Biology subjects

Kaufmann, T.

Publications and source records attributed to Kaufmann, T..

15 recordsLinked to original sources

bioRxiv2018

Genetic control of variability in subcortical and intracranial volumes

Sensitivity to external demands is essential for adaptation to dynamic environments, but comes at the cost of increased risk of adverse outcomes when facing poor environmental conditions. Here, we apply a novel methodology to perform genome-wide association analysis of mean and variance in nine key brain features (accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus, intracranial volume and cortical thickness), integrating genetic and neuroanatomical data from a large lifespan sample (n=25,575 individuals; 8 to 89 years, mean age 51.9 years). We identify genetic loci associated with phenotypic variability in cortical thickness, thalamus, pallidum, and intracranial volumes. The variance-controlling loci included genes with a documented role in brain and mental health and were not associated with the mean anatomical volumes. This proof-of-principle of the hypothesis of a genetic regulation of brain volume variability contributes to establishing the genetic basis of phenotypic variance (i.e., heritability), allows identifying different degrees of brain robustness across individuals, and opens new research avenues in the search for mechanisms controlling brain and mental health.

neuroscience↗

bioRxiv2018

Cross-sectional and longitudinal brain scans reveal accelerated brain aging in multiple sclerosis

Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. By combining longitudinal MRI-based brain morphometry and brain age estimation using machine learning, we tested the hypothesis that MS patients have higher brain age relative to chronological age than healthy controls (HC) and that longitudinal rate of brain aging in MS patients is associated with clinical course.\n\nSeventy-six MS patients, 71 % females and mean age 34.8 years (range 21-49) at inclusion, were examined with brain MRI at three time points with a mean total follow up period of 4.4 years. A machine learning model was applied on an independent training set of 3208 HC, estimating individual brain age and calculating the difference between estimated brain age and chronological age, termed brain age gap (BAG). We also assessed the longitudinal change rate in BAG in MS individuals. We used additional cross-sectional MRI data from 235 HC for case-control comparison.\n\nMS patients showed increased BAG (4.4 {+/-}6.6 years) compared to HC (Cohens D = 0.69, p = 4.0 x 10-6). Longitudinal estimates of BAG in MS patients suggested an accelerated rate of brain aging corresponding to an annual increase of 0.41 ({+/-}1.23) years compared to chronological aging for the MS patients (p = 0.008).\n\nOn average, patients with MS have significantly higher BAG compared to HC and accelerated rate of brain aging compared to chronological aging. Brain age estimation represents a promising method for evaluation of brain changes in MS, with potential for predicting future outcome and guide treatment.

neuroscience↗

bioRxiv2018

The dark side of the mean: brain structural heterogeneity in schizophrenia and its polygenic risk.

ImportanceBetween-subject variability in brain structure is determined by gene-environment interactions, possibly reflecting differential sensitivity to environmental and genetic perturbations. Magnetic resonance imaging (MRI) studies have revealed thinner cortices and smaller subcortical volumes in patients. However, such group-level comparisons may mask considerable within-group heterogeneity, which has largely remained unnoticed in the literature\n\nObjectiveTo compare brain structural variability between individuals with SZ and healthy controls (HC) and to test if respective variability reflects the polygenic risk for SZ (PRS) in HC.\n\nDesign, Setting, and ParticipantsWe compared MRI derived cortical thickness and subcortical volumes between 2,010 healthy controls and 1,151 patients with SZ across 16 cohorts. Secondly, we tested for associations between PRS and MRI features in 12,490 participants from UK Biobank.\n\nMain Outcomes and MeasuresWe modeled mean and dispersion effects of SZ and PRS using double generalized linear models. We performed vertex-wise analyses for thickness, and region-of-interest analysis for cortical, subcortical and hippocampal subfield volumes. Follow-up analyses included within-sample analysis, controlling for intracranial volume and population covariates, test of robustness of PRS threshold, and outlier removal.\n\nResultsCompared to controls, patients with SZ showed higher heterogeneity in cortical thickness, cortical and ventricle volumes, and hippocampal subfields. Higher PRS was associated with thinner frontal and temporal cortices, as well as smaller left CA2/3, but was not significantly associated with dispersion.\n\nConclusion and relevanceSZ is associated with substantial brain structural heterogeneity beyond the mean differences. These findings possibly reflect higher differential sensitivity to environmental and genetic perturbations in patients, supporting the heterogeneous nature of SZ. Higher PRS for SZ was associated with thinner fronto-temporal cortices and smaller subcortical volumes, but there were no significant associations with the heterogeneity in these measures, i.e. the variability among individuals with high PRS were comparable to the variability among individuals with low PRS. This suggests that brain variability in SZ results from interactions between environmental and genetic factors that are not captured by the PGR. Factors contributing to heterogeneity in fronto-temporal cortices and hippocampus are thus key to further our understanding of how genetic and environmental factors shape brain biology in SZ.\n\nKey PointsQuestion: Is schizophrenia and its polygenic risk associated with brain structural heterogeneity in addition to mean changes?\n\nFindings: In a sample of 1151 patients and 2010 controls, schizophrenia was associated with increased heterogeneity in fronto-temporal thickness, cortical, ventricle, and hippocampal volumes, besides robust reductions in mean estimates. In an independent sample of 12,490 controls, polygenic risk for schizophrenia was associated with thinner fronto-temporal cortices and smaller CA2/3 of the left hippocampus, but not with heterogeneity.\n\nMeaning: Schizophrenia is associated with increased inter-individual differences in brainstructure, possibly reflecting clinical heterogeneity, gene-environment interactions, or secondary disease factors.

neuroscience↗

bioRxiv2018

White matter aberrations and age-related trajectories in patients with schizophrenia and bipolar disorder revealed by diffusion tensor imaging

Supported by histological and genetic evidence implicating myelin, neuroinflammation and oligodendrocyte dysfunction in schizophrenia spectrum disorders (SZ), diffusion tensor imaging (DTI) studies have consistently shown white matter (WM) abnormalities when compared to healthy controls (HC). The diagnostic specificity remains unclear, with bipolar disorders (BD) frequently conceptualized as a less severe clinical manifestation along a psychotic spectrum. Further, the age-related dynamics and possible sex differences of WM abnormalities in SZ and BD are currently understudied.\n\nUsing tract-based spatial statistics (TBSS) we compared DTI-based microstructural indices between SZ (n=128), BD (n=61), and HC (n=293). We tested for age-by-group and sex-by-group interactions, computed effect sizes within different age-bins and within genders.\n\nTBSS revealed global reductions in fractional anisotropy (FA) and increases in radial (RD) diffusivity in SZ compared to HC, with strongest effects in the body and splenium of the corpus callosum, and lower FA in SZ compared to BD in right inferior longitudinal fasciculus and right inferior fronto-occipital fasciculus, and no significant differences between BD and HC. The results were not strongly dependent on age or sex. Despite lack of significant group-by-age interactions, a sliding-window approach supported widespread WM involvement in SZ with most profound differences in FA from the late 20s.

neuroscience↗

bioRxiv2018

Assessing distinct patterns of cognitive aging using tissue-specific brain age prediction based on diffusion tensor imaging and brain morphometry

Multimodal imaging enables sensitive measures of the architecture and integrity of the human brain, but the high-dimensional nature of advanced brain imaging features poses inherent challenges for the analyses and interpretations. Multivariate age prediction reduces the dimensionality to one biologically informative summary measure with potential for assessing deviations from normal lifespan trajectories. A number of studies documented remarkably accurate age prediction, but the differential age trajectories and the cognitive sensitivity of distinct brain tissue classes have to a lesser extent been characterized.\n\nExploring differential brain age models driven by tissue-specific classifiers provides a hitherto unexplored opportunity to disentangle independent sources of heterogeneity in brain biology. We trained machine-learning models to estimate brain age using various combinations of FreeSurfer based morphometry and diffusion tensor imaging based indices of white matter microstructure in 612 healthy controls aged 18-87 years. To compare the tissue- specific brain ages and their cognitive sensitivity we applied each of the 11 models in an independent and cognitively well-characterized sample (n=265, 20-88 years). Correlations between true and estimated age in our test sample were highest for the most comprehensive brain morphometry (r=0.83, CI:0.78-0.86) and white matter microstructure (r=0.79, CI:0.74-0.83) models, confirming sensitivity and generalizability. The deviance from the chronological age were sensitive to performance on several cognitive tests for various models, including spatial Stroop and symbol coding, indicating poorer performance in individuals with an over-estimated age. Tissue-specific brain age models provide sensitive measures of brain integrity, with implications for the study of a range of brain disorders.

neuroscience↗

bioRxiv2018

Probing developmental patterns of intracortical myelination using gray/white matter contrast and associations with cognitive abilities and psychopathology in youth

BackgroundCerebral myeloarchitecture shows substantial development across childhood and adolescence, and aberrations in these trajectories are relevant for a range of mental disorders. Differential myelination between intracortical and subjacent white matter can be approximated using signal intensities in T1-weighted magnetic resonance images (MRI).\n\nMethodsTo test the sensitivity of gray/white matter contrast (GWC) to age and individual differences in psychopathology and general cognitive ability in youth (8-23 years), we formed data-driven psychopathology and cognitive components using a large population-based sample, the Philadelphia Neurodevelopmental Cohort (PNC) (n=6487, 52% females). We then tested for associations with regional GWC defined by an independent component analysis (ICA) in a subsample with available MRI data (n=1467, 53% females).\n\nResultsThe analyses revealed a global GWC component, which showed an age-related decrease from late childhood and across adolescence. In addition, we found regional anatomically meaningful components with differential age associations explaining variance beyond the global component. When accounting for age and sex, both higher symptom levels of anxiety or prodromal psychosis and lower cognitive ability were associated with higher GWC in insula and cingulate cortices and with lower GWC in pre- and postcentral cortices. We also found several additional regional associations with anxiety, prodromal psychosis and cognitive ability.\n\nConclusionIndependent modes of GWC variation are sensitive to global and regional brain developmental processes, possibly related to differences between intracortical and subjacent white matter myelination, and individual differences in regional GWC are associated with both mental health and general cognitive functioning.

neuroscience↗

bioRxiv2018

Genetics of brain age suggest an overlap with common brain disorders

Numerous genetic and environmental factors contribute to psychiatric disorders and other brain disorders. Common risk factors likely converge on biological pathways regulating the optimization of brain structure and function across the lifespan. Here, using structural magnetic resonance imaging and machine learning, we estimated the gap between brain age and chronological age in 36,891 individuals aged 3 to 96 years, including individuals with different brain disorders. We show that several disorders are associated with accentuated brain aging, with strongest effects in schizophrenia, multiple sclerosis and dementia, and document differential regional patterns of brain age gaps between disorders. In 16,269 healthy adult individuals, we show that brain age gap is heritable with a polygenic architecture overlapping those observed in common brain disorders. Our results identify brain age gap as a genetically modulated trait that offers a window into shared and distinct mechanisms in different brain disorders.

neuroscience↗

bioRxiv2018

Brain scans from 21297 individuals reveal the genetic architecture of hippocampal subfield volumes

The hippocampus is a heterogeneous structure, comprising histologically distinguishable subfields. These subfields are differentially involved in memory consolidation, spatial navigation and pattern separation, complex functions often impaired in individuals with brain disorders characterized by reduced hippocampal volume, including Alzheimers disease (AD) and schizophrenia. Given the structural and functional heterogeneity of the hippocampal formation, we sought to characterize the subfields genetic architecture. T1-weighted brain scans (n=21297, 16 cohorts) were processed with the hippocampal subfields algorithm in FreeSurfer v6.0. We ran a genome-wide association analysis on each subfield, covarying for total hippocampal volume. We further calculated the single nucleotide polymorphism (SNP)-based heritability of twelve subfields, as well as their genetic correlation with each other, with other structural brain features, and with AD and schizophrenia. All outcome measures were corrected for age, sex, and intracranial volume. We found 15 unique genome-wide significant loci across six subfields, of which eight had not been previously linked to the hippocampus. Top SNPs were mapped to genes associated with neuronal differentiation, locomotor behaviour, schizophrenia and AD. The volumes of all the subfields were estimated to be heritable (h2 from .14 to .27, all p< 1x10-16) and clustered together based on their genetic correlations compared to other structural brain features. There was also evidence of genetic overlap of subicular subfield volumes with schizophrenia. We conclude that hippocampal subfields have partly distinct genetic determinants associated with specific biological processes and traits. Taking into account this specificity may increase our understanding of hippocampal neurobiology and associated pathologies.

genetics↗

bioRxiv2018

Cerebellar grey matter volume in adolescence is associated with prodromal psychotic symptoms and norm-violating behavior

ImportanceAccumulating evidence supports cerebellar involvement in mental disorders such as schizophrenia, bipolar disorder, depression, anxiety disorders and attention-deficit hyperactivity disorder. However, little is known about cerebellar involvement in the developmental stages of these disorders. In particular, whether cerebellar morphology is associated with early expression of specific symptom domains remains unclear.\n\nObjectiveTo determine the robustness and specificity of associations between cerebellar morphology, general cognitive function, general psychopathology and sub-clinical psychiatric symptom domains in adolescence.\n\nDesign, setting and participantsAssessment of parametric structure-function associations between MR-based brain morphometric features and data-driven cognitive and clinical phenotypes in the Philadelphia Neurodevelopmental Cohort (N=1401, age-range: 8 - 23).\n\nMain outcomes and measuresRobust prediction of cognitive and clinical symptom domain scores from cerebellar, subcortical and cerebro-cortical brain features using machine learning with 10-fold internal cross-validation and permutation-based statistical inference.\n\nResultsCerebellar morphology predicted both general cognitive function and general psychopathology (mean Pearson correlation coefficients between predicted and observed values: r = .20 and r = .13, respectively; corrected p-values < .0009). Analyses of specific sub-clinical symptom domains revealed significant associations with rates of norm-violating behavior (r = .17; p < .0009), prodromal psychotic symptoms (r = .12; p < .0009) and anxiety symptoms (r = .09; p =.0117). In contrast, we observed no significant associations between cerebellar features and the severity of attention deficits, depressive, manic or obsessive-compulsive symptoms (all rs =< .03, all ps => .1). Associations with norm-violating behavior and prodromal psychotic symptoms were stronger for the cerebellum than for subcortical and cerebro-cortical regions, while anxiety and general cognitive function were related to more global brain morphology patterns. The association between cerebellar volume and prodromal psychotic symptoms, and to a lesser extent norm violating behavior, remained significant when adjusting for potentially confounding factors such as general cognitive function, general psychopathology, parental education level and use of psychoactive substances.\n\nConclusions and relevanceThe robust associations with sub-clinical psychiatric symptoms in the age range when these typically emerge highlight the cerebellum as a key brain structure in the development of severe mental disorders.\n\nKey pointsO_ST_ABSQuestionsC_ST_ABSIs cerebellar morphology associated with sub-clinical psychiatric symptoms in adolescence? Do such associations show symptom domain specificity or do they rather constitute a marker of general psychopathology?\n\nFindingsMachine learning utilizing cerebellar morphology features significantly predicted the severity of prodromal psychotic symptoms, norm-violating behavior and anxiety, but not attention deficits, depressive, manic or obsessive-compulsive sub-clinical symptoms. Associations with prodromal psychotic symptoms were stronger for the cerebellum than for cerebral subcortical and cerebro-cortical regions, and remained significant when adjusting for several potentially confounding factors.\n\nMeaningThe cerebellum appears to play a key role in the development of severe mental illness.

neuroscience↗

bioRxiv2018

Data-driven clustering reveals a link between symptoms and functional brain connectivity in depression

BackgroundDepression is a complex disorder with large inter-individual variability in symptom profiles that often occur alongside symptoms of other psychiatric domains such as anxiety. A dimensional and symptom-based approach may help refine the characterization and classification of depressive and anxiety disorders and thus aid in establishing robust biomarkers. We assess the brain functional connectivity correlates of a symptom-based clustering of individuals using functional brain imaging data.\n\nMethodsWe assessed symptoms of depression and anxiety using Becks Depression and Becks Anxiety inventories in individuals with or without a history of depression, and high dimensional data clustering to form subgroups based on symptom profiles. To assess the biological relevance of this subtyping, we compared functional magnetic resonance imaging-based dynamic and static functional connectivity between subgroups in a subset of the total sample.\n\nResultsWe identified five subgroups with distinct symptom profiles, cutting across diagnostic boundaries and differing in terms of total severity, symptom patterns and centrality. For instance, inability to relax, fear of the worst, and feelings of guilt were among the most severe symptoms in subgroup 1, 2 and 3, respectively. These subgroups showed evidence of differential static brain connectivity patterns, in particular comprising a fronto-temporal network. In contrast, we found no significant associations with clinical sum scores, dynamic functional connectivity or global connectivity measures.\n\nConclusionAdding to the ongoing pursuit of individual-based treatment, the results show subtyping based on a dimensional conceptualization and unique constellations of anxiety and depression symptoms is supported by distinct brain static functional connectivity patterns.

neuroscience↗

bioRxiv2018

An Augmented Aging Process in Brain White Matter in HIV

ObjectiveHIV infection and aging are both associated with neurodegeneration. However, whether the aging process alone or other factors associated with advanced age account for the progression of neurodegeneration in the aging HIV-positive (HIV+) population remains unclear.\n\nMethodsHIV+ (n=70) and HIV-negative (HIV-, n=34) participants underwent diffusion tensor imaging (DTI) and metrics of microstructural properties were extracted from regions of interest (ROIs). A support vector regression model was trained on two independent datasets of healthy adults across the adult life-span (n=765, Cam-CAN = 588; UiO = 177) to predict participant age from DTI metrics, and applied to the HIV dataset. Predicted brain age gap (BAG) was computed as the difference between predicted age and chronological age, and statistically compared between HIV groups. Regressions assessed the relationship between BAG and HIV severity/medical comorbidities. Finally, correlation analyses tested for associations between BAG and cognitive performance.\n\nResultsBAG was significantly higher in the HIV+ group than the HIV-group F (1, 103) = 12.408, p = 0.001). HIV RNA viral load was significantly associated with BAG, particularly in older HIV+ individuals (R2 = 0.29, F(7, 70) = 2.66, p = 0.021). Further, BAG was negatively correlated with domain-level cognitive function (learning: r = -0.26, p = 0.008; memory: r = -0.21, p = 0.034).\n\nConclusionsHIV infection is associated with augmented white matter aging, and greater brain aging is associated with worse cognitive performance in multiple domains.

neuroscience↗

bioRxiv2017

Heritable cognitive and psychopathology factors in youth are predicted by brain fronto-temporal white matter pathway

A healthy transition from adolescence to adulthood relies on a continuous individual adaptation to a dynamic environment. Here, we employed data driven multivariate approaches to derive both general cognitive and psychopathology factors as well as brain phenotypes in children and adolescents in the publicly available PNC sample. We identified a distinct brain white matter pattern which proved central for prediction of heritable cognition and psychopathology scores, highlighting the importance of fronto-temporal connections for intellectual and mental development.

neuroscience↗

bioRxiv2017

Effects of autozygosity and schizophrenia polygenic risk on cognitive and brain developmental trajectories

Cognitive and brain development are determined by dynamic interactions between genes and environment across the lifespan. Aside from marker-by-marker analyses of polymorphisms, biologically meaningful features of the whole-genome (derived from the combined effect of individual markers) have been postulated to inform on human phenotypes including cognitive traits and their underlying biological substrate.\n\nHere, estimates of inbreeding and genetic susceptibility for schizophrenia calculated from genome-wide data -runs of homozygosity (ROH) and schizophrenia polygenic risk score (PGRS)- are analyzed in relation to cognitive abilities (n=4183) and brain structure (n=516) in a general-population sample of European-ancestry participants aged 8-22, from the Philadelphia Neurodevelopmental Cohort.\n\nThe findings suggest that a higher ROH burden and higher schizophrenia PGRS are associated with higher intelligence. Cognition~ROH and cognition~PGRS associations obtained in this cohort may respectively evidence that assortative mating influences intelligence, and that individuals with high schizophrenia genetic risk who do not transition to disease status are cognitively resilient.\n\nNeuroanatomical data showed that the effects of schizophrenia PGRS on cognition could be modulated by brain structure, although larger imaging datasets are needed to accurately disentangle the underlying neural mechanisms linking IQ with both inbreeding and the genetic burden for schizophrenia.

neuroscience↗

bioRxiv2017

PARP inhibition causes premature loss of cohesion in cancer cells

Poly(ADP-ribose) polymerases (PARPs) regulate various aspects of cellular function including mitotic progression. Although PARP inhibitors have been undergoing various clinical trials and the PARP1/2 inhibitor olaparib was approved as monotherapy for BRCA-mutated ovarian cancer, their mode of action in killing tumour cells is not fully understood. We investigated the effect of PARP inhibition on mitosis in cancerous (cervical, ovary, breast and osteosarcoma) and non-cancerous cells by live-cell imaging. The clinically relevant inhibitor olaparib induced strong perturbations in mitosis, including problems with chromosome alignment at the metaphase plate, anaphase delay, and premature loss of cohesion (cohesion fatigue) after a prolonged metaphase arrest, resulting in sister chromatid scattering. PARP1 and PARP2 depletion suppressed the phenotype while PARP2 overexpression enhanced it, suggesting that olaparib-bound PARP1 and PARP2 rather than the lack of catalytic activity causes this phenotype. Olaparib-induced mitotic chromatid scattering was observed in various cancer cell lines with increased protein levels of PARP1 and PARP2, but not in non-cancer or cancer cell lines that expressed lower levels of PARP1 or PARP2. Interestingly, the sister chromatid scattering phenotype occurred only when olaparib was added during the S-phase preceding mitosis, suggesting that PARP1 and PARP2 entrapment at replication forks impairs sister chromatid cohesion. Clinically relevant DNA-damaging agents that impair replication progression such as topoisomerase inhibitors and cisplatin were also found to induce sister chromatid scattering and metaphase plate alignment problems, suggesting that these mitotic phenotypes are a common outcome of replication perturbation.

cell biology↗

bioRxiv2017

Genetic networks of the oxytocin system in the human brain: A gene expression and large-scale fMRI meta-analysis study

Oxytocin is a neuropeptide involved in animal and human reproductive and social behaviour, with potential implications for a range of psychiatric disorders. However, the therapeutic potential of oxytocin in mental health care suggested by animal research has not been successfully translated into clinical practice, partly due to a poor understanding of the expression and distribution of the oxytocin signaling pathway in the human brain, and its complex interactions with other biological systems. Among the genes involved in the oxytocin signaling pathway, three genes have been frequently implicated in human social behavior: OXT (structural gene for oxytocin), OXTR (oxytocin receptor), and CD38 (central oxytocin secretion). We characterized the distribution of the OXT, OXTR, and CD38 mRNA across the brain, identified putative gene pathway interactions by comparing gene expression patterns across 29131 genes, and assessed associations between gene expression patterns and cognitive states via large-scale fMRI meta-analysis. In line with the animal literature, oxytocin pathway gene expression was enriched in central, temporal, and olfactory regions. Across the brain, there was high co-expression of the oxytocin pathway genes with both dopaminergic (DRD2) and muscarinic acetylcholine (CHRM4) genes, reflecting an anatomical basis for critical gene pathway interactions. Finally, fMRI meta-analysis revealed that oxytocin pathway maps correspond with motivation and emotion processing, demonstrating the value of probing gene expression maps to identify brain functional targets for future pharmacological trials.

neuroscience↗