Investigating intra-individual variability of face scanning in autistic children

We investigated the intra-individual variability of face scanning in autistic children to represent a new avenue for understanding abnormal face scanning in autism spectrum condition. Across four studies, we used eye-tracking techniques to systematically examine the variability of face scanning patterns in autistic children when performing different tasks and scanning different types of faces. Autistic and non-autistic children were asked to complete a face judgment task (Study 1, age range: 4.9–7.2 years), a face recognition task (Study 2, age range: 4.7–7.6 years), a facial expression recognition task (Study 3, age range: 4.3–7.4 years), and a dynamic facial expression free viewing task (Study 4, age range: 2.5–5.6 years). In addition, we conducted Study 5 using houses as stimuli to test the specificity of the results to faces (age range: 4.9–7.2 years). We found that scan pattern similarity between different face presentations was lower in autistic children than non-autistic children, which was robust to variations in experimental methods. Furthermore, the decreased scan pattern similarity in autism spectrum condition was evident in both viewing faces and houses. These results suggest that the scanning patterns of autistic children are noisier and variable. It might represent a new avenue for the understanding of core symptoms in autism spectrum condition. Lay abstract Atypical face scanning is suggested to be related to social interactions and communicative deficits in autistic children. We systematically examined whether autistic and non-autistic children used consistent scanning patterns when performing different tasks and scanning different types of faces. We found that autistic children scanned faces more variably than non-autistic children: While non-autistic children used more consistent scanning patterns, autistic children’s scanning patterns changed frequently when watching different faces. Autistic children’s variable face scanning patterns might delay and impair face processing, resulting in a social interaction deficit. What’s more, variable scanning patterns may create an unstable and unpredictable perception of the environment for autistic children. Developing in such an unstable environment might motivate autistic children to retract from the environment, avoid social interaction, and focus instead on the performance of repetitive behavior. Therefore, studying face scanning variability might represent a new avenue for understanding core symptoms in autistic people.

Pattern similarity in the frontoparietal control network reflects an "off-veridical" template that optimizes target-match decisions during visual search

Theories of attention hypothesize the existence of an "attentional" or "target" template that contains task-relevant information in memory when searching for an object. The target template contributes to visual search by directing visual attention towards potential targets and serving as a decisional boundary for target identification. However, debate still exists regarding how template information is stored in the human brain. Here, we conducted a pattern-based fMRI study to assess how template information is encoded to optimize target-match decisions during visual search. To ensure that match decisions reflect visual search demands, we used a visual search paradigm in which all distractors were linearly separable but highly similar to the target and were known to shift the target representation away from the distractor features (Yu & Geng, 2019). In a separate match-to-sample probe task, we measured the target representation used for match decisions across two resting state networks that have long been hypothesized to maintain and control target information: the frontoparietal control network (FPCN) and the visual network (VisN). Our results showed that lateral prefrontal cortex in FPCN maintained the context-dependent "off-veridical" template; in contrast, VisN encoded a veridical copy of the target feature during match decisions. By using behavioral drift diffusion modeling, we verified that the decision criterion during visual search and the probe task relied on a common biased target template. Taken together, our results suggest that sensory-veridical information is transformed in lateral prefrontal cortex into an adaptive code of target-relevant information that optimizes decision processes during visual search.

Expression of CRY2 Gene in the Brain Is Related to Human Navigation

Navigation is a complex cognitive process. CRY2 gene has been proposed to play an important role in navigation behaviors in various non-human animal species. Utilizing a recently developed neuroimaging-transcriptomics approach, the present study reported a tentative link between the CRY2 gene and human navigation. Specifically, we showed a significant pattern similarity between CRY2 gene expression in the human brain and navigation-related neural activation in functional magnetic resonance imaging. To further illuminate the functionality of CRY2 in human navigation, we examined the correlation between CRY2 expression and various cognitive processes underlying navigation, and found high correlation of CRY2 expression with neural activity of multiple cognitive domains, particularly object and shape perception and spatial memory. Further analyses on the relation between the neural activity of human navigation and the expression maps of genes of two CRY2-related pathways, i.e., the magnetoreceptive and circadian-related functions, found a trend of correlation for the CLOCK gene, a core circadian regulator gene, suggesting that CRY2 may modulate human navigation through its role in circadian rhythm. This observation was further confirmed by a behavioral study where individuals with better circadian regularity in daily life showed better sense of direction. Taken together, our study presents the first neural evidence that links CRY2 with human navigation, possibly through the modulation of circadian rhythm.

Can a Data-Driven Measure of Neuroanatomic Dementia Risk be Considered a Measure of Brain Aging?

There is an increasing interest in identifying aging-related factors which may be permissive of Alzheimer’s Disease (AD) emergence. We previously used machine learning to derive an index of neuroanatomic risk of dementia called AD pattern similarity (AD-PS) score using MRIs obtained in the Atherosclerosis Risk in Communities (ARIC) study. Here, we investigate the potential of the AD-PS scores as a brain-focused measure of biologic age. Among 1970 ARIC participants with MRI collected at ARIC Visit 5, we related AD-PS scores to three measures of aging: mortality (n=356) over 8 years of follow-up; an a priori panel of 32 proteins related to aging (N=1647); and a deficit accumulation index (DAI) based on 38 health-related measures. We found lower AD-PS scores associated with significantly lower mortality (HR=0.58, CI-95%, [0.45 - 0.75], p < 0.001) after adjusting for age, race, smoking and hypertension. Among the 32 proteins, nine were significantly associated to AD-PS scores (p < 0.05) with 4 remaining significant adjusting for multiple comparisons (Growth/differentiation factor 15, Tumor necrosis factor receptor superfamily member 1A and 1B and Collagen alpha-1(XVIII) chain). Finally, in a linear regression model after adjusting for age, race, sex, hypertension and smoking, AD-PS scores were associated with the DAI (p < 0.001). The consistent patterns of associations suggest that a data-driven measure of AD neuroanatomic risk may be capturing aspects of biologic age in older adults.

Temporal self-compression: Behavioral and neural evidence that past and future selves are compressed as they move away from the present

A basic principle of perception is that as objects increase in distance from an observer, they also become logarithmically compressed in perception (i.e., not differentiated from one another), making them hard to distinguish. Could this basic principle apply to perhaps our most meaningful mental representation: our own sense of self? Here, we report four studies that suggest selves are increasingly non-discriminable with temporal distance from the present as well. In Studies 1 through 3, participants made trait ratings across various time points in the past and future. We found that participants compressed their past and future selves, relative to their present self. This effect was preferential to the self and could not be explained by the alternative possibility that individuals simply perceive arbitrary self-change with time irrespective of temporal distance. In Study 4, we tested for neural evidence of temporal self-compression by having participants complete trait ratings across time points while undergoing functional MRI. Representational similarity analysis was used to determine whether neural self-representations are compressed with temporal distance as well. We found evidence of temporal self-compression in areas of the default network, including medial prefrontal cortex and posterior cingulate cortex. Specifically, neural pattern similarity between self-representations was logarithmically compressed with temporal distance. Taken together, these findings reveal a “temporal self-compression” effect, with temporal selves becoming increasingly non-discriminable with distance from the present.

Rare cutaneous manifestation of COVID-19 infection and Pfizer-BioNTech COVID-19 vaccine with a unique pattern similarity

In December 2019, a new emerging virus causing mild-to-severe pneumonia was detected in China. The virus was described as a variant of SARS-CoV and was called SARS-CoV-2, then declared a pandemic by the WHO on 11 March 2020. Millions of people contracted the virus and presented with a symptomatology of variable severity, including upper respiratory tract symptoms, systemic symptoms and diarrhea. We herein report a rare skin presentation in a 33-year-old female that occurred both during COVID-19 infection and after receiving the first dose of COVID-19 vaccine.

Noradrenergic arousal after encoding reverses the course of systems consolidation in humans

It is commonly assumed that episodic memories undergo a time-dependent systems consolidation process, during which hippocampus-dependent memories eventually become reliant on neocortical areas. Here we show that systems consolidation dynamics can be experimentally manipulated and even reversed. We combined a single pharmacological elevation of post-encoding noradrenergic activity through the α2-adrenoceptor antagonist yohimbine with fMRI scanning both during encoding and recognition testing either 1 or 28 days later. We show that yohimbine administration, in contrast to placebo, leads to a time-dependent increase in hippocampal activity and multivariate encoding-retrieval pattern similarity, an indicator of episodic reinstatement, between 1 and 28 days. This is accompanied by a time-dependent decrease in neocortical activity. Behaviorally, these neural changes are linked to a reduced memory decline over time after yohimbine intake. These findings indicate that noradrenergic activity shortly after encoding may alter and even reverse systems consolidation in humans, thus maintaining vividness of memories over time.

Application of Data Stream Pattern Evolution Analysis in Energy Control System

Aiming at the data stream obtained in the energy control system of a cigarette factory, a data stream pattern analysis method is proposed which provides support for anomaly detection and other applications by detecting and tracking the pattern and extracting the evolution between the patterns. In this paper, the concept and definition of pattern tracking method for data streams are proposed, as well as the measurement criteria of pattern similarity. On this basis, the paper introduces in detail how to generate and cluster the hypercube grids, store the grid, generate the pattern and track the pattern on the real-time data stream. The paper also defines and describes the dynamic process of the generating, retreating, mutating, dividing and merging of the data stream pattern. The algorithm in this paper is applied to the real data stream collected in the energy control system of Ningbo cigarette factory, identifying and analyzing various feature of the data stream pattern, which can effectively describe the physical changes of the energy system.