In autistic children aged 1\u0026ndash;9 years, increased WM-PVS count and volume fraction have been associated with greater clinical severity of ASD.\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) of deep medullary veins has further revealed lower indices in autistic children aged 2\u0026ndash;5 years compared with neurotypical controls, suggesting reduced fluid transport along these pathways.\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e Converging structural MRI studies have also demonstrated enlarged lateral ventricles and choroid plexus in autistic children and adolescents relative to age-matched peers,\u003csup\u003e54,55,57\u003c/sup\u003e suggesting that altered CSF-based mechanisms in ASD are reflected not only in downstream PVS morphology and fluid transport, but also in upstream CSF-handling. Thus, enlargement of the lateral ventricles and choroid plexus implicates abnormalities in structures involved in both CSF handling as well as solute filtration and immune signaling between blood and CSF.\u003csup\u003e\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Despite ample evidence of early-life alterations, little is known about how CSF-based features manifest in ASD after childhood, particularly in middle-aged and older autistic adults. Given the critical role of CSF regulation in brain metabolic waste clearance,\u003csup\u003e7,27\u003c/sup\u003e this knowledge gap limits the development of a mechanistic framework for understanding the heightened vulnerability to dementia in this understudied clinical population.\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn this multimodal imaging study, we used structural and diffusion-based MRI approaches to compare WM-PVS count (the total number of PVS lesions), volume (total lesion volume), and diameter (mean diameter of detected PVS) across the frontal, parietal, temporal, and occipital lobes, as well as DTI-ALPS indices and volumes of the lateral ventricles and choroid plexus, between middle-aged and older autistic adults and matched neurotypical controls. To our knowledge, no prior study has jointly examined WM-PVS morphology, DTI-ALPS, and volumes of the lateral ventricles and choroid plexus in ageing autistic adults. Consistent with findings in autistic infants and children,\u003csup\u003e47,53\u0026ndash;55\u003c/sup\u003e we predicted that CSF dysregulation would persist beyond early development and hypothesized that autistic adults would exhibit 1) greater WM-PVS volume, count, and diameter; 2) lower DTI-ALPS indices; and 3) increased lateral ventricle and choroid plexus volumes relative to controls. We further hypothesized that age-associated changes in these measures would be more pronounced in autistic adults compared with neurotypical controls, reflecting the life-course neurobiological vulnerability associated with ASD.\u003c/p\u003e"},{"header":"Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eA total of 49 autistic adults and 61 neurotypical controls (NT) participated in this cross-sectional study. The two groups were matched on age, sex, and full-scale IQ (FSIQ), performance IQ (pIQ), and verbal IQ (vIQ) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Autistic participants were recruited through the Center for Autism and Related Disabilities (CARD) at the University of Florida, the University of Central Florida, and the University of South Florida, as well as the SPARK Research Match program (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sfari.org/resource/research-match\u003c/span\u003e\u003cspan address=\"https://www.sfari.org/resource/research-match\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Controls were primarily recruited through local flyers and word of mouth referrals. All study procedures were approved by the Institutional Review Boards (IRB) at the University of Florida (IRB202100659, approved September 23, 2021). In accordance with the Declaration of Helsinki, written informed consent was obtained from all participants after they received a complete description of the study and its procedures.\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\u003eDemographic characteristics between autistic participants (ASD) and neurotypical controls (NT)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eASD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et/χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample size (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026thinsp;\u0026minus;\u0026thinsp;73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u0026thinsp;\u0026minus;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (M/F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28/21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29/32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFSIQ score\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77\u0026thinsp;\u0026minus;\u0026thinsp;143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82\u0026thinsp;\u0026minus;\u0026thinsp;126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evIQ score\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71\u0026thinsp;\u0026minus;\u0026thinsp;136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83\u0026thinsp;\u0026minus;\u0026thinsp;130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epIQ score\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72\u0026thinsp;\u0026minus;\u0026thinsp;142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79\u0026thinsp;\u0026minus;\u0026thinsp;142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeTIV\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1545\u0026thinsp;\u0026plusmn;\u0026thinsp;149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1512\u0026thinsp;\u0026plusmn;\u0026thinsp;157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative head motion (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADOS-2 SA score\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026thinsp;\u0026minus;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADOS-2 RRB\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026thinsp;\u0026minus;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003eAge, FSIQ, pIQ, vIQ and eTIV shown as mean and standard deviation\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003eb\u003c/sup\u003eADOS-2 Social Affect (SA) and Restricted and Repetitive Behavior (RRB) are derived from the Module 4 revised algorithm and shown as mean and standard deviation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here]\u003c/p\u003e \u003cp\u003eScreening criteria for participants were consistent with those used in previous studies from our lab and others \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Autistic adults with a clinical diagnosis of ASD were required to score\u0026thinsp;\u0026gt;\u0026thinsp;32 on the Autism Spectrum Quotient for Adults (AQ-50)\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e65 on the Social Responsiveness Scale Adult Self-Report (SRS-2)\u003csup\u003e61\u003c/sup\u003e to be invited for a comprehensive in-person diagnostic evaluation using the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) \u003csup\u003e62\u003c/sup\u003e. Diagnosis of ASD was confirmed through an integrative review of AQ-50, SRS-2, and ADOS-2 scores, in conjunction with expert clinical judgement by a licensed clinician, following the DSM-5 criteria.\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eNeurotypical controls were recruited if they scored \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e 22 on the AQ-50 and \u0026lt; 60 on the SRS-2. Exclusion criteria for controls included a family history of ASD or related neurodevelopmental conditions among first- and second-degree relatives.\u003c/p\u003e \u003cp\u003eExclusion criteria for both groups included: (1) a diagnosis of intellectual disability (including non-specific developmental delay), mild cognitive impairment, or dementia; (2) a current or past major psychiatric condition (e.g., schizophrenia, bipolar disorder, or post-traumatic stress disorder); (3) a current or past illness involving the central nervous system (e.g., brain tumor, thyroid disease, Cushing\u0026rsquo;s disease, or HIV infection); (4) a diagnosed neurological disorder (e.g., Parkinson\u0026rsquo;s disease, cerebellar ataxia, seizure, dystonia, or stroke); (5) a family history of heritable neurological disease (e.g., Huntington\u0026rsquo;s disease, Wilson\u0026rsquo;s disease, or amyotrophic lateral sclerosis); (6) implanted medical devices incompatible with MRI (e.g., cardiac pacemakers, infusion pumps, cochlear implants); (7) current pregnancy; (8) FSIQ\u0026thinsp;\u0026lt;\u0026thinsp;75 as measured by the Wechsler Abbreviated Scales of Intelligence, Second Edition \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, or (9) non-English-speaking status.\u003c/p\u003e \u003cp\u003eMedication use and polypharmacy are prevalent in ASD \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. To allow an ecologically representative sample of autistic adults, routine medication use was not an exclusion criterion in this study. Participants taking psychotropic medication included those prescribed antidepressants (ASD\u0026thinsp;=\u0026thinsp;25, NT\u0026thinsp;=\u0026thinsp;4), antipsychotics/neuroleptics (ASD\u0026thinsp;=\u0026thinsp;4), sedatives/hypnotics/anxiolytics (ASD\u0026thinsp;=\u0026thinsp;7, NT\u0026thinsp;=\u0026thinsp;2), stimulants (ASD\u0026thinsp;=\u0026thinsp;9), and anticonvulsants (ASD\u0026thinsp;=\u0026thinsp;6, NT\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMRI data acquisition\u003c/h3\u003e\n\u003cp\u003eMRI scans were acquired using a 3T Siemens Prisma scanner equipped with a 64-channel head coil at the University of Florida McKnight Brain Institute. Participants were screened before scanning, and hearing protection was provided prior to entering the scanner room. Foam padding was securely placed around the head to minimize motion during image acquisition. For T1-weighted imaging, the MPRAGE sequence was acquired with the following parameters: repetition time (TR)\u0026thinsp;=\u0026thinsp;2000 ms, echo time (TE)\u0026thinsp;=\u0026thinsp;2.99 ms, flip angle\u0026thinsp;=\u0026thinsp;8\u0026deg;, field of view (FOV)\u0026thinsp;=\u0026thinsp;256 \u0026times; 256 mm, matrix size\u0026thinsp;=\u0026thinsp;320 \u0026times; 320, 208 sagittal slices, and isotropic voxel size\u0026thinsp;=\u0026thinsp;0.8 \u0026times; 0.8 \u0026times; 0.8 mm\u003csup\u003e3\u003c/sup\u003e. For T2-weighted imaging, a three-dimensional SPACE sequence was collected with the following parameters: TR\u0026thinsp;=\u0026thinsp;2500 ms, TE\u0026thinsp;=\u0026thinsp;370 ms, variable refocusing flip angle, FOV\u0026thinsp;=\u0026thinsp;256 \u0026times; 256 mm, matrix size\u0026thinsp;=\u0026thinsp;320 \u0026times; 320, 208 sagittal slices, and isotropic voxel size\u0026thinsp;=\u0026thinsp;0.8 \u0026times; 0.8 \u0026times; 0.8 mm\u003csup\u003e3\u003c/sup\u003e. Diffusion weighted scans were acquired with an echo-planar imaging sequence with the following parameters: TR\u0026thinsp;=\u0026thinsp;6400 ms, TE\u0026thinsp;=\u0026thinsp;58 ms, b-values: 5 \u0026times; 0 s/mm\u003csup\u003e2\u003c/sup\u003e and 64 \u0026times; 1000 s/mm\u003csup\u003e2\u003c/sup\u003e, bandwidth\u0026thinsp;=\u0026thinsp;2442 Hz/pixel, FOV\u0026thinsp;=\u0026thinsp;256 \u0026times; 256, matrix size\u0026thinsp;=\u0026thinsp;128 \u0026times; 128, 69 axial slices, and isotropic voxel size\u0026thinsp;=\u0026thinsp;2.0 \u0026times; 2.0 \u0026times; 2.0 mm\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here]\u003c/p\u003e\n\u003ch3\u003eWhite matter perivascular spaces\u003c/h3\u003e\n\u003cp\u003eT2-weighted images were visually assessed by the team\u0026rsquo;s physician-scientist (GB) for PVS segmentation. Cases with prominent imaging artifacts that significantly compromised accurate PVS quantification were excluded from further analysis (ASD\u0026thinsp;=\u0026thinsp;2). T2-weighted images were rigidly registered to the processed T1-weighted images using a six-degree-of-freedom transformation and boundary-based cost function, as implemented by the \u003cem\u003ebbregister\u003c/em\u003e command using FreeSurfer version 8.0.0. We segmented MRI-visible WM-PVS using a robust, fully automated pipeline that has been previously validated across multiple independent datasets of healthy aging and neurodegeneration \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Briefly, we first generated enhanced perivascular contrast (EPC) images for each participant by computing the voxel-wise ratio of T1-weighted to T2-weighted images \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Prior work has demonstrated that this approach enhances PVS contrast and increases the sensitivity of both visual detection and automated PVS segmentation \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. We then applied a multiscale \u003cem\u003eFrangi vesselness\u003c/em\u003e filter \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e to the white matter mask applied on the EPC images to enhance tubular structures consistent with PVS morphology. We set the filter parameters to α\u0026thinsp;=\u0026thinsp;0.5 and β\u0026thinsp;=\u0026thinsp;0.5, with c defined as half the maximum Hessian norm, in accordance with established recommendations \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. The \u003cem\u003eFrangi\u003c/em\u003e filter assigns a \u003cem\u003evesselness\u003c/em\u003e value to each voxel based on the eigenvalues of the Hessian matrix, thereby preferentially improving elongated, vessel-like structures while suppressing non-tubular signal.\u003c/p\u003e \u003cp\u003eWe applied the \u003cem\u003eFrangi\u003c/em\u003e filter within 32 bilateral white matter regions generated using FreeSurfer according to the Desikan-Killiany atlas. To minimize contamination from non-PVS pathology, we first segmented white matter lesions unrelated to PVS using a fully automated approach \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e and excluded these lesions from the white matter masks prior to \u003cem\u003evesselness\u003c/em\u003e filtering, consistent with prior work \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. We then automatically derived PVS masks from the resulting \u003cem\u003evesselness\u003c/em\u003e maps using a percentile-based thresholding approach that has been previously validated \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor each participant, we quantified PVS metrics including total count, total volume, and mean diameter. Total count and total volume refer to the occurrence and spatial occupancy that visible PVS exhibit in a mask. Mean diameter refers to the average cross-sectional width of detected PVS \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. We derived these measures using MATLAB\u0026rsquo;s \u003cem\u003eregionprops3\u003c/em\u003e function applied to the binarized PVS masks. Because both PVS count and volume scale with the volume of the underlying white matter region, we normalized these measures by the corresponding regional white matter mask volume to derive PVS count fraction and PVS volume fraction, respectively \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe grouped white matter masks from the left and right hemisphere by anatomical lobe, consistent with prior studies in autistic children \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. The frontal lobe comprised 11 masks, including the superior frontal gyrus, rostral middle frontal gyrus, caudal middle frontal gyrus, pars opercularis, pars orbitalis, pars triangularis, lateral orbitofrontal gyrus, medial orbitofrontal gyrus, precentral gyrus, paracentral lobule, and frontal pole. The parietal lobe consisted of 5 masks encompassing the postcentral gyrus, supramarginal gyrus, superior parietal lobule, inferior parietal lobule, and precuneus regions. The temporal lobe included 8 masks incorporating the entorhinal cortex, parahippocampal gyrus, fusiform gyrus, superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, transverse temporal gyrus, and temporal pole. Finally, the occipital lobe consisted of 4 masks encompassing the lingual gyrus, pericalcarine cortex, cuneus, and lateral occipital cortex. Across all bilateral regions of interest (ROIs) and PVS metrics, each participant yielded 168 PVS variables (56 ROIs \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e 3 PVS metrics).\u003c/p\u003e\n\u003ch3\u003eDTI-ALPS index\u003c/h3\u003e\n\u003cp\u003eWe calculated the DTI-ALPS index using an MNI-space approach \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) and processed diffusion weighted MRI data following pipelines used in previous published work \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Specifically, we performed denoising and Gibbs artefact removal using MRtrix3 \u003csup\u003e71\u003c/sup\u003e. We then generated brain masks directly from the diffusion weighted images using the \u003cem\u003edwi2mask\u003c/em\u003e function in MRtrix3 \u003csup\u003e71\u003c/sup\u003e. Next, we corrected for eddy current induced image distortions and head motion, with corresponding adjustment to gradient directions (i.e. b-vectors) using Eddy, a fully automated quality control framework in FSL \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe subsequently reconstructed diffusion tensors using FSL's \u003cem\u003edtifit\u003c/em\u003e function to generate fractional anisotropy (FA) images in native subject space. We registered the resulting FA images to the HCP1065 FA template \u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e using the symmetric normalization (SyN) algorithm implemented in Advanced Normalization Tools (ANTs) \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e, which applies both an affine transformation and a nonlinear diffeomorphic warp. These registered FA images in MNI space were visually inspected to confirm appropriate data quality, as in our prior work \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. We then applied the resulting transformation matrices to each of the six unique tensor elements (Dxx, Dxy, Dxz, Dyy, Dyz, and Dzz) to transform the diffusion tensor into MNI space.\u003c/p\u003e \u003cp\u003eTo compute the index, we defined four bilateral spherical regions of interest (ROIs; 5mm-diameter) at the level of the lateral ventricles to sample projection and association white matter fibers. We centered projection fiber ROIs at MNI coordinates (\u0026plusmn;\u0026thinsp;25, \u0026minus;\u0026thinsp;20, 28) and association fiber ROIs at (\u0026plusmn;\u0026thinsp;39, \u0026minus;\u0026thinsp;20, 28), based on the HCP1065 1mm FA template and corresponding tensor maps overlaid in RGB to confirm proper orientation. We then calculated the DTI-ALPS index as the ratio of diffusivity along the x-axis to diffusivity perpendicular to the primary fiber direction, defined as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:DTI-ALPS=\\:\\frac{\\text{m}\\text{e}\\text{a}\\text{n}\\:(\\text{D}\\text{x}\\text{x}\\_\\text{p}\\text{r}\\text{o}\\text{j},\\:\\text{D}\\text{x}\\text{x}\\_\\text{a}\\text{s}\\text{s}\\text{o}\\text{c})\\:}{\\text{m}\\text{e}\\text{a}\\text{n}\\:(\\text{D}\\text{y}\\text{y}\\_\\text{p}\\text{r}\\text{o}\\text{j},\\:\\text{D}\\text{z}\\text{z}\\_\\text{a}\\text{s}\\text{s}\\text{o}\\text{c})}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere Dxx represents diffusivity along projection and association fibers, Dyy represents perpendicular diffusivity in projection fibers, and Dzz represents perpendicular diffusivity in association fibers. We averaged left and right hemisphere values to obtain a single bilateral ALPS index for each participant. Due to the timing in which individuals were recruited in this study, five participants in our sample were unable to receive the diffusion MRI necessary for DTI-ALPS analysis (ASD\u0026thinsp;=\u0026thinsp;4; NT\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e\n\u003ch3\u003eLateral ventricle and choroid plexus volume\u003c/h3\u003e\n\u003cp\u003eWe performed quantification of lateral ventricle and choroid plexus volume using FreeSurfer versions 7.2.0 \u003csup\u003e75\u003c/sup\u003e and FSL version 6.0 \u003csup\u003e72\u003c/sup\u003e on a Linux-based computing system. First, T1-weighted images underwent slice-by-slice visual inspection across axial, coronal, and sagittal planes by multiple trained raters. Images that exhibited significant segmentation inaccuracies were corrected using the \u003cem\u003eRecon Edit\u003c/em\u003e tool in FreeSurfer and subsequently cross-validated by an independent rater to ensure consistency. T1-weighted images that did not meet quality control criteria were excluded from further analysis (ASD\u0026thinsp;=\u0026thinsp;1). Images that passed quality control were processed for volumetric segmentation and cortical surface reconstruction following the standard \u003cem\u003erecon-all\u003c/em\u003e pipeline, which includes motion correction, intensity normalization, transformation to Talairach space, skull stripping and brain extraction, white matter segmentation, and cortical surface parcellation. We derived volumes of the left and right lateral ventricles and choroid plexus (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) from T1-weighted MRI scans using automated segmentation outputs generated by FreeSurfer. To account for inter-individual differences in brain size, we normalized these ROIs by the estimated total intracranial volume (eTIV) provided by FreeSurfer.\u003c/p\u003e \u003cp\u003eParticipant sample size for each MRI measure was 49 ASD/61 NT for WM-PVS and T1-weighted volume, and 46 ASD/59 NT for DTI-ALPS.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted in RStudio version 4.4.1. Demographic characteristics between autistic participants and neurotypical controls were compared using independent-sample \u003cem\u003et\u003c/em\u003e-tests for continuous variables, including age, FSIQ, pIQ, vIQ, and eTIV. Group differences in sex were assessed using a Chi-square test.\u003c/p\u003e \u003cp\u003eNormality of outcome variables was assessed using the Shapiro-Wilk test, which indicated non-normal distributions for several measures, including lateral ventricle and choroid plexus volumes, and approximately 40% of PVS variables. Given the non-normal distribution properties, linear regression models with 5000 permutations were implemented to examine main effects of group (ASD vs. NT), age, and their interaction (group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e age). Each MRI metric served as the dependent variable in separate models. Age was mean-centered to improve interpretability of group and group-by-age effects and reduce multicollinearity among predictors.\u003c/p\u003e \u003cp\u003eBonferroni correction was applied to adjust for multiple comparisons \u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. For WM-PVS, Bonferroni thresholds were set within lobes (frontal, parietal, temporal, and occipital) to control the family-wise error rate while preserving anatomical specificity. Specifically, significance was defined as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.00227 for the frontal lobe (22 masks), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.00500 for the parietal lobe (10 masks), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.00313 for the temporal lobe (16 masks), and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.00625 for the occipital lobe (8 masks). Significance was defined as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.025 for the lateral ventricles and choroid plexus volume, as each consisted of two ROIs (left and right), and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for the DTI-ALPS index.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eParticipant demographics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the demographic characteristics of the groups. Autistic adults and neurotypical controls were matched on age, sex, all IQ scores, and eTIV. Relative head motion also did not differ between groups.\u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWhite matter perivascular spaces\u003c/h2\u003e \u003cp\u003eAutistic adults exhibited significantly steeper age-associated increases in WM-PVS volume fraction within the left inferior parietal lobule compared to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Furthermore, both groups demonstrated significant age-associated increases in WM-PVS volume fraction within the right pars triangularis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Multiple lobar patterns in WM-PVS were demonstrated at the uncorrected level (\u003cem\u003ep-perm\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For example, 1) group effects were most frequent in the temporal lobe (50%), followed by the parietal (25%) and occipital lobes (25%), with no group effect observed in the frontal lobe; 2) group \u0026times; age interaction effects were most frequent in the parietal lobe (62.5%), followed by the temporal (25%) and frontal lobes (12.5%), and no interaction effects observed in the occipital lobe; and 3) age effects were predominantly found in the frontal lobe (55.6%), followed by the parietal (22.2%), temporal (18.5%), and occipital lobes (3.7%). Finally, hemispheric distribution showed that age and group main effects were bilaterally distributed, whereas all group \u0026times; age interaction effects were left-lateralized. No other significant Bonferroni-corrected group, age, or group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e age effects were observed (Supplementary Tables\u0026nbsp;1\u0026ndash;4).\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\u003eLinear regression results of p-perm\u0026thinsp;\u0026lt;\u0026thinsp;0.05 WM-PVS metrics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLobe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMask\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep-perm\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrontal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-parsopercularis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup\u0026sdot;Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-caudalmiddlefrontal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-caudalmiddlefrontal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-parsopercularis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-parstriangularis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-precentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-rostralmiddlefrontal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-superiorfrontal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR-parsopercularis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR-parsopercularis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR-parstriangularis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR-parstriangularis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eR-parstriangularis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e3.75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR-precentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR-rostralmiddlefrontal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR-superiorfrontal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParietal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR-inferiorparietal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR-supramarginal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-inferiorparietal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup\u0026sdot;Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-inferiorparietal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup\u0026sdot;Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eL-inferiorparietal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eGroup\u0026sdot;Age\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e3.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-precuneus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup\u0026sdot;Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-precuneus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup\u0026sdot;Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-precuneus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-supramarginal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR-inferiorparietal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR-postcentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR-precuneus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR-supramarginal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemporal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-entorhinal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-parahippocampal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.4256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR-transversetemporal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR-transversetemporal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-fusiform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup\u0026sdot;Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-inferiortemporal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup\u0026sdot;Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-inferiortemporal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-middletemporal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-middletemporal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR-inferiortemporal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR-middletemporal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccipital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-lingual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.3454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-pericalcarine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-lingual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eBold indicates significance after Bonferroni correction\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eDM\u0026thinsp;=\u0026thinsp;Diameter mean; VF\u0026thinsp;=\u0026thinsp;volume fraction; CF\u0026thinsp;=\u0026thinsp;count fraction; L\u0026thinsp;=\u0026thinsp;Left; R\u0026thinsp;=\u0026thinsp;Right\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDTI-ALPS index\u003c/h2\u003e \u003cp\u003eAutistic adults showed significantly lower DTI-ALPS indices compared to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Both groups showed significant age-associated decreases in DTI-ALPS indices, although no group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e age interaction was observed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear regression results of DTI-ALPS index\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep-perm\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTI-ALPS index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGroup\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.0624\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0282\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-2.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u0026sdot;Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.0043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-2.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBold indicates significance after Bonferroni correction\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLateral ventricle and choroid plexus volumes\u003c/h2\u003e \u003cp\u003eAutistic adults showed significantly larger lateral ventricle volumes in both the left and right hemispheres relative to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Significant age-associated increases in left and right lateral ventricle volumes were observed in both groups; however, no significant group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e age interactions were identified (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Autistic adults also demonstrated significantly larger left and right choroid plexus volume compared to controls, and both groups showed age-associated volume increases in the left choroid plexus. No significant group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e age effect was observed in either the left or right choroid plexus.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear regression results of lateral ventricles and choroid plexus volumes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep-perm\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-Lateral ventricle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGroup\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup*Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-Lateral ventricle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGroup\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup*Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-Choroid plexus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGroup\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup*Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-Choroid plexus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGroup\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup*Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBold indicates significance after Bonferroni correction\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eL\u0026thinsp;=\u0026thinsp;Left; R\u0026thinsp;=\u0026thinsp;Right\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study examined CSF-based mechanisms critical for brain metabolic waste clearance using multimodal imaging in ageing autistic adults and neurotypical controls. We report several novel findings. First, autistic adults exhibited an age-associated increase in WM-PVS volume fraction in the left inferior parietal lobule that was not observed in controls. Second, autistic adults demonstrated lower DTI-ALPS indices compared to controls. Third, autistic adults showed larger left and right lateral ventricles compared to controls. Fourth, autistic adults showed larger left and right choroid plexus volumes relative to controls. Fifth, both groups demonstrated greater WM-PVS volume fraction in the right pars triangularis, reduced DTI-ALPS indices, and increased bilateral lateral ventricle and left choroid plexus volumes with increasing age. Collectively, these findings provide evidence for a mechanistic overlap between CSF-based alterations observed in ageing autistic adults and those reported across multiple subtypes of dementia. Therefore, CSF-based alterations evident from early development through middle and older adulthood may support the framework of a lifelong neurobiological vulnerability contributing to increased dementia risk in ASD.\u003c/p\u003e \u003cp\u003eCompared to controls, autistic adults demonstrated an age-associated increase in WM-PVS volume fraction localized to the left inferior parietal lobule. The inferior parietal lobule is a densely vascularized region within the default mode network and serves as a major hub for sensory integration \u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e, language \u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e, and motor processes \u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e through long-range connectivity across multiple cortical networks \u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. Lateralized alterations of the left inferior parietal lobule have been reported in autistic children and adolescents in MRI studies of brain structure and functional connectivity, and have been strongly associated with clinical severity \u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e,\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Our finding extends this regional atypicality into middle and older adulthood, suggesting increased susceptibility of this integrative association cortex to perivascular burden with aging in ASD. As a metabolically demanding region, the inferior parietal lobule is particularly sensitive to vascular alterations that influence perivascular fluid dynamics, processes that are strongly modulated by arterial stiffness and pulsatility \u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. Notably, the inferior parietal lobule is also recognized as an early site of vascular and metabolic dysfunction in Alzheimer\u0026rsquo;s disease, as evidenced by hypoperfusion, hypometabolism, and volume atrophy \u003csup\u003e\u003cspan additionalcitationids=\"CR84\" citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. Volumetric reductions of the left inferior parietal lobule have been shown to differentiate Alzheimer's disease from normal cognition, with left-lateralized cortical thinning in this region identified as a prominent marker of early neurodegeneration \u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. Although region-specific assessments of WM-PVS in Alzheimer\u0026rsquo;s disease remain limited, increased WM-PVS burden has been consistently documented \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Taken together, these findings highlight a shared neurobiological substrate involving the left inferior parietal lobule and underscore its heightened vulnerability to pathological brain aging in autistic adults, warranting further investigation to clarify the mechanistic linkage between perivascular dysfunction and neurodegenerative risk in ASD.\u003c/p\u003e \u003cp\u003eAutistic adults exhibited lower DTI-ALPS indices relative to controls. The deep medullary veins, medullary arteries, and their associated perivascular spaces run predominantly in the x-direction (left\u0026ndash;right orientation) at the level of the lateral ventricles. DTI-ALPS indices serve as a proxy measure of CSF-mediated fluid diffusivity along the perivascular pathways, supporting bulk CSF transport into and out of the brain parenchyma, capturing fluid movement toward both the brain surface and the ventricular system \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. Our findings indicate that reduced perivascular fluid diffusivity in ASD persists beyond childhood \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e and extends into middle and older adulthood. Lower DTI-ALPS indices have been reported across the dementia spectrum, including Alzheimer\u0026rsquo;s disease \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e, vascular dementia \u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e, and Lewy body disease \u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e. In middle-aged adults, reduced DTI-ALPS indices predict future cognitive impairment and increased risk of dementia \u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e,\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e, highlighting the downstream consequences of chronically impaired fluid diffusivity. In Alzheimer\u0026rsquo;s disease, lower DTI-ALPS indices have been associated with increased cerebral amyloid-β deposition and worsened clinical outcomes, consistent with a disrupted capacity to regulate the accumulation of neurotoxic species \u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e. As a diffusion-based metric, DTI-ALPS indices may also reflect contributions from white matter microstructure alterations and vascular changes that are not specific to perivascular clearance \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Nevertheless, reduced perivascular fluid diffusivity in ASD from early life \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e through older adulthood reflect enduring alterations in perivascular fluid regulation that may present lifelong altered clearance of neurotoxic byproducts along major medullary vessels within the brain parenchyma. This impairment aligns with converging evidence of central neuroimmune activation observed in the CSF \u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e and brain tissue \u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e of autistic children and adults. These findings emphasize functionally relevant alterations in perivascular fluid dynamics in aging autistic adults that may overlap with established biological vulnerabilities associated with dementia progression and coincide with observed differences in WM-PVS.\u003c/p\u003e \u003cp\u003eAutistic adults also demonstrated larger left and right lateral ventricle volumes relative to controls. This finding is consistent with prior studies reporting enlarged lateral ventricles in autistic children \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, adolescents \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, and adults \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, indicating that ventricular enlargement represents a persistent neurodevelopmental vulnerability in ASD across the lifespan. Expansion of the cerebral ventricles\u0026mdash;particularly in cohorts in which ventriculomegaly is evident early in development\u0026mdash;has been interpreted as reflecting compromised CSF homeostasis due to altered circulation, distribution, or resorption dynamics \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. This interpretation is particularly plausible in ASD, as ventricular enlargement has been observed very early in life, well before the typical onset of aging-related neurodegenerative processes. In parallel, ventricular enlargement has also been linked to broader structural alterations within surrounding brain regions \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e and has been observed in Alzheimer\u0026rsquo;s disease\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, frontotemporal dementia \u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e, and Lewy body disease \u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e. Prior work from our group identified significant associations between enlarged ventricle volumes and reduced volumes in adjacent subcortical hippocampus and amygdala beginning in middle adulthood in ASD, but not during early development, highlighting accelerated structural deviations involving ventricular expansion and neighboring tissue volume reduction later in life \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Notably, none of the autistic adults in the current study had a clinical diagnosis of mild cognitive impairment or dementia. Ventricular expansion observed in this cohort may reflect a persistent vulnerability in brain morphogenesis that may confer increased susceptibility to aging-related neurodegeneration rather than dementia-specific pathology in ASD \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Future longitudinal studies are needed to disentangle the combined effects of early developmental vulnerability and aging-associated structural dynamics on ventricular enlargement in ASD.\u003c/p\u003e \u003cp\u003eFinally, autistic adults showed larger left and right choroid plexus compared to controls. This finding of choroid plexus enlargement is consistent with early childhood and adolescent studies of ASD \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, in which early brain overgrowth \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e and neuroimmune dysregulation \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e have been proposed as potential underlying mechanisms. Enlargement of the choroid plexus has also been commonly reported in aging-related neurodegenerative disorders, including Alzheimer\u0026rsquo;s disease\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, vascular dementia \u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e, and frontotemporal dementia \u003csup\u003e\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e, implicating compromised CSF production, nutrient transport, metabolic exchange, neuroimmune signaling, and clearance of neurotoxic peptides between the blood and CSF compartments. Our finding in autistic adults suggests that choroid plexus enlargement is observable across middle and older adulthood, and may align with longstanding disruptions in CSF production, homeostasis and neuroimmune signaling contributing to increased vulnerability to aging-related neurodegeneration in autistic adults.\u003c/p\u003e \u003cp\u003eAge-associated patterns were observed in both groups across multiple neuroimaging markers. The age-associated increase in WM-PVS volume fraction in the right pars triangularis across both groups suggests that perivascular morphology within this region is particularly sensitive to aging independent of diagnosis. Furthermore, the presence of age-associated reductions in DTI-ALPS demonstrates that decreased fluid diffusivity along perivascular pathways represents a general feature of aging for both groups. The age-associated increases in left and right lateral ventricle volumes we observed in both groups is consistent with studies of neurobiological aging in the general population showing bilateral ventricular enlargement as a common feature of advancing age \u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e. Finally, both groups demonstrated a lateralized age-associated volume increase in the left choroid plexus, suggesting an asymmetric susceptibility to enlargement in this CSF-producing and immune-signaling region with increasing age.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eOur findings should be interpreted alongside several considerations. First, the cross-sectional design limited our ability to quantify intra-individual variability and capture longitudinal trajectories \u003csup\u003e\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e. Therefore, age- and group-by-age\u0026ndash;associated CSF-based alterations should be interpreted cautiously. Longitudinal studies spanning the lifespan are needed to clarify the role of CSF-based mechanisms in neurobiological vulnerability contributing to increased dementia risk in ASD. Second, the sample primarily comprised cognitively unimpaired autistic adults. While this allowed characterization of predisposed CSF-related vulnerability in middle and older adulthood, it limited our ability to disentangle neurodevelopmental from neurodegenerative processes contributing to pathological aging in ASD. Studies including autistic adults across the dementia spectrum will be essential to address this gap. Third, participants were predominantly of average or above-average intelligence; thus, findings may not generalize to autistic adults with ID. Although ID modifies dementia risk in ASD\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, our results suggest that ASD itself\u0026mdash;independent of ID\u0026mdash;may be associated with altered CSF-related neurobiology linked to pathological brain aging. Finally, health factors including sleep disturbance, cardiovascular and metabolic disease, polypharmacy, and lifelong medication exposure, may have influenced these findings. Future large-scale studies should systematically evaluate their potential confounding and moderating effects.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study represents the first multimodal MRI investigation of CSF-based markers in ageing autistic adults. We identified convergent impairments of CSF\u0026ndash;based clearance mechanisms in ASD, including region-specific alterations in white matter perivascular spaces, reduced perivascular fluid diffusivity, and lateral ventricular enlargement. Considered alongside prior findings in autistic children,\u003csup\u003e54,57\u003c/sup\u003e these results support a lifelong pattern of altered CSF regulation that extends into the pathologically vulnerable periods of middle and older adulthood. Collectively, these findings establish a conceptual foundation for future research aimed at disentangling lifelong neurobiological vulnerabilities that may contribute to increased dementia risk in ASD.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocols were approved by the Institutional Review Boards (IRB) at UF (IRB201801378; approval date: July 26, 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the submission.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe are deeply grateful to all individuals who participated in this study. We thank CARD directors Ann-Marie Orlando, Ph.D., at the University of Florida, Teresa Daly, Ph.D., at the University of Central Florida, Beth Boone, Ph.D., at the University of South Florida, as well as the SPARK Research Match team, for their endless efforts in supporting participant identification and recruitment.\u0026nbsp;We also thank the undergraduate trainees in the Department of Applied Physiology and Kinesiology and the Department of Psychology, as well as professional graduate trainees in the Department of Occupational Therapy, at the University of Florida for their valuable assistance with MRI data inspection and post-processing.\u003c/p\u003e\n\u003cp\u003eFunding sources\u003c/p\u003e\n\u003cp\u003eDr. Zheng Wang receives funding from the National Institute on Aging (R21AG065621 and R01AG086493), the National Institute of Neurological Disorders and Stroke (R01NS121120), and the University of Florida APK Research Investment Grants Award. Dr. Giuseppe Barisano receives funding from the Alzheimer\u0026rsquo;s Drug Discovery Foundation (RC-202405-2026586). The REDCap application used in this study was supported by funding from the National Center for Advancing Translational Sciences (UL1T001427).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConflicts of interest\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no competing interests.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent statement\u003c/p\u003e\n\u003cp\u003eAll authors have read and consented to the submission of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVivanti G, Tao S, Lyall K, Robins DL, Shea LL (2021) The prevalence and incidence of early-onset dementia among adults with autism spectrum disorder. 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Neurosci Biobehav Rev 105782. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neubiorev.2024.105782\u003c/span\u003e\u003cspan address=\"10.1016/j.neubiorev.2024.105782\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":"University of Florida","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-9450979/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9450979/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground.\u003c/h2\u003e \u003cp\u003eAutistic adults demonstrate a 4\u0026ndash;6-fold increased risk of unspecified dementia compared with the general population; however, the neurobiological substrates underlying this elevated risk remain unexplored. Alterations in cerebrospinal fluid\u0026ndash;based mechanisms involved in brain metabolic waste clearance may represent a shared neuropathological pathway between autism spectrum disorder and dementia. Specifically, developmental deviations in cerebrospinal fluid-related imaging markers have been consistently reported in autistic infants, children, and adolescents, and brain amyloid and other metabolic waste accumulation is a hallmark of Alzheimer\u0026rsquo;s disease and related dementias. Despite this overlap, cerebrospinal fluid-based regulatory mechanisms have not been systematically examined in ageing autistic adults. Here, we used a multimodal magnetic resonance imaging approach to quantify structural and diffusion-based markers of cerebrospinal fluid regulation in middle-aged and older autistic adults compared with matched controls.\u003c/p\u003e\u003ch2\u003eMethods.\u003c/h2\u003e \u003cp\u003eForty-nine autistic adults aged 30\u0026ndash;73 years and 61 age-, sex-, and intelligence quotient\u0026ndash;matched controls underwent T1-, T2-, and diffusion-weighted imaging. Measures included white matter perivascular space volume fraction, count fraction, and mean diameter; diffusion-based indices of fluid movement along perivascular pathways; and volumes of the lateral ventricles and choroid plexus.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e \u003cp\u003eWith increasing age, autistic adults exhibited significantly greater increases in white matter perivascular volume fraction within the left inferior parietal lobule compared with controls. Autistic adults also showed significantly reduced diffusion indices and larger bilateral lateral ventricle and choroid plexus volumes relative to controls. Across both groups, increasing age was associated with higher white matter perivascular volume fraction in the right pars triangularis, reduced diffusion indices, and enlargement of the bilateral lateral ventricles and left choroid plexus.\u003c/p\u003e\u003ch2\u003eLimitations.\u003c/h2\u003e \u003cp\u003eFirst, the cross-sectional design limited our ability to quantify intra-individual variability and capture longitudinal trajectories. Second, the sample primarily comprised cognitively unimpaired autistic adults. Third, participants were predominantly of average or above-average intelligence; thus, findings may not generalize to autistic adults with ID. Finally, health factors including sleep disturbance, cardiovascular and metabolic disease, polypharmacy, and lifelong medication exposure, may have influenced these findings. Future large-scale studies should systematically evaluate their potential confounding and moderating effects.\u003c/p\u003e\u003ch2\u003eConclusions.\u003c/h2\u003e \u003cp\u003eThese findings demonstrate that ageing autistic adults exhibit convergent alterations in cerebrospinal fluid regulatory mechanisms, reflected in perivascular space morphology, diffusion-based fluid dynamics, and ventricular and choroid plexus enlargement. Together, the results link early developmental deviations to later-life vulnerability and highlight cerebrospinal fluid dysregulation as a potential candidate neurobiological substrate contributing to the increased prevalence of dementia in autistic adults.\u003c/p\u003e","manuscriptTitle":"Altered cerebrospinal fluid-based clearance mechanisms in aging autistic adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 08:05:00","doi":"10.21203/rs.3.rs-9450979/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":"6a133dd2-083e-4101-965b-f59ad57661e9","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T08:05:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 08:05:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9450979","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9450979","identity":"rs-9450979","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}