Cortical complexity as a measure of age-related brain atrophy

The structure of the human brain changes in a variety of ways as we age. While a sizeable literature has examined age-related differences in cortical thickness, and to a lesser degree, gyrification, here we examined differences in cortical complexity, as indexed by fractal dimensionality in a sample of over 400 individuals across the adult lifespan. While prior studies have shown differences in fractal dimensionality between patient populations and age-matched, healthy controls, it is unclear how well this measure would relate to age-related cortical atrophy. Initially computing a single measure for the entire cortical ribbon, i.e., unparcellated gray matter, we found fractal dimensionality to be more sensitive to age-related differences than either cortical thickness or gyrification index. We additionally observed regional differences in age-related atrophy between the three measures, suggesting that they may index distinct differences in cortical structure. We also provide a freely available MATLAB toolbox for calculating fractal dimensionality.

Test-retest reliability of brain morphology estimates

Metrics of brain morphology are increasingly being used to examine inter-individual differences, making it important to evaluate the reliability of these structural measures. Here we used two open-access datasets to assess the intersession reliability of three cortical measures (thickness, gyrification, and fractal dimensionality) and two subcortical measures (volume and fractal dimensionality). Reliability was generally good, particularly with the gyrification and fractal dimensionality measures. One dataset used a sequence previously optimized for brain morphology analyses and had particularly high reliability. Examining the reliability of morphological measures is critical before the measures can be validly used to investigate inter-individual differences.

Age differences in head motion and estimates of cortical morphology

Cortical morphology is known to differ with age, as measured by cortical thickness, fractal dimensionality, and gyrification. However, head motion during MRI scanning has been shown to influence estimates of cortical thickness as well as increase with age. Studies have also found task-related differences in head motion and relationships between body–mass index (BMI) and head motion. Here I replicated these prior findings, as well as several others, within a large, open-access dataset (Centre for Ageing and Neuroscience, CamCAN). This is a larger dataset than these results have been demonstrated previously, within a sample size of more than 600 adults across the adult lifespan. While replicating prior findings is important, demonstrating these key findings concurrently also provides an opportunity for additional related analyses: critically, I test for the influence of head motion on cortical fractal dimensionality and gyrification; effects were statistically significant in some cases, but small in magnitude.

Robust estimation of sulcal morphology

While it is well established that cortical morphology differs in relation to a variety of inter-individual factors, it is often characterized using estimates of volume, thickness, surface area, or gyrification. Here we developed a computational approach for estimating sulcal width and depth that relies on cortical surface reconstructions output by FreeSurfer. While other approaches for estimating sulcal morphology exist, studies often require the use of multiple brain morphology programs that have been shown to differ in their approaches to localize sulcal landmarks, yielding morphological estimates based on inconsistent boundaries. To demonstrate the approach, sulcal morphology was estimated in three large sample of adults across the lifespan, in relation to aging. A fourth sample is additionally used to estimate test–retest reliability of the approach. This toolbox is now made freely available as supplemental to this paper: https://cmadan.github.io/calcSulc/.

Preserving and restoring bone with continuous insulin infusion therapy in a mouse model of type 1 diabetes

Those with type 1 diabetes (T1D) are more likely to suffer a fracture than age- and sex-matched individuals without diabetes, despite daily insulin therapy. In rodent studies examining the effect of bone- or glucose-targeting therapies on preventing the T1D-related decrease in bone strength, insulin co-therapy is often not included, despite the known importance of insulin signaling to bone mass accrual. Therefore, working toward a relevant pre-clinical model of diabetic bone disease, we assessed the effect of continuous subcutaneous insulin infusion (CSII) therapy at escalating doses on preserving bone and the effect of delayed CSII on rescuing the T1D-related bone deterioration in an established murine model of T1D. Osmotic minipumps were implanted in male DBA/2 J mice 2 weeks (prevention study) and 6 weeks (rescue study) after the first injection of streptozotocin (STZ) to deliver insulin at 0, 0.0625, 0.125, or 0.25 IU/day (prevention study; n = 4-5 per dose) and 0 or 0.25 IU/day (rescue study; n = 10 per group). CSII lasted 4 weeks in both studies, which also included age-matched, non-diabetic DBA/2 J mice (n = 8-12 per study). As the insulin dose increased, blood glucose decreased, body weight increased, a serum maker of bone resorption decreased, and a serum marker of bone formation increased such that each end-point characteristic was linearly correlated with dose. There were insulin dose-dependent relationships (femur diaphysis) with cross-sectional area of cortical bone and cortical thickness (micro-computed tomography) as well as structural strength (peak force endured by the mid-shaft during three-point bending). Likewise, trabecular bone volume fraction (BV/TV), thickness, and number (distal femur metaphysis) increased as the insulin dose increased. Delayed CSII improved glycated hemoglobin (HbA1c), but blood glucose levels remained relatively high (well above non-diabetic levels). Interestingly, it returned the resorption and formation markers to similar levels as those seen in non-T1D control mice. This apparent return after 4 weeks of CSII translated to a partial rescue of the structural strength of the femur mid-shaft. Delayed CSII also increased Tb.Th to levels seen in non-T1D controls but did not fully restore BV/TV. The use of exogenous insulin should be considered in pre-clinical studies investigating the effect of T1D on bone as insulin therapy maintains bone structure without necessarily lowering glucose below diabetic levels.

Behavioral, Anatomical and Heritable Convergence of Affect and Cognition in Superior Frontal Cortex

Cognitive abilities and affective experience are key human traits that are interrelated in behavior and brain. Individual variation of cognitive and affective traits, as well as brain structure, has been shown to partly underlie genetic effects. However, to what extent affect and cognition have a shared genetic relationship with local brain structure is incompletely understood. Here we studied phenotypic and genetic correlations of cognitive and affective traits in behavior and brain structure (cortical thickness, surface area and subcortical volumes) in the pedigree-based Human Connectome Project sample (N = 1091). Both cognitive and affective trait scores were highly heritable and showed significant phenotypic correlation on the behavioral level. Cortical thickness in the left superior frontal cortex showed a phenotypic association with both affect and cognition. Decomposing the phenotypic correlations into genetic and environmental components showed that the associations were accounted for by shared genetic effects between the traits. Quantitative functional decoding of the left superior frontal cortex further indicated that this region is associated with cognitive and emotional functioning. This study provides a multi-level approach to study the association between affect and cognition and suggests a convergence of both in superior frontal cortical thickness.

Lower gestational age is associated with lower cortical volume and cognitive and educational performance in adolescence

BACKGROUND

Gestational age (GA) is associated with later cognition and behavior. However, it is unclear how specific cognitive domains and brain structural development varies with the stepwise change of gestational duration.

METHODS

This large-scale longitudinal cohort study analyzed 11,878 early adolescents' brain volume maps at 9-10 years (baseline) and 5685 at 11-12 years (a 2-year follow-up) from the Adolescent Brain Cognitive Development (ABCD) study. According to gestational age, adolescents were divided into five categorical groups: ≤ 33 weeks, 34-35 weeks, 36 weeks, 37-39 weeks, and ≥ 40 weeks. The NIH Toolbox was used to estimate neurocognitive performance, including crystallized and fluid intelligence, which was measured for 11,878 adolescents at baseline with crystallized intelligence and relevant subscales obtained at 2-year follow-up (with participant numbers ranging from 6185 to 6310 depending on the cognitive domain). An additional large population-based cohort of 618,070 middle adolescents at ninth-grade (15-16 years) from the Danish national register was utilized to validate the association between gestational age and academic achievements. A linear mixed model was used to examine the group differences between gestational age and neurocognitive performance, school achievements, and grey matter volume. A mediation analysis was performed to examine whether brain structural volumes mediated the association between GA and neurocognition, followed with a longitudinal analysis to track the changes.

RESULTS

Significant group differences were found in all neurocognitive scores, school achievements, and twenty-five cortical regional volumes (P < 0.05, Bonferroni corrected). Specifically, lower gestational ages were associated with graded lower cognition and school achievements and with smaller brain volumes of the fronto-parieto-temporal, fusiform, cingulate, insula, postcentral, hippocampal, thalamic, and pallidal regions. These lower brain volumes mediated the association between gestational age and cognitive function (P = 1 × 10^-8, β = 0.017, 95% CI: 0.007-0.028). Longitudinal analysis showed that compared to full term adolescents, preterm adolescents still had smaller brain volumes and crystallized intelligence scores at 11-12 years.

CONCLUSIONS

These results emphasize the relationships between gestational age at birth and adolescents' lower brain volume, and lower cognitive and educational performance, measured many years later when 9-10 and 11-12 years old. The study indicates the importance of early screening and close follow-up for neurocognitive and behavioral development for children and adolescents born with gestational ages that are even a little lower than full term.

Application of a diathesis-stress model to the interplay of cortical structural development and emerging depression in youth

Cross-sectional studies in adults have long identified differences in cortical structure in adults with depression compared to healthy adults, with most studies identifying reductions in grey matter volume, cortical thickness, and surface area in primarily frontal cortical regions including the OFC, ACC, and variable sub-regions of the PFC. However, when, why, and for whom these neural correlates of depression emerge remains poorly understood, necessitating developmental study of associations between depression and cortical structure. We systematically reviewed studies examining these associations in child/adolescent samples, and applied a developmentally-focused diathesis-stress model to understand the impacts of depressogenic risk-factors and stressors on the development of structural neural correlates of depression. Cross-sectional findings in youth are generally similar to those found in adults, but vary in magnitude and direction of effects. Preliminary evidence suggests that age, sex, severity, and comorbidity moderate these associations. Longitudinal studies show depression prospectively predicting cortical structure and structure predicting emerging depression. Consistent with a diathesis-stress model, associations have been noted between risk-factors for depression (e.g., genetic risk, family risk) and environmental stressors (e.g., early life stress) and structural neural correlates. Further investigation of these associations across development with attention to vulnerability factors and stressors is indicated.

Severe nausea and vomiting in pregnancy: psychiatric and cognitive problems and brain structure in children

BACKGROUND

Two studies have suggested that severe prolonged nausea and vomiting during pregnancy is associated with emotional and behavioral problems in offspring, with smaller sample size and short-term follow-up. Moreover, little information is available on the role of the brain structure in the associations.

METHODS

In a US-based cohort, the association was investigated between severe prolonged nausea and vomiting in pregnancy (extending after the second trimester and termed SNVP), psychiatric and cognitive problems, and brain morphology, from the Adolescent Brain Cognitive Development (ABCD) study, from 10,710 children aged 9-11 years. We validated the emotional including psychiatric findings using the Danish National Cohort Study with 2,092,897 participants.

RESULTS

SNVP was significantly associated with emotional and psychiatric problems (t = 8.89, Cohen's d = 0.172, p = 6.9 × 10-19) and reduced global cognitive performance (t = - 4.34, d = - 0.085, p = 1.4 × 10-5) in children. SNVP was associated with low cortical area and volume, especially in the cingulate cortex, precuneus, and superior medial prefrontal cortex. These lower cortical areas and volumes significantly mediated the relation between SNVP and the psychiatric and cognitive problems in children. In the Danish National Cohort, severe nausea and vomiting in pregnancy were significantly associated with increased risks of behavioral and emotional disorders in children (hazard ratio, 1.24; 95% confidence interval, 1.16-1.33).

CONCLUSIONS

SNVP is strongly associated with psychiatric and cognitive problems in children, with mediation by brain structure. These associations highlight the clinical importance and potential benefits of the treatment of SNVP, which could reduce the risk of psychiatric disorder in the next generation.

Sex effects on cortical morphological networks in healthy young adults

Understanding sex-related differences across the human cerebral cortex is an important step in elucidating the basis of psychological, behavioural and clinical differences between the sexes. Prior structural neuroimaging studies primarily focused on regional sex differences using univariate analyses. Here we focus on sex differences in cortical morphological networks (CMNs) derived using multivariate modelling of regional cortical measures of volume and surface from high-quality structural MRI scans from healthy participants in the Human Connectome Project (HCP) (n = 1,063) and the Southwest University Longitudinal Imaging Multimodal (SLIM) study (n = 549). The functional relevance of the CMNs was inferred using the NeuroSynth decoding function. Sex differences were widespread but not uniform. In general, females had higher volume, thickness and cortical folding in networks that involve prefrontal (both ventral and dorsal regions including the anterior cingulate) and parietal regions while males had higher volume, thickness and cortical folding in networks that primarily include temporal and posterior cortical regions. CMN loading coefficients were used as input features to linear discriminant analyses that were performed separately in the HCP and SLIM; sex was predicted with a high degree of accuracy (81%-85%) across datasets. The availability of behavioral data in the HCP enabled us to show that male-biased surface-based CMNs were associated with externalizing behaviors. These results extend previous literature on regional sex-differences by identifying CMNs that can reliably predict sex, are relevant to the expression of psychopathology and provide the foundation for the future investigation of their functional significance in clinical populations.

Vulnerability to mood degradation during sleep deprivation is influenced by white-matter compactness of the triple-network model

Sleep deprivation (SD) is often associated with significant shifts in mood state relative to baseline functioning. Prior work suggests that there are consistent trait-like differences among individuals in the degree to which their mood and performances are affected by sleep loss. The goal of this study was to determine the extent to which trait-like individual differences in vulnerability/resistance to mood degradation during a night of SD are dependent upon region-specific white and grey matter (WM/GM) characteristics of a triple-network model, including the default-mode network (DMN), control-execution network (CEN) and salience network (SN). Diffusion-weighted and anatomical brain data were collected from 45 healthy individuals several days prior to a 28-h overnight SD protocol. During SD, a visual analog mood scale was administered every hour from 19:15 (time point1; TP1) to 11:15 (TP17) the following morning to measure two positive and six negative mood states. Four core regions within the DMN, five within the CEN, and seven within the SN were used as regions of interest (ROIs). An index of mood resistance (IMR) was defined as the averaged differences between positive and negative mood states over 12 TPs (TP5 to TP16) relative to baseline (TP1 to TP4). For each ROI, characteristics of WM - quantitative anisotropy (QA) and mean curvature index (WM-MCI), and GM - cortical volume (CV) and GM-MCI were estimated, and used to predict IMR. WM characteristics, particularly QA, of all of regions within the DMN, and most of the regions within the CEN and SN predicted IMR during SD. In contrast, most ROIs did not show significant association between IMR and any of the GM characteristics (CV and MCI) or WM MCI. Our findings suggest that greater resilience to mood degradation induced by total SD appears to be associated with more compact axonal pathways within the DMN, CEN and SN.

Unravelling the liver-brain connection: A two-sample Mendelian randomization study investigating the causal relationship between NAFLD and cortical structure

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) has been linked to cognitive decline and neuropsychiatric conditions, implying a potential connection between NAFLD and brain health. However, the causal association between NAFLD and cortical changes remains uncertain. This study aimed to examine the causal impact of NAFLD on cortical structures using a two-sample Mendelian randomization (MR) approach.

METHODS

Summary data from genome-wide association studies (GWAS) for NAFLD were gathered from large-scale cohorts. Surface area (SA) and cortical thickness (TH) measurements were derived from Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium magnetic resonance imaging (MRI) data of 33,992 participants. Inverse-variance weighted (IVW) served as the primary method. Additional sensitivity analyses, including MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO), MR-Egger, and weighted median procedures, were conducted to detect heterogeneity and pleiotropy.

RESULTS

Our MR analysis revealed that NAFLD led to notable alterations in cortical structures, particularly in the pars orbitalis gyrus. Specifically, genetically predicted NAFLD was linked to a decrease in TH (β = -0.008 mm, 95 % CI: -0.013 mm to -0.004 mm, P = 3.00 × 10-4) within this region. No significant heterogeneity and pleiotropy were identified.

CONCLUSION

The two-sample MR study supports the existence of a liver-brain axis by demonstrating a causal association between NAFLD and changes in cortical structures. These findings emphasize the potential association between NAFLD and brain health, which could have implications for preventing and treating cognitive deficits and neuropsychiatric conditions in patients with NAFLD.

Bidirectional two-sample mendelian randomization analysis identifies causal associations of MRI-based cortical thickness and surface area relation to NAFLD

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) patients have exhibited extra-hepatic neurological changes, but the causes and mechanisms remain unclear. This study investigates the causal effect of NAFLD on cortical structure through bidirectional two-sample Mendelian randomization analysis.

METHODS

Genetic data from 778,614 European individuals across four NAFLD studies were used to determine genetically predicted NAFLD. Abdominal MRI scans from 32,860 UK Biobank participants were utilized to evaluate genetically predicted liver fat and volume. Data from the ENIGMA Consortium, comprising 51,665 patients, were used to evaluate the associations between genetic susceptibility, NAFLD risk, liver fat, liver volume, and alterations in cortical thickness (TH) and surface area (SA). Inverse-variance weighted (IVW) estimation, Cochran Q, and MR-Egger were employed to assess heterogeneity and pleiotropy.

RESULTS

Overall, NAFLD did not significantly affect cortical SA or TH. However, potential associations were noted under global weighting, relating heightened NAFLD risk to reduced parahippocampal SA and decreased cortical TH in the caudal middle frontal, cuneus, lingual, and parstriangularis regions. Liver fat and volume also influenced the cortical structure of certain regions, although no Bonferroni-adjusted p-values reached significance. Two-step MR analysis revealed that liver fat, AST, and LDL levels mediated the impact of NAFLD on cortical structure. Multivariable MR analysis suggested that the impact of NAFLD on the cortical TH of lingual and parstriangularis was independent of BMI, obesity, hyperlipidemia, and diabetes.

CONCLUSION

This study provides evidence that NAFLD causally influences the cortical structure of the brain, suggesting the existence of a liver-brain axis in the development of NAFLD.

Associations of polygenic risk scores differentiating attention-deficit hyperactivity disorder from autism spectrum disorder with cognitive and cortical alterations in Schizophrenia patients

Schizophrenia (SCZ) is a clinically and genetically heterogeneous disorder that shares genetic factors with autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). A genome-wide association study (GWAS) differentiating ADHD from ASD was performed recently. In this study, we investigated whether polygenic risk scores (PRSs) differentiating ASD from ADHD are associated with cognitive impairments and alterations in cortical structures in SCZ patients. Based on the GWAS data (9,315 ASD and 11,964 ADHD patients), PRSs differentiating ADHD from ASD (indicating a greater risk of ADHD and a lower risk of ASD) were calculated for SCZ patients (n = 168). Cognitive performance, including verbal comprehension (VC), perceptual organization (PO), working memory (WM), and processing speed (PS), was assessed using the WAIS-III (n = 145). The surface areas and cortical thicknesses of 34 bilateral brain regions were extracted using FreeSurfer (n = 126). We examined the associations of these PRSs with cognitive performance and cortical structures in SCZ patients. Among the four cognitive domains, a higher PRS, indicating a greater risk of ADHD, was associated with impaired WM in SCZ patients (beta=-0.21, p = 0.012). A lower PRS, indicating a greater risk of ASD, was associated with decreased surface areas of the left medial orbitofrontal (beta = 0.21, p = 8.29 × 10- 4), left entorhinal (beta = 0.21, p = 0.025), left postcentral (beta = 0.18, p = 7.52 × 10- 3), right fusiform (beta = 0.17, p = 6.64 × 10- 3), and left fusiform cortices (beta = 0.17, p = 7.77 × 10- 3) in SCZ patients. A higher PRS, indicating a greater risk of ADHD, was associated with decreased cortical thickness in the bilateral transverse temporal regions (left, beta=-0.17, p = 0.039; right, beta=-0.17, p = 0.045). Our study revealed a relationship between genetic factors that differentiate ADHD patients from ASD patients and both cortical structure and cognitive performance in SCZ patients. These findings suggest that the heterogeneity of SCZ might be partly derived from genetic factors related to neurodevelopmental and psychiatric disorders other than SCZ.

Dynamic reorganization of cortical structure in multi-domain regions after capsular and pontine stroke

Subcortical stroke may cause widespread structural changes to the cerebral cortex in multiple domains; however, the details of this process remain unclear. In this prospective observational study, we acquired two datasets to investigate the effect of lesion location on cortical structure. One was cross-sectional, comprising 269 patients with chronic stroke, either capsular stroke (CS) or pontine stroke (PS), and the other was longitudinal, comprising 119 patients with CS or PS. In the chronic-stage data, both CS and PS exhibited reduced cortical thickness in the precentral gyrus and increased cortical thickness and area in the frontal, temporal, occipital and insular cortices. Cortical thicknesses were correlated with motor outcomes in the precentral and lingual gyri, and early impairment of the corticospinal tract was associated with cortical thickness in the middle frontal gyrus. In the longitudinal dataset, CS showed gradually decreasing cortical thickness in the precentral gyrus, and both CS and PS showed gradually increasing cortical thickness and area in regions with significant structural reorganization. Subcortical stroke can therefore cause complex cortical structural changes in multi-domain regions involved in motor, primary and higher cognitive areas and have different evolution patterns depending on the subcortical level of the lesion affecting the motor pathways.

Investigation of causal associations between cerebral cortical structure and Barrett's esophagus: insights from Mendelian randomization and meta-analysis

Background

Barrett's esophagus (BE) is a precancerous condition often associated with esophageal adenocarcinoma, influenced by both genetic and environmental factors. However, there is controversy regarding the causal relationship between cerebral cortical structures and BE, with recent studies suggesting a potential neurobiological component to its multifactorial etiology. This study aims to clarify this relationship by utilizing Mendelian randomization (MR) analysis to investigate the potential causal effects of cortical structure variations on BE risk.

Methods

Comprehensive MR analyses was utilized to examine the potential causal associations between variations in cerebral cortical structure, specifically cortical thickness (TH) and surface area (SA), and the susceptibility to developing BE. Data were obtained from two genome-wide association study (GWAS) repositories. Instrumental variables were chosen using rigorous criteria, and the analysis was enhanced by employing inverse variance weighting and three additional methods, as well as conducting sensitivity analyses to evaluate the reliability of our results. In the validation stage, we used meta-analysis to combine the effect sizes to obtain robust causal relationships.

Results

Initial MR findings indicated significant associations between cortical structural features in several specific regions and BE. The meta-analysis confirmed a consistent negative correlation with BE for increased cortical TH in the supramarginal and pars orbitalis regions, and a positive correlation for increased SA in the middle temporal region. Additional initial positive findings did not maintain significance in the meta-analysis, suggesting the need for cautious interpretation and further validation.

Conclusions

Our study underscores the gastrointestinal-brain axis hypothesis, identifying cortical structure integrity as a potential modifier of BE risk, highlighting the importance of considering neurobiological factors in its pathogenesis. Understanding these associations could have significant clinical implications, particularly in developing targeted interventions to modify BE risk based on neurological pathways.

Spatial patterns of individual morphological deformation in schizophrenia: Putative cortical compensatory of unaffected sibling

BACKGROUND

Neuroimaging advancements have revealed morphological deformation across various indicators, illuminating the neuropathological origins of schizophrenia. However, consolidating the findings across indicators and assessing regional global deformation at individual-level poses a significant challenge.

METHODS

We propose individual morphological deformation index (IMDI) as potential biomarker for schizophrenia leveraging a distance algorithm that incorporates three key indicators (cortical thickness, gyrification, and volume), and applied it for 199 schizophrenia patients, 218 healthy controls, and 47 unaffected siblings. Additionally, we studied the relationships between polygenic risks, symptomology, cognition, social functioning and regional IMDI.

RESULTS

Our findings reveal significantly higher IMDI in specific brain regions (bilateral pars opercularis, lateral orbitofrontal, left superior parietal, right pars orbitalis, and superior temporal) in patients, demonstrating two distinct spatial patterns linked to either isolated indicator reduction or concurrent declines across multiple indicators. Notably, unaffected siblings exhibited higher IMDI than controls, primarily due to cortical volume expansion in the right pars opercularis and superior temporal regions. Patients with higher IMDI had more severe positive symptoms, impaired cognition, reduced social functioning and selfcare ability. Participants with higher polygenic scores showed higher IMDI specifically in left caudal middle frontal regions.

CONCLUSIONS

The proposed IMDI biomarker offers an objective, interpretable way to quantify global regional deformation and integrate disparate neuroimaging indicators. Our results indicate that schizophrenia-related cortical deformations encompass sensorimotor, attention, default mode, and frontoparietal networks, exhibiting at least two spatial patterns. Moreover, siblings may exhibit compensation in cortical volume. These insights offer a novel perspective on the neuroanatomical underpinnings of schizophrenia.

Heschl's gyrus and the temporal pole: The cortical lateralization of language

The left lateralization of language has been attributed to hemispheric specialization for processing rapidly changing information. While interhemispheric differences in auditory cortex organization support this view, the macrostructure of the entire cerebral cortex has not been thoroughly examined from this perspective. This study investigated hemispheric asymmetries in cortical surface area and thickness and their relationship to pronunciation scores from oral reading using the Human Connectome Project Young Adult dataset (N=1113). Heschl's gyrus had the most left-lateralized surface area, while the temporal pole showed the strongest right-lateralization in thickness. These areas correspond to the core components of speech: sound and meaning. Notably, their structural features were the only ones also yielding a significant correlation with pronunciation scores. Additionally, Broca's area's posterior region (pars opercularis), involved in articulatory phonological processing, showed leftward lateralization, contrasting with the right-lateralized anterior portions. Left-hemisphere language areas were largely thinner and more extended than their right-sided homologs with a larger white-to-gray matter ratio. Cortical thickness was inversely related to surface area. The lateralization of auditory-related language areas and their structure's correlation with pronunciation in oral reading supports a genetically based auditory foundation for language. A thinner, more efficient cortex with larger surface areas and increased myelination likely underlies the left-hemispheric dominance of language. Thinner, more extended brain areas have been linked to more myelination and wider cortical columns and intercolumnar space. This provides the potential for a fast network of interconnected, discrete information units able to support language's demands of rapid categorical processing.

Cortical structure reorganization and correlation with attention deficit in subcortical stroke: An underlying pattern analysis

BACKGROUND

Subcortical stroke may significantly alter the cerebral cortical structure and affect attention function, but the details of this process remain unclear. The study aimed to investigate the neural substrates underlying attention impairment in patients with subcortical stroke.

MATERIALS AND METHODS

In this prospective observational study, two distinct datasets were acquired to identify imaging biomarkers underlying attention deficit. The first dataset consisted of 86 patients with subcortical stroke, providing a cross-sectional perspective, whereas the second comprised 108 patients with stroke, offering longitudinal insights. All statistical analyses were subjected to false discovery rate correction upon P < 0.05.

RESULTS

In the chronic-stage data, the stroke group exhibited significantly poorer attention function compared with that of the control group. The cortical structure analysis showed that patients with stroke exhibited decreased cortical thickness of the precentral gyrus and surface area of the cuneus, along with an increase in various frontal, occipital, and parietal cortices regions. The declined attention function positively correlated with the superior frontal gyrus cortical thickness and supramarginal gyrus surface area. In the longitudinal dataset, patients with stroke showed gradually increasing cortical thickness and surface area within regions of obvious structural reorganization. Furthermore, deficient attention positively correlated with supramarginal gyrus surface area both at the subacute and chronic stages post-stroke.

CONCLUSIONS

Subcortical stroke can elicit dynamic reorganization of cortical areas associated with attention impairment. Moreover, the altered surface area of the supramarginal gyrus is a potential neuroimaging biomarker for attention deficits.

Joint modeling of human cortical structure: Genetic correlation network and composite-trait genetic correlation

Heritability and genetic covariance/correlation quantify the marginal and shared genetic effects across traits. They offer insights on the genetic architecture of complex traits and diseases. To explore how genetic variations contribute to brain function variations, we estimated heritability and genetic correlation across cortical thickness, surface area, and volume of 33 anatomically predefined regions in left and right hemispheres, using summary statistics of genome-wide association analyses of 31,968 participants in the UK Biobank. To characterize the relationships between these regions of interest, we constructed a genetic network for these regions using recursive two-way cut-offs in similarity matrices defined by genetic correlations. The inferred genetic network matches the brain lobe mapping more closely than the network inferred from phenotypic similarities. We further studied the associations between the genetic network for brain regions and 30 complex traits through a novel composite-linkage disequilibrium score regression method. We identified seven significant pairs, which offer insights on the genetic basis for regions of interest mediated by cortical measures.

Causal relationship between multiple sclerosis and cortical structure: a Mendelian randomization study

BACKGROUND

Observational studies have suggested an association between multiple sclerosis (MS) and cortical structure, but the results have been inconsistent.

OBJECTIVE

We used two-sample Mendelian randomization (MR) to assess the causal relationship between MS and cortical structure.

METHODS

MS data as the exposure trait, including 14,498 cases and 24,091 controls, were obtained from the International Multiple Sclerosis Genetics Consortium. Genome-wide association study (GWAS) data for cortical surface area (SAw/nw) and thickness (THw/nw) in 51,665 individuals of European ancestry were obtained from the ENIGMA Consortium. The inverse-variance weighted (IVW) method was used as the primary analysis for MR. Sensitivity analyses were conducted to evaluate heterogeneity and pleiotropy. Enrichment analysis was performed on MR analyses filtered by sensitivity analysis.

RESULTS

After IVW and sensitivity analysis filtering, only six surviving MR results provided suggestive evidence supporting a causal relationship between MS and cortical structure, including lingual SAw (p = .0342, beta (se) = 5.7127 (2.6969)), parahippocampal SAw (p = .0224, beta (se) = 1.5577 (0.6822)), rostral middle frontal SAw (p = .0154, beta (se) = - 9.0301 (3.7281)), cuneus THw (p = .0418, beta (se) =  - 0.0020 (0.0010)), lateral orbitofrontal THw (p = .0281, beta (se) = 0.0025 (0.0010)), and lateral orbitofrontal THnw (p = .0417, beta (se) = 0.0029 (0.0014)). Enrichment analysis suggested that leukocyte cell-related pathways, JAK-STAT signaling pathway, NF-kappa B signaling pathway, cytokine-cytokine receptor interaction, and prolactin signaling pathway may be involved in the effect of MS on cortical morphology.

CONCLUSION

Our results provide evidence supporting a causal relationship between MS and cortical structure. Enrichment analysis suggests that the pathways mediating brain morphology abnormalities in MS patients are mainly related to immune and inflammation-driven pathways.

Cortical structure and the risk of amyotrophic lateral sclerosis: A bidirectional Mendelian randomization study

BACKGROUND

Current observational studies indicate progressive brain atrophy is closely associated with the clinical feature of amyotrophic lateral sclerosis. However, it is unclear whether the changes in cortical structure are the cause or result of ALS. Our study aimed to investigate the causal relationship between cortical structure and ALS risk using a bidirectional two-sample MR study.

METHODS

We collected publicly available genome-wide association studies' summary statistics for cortical structure from UK Biobank and ENIGMA consortium (n = 33,992) and ALS from the Project MinE (n = 138,086). We used the inverse variance weighted method (IVW) as primary analysis in order to evaluate the causal effects. In addition, the weighted median and MR Egger methods were performed to ensure the robustness and reliability of the IVW results.

RESULTS

We found the decreased surface of the paracentral lobule and thickness of the frontal pole and middle temporal lobe were suggestively associated with an increased risk of ALS as well as the increased surface of medial orbitofrontal and middle temporal lobe. In another aspect, the causalities between the susceptibility to ALS and the volume of the transverse temporal gyrus and superior temporal gyrus were negative. Besides, the susceptibility to ALS might also contribute to an increased thickness of the postcentral gyrus and superior parietal gyrus.

CONCLUSION

In this two-sample MR analysis, we observed that multiple cortical brain regions are associated with a higher ALS risk. Further research into the underlying mechanisms is required to back up our findings.

The causal effects between selenium levels and the brain cortical structure: A two-sample Mendelian randomization study

Extensive research has demonstrated the critical role of selenium (Se) and selenoproteins in brain function and cognition. However, the impact of Se on brain cortical structure remains enigmatic. Therefore, this study used Mendelian randomization (MR) analysis to investigate the causal effect between Se levels and brain cortical structure.

METHODS

This study utilizes 11 genetic variants associated with Se level variations, extracted from a large-scale genome-wide association study (GWAS) encompassed circulating Se levels (n = 5477) and toenail Se levels (n = 4162) in the European population. Outcome data were sourced from the summary statistics of the ENIGMA Consortium, comprising GWAS data from 51,666 individuals. The variables include cortical surface area (SA), thickness (TH) at the global level, and 34 functional cortical regions evaluated by magnetic resonance imaging. The inverse-variance-weighted method was used as the primary estimate. Additionally, sensitivity analyses were conducted to detect potential violations of assumptions underlying MR.

RESULTS

At the global level, Se levels were not correlated with SA but showed a significant negative correlation with TH (β = -0.00485 mm, SE = 0.00192, p = .0115). Heterogeneity was observed across different brain regions, with positive correlations found between Se levels and the TH of the parahippocampal gyrus, superior frontal gyrus, and frontal pole, whereas negative correlations were found with the TH of the inferior parietal lobe and middle temporal lobe. Regarding SA, Se levels exhibit positive correlations with the pars triangularis, caudal anterior cingulate, inferior parietal lobe, and banks of the superior temporal sulcus. Conversely, negative correlations were observed with the medial orbitofrontal cortex, posterior cingulate gyrus, insula, and the middle, superior, and transverse gyrus of the temporal lobe. No pleiotropy was detected.

RESULTS

This MR study indicated that Se levels causally influence the brain cortical structure.