Brain imaging and neuropsychological assessment of individuals recovered from a mild to moderate SARS-CoV-2 infection.

As severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infections have been shown to affect the central nervous system, the investigation of associated alterations of brain structure and neuropsychological sequelae is crucial to help address future health care needs. Therefore, we performed a comprehensive neuroimaging and neuropsychological assessment of 223 nonvaccinated individuals recovered from a mild to moderate SARS-CoV-2 infection (100 female/123 male, age [years], mean ± SD, 55.54 ± 7.07; median 9.7 mo after infection) in comparison with 223 matched controls (93 female/130 male, 55.74 ± 6.60) within the framework of the Hamburg City Health Study. Primary study outcomes were advanced diffusion MRI measures of white matter microstructure, cortical thickness, white matter hyperintensity load, and neuropsychological test scores. Among all 11 MRI markers tested, significant differences were found in global measures of mean diffusivity (MD) and extracellular free water which were elevated in the white matter of post-SARS-CoV-2 individuals compared to matched controls (free water: 0.148 ± 0.018 vs. 0.142 ± 0.017, P < 0.001; MD [10-3 mm2/s]: 0.747 ± 0.021 vs. 0.740 ± 0.020, P < 0.001). Group classification accuracy based on diffusion imaging markers was up to 80%. Neuropsychological test scores did not significantly differ between groups. Collectively, our findings suggest that subtle changes in white matter extracellular water content last beyond the acute infection with SARS-CoV-2. However, in our sample, a mild to moderate SARS-CoV-2 infection was not associated with neuropsychological deficits, significant changes in cortical structure, or vascular lesions several months after recovery. External validation of our findings and longitudinal follow-up investigations are needed.

Larger cerebral cortex is genetically correlated with greater frontal area and dorsal thickness.

Human cortical expansion has occurred non-uniformly across the brain. We assessed the genetic architecture of cortical global expansion and regionalization by comparing two sets of genome-wide association studies of 24 cortical regions with and without adjustment for global measures (i.e., total surface area, mean cortical thickness) using a genetically informed parcellation in 32,488 adults. We found 393 and 756 significant loci with and without adjusting for globals, respectively, where 8% and 45% loci were associated with more than one region. Results from analyses without adjustment for globals recovered loci associated with global measures. Genetic factors that contribute to total surface area of the cortex particularly expand anterior/frontal regions, whereas those contributing to thicker cortex predominantly increase dorsal/frontal-parietal thickness. Interactome-based analyses revealed significant genetic overlap of global and dorsolateral prefrontal modules, enriched for neurodevelopmental and immune system pathways. Consideration of global measures is important in understanding the genetic variants underlying cortical morphology.

A generalizable data-driven model of atrophy heterogeneity and progression in memory clinic settings.

Memory clinic patients are a heterogeneous population representing various aetiologies of pathological ageing. It is not known whether divergent spatiotemporal progression patterns of brain atrophy, as previously described in Alzheimer's disease patients, are prevalent and clinically meaningful in this group of older adults. To uncover distinct atrophy subtypes, we applied the Subtype and Stage Inference (SuStaIn) algorithm to baseline structural MRI data from 813 participants enrolled in the DELCODE cohort (mean ± standard deviation, age = 70.67 ± 6.07 years, 52% females). Participants were cognitively unimpaired (n = 285) or fulfilled diagnostic criteria for subjective cognitive decline (n = 342), mild cognitive impairment (n = 118) or dementia of the Alzheimer's type (n = 68). Atrophy subtypes were compared in baseline demographics, fluid Alzheimer's disease biomarker levels, the Preclinical Alzheimer Cognitive Composite (PACC-5) as well as episodic memory and executive functioning. PACC-5 trajectories over up to 240 weeks were examined. To test whether baseline atrophy subtype and stage predicted clinical trajectories before manifest cognitive impairment, we analysed PACC-5 trajectories and mild cognitive impairment conversion rates of cognitively unimpaired participants and those with subjective cognitive decline. Limbic-predominant and hippocampal-sparing atrophy subtypes were identified. Limbic-predominant atrophy initially affected the medial temporal lobes, followed by further temporal regions and, finally, the remaining cortical regions. At baseline, this subtype was related to older age, more pathological Alzheimer's disease biomarker levels, APOE ε4 carriership and an amnestic cognitive impairment. Hippocampal-sparing atrophy initially occurred outside the temporal lobe, with the medial temporal lobe spared up to advanced atrophy stages. This atrophy pattern also affected individuals with positive Alzheimer's disease biomarkers and was associated with more generalized cognitive impairment. Limbic-predominant atrophy, in all participants and in only unimpaired participants, was linked to more negative longitudinal PACC-5 slopes than observed in participants without or with hippocampal-sparing atrophy and increased the risk of mild cognitive impairment conversion. SuStaIn modelling was repeated in a sample from the Swedish BioFINDER-2 cohort. Highly similar atrophy progression patterns and associated cognitive profiles were identified. Cross-cohort model generalizability, at both the subject and the group level, was excellent, indicating reliable performance in previously unseen data. The proposed model is a promising tool for capturing heterogeneity among older adults at early at-risk states for Alzheimer's disease in applied settings. The implementation of atrophy subtype- and stage-specific end points might increase the statistical power of pharmacological trials targeting early Alzheimer's disease.

Brain morphometry in former American football players: Findings from the DIAGNOSE CTE research project.

Exposure to repetitive head impacts (RHIs) in contact sports is associated with neurodegenerative disorders including chronic traumatic encephalopathy (CTE) which currently can be diagnosed only at postmortem. American football players are at higher risk of developing CTE given their exposure to RHIs. One promising approach for diagnosing CTE in vivo is to explore known neuropathological abnormalities at postmortem in living individuals using structural magnetic resonance imaging (MRI). MRI brain morphometry was evaluated in 170 male former American football players ages 45-74 years (n = 114 professional; n = 56 college) and 54 same-age unexposed asymptomatic male controls (n = 58 age range 45-74). Cortical thickness and volume of regions of interest were selected based on established CTE pathology findings and were assessed using FreeSurfer. Group differences and interactions with age and exposure factors were evaluated using a generalized least squares model. A separate logistic regression and independent multinomial model were performed to predict each Traumatic Encephalopathy Syndrome (TES) diagnosis core clinical features and provisional level of certainty for CTE pathology using brain regions of interest. Former college and professional American football players (combined) showed significant cortical thickness and/or volume reductions compared to unexposed asymptomatic controls in the hippocampus amygdala entorhinal cortex parahippocampal gyrus insula temporal pole and superior frontal gyrus. Post-hoc analyses identified group-level differences between former professional players and unexposed asymptomatic controls in the hippocampus amygdala entorhinal cortex parahippocampal gyrus insula and superior frontal gyrus. Former college players showed significant volume reductions in the hippocampus amygdala and superior frontal gyrus compared to the unexposed asymptomatic controls. We did not observe age-by-group interactions for brain morphometric measures. Interactions between morphometry and exposure measures were limited to a single significant positive association between the age of first exposure to organized tackle football and right insular volume. We found no significant relationship between brain morphometric measures and the TES diagnosis core clinical features and provisional level of certainty for CTE pathology outcomes. These findings suggest that MRI morphometrics detects abnormalities in individuals with a history of RHI exposure that resemble the anatomic distribution of pathological findings from postmortem CTE studies. The lack of findings associating MRI measures with exposure metrics (except for one significant relationship) or TES diagnosis and core clinical features suggests that brain morphometry must be complemented by other types of measures to characterize individuals with RHIs.

Late-onset temporal lobe epilepsy: insights from brain atrophy and Alzheimer's disease biomarkers.

Considering the growing age of the world population, the incidence of epilepsy in older adults is expected to increase significantly. It has been suggested that late-onset temporal lobe epilepsy (LO-TLE) may be neurodegenerative in origin and overlap with Alzheimer's Disease (AD). Herein, we aimed to characterize the pattern of cortical atrophy and cerebrospinal fluid (CSF) biomarkers of AD (total and phosphorylated tau, and ß-amyloid) in a selected population of LO-TLE of unknown origin. We prospectively enrolled individuals with temporal lobe epilepsy onset after the age of 50 and no cognitive impairment. They underwent a structural MRI scan and CSF biomarkers measurement. Imaging and biomarkers data were compared to three retrospectively collected groups: (i) age-sex-matched healthy controls, (ii) patients with Mild Cognitive Impairment (MCI) and abnormal CSF AD biomarkers (MCI-AD), and (iii) patients with MCI and normal CSF AD biomarkers (MCI-noAD). From a pool of 52 patients, twenty consecutive eligible LO-TLE patients with a mean disease duration of 1.8 years were recruited. As control populations, 25 patients with MCI-AD, 25 patients with MCI-noAD, and 25 healthy controls were enrolled. CSF biomarkers returned normal values in LO-TLE, significantly different from patients with MCI due to AD. There were no differences in cortico-subcortical atrophy between epilepsy patients and healthy controls, while patients with MCI demonstrated widespread injuries of cortico-subcortical structures. Individuals with a late-onset form of temporal lobe epilepsy, characterized by short disease duration and normal CSF ß-amyloid and tau protein levels, showed patterns of cortical thickness and subcortical volumes not significantly different from healthy controls, but highly different from patients with MCI, either due to Alzheimer's Disease or not.

Leveraging the adolescent brain cognitive development study to improve behavioral prediction from neuroimaging in smaller replication samples.

Neuroimaging is a popular method to map brain structural and functional patterns to complex human traits. Recently published observations cast doubt upon these prospects, particularly for prediction of cognitive traits from structural and resting state functional magnetic resonance imaging (MRI). We leverage baseline data from thousands of children in the Adolescent Brain Cognitive DevelopmentSM Study to inform the replication sample size required with univariate and multivariate methods across different imaging modalities to detect reproducible brain-behavior associations. We demonstrate that by applying multivariate methods to high-dimensional brain imaging data, we can capture lower dimensional patterns of structural and functional brain architecture that correlate robustly with cognitive phenotypes and are reproducible with only 41 individuals in the replication sample for working memory-related functional MRI, and ~ 100 subjects for structural and resting state MRI. Even with 100 random re-samplings of 100 subjects in discovery, prediction can be adequately powered with 66 subjects in replication for multivariate prediction of cognition with working memory task functional MRI. These results point to an important role for neuroimaging in translational neurodevelopmental research and showcase how findings in large samples can inform reproducible brain-behavior associations in small sample sizes that are at the heart of many research programs and grants.

Gender and age related brain structural and functional alterations in children with autism spectrum disorder.

To explore the effects of age and gender on the brain in children with autism spectrum disorder using magnetic resonance imaging. 185 patients with autism spectrum disorder and 110 typically developing children were enrolled. In terms of gender, boys with autism spectrum disorder had increased gray matter volumes in the insula and superior frontal gyrus and decreased gray matter volumes in the inferior frontal gyrus and thalamus. The brain regions with functional alterations are mainly distributed in the cerebellum, anterior cingulate gyrus, postcentral gyrus, and putamen. Girls with autism spectrum disorder only had increased gray matter volumes in the right cuneus and showed higher amplitude of low-frequency fluctuation in the paracentral lobule, higher regional homogeneity and degree centrality in the calcarine fissure, and greater right frontoparietal network-default mode network connectivity. In terms of age, preschool-aged children with autism spectrum disorder exhibited hypo-connectivity between and within auditory network, somatomotor network, and visual network. School-aged children with autism spectrum disorder showed increased gray matter volumes in the rectus gyrus, superior temporal gyrus, insula, and suboccipital gyrus, as well as increased amplitude of low-frequency fluctuation and regional homogeneity in the calcarine fissure and precentral gyrus and decreased in the cerebellum and anterior cingulate gyrus. The hyper-connectivity between somatomotor network and left frontoparietal network and within visual network was found. It is essential to consider the impact of age and gender on the neurophysiological alterations in autism spectrum disorder children when analyzing changes in brain structure and function.

Differential effects of early or late exposure to prenatal maternal immune activation on mouse embryonic neurodevelopment.

Exposure to maternal immune activation (MIA) in utero is a risk factor for neurodevelopmental and psychiatric disorders. MIA-induced deficits in adolescent and adult offspring have been well characterized; however, less is known about the effects of MIA exposure on embryo development. To address this gap, we performed high-resolution ex vivo MRI to investigate the effects of early (gestational day [GD]9) and late (GD17) MIA exposure on embryo (GD18) brain structure. We identify striking neuroanatomical changes in the embryo brain, particularly in the late-exposed offspring. We further examined the putative neuroanatomical underpinnings of MIA timing in the hippocampus using electron microscopy and identified differential effects due to MIA timing. An increase in apoptotic cell density was observed in the GD9-exposed offspring, while an increase in the density of neurons and glia with ultrastructural features reflective of increased neuroinflammation and oxidative stress was observed in GD17-exposed offspring, particularly in females. Overall, our findings integrate imaging techniques across different scales to identify differential impact of MIA timing on the earliest stages of neurodevelopment.

Automated protocols for delineating human hippocampal subfields from 3 Tesla and 7 Tesla magnetic resonance imaging data.

Researchers who study the human hippocampus are naturally interested in how its subfields function. However, many researchers are precluded from examining subfields because their manual delineation from magnetic resonance imaging (MRI) scans (still the gold standard approach) is time consuming and requires significant expertise. To help ameliorate this issue, we present here two protocols, one for 3T MRI and the other for 7T MRI, that permit automated hippocampus segmentation into six subregions, namely dentate gyrus/cornu ammonis (CA)4, CA2/3, CA1, subiculum, pre/parasubiculum, and uncus along the entire length of the hippocampus. These protocols are particularly notable relative to existing resources in that they were trained and tested using large numbers of healthy young adults (n = 140 at 3T, n = 40 at 7T) whose hippocampi were manually segmented by experts from MRI scans. Using inter-rater reliability analyses, we showed that the quality of automated segmentations produced by these protocols was high and comparable to expert manual segmenters. We provide full open access to the automated protocols, and anticipate they will save hippocampus researchers a significant amount of time. They could also help to catalyze subfield research, which is essential for gaining a full understanding of how the hippocampus functions.

Neurite density of the hippocampus is associated with trace eyeblink conditioning latency in 4- to 6-year-olds.

Limited options exist to evaluate the development of hippocampal function in young children. Research has established that trace eyeblink conditioning (EBC) relies on a functional hippocampus. Hence, we set out to investigate whether trace EBC is linked to hippocampal structure, potentially serving as a valuable indicator of hippocampal development. Our study explored potential associations between individual differences in hippocampal volume and neurite density with trace EBC performance in young children. We used onset latency of conditioned responses (CR) and percentage of conditioned responses (% CR) as measures of hippocampal-dependent associative learning. Using a sample of typically developing children aged 4 to 6 years (N = 30; 14 girls; M = 5.70 years), participants underwent T1- and diffusion-weighted MRI scans and completed a 15-min trace eyeblink conditioning task conducted outside the MRI. % CR and CR onset latency were calculated based on all trials involving tone-puff presentations and tone-alone trials. Findings revealed a connection between greater left hippocampal neurite density and delayed CR onset latency. Children with higher neurite density in the left hippocampus tended to blink closer to the onset of the unconditioned stimulus, indicating that structural variations in the hippocampus were associated with more precise timing of conditioned responses. No other relationships were observed between hippocampal volume, cerebellum volume or neurite density, hippocampal white matter connectivity and any EBC measures. Preliminary results suggest that trace EBC may serve as a straightforward yet innovative approach for studying hippocampal development in young children and populations with atypical development.

Attention-deficit hyperactivity disorder symptoms and brain morphology: Addressing potential selection bias with inverse probability weighting.

The goal of this study was to examine what happens to established associations between attention deficit hyperactivity disorder (ADHD) symptoms and cortical surface and thickness regions once we apply inverse probability of censoring weighting (IPCW) to address potential selection bias. Moreover, we illustrate how different factors that predict participation contribute to potential selection bias. Participants were 9- to 11-year-old children from the Generation R study (N = 2707). Cortical area and thickness were measured with magnetic resonance imaging (MRI) and ADHD symptoms with the Child Behavior Checklist. We examined how associations between ADHD symptoms and brain morphology change when we weight our sample back to either follow-up (ages 9-11), baseline (cohort at birth), or eligible (population of Rotterdam at time of recruitment). Weights were derived using IPCW or raking and missing predictors of participation used to estimate weights were imputed. Weighting analyses to baseline and eligible increased beta coefficients for the middle temporal gyrus surface area, as well as fusiform gyrus cortical thickness. Alternatively, the beta coefficient for the rostral anterior cingulate decreased. Removing one group of variables used for estimating weights resulted in the weighted regression coefficient moving closer to the unweighted regression coefficient. In addition, we found considerably different beta coefficients for most surface area regions and all thickness measures when we did not impute missing covariate data. Our findings highlight the importance of using inverse probability weighting (IPW) in the neuroimaging field, especially in the context of mental health-related research. We found that including all variables related to exposure-outcome in the IPW model and combining IPW with multiple imputations can help reduce bias. We encourage future psychiatric neuroimaging studies to define their target population, collect information on eligible but not included participants and use inverse probability of censoring weighting (IPCW) to reduce selection bias.

BrainAGE: Revisited and reframed machine learning workflow.

Since the introduction of the BrainAGE method, novel machine learning methods for brain age prediction have continued to emerge. The idea of estimating the chronological age from magnetic resonance images proved to be an interesting field of research due to the relative simplicity of its interpretation and its potential use as a biomarker of brain health. We revised our previous BrainAGE approach, originally utilising relevance vector regression (RVR), and substituted it with Gaussian process regression (GPR), which enables more stable processing of larger datasets, such as the UK Biobank (UKB). In addition, we extended the global BrainAGE approach to regional BrainAGE, providing spatially specific scores for five brain lobes per hemisphere. We tested the performance of the new algorithms under several different conditions and investigated their validity on the ADNI and schizophrenia samples, as well as on a synthetic dataset of neocortical thinning. The results show an improved performance of the reframed global model on the UKB sample with a mean absolute error (MAE) of less than 2 years and a significant difference in BrainAGE between healthy participants and patients with Alzheimer's disease and schizophrenia. Moreover, the workings of the algorithm show meaningful effects for a simulated neocortical atrophy dataset. The regional BrainAGE model performed well on two clinical samples, showing disease-specific patterns for different levels of impairment. The results demonstrate that the new improved algorithms provide reliable and valid brain age estimations.

A volumetric asymmetry study of gray matter in individuals with and without dyslexia.

Brain imaging work aimed at increased classification of dyslexia has underscored an important relationship between anterior (i.e., the inferior frontal gyrus; IFG) and posterior (i.e., superior temporal gyrus and supramarginal gyrus) brain regions. The extent to which the three components of the inferior frontal gyrus, namely the pars orbitalis, triangularis, and opercularis, are differentially related to the posterior regions, namely the superior temporal gyrus and supramarginal gyrus, needs further elucidation. Information about the nature of the anterior-posterior connections would facilitate our understanding of the neural underpinnings associated with dyslexia. Adult participants (N = 38; 16 with dyslexia) took part in an MRI study, whereby high-resolution structural scans were obtained. Volumetric asymmetry of the three regions of the IFG, the superior temporal gyrus, and the supramarginal gyrus was extracted. Significant differences were found for each of the three IFG regions, such that skilled readers had a greater leftward asymmetry of the orbitalis and triangularis, and greater rightward asymmetry of the opercularis, when compared to individuals with dyslexia. Furthermore, the pars triangularis was significantly associated with leftward asymmetry of the superior temporal gyrus for skilled but not dyslexic participants. For individuals with dyslexia, the cortical asymmetry of the IFG, and the corresponding connections with other reading-related brain regions, is inherently different from skilled readers. We discuss our findings in the context of the print-to-speech framework to further our understanding of the neural underpinnings associated with dyslexia.

Aging modulates frontal lobes involvement in emotion regulation processing.

Emotion regulation (ER) is the process by which individuals can modulate the intensity of their emotional experience and it plays a crucial role in daily life. So far, behavioral analyses seem to suggest that ER ability remains stable throughout the lifespan. However, imaging studies evaluating the neural correlates of ER performance during the aging process have shown mixed results. In this study, we used the "Cambridge Centre for Ageing and Neuroscience cohort sample" to investigate: (1) ER behavioral performance and (2) the differential association between brain measures (based on both structural and functional connectivity data) and ER performance, in a group of younger/middle-aged participants (N = 159; age range: 18y < x < 58y) relative to a group of older healthy subjects (N = 136; age range: 58y < =x < 89y). Whereas we found no group-related differences either in ER behavioral data or the association between ER performance and structural data, we did observe that ER performance was differentially correlated in our two study groups to functional connectivity measures in the fronto-insular-temporal network, which has been shown to be involved in emotional processing. Group-related differences were specifically localized in a cluster of voxels within the anterior cingulate areas which revealed a reverse pattern between our study groups: in younger/middle-aged participants better ER performance was associated with increase connectivity, whereas among older participants better ER performance was related to reduced connectivity. Based on our results, we suggest that a de-differentiation mechanism, known to affect the frontal lobes brain activity and connectivity in older subjects, might explain our findings.

Normative Magnetic Resonance Imaging Data Increase the Sensitivity to Brain Volume Abnormalities in the Classification of Fetal Alcohol Spectrum Disorder.

OBJECTIVE: To evaluate the use of a large magnetic resonance imaging (MRI) normative dataset to quantify structural brain anomalies that may improve diagnostic sensitivity for atypical brain volume in youth with fetal alcohol spectrum disorder (FASD). STUDY DESIGN: Participants included 48 children with prenatal alcohol exposure (PAE) and 43 controls, ages 8-17 years, from the longitudinal Collaborative Initiative on FASD s. Recently published lifespan brain charts were used to quantify participants' (per)centile for brain volumes (cortical and subcortical gray matter and cortical white matter), providing an index of (dis)similarity to typically developing individuals of the same age and sex. RESULTS: Participants with PAE demonstrated lower mean centile scores compared with controls. Participants with PAE and scores ≤ 10th centile on at least 1 brain volume metric demonstrated significantly lower performance on measures of intellectual function and aspects of executive functioning compared with participants with PAE and "typical" volumes (>10th centile). Brain volume centiles explained a greater amount of variance in IQ and improved sensitivity to brain volume anomalies in FASD compared with the most commonly used diagnostic criterion of occipitofrontal circumference (OFC) ≤ 10th. CONCLUSION: Age- and sex-adjusted brain volumes based on a large normative dataset may be useful predictors of functional outcomes and may identify a greater number of individuals with FASD than the currently used criterion of OFC.

Prediction of estimated risk for bipolar disorder using machine learning and structural MRI features.

BACKGROUND: Individuals with bipolar disorder are commonly correctly diagnosed a decade after symptom onset. Machine learning techniques may aid in early recognition and reduce the disease burden. As both individuals at risk and those with a manifest disease display structural brain markers, structural magnetic resonance imaging may provide relevant classification features. METHODS: Following a pre-registered protocol, we trained linear support vector machine (SVM) to classify individuals according to their estimated risk for bipolar disorder using regional cortical thickness of help-seeking individuals from seven study sites (N = 276). We estimated the risk using three state-of-the-art assessment instruments (BPSS-P, BARS, EPIbipolar). RESULTS: For BPSS-P, SVM achieved a fair performance of Cohen's κ of 0.235 (95% CI 0.11-0.361) and a balanced accuracy of 63.1% (95% CI 55.9-70.3) in the 10-fold cross-validation. In the leave-one-site-out cross-validation, the model performed with a Cohen's κ of 0.128 (95% CI -0.069 to 0.325) and a balanced accuracy of 56.2% (95% CI 44.6-67.8). BARS and EPIbipolar could not be predicted. In post hoc analyses, regional surface area, subcortical volumes as well as hyperparameter optimization did not improve the performance. CONCLUSIONS: Individuals at risk for bipolar disorder, as assessed by BPSS-P, display brain structural alterations that can be detected using machine learning. The achieved performance is comparable to previous studies which attempted to classify patients with manifest disease and healthy controls. Unlike previous studies of bipolar risk, our multicenter design permitted a leave-one-site-out cross-validation. Whole-brain cortical thickness seems to be superior to other structural brain features.

Longitudinal changes in brain-derived neurotrophic factor (BDNF) but not cytokines contribute to hippocampal recovery in anorexia nervosa above increases in body mass index.

BACKGROUND: Physical sequelae of anorexia nervosa (AN) include a marked reduction in whole brain volume and subcortical structures such as the hippocampus. Previous research has indicated aberrant levels of inflammatory markers and growth factors in AN, which in other populations have been shown to influence hippocampal integrity. METHODS: Here we investigated the influence of concentrations of two pro-inflammatory cytokines (tumor necrosis factor-alpha [TNF-α] and interleukin-6 [IL-6]) and brain-derived neurotrophic factor (BDNF) on the whole hippocampal volume, as well as the volumes of three regions (the hippocampal body, head, and tail) and 18 subfields bilaterally. Investigations occurred both cross-sectionally between acutely underweight adolescent/young adult females with AN (acAN; n = 82) and people recovered from AN (recAN; n = 20), each independently pairwise age-matched with healthy controls (HC), and longitudinally in acAN after partial renourishment (n = 58). Hippocampal subfield volumes were quantified using FreeSurfer. Concentrations of molecular factors were analyzed in linear models with hippocampal (subfield) volumes as the dependent variable. RESULTS: Cross-sectionally, there was no evidence for an association between IL-6, TNF-α, or BDNF and between-group differences in hippocampal subfield volumes. Longitudinally, increasing concentrations of BDNF were positively associated with longitudinal increases in bilateral global hippocampal volumes after controlling for age, age2, estimated total intracranial volume, and increases in body mass index (BMI). CONCLUSIONS: These findings suggest that increases in BDNF may contribute to global hippocampal recovery over and above increases in BMI during renourishment. Investigations into treatments targeted toward increasing BDNF in AN may be warranted.

Atypical procedural learning in children with developmental coordination disorder: A combined behavioral and neuroimaging study.

While procedural learning (PL) has been implicated in delayed motor skill observed in developmental coordination disorder (DCD), few studies have considered the impact of co-occurring attentional problems. Furthermore, the neurostructural basis of PL in children remains unclear. We investigated PL in children with DCD while controlling for inattention symptoms, and examined the role of fronto-basal ganglia-cerebellar morphology in PL. Fifty-nine children (6-14 years; nDCD = 19, ncontrol = 40) completed the serial reaction time (SRT) task to measure PL. The Attention-Deficit Hyperactivity Disorder Rating Scale-IV was administered to measure inattention symptoms. Structural T1 images were acquired for a subset of participants (nDCD = 10, ncontrol = 28), and processed using FreeSurfer. Volume was extracted for the cerebellum, basal ganglia, and frontal regions. After controlling for inattention symptoms, the reaction time profile of controls was consistent with learning on the SRT task. This was not the case for those with DCD. SRT task performance was positively correlated with cerebellar cortical volume, and children with DCD trended towards lower cerebellar volume compared to controls. Children with DCD may not engage in PL during the SRT task in the same manner as controls, with this differential performance being associated with atypical cerebellar morphology.

A systematic review of (semi-)automatic quality control of T1-weighted MRI scans.

PURPOSE: Artifacts in magnetic resonance imaging (MRI) scans degrade image quality and thus negatively affect the outcome measures of clinical and research scanning. Considering the time-consuming and subjective nature of visual quality control (QC), multiple (semi-)automatic QC algorithms have been developed. This systematic review presents an overview of the available (semi-)automatic QC algorithms and software packages designed for raw, structural T1-weighted (T1w) MRI datasets. The objective of this review was to identify the differences among these algorithms in terms of their features of interest, performance, and benchmarks. METHODS: We queried PubMed, EMBASE (Ovid), and Web of Science databases on the fifth of January 2023, and cross-checked reference lists of retrieved papers. Bias assessment was performed using PROBAST (Prediction model Risk Of Bias ASsessment Tool). RESULTS: A total of 18 distinct algorithms were identified, demonstrating significant variations in methods, features, datasets, and benchmarks. The algorithms were categorized into rule-based, classical machine learning-based, and deep learning-based approaches. Numerous unique features were defined, which can be roughly divided into features capturing entropy, contrast, and normative measures. CONCLUSION: Due to dataset-specific optimization, it is challenging to draw broad conclusions about comparative performance. Additionally, large variations exist in the used datasets and benchmarks, further hindering direct algorithm comparison. The findings emphasize the need for standardization and comparative studies for advancing QC in MR imaging. Efforts should focus on identifying a dataset-independent measure as well as algorithm-independent methods for assessing the relative performance of different approaches.

Cortical thickness, gray matter volume, and cognitive performance: a crosssectional study of the moderating effects of age on their interrelationships.

In a sample comprising younger, middle-aged, and older cognitively healthy adults (N = 375), we examined associations between mean cortical thickness, gray matter volume (GMV), and performance in 4 cognitive domains-memory, speed, fluency, and crystallized intelligence. In almost all cases, the associations were moderated significantly by age, with the strongest associations in the older age group. An exception to this pattern was identified in a younger adult subgroup aged <23 years when a negative association between cognitive performance and cortical thickness was identified. Other than for speed, all associations between structural metrics and performance in specific cognitive domains were fully mediated by mean cognitive ability. Cortical thickness and GMV explained unique fractions of the variance in mean cognitive ability, speed, and fluency. In no case, however, did the amount of variance jointly explained by the 2 metrics exceed 7% of the total variance. These findings suggest that cortical thickness and GMV are distinct correlates of domain-general cognitive ability, that the strength and, for cortical thickness, the direction of these associations are moderated by age, and that these structural metrics offer only limited insights into the determinants of individual differences in cognitive performance across the adult lifespan.