Missing in Action: African Ancestry Brain Research.

Individuals of African ancestry have been starkly underrepresented in the pursuit of personalized medicine for brain illnesses. The African Ancestry Neuroscience Research Initiative will seek to generate much-needed brain gene and protein expression profiles for people of African ancestry.

Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods.

BACKGROUND: RNA sequencing offers advantages over other quantification methods for microRNA (miRNA), yet numerous biases make reliable quantification challenging. Previous evaluations of these biases have focused on adapter ligation bias with limited evaluation of reverse transcription bias or amplification bias. Furthermore, evaluations of the quantification of isomiRs (miRNA isoforms) or the influence of starting amount on performance have been very limited. No study had yet evaluated the quantification of isomiRs of altered length or compared the consistency of results derived from multiple moderate starting inputs. We therefore evaluated quantifications of miRNA and isomiRs using four library preparation kits, with various starting amounts, as well as quantifications following removal of duplicate reads using unique molecular identifiers (UMIs) to mitigate reverse transcription and amplification biases. RESULTS: All methods resulted in false isomiR detection; however, the adapter-free method tested was especially prone to false isomiR detection. We demonstrate that using UMIs improves accuracy and we provide a guide for input amounts to improve consistency. CONCLUSIONS: Our data show differences and limitations of current methods, thus raising concerns about the validity of quantification of miRNA and isomiRs across studies. We advocate for the use of UMIs to improve accuracy and reliability of miRNA quantifications.

Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm.

High-resolution three-dimensional magnetic resonance imaging (3D-MRI) is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM) algorithm in the quality assessment of structural brain images, using global and region of interest (ROI) automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy) of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

Subjective socioeconomic status predicts human ventral striatal responses to social status information.

The enormous influence of hierarchical rank on social interactions [1] suggests that neural mechanisms exist to process status-related information [2] and ascribe value to it. The ventral striatum is prominently implicated in processing value and salience, independent of hedonic properties [3, 4], and a functional magnetic resonance imaging (fMRI) study of social status perception in humans demonstrated that viewing higher-ranked compared to lower-ranked individuals evokes a ventral striatal response [5], indicative of a greater assignment of value/salience to higher status. Consistent with this interpretation, nonhuman primates value information associated with higher-ranked conspecifics more than lower-ranked, as illustrated using a choice paradigm in which monkeys preferentially take the opportunity to view high-status monkeys [6]. Interestingly, this status-related value assignment in nonhuman primates is influenced by one's own hierarchical rank: high-status monkeys preferentially attend to conspecifics of high status, whereas low-status monkeys will also attend to other low-status monkeys [7]. Complementary to these findings, using fMRI and a social status judgment task in humans, we suggest a neurobiological mechanism by which one's own relative hierarchical rank influences the value attributed to particular social status information by demonstrating that one's subjective socioeconomic status differentially influences ventral striatal activity during processing of status-related information.

Evidence of statistical epistasis between DISC1, CIT and NDEL1 impacting risk for schizophrenia: biological validation with functional neuroimaging.

The etiology of schizophrenia likely involves genetic interactions. DISC1, a promising candidate susceptibility gene, encodes a protein which interacts with many other proteins, including CIT, NDEL1, NDE1, FEZ1 and PAFAH1B1, some of which also have been associated with psychosis. We tested for epistasis between these genes in a schizophrenia case-control study using machine learning algorithms (MLAs: random forest, generalized boosted regression andMonteCarlo logic regression). Convergence of MLAs revealed a subset of seven SNPs that were subjected to 2-SNP interaction modeling using likelihood ratio tests for nested unconditional logistic regression models. Of the 7C2 = 21 interactions, four were significant at the α = 0.05 level: DISC1 rs1411771-CIT rs10744743 OR = 3.07 (1.37, 6.98) p = 0.007; CIT rs3847960-CIT rs203332 OR = 2.90 (1.45, 5.79) p = 0.003; CIT rs3847960-CIT rs440299 OR = 2.16 (1.04, 4.46) p = 0.038; one survived Bonferroni correction (NDEL1 rs4791707-CIT rs10744743 OR = 4.44 (2.22, 8.88) p = 0.00013). Three of four interactions were validated via functional magnetic resonance imaging (fMRI) in an independent sample of healthy controls; risk associated alleles at both SNPs predicted prefrontal cortical inefficiency during the N-back task, a schizophrenia-linked intermediate biological phenotype: rs3847960-rs440299; rs1411771-rs10744743, rs4791707-rs10744743 (SPM5 p < 0.05, corrected), although we were unable to statistically replicate the interactions in other clinical samples. Interestingly, the CIT SNPs are proximal to exons that encode theDISC1 interaction domain. In addition, the 3' UTR DISC1 rs1411771 is predicted to be an exonic splicing enhancer and the NDEL1 SNP is ~3,000 bp from the exon encoding the region of NDEL1 that interacts with the DISC1 protein, giving a plausible biological basis for epistasis signals validated by fMRI.

Preferential amygdala reactivity to the negative assessment of neutral faces.

BACKGROUND: Prior studies suggest that the amygdala shapes complex behavioral responses to socially ambiguous cues. We explored human amygdala function during explicit behavioral decision making about discrete emotional facial expressions that can represent socially unambiguous and ambiguous cues. METHODS: During functional magnetic resonance imaging, 43 healthy adults were required to make complex social decisions (i.e., approach or avoid) about either relatively unambiguous (i.e., angry, fearful, happy) or ambiguous (i.e., neutral) facial expressions. Amygdala activation during this task was compared with that elicited by simple, perceptual decisions (sex discrimination) about the identical facial stimuli. RESULTS: Angry and fearful expressions were more frequently judged as avoidable and happy expressions most often as approachable. Neutral expressions were equally judged as avoidable and approachable. Reaction times to neutral expressions were longer than those to angry, fearful, and happy expressions during social judgment only. Imaging data on stimuli judged to be avoided revealed a significant task by emotion interaction in the amygdala. Here, only neutral facial expressions elicited greater activity during social judgment than during sex discrimination. Furthermore, during social judgment only, neutral faces judged to be avoided were associated with greater amygdala activity relative to neutral faces that were judged as approachable. Moreover, functional coupling between the amygdala and both dorsolateral prefrontal (social judgment > sex discrimination) and cingulate (sex discrimination > social judgment) cortices was differentially modulated by task during processing of neutral faces. CONCLUSIONS: Our results suggest that increased amygdala reactivity and differential functional coupling with prefrontal circuitries may shape complex decisions and behavioral responses to socially ambiguous cues.

Cognitive fitness of cost-efficient brain functional networks.

The human brain's capacity for cognitive function is thought to depend on coordinated activity in sparsely connected, complex networks organized over many scales of space and time. Recent work has demonstrated that human brain networks constructed from neuroimaging data have economical small-world properties that confer high efficiency of information processing at relatively low connection cost. However, it has been unclear how the architecture of complex brain networks functioning at different frequencies can be related to behavioral performance on cognitive tasks. Here, we show that impaired accuracy of working memory could be related to suboptimal cost efficiency of brain functional networks operating in the classical beta frequency band, 15-30 Hz. We analyzed brain functional networks derived from magnetoencephalography data recorded during working-memory task performance in 29 healthy volunteers and 28 people with schizophrenia. Networks functioning at higher frequencies had greater global cost efficiency than low-frequency networks in both groups. Superior task performance was positively correlated with global cost efficiency of the beta-band network and specifically with cost efficiency of nodes in left lateral parietal and frontal areas. These results are consistent with biophysical models highlighting the importance of beta-band oscillations for long-distance functional connections in brain networks and with pathophysiological models of schizophrenia as a dysconnection syndrome. More generally, they echo the saying that "less is more": The information processing performance of a network can be enhanced by a sparse or low-cost configuration with disproportionately high efficiency.