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Intrinsic brain activity is characterized by pervasive long-range temporal correlations. While these scale-invariant dynamics are a fundamental hallmark of brain function, their implications for individual-level metabolic regulation remain poorly understood. Here, we address this gap by integrating resting-state functional Magnetic Resonance Imaging (fMRI) and dynamic [F]FDG Positron Emission Tomography (PET) data acquired from the same cohort of participants. We uncover a systematic relationship between long-range temporal correlations, quantified via the Hurst exponent, and glucose metabolism. Our findings reveal that persistent temporal dependencies impose a measurable metabolic cost, with brains exhibiting higher long-range temporal correlations incurring greater energetic demands. Beyond glucose metabolism, we also show that these dynamics are likely supported by continuous biosynthetic processes, such as protein synthesis, which are critical for neural circuit maintenance and remodeling. Overall, our results suggest that a significant fraction of the brain's so-called "Dark Energy" is actively spent to power spontaneous long-range temporal correlations.
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http://dx.doi.org/10.1101/2025.07.29.667370 | DOI Listing |
PLoS One
September 2025
Grupo Interdisciplinario de Biología Teórica, Instituto de Neurociencia Cognitiva y Traslacional (INCyT), Universidad Favaloro, INECO, CONICET, Buenos Aires, Argentina.
The present paper analyzes the sounds emitted by pre-hatching chicks, focusing on those named as "clicks," which are thought to mediate pre-hatching social interactions and hatching synchronization. Representative acoustic signals were analyzed under three incubation conditions: (1) isolated pre-hatching chicks (n = 13), (2) pre-hatching chicks in contact with others of the same age (n = 14), and (3) pre-hatching chicks in contact with other of different age (n = 10 for each group: leader and follower). Customized MATLAB software was developed to (a) identify and isolate clicks from other recorded sounds, (b) represent them as temporal series of stochastic point processes, and (c) determine whether click emission dynamics resembled white noise or exhibited characteristics of informative signals.
View Article and Find Full Text PDFSci Rep
September 2025
Henan Xj Metering Co., Ltd, Xuchang, 461000, Henan, China.
The precise estimation of the Remaining Useful Life (RUL) of lithium-ion batteries is essential for averting unforeseen failures and enhancing operational efficiency and maintenance planning. This paper presents an advanced deep learning framework that couples a spatial-attention mechanism with a Transductive Long Short-Term Memory (TLSTM) model, augmented by one-dimensional dilated convolutional layers to capture long-range temporal dependencies. In contrast to traditional LSTM or GRU models, our methodology utilizes one-dimensional dilated convolutional layers to effectively capture long-range temporal relationships and implements a clustering-based Differential Evolution (DE) strategy for resilient weight initialization and optimization.
View Article and Find Full Text PDFSensors (Basel)
August 2025
Department of Computer Science and Technology, College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010011, China.
Time series clustering finds wide application but is often limited by data quality and the inherent limitations of existing methods. Compared to high-dimensional structured data like images, the low-dimensional features of time series contain less information, and endogenous noise can easily obscure important patterns. When dealing with massive time series data, existing clustering methods often focus on mining associations between sequences.
View Article and Find Full Text PDFEntropy (Basel)
August 2025
Big Data Analytics Laboratory, Chengyi College, Jimei University, Xiamen 361021, China.
Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide. Electroencephalogram (EEG) signals play a vital role in the diagnosis and analysis of epileptic seizures. However, traditional machine learning techniques often rely on handcrafted features, limiting their robustness and generalizability across diverse EEG acquisition settings, seizure types, and patients.
View Article and Find Full Text PDFBioengineering (Basel)
July 2025
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200230, China.
The electroencephalogram (EEG), widely used for measuring the brain's electrophysiological activity, has been extensively applied in the automatic detection of epileptic seizures. However, several challenges remain unaddressed in prior studies on automated seizure detection: (1) Methods based on CNN and LSTM assume that EEG signals follow a Euclidean structure; (2) Algorithms leveraging graph convolutional networks rely on adjacency matrices constructed with fixed edge weights or predefined connection rules. To address these limitations, we propose a novel algorithm: Dynamic Graph Convolutional Network with Dilated Convolution (DGDCN).
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