Brain States That Encode Perceived Emotion Are Reproducible but Their Classification Accuracy Is Stimulus-Dependent

The brain state hypothesis of image-induced affect processing, which posits that a one-to-one mapping exists between each image stimulus and its induced functional magnetic resonance imaging (fMRI)-derived neural activation pattern (i.e., brain state), has recently received support from several multivariate pattern analysis (MVPA) studies. Critically, however, classification accuracy differences across these studies, which largely share experimental designs and analyses, suggest that there exist one or more unaccounted sources of variance within MVPA studies of affect processing. To explore this possibility, we directly demonstrated strong inter-study correlations between image-induced affective brain states acquired 4 years apart on the same MRI scanner using near-identical methodology with studies differing only by the specific image stimuli and subjects. We subsequently developed a plausible explanation for inter-study differences in affective valence and arousal classification accuracies based on the spatial distribution of the perceived affective properties of the stimuli. Controlling for this distribution improved valence classification accuracy from 56% to 85% and arousal classification accuracy from 61% to 78%, which mirrored the full range of classification accuracy across studies within the existing literature. Finally, we validated the predictive fidelity of our image-related brain states according to an independent measurement, autonomic arousal, captured via skin conductance response (SCR). Brain states significantly but weakly (r = 0.08) predicted the SCRs that accompanied individual image stimulations. More importantly, the effect size of brain state predictions of SCR increased more than threefold (r = 0.25) when the stimulus set was restricted to those images having group-level significantly classifiable arousal properties

Diabetic cardiomyopathy

Diabetic cardiomyopathy is a distinct primary disease process, independent of coronary artery disease, which leads to heart failure in diabetic patients. Epidemiological and clinical trial data have confirmed the greater incidence and prevalence of heart failure in diabetes. Novel echocardiographic and MR (magnetic resonance) techniques have enabled a more accurate means of phenotyping diabetic cardiomyopathy. Experimental models of diabetes have provided a range of novel molecular targets for this condition, but none have been substantiated in humans. Similarly, although ultrastructural pathology of the microvessels and cardiomyocytes is well described in animal models, studies in humans are small and limited to light microscopy. With regard to treatment, recent data with thiazoledinediones has generated much controversy in terms of the cardiac safety of both these and other drugs currently in use and under development. Clinical trials are urgently required to establish the efficacy of currently available agents for heart failure, as well as novel therapies in patients specifically with diabetic cardiomyopathy

Large-scale brain organization during facial emotion processing as a function of early life trauma among adolescent girls

Background: A wealth of research has investigated the impact of early life trauma exposure on functional brain activation during facial emotion processing and has often demonstrated amygdala hyperactivity and weakened connectivity between amygdala and medial PFC (mPFC). There have been notably limited investigations linking these previous node-specific findings into larger-scale network models of brain organization. Method: To address these gaps, we applied graph theoretical analyses to fMRI data collected during a facial emotion processing task among 88 adolescent girls (n=59 exposed to direct physical or sexual assault; n=29 healthy controls), aged 11–17, during fMRI. Large-scale organization indices of modularity, assortativity, and global efficiency were calculated for stimulus-specific functional connectivity using an 883 region-of-interest parcellation. Results: Among the entire sample, more severe early life trauma was associated with more modular and assortative, but less globally efficient, network organization across all stimulus categories. Among the assaulted girls, severity of early life trauma and PTSD diagnoses were both simultaneously related to increased modular brain organization. We also found that more modularized network organization was related both to amygdala hyperactivation and weakened connectivity between amygdala and medial PFC. Conclusions: These results demonstrate that early life trauma is associated with enhanced brain organization during facial emotion processing and that this pattern of brain organization might explain the commonly observed association between childhood trauma and amygdala hyperactivity and weakened connectivity with mPFC. Implications of these results for neurocircuitry models are discussed

Individual differences in rate of acquiring stable neural representations of tasks in fMRI.

Task-related functional magnetic resonance imaging (fMRI) is a widely-used tool for studying the neural processing correlates of human behavior in both healthy and clinical populations. There is growing interest in mapping individual differences in fMRI task behavior and neural responses. By utilizing neuroadaptive task designs accounting for such individual differences, task durations can be personalized to potentially optimize neuroimaging study outcomes (e.g., classification of task-related brain states). To test this hypothesis, we first retrospectively tracked the volume-by-volume changes of beta weights generated from general linear models (GLM) for 67 adult subjects performing a stop-signal task (SST). We then modeled the convergence of the volume-by-volume changes of beta weights according to their exponential decay (ED) in units of half-life. Our results showed significant differences in beta weight convergence estimates of optimal stopping times (OSTs) between go following successful stop trials and failed stop trials for both cocaine dependent (CD) and control group (Con), and between go following successful stop trials and go following failed stop trials for Con group. Further, we implemented support vector machine (SVM) classification for 67 CD/Con labeled subjects and compared the classification accuracies of fMRI-based features derived from (1) the full fMRI task versus (2) the fMRI task truncated to multiples of the unit of half-life. Among the computed binary classification accuracies, two types of task durations based on 2 half-lives significantly outperformed the accuracies using fully acquired trials, supporting this length as the OST for the SST. In conclusion, we demonstrate the potential of a neuroadaptive task design that can be widely applied to personalizing other task-based fMRI experiments in either dynamic real-time fMRI applications or within fMRI preprocessing pipelines

Neural encodings of affect processing.

Color gradations indicate the group-level t-scores of the encoding parameters (red indicating positive valence or high arousal, blue indicating negative valence or low arousal). T-scores are presented only for those voxels in which encoding parameters survived global permutation testing (p (TIF)</p

Comparison of support vector machine predictions based upon whole-brain gray matter versus Gram-Schmidt reduced dimensionality features.

Gram-Schmidt dimensionality reduction projects the original whole-brain gray matter features (n~30,000–40,000) onto an orthogonal basis in which the coordinate dimension is less than or equal to the number of sample features (n≤90). We report the effect size of the reduced dimensional predictions in explaining predictions in the original feature space using a linear mixed-effects model in which random effects are modeled subject-wise. Gray symbols depict individual trials. The bold red line depicts the group-level effect. Bold gray lines depict significant subject-level effects whereas light gray lines depict subject-level effects that were not significant. Valence. The fixed effect (R2 = .71) is significant (p0: β = 0). Random effects significantly improve effect-size (p0: observed responses generated by fixed-effects only). Arousal. The fixed effect (R2 = .72) is significant (p0: β = 0). Random effects significantly improve effect-size (p0: observed responses generated by fixed-effects only). (TIF)</p

Facial electromyography sensitivity analysis in the prediction of normative valence.

(A) Valence prediction effect-size, measured as adjusted R2, as a function of the polar extremes of affectively valent stimuli used to construct the prediction, plotted separately for facial EMG signals recorded from the corrugator supercilii (blue) and zygomaticus major (red). Polar-extremity is reported as a factor of the standard deviation of the normative valence scores used to threshold stimuli for exclusion from the prediction. The symbols represent thresholds for which the plotted effect-size is statistically significant. (B) The fraction of the total number of image stimuli remaining in the set after thresholding. The symbol denotes the minimum threshold level for which the corrugator signal significantly predicted normative valence score of the remaining stimuli. (TIF)</p