Computer methods and programs in biomedicine最新文献

{"title":"Lazy Resampling: Fast and information preserving preprocessing for deep learning","authors":"","doi":"10.1016/j.cmpb.2024.108422","DOIUrl":"10.1016/j.cmpb.2024.108422","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Preprocessing of data is a vital step for almost all deep learning workflows. In computer vision, manipulation of data intensity and spatial properties can improve network stability and can provide an important source of generalisation for deep neural networks. Models are frequently trained with preprocessing pipelines composed of many stages, but these pipelines come with a drawback; each stage that resamples the data costs time, degrades image quality, and adds bias to the output. Long pipelines can also be complex to design, especially in medical imaging, where cropping data early can cause significant artifacts.</div></div><div><h3>Methods:</h3><div>We present Lazy Resampling, a software that rephrases spatial preprocessing operations as a graphics pipeline. Rather than each transform individually modifying the data, the transforms generate transform descriptions that are composited together into a single resample operation wherever possible. This reduces pipeline execution time and, most importantly, limits signal degradation. It enables simpler pipeline design as crops and other operations become non-destructive. Lazy Resampling is designed in such a way that it provides the maximum benefit to users without requiring them to understand the underlying concepts or change the way that they build pipelines.</div></div><div><h3>Results:</h3><div>We evaluate Lazy Resampling by comparing traditional pipelines and the corresponding lazy resampling pipeline for the following tasks on Medical Segmentation Decathlon datasets. We demonstrate lower information loss in lazy pipelines vs. traditional pipelines. We demonstrate that Lazy Resampling can avoid catastrophic loss of semantic segmentation label accuracy occurring in traditional pipelines when passing labels through a pipeline and then back through the inverted pipeline. Finally, we demonstrate statistically significant improvements when training UNets for semantic segmentation.</div></div><div><h3>Conclusion:</h3><div>Lazy Resampling reduces the loss of information that occurs when running processing pipelines that traditionally have multiple resampling steps and enables researchers to build simpler pipelines by making operations such as rotation and cropping effectively non-destructive. It makes it possible to invert labels back through a pipeline without catastrophic loss of accuracy.</div><div>A reference implementation for Lazy Resampling can be found at <span><span>https://github.com/KCL-BMEIS/LazyResampling</span><svg><path></path></svg></span>. Lazy Resampling is being implemented as a core feature in MONAI, an open source python-based deep learning library for medical imaging, with a roadmap for a full integration.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A robust myoelectric pattern recognition framework based on individual motor unit activities against electrode array shifts","authors":"","doi":"10.1016/j.cmpb.2024.108434","DOIUrl":"10.1016/j.cmpb.2024.108434","url":null,"abstract":"<div><h3>Background and objective</h3><div>Electrode shift is always one of the critical factors to compromise the performance of myoelectric pattern recognition (MPR) based on surface electromyogram (SEMG). However, current studies focused on the global features of SEMG signals to mitigate this issue but it is just an oversimplified description of the human movements without incorporating microscopic neural drive information. The objective of this work is to develop a novel method for calibrating the electrode array shifts toward achieving robust MPR, leveraging individual motor unit (MU) activities obtained through advanced SEMG decomposition.</div></div><div><h3>Methods</h3><div>All of the MUs from decomposition of SEMG data recorded at the original electrode array position were first initialized to train a neural network for pattern recognition. A part of decomposed MUs could be tracked and paired with MUs obtained at the original position based on spatial distribution of their MUAP waveforms, so as to determine the shift vector (describing both the orientation and distance of the shift) implicated consistently by these multiple MU pairs. Given the known shift vector, the features of the after-shift decomposed MUs were corrected accordingly and then fed into the network to finalize the MPR task. The performance of the proposed method was evaluated with data recorded by a 16 × 8 electrode array placed over the finger extensor muscles of 8 subjects performing 10 finger movement patterns.</div></div><div><h3>Results</h3><div>The proposed method achieved a shift detection accuracy of 100 % and a pattern recognition accuracy approximating to 100 %, significantly outperforming the conventional methods with lower shift detection accuracies and lower pattern recognition accuracies (<em>p</em> &lt; 0.05).</div></div><div><h3>Conclusions</h3><div>Our method demonstrated the feasibility of using decomposed MUAP waveforms’ spatial distributions to calibrate electrode shift. This study provides a new tool to enhance the robustness of myoelectric control systems via microscopic neural drive information at an individual MU level.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142326264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding motor imagery loaded on steady-state somatosensory evoked potential based on complex task-related component analysis","authors":"","doi":"10.1016/j.cmpb.2024.108425","DOIUrl":"10.1016/j.cmpb.2024.108425","url":null,"abstract":"<div><h3>Background and objective</h3><div>Motor Imagery (MI) recognition is one of the most critical decoding problems in brain- computer interface field. Combined with the steady-state somatosensory evoked potential (MI-SSSEP), this new paradigm can achieve higher recognition accuracy than the traditional MI paradigm. Typical algorithms do not fully consider the characteristics of MI-SSSEP signals. Developing an algorithm that fully captures the paradigm's characteristics to reduce false triggering rate is the new step in improving performance.</div></div><div><h3>Methods</h3><div>The idea to use complex signal task-related component analysis (cTRCA) algorithm for spatial filtering processing has been proposed in this paper according to the features of SSSEP signal. In this research, it's proved from the analysis of simulation signals that task-related component analysis (TRCA) as typical method is affected when the response between stimuli has reduced correlation and the proposed algorithm can effectively overcome this problem. The experimental data under the MI-SSSEP paradigm have been used to identify right-handed target tasks and three unique interference tasks are used to test the false triggering rate. cTRCA demonstrates superior performance as confirmed by the Wilcoxon signed-rank test.</div></div><div><h3>Results</h3><div>The recognition algorithm of cTRCA combined with mutual information-based best individual feature (MIBIF) and minimum distance to mean (MDM) can obtain AUC value up to 0.89, which is much higher than traditional algorithm common spatial pattern (CSP) combined with support vector machine (SVM) (the average AUC value is 0.77, <em>p</em> &lt; 0.05). Compared to CSP+SVM, this algorithm model reduced the false triggering rate from 38.69 % to 20.74 % (<em>p</em> &lt; 0.001).</div></div><div><h3>Conclusions</h3><div>The research prove that TRCA is influenced by MI-SSSEP signals. The results further prove that the motor imagery task in the new paradigm MI-SSSEP causes the phase change in evoked potential. and the cTRCA algorithm based on such phase change is more suitable for this hybrid paradigm and more conducive to decoding the motor imagery task and reducing false triggering rate.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does GLP-1 cause post-bariatric hypoglycemia: ‘Computer says no’","authors":"","doi":"10.1016/j.cmpb.2024.108424","DOIUrl":"10.1016/j.cmpb.2024.108424","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Patients who underwent Roux-en-Y Gastric Bypass surgery for treatment of obesity or diabetes can suffer from post-bariatric hypoglycemia (PBH). It has been assumed that PBH is caused by increased levels of the hormone GLP-1. In this research, we elucidate the role of GLP-1 in PBH with a physiology-based mathematical model.</div></div><div><h3>Methods:</h3><div>The Eindhoven Diabetes Simulator (EDES) model, simulating postprandial glucose homeostasis, was adapted to include the effect of GLP-1 on insulin secretion. Parameter sensitivity analysis was used to identify parameters that could cause PBH. Virtual patient models were created by defining sets of models parameters based on 63 participants from the HypoBaria study cohort, before and one year after bariatric surgery.</div></div><div><h3>Results:</h3><div>Simulations with the virtual patient models showed that glycemic excursions can be correctly simulated for the study population, despite heterogeneity in the glucose, insulin and GLP-1 data. Sensitivity analysis showed that GLP-1 stimulated insulin secretion alone was not able to cause PBH. Instead, analyses showed the increased transit speed of the ingested food resulted in quick and increased glucose absorption in the gut after surgery, which in turn induced postprandial glycemic dips. Furthermore, according to the model post-bariatric increased rate of glucose absorption in combination with different levels of insulin sensitivity can result in PBH.</div></div><div><h3>Conclusions:</h3><div>Our model findings implicate that if initial rapid improvement in insulin sensitivity after gastric bypass surgery is followed by a more gradual decrease in insulin sensitivity, this may result in the emergence of PBH after prolonged time (months to years after surgery).</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NecroGlobalGCN: Integrating micronecrosis information in HCC prognosis prediction via graph convolutional neural networks","authors":"","doi":"10.1016/j.cmpb.2024.108435","DOIUrl":"10.1016/j.cmpb.2024.108435","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Hepatocellular carcinoma (HCC) ranks fourth in cancer mortality, underscoring the importance of accurate prognostic predictions to improve postoperative survival rates in patients. Although micronecrosis has been shown to have high prognostic value in HCC, its application in clinical prognosis prediction requires specialized knowledge and complex calculations, which poses challenges for clinicians. It would be of interest to develop a model to help clinicians make full use of micronecrosis to assess patient survival.</div></div><div><h3>Methods</h3><div>To address these challenges, we propose a HCC prognosis prediction model that integrates pathological micronecrosis information through Graph Convolutional Neural Networks (GCN). This approach enables GCN to utilize micronecrosis, which has been shown to be highly correlated with prognosis, thereby significantly enhancing prognostic stratification quality. We developed our model using 3622 slides from 752 patients with primary HCC from the FAH-ZJUMS dataset and conducted internal and external validations on the FAH-ZJUMS and TCGA-LIHC datasets, respectively.</div></div><div><h3>Results</h3><div>Our method outperformed the baseline by 8.18% in internal validation and 9.02% in external validations. Overall, this paper presents a deep learning research paradigm that integrates HCC micronecrosis, enhancing both the accuracy and interpretability of prognostic predictions, with potential applicability to other pathological prognostic markers.</div></div><div><h3>Conclusions</h3><div>This study proposes a composite GCN prognostic model that integrates information on HCC micronecrosis, collecting large dataset of HCC histopathological images. This approach could assist clinicians in analyzing HCC patient survival and precisely locating and visualizing necrotic tissues that affect prognosis. Following the research paradigm outlined in this paper, other prognostic biomarker integration models with GCN could be developed, significantly enhancing the predictive performance and interpretability of prognostic model.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142364650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combining clinical and molecular data for personalized treatment in acute myeloid leukemia: A machine learning approach","authors":"","doi":"10.1016/j.cmpb.2024.108432","DOIUrl":"10.1016/j.cmpb.2024.108432","url":null,"abstract":"<div><h3>Background and Objective</h3><div>The standard of care in <em>Acute Myeloid Leukemia</em> patients has remained essentially unchanged for nearly 40 years. Due to the complicated mutational patterns within and between individual patients and a lack of targeted agents for most mutational events, implementing individualized treatment for AML has proven difficult. We reanalysed the <em>BeatAML dataset</em> employing <em>Machine Learning algorithms</em>. The BeatAML project entails patients extensively characterized at the molecular and clinical levels and linked to drug sensitivity outputs. Our approach capitalizes on the molecular and clinical data provided by the <em>BeatAML dataset</em> to predict the <em>ex vivo</em> drug sensitivity for the 122 drugs evaluated by the project.</div></div><div><h3>Methods</h3><div>We utilized ElasticNet, which produces fully interpretable models, in combination with a two-step training protocol that allowed us to narrow down computations. We automated the genes’ filtering step by employing two metrics, and we evaluated all possible data combinations to identify the best training configuration settings per drug.</div></div><div><h3>Results</h3><div>We report a Pearson correlation across all drugs of 0.36 when clinical and RNA sequencing data were combined, with the best-performing models reaching a Pearson correlation of 0.67. When we trained using the datasets in isolation, we noted that RNA Sequencing data (Pearson: 0.36) attained three times the predictive power of whole exome sequencing data (Pearson: 0.11), with clinical data falling somewhere in between (Pearson 0.26). Lastly, we present a paradigm of clinical significance. We used our models’ prediction as a <em>drug sensitivity score</em> to rank an individual's expected response to treatment. We identified 78 patients out of 89 (88 %) that the proposed drug was more potent than the administered one based on their <em>ex vivo</em> drug sensitivity data.</div></div><div><h3>Conclusions</h3><div>In conclusion, our reanalysis of the BeatAML dataset using Machine Learning algorithms demonstrates the potential for individualized treatment prediction in Acute Myeloid Leukemia patients, addressing the longstanding challenge of treatment personalization in this disease. By leveraging molecular and clinical data, our approach yields promising correlations between predicted drug sensitivity and actual responses, highlighting a significant step forward in improving therapeutic outcomes for AML patients.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169260724004255/pdfft?md5=2452e4d62b109d4cc19e47265be2ee8e&pid=1-s2.0-S0169260724004255-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development, calibration and validation of impact-specific cervical spine models: A novel approach using hybrid multibody and finite-element methods","authors":"","doi":"10.1016/j.cmpb.2024.108430","DOIUrl":"10.1016/j.cmpb.2024.108430","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Spinal cord injuries can have a severe impact on athletes’ or patients’ lives. High axial impact scenarios like tackling and scrummaging can cause hyperflexion and buckling of the cervical spine, which is often connected with bilateral facet dislocation. Typically, finite-element (FE) or musculoskeletal models are applied to investigate these scenarios, however, they have the drawbacks of high computational cost and lack of soft tissue information, respectively. Moreover, material properties of the involved tissues are commonly tested in quasi-static conditions, which do not accurately capture the mechanical behavior during impact scenarios. Thus, the aim of this study was to develop, calibrate and validate an approach for the creation of impact-specific hybrid, rigid body - finite-element spine models for high-dynamic axial impact scenarios.</div></div><div><h3>Methods:</h3><div>Five porcine cervical spine models were used to replicate in-vitro experiments to calibrate stiffness and damping parameters of the intervertebral joints by matching the kinematics of the in-vitro with the in-silico experiments. Afterwards, a five-fold cross-validation was conducted. Additionally, the von Mises stress of the lumped FE-discs was investigated during impact.</div></div><div><h3>Results:</h3><div>The results of the calibration and validation of our hybrid approach agree well with the in-vitro experiments. The stress maps of the lumped FE-discs showed that the highest stress of the most superior lumped disc was located anterior while the remaining lumped discs had their maximum in the posterior portion.</div></div><div><h3>Conclusion:</h3><div>Our hybrid method demonstrated the importance of impact-specific modeling. Overall, our hybrid modeling approach enhances the possibilities of identifying spine injury mechanisms by facilitating dynamic, impact-specific computational models.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169260724004231/pdfft?md5=ce1e473dd9a8e164ca33393fec592b86&pid=1-s2.0-S0169260724004231-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient-specific pulmonary venous flow characterization and its impact on left atrial appendage thrombosis in atrial fibrillation patients","authors":"","doi":"10.1016/j.cmpb.2024.108428","DOIUrl":"10.1016/j.cmpb.2024.108428","url":null,"abstract":"<div><h3>Background</h3><div>Cardioembolic strokes are commonly occurred in non-valvular atrial fibrillation (AF) patients, with over 90% of cases originating from clot in left atrial appendage (LAA), which is believed to be greatly related with hemodynamic characters. Numerical simulation is widely accepted in the hemodynamic analysis, and patient-specific boundaries are required for realistic numerical simulations.</div></div><div><h3>Method</h3><div>This paper firstly proposed a method that maps personalized pulmonary venous flow (PVF) by utilizing the volume changes of the left atrium (LA) over the cardiac cycle. Then we used data from patients with AF to investigate the correlation between PVF patterns and hemodynamics within the LAA. Meanwhile, we conducted a fluid-structure interaction analysis to assess the impact of velocity- and time-related PVF parameters on LAA hemodynamic characters.</div></div><div><h3>Results</h3><div>The analysis reveal that the ratio of systolic to diastolic peak velocity (<em>V</em>_S/<em>V</em>_D), and systolic velocity-time integral (VTI) showed a significant influence on LAA velocity in patients with atrial fibrillation, and the increases of velocity- and time-related parameters were found to be positively correlated with the blood update in the LAA.</div></div><div><h3>Conclusions</h3><div>This study established a method for mapping patient-specific PVF based on LA volume change, and evaluated the relationship between PVF parameters and thrombosis risk. The present work provides an insight from PVF characters to evaluate the risk of thrombus formation within LAA in patients with AF.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel GAN-based three-axis mutually supervised super-resolution reconstruction method for rectal cancer MR image","authors":"","doi":"10.1016/j.cmpb.2024.108426","DOIUrl":"10.1016/j.cmpb.2024.108426","url":null,"abstract":"<div><h3>Background and objective</h3><div>This study aims to enhance the resolution in the axial direction of rectal cancer magnetic resonance (MR) imaging scans to improve the accuracy of visual interpretation and quantitative analysis. MR imaging is a critical technique for the diagnosis and treatment planning of rectal cancer. However, obtaining high-resolution MR images is both time-consuming and costly. As a result, many hospitals store only a limited number of slices, often leading to low-resolution MR images, particularly in the axial plane. Given the importance of image resolution in accurate assessment, these low-resolution images frequently lack the necessary detail, posing substantial challenges for both human experts and computer-aided diagnostic systems. Image super-resolution (SR), a technique developed to enhance image resolution, was originally applied to natural images. Its success has since led to its application in various other tasks, especially in the reconstruction of low-resolution MR images. However, most existing SR methods fail to account for all anatomical planes during reconstruction, leading to unsatisfactory results when applied to rectal cancer MR images.</div></div><div><h3>Methods</h3><div>In this paper, we propose a GAN-based three-axis mutually supervised super-resolution reconstruction method tailored for low-resolution rectal cancer MR images. Our approach involves performing one-dimensional (1D) intra-slice SR reconstruction along the axial direction for both the sagittal and coronal planes, coupled with inter-slice SR reconstruction based on slice synthesis in the axial direction. To further enhance the accuracy of super-resolution reconstruction, we introduce a consistency supervision mechanism across the reconstruction results of different axes, promoting mutual learning between each axis. A key innovation of our method is the introduction of Depth-GAN for synthesize intermediate slices in the axial plane, incorporating depth information and leveraging Generative Adversarial Networks (GANs) for this purpose. Additionally, we enhance the accuracy of intermediate slice synthesis by employing a combination of supervised and unsupervised interactive learning techniques throughout the process.</div></div><div><h3>Results</h3><div>We conducted extensive ablation studies and comparative analyses with existing methods to validate the effectiveness of our approach. On the test set from Shanxi Cancer Hospital, our method achieved a Peak Signal-to-Noise Ratio (PSNR) of 34.62 and a Structural Similarity Index (SSIM) of 96.34 %. These promising results demonstrate the superiority of our method.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142379216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the effect of brain atrophy on transcranial direct current stimulation: A computational study using ADNI dataset","authors":"","doi":"10.1016/j.cmpb.2024.108429","DOIUrl":"10.1016/j.cmpb.2024.108429","url":null,"abstract":"<div><h3>Background</h3><div>Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that uses weak electrical currents to modulate brain activity, thus potentially aiding the treatment of brain diseases. Although tDCS offers convenience, it yields inconsistent electric-field distributions among individuals. This inconsistency may be attributed to certain factors, such as brain atrophy. Brain atrophy is accompanied by increased cerebrospinal fluid (CSF) volume. Owing to the high electrical conductivity of CSF, its increased volume complicates current delivery to the brain, thus resulting in greater inter-subject variability.</div></div><div><h3>Objective</h3><div>We aim to investigate the differences in tDCS-induced electric fields between groups with different severities of brain atrophy.</div></div><div><h3>Methods</h3><div>We classified 180 magnetic resonance images into four groups based on the presence of Alzheimer's disease and sex. We used two montages, i.e., F-3 &amp; Fp-2 and TP-9 &amp; TP-10, to target the left rostral middle frontal gyrus and the hippocampus/amygdala complex, respectively. Differences between the groups in terms of regional volume variation, stimulation effect, and correlation were analyzed.</div></div><div><h3>Results</h3><div>Significant differences were observed in the geometrical variations of the CSF and two target regions. Electric fields induced by tDCS were similar in both sexes. Unique patterns were observed in each group in the correlation analysis.</div></div><div><h3>Conclusion</h3><div>Our findings show that factors such as brain atrophy affect the tDCS results and that the factors present complex relationships. Further studies are necessary to better understand the relationships between these factors and optimize tDCS as a therapeutic tool.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142307292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}