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The cell—cell signaling gene CDH13 is associated with a wide spectrum of neuropsychiatric disorders, including attention-deficit/hyperactivity disorder (ADHD), autism, and major depression. CDH13 regulates axonal outgrowth and synapse formation, substantiating its relevance for neurodevelopmental processes. Several studies support the influence of CDH13 on personality traits, behavior, and executive functions. However, evidence for functional effects of common gene variation in the CDH13 gene in humans is sparse. Therefore, we tested for association of a functional intronic CDH13 SNP rs2199430 with ADHD in a sample of 998 adult patients and 884 healthy controls. The Big Five personality traits were assessed by the NEO-PI-R questionnaire. Assuming that altered neural correlates of working memory and cognitive response inhibition show genotype-dependent alterations, task performance and electroencephalographic event-related potentials were measured by n-back and continuous performance (Go/NoGo) tasks. The rs2199430 genotype was not associated with adult ADHD on the categorical diagnosis level. However, rs2199430 was significantly associated with agreeableness, with minor G allele homozygotes scoring lower than A allele carriers. Whereas task performance was not affected by genotype, a significant heterosis effect limited to the ADHD group was identified for the n-back task. Heterozygotes (AG) exhibited significantly higher N200 amplitudes during both the 1-back and 2-back condition in the central electrode position Cz. Consequently, the common genetic variation of CDH13 is associated with personality traits and impacts neural processing during working memory tasks. Thus, CDH13 might contribute to symptomatic core dysfunctions of social and cognitive impairment in ADHD.
In psychiatry, there has been a growing focus on identifying at-risk populations. For schizophrenia, these efforts have led to the development of early recognition and intervention measures. Despite a similar disease burden, the populations at risk of bipolar disorder have not been sufficiently characterized. Within the BipoLife consortium, we used magnetic resonance imaging (MRI) data from a multicenter study to assess structural gray matter alterations in N = 263 help-seeking individuals from seven study sites. We defined the risk using the EPIbipolar assessment tool as no-risk, low-risk, and high-risk and used a region-of-interest approach (ROI) based on the results of two large-scale multicenter studies of bipolar disorder by the ENIGMA working group. We detected significant differences in the thickness of the left pars opercularis (Cohen’s d = 0.47, p = 0.024) between groups. The cortex was significantly thinner in high-risk individuals compared to those in the no-risk group (p = 0.011). We detected no differences in the hippocampal volume. Exploratory analyses revealed no significant differences in other cortical or subcortical regions. The thinner cortex in help-seeking individuals at risk of bipolar disorder is in line with previous findings in patients with the established disorder and corresponds to the region of the highest effect size in the ENIGMA study of cortical alterations. Structural alterations in prefrontal cortex might be a trait marker of bipolar risk. This is the largest structural MRI study of help-seeking individuals at increased risk of bipolar disorder.
Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). In addition, the proposed framework is optimized in terms of both predictive power and reproducibility/stability of the patterns. Our approach aims to provide better pattern interpretation than voxel-based whole-brain approaches by yielding stable brain connectivity patterns that underlie discriminative changes in brain function between the groups. We illustrate our technique by classifying patients with major depressive disorder (MDD) and healthy participants, in two (event- and block-related) fMRI datasets acquired while participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces.