The existence of facial aftereffects suggests that shape-selective mechanisms at the higher stages of visual object coding -- similarly to the early processing of low-level visual features -- are adaptively recalibrated. Our goal was to uncover the ERP correlates of shape-selective adaptation and to test whether it is also involved in the visual processing of human body parts. We found that prolonged adaptation to female hands -- similarly to adaptation to female faces -- biased the judgements about the subsequently presented hand test stimuli: they were perceived more masculine than in the control conditions. We also showed that these hand aftereffects are size and orientation invariant. However, no aftereffects were found when the adaptor and test stimuli belonged to different categories (i.e. face adaptor and hand test, or vice versa), suggesting that the underlying adaptation mechanisms are category-specific. In accordance with the behavioral results, both adaptation to faces and hands resulted in a strong and category-specific modulation -- reduced amplitude and increased latency -- of the N170 component of ERP responses. Our findings suggest that shape-selective adaptation is a general mechanism of visual object processing and its neural effects are primarily reflected in the N170 component of the ERP responses.

We investigated neural mechanisms that support voice recognition in a training paradigm with fMRI. The same listeners were trained on different weeks to categorize the mid-regions of voice-morph continua as an individual's voice. Stimuli implicitly defined a voice-acoustics space, and training explicitly defined a voice-identity space. The pre-defined centre of the voice category was shifted from the acoustic centre each week in opposite directions, so the same stimuli had different training histories on different tests. Cortical sensitivity to voice similarity appeared over different time-scales and at different representational stages. First, there were short-term adaptation effects: increasing acoustic similarity to the directly preceding stimulus led to haemodynamic response reduction in the middle/posterior STS and in right ventrolateral prefrontal regions. Second, there were longer-term effects: response reduction was found in the orbital/insular cortex for stimuli that were most versus least similar to the acoustic mean of all preceding stimuli, and, in the anterior temporal pole, the deep posterior STS and the amygdala, for stimuli that were most versus least similar to the trained voice-identity category mean. These findings are interpreted as effects of neural sharpening of long-term stored typical acoustic and category-internal values. The analyses also reveal anatomically separable voice representations: one in a voice-acoustics space and one in a voice-identity space. Voice-identity representations flexibly followed the trained identity shift, and listeners with a greater identity effect were more accurate at recognizing familiar voices. Voice recognition is thus supported by neural voice spaces that are organized around flexible 'mean voice' representations.

Previously several functional magnetic resonance imaging (fMRI) studies point toward the role of perceptual expectations in determining adaptation or repetition suppression (RS) in humans. These studies showed that the probability of repetitions of faces within a block influences the magnitude of adaptation in face-related areas of the human brain (Summerfield et al., 2008). However, a current macaque single-cell/local field potential (LFP) recording study using objects as stimuli found no evidence for the modulation of the neural response by the repetition probability in the inferior temporal cortex (Kaliukhovich and Vogels, 2010). Here we examined whether stimulus repetition probability affects fMRI repetition suppression for nonface object stimuli in the human brain. Subjects were exposed to either two identical [repetition trials (RTs)] or two different [alternation trials (ATs)] object stimuli. Both types of trials were presented blocks consisting of either 75% [repetition blocks (RBs)] or 25% [alternation blocks (ABs)] of RTs. We found strong RS, i.e., a lower signal for RTs compared to ATs, in the object sensitive lateral occipital cortex as well as in the face-sensitive occipital and fusiform face areas. More importantly, however, there was no significant difference in the magnitude of RS between RBs and ABs in each of the areas. This is in agreement with the previous monkey single-unit/LFP findings and suggests that RS in the case of nonface visual objects is not modulated by the repetition probability in humans. Our results imply that perceptual expectation effects vary for different visual stimulus categories.

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Aim:Hypothermia is often induced to reduce brain injury in newborns, following perinatal hypoxic–ischaemic events, and in adults following traumatic brain injury, stroke or cardiac arrest. We aimed to devise a method, based on diffusion-weighted MRI, to measure non-invasively the temperature of the cerebrospinal fluid in the lateral ventricles.Methods:The well-known temperature dependence of the water diffusion constant was used for the estimation of temperature. We carried out diffusion MRI measurements on a 3T Philips Achieva Scanner involving phantoms (filled with water or artificial cerebrospinal fluid while slowly cooling from 41 to 32°C) and healthy adult volunteers.Results:The estimated temperature of water phantoms followed that measured using a mercury thermometer, but the estimates for artificial cerebrospinal fluid were 1.04°C lower. After correcting for this systematic difference, the estimated temperature within the lateral ventricles of volunteers was 39.9°C. Using diffusion directions less sensitive to cerebrospinal fluid flow, it was 37.7°C, which was in agreement with the literature.Conclusion:Although further improvements are needed, measuring the temperature within the lateral ventricles using diffusion MRI is a viable method that may be useful for clinical applications. We introduced the method, identified sources of error and offered remedies for each.

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 ϳ220 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.

Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network.

Traditionally, research on visual attention has been focused on the processes involved in conscious, explicit selection of task-relevant sensory input. Recently, however, it has been shown that attending to a specific feature of an object automatically increases neural sensitivity to this feature throughout the visual field. Here we show that directing attention to a specific color of an object results in attentional modulation of the processing of task-irrelevant and not consciously perceived motion signals that are spatiotemporally associated with this color throughout the visual field. Such implicit cross-feature spreading of attention takes place according to the veridical physical associations between the color and motion signals, even under special circumstances when they are perceptually misbound. These results imply that the units of implicit attentional selection are spatiotemporally colocalized feature clusters that are automatically bound throughout the visual field.