Pub Date : 2024-09-24DOI: 10.1016/j.neuroimage.2024.120864
Hao Cheng , Kaiyan He , Congcong Li , Xiao Ma , Fufu Zheng , Wei Xu , Pan Liao , Rui Yang , Dongxu Li , Lang Qin , Shuai Na , Bingjiang Lyu , Jia-Hong Gao
The current magnetoencephalography (MEG) systems, which rely on cables for control and signal transmission, do not fully realize the potential of wearable optically pumped magnetometers (OPM). This study presents a significant advancement in wireless OPM-MEG by reducing magnetization in the electronics and developing a tailored wireless communication protocol. Our protocol effectively eliminates electromagnetic interference, particularly in the critical frequency bands of MEG signals, and accurately synchronizes the acquisition and stimulation channels with the host computer's clock. We have successfully achieved single-channel wireless OPM-MEG measurement and demonstrated its reliability by replicating three well-established experiments: The alpha rhythm, auditory evoked field, and steady-state visual evoked field in the human brain. Our prototype wireless OPM-MEG system not only streamlines the measurement process but also represents a major step forward in the development of wearable OPM-MEG applications in both neuroscience and clinical research.
{"title":"Wireless optically pumped magnetometer MEG","authors":"Hao Cheng , Kaiyan He , Congcong Li , Xiao Ma , Fufu Zheng , Wei Xu , Pan Liao , Rui Yang , Dongxu Li , Lang Qin , Shuai Na , Bingjiang Lyu , Jia-Hong Gao","doi":"10.1016/j.neuroimage.2024.120864","DOIUrl":"10.1016/j.neuroimage.2024.120864","url":null,"abstract":"<div><div>The current magnetoencephalography (MEG) systems, which rely on cables for control and signal transmission, do not fully realize the potential of wearable optically pumped magnetometers (OPM). This study presents a significant advancement in wireless OPM-MEG by reducing magnetization in the electronics and developing a tailored wireless communication protocol. Our protocol effectively eliminates electromagnetic interference, particularly in the critical frequency bands of MEG signals, and accurately synchronizes the acquisition and stimulation channels with the host computer's clock. We have successfully achieved single-channel wireless OPM-MEG measurement and demonstrated its reliability by replicating three well-established experiments: The alpha rhythm, auditory evoked field, and steady-state visual evoked field in the human brain. Our prototype wireless OPM-MEG system not only streamlines the measurement process but also represents a major step forward in the development of wearable OPM-MEG applications in both neuroscience and clinical research.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"300 ","pages":"Article 120864"},"PeriodicalIF":4.7,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322095","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}
Pub Date : 2024-09-24DOI: 10.1016/j.neuroimage.2024.120861
Xiaotong Wu , Chenxin Xie , Fangxiao Cheng , Zhuoshuo Li , Ruizhuo Li , Duan Xu , Hosung Kim , Jianjia Zhang , Hongsheng Liu , Mengting Liu
Significant changes in brain morphology occur during the third trimester of gestation. The capability of deep learning in leveraging these morphological features has enhanced the accuracy of brain age predictions for this critical period. Yet, the opaque nature of deep learning techniques, often described as "black box" approaches, limits their interpretability, posing challenges in clinical applications. Traditional interpretable methods developed for computer vision and natural language processing may not directly translate to the distinct demands of neuroimaging. In response, our research evaluates the effectiveness and adaptability of two interpretative methods—regional age prediction and the perturbation-based saliency map approach—for predicting the brain age of neonates. Analyzing 664 T1 MRI scans with the NEOCIVET pipeline to extract brain surface and cortical features, we assess how these methods illuminate key brain regions for age prediction, focusing on technical analysis with clinical insight. Through a comparative analysis of the saliency index (SI) with relative brain age (RBA) and the examination of structural covariance networks, we uncover the saliency index's enhanced ability to pinpoint regions vital for accurate indication of clinical factors. Our results highlight the advantages of perturbation techniques in addressing the complexities of medical data, steering clinical interventions for premature neonates towards more personalized and interpretable approaches. This study not only reveals the promise of these methods in complex medical scenarios but also offers a blueprint for implementing more interpretable and clinically relevant deep learning models in healthcare settings.
{"title":"Comparative evaluation of interpretation methods in surface-based age prediction for neonates","authors":"Xiaotong Wu , Chenxin Xie , Fangxiao Cheng , Zhuoshuo Li , Ruizhuo Li , Duan Xu , Hosung Kim , Jianjia Zhang , Hongsheng Liu , Mengting Liu","doi":"10.1016/j.neuroimage.2024.120861","DOIUrl":"10.1016/j.neuroimage.2024.120861","url":null,"abstract":"<div><div>Significant changes in brain morphology occur during the third trimester of gestation. The capability of deep learning in leveraging these morphological features has enhanced the accuracy of brain age predictions for this critical period. Yet, the opaque nature of deep learning techniques, often described as \"black box\" approaches, limits their interpretability, posing challenges in clinical applications. Traditional interpretable methods developed for computer vision and natural language processing may not directly translate to the distinct demands of neuroimaging. In response, our research evaluates the effectiveness and adaptability of two interpretative methods—regional age prediction and the perturbation-based saliency map approach—for predicting the brain age of neonates. Analyzing 664 T1 MRI scans with the NEOCIVET pipeline to extract brain surface and cortical features, we assess how these methods illuminate key brain regions for age prediction, focusing on technical analysis with clinical insight. Through a comparative analysis of the saliency index (SI) with relative brain age (RBA) and the examination of structural covariance networks, we uncover the saliency index's enhanced ability to pinpoint regions vital for accurate indication of clinical factors. Our results highlight the advantages of perturbation techniques in addressing the complexities of medical data, steering clinical interventions for premature neonates towards more personalized and interpretable approaches. This study not only reveals the promise of these methods in complex medical scenarios but also offers a blueprint for implementing more interpretable and clinically relevant deep learning models in healthcare settings.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"300 ","pages":"Article 120861"},"PeriodicalIF":4.7,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142350916","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}
Pub Date : 2024-09-24DOI: 10.1016/j.neuroimage.2024.120866
Xiaolong Peng , Lucas R. Trambaiolli , Eun Young Choi , Julia F. Lehman , Gary Linn , Brian E. Russ , Charles E. Schroeder , Suzanne N. Haber , Hesheng Liu
Corticostriatal connections are essential for motivation, cognition, and behavioral flexibility. There is broad interest in using resting-state functional magnetic resonance imaging (rs-fMRI) to link circuit dysfunction in these connections with neuropsychiatric disorders. In this paper, we used tract-tracing data from non-human primates (NHPs) to assess the likelihood of monosynaptic connections being represented in rs-fMRI data of NHPs and humans. We also demonstrated that existing hub locations in the anatomical data can be identified in the rs-fMRI data from both species. To characterize this in detail, we mapped the complete striatal projection zones from 27 tract-tracer injections located in the orbitofrontal cortex (OFC), dorsal anterior cingulate cortex (dACC), ventromedial prefrontal cortex (vmPFC), ventrolateral PFC (vlPFC), and dorsal PFC (dPFC) of macaque monkeys. Rs-fMRI seeds at the same regions of NHP and homologous regions of human brains showed connectivity maps in the striatum mostly consistent with those observed in the tracer data. We then examined the location of overlap in striatal projection zones. The medial rostral dorsal caudate connected with all five frontocortical regions evaluated in this study in both modalities (tract-tracing and rs-fMRI) and species (NHP and human). Other locations in the caudate also presented an overlap of four frontocortical regions, suggesting the existence of different locations with lower levels of input diversity. Small retrograde tracer injections and rs-fMRI seeds in the striatum confirmed these cortical input patterns. This study sets the ground for future studies evaluating rs-fMRI in clinical samples to measure anatomical corticostriatal circuit dysfunction and identify connectional hubs to provide more specific treatment targets for neurological and psychiatric disorders.
{"title":"Cross-species striatal hubs: Linking anatomy to resting-state connectivity","authors":"Xiaolong Peng , Lucas R. Trambaiolli , Eun Young Choi , Julia F. Lehman , Gary Linn , Brian E. Russ , Charles E. Schroeder , Suzanne N. Haber , Hesheng Liu","doi":"10.1016/j.neuroimage.2024.120866","DOIUrl":"10.1016/j.neuroimage.2024.120866","url":null,"abstract":"<div><div>Corticostriatal connections are essential for motivation, cognition, and behavioral flexibility. There is broad interest in using resting-state functional magnetic resonance imaging (rs-fMRI) to link circuit dysfunction in these connections with neuropsychiatric disorders. In this paper, we used tract-tracing data from non-human primates (NHPs) to assess the likelihood of monosynaptic connections being represented in rs-fMRI data of NHPs and humans. We also demonstrated that existing hub locations in the anatomical data can be identified in the rs-fMRI data from both species. To characterize this in detail, we mapped the complete striatal projection zones from 27 tract-tracer injections located in the orbitofrontal cortex (OFC), dorsal anterior cingulate cortex (dACC), ventromedial prefrontal cortex (vmPFC), ventrolateral PFC (vlPFC), and dorsal PFC (dPFC) of macaque monkeys. Rs-fMRI seeds at the same regions of NHP and homologous regions of human brains showed connectivity maps in the striatum mostly consistent with those observed in the tracer data. We then examined the location of overlap in striatal projection zones. The medial rostral dorsal caudate connected with all five frontocortical regions evaluated in this study in both modalities (tract-tracing and rs-fMRI) and species (NHP and human). Other locations in the caudate also presented an overlap of four frontocortical regions, suggesting the existence of different locations with lower levels of input diversity. Small retrograde tracer injections and rs-fMRI seeds in the striatum confirmed these cortical input patterns. This study sets the ground for future studies evaluating rs-fMRI in clinical samples to measure anatomical corticostriatal circuit dysfunction and identify connectional hubs to provide more specific treatment targets for neurological and psychiatric disorders.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"301 ","pages":"Article 120866"},"PeriodicalIF":4.7,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142350973","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}
Pub Date : 2024-09-23DOI: 10.1016/j.neuroimage.2024.120863
Kazuki Sakakura , Matthew Brennan , Masaki Sonoda , Takumi Mitsuhashi , Aimee F Luat , Neena I Marupudi , Sandeep Sood , Eishi Asano
Cognitive control processes enable the suppression of automatic behaviors and the initiation of appropriate responses. The Stroop color naming task serves as a benchmark paradigm for understanding the neurobiological model of verbal cognitive control. Previous research indicates a predominant engagement of the prefrontal and premotor cortex during the Stroop task compared to reading. We aim to further this understanding by creating a dynamic atlas of task-preferential modulations of functional connectivity through white matter. Patients undertook word-reading and Stroop tasks during intracranial EEG recording. We quantified task-related high-gamma amplitude modulations at 547 nonepileptic electrode sites, and a mixed model analysis identified regions and timeframes where these amplitudes differed between tasks. We then visualized white matter pathways with task-preferential functional connectivity enhancements at given moments. Word reading, compared to the Stroop task, exhibited enhanced functional connectivity in inter- and intra-hemispheric white matter pathways from the left occipital-temporal region 350–600 ms before response, including the posterior callosal fibers as well as the left vertical occipital, inferior longitudinal, inferior fronto-occipital, and arcuate fasciculi. The Stroop task showed enhanced functional connectivity in the pathways from the left middle-frontal pre-central gyri, involving the left frontal u-fibers and anterior callosal fibers. Automatic word reading largely utilizes the left occipital-temporal cortices and associated white matter tracts. Verbal cognitive control predominantly involves the left middle frontal and precentral gyri and its connected pathways. Our dynamic tractography atlases may serve as a novel resource providing insights into the unique neural dynamics and pathways of automatic reading and verbal cognitive control.
{"title":"Dynamic functional connectivity in verbal cognitive control and word reading","authors":"Kazuki Sakakura , Matthew Brennan , Masaki Sonoda , Takumi Mitsuhashi , Aimee F Luat , Neena I Marupudi , Sandeep Sood , Eishi Asano","doi":"10.1016/j.neuroimage.2024.120863","DOIUrl":"10.1016/j.neuroimage.2024.120863","url":null,"abstract":"<div><div>Cognitive control processes enable the suppression of automatic behaviors and the initiation of appropriate responses. The Stroop color naming task serves as a benchmark paradigm for understanding the neurobiological model of verbal cognitive control. Previous research indicates a predominant engagement of the prefrontal and premotor cortex during the Stroop task compared to reading. We aim to further this understanding by creating a dynamic atlas of task-preferential modulations of functional connectivity through white matter. Patients undertook word-reading and Stroop tasks during intracranial EEG recording. We quantified task-related high-gamma amplitude modulations at 547 nonepileptic electrode sites, and a mixed model analysis identified regions and timeframes where these amplitudes differed between tasks. We then visualized white matter pathways with task-preferential functional connectivity enhancements at given moments. Word reading, compared to the Stroop task, exhibited enhanced functional connectivity in inter- and intra-hemispheric white matter pathways from the left occipital-temporal region 350–600 ms before response, including the posterior callosal fibers as well as the left vertical occipital, inferior longitudinal, inferior fronto-occipital, and arcuate fasciculi. The Stroop task showed enhanced functional connectivity in the pathways from the left middle-frontal pre-central gyri, involving the left frontal u-fibers and anterior callosal fibers. Automatic word reading largely utilizes the left occipital-temporal cortices and associated white matter tracts. Verbal cognitive control predominantly involves the left middle frontal and precentral gyri and its connected pathways. Our dynamic tractography atlases may serve as a novel resource providing insights into the unique neural dynamics and pathways of automatic reading and verbal cognitive control.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"300 ","pages":"Article 120863"},"PeriodicalIF":4.7,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319769","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}
Pub Date : 2024-09-22DOI: 10.1016/j.neuroimage.2024.120859
Breanna K. Nelson , Lea N. Farah , Ava Grier , Wayne Su , Johnson Chen , Vesna Sossi , Mypinder S. Sekhon , A. Jon Stoessl , Cheryl Wellington , William G. Honer , Donna Lang , Noah D. Silverberg , William J. Panenka
Background
The pathophysiology of protracted symptoms after COVID-19 is unclear. This study aimed to determine if long-COVID is associated with differences in baseline characteristics, markers of white matter diffusivity in the brain, and lower scores on objective cognitive testing.
Methods
Individuals who experienced COVID-19 symptoms for more than 60 days post-infection (long-COVID) (n = 56) were compared to individuals who recovered from COVID-19 within 60 days of infection (normal recovery) (n = 35). Information regarding physical and mental health, and COVID-19 illness was collected. The National Institute of Health Toolbox Cognition Battery was administered. Participants underwent magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI). Tract-based spatial statistics were used to perform a whole-brain voxel-wise analysis on standard DTI metrics (fractional anisotropy, axial diffusivity, mean diffusivity, radial diffusivity), controlling for age and sex. NIH Toolbox Age-Adjusted Fluid Cognition Scores were used to compare long-COVID and normal recovery groups, covarying for Age-Adjusted Crystallized Cognition Scores and years of education. False discovery rate correction was applied for multiple comparisons.
Results
There were no significant differences in age, sex, or history of neurovascular risk factors between the groups. The long-COVID group had significantly (p < 0.05) lower mean diffusivity than the normal recovery group across multiple white matter regions, including the internal capsule, anterior and superior corona radiata, corpus callosum, superior fronto-occiptal fasciculus, and posterior thalamic radiation. However, the effect sizes of these differences were small (all <|0.3|) and no significant differences were found for the other DTI metrics. Fluid cognition composite scores did not differ significantly between the long-COVID and normal recovery groups (p > 0.05).
Conclusions
Differences in diffusivity between long-COVID and normal recovery groups were found on only one DTI metric. This could represent subtle areas of pathology such as gliosis or edema, but the small effect sizes and non-specific nature of the diffusion indices make pathological inference difficult. Although long-COVID patients reported many neuropsychiatric symptoms, significant differences in objective cognitive performance were not found.
{"title":"Differences in brain structure and cognitive performance between patients with long-COVID and those with normal recovery","authors":"Breanna K. Nelson , Lea N. Farah , Ava Grier , Wayne Su , Johnson Chen , Vesna Sossi , Mypinder S. Sekhon , A. Jon Stoessl , Cheryl Wellington , William G. Honer , Donna Lang , Noah D. Silverberg , William J. Panenka","doi":"10.1016/j.neuroimage.2024.120859","DOIUrl":"10.1016/j.neuroimage.2024.120859","url":null,"abstract":"<div><h3>Background</h3><div>The pathophysiology of protracted symptoms after COVID-19 is unclear. This study aimed to determine if long-COVID is associated with differences in baseline characteristics, markers of white matter diffusivity in the brain, and lower scores on objective cognitive testing.</div></div><div><h3>Methods</h3><div>Individuals who experienced COVID-19 symptoms for more than 60 days post-infection (long-COVID) (<em>n</em> = 56) were compared to individuals who recovered from COVID-19 within 60 days of infection (normal recovery) (<em>n</em> = 35). Information regarding physical and mental health, and COVID-19 illness was collected. The National Institute of Health Toolbox Cognition Battery was administered. Participants underwent magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI). Tract-based spatial statistics were used to perform a whole-brain voxel-wise analysis on standard DTI metrics (fractional anisotropy, axial diffusivity, mean diffusivity, radial diffusivity), controlling for age and sex. NIH Toolbox Age-Adjusted Fluid Cognition Scores were used to compare long-COVID and normal recovery groups, covarying for Age-Adjusted Crystallized Cognition Scores and years of education. False discovery rate correction was applied for multiple comparisons.</div></div><div><h3>Results</h3><div>There were no significant differences in age, sex, or history of neurovascular risk factors between the groups. The long-COVID group had significantly (<em>p</em> < 0.05) lower mean diffusivity than the normal recovery group across multiple white matter regions, including the internal capsule, anterior and superior corona radiata, corpus callosum, superior fronto-occiptal fasciculus, and posterior thalamic radiation. However, the effect sizes of these differences were small (all <span><math><mi>β</mi></math></span><|0.3|) and no significant differences were found for the other DTI metrics. Fluid cognition composite scores did not differ significantly between the long-COVID and normal recovery groups (<em>p</em> > 0.05).</div></div><div><h3>Conclusions</h3><div>Differences in diffusivity between long-COVID and normal recovery groups were found on only one DTI metric. This could represent subtle areas of pathology such as gliosis or edema, but the small effect sizes and non-specific nature of the diffusion indices make pathological inference difficult. Although long-COVID patients reported many neuropsychiatric symptoms, significant differences in objective cognitive performance were not found.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"300 ","pages":"Article 120859"},"PeriodicalIF":4.7,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319767","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}
Pub Date : 2024-09-22DOI: 10.1016/j.neuroimage.2024.120858
Yuxing Li , Zhizheng Zhuo , Chenghao Liu , Yunyun Duan , Yulu Shi , Tingting Wang , Runzhi Li , Yanli Wang , Jiwei Jiang , Jun Xu , Decai Tian , Xinghu Zhang , Fudong Shi , Xiaofeng Zhang , Aaron Carass , Frederik Barkhof , Jerry L Prince , Chuyang Ye , Yaou Liu
Diffusion magnetic resonance imaging (dMRI) allows non-invasive assessment of brain tissue microstructure. Current model-based tissue microstructure reconstruction techniques require a large number of diffusion gradients, which is not clinically feasible due to imaging time constraints, and this has limited the use of tissue microstructure information in clinical settings. Recently, approaches based on deep learning (DL) have achieved promising tissue microstructure reconstruction results using clinically feasible dMRI. However, it remains unclear whether the subtle tissue changes associated with disease or age are properly preserved with DL approaches and whether DL reconstruction results can benefit clinical applications. Here, we provide the first evidence that DL approaches to tissue microstructure reconstruction yield reliable brain tissue microstructure analysis based on clinically feasible dMRI scans. Specifically, we reconstructed tissue microstructure from four different brain dMRI datasets with only 12 diffusion gradients, a clinically feasible protocol, and the neurite orientation dispersion and density imaging (NODDI) and spherical mean technique (SMT) models were considered. With these results we show that disease-related and age-dependent alterations of brain tissue were accurately identified. These findings demonstrate that DL tissue microstructure reconstruction can accurately quantify microstructural alterations in the brain based on clinically feasible dMRI.
{"title":"Deep learning enables accurate brain tissue microstructure analysis based on clinically feasible diffusion magnetic resonance imaging","authors":"Yuxing Li , Zhizheng Zhuo , Chenghao Liu , Yunyun Duan , Yulu Shi , Tingting Wang , Runzhi Li , Yanli Wang , Jiwei Jiang , Jun Xu , Decai Tian , Xinghu Zhang , Fudong Shi , Xiaofeng Zhang , Aaron Carass , Frederik Barkhof , Jerry L Prince , Chuyang Ye , Yaou Liu","doi":"10.1016/j.neuroimage.2024.120858","DOIUrl":"10.1016/j.neuroimage.2024.120858","url":null,"abstract":"<div><div><em>Diffusion magnetic resonance imaging</em> (dMRI) allows non-invasive assessment of brain tissue microstructure. Current model-based tissue microstructure reconstruction techniques require a large number of diffusion gradients, which is not clinically feasible due to imaging time constraints, and this has limited the use of tissue microstructure information in clinical settings. Recently, approaches based on <em>deep learning</em> (DL) have achieved promising tissue microstructure reconstruction results using clinically feasible dMRI. However, it remains unclear whether the subtle tissue changes associated with disease or age are properly preserved with DL approaches and whether DL reconstruction results can benefit clinical applications. Here, we provide the first evidence that DL approaches to tissue microstructure reconstruction yield reliable brain tissue microstructure analysis based on clinically feasible dMRI scans. Specifically, we reconstructed tissue microstructure from four different brain dMRI datasets with only 12 diffusion gradients, a clinically feasible protocol, and the <em>neurite orientation dispersion and density imaging</em> (NODDI) and <em>spherical mean technique</em> (SMT) models were considered. With these results we show that disease-related and age-dependent alterations of brain tissue were accurately identified. These findings demonstrate that DL tissue microstructure reconstruction can accurately quantify microstructural alterations in the brain based on clinically feasible dMRI.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"300 ","pages":"Article 120858"},"PeriodicalIF":4.7,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327987","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}
Pub Date : 2024-09-19DOI: 10.1016/j.neuroimage.2024.120862
Giovanni Pellegrino , Silvia L. Isabella , Giulio Ferrazzi , Laura Gschwandtner , Martin Tik , Giorgio Arcara , Daniele Marinazzo , Anna-Lisa Schuler
Objective
Auditory-driven gamma synchrony (GS) is linked to the function of a specific cortical circuit based on a parvalbumin+ and pyramidal neuron loop. This circuit is impaired in neuropsychiatric conditions (i.e. schizophrenia, Alzheimer's disease, stroke etc.) and its relevance in clinical practice is increasingly being recognized. Auditory stimulation at a typical gamma frequency of 40 Hz can be applied as a ‘stress test’ of excitation/inhibition (E/I) of the entire cerebral cortex, to drive GS and record it with magnetoencephalography (MEG) or high-density electroencephalography (EEG). However, these two techniques are costly and not widely available. Therefore, we assessed whether a single EEG electrode is sufficient to provide an accurate estimate of the auditory-driven GS level of the entire cortical surface while expecting the highest correspondence in the auditory and somatosensory cortices.
Methods
We measured simultaneous EEG-MEG in 29 healthy subjects, utilizing 3 EEG electrodes (C4, F4, O2) and a full MEG setup. Recordings were performed during binaural exposure to auditory gamma stimulation and during silence. We compared GS measurement of each of the three EEG electrodes separately against full MEG mapping. Time-resolved phase locking value (PLVt) was computed between EEG signals and cortex reconstructed MEG signals.
Results
During auditory stimulation, but not at rest, EEG captures a significant amount of GS, especially from both auditory cortices and motor-premotor regions. This was especially true for frontal (C4) and central electrodes (F4).
Discussion and Conclusions
While hd-EEG and MEG are necessary for accurate spatial mapping of GS at rest and during auditory stimulation, a single EEG channel is sufficient to detect the global level of GS. These results have great translational potential for mapping GS in standard clinical settings.
目的：听觉驱动的伽马同步（GS）与基于副白质+和锥体神经元环路的特定皮层回路的功能有关。在神经精神疾病（如精神分裂症、阿尔茨海默病、中风等）中，这一环路的功能受到损害，其与临床实践的相关性也日益得到认可。典型伽马频率为 40Hz 的听觉刺激可以作为一种 "压力测试"，对整个大脑皮层进行有效的兴奋/抑制（E/I），从而引发 GS，并通过脑磁图（MEG）或高密度脑电图（EEG）进行记录。然而，这两种技术成本高昂，且并不普及。因此，我们评估了单个脑电图电极是否足以准确估计整个皮层表面由听觉驱动的 GS 水平，同时期望听觉皮层和体感皮层具有最高的对应性：我们利用 3 个 EEG 电极（C4、F4、O2）和完整的 MEG 设置，对 29 名健康受试者进行了 EEG-MEG 同步测量。在听觉伽马刺激的双耳暴露和安静时进行记录。我们将三个脑电图电极分别进行的 GS 测量与完整的 MEG 图谱进行了比较。在脑电信号和皮层重建的 MEG 信号之间计算时间分辨锁相值（PLVt）：结果：在听觉刺激过程中，脑电图捕获了大量的 GS，尤其是来自听觉皮层和运动-运动区域的 GS。额叶（C4）和中央电极（F4）尤其如此：讨论和结论：虽然在静息状态和听觉刺激过程中，高级脑电图和脑电图是准确绘制GS空间图的必要条件，但单个脑电图通道足以检测GS的整体水平。这些结果对于在标准临床环境中绘制GS图具有巨大的转化潜力。
{"title":"Reliable measurement of auditory-driven gamma synchrony with a single EEG electrode: A simultaneous EEG-MEG study","authors":"Giovanni Pellegrino , Silvia L. Isabella , Giulio Ferrazzi , Laura Gschwandtner , Martin Tik , Giorgio Arcara , Daniele Marinazzo , Anna-Lisa Schuler","doi":"10.1016/j.neuroimage.2024.120862","DOIUrl":"10.1016/j.neuroimage.2024.120862","url":null,"abstract":"<div><h3>Objective</h3><div>Auditory-driven gamma synchrony (GS) is linked to the function of a specific cortical circuit based on a parvalbumin+ and pyramidal neuron loop. This circuit is impaired in neuropsychiatric conditions (i.e. schizophrenia, Alzheimer's disease, stroke etc.) and its relevance in clinical practice is increasingly being recognized. Auditory stimulation at a typical gamma frequency of 40 Hz can be applied as a ‘stress test’ of excitation/inhibition (E/I) of the entire cerebral cortex, to drive GS and record it with magnetoencephalography (MEG) or high-density electroencephalography (EEG). However, these two techniques are costly and not widely available. Therefore, we assessed whether a single EEG electrode is sufficient to provide an accurate estimate of the auditory-driven GS level of the entire cortical surface while expecting the highest correspondence in the auditory and somatosensory cortices.</div></div><div><h3>Methods</h3><div>We measured simultaneous EEG-MEG in 29 healthy subjects, utilizing 3 EEG electrodes (C4, F4, O2) and a full MEG setup. Recordings were performed during binaural exposure to auditory gamma stimulation and during silence. We compared GS measurement of each of the three EEG electrodes separately against full MEG mapping. Time-resolved phase locking value (PLVt) was computed between EEG signals and cortex reconstructed MEG signals.</div></div><div><h3>Results</h3><div>During auditory stimulation, but not at rest, EEG captures a significant amount of GS, especially from both auditory cortices and motor-premotor regions. This was especially true for frontal (C4) and central electrodes (F4).</div></div><div><h3>Discussion and Conclusions</h3><div>While hd-EEG and MEG are necessary for accurate spatial mapping of GS at rest and during auditory stimulation, a single EEG channel is sufficient to detect the global level of GS. These results have great translational potential for mapping GS in standard clinical settings.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"300 ","pages":"Article 120862"},"PeriodicalIF":4.7,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142292259","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}
Pub Date : 2024-09-19DOI: 10.1016/j.neuroimage.2024.120857
Qiuyun Xu , Yongsheng Chen , Stephan Miller , Kunal Bajaj , Jairo Santana , Mohamed Badawy , Haiying Lyu , Yu Liu , Naying He , Fuhua Yan , E. Mark Haacke
Background
White matter (WM) fiber tracts in the brainstem communicate with various regions in the cerebrum, cerebellum, and spinal cord. Clinically, small lesions, malformations, or histopathological changes in the brainstem can cause severe neurological disorders. A direct and non-invasive assessment approach could bring valuable information about the intricate anatomical variations of the white matter fiber tracts and nuclei. Although tractography from diffusion tensor imaging has been commonly used to map the WM fiber tracts connectivity, it is difficult to differentiate the complex WM tracts anatomically. Both high field MRI methods and ultrahigh-field MRI methods at 7T and 11.7 T have been used to enhance the contrast of WM fiber tracts. Despite their promising results, it is still challenging to achieve wide clinical adoption at 3T. In this study, we explored a clinically feasible method using a proton density weighted (PDW) 3D gradient echo (GRE) sequence to directly image the WM fiber tracts in the brainstem at 3T in vivo.
Methods
We optimized a 3D high resolution, double echo, short TR, PDW GRE sequence on 5 healthy volunteers using a clinical 3T scanner to visualize the complicated anatomy of WM fiber tracts in the brain stem. Tissue properties including T1, proton density and T2* from in vivo quantitative MRI data were used for simulations to determine the optimal flip angle for the sequence. The visualization of multiple WM fiber tracts in the brainstem was assessed qualitatively and quantitatively using relative contrast and contrast-to-noise ratio (CNR). To improve the CNR, the final images were created by averaging over all echoes from two consecutive scans at the optimal flip angle. The results were compared to anatomical atlases and histology sections to identify the major fiber tracts. All the identified major fiber tracts were labeled on axial, sagittal and coronal slices.
Results
The WM fiber tracts were found to have distinct hypointense signal throughout the brainstem and most of the major WM fiber tracts, such as the corticospinal tract, medial lemniscus, medial longitudinal fasciculus, and central tegmental tract, in the brainstem up to and including the thalamus were identified in all subjects. Both qualitative and quantitative evaluations showed that the 3° scan offered the best contrast for WM fiber tracts for a TR of 20 ms. The average over the first two echo times and two consecutive 3° scans gave a CNR of 47.8 ± 6.2 for the pyramidal tracts in particular and CNRs values greater than 6.5 ± 2.4 for the rest of the fiber tracts.
Conclusions
All the major fiber tracts in the brainstem could be visualized. Given the reasonably short scan time of 10 min at 3T, double echo PDW GRE sequence is a very practical approach for clinical adoption.
背景：脑干中的白质（WM）纤维束与大脑、小脑和脊髓的各个区域相通。临床上，脑干的微小病变、畸形或组织病理学改变都可能导致严重的神经系统疾病。直接和非侵入性的评估方法可以为白质纤维束和核团错综复杂的解剖学变化提供有价值的信息。虽然扩散张量成像的束成像技术已被普遍用于绘制白质纤维束连接图，但很难从解剖学角度区分复杂的白质纤维束。7 T 和 11.7 T 的高磁场 MRI 方法和超高磁场 MRI 方法已被用于增强 WM 纤维束的对比度。尽管这些方法取得了可喜的成果，但要在 3 T 下广泛应用于临床仍具有挑战性。在这项研究中，我们探索了一种临床上可行的方法，使用质子密度加权（PDW）三维梯度回波（GRE）序列在 3 T 下直接对脑干的 WM 纤维束进行活体成像：我们使用临床3T扫描仪在5名健康志愿者身上优化了三维高分辨率、双回波、短TR、质子密度加权GRE序列，以观察脑干中WM纤维束的复杂解剖结构。组织属性包括活体定量磁共振成像数据中的 T1、质子密度和 T2*，用于模拟确定序列的最佳翻转角。利用相对对比度和对比-噪声比（CNR）对脑干中多个 WM 纤维束的可视化进行定性和定量评估。为了提高 CNR，在最佳翻转角度下对两次连续扫描的所有回波进行平均，以创建最终图像。扫描结果与解剖图谱和组织学切片进行比较，以确定主要纤维束。所有确定的主要纤维束都在轴向、矢状和冠状切片上做了标记：结果：所有受试者的脑干中都发现了明显的低密度信号，脑干中的大多数主要脑干纤维束，如皮质脊髓束、内侧大脑半月板、内侧纵筋膜和中央被盖束，直至丘脑。定性和定量评估均显示，在20毫秒的TR时间内，3°扫描为WM纤维束提供了最佳对比度。前两次回波时间和连续两次3°扫描的平均值显示，锥体束的CNR值为（47.8±6.2），其余纤维束的CNR值大于（6.5±2.4）：结论：脑干的所有主要纤维束均可视化。双回波 PDW GRE 序列在 3T 下的扫描时间很短，仅需 10 分钟，是一种非常实用的临床应用方法。
{"title":"In Vivo visualization of white matter fiber tracts in the brainstem using low flip angle double echo 3D gradient echo imaging at 3T","authors":"Qiuyun Xu , Yongsheng Chen , Stephan Miller , Kunal Bajaj , Jairo Santana , Mohamed Badawy , Haiying Lyu , Yu Liu , Naying He , Fuhua Yan , E. Mark Haacke","doi":"10.1016/j.neuroimage.2024.120857","DOIUrl":"10.1016/j.neuroimage.2024.120857","url":null,"abstract":"<div><h3>Background</h3><div>White matter (WM) fiber tracts in the brainstem communicate with various regions in the cerebrum, cerebellum, and spinal cord. Clinically, small lesions, malformations, or histopathological changes in the brainstem can cause severe neurological disorders. A direct and non-invasive assessment approach could bring valuable information about the intricate anatomical variations of the white matter fiber tracts and nuclei. Although tractography from diffusion tensor imaging has been commonly used to map the WM fiber tracts connectivity, it is difficult to differentiate the complex WM tracts anatomically. Both high field MRI methods and ultrahigh-field MRI methods at 7T and 11.7 T have been used to enhance the contrast of WM fiber tracts. Despite their promising results, it is still challenging to achieve wide clinical adoption at 3T. In this study, we explored a clinically feasible method using a proton density weighted (PDW) 3D gradient echo (GRE) sequence to directly image the WM fiber tracts in the brainstem at 3T <em>in vivo</em>.</div></div><div><h3>Methods</h3><div>We optimized a 3D high resolution, double echo, short TR, PDW GRE sequence on 5 healthy volunteers using a clinical 3T scanner to visualize the complicated anatomy of WM fiber tracts in the brain stem. Tissue properties including T1, proton density and T2* from <em>in vivo</em> quantitative MRI data were used for simulations to determine the optimal flip angle for the sequence. The visualization of multiple WM fiber tracts in the brainstem was assessed qualitatively and quantitatively using relative contrast and contrast-to-noise ratio (CNR). To improve the CNR, the final images were created by averaging over all echoes from two consecutive scans at the optimal flip angle. The results were compared to anatomical atlases and histology sections to identify the major fiber tracts. All the identified major fiber tracts were labeled on axial, sagittal and coronal slices.</div></div><div><h3>Results</h3><div>The WM fiber tracts were found to have distinct hypointense signal throughout the brainstem and most of the major WM fiber tracts, such as the corticospinal tract, medial lemniscus, medial longitudinal fasciculus, and central tegmental tract, in the brainstem up to and including the thalamus were identified in all subjects. Both qualitative and quantitative evaluations showed that the 3° scan offered the best contrast for WM fiber tracts for a TR of 20 ms. The average over the first two echo times and two consecutive 3° scans gave a CNR of 47.8 ± 6.2 for the pyramidal tracts in particular and CNRs values greater than 6.5 ± 2.4 for the rest of the fiber tracts.</div></div><div><h3>Conclusions</h3><div>All the major fiber tracts in the brainstem could be visualized. Given the reasonably short scan time of 10 min at 3T, double echo PDW GRE sequence is a very practical approach for clinical adoption.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"300 ","pages":"Article 120857"},"PeriodicalIF":4.7,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1053811924003549/pdfft?md5=bdbb3734d5d26e1c2262279b85f57e03&pid=1-s2.0-S1053811924003549-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142292257","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}
Pub Date : 2024-09-18DOI: 10.1016/j.neuroimage.2024.120856
Keying Jiang , Jinlian Wang , Yuanyuan Gao , Xiang Li , Hohjin Im , Yingying Zhu , Hanxiao Du , Lei Feng , Wenwei Zhu , Guang Zhao , Ying Hu , Peng Zhu , Wenfeng Zhu , He Wang , Qiang Wang
The interplay between personality traits and impulsivity has long been a central theme in psychology and psychiatry. However, the potential association between Greed Personality Traits (GPT) and impulsivity, encompassing both trait and state impulsivity and future time perspective, remains largely unexplored. To address these issues, we employed questionnaires and an inter-temporal choice task to estimate corresponding trait/state impulsivity and collected multi-modal neuroimaging data (resting-state functional imaging: n = 430; diffusion-weighted imaging: n = 426; task-related functional imaging: n = 53) to investigate the underlying microstructural and functional substrates. Behavioral analyses revealed that GPT mediated the association between time perspective (e.g., present fatalism) and trait impulsivity (e.g., motor impulsivity). Functional imaging analyses further identified that brain activation strengths and patterns related to delay length, particularly in the dorsomedial prefrontal cortex, superior parietal lobule, and cerebellum, were associated with GPT. Moreover, individuals with similar levels of greed exhibited analogous spontaneous brain activity patterns, predominantly in the Default Mode Network (DMN), Fronto-Parietal Network (FPN), and Visual Network (VIS). Diffusion imaging analysis observed specific microstructural characteristics in the spinocerebellar/pontocerebellar fasciculus, internal/external capsule, and corona radiata that support the formation of GPT. Furthermore, the corresponding neural activation pattern, spontaneous neural activity pattern, and analogous functional couplings among the aforementioned brain regions mediated the relationships between time perspective and GPT and between GPT and motor impulsivity. These findings provide novel insights into the possible pathway such as time perspective → dispositional greed → impulsivity and uncover their underlying microstructural and functional substrates.
{"title":"Microstructural and functional substrates underlying dispositional greed and its link with trait but not state impulsivity","authors":"Keying Jiang , Jinlian Wang , Yuanyuan Gao , Xiang Li , Hohjin Im , Yingying Zhu , Hanxiao Du , Lei Feng , Wenwei Zhu , Guang Zhao , Ying Hu , Peng Zhu , Wenfeng Zhu , He Wang , Qiang Wang","doi":"10.1016/j.neuroimage.2024.120856","DOIUrl":"10.1016/j.neuroimage.2024.120856","url":null,"abstract":"<div><p>The interplay between personality traits and impulsivity has long been a central theme in psychology and psychiatry. However, the potential association between Greed Personality Traits (GPT) and impulsivity, encompassing both trait and state impulsivity and future time perspective, remains largely unexplored. To address these issues, we employed questionnaires and an inter-temporal choice task to estimate corresponding trait/state impulsivity and collected multi-modal neuroimaging data (resting-state functional imaging: <em>n</em> = 430; diffusion-weighted imaging: <em>n</em> = 426; task-related functional imaging: <em>n</em> = 53) to investigate the underlying microstructural and functional substrates. Behavioral analyses revealed that GPT mediated the association between time perspective (e.g., present fatalism) and trait impulsivity (e.g., motor impulsivity). Functional imaging analyses further identified that brain activation strengths and patterns related to delay length, particularly in the dorsomedial prefrontal cortex, superior parietal lobule, and cerebellum, were associated with GPT. Moreover, individuals with similar levels of greed exhibited analogous spontaneous brain activity patterns, predominantly in the Default Mode Network (DMN), Fronto-Parietal Network (FPN), and Visual Network (VIS). Diffusion imaging analysis observed specific microstructural characteristics in the spinocerebellar/pontocerebellar fasciculus, internal/external capsule, and corona radiata that support the formation of GPT. Furthermore, the corresponding neural activation pattern, spontaneous neural activity pattern, and analogous functional couplings among the aforementioned brain regions mediated the relationships between time perspective and GPT and between GPT and motor impulsivity. These findings provide novel insights into the possible pathway such as time perspective → dispositional greed → impulsivity and uncover their underlying microstructural and functional substrates.</p></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"300 ","pages":"Article 120856"},"PeriodicalIF":4.7,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1053811924003537/pdfft?md5=709db1bb77b0071c538b6a24ae915429&pid=1-s2.0-S1053811924003537-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142270682","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}
Pub Date : 2024-09-18DOI: 10.1016/j.neuroimage.2024.120842
Shuai Dou , Xikai Liu , Ya Deng , Yimin Chen , Pengfei Song , Tong Wen , Bangcheng Han
Magnetoencephalography based on optically pumped magnetometers can passively detect the ultra-weak brain magnetic field signals, which has significant clinical application prospects for the diagnosis and treatment of cerebral disorders. This paper proposes a brain magnetic signal measurement method on the basis of the active–passive coupling magnetic shielding strategy and helmet-mounted detection array, which has lower cost and comparable performance over the existing ones. We first utilized the spatially-grid constrained coils and biplanar coils with proportion–integration–differentiation controller with tracking differentiator to ensure a near-zero and stable magnetic field environment with large uniform region. Subsequently, we implemented the brain magnetic signal measurement with the subject randomly moving fingers through tapping a keyboard and with the condition of opening and closing the eyes. Effectively induced brain magnetic signals were detected at the motor functional area and occipital lobe area in the two experiments, respectively. The proposed method will contribute to the development of functional brain imaging.
{"title":"Lightweight and wearable magnetoencephalography system based on spatially-grid constrained coils and compact magnetically shielded room","authors":"Shuai Dou , Xikai Liu , Ya Deng , Yimin Chen , Pengfei Song , Tong Wen , Bangcheng Han","doi":"10.1016/j.neuroimage.2024.120842","DOIUrl":"10.1016/j.neuroimage.2024.120842","url":null,"abstract":"<div><div>Magnetoencephalography based on optically pumped magnetometers can passively detect the ultra-weak brain magnetic field signals, which has significant clinical application prospects for the diagnosis and treatment of cerebral disorders. This paper proposes a brain magnetic signal measurement method on the basis of the active–passive coupling magnetic shielding strategy and helmet-mounted detection array, which has lower cost and comparable performance over the existing ones. We first utilized the spatially-grid constrained coils and biplanar coils with proportion–integration–differentiation controller with tracking differentiator to ensure a near-zero and stable magnetic field environment with large uniform region. Subsequently, we implemented the brain magnetic signal measurement with the subject randomly moving fingers through tapping a keyboard and with the condition of opening and closing the eyes. Effectively induced brain magnetic signals were detected at the motor functional area and occipital lobe area in the two experiments, respectively. The proposed method will contribute to the development of functional brain imaging.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"300 ","pages":"Article 120842"},"PeriodicalIF":4.7,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1053811924003392/pdfft?md5=4208d7189b7a2abe691e02c6d4d27b6d&pid=1-s2.0-S1053811924003392-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142292258","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}
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