Large Common Mitochondrial DNA Deletions Are Associated with a Mitochondrial SNP T14798C Near the 3' Breakpoints.

Introduction: Large somatic deletions of mitochondrial DNA (mtDNA) accumulate with aging in metabolically active tissues such as the brain. We have cataloged the breakpoints and frequencies of large mtDNA deletions in the human brain. Methods: We quantified 112 high-frequency mtDNA somatic deletions across four human brain regions with the Splice-Break2 pipeline. In addition, we utilized PLINK/Seq to test the association of mitochondrial genotypes with the abundance of these high-frequency mtDNA deletions. A conservative p value threshold of 5E-08 was used to find the significant loci. Results: One mtDNA SNP (T14798C) was significantly associated with mtDNA deletions in two brain regions, the dorsolateral prefrontal cortex (DLPFC) and the superior temporal gyrus. Since the DLPFC showed the most robust association between T14798C and two deletion breakpoints (7816-14807 and 5462-14807), this association was tested in the DLPFC of a replication sample and validated the first results. Incorporating the C allele at 14,798 bp increased the perfect/imperfect length of the repeat at the 3' breakpoint of the two associated deletions. Conclusion: This is the first study to identify the association of mtDNA SNP with large mtDNA deletions in the human brain. The T14798C allele located in the MT-CYB gene is a common polymorphism that occurs in several mitochondrial haplogroups. We hypothesize that the T14798C association with two deletions occurs by extending the repeat length around the 3' deletion breakpoints. This simple mechanism suggests that mtDNA SNPs can affect the mitochondrial genome structure, especially in brain where high levels of reactive oxygen species lead to deletion accumulation with aging.

Biased belief priors versus biased belief updating: Differential correlates of depression and anxiety.

Individuals prone to anxiety and depression often report beliefs and make judgements about themselves that are more negative than those reported by others. We use computational modeling of a richly naturalistic task to disentangle the role of negative priors versus negatively biased belief updating and to investigate their association with different dimensions of Internalizing psychopathology. Undergraduate participants first provided profiles for a hypothetical tech internship. They then viewed pairs of other profiles and selected the individual they would prefer to work alongside out of each pair. In a subsequent phase of the experiment, participants made judgments about their relative popularity as hypothetical internship partners both before any feedback and after each of 20 items of feedback revealing whether or not they had been selected as the preferred teammate from a given pairing. Scores on latent factors of general negative affect, anxiety-specific affect and depression-specific affect were estimated using participants' self-report scores on standardized measures of anxiety and depression together with factor loadings from a bifactor analysis conducted previously. Higher scores on the depression-specific factor were linked to more negative prior beliefs but were not associated with differences in belief updating. In contrast, higher scores on the anxiety-specific factor were associated with a negative bias in belief updating but no difference in prior beliefs. These findings indicate that, to at least some extent, distinct processes may impact the formation of belief priors and in-the-moment belief updating and that these processes may be differentially disrupted in depression and anxiety. Future directions for enquiry include examination of the possibility that prior beliefs biases in depression might reflect generalization from prior experiences or global schema whereas belief updating biases in anxiety might be more situationally specific.

Algorithms in the historical emergence of word senses.

Human language relies on a finite lexicon to express a potentially infinite set of ideas. A key result of this tension is that words acquire novel senses over time. However, the cognitive processes that underlie the historical emergence of new word senses are poorly understood. Here, we present a computational framework that formalizes competing views of how new senses of a word might emerge by attaching to existing senses of the word. We test the ability of the models to predict the temporal order in which the senses of individual words have emerged, using an historical lexicon of English spanning the past millennium. Our findings suggest that word senses emerge in predictable ways, following an historical path that reflects cognitive efficiency, predominantly through a process of nearest-neighbor chaining. Our work contributes a formal account of the generative processes that underlie lexical evolution.