Hypnotic suggestions cognitively penetrate tactile perception through top-down modulation of semantic contents.

Perception is subject to ongoing alterations by learning and top-down influences. Although abundant studies have shown modulation of perception by attention, motivation, content and context, there is an unresolved controversy whether these examples provide true evidence that perception is penetrable by cognition. Here we show that tactile perception assessed as spatial discrimination can be instantaneously and systematically altered merely by the semantic content during hypnotic suggestions. To study neurophysiological correlates, we recorded EEG and SEPs. We found that the suggestion "your index finger becomes bigger" led to improved tactile discrimination, while the suggestion "your index finger becomes smaller" led to impaired discrimination. A hypnosis without semantic suggestions had no effect but caused a reduction of phase-locking synchronization of the beta frequency band between medial frontal cortex and the finger representation in somatosensory cortex. Late SEP components (P80-N140 complex) implicated in attentional processes were altered by the semantic contents, but processing of afferent inputs in SI remained unaltered. These data provide evidence that the psychophysically observed modifiability of tactile perception by semantic contents is not simply due to altered perception-based judgments, but instead is a consequence of modified perceptual processes which change the perceptual experience.

3D Automated Segmentation of Lower Leg Muscles Using Machine Learning on a Heterogeneous Dataset.

Quantitative MRI combines non-invasive imaging techniques to reveal alterations in muscle pathophysiology. Creating muscle-specific labels manually is time consuming and requires an experienced examiner. Semi-automatic and fully automatic methods reduce segmentation time significantly. Current machine learning solutions are commonly trained on data from healthy subjects using homogeneous databases with the same image contrast. While yielding high Dice scores (DS), those solutions are not applicable to different image contrasts and acquisitions. Therefore, the aim of our study was to evaluate the feasibility of automatic segmentation of a heterogeneous database. To create a heterogeneous dataset, we pooled lower leg muscle images from different studies with different contrasts and fields-of-view, containing healthy controls and diagnosed patients with various neuromuscular diseases. A second homogenous database with uniform contrasts was created as a subset of the first database. We trained three 3D-convolutional neuronal networks (CNN) on those databases to test performance as compared to manual segmentation. All networks, training on heterogeneous data, were able to predict seven muscles with a minimum average DS of 0.75. U-Net performed best when trained on the heterogeneous dataset (DS: 0.80 ± 0.10, AHD: 0.39 ± 0.35). ResNet and DenseNet yielded higher DS, when trained on a heterogeneous dataset (both DS: 0.86), as compared to a homogeneous dataset (ResNet DS: 0.83, DenseNet DS: 0.76). In conclusion, a CNN trained on a heterogeneous dataset achieves more accurate labels for predicting a heterogeneous database of lower leg muscles than a CNN trained on a homogenous dataset. We propose that a large heterogeneous database is needed, to make automated segmentation feasible for different kinds of image acquisitions.