Chuan Bi, Thomas E. Nichols, Hwiyoung Lee, Yifan Yang, Zhenyao Ye, Yezhi Pan, Elliot Hong, P. Kochunov, Shuo Chen
Abstract Network analysis of whole-brain connectome data is widely employed to examine systematic changes in connections among brain areas caused by clinical and experimental conditions. In these analyses, the connectome data, represented as a matrix, are treated as outcomes, while the subject conditions serve as predictors. The objective of network analysis is to identify connectome subnetworks whose edges are associated with the predictors. Data-driven network analysis is a powerful approach that automatically organizes individual predictor-related connections (edges) into subnetworks, rather than relying on pre-specified subnetworks, thereby enabling network-level inference. However, power calculation for data-driven network analysis presents a challenge due to the data-driven nature of subnetwork identification, where nodes, edges, and model parameters cannot be pre-specified before the analysis. Additionally, data-driven network analysis involves multivariate edge variables and may entail multiple subnetworks, necessitating the correction for multiple testing (e.g., family-wise error rate (FWER) control). To address this issue, we developed BNPower, a user-friendly power calculation tool for data-driven network analysis. BNPower utilizes simulation analysis, taking into account the complexity of the data-driven network analysis model. We have implemented efficient computational strategies to facilitate data-driven network analysis, including subnetwork extraction and permutation tests for controlling FWER, while maintaining low computational costs. The toolkit, which includes a graphical user interface and source codes, is publicly available at the following GitHub repository: https://github.com/bichuan0419/brain_connectome_power_tool
{"title":"BNPower: a power calculation tool for data-driven network analysis for whole-brain connectome data","authors":"Chuan Bi, Thomas E. Nichols, Hwiyoung Lee, Yifan Yang, Zhenyao Ye, Yezhi Pan, Elliot Hong, P. Kochunov, Shuo Chen","doi":"10.1162/imag_a_00099","DOIUrl":"https://doi.org/10.1162/imag_a_00099","url":null,"abstract":"Abstract Network analysis of whole-brain connectome data is widely employed to examine systematic changes in connections among brain areas caused by clinical and experimental conditions. In these analyses, the connectome data, represented as a matrix, are treated as outcomes, while the subject conditions serve as predictors. The objective of network analysis is to identify connectome subnetworks whose edges are associated with the predictors. Data-driven network analysis is a powerful approach that automatically organizes individual predictor-related connections (edges) into subnetworks, rather than relying on pre-specified subnetworks, thereby enabling network-level inference. However, power calculation for data-driven network analysis presents a challenge due to the data-driven nature of subnetwork identification, where nodes, edges, and model parameters cannot be pre-specified before the analysis. Additionally, data-driven network analysis involves multivariate edge variables and may entail multiple subnetworks, necessitating the correction for multiple testing (e.g., family-wise error rate (FWER) control). To address this issue, we developed BNPower, a user-friendly power calculation tool for data-driven network analysis. BNPower utilizes simulation analysis, taking into account the complexity of the data-driven network analysis model. We have implemented efficient computational strategies to facilitate data-driven network analysis, including subnetwork extraction and permutation tests for controlling FWER, while maintaining low computational costs. The toolkit, which includes a graphical user interface and source codes, is publicly available at the following GitHub repository: https://github.com/bichuan0419/brain_connectome_power_tool","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"29 3","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140464173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhengguo Tan, Patrick Alexander Liebig, R. Heidemann, F. B. Laun, Florian Knoll
Abstract The pursuit of high spatial-angular-temporal resolution for in vivo diffusion-weighted magnetic resonance imaging (DW-MRI) at ultra-high field strength (7 T and above) is important in understanding brain microstructure and function. Such pursuit, however, faces several technical challenges. First, increased off-resonance and shorter T2 relaxation require faster echo train readouts. Second, existing high-resolution DW-MRI techniques usually employ in-plane fully-sampled multi-shot EPI, which not only prolongs the scan time but also induces a high specific absorption rate (SAR) at 7 T. To address these challenges, we develop in this work navigator-based interleaved EPI (NAViEPI) which enforces the same effective echo spacing (ESP) between the imaging and the navigator echo. First, NAViEPI renders no distortion mismatch between the two echoes, and thus simplifies shot-to-shot phase variation correction. Second, NAViEPI allows for a large number of shots (e.g., >4) with undersampled iEPI acquisition, thereby rendering clinically-feasible high-resolution sub-milliemeter protocols. To retain signal-to-noise ratio (SNR) and to reduce undersampling artifacts, we developed a ky-shift encoding among diffusion encodings to explore complementary k- q-space sampling. Moreover, we developed a novel joint reconstruction with overlapping locally low-rank regularization generalized to the multi-band multi-shot acquisition at 7 T (dubbed JETS-NAViEPI). Our method was demonstrated, with experimental results covering 1 mm isotropic resolution multi b-value DWI and sub-millimeter in-plane resolution fast TRACE acquisition.
摘要 在超高场强（7 T 及以上）下进行活体弥散加权磁共振成像（DW-MRI），追求高空间-矩形-时间分辨率对于了解大脑微观结构和功能非常重要。然而，这种追求面临着几项技术挑战。首先，由于非共振的增加和 T2 松弛的缩短，需要更快的回波列读取速度。其次，现有的高分辨率 DW-MRI 技术通常采用平面内全采样多拍 EPI，这不仅会延长扫描时间，还会在 7 T 时产生较高的比吸收率（SAR）。为了应对这些挑战，我们在这项工作中开发了基于导航器的交错 EPI（NAViEPI），它能在成像和导航器回波之间实现相同的有效回波间隔（ESP）。首先，NAViEPI 使两个回波之间没有失真错配，从而简化了镜头到镜头的相位变化校正。其次，NAViEPI 允许使用欠采样 iEPI 采集大量镜头（例如大于 4 个），从而实现临床上可行的高分辨率亚毫米方案。为了保持信噪比（SNR）并减少欠采样伪影，我们在扩散编码中开发了一种 ky 移位编码，以探索互补的 k- q 空间采样。此外，我们还开发了一种新的联合重建方法，该方法采用重叠局部低秩正则化，适用于 7 T 的多波段多拍采集（命名为 JETS-NAViEPI）。我们的方法得到了验证，实验结果涵盖了 1 毫米各向同性分辨率的多 b 值 DWI 和亚毫米平面分辨率的快速 TRACE 采集。
{"title":"Accelerated diffusion-weighted magnetic resonance imaging at 7 T: Joint reconstruction for shift-encoded navigator-based interleaved echo planar imaging (JETS-NAViEPI)","authors":"Zhengguo Tan, Patrick Alexander Liebig, R. Heidemann, F. B. Laun, Florian Knoll","doi":"10.1162/imag_a_00085","DOIUrl":"https://doi.org/10.1162/imag_a_00085","url":null,"abstract":"Abstract The pursuit of high spatial-angular-temporal resolution for in vivo diffusion-weighted magnetic resonance imaging (DW-MRI) at ultra-high field strength (7 T and above) is important in understanding brain microstructure and function. Such pursuit, however, faces several technical challenges. First, increased off-resonance and shorter T2 relaxation require faster echo train readouts. Second, existing high-resolution DW-MRI techniques usually employ in-plane fully-sampled multi-shot EPI, which not only prolongs the scan time but also induces a high specific absorption rate (SAR) at 7 T. To address these challenges, we develop in this work navigator-based interleaved EPI (NAViEPI) which enforces the same effective echo spacing (ESP) between the imaging and the navigator echo. First, NAViEPI renders no distortion mismatch between the two echoes, and thus simplifies shot-to-shot phase variation correction. Second, NAViEPI allows for a large number of shots (e.g., >4) with undersampled iEPI acquisition, thereby rendering clinically-feasible high-resolution sub-milliemeter protocols. To retain signal-to-noise ratio (SNR) and to reduce undersampling artifacts, we developed a ky-shift encoding among diffusion encodings to explore complementary k- q-space sampling. Moreover, we developed a novel joint reconstruction with overlapping locally low-rank regularization generalized to the multi-band multi-shot acquisition at 7 T (dubbed JETS-NAViEPI). Our method was demonstrated, with experimental results covering 1 mm isotropic resolution multi b-value DWI and sub-millimeter in-plane resolution fast TRACE acquisition.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"125 ","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139825423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Skylar E. Stolte, A. Indahlastari, Jason Chen, Alejandro Albizu, Ayden L. Dunn, Samantha Pedersen, Kyle B. See, Adam J. Woods, Ruogu Fang
Abstract Whole-head segmentation from Magnetic Resonance Images (MRI) establishes the foundation for individualized computational models using finite element method (FEM). This foundation paves the path for computer-aided solutions in fields such as non-invasive brain stimulation. Most current automatic head segmentation tools are developed using healthy young adults. Thus, they may neglect the older population that is more prone to age-related structural decline such as brain atrophy. In this work, we present a new deep learning method called GRACE, which stands for General, Rapid, And Comprehensive whole-hEad tissue segmentation. GRACE is trained and validated on a novel dataset that consists of 177 manually corrected MR-derived reference segmentations that have undergone meticulous manual review. Each T1-weighted MRI volume is segmented into 11 tissue types, including white matter, grey matter, eyes, cerebrospinal fluid, air, blood vessel, cancellous bone, cortical bone, skin, fat, and muscle. To the best of our knowledge, this work contains the largest manually corrected dataset to date in terms of number of MRIs and segmented tissues. GRACE outperforms five freely available software tools and a traditional 3D U-Net on a five-tissue segmentation task. On this task, GRACE achieves an average Hausdorff Distance of 0.21, which exceeds the runner-up at an average Hausdorff Distance of 0.36. GRACE can segment a whole-head MRI in about 3 seconds, while the fastest software tool takes about 3 minutes. In summary, GRACE segments a spectrum of tissue types from older adults’ T1-MRI scans at favorable accuracy and speed. The trained GRACE model is optimized on older adult heads to enable high-precision modeling in age-related brain disorders. To support open science, the GRACE code and trained weights are made available online and open to the research community upon publication at https://github.com/lab-smile/GRACE.
{"title":"Precise and rapid whole-head segmentation from magnetic resonance images of older adults using deep learning","authors":"Skylar E. Stolte, A. Indahlastari, Jason Chen, Alejandro Albizu, Ayden L. Dunn, Samantha Pedersen, Kyle B. See, Adam J. Woods, Ruogu Fang","doi":"10.1162/imag_a_00090","DOIUrl":"https://doi.org/10.1162/imag_a_00090","url":null,"abstract":"Abstract Whole-head segmentation from Magnetic Resonance Images (MRI) establishes the foundation for individualized computational models using finite element method (FEM). This foundation paves the path for computer-aided solutions in fields such as non-invasive brain stimulation. Most current automatic head segmentation tools are developed using healthy young adults. Thus, they may neglect the older population that is more prone to age-related structural decline such as brain atrophy. In this work, we present a new deep learning method called GRACE, which stands for General, Rapid, And Comprehensive whole-hEad tissue segmentation. GRACE is trained and validated on a novel dataset that consists of 177 manually corrected MR-derived reference segmentations that have undergone meticulous manual review. Each T1-weighted MRI volume is segmented into 11 tissue types, including white matter, grey matter, eyes, cerebrospinal fluid, air, blood vessel, cancellous bone, cortical bone, skin, fat, and muscle. To the best of our knowledge, this work contains the largest manually corrected dataset to date in terms of number of MRIs and segmented tissues. GRACE outperforms five freely available software tools and a traditional 3D U-Net on a five-tissue segmentation task. On this task, GRACE achieves an average Hausdorff Distance of 0.21, which exceeds the runner-up at an average Hausdorff Distance of 0.36. GRACE can segment a whole-head MRI in about 3 seconds, while the fastest software tool takes about 3 minutes. In summary, GRACE segments a spectrum of tissue types from older adults’ T1-MRI scans at favorable accuracy and speed. The trained GRACE model is optimized on older adult heads to enable high-precision modeling in age-related brain disorders. To support open science, the GRACE code and trained weights are made available online and open to the research community upon publication at https://github.com/lab-smile/GRACE.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"116 1","pages":"1-21"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139884733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Valenza, Francesco Di Ciò, Nicola Toschi, Riccardo Barbieri
Abstract The central-autonomic network (CAN) comprises brain regions that are functionally linked to the activity of peripheral autonomic nerves. While parasympathetic CAN (i.e., the CAN projecting onto parasympathetic branches) has recently been investigated and is known to be involved in neurological and neuropsychiatric disorders, sympathetic CAN (i.e., the CAN projecting onto sympathetic nerves) has not been fully characterized. Using functional magnetic resonance imaging (fMRI) data from the Human Connectome Project in conjunction with heartbeat dynamics and its orthonormal autoregressive descriptors as a proxy for sympathetic activity estimation, namely, the sympathetic activity index (SAI), we uncover brain regions belonging to the sympathetic CAN at rest. We uncover a widespread CAN comprising both cortical (in all lobes) and subcortical areas, including the cerebellum and brainstem, which is functionally linked to sympathetic activity and overlaps with brain regions driving parasympathetic activity. These findings may constitute fundamental knowledge linking brain and bodily dynamics, including the link between neurological and psychiatric disorders and autonomic dysfunctions.
{"title":"Sympathetic and parasympathetic central autonomic networks","authors":"G. Valenza, Francesco Di Ciò, Nicola Toschi, Riccardo Barbieri","doi":"10.1162/imag_a_00094","DOIUrl":"https://doi.org/10.1162/imag_a_00094","url":null,"abstract":"Abstract The central-autonomic network (CAN) comprises brain regions that are functionally linked to the activity of peripheral autonomic nerves. While parasympathetic CAN (i.e., the CAN projecting onto parasympathetic branches) has recently been investigated and is known to be involved in neurological and neuropsychiatric disorders, sympathetic CAN (i.e., the CAN projecting onto sympathetic nerves) has not been fully characterized. Using functional magnetic resonance imaging (fMRI) data from the Human Connectome Project in conjunction with heartbeat dynamics and its orthonormal autoregressive descriptors as a proxy for sympathetic activity estimation, namely, the sympathetic activity index (SAI), we uncover brain regions belonging to the sympathetic CAN at rest. We uncover a widespread CAN comprising both cortical (in all lobes) and subcortical areas, including the cerebellum and brainstem, which is functionally linked to sympathetic activity and overlaps with brain regions driving parasympathetic activity. These findings may constitute fundamental knowledge linking brain and bodily dynamics, including the link between neurological and psychiatric disorders and autonomic dysfunctions.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"588 2","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140469687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elinor Thompson, A. Schroder, Tiantian He, Cameron Shand, Sonja Soskic, N. Oxtoby, F. Barkhof, Daniel C. Alexander
Abstract Cortical atrophy and aggregates of misfolded tau proteins are key hallmarks of Alzheimer’s disease. Computational models that simulate the propagation of pathogens between connected brain regions have been used to elucidate mechanistic information about the spread of these disease biomarkers, such as disease epicentres and spreading rates. However, the connectomes that are used as substrates for these models are known to contain modality-specific false positive and false negative connections, influenced by the biases inherent to the different methods for estimating connections in the brain. In this work, we compare five types of connectomes for modelling both tau and atrophy patterns with the network diffusion model, which are validated against tau PET and structural MRI data from individuals with either mild cognitive impairment or dementia. We then test the hypothesis that a joint connectome, with combined information from different modalities, provides an improved substrate for the model. We find that a combination of multimodal information helps the model to capture observed patterns of tau deposition and atrophy better than any single modality. This is validated with data from independent datasets. Overall, our findings suggest that combining connectivity measures into a single connectome can mitigate some of the biases inherent to each modality and facilitate more accurate models of pathology spread, thus aiding our ability to understand disease mechanisms, and providing insight into the complementary information contained in different measures of brain connectivity
摘要 皮层萎缩和折叠错误的 tau 蛋白聚集是阿尔茨海默病的主要特征。模拟病原体在相连脑区之间传播的计算模型已被用于阐明这些疾病生物标志物传播的机理信息，如疾病的中心和传播速度。然而，众所周知，作为这些模型基底的连接组包含特定模式的假阳性和假阴性连接，这是受不同大脑连接估计方法固有偏差的影响。在这项研究中，我们比较了五种类型的连接组，以网络扩散模型来模拟tau和萎缩模式，并通过轻度认知障碍或痴呆症患者的tau PET和结构性核磁共振成像数据进行了验证。然后，我们检验了一个假设，即结合了不同模式信息的联合连接组能为模型提供更好的基底。我们发现，与任何单一模式相比，多模式信息的组合有助于模型更好地捕捉观察到的 tau 沉积和萎缩模式。这一点通过独立数据集的数据得到了验证。总之，我们的研究结果表明，将连通性测量结合到一个单一的连通组中可以减轻每种模式固有的一些偏差，有助于建立更准确的病理扩散模型，从而帮助我们理解疾病机制，并深入了解不同大脑连通性测量所包含的互补信息。
{"title":"Combining multimodal connectivity information improves modelling of pathology spread in Alzheimer’s disease","authors":"Elinor Thompson, A. Schroder, Tiantian He, Cameron Shand, Sonja Soskic, N. Oxtoby, F. Barkhof, Daniel C. Alexander","doi":"10.1162/imag_a_00089","DOIUrl":"https://doi.org/10.1162/imag_a_00089","url":null,"abstract":"Abstract Cortical atrophy and aggregates of misfolded tau proteins are key hallmarks of Alzheimer’s disease. Computational models that simulate the propagation of pathogens between connected brain regions have been used to elucidate mechanistic information about the spread of these disease biomarkers, such as disease epicentres and spreading rates. However, the connectomes that are used as substrates for these models are known to contain modality-specific false positive and false negative connections, influenced by the biases inherent to the different methods for estimating connections in the brain. In this work, we compare five types of connectomes for modelling both tau and atrophy patterns with the network diffusion model, which are validated against tau PET and structural MRI data from individuals with either mild cognitive impairment or dementia. We then test the hypothesis that a joint connectome, with combined information from different modalities, provides an improved substrate for the model. We find that a combination of multimodal information helps the model to capture observed patterns of tau deposition and atrophy better than any single modality. This is validated with data from independent datasets. Overall, our findings suggest that combining connectivity measures into a single connectome can mitigate some of the biases inherent to each modality and facilitate more accurate models of pathology spread, thus aiding our ability to understand disease mechanisms, and providing insight into the complementary information contained in different measures of brain connectivity","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"15 3","pages":"1-19"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139883962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Adam Berlijn, Dana M. Huvermann, S. Groiss, Alfons Schnitzler, Manfred Mittelstaedt, Christian Bellebaum, Dagmar Timmann, Martina Minnerop, Jutta Peterburs
Abstract The present study investigated temporal aspects of cerebellar contributions to the processing of performance errors as indexed by the error-related negativity (ERN) in the response-locked event-related potential (ERP). We co-registered EEG and applied single-pulse transcranial magnetic stimulation (spTMS) to the left posterolateral cerebellum and an extra-cerebellar control region (vertex) while healthy adult volunteers performed a Go/Nogo Flanker Task. In Go trials, TMS pulses were applied at four different time points, with temporal shifts of -100 ms, -50 ms, 0 ms, or +50 ms relative to the individual error latency (IEL, i.e., individual ERN peak latency + median error response time). These stimulation timings were aggregated into early (-100 ms, -50 ms) and late (0 ms, +50 ms) stimulation for the analysis. In Nogo trials, TMS pulses occurred 0 ms, 100 ms, or 300 ms after stimulus onset. Mixed linear model analyses revealed that cerebellar stimulation did not affect error rates overall. No effects were found for response times. As hypothesized, ERN amplitudes were decreased for cerebellar stimulation. No significant differences were found for the error positivity (Pe). Similar to TMS application to probe cerebellar-brain inhibition in the motor domain, the inhibitory tone of the cerebellar cortex may have been disrupted by the pulses. Reduced inhibitory output of the cerebellar cortex may have facilitated the processing of error information for response selection, which is reflected in a decreased ERN.
{"title":"The effect of cerebellar TMS on error processing: A combined single-pulse TMS and ERP study","authors":"Adam Berlijn, Dana M. Huvermann, S. Groiss, Alfons Schnitzler, Manfred Mittelstaedt, Christian Bellebaum, Dagmar Timmann, Martina Minnerop, Jutta Peterburs","doi":"10.1162/imag_a_00080","DOIUrl":"https://doi.org/10.1162/imag_a_00080","url":null,"abstract":"Abstract The present study investigated temporal aspects of cerebellar contributions to the processing of performance errors as indexed by the error-related negativity (ERN) in the response-locked event-related potential (ERP). We co-registered EEG and applied single-pulse transcranial magnetic stimulation (spTMS) to the left posterolateral cerebellum and an extra-cerebellar control region (vertex) while healthy adult volunteers performed a Go/Nogo Flanker Task. In Go trials, TMS pulses were applied at four different time points, with temporal shifts of -100 ms, -50 ms, 0 ms, or +50 ms relative to the individual error latency (IEL, i.e., individual ERN peak latency + median error response time). These stimulation timings were aggregated into early (-100 ms, -50 ms) and late (0 ms, +50 ms) stimulation for the analysis. In Nogo trials, TMS pulses occurred 0 ms, 100 ms, or 300 ms after stimulus onset. Mixed linear model analyses revealed that cerebellar stimulation did not affect error rates overall. No effects were found for response times. As hypothesized, ERN amplitudes were decreased for cerebellar stimulation. No significant differences were found for the error positivity (Pe). Similar to TMS application to probe cerebellar-brain inhibition in the motor domain, the inhibitory tone of the cerebellar cortex may have been disrupted by the pulses. Reduced inhibitory output of the cerebellar cortex may have facilitated the processing of error information for response selection, which is reflected in a decreased ERN.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"1 2","pages":"1-19"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139685807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The subsequent memory paradigm is a fundamental tool in neuroimaging investigations of encoding processes. Although some studies have contrasted remembered trials with forgotten ones, others have focused on strongly remembered trials versus forgotten ones. This study employed a meta-analytic approach to juxtapose the effects observed in the two types of contrast. Three distinct perspectives on memory formation—semantic elaboration, attentional focus, and hippocampal processing—yield diverse hypotheses about the regions responsible for the formation of strong memories. The meta-analysis yielded evidence supporting the attentional and semantic hypotheses while failing to substantiate the hippocampal hypothesis. The discussion section integrates these varied perspectives into a coherent view, culminating in the proposal of a model called the Significance-driven and Attention-driven Memory (SAM). Several pivotal postulates underpin the SAM model. First, it establishes a link between fluctuations in the trial-to-trial encoding performance and continuous variations in sustained attention. Second, the model contends that attention exerts a potent influence on both perceptual and semantic processing, while its impact on hippocampal processing remains moderate. Lastly, the model accentuates the heightened role of the hippocampus in significance-driven encoding, as opposed to attention-driven encoding. From a specific perspective, the model’s value lies in promoting a holistic understanding of the current extensive meta-analytic results. In a more comprehensive context, the model introduces an integrated framework that synthesizes various encoding-related cognitive and neural processes into a cohesive and unified perspective.
{"title":"Neural and cognitive dynamics leading to the formation of strong memories: A meta-analysis and the SAM model","authors":"Hongkeun Kim","doi":"10.1162/imag_a_00098","DOIUrl":"https://doi.org/10.1162/imag_a_00098","url":null,"abstract":"Abstract The subsequent memory paradigm is a fundamental tool in neuroimaging investigations of encoding processes. Although some studies have contrasted remembered trials with forgotten ones, others have focused on strongly remembered trials versus forgotten ones. This study employed a meta-analytic approach to juxtapose the effects observed in the two types of contrast. Three distinct perspectives on memory formation—semantic elaboration, attentional focus, and hippocampal processing—yield diverse hypotheses about the regions responsible for the formation of strong memories. The meta-analysis yielded evidence supporting the attentional and semantic hypotheses while failing to substantiate the hippocampal hypothesis. The discussion section integrates these varied perspectives into a coherent view, culminating in the proposal of a model called the Significance-driven and Attention-driven Memory (SAM). Several pivotal postulates underpin the SAM model. First, it establishes a link between fluctuations in the trial-to-trial encoding performance and continuous variations in sustained attention. Second, the model contends that attention exerts a potent influence on both perceptual and semantic processing, while its impact on hippocampal processing remains moderate. Lastly, the model accentuates the heightened role of the hippocampus in significance-driven encoding, as opposed to attention-driven encoding. From a specific perspective, the model’s value lies in promoting a holistic understanding of the current extensive meta-analytic results. In a more comprehensive context, the model introduces an integrated framework that synthesizes various encoding-related cognitive and neural processes into a cohesive and unified perspective.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"56 11","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139966642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Michael Schaefer, Esther Kühn, Felix Schweitzer, Markus Muehlhan
Abstract Studies have consistently demonstrated that the mere observation of touch engages our own somatosensory cortices. However, a systematic evaluation of the involved networks is missing. Here, we present results of a meta-analytic connectivity modeling (MACM) approach based on clusters revealed by activation likelihood estimation (ALE) combined with resting-state analysis to detect networks subserving our ability to empathize with tactile experiences of other people. ALE analysis revealed 8 clusters in frontal, temporal, and parietal brain areas, which behavioral domain profiles predominantly refer to cognition and perception. The MACM analysis further identified distinct networks that are subserved by subcortical structures, revealed that all clusters involved in touch observation are connected to dorso-medial frontal and anterior cingulate cortex control regions, and showed that medial temporal lobe memory structures do not inform network activation during touch observation (confirmed by post hoc resting-state connectivity analyses). Our data highlight the importance of higher-level control areas and suggest only a minor role for past bodily experiences in the ad hoc perception of other people’s experiences.
{"title":"The neural networks of touch observation","authors":"Michael Schaefer, Esther Kühn, Felix Schweitzer, Markus Muehlhan","doi":"10.1162/imag_a_00065","DOIUrl":"https://doi.org/10.1162/imag_a_00065","url":null,"abstract":"Abstract Studies have consistently demonstrated that the mere observation of touch engages our own somatosensory cortices. However, a systematic evaluation of the involved networks is missing. Here, we present results of a meta-analytic connectivity modeling (MACM) approach based on clusters revealed by activation likelihood estimation (ALE) combined with resting-state analysis to detect networks subserving our ability to empathize with tactile experiences of other people. ALE analysis revealed 8 clusters in frontal, temporal, and parietal brain areas, which behavioral domain profiles predominantly refer to cognition and perception. The MACM analysis further identified distinct networks that are subserved by subcortical structures, revealed that all clusters involved in touch observation are connected to dorso-medial frontal and anterior cingulate cortex control regions, and showed that medial temporal lobe memory structures do not inform network activation during touch observation (confirmed by post hoc resting-state connectivity analyses). Our data highlight the importance of higher-level control areas and suggest only a minor role for past bodily experiences in the ad hoc perception of other people’s experiences.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"9 9","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139455498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nathan M. Petro, G. Picci, Lauren R. Ott, Maggie P. Rempe, C. Embury, Samantha H. Penhale, Yu-Ping Wang, Julia M. Stephen, V. Calhoun, Brittany K. Taylor, Tony W. Wilson
Abstract Psychiatric disorders frequently emerge during adolescence, with girls at nearly twice the risk compared to boys. These sex differences have been linked to structural brain differences in association regions, which undergo profound development during childhood and adolescence. However, the relationship between functional activity in these cortical regions and the emergence of psychiatric disorders more broadly remains poorly understood. Herein, we investigated whether differences in internalizing and externalizing symptoms among youth are related to multispectral spontaneous neural activity. Spontaneous cortical activity was recorded using magnetoencephalography (MEG) in 105 typically-developing youth (9-15 years-old; 54 female) during eyes-closed rest. The strength of spontaneous neural activity within canonical frequency bands was estimated at each cortical vertex. The resulting functional maps were submitted to vertex-wise regressions to identify spatially specific effects whereby sex moderated the relationship between externalizing and internalizing symptoms, age, and spontaneous neural activity. The interaction between sex, age, and internalizing symptoms was significant in the theta frequency band, wherein theta activity was weaker for older relative to younger girls (but not boys) with greater internalizing symptoms. This relationship was strongest in the temporoparietal junction, with areas of the cingulate cortex exhibiting a similar relationship. The moderating role of sex in the relationship between age, internalizing symptoms, and spontaneous theta activity predominantly implicated association cortices. The negative relationship between theta and internalizing symptoms may reflect negative rumination with anxiety and depression. The specificity of this effect to older girls may reflect the selective emergence of psychiatric symptoms during adolescence in this subgroup.
{"title":"Sexual dimorphism in cortical theta rhythms relates to elevated internalizing symptoms during adolescence","authors":"Nathan M. Petro, G. Picci, Lauren R. Ott, Maggie P. Rempe, C. Embury, Samantha H. Penhale, Yu-Ping Wang, Julia M. Stephen, V. Calhoun, Brittany K. Taylor, Tony W. Wilson","doi":"10.1162/imag_a_00062","DOIUrl":"https://doi.org/10.1162/imag_a_00062","url":null,"abstract":"Abstract Psychiatric disorders frequently emerge during adolescence, with girls at nearly twice the risk compared to boys. These sex differences have been linked to structural brain differences in association regions, which undergo profound development during childhood and adolescence. However, the relationship between functional activity in these cortical regions and the emergence of psychiatric disorders more broadly remains poorly understood. Herein, we investigated whether differences in internalizing and externalizing symptoms among youth are related to multispectral spontaneous neural activity. Spontaneous cortical activity was recorded using magnetoencephalography (MEG) in 105 typically-developing youth (9-15 years-old; 54 female) during eyes-closed rest. The strength of spontaneous neural activity within canonical frequency bands was estimated at each cortical vertex. The resulting functional maps were submitted to vertex-wise regressions to identify spatially specific effects whereby sex moderated the relationship between externalizing and internalizing symptoms, age, and spontaneous neural activity. The interaction between sex, age, and internalizing symptoms was significant in the theta frequency band, wherein theta activity was weaker for older relative to younger girls (but not boys) with greater internalizing symptoms. This relationship was strongest in the temporoparietal junction, with areas of the cingulate cortex exhibiting a similar relationship. The moderating role of sex in the relationship between age, internalizing symptoms, and spontaneous theta activity predominantly implicated association cortices. The negative relationship between theta and internalizing symptoms may reflect negative rumination with anxiety and depression. The specificity of this effect to older girls may reflect the selective emergence of psychiatric symptoms during adolescence in this subgroup.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"54 9","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139456291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nawal Kinany, Caroline Landelle, B. De Leener, O. Lungu, Julien Doyon, D. Van de Ville
Abstract The spinal cord is a critical component of the central nervous system, transmitting and integrating signals between the brain and the periphery via topographically organized functional levels. Despite its central role in sensorimotor processes and several neuromotor disorders, mapping the functional organization of the spinal cord in vivo in humans has been a long-standing challenge. Here, we test the efficacy of two data-driven connectivity approaches to produce a reliable and temporally stable functional parcellation of the cervical spinal cord through resting-state networks in two different functional magnetic resonance imaging (fMRI) datasets. Our results demonstrate robust and replicable patterns across methods and datasets, effectively capturing the spinal functional levels. Furthermore, we present the first evidence of spinal resting-state networks organized in functional levels in individual participants, unveiling personalized maps of the spinal functional organization. These findings underscore the potential of non-invasive, data-driven approaches to reliably outline the spinal cord’s functional architecture. The implications are far-reaching, from spinal cord fMRI processing to personalized investigations of healthy and impaired spinal cord function.
{"title":"In vivo parcellation of the human spinal cord functional architecture","authors":"Nawal Kinany, Caroline Landelle, B. De Leener, O. Lungu, Julien Doyon, D. Van de Ville","doi":"10.1162/imag_a_00059","DOIUrl":"https://doi.org/10.1162/imag_a_00059","url":null,"abstract":"Abstract The spinal cord is a critical component of the central nervous system, transmitting and integrating signals between the brain and the periphery via topographically organized functional levels. Despite its central role in sensorimotor processes and several neuromotor disorders, mapping the functional organization of the spinal cord in vivo in humans has been a long-standing challenge. Here, we test the efficacy of two data-driven connectivity approaches to produce a reliable and temporally stable functional parcellation of the cervical spinal cord through resting-state networks in two different functional magnetic resonance imaging (fMRI) datasets. Our results demonstrate robust and replicable patterns across methods and datasets, effectively capturing the spinal functional levels. Furthermore, we present the first evidence of spinal resting-state networks organized in functional levels in individual participants, unveiling personalized maps of the spinal functional organization. These findings underscore the potential of non-invasive, data-driven approaches to reliably outline the spinal cord’s functional architecture. The implications are far-reaching, from spinal cord fMRI processing to personalized investigations of healthy and impaired spinal cord function.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"15 7","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139456931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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