Gene-environment pathways to cognitive intelligence and psychotic-like experiences in children.

In children, psychotic-like experiences (PLEs) are related to risk of psychosis, schizophrenia, and other mental disorders. Maladaptive cognitive functioning, influenced by genetic and environmental factors, is hypothesized to mediate the relationship between these factors and childhood PLEs. Using large-scale longitudinal data, we tested the relationships of genetic and environmental factors (such as familial and neighborhood environment) with cognitive intelligence and their relationships with current and future PLEs in children. We leveraged large-scale multimodal data of 6,602 children from the Adolescent Brain and Cognitive Development Study. Linear mixed model and a novel structural equation modeling (SEM) method that allows estimation of both components and factors were used to estimate the joint effects of cognitive phenotypes polygenic scores (PGSs), familial and neighborhood socioeconomic status (SES), and supportive environment on NIH Toolbox cognitive intelligence and PLEs. We adjusted for ethnicity (genetically defined), schizophrenia PGS, and additionally unobserved confounders (using computational confound modeling). Our findings indicate that lower cognitive intelligence and higher PLEs are significantly associated with lower PGSs for cognitive phenotypes, lower familial SES, lower neighborhood SES, and less supportive environments. Specifically, cognitive intelligence mediates the effects of these factors on PLEs, with supportive parenting and positive school environments showing the strongest impact on reducing PLEs. This study underscores the influence of genetic and environmental factors on PLEs through their effects on cognitive intelligence. Our findings have policy implications in that improving school and family environments and promoting local economic development may enhance cognitive and mental health in children.

Childhood is a critical period for brain development. Difficult experiences during this developmental phase may contribute to reduced intelligence and poorer mental health later in life. Genetics and environmental factors also play roles. For example, having family support or a higher family income has been linked to better brain health outcomes for children. Delusions or hallucinations, or other psychotic-like experiences during childhood, are linked with poor mental health later in life. Children who experience psychotic-like episodes between the ages of nine and eleven have a higher risk of developing schizophrenia or related conditions. Environmental circumstances during childhood also appear to play a crucial role in shaping the risk of schizophrenia or related conditions. Park, Lee et al. show that positive parenting and supportive school and neighborhood environments boost child intelligence and mental health. In the experiments, Park, Lee et al. analyzed data on 6,602 children to determine how genetics and environmental factors shaped their intelligence and mental health. The models show that children with higher intelligence have a lower risk of psychosis. Both genetics and supportive environments contribute to higher intelligence. Complex interactions between biology and social factors shape children's intelligence and mental health. Beneficial genetics and coming from a family with more financial resources are helpful. Yet, social environments, such as having parents who use positive child-rearing practices, or having supportive schools or neighborhoods, have protective effects that can offset other disadvantages. Policies that help parents, encourage supportive school environments, and strengthen neighborhoods may boost children's intelligence and mental health later in life.

Early life stress modulates the genetic influence on brain structure and cognitive function in children.

The enduring influence of early life stress (ELS) on brain and cognitive development has been widely acknowledged, yet the precise mechanisms underlying this association remain elusive. We hypothesize that ELS might disrupt the genome-wide influence on brain morphology and connectivity development, consequently exerting a detrimental impact on children's cognitive ability. We analyzed the multimodal data of DNA genotypes, brain imaging (structural and diffusion MRI), and neurocognitive battery (NIH Toolbox) of 4276 children (ages 9-10 years, European ancestry) from the Adolescent Brain Cognitive Development (ABCD) study. The genome-wide influence on cognitive function was estimated using the polygenic score (GPS). By using brain morphometry and tractography, we identified the brain correlates of the cognition GPSs. Statistical analyses revealed relationships for the gene-brain-cognition pathway. The brain structural variance significantly mediated the genetic influence on cognition (indirect effect = 0.016, PFDR < 0.001). Of note, this gene-brain relationship was significantly modulated by abuse, resulting in diminished cognitive capacity (Index of Moderated Mediation = -0.007; 95 % CI = -0.012 âˆ¼ -0.002). Our results support a novel gene-brain-cognition model likely elucidating the long-lasting negative impact of ELS on children's cognitive development.

Dentate Gyrus Microstructure Is Associated With Resilience After Exposure to Maternal Stress Across Two Human Cohorts.

BACKGROUND: Maternal stress (MS) is a well-documented risk factor for impaired emotional development in offspring. Rodent models implicate the dentate gyrus (DG) of the hippocampus in the effects of MS on offspring depressive-like behaviors, but mechanisms in humans remain unclear. Here, we tested whether MS was associated with depressive symptoms and DG micro- and macrostructural alterations in offspring across 2 independent cohorts. METHODS: We analyzed DG diffusion tensor imaging-derived mean diffusivity (DG-MD) and volume in a three-generation family risk for depression study (TGS; n = 69, mean age = 35.0 years) and in the Adolescent Brain Cognitive Development (ABCD) Study (n = 5196, mean age = 9.9 years) using generalized estimating equation models and mediation analysis. MS was assessed by the Parenting Stress Index (TGS) and a measure compiled from the Adult Response Survey from the ABCD Study. The Patient Health Questionnaire-9 and rumination scales (TGS) and the Child Behavior Checklist (ABCD Study) measured offspring depressive symptoms at follow-up. The Schedule for Affective Disorders and Schizophrenia-Lifetime interview was used to assign depression diagnoses. RESULTS: Across cohorts, MS was associated with future symptoms and higher DG-MD (indicating disrupted microstructure) in offspring. Higher DG-MD was associated with higher symptom scores measured 5 years (in the TGS) and 1 year (in the ABCD Study) after magnetic resonance imaging. In the ABCD Study, DG-MD was increased in high-MS offspring who had depressive symptoms at follow-up, but not in offspring who remained resilient or whose mother had low MS. CONCLUSIONS: Converging results across 2 independent samples extend previous rodent studies and suggest a role for the DG in exposure to MS and offspring depression.

Differences in White Matter Structural Networks in Family Risk of Major Depressive Disorder and Suicidality: A Connectome Analysis.

Background: Depression and suicide are leading global causes of disability and death and are highly familial. Family and individual history of depression are associated with neurobiological differences including decreased white matter connectivity; however, this has only been shown for individual regions. We use graph theory models to account for the network structure of the brain with high levels of specialization and integration and examine whether they differ by family history of depression or of suicidality within a three-generation longitudinal family study with well-characterized clinical histories. Methods: Clinician interviews across three generations were used to classify family risk of depression and suicidality. Then, we created weighted network models using 108 cortical and subcortical regions of interest for 96 individuals using diffusion tensor imaging derived fiber tracts. Global and local summary measures (clustering coefficient, characteristic path length, and global and local efficiencies) and network-based statistics were utilized for group comparison of family history of depression and, separately, of suicidality, adjusted for personal psychopathology. Results: Clustering coefficient (connectivity between neighboring regions) was lower in individuals at high family risk of depression and was associated with concurrent clinical symptoms. Network-based statistics showed hypoconnected subnetworks in individuals with high family risk of depression and of suicidality, after controlling for personal psychopathology. These subnetworks highlighted cortical-subcortical connections including between the superior frontal cortex, thalamus, precuneus, and putamen. Conclusions: Family history of depression and of suicidality are associated with hypoconnectivity between subcortical and cortical regions, suggesting brain-wide impaired information processing, even in those personally unaffected.

Overestimated prediction using polygenic prediction derived from summary statistics.

BACKGROUND: When polygenic risk score (PRS) is derived from summary statistics, independence between discovery and test sets cannot be monitored. We compared two types of PRS studies derived from raw genetic data (denoted as rPRS) and the summary statistics for IGAP (sPRS). RESULTS: Two variables with the high heritability in UK Biobank, hypertension, and height, are used to derive an exemplary scale effect of PRS. sPRS without APOE is derived from International Genomics of Alzheimer's Project (IGAP), which records ΔAUC and ΔR2 of 0.051 ± 0.013 and 0.063 ± 0.015 for Alzheimer's Disease Sequencing Project (ADSP) and 0.060 and 0.086 for Accelerating Medicine Partnership - Alzheimer's Disease (AMP-AD). On UK Biobank, rPRS performances for hypertension assuming a similar size of discovery and test sets are 0.0036 ± 0.0027 (ΔAUC) and 0.0032 ± 0.0028 (ΔR2). For height, ΔR2 is 0.029 ± 0.0037. CONCLUSION: Considering the high heritability of hypertension and height of UK Biobank and sample size of UK Biobank, sPRS results from AD databases are inflated. Independence between discovery and test sets is a well-known basic requirement for PRS studies. However, a lot of PRS studies cannot follow such requirements because of impossible direct comparisons when using summary statistics. Thus, for sPRS, potential duplications should be carefully considered within the same ethnic group.

Sex-Specific Effects of Anxiety on Cognition and Activity-Dependent Neural Networks: Insights from (Female) Mice and (Wo)Men.

INTRODUCTION: Neuropsychiatric symptoms (NPS), such as depression and anxiety, are observed in 90% of Alzheimer's disease (AD) patients, two-thirds of whom are women. NPS usually manifest long before AD onset creating a therapeutic opportunity. Here, we examined the impact of anxiety on AD progression and the underlying brain-wide neuronal mechanisms. METHODS: To gain mechanistic insight into how anxiety impacts AD progression, we performed a cross-sectional analysis on mood, cognition, and neural activity utilizing the ArcCreERT2 x enhanced yellow fluorescent protein (eYFP) x APP/PS1 (AD) mice. The ADNI dataset was used to determine the impact of anxiety on AD progression in human subjects. RESULTS: Female AD mice exhibited anxiety-like behavior and cognitive decline at an earlier age than control (Ctrl) mice and male mice. Brain-wide analysis of c-Fos+ revealed changes in regional correlations and overall network connectivity in AD mice. Sex-specific memory trace changes were observed; female AD mice exhibited impaired memory traces in dorsal CA3 (dCA3), while male AD mice exhibited impaired memory traces in the dorsal dentate gyrus (dDG). In the ADNI dataset, anxiety predicted transition to dementia. Female subjects positive for anxiety and amyloid transitioned more quickly to dementia than male subjects. CONCLUSIONS: While future studies are needed to understand whether anxiety is a predictor, a neuropsychiatric biomarker, or a comorbid symptom that occurs during disease onset, these results suggest that AD network dysfunction is sexually dimorphic, and that personalized medicine may benefit male and female AD patients rather than a one size fits all approach.

Maternal age at birth and child attention-deficit hyperactivity disorder: causal association or familial confounding?

BACKGROUND: Causal explanations for the association of young motherhood with increased risk for child attention-deficit hyperactivity disorder (ADHD) remain unclear. METHODS: The ABCD Study recruited 11,878 youth from 22 sites across the United States between June 1, 2016 and October 15, 2018. This cross-sectional analysis of 8,514 children aged 8-11 years excluded 2,260 twins/triplets, 265 adopted children, and 839 younger siblings. We examined associations of maternal age with ADHD clinical range diagnoses based on the Child Behavior Checklist and NIH Toolbox Flanker Attention Scores using mixed logistic and linear regression models, respectively. We conducted confounding and causal mediation analyses using genotype array, demographic, socioeconomic, and prenatal environment data to investigate which genetic and environmental variables may explain the association between young maternal age and child ADHD. RESULTS: In crude models, each 10-year increase in maternal age was associated with 32% decreased odds of ADHD clinical range diagnosis (OR = 0.68; 95% CI [0.59, 0.78]) and 1.09-points increased NIH Flanker Attention Scores (ß = 1.09; 95% CI [0.76, 1.41]), indicating better child visual selective attention. However, adjustment for confounders weakened these associations. The strongest confounders were family income, caregiver education, and ADHD polygenic risk score for ADHD clinical range diagnoses, and family income, caregiver education, and race/ethnicity for NIH Flanker Attention Scores. Breastfeeding duration, prenatal alcohol exposure, and prenatal tobacco exposure were responsible for up to 18%, 6%, and 4% mediation, respectively. CONCLUSIONS: Socioeconomic disadvantages were likely the primary explanation for the association of young maternal age with child ADHD, although genetics and modifiable environmental factors also played a role. Public policies aimed at reducing the burden of ADHD associated with young motherhood should target socioeconomic inequalities and support young pregnant women by advocating for reduced prenatal tobacco exposure and healthy breastfeeding practices after childbirth.

Cognitive Capacity Genome-Wide Polygenic Scores Identify Individuals with Slower Cognitive Decline in Aging.

The genetic protective factors for cognitive decline in aging remain unknown. Predicting an individual's rate of cognitive decline-or with better cognitive resilience-using genetics will allow personalized intervention for cognitive enhancement and the optimal selection of target samples in clinical trials. Here, using genome-wide polygenic scores (GPS) of cognitive capacity as the genomic indicators for variations of human intelligence, we analyzed the 18-year records of cognitive and behavioral data of 8511 European-ancestry adults from the Wisconsin Longitudinal Study (WLS), specifically focusing on the cognitive assessments that were repeatedly administered to the participants with their average ages of 64.5 and 71.5. We identified a significant interaction effect between age and cognitive capacity GPS, which indicated that a higher cognitive capacity GPS significantly correlated with a slower cognitive decline in the domain of immediate memory recall (ß = 1.86 × 10-1, p-value = 1.79 × 10-3). The additional phenome-wide analyses identified several associations between cognitive capacity GPSs and cognitive/behavioral phenotypes, such as similarities task (ß = 1.36, 95% CI = (1.22, 1.51), p-value = 3.59 × 10-74), number series task (ß = 0.94, 95% CI = (0.85, 1.04), p-value = 2.55 × 10-78), IQ scores (ß = 1.42, 95% CI = (1.32, 1.51), p-value = 7.74 × 10-179), high school classrank (ß = 1.86, 95% CI = (1.69, 2.02), p-value = 3.07 × 10-101), Openness from the BIG 5 personality factor (p-value = 2.19 × 10-14, ß = 0.57, 95% CI = (0.42, 0.71)), and leisure activity of reading books (ß = 0.50, 95% CI = (0.40, 0.60), p-value = 2.03 × 10-21), attending cultural events, such as concerts, plays, or museums (ß = 0.60, 95% CI = (0.49, 0.72), p-value = 2.06 × 10-23), and watching TV (ß = -0.48, 95% CI = (-0.59, -0.37), p-value = 4.16 × 10-18). As the first phenome-wide analysis of cognitive and behavioral phenotypes, this study presents the novel genetic protective effects of cognitive ability on the decline of memory recall in an aging population.

The sexual brain, genes, and cognition: A machine-predicted brain sex score explains individual differences in cognitive intelligence and genetic influence in young children.

Sex impacts the development of the brain and cognition differently across individuals. However, the literature on brain sex dimorphism in humans is mixed. We aim to investigate the biological underpinnings of the individual variability of sexual dimorphism in the brain and its impact on cognitive performance. To this end, we tested whether the individual difference in brain sex would be linked to that in cognitive performance that is influenced by genetic factors in prepubertal children (N = 9,658, ages 9-10 years old; the Adolescent Brain Cognitive Development study). To capture the interindividual variability of the brain, we estimated the probability of being male or female based on the brain morphometry and connectivity features using machine learning (herein called a brain sex score). The models accurately classified the biological sex with a test ROC-AUC of 93.32%. As a result, a greater brain sex score correlated significantly with greater intelligence (pfdr < .001, ηp2  = .011-.034; adjusted for covariates) and higher cognitive genome-wide polygenic scores (GPSs) (pfdr < .001, ηp2 < .005). Structural equation models revealed that the GPS-intelligence association was significantly modulated by the brain sex score, such that a brain with a higher maleness score (or a lower femaleness score) mediated a positive GPS effect on intelligence (indirect effects = .006-.009; p = .002-.022; sex-stratified analysis). The finding of the sex modulatory effect on the gene-brain-cognition relationship presents a likely biological pathway to the individual and sex differences in the brain and cognitive performance in preadolescence.

Therapeutic Effect of Repetitive Transcranial Magnetic Stimulation for Post-stroke Vascular Cognitive Impairment: A Prospective Pilot Study.

Objective: Post-stroke cognitive impairment (PSCI) is resistant to treatment. Recent studies have widely applied repetitive transcranial magnetic stimulation (rTMS) to treat various brain dysfunctions, such as post-stroke syndromes. Nonetheless, a protocol for PSCI has not been established. Therefore, this study is aimed to evaluate the therapeutic effect of our high-frequency rTMS protocol for PSCI during the chronic phase of stroke. Methods: In this prospective study, ten patients with PSCI were enrolled and received high-frequency rTMS on the ipsilesional dorsolateral prefrontal cortex (DLPFC) for 10 sessions (5 days per week for 2 weeks). Cognitive and affective abilities were assessed at baseline and 2 and 14 weeks after rTMS initiation. To investigate the therapeutic mechanism of rTMS, the mRNA levels of pro-inflammatory cytokines (interleukin (IL)-6, IL-1ß, transforming growth factor beta [TGF-ß], and tumor necrosis factor alpha [TNF-α]) in peripheral blood samples were quantified using reverse transcription polymerase chain reaction, and cognitive functional magnetic resonance imaging (fMRI) was conducted at baseline and 14 weeks in two randomly selected patients after rTMS treatment. Results: The scores of several cognitive evaluations, i.e., the Intelligence Quotient (IQ) of Wechsler Adult Intelligence Scale, auditory verbal learning test (AVLT), and complex figure copy test (CFT), were increased after completion of the rTMS session. After 3 months, these improvements were sustained, and scores on the Mini-Mental Status Examination and Montreal Cognitive Assessment (MoCA) were also increased (p < 0.05). While the Geriatric Depression Scale (GeDS) did not show change among all patients, those with moderate-to-severe depression showed amelioration of the score, with marginal significance. Expression of pro-inflammatory cytokines was decreased immediately after the ten treatment sessions, among which, IL-1ß remained at a lower level after 3 months. Furthermore, strong correlations between the decrease in IL-6 and increments in AVLT (r = 0.928) and CFT (r = 0.886) were found immediately after the rTMS treatment (p < 0.05). Follow-up fMRI revealed significant activation in several brain regions, such as the medial frontal lobe, hippocampus, and angular area. Conclusions: High-frequency rTMS on the ipsilesional DLPFC may exert immediate efficacy on cognition with the anti-inflammatory response and changes in brain network in PSCI, lasting at least 3 months.

Association of Genome-Wide Polygenic Scores for Multiple Psychiatric and Common Traits in Preadolescent Youths at Risk of Suicide.

Importance: Suicide is the second leading cause of death among youths worldwide, but no available means exist to identify the risk of suicide in this population. Objective: To assess whether genome-wide polygenic scores for psychiatric and common traits are associated with the risk of suicide among preadolescent children and to investigate whether and to what extent the interaction between early life stress (a major environmental risk factor) and polygenic factors is associated with suicidal thoughts and behaviors among youths. Design, Setting, and Participants: This cohort study analyzed the genotype-phenotype data of 11 869 preadolescent children aged 9 to 10 years from the Adolescent Brain and Cognitive Development study. Data were collected from September 1, 2016, to October 21, 2018, and analyzed from August 1, 2020, to January 3, 2021. Using machine learning approaches, genome-wide polygenic scores of 24 complex traits were estimated to investigate their phenome-wide associations and utility for assessing risk of suicidal thoughts and behaviors (suicidal ideation [active, passive, and overall] and suicide attempt). Main Outcomes and Measures: Genome-wide polygenic scores were used to measure 24 traits, including psychiatric disorders, cognitive capacity, and personality and psychological characteristics. The Child Behavior Checklist was used to measure early life stress, and the Family Environment Scale was used to assess family environment. Suicidal ideation and suicide attempts were derived from the computerized version of the Kiddie Schedule for Affective Disorders and Schizophrenia. Results: Among 11 869 preadolescent children in the US, complete data for phenotypic outcomes, genotypes, and covariates were available for 7140 participants in the multiethnic cohort (mean [SD] age, 9.9 [0.6] years; 3588 girls [50.3%]), including 925 participants with suicidal ideation and 63 participants with suicide attempts. Among those 7140 participants, 729 had African ancestry (self-reported race or ethnicity: 569 Black, 71 Hispanic, and 89 other), 276 had admixed American ancestry (self-reported race or ethnicity: 265 Hispanic, 3 White, and 8 other), 150 had East Asian ancestry (self-reported race or ethnicity: 67 Asian, 18 Hispanic, and 65 other), 5718 had European ancestry (self-reported race or ethnicity: 7 Asian, 39 Black, 1142 Hispanic, 3934 White, and 596 other), and 267 had other ancestries (self-reported race or ethnicity: 70 Asian, 13 Black, 126 Hispanic, 48 White, and 10 other). Three genome-wide polygenic scores were significantly associated (false discovery rate P < .05) with suicidal thoughts and behaviors among all participants: attention-deficit/hyperactivity disorder (odds ratio [OR], 1.12; 95% CI, 1.05-1.21; P = .001), schizophrenia (OR, 1.50; 95% CI, 1.17-1.93; P = .002), and general happiness (OR, 0.89; 95% CI, 0.83-0.96; P = .002). In the analysis including only children with European ancestry, 3 additional genome-wide polygenic scores with false discovery rate significance were associated with suicidal thoughts and behaviors: autism spectrum disorder (OR, 1.18; 95% CI, 1.06-1.31; P = .002), major depressive disorder (OR, 1.12; 95% CI, 1.04-1.21; P = .003), and posttraumatic stress disorder (OR, 1.12; 95% CI, 1.04-1.21; P = .004). A significant interaction between genome-wide polygenic scores and environment was found, with genetic risk factors for autism spectrum disorder and the level of early life stress associated with increases in the risk of overall suicidal ideation and overall suicidal thoughts and behaviors (OR, 1.20; 95% CI, 1.07-1.35; P = .002). A machine learning model using multitrait genome-wide polygenic scores and additional self-reported questionnaire data (Child Behavior Checklist and Family Environment Scale) produced a moderately accurate estimate of overall suicidal thoughts and behaviors (area under the receiver operating characteristic curve [AUROC], 0.77; 95% CI, 0.73-0.81; accuracy, 0.67) and suicidal ideation (AUROC, 0.76; 95% CI, 0.72-0.80; accuracy, 0.66) among children with European ancestry only. Among all children in the multiethnic cohort, the integrated model also outperformed the baseline model in estimating the risk of overall suicidal thoughts and behaviors (AUROC, 0.71; 95% CI, 0.67-0.75; accuracy, 0.68) and suicidal ideation (AUROC, 0.75; 95% CI, 0.71-0.78; accuracy, 0.67). Conclusions and Relevance: In this cohort study of preadolescent youths in the US, higher genome-wide polygenic scores for psychiatric disorders, such as attention-deficit/hyperactivity disorder, autism spectrum disorder, posttraumatic stress disorder, and schizophrenia, were significantly associated with a greater risk of suicidal ideation and suicide attempt. The findings and quantitative models from this study may help to identify children with a high risk of suicide, potentially assisting with early screening, intervention, and prevention.

Maturity of gray matter structures and white matter connectomes, and their relationship with psychiatric symptoms in youth.

Brain predicted age difference, or BrainPAD, compares chronological age to an age estimate derived by applying machine learning (ML) to MRI brain data. BrainPAD studies in youth have been relatively limited, often using only a single MRI modality or a single ML algorithm. Here, we use multimodal MRI with a stacked ensemble ML approach that iteratively applies several ML algorithms (AutoML). Eligible participants in the Healthy Brain Network (N = 489) were split into training and test sets. Morphometry estimates, white matter connectomes, or both were entered into AutoML to develop BrainPAD models. The best model was then applied to a held-out evaluation dataset, and associations with psychometrics were estimated. Models using morphometry and connectomes together had a mean absolute error of 1.18 years, outperforming models using a single MRI modality. Lower BrainPAD values were associated with more symptoms on the CBCL (pcorr  = .012) and lower functioning on the Children's Global Assessment Scale (pcorr  = .012). Higher BrainPAD values were associated with better performance on the Flanker task (pcorr  = .008). Brain age prediction was more accurate using ComBat-harmonized brain data (MAE = 0.26). Associations with psychometric measures remained consistent after ComBat harmonization, though only the association with CGAS reached statistical significance in the reduced sample. Our findings suggest that BrainPAD scores derived from unharmonized multimodal MRI data using an ensemble ML approach may offer a clinically relevant indicator of psychiatric and cognitive functioning in youth.

Altered white matter microstructural organization in posttraumatic stress disorder across 3047 adults: results from the PGC-ENIGMA PTSD consortium.

A growing number of studies have examined alterations in white matter organization in people with posttraumatic stress disorder (PTSD) using diffusion MRI (dMRI), but the results have been mixed which may be partially due to relatively small sample sizes among studies. Altered structural connectivity may be both a neurobiological vulnerability for, and a result of, PTSD. In an effort to find reliable effects, we present a multi-cohort analysis of dMRI metrics across 3047 individuals from 28 cohorts currently participating in the PGC-ENIGMA PTSD working group (a joint partnership between the Psychiatric Genomics Consortium and the Enhancing NeuroImaging Genetics through Meta-Analysis consortium). Comparing regional white matter metrics across the full brain in 1426 individuals with PTSD and 1621 controls (2174 males/873 females) between ages 18-83, 92% of whom were trauma-exposed, we report associations between PTSD and disrupted white matter organization measured by lower fractional anisotropy (FA) in the tapetum region of the corpus callosum (Cohen's d = -0.11, p = 0.0055). The tapetum connects the left and right hippocampus, for which structure and function have been consistently implicated in PTSD. Results were consistent even after accounting for the effects of multiple potentially confounding variables: childhood trauma exposure, comorbid depression, history of traumatic brain injury, current alcohol abuse or dependence, and current use of psychotropic medications. Our results show that PTSD may be associated with alterations in the broader hippocampal network.

Altered Dentate Gyrus Microstructure in Individuals at High Familial Risk for Depression Predicts Future Symptoms.

BACKGROUND: Offspring of individuals with major depressive disorder (MDD) are at increased risk for developing MDD themselves. Altered hippocampal, and specifically dentate gyrus (DG), structure and function may be involved in depression development. However, hippocampal abnormalities could also be a consequence of the disease. For the first time, we tested whether abnormal DG micro- and macrostructure were present in offspring of individuals with MDD and whether these abnormalities predicted future symptomatology. METHODS: We measured the mean diffusivity of gray matter, a measure of microstructure, via diffusion tensor imaging and volume of the DG via structural magnetic resonance imaging in 102 generation 2 and generation 3 offspring at high and low risk for depression, defined by the presence or absence, respectively, of moderate to severe MDD in generation 1. Prior, current, and future depressive symptoms were tested for association with hippocampal structure. RESULTS: DG mean diffusivity was higher in individuals at high risk for depression, regardless of a lifetime history of MDD. While DG mean diffusivity was not associated with past or current depressive symptoms, higher mean diffusivity predicted higher symptom scores 8 years later. DG microstructure partially mediated the association between risk and future symptoms. DG volume was smaller in high-risk generation 2 but not in high-risk generation 3. CONCLUSIONS: Together, these findings suggest that the DG has a role in the development of depression. Furthermore, DG microstructure, more than macrostructure, is a sensitive risk marker for depression and partially mediates future depressive symptoms.

Concordance in parent and offspring cortico-basal ganglia white matter connectivity varies by parental history of major depressive disorder and early parental care.

Social behavior is transmitted cross-generationally through coordinated behavior within attachment bonds. Parental depression and poor parental care are major risks for disruptions of such coordination and are associated with offspring's psychopathology and interpersonal dysfunction. Given the key role of the cortico-basal ganglia (CBG) circuits in social communication, we examined similarities (concordance) of parent-offspring CBG white matter (WM) connections and how parental history of major depressive disorder (MDD) and early parental care moderate these similarities. We imaged 44 parent-offspring dyads and investigated WM connections between basal-ganglia seeds and selected regions in temporal cortex using diffusion tensor imaging (DTI) tractography. We found significant concordance in parent-offspring strength of CBG WM connections, moderated by parental lifetime-MDD and care. The results showed diminished neural concordance among dyads with a depressed parent and that better parental care predicted greater concordance, which also provided a protective buffer against attenuated concordance among dyads with a depressed parent. Our findings provide the first neurobiological evidence of concordance between parents-offspring in WM tracts and that concordance is diminished in families where parents have lifetime-MDD. This disruption may be a risk factor for intergenerational transmission of psychopathology. Findings emphasize the long-term role of early caregiving in shaping the neural concordance among at-risk and affected dyads.

Differences in brain structure and function in children with the FTO obesity-risk allele.

OBJECTIVE: Noncoding alleles of the fat mass and obesity-associated (FTO) gene have been associated with obesity risk, yet the underlying mechanisms remain unknown. Risk allele carriers show alterations in brain structure and function, but previous studies have not disassociated the effects of genotype from those of body mass index (BMI). METHODS: Differences in brain structure and function were examined in children without obesity grouped by their number of copies (0,1,2) of the FTO obesity-risk single-nucleotide polymorphism (SNP) rs1421085. One hundred five 5- to 10-year-olds (5th-95th percentile body fat) were eligible to participate. Usable scans were obtained from 93 participants (15 CC [homozygous risk], 31 CT [heterozygous] and 47 TT [homozygous low risk]). RESULTS: Homozygous C allele carriers (CCs) showed greater grey matter volume in the cerebellum and temporal fusiform gyrus. CCs also demonstrated increased bilateral cerebellar white matter fibre density and increased resting-state functional connectivity between the bilateral cerebellum and regions in the frontotemporal cortices. CONCLUSIONS: This is the first study to examine brain structure and function related to FTO alleles in young children not yet manifesting obesity. This study lends support to the notion that the cerebellum may be involved in FTO-related risk for obesity, yet replication and further longitudinal study are required.

Machine learning prediction of incidence of Alzheimer's disease using large-scale administrative health data.

Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals' history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer's disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: "definite AD" with diagnostic codes and dementia medication (n = 614) and "probable AD" with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on "definite AD" and "probable AD" outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

Structural neural markers of response to cognitive behavioral therapy in pediatric obsessive-compulsive disorder.

BACKGROUND: Cognitive behavioral therapy (CBT) is an effective, first-line treatment for pediatric obsessive-compulsive disorder (OCD). While neural predictors of treatment outcomes have been identified in adults with OCD, robust predictors are lacking for pediatric patients. Herein, we sought to identify brain structural markers of CBT response in youth with OCD. METHODS: Twenty-eight children/adolescents with OCD and 27 matched healthy participants (7- to 18-year-olds, M = 11.71 years, SD = 3.29) completed high-resolution structural and diffusion MRI (all unmedicated at time of scanning). Patients with OCD then completed 12-16 sessions of CBT. Subcortical volume and cortical thickness were estimated using FreeSurfer. Structural connectivity (streamline counts) was estimated using MRtrix. RESULTS: Thinner cortex in nine frontoparietal regions significantly predicted improvement in Children's Yale-Brown Obsessive-Compulsive Scale (CY-BOCS) scores (all ts > 3.4, FDR-corrected ps < .05). These included middle and superior frontal, angular, lingual, precentral, superior temporal, and supramarginal gyri (SMG). Vertex-wise analyses confirmed a significant left SMG cluster, showing large effect size (Cohen's d = 1.42) with 72.22% specificity and 90.00% sensitivity in predicting CBT response. Ten structural connections between cingulo-opercular regions exhibited fewer streamline counts in OCD (all ts > 3.12, Cohen's ds > 0.92) compared with healthy participants. These connections predicted post-treatment CY-BOCS scores, beyond pretreatment severity and demographics, though not above and beyond cortical thickness. CONCLUSIONS: The current study identified group differences in structural connectivity (reduced among cingulo-opercular regions) and cortical thickness predictors of CBT response (thinner frontoparietal cortices) in unmedicated children/adolescents with OCD. These data suggest, for the first time, that cortical and white matter features of task control circuits may be useful in identifying which pediatric patients respond best to individual CBT.

Diagnosis and prognosis of Alzheimer's disease using brain morphometry and white matter connectomes.

Accurate, reliable prediction of risk for Alzheimer's disease (AD) is essential for early, disease-modifying therapeutics. Multimodal MRI, such as structural and diffusion MRI, is likely to contain complementary information of neurodegenerative processes in AD. Here we tested the utility of the multimodal MRI (T1-weighted structure and diffusion MRI), combined with high-throughput brain phenotyping-morphometry and structural connectomics-and machine learning, as a diagnostic tool for AD. We used, firstly, a clinical cohort at a dementia clinic (National Health Insurance Service-Ilsan Hospital [NHIS-IH]; N = 211; 110 AD, 64 mild cognitive impairment [MCI], and 37 cognitively normal with subjective memory complaints [SMC]) to test the diagnostic models; and, secondly, Alzheimer's Disease Neuroimaging Initiative (ADNI)-2 to test the generalizability. Our machine learning models trained on the morphometric and connectome estimates (number of features = 34,646) showed optimal classification accuracy (AD/SMC: 97% accuracy, MCI/SMC: 83% accuracy; AD/MCI: 97% accuracy) in NHIS-IH cohort, outperforming a benchmark model (FLAIR-based white matter hyperintensity volumes). In ADNI-2 data, the combined connectome and morphometry model showed similar or superior accuracies (AD/HC: 96%; MCI/HC: 70%; AD/MCI: 75% accuracy) compared with the CSF biomarker model (t-tau, p-tau, and Amyloid ß, and ratios). In predicting MCI to AD progression in a smaller cohort of ADNI-2 (n = 60), the morphometry model showed similar performance with 69% accuracy compared with CSF biomarker model with 70% accuracy. Our comparisons of the classifiers trained on structural MRI, diffusion MRI, FLAIR, and CSF biomarkers showed the promising utility of the white matter structural connectomes in classifying AD and MCI in addition to the widely used structural MRI-based morphometry, when combined with machine learning.