Bayesian Brain

Cognitive fatigue is a major symptom of Multiple Sclerosis (MS), from the early stages of the disease. This study aims to detect if brain microstructure is altered early in the disease course and is associated with cognitive fatigue in people with MS (pwMS) compared to matched healthy controls (HC). Recently diagnosed pwMS (N = 18, age < 45 years old) with either a Relapsing-Remitting or a Clinically Isolated Syndrome course of the disease, and HC (N = 19) matched for sex, age and education were analyzed...

Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, that provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian decoders are overconfident. That is, the estimates for the decoded stimulus or movement variables are more certain than they should be. We then show how Bayesian decoding with latent variables, taking account of low-dimensional shared variability in the observations, can improve calibration, although additional correction for overconfidence is still needed...

Functional magnetic resonance imaging (fMRI) plays a crucial role in neuroimaging, enabling the exploration of brain activity through complex-valued signals. These signals, composed of magnitude and phase, offer a rich source of information for understanding brain functions. Traditional fMRI analyses have largely focused on magnitude information, often overlooking the potential insights offered by phase data. In this paper, we propose a novel fully Bayesian model designed for analyzing single-subject complex-valued fMRI (cv-fMRI) data...

The brain can be seen as a predictive system continuously computing prior information to guess posterior probabilities minimizing sources of uncertainty. To test this Bayesian view of the brain, event-related potentials (ERP) methods have been used focusing on the well-known P3 component, traditionally associated with decision-making processes and sources of uncertainty regarding target probability. Another ERP component linked with decision-making is the prefrontal P2 (pP2) component, which has never been considered within the Bayesian framework...

This paper evaluates the ethical implications of utilizing artificial intelligence (AI) algorithms in neurological diagnostic examinations. Applications of AI technology have been utilized to aid in the determination of pharmacological dosages of gadolinium for brain lesion detection, localization of seizure foci, and the characterization of large vessel occlusion in ischemic stroke patients. Multiple subtypes of AI- machine learning (AI/ML) algorithms are analyzed as AI-assisted neurology utilizes supervised, unsupervised, artificial neural network (ANN), and deep neural network (DNN) learning models...

As humans increasingly modify the natural world, many animals have responded by changing their behaviour. Understanding and predicting the extent of these responses is a key step in conserving these species. For example, the tendency for some species of birds to incorporate anthropogenic items-particularly plastic material-into their nests is of increasing concern, as in some cases, this behaviour has harmful effects on adults, young and eggs. Studies of this phenomenon, however, have to date been largely limited in geographic and taxonomic scope...

Connectome spectrum electromagnetic tomography (CSET) combines diffusion MRI-derived structural connectivity data with well-established graph signal processing tools to solve the M/EEG inverse problem. Using simulated EEG signals from fMRI responses, and two EEG datasets on visual-evoked potentials, we provide evidence supporting that (i) CSET captures realistic neurophysiological patterns with better accuracy than state-of-the-art methods, (ii) CSET can reconstruct brain responses more accurately and with more robustness to intrinsic noise in the EEG signal...

BACKGROUND: Inhaled treprostinil (iTre) is the only treatment approved for pulmonary hypertension due to interstitial lung disease (PH-ILD) to improve exercise capacity. This post hoc analysis evaluated clinical worsening and PH-ILD exacerbations from the 16-week INCREASE study and change in 6-minute walking distance (6MWD) in the INCREASE open-label extension (OLE) in patients with less severe haemodynamics. METHODS: Patients were stratified by baseline pulmonary vascular resistance (PVR) of <4 Wood units (WU) versus ≥4 WU and <5 WU versus ≥5 WU...

Different kinds of traumatic experiences like natural catastrophes vs. relational traumatic experiences (e.g., sex/physical abuse, interpersonal partner violence) are involved in the development of the self and PTSD psychopathological manifestations. Looking at a neuroscience approach, it has been proposed a nested hierarchical model of self, which identifies three neural-mental networks: (i) interoceptive; (ii) exteroceptive; (iii) mental. However, it is still unclear how the self and its related brain networks might be affected by non-relational vs relational traumatic experiences...

Prediction of upcoming words is thought to be crucial for language comprehension. Here, we are asking whether bilingualism entails changes to the electrophysiological substrates of prediction. Prior findings leave it open whether monolingual and bilingual speakers predict upcoming words to the same extent and in the same manner. We address this issue with a naturalistic approach, employing an information-theoretic metric, surprisal, to predict and contrast the N400 brain potential in monolingual and bilingual speakers...

Deep learning models have demonstrated great potential in medical imaging but are limited by the expensive, large volume of annotations required. To address this, we compared different active learning strategies by training models on subsets of the most informative images using real-world clinical datasets for brain tumor segmentation and proposing a framework that minimizes the data needed while maintaining performance. Then, 638 multi-institutional brain tumor magnetic resonance imaging scans were used to train three-dimensional U-net models and compare active learning strategies...

MOTIVATION: Due to the link between microglial morphology and function, morphological changes in microglia are frequently used to identify pathological immune responses in the central nervous system. In the absence of pathology, microglia are responsible for maintaining homeostasis, and their morphology can be indicative of how the healthy brain behaves in the presence of external stimuli and genetic differences. Despite recent interest in high throughput methods for morphological analysis, Sholl analysis is still widely used for quantifying microglia morphology via imaging data...

Procedural circuit Deficit Hypothesis (PDH) of Developmental Language Disorder (DLD) predicts problems with learning and retention of grammar. Twenty 7- to 9-year-old Cantonese-speaking children with DLD and their typically developing (TD) age peers participated in a syntactic priming task that was given in two sessions one week apart. Production of Indirect Object Relative Clause (IORC) was tested using a probe test before and after the priming task, and one week later. The study involved two cycles of learning and retention, and two levels of prior knowledge...

In the realm of law enforcement, the "police hunch" has long been a mysterious but crucial aspect of decision-making. Drawing on the developing framework of Active Inference from cognitive science, this theoretical article examines the genesis, mechanics, and implications of the police hunch. It argues that hunches - often vital in high-stakes situations - should not be described as mere intuitions, but as intricate products of our mind's generative models. These models, shaped by observations of the social world and assimilated and enacted through active inference, seek to reduce surprise and make hunches an indispensable tool for officers, in exactly the same way that hypotheses are indispensable for scientists...

Transcranial static magnetic field stimulation (tSMS) is a non-invasive brain stimulation technique that is portable and easy to use. Long-term, home-based treatments with tSMS of the supplementary motor area (SMA) are promising for movement disorders and other brain diseases. The aim of the present work was to investigate the potential of SMA-tSMS for reducing corticospinal excitability. We completed an open pilot study in which twenty right-handed healthy subjects (8 females; age: 31.3 ± 5...

Breathlessness is among the most common post-COVID symptoms. In a considerable number of patients, severe breathlessness cannot be explained by peripheral organ impairment. Recent concepts have described how such persistent breathlessness could arise from dysfunctional processing of respiratory information in the brain. In this paper, we present a first quantitative and testable mathematical model of how processing of respiratory-related signals could lead to breathlessness perception. The model is based on recent theories that the brain holds an adaptive and dynamic internal representation of a respiratory state that is based on previous experiences and comprises gas exchange between environment, lung and tissue cells...

IMPORTANCE: US government personnel stationed internationally have reported anomalous health incidents (AHIs), with some individuals experiencing persistent debilitating symptoms. OBJECTIVE: To assess the potential presence of magnetic resonance imaging (MRI)-detectable brain lesions in participants with AHIs, with respect to a well-matched control group. DESIGN, SETTING, AND PARTICIPANTS: This exploratory study was conducted at the National Institutes of Health (NIH) Clinical Center and the NIH MRI Research Facility between June 2018 and November 2022...

Genetic association studies for brain connectivity phenotypes have gained prominence due to advances in noninvasive imaging techniques and quantitative genetics. Brain connectivity traits, characterized by network configurations and unique biological structures, present distinct challenges compared to other quantitative phenotypes. Furthermore, the presence of sample relatedness in the most imaging genetics studies limits the feasibility of adopting existing network-response modeling. In this article, we fill this gap by proposing a Bayesian network-response mixed-effect model that considers a network-variate phenotype and incorporates population structures including pedigrees and unknown sample relatedness...

Dealing with social rejection is challenging, especially during childhood when behavioral and neural responses to social rejection are still developing. In the current longitudinal study, we used a Bayesian multilevel growth curve model to describe individual differences in the development of behavioral and neural responses to social rejection in a large sample (n > 500). We found a peak in aggression following negative feedback (compared to neutral feedback) during late childhood, as well as individual differences during this developmental phase, possibly suggesting a sensitive window for dealing with social rejection across late childhood...

Face-to-face communication relies on the integration of acoustic speech signals with the corresponding facial articulations. In the McGurk illusion, an auditory /ba/ phoneme presented simultaneously with a facial articulation of a /ga/ (i.e., viseme), is typically fused into an illusory 'da' percept. Despite its widespread use as an index of audiovisual speech integration, critics argue that it arises from perceptual processes that differ categorically from natural speech recognition. Conversely, Bayesian theoretical frameworks suggest that both the illusory McGurk and the veridical audiovisual congruent speech percepts result from probabilistic inference based on noisy sensory signals...