Between prediction and reality: top-down propagation, communication and modulation

Expectations predict the upcoming visual information, facilitating its disambiguation from the noisy input to speed up behavior. However, the neural mechanisms that support such dynamic feature flow and how they facilitate behavior remain unclear. In this thesis, I will trace the propagation, communication and modulation effect related to the prediction. In the first study (Chapter 2), I initiated a cueing-categorization design and validated its feasibility. I first detected the sensibility of participants in distinguishing auditory pitches (i.e., the cues) by estimating their d-primes. Next, in two phases, I instructed participants to build the coupling relationship between auditory cues and stimuli, then proved that compared with non-informative prediction, informative ones can significantly reduce the reaction time. Recognizing confounding factors in the first study, I further improved the design by manipulating two specific and separate predicted contents. I used a prediction experiment that cued participants (N = 11) to the spatial location (left vs. right) and spatial frequency (SF, Low, LSF, vs. High, HSF) contents of an upcoming Gabor patch. I reconstructed two networks (prediction network and categorization network) in the following two studies with simultaneous MEG recordings of each participant’s neural activity. In the second study (Chapter 3), focusing on the pre-stimulus prediction stage, I answered when, where and how predictions dynamically propagate through a network of brain regions. I traced the dynamic neural representation of predictive cues and reconstructed the communications about predicted contents in a functional network, sequentially from temporal lobe at 90-120ms, to occipital cortex after 200ms, with modulatory supervision of frontal regions at 120-200ms. In the third study (Chapter 4), turning to the post-stimulus stage, I reconstructed the communication network propagating the stimulus feature from occipital-ventral regions (150-250ms) to parietal lobe (250-350ms), finally arriving premotor cortex (>350ms) which modulates behavioral categorization. I found the previous prediction previewed and then sharpen stimulus representation across the categorization network, leading to a faster reaction time. I discussed the generalization of the findings to other stimulus features and sensory modalities. Putting forward the plans about developing a series of structured studies on predicting higher-dimensional features, in the future, I aim to understand the neural mechanisms about how prediction tunes perception and to trace the concrete predicted contents in laminar layers with the fusion of E/MEG and fMRI

Different computations over the same inputs produce selective behavior in algorithmic brain networks

A key challenge in neuroimaging remains to understand where, when, and now particularly how human brain networks compute over sensory inputs to achieve behavior. To study such dynamic algorithms from mass neural signals, we recorded the magnetoencephalographic (MEG) activity of participants who resolved the classic XOR, OR, and AND functions as overt behavioral tasks (N = 10 participants/task, N-of-1 replications). Each function requires a different computation over the same inputs to produce the task-specific behavioral outputs. In each task, we found that source-localized MEG activity progresses through four computational stages identified within individual participants: (1) initial contralateral representation of each visual input in occipital cortex, (2) a joint linearly combined representation of both inputs in midline occipital cortex and right fusiform gyrus, followed by (3) nonlinear task-dependent input integration in temporal-parietal cortex, and finally (4) behavioral response representation in postcentral gyrus. We demonstrate the specific dynamics of each computation at the level of individual sources. The spatiotemporal patterns of the first two computations are similar across the three tasks; the last two computations are task specific. Our results therefore reveal where, when, and how dynamic network algorithms perform different computations over the same inputs to produce different behaviors

Network communications flexibly predict visual contents that enhance representations for faster visual categorization

Models of visual cognition generally assume that brain networks predict the contents of a stimulus to facilitate its subsequent categorization. However, understanding prediction and categorization at a network level has remained challenging, partly because we need to reverse engineer their information processing mechanisms from the dynamic neural signals. Here, we used connectivity measures that can isolate the communications of a specific content to reconstruct these network mechanisms in each individual participant (N=11, both sexes). Each was cued to the spatial location (left vs. right) and contents (Low vs. High Spatial Frequency, LSF vs. HSF) of a predicted Gabor stimulus that they then categorized. Using each participant’s concurrently measured MEG, we reconstructed networks that predict and categorize LSF vs. HSF contents for behavior. We found that predicted contents flexibly propagate top-down from temporal to lateralized occipital cortex, depending on task demands, under supervisory control of prefrontal cortex. When they reach lateralized occipital cortex, predictions enhance the bottom-up LSF vs. HSF representations of the stimulus, all the way from occipital-ventral-parietal to pre-motor cortex, in turn producing faster categorization behavior. Importantly, content communications are subsets (i.e. 55-75%) of the signal-to-signal communications typically measured between brain regions. Hence, our study isolates functional networks that process the information of cognitive functions

The Influence of Action Video Gaming Experience on the Perception of Emotional Faces and Emotional Word Meaning

Action video gaming (AVG) experience has been found related to sensorimotor and attentional development. However, the influence of AVG experience on the development of emotional perception skills is still unclear. Using behavioral and ERP measures, this study examined the relationship between AVG experience and the ability to decode emotional faces and emotional word meanings. AVG experts and amateurs completed an emotional word-face Stroop task prior to (the pregaming phase) and after (the postgaming phase) a 1 h AVG session. Within-group comparisons showed that after the 1 h AVG session, a more negative N400 was observed in both groups of participants, and a more negative N170 was observed in the experts. Between-group comparisons showed that the experts had a greater change of N170 and N400 amplitudes across phases than the amateurs. The results suggest that both the 1 h and long-term AVG experiences may be related to an increased difficulty of emotional perception. Furthermore, certain behavioral and ERP measures showed neither within- nor between-group differences, suggesting that the relationship between AVG experience and emotional perception skills still needs further research

Computer-assisted detection of cemento-enamel junction in intraoral ultrasonographs

The cemento-enamel junction (CEJ) is an important reference point for various clinical measurements in oral health assessment. Identifying CEJ in ultrasound images is a challenging task for dentists. In this study, a computer-assisted detection method is proposed to identify the CEJ in ultrasound images, based on the curvature change of the junction outlining the upper edge of the enamel and cementum at the cementum–enamel intersection. The technique consists of image preprocessing steps for image enhancement, segmentation, and edge detection to locate the boundary of the enamel and cementum. The effects of the image preprocessing and the sizes of the bounding boxes enclosing the CEJ were studied. For validation, the algorithm was applied to 120 images acquired from human volunteers. The mean difference of the best performance between the proposed method and the two raters’ measurements was an average of 0.25 mm with reliability ≥ 0.98. The proposed method has the potential to assist dental professionals in CEJ identification on ultrasonographs to provide better patient care

The establishment and application of a dual Nano-PCR detection method for feline calicivirus and feline herpesvirus type I

Feline calicivirus (FCV) and Feline herpesvirus type I (FHV-I) are the main pathogens causing upper respiratory tract infections in cats, and some wild animals. These two viruses always coinfection and cause serious harm to pet industry and wild animals protection. Established a rapid and accurate differential diagnosis method is crucial for prevention and control of disease, however, the current main detection method for these two viruses, either is low sensitivity (immunochromatographic strip), or is time-consuming and cannot differential diagnosis (conventional single PCR). Nanoparticle-assisted polymerase chain reaction (Nano-PCR) is a recently developed technique for rapid detection method of virus and bacteria. In this study, we described a dual Nano-PCR assay through combining the nanotechnology and PCR technology, which for the clinical simultaneous detection of FCV and FHV-I and differential diagnosis of upper respiratory tract infections in cats or other animals. Under optimized conditions, the optimal annealing temperature for dual Nano-PCR was 51.5°C, and specificity test results showed it had no cross reactivity to related virus, such as feline panleukopenia virus (FPV), feline Infectious peritonitis virus (FIPV) and rabies virus (RABV). Furthermore, the detection limit of dual Nano-PCR for FCV and FHV-I both were 1 × 10−8 ng/μL, convert to number of copies of virus DNA was 6.22 × 103copies/μL (FCV) and 2.81 × 103copies/μL (FHV-I), respectively. The dual Nano-PCR detected result of 52 cat clinical samples, including ocular, nasal and faecal swabs, and (3 FCV-positive samples), was consistent with ordinary PCR and the clinical detection results. The dual Nano-PCR method established in this study with strong specificity and high sensitivity can be used for virus nucleic acid (FCV and FHV-I) detection of clinical samples of feline upper respiratory tract infections feline calicivirus and feline herpesvirus while providing support for the early diagnosis of cats that infected by FCV and FHV-I

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Impacts of electronic cigarettes usage on air quality of vape shops and their nearby areas.

With the rapid growth of the electronic cigarette (e-cig) market, there is an increasing number of vape shops that exclusively sell e-cigs. The use of e-cigs in the vape shop is a primary source of indoor particles, which might transport to its nearby indoor spaces in the multiunit setting. In this study, six pairs of vape shops and neighboring businesses in Southern California were recruited for real-time measurements of particulate pollutants between February 2017 and October 2019. The mean (SD) particle number concentration (PNC) and PM2.5 concentration in the studied vape shops were 2.8 × 104 (2.3 × 104) particles/cm3 and 276 (546) μg/m3, which were substantially higher than those in neighboring businesses and outdoor areas. In addition, 24-h time-weighted average (TWA) nicotine sampling was conducted in the six pairs and three additional pairs. Nicotine was detected in the air of all the studied vape shops and neighboring businesses, in which the mean (SD) concentration was 2.59 (1.02) and 0.17 (0.13) μg/m3, respectively. Inside vape shops, the dilution-corrected vaping density (puffs/h/100 m3) is a strong predictor of the particle concentration, and nicotine concentration highly depends on the air exchange rate (AER). Out of the six studied pairs, PNCs in five vape shops and PM2.5 in two vape shops were significantly correlated with those in their neighboring businesses. This correlation was stronger when the door of the vape shop was closed. When the door was open, environmental electronic vaping (EEV) aerosols, especially smaller particles, could transport from the vape shop to the outdoor environment. Overall, e-cig usage in the vape shop impacts both its own and nearby air quality, raising concerns regarding the risk of exposure to EEV aerosols in the surrounding environments

Strength of predicted information content in the brain biases decision behavior

Prediction-for-perception theories suggest that the brain predicts incoming stimuli to facilitate their categorization.1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17 However, it remains unknown what the information contents of these predictions are, which hinders mechanistic explanations. This is because typical approaches cast predictions as an underconstrained contrast between two categories18,19,20,21,22,23,24—e.g., faces versus cars, which could lead to predictions of features specific to faces or cars, or features from both categories. Here, to pinpoint the information contents of predictions and thus their mechanistic processing in the brain, we identified the features that enable two different categorical perceptions of the same stimuli. We then trained multivariate classifiers to discern, from dynamic MEG brain responses, the features tied to each perception. With an auditory cueing design, we reveal where, when, and how the brain reactivates visual category features (versus the typical category contrast) before the stimulus is shown. We demonstrate that the predictions of category features have a more direct influence (bias) on subsequent decision behavior in participants than the typical category contrast. Specifically, these predictions are more precisely localized in the brain (lateralized), are more specifically driven by the auditory cues, and their reactivation strength before a stimulus presentation exerts a greater bias on how the individual participant later categorizes this stimulus. By characterizing the specific information contents that the brain predicts and then processes, our findings provide new insights into the brain’s mechanisms of prediction for perception