Mapping developmental regionalization and patterns of cortical surface area from 29 post-menstrual weeks to 2 years of age.

Surface area of the human cerebral cortex expands extremely dynamically and regionally heterogeneously from the third trimester of pregnancy to 2 y of age, reflecting the spatial heterogeneity of the underlying microstructural and functional development of the cerebral cortex. However, little is known about the developmental patterns and regionalization of cortical surface area during this critical stage, due to the lack of high-quality imaging data and accurate computational tools for pediatric brain MRI data. To fill this critical knowledge gap, by leveraging 1,037 high-quality MRI scans with the age between 29 post-menstrual weeks and 24 mo from 735 pediatric subjects in two complementary datasets, i.e., the Baby Connectome Project (BCP) and the developing Human Connectome Project (dHCP), and state-of-the-art dedicated image-processing tools, we unprecedentedly parcellate the cerebral cortex into a set of distinct subdivisions purely according to the developmental patterns of the cortical surface. Our discovered developmentally distinct subdivisions correspond well to structurally and functionally meaningful regions and reveal spatially contiguous, hierarchical, and bilaterally symmetric patterns of early cortical surface expansion. We also show that high-order association subdivisions, where cortical folds emerge later during prenatal stages, undergo more dramatic cortical surface expansion during infancy, compared with the central regions, especially the sensorimotor and insula cortices, thus forming a distinct central-pole division in early cortical surface expansion. These results provide an important reference for exploring and understanding dynamic early brain development in health and disease.

Sight restoration in congenitally blind humans does not restore visual brain structure.

It is unknown whether impaired brain structure after congenital blindness is reversible if sight is restored later in life. Using structural magnetic resonance imaging, visual cortical surface area and cortical thickness were assessed in a large group of 21 sight-recovery individuals who had been born blind and who months or years later gained sight through cataract removal surgery. As control groups, we included 27 normally sighted individuals, 10 individuals with permanent congenital blindness, and 11 sight-recovery individuals with a late onset of cataracts. Congenital cataract-reversal individuals had a lower visual cortical surface area and a higher visual cortical thickness than normally sighted controls. These results corresponded to those of congenitally permanently blind individuals suggesting that impaired brain structure did not recover. Crucially, structural brain alterations in congenital-cataract reversal individuals were associated with a lower post-surgery visual acuity. No significant changes in visual cortex structure were observed in sight-recovery individuals with late onset cataracts. The results demonstrate that impaired structural brain development due to visual deprivation from birth is not fully reversible and limits functional recovery. Additionally, they highlight the crucial importance of prevention measures in the context of other types of aberrant childhood environments including low socioeconomic status and adversity.

Gyral peaks and patterns in human brains.

Cortical folding patterns are related to brain function, cognition, and behavior. Since the relationship has not been fully explained on a coarse scale, many efforts have been devoted to the identification of finer grained cortical landmarks, such as sulcal pits and gyral peaks, which were found to remain invariant across subjects and ages and the invariance may be related to gene mediated proto-map. However, gyral peaks were only investigated on macaque monkey brains, but not on human brains where the investigation is challenged due to high inter-individual variabilities. To this end, in this work, we successfully identified 96 gyral peaks both on the left and right hemispheres of human brains, respectively. These peaks are spatially consistent across individuals. Higher or sharper peaks are more consistent across subjects. Both structural and functional graph metrics of peaks are significantly different from other cortical regions, and more importantly, these nodal graph metrics are anti-correlated with the spatial consistency metrics within peaks. In addition, the distribution of peaks and various cortical anatomical, structural/functional connective features show hemispheric symmetry. These findings provide new clues to understanding the cortical landmarks, as well as their relationship with brain functions, cognition, behavior in both healthy and aberrant brains.

Relationship between autism and brain cortex surface area: genetic correlation and a two-sample Mendelian randomization study.

BACKGROUND: Alterations in surface area (SA) in specific regions of the cortex have been reported in many individuals with autism spectrum disorder (ASD), however, the genetic background between ASD and SA is still unclear. This study estimated the genetic correlation and causal effect of ASD and cortical SA. METHODS: Summarized data of genome-wide association studies (GWAS) were separately downloaded from the Psychiatric Genomics Consortium (18,381 cases of ASD, and 27,969 controls) and the Enhancing Neuroimaging Genetics through Meta-Analysis Consortium (33,992 participants of Europeans). We used Linkage disequilibrium score regression (LDSC) and Heritability Estimation from Summary Statistics (HESS) to calculate the heritability of each trait. As for the genetic correlation between ASD and SA, LDSC was used for global correlation and HESS was used to examine the local genetic covariance further. We used three Mendelian randomization (MR) methods, Inverse-variance weighted, MR-Egger, and weighted median to estimate the causal relationship. RESULTS: LDSC observed a nominal significant genetic correlation (rg = 0.1229, P-value = 0.0346) between ASD and SA of the rostral anterior cingulate gyrus whereas analysis through HESS did not reveal any significant loci having genetic covariance. Based on MR results, statistically meaningful estimations were found in the following areas, postcentral cortex (ß (SE) = 21.82 (7.84) mm, 95% CI: 6.46 to 37.19 mm, PIVW = 5.38 × 10- 3, PFDR = 3.09 × 10- 2), posterior cingulate gyrus (ß (SE) = 6.23 (2.69) mm, 95% CI: 0.96 to 11.49 mm, PIVW = 2.05 × 10- 2, PFDR = 4.26 × 10- 2), supramarginal gyrus (ß (SE) = 19.25 (8.43) mm, 95% CI: 29.29 to 35.77 mm, PIVW = 2.24 × 10- 2, PFDR = 4.31 × 10- 2). CONCLUSION: Our results provided genetic evidence to support the opinion that individuals with ASD tend to develop differences in cortical SA of special areas. The findings contributed to understanding the genetic relationship between ASD and cortical SA.

Identifying and reverting the adverse effects of white matter hyperintensities on cortical surface analyses.

The Human Connectome Project (HCP)-style surface-based brain MRI analysis is a powerful technique that allows precise mapping of the cerebral cortex. However, the strength of its surface-based analysis has not yet been tested in the older population that often presents with white matter hyperintensities (WMHs) on T2-weighted (T2w) MRI (hypointensities on T1w MRI). We investigated T1-weighted (T1w) and T2w structural MRI in 43 healthy middle-aged to old participants. Juxtacortical WMHs were often misclassified by the default HCP pipeline as parts of the gray matter in T1w MRI, leading to incorrect estimation of the cortical surfaces and cortical metrics. To revert the adverse effects of juxtacortical WMHs, we incorporated the Brain Intensity AbNormality Classification Algorithm into the HCP pipeline (proposed pipeline). Blinded radiologists performed stereological quality control (QC) and found a decrease in the estimation errors in the proposed pipeline. The superior performance of the proposed pipeline was confirmed using an originally-developed automated surface QC based on a large database. Here we showed the detrimental effects of juxtacortical WMHs for estimating cortical surfaces and related metrics and proposed a possible solution for this problem. The present knowledge and methodology should help researchers identify adequate cortical surface biomarkers for aging and age-related neuropsychiatric disorders.

Robust hierarchically organized whole-brain patterns of dysconnectivity in schizophrenia spectrum disorders observed after personalized intrinsic network topography.

BACKGROUND: Spatial patterns of brain functional connectivity can vary substantially at the individual level. Applying cortical surface-based approaches with individualized rather than group templates may accelerate the discovery of biological markers related to psychiatric disorders. We investigated cortico-subcortical networks from multi-cohort data in people with schizophrenia spectrum disorders (SSDs) and healthy controls (HC) using individualized connectivity profiles. METHODS: We utilized resting-state and anatomical MRI data from n = 406 participants (n = 203 SSD, n = 203 HC) from four cohorts. Functional timeseries were extracted from previously defined intrinsic network subregions of the striatum, thalamus, and cerebellum as well as 80 cortical regions of interest, representing six intrinsic networks using (1) volume-based approaches, (2) a surface-based group atlas approaches, and (3) Personalized Intrinsic Network Topography (PINT). RESULTS: The correlations between all cortical networks and the expected subregions of the striatum, cerebellum, and thalamus were increased using a surface-based approach (Cohen's D volume vs. surface 0.27-1.00, all p < 10-6 ) and further increased after PINT (Cohen's D surface vs. PINT 0.18-0.96, all p < 10-4 ). In SSD versus HC comparisons, we observed robust patterns of dysconnectivity that were strengthened using a surface-based approach and PINT (Number of differing pairwise-correlations: volume: 404, surface: 570, PINT: 628, FDR corrected). CONCLUSION: Surface-based and individualized approaches can more sensitively delineate cortical network dysconnectivity differences in people with SSDs. These robust patterns of dysconnectivity were visibly organized in accordance with the cortical hierarchy, as predicted by computational models.

Neurodevelopmental risk and adaptation as a model for comorbidity among internalizing and externalizing disorders: genomics and cell-specific expression enriched morphometric study.

BACKGROUND: Comorbidity is the rule rather than the exception for childhood and adolescent onset mental disorders, but we cannot predict its occurrence and do not know the neural mechanisms underlying comorbidity. We investigate if the effects of comorbid internalizing and externalizing disorders on anatomical differences represent a simple aggregate of the effects on each disorder and if these comorbidity-associated cortical surface differences relate to a distinct genetic underpinning. METHODS: We studied the cortical surface area (SA) and thickness (CT) of 11,878 preadolescents (9-10 years) from the Adolescent Brain and Cognitive Development Study. Linear mixed models were implemented in comparative and association analyses among internalizing (dysthymia, major depressive disorder, disruptive mood dysregulation disorder, agoraphobia, panic disorder, specific phobia, separation anxiety disorder, social anxiety disorder, generalized anxiety disorder, post-traumatic stress disorder), externalizing (attention-deficit/hyperactivity disorder, oppositional defiant disorder, conduct disorder) diagnostic groups, a group with comorbidity of the two and a healthy control group. Genome-wide association analysis (GWAS) and cell type specificity analysis were performed on 4468 unrelated European participants from this cohort. RESULTS: Smaller cortical surface area but higher thickness was noted across patient groups when compared to controls. Children with comorbid internalizing and externalizing disorders had more pronounced areal reduction than those without comorbidity, indicating an additive burden. In contrast, cortical thickness had a non-linear effect with comorbidity: the comorbid group had no significant CT differences, while those patient groups without comorbidity had significantly higher thickness compare to healthy controls. Distinct biological pathways were implicated in regional SA and CT differences. Specifically, CT differences were associated with immune-related processes implicating astrocytes and oligodendrocytes, while SA-related differences related mainly to inhibitory neurons. CONCLUSION: The emergence of comorbidity across distinct clusters of psychopathology is unlikely to be due to a simple additive neurobiological effect alone. Distinct developmental risk moderated by immune-related adaptation processes, with unique genetic and cell-specific factors, may contribute to underlying SA and CT differences. Children with the highest risk but lowest resilience, both captured in their developmental morphometry, may develop a comorbid illness pattern.

Influence of gonadal steroids on cortical surface area in infancy.

Sex differences in the human brain emerge as early as mid-gestation and have been linked to sex hormones, particularly testosterone. Here, we analyzed the influence of markers of early sex hormone exposure (polygenic risk score (PRS) for testosterone, salivary testosterone, number of CAG repeats, digit ratios, and PRS for estradiol) on the growth pattern of cortical surface area in a longitudinal cohort of 722 infants. We found PRS for testosterone and right-hand digit ratio to be significantly associated with surface area, but only in females. PRS for testosterone at the most stringent P value threshold was positively associated with surface area development over time. Higher right-hand digit ratio, which is indicative of low prenatal testosterone levels, was negatively related to surface area in females. The current work suggests that variation in testosterone levels during both the prenatal and postnatal period may contribute to cortical surface area development in female infants.

Cyto/myeloarchitecture of cortical gray matter and superficial white matter in early neurodevelopment: multimodal MRI study in preterm neonates.

The cerebral cortex undergoes rapid microstructural changes throughout the third trimester. Recently, there has been growing interest on imaging features that represent cyto/myeloarchitecture underlying intracortical myelination, cortical gray matter (GM), and its adjacent superficial whitematter (sWM). Using 92 magnetic resonance imaging scans from 78 preterm neonates, the current study used combined T1-weighted/T2-weighted (T1w/T2w) intensity ratio and diffusion tensor imaging (DTI) measurements, including fractional anisotropy (FA) and mean diffusivity (MD), to characterize the developing cyto/myeloarchitectural architecture. DTI metrics showed a linear trajectory: FA decreased in GM but increased in sWM with time; and MD decreased in both GM and sWM. Conversely, T1w/T2w measurements showed a distinctive parabolic trajectory, revealing additional cyto/myeloarchitectural signature inferred. Furthermore, the spatiotemporal courses were regionally heterogeneous: central, ventral, and temporal regions of GM and sWM exhibited faster T1w/T2w changes; anterior sWM areas exhibited faster FA increases; and central and cingulate areas in GM and sWM exhibited faster MD decreases. These results may explain cyto/myeloarchitectural processes, including dendritic arborization, synaptogenesis, glial proliferation, and radial glial cell organization and apoptosis. Finally, T1w/T2w values were significantly associated with 1-year language and cognitive outcome scores, while MD significantly decreased with intraventricular hemorrhage.

Impaired topological properties of cortical morphological brain networks correlate with motor symptoms in Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is characterized by loss of selectively vulnerable neurons within the basal ganglia circuit and progressive atrophy in subcortical and cortical regions. However, the impact of neurodegenerative pathology on the topological organization of cortical morphological networks has not been explored. The aims of this study were to investigate altered network patterns of covariance in cortical thickness and complexity, and to evaluate how morphological network integrity in PD is related to motor impairment. METHODS: Individual morphological networks were constructed for 50 PD patients and 46 healthy controls (HCs) by estimating interregional similarity distributions in surface-based indices. We performed graph theoretical analysis and network-based statistics to detect PD-related alterations and further examined the correlation of network metrics with clinical scores. Furthermore, support vector regression based on topological characteristics was applied to predict the severity of motor impairment in PD. RESULTS: Compared with HCs, PD patients showed lower local efficiency (p = 0.004), normalized characteristic path length (p = 0.022), and clustering coefficient (p = 0.005) for gyrification index-based morphological brain networks. Nodal topological abnormalities were mainly in the frontal, parietal and temporal regions, and impaired morphological connectivity was involved in the sensorimotor and default mode networks. The support vector regression model using network-based features allowed prediction of motor symptom severity with a correlation coefficient of 0.606. CONCLUSIONS: This study identified a disrupted topological organization of cortical morphological networks that could substantially advance our understanding of the network degeneration mechanism of PD and might offer indicators for monitoring disease progression.

Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups.

The general linear model (GLM) is a widely popular and convenient tool for estimating the functional brain response and identifying areas of significant activation during a task or stimulus. However, the classical GLM is based on a massive univariate approach that does not explicitly leverage the similarity of activation patterns among neighboring brain locations. As a result, it tends to produce noisy estimates and be underpowered to detect significant activations, particularly in individual subjects and small groups. A recently proposed alternative, a cortical surface-based spatial Bayesian GLM, leverages spatial dependencies among neighboring cortical vertices to produce more accurate estimates and areas of functional activation. The spatial Bayesian GLM can be applied to individual and group-level analysis. In this study, we assess the reliability and power of individual and group-average measures of task activation produced via the surface-based spatial Bayesian GLM. We analyze motor task data from 45 subjects in the Human Connectome Project (HCP) and HCP Retest datasets. We also extend the model to multi-run analysis and employ subject-specific cortical surfaces rather than surfaces inflated to a sphere for more accurate distance-based modeling. Results show that the surface-based spatial Bayesian GLM produces highly reliable activations in individual subjects and is powerful enough to detect trait-like functional topologies. Additionally, spatial Bayesian modeling enhances reliability of group-level analysis even in moderately sized samples (n=45). Notably, the power of the spatial Bayesian GLM to detect activations above a scientifically meaningful effect size is nearly invariant to sample size, exhibiting high power even in small samples (n=10). The spatial Bayesian GLM is computationally efficient in individuals and groups and is convenient to implement with the open-source BayesfMRI R package.

Comparing brain asymmetries independently of brain size.

Studies examining cerebral asymmetries typically divide the l-R Measure (e.g., Left-Right Volume) by the L + R Measure to obtain an Asymmetry Index (AI). However, contrary to widespread belief, such a division fails to render the AI independent from the L + R Measure and/or from total brain size. As a result, variations in brain size may bias correlation estimates with the AI or group differences in AI. We investigated how to analyze brain asymmetries in to distinguish global from regional effects, and report unbiased group differences in cerebral asymmetries in the UK Biobank (N = 40, 028). We used 306 global and regional brain measures provided by the UK Biobank. Global gray and white matter volumes were taken from Freesurfer ASEG, subcortical gray matter volumes from Freesurfer ASEG and subsegmentation, cortical gray matter volumes, mean thicknesses, and surface areas from the Destrieux atlas applied on T1-and T2-weighted images, cerebellar gray matter volumes from FAST FSL, and regional white matter volumes from Freesurfer ASEG. We analyzed the extent to which the L + R Measure, Total Cerebral Measure (TCM, e.g., Total Brain Volume), and l-R TCM predict regional asymmetries. As a case study, we assessed the consequences of omitting each of these predictors on the magnitude and significance of sex differences in asymmetries. We found that the L + R Measure, the TCM, and the l-R TCM predicted the AI of more than 89% of regions and that their relationships were generally linear. Removing any of these predictors changed the significance of sex differences in 33% of regions and the magnitude of sex differences across 13-42% of regions. Although we generally report similar sex and age effects on cerebral asymmetries to those of previous large-scale studies, properly adjusting for regional and global brain size revealed additional sex and age effects on brain asymmetry.

Proportional intracranial volume correction differentially biases behavioral predictions across neuroanatomical features, sexes, and development.

Individual differences in brain anatomy can be used to predict variations in cognitive ability. Most studies to date have focused on broad population-level trends, but the extent to which the observed predictive features are shared across sexes and age groups remains to be established. While it is standard practice to account for intracranial volume (ICV) using proportion correction in both regional and whole-brain morphometric analyses, in the context of brain-behavior predictions the possible differential impact of ICV correction on anatomical features and subgroups within the population has yet to be systematically investigated. In this work, we evaluate the effect of proportional ICV correction on sex-independent and sex-specific predictive models of individual cognitive abilities across multiple anatomical properties (surface area, gray matter volume, and cortical thickness) in healthy young adults (Human Connectome Project; n = 1013, 548 females) and typically developing children (Adolescent Brain Cognitive Development study; n = 1823, 979 females). We demonstrate that ICV correction generally reduces predictive accuracies derived from surface area and gray matter volume, while increasing predictive accuracies based on cortical thickness in both adults and children. Furthermore, the extent to which predictive models generalize across sexes and age groups depends on ICV correction: models based on surface area and gray matter volume are more generalizable without ICV correction, while models based on cortical thickness are more generalizable with ICV correction. Finally, the observed neuroanatomical features predictive of cognitive abilities are unique across age groups regardless of ICV correction, but whether they are shared or unique across sexes (within age groups) depends on ICV correction. These findings highlight the importance of considering individual differences in ICV, and show that proportional ICV correction does not remove the effects of cranial volume from anatomical measurements and can introduce ICV bias where previously there was none. ICV correction choices affect not just the strength of the relationships captured, but also the conclusions drawn regarding the neuroanatomical features that underlie those relationships.

Patterns of change in cortical morphometry following traumatic brain injury in adults.

Progressive cortical volumetric loss following moderate-severe traumatic brain injury (TBI) has been observed; however, regionally specific changes in the structural determinants of cortical volume, namely, cortical thickness (CT) and cortical surface area (CSA), are unknown and may inform the patterns and neural substrates of neurodegeneration and plasticity following injury. We aimed to (a) assess differences in CT and CSA between TBI participants and controls in the early chronic stage post-injury, (b) describe longitudinal changes in cortical morphometry following TBI, and (c) examine how regional changes in CT and CSA are associated. We acquired magnetic resonance images for 67 participants with TBI at up to 4 time-points spanning 5 months to 7 years post-injury, and 18 controls at 2 time-points. In the early chronic stage, TBI participants displayed thinner cortices than controls, predominantly in frontal regions, but no CSA differences. Throughout the chronic period, TBI participants showed widespread CT reductions in posterior cingulate/precuneus regions and moderate CT increase in frontal regions. Additionally, CSA showed a significant decrease in the orbitofrontal cortex and circumscribed increase in posterior regions. No changes were identified in controls. Relationships between regional cortical changes in the same morphological measure revealed coordinated patterns within participants, whereas correlations between regions with CT and CSA change yielded bi-directional relationships. This suggests that these measures may be differentially affected by neurodegenerative mechanisms such as transneuronal degeneration following TBI and that degeneration may be localized to the depths of cortical sulci. These findings emphasize the importance of dissecting morphometric contributions to cortical volume change.

What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group.

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.

Critical factors in achieving fine-scale functional MRI: Removing sources of inadvertent spatial smoothing.

Ultra-high Field (≥7T) functional magnetic resonance imaging (UHF-fMRI) provides opportunities to resolve fine-scale features of functional architecture such as cerebral cortical columns and layers, in vivo. While the nominal resolution of modern fMRI acquisitions may appear to be sufficient to resolve these features, several common data preprocessing steps can introduce unwanted spatial blurring, especially those that require interpolation of the data. These resolution losses can impede the detection of the fine-scale features of interest. To examine quantitatively and systematically the sources of spatial resolution losses occurring during preprocessing, we used synthetic fMRI data and real fMRI data from the human visual cortex-the spatially interdigitated human V2 "thin" and "thick" stripes. The pattern of these cortical columns lies along the cortical surface and thus can be best appreciated using surface-based fMRI analysis. We used this as a testbed for evaluating strategies that can reduce spatial blurring of fMRI data. Our results show that resolution losses can be mitigated at multiple points in preprocessing pathway. We show that unwanted blur is introduced at each step of volume transformation and surface projection, and can be ameliorated by replacing multi-step transformations with equivalent single-step transformations. Surprisingly, the simple approaches of volume upsampling and of cortical mesh refinement also helped to reduce resolution losses caused by interpolation. Volume upsampling also serves to improve motion estimation accuracy, which helps to reduce blur. Moreover, we demonstrate that the level of spatial blurring is nonuniform over the brain-knowledge which is critical for interpreting data in high-resolution fMRI studies. Importantly, our study provides recommendations for reducing unwanted blurring during preprocessing as well as methods that enable quantitative comparisons between preprocessing strategies. These findings highlight several underappreciated sources of a spatial blur. Individually, the factors that contribute to spatial blur may appear to be minor, but in combination, the cumulative effects can hinder the interpretation of fine-scale fMRI and the detectability of these fine-scale features of functional architecture.

The Evolutionary History of Common Genetic Variants Influencing Human Cortical Surface Area.

Structural brain changes along the lineage leading to modern Homo sapiens contributed to our distinctive cognitive and social abilities. However, the evolutionarily relevant molecular variants impacting key aspects of neuroanatomy are largely unknown. Here, we integrate evolutionary annotations of the genome at diverse timescales with common variant associations from large-scale neuroimaging genetic screens. We find that alleles with evidence of recent positive polygenic selection over the past 2000-3000 years are associated with increased surface area (SA) of the entire cortex, as well as specific regions, including those involved in spoken language and visual processing. Therefore, polygenic selective pressures impact the structure of specific cortical areas even over relatively recent timescales. Moreover, common sequence variation within human gained enhancers active in the prenatal cortex is associated with postnatal global SA. We show that such variation modulates the function of a regulatory element of the developmentally relevant transcription factor HEY2 in human neural progenitor cells and is associated with structural changes in the inferior frontal cortex. These results indicate that non-coding genomic regions active during prenatal cortical development are involved in the evolution of human brain structure and identify novel regulatory elements and genes impacting modern human brain structure.

Improving in vivo human cerebral cortical surface reconstruction using data-driven super-resolution.

Accurate and automated reconstruction of the in vivo human cerebral cortical surface from anatomical magnetic resonance (MR) images facilitates the quantitative analysis of cortical structure. Anatomical MR images with sub-millimeter isotropic spatial resolution improve the accuracy of cortical surface and thickness estimation compared to the standard 1-millimeter isotropic resolution. Nonetheless, sub-millimeter resolution acquisitions require averaging multiple repetitions to achieve sufficient signal-to-noise ratio and are therefore long and potentially vulnerable to subject motion. We address this challenge by synthesizing sub-millimeter resolution images from standard 1-millimeter isotropic resolution images using a data-driven supervised machine learning-based super-resolution approach achieved via a deep convolutional neural network. We systematically characterize our approach using a large-scale simulated dataset and demonstrate its efficacy in empirical data. The super-resolution data provide improved cortical surfaces similar to those obtained from native sub-millimeter resolution data. The whole-brain mean absolute discrepancy in cortical surface positioning and thickness estimation is below 100 µm at the single-subject level and below 50 µm at the group level for the simulated data, and below 200 µm at the single-subject level and below 100 µm at the group level for the empirical data, making the accuracy of cortical surfaces derived from super-resolution sufficient for most applications.

A Cortical Surface-Based Meta-Analysis of Human Reasoning.

Recent advances in neuroimaging have augmented numerous findings in the human reasoning process but have yielded varying results. One possibility for this inconsistency is that reasoning is such an intricate cognitive process, involving attention, memory, executive functions, symbolic processing, and fluid intelligence, whereby various brain regions are inevitably implicated in orchestrating the process. Therefore, researchers have used meta-analyses for a better understanding of neural mechanisms of reasoning. However, previous meta-analysis techniques include weaknesses such as an inadequate representation of the cortical surface's highly folded geometry. Accordingly, we developed a new meta-analysis method called Bayesian meta-analysis of the cortical surface (BMACS). BMACS offers a fast, accurate, and accessible inference of the spatial patterns of cognitive processes from peak brain activations across studies by applying spatial point processes to the cortical surface. Using BMACS, we found that the common pattern of activations from inductive and deductive reasoning was colocalized with the multiple-demand system, indicating that reasoning is a high-level convergence of complex cognitive processes. We hope surface-based meta-analysis will be facilitated by BMACS, bringing more profound knowledge of various cognitive processes.

Aberrant cortical surface and cognition function in drug-naive first-episode schizophrenia.

OBJECTIVE: Impaired cognitive function is a central symptom of schizophrenia and is often correlated with inferior global functional outcomes. However, the role of some neurobiological factors such as cortical structure alterations in the underlying cognitive damages in schizophrenia remains unclear. The present study attempted to explore the neurobiomarkers of cognitive function in drug-naive, first-episode schizophrenia by using structural magnetic resonance imaging (MRI). METHODS: The present study was conducted in patients with drug-naive, first-episode schizophrenia (SZ) and healthy controls (HCs). MRI T1 images were pre-processed using CAT12. Surface-based morphometry (SBM) was utilised to evaluate structural parameters such as cortical thickness and sulcus depth. The positive and negative syndrome scale (PANSS) and Chinese version of the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) consensus cognitive battery (MCCB) were employed to estimate the psychotic symptoms and cognition, respectively. RESULTS: A total of 117 patients with drug-naive first-episode schizophrenia (SZ) and 98 healthy controls (HCs) were included. Both the cortical thickness and sulcus depth in the frontal lobe were lower in patients with SZ than in the HCs under family-wise error correction (p < 0.05). Attention and visual learning in MCCB were positively correlated with the right lateral orbitofrontal cortical thickness in the patients with SZ (p < 0.01). CONCLUSIONS: The reduced surface value of multiple cortical structures, particularly the cortical thickness and sulcus depth in the frontal lobe, could be the potential biomarkers for cognitive impairment in SZ.