AI-based localization of the epileptogenic zone using intracranial EEG.

Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end-to-end learning.

Self-supervised data-driven approach defines pathological high-frequency oscillations in epilepsy.

Objective: Interictal high-frequency oscillations (HFOs) are a promising neurophysiological biomarker of the epileptogenic zone (EZ). However, objective criteria for distinguishing pathological from physiological HFOs remain elusive, hindering clinical application. We investigated whether the distinct mechanisms underlying pathological and physiological HFOs are encapsulated in their signal morphology in intracranial electroencephalographic (iEEG) recordings and whether this distinction could be captured by a deep generative model.

Reading specific memories from human neurons before and after sleep.

The ability to retrieve a single episode encountered just once is a hallmark of human intelligence and episodic memory[1]. Yet, decoding a specific memory from neuronal activity in the human brain remains a formidable challenge. Here, we develop a transformer neural network model[2, 3] trained on neuronal spikes from intracranial microelectrodes recorded during a single viewing of an audiovisual episode.

Evidence of thalamocortical network activation during epileptic spasms: A thalamic stereotactic EEG study.

Objective: Although the role of subcortical structures in the generation of epileptic spasms has been proposed, supporting evidence remains limited. This study aimed to provide neurophysiological evidence of thalamocortical network involvement during epileptic spasms.

Methods: We analyzed four patients (ages 2.

Mini-Seizures: Novel Interictal iEEG Biomarker Capturing Synchronization Network Dynamics at the Epileptogenic Zone.

Epilepsy affects 1% of the population, with up to one-third of patients being medication-resistant. Surgery is the only curative treatment, yet over one-third of surgical patients fail to achieve seizure freedom due to the lack of a reliable epileptogenic zone (EZ) biomarker. We introduced and validated mini-seizures, frequent hypersynchronization events at EZ hubs that mirror seizure network dynamics, as a novel interictal EEG biomarker.

Fast ripple band high-frequency activity associated with thalamic sleep spindles in pediatric epilepsy.

Objective: To investigate high-frequency activities (HFA) associated with thalamic sleep spindles.

Methods: We studied a cohort of ten pediatric patients with medication resistant epilepsy who were identified as potential candidates for thalamic neuromodulation. These patients had thalamic sampling as well as presumed epileptogenic zones, using stereotactic EEG (SEEG) with a sampling frequency of 2,000 Hz.

Embed-Search-Align: DNA sequence alignment using Transformer models.

Motivation: DNA sequence alignment, an important genomic task, involves assigning short DNA reads to the most probable locations on an extensive reference genome. Conventional methods tackle this challenge in two steps: genome indexing followed by efficient search to locate likely positions for given reads. Building on the success of Large Language Models in encoding text into embeddings, where the distance metric captures semantic similarity, recent efforts have encoded DNA sequences into vectors using Transformers and have shown promising results in tasks involving classification of short DNA sequences.

Diverse Imitation Learning via Self-Organizing Generative Models.

Imitation learning (IL) is a well-known problem in the field of Markov decision process (MDP), where one is given multiple demonstration trajectories generated by expert(s), and the goal is to replicate the hidden expert-policies so that when the MDP is run independently, it generates trajectories close to the demonstrated ones. IL is one of the most useful tools used in building versatile robots that can learn from examples. This task becomes particularly challenging when the expert exhibits a mixture of behavior modes.

PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application.

. This study aims to develop and validate an end-to-end software platform, PyHFO, that streamlines the application of deep learning (DL) methodologies in detecting neurophysiological biomarkers for epileptogenic zones from EEG recordings..

Emergent invariance and scaling properties in the collective return dynamics of a stock market.

A key metric to determine the performance of a stock in a market is its return over different investment horizons (τ). Several works have observed heavy-tailed behavior in the distributions of returns in different markets, which are observable indicators of underlying complex dynamics. Such prior works study return distributions that are marginalized across the individual stocks in the market, and do not track statistics about the joint distributions of returns conditioned on different stocks, which would be useful for optimizing inter-stock asset allocation strategies.

AI computing reaches for the edge.

A chip design integrates computation and memory to efficiently process data at low energy cost.

Metalearning Generalizable Dynamics from Trajectories.

We present the interpretable meta neural ordinary differential equation (iMODE) method to rapidly learn generalizable (i.e., not parameter-specific) dynamics from trajectories of multiple dynamical systems that vary in their physical parameters.

Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy.

Objective: This study aimed to explore sensitive detection methods for pathological high-frequency oscillations (HFOs) to improve seizure outcomes in epilepsy surgery.

Methods: We analyzed interictal HFOs (80-500 Hz) in 15 children with medication-resistant focal epilepsy who underwent chronic intracranial electroencephalogram via subdural grids. The HFOs were assessed using the short-term energy (STE) and Montreal Neurological Institute (MNI) detectors and examined for spike association and time-frequency plot characteristics.

Quantum advantage in variational Bayes inference.

Variational Bayes (VB) inference algorithm is used widely to estimate both the parameters and the unobserved hidden variables in generative statistical models. The algorithm-inspired by variational methods used in computational physics-is iterative and can get easily stuck in local minima, even when classical techniques, such as deterministic annealing (DA), are used. We study a VB inference algorithm based on a nontraditional quantum annealing approach-referred to as quantum annealing variational Bayes (QAVB) inference-and show that there is indeed a quantum advantage to QAVB over its classical counterparts.

Optimizing Detection and Deep Learning-based Classification of Pathological High-Frequency Oscillations in Epilepsy.

Objective: This study aimed to explore sensitive detection methods and deep learning (DL)-based classification for pathological high-frequency oscillations (HFOs).

Methods: We analyzed interictal HFOs (80-500 Hz) in 15 children with medication-resistant focal epilepsy who underwent resection after chronic intracranial electroencephalogram via subdural grids. The HFOs were assessed using the short-term energy (STE) and Montreal Neurological Institute (MNI) detectors and examined for pathological features based on spike association and time-frequency plot characteristics.

Decoding of human identity by computer vision and neuronal vision.

Extracting meaning from a dynamic and variable flow of incoming information is a major goal of both natural and artificial intelligence. Computer vision (CV) guided by deep learning (DL) has made significant strides in recognizing a specific identity despite highly variable attributes. This is the same challenge faced by the nervous system and partially addressed by the concept cells-neurons exhibiting selective firing in response to specific persons/places, described in the human medial temporal lobe (MTL) ⁠.

Characterizing physiological high-frequency oscillations using deep learning.

Intracranially-recorded interictal high-frequency oscillations (HFOs) have been proposed as a promising spatial biomarker of the epileptogenic zone. However, HFOs can also be recorded in the healthy brain regions, which complicates the interpretation of HFOs. The present study aimed to characterize salient features of physiological HFOs using deep learning (DL).

Ansatz-Independent Variational Quantum Classifiers and the Price of Ansatz.

The paradigm of variational quantum classifiers (VQCs) encodes classical information as quantum states, followed by quantum processing and then measurements to generate classical predictions. VQCs are promising candidates for efficient utilizations of noisy intermediate scale quantum (NISQ) devices: classifiers involving M-dimensional datasets can be implemented with only [Formula: see text] qubits by using an amplitude encoding. A general framework for designing and training VQCs, however, is lacking.

Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach.

Intracranially recorded interictal high-frequency oscillations have been proposed as a promising spatial biomarker of the epileptogenic zone. However, its visual verification is time-consuming and exhibits poor inter-rater reliability. Furthermore, no method is currently available to distinguish high-frequency oscillations generated from the epileptogenic zone (epileptogenic high-frequency oscillations) from those generated from other areas (non-epileptogenic high-frequency oscillations).

Modelling social readers: novel tools for addressing reception from online book reviews.

Social reading sites offer an opportunity to capture a segment of readers' responses to literature, while data-driven analysis of these responses can provide new critical insight into how people 'read'. Posts discussing an individual book on the social reading site, , are referred to as 'reviews', and consist of summaries, opinions, quotes or some mixture of these. Computationally modelling these reviews allows one to discover the non-professional discussion space about a work, including an aggregated summary of the work's plot, an implicit sequencing of various subplots and readers' impressions of main characters.

Conspiracy in the time of corona: automatic detection of emerging COVID-19 conspiracy theories in social media and the news.

Rumors and conspiracy theories thrive in environments of low confidence and low trust. Consequently, it is not surprising that ones related to the COVID-19 pandemic are proliferating given the lack of scientific consensus on the virus's spread and containment, or on the long-term social and economic ramifications of the pandemic. Among the stories currently circulating in US-focused social media forums are ones suggesting that the 5G telecommunication network activates the virus, that the pandemic is a hoax perpetrated by a global cabal, that the virus is a bio-weapon released deliberately by the Chinese, or that Bill Gates is using it as cover to launch a broad vaccination program to facilitate a global surveillance regime.

An automated pipeline for the discovery of conspiracy and conspiracy theory narrative frameworks: Bridgegate, Pizzagate and storytelling on the web.

Although a great deal of attention has been paid to how conspiracy theories circulate on social media, and the deleterious effect that they, and their factual counterpart conspiracies, have on political institutions, there has been little computational work done on describing their narrative structures. Predicating our work on narrative theory, we present an automated pipeline for the discovery and description of the generative narrative frameworks of conspiracy theories that circulate on social media, and actual conspiracies reported in the news media. We base this work on two separate comprehensive repositories of blog posts and news articles describing the well-known conspiracy theory Pizzagate from 2016, and the New Jersey political conspiracy Bridgegate from 2013.

Brain-inspired automated visual object discovery and detection.

Despite significant recent progress, machine vision systems lag considerably behind their biological counterparts in performance, scalability, and robustness. A distinctive hallmark of the brain is its ability to automatically discover and model objects, at multiscale resolutions, from repeated exposures to unlabeled contextual data and then to be able to robustly detect the learned objects under various nonideal circumstances, such as partial occlusion and different view angles. Replication of such capabilities in a machine would require three key ingredients: () access to large-scale perceptual data of the kind that humans experience, () flexible representations of objects, and () an efficient unsupervised learning algorithm.

"Mommy Blogs" and the Vaccination Exemption Narrative: Results From A Machine-Learning Approach for Story Aggregation on Parenting Social Media Sites.

Background: Social media offer an unprecedented opportunity to explore how people talk about health care at a very large scale. Numerous studies have shown the importance of websites with user forums for people seeking information related to health. Parents turn to some of these sites, colloquially referred to as "mommy blogs," to share concerns about children's health care, including vaccination.