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21 papers · ranked by Valyu relevance

01
PPubMed14 Nov 2025
GEMReg: a spatio-temporal grayordinate ensemble modelling framework for predicting task activation maps from resting-state fMRI
Sasideep Pasumarthi, Satwik Bathula, Nitya Tiwari, Himanshu Padole
Objective Recent advances in neuroimaging have highlighted the growing utility of resting-state functional magnetic resonance imaging (rs-fMRI) as an alternative to task-based fMRI. In addition to being simpler, cost-effective, and time-efficient, rs-fMRI is particularly advantageous for non-compliant populations such…
86relevance
02
BbioRxiv21 Jun 2019
Predicting dysfunctional age-related task activations from resting-state network alterations
Ravi D. Mill, Brian A. Gordon, David A. Balota, Jeffrey M. Zacks + 1 more
Alzheimer’s disease (AD) is linked to changes in fMRI task activations and fMRI resting-state functional connectivity (restFC), which can emerge early in the timecourse of illness. Study of these fMRI correlates of unhealthy aging has been conducted in largely separate subfields. Taking inspiration from neural network…
86relevance
03
PPubMed22 Oct 2019
Regression‐based machine‐learning approaches to predict task activation using resting‐state fMRI Cohen et al.
Alexander D. Cohen, Ziyi Chen, Oiwi Parker Jones, Chen Niu + 1 more
'Yang Wang'] Title: Abstract Resting-state fMRI has shown the ability to predict task activation on an individual basis by using a general linear model (GLM) to map resting-state network features to activation z-scores. The question remains whether the relatively simplistic GLM is the best approach to accomplish this…
86relevance
04
PPubMed4 May 2020
Toward a “treadmill test” for cognition: Improved prediction of general cognitive ability from the task activated brain Sripada et al.
Chandra Sripada, Mike Angstadt, Saige Rutherford, Aman Taxali + 1 more
'Kerby Shedden'] Title: Abstract General cognitive ability (GCA) refers to a trait-like ability that contributes to performance across diverse cognitive tasks. Identifying brain-based markers of GCA has been a longstanding goal of cognitive and clinical neuroscience. Recently, predictive modeling methods have emerged…
85relevance
05
PPubMed26 May 2026
TADM-CGAN: a resting-state to task activation map prediction framework using temporal attention-driven diffusion models and conditional generative adversarial networks
Sasideep Pasumarthi, Nitya Tiwari, Himanshu Padole
Predicting task-induced brain activation from resting-state fMRI (rs-fMRI) remains a significant challenge in computational neuroimaging, primarily due to the difficulty in simultaneously modeling the detailed temporal evolution and high spatial resolution of intrinsic neural activity. Most existing literature relies…
84relevance
06
BbioRxiv8 Jul 2020
The functional relevance of task-state functional connectivity
Michael W. Cole, Takuya Ito, Carrisa Cocuzza, Ruben Sanchez-Romero
Resting-state functional connectivity has provided substantial insight into intrinsic brain network organization, yet the functional importance of task-related change from that intrinsic network organization remains unclear. Indeed, such task-related changes are known to be small, suggesting they may have only minimal…
83relevance
07
BbioRxiv10 Dec 2019
How tasks change whole-brain functional organization to reveal brain-phenotype relationships
Abigail S. Greene, Siyuan Gao, Stephanie Noble, Dustin Scheinost + 1 more
Functional connectivity (FC) calculated from task fMRI data better reveals brain-phenotype relationships than rest-based FC, but why is unknown. In over 700 individuals performing 7 tasks, we use psychophysiological interaction (PPI) and predictive modeling analyses to demonstrate that FC and overall degree of…
77relevance
08
BbioRxiv24 May 2016
Activity flow over resting-state networks shapes cognitive task activations
Michael W. Cole, Takuya Ito, Danielle S. Bassett, Douglas H. Schultz
Resting-state functional connectivity (FC) has helped reveal the intrinsic network organization of the human brain, yet its relevance to cognitive task activations has been unclear. Uncertainty remains despite evidence that resting-state FC patterns are highly similar to cognitive task activation patterns. Identifying…
75relevance
09
AarXiv6 Dec 2024
Modeling Task Immersion based on Goal Activation Mechanism
Kazuma Nagashima, Jumpei Nishikawa, Junya Morita
Immersion in a task is a prerequisite for creativity. However, excessive arousal in a single task has drawbacks, such as overlooking events outside of the task. To examine such a negative aspect, this study constructs a computational model of arousal dynamics where the excessively increased arousal makes the task…
69relevance
10
BbioRxiv21 Oct 2020
Temporal prediction elicits rhythmic pre-activation of relevant sensory cortices
Louise Catheryne Barne, André Mascioli Cravo, Floris P. de Lange, Eelke Spaak
Being able to anticipate events before they happen facilitates stimulus processing. The anticipation of the contents of events is thought to be implemented by the elicitation of prestimulus templates in sensory cortex. In contrast, the anticipation of the timing of events is typically associated with entrainment of…
69relevance
11
BbioRxiv18 Jun 2020
Optimisation of functional network resources when learning behavioural strategies for performing complex tasks
Richard E. Daws, Gregory Scott, Eyal Soreq, Robert Leech + 2 more
We developed two novel self-ordered switching (SOS) fMRI paradigms to investigate how human behaviour and underlying network resources are optimised when learning to perform complex tasks with multiple goals. SOS was performed with detailed feedback and minimal pretraining (study 1) or with minimal feedback and…
67relevance
12
CchemRxiv13 Dec 2024
Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks
Authors not listed
Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, leading to a scarcity of high-quality activation energy data. In this…
66relevance
13
CchemRxiv1 Nov 2023
Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability
Sai Mahit Vadaddi, Qiyuan Zhao, Brett M Savoie
Activation energy characterization of competing reactions is a costly, but crucial step for understanding the kinetic relevance of distinct reaction pathways, product yields, and myriad other properties of reacting systems. The standard methodology for activation energy characterization has historically been a…
66relevance
14
AarXiv24 Sep 2020
Deep Learning for Predictive Business Process Monitoring: Review and Benchmark.
Efrén Rama-Maneiro, Juan C. Vidal, Manuel Lama
—Predictive monitoring of business processes is concerned with the prediction of ongoing cases on a business process. Lately, the popularity of deep learning techniques has propitiated an ever-growing set of approaches focused on predictive monitoring based on these techniques. However, the high disparity of process…
65relevance
15
AarXiv24 Nov 2016
General human activity patterns.
Anders Mollgaard, Sune Lehmann, Joachim Mathiesen
A day in the life of a person involves a broad range of activities which are common across many people. Going beyond diurnal cycles, a central question is: to what extent do individuals act according to patterns shared across an entire population? Here we investigate the interplay between different activity types…
63relevance
16
PPubMed18 Jul 2023
Human Activity Prediction Based on Forecasted IMU Activity Signals by Sequence-to-Sequence Deep Neural Networks
Ismael Espinoza Jaramillo, Channabasava Chola, Jin-Gyun Jeong, Ji-Heon Oh + 9 more
'Ji-Heon Oh' 'Hwanseok Jung' 'Jin-Hyuk Lee' 'Won Hee Lee' 'Tae-Seong Kim' 'Athina Tsanousa' 'Georgios Meditskos' 'Stefanos Vrochidis' 'Periklis Chatzimisios' 'Ioannis Yiannis Kompatsiaris'] Human Activity Recognition (HAR) has gained significant attention due to its broad range of applications, such as healthcare…
61relevance
17
BbioRxiv28 Jul 2026
Goal-directed action selection relies on conjunctive representations that bind goals, actions and expected sensory outcomes
Jet Lageman, Atsushi Kikumoto, David Badre, Johannes J. Fahrenfort + 1 more
Goal-directed actions are performed to obtain specific sensory outcomes. However, theories of action control diverge on whether anticipated outcomes are only consequences of motor command specification or serve as instrumental determinants of action selection. Here, we directly tested whether predicted sensory outcomes…
61relevance
18
CchemRxiv18 Oct 2022
Machine Learning Identification of Active Sites in Graphite-Conjugated Catalysts
Kunal Lodaya, Nathan Ricke, Kelly Chen, Troy Van Voorhis
Graphite-conjugated catalysts (GCCs) are a promising class of materials that combine many of the advantages of heterogenous and homogeneous catalysts. In particular, GCCs containing an aryl-pyridinium active site appear to be effective nonmetal catalysts for the oxygen reduction reaction (ORR). In this study, we…
57relevance
19
CchemRxiv30 Jun 2021
Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction
Simon Viet Johansson, Hampus Gummesson Svensson, Esben Bjerrum, Alexander Schliep + 3 more
Computer aided synthesis planning is a rapidly growing field for suggesting synthetic routes for molecules of interest. The methods used are usually dependent on access to large datasets for training, but with a finite experimental budget there are limitations on how much data can be obtained from experiments. Active…
57relevance
20
AarXiv2019
Online Human Activity Recognition Employing Hierarchical Hidden Markov Models
Parviz Asghari, Elnaz Soelimani, Ehsan Nazerfard
—In the last few years there has been a growing interest in Human Activity Recognition (HAR) topic. Sensor-based HAR approaches, in particular, has been gaining more popularity owing to their privacy preserving nature. Furthermore, due to the widespread accessibility of the internet, a broad range of streaming-based…
56relevance
21
AarXiv11 Mar 2021
A systematic literature review on state-of-the-art deep learning methods for process prediction
Dominic Alexander Neu, Johannes Lahann, Peter Fettke
Process mining enables the reconstruction and evaluation of business processes based on digital traces in IT systems. An increasingly important technique in this context is process prediction. Given a sequence of events of an ongoing trace, process prediction allows forecasting upcoming events or performance…
51relevance

Results for task activation prediction

GEMReg: a spatio-temporal grayordinate ensemble modelling framework for predicting task activation maps from resting-state fMRI

Predicting dysfunctional age-related task activations from resting-state network alterations

Regression‐based machine‐learning approaches to predict task activation using resting‐state fMRI Cohen et al.

Toward a “treadmill test” for cognition: Improved prediction of general cognitive ability from the task activated brain Sripada et al.

TADM-CGAN: a resting-state to task activation map prediction framework using temporal attention-driven diffusion models and conditional generative adversarial networks

The functional relevance of task-state functional connectivity

How tasks change whole-brain functional organization to reveal brain-phenotype relationships

Activity flow over resting-state networks shapes cognitive task activations

Modeling Task Immersion based on Goal Activation Mechanism

Temporal prediction elicits rhythmic pre-activation of relevant sensory cortices

Optimisation of functional network resources when learning behavioural strategies for performing complex tasks

Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks

Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability

Deep Learning for Predictive Business Process Monitoring: Review and Benchmark.

General human activity patterns.

Human Activity Prediction Based on Forecasted IMU Activity Signals by Sequence-to-Sequence Deep Neural Networks

Goal-directed action selection relies on conjunctive representations that bind goals, actions and expected sensory outcomes

Machine Learning Identification of Active Sites in Graphite-Conjugated Catalysts

Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction

Online Human Activity Recognition Employing Hierarchical Hidden Markov Models

A systematic literature review on state-of-the-art deep learning methods for process prediction

GEMReg: a spatio-temporal grayordinate ensemble modelling framework for predicting task activation maps from resting-state fMRI

Predicting dysfunctional age-related task activations from resting-state network alterations

Regression‐based machine‐learning approaches to predict task activation using resting‐state fMRI Cohen et al.

Toward a “treadmill test” for cognition: Improved prediction of general cognitive ability from the task activated brain Sripada et al.

TADM-CGAN: a resting-state to task activation map prediction framework using temporal attention-driven diffusion models and conditional generative adversarial networks

The functional relevance of task-state functional connectivity

How tasks change whole-brain functional organization to reveal brain-phenotype relationships

Activity flow over resting-state networks shapes cognitive task activations

Modeling Task Immersion based on Goal Activation Mechanism

Temporal prediction elicits rhythmic pre-activation of relevant sensory cortices

Optimisation of functional network resources when learning behavioural strategies for performing complex tasks

Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks

Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability

Deep Learning for Predictive Business Process Monitoring: Review and Benchmark.

General human activity patterns.

Human Activity Prediction Based on Forecasted IMU Activity Signals by Sequence-to-Sequence Deep Neural Networks

Goal-directed action selection relies on conjunctive representations that bind goals, actions and expected sensory outcomes

Machine Learning Identification of Active Sites in Graphite-Conjugated Catalysts

Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction

Online Human Activity Recognition Employing Hierarchical Hidden Markov Models

A systematic literature review on state-of-the-art deep learning methods for process prediction