Heritability of the human connectome: A connectotyping study.
ABSTRACT: Recent progress in resting-state neuroimaging demonstrates that the brain exhibits highly individualized patterns of functional connectivity-a "connectotype." How these individualized patterns may be constrained by environment and genetics is unknown. Here we ask whether the connectotype is familial and heritable. Using a novel approach to estimate familiality via a machine-learning framework, we analyzed resting-state fMRI scans from two well-characterized samples of child and adult siblings. First we show that individual connectotypes were reliably identified even several years after the initial scanning timepoint. Familial relationships between participants, such as siblings versus those who are unrelated, were also accurately characterized. The connectotype demonstrated substantial heritability driven by high-order systems including the fronto-parietal, dorsal attention, ventral attention, cingulo-opercular, and default systems. This work suggests that shared genetics and environment contribute toward producing complex, individualized patterns of distributed brain activity, rather than constraining local aspects of function. These insights offer new strategies for characterizing individual aberrations in brain function and evaluating heritability of brain networks.
Project description:A resting-state functional connectivity (rsFC)-constructed functional network (FN) derived from functional magnetic resonance imaging (fMRI) data can effectively mine alterations in brain function during aging due to the non-invasive and effective advantages of fMRI. With global health research focusing on aging, several open fMRI datasets have been made available that combine deep learning with big data and are a new, promising trend and open issue for brain information detection in fMRI studies of brain aging. In this study, we proposed a new method based on deep learning from the perspective of big data, named Deep neural network (DNN) with Autoencoder (AE) pretrained Functional connectivity Analysis (DAFA), to deeply mine the important functional connectivity changes in fMRI during brain aging. First, using resting-state fMRI data from 421 subjects from the CamCAN dataset, functional connectivities were calculated using sliding window method, and the complex functional patterns were mined by an AE. Then, to increase the statistical power and reliability of the results, we used an AE-pretrained DNN to relabel the functional connectivities of each subject to classify them as belonging to the attributes of young or old individuals. A method called search-back analysis was performed to find alterations in brain function during aging according to the relabeled functional connectivities. Finally, behavioral data regarding fluid intelligence and response time were used to verify the revealed functional changes. Compared to traditional methods, DAFA revealed additional, important aged-related changes in FC patterns [e.g., FC connections within the default mode (DMN) and the sensorimotor and cingulo-opercular networks, as well as connections between the frontoparietal and cingulo-opercular networks, between the DMN and the frontoparietal/cingulo-opercular/sensorimotor/occipital/cerebellum networks, and between the sensorimotor and frontoparietal/cingulo-opercular networks], which were correlated to behavioral data. These findings demonstrated that the proposed DAFA method was superior to traditional FC-determining methods in discovering changes in brain functional connectivity during aging. In addition, it may be a promising method for exploring important information in other fMRI studies.
Project description:Familiality in brain tumors is not definitively substantiated.We used the Utah Population Data Base (UPDB), a genealogy representing the Utah pioneers and their descendants, record-linked to statewide cancer records, to describe the familial nature of primary brain cancer. We examined the familial clustering of primary brain tumors, including subgroups defined by histologic type and age at diagnosis. The UPDB includes 1,401 primary brain tumor cases defined as astrocytoma or glioblastoma, all with at least three generations of genealogy data. We tested the hypothesis of excess relatedness of brain tumor cases using the Genealogical Index of Familiality method. We estimated relative risks for brain tumors in relatives using rates of brain tumors estimated internally.Significant excess relatedness was observed for astrocytomas and glioblastomas considered as a group (n = 1,401), for astrocytomas considered separately (n = 744), but not for glioblastomas considered separately (n = 658). Significantly increased risks to first- and second-degree relatives for astrocytomas were identified for relatives of astrocytomas considered separately. Significantly increased risks to first-degree relatives, but not second degree, were observed for astrocytoma and glioblastoma cases considered together, and for glioblastoma cases considered separately.This study provides strong evidence for a familial contribution to primary brain cancer risk. There is evidence that this familial aspect includes not only shared environment, but also a heritable component. Extended high-risk brain tumor pedigrees identified in the UPDB may provide the opportunity to identify predisposition genes responsible for familial brain tumors.
Project description:Family and twin studies of Borderline Personality Disorder (BPD) have found familial aggregation and genetic propensity for BPD, but estimates vary widely. Large-scale family studies of clinically diagnosed BPD are lacking. Therefore, we performed a total-population study estimating the familial aggregation and heritability of clinically diagnosed BPD. We followed 1,851,755 individuals born 1973-1993 in linked Swedish national registries. BPD-diagnosis was ascertained between 1997 and 2013, 11,665 received a BPD-diagnosis. We identified relatives and estimated sex and birth year adjusted hazard ratios, i.e., the rate of BPD-diagnoses in relatives to individuals with BPD-diagnosis compared to individuals with unaffected relatives, and used structural equation modeling to estimate heritability. The familial association decreased along with genetic relatedness. The hazard ratio was 11.5 (95% confidence interval (CI) = 1.6-83.8) for monozygotic twins; 7.4 (95% CI = 1.0-55.3) for dizygotic twins; 4.7 (95% CI = 3.9-5.6) for full siblings; 2.1 (95% CI = 1.5-3.0) for maternal half-siblings; 1.3 (95% CI = 0.9-2.1) for paternal half-siblings; 1.7 (95% CI = 1.4-2.0) for cousins whose parents were full siblings; 1.1 (95% CI = 0.7-1.8) for cousins whose parents were maternal half-siblings; and 1.9 (95% CI = 1.2-2.9) for cousins whose parents were paternal half-siblings. Heritability was estimated at 46% (95% CI = 39-53), and the remaining variance was explained by individually unique environmental factors. Our findings pave the way for further research into specific genetic variants, unique environmental factors implicated, and their interplay in risk for BPD.
Project description:Phenotype definition of psychotic disorders has a strong impact on the degree of familial aggregation. Nevertheless, the extent to which distinct classification systems affect familial aggregation (ie, familiality) remains an open question. This study was aimed at examining the familiality associated with 4 nosologic systems of psychotic disorders (DSM-IV, ICD-10, Leonhard's classification and a data-driven approach) and their constituting diagnoses in a sample of multiplex families with psychotic disorders.Participants were probands with a psychotic disorder, their parents and at least one first-degree relative with a psychotic disorder. The sample was made of 441 families comprising 2703 individuals, of whom 1094 were affected and 1709 unaffected.The Leonhard classification system had the highest familiality (h (2) = 0.64), followed by the empirical (h (2) = 0.55), DSM-IV (h (2) = 0.50), and ICD-10 (h (2) = 0.48). Familiality estimates for individual diagnoses varied considerably (h (2) = 0.25-0.79). Regarding schizophrenia diagnoses, Leonhard's systematic schizophrenia (h (2) = 0.78) had the highest familiality, followed by latent class core schizophrenia (h (2) = 0.74), DSM-IV schizophrenia (h (2) = 0.48), and ICD-10 schizophrenia (h (2) = 0.41). Psychotic mood disorders showed substantial familiality across nosologic systems (h (2) = 0.60-0.77). Domains of psychopathology other than reality-distortion symptoms showed moderate familiality irrespective of diagnosis (h (2) = 0.22-0.52) with the deficit syndrome of schizophrenia showing the highest familiality (h (2) = 0.66).While affective psychoses showed relatively high familiality estimates across classification schemes, those of nonaffective psychoses varied markedly as a function of the diagnostic scheme with a narrow schizophrenia phenotype maximizing its familial aggregation. Leonhard's classification of psychotic disorders may be better suited for molecular genetic studies than the official diagnostic systems.
Project description:Executive functions (EFs) are regulatory cognitive processes that support goal-directed thoughts and behaviors and that involve two primary networks of functional brain activity in adulthood: the fronto-parietal and cingulo-opercular networks. The current study assessed whether the same networks identified in adulthood underlie child EFs. Using task-based fMRI data from a diverse sample of N?=?117 children and early adolescents (M age?=?10.17 years), we assessed the extent to which neural activity was shared across switching, updating, and inhibition domains, and whether these patterns were qualitatively consistent with adult EF-related activity. Brain regions that were consistently engaged across switching, updating, and inhibition tasks closely corresponded to the cingulo-opercular and fronto-parietal networks identified in studies of adults. Isolating brain activity during more demanding task periods highlighted contributions of the dorsal anterior cingulate and anterior insular regions of the cingulo-opercular network. Results were independent of age and time-on-task effects. These results indicate that the two core brain networks that support EFs are in place by middle childhood, in agreement with resting-state findings of adultlike brain network organization. Improvement in EFs from middle childhood to adulthood, therefore, are likely due to quantitative changes in activity within these networks, rather than qualitative changes in the organization of the networks themselves. Improved knowledge of how the brain's functional organization supports EF in childhood has critical implications for understanding the maturation of cognitive abilities.
Project description:<h4>Background</h4>ASD and ADHD are prevalent neurodevelopmental disorders that frequently co-occur and have strong evidence for a degree of shared genetic aetiology. Behavioural and neurocognitive heterogeneity in ASD and ADHD has hampered attempts to map the underlying genetics and neurobiology, predict intervention response, and improve diagnostic accuracy. Moving away from categorical conceptualisations of psychopathology to a dimensional approach is anticipated to facilitate discovery of data-driven clusters and enhance our understanding of the neurobiological and genetic aetiology of these conditions. The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project is one of the first large-scale, family-based studies to take a truly transdiagnostic approach to ASD and ADHD. Using a comprehensive phenotyping protocol capturing dimensional traits central to ASD and ADHD, the MAGNET project aims to identify data-driven clusters across ADHD-ASD spectra using deep phenotyping of symptoms and behaviours; investigate the degree of familiality for different dimensional ASD-ADHD phenotypes and clusters; and map the neurocognitive, brain imaging, and genetic correlates of these data-driven symptom-based clusters.<h4>Methods</h4>The MAGNET project will recruit 1,200 families with children who are either typically developing, or who display elevated ASD, ADHD, or ASD-ADHD traits, in addition to affected and unaffected biological siblings of probands, and parents. All children will be comprehensively phenotyped for behavioural symptoms, comorbidities, neurocognitive and neuroimaging traits and genetics.<h4>Conclusion</h4>The MAGNET project will be the first large-scale family study to take a transdiagnostic approach to ASD-ADHD, utilising deep phenotyping across behavioural, neurocognitive, brain imaging and genetic measures.
Project description:Hemispheric integration and specialization are two prominent organizational principles for macroscopic brain function. Impairments of interhemispheric cooperation have been reported in schizophrenia patients, but whether such abnormalities should be attributed to effects of illness or familial risk remains inconclusive. Moreover, it is unclear how abnormalities in interhemispheric connectivity impact hemispheric specialization. To address these questions, we performed magnetic resonance imaging (MRI) in a large cohort of 253 participants, including 84 schizophrenia patients, 106 of their unaffected siblings and 63 healthy controls. Interhemispheric connectivity and hemispheric specialization were calculated from resting-state functional connectivity, and compared across groups. Results showed that schizophrenia patients exhibit lower interhemispheric connectivity as compared to controls and siblings. In addition, patients showed higher levels of hemispheric specialization as compared to siblings. Level of interhemispheric connectivity and hemispheric specialization correlated with duration of illness in patients. No significant alterations were identified in siblings relative to controls on both measurements. Furthermore, alterations in interhemispheric connectivity correlated with changes in hemispheric specialization in patients relative to controls and siblings. Taken together, these results suggest that lower interhemispheric connectivity and associated abnormalities in hemispheric specialization are features of established illness, rather than an expression of preexistent familial risk for schizophrenia.
Project description:A growing body of research has suggested that people with schizophrenia (SZ) exhibit altered patterns of functional and anatomical brain connectivity. For example, many previous resting state functional connectivity (rsFC) studies have shown that, compared to healthy controls (HC), people with SZ demonstrate hyperconnectivity between subregions of the thalamus and sensory cortices, as well as hypoconnectivity between subregions of the thalamus and prefrontal cortex. In addition to thalamic findings, hypoconnectivity between cingulo-opercular brain regions thought to be involved in salience detection has also been commonly reported in people with SZ. However, previous studies have largely relied on seed-based analyses. Seed-based approaches require researchers to define a single a priori brain region, which is then used to create a rsFC map across the entire brain. While useful for testing specific hypotheses, these analyses are limited in that only a subset of connections across the brain are explored. In the current manuscript, we leverage novel network statistical techniques in order to detect latent functional connectivity networks with organized topology that successfully differentiate people with SZ from HCs. Importantly, these techniques do not require a priori seed selection and allow for whole brain investigation, representing a comprehensive, data-driven approach to determining differential connectivity between diagnostic groups. Across two samples, (Sample 1: 35 SZ, 44 HC; Sample 2: 65 SZ, 79 HC), we found evidence for differential rsFC within a network including temporal and thalamic regions. Connectivity in this network was greater for people with SZ compared to HCs. In the second sample, we also found evidence for hypoconnectivity within a cingulo-opercular network of brain regions in people with SZ compared to HCs. In summary, our results replicate and extend previous studies suggesting hyperconnectivity between the thalamus and sensory cortices and hypoconnectivity between cingulo-opercular regions in people with SZ using data-driven statistical and graph theoretical techniques.
Project description:Developmental disorders of spoken and written language are heterogeneous in nature with impairments observed across various linguistic, cognitive, and sensorimotor domains. These disorders are also associated with characteristic patterns of atypical neural structure and function that are observable early in development, often before formal schooling begins. Established patterns of heritability point toward genetic contributions, and molecular genetics approaches have identified genes that play a role in these disorders. Still, identified genes account for only a limited portion of phenotypic variance in complex developmental disorders, described as the problem of "missing heritability." The characterization of intermediate phenotypes at the neural level may fill gaps in our understanding of heritability patterns in complex disorders, and the emerging field of neuroimaging genetics offers a promising approach to accomplish this goal. The neuroimaging genetics approach is gaining prevalence in language- and reading-related research as it is well-suited to incorporate behavior, genetics, and neurobiology into coherent etiological models of complex developmental disorders. Here, we review research applying the neuroimaging genetics approach to the study of specific reading disability (SRD) and developmental language disorder (DLD), much of which links genes with known neurodevelopmental function to functional and structural abnormalities in the brain.
Project description:We presented a risk assessment model to distinguish between type 1 diabetes (T1D) affected and unaffected siblings using only three single nucleotide polymorphism (SNP) genotypes. In addition we calculated the heritability from genome-wide identity-by-descent (IBD) sharing between full siblings. We analyzed 1,253 pairs of affected individuals and their unaffected siblings (750 pairs from a discovery set and 503 pairs from a validation set) from the T1D Genetics Consortium (T1DGC), applying a logistic regression to analyze the area under the receiver operator characteristic (ROC) curve (AUC). To calculate the heritability of T1D we used the Haseman-Elston regression analysis of the squared difference between the phenotypes of the pairs of siblings on the estimate of their genome-wide IBD proportion. The model with only 3 SNPs achieving an AUC of 0.75 in both datasets outperformed the model using the presence of the high-risk DR3/4 HLA genotype, namely AUC of 0.60. The heritability on the liability scale of T1D was approximately from 0.53 to 0.92, close to the results obtained from twin studies, ranging from 0.4 to 0.88.