Visual number sense for real-world scenes shared by deep neural networks and humans.

Recently, visual number sense has been identified from deep neural networks (DNNs). However, whether DNNs have the same capacity for real-world scenes, rather than the simple geometric figures that are often tested, is unclear. In this study, we explore the number perception of scenes using AlexNet and find that numerosity can be represented by the pattern of group activation of the category layer units. The global activation of these units increases with the number of objects in the scene, and the variations in their activation decrease accordingly. By decoding the numerosity from this pattern, we reveal that the embedding coefficient of a scene determines the likelihood of potential objects to contribute to numerical perception. This was demonstrated by the more optimized performance for pictures with relatively high embedding coefficients in both DNNs and humans. This study for the first time shows that a distinct feature in visual environments, revealed by DNNs, can modulate human perception, supported by a group-coding mechanism.

Form Properties of Moving Targets Bias Smooth Pursuit Target Selection in Monkeys / 神经科学通报·英文版

During natural viewing, we often recognize multiple objects, detect their motion, and select one object as the target to track. It remains to be determined how such behavior is guided by the integration of visual form and motion perception. To address this, we studied how monkeys made a choice to track moving targets with different forms by smooth pursuit eye movements in a two-target task. We found that pursuit responses were biased toward the motion direction of a target with a hole. By computing the relative weighting, we found that the target with a hole exhibited a larger weight for vector computation. The global hole feature dominated other form properties. This dominance failed to account for changes in pursuit responses to a target with different forms moving singly. These findings suggest that the integration of visual form and motion perception can reshape the competition in sensorimotor networks to guide behavioral selection.

Altered functional connectivity within and between brain modules in absence epilepsy: a resting-state functional magnetic resonance imaging study.

Functional connectivity has been correlated with a patient's level of consciousness and has been found to be altered in several neuropsychiatric disorders. Absence epilepsy patients, who experience a loss of consciousness, are assumed to suffer from alterations in thalamocortical networks; however, previous studies have not explored the changes at a functional module level. We used resting-state functional magnetic resonance imaging to examine the alteration in functional connectivity that occurs in absence epilepsy patients. By parcellating the brain into 90 brain regions/nodes, we uncovered an altered functional connectivity within and between functional modules. Some brain regions had a greater number of altered connections and therefore behaved as key nodes in the changed network pattern; these regions included the superior frontal gyrus, the amygdala, and the putamen. In particular, the superior frontal gyrus demonstrated both an increased value of connections with other nodes of the frontal default mode network and a decreased value of connections with the limbic system. This divergence is positively correlated with epilepsy duration. These findings provide a new perspective and shed light on how functional connectivity and the balance of within/between module connections may contribute to both the state of consciousness and the development of absence epilepsy.

Atrophy and primary somatosensory cortical reorganization after unilateral thoracic spinal cord injury: a longitudinal functional magnetic resonance imaging study.

The effects of traumatic spinal cord injury (SCI) on the changes in the central nervous system (CNS) over time may depend on the dynamic interaction between the structural integrity of the spinal cord and the capacity of the brain plasticity. Functional magnetic resonance imaging (fMRI) was used in a longitudinal study on five rhesus monkeys to observe cerebral activation during upper limb somatosensory tasks in healthy animals and after unilateral thoracic SCI. The changes in the spinal cord diameters were measured, and the correlations among time after the lesion, structural changes in the spinal cord, and primary somatosensory cortex (S1) reorganization were also determined. After SCI, activation of the upper limb in S1 shifted to the region which generally dominates the lower limb, and the rostral spinal cord transverse diameter adjacent to the lesion exhibited obvious atrophy, which reflects the SCI-induced changes in the CNS. A significant correlation was found among the time after the lesion, the spinal cord atrophy, and the degree of contralateral S1 reorganization. The results indicate the structural changes in the spinal cord and the dynamic reorganization of the cerebral activation following early SCI stage, which may help to further understand the neural plasticity in the CNS.

A new method based on sparse component decomposition to remove MRI artifacts in the continuous EEG recordings / 生物医学工程学杂志

How to effectively remove the magnetic resonance imaging (MRI) artifacts in the electroencephalography (EEG) recordings, when EEG and functional magnetic resonance imaging (FMRI) are simultaneous recorded, is a challenge for integration of EEG and FMRI. According to the temporal-spatial difference between MRI artifacts and EEG, a new method based on sparse component decomposition in the mixed over-complete dictionary is proposed in this paper to remove MR artifacts. A mixed over-complete dictionary (MOD) of waveletes and discrete cosine which can exhibit the temporal-spatial discrepancy between MRI artificats and EEG is constructed first, and then the signals are separated by learning in this MOD with matching pursuit (MP) algorithm. The method is applied to the MRI artifacts corrupted EEG recordings and the decomposition result shows its validation.