Admixture mapping of cognitive function in diverse Hispanic and Latino adults: Results from the Hispanic Community Health Study/Study of Latinos.

INTRODUCTION: We conducted admixture mapping and fine-mapping analyses to identify ancestry-of-origin loci influencing cognitive abilities. METHODS: We estimated the association of local ancestry intervals across the genome with five neurocognitive measures in 7140 diverse Hispanic and Latino adults (mean age 55 years). We prioritized genetic variants in associated loci and tested them for replication in four independent cohorts. RESULTS: We identified nine local ancestry-associated regions for the five neurocognitive measures. There was strong biological support for the observed associations to cognitive function at all loci and there was statistical evidence of independent replication at 4q12, 9p22.1, and 13q12.13. DISCUSSION: Our study identified multiple novel loci harboring genes implicated in cognitive functioning and dementia, and uncovered ancestry-relevant genetic variants. It adds to our understanding of the genetic architecture of cognitive function in Hispanic and Latino adults and demonstrates the power of admixture mapping to discover unique haplotypes influencing cognitive function, complementing genome-wide association studies. HIGHLIGHTS: We identified nine ancestry-of-origin chromosomal regions associated with five neurocognitive traits. In each associated region, we identified single nucleotide polymorphisms (SNPs) that explained, at least in part, the admixture signal and were tested for replication in independent samples of Black, non-Hispanic White, and Hispanic/Latino adults with the same or similar neurocognitive tests. Statistical evidence of independent replication of the prioritized SNPs was observed for three of the nine associations, at chr4q12, chr9p22.1, and chr13q12.13. At all loci, there was strong biological support for the observed associations to cognitive function and dementia, prioritizing genes such as KIT, implicated in autophagic clearance of neurotoxic proteins and on mast cell and microglial-mediated inflammation; SLC24A2, implicated in synaptic plasticity associated with learning and memory; and MTMR6, implicated in phosphoinositide lipids metabolism.

Obesity and brain structure in schizophrenia - ENIGMA study in 3021 individuals.

Schizophrenia is frequently associated with obesity, which is linked with neurostructural alterations. Yet, we do not understand how the brain correlates of obesity map onto the brain changes in schizophrenia. We obtained MRI-derived brain cortical and subcortical measures and body mass index (BMI) from 1260 individuals with schizophrenia and 1761 controls from 12 independent research sites within the ENIGMA-Schizophrenia Working Group. We jointly modeled the statistical effects of schizophrenia and BMI using mixed effects. BMI was additively associated with structure of many of the same brain regions as schizophrenia, but the cortical and subcortical alterations in schizophrenia were more widespread and pronounced. Both BMI and schizophrenia were primarily associated with changes in cortical thickness, with fewer correlates in surface area. While, BMI was negatively associated with cortical thickness, the significant associations between BMI and surface area or subcortical volumes were positive. Lastly, the brain correlates of obesity were replicated among large studies and closely resembled neurostructural changes in major depressive disorders. We confirmed widespread associations between BMI and brain structure in individuals with schizophrenia. People with both obesity and schizophrenia showed more pronounced brain alterations than people with only one of these conditions. Obesity appears to be a relevant factor which could account for heterogeneity of brain imaging findings and for differences in brain imaging outcomes among people with schizophrenia.

Imaging local genetic influences on cortical folding.

Recent progress in deciphering mechanisms of human brain cortical folding leave unexplained whether spatially patterned genetic influences contribute to this folding. High-resolution in vivo brain MRI can be used to estimate genetic correlations (covariability due to shared genetic factors) in interregional cortical thickness, and biomechanical studies predict an influence of cortical thickness on folding patterns. However, progress has been hampered because shared genetic influences related to folding patterns likely operate at a scale that is much more local (<1 cm) than that addressed in prior imaging studies. Here, we develop methodological approaches to examine local genetic influences on cortical thickness and apply these methods to two large, independent samples. We find that such influences are markedly heterogeneous in strength, and in some cortical areas are notably stronger in specific orientations relative to gyri or sulci. The overall, phenotypic local correlation has a significant basis in shared genetic factors and is highly symmetric between left and right cortical hemispheres. Furthermore, the degree of local cortical folding relates systematically with the strength of local correlations, which tends to be higher in gyral crests and lower in sulcal fundi. The relationship between folding and local correlations is stronger in primary sensorimotor areas and weaker in association areas such as prefrontal cortex, consistent with reduced genetic constraints on the structural topology of association cortex. Collectively, our results suggest that patterned genetic influences on cortical thickness, measurable at the scale of in vivo MRI, may be a causal factor in the development of cortical folding.

Regional White Matter Scaling in the Human Brain.

Anatomical organization of the primate cortex varies as a function of total brain size, where possession of a larger brain is accompanied by disproportionate expansion of associative cortices alongside a relative contraction of sensorimotor systems. However, equivalent scaling maps are not yet available for regional white matter anatomy. Here, we use three large-scale neuroimaging datasets to examine how regional white matter volume (WMV) scales with interindividual variation in brain volume among typically developing humans (combined N = 2391: 1247 females, 1144 males). We show that WMV scaling is regionally heterogeneous: larger brains have relatively greater WMV in anterior and posterior regions of cortical white matter, as well as the genu and splenium of the corpus callosum, but relatively less WMV in most subcortical regions. Furthermore, regions of positive WMV scaling tend to connect previously-defined regions of positive gray matter scaling in the cortex, revealing a coordinated coupling of regional gray and white matter organization with naturally occurring variations in human brain size. However, we also show that two commonly studied measures of white matter microstructure, fractional anisotropy (FA) and magnetization transfer (MT), scale negatively with brain size, and do so in a manner that is spatially unlike WMV scaling. Collectively, these findings provide a more complete view of anatomic scaling in the human brain, and offer new contexts for the interpretation of regional white matter variation in health and disease.SIGNIFICANCE STATEMENT Recent work has shown that, in humans, regional cortical and subcortical anatomy show systematic changes as a function of brain size variation. Here, we show that regional white matter structures also show brain-size related changes in humans. Specifically, white matter regions connecting higher-order cortical systems are relatively expanded in larger human brains, while subcortical and cerebellar white matter tracts responsible for unimodal sensory or motor functions are relatively contracted. This regional scaling of white matter volume (WMV) is coordinated with regional scaling of cortical anatomy, but is distinct from scaling of white matter microstructure. These findings provide a more complete view of anatomic scaling of the human brain, with relevance for evolutionary, basic, and clinical neuroscience.

Antisaccade error rates and gap effects in psychosis syndromes from bipolar-schizophrenia network for intermediate phenotypes 2 (B-SNIP2).

BACKGROUND: Antisaccade tasks can be used to index cognitive control processes, e.g. attention, behavioral inhibition, working memory, and goal maintenance in people with brain disorders. Though diagnoses of schizophrenia (SZ), schizoaffective (SAD), and bipolar I with psychosis (BDP) are typically considered to be distinct entities, previous work shows patterns of cognitive deficits differing in degree, rather than in kind, across these syndromes. METHODS: Large samples of individuals with psychotic disorders were recruited through the Bipolar-Schizophrenia Network on Intermediate Phenotypes 2 (B-SNIP2) study. Anti- and pro-saccade task performances were evaluated in 189 people with SZ, 185 people with SAD, 96 people with BDP, and 279 healthy comparison participants. Logistic functions were fitted to each group's antisaccade speed-performance tradeoff patterns. RESULTS: Psychosis groups had higher antisaccade error rates than the healthy group, with SZ and SAD participants committing 2 times as many errors, and BDP participants committing 1.5 times as many errors. Latencies on correctly performed antisaccade trials in SZ and SAD were longer than in healthy participants, although error trial latencies were preserved. Parameters of speed-performance tradeoff functions indicated that compared to the healthy group, SZ and SAD groups had optimal performance characterized by more errors, as well as less benefit from prolonged response latencies. Prosaccade metrics did not differ between groups. CONCLUSIONS: With basic prosaccade mechanisms intact, the higher speed-performance tradeoff cost for antisaccade performance in psychosis cases indicates a deficit that is specific to the higher-order cognitive aspects of saccade generation.

Associations of cannabis use disorder with cognition, brain structure, and brain function in African Americans.

Although previous studies have highlighted associations of cannabis use with cognition and brain morphometry, critical questions remain with regard to the association between cannabis use and brain structural and functional connectivity. In a cross-sectional community sample of 205 African Americans (age 18-70) we tested for associations of cannabis use disorder (CUD, n = 57) with multi-domain cognitive measures and structural, diffusion, and resting state brain-imaging phenotypes. Post hoc model evidence was computed with Bayes factors (BF) and posterior probabilities of association (PPA) to account for multiple testing. General cognitive functioning, verbal intelligence, verbal memory, working memory, and motor speed were lower in the CUD group compared with non-users (p < .011; 1.9 < BF < 3,217). CUD was associated with altered functional connectivity in a network comprising the motor-hand region in the superior parietal gyri and the anterior insula (p < .04). These differences were not explained by alcohol, other drug use, or education. No associations with CUD were observed in cortical thickness, cortical surface area, subcortical or cerebellar volumes (0.12 < BF < 1.5), or graph-theoretical metrics of resting state connectivity (PPA < 0.01). In a large sample collected irrespective of cannabis used to minimize recruitment bias, we confirm the literature on poorer cognitive functioning in CUD, and an absence of volumetric brain differences between CUD and non-CUD. We did not find evidence for or against a disruption of structural connectivity, whereas we did find localized resting state functional dysconnectivity in CUD. There was sufficient proof, however, that organization of functional connectivity as determined via graph metrics does not differ between CUD and non-user group.

Heritability and Cognitive Relevance of Structural Brain Controllability.

Cognition and behavior are thought to emerge from the connections and interactions among brain regions. The precise nature of these relationships remains elusive. Here we use tools provided by network control theory to determine how the structural connectivity profile of brain regions may shape individual variation in cognition. In a cohort of healthy young adults (n = 1066), we computed two fundamental brain regional control patterns, average and modal controllability, which index the degree of influence of a region over others. We first established that regional brain controllability measures were both reproducible and heritable. Regions with controllability profiles theoretically conducive to facilitating multiple cognitive operations were over-represented in higher-order resting-state networks. Finally, variation in regional controllability accounted for about 50% of interindividual variability in multiple cognitive domains. We conclude that controllability is a biologically plausible property of the structural connectome and provides a mechanistic explanation for how brain structural architecture may influence cognitive functions.

Structural Brain Architectures Match Intrinsic Functional Networks and Vary across Domains: A Study from 15 000+ Individuals.

Brain structural networks have been shown to consistently organize in functionally meaningful architectures covering the entire brain. However, to what extent brain structural architectures match the intrinsic functional networks in different functional domains remains under explored. In this study, based on independent component analysis, we revealed 45 pairs of structural-functional (S-F) component maps, distributing across nine functional domains, in both a discovery cohort (n = 6005) and a replication cohort (UK Biobank, n = 9214), providing a well-match multimodal spatial map template for public use. Further network module analysis suggested that unimodal cortical areas (e.g., somatomotor and visual networks) indicate higher S-F coherence, while heteromodal association cortices, especially the frontoparietal network (FPN), exhibit more S-F divergence. Collectively, these results suggest that the expanding and maturing brain association cortex demonstrates a higher degree of changes compared with unimodal cortex, which may lead to higher interindividual variability and lower S-F coherence.

Genetic Contributions to Multivariate Data-Driven Brain Networks Constructed via Source-Based Morphometry.

Identifying genetic factors underlying neuroanatomical variation has been difficult. Traditional methods have used brain regions from predetermined parcellation schemes as phenotypes for genetic analyses, although these parcellations often do not reflect brain function and/or do not account for covariance between regions. We proposed that network-based phenotypes derived via source-based morphometry (SBM) may provide additional insight into the genetic architecture of neuroanatomy given its data-driven approach and consideration of covariance between voxels. We found that anatomical SBM networks constructed on ~ 20 000 individuals from the UK Biobank were heritable and shared functionally meaningful genetic overlap with each other. We additionally identified 27 unique genetic loci that contributed to one or more SBM networks. Both GWA and genetic correlation results indicated complex patterns of pleiotropy and polygenicity similar to other complex traits. Lastly, we found genetic overlap between a network related to the default mode and schizophrenia, a disorder commonly associated with neuroanatomic alterations.

Minimal Relationship between Local Gyrification and General Cognitive Ability in Humans.

Previous studies suggest that gyrification is associated with superior cognitive abilities in humans, but the strength of this relationship remains unclear. Here, in two samples of related individuals (total N = 2882), we calculated an index of local gyrification (LGI) at thousands of cortical surface points using structural brain images and an index of general cognitive ability (g) using performance on cognitive tests. Replicating previous studies, we found that phenotypic and genetic LGI-g correlations were positive and statistically significant in many cortical regions. However, all LGI-g correlations in both samples were extremely weak, regardless of whether they were significant or nonsignificant. For example, the median phenotypic LGI-g correlation was 0.05 in one sample and 0.10 in the other. These correlations were even weaker after adjusting for confounding neuroanatomical variables (intracranial volume and local cortical surface area). Furthermore, when all LGIs were considered together, at least 89% of the phenotypic variance of g remained unaccounted for. We conclude that the association between LGI and g is too weak to have profound implications for our understanding of the neurobiology of intelligence. This study highlights potential issues when focusing heavily on statistical significance rather than effect sizes in large-scale observational neuroimaging studies.

Neurocognitive impairment in type 2 diabetes: evidence for shared genetic aetiology.

AIMS/HYPOTHESIS: Type 2 diabetes is associated with cognitive impairments, but it is unclear whether common genetic factors influence both type 2 diabetes risk and cognition. METHODS: Using data from 1892 Mexican-American individuals from extended pedigrees, including 402 with type 2 diabetes, we examined possible pleiotropy between type 2 diabetes and cognitive functioning, as measured by a comprehensive neuropsychological test battery. RESULTS: Negative phenotypic correlations (ρp) were observed between type 2 diabetes and measures of attention (Continuous Performance Test [CPT d']: ρp = -0.143, p = 0.001), verbal memory (California Verbal Learning Test [CVLT] recall: ρp = -0.111, p = 0.004) and face memory (Penn Face Memory Test [PFMT]: ρp = -0.127, p = 0.002; PFMT Delayed: ρp = -0.148, p = 2 × 10-4), replicating findings of cognitive impairment in type 2 diabetes. Negative genetic correlations (ρg) were also observed between type 2 diabetes and measures of attention (CPT d': ρg = -0.401, p = 0.001), working memory (digit span backward test: ρg = -0.380, p = 0.005), and face memory (PFMT: ρg = -0.476, p = 2 × 10-4; PFMT Delayed: ρg = -0.376, p = 0.005), suggesting that the same genetic factors underlying risk for type 2 diabetes also influence poor cognitive performance in these domains. Performance in these domains was also associated with type 2 diabetes risk using an endophenotype ranking value approach. Specifically, on measures of attention (CPT d': ß = -0.219, p = 0.005), working memory (digit span backward: ß = -0.326, p = 0.035), and face memory (PFMT: ß = -0.171, p = 0.023; PFMT Delayed: ß = -0.215, p = 0.005), individuals with type 2 diabetes showed the lowest performance, while unaffected/unrelated individuals showed the highest performance, and those related to an individual with type 2 diabetes performed at an intermediate level. CONCLUSIONS/INTERPRETATION: These findings suggest that cognitive impairment may be a useful endophenotype of type 2 diabetes and, therefore, help to elucidate the pathophysiological underpinnings of this chronic disease. DATA AVAILABILITY: The data analysed in this study is available in dbGaP: www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001215.v2.p2.

Cognitive impairment from early to middle adulthood in patients with affective and nonaffective psychotic disorders.

BACKGROUND: Cognitive impairment is a core feature of psychotic disorders, but the profile of impairment across adulthood, particularly in African-American populations, remains unclear. METHODS: Using cross-sectional data from a case-control study of African-American adults with affective (n = 59) and nonaffective (n = 68) psychotic disorders, we examined cognitive functioning between early and middle adulthood (ages 20-60) on measures of general cognitive ability, language, abstract reasoning, processing speed, executive function, verbal memory, and working memory. RESULTS: Both affective and nonaffective psychosis patients showed substantial and widespread cognitive impairments. However, comparison of cognitive functioning between controls and psychosis groups throughout early (ages 20-40) and middle (ages 40-60) adulthood also revealed age-associated group differences. During early adulthood, the nonaffective psychosis group showed increasing impairments with age on measures of general cognitive ability and executive function, while the affective psychosis group showed increasing impairment on a measure of language ability. Impairments on other cognitive measures remained mostly stable, although decreasing impairments on measures of processing speed, memory and working memory were also observed. CONCLUSIONS: These findings suggest similarities, but also differences in the profile of cognitive dysfunction in adults with affective and nonaffective psychotic disorders. Both affective and nonaffective patients showed substantial and relatively stable impairments across adulthood. The nonaffective group also showed increasing impairments with age in general and executive functions, and the affective group showed an increasing impairment in verbal functions, possibly suggesting different underlying etiopathogenic mechanisms.

Person-Based Brain Morphometric Similarity is Heritable and Correlates With Biological Features.

The characterization of the functional significance of interindividual variation in brain morphometry is a core aim of cognitive neuroscience. Prior research has focused on interindividual variation at the level of regional brain measures thus overlooking the fact that each individual brain is a person-specific ensemble of interdependent regions. To expand this line of inquiry we introduce the person-based similarity index (PBSI) for brain morphometry. The conceptual unit of the PBSI is the individual person's brain structural profile which considers all relevant morphometric measures as features of a single vector. In 2 independent cohorts (total of 1756 healthy participants), we demonstrate the foundational validity of this approach by affirming that the PBSI scores for subcortical volume and cortical thickness in healthy individuals differ between men and women, are heritable, and robust to variation in neuroimaging parameters, sample composition, and regional brain morphometry. Moreover, the PBSI scores correlate with age, body mass index, and fluid intelligence. Collectively, these results suggest that the person-based measures of brain morphometry are biologically and functionally meaningful and have the potential to advance the study of human variation in multivariate brain imaging phenotypes in healthy and clinical populations.

Regularized aggregation of statistical parametric maps.

Combining statistical parametric maps (SPM) from individual subjects is the goal in some types of group-level analyses of functional magnetic resonance imaging data. Brain maps are usually combined using a simple average across subjects, making them susceptible to subjects with outlying values. Furthermore, t tests are prone to false positives and false negatives when outlying values are observed. We propose a regularized unsupervised aggregation method for SPMs to find an optimal weight for aggregation, which aids in detecting and mitigating the effect of outlying subjects. We also present a bootstrap-based weighted t test using the optimal weights to construct an activation map robust to outlying subjects. We validate the performance of the proposed aggregation method and test using simulated and real data examples. Results show that the regularized aggregation approach can effectively detect outlying subjects, lower their weights, and produce robust SPMs.

Evidence for genetic correlation between human cerebral white matter microstructure and inflammation.

White matter microstructure is affected by immune system activity via the actions of circulating pro-inflammatory cytokines. Although white matter microstructure and inflammatory measures are significantly heritable, it is unclear if overlapping genetic factors influence these traits in humans. We conducted genetic correlation analyses of these traits using randomly ascertained extended pedigrees from the Genetics of Brain Structure and Function Study (N = 1862, 59% females, ages 18-97 years; 42 ± 15.7). White matter microstructure was assessed using fractional anisotropy (FA) calculated from diffusion tensor imaging (DTI). Circulating levels (pg/mL) of pro-inflammatory cytokines (IL-6, IL-8, and TNFα) phenotypically associated with white matter microstructure were quantified from blood serum. All traits were significantly heritable (h2 ranging from 0.41 to 0.66 for DTI measures and from 0.18 to 0.30 for inflammatory markers). Phenotypically, higher levels of circulating inflammatory markers were associated with lower FA values across the brain (r = -.03 to r = -.17). There were significant negative genetic correlations between most DTI measures and IL-8 and TNFα, although effects for TNFα were no longer significant when covarying for body mass index. Genetic correlations between DTI measures and IL-6 were not significant. Understanding the genetic correlation between specific inflammatory markers and DTI measures may help researchers focus questions related to inflammatory processes and brain structure.

Family-based analyses reveal novel genetic overlap between cytokine interleukin-8 and risk for suicide attempt.

BACKGROUND: Suicide is major public health concern. It is imperative to find robust biomarkers so that at-risk individuals can be identified in a timely and reliable manner. Previous work suggests mechanistic links between increased cytokines and risk for suicide, but questions remain regarding the etiology of this association, as well as the roles of sex and BMI. METHODS: Analyses were conducted using a randomly-ascertained extended-pedigree sample of 1882 Mexican-American individuals (60% female, mean age = 42.04, range = 18-97). Genetic correlations were calculated using a variance components approach between the cytokines TNF-α, IL-6 and IL-8, and Lifetime Suicide Attempt and Current Suicidal Ideation. The potentially confounding effects of sex and BMI were considered. RESULTS: 159 individuals endorse a Lifetime Suicide Attempt. IL-8 and IL-6 shared significant genetic overlap with risk for suicide attempt (ρg = 0.49, pFDR = 7.67 × 10-03; ρg = 0.53, pFDR = 0.01), but for IL-6 this was attenuated when BMI was included as a covariate (ρg = 0.37, se = 0.23, pFDR = 0.12). Suicide attempts were significantly more common in females (pFDR = 0.01) and the genetic overlap between IL-8 and risk for suicide attempt was significant in females (ρg = 0.56, pFDR = 0.01), but not in males (ρg = 0.44, pFDR = 0.30). DISCUSSION: These results demonstrate that: IL-8 shares genetic influences with risk for suicide attempt; females drove this effect; and BMI should be considered when assessing the association between IL-6 and suicide. This finding represents a significant advancement in knowledge by demonstrating that cytokine alterations are not simply a secondary manifestation of suicidal behavior, but rather, the pathophysiology of suicide attempts is, at least partly, underpinned by the same biological mechanisms responsible for regulating inflammatory response.

Improved frontoparietal white matter integrity in overweight children is associated with attendance at an after-school exercise program.

Aerobic fitness is associated with white matter integrity (WMI) in adults as measured by diffusion tensor imaging (DTI). This study examined the effect of an 8-month exercise intervention on WMI in children. Participants were 18 sedentary, overweight (BMI≥85th percentile) 8- to 11-year-old children (94% Black), randomly assigned to either an aerobic exercise (n=10) or sedentary attention control group (n=8). Each group was offered an instructor-led after-school program every school day for approximately 8 months. Before and after the program, all subjects participated in DTI scans. Tractography was conducted to isolate the superior longitudinal fasciculus and investigate whether the exercise intervention affected WMI in this region. There was no group by time interaction for WMI in the superior longitudinal fasciculus. There was a group by time by attendance interaction, however, such that higher attendance at the exercise intervention, but not the control intervention, was associated with increased WMI. Heart rate and the total dose of exercise correlated with WMI changes in the exercise group. In the overall sample, increased WMI was associated with improved scores on a measure of attention and improved teacher ratings of executive function. This study indicates that participating in an exercise intervention improves WMI in children as compared to a sedentary after-school program.

Lifespan reference curves for harmonizing multi-site regional brain white matter metrics from diffusion MRI.

Age-related white matter (WM) microstructure maturation and decline occur throughout the human lifespan, complementing the process of gray matter development and degeneration. Here, we create normative lifespan reference curves for global and regional WM microstructure by harmonizing diffusion MRI (dMRI)-derived data from ten public datasets (N = 40,898 subjects; age: 3-95 years; 47.6% male). We tested three harmonization methods on regional diffusion tensor imaging (DTI) based fractional anisotropy (FA), a metric of WM microstructure, extracted using the ENIGMA-DTI pipeline. ComBat-GAM harmonization provided multi-study trajectories most consistent with known WM maturation peaks. Lifespan FA reference curves were validated with test-retest data and used to assess the effect of the ApoE4 risk factor for dementia in WM across the lifespan. We found significant associations between ApoE4 and FA in WM regions associated with neurodegenerative disease even in healthy individuals across the lifespan, with regional age-by-genotype interactions. Our lifespan reference curves and tools to harmonize new dMRI data to the curves are publicly available as eHarmonize (https://github.com/ahzhu/eharmonize).