Brain Heterogeneity in Schizophrenia and Its Association With Polygenic Risk

A principal component approach to improve association testing with polygenic risk scores

Polygenic risk scores (PRSs) have become an increasingly popular approach for demonstrating polygenic influences on complex traits and for establishing common polygenic signals between different traits. PRSs are typically constructed using pruning and thresholding (P+T), but the best choice of parameters is uncertain; thus multiple settings are used and the best is chosen. Optimization can lead to inflated Type I error. Permutation procedures can correct this, but they can be computationally intensive. Alternatively, a single parameter setting can be chosen a priori for the PRS, but choosing suboptimal settings results in loss of power. We propose computing PRSs under a range of parameter settings, performing principal component analysis (PCA) on the resulting set of PRSs, and using the first PRS-PC in association tests. The first PC reweights the variants included in the PRS to achieve maximum variation over all PRS settings used. Using simulations and a real data application to study PRS association with bipolar disorder and psychosis in bipolar disorder, we compare the performance of the proposed PRS-PCA approach with a permutation test and an a priori selected p-value threshold. The PRS-PCA approach is simple to implement, outperforms the other strategies in most scenarios, and provides an unbiased estimate of prediction performance.

Brain Age Prediction Reveals Aberrant Brain White Matter in Schizophrenia and Bipolar Disorder: A Multisample Diffusion Tensor Imaging Study

BACKGROUND

Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts.

METHODS

We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18-94 years of age) and applied the models to the test sets including 648 patients with SZ (18-66 years of age), 185 patients with BD (18-64 years of age), and 990 HC subjects (17-68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results.

RESULTS

Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen's d = -0.29) and patients with BD (Cohen's d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy-based models showed larger group differences than the models based on other DTI-derived metrics.

CONCLUSIONS

Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.

Anatomic abnormalities of hippocampal subfields in never-treated and antipsychotic-treated patients with long-term schizophrenia

Hippocampal volume deficits have been reported in chronically-treated schizophrenia patients, however, the longer-term effects of antipsychotic medications on hippocampal anatomy are unclear. This case-control study investigated volume differences in hippocampal subfields of never-treated and antipsychotic-treated patients with long-term schizophrenia. High spatial-resolution T1-weighted magnetic resonance images were collected from 29 never-treated and 40 antipsychotic-treated patients with long-term schizophrenia matched for illness duration (all ≥ 5 years), and 40 demographically-matched healthy controls. Hippocampal subfield volumes were measured using FreeSurfer v6.0, compared across groups and between hemispheres, and correlated with clinical features. Volume reductions were found in both patient groups compared to healthy controls in 8 of 26 hippocampal subfields (Cohen's d = 0.46 - 1.17, P = < .001 - .03), and more diffusely and obviously in never-treated than treated patients (Cohen's d = 0.50 - 0.90, P = < .001 - .04). Greater right-than-left volumes were seen in treated patients and healthy controls in 11 of 13 subfields (T = 2.30 - 7.29, P = < .001 - .03), but not in never-treated patients, in whom the volumes were reduced more on the right than on the left. Subfield volumes were negatively correlated with symptom severity and illness duration, and declined with age in never-treated patients. Findings indicate clinically-relevant and age-related volume reductions in hippocampal subfields of never-treated patients with long-term schizophrenia. Broader and greater subfield deficits in never-treated than treated patients, especially in the right hippocampus, suggest that long-term antipsychotic treatment may benefit hippocampal structures over the longer-term course of illness.

Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders

A pivotal aim of psychiatric and neurological research is to promote the translation of the findings into clinical practice to improve diagnostic and prognostic assessment of individual patients. Structural neuroimaging holds much promise, with neuroanatomical measures accounting for up to 40% of the variance in clinical outcome. Building on these findings, a number of imaging-based clinical tools have been developed to make diagnostic and prognostic inferences about individual patients from their structural Magnetic Resonance Imaging scans. This systematic review describes and compares the technical characteristics of the available tools, with the aim to assess their translational potential into real-world clinical settings. The results reveal that a total of eight tools. All of these were specifically developed for neurological disorders, and as such are not suitable for application to psychiatric disorders. Furthermore, most of the tools were trained and validated in a single dataset, which can result in poor generalizability, or using a small number of individuals, which can cause overoptimistic results. In addition, all of the tools rely on two strategies to detect brain abnormalities in single individuals, one based on univariate comparison, and the other based on multivariate machine-learning algorithms. We discuss current barriers to the adoption of these tools in clinical practice and propose a checklist of pivotal characteristics that should be included in an "ideal" neuroimaging-based clinical tool for brain disorders.

Brain Connectome Mapping of Complex Human Traits and Their Polygenic Architecture Using Machine Learning

BACKGROUND

Mental disorders and individual characteristics such as intelligence and personality are complex traits sharing a largely unknown neuronal basis. Their genetic architectures are highly polygenic and overlapping, which is supported by heterogeneous phenotypic expression and substantial clinical overlap. Brain network analysis provides a noninvasive means of dissecting biological heterogeneity, yet its sensitivity, specificity, and validity in assessing individual characteristics relevant for brain function and mental health and their genetic underpinnings in clinical applications remain a challenge.

METHODS

In a machine learning approach, we predicted individual scores for educational attainment, fluid intelligence and dimensional measures of depression, anxiety, and neuroticism using functional magnetic resonance imaging-based static and dynamic temporal synchronization between large-scale brain network nodes in 10,343 healthy individuals from the UK Biobank. In addition to using age and sex to serve as our reference point, we also predicted individual polygenic scores for related phenotypes and 13 different neuroticism traits and schizophrenia.

RESULTS

Beyond high accuracy for age and sex, supporting the biological sensitivity of the connectome-based features, permutation tests revealed above chance-level prediction accuracy for trait-level educational attainment and fluid intelligence. Educational attainment and fluid intelligence were mainly negatively associated with static brain connectivity in frontal and default mode networks, whereas age showed positive correlations with a more widespread pattern. In contrast, prediction accuracy was at chance level for depression, anxiety, neuroticism, and polygenic scores across traits.

CONCLUSIONS

These novel findings provide a benchmark for future studies linking the genetic architecture of individual and mental health traits with functional magnetic resonance imaging-based brain connectomics.

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.

Heterogeneity of Striatal Dopamine Function in Schizophrenia: Meta-analysis of Variance

BACKGROUND

It has been hypothesized that dopamine function in schizophrenia exhibits heterogeneity in excess of that seen in the general population. However, no previous study has systematically tested this hypothesis.

METHODS

We employed meta-analysis of variance to investigate interindividual variability of striatal dopaminergic function in patients with schizophrenia and in healthy control subjects. We included 65 studies that reported molecular imaging measures of dopamine synthesis or release capacities, dopamine D_2/3 receptor (D2/3R) or dopamine transporter (DAT) availabilities, or synaptic dopamine levels in 983 patients and 968 control subjects. Variability differences were quantified using variability ratio (VR) and coefficient of variation ratio.

RESULTS

Interindividual variability of striatal D2/3R (VR = 1.26, p < .0001) and DAT (VR = 1.31, p = .01) availabilities and synaptic dopamine levels (VR = 1.38, p = .045) but not dopamine synthesis (VR = 1.12, p = .13) or release (VR = 1.08, p = .70) capacities were significantly greater in patients than in control subjects. Findings were robust to variability measure. Mean dopamine synthesis (g = 0.65, p = .004) and release (g = 0.66, p = .03) capacities, as well as synaptic levels (g = 0.78, p = .0006), were greater in patients overall, but mean synthesis capacity did not differ from that of control subjects in treatment-resistant patients (p > .3). Mean D2/3R (g = 0.17, p = .14) and DAT (g = -0.20, p = .28) availabilities did not differ between groups.

CONCLUSIONS

Our findings demonstrate significant heterogeneity of striatal dopamine function in schizophrenia. They suggest that while elevated dopamine synthesis and release capacities may be core features of the disorder, altered D2/3R and DAT availabilities and synaptic dopamine levels may occur only in a subgroup of patients. This heterogeneity may contribute to variation in treatment response and side effects.

Assessing the information content of ERP signals in schizophrenia using multivariate decoding methods

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This study took multivariate decoding methods that are widely used to assess the nature of neural representations in neurotypical people and applied them to a comparison of people with schizophrenia and matched control subjects.

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Participants performed a visual working memory task that required remembering 1–5 items from one side of the display and ignoring an equal number of items on the other side of the display.

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We attempted to decode which side was being held in working memory from the scalp distribution of the ERP activity during the delay period of the working memory task, and we found greater decoding accuracy in people with schizophrenia than in control subjects when a single item was being held in memory.

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These results support the hyperfocusing hypothesis of cognitive dysfunction in schizophrenia, and they provide an important proof of concept for applying multivariate decoding methods to comparisons of neural representations in psychiatric and non-psychiatric populations.

Multivariate pattern classification (decoding) methods are commonly employed to study mechanisms of neurocognitive processing in typical individuals, where they can be used to quantify the information that is present in single-participant neural signals. These decoding methods are also potentially valuable in determining how the representation of information differs between psychiatric and non-psychiatric populations. Here, we examined ERPs from people with schizophrenia (PSZ) and healthy control subjects (HCS) in a working memory task that involved remembering 1, 3, or 5 items from one side of the display and ignoring the other side. We used the spatial pattern of ERPs to decode which side of the display was being held in working memory. One might expect that decoding accuracy would be inevitably lower in PSZ as a result of increased noise (i.e., greater trial-to-trial variability). However, we found that decoding accuracy was greater in PSZ than in HCS at memory load 1, consistent with previous research in which memory-related ERP signals were larger in PSZ than in HCS at memory load 1. We also observed that decoding accuracy was strongly related to the ratio of the memory-related ERP activity and the noise level. In addition, we found similar noise levels in PSZ and HCS, counter to the expectation that PSZ would exhibit greater trial-to-trial variability. Together, these results demonstrate that multivariate decoding methods can be validly applied at the individual-participant level to understand the nature of impaired cognitive function in a psychiatric population.

[Brain imaging in schizophrenia : A review of current trends and developments]

Imaging methods have become the main approach for identifying dysfunctional neuronal networks in schizophrenia. This review article presents recent results of disorders of neuronal networks at structural and functional levels and summarizes the current developments. Large multicenter analyses have further established patterns of regional brain alterations, while novel methods in magnetic resonance (MR) morphometry have contributed to differentiating early from delayed brain structural changes. The use of machine learning approaches has not only enabled the establishment of classification models using biological data for future differential diagnostic use, it has also facilitated multivariate models for outcome prediction following therapeutic interventions. Novel methods, such as BrainAGE, a surrogate marker of accelerated brain aging processes, have added to longitudinal studies to gain insights into the brain structural dynamics from early brain developmental alterations to progressive structural brain changes after disease onset.

Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis

Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity and neurobiological complexity. A dimensional approach and fusion of imaging modalities may yield a more coherent view of the neuronal correlates of depression. We used linked independent component analysis to fuse cortical macrostructure (thickness, area, gray matter density), white matter diffusion properties and resting-state functional magnetic resonance imaging default mode network amplitude in patients with a history of depression (n = 170) and controls (n = 71). We used univariate and machine learning approaches to assess the relationship between age, sex, case-control status, and symptom loads for depression and anxiety with the resulting brain components. Univariate analyses revealed strong associations between age and sex with mainly global but also regional specific brain components, with varying degrees of multimodal involvement. In contrast, there were no significant associations with case-control status, nor symptom loads for depression and anxiety with the brain components, nor any interaction effects with age and sex. Machine learning revealed low model performance for classifying patients from controls and predicting symptom loads for depression and anxiety, but high age prediction accuracy. Multimodal fusion of brain imaging data alone may not be sufficient for dissecting the clinical and neurobiological heterogeneity of depression. Precise clinical stratification and methods for brain phenotyping at the individual level based on large training samples may be needed to parse the neuroanatomy of depression.

Polygenic burden associated to oligodendrocyte precursor cells and radial glia influences the hippocampal volume changes induced by aerobic exercise in schizophrenia patients

Hippocampal volume decrease is a structural hallmark of schizophrenia (SCZ), and convergent evidence from postmortem and imaging studies suggests that it may be explained by changes in the cytoarchitecture of the cornu ammonis 4 (CA4) and dentate gyrus (DG) subfields. Increasing evidence indicates that aerobic exercise increases hippocampal volume in CA subfields and improves cognition in SCZ patients. Previous studies showed that the effects of exercise on the hippocampus might be connected to the polygenic burden of SCZ risk variants. However, little is known about cell type-specific genetic contributions to these structural changes. In this secondary analysis, we evaluated the modulatory role of cell type-specific SCZ polygenic risk scores (PRS) on volume changes in the CA1, CA2/3, and CA4/DG subfields over time. We studied 20 multi-episode SCZ patients and 23 healthy controls who performed aerobic exercise, and 21 multi-episode SCZ patients allocated to a control intervention (table soccer) for 3 months. Magnetic resonance imaging-based assessments were performed with FreeSurfer at baseline and after 3 months. The analyses showed that the polygenic burden associated with oligodendrocyte precursor cells (OPC) and radial glia (RG) significantly influenced the volume changes between baseline and 3 months in the CA4/DG subfield in SCZ patients performing aerobic exercise. A higher OPC- or RG-associated genetic risk burden was associated with a less pronounced volume increase or even a decrease in CA4/DG during the exercise intervention. We hypothesize that SCZ cell type-specific polygenic risk modulates the aerobic exercise-induced neuroplastic processes in the hippocampus.

Population-based neuroimaging reveals traces of childbirth in the maternal brain

Maternal brain adaptations have been found across pregnancy and postpartum, but little is known about the long-term effects of parity on the maternal brain. Using neuroimaging and machine learning, we investigated structural brain characteristics in 12,021 middle-aged women from the UK Biobank, demonstrating that parous women showed less evidence of brain aging compared to their nulliparous peers. The relationship between childbirths and a "younger-looking" brain could not be explained by common genetic variation or relevant confounders. Although prospective longitudinal studies are needed, the results suggest that parity may involve neural changes that could influence women's brain aging later in life.