Race and ethnicity in multiple sclerosis phase 3 clinical trials: A systematic review.

BACKGROUND: Distinctive differences in multiple sclerosis (MS) have been observed by race and ethnicity. We aim to (1) assess how often race and ethnicity were reported in clinical trials registered on ClinicalTrials.gov, (2) evaluate whether the population was diverse enough, and (3) compare with publications. METHODS: We included phase 3 clinical trials registered with results on ClinicalTrials.gov between 2007 and 2023. When race and/or ethnicity were reported, we searched for the corresponding publications. RESULTS: Out of the 99 included studies, 56% reported race and/or ethnicity, of which only 26% of those primarily completed before 2017. Studies reporting race or ethnicity contributed to a total of 33,891 participants, mainly enrolled in Eastern Europe. Most were White (93%), and the median percentage of White participants was 93% (interquartile range (IQR) = 86%-98%), compared to 3% for Black (IQR = 1%-12%) and 0.2% for Asian (IQR = 0%-1%). Four trials omitted race and ethnicity in publications and even when information was reported, some discrepancies in terminology were identified and categories with fewer participants were often collapsed. CONCLUSION: More efforts should be done to improve transparency, accuracy, and representativeness, in publications and at a design phase, by addressing social determinants of health that historically limit the enrollment of underrepresented population.

Therapeutic lag: Is treatment effect delayed in progressive MS?

BACKGROUND: Randomized clinical trials (RCTs) in progressive multiple sclerosis (MS) often revealed non-significant treatment effects on disability progression. OBJECTIVES: To investigate whether the failure to detect a significant benefit from treatment may be motivated by a delay in treatment effect, possibly related to baseline characteristics. METHODS: We re-analyzed data from two RCTs testing interferon-beta and glatiramer-acetate versus placebo in progressive MS with no significant effect on EDSS progression. We first designed a time-dependent Cox model with no treatment effect up to time = t0, and constant hazard ratio (HR) after time = t0. We selected the best-fitting t0 from 0 (standard Cox model) to 2.5 years. Furthermore, we modeled the delay as a function of baseline EDSS and fitted the resulting Cox model to the merged dataset. RESULTS: The time-dependent Cox model revealed a significant benefit of treatment delayed by t0 = 2.5 years for the SPECTRIMS study (HR = 0.65 (0.43-0.98), p = 0.041), and delayed by t0 = 2 years for the PROMISE study (HR = 0.65, (0.42-0.99), p = 0.044). In the merged dataset, the HR for the EDSS-dependent delayed effect was 0.68 (0.56, 0.82), p < 0.001. CONCLUSION: The assumption of a delayed treatment effect improved the fit to the data of the two examined RCTs, uncovering a significant, although shifted, benefit of treatment.

Action prediction in psychosis.

Aberrant motor-sensory predictive functions have been linked to symptoms of psychosis, particularly reduced attenuation of self-generated sensations and misattribution of self-generated actions. Building on the parallels between prediction of self- and other-generated actions, this study aims to investigate whether individuals with psychosis also demonstrate abnormal perceptions and predictions of others' actions. Patients with psychosis and matched controls completed a two-alternative object size discrimination task. In each trial, they observed reaching actions towards a small and a large object, with varying levels of temporal occlusion ranging from 10% to 80% of movement duration. Their task was to predict the size of the object that would be grasped. We employed a novel analytic approach to examine how object size information was encoded and read out across progressive levels of occlusion with single-trial resolution. Patients with psychosis exhibited an overall pattern of reduced and discontinuous evidence integration relative to controls, characterized by a period of null integration up to 20% of movement duration, during which they did not read any size information. Surprisingly, this drop in accuracy in the initial integration period was not accompanied by a reduction in confidence. Difficulties in action prediction were correlated with the severity of negative symptoms and impaired functioning in social relationships.

Intersecting kinematic encoding and readout of intention in autism.

Observers with autism spectrum disorders (ASDs) find it difficult to read intentions from movements. However, the computational bases of these difficulties are unknown. Do these difficulties reflect an intention readout deficit, or are they more likely rooted in kinematic (dis-)similarities between typical and ASD kinematics? We combined motion tracking, psychophysics, and computational analyses to uncover single-trial intention readout computations in typically developing (TD) children (n = 35) and children with ASD (n = 35) who observed actions performed by TD children and children with ASD. Average intention discrimination performance was above chance for TD observers but not for ASD observers. However, single-trial analysis showed that both TD and ASD observers read single-trial variations in movement kinematics. TD readers were better able to identify intention-informative kinematic features during observation of TD actions; conversely, ASD readers were better able to identify intention-informative features during observation of ASD actions. Crucially, while TD observers were generally able to extract the intention information encoded in movement kinematics, those with autism were unable to do so. These results extend existing conceptions of mind reading in ASD by suggesting that intention reading difficulties reflect both an interaction failure, rooted in kinematic dissimilarity between TD and ASD kinematics (at the level of feature identification), and an individual readout deficit (at the level of information extraction), accompanied by an overall reduced sensitivity of intention readout to single-trial variations in movement kinematics.

Emergence of Lie Symmetries in Functional Architectures Learned by CNNs.

In this paper we study the spontaneous development of symmetries in the early layers of a Convolutional Neural Network (CNN) during learning on natural images. Our architecture is built in such a way to mimic some properties of the early stages of biological visual systems. In particular, it contains a pre-filtering step ℓ0 defined in analogy with the Lateral Geniculate Nucleus (LGN). Moreover, the first convolutional layer is equipped with lateral connections defined as a propagation driven by a learned connectivity kernel, in analogy with the horizontal connectivity of the primary visual cortex (V1). We first show that the ℓ0 filter evolves during the training to reach a radially symmetric pattern well approximated by a Laplacian of Gaussian (LoG), which is a well-known model of the receptive profiles of LGN cells. In line with previous works on CNNs, the learned convolutional filters in the first layer can be approximated by Gabor functions, in agreement with well-established models for the receptive profiles of V1 simple cells. Here, we focus on the geometric properties of the learned lateral connectivity kernel of this layer, showing the emergence of orientation selectivity w.r.t. the tuning of the learned filters. We also examine the short-range connectivity and association fields induced by this connectivity kernel, and show qualitative and quantitative comparisons with known group-based models of V1 horizontal connections. These geometric properties arise spontaneously during the training of the CNN architecture, analogously to the emergence of symmetries in visual systems thanks to brain plasticity driven by external stimuli.

Correction to: From receptive profiles to a metric model of V1.

The authors would like to note an omission, in the published paper, of the Matlab code initially included as Electronic Supplementary Material. Therefore, we hereby re-submit the code in question.

From receptive profiles to a metric model of V1.

In this work we show how to construct connectivity kernels induced by the receptive profiles of simple cells of the primary visual cortex (V1). These kernels are directly defined by the shape of such profiles: this provides a metric model for the functional architecture of V1, whose global geometry is determined by the reciprocal interactions between local elements. Our construction adapts to any bank of filters chosen to represent a set of receptive profiles, since it does not require any structure on the parameterization of the family. The connectivity kernel that we define carries a geometrical structure consistent with the well-known properties of long-range horizontal connections in V1, and it is compatible with the perceptual rules synthesized by the concept of association field. These characteristics are still present when the kernel is constructed from a bank of filters arising from an unsupervised learning algorithm.