Sediment subduction in Hadean revealed by machine learning.

Due to the scarcity of rock samples, the Hadean Era predating 4 billion years ago (Ga) poses challenges in understanding geological processes like subaerial weathering and plate tectonics that are critical for the evolution of life. The Jack Hills zircon from Western Australia, the primary Hadean samples available, offer valuable insights into magma sources and tectonic genesis through trace element signatures. However, a consensus on these signatures has not been reached. To address this, we developed a machine learning classifier capable of deciphering the geochemical fingerprints of zircon. This allowed us to identify the oldest detrital zircon originating from sedimentary-derived "S-type" granites. Our results indicate the presence of S-type granites as early as 4.24 Ga, persisting throughout the Hadean into the Archean. Examining global detrital zircon across Earth's history reveals consistent supercontinent-like cycles from the present back to the Hadean. These findings suggest that a significant amount of Hadean continental crust was exposed, weathered into sediments, and incorporated into the magma sources of Jack Hills zircon. Only the early operation of both subaerial weathering and plate subduction can account for the prevalence of S-type granites we observe. Additionally, the periodic evolution of S-type granite proportions implies that subduction-driven tectonic cycles were active during the Hadean, at least around 4.2 Ga. The evidence thus points toward an early Earth resembling the modern Earth in terms of active tectonics and habitable surface conditions. This suggests the potential for life to originate in environments like warm ponds rather than extreme hydrothermal settings.

Ultra-high-resolution time-frequency analysis of EEG to characterise brain functional connectivity with the application in Alzheimer's disease.

Objective.This study aims to explore the potential of high-resolution brain functional connectivity based on electroencephalogram, a non-invasive low-cost technique, to be translated into a long-overdue biomarker and a diagnostic method for Alzheimer's disease (AD).Approach.The paper proposes a novel ultra-high-resolution time-frequency nonlinear cross-spectrum method to construct a promising biomarker of AD pathophysiology. Specifically, using the peak frequency estimated from a revised Hilbert-Huang transformation (RHHT) cross-spectrum as a biomarker, the support vector machine classifier is used to distinguish AD from healthy controls (HCs).Main results.With the combinations of the proposed biomarker and machine learning, we achieved a promising accuracy of 89%. The proposed method performs better than the wavelet cross-spectrum and other functional connectivity measures in the temporal or frequency domain, particularly in the Full, Delta and Alpha bands. Besides, a novel visualisation approach developed from topography is introduced to represent the brain functional connectivity, with which the difference between AD and HCs can be clearly displayed. The interconnections between posterior and other brain regions are obviously affected in AD.Significance.Those findings imply that the proposed RHHT approach could better track dynamic and nonlinear functional connectivity information, paving the way for the development of a novel diagnostic approach.

Spatial-temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram.

Functional connectivity of the human brain, representing statistical dependence of information flow between cortical regions, significantly contributes to the study of the intrinsic brain network and its functional mechanism. To fully explore its potential in the early diagnosis of Alzheimer's disease (AD) using electroencephalogram (EEG) recordings, this article introduces a novel dynamical spatial-temporal graph convolutional neural network (ST-GCN) for better classification performance. Different from existing studies that are based on either topological brain function characteristics or temporal features of EEG, the proposed ST-GCN considers both the adjacency matrix of functional connectivity from multiple EEG channels and corresponding dynamics of signal EEG channel simultaneously. Different from the traditional graph convolutional neural networks, the proposed ST-GCN makes full use of the constrained spatial topology of functional connectivity and the discriminative dynamic temporal information represented by the 1D convolution. We conducted extensive experiments on the clinical EEG data set of AD patients and Healthy Controls. The results demonstrate that the proposed method achieves better classification performance (92.3%) than the state-of-the-art methods. This approach can not only help diagnose AD but also better understand the effect of normal ageing on brain network characteristics before we can accurately diagnose the condition based on resting-state EEG.

Brain functional and effective connectivity based on electroencephalography recordings: A review.

Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG-based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools. This article reviews EEG-based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric or nonparametric, time-based, and frequency-based or time-frequency-based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics, and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented.

A Revised Hilbert-Huang Transformation to Track Non-Stationary Association of Electroencephalography Signals.

The time-varying cross-spectrum method has been used to effectively study transient and dynamic brain functional connectivity between non-stationary electroencephalography (EEG) signals. Wavelet-based cross-spectrum is one of the most widely implemented methods, but it is limited by the spectral leakage caused by the finite length of the basic function that impacts the time and frequency resolutions. This paper proposes a new time-frequency brain functional connectivity analysis framework to track the non-stationary association of two EEG signals based on a Revised Hilbert-Huang Transform (RHHT). The framework can estimate the cross-spectrum of decomposed components of EEG, followed by a surrogate significance test. The results of two simulation examples demonstrate that, within a certain statistical confidence level, the proposed framework outperforms the wavelet-based method in terms of accuracy and time-frequency resolution. A case study on classifying epileptic patients and healthy controls using interictal seizure-free EEG data is also presented. The result suggests that the proposed method has the potential to better differentiate these two groups benefiting from the enhanced measure of dynamic time-frequency association.