Article
Multidisciplinary Sciences
Martin Arguin, Roxanne Ferrandez, Justine Masse
Summary: This report introduces a random temporal sampling technique for studying oscillatory visual mechanisms, with results showing variations in perceptual effectiveness based on temporal features of stimulus visibility. The power spectra of classification images are highly generalizable, and stimulus class can be reliably decoded from the power spectrum of individual classification images.
SCIENTIFIC REPORTS
(2021)
Article
Clinical Neurology
Yujiang Wang, Gabrielle M. Schroeder, Jonathan J. Horsley, Mariella Panagiotopoulou, Fahmida A. Chowdhury, Beate Diehl, John S. Duncan, Andrew W. McEvoy, Anna Miserocchi, Jane de Tisi, Peter N. Taylor
Summary: Comparing patient data to a normative map has shown promise in identifying abnormalities on interictal intracranial electroencephalogram (iEEG) for localization of epileptogenic tissue and outcome prediction. However, the temporal stability of these findings has not been established.
Article
Multidisciplinary Sciences
Alon Zivony, Martin Eimer
Summary: When identifying an object in a rapid serial visual presentation (RSVP) stream, observers often make errors by reporting a distractor instead of the target. Two experiments examined whether these errors are associated with the speed of attentional engagement. The results showed that distractor intrusions are closely linked to the timing of selective attention allocation, making the intrusion paradigm a valuable tool for studying the temporal dynamics of attention.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Information Systems
K. Jyothi Upadhya, Aman Paleja, M. Geetha, B. Dinesh Rao, Mini Shail Chhabra
Summary: This paper introduces a depth-first search framework, 3P-BitVectorMiner, for extracting partial periodic patterns from temporal databases. Two variations are proposed to mine rare fully periodic patterns and rare partial periodic patterns. Experiments demonstrate that 3P-BitVectorMiner outperforms the state-of-the-art algorithm 3P-Growth in terms of performance and scalability.
Article
Mathematics, Applied
Quynh-Anh Nguyen, Leonid L. Rubchinsky
Summary: Synchronization in neural systems, particularly in the gamma frequency band, plays a crucial role in cognitive phenomena; the strength of synchronization is often low and intermittent, with intervals of synchronization followed by desynchronization. Changes in synaptic strength can alter the temporal patterning of synchrony, independent of changes in synchrony strength, potentially leading to efficient formation and breakup of transient neural assemblies.
Review
Neurosciences
Guillaume Etter, James E. Carmichael, Sylvain Williams
Summary: Oscillations in neural activity, particularly theta rhythms in the hippocampus, play a crucial role in learning and memory. While loss of theta rhythmicity impairs memory, spatial-temporal representations persist in altered hippocampal oscillations. This review aims to investigate the contribution of hippocampal oscillations and single-neuron activity to memory function, and propose hypotheses for how septohippocampal oscillations support memory function without directly contributing to hippocampal sequences.
FRONTIERS IN CELLULAR NEUROSCIENCE
(2023)
Article
Acoustics
Brendon Samuels, Jessica Grahn, Molly J. Henry, Scott A. MacDougall-Shackleton
Summary: Starlings trained to discriminate between rhythmic patterns with or without a regular beat were unable to do so effectively. They relied on absolute temporal features to categorize sounds as fast or slow, demonstrating a focus on local features in rhythms rather than global temporal organization.
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA
(2021)
Article
Computer Science, Artificial Intelligence
Bahareh Nikpour, Narges Armanfard
Summary: The use of skeleton data for activity recognition has become widespread due to its advantages over RGB data. In this paper, a novel framework called STH-DRL is proposed for activity recognition, which includes a temporal agent and a spatial agent. The agents are trained using deep reinforcement learning to find key frames and key joints, respectively, by formulating the search problems as Markov decision processes. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed method.
PATTERN RECOGNITION
(2023)
Article
Neurosciences
Brendan Brady, Tim Bardouille
Summary: This study integrates the periodic/aperiodic parameterization of neural power spectra and the transient events framework of oscillatory activity to analyze extracranial neurophysiological signals. The novel technique called PAPTO is applied to resting-state sensorimotor magnetoencephalography recordings. The results show that PAPTO is more sensitive to neocortical transient beta rhythms and captures more variance in the resting-state occurrence rate of beta events compared to conventional transient event detection algorithms.
Article
Computer Science, Artificial Intelligence
Kanchan Keisham, Amin Jalali, Minho Lee
Summary: This study proposes a novel spatio-temporal attention network for online action proposal generation, which can generate precise action boundaries and handle noisy features effectively, suitable for online tasks.
Article
Computer Science, Artificial Intelligence
Hongchao Qin, Rong-Hua Li, Ye Yuan, Yongheng Dai, Guoren Wang
Summary: This paper proposes a novel model for finding the densest periodic subgraph in temporal graphs. By developing pruning techniques and efficient algorithms, efficient computation of periodic subgraphs is achieved.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Review
Nutrition & Dietetics
Sergio Garbarino, Emanuela Garbarino, Paola Lanteri
Summary: This paper discusses the effect of chrononutrition on circadian rhythm regulation, specifically focusing on the use of chocolate to resynchronize internal biological clocks with external synchronizers. The high flavonoid content in chocolate promotes various health benefits and may serve as a strategy to reduce the negative effects of desynchronization. While the exact mechanisms of action are not fully understood, available literature suggests that chocolate intake, in compliance with chrononutrition, could improve work ability and daily life.
Article
Geochemistry & Geophysics
Yanheng Wang, Danfeng Hong, Jianjun Sha, Lianru Gao, Lian Liu, Yonggang Zhang, Xianhui Rong
Summary: This article presents a joint spectral, spatial, and temporal transformer for hyperspectral image change detection. The proposed method encodes the positions of pixels to remember spectral and spatial sequences and uses transformer encoders to extract features. Experimental results show that the method outperforms other approaches both visually and qualitatively.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Biochemical Research Methods
Francis C. Motta, Robert C. Moseley, Bree Cummins, Anastasia Deckard, Steven B. Haase
Summary: This study demonstrates that a unified set of dynamic features of high-throughput time series gene expression data is more prominent in the core transcriptional regulators of cell and circadian cycles than in their outputs, even in the presence of external periodic stimuli. Additionally, the ability to discriminate between core and non-core genes is largely insensitive to the particular choice of quantification of these features.
BMC BIOINFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Lili Guo, Shifei Ding, Longbiao Wang, Jianwu Dang
Summary: This article introduces a deep spectro-temporal-channel network (DSTCNet) for speech emotion recognition, which improves the representation ability by integrating multiple spectro-temporal-channel attention modules. Experimental results show that DSTCNet outperforms traditional CNN-based methods and several state-of-the-art methods in emotion recognition.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Neurosciences
Anna Fiveash, Daniele Schon, Laure-Helene Canette, Benjamin Morillon, Nathalie Bedoin, Barbara Tillmann
BRAIN AND COGNITION
(2020)
Article
Neurosciences
Julie Courtiol, Maxime Guye, Fabrice Bartolomei, Spase Petkoski, Viktor K. Jirsa
JOURNAL OF NEUROSCIENCE
(2020)
Article
Neurosciences
Jacques Pesnot Lerousseau, Agnes Trebuchon, Benjamin Morillon, Daniele Schon
Summary: The study found that cortical neural oscillations in response to tones exhibit persistent activity, while there was no persistent activity in response to a 2.5 Hz stream in a passive perception paradigm. The data were well captured by a damped harmonic oscillator model and classified into three classes of neural dynamics with distinct damping properties and eigenfrequencies, providing a mechanistic explanation of frequency selectivity in auditory neural entrainment in the human cortex.
JOURNAL OF NEUROSCIENCE
(2021)
Article
Neurosciences
Spase Petkoski, Viktor K. Jirsa
Summary: This study extends traditional graph theory to the domain of oscillations and proposes a novel network normalization method. By analyzing the Human Connectome Project database, it explains the emergence of frequency-specific network cores. These findings are robust across human subjects and provide a new perspective for neuroscience research.
NETWORK NEUROSCIENCE
(2022)
Article
Neurosciences
Michael Schirner, Lia Domide, Dionysios Perdikis, Paul Triebkorn, Leon Stefanovski, Roopa Pai, Paula Prodan, Bogdan Valean, Jessica Palmer, Chloe Langford, Andre Blickensdoerfer, Michiel van der Vlag, Sandra Diaz-Pier, Alexander Peyser, Wouter Klijn, Dirk Pleiter, Anne Nahm, Oliver Schmid, Marmaduke Woodman, Lyuba Zehl, Jan Fousek, Spase Petkoski, Lionel Kusch, Meysam Hashemi, Daniele Marinazzo, Jean-Francois Mangin, Agnes Floeel, Simisola Akintoye, Bernd Carsten Stahl, Michael Cepic, Emily Johnson, Gustavo Deco, Anthony R. McIntosh, Claus C. Hilgetag, Marc Morgan, Bernd Schuller, Alex Upton, Colin McMurtrie, Timo Dickscheid, Jan G. Bjaalie, Katrin Amunts, Jochen Mersmann, Viktor Jirsa, Petra Ritter
Summary: The Virtual Brain (TVB) is an open-source service on the cloud research platform EBRAINS, providing software for constructing, simulating and analyzing brain network models. It offers features such as MRI processing pipelines and automatic conversion of model equations, facilitating online collaboration and data discovery.
Editorial Material
Mathematical & Computational Biology
Andrea Duggento, Spase Petkoski, Tomislav Stankovski, Nicola Toschi
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
(2022)
Editorial Material
Biochemistry & Molecular Biology
Benjamin Morillon, Luc H. Arnal, Pascal Belin
Summary: Categorizing voices is essential for social interactions based on auditory perception. A recent study investigated the spatiotemporal patterns of neural activity in the associative auditory cortex that lead to voice-specific responses using human intracranial recordings.
Article
Neurosciences
P. Sorrentino, S. Petkoski, M. Sparaco, E. Troisi Lopez, E. Signoriello, F. Baselice, S. Bonavita, M. A. Pirozzi, M. Quarantelli, G. Sorrentino, V. Jirsa
Summary: Two structurally connected brain regions are more likely to interact, with the properties of the structural bundles and the topology of the structural connectome affecting the timing of the interactions. Using magneto/electroencephalography (MEG/EEG) and integrating them with the structural bundles, researchers measured functional delays across the entire human brain and created a topochronic map. The study also found that patients with multiple sclerosis had greater delays across the network compared to controls.
JOURNAL OF NEUROSCIENCE
(2022)
Article
Neurosciences
Spase Petkoski, Petra Ritter, Viktor K. Jirsa
Summary: Diffusion-weighted magnetic resonance imaging (MRI) data is used to analyze the structural connectivity of the brain at different ages. The study finds that there is a significant decrease in streamlines in frontal regions and long inter-hemispheric links. The average length of tracts also decreases, but clustering remains unaffected. Age-related changes in functional connectivity (FC) are identified through functional MRI, indicating a more stable dynamic functional connectivity (dFC) but wider range and variance of metaconnectivity (MC) features.
Article
Multidisciplinary Sciences
Viktor Sip, Meysam Hashemi, Timo Dickscheid, Katrin Amunts, Spase Petkoski, Viktor Jirsa
Summary: Model-based data analysis is used to link observed data to model parameters in a network of neural masses, with recent studies focusing on the role of regional variance of model parameters. This study introduces a method to infer the neural mass model representing regional dynamics and region- and subject-specific parameters from functional data, while considering the known network structure. Applied to human resting-state fMRI, the study reveals that the underlying dynamics can be described as noisy fluctuations around a fixed point, and identifies three regional parameters with distinct roles in the dynamics, one of which is strongly correlated with gene expression spatial patterns. This approach provides a novel way to analyze resting-state fMRI and has potential applications in understanding brain dynamics during aging or neurodegeneration.
Article
Psychology, Experimental
Jeremy Giroud, Jacques Pesnot Lerousseau, Francois Pellegrino, Benjamin Morillon
Summary: Humans are skilled at processing speech, but the mechanism behind it is still unknown. This study developed a framework to measure the influences of different acoustic and linguistic features on speech comprehension, finding that comprehension is impacted by these features to varying degrees, with syllabic rate having the most influence. Additionally, contextual information at the supra-lexical level significantly reduces the impact of other features. The study also estimated the channel capacity associated with each linguistic feature, revealing that they have distinct processing bottlenecks.
Review
Psychiatry
Gunter Schumann, Ole A. Andreassen, Tobias Banaschewski, Vince D. Calhoun, Nicholas Clinton, Sylvane Desrivieres, Ragnhild Eek Brandlistuen, Jianfeng Feng, Soeren Hese, Esther Hitchen, Per Hoffmann, Tianye Jia, Viktor Jirsa, Andre F. Marquand, Frauke Nees, Markus M. Noethen, Gaia Novarino, Elli Polemiti, Markus Ralser, Michael Rapp, Kerstin Schepanski, Tamara Schikowski, Mel Slater, Peter Sommer, Bernd Carsten Stahl, Paul M. Thompson, Sven Twardziok, Dennis van der Meer, Henrik Walter, Lars Westlye
Summary: This article introduces a research strategy that leverages innovative tools and techniques such as data federation, geospatial observation, and data integration to understand the impact of environmental factors on the brain and mental illness. The presented example demonstrates the potential of this research to identify brain mechanisms underlying mental health symptoms related to environmental adversity and develop targeted interventions. The development of objective biomarkers and evidence-based interventions can greatly improve outcomes of environment-related mental illness.
Article
Neurosciences
Lucas Arbabyazd, Spase Petkoski, Michael Breakspear, Ana Solodkin, Demian Battaglia, Viktor Jirsa
Summary: Resting-state fluctuations of functional links in older adults, including those with amnesic mild cognitive impairment (aMCI) and Alzheimer's disease (AD), are not independent but constrained by high-order correlations. The dynamic functional connectivity (dFC) becomes increasingly bursty and intermittent in patients with AD, while regions affected at early stages of AD pathology are less involved in higher order interactions.
NETWORK NEUROSCIENCE
(2023)
Article
Neurosciences
Mario Lavanga, Johanna Stumme, Bahar Hazal Yalcinkaya, Jan Fousek, Christiane Jockwitz, Hiba Sheheitli, Nora Bittner, Meysam Hashemi, Spase Petkoski, Svenja Caspers, Viktor Jirsa
Summary: The study used a brain network modeling framework to infer the causal link between structural connectivity and functional architecture and demonstrated that global modulation of brain dynamics increases with age, particularly in older adults with poor cognitive performance. The researchers validated their hypothesis using a deep-learning Bayesian approach, providing mechanistic evidence of dedifferentiation leading to cognitive decline during aging.
Review
Psychiatry
Gunter Schumann, Ole A. Andreassen, Tobias Banaschewski, Vince D. Calhoun, Nicholas Clinton, Sylvane Desrivieres, Ragnhild Eek Brandlistuen, Jianfeng Feng, Soeren Hese, Esther Hitchen, Per Hoffmann, Tianye Jia, Viktor Jirsa, Andre F. Marquand, Frauke Nees, Markus M. Noethen, Gaia Novarino, Elli Polemiti, Markus Ralser, Michael Rapp, Kerstin Schepanski, Tamara Schikowski, Mel Slater, Peter Sommer, Bernd Carsten Stahl, Paul M. Thompson, Sven Twardziok, Dennis van der Meer, Henrik Walter, Lars Westlye
Summary: Climate change, pollution, urbanization, socioeconomic inequality, and the COVID-19 pandemic have affected brain health. Research using innovative data integration tools can identify brain mechanisms underlying environment-related mental illness and inform innovative treatments. This research will lead to the development of objective biomarkers and evidence-based interventions to improve outcomes of environment-related mental illness.
