An fNIRS representation and fNIRS-scales multimodal fusion method for auxiliary diagnosis of amnestic mild cognitive impairment DOI Creative Commons
Shiyu Cheng, Pan Shang, Yingwei Zhang

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 96, P. 106646 - 106646

Published: July 18, 2024

Amnestic mild cognitive impairment (aMCI) is the prodromal period of more serious neurodegenerative diseases (e.g., Alzheimer's disease), characterized by declines in memory and thinking abilities. Auxiliary assessment early diagnosis aMCI are crucial preventing continued deterioration abilities; nevertheless, this task poses a formidable challenge due to inconspicuous nature symptoms. Functional near-infrared spectroscopy (fNIRS) non-invasive, low-cost, user-friendly neuroimaging technique, which capable detecting subtle changes brain activity among different subjects. Moreover, multimodal fusion can assess cognition status from perspectives enhance auxiliary accuracy significantly. This paper proposes an fNIRS representation fNIRS-scales method for aMCI. Specifically, we convert one-dimensional time-series signals into two-dimensional images with Gramian Angular Field achieve end-to-end convolutional neural network. Then, integrate extracted features scales at decision-making level improve aMCI, employing data balance strategy prevent biased prediction. What more, based on features, also propose data-driven scales-screening help physician higher efficiency. We conducted experiments 86 subjects (including 53 patients 33 normal controls) recruited Foshan First People's Hospital. The reaches 88.02% 93.90% further fusion, respectively. With scales-screening, delete 50% scales, reducing test time but only losing 2.54% accuracy.

Language: Английский

Theta-burst stimulation of TMS treatment for anxiety and depression: A FNIRS study DOI
Yan Zhang, Li Li, Yueran Bian

et al.

Journal of Affective Disorders, Journal Year: 2023, Volume and Issue: 325, P. 713 - 720

Published: Jan. 20, 2023

Language: Английский

Citations

14
Feasibility of local interpretable model-agnostic explanations (LIME) algorithm as an effective and interpretable feature selection method: comparative fNIRS study DOI
Jaeyoung Shin

Biomedical Engineering Letters, Journal Year: 2023, Volume and Issue: 13(4), P. 689 - 703

Published: June 7, 2023

Language: Английский

Citations

13
Classification Algorithm for fNIRS-based Brain Signals Using Convolutional Neural Network with Spatiotemporal Feature Extraction Mechanism DOI
Yuxin Qin, Baojiang Li, Wenlong Wang

et al.

Neuroscience, Journal Year: 2024, Volume and Issue: 542, P. 59 - 68

Published: Feb. 17, 2024

Language: Английский

Citations

5
Lights on music cognition: A systematic and critical review of fNIRS applications and future perspectives DOI Creative Commons
Federico Curzel, Barbara Tillmann, Laura Ferreri

et al.

Brain and Cognition, Journal Year: 2024, Volume and Issue: 180, P. 106200 - 106200

Published: June 21, 2024

Research investigating the neural processes related to music perception and production constitutes a well-established field within cognitive neurosciences. While most neuroimaging tools have limitations in studying complexity of musical experiences, functional Near-Infrared Spectroscopy (fNIRS) represents promising, relatively new tool for both laboratory ecological settings, which is also suitable typical pathological populations across development. Here we systematically review fNIRS studies on cognition, highlighting prospects potentialities. We include an overview basic theory, together with brief comparison characteristics other tools. Fifty-nine meeting inclusion criteria (i.e., using as primary stimulus) are presented five thematic sections. Critical discussion methodology leads us propose guidelines good practices aiming robust signal analyses reproducibility. A continuously updated world map proposed, including information from criteria. It provides organized, accessible, updatable reference database, could serve catalyst future collaborations community. In conclusion, shows potential music, particularly contexts special populations, aligning current research priorities cognition.

Language: Английский

Citations

4
EEG–fNIRS-Based Emotion Recognition Using Graph Convolution and Capsule Attention Network DOI Creative Commons
Guijun Chen, Yue Liu, Xueying Zhang

et al.

Brain Sciences, Journal Year: 2024, Volume and Issue: 14(8), P. 820 - 820

Published: Aug. 16, 2024

Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) can objectively reflect a person’s emotional state have been widely studied in emotion recognition. However, the effective feature fusion discriminative learning from EEG–fNIRS data is challenging. In order to improve accuracy of recognition, graph convolution capsule attention network model (GCN-CA-CapsNet) proposed. Firstly, signals are collected 50 subjects induced by video clips. And then, features EEG fNIRS extracted; fused generate higher-quality primary capsules with Pearson correlation adjacency matrix. Finally, module introduced assign different weights capsules, selected better classification dynamic routing mechanism. We validate efficacy proposed method on our dataset an ablation study. Extensive experiments demonstrate that GCN-CA-CapsNet achieves more satisfactory performance against state-of-the-art methods, average increase 3–11%.

Language: Английский

Citations

4
Intermittent theta burst stimulation for negative symptoms in schizophrenia patients with mild cognitive impairment: a randomized controlled trail DOI Creative Commons
Jing Li,

Xian Mo,

Dan Jiang

et al.

Frontiers in Psychiatry, Journal Year: 2025, Volume and Issue: 15

Published: Jan. 3, 2025

This study aims to evaluate the intervention effect of intermittent Theta burst stimulation (iTBS) on bilateral dorsomedial prefrontal cortex (DMPFC) for negative symptoms in schizophrenia using functional near-infrared spectroscopy (fNIRS) confirm therapeutic significance DMPFC treating and provide new evidence treatment research. Thirty-nine patients with mild cognitive impairment were randomly divided into a group (n=20) control (n=19). The received iTBS DMPFC. sham treatment. Negative symptoms, function, emotional state, social function assessed at pre-treatment, post-treatment, 4-, 8-, 12-week follow-ups. Brain activation regions interest (ROIs) was evaluated through verbal fluency tasks. Changes scale scores analyzed by repeated measures ANOVA. After 20 sessions iTBS, Scale Assessment Symptoms (SANS) total sub-scale significantly improved group, statistically significant differences. SANS differed between pre- post-treatment both groups, markedly lower than pre-treatment better efficacy group. However, there no difference function. ROIs did not differ groups before intervention. treatment, higher controls, difference. Regarding connectivity, small-world properties Sigma Gamma enhanced. can effectively alleviate enhance schizophrenia.

Language: Английский

Citations

0
Near-Infrared Spectroscopy Technique and Its Application in Biomedical Fields DOI Creative Commons
Ziyi Huang, Haofeng Zhang

IntechOpen eBooks, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 10, 2025

Near-infrared spectroscopy (NIRS) is a non-invasive monitoring technique that utilizes light transmission and absorption to continuously evaluate regional tissue oxygen saturation, delivery, metabolism. Widely adopted in modern clinical practice, NIRS particularly effective assessing cerebral oxygenation, enabling the early detection of impaired perfusion. Grounded Beer–Lambert law, relies on characteristics oxyhemoglobin deoxyhemoglobin as primary chromophores biological tissues. This chapter provides comprehensive overview technology its applications biomedical fields. It begins by discussing fundamental assumptions, advantages, limitations NIRS, along with typical structure system. Following this, principles properties are explored depth. The then delves into brain monitoring, focusing oxygenation hemodynamics. Additionally, it examines use cardiac applications, highlighting both potential challenges involved. role machine learning signal processing also demonstrated. Finally, integration other imaging modalities, including optical coherence tomography, electroencephalography, ultrasound, introduced illustrate capabilities multi-modality systems.

Language: Английский

Citations

0
Accurate identification of anxiety and depression based on the dlPFC in an emotional autobiographical memory task: A machine learning approach DOI
Guixiang Wang, Yusen Huang, Yan Zhang

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 104, P. 107503 - 107503

Published: Jan. 18, 2025

Language: Английский

Citations

0
FCS-TPNet: Fusion of fNIRS chromophore signals to construct temporal-spatial graph representation for topological networks DOI
Lin F. Yang, Jiang Gu, Jun Chen

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 104, P. 107528 - 107528

Published: Jan. 27, 2025

Language: Английский

Citations

0
Case report: Monitoring consciousness with fNIRS in a patient with prolonged reduced consciousness following hemorrhagic stroke undergoing adjunct taVNS therapy DOI Creative Commons
Fei Gao,

Likai Wang,

Zhan Wang

et al.

Frontiers in Neuroscience, Journal Year: 2025, Volume and Issue: 19

Published: Feb. 4, 2025

Disorders of consciousness (DoC) resulting from severe brain injury present substantial challenges in rehabilitation due to disruptions network connectivity, particularly within the frontal-parietal critical for awareness. Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a promising non-invasive intervention; however, precise mechanisms through which it influences cortical function DoC patients remain unclear. This study describes effects taVNS on fronto-parietal connectivity and arousal 77-year-old female patient with unresponsive wakefulness syndrome (UWS). The received bilateral 1 h daily over 3 months, functional (FC) frontoparietal assessed using near-infrared spectroscopy (fNIRS) behavioral responsiveness evaluated Coma Recovery Scale-Revised (CRS-R). After intervention, mean FC was enhanced 0.06 (SD = 0.31) 0.33 0.28) network. were subdivided into 12 regions interest (ROIs) determined that between left dorsolateral prefrontal cortex (DLPFC) ROIs ± 0.41 before intervention 0.55 0.24 after intervention. Behavioral improvements evidenced by an increase CRS-R scores 2 14, marking patient's transition UWS minimally conscious state plus (MCS+). Additionally, associated auditory sensory processing showed increased engagement, supporting positive impact responsiveness. suggests its value adjunctive therapy patients. Further studies are necessary confirm these wider population refine strategy clinical application taVNS.

Language: Английский

Citations

0