In primary visual cortex fMRI responses to chromatic and achromatic stimuli are interdependent and predict contrast detection thresholds.

Chromatic and achromatic signals in primary visual cortex have historically been considered independent of each other but have since shown evidence of interdependence. Here, we investigated the combination of two components of a stimulus; an achromatic dynamically changing check background and a chromatic (L-M or S cone) target grating. We found that combinations of chromatic and achromatic signals in primary visual cortex were interdependent, with the dynamic range of responses to chromatic contrast decreasing as achromatic contrast increased. A contrast detection threshold study also revealed interdependence of background and target, with increasing chromatic contrast detection thresholds as achromatic background contrast increased. A model that incorporated a normalising effect of achromatic contrast on chromatic responses, but not vice versa, best predicted our V1 data as well as behavioural thresholds. Further along the visual hierarchy, the dynamic range of chromatic responses was maintained when compared to achromatic responses, which became increasingly compressive.

Temporal dynamics of normalization reweighting.

For decades, neural suppression in early visual cortex has been thought to be fixed. But recent work has challenged this assumption by showing that suppression can be reweighted based on recent history; when pairs of stimuli are repeatedly presented together, suppression between them strengthens. Here we investigate the temporal dynamics of this process using a steady-state visual evoked potential (SSVEP) paradigm that provides a time-resolved, direct index of suppression between pairs of stimuli flickering at different frequencies (5 and 7 Hz). Our initial analysis of an existing electroencephalography (EEG) dataset (N = 100) indicated that suppression increases substantially during the first 2-5 seconds of stimulus presentation (with some variation across stimulation frequency). We then collected new EEG data (N = 100) replicating this finding for both monocular and dichoptic mask arrangements in a preregistered study designed to measure reweighting. A third experiment (N = 20) used source-localized magnetoencephalography and found that these effects are apparent in primary visual cortex (V1), consistent with results from neurophysiological work. Because long-standing theories propose inhibition/excitation differences in autism, we also compared reweighting between individuals with high versus low autistic traits, and with and without an autism diagnosis, across our three datasets (total N = 220). We find no compelling differences in reweighting that are associated with autism. Our results support the normalization reweighting model and indicate that for prolonged stimulation, increases in suppression occur on the order of 2-5 seconds after stimulus onset.

A Multichannel Cross-Modal Fusion Framework for Electron Tomography.

In this paper, we present a multichannel cross-modal fusion algorithm to combine two complementary modalities in electron tomography: X-ray spectroscopy and scanning transmission electron microscopy (STEM). The former reveals compositions with high elemental specificity but low signal-to-noise ratio (SNR), while the latter characterizes the structure with high SNR but little chemical information. We use a multivariate regression to build a cross-modal fusion framework for these two modalities to simultaneously achieve high elemental specificity and high SNR for a target element chosen from the sample under study. Specifically, we first compute three-dimensional tomograms from tilt-series datasets of X-ray and STEM using different reconstruction algorithms. Then, we generate many feature images from each tomogram. Finally, we adopt partial least squares regression to assess the connection between these feature images and the reconstruction of the target element. Based on the simulated and experimental datasets of semiconductor devices, we demonstrate that our algorithm can not only produce continuous edges, homogeneous foreground, and clean background in its element-specific reconstructions but also can more accurately preserve fine structures than state-of-the-art tomography techniques. Moreover, we show that it can deliver results with high fidelity even for X-ray datasets with limited tilts or low counts. This property is highly desired in the semiconductor industry where acquisition time and sample damage are essential.

Automatic correction of nonlinear damping effects in HAADF-STEM tomography for nanomaterials of discrete compositions.

HAADF-STEM tomography is a common technique for characterizing the three-dimensional morphology of nanomaterials. In conventional tomographic reconstruction algorithms, the image intensity is assumed to be a linear projection of a physical property of the specimen. However, this assumption of linearity is not completely valid due to the nonlinear damping of signal intensities. The nonlinear damping effects increase w.r.t the specimen thickness and lead to so-called "cupping artifacts", due to a mismatch with the linear model used in the reconstruction algorithm. Moreover, nonlinear damping effects can strongly limit the applicability of advanced reconstruction approaches such as Total Variation Minimization and discrete tomography. In this paper, we propose an algorithm for automatically correcting the nonlinear effects and the subsequent cupping artifacts. It is applicable to samples in which chemical compositions can be segmented based on image gray levels. The correction is realized by iteratively estimating the nonlinear relationship between projection intensity and sample thickness, based on which the projections are linearized. The correction and reconstruction algorithms are tested on simulated and experimental data.

Modeling nanoscale inhomogeneities for quantitative HAADF STEM imaging.

High-angle annular dark-field scanning transmission electron microscopy in conjunction with image simulation is an important tool to determine the structure of nanomaterials. We show that molecular dynamics calculations can be combined with multislice image simulations to account for the large effects of surface-enhanced thermal vibrations and structural relaxation on image intensities. Application to a catalytically important gold cluster shows that the image intensity is sensitive to these surface dominated effects with important implications for three-dimensional structural characterizations.