Relative preservation of facial expression recognition in posterior cortical atrophy.

OBJECTIVE: To compare recognition of facial expression (FE) vs recognition of facial identity (FI) in posterior cortical atrophy (PCA), with the hypothesis that FE recognition would be relatively preserved in PCA. METHODS: In this observational study, FI and expression recognition tasks were performed by 194 participants in 4 groups, including 39 with Alzheimer disease (AD) (non-PCA), 49 with behavioral variant frontotemporal dementia (bvFTD), 15 with PCA, and 91 healthy controls. Between-group differences in test scores were compared. RESULTS: Patients with PCA performed worse than healthy controls in FI and emotion recognition tasks (p < 0.001 for all). Patients with PCA also performed worse than AD and bvFTD groups in FI recognition, with no difference in FE recognition. CONCLUSIONS: Patients with PCA have relatively preserved FE recognition compared to FI recognition, as seen in affective blindsight.

Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study.

BACKGROUND: Pneumothorax can precipitate a life-threatening emergency due to lung collapse and respiratory or circulatory distress. Pneumothorax is typically detected on chest X-ray; however, treatment is reliant on timely review of radiographs. Since current imaging volumes may result in long worklists of radiographs awaiting review, an automated method of prioritizing X-rays with pneumothorax may reduce time to treatment. Our objective was to create a large human-annotated dataset of chest X-rays containing pneumothorax and to train deep convolutional networks to screen for potentially emergent moderate or large pneumothorax at the time of image acquisition. METHODS AND FINDINGS: In all, 13,292 frontal chest X-rays (3,107 with pneumothorax) were visually annotated by radiologists. This dataset was used to train and evaluate multiple network architectures. Images showing large- or moderate-sized pneumothorax were considered positive, and those with trace or no pneumothorax were considered negative. Images showing small pneumothorax were excluded from training. Using an internal validation set (n = 1,993), we selected the 2 top-performing models; these models were then evaluated on a held-out internal test set based on area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value (PPV). The final internal test was performed initially on a subset with small pneumothorax excluded (as in training; n = 1,701), then on the full test set (n = 1,990), with small pneumothorax included as positive. External evaluation was performed using the National Institutes of Health (NIH) ChestX-ray14 set, a public dataset labeled for chest pathology based on text reports. All images labeled with pneumothorax were considered positive, because the NIH set does not classify pneumothorax by size. In internal testing, our "high sensitivity model" produced a sensitivity of 0.84 (95% CI 0.78-0.90), specificity of 0.90 (95% CI 0.89-0.92), and AUC of 0.94 for the test subset with small pneumothorax excluded. Our "high specificity model" showed sensitivity of 0.80 (95% CI 0.72-0.86), specificity of 0.97 (95% CI 0.96-0.98), and AUC of 0.96 for this set. PPVs were 0.45 (95% CI 0.39-0.51) and 0.71 (95% CI 0.63-0.77), respectively. Internal testing on the full set showed expected decreased performance (sensitivity 0.55, specificity 0.90, and AUC 0.82 for high sensitivity model and sensitivity 0.45, specificity 0.97, and AUC 0.86 for high specificity model). External testing using the NIH dataset showed some further performance decline (sensitivity 0.28-0.49, specificity 0.85-0.97, and AUC 0.75 for both). Due to labeling differences between internal and external datasets, these findings represent a preliminary step towards external validation. CONCLUSIONS: We trained automated classifiers to detect moderate and large pneumothorax in frontal chest X-rays at high levels of performance on held-out test data. These models may provide a high specificity screening solution to detect moderate or large pneumothorax on images collected when human review might be delayed, such as overnight. They are not intended for unsupervised diagnosis of all pneumothoraces, as many small pneumothoraces (and some larger ones) are not detected by the algorithm. Implementation studies are warranted to develop appropriate, effective clinician alerts for the potentially critical finding of pneumothorax, and to assess their impact on reducing time to treatment.

Neuroanatomy of Shared Conversational Laughter in Neurodegenerative Disease.

Perceiving another person's emotional expression often sparks a corresponding signal in the observer. Shared conversational laughter is a familiar example. Prior studies of shared laughter have made use of task-based functional neuroimaging. While these methods offer insight in a controlled setting, the ecological validity of such controlled tasks has limitations. Here, we investigate the neural correlates of shared laughter in patients with one of a variety of neurodegenerative disease syndromes (N = 75), including Alzheimer's disease (AD), behavioral variant frontotemporal dementia (bvFTD), right and left temporal variants of semantic dementia (rtvFTD, svPPA), nonfluent/agrammatic primary progressive aphasia (nfvPPA), corticobasal syndrome (CBS), and progressive supranuclear palsy (PSP). Patients were recorded in a brief unrehearsed conversation with a partner (e.g., a friend or family member). Laughter was manually labeled, and an automated system was used to assess the timing of that laughter relative to the partner's laughter. The probability of each participant with neurodegenerative disease laughing during or shortly after his or her partners' laughter was compared to differences in brain morphology using voxel-based morphometry, thresholded based on cluster size and a permutation method and including age, sex, magnet strength, disease-specific atrophy and total intracranial volumes as covariates. While no significant correlations were found at the critical T value, at a corrected voxelwise threshold of p < 0.005, a cluster in the left posterior cingulate gyrus demonstrated a trend at p = 0.08 (T = 4.54). Exploratory analysis with a voxelwise threshold of p = 0.001 also suggests involvement of the left precuneus (T = 3.91) and right fusiform gyrus (T = 3.86). The precuneus has been previously implicated in the detection of socially complex laughter, and the fusiform gyrus has a well-described role in the recognition and processing of others' emotional cues. This study is limited by a relatively small sample size given the number of covariates. While further investigation is needed, these results support our understanding of the neural underpinnings of shared conversational laughter.

Deconstructing empathy: Neuroanatomical dissociations between affect sharing and prosocial motivation using a patient lesion model.

Affect sharing and prosocial motivation are integral parts of empathy that are conceptually and mechanistically distinct. We used a neurodegenerative disease (NDG) lesion model to examine the neural correlates of these two aspects of real-world empathic responding. The study enrolled 275 participants, including 44 healthy older controls and 231 patients diagnosed with one of five neurodegenerative diseases (75 Alzheimer's disease, 58 behavioral variant frontotemporal dementia (bvFTD), 42 semantic variant primary progressive aphasia (svPPA), 28 progressive supranuclear palsy, and 28 non-fluent variant primary progressive aphasia (nfvPPA). Informants completed the Revised Self-Monitoring Scale's Sensitivity to the Expressive Behavior of Others (RSMS-EX) subscale and the Interpersonal Reactivity Index's Empathic Concern (IRI-EC) subscale describing the typical empathic behavior of the participants in daily life. Using regression modeling of the voxel based morphometry of T1 brain scans prepared using SPM8 DARTEL-based preprocessing, we isolated the variance independently contributed by the affect sharing and the prosocial motivation elements of empathy as differentially measured by the two scales. We found that the affect sharing component uniquely correlated with volume in right>left medial and lateral temporal lobe structures, including the amygdala and insula, that support emotion recognition, emotion generation, and emotional awareness. Prosocial motivation, in contrast, involved structures such as the nucleus accumbens (NaCC), caudate head, and inferior frontal gyrus (IFG), which suggests that an individual must maintain the capacity to experience reward, to resolve ambiguity, and to inhibit their own emotional experience in order to effectively engage in spontaneous altruism as a component of their empathic response to others.

Gene and MicroRNA Expression Responses to Exercise; Relationship with Insulin Sensitivity.

BACKGROUND: Healthy individuals on the lower end of the insulin sensitivity spectrum also have a reduced gene expression response to exercise for specific genes. The goal of this study was to determine the relationship between insulin sensitivity and exercise-induced gene expression in an unbiased, global manner. METHODS AND FINDINGS: Euglycemic clamps were used to measure insulin sensitivity and muscle biopsies were done at rest and 30 minutes after a single acute exercise bout in 14 healthy participants. Changes in mRNA expression were assessed using microarrays, and miRNA analysis was performed in a subset of 6 of the participants using sequencing techniques. Following exercise, 215 mRNAs were changed at the probe level (Bonferroni-corrected P<0.00000115). Pathway and Gene Ontology analysis showed enrichment in MAP kinase signaling, transcriptional regulation and DNA binding. Changes in several transcription factor mRNAs were correlated with insulin sensitivity, including MYC, r=0.71; SNF1LK, r=0.69; and ATF3, r= 0.61 (5 corrected for false discovery rate). Enrichment in the 5'-UTRs of exercise-responsive genes suggested regulation by common transcription factors, especially EGR1. miRNA species of interest that changed after exercise included miR-378, which is located in an intron of the PPARGC1B gene. CONCLUSIONS: These results indicate that transcription factor gene expression responses to exercise depend highly on insulin sensitivity in healthy people. The overall pattern suggests a coordinated cycle by which exercise and insulin sensitivity regulate gene expression in muscle.

Adenine nucleotide translocase is acetylated in vivo in human muscle: Modeling predicts a decreased ADP affinity and altered control of oxidative phosphorylation.

Proteomics techniques have revealed that lysine acetylation is abundant in mitochondrial proteins. This study was undertaken (1) to determine the relationship between mitochondrial protein acetylation and insulin sensitivity in human skeletal muscle, identifying key acetylated proteins, and (2) to use molecular modeling techniques to understand the functional consequences of acetylation of adenine nucleotide translocase 1 (ANT1), which we found to be abundantly acetylated. Eight lean and eight obese nondiabetic subjects had euglycemic clamps and muscle biopsies for isolation of mitochondrial proteins and proteomics analysis. A number of acetylated mitochondrial proteins were identified in muscle biopsies. Overall, acetylation of mitochondrial proteins was correlated with insulin action (r = 0.60; P < 0.05). Of the acetylated proteins, ANT1, which catalyzes ADP-ATP exchange across the inner mitochondrial membrane, was acetylated at lysines 10, 23, and 92. The extent of acetylation of lysine 23 decreased following exercise, depending on insulin sensitivity. Molecular dynamics modeling and ensemble docking simulations predicted the ADP binding site of ANT1 to be a pocket of positively charged residues, including lysine 23. Calculated ADP-ANT1 binding affinities were physiologically relevant and predicted substantial reductions in affinity upon acetylation of lysine 23. Insertion of these derived binding affinities as parameters into a complete mathematical description of ANT1 kinetics predicted marked reductions in adenine nucleotide flux resulting from acetylation of lysine 23. Therefore, acetylation of ANT1 could have dramatic physiological effects on ADP-ATP exchange. Dysregulation of acetylation of mitochondrial proteins such as ANT1 therefore could be related to changes in mitochondrial function that are associated with insulin resistance.

AMASS: a database for investigating protein structures.

MOTIVATION: Modern techniques have produced many sequence annotation databases and protein structure portals, but these Web resources are rarely integrated in ways that permit straightforward exploration of protein functional residues and their co-localization. RESULTS: We have created the AMASS database, which maps 1D sequence annotation databases to 3D protein structures with an intuitive visualization interface. Our platform also provides an analysis service that screens mass spectrometry sequence data for post-translational modifications that reside in functionally relevant locations within protein structures. The system is built on the premise that functional residues such as active sites, cancer mutations and post-translational modifications within proteins may co-localize and share common functions. AVAILABILITY AND IMPLEMENTATION: AMASS database is implemented with Biopython and Apache as a freely available Web server at amass-db.org.

Differential expression of microRNAs as predictors of glioblastoma phenotypes.

BACKGROUND: Glioblastoma is the most aggressive primary central nervous tumor and carries a very poor prognosis. Invasion precludes effective treatment and virtually assures tumor recurrence. In the current study, we applied analytical and bioinformatics approaches to identify a set of microRNAs (miRs) from several different human glioblastoma cell lines that exhibit significant differential expression between migratory (edge) and migration-restricted (core) cell populations. The hypothesis of the study is that differential expression of miRs provides an epigenetic mechanism to drive cell migration and invasion. RESULTS: Our research data comprise gene expression values for a set of 805 human miRs collected from matched pairs of migratory and migration-restricted cell populations from seven different glioblastoma cell lines. We identified 62 down-regulated and 2 up-regulated miRs that exhibit significant differential expression in the migratory (edge) cell population compared to matched migration-restricted (core) cells. We then conducted target prediction and pathway enrichment analysis with these miRs to investigate potential associated gene and pathway targets. Several miRs in the list appear to directly target apoptosis related genes. The analysis identifies a set of genes that are predicted by 3 different algorithms, further emphasizing the potential validity of these miRs to promote glioblastoma. CONCLUSIONS: The results of this study identify a set of miRs with potential for decreased expression in invasive glioblastoma cells. The verification of these miRs and their associated targeted proteins provides new insights for further investigation into therapeutic interventions. The methodological approaches employed here could be applied to the study of other diseases to provide biomedical researchers and clinicians with increased opportunities for therapeutic interventions.