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Converging intracortical signatures of two separated processing timescales in human early auditory cortex.

In NeuroImage ; h5-index 117.0

Neural oscillations in auditory cortex are argued to support parsing and representing speech constituents at their corresponding temporal scales. Yet, how incoming sensory information interacts with ongoing spontaneous brain activity, what features of the neuronal microcircuitry underlie spontaneous and stimulus-evoked spectral fingerprints, and what these fingerprints entail for stimulus encoding, remain largely open questions. We used a combination of human invasive electrophysiology, computational modeling and decoding techniques to assess the information encoding properties of brain activity and to relate them to a plausible underlying neuronal microarchitecture. We analyzed intracortical auditory EEG activity from 10 patients while they were listening to short sentences. Pre-stimulus neural activity in early auditory cortical regions often exhibited power spectra with a shoulder in the delta range and a small bump in the beta range. Speech decreased power in the beta range, and increased power in the delta-theta and gamma ranges. Using multivariate machine learning techniques, we assessed the spectral profile of information content for two aspects of speech processing: detection and discrimination. We obtained better phase than power information decoding, and a bimodal spectral profile of information content with better decoding at low (delta-theta) and high (gamma) frequencies than at intermediate (beta) frequencies. These experimental data were reproduced by a simple rate model made of two subnetworks with different timescales, each composed of coupled excitatory and inhibitory units, and connected via a negative feedback loop. Modeling and experimental results were similar in terms of pre-stimulus spectral profile (except for the iEEG beta bump), spectral modulations with speech, and spectral profile of information content. Altogether, we provide converging evidence from both univariate spectral analysis and decoding approaches for a dual timescale processing infrastructure in human auditory cortex, and show that it is consistent with the dynamics of a simple rate model.

Baroni Fabiano, Morillon Benjamin, Trébuchon Agnès, Liégeois-Chauvel Catherine, Olasagasti Itsaso, Giraud Anne-Lise

2020-May-18

auditory cortex, brain decoding, computational modeling, iEEG, spectral analysis, speech perception

General General

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states.

In NeuroImage ; h5-index 117.0

Predicting biomedical outcomes from Magnetoencephalography and Electroencephalography (M/EEG) is central to applications like decoding, brain-computer-interfaces (BCI) or biomarker development and is facilitated by supervised machine learning. Yet most of the literature is concerned with classification of outcomes defined at the event-level. Here, we focus on predicting continuous outcomes from M/EEG signal defined at the subject-level, and analyze about 600 MEG recordings from Cam-CAN dataset and about 1000 EEG recordings from TUH dataset. Considering different generative mechanisms for M/EEG signals and the biomedical outcome, we propose statistically-consistent predictive models that avoid source-reconstruction based on the covariance as representation. Our mathematical analysis and ground truth simulations demonstrated that consistent function approximation can be obtained with supervised spatial filtering or by embedding with Riemannian geometry. Additional simulations revealed that Riemannian methods were more robust to model violations, in particular geometric distortions induced by individual anatomy. To estimate the relative contribution of brain dynamics and anatomy to prediction performance, we propose a novel model inspection procedure based on biophysical forward modeling. Applied to prediction of outcomes at the subject-level, the analysis revealed that the Riemannian model better exploited anatomical information while sensitivity to brain dynamics was similar across methods. We then probed the robustness of the models across different data cleaning options. Environmental denoising was globally important but Riemannian models were strikingly robust and continued performing well even without preprocessing. Our results suggest each method has its niche: supervised spatial filtering is practical for event-level prediction while the Riemannian model may enable simple end-to-end learning.

Sabbagh David, Ablin Pierre, Varoquaux Gaël, Gramfort Alexandre, Engemann Denis A

2020-May-18

Covariance, MEG/EEG, Machine Learning, Neuronal oscillations, Riemannian Geometry, Spatial Filters

General General

Summarization of biomedical articles using domain-specific word embeddings and graph ranking.

In Journal of biomedical informatics ; h5-index 55.0

Text summarization tools can help biomedical researchers and clinicians reduce the time and effort needed for acquiring important information from numerous documents. It has been shown that the input text can be modeled as a graph, and important sentences can be selected by identifying central nodes within the graph. However, the effective representation of documents, quantifying the relatedness of sentences, and selecting the most informative sentences are main challenges that need to be addressed in graph-based summarization. In this paper, we address these challenges in the context of biomedical text summarization. We evaluate the efficacy of a graph-based summarizer using different types of context-free and contextualized embeddings. The word representations are produced by pre-training neural language models on large corpora of biomedical texts. The summarizer models the input text as a graph in which the strength of relations between sentences is measured using the domain specific vector representations. We also assess the usefulness of different graph ranking techniques in the sentence selection step of our summarization method. Using the common Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics, we evaluate the performance of our summarizer against various comparison methods. The results show that when the summarizer utilizes proper combinations of context-free and contextualized embeddings, along with an effective ranking method, it can outperform the other methods. We demonstrate that the best settings of our graph-based summarizer can efficiently improve the informative content of summaries and decrease the redundancy.

Moradi Milad, Dashti Maedeh, Samwald Matthias

2020-May-18

Deep learning, Graph ranking, Medical text mining, Natural language processing, Text summarization, Word embedding

General General

How Artificial Intelligence Will Impact Colonoscopy and Colorectal Screening.

In Gastrointestinal endoscopy clinics of North America

Artificial intelligence may improve value in colonoscopy-based colorectal screening and surveillance by improving quality and decreasing unnecessary costs. The quality of screening and surveillance as measured by adenoma detection rates can be improved through real-time computer-assisted detection of polyps. Unnecessary costs can be decreased with optical biopsies to identify low-risk polyps using computer-assisted diagnosis that can undergo the resect-and-discard or diagnose-and-leave strategy. Key challenges include the clinical integration of artificial intelligence-based technology into the endoscopists' workflow, the effect of this technology on endoscopy center efficiency, and the interpretability of the underlying deep learning algorithms. The future for image-based artificial intelligence in gastroenterology will include applications to improve the diagnosis and treatment of cancers throughout the gastrointestinal tract.

Shung Dennis L, Byrne Michael F

2020-Jul

Artificial intelligence, Colonoscopy, Value-based care

General General

The Case for High-Quality Colonoscopy Remaining a Premier Colorectal Cancer Screening Strategy in the United States.

In Gastrointestinal endoscopy clinics of North America

Most colorectal cancer screening in the United States occurs in the opportunistic setting, where screening is initiated by a patient-provider interaction. Colonoscopy provides the longest-interval protection, and high-quality colonoscopy is ideally suited to the opportunistic setting. Both detection and colonoscopic resection have improved as a result of intense scientific investigation. Further improvements in detection are expected with the introduction of artificial intelligence programs into colonoscopy platforms. We may expect recommended intervals or colonoscopy after negative examinations performed by high-quality detectors to expand beyond 10 years. Thus, high-quality colonoscopy remains an excellent approach to colorectal cancer screening in the opportunistic setting.

Rex Douglas K

2020-Jul

Colonoscopy, Colorectal adenomas, Colorectal cancer, Colorectal cancer screening, Colorectal polyps

Public Health Public Health

Risk Stratification Strategies for Colorectal Cancer Screening: From Logistic Regression to Artificial Intelligence.

In Gastrointestinal endoscopy clinics of North America

Risk stratification is a system by which clinically meaningful separation of risk is achieved in a group of otherwise similar persons. Although parametric logistic regression dominates risk prediction, use of nonparametric and semiparametric methods, including artificial neural networks, is increasing. These statistical-learning and machine-learning methods, along with simple rules, are collectively referred to as "artificial intelligence" (AI). AI requires knowledge of study validity, understanding of model metrics, and determination of whether and to what extent the model can and should be applied to the patient or population under consideration. Further investigation is needed, especially in model validation and impact assessment.

Imperiale Thomas F, Monahan Patrick O

2020-Jul

Cancer prevention, Colorectal cancer screening, Machine learning methods, Multivariate methods, Risk prediction models, Risk stratification