Viktor Gál

Dissociating the Effect of Noise on Sensory Processing and Overall Decision Difficulty

It has been proposed that perceptual decision making involves a task-difficulty component, which detects perceptual uncertainty and guides allocation of attentional resources. It is thought to take place immediately after the early extraction of sensory information and is specifically reflected in a positive component of the event related potentials, peaking at ∼220 ms after stimulus onset. However, in the previous research, neural processes associated with the monitoring of overall task difficulty were confounded by those associated with the increased sensory processing demands as a result of adding noise to the stimuli. Here we dissociated the effect of phase noise on sensory processing and overall decision difficulty using a face gender categorization task. Task difficulty was manipulated either by adding noise to the stimuli or by adjusting the female/male characteristics of the face images. We found that it is the presence of noise and not the increased overall task difficulty that affects the electrophysiological responses in the first 300 ms following stimulus onset in humans. Furthermore, we also showed that processing of phase-randomized as compared to intact faces is associated with increased fMRI responses in the lateral occipital cortex. These results revealed that noise-induced modulation of the early electrophysiological responses reflects increased visual cortical processing demands and thus failed to provide support for a task-difficulty component taking place between the early sensory processing and the later sensory accumulation stages of perceptual decision making.

Cross-modal plasticity of the corticothalamic circuits in rats enucleated on the first postnatal day

Reorganization of the reciprocal corticothalamic connections was studied as a possible anatomical substrate of the cross‐modal compensation of the missing visual input of the visual cortex by somatosensory‐evoked activities in neonatally enucleated rats. The use of quantitative retrograde tract‐tracing techniques revealed that the contribution of the lateral posterior thalamic nucleus (LP) is significantly increased following enucleation, while that of the dorsolateral geniculate and the lateral dorsal nuclei is decreased in the thalamocortical afferentation of a region in visual cortical area 17. In contrast with the control rats, a dense terminal arborization of afferents was labelled in the LP after the injection of anterograde tracer into the barrel cortex of the enucleated rats. The injection of anterograde tracer into the visual cortex also demonstrated a massive afferentation into the LP of the enucleated rats. Visual and somatosensory corticothalamic afferents exhibited similar ultrastructural features in the LP after enucleation, but their synaptic organizations differed as regards the diameter of the postsynaptic dendrites. Taken together with the previous observations, these results suggest a central role for the LP in the transmission of the somatosensory‐evoked activities to the visual cortex after early blindness.

Learning to filter out visual distractors

When learning to master a visual task in a cluttered natural environment, it is important to optimize the processing of task‐relevant information and to efficiently filter out distractors. However, the mechanisms that suppress task‐irrelevant information are not well understood. Here we show that training leads to a selective increase in motion coherence detection thresholds for task‐irrelevant motion directions that interfered with the processing of task‐relevant directions during training. Furthermore, using functional magnetic resonance imaging we found that training attenuated neural responses associated with the task‐irrelevant direction compared with the task‐relevant direction in the visual cortical areas involved in processing of visual motion. The strongest suppression of functional magnetic resonance imaging responses to task‐irrelevant motion information was observed in human area MT+. These findings reveal that perceptual learning leads to the suppression and efficient filtering of task‐irrelevant visual information.

Flawless visual short-term memory for facial emotional expressions

Facial emotions are important cues of human social interactions. Emotional expressions are continuously changing and thus should be monitored, memorized, and compared from time to time during social intercourse. However, it is not known how efficiently emotional expressions can be stored in short-term memory. Here we show that emotion discrimination is not impaired when the faces to be compared are separated by several seconds, requiring storage of fine-grained emotion-related information in short-term memory. Likewise, we found no significant effect of increasing the delay between the sample and the test face in the case of facial identity discrimination. Furthermore, a second experiment conducted on a large subject sample (N = 160) revealed flawless short-term memory for both facial emotions and facial identity also when observers performed the discrimination tasks only twice with novel faces. We also performed an fMRI experiment, which confirmed that discrimination of fine-grained emotional expressions in our experimental paradigm involved processing of high-level facial emotional attributes. Significantly stronger fMRI responses were found in a cortical network--including the posterior superior temporal sulcus--that is known to be involved in processing of facial emotional expression during emotion discrimination than during identity discrimination. These findings reveal flawless, high-resolution visual short-term memory for emotional expressions, which might underlie efficient monitoring of continuously changing facial emotions.

Attentional modulation of perceived pain intensity in capsaicin-induced secondary hyperalgesia

Perceived pain intensity is modulated by attention. However, it is not known that how pain intensity ratings are affected by attention in capsaicin-induced secondary hyperalgesia. Here we show that perceived pain intensity in secondary hyperalgesia is decreased when attention is distracted away from the painful pinprick stimulus with a visual task. Furthermore, it was found that the magnitude of attentional modulation in secondary hyperalgesia is very similar to that of capsaicin-untreated, control condition. Our findings, showing no interaction between capsaicin treatment and attentional modulation suggest that capsaicin-induced secondary hyperalgesia and attention might affect mechanical pain through independent mechanisms.

Resting State fMRI Functional Connectivity Analysis Using Dynamic Time Warping

Traditional resting-state network concept is based on calculating linear dependence of spontaneous low frequency fluctuations of the BOLD signals of different brain areas, which assumes temporally stable zero-lag synchrony across regions. However, growing amount of experimental findings suggest that functional connectivity exhibits dynamic changes and a complex time-lag structure, which cannot be captured by the static zero-lag correlation analysis. Here we propose a new approach applying Dynamic Time Warping (DTW) distance to evaluate functional connectivity strength that accounts for non-stationarity and phase-lags between the observed signals. Using simulated fMRI data we found that DTW captures dynamic interactions and it is less sensitive to linearly combined global noise in the data as compared to traditional correlation analysis. We tested our method using resting-state fMRI data from repeated measurements of an individual subject and showed that DTW analysis results in more stable connectivity patterns by reducing the within-subject variability and increasing robustness for preprocessing strategies. Classification results on a public dataset revealed a superior sensitivity of the DTW analysis to group differences by showing that DTW based classifiers outperform the zero-lag correlation and maximal lag cross-correlation based classifiers significantly. Our findings suggest that analysing resting-state functional connectivity using DTW provides an efficient new way for characterizing functional networks.

Classification of fMRI data using dynamic time warping based functional connectivity analysis

The synchronized spontaneous low frequency fluctuations of the BOLD signal, as captured by functional MRI measurements, is known to represent the functional connections of different brain areas. The aforementioned MRI measurements result in high-dimensional time series, the dimensions of which correspond to the activity of different brain regions. Recently we have shown that Dynamic Time Warping (DTW) distance can be used as a similarity measure between BOLD signals of brain regions as an alternative of the traditionally used correlation coefficient. We have characterized the new metrics stability in multiple measurements, and between subjects in homogenous groups. In this paper we investigated the DTW metrics sensitivity and demonstrated that DTW-based models outperform correlation-based models in resting-state fMRI data classification tasks. Additionally, we show that functional connectivity networks resulting from DTW-based models as compared to the correlation-based models are more stable and sensitive to differences between healthy subjects and patient groups.

Collision prediction via the CNN Universal Machine

We present an analogic CNN algorithm that estimates the time to an impending collision between an approaching object and the observer. Calculation is based on a context insensitive method, which is well known in neurobiology, using only two specific cues of the expanding two-dimensional image of the looming object.

A Model for Classification Based on the Functional Connectivity Pattern Dynamics of the Brain

Synchronized spontaneous low frequency fluctuations of the so called BOLD signal, as measured by functional Magnetic Resonance Imaging (fMRI), are known to represent the functional connections of different brain areas. Dynamic Time Warping (DTW) distance can be used as a similarity measure between BOLD signals of brain regions as an alternative of the traditionally used correlation coefficient and the usage of the DTW algorithm has further advantages: beside the DTW distance, the algorithm generates the warping path, i.e. the time-delay function between the compared two time-series. In this paper, we propose to use the relative length of the warping path as classification feature and demonstrate that the warping path itself carries important information when classifying patients according to cannabis addiction. We discuss biomedical relevance of our findings as well.

Electrophysiological correlates of learning-induced modulation of visual motion processing in humans

Training on a visual task leads to increased perceptual and neural responses to visual features that were attended during training as well as decreased responses to neglected distractor features. However, the time course of these attention-based modulations of neural sensitivity for visual features has not been investigated before. Here we measured event related potentials (ERP) in response to motion stimuli with different coherence levels before and after training on a speed discrimination task requiring object-based attentional selection of one of the two competing motion stimuli. We found that two peaks on the ERP waveform were modulated by the strength of the coherent motion signal; the response amplitude associated with motion directions that were neglected during training was smaller than the response amplitude associated with motion directions that were attended during training. The first peak of motion coherence-dependent modulation of the ERP responses was at 300 ms after stimulus onset and it was most pronounced over the occipitotemporal cortex. The second peak was around 500 ms and was focused over the parietal cortex. A control experiment suggests that the earlier motion coherence-related response modulation reflects the extraction of the coherent motion signal whereas the later peak might index accumulation and readout of motion signals by parietal decision mechanisms. These findings suggest that attention-based learning affects neural responses both at the sensory and decision processing stages.