From brain to education through machine learning: Predicting literacy and numeracy skills from neuroimaging data DOI Open Access
Tomoya Nakai, Jérôme Prado

Published: Nov. 13, 2023

The potential of using neural data to predict academic outcomes has always been at the heart educational neuroscience, an emerging field crossroad psychology, neuroscience and education sciences. Although this prospect long elusive, exponential use advanced techniques in machine learning artificial intelligence neuroimaging may change state affairs. Here we provide a review studies that have used literacy numeracy adults children, both context disability typical performance. We notably cross-sectional longitudinal designs such studies, describe how they can be coupled with regression classification approaches. Our highlights promise these methods for predicting (and difficulties). However, also found large variability terms algorithms underlying brain circuits across relative lack investigating prediction young children before onset formal education. This calls standardization field, as well greater accessible portable more applicability than lab-based techniques.

Language: Английский

The intrinsic functional connectivity patterns of the phonological and semantic networks in word reading DOI
Yuan Feng, Shuo Zhang, Aqian Li

et al.

Neuroscience, Journal Year: 2025, Volume and Issue: 571, P. 139 - 150

Published: Feb. 22, 2025

Language: Английский

Citations

0
Distinct distributed brain networks dissociate self-generated mental states DOI Creative Commons
Nathan Anderson, Joseph J. Salvo, Jonathan Smallwood

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 27, 2025

Human cognition relies on two modes: a perceptually-coupled mode where mental states are driven by sensory input and perceptually-decoupled featuring self-generated content. Past work suggests that imagined supported the reinstatement of activity in cortex, but transmodal systems within canonical default network also implicated mind-wandering, recollection, imagining future. We identified brain supporting using precision fMRI. Participants different scenarios scanner, then rated their several properties multi-dimensional experience sampling. found thinking involving scenes evoked or near network, while speech language network. Imagining-related regions overlapped with viewing listening to speech, respectively; however, this overlap was predominantly association networks, rather than adjacent unimodal networks. The results suggest networks support high visual auditory vividness. Different large-scale audiolinguistic

Language: Английский

Citations

0
Natural rhythmic speech activates network reorganization with frontal community enhancing communication efficiency in patients with intrinsic brain tumor DOI Creative Commons

Leyan Gao,

Zhirui Yang,

Yuyao Zhou

et al.

NeuroImage, Journal Year: 2025, Volume and Issue: 310, P. 121112 - 121112

Published: March 4, 2025

Brain tumors provide unique insights into brain plasticity due to their slow growth compared acute cerebrovascular diseases. Despite relying on sophisticated functional networks, patients with exhibit minimal deficits in higher language functions and demonstrate positive post-injury plasticity; however, the underlying neural mechanisms remain unclear. We utilized high-density electroencephalography investigate network tumor without evident deficits. Natural rhythmic sentences non-rhythmic contrasting speech prosodic harmony were employed examine impact of task integrativeness reorganization. Our study reveals that perception, characterized by processing integrativeness, induced inhibited engagement frontal lobe but evoked enhanced hubness modularity, which supported generation new connections promoted efficiency global connectivity. Furthermore, local invasion prompted adjacent hubs generate enriched during early phase, facilitating later findings underscore significant role reveal importance highly integrated tasks for reorganization rehabilitation.

Language: Английский

Citations

0
NaDyNet: A Toolbox for Dynamic Network Analysis of Naturalistic Stimuli DOI Creative Commons

Junjie Yang,

Zhe Hu, Junjing Li

et al.

NeuroImage, Journal Year: 2025, Volume and Issue: unknown, P. 121203 - 121203

Published: April 1, 2025

Experiments with naturalistic stimuli (e.g., listening to stories or watching movies) are emerging paradigms in brain function research. The content of is rich and continuous. fMRI signals complex include different components. A major challenge isolate the stimuli-induced while simultaneously tracking brain's responses these real-time. To this end, we have developed a user-friendly graphical interface toolbox called NaDyNet (Naturalistic Dynamic Network Toolbox), which integrates existing dynamic network analysis methods their improved versions. main features are: 1) extracting interest from signals; 2) incorporating six commonly used three static methods; 3) versions by adopting inter-subject eliminate effects non-interest 4) performing K-means clustering identify temporally reoccurring states along temporal spatial attributes; 5) Visualization spatiotemporal results. We then introduced rationale for improve presented examples analyzing data. hope that will promote development neuroscience. available at https://github.com/yuanbinke/Naturalistic-Dynamic-Network-Toolbox.

Language: Английский

Citations

0
Stage-dependent differential impact of network communication on cognitive function across the continuum of cognitive decline in Parkinson's disease DOI Creative Commons
Xiaolu Li,

Huize Pang,

Shuting Bu

et al.

Neurobiology of Disease, Journal Year: 2024, Volume and Issue: 199, P. 106578 - 106578

Published: June 25, 2024

Our objective was to explore the patterns of resting-state network (RSN) connectivity alterations and investigate how influences individual-level connections on cognition varied across clinical stages without assuming a constant relationship. 108 PD patients with continuum cognitive decline (PD-NC = 46, PD-MCI 43, PDD 19) 34 healthy controls (HCs) underwent functional MRI neuropsychological tests. Independent component analysis (ICA) graph theory analyses (GTA) were employed RSN connection changes. Additionally, stage-dependent differential impact communication performance examined using sparse varying coefficient modeling. Compared HCs, dorsal attention (DAN) sensorimotor (dSMN) central networks decreased in PD-NC stage, while lateral visual (LVN) emerged as dementia. cerebellum (CBN) increased stages. GTA demonstrated nodal metrics for DAN dSMN, coupled an increase CBN. Moreover, degree centrality (DC) values dSMN exhibited decline. findings suggest that progression impairment, LVN gradually transitions into core node reduced connectivity, enhancement CBN diminishes. Furthermore, non-linear relationship between DC RSNs indicates potential tailored interventions targeting specific

Language: Английский

Citations

3
Unveiling altered connectivity between cognitive networks and cerebellum in schizophrenia DOI

Margherita Biondi,

Marco Marino, Dante Mantini

et al.

Schizophrenia Research, Journal Year: 2024, Volume and Issue: 271, P. 47 - 58

Published: July 15, 2024

Language: Английский

Citations

3
Inter-subject dynamic conditional correlation: A novel method to track the framewise network implication during naturalistic stimuli DOI
Lifeng Chen,

Shiyao Tan,

Chaoqun Li

et al.

Brain Connectivity, Journal Year: 2024, Volume and Issue: 14(9), P. 471 - 488

Published: Sept. 20, 2024

Naturalistic stimuli have become increasingly popular in modern cognitive neuroscience. These high ecological validity due to their rich and multilayered features. However, complexity also presents methodological challenges for uncovering neural network reconfiguration. Dynamic functional connectivity using the sliding-window technique is commonly used but has several limitations. In this study, we introduce a new method called intersubject dynamic conditional correlation (ISDCC).

Language: Английский

Citations

2
Multilayer Network Instability Underlying Persistent Auditory Verbal Hallucinations in Schizophrenia DOI
Jinguang Li,

Jingqi He,

Honghong Ren

et al.

Psychiatry Research, Journal Year: 2024, Volume and Issue: 344, P. 116351 - 116351

Published: Dec. 31, 2024

Language: Английский

Citations

1
Compensatory Mechanisms for Preserving Speech-in-Noise Comprehension Involve Prefrontal Cortex in Older Adults DOI Creative Commons
Zhuoran Li, Yi Liu, Xinmiao Zhang

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: March 13, 2024

Abstract The capacity of comprehending others amidst noise is essential for human communication. However, it presents significant challenges the elderly who often face progressive declines in peripheral auditory system and whole brain. While previous studies have suggested existence neural reserve compensation as potential mechanisms preserving cognitive abilities aging, specific supporting speech-in-noise comprehension among remain unclear. To address this question, present study employs an inter-brain neuroscience approach by analyzing coupling between brain activities older adults those speakers under noisy conditions. Results showed that encompassed more extensive regions listeners compared to young listeners, with a notable engagement prefrontal cortex. Moreover, from cortex was coordinated classical language-related regions. More importantly, background increases, listener’s speech performance closely associated Taken together, reveals compensatory recruitment neurocognitive resources, particularly within cortex, facilitate processing aging brain, further highlights critical role maintaining elderly’s ability comprehend environments. It supports hypothesis, extending knowledge about basis underlies preservation population.

Language: Английский

Citations

0
Acoustic Signal Generation Techniques for Improved Coconut Maturity Classification System DOI

June Anne Caladcad,

Eduardo Piedad

Published: Jan. 1, 2024

Language: Английский

Citations

0