Transcranial Direct Current Stimulation (tDCS) and Transcranial Magnetic Stimulation (tMS) have received widespread clinical use as techniques within a Non-Invasive Brain Stimulation (NIBS) domain, whose primary focus is modulation of neural activity to treat neurological and psychiatric disorders. Despite these advancements, precision targeting of deep brain structures remains a challenge faced with great need of another innovation that will improve precision and reduce the risks. A novel methodology integrating transcranial Focused Ultrasound (tFUS) with real-time functional imaging modalities, including functional Magnetic Resonance Imaging (fMRI) and Near-Infra-Red Spectroscopy (NIRS), is proposed in this study as the Integrated Focused Ultrasound and Real-Time Imaging Control System (IFURTICS).
Principle results
Closed loop algorithms employed by IFURTICS allow it to dynamically vary stimulation parameters in response to real-time feedback on neural activity, allowing for accurate targeting of sensitive networks while minimizing deleterious collateral effects.
Conclusions
Clinical trials using standard datasets of fMRI and NIRS have proved that the approach improved targeting accuracy by ∼18 %, reduced off-target effects by ∼55 % and enhanced therapeutic outcomes by 50 % over current methods, suggesting its potential as a transformative approach to precision neuro-modulation.
{"title":"Integration of focused ultrasound and dynamic imaging control system for targeted neuro-modulation","authors":"K.M. Karthick Raghunath , Surbhi Bhatia Khan , T.R. Mahesh , Ahlam Almusharraf , Rubal Jeet , Mohammad Tabrez Quasim , Azeem Irshad , Fatima Asiri","doi":"10.1016/j.jneumeth.2025.110391","DOIUrl":"10.1016/j.jneumeth.2025.110391","url":null,"abstract":"<div><h3>Background</h3><div>Transcranial Direct Current Stimulation (tDCS) and Transcranial Magnetic Stimulation (tMS) have received widespread clinical use as techniques within a Non-Invasive Brain Stimulation (NIBS) domain, whose primary focus is modulation of neural activity to treat neurological and psychiatric disorders. Despite these advancements, precision targeting of deep brain structures remains a challenge faced with great need of another innovation that will improve precision and reduce the risks. A novel methodology integrating transcranial Focused Ultrasound (tFUS) with real-time functional imaging modalities, including functional Magnetic Resonance Imaging (fMRI) and Near-Infra-Red Spectroscopy (NIRS), is proposed in this study as the Integrated Focused Ultrasound and Real-Time Imaging Control System (IFURTICS).</div></div><div><h3>Principle results</h3><div>Closed loop algorithms employed by IFURTICS allow it to dynamically vary stimulation parameters in response to real-time feedback on neural activity, allowing for accurate targeting of sensitive networks while minimizing deleterious collateral effects.</div></div><div><h3>Conclusions</h3><div>Clinical trials using standard datasets of fMRI and NIRS have proved that the approach improved targeting accuracy by ∼18 %, reduced off-target effects by ∼55 % and enhanced therapeutic outcomes by 50 % over current methods, suggesting its potential as a transformative approach to precision neuro-modulation.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"417 ","pages":"Article 110391"},"PeriodicalIF":2.7,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-19DOI: 10.1016/j.jneumeth.2025.110402
Minjie Wang , Yuan Zhang , Aiping Wang , Zhongxue Gan , Lihua Zhang , Xiaoyang Kang
Background
Spinal cord stimulation (SCS) plays a crucial role in treating various neurological diseases. Utilizing soft spinal cord electrodes in SCS allows for a better fit with the physiological structure of the spinal cord and reduces tissue damage. Polydimethylsiloxane (PDMS) has emerged as an ideal material for soft bioelectronics. However, micromachining soft PDMS bioelectronics devices with low thermal effects and high uniformity remains challenging.
New method
Here, we demonstrated a fully laser-micromachined soft neural interface for SCS. The native and color adjusted PDMS with variable absorbance characteristics were investigated in laser processing. In addition, we systematically evaluated the impact of electrode sizes on the electrochemical performance of neural interface. By fitting the equivalent circuit model, the electrochemical process of neural interface was revealed and the performance of the electrode was evaluated. The biocompatibility of color adjusted PDMS was confirmed by cytotoxicity assays. Finally, we validated the neural interface in mice.
Results
Color adjusted PDMS has good biocompatibility and can significantly reduce the damage caused by thermal effects, enhancing the electrochemical performance of bioelectronic devices. The soft neural interface with color adjusted PDMS encapsulation layer can activate the motor function safely.
Comparison with existing methods
The fully laser-micromachined soft neural interface was proposed for the first time. Compared with existing methods, this method showed low thermal effects, high uniformity, and could be easily scaled up.
Conclusions
The fully laser-micromachined soft neural interface device with color adjusted PDMS encapsulation layer shows great promise for applications in SCS.
{"title":"Soft neural interface with color adjusted PDMS encapsulation layer for spinal cord stimulation","authors":"Minjie Wang , Yuan Zhang , Aiping Wang , Zhongxue Gan , Lihua Zhang , Xiaoyang Kang","doi":"10.1016/j.jneumeth.2025.110402","DOIUrl":"10.1016/j.jneumeth.2025.110402","url":null,"abstract":"<div><h3>Background</h3><div>Spinal cord stimulation (SCS) plays a crucial role in treating various neurological diseases. Utilizing soft spinal cord electrodes in SCS allows for a better fit with the physiological structure of the spinal cord and reduces tissue damage. Polydimethylsiloxane (PDMS) has emerged as an ideal material for soft bioelectronics. However, micromachining soft PDMS bioelectronics devices with low thermal effects and high uniformity remains challenging.</div></div><div><h3>New method</h3><div>Here, we demonstrated a fully laser-micromachined soft neural interface for SCS. The native and color adjusted PDMS with variable absorbance characteristics were investigated in laser processing. In addition, we systematically evaluated the impact of electrode sizes on the electrochemical performance of neural interface. By fitting the equivalent circuit model, the electrochemical process of neural interface was revealed and the performance of the electrode was evaluated. The biocompatibility of color adjusted PDMS was confirmed by cytotoxicity assays. Finally, we validated the neural interface in mice.</div></div><div><h3>Results</h3><div>Color adjusted PDMS has good biocompatibility and can significantly reduce the damage caused by thermal effects, enhancing the electrochemical performance of bioelectronic devices. The soft neural interface with color adjusted PDMS encapsulation layer can activate the motor function safely.</div></div><div><h3>Comparison with existing methods</h3><div>The fully laser-micromachined soft neural interface was proposed for the first time. Compared with existing methods, this method showed low thermal effects, high uniformity, and could be easily scaled up.</div></div><div><h3>Conclusions</h3><div>The fully laser-micromachined soft neural interface device with color adjusted PDMS encapsulation layer shows great promise for applications in SCS.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"417 ","pages":"Article 110402"},"PeriodicalIF":2.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143472567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-18DOI: 10.1016/j.jneumeth.2025.110404
Eleni Tzekaki , Chryssa Bekiari , Anastasia Pantazaki , Maria Tsantarliotou , Magda Tsolaki , Sophia N. Lavrentiadou
Background
Brain organoids have emerged as powerful models for studying brain development and neurological disorders
Comparison with existing methods
Current models rely on stem cell isolation and differentiation using different growth factors. Thus, their composition varies according to the protocol followed.
New method
We developed a simple protocol to generate organoids from newborn rat whole brain. It is a one-step procedure that yields organoids of consistent composition. The whole brains from 3-day old pups were digested enzymatically. All isolated cells were seeded in culture plates using a basement membrane extract (BME) matrix as a scaffold and cultured in the presence of the appropriate medium.
Results
Hematoxylin-eosin staining of 28-day-old cultured domes revealed their structural integrity, while immunohistochemistry confirmed the presence of neurons, astrocytes, microglia, and progenitor stem cells in the structures. To assess whether these organoids can serve as a model to study brain physiopathology, and in particular neurodegenerative diseases such as Alzheimer’s disease (AD), we determined how these organoids respond upon their exposure to lipopolysaccharides (LPS), a potent neuroinflammatory factor. LPS-induced amyloid precursor protein (APP), tau protein and glial fibrillary acidic protein (GFAP) expression. Moreover, the intracellular levels of IL-1β and the extracellular levels of amyloid-beta (Aβ) were also elevated.
Conclusions
Therefore, this simple protocol results in the generation of functional brain organoids with a consistent structure, that requires no use of varying factors that may affect the structure and function of the produced organoids, thus providing a valuable tool for the study of the physiopathology of neurodegenerative disorders.
{"title":"A new protocol for the development of organoids based on molecular mechanisms in the developing newborn rat brain: Prospective applications in the study of Alzheimer’s disease","authors":"Eleni Tzekaki , Chryssa Bekiari , Anastasia Pantazaki , Maria Tsantarliotou , Magda Tsolaki , Sophia N. Lavrentiadou","doi":"10.1016/j.jneumeth.2025.110404","DOIUrl":"10.1016/j.jneumeth.2025.110404","url":null,"abstract":"<div><h3>Background</h3><div>Brain organoids have emerged as powerful models for studying brain development and neurological disorders</div></div><div><h3>Comparison with existing methods</h3><div>Current models rely on stem cell isolation and differentiation using different growth factors. Thus, their composition varies according to the protocol followed.</div></div><div><h3>New method</h3><div>We developed a simple protocol to generate organoids from newborn rat whole brain. It is a one-step procedure that yields organoids of consistent composition. The whole brains from 3-day old pups were digested enzymatically. All isolated cells were seeded in culture plates using a basement membrane extract (BME) matrix as a scaffold and cultured in the presence of the appropriate medium.</div></div><div><h3>Results</h3><div>Hematoxylin-eosin staining of 28-day-old cultured domes revealed their structural integrity, while immunohistochemistry confirmed the presence of neurons, astrocytes, microglia, and progenitor stem cells in the structures. To assess whether these organoids can serve as a model to study brain physiopathology, and in particular neurodegenerative diseases such as Alzheimer’s disease (AD), we determined how these organoids respond upon their exposure to lipopolysaccharides (LPS), a potent neuroinflammatory factor. LPS-induced amyloid precursor protein (APP), tau protein and glial fibrillary acidic protein (GFAP) expression. Moreover, the intracellular levels of IL-1β and the extracellular levels of amyloid-beta (Aβ) were also elevated.</div></div><div><h3>Conclusions</h3><div>Therefore, this simple protocol results in the generation of functional brain organoids with a consistent structure, that requires no use of varying factors that may affect the structure and function of the produced organoids, thus providing a valuable tool for the study of the physiopathology of neurodegenerative disorders.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"417 ","pages":"Article 110404"},"PeriodicalIF":2.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143468252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-18DOI: 10.1016/j.jneumeth.2025.110403
William D. Reeves , Ishfaque Ahmed , Brooke S. Jackson , Wenwu Sun , Celestine F. Williams , Catherine L. Davis , Jennifer E. McDowell , Nathan E. Yanasak , Shaoyong Su , Qun Zhao
Background
Studies using functional magnetic resonance imaging (fMRI) broadly require a method of parcellating the brain into regions of interest (ROIs). Parcellations can be based on standardized brain anatomy, such as the Montreal Neurological Institute’s (MNI) 152 atlas, or an individual’s functional activity patterns, such as the Personode software.
New method
This work outlines and tests the independent component analysis (ICA)-based parcellation algorithm (IPA) when applied to a hypertension study (n = 48) that uses the independent components (ICs) output from group ICA (gICA) to build ROIs which are ideally spatially consistent and functionally homogeneous. After regression of ICs to all subjects, the IPA builds individualized parcellations while simultaneously obtaining a gICA-derived parcellation.
Results
ROI spatial consistency quantified by dice similarity coefficients (DSCs) show individualized parcellations exhibit mean DSCs of 0.69 ± 0.14. Functional homogeneity, calculated as mean Pearson correlation value of all voxels comprising a ROI, shows individualized parcellations with a mean of 0.30 ± 0.14 and gICA-derived parcellations’ mean of 0.38 ± 0.15.
Comparison with existing method(s)
Individualized Personode parcellations show decreased mean DSCs (0.43 ± 0.11) with the individualized parcellations, gICA-derived parcellations, and the MNI atlas having decreased homogeneity values of 0.28 ± 0.14, 0.31 ± 0.15, and 0.20 ± 0.11 respectively.
Conclusions
Results show that the IPA can more reliably define a ROI and does so with higher functional homogeneity. Given these findings, the IPA shows promise as a novel parcellation technique that could aid the analysis of fMRI data.
{"title":"fMRI-based data-driven brain parcellation using independent component analysis","authors":"William D. Reeves , Ishfaque Ahmed , Brooke S. Jackson , Wenwu Sun , Celestine F. Williams , Catherine L. Davis , Jennifer E. McDowell , Nathan E. Yanasak , Shaoyong Su , Qun Zhao","doi":"10.1016/j.jneumeth.2025.110403","DOIUrl":"10.1016/j.jneumeth.2025.110403","url":null,"abstract":"<div><h3>Background</h3><div>Studies using functional magnetic resonance imaging (fMRI) broadly require a method of parcellating the brain into regions of interest (ROIs). Parcellations can be based on standardized brain anatomy, such as the Montreal Neurological Institute’s (MNI) 152 atlas, or an individual’s functional activity patterns, such as the Personode software.</div></div><div><h3>New method</h3><div>This work outlines and tests the independent component analysis (ICA)-based parcellation algorithm (IPA) when applied to a hypertension study (n = 48) that uses the independent components (ICs) output from group ICA (gICA) to build ROIs which are ideally spatially consistent and functionally homogeneous. After regression of ICs to all subjects, the IPA builds individualized parcellations while simultaneously obtaining a gICA-derived parcellation.</div></div><div><h3>Results</h3><div>ROI spatial consistency quantified by dice similarity coefficients (DSCs) show individualized parcellations exhibit mean DSCs of 0.69 ± 0.14. Functional homogeneity, calculated as mean Pearson correlation value of all voxels comprising a ROI, shows individualized parcellations with a mean of 0.30 ± 0.14 and gICA-derived parcellations’ mean of 0.38 ± 0.15.</div></div><div><h3>Comparison with existing method(s)</h3><div>Individualized Personode parcellations show decreased mean DSCs (0.43 ± 0.11) with the individualized parcellations, gICA-derived parcellations, and the MNI atlas having decreased homogeneity values of 0.28 ± 0.14, 0.31 ± 0.15, and 0.20 ± 0.11 respectively.</div></div><div><h3>Conclusions</h3><div>Results show that the IPA can more reliably define a ROI and does so with higher functional homogeneity. Given these findings, the IPA shows promise as a novel parcellation technique that could aid the analysis of fMRI data.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"417 ","pages":"Article 110403"},"PeriodicalIF":2.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-18DOI: 10.1016/j.jneumeth.2025.110399
Qing Ye , Xin Wang , Ting Li , Jing Xu , Xiangming Ye
Background
For stroke patients, a therapeutic approach named Non-invasive brain stimulation (NIBS) was applied and it has gained attention. This NIBS approach enhances the neuroplasticity and facilitates in functional Stroke Rehabilitation (SR) through regulating the brain activity. This NIBS has several advantages, but, the variability in patient responses, poor personalized treatment plans, and challenges in predicting rehabilitation stages may limit its clinical application. The generalized approaches are usually employed by the current SR methods. Here, the Patient-Centric (PC) factors that impacts neuroplasticity fails to be considered by the current SR methods. Thus, Real-Time mechanisms in monitoring and adapting to neural responses are lacking in the current SR methods.
Methods
A novel SR with Machine Learning (ML), (SR-ML) framework is suggested in this study. This suggested study integrates the patient-specific data, neuroimaging, and NIBS intervention models for the purpose of overcoming those issues. By optimising stimulation parameters based on patient profiles, the SR-ML framework uses ML algorithms. This integration will enhance the efficacy and facilitates the customized NIBS therapies. During NIBS sessions, the Time-Series (TS) neural data is analyzed and classified by the application of the Recurrent (NN) Neural Network (RNN). The temporal relationships and patterns indicating neuroplastic variations were effectively identified by this RNN.
Results
The stroke patients neuroplasticity signs was improved, and effective rehabilitation outcomes was attained by the suggested SR-ML model, and it was demonstrated by the outcomes of the simulation. The accuracy and adaptability of NIBS interventions were enhanced by the potential of ML, and it is highlighted by the outcomes.
Conclusion
A revolutionized development in the customized SR was facilitated by the suggested SR-ML framework, as it integrates ML with NIBS. More effective and PC neurotherapeutic approaches was attained by RT classification and optimization of simulation protocols. Thus, the limitations in the current SR methods was addressed by the effective method
{"title":"Clinical efficacy of NIBS in enhancing neuroplasticity for stroke recovery","authors":"Qing Ye , Xin Wang , Ting Li , Jing Xu , Xiangming Ye","doi":"10.1016/j.jneumeth.2025.110399","DOIUrl":"10.1016/j.jneumeth.2025.110399","url":null,"abstract":"<div><h3>Background</h3><div>For stroke patients, a therapeutic approach named Non-invasive brain stimulation (NIBS) was applied and it has gained attention. This NIBS approach enhances the neuroplasticity and facilitates in functional Stroke Rehabilitation (SR) through regulating the brain activity. This NIBS has several advantages, but, the variability in patient responses, poor personalized treatment plans, and challenges in predicting rehabilitation stages may limit its clinical application. The generalized approaches are usually employed by the current SR methods. Here, the Patient-Centric (PC) factors that impacts neuroplasticity fails to be considered by the current SR methods. Thus, Real-Time mechanisms in monitoring and adapting to neural responses are lacking in the current SR methods.</div></div><div><h3>Methods</h3><div>A novel SR with Machine Learning (ML), (SR-ML) framework is suggested in this study. This suggested study integrates the patient-specific data, neuroimaging, and NIBS intervention models for the purpose of overcoming those issues. By optimising stimulation parameters based on patient profiles, the SR-ML framework uses ML algorithms. This integration will enhance the efficacy and facilitates the customized NIBS therapies. During NIBS sessions, the Time-Series (TS) neural data is analyzed and classified by the application of the Recurrent (NN) Neural Network (RNN). The temporal relationships and patterns indicating neuroplastic variations were effectively identified by this RNN.</div></div><div><h3>Results</h3><div>The stroke patients neuroplasticity signs was improved, and effective rehabilitation outcomes was attained by the suggested SR-ML model, and it was demonstrated by the outcomes of the simulation. The accuracy and adaptability of NIBS interventions were enhanced by the potential of ML, and it is highlighted by the outcomes.</div></div><div><h3>Conclusion</h3><div>A revolutionized development in the customized SR was facilitated by the suggested SR-ML framework, as it integrates ML with NIBS. More effective and PC neurotherapeutic approaches was attained by RT classification and optimization of simulation protocols. Thus, the limitations in the current SR methods was addressed by the effective method</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"417 ","pages":"Article 110399"},"PeriodicalIF":2.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143468254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-18DOI: 10.1016/j.jneumeth.2025.110406
Peishan Dai , Zhuang He , Jialin Luo , Kaineng Huang , Ting Hu , Qiongpu Chen , Shenghui Liao , Xiaoping Yi , the REST-meta-MDD Consortium
Background
Major depressive disorder (MDD) is a severe mental illness, and the Hamilton Depression Rating Scale (HAMD) is commonly used to quantify its severity. Our aim is to develop a predictive model for MDD symptoms using machine learning techniques based on effective connectivity (EC) from resting-state functional magnetic resonance imaging (rs-fMRI).
New method
We obtained large-scale rs-fMRI data and HAMD scores from the multi-site REST-meta-MDD dataset. Average time series were extracted using different atlases. Brain EC features were computed using Granger causality analysis based on symbolic path coefficients, and a machine learning model based on EC was constructed to predict HAMD scores. Finally, the most predictive features were identified and visualized.
Results
Experimental results indicate that different brain atlases significantly impact predictive performance, with the Dosenbach atlas performing best. EC-based models outperformed functional connectivity, achieving the best predictive accuracy (r = 0.81, p < 0.001, Root Mean Squared Error=3.55). Among various machine learning methods, support vector regression demonstrated superior performance.
Comparison with existing methods
Current phenotype score prediction primarily relies on FC, which cannot indicate the direction of information flow within brain networks. Our method is based on EC, which contains more comprehensive brain network information and has been validated on large-scale multi-site data.
Conclusions
Brain network connectivity features effectively predict HAMD scores in MDD patients. The identified EC feature network may serve as a biomarker for predicting symptom severity. Our work may provide clinically significant insights for the early diagnosis of MDD, thereby facilitating the development of personalized diagnostic tools and therapeutic interventions.
{"title":"Using effective connectivity-based predictive modeling to predict MDD scale scores from multisite rs-fMRI data","authors":"Peishan Dai , Zhuang He , Jialin Luo , Kaineng Huang , Ting Hu , Qiongpu Chen , Shenghui Liao , Xiaoping Yi , the REST-meta-MDD Consortium","doi":"10.1016/j.jneumeth.2025.110406","DOIUrl":"10.1016/j.jneumeth.2025.110406","url":null,"abstract":"<div><h3>Background</h3><div>Major depressive disorder (MDD) is a severe mental illness, and the Hamilton Depression Rating Scale (HAMD) is commonly used to quantify its severity. Our aim is to develop a predictive model for MDD symptoms using machine learning techniques based on effective connectivity (EC) from resting-state functional magnetic resonance imaging (rs-fMRI).</div></div><div><h3>New method</h3><div>We obtained large-scale rs-fMRI data and HAMD scores from the multi-site REST-meta-MDD dataset. Average time series were extracted using different atlases. Brain EC features were computed using Granger causality analysis based on symbolic path coefficients, and a machine learning model based on EC was constructed to predict HAMD scores. Finally, the most predictive features were identified and visualized.</div></div><div><h3>Results</h3><div>Experimental results indicate that different brain atlases significantly impact predictive performance, with the Dosenbach atlas performing best. EC-based models outperformed functional connectivity, achieving the best predictive accuracy (r = 0.81, p < 0.001, Root Mean Squared Error=3.55). Among various machine learning methods, support vector regression demonstrated superior performance.</div></div><div><h3>Comparison with existing methods</h3><div>Current phenotype score prediction primarily relies on FC, which cannot indicate the direction of information flow within brain networks. Our method is based on EC, which contains more comprehensive brain network information and has been validated on large-scale multi-site data.</div></div><div><h3>Conclusions</h3><div>Brain network connectivity features effectively predict HAMD scores in MDD patients. The identified EC feature network may serve as a biomarker for predicting symptom severity. Our work may provide clinically significant insights for the early diagnosis of MDD, thereby facilitating the development of personalized diagnostic tools and therapeutic interventions.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"417 ","pages":"Article 110406"},"PeriodicalIF":2.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-18DOI: 10.1016/j.jneumeth.2025.110400
Miriam Paola Pili , Livio Provenzi , Lucia Billeci , Valentina Riva , Maddalena Cassa , Eleonora Siri , Giorgia Procissi , Elisa Roberti , Elena Capelli
Background
Electroencephalograph (EEG) hyperscanning allows studying Interpersonal Neural Synchrony (INS) between two or more individuals across different social conditions, including parent-infant interactions. Signal pre-processing is crucial to optimize computation of INS estimates; however, few attempts have been made at comparing the impact of different dyadic EEG data pre-processing methods on INS estimates.
New methods
EEG data collected on 31 mother-infant dyads (8–10 months) engaged in a Face-to-Face Still-Face Procedure were pre-processed with two versions of the same pipeline, the “automated” and the “manual”. Cross-frequency PLV in the theta (3–5 Hz, 4–7 Hz) and alpha (6–9 Hz, 8–12 Hz) frequency bands were computed after automated and manual pre-processing and compared through Pearson’s correlations and Repeated Measures ANOVAs.
Results
PLVs computed in the theta, but not alpha, frequency band were significantly higher after automated pre-processing than after manual pre-processing. Moreover, the automated pipeline rejected a significantly lower percentage of ICs and epochs compared to the manual pipeline.
Comparison with existing methods
While no direct comparison with existing dyadic EEG data pre-processing pipelines was made, this is the first study assessing the impact of different methodological decisions, particularly of the degree of pre-processing automatization, on cross-frequency PLV computed on a dataset of parent-infant dyads.
Conclusions
Non-directional phase-based INS indexes such as the PLV seem to be affected by the degree of automatization of the pre-processing pipeline. Future research should strive for standardization of dyadic EEG pre-processing methods.
{"title":"Exploring the impact of manual and automatic EEG pre-processing methods on interpersonal neural synchrony measures in parent-infant hyperscanning studies","authors":"Miriam Paola Pili , Livio Provenzi , Lucia Billeci , Valentina Riva , Maddalena Cassa , Eleonora Siri , Giorgia Procissi , Elisa Roberti , Elena Capelli","doi":"10.1016/j.jneumeth.2025.110400","DOIUrl":"10.1016/j.jneumeth.2025.110400","url":null,"abstract":"<div><h3>Background</h3><div>Electroencephalograph (EEG) hyperscanning allows studying Interpersonal Neural Synchrony (INS) between two or more individuals across different social conditions, including parent-infant interactions. Signal pre-processing is crucial to optimize computation of INS estimates; however, few attempts have been made at comparing the impact of different dyadic EEG data pre-processing methods on INS estimates.</div></div><div><h3>New methods</h3><div>EEG data collected on 31 mother-infant dyads (8–10 months) engaged in a Face-to-Face Still-Face Procedure were pre-processed with two versions of the same pipeline, the “automated” and the “manual”. Cross-frequency PLV in the theta (3–5 Hz, 4–7 Hz) and alpha (6–9 Hz, 8–12 Hz) frequency bands were computed after automated and manual pre-processing and compared through Pearson’s correlations and Repeated Measures ANOVAs.</div></div><div><h3>Results</h3><div>PLVs computed in the theta, but not alpha, frequency band were significantly higher after automated pre-processing than after manual pre-processing. Moreover, the automated pipeline rejected a significantly lower percentage of ICs and epochs compared to the manual pipeline.</div></div><div><h3>Comparison with existing methods</h3><div>While no direct comparison with existing dyadic EEG data pre-processing pipelines was made, this is the first study assessing the impact of different methodological decisions, particularly of the degree of pre-processing automatization, on cross-frequency PLV computed on a dataset of parent-infant dyads.</div></div><div><h3>Conclusions</h3><div>Non-directional phase-based INS indexes such as the PLV seem to be affected by the degree of automatization of the pre-processing pipeline. Future research should strive for standardization of dyadic EEG pre-processing methods.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"417 ","pages":"Article 110400"},"PeriodicalIF":2.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143468257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Although functional MRI (fMRI) in awake marmosets (Callithrix jacchus) is fascinating for functional brain mapping and evaluation of brain disease models, it is difficult to launch awake fMRI on scanners with bore sizes of less than 16 cm. A universal marmoset holder for the small-bore size MRI was designed, and it was evaluated whether this holder could conduct auditory stimulation fMRI in the awake state using 16 cm bore size MRI scanner.
New method
The marmoset holder was designed with an outer diameter of 71.9 mm. A holder was designed to allow adjustment according to the individual head shape, enabling the use of the holder universally. An awake fMRI study of auditory response was conducted to evaluate the practicality of the new holder. Whole-brain activation was investigated when marmosets heard the marmoset social communication “phee call” an artificial tone sound and reversed of those.
Results
The prefrontal cortex was significantly activated in response to phee calls, whereas only the auditory cortex was activated in response to pure tones. In contrast, the auditory response was decreased when marmosets heard phee call. Their stimulus-specific responses indicated they perceived and differentiated sound characteristics in the fMRI environment.
Comparison with existing methods
A holder does not require surgical intervention or a custom-made helmet to minimize head movement in a small space.
Conclusion
Our newly developed holder made it possible to perform longitudinal fMRI experiments on multiple marmosets in a less invasive manner.
{"title":"Development of a non-invasive novel individual marmoset holder for evaluation by awake functional magnetic resonance brain imaging","authors":"Fumiko Seki , Terumi Yurimoto , Michiko Kamioka , Takashi Inoue , Yuji Komaki , Atsushi Iriki , Erika Sasaki , Yumiko Yamazaki","doi":"10.1016/j.jneumeth.2025.110390","DOIUrl":"10.1016/j.jneumeth.2025.110390","url":null,"abstract":"<div><h3>Background</h3><div>Although functional MRI (fMRI) in awake marmosets (<em>Callithrix jacchus</em>) is fascinating for functional brain mapping and evaluation of brain disease models, it is difficult to launch awake fMRI on scanners with bore sizes of less than 16 cm. A universal marmoset holder for the small-bore size MRI was designed, and it was evaluated whether this holder could conduct auditory stimulation fMRI in the awake state using 16 cm bore size MRI scanner.</div></div><div><h3>New method</h3><div>The marmoset holder was designed with an outer diameter of 71.9 mm. A holder was designed to allow adjustment according to the individual head shape, enabling the use of the holder universally. An awake fMRI study of auditory response was conducted to evaluate the practicality of the new holder. Whole-brain activation was investigated when marmosets heard the marmoset social communication “phee call” an artificial tone sound and reversed of those.</div></div><div><h3>Results</h3><div>The prefrontal cortex was significantly activated in response to phee calls, whereas only the auditory cortex was activated in response to pure tones. In contrast, the auditory response was decreased when marmosets heard phee call. Their stimulus-specific responses indicated they perceived and differentiated sound characteristics in the fMRI environment.</div></div><div><h3>Comparison with existing methods</h3><div>A holder does not require surgical intervention or a custom-made helmet to minimize head movement in a small space.</div></div><div><h3>Conclusion</h3><div>Our newly developed holder made it possible to perform longitudinal fMRI experiments on multiple marmosets in a less invasive manner.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"417 ","pages":"Article 110390"},"PeriodicalIF":2.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143433025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-12DOI: 10.1016/j.jneumeth.2025.110389
Max A van den Boom, Nicholas M Gregg, Gabriela Ojeda Valencia, Brian N Lundstrom, Kai J Miller, Dorien van Blooijs, Geertjan J M Huiskamp, Frans S S Leijten, Gregory A Worrell, Dora Hermes
Background: Human brain connectivity can be measured in different ways. Intracranial EEG (iEEG) measurements during single pulse electrical stimulation provide a unique way to assess the spread of electrical information with millisecond precision. However, the methods used for the detection of responses in cortico-cortical evoked potential (CCEP) data vary across studies, from visual inspection with manual annotation to a variety of automated methods.
New method: To provide a robust workflow to process CCEP data and detect early evoked responses in a fully automated and reproducible fashion, we developed the Early Response (ER)-detect toolbox. ER-detect is an open-source Python package and Docker application to preprocess BIDS structured iEEG data and detect early evoked CCEP responses. ER-detect can use three early response detection methods, which were validated against 14 manually annotated CCEP datasets from two different clinical sites by four independent raters.
Results: and comparison with existing methods: ER-detect's automated detection performed on par with the inter-rater reliability (Cohen's Kappa of ~0.6). Moreover, ER-detect was optimized for processing large CCEP datasets, to be used in conjunction with other connectomic investigations.
Conclusion: ER-detect provides a highly efficient standardized workflow such that iEEG-BIDS data can be processed in a consistent manner and enhance the reproducibility of CCEP based connectivity results for both research and clinical purposes.
{"title":"ER-detect: a pipeline for robust detection of early evoked responses in BIDS-iEEG electrical stimulation data.","authors":"Max A van den Boom, Nicholas M Gregg, Gabriela Ojeda Valencia, Brian N Lundstrom, Kai J Miller, Dorien van Blooijs, Geertjan J M Huiskamp, Frans S S Leijten, Gregory A Worrell, Dora Hermes","doi":"10.1016/j.jneumeth.2025.110389","DOIUrl":"10.1016/j.jneumeth.2025.110389","url":null,"abstract":"<p><strong>Background: </strong>Human brain connectivity can be measured in different ways. Intracranial EEG (iEEG) measurements during single pulse electrical stimulation provide a unique way to assess the spread of electrical information with millisecond precision. However, the methods used for the detection of responses in cortico-cortical evoked potential (CCEP) data vary across studies, from visual inspection with manual annotation to a variety of automated methods.</p><p><strong>New method: </strong>To provide a robust workflow to process CCEP data and detect early evoked responses in a fully automated and reproducible fashion, we developed the Early Response (ER)-detect toolbox. ER-detect is an open-source Python package and Docker application to preprocess BIDS structured iEEG data and detect early evoked CCEP responses. ER-detect can use three early response detection methods, which were validated against 14 manually annotated CCEP datasets from two different clinical sites by four independent raters.</p><p><strong>Results: </strong>and comparison with existing methods: ER-detect's automated detection performed on par with the inter-rater reliability (Cohen's Kappa of ~0.6). Moreover, ER-detect was optimized for processing large CCEP datasets, to be used in conjunction with other connectomic investigations.</p><p><strong>Conclusion: </strong>ER-detect provides a highly efficient standardized workflow such that iEEG-BIDS data can be processed in a consistent manner and enhance the reproducibility of CCEP based connectivity results for both research and clinical purposes.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110389"},"PeriodicalIF":2.7,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143425460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-11DOI: 10.1016/j.jneumeth.2025.110392
B. Vijayalakshmi , S. Anand
Background
Brain tumor classification from magnetic resonance (MR) images is crucial for early diagnosis and effective treatment planning. However, the homogeneity of tumors across different categories poses a challenge. Although, attention-based convolutional neural networks (CNNs) approaches have shown promising results in brain tumor classification, simultaneous consideration of both spatial and channel-specific features remains limited.
Methods
This study proposes a novel model that integrates Bi-FocusNet with correlated learning and CB-Attention. Bi-FocusNet is designed to concentrate on both spatial and channel-wise tumor features by using two complementary learning methodologies: correlated spatial inception learning and correlated channel residual learning. These learnings extract richer and more diverse feature representations from tumor lesions of varied sizes, significantly enhancing the model’s learning capacity. The CB-Attention mechanism works as a cross-learning module, facilitating interaction between the two learning methods to capture the missing information across spatial and channel-wise features.
Results
Ablation studies and experiments were conducted using the BT-large-2c, Figshare, and Kaggle datasets. The proposed model outperformed existing classification methods in accuracy and other metrics, demonstrating enhanced performance on all three datasets with accuracies of 99.02 %, 97.06 %, and 96.44 %, respectively. Additionally, the BT-Merged-4c dataset was used to assess the ability to handle class variation, and 96.28 % accuracy was achieved.
Conclusion
The CB-CIRL Net improves the extraction of spatial and channel-wise features through the utilization of Bi-FocusNet with correlated learning and CB-Attention, resulting in enhanced classification accuracy across various datasets. The model's outstanding performance demonstrates its capacity to improve brain tumor diagnosis and clinical application.
{"title":"Cross prior Bayesian attention with correlated inception and residual learning for brain tumor classification using MR images (CB-CIRL Net)","authors":"B. Vijayalakshmi , S. Anand","doi":"10.1016/j.jneumeth.2025.110392","DOIUrl":"10.1016/j.jneumeth.2025.110392","url":null,"abstract":"<div><h3>Background</h3><div>Brain tumor classification from magnetic resonance (MR) images is crucial for early diagnosis and effective treatment planning. However, the homogeneity of tumors across different categories poses a challenge. Although, attention-based convolutional neural networks (CNNs) approaches have shown promising results in brain tumor classification, simultaneous consideration of both spatial and channel-specific features remains limited.</div></div><div><h3>Methods</h3><div>This study proposes a novel model that integrates Bi-FocusNet with correlated learning and CB-Attention. Bi-FocusNet is designed to concentrate on both spatial and channel-wise tumor features by using two complementary learning methodologies: correlated spatial inception learning and correlated channel residual learning. These learnings extract richer and more diverse feature representations from tumor lesions of varied sizes, significantly enhancing the model’s learning capacity. The CB-Attention mechanism works as a cross-learning module, facilitating interaction between the two learning methods to capture the missing information across spatial and channel-wise features.</div></div><div><h3>Results</h3><div>Ablation studies and experiments were conducted using the BT-large-2c, Figshare, and Kaggle datasets. The proposed model outperformed existing classification methods in accuracy and other metrics, demonstrating enhanced performance on all three datasets with accuracies of 99.02 %, 97.06 %, and 96.44 %, respectively. Additionally, the BT-Merged-4c dataset was used to assess the ability to handle class variation, and 96.28 % accuracy was achieved.</div></div><div><h3>Conclusion</h3><div>The CB-CIRL Net improves the extraction of spatial and channel-wise features through the utilization of Bi-FocusNet with correlated learning and CB-Attention, resulting in enhanced classification accuracy across various datasets. The model's outstanding performance demonstrates its capacity to improve brain tumor diagnosis and clinical application.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"417 ","pages":"Article 110392"},"PeriodicalIF":2.7,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143414309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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