Investigating the group-level impact of advanced dual-echo fMRI combinations

Multi-echo fMRI data acquisition has been widely investigated and suggested to optimize sensitivity for detecting the BOLD signal. Several methods have also been proposed for the combination of data with different echo times. The aim of the present study was to investigate whether these advanced echo combination methods provide advantages over the simple averaging of echoes when state-of-the-art group-level random-effect analyses are performed. Both resting-state and task-based dual-echo fMRI data were collected from 27 healthy adult individuals (14 male, mean age = 25.75 years) using standard echo-planar acquisition methods at 3T. Both resting-state and task-based data were subjected to a standard image pre-processing pipeline. Subsequently the two echoes were combined as a weighted average, using four different strategies for calculating the weights: (1) simple arithmetic averaging, (2) BOLD sensitivity weighting, (3) temporal-signal-to-noise ratio weighting and (4) temporal BOLD sensitivity weighting. Our results clearly show that the simple averaging of data with the different echoes is sufficient. Advanced echo combination methods may provide advantages on a single-subject level but when considering random-effects group level statistics they provide no benefit regarding sensitivity (i.e., group-level t-values) compared to the simple echo-averaging approach. One possible reason for the lack of clear advantages may be that apart from increasing the average BOLD sensitivity at the single-subject level, the advanced weighted averaging methods also inflate the inter-subject variance. As the echo combination methods provide very similar results, the recommendation is to choose between them depending on the availability of time for collecting additional resting-state data or whether subject-level or group-level analyses are planned

General and specific responsiveness of the amygdala during explicit emotion recognition in females and males

<p>Abstract</p> <p>Background</p> <p>The ability to recognize emotions in facial expressions relies on an extensive neural network with the amygdala as the key node as has typically been demonstrated for the processing of fearful stimuli. A sufficient characterization of the factors influencing and modulating amygdala function, however, has not been reached now. Due to lacking or diverging results on its involvement in recognizing all or only certain negative emotions, the influence of gender or ethnicity is still under debate.</p> <p>This high-resolution fMRI study addresses some of the relevant parameters, such as emotional valence, gender and poser ethnicity on amygdala activation during facial emotion recognition in 50 Caucasian subjects. Stimuli were color photographs of emotional Caucasian and African American faces.</p> <p>Results</p> <p>Bilateral amygdala activation was obtained to all emotional expressions (anger, disgust, fear, happy, and sad) and neutral faces across all subjects. However, only in males a significant correlation of amygdala activation and behavioral response to fearful stimuli was observed, indicating higher amygdala responses with better fear recognition, thus pointing to subtle gender differences. No significant influence of poser ethnicity on amygdala activation occurred, but analysis of recognition accuracy revealed a significant impact of poser ethnicity that was emotion-dependent.</p> <p>Conclusion</p> <p>Applying high-resolution fMRI while subjects were performing an explicit emotion recognition task revealed bilateral amygdala activation to all emotions presented and neutral expressions. This mechanism seems to operate similarly in healthy females and males and for both in-group and out-group ethnicities. Our results support the assumption that an intact amygdala response is fundamental in the processing of these salient stimuli due to its relevance detecting function.</p

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Converging evidence has accumulated that menstrual cycle and thus hormonal levels can affect emotional behavior, in particular facial emotion recognition. Here we explored the association of ovarian hormone levels and amygdala activation during an explicit emotion recognition task in two groups of healthy young females: one group was measured while in their follicular phase (n = 11) and the other during their luteal phase (n = 11). Using a 3T scanner in combination with a protocol specifically optimized to reliably detect amygdala activation we found significantly stronger amygdala activation in females during their follicular phase. Also, emotion recognition performance was significantly better in the follicular phase. We observed significant negative correlations between progesterone levels and amygdala response to fearful, sad and neutral faces, further supporting a significant modulation of behavior and neural response by hormonal changes during the menstrual cycle. From an evolutionary point of view this significant influence of ovarian hormone level on emotion processing and an important neural correlate, the amygdala, may enable a higher social sensitivity in females during their follicular phase, thus facilitating socio-emotional behavior (and social interaction) which may possibly facilitate mating behavior as well