Cortical Thickness Estimates Research Articles

Greater baseline cortical atrophy in the dorsal attention network predicts faster clinical decline in Posterior Cortical Atrophy.

Posterior Cortical Atrophy (PCA) is a clinical syndrome characterized by progressive visuospatial and visuoperceptual impairment. As the neurodegenerative disease progresses, patients lose independent functioning due to the worsening of initial symptoms and development of symptoms in other cognitive domains. The timeline of clinical progression is variable across patients, and the field currently lacks robust methods for prognostication. Here, evaluated the utility of MRI-based cortical atrophy as a predictor of longitudinal clinical decline in a sample of PCA patients. PCA patients were recruited through the Massachusetts General Hospital Frontotemporal Disorders Unit PCA Program. All patients had cortical thickness estimates from baseline MRI scans, which were used to predict longitudinal change in clinical impairment assessed by the CDR Sum-of-Boxes (CDR-SB) score. Multivariable linear regression was used to estimate the magnitude of cortical atrophy in PCA patients relative to a group of amyloid-negative cognitively unimpaired participants. Linear mixed-effects models were used to test hypotheses about the utility of baseline cortical atrophy for predicting longitudinal clinical decline. Data acquired from 34 PCA patients (mean age = 65.41 ± 7.90, 71% females) and 24 controls (mean age = 67.34 ± 4.93, 50% females) were analyzed. Sixty-two percent of the PCA patients were classified as having mild cognitive impairment (CDR 0.5) at baseline, with the rest having mild dementia (CDR 1). Each patient had at least one clinical follow-up, with the mean duration of 2.78 ± 1.62 years. Relative to controls, PCA patients showed prominent baseline atrophy in the posterior cortical regions, with the largest effect size observed in the visual network of the cerebral cortex. Cortical atrophy localized to the dorsal attention network, which supports higher-order visuospatial function, selectively predicted the rate of subsequent clinical decline. These results demonstrate the utility of a snapshot measure of cortical atrophy of the dorsal attention network for predicting the rate of subsequent clinical decline in PCA. If replicated, this topographically-specific MRI-based biomarker could be useful as a clinical prognostication tool that facilitates personalized care planning.

Children had smaller brain volumes and cortical surface areas after prenatal opioid maintenance therapy exposure.

The studies have shown that infants with prenatal OMT exposure had smaller brain volumes than non-exposed controls, but long-term outcome data are lacking. We examined 5-13-year-old OMT-exposed children with brain MRI and tested motor and visual-motor functions and possible associations between brain morphology and outcome. To this retrospective cohort study, we recruited 55 children with prenatal OMT exposure and 59 age- and gender-matched controls. They were examined with brain MRI, Movement-ABC and Beery-VMI. MRI images were processed with the Free Surfer® software to obtain volumetrics and estimates of cortical surface area and thickness. We used a general linear regression model (GLM) to calculate group differences. The children in the OMT group had smaller mean total intracranial volume (ICV), 1407 cm3 (CI 95% 1379-1434) versus 1450 cm3 (CI 95% 1423-1476) in the control group (p = 0.026). After adjusting for ICV, significant group differences persisted for volumes of amygdala, basal ganglia and mid-posterior part of corpus callosum. Cortical surface area was smaller in the left caudal middle frontal gyrus and the right inferior parietal lobule in the OMT-group. Visual-motor function was significantly correlated with ICV. Prenatal OMT exposure may alter early brain development with possible negative long-term functional consequences.

Combined cognitive assessment and automated MRI volumetry improves the diagnostic accuracy of detecting MCI due to Alzheimer's disease

BackgroundMild cognitive impairment (MCI) confers a high annual risk of 10–15 % of conversion to Alzheimer's disease (AD) dementia. MRI atrophy patterns derived from automated ROI analysis, particularly hippocampal subfield volumes, were reported to be useful in diagnosing early clinical stages of Alzheimer's disease. ObjectiveThe aim of the present study was to combine automated ROI MRI morphometry of hippocampal subfield volumes and cortical thickness estimates using FreeSurfer 6.0 with cognitive measures to predict disease progression and time to conversion from MCI to AD dementia. MethodsBaseline (Neuropsychology, MRI) and clinical follow-up data from 62 MCI patients were analysed retrospectively. Individual cortical thickness and volumetric measures were obtained from T1-weighted MRI. Linear discriminant analysis (LDA) of both, cognitive measures and MRI measures (hippocampal subfields, temporal and parietal lobe volumes), were performed to differentiate MCI converters from stable MCI patients. ResultsOut of 62 MCI patients 21 (34 %) converted to AD dementia within a mean follow-up time of 74.7 ± 36.8 months (mean ± SD, range 12 to 130 months). LDA identified temporal lobe atrophy and hippocampal subfield volumes in combination with cognitive measures of verbal memory, verbal fluency and executive functions to correctly classify 71.4.% of MCI subjects converting to AD dementia and 92.7 % with stable MCI. Lower baseline GM volume of the subiculum and the superior temporal gyrus was associated with faster disease progression of MCI converters. ConclusionCombining cognitive assessment with automated ROI MRI morphometry is superior to using a single test in order to distinguish MCI due to AD from non converting MCI patients.

A - 1 Informant Report of Practical Judgment Is Associated with Mild Cognitive Impairment and Measures of Brain Structure in a Nondemented Sample of Older Adults

Abstract Objective Given the potential risks associated with compromised judgment in older adulthood, we examined whether informant-reported practical judgment abilities were associated with cognitive impairment and measures of brain structure. Method 97 community-dwelling older adults with informants (Mage = 74.64 ± 7.42, Medu = 15.76 ± 2.42, 74%female, 92%white) underwent neuropsychological evaluation, including the Test of Practical Judgment-Informant (TOP-J-INF), a 15-item informant-reported scale of judgment ability. Participants were diagnosed with mild cognitive impairment (MCI; n = 33) or as neuropsychologically unimpaired (n = 64). Participants completed an MRI study including T1-weighted image processed with FreeSurfer software (v 6.0.0) to obtain estimates of gray matter volume and cortical thickness (parcellated with the Desikan-Killiany atlas). Logistic and linear models analyzed the association of TOP-J-INF with MCI diagnosis and brain volume/thickness (in separate models). To better support the exploratory nature of this analysis of associations with structural brain variables, we did not apply correction for multiple comparisons. Results Each additional point on the TOP-J-INF increased the odds of MCI diagnosis by 16.8% (OR = 1.168, p = 0.004). Worse judgment was associated with reduced brain volume in the right parahippocampal gyrus (β = −11.688, p = 0.032), superior temporal gyrus (β = −51.330, p = 0.040), rostral anterior cingulate cortex (β = −25.704, p = 0.004); and reduced thickness of the left transverse temporal gyrus (β = −0.010, p = 0.041). Conclusions To our knowledge, this is the first study to investigate the relationship between informant report of judgment and measures of brain structure, providing support that informant concerns about a loved one’s compromised judgment is associated with volume/thickness reduction in key brain regions. Assessing practical judgment abilities via informants could provide critical clinical information when working with vulnerable older individuals.

Comparing the stability and reproducibility of brain-behavior relationships found using canonical correlation analysis and partial least squares within the ABCD sample.

Canonical correlation analysis (CCA) and partial least squares correlation (PLS) detect linear associations between two data matrices by computing latent variables (LVs) having maximal correlation (CCA) or covariance (PLS). This study compared the similarity and generalizability of CCA- and PLS-derived brain-behavior relationships. Data were accessed from the baseline Adolescent Brain Cognitive Development (ABCD) dataset (N > 9,000, 9-11 years). The brain matrix consisted of cortical thickness estimates from the Desikan-Killiany atlas. Two phenotypic scales were examined separately as the behavioral matrix; the Child Behavioral Checklist (CBCL) subscale scores and NIH Toolbox performance scores. Resampling methods were used to assess significance and generalizability of LVs. LV1 for the CBCL brain relationships was found to be significant, yet not consistently stable or reproducible, across CCA and PLS models (singular value: CCA = .13, PLS = .39, p < .001). LV1 for the NIH brain relationships showed similar relationships between CCA and PLS and was found to be stable and reproducible (singular value: CCA = .21, PLS = .43, p < .001). The current study suggests that stability and reproducibility of brain-behavior relationships identified by CCA and PLS are influenced by the statistical characteristics of the phenotypic measure used when applied to a large population-based pediatric sample.

Sex differences in trajectories of cortical development in autistic children from 2–13 years of age

Previous studies have reported alterations in cortical thickness in autism. However, few have included enough autistic females to determine if there are sex specific differences in cortical structure in autism. This longitudinal study aimed to investigate autistic sex differences in cortical thickness and trajectory of cortical thinning across childhood. Participants included 290 autistic (88 females) and 139 nonautistic (60 females) individuals assessed at up to 4 timepoints spanning ~2–13 years of age (918 total MRI timepoints). Estimates of cortical thickness in early and late childhood as well as the trajectory of cortical thinning were modeled using spatiotemporal linear mixed effects models of age-by-sex-by-diagnosis. Additionally, the spatial correspondence between cortical maps of sex-by-diagnosis differences and neurotypical sex differences were evaluated. Relative to their nonautistic peers, autistic females had more extensive cortical differences than autistic males. These differences involved multiple functional networks, and were mainly characterized by thicker cortex at ~3 years of age and faster cortical thinning in autistic females. Cortical regions in which autistic alterations were different between the sexes significantly overlapped with regions that differed by sex in neurotypical development. Autistic females and males demonstrated some shared differences in cortical thickness and rate of cortical thinning across childhood relative to their nonautistic peers, however these areas were relatively small compared to the widespread differences observed across the sexes. These results support evidence of sex-specific neurobiology in autism and suggest that processes that regulate sex differentiation in the neurotypical brain contribute to sex differences in the etiology of autism.

Bridging the gap: improving correspondence between low-field and high-field magnetic resonance images in young people.

Portable low-field-strength magnetic resonance imaging (MRI) systems represent a promising alternative to traditional high-field-strength systems with the potential to make MR technology available at scale in low-resource settings. However, lower image quality and resolution may limit the research and clinical potential of these devices. We tested two super-resolution methods to enhance image quality in a low-field MR system and compared their correspondence with images acquired from a high-field system in a sample of young people. T1- and T2-weighted structural MR images were obtained from a low-field (64mT) Hyperfine and high-field (3T) Siemens system in N = 70 individuals (mean age = 20.39 years, range 9-26 years). We tested two super-resolution approaches to improve image correspondence between images acquired at high- and low-field: (1) processing via a convolutional neural network ('SynthSR'), and (2) multi-orientation image averaging. We extracted brain region volumes, cortical thickness, and cortical surface area estimates. We used Pearson correlations to test the correspondence between these measures, and Steiger Z tests to compare the difference in correspondence between standard imaging and super-resolution approaches. Single pairs of T1- and T2-weighted images acquired at low field showed high correspondence to high-field-strength images for estimates of total intracranial volume, surface area cortical volume, subcortical volume, and total brain volume (r range = 0.60-0.88). Correspondence was lower for cerebral white matter volume (r = 0.32, p = 0.007, q = 0.009) and non-significant for mean cortical thickness (r = -0.05, p = 0.664, q = 0.664). Processing images with SynthSR yielded significant improvements in correspondence for total brain volume, white matter volume, total surface area, subcortical volume, cortical volume, and total intracranial volume (r range = 0.85-0.97), with the exception of global mean cortical thickness (r = 0.14). An alternative multi-orientation image averaging approach improved correspondence for cerebral white matter and total brain volume. Processing with SynthSR also significantly improved correspondence across widespread regions for estimates of cortical volume, surface area and subcortical volume, as well as within isolated prefrontal and temporal regions for estimates of cortical thickness. Applying super-resolution approaches to low-field imaging improves regional brain volume and surface area accuracy in young people. Finer-scale brain measurements, such as cortical thickness, remain challenging with the limited resolution of low-field systems.

The Combined Effects of Nicotine and Cannabis on Cortical Thickness Estimates in Adolescents and Emerging Adults.

Early life substance use, including cannabis and nicotine, may result in deleterious effects on the maturation of brain tissue and gray matter cortical development. The current study employed linear regression models to investigate the main and interactive effects of past-year nicotine and cannabis use on gray matter cortical thickness estimates in 11 bilateral independent frontal cortical regions in 223 16-22-year-olds. As the frontal cortex develops throughout late adolescence and young adulthood, this period becomes crucial for studying the impact of substance use on brain structure. The distinct effects of nicotine and cannabis use status on cortical thickness were found bilaterally, as cannabis and nicotine users both had thinner cortices than non-users. Interactions between nicotine and cannabis were also observed, in which cannabis use was associated with thicker cortices for those with a history of nicotine and tobacco product (NTP) use in three left frontal regions. This study sheds light on the intricate relationship between substance use and brain structure, suggesting a potential modulation of cannabis' impact on cortical thickness by nicotine exposure, and emphasizing the need for further longitudinal research to characterize these interactions and their implications for brain health and development.

Utility of Cortical Atrophy in Functional Networks as a Predictor of Progression in bvFTD

AbstractBackgroundBehavioral variant frontotemporal dementia (bvFTD) is a clinically heterogeneous neurodegenerative disorder with variable rates of disease progression. Predictors of disease progression in bvFTD are significantly lacking, limiting the ability of clinical care planning. We recently showed that cortical atrophy in the frontoparietal control network (FPN) at baseline predicts the conversion to dementia in patients with Primary Progressive Aphasia (PPA). Leveraging this methodology, we hypothesized that FPN atrophy would predict progression of dementia in patients diagnosed with bvFTD.MethodWe analyzed data from 48 bvFTD patients (mean age 59.9 years, 20 Female) with very mild or mild dementia from our Frontotemporal Disorders Unit cohort and the Frontotemporal Lobar Degeneration Neuroimaging Initiative. Our primary outcome measure was change in the Clinical Dementia Rating‐FTLD Sum‐of‐Boxes (SoB) at baseline and at least one year later. Each participant underwent baseline MRI scanning for cortical thickness estimates. We ran a Pearson correlation analysis between the annualized change of CDR‐FTLD SoB from baseline to last observation, and baseline cortical network atrophy (quantified as network‐wise W scores). Vertex‐wise GLM analyses and network‐wise bivariate correlation analyses were also used to assess the relationship between network cortical atrophy and annualized CDR changes.ResultOf the 7 networks investigated, only FPN atrophy predicted annualized change in CDR‐FTLD SoB scores (r = ‐0.31, p = 0.03). That is, in bvFTD patients at the very mild/mild dementia stage, the greater the baseline FPN atrophy, the more rapid the clinical decline. Vertex‐wise GLM maps did not show that atrophy across the entire FPN predicted clinical decline. Rather, baseline atrophy in the dorsal precuneus and supramarginal gyrus nodes of the FPN was most predictive of clinical disease progression.ConclusionCortical atrophy in the FPN, and particularly in posterior regions of this network, is predictive of the rate of clinical decline in bvFTD patients, possibly due to the critical roles this network serves in executive function and affective regulation. This work could promote the use of structural imaging biomarkers to guide prognostication for bvFTD patients and their families, and to inform the design of clinical trials.

BrainChart cortical thickness centile scores: a generalizable tool to detect age‐inappropriate cortical neurodegeneration in Alzheimer’s disease

AbstractBackgroundIdentifying age‐inappropriate cortical neurodegeneration on structural MRI is a key component of Alzheimer’s disease (AD) assessment. However, widely available reference standards that enable objective interpretation are lacking. BrainChart (www.BrainChart.io; https://github.com/BrainChart/Lifespan) calculates age and sex adjusted centile scores for structural MRI metrics using normative data from 101,457 participants across the lifecourse and can be applied across different datasets. We applied this approach to regional cortical thickness estimates and explored its utility in identifying patients with AD associated neurodegeneration.MethodsCross‐sectional BrainChart cortical thickness centile scores were calculated for 241 individuals with pathologically confirmed AD (median scan time prior to death 5.8 years), as well as 1432 cognitively normal participants (CN) from the NACC dataset. Area under the receiver operating characteristic curve (AUC) analyses were performed to identify the cortical regions that best identified patients with pathologically confirmed AD. This approach was then applied to 305 participants from the ADNI‐3 cohort, all of whom underwent clinical amyloid (florbetapir) PET and tau (flortaucipir) PET within 6 months of structural MRI (CN = 185; Mild Cognitive Impairment (MCI) = 95); AD = 26).ResultsThe top ten performing regions for differentiating pathologically confirmed AD from CN in the NACC dataset were entorhinal, middle temporal, inferior parietal, isthmus cingulate, fusiform, supramarginal, inferior temporal, superior temporal, precuneus and temporal pole. AUCs ranged from 0.72 – 0.77 (figure 1 and table 1). Combining these ten regions into a single model significantly improved diagnostic separability (AUC = 0.81, figure 1). Optimal cut‐points for individual regions ranged from the 25th centile to the 35th centile, with sensitivities ranging from 57.7% to 68.9%, and specificities ranging from 72.6% to 80.9%. Using progressively lower centile scores increased specificity but decreased sensitivity (table 1).In ADNI‐3 participants, a combined model of the same ten regions derived from the NACC dataset was able to discriminate amyloid and tau pet positive AD patients from amyloid and tau negative CN (AUC = 0.98), as well as amyloid and tau positive MCI patients from amyloid and tau negative CN (AUC = 0.85) (figure 2).ConclusionBrainChart age and sex adjusted cortical thickness centile scores, derived from an extensive normative dataset, represent a generalizable method for objectively identifying cortical neurodegeneration in AD.

BrainChart cortical thickness centile scores: a generalizable tool to detect age‐inappropriate cortical neurodegeneration in Alzheimer’s disease

AbstractBackgroundIdentifying age‐inappropriate cortical neurodegeneration on structural MRI is a key component of Alzheimer’s disease (AD) assessment. However, widely available reference standards that enable objective interpretation are lacking. BrainChart (www.BrainChart.io; https://github.com/BrainChart/Lifespan) calculates age and sex adjusted centile scores for structural MRI metrics using normative data from 101,457 participants across the lifecourse and can be applied across different datasets. We applied this approach to regional cortical thickness estimates and explored its utility in identifying patients with AD associated neurodegeneration.MethodCross‐sectional BrainChart cortical thickness centile scores were calculated for 241 individuals with pathologically confirmed AD (median scan time prior to death 5.8 years), as well as 1432 cognitively normal participants (CN) from the NACC dataset. Area under the receiver operating characteristic curve (AUC) analyses were performed to identify the cortical regions that best identified patients with pathologically confirmed AD. This approach was then applied to 305 participants from the ADNI‐3 cohort, all of whom underwent clinical amyloid (florbetapir) PET and tau (flortaucipir) PET within 6 months of structural MRI (CN = 185; Mild Cognitive Impairment (MCI) = 95); AD = 26).ResultThe top ten performing regions for differentiating pathologically confirmed AD from CN in the NACC dataset were entorhinal, middle temporal, inferior parietal, isthmus cingulate, fusiform, supramarginal, inferior temporal, superior temporal, precuneus and temporal pole. AUCs ranged from 0.72 – 0.77 (figure 1 and table 1). Combining these ten regions into a single model significantly improved diagnostic separability (AUC = 0.81, figure 1). Optimal cut‐points for individual regions ranged from the 25th centile to the 35th centile, with sensitivities ranging from 57.7% to 68.9%, and specificities ranging from 72.6% to 80.9%. Using progressively lower centile scores increased specificity but decreased sensitivity (table 1). In ADNI‐3 participants, a combined model of the same ten regions derived from the NACC dataset was able to discriminate amyloid and tau pet positive AD patients from amyloid and tau negative CN (AUC = 0.98), as well as amyloid and tau positive MCI patients from amyloid and tau negative CN (AUC = 0.85) (figure 2).ConclusionBrainChart age and sex adjusted cortical thickness centile scores, derived from an extensive normative dataset, represent a generalizable method for objectively identifying cortical neurodegeneration in AD.

The effect of processing pipelines, input images and age on automatic cortical morphology estimates

Background and ObjectiveMagnetic resonance imaging of the brain allows to enrich the study of the relationship between cortical morphology, healthy ageing, diseases and cognition. Since manual segmentation of the cerebral cortex is time consuming and subjective, many software packages have been developed. FreeSurfer (FS) and Advanced Normalization Tools (ANTs) are the most used and allow as inputs a T1-weighted (T1w) image or its combination with a T2-weighted (T2w) image. In this study we evaluated the impact of different software and input images on cortical estimates. Additionally, we investigated whether the variation of the results depending on software and inputs is also influenced by age. MethodsFor 240 healthy subjects, cortical thickness was computed with ANTs and FreeSurfer. Estimates were derived using both the T1w image and adding the T2w image. Significant effects due to software, input images and age range were investigated with ANOVA statistical analysis. Moreover, the accuracy of the cortical thickness estimates was assessed based on their age-prediction precision. ResultsUsing FreeSurfer and ANTs with T1w or T1w-T2w images resulted in significant differences in the cortical thickness estimates. These differences change with the age range of the subjects. Regardless of the images used, the more recent FS version tested exhibited the best performances in terms of age prediction. ConclusionsOur study points out the importance of i) consistently processing data using the same tool; ii) considering the software, input images and the age range of the subjects when comparing multiple studies.

Differential Cortical Volume and Surface Morphometry in Youth With Chronic Health Conditions.

Up to 1 in 3 youth in the United States have a childhood-onset chronic health condition (CHC), which can lead to neurodevelopmental disruptions in cognitive functioning and brain structure. However, the nature and extent of structural neurobiomarkers that may be consistent across a broad spectrum of CHCs are unknown. Thus, the purpose of this study was to identify potential differences in brain structure in youth with and without chronic physical health conditions (e.g., diabetes, hemophilia). Here, 49 T1 structural magnetic resonance imaging (MRI) images were obtained from youth with (n = 26) and without (n = 23) CHCs. Images were preprocessed using voxel-based morphometry (VBM) to generate whole-brain voxel-wise gray matter volume maps and whole-brain extracted estimates of cortical surface area and cortical thickness. Multi-scanner harmonization was implemented on surface-based estimates and linear models were used to estimate significant main effects of the group. We detected widespread decreases in brain structure in youth with CHCs as compared to controls in regions of the prefrontal, cingulate, and visual association areas. The insula exhibited the opposite effect, with cases having increased surface area as compared to controls. To our knowledge, these findings identify a novel structural biomarker of childhood-onset CHCs, with consistent alterations identified in gray matter of regions in the prefrontal cortex and insula involved in emotion regulation and executive function. These findings, while exploratory, may reflect an impact of chronic health stress in the adolescent brain, and suggest that more comprehensive assessment of stress and neurodevelopment in youth with CHCs may be appropriate.

Excellent Interrater Reliability for Manual Segmentation of the Medial Perirhinal Cortex.

Objective: Evaluation of interrater reliability for manual segmentation of brain structures that are affected first by neurofibrillary tau pathology in Alzheimer's disease. Method: Medial perirhinal cortex, lateral perirhinal cortex, and entorhinal cortex were manually segmented by two raters on structural magnetic resonance images of 44 adults (20 men; mean age = 69.2 ± 10.4 years). Intraclass correlation coefficients (ICC) of cortical thickness and volumes were calculated. Results: Very high ICC values of manual segmentation for the cortical thickness of all regions (0.953-0.986) and consistently lower ICC values for volume estimates of the medial and lateral perirhinal cortex (0.705-0.874). Conclusions: The applied manual segmentation protocol allows different raters to achieve remarkably similar cortical thickness estimates for regions of the parahippocampal gyrus. In addition, the results suggest a preference for cortical thickness over volume as a reliable measure of atrophy, especially for regions affected by collateral sulcus variability (i.e., medial and lateral perirhinal cortex). The results provide a basis for future automated segmentation and collection of normative data.

Systematic comparisons of different quality control approaches applied to three large pediatric neuroimaging datasets

IntroductionPoor quality T1-weighted brain scans systematically affect the calculation of brain measures. Removing the influence of such scans requires identifying and excluding scans with noise and artefacts through a quality control (QC) procedure. While QC is critical for brain imaging analyses, it is not yet clear whether different QC approaches lead to the exclusion of the same participants. Further, the removal of poor-quality scans may unintentionally introduce a sampling bias by excluding the subset of participants who are younger and/or feature greater clinical impairment. This study had two aims: (1) examine whether different QC approaches applied to T1-weighted scans would exclude the same participants, and (2) examine how exclusion of poor-quality scans impacts specific demographic, clinical and brain measure characteristics between excluded and included participants in three large pediatric neuroimaging samples. MethodsWe used T1-weighted, resting-state fMRI, demographic and clinical data from the Province of Ontario Neurodevelopmental Disorders network (Aim 1: n = 553, Aim 2: n = 465), the Healthy Brain Network (Aim 1: n = 1051, Aim 2: n = 558), and the Philadelphia Neurodevelopmental Cohort (Aim 1: n = 1087; Aim 2: n = 619). Four different QC approaches were applied to T1-weighted MRI (visual QC, metric QC, automated QC, fMRI-derived QC). We used tetrachoric correlation and inter-rater reliability analyses to examine whether different QC approaches excluded the same participants. We examined differences in age, mental health symptoms, everyday/adaptive functioning, IQ and structural MRI-derived brain indices between participants that were included versus excluded following each QC approach. ResultsDataset-specific findings revealed mixed results with respect to overlap of QC exclusion. However, in POND and HBN, we found a moderate level of overlap between visual and automated QC approaches (rtet=0.52–0.59). Implementation of QC excluded younger participants, and tended to exclude those with lower IQ, and lower everyday/adaptive functioning scores across several approaches in a dataset-specific manner. Across nearly all datasets and QC approaches examined, excluded participants had lower estimates of cortical thickness and subcortical volume, but this effect did not differ by QC approach. ConclusionThe results of this study provide insight into the influence of QC decisions on structural pediatric imaging analyses. While different QC approaches exclude different subsets of participants, the variation of influence of different QC approaches on clinical and brain metrics is minimal in large datasets. Overall, implementation of QC tends to exclude participants who are younger, and those who have more cognitive and functional impairment. Given that automated QC is standardized and can reduce between-study differences, the results of this study support the potential to use automated QC for large pediatric neuroimaging datasets.

Spatial patterns of gray and white matter compromise relate to age of seizure onset in temporal lobe epilepsy

ObjectiveTemporal Lobe Epilepsy (TLE) is frequently a neurodevelopmental disorder, involving subcortical volume loss, cortical atrophy, and white matter (WM) disruption. However, few studies have addressed how these pathological changes in TLE relate to one another. In this study, we investigate spatial patterns of gray and white matter degeneration in TLE and evaluate the hypothesis that the relationship among these patterns varies as a function of the age at which seizures begin. MethodsEighty-two patients with TLE and 59 healthy controls were enrolled. T1-weighted images were used to obtain hippocampal volumes and cortical thickness estimates. Diffusion-weighted imaging was used to obtain fractional anisotropy (FA) and mean diffusivity (MD) of the superficial WM (SWM) and deep WM tracts. Analysis of covariance was used to examine patterns of WM and gray matter alterations in TLE relative to controls, controlling for age and sex. Sliding window correlations were then performed to examine the relationships between SWM degeneration, cortical thinning, and hippocampal atrophy across ages of seizure onset. ResultsCortical thinning in TLE followed a widespread, bilateral pattern that was pronounced in posterior centroparietal regions, whereas SWM and deep WM loss occurred mostly in ipsilateral, temporolimbic regions compared to controls. Window correlations revealed a relationship between hippocampal volume loss and whole brain SWM disruption in patients who developed epilepsy during childhood. On the other hand, in patients with adult-onset TLE, co-occurring cortical and SWM alterations were observed in the medial temporal lobe ipsilateral to the seizure focus. SignificanceOur results suggest that although cortical, hippocampal and WM alterations appear spatially discordant at the group level, the relationship among these features depends on the age at which seizures begin. Whereas neurodevelopmental aspects of TLE may result in co-occurring WM and hippocampal degeneration near the epileptogenic zone, the onset of seizures in adulthood may set off a cascade of SWM microstructural loss and cortical atrophy of a neurodegenerative nature.

Access to quality health resources and environmental toxins affect the relationship between brain structure and BMI in a sample of pre and early adolescents.

Environmental resources are related to childhood obesity risk and altered brain development, but whether these relationships are stable or if they have sustained impact is unknown. Here, we utilized a multidimensional index of childhood neighborhood conditions to compare the influence of various social and environmental disparities (SED) on body mass index (BMI)-brain relationships over a 2-year period in early adolescence. Data were gathered the Adolescent Brain Cognitive Development Study® (n = 2,970, 49.8% female, 69.1% White, no siblings). Structure magnetic resonance imaging (sMRI), anthropometrics, and demographic information were collected at baseline (9/10-years-old) and the 2-year-follow-up (11/12-years-old). Region of interest (ROIs; 68 cortical, 18 subcortical) estimates of cortical thickness and subcortical volume were extracted from sMRI T1w images using the Desikan atlas. Residential addresses at baseline were used to obtain geocoded estimates of SEDs from 3 domains of childhood opportunity index (COI): healthy environment (COIHE), social/economic (COISE), and education (COIED). Nested, random-effects mixed models were conducted to evaluate relationships of BMI with (1) ROI * COI[domain] and (2) ROI * COI[domain] * Time. Models controlled for sex, race, ethnicity, puberty, and the other two COI domains of non-interest, allowing us to estimate the unique variance explained by each domain and its interaction with ROI and time. Youth living in areas with lower COISE and COIED scores were heavier at the 2-year follow-up than baseline and exhibited greater thinning in the bilateral occipital cortex between visits. Lower COISE scores corresponded with larger volume of the bilateral caudate and greater BMI at the 2-year follow-up. COIHE scores showed the greatest associations (n = 20 ROIs) with brain-BMI relationships: youth living in areas with lower COIHE had thinner cortices in prefrontal regions and larger volumes of the left pallidum and Ventral DC. Time did not moderate the COIHE x ROI interaction for any brain region during the examined 2-year period. Findings were independent of family income (i.e., income-to-needs). Collectively our findings demonstrate that neighborhood SEDs for health-promoting resources play a particularly important role in moderating relationships between brain and BMI in early adolescence regardless of family-level financial resources.

Comparison of FreeSurfer inputs on estimates and reliability of volume and thickness measures

AbstractBackgroundAnatomical volume and cortical thickness are measures of neurodegeneration to track disease progression and evaluation of treatment efficacy. FreeSurfer software provides automatic structural parcellation and segmentation to estimate volume and thickness. T1‐weighted inputs are commonly used, but T2 or T2*FLAIR images can be added. This study compared FreeSurfer estimates for T1 only vs T1+T2 or T1+T2*FLAIR inputs with regard to reliability and its relation to amyloidosis.Method3‐Tesla MRI data were included for 379 scans (T1: 35%, T1+T2: 35%, T1+T2*FLAIR: 31% ‐ session 1: 39%, 2: 34%, 3: 27%) from 60 healthy older adults (mean age 71, 67% female). FreeSurfer‐derived estimates of volume and cortical thickness were obtained for T1, T1+T2, and T1+T2*FLAIR inputs. Kruskal Wallis tests followed by Dunn post hoc test were used to identify estimates that were significantly different across sequences. Two‐way mixed model analyses were performed on each estimate, including subject and session as grouping factors. Interclass correlation coefficients measured similarity and reliability. Amyloid‐PET SUVR levels were available for 54% of subjects. Bonferroni correction and estimated marginal means were used to correct for multiple comparisons.ResultFor volume estimates, 17% of pairwise combinations (7% T1+T2vsT1+T2*Flair, 6% T1vsT1+T2, and 4% T1vsT1+T2*FLAIR) showed significant differences between sequences adjusting for multiple comparisons. The cross‐sectional similarity between sequences had median ICC=0.87. Test‐retest reliability within sequences had median ICC=0.88 for T1, 0.87 for T1+T2 and 0.78 for T1+T2*FLAIR. For thickness estimates, 73% of pairwise combinations (27% T1+T2vsT1+T2*FLAIR, 23% T1vsT1+T2*FLAIR, and 22% T1vsT1+T2) showed significant differences between sequences after adjusting for multiple comparisons. The cross‐sectional similarity between sequences had median ICC=0.43. Test‐retest reliability within sequences had median ICC=0.78 for T1, 0.74 for T1+T2 and 0.30 for T1+T2*FLAIR. Amyloid SUVRs had non‐significant correlations with all ICC measures.ConclusionOverall, volumes had high similarity across sequence inputs and high test‐retest reliability. Thickness showed less reliability across sequences and sessions driven by low similarity and low reliability for T1+T2*FLAIR. Amyloid deposition did not impact reliability estimates. These results suggest that T1 only or T1+T2 inputs have high reliability across sessions and should be considered in clinical trials designs that measure change in volume or thickness.

Sex-based variations of prefrontal structure and longitudinal symptoms in pediatric posttraumatic stress disorder.

Pediatric posttraumatic stress disorder (pPTSD) is more than three times as likely to develop in trauma-exposed female youth than males. Despite the staggering sex differences in the prevalence rates of pPTSD and symptom expression, relatively little is known about the underlying biomarkers of these sex-based variations in pPTSD as compared to typically development. The Youth PTSD study recruited 97 youth, ages of 7 and 18, to undergo comprehensive clinical assessments and T1-weighted MRI to evaluate the extent to which sex can explain PTSD-related variations in brain structure. Whole-brain VBM as well as whole-brain estimates of cortical thickness and surface area were analyzed to identify group-by-sex interactions. Finally, we tested whether current or future symptom severity was predictive of regions exhibiting sex-based variations. Clinically, females with PTSD were significantly more likely to report exposure to and higher severity of interpersonal violence and symptoms of hyperarousal. Sex and PTSD status were predictive of gray matter across the lateral prefrontal cortex (PFC), including the ventrolateral PFC and frontal pole, where increased volume and surface area was found in PTSD females as compared to PTSD males. Interestingly, the ventrolateral prefrontal cortex and frontal pole were negatively predictive of symptoms 1 year later in only males with PTSD. Together, these results establish that youth with PTSD exhibit sex-based variations in clinical and trauma characteristics and prefrontal cortical structure relative to normative development. This work demonstrates the importance of examining the role that sex may play in the behavioral and neurobiological presentation of pPTSD.

Morphometric features of drug-resistant essential tremor and recovery after stereotactic radiosurgical thalamotomy.

Essential tremor (ET) is the most common movement disorder. Its neural underpinnings remain unclear. Here, we quantified structural covariance between cortical thickness (CT), surface area (SA), and mean curvature (MC) estimates in patients with ET before and 1 year after ventro-intermediate nucleus stereotactic radiosurgical thalamotomy, and contrasted the observed patterns with those from matched healthy controls. For SA, complex rearrangements within a network of motion-related brain areas characterized patients with ET. This was complemented by MC alterations revolving around the left middle temporal cortex and the disappearance of positive-valued covariance across both modalities in the right fusiform gyrus. Recovery following thalamotomy involved MC readjustments in frontal brain centers, the amygdala, and the insula, capturing nonmotor characteristics of the disease. The appearance of negative-valued CT covariance between the left parahippocampal gyrus and hippocampus was another recovery mechanism involving high-level visual areas. This was complemented by the appearance of negative-valued CT/MC covariance, and positive-valued SA/MC covariance, in the right inferior temporal cortex and bilateral fusiform gyrus. Our results demonstrate that different morphometric properties provide complementary information to understand ET, and that their statistical cross-dependences are also valuable. They pinpoint several anatomical features of the disease and highlight routes of recovery following thalamotomy.