Specialized Networks for Social Cognition in the Primate Brain.

Primates have evolved diverse cognitive capabilities to navigate their complex social world. To understand how the brain implements critical social cognitive abilities, we describe functional specialization in the domains of face processing, social interaction understanding, and mental state attribution. Systems for face processing are specialized from the level of single cells to populations of neurons within brain regions to hierarchically organized networks that extract and represent abstract social information. Such functional specialization is not confined to the sensorimotor periphery but appears to be a pervasive theme of primate brain organization all the way to the apex regions of cortical hierarchies. Circuits processing social information are juxtaposed with parallel systems involved in processing nonsocial information, suggesting common computations applied to different domains. The emerging picture of the neural basis of social cognition is a set of distinct but interacting subnetworks involved in component processes such as face perception and social reasoning, traversing large parts of the primate brain.

Face Processing Systems: From Neurons to Real-World Social Perception.

Primate face processing depends on a distributed network of interlinked face-selective areas composed of face-selective neurons. In both humans and macaques, the network is divided into a ventral stream and a dorsal stream, and the functional similarities of the areas in humans and macaques indicate they are homologous. Neural correlates for face detection, holistic processing, face space, and other key properties of human face processing have been identified at the single neuron level, and studies providing causal evidence have established firmly that face-selective brain areas are central to face processing. These mechanisms give rise to our highly accurate familiar face recognition but also to our error-prone performance with unfamiliar faces. This limitation of the face system has important implications for consequential situations such as eyewitness identification and policing.

Amyloid-ß accumulation in relation to functional connectivity in aging: A longitudinal study.

The brain undergoes many changes at pathological and functional levels in healthy aging. This study employed a longitudinal and multimodal imaging dataset from the OASIS-3 study (n = 300) and explored possible relationships between amyloid beta (Aß) accumulation and functional brain organization over time in healthy aging. We used positron emission tomography (PET) with Pittsburgh compound-B (PIB) to quantify the Aß accumulation in the brain and resting-state functional MRI (rs-fMRI) to measure functional connectivity (FC) among brain regions. Each participant had at least 2 to 3 follow-up visits. A linear mixed-effect model was used to examine longitudinal changes of Aß accumulation and FC throughout the whole brain. We found that the limbic and frontoparietal networks had a greater annual Aß accumulation and a slower decline in FC in aging. Additionally, the amount of the Aß deposition in the amygdala network at baseline slowed down the decline in its FC in aging. Furthermore, the functional connectivity of the limbic, default mode network (DMN), and frontoparietal networks accelerated the Aß propagation across their functionally highly connected regions. The functional connectivity of the somatomotor and visual networks accelerated the Aß propagation across the brain regions in the limbic, frontoparietal, and DMN networks. These findings suggested that the slower decline in the functional connectivity of the functional hubs may compensate for their greater Aß accumulation in aging. The Aß propagation from one brain region to the other may depend on their functional connectivity strength.

Brain reactivity differentiates subjects with high and low dream recall frequencies during both sleep and wakefulness.

The neurophysiological correlates of dreaming remain unclear. According to the "arousal-retrieval" model, dream encoding depends on intrasleep wakefulness. Consistent with this model, subjects with high and low dream recall frequency (DRF) report differences in intrasleep awakenings. This suggests a possible neurophysiological trait difference between the 2 groups. To test this hypothesis, we compared the brain reactivity (evoked potentials) of subjects with high (HR, N = 18) and low (LR, N = 18) DRF during wakefulness and sleep. During data acquisition, the subjects were presented with sounds to be ignored (first names randomly presented among pure tones) while they were watching a silent movie or sleeping. Brain responses to first names dramatically differed between the 2 groups during both sleep and wakefulness. During wakefulness, the attention-orienting brain response (P3a) and a late parietal response were larger in HR than in LR. During sleep, we also observed between-group differences at the latency of the P3a during N2 and at later latencies during all sleep stages. Our results demonstrate differences in the brain reactivity of HR and LR during both sleep and wakefulness. These results suggest that the ability to recall dreaming is associated with a particular cerebral functional organization, regardless of the state of vigilance.

Using artificial neural networks to ask 'why' questions of minds and brains.

Neuroscientists have long characterized the properties and functions of the nervous system, and are increasingly succeeding in answering how brains perform the tasks they do. But the question 'why' brains work the way they do is asked less often. The new ability to optimize artificial neural networks (ANNs) for performance on human-like tasks now enables us to approach these 'why' questions by asking when the properties of networks optimized for a given task mirror the behavioral and neural characteristics of humans performing the same task. Here we highlight the recent success of this strategy in explaining why the visual and auditory systems work the way they do, at both behavioral and neural levels.

EEG Effective Source Projections Are More Bilaterally Symmetric in Infants Than in Adults.

Although anatomical brain hemispheric asymmetries have been clearly documented in the infant brain, findings concerning functional hemispheric specialization have been inconsistent. The present report aims to assess whether bilaterally symmetric synchronous activity between the two hemispheres is a characteristic of the infant brain. To asses cortical bilateral synchronicity, we used decomposition by independent component analysis (ICA) of high-density electroencephalographic (EEG) data collected in an auditory passive oddball paradigm. Decompositions of concatenated 64-channel EEG data epochs from each of 34 typically developing 6-month-old infants and from 18 healthy young adults participating in the same passive auditory oddball protocol were compared to characterize differences in functional brain organization between early life and adulthood. Our results show that infant EEG decompositions comprised a larger number of independent component (IC) effective source processes compatible with a cortical origin and having bilaterally near-symmetric scalp projections (13.8% of the infant data ICs presented a bilateral pattern vs. 4.3% of the adult data ICs). These IC projections could be modeled as the sum of potentials volume-conducted to the scalp from synchronous locally coherent field activities in corresponding left and right cortical source areas. To conclude, in this paradigm, source-resolved infant brain EEG exhibited more bilateral synchronicity than EEG produced by the adult brain, supporting the hypothesis that more strongly unilateral and likely more functionally specialized unihemispheric cortical field activities are concomitants of brain maturation.