Single-Trial {MEG} Data Can Be Denoised Through Cross-Subject Predictive Modeling

A pervasive challenge in brain imaging is the presence of noise that hinders investigation of underlying neural processes, with Magnetoencephalography (MEG) in particular having very low Signal-to-Noise Ratio (SNR). The established strategy to increase MEG's SNR involves averaging multiple repetitions of data corresponding to the same stimulus. However, repetition of stimulus can be undesirable, because underlying neural activity has been shown to change across trials, and repeating stimuli limits the breadth of the stimulus space experienced by subjects. In particular, the rising popularity of naturalistic studies with a single viewing of a movie or story necessitates the discovery of new approaches to increase SNR. We introduce a simple framework to reduce noise in single-trial MEG data by leveraging correlations in neural responses across subjects as they experience the same stimulus. We demonstrate its use in a naturalistic reading comprehension task with 8 subjects, with MEG data collected while they read the same story a single time. We find that our procedure results in data with reduced noise and allows for better discovery of neural phenomena. As proof-of-concept, we show that the N400m's correlation with word surprisal, an established finding in literature, is far more clearly observed in the denoised data than the original data. The denoised data also shows higher decoding and encoding accuracy than the original data, indicating that the neural signals associated with reading are either preserved or enhanced after the denoising procedure

Developpement des techniques d'analyse par faisceaux d'ions acceleres elementaires (PIXE PIGE) et structurales (HSF-SIMS): - applications a l'analyse des sols sans traitement prealable

BIASPas de résum

An Integrated Review of Developmental Outcomes and Late‐Preterm Birth

Objective: To evaluate existing evidence on long‐term developmental outcomes of late‐preterm infants (LPI; infants born 34‐36 6/7 weeks gestation). Data Sources: Computerized bibliographic databases and hand search for English language articles published between January 1995 and November 2010 yielded 817 articles. Study Selection: Twelve studies (10 cohort and two cross‐sectional) were identified that defined late‐preterm (LP) birth as 34 to 36 6/7 weeks gestation and addressed growth and neurodevelopmental outcomes in LPI. Data Extraction: Using a modified Downs and Black scale for assessing the quality of experimental and observational studies, two reviewers who were blind to each other's ratings assessed study quality. Ratings ranged from 12.5 to 14 with moderate to very good interrater agreement. Kappa (κ) values were 0.83 (reporting), 0.63 (external validity), 0.73 (internal validity), and 0.83 (design) for the four subscales and 0.56 for the whole scale, with no major systematic disagreements between reviewers. Data Synthesis: Studies were divided into five categories to include the following developmental outcomes: neurodevelopment, behavioral, cognitive, growth, and function. Using the Meta‐analysis of Observational Studies in Epidemiology (MOOSE) guidelines, synthesis of the findings is provided as an integrative review. Conclusion: Significant variations in study populations, methodology, and definition of LP exist. Due to paucity and heterogeneity of the existing data especially in infants born 34 to 36 6/7 weeks, there is no clear characterization of the long‐term risks, and future research is needed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86856/1/j.1552-6909.2011.01270.x.pd

Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models

A long standing goal in neuroscience has been to elucidate the functional organization of the brain. Within higher visual cortex, functional accounts have remained relatively coarse, focusing on regions of interest (ROIs) and taking the form of selectivity for broad categories such as faces, places, bodies, food, or words. Because the identification of such ROIs has typically relied on manually assembled stimulus sets consisting of isolated objects in non-ecological contexts, exploring functional organization without robust a priori hypotheses has been challenging. To overcome these limitations, we introduce a data-driven approach in which we synthesize images predicted to activate a given brain region using paired natural images and fMRI recordings, bypassing the need for category-specific stimuli. Our approach -- Brain Diffusion for Visual Exploration ("BrainDiVE") -- builds on recent generative methods by combining large-scale diffusion models with brain-guided image synthesis. Validating our method, we demonstrate the ability to synthesize preferred images with appropriate semantic specificity for well-characterized category-selective ROIs. We then show that BrainDiVE can characterize differences between ROIs selective for the same high-level category. Finally we identify novel functional subdivisions within these ROIs, validated with behavioral data. These results advance our understanding of the fine-grained functional organization of human visual cortex, and provide well-specified constraints for further examination of cortical organization using hypothesis-driven methods.Comment: NeurIPS 2023 (Oral). Project page: https://www.cs.cmu.edu/~afluo/BrainDiVE

BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity

Understanding the functional organization of higher visual cortex is a central focus in neuroscience. Past studies have primarily mapped the visual and semantic selectivity of neural populations using hand-selected stimuli, which may potentially bias results towards pre-existing hypotheses of visual cortex functionality. Moving beyond conventional approaches, we introduce a data-driven method that generates natural language descriptions for images predicted to maximally activate individual voxels of interest. Our method -- Semantic Captioning Using Brain Alignments ("BrainSCUBA") -- builds upon the rich embedding space learned by a contrastive vision-language model and utilizes a pre-trained large language model to generate interpretable captions. We validate our method through fine-grained voxel-level captioning across higher-order visual regions. We further perform text-conditioned image synthesis with the captions, and show that our images are semantically coherent and yield high predicted activations. Finally, to demonstrate how our method enables scientific discovery, we perform exploratory investigations on the distribution of "person" representations in the brain, and discover fine-grained semantic selectivity in body-selective areas. Unlike earlier studies that decode text, our method derives voxel-wise captions of semantic selectivity. Our results show that BrainSCUBA is a promising means for understanding functional preferences in the brain, and provides motivation for further hypothesis-driven investigation of visual cortex

Twistor theory on a finite graph

We show how the description of a shear-free ray congruence in Minkowski space as an evolving family of semi-conformal mappings can naturally be formulated on a finite graph. For this, we introduce the notion of holomorphic function on a graph. On a regular coloured graph of degree three, we recover the space-time picture. In the spirit of twistor theory, where a light ray is the more fundamental object from which space-time points should be derived, the line graph, whose points are the edges of the original graph, should be considered as the basic object. The Penrose twistor correspondence is discussed in this context

Intense emission of cluster anions from gold targets under impact of keV/u gold clusters

CAS, BIASPas de résum

Repurposing Ivermectin for COVID-19: Molecular Aspects and Therapeutic Possibilities

As of January 2021, SARS-CoV-2 has killed over 2 million individuals across the world. As such, there is an urgent need for vaccines and therapeutics to reduce the burden of COVID-19. Several vaccines, including mRNA, vector-based vaccines, and inactivated vaccines, have been approved for emergency use in various countries. However, the slow roll-out of vaccines and insufficient global supply remains a challenge to turn the tide of the pandemic. Moreover, vaccines are important tools for preventing the disease but therapeutic tools to treat patients are also needed. As such, since the beginning of the pandemic, repurposed FDA-approved drugs have been sought as potential therapeutic options for COVID-19 due to their known safety profiles and potential anti-viral effects. One of these drugs is ivermectin (IVM), an antiparasitic drug created in the 1970s. IVM later exerted antiviral activity against various viruses including SARS-CoV-2. In this review, we delineate the story of how this antiparasitic drug was eventually identified as a potential treatment option for COVID-19. We review SARS-CoV-2 lifecycle, the role of the nucleocapsid protein, the turning points in past research that provided initial 'hints' for IVM's antiviral activity and its molecular mechanism of action- and finally, we culminate with the current clinical findings

Correlations from gadopentetate dimeglumine-enhanced magnetic resonance imaging after methotrexate chemotherapy for hemorrhagic placenta increta

OBJECTIVE: To describe pre- and post-methotrexate (MTX) therapy images from pelvic magnetic resonance imaging (MRI) with gadopentetate dimeglumine contrast following chemotherapy for post-partum hemorrhage secondary to placenta increta. MATERIAL AND METHOD: A 28-year-old Caucasian female presented 4 weeks post-partum complaining of intermittent vaginal bleeding. She underwent dilatation and curettage immediately after vaginal delivery for suspected retained placental tissue but 28 d after delivery, the serum β-hCG persisted at 156 IU/mL. Office transvaginal sonogram (4 mHz B-mode) was performed, followed by pelvic MRI using a 1.5 Tesla instrument after administration of gadolinium-based contrast agent. MTX was administered intramuscularly, and MRI was repeated four weeks later. RESULTS: While transvaginal sonogram suggested retained products of conception confined to the endometrial compartment, an irregular 53 × 34 × 28 mm heterogeneous intrauterine mass was noted on MRI to extend into the anterior myometrium, consistent with placenta increta. Vaginal bleeding diminished following MTX treatment, with complete discontinuation of bleeding achieved by ~20 d post-injection. MRI using identical technique one month later showed complete resolution of the uterine lesion. Serum β-hCG was <5 IU/mL. CONCLUSION: Reduction or elimination of risks associated with surgical management of placenta increta is important to preserve uterine function and reproductive potential. For selected hemodynamically stable patients, placenta increta may be treated non-operatively with MTX as described here. A satisfactory response to MTX can be ascertained by serum hCG measurements with pre- and post-treatment pelvic MRI with gadopentetate dimeglumine enhancement, which offers advantages over standard transvaginal sonography