Brain Morphometry Research Articles

Longitudinal Patterns of Brain Changes in a Community Sample in Relation to Aging and Cognitive Status.

Aging and Alzheimer's disease (AD) are characterized by widespread cortical and subcortical atrophy. Though atrophy patterns between aging and AD overlap considerably, regional differences between these two conditions may exist. Few studies, however, have investigated these patterns in large community samples. Elaborate longitudinal changes in brain morphometry in relation to aging and cognitive status in a well-characterized community cohort. Clinical and neuroimaging data were compiled from 72 participants from the Cardiovascular Health Study-Cognition Study, a community cohort of healthy aging and probable AD participants. Two time points were identified for each participant with a mean follow-up time of 5.36 years. MRI post-processing, morphometric measurements, and statistical analyses were performed using FreeSurfer, Version 7.1.1. Cortical volume was significantly decreased in the bilateral superior frontal, bilateral inferior parietal, and left superior parietal regions, among others. Cortical thickness was significantly reduced in the bilateral superior frontal and left inferior parietal regions, among others. Overall gray and white matter volumes and hippocampal subfields also demonstrated significant reductions. Cortical volume atrophy trajectories between cognitively stable and cognitively declined participants were significantly different in the right postcentral region. Observed volume reductions were consistent with previous studies investigating morphometric brain changes. Patterns of brain atrophy between AD and aging may be different in magnitude but exhibit widespread spatial overlap. These findings help characterize patterns of brain atrophy that may reflect the general population. Larger studies may more definitively establish population norms of aging and AD-related neuroimaging changes.

Routine Brain MRI Findings on the Long-Term Effects of COVID-19: A Scoping Review.

Post-COVID condition (PCC) is associated with long-term neuropsychiatric symptoms. Magnetic resonance imaging (MRI) in PCC examines the brain metabolism, connectivity, and morphometry. Such techniques are not easily available in routine practice. We conducted a scoping review to determine what is known about the routine MRI findings in PCC patients. The PubMed database was searched up to 11 April 2023. We included cohort, cross-sectional, and before-after studies in English. Articles with only advanced MRI sequences (DTI, fMRI, VBM, PWI, ASL), preprints, and case reports were excluded. The National Heart, Lung, and Blood Institute and PRISMA Extension tools were used for quality assurance. A total of 7 citations out of 167 were included. The total sample size was 451 patients (average age 51 ± 8 years; 67% female). Five studies followed a single recovering cohort, while two studies compared findings between two severity groups. The MRI findings were perivascular spaces (47%), microbleeds (27%) and white matter lesions (10%). All the studies agreed that PCC manifestations are not associated with specific MRI findings. The results of the included studies are heterogeneous due to the low agreement on the types of MRI abnormalities in PCC. Our findings indicate that the routine brain MRI protocol has little value for long COVID diagnostics.

Associations between body mass index, sleep-disordered breathing, brain structure, and behavior in healthy children.

Pediatric overweight/obesity can lead to sleep-disordered breathing (SDB), abnormal neurological and cognitive development, and psychiatric problems, but the associations and interactions between these factors have not been fully explored. Therefore, we investigated the associations between body mass index (BMI), SDB, psychiatric and cognitive measures, and brain morphometry in 8484 children 9-11years old using the Adolescent Brain Cognitive Development dataset. BMI was positively associated with SDB, and both were negatively correlated with cortical thickness in lingual gyrus and lateral orbitofrontal cortex, and cortical volumes in postcentral gyrus, precentral gyrus, precuneus, superior parietal lobule, and insula. Mediation analysis showed that SDB partially mediated the effect of overweight/obesity on these brain regions. Dimensional psychopathology (including aggressive behavior and externalizing problem) and cognitive function were correlated with BMI and SDB. SDB and cortical volumes in precentral gyrus and insula mediated the correlations between BMI and externalizing problem and matrix reasoning ability. Comparisons by sex showed that obesity and SDB had a greater impact on brain measures, cognitive function, and mental health in girls than in boys. These findings suggest that preventing childhood obesity will help decrease SDB symptom burden, abnormal neurological and cognitive development, and psychiatric problems.

Unraveling Lifelong Brain Morphometric Dynamics: A Protocol for Systematic Review and Meta-Analysis in Healthy Neurodevelopment and Ageing.

A high incidence and prevalence of neurodegenerative diseases and neurodevelopmental disorders justify the necessity of well-defined criteria for diagnosing these pathologies from brain imaging findings. No easy-to-apply quantitative markers of abnormal brain development and ageing are available. We aim to find the characteristic features of non-pathological development and degeneration in distinct brain structures and to work out a precise descriptive model of brain morphometry in age groups. We will use four biomedical databases to acquire original peer-reviewed publications on brain structural changes occurring throughout the human life-span. Selected publications will be uploaded to Covidence systematic review software for automatic deduplication and blinded screening. Afterwards, we will manually review the titles, abstracts, and full texts to identify the papers matching eligibility criteria. The relevant data will be extracted to a 'Summary of findings' table. This will allow us to calculate the annual rate of change in the volume or thickness of brain structures and to model the lifelong dynamics in the morphometry data. Finally, we will adjust the loss of weight/thickness in specific brain areas to the total intracranial volume. The systematic review will synthesise knowledge on structural brain change across the life-span.

Prenatal tobacco exposure on brain morphometry partially mediated poor cognitive performance in preadolescent children.

To evaluate whether prenatal tobacco exposure (PTE) is related to poorer cognitive performance, abnormal brain morphometry, and whether poor cognitive performance is mediated by PTE-related structural brain differences. The Adolescent Brain Cognitive Development study dataset was used to compare structural MRI data and neurocognitive (NIH Toolbox®) scores in 9-to-10-year-old children with (n=620) and without PTE (n=10,989). We also evaluated whether PTE effects on brain morphometry mediated PTE effects on neurocognitive scores. Group effects were evaluated using Linear Mixed Models, covaried for socio-demographics and prenatal exposures to alcohol and/or marijuana, and corrected for multiple comparisons using the false-discovery rate (FDR). Compared to unexposed children, those with PTE had poorer performance (all p-values <0.05) on executive function, working memory, episodic memory, reading decoding, crystallized intelligence, fluid intelligence and overall cognition. Exposed children also had thinner parahippocampal gyri, smaller surface areas in the posterior-cingulate and pericalcarine cortices; the lingual and inferior parietal gyri, and smaller thalamic volumes (all p-values <0.001). Furthermore, among children with PTE, girls had smaller surface areas in the superior-frontal (interaction-FDR-p=0.01), precuneus (interaction-FDR-p=0.03) and postcentral gyri (interaction-FDR-p=0.02), while boys had smaller putamen volumes (interaction-FDR-p=0.02). Smaller surface areas across regions of the frontal and parietal lobes, and lower thalamic volumes, partially mediated the associations between PTE and poorer neurocognitive scores (p-values <0.001). Our findings suggest PTE may lead to poorer cognitive performance and abnormal brain morphometry, with sex-specific effects in some brain regions, in pre-adolescent children. The poor cognition in children with PTE may result from the smaller areas and subcortical brain volumes.

Ensemble method employing quantitative biomarkers and deep learning approach from structural magnetic resonance imaging to diagnose Alzheimer’s disease

Abstract Introduction: Alzheimer’s disease (AD) is a neurodegenerative condition that impairs activities of daily living and sharply declines gross cognitive ability. Over 152 million individuals worldwide will live with the dreaded consequence of a longer lifespan by the year 2050, making it a pressing public health issue. Magnetic resonance imaging (MRI) provides excellent soft tissue contrast and helps image the brain in vivo, non-invasively. Aims and Objectives: To summarize AD’s anatomical, physiological, and pathophysiological changes and derivation of quantifiable biomarkers from MRI to develop artificial intelligence (AI) based computer-aided detection (CAD) system to classify subjects among AD, mild cognitive impairment (MCI), and cognitively normal (CN). Materials and Methods: This retrospective study uses clinical and standardized, pre-processed, quality-controlled, and quality-checked—structural MRI imaging (diagnosed/labeled) data of 1069 subjects, age, gender, and class matched, taken from Alzheimer’s disease neuroimaging initiative. A pipeline is developed to get quantified biomarkers from the assessment of (1) cortical thickness, (2) volumetric segmentation for whole brain volumes, and (3) region of interest (ROI) areas most affected in AD. A gradient boosting method is used to predict class labels. The second approach implements a convolution neural network (CNN) model comprising 3D ROI. Results: Implemented CAD system using an ensemble gradient boosting approach has demonstrated good receiver operating characteristics characteristic and yielded balanced accuracy (BA) of 82.31%, 78.52%, and 72.73%, and the CNN approach has given better results 88.44%, 82.96%, and 74.34% for classification task AD versus CN, AD versus MCI, and MCI versus CN, respectively. Conclusion: This study has used a substantially large dataset of 1069 subjects. The deep learning-based efficient and optimal CNN model has used significantly large ROI-based 3-Dimentional volume, resulting in impressive performance improvements over comparable methods. The CNN model had given higher accuracy (6.13% for AD vs. CN, 4.44% for AD vs. MCI and 1.61% for MCI vs. CN) over gradient boosting, as the model uses significantly large ROI-based 3D brain volume and an inherent capability of it in learning most discriminative features automatically. However, quantitative biomarkers derived from brain morphometry, which accesses structural changes, yield reasonable estimates over pathophysiological alterations across the brain and augment a clinician with insightful and a holistic view, resulting in higher confidence over predicated class label by CNN and is a step closer to explainable AI. Accuracy for MCI versus CN drops as these classes share similar features and characteristics and can be improved by integrating biomarkers from other MRI modalities.

Regional gray matter volume correlates with anxiety, apathy, and resilience in geriatric depression.

Geriatric depression (GD) is associated with significant medical comorbidity, cognitive impairment, brain atrophy, premature mortality, and suboptimal treatment response. While apathy and anxiety are common comorbidities, resilience is a protective factor. Understanding the relationships between brain morphometry, depression, and resilience in GD could inform clinical treatment. Only few studies have addressed gray matter volume (GMV) associations with mood and resilience. Forty-nine adults aged >60 years (38 women) with major depressive disorder undergoing concurrent antidepressant treatment participated in the study. Anatomical T1-weighted scans, apathy, anxiety, and resilience data were collected. Freesurfer 6.0 was used to preprocess T1-weighted images and qdec to perform voxel-wise whole-brain analyses. Partial Spearman correlations controlling for age and sex tested the associations between clinical scores, and general linear models identified clusters of associations between GMV and clinical scores, with age and sex as covariates. Cluster correction and Monte-Carlo simulations were applied (corrected alpha = 0.05). Greater depression severity was associated with greater anxiety (r = 0.53, p = 0.0001), lower resilience (r = -0.33, p = 0.03), and greater apathy (r = 0.39, p = 0.01). Greater GMV in widespread, partially overlapping clusters across the brain was associated with reduced anxiety and apathy, as well as increased resilience. Our results suggest that greater GMV in extended brain regions is a potential marker for resilience in GD, while GMV in more focal and overlapping regions may be markers for depression and anxiety. Interventions focused on improving symptoms in GD may seek to examine their effects on these brain regions.

Is Short Sleep Bad for the Brain? Brain Structure and Cognitive Function in Short Sleepers.

Many sleep less than recommended without experiencing daytime sleepiness. According to prevailing views, short sleep increases risk of lower brain health and cognitive function. Chronic mild sleep deprivation could cause undetected sleep debt, negatively affecting cognitive function and brain health. However, it is possible that some have less sleep need and are more resistant to negative effects of sleep loss. We investigated this using a cross-sectional and longitudinal sample of 47,029 participants of both sexes (20-89 years) from the Lifebrain consortium, Human Connectome project (HCP) and UK Biobank (UKB), with measures of self-reported sleep, including 51,295 MRIs of the brain and cognitive tests. A total of 740 participants who reported to sleep <6 h did not experience daytime sleepiness or sleep problems/disturbances interfering with falling or staying asleep. These short sleepers showed significantly larger regional brain volumes than both short sleepers with daytime sleepiness and sleep problems (n = 1742) and participants sleeping the recommended 7-8 h (n = 3886). However, both groups of short sleepers showed slightly lower general cognitive function (GCA), 0.16 and 0.19 SDs, respectively. Analyses using accelerometer-estimated sleep duration confirmed the findings, and the associations remained after controlling for body mass index, depression symptoms, income, and education. The results suggest that some people can cope with less sleep without obvious negative associations with brain morphometry and that sleepiness and sleep problems may be more related to brain structural differences than duration. However, the slightly lower performance on tests of general cognitive abilities warrants closer examination in natural settings.SIGNIFICANCE STATEMENT Short habitual sleep is prevalent, with unknown consequences for brain health and cognitive performance. Here, we show that daytime sleepiness and sleep problems are more strongly related to regional brain volumes than sleep duration. However, participants sleeping ≤6 h had slightly lower scores on tests of general cognitive function (GCA). This indicates that sleep need is individual and that sleep duration per se is very weakly if at all related brain health, while daytime sleepiness and sleep problems may show somewhat stronger associations. The association between habitual short sleep and lower scores on tests of general cognitive abilities must be further scrutinized in natural settings.

High field brain proton magnetic resonance spectroscopy and volumetry in children with chronic, compensated liver disease – A pilot study

BackgroundThere is increasing evidence that children or young adults having acquired liver disease in childhood display neurocognitive impairment which may become more apparent as they grow older. The molecular, cellular and morphological underpinnings of this clinical problem are incompletely understood. AimTherefore, we used the advantages of highly-resolved proton magnetic resonance spectroscopy at ultra-high magnetic field to analyze the neurometabolic profile and brain morphometry of children with chronic, compensated liver disease, hypothesizing that with high field spectroscopy we would identify early evidence of rising brain glutamine and decreased myoinositol, such as has been described both in animals and humans with more significant liver disease. MethodsPatients (n = 5) and age-matched controls (n = 19) underwent 7T MR scans and short echo time 1H MR spectra were acquired using the semi-adiabatic SPECIAL sequence in two voxels located in gray and white matter dominated prefrontal cortex, respectively. A 3D MP2RAGE sequence was also acquired for brain volumetry and T1 mapping. Liver disease had to have developed at least 6 months before entering the study. Subjects underwent routine blood analysis and neurocognitive testing using validated methods within 3 months of MRI and MRS. ResultsFive children aged 8–16 years with liver disease acquired in childhood were included. Baseline biological characteristics were similar among patients. There were no statistically significant differences between subjects and controls in brain metabolite levels or brain volumetry. Finally, there were minor neurocognitive fluctuations including attention deficit in one child, but none fell in the statistically significant range. ConclusionChildren with chronic, compensated liver disease did not display an abnormal neurometabolic profile, neurocognitive abnormalities, or signal intensity changes in the globus pallidus. Despite the absence of neurometabolic changes, it is an opportunity to emphasize that it is only by developing the use of 1H MRS at high field in the clinical arena that we will understand the significance and generalizability of these findings in children with CLD. Healthy children displayed neurometabolic regional differences as previously reported in adult subjects.

Associations Between the Wisconsin Inventory of Smoking Dependence Motives and Regional Brain Volumes in Adult Smokers.

The Wisconsin Inventory of Smoking Dependence Motives (WISDM-68) is a 68-item questionnaire to assess nicotine dependence as a multifactorial construct based on 13 theoretically derived smoking motives. Chronic smoking is associated with structural changes in brain regions implicated in the maintenance of smoking behavior; however, associations between brain morphometry and the various reinforcing components of smoking behavior remain unexamined. The present study investigated the potential association between smoking dependence motives and regional brain volumes in a cohort of 254 adult smokers. The WISDM-68 was administered to participants at the baseline session. Structural magnetic resonance brain imaging (MRI) data from 254 adult smokers (Mage = 42.7 ± 11.4) with moderate to severe nicotine dependence (MFTND = 5.4 ± 2.0) smoking for at least 2 years (Myears = 24.3 ± 11.8) were collected and analyzed with Freesurfer. Vertex-wise cluster analysis revealed that high scores on the WISDM-68 composite, secondary dependence motives (SDM) composite, and multiple SDM subscales were associated with lower cortical volume in the right lateral prefrontal cortex (cluster-wise p's < .035). Analysis of subcortical volumes (ie, nucleus accumbens, amygdala, caudate, and pallidum) revealed several significant associations with WISDM-68 subscales, dependence severity (Fagerström Test for Nicotine Dependence), and overall exposure (pack-years). No significant associations between cortical volume and other nicotine dependence measures or pack-years were observed. Results suggest that smoking motives may play a larger role in cortical abnormalities than addiction severity and smoking exposure per se, whereas subcortical volumes are associated with smoking motives, addiction severity, and smoking exposure. The present study reports novel associations between the various reinforcing components of smoking behavior assessed by the WISDM-68 and regional brain volumes. Results suggest that the underlying emotional, cognitive, and sensory processes that drive non-compulsive smoking behaviors may play a larger role in gray matter abnormalities of smokers than smoking exposure or addiction severity.

Abnormal volumetric brain morphometry and cerebral blood flow in adolescents with depression

Abnormal volumetric brain morphometry and cerebral blood flow in adolescents with depression

Accuracy of skull stripping in a single-contrast convolutional neural network model using eight-contrast magnetic resonance images.

In automated analyses of brain morphometry, skull stripping or brain extraction is a critical first step because it provides accurate spatial registration and signal-intensity normalization. Therefore, it is imperative to develop an ideal skull-stripping method in the field of brain image analysis. Previous reports have shown that convolutional neural network (CNN) method is better at skull stripping than non-CNN methods. We aimed to evaluate the accuracy of skull stripping in a single-contrast CNN model using eight-contrast magnetic resonance (MR) images. A total of 12 healthy participants and 12 patients with a clinical diagnosis of unilateral Sturge-Weber syndrome were included in our study. A 3-T MR imaging system and QRAPMASTER were used for data acquisition. We obtained eight-contrast images produced by post-processing T1, T2, and proton density (PD) maps. To evaluate the accuracy of skull stripping in our CNN method, gold-standard intracranial volume (ICVG) masks were used to train the CNN model. The ICVG masks were defined by experts using manual tracing. The accuracy of the intracranial volume obtained from the single-contrast CNN model (ICVE) was evaluated using the Dice similarity coefficient [= 2(ICVE ⋂ ICVG)/(ICVE + ICVG)]. Our study showed significantly higher accuracy in the PD-weighted image (WI), phase-sensitive inversion recovery (PSIR), and PD-short tau inversion recovery (STIR) compared to the other three contrast images (T1-WI, T2-fluid-attenuated inversion recovery [FLAIR], and T1-FLAIR). In conclusion, PD-WI, PSIR, and PD-STIR should be used instead of T1-WI for skull stripping in the CNN models.

Brain structure underlying the empathizing-systemizing difference in children with autism spectrum disorder.

Behavioral research has shown that children with autism spectrum disorder (ASD) havea higher empathizing-systemizing difference(D score) than normal children. However, there is no research about the neuroanatomical mechanisms of the empathizing-systemizing difference in children with ASD. Participants comprised 41 children with ASD and 39 typically developing (TD) children aged 6‒12years. Empathizing-systemizing difference was estimated using the D score from the Chinese version of Children's Empathy Quotient and Systemizing Quotient. We quantified brain morphometry, including global and regional brain volumes and surface-based cortical measures (cortical thickness, surface area, and gyrification) via structural magnetic resonance imaging. We found that the D score was significantly negatively associated with amygdala gray matter volume [β = -0.16; 95% confidence interval (CI): -0.30, -0.02; P value = 0.030] in children with ASD. There was a significantly negative association between D score and gyrification in the left lateral occipital cortex (LOC) in children with ASD (B = -0.10; SE = 0.03; cluster-wise P value = 0.006) and a significantly positive association between D score and gyrification in the right fusiform in TD children (B = 0.10; SE = 0.03; cluster-wise P value = 0.022). Moderation analyses demonstrated significant interactions between D score and diagnosed group in amygdala gray matter volume (β = 0.19; 95% CI 0.04, 0.35; P value = 0.013) and left LOC gyrification (β = 0.11; 95% CI 0.05, 0.17; P value = 0.001) but not in right fusiform gyrification (β = 0.08; 95% CI -0.02, 0.17; P value = 0.105). Neuroanatomical variation in amygdala volume and gyrification of LOC could be potential biomarkers for the empathizing-systemizing difference in children with ASD but not in TD children. Large-scale neuroimaging studies are necessary to test the replicability of our findings.

Identifying network hubs from brain anatomical similarity networks

AbstractBackgroundBrain hubs are highly connected regions important for integrating cognitive processing in the healthy brain. Brain hubs also play role in neurodegeneration causing dementia. However, it is not clear what brain morphometry is most useful in identifying brain regions critical to their involvement in neurodegeneration.MethodWe examined brain network hubs from four measures of cortical morphometry, derived using anatomical T1‐weigher MRI. From a large cohort of healthy participants (N = 260) from 20 to 85 years of age, we generated 200 networks for each measure of cortical morphometry using bootstrapping. We then tested how two measures of brain network hubness – nodal strength and clustering coefficient – were mapped onto brain network hubs across all 200 networks for each morphometric feature. Regions were identified as brain hubs if ranked two standard deviations away from the corresponding group mean.ResultWe mapped out brain hubs for each four measures (features) of brain morphometry. Nodal strength and clustering coefficient showed similarity in the behaviour across the features: gray volume, regional surface area and its thickness and thickness standard deviation. However, the differences were found in what brain region qualify as a network hub for each feature.ConclusionCortical morphometry extracted from anatomical brain scans can be used as a potential imaging marker for identification of brain hubs potentially involved in early differentiation of dementia subtypes.

Aerobic exercise prevents white matter microstructural degeneration in MCI adults

AbstractBackgroundWhile the use of non‐pharmacological interventions—such as physical activity, nutritional supplementation and cognitive stimulation—to prevent or delay cognitive decline and the onset of Alzheimer’s disease (AD) has received increasing attention, evidence supporting the benefits of these interventions have been mixed, and underlying mechanisms are not well‐understood.MethodIn participants enrolled in the PACE‐2 trial, which examined the impacts of 6‐month physical activity intervention on brain and cognitive outcomes, we evaluated the effects of aerobic exercise (n = 15, 63±10 yrs) on longitudinal changes in brain morphometry and white matter microstructure compared to stretching exercise controls (n = 17, 67±7 yrs.) using MRI among pre‐diabetic mild cognitive impairment adults. High‐resolution structural T1‐weighted and diffusion‐weighted images (DTI) were acquired before and after exercise intervention. Group differences in MRI markers (cortical volume and thickness, white matter DTI and free water metrics) after intervention and their longitudinal changes were compared for 68 cortical (Desikan‐Killany Atlas) and 48 white matter (JHU WM Atlas) regions using FreeSurfer SPM, FSL and Nextflow.ResultNo statistically significant group difference in cortical thickness, volumes and conventional DTI metrics was observed. On the other hand, free water (FW)‐corrected fractional anisotropy (FA) remained unchanged in aerobic exercise group (p = 0.32) while FA decreased in stretching controls in the right cerebral peduncle(p&lt;0.001), resulting in group differences in FW‐corrected FA after 6‐month exercise intervention (p = 0.02 after Bonferroni correction). These changes in stretching exercise group were negatively correlated with FW fraction changes (marker related with neuroinflammation) (r = ‐0.71, p = 0.001).ConclusionAerobic exercise could have neuroprotective effects by preventing white matter degeneration, and FW fraction and FW‐corrected imaging markers could be useful for more sensitive detection of early AD sign.

Relative importance of multimodal risk factors to discriminate memory trajectories: Similarities and differences across ethnoracial groups in cognitively normal older adults

AbstractBackgroundInequalities in dementia incidence and heterogeneities observed on memory decline are present across ethnic/racial groups. Research suggests that inequities in risk/protective factors contribute towards these differences. We examine risk factors across multiple domains to identify those that best discriminate differences in memory trajectories across ethnoracial groups.MethodWe used an ethnoracially diverse longitudinal cohort of cognitively normal older adults (n = 299; mean age = 73.47 (6.90) years; 63.9% female) from the University of California, Davis‐Alzheimer’s Disease Research Center. First, we applied latent class growth analysis to objectively classify memory trajectories into distinct latent groups across a maximum of 10 follow‐ups. Second, we used random forest analyses (RFA) to examine relative importance of 22 risk/protective factors from seven domains (demographic, genetic, brain morphometry, plasma and functional biomarkers, psychological and lifestyle factors) to discriminate low/high memory performers as stratified by three ethnoracial groups (African Americans [AA; n = 111]; Hispanics [n = 108], Whites [n = 66]).ResultFirst, we observed that four latent classes (low, intermediate, high, very high) best represented memory trajectories (Figure 1). Second, for our RFA analyses, we collapsed low and intermediate groups to a low group (n = 136; 40.4% AA, 25.6% Hispanics, 28.8% Whites), and high and very high to a high performing group (n = 148; 34.6% AA, 50% Hispanics, 15.4% Whites). High memory class was predicted (Figure 2) in AA by lower smoking years, lower Functional Assessment Questionnaire (FAQ), younger age, higher education, lower neurofilament light (NfL), Apolipoprotein E non‐carriers, low neuropsychiatric inventory (NPI) severity, men, and higher hippocampal volume (AUC = 0.73); in Hispanics with men, lower FAQ, lower Geriatric Depression Scale (GDS), smaller third ventricle, larger hippocampal volume, lower NPI severity, smaller lateral ventricle, and lower NfL (AUC = 0.86); and in Whites, with lower FAQ, higher education, higher alcohol use frequency, lower NfL, lower GDS, smaller lateral ventricles, and low white matter hyperintensities (AUC = 0.82).ConclusionWe observed similarities and differences in predictors to discriminate low versus high memory performers in three ethnoracial groups. Identifying relative importance of risk factors will promote the integration of multimodal risk factors across ethnoracial groups to develop novel and complex risk profiles leading to early detection and identification of asymptomatic or preclinical older adults.

Patterns of Impaired Neurocognitive Performance on the Global Neuropsychological Assessment, and Their Brain Structural Correlates in Recent-onset and Chronic Schizophrenia.

Schizophrenia is associated with impairment in multiple cognitive domains. There is a paucity of research on the effect of prolonged illness duration (≥ 15 years) on cognitive performance along multiple domains. In this pilot study, we used the Global Neuropsychological Assessment (GNA), a brief cognitive battery, to explore the patterns of cognitive impairment in recent-onset (≤ 2 years) compared to chronic schizophrenia (≥ 15 years), and correlate cognitive performance with brain morphometry in patients and healthy adults. We assessed cognitive performance in patients with recent-onset (n = 17, illness duration ≤ 2 years) and chronic schizophrenia (n = 14, duration ≥ 15 years), and healthy adults (n = 16) using the GNA and examined correlations between cognitive scores and gray matter volumes computed from T1-weighted magnetic resonance imaging images. We observed cognitive deficits affecting multiple domains in the schizophrenia samples. Selectively greater impairment of perceptual comparison speed was found in adults with chronic schizophrenia (p = 0.009, η2partial = 0.25). In the full sample (n = 47), perceptual comparison speed correlated significantly with gray matter volumes in the anterior and medial temporal lobes (TFCE, FWE p < 0.01). Along with generalized deficit across multiple cognitive domains, selectively greater impairment of perceptual comparison speed appears to characterize chronic schizophrenia. This pattern might indicate an accelerated or premature cognitive aging. Anterior-medial temporal gray matter volumes especially of the left hemisphere might underlie the impairment noted in this domain in schizophrenia.

Morphological Features of Language Regions in Individuals with Tuberous Sclerosis Complex.

A significant number of individuals with tuberous sclerosis complex (TSC) exhibit language difficulties. Here, we examined the language-related brain morphometry in 59 participants (7 participants with TSC and comorbid autism spectrum disorder (ASD) (TSC + ASD), 13 with TSC but no ASD (TSC-ASD), 10 with ASD-only (ASD), and 29 typically developing (TD) controls). A hemispheric asymmetry was noted in surface area and gray matter volume of several cortical language areas in TD, ASD, and TSC-ASD groups, but not in TSC + ASD group. TSC + ASD group demonstrated increased cortical thickness and curvature values in multiple language regions for both hemispheres, compared to other groups. After controlling for tuber load in the TSC groups, within-group differences stayed the same but the differences between TSC-ASD and TSC + ASD were no longer statistically significant. These preliminary findings suggest that comorbid ASD in TSC as well as tuber load in TSC is associated with changes in the morphometry of language regions. Future studies with larger sample sizes will be needed to confirm these findings.

Brain cortical assessment by MRI in fetuses with left congenital diaphragmatic hernia Brain development in left congenital diaphragmatic hernia.

To evaluate fetal brain development using MRI (magnetic resonance imaging) in CDH (congenital diaphragmatic hernia). 52 isolated left CDH and 104 control fetuses were imaged using MRI. Brain morphometry (Biparietal diameter - BPD, brain fronto-occipital diameter - BFOD, third ventricle, posterior ventricles, transcerebellar diameter - TCD, anteroposterior and craniocaudal cerebellar vermis diameter - AP and CC) and cortical structures (bilateral cingulate fissure - CF, insular fissure - IF, insular depth - ID) were compared with controls using Mann-Whitney test . Median gestational age at MRI was comparable (p=0.95), as well as was the median biparietal diameter (p=0.737). Among morphometric parameters only the brain fronto-occipital diameter was significantly smaller in CDH (p= 0.001) and the third ventricle was significantly greater in CDH (<0.0001). Among cortical structures, the cingulate and insular fissure were significantly deeper in CDH fetuses (p<0.0001), as the insular depth ID was smaller in CDH (p< 0.03). CDH fetuses have a smaller fronto-occipitaldiameter, reduced insular depth, and deeper cingulate and insular fissure, and greater third ventricle width as compared to controls. These findings suggest that left CDH may have an impact on fetal brain development with an overall reduction of brain volume. This article is protected by copyright. All rights reserved.

Brain morphometry in older adults with and without dementia using extremely rapid structural scans

T1-weighted structural MRI is widely used to measure brain morphometry (e.g., cortical thickness and subcortical volumes). Accelerated scans as fast as one minute or less are now available but it is unclear if they are adequate for quantitative morphometry. Here we compared the measurement properties of a widely adopted 1.0 mm resolution scan from the Alzheimer's Disease Neuroimaging Initiative (ADNI = 5′12’’) with two variants of highly accelerated 1.0 mm scans (compressed-sensing, CSx6 = 1′12’’; and wave-controlled aliasing in parallel imaging, WAVEx9 = 1′09’’) in a test-retest study of 37 older adults aged 54 to 86 (including 19 individuals diagnosed with a neurodegenerative dementia). Rapid scans produced highly reliable morphometric measures that largely matched the quality of morphometrics derived from the ADNI scan. Regions of lower reliability and relative divergence between ADNI and rapid scan alternatives tended to occur in midline regions and regions with susceptibility-induced artifacts. Critically, the rapid scans yielded morphometric measures similar to the ADNI scan in regions of high atrophy. The results converge to suggest that, for many current uses, extremely rapid scans can replace longer scans. As a final test, we explored the possibility of a 0′49’’ 1.2 mm CSx6 structural scan, which also showed promise. Rapid structural scans may benefit MRI studies by shortening the scan session and reducing cost, minimizing opportunity for movement, creating room for additional scan sequences, and allowing for the repetition of structural scans to increase precision of the estimates.