Sangcheon Choi, David Hike, R. Pohmann, Nikolai Avdievich, Lidia Gomez-Cid, Weitao Man, Klaus Scheffler, Xin Yu
Abstract Laminar-specific functional magnetic resonance imaging (fMRI) has been widely used to study circuit-specific neuronal activity by mapping spatiotemporal fMRI response patterns across cortical layers. Hemodynamic responses reflect indirect neuronal activity given the limitation of spatial and temporal resolution. Previously, a gradient-echo-based line-scanning fMRI (GELINE) method was proposed with high temporal (50 ms) and spatial (50 µm) resolution to better characterize the fMRI onset time across cortical layers by employing two saturation RF pulses. However, the imperfect RF saturation performance led to poor boundary definition of the reduced region of interest (ROI) and aliasing problems outside of the ROI. Here, we propose an α (alpha)-180 spin-echo-based line-scanning fMRI (SELINE) method in animals to resolve this issue by employing a refocusing 180˚ RF pulse perpendicular to the excitation slice (without any saturation RF pulse) and also achieve high spatiotemporal resolution. In contrast to GELINE signals which peaked at the superficial layer, we detected varied peaks of laminar-specific BOLD signals across deeper cortical layers using the SELINE method, indicating the well-defined exclusion of the large draining-vein effect using the spin-echo sequence. Furthermore, we applied the SELINE method with a 200 ms repetition time (TR) to sample the fast hemodynamic changes across cortical layers with a less draining vein effect. In summary, this SELINE method provides a novel acquisition scheme to identify microvascular-sensitive laminar-specific BOLD responses across cortical depth.
{"title":"Alpha-180 spin-echo-based line-scanning method for high-resolution laminar-specific fMRI in animals","authors":"Sangcheon Choi, David Hike, R. Pohmann, Nikolai Avdievich, Lidia Gomez-Cid, Weitao Man, Klaus Scheffler, Xin Yu","doi":"10.1162/imag_a_00120","DOIUrl":"https://doi.org/10.1162/imag_a_00120","url":null,"abstract":"Abstract Laminar-specific functional magnetic resonance imaging (fMRI) has been widely used to study circuit-specific neuronal activity by mapping spatiotemporal fMRI response patterns across cortical layers. Hemodynamic responses reflect indirect neuronal activity given the limitation of spatial and temporal resolution. Previously, a gradient-echo-based line-scanning fMRI (GELINE) method was proposed with high temporal (50 ms) and spatial (50 µm) resolution to better characterize the fMRI onset time across cortical layers by employing two saturation RF pulses. However, the imperfect RF saturation performance led to poor boundary definition of the reduced region of interest (ROI) and aliasing problems outside of the ROI. Here, we propose an α (alpha)-180 spin-echo-based line-scanning fMRI (SELINE) method in animals to resolve this issue by employing a refocusing 180˚ RF pulse perpendicular to the excitation slice (without any saturation RF pulse) and also achieve high spatiotemporal resolution. In contrast to GELINE signals which peaked at the superficial layer, we detected varied peaks of laminar-specific BOLD signals across deeper cortical layers using the SELINE method, indicating the well-defined exclusion of the large draining-vein effect using the spin-echo sequence. Furthermore, we applied the SELINE method with a 200 ms repetition time (TR) to sample the fast hemodynamic changes across cortical layers with a less draining vein effect. In summary, this SELINE method provides a novel acquisition scheme to identify microvascular-sensitive laminar-specific BOLD responses across cortical depth.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"31 4","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140398452","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}
Ryan M. Hill, Holly Schofield, E. Boto, Lukas Rier, James Osborne, Cody Doyle, Frank Worcester, Tyler Hayward, N. Holmes, Richard Bowtell, V. Shah, Matthew J. Brookes
Abstract The measurement of electrophysiology is of critical importance to our understanding of brain function. However, current non-invasive measurements—electroencephalography (EEG) and magnetoencephalography (MEG)—have limited sensitivity, particularly compared to invasive recordings. Optically-Pumped Magnetometers (OPMs) are a new type of magnetic field sensor which ostensibly promise MEG systems with higher sensitivity; however, the noise floor of current OPMs remains high compared to cryogenic instrumentation and this limits the achievable signal-to-noise ratio of OPM-MEG recordings. Here, we investigate how sensor array design affects sensitivity, and whether judicious sensor placement could compensate for the higher noise floor. Through theoretical analyses, simulations, and experiments, we use a beamformer framework to show that increasing the total signal measured by an OPM array—either by increasing the number of sensors and channels, or by optimising the placement of those sensors—affords a linearly proportional increase in signal-to-noise ratio (SNR) following beamformer reconstruction. Our experimental measurements confirm this finding, showing that by changing sensor locations in a 90-channel array, we could increase the SNR of visual gamma oscillations from 4.8 to 10.5. Using a 180-channel optimised OPM-array, we capture broadband gamma oscillations induced by a naturalistic visual paradigm, with an SNR of 3; a value that compares favourably to similar measures made using conventional MEG. Our findings show how an OPM-MEG array can be optimised to measure brain electrophysiology with the highest possible sensitivity. This is important for the design of future OPM-based instrumentation.
{"title":"Optimising the sensitivity of optically-pumped magnetometer magnetoencephalography to gamma band electrophysiological activity","authors":"Ryan M. Hill, Holly Schofield, E. Boto, Lukas Rier, James Osborne, Cody Doyle, Frank Worcester, Tyler Hayward, N. Holmes, Richard Bowtell, V. Shah, Matthew J. Brookes","doi":"10.1162/imag_a_00112","DOIUrl":"https://doi.org/10.1162/imag_a_00112","url":null,"abstract":"Abstract The measurement of electrophysiology is of critical importance to our understanding of brain function. However, current non-invasive measurements—electroencephalography (EEG) and magnetoencephalography (MEG)—have limited sensitivity, particularly compared to invasive recordings. Optically-Pumped Magnetometers (OPMs) are a new type of magnetic field sensor which ostensibly promise MEG systems with higher sensitivity; however, the noise floor of current OPMs remains high compared to cryogenic instrumentation and this limits the achievable signal-to-noise ratio of OPM-MEG recordings. Here, we investigate how sensor array design affects sensitivity, and whether judicious sensor placement could compensate for the higher noise floor. Through theoretical analyses, simulations, and experiments, we use a beamformer framework to show that increasing the total signal measured by an OPM array—either by increasing the number of sensors and channels, or by optimising the placement of those sensors—affords a linearly proportional increase in signal-to-noise ratio (SNR) following beamformer reconstruction. Our experimental measurements confirm this finding, showing that by changing sensor locations in a 90-channel array, we could increase the SNR of visual gamma oscillations from 4.8 to 10.5. Using a 180-channel optimised OPM-array, we capture broadband gamma oscillations induced by a naturalistic visual paradigm, with an SNR of 3; a value that compares favourably to similar measures made using conventional MEG. Our findings show how an OPM-MEG array can be optimised to measure brain electrophysiology with the highest possible sensitivity. This is important for the design of future OPM-based instrumentation.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"48 4","pages":"1-19"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140283090","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}
Sam Parsons, A. Brandmaier, U. Lindenberger, R. Kievit
Abstract Magnetic resonance imaging (MRI) is a vital tool for the study of brain structure and function. It is increasingly being used in individual differences research to examine brain-behaviour associations. Prior work has demonstrated low test-retest stability of functional MRI measures, highlighting the need to examine the longitudinal stability (test-retest reliability across long timespans) of MRI measures across brain regions and imaging metrics, particularly in adolescence. In this study, we examined the longitudinal stability of grey matter measures (cortical thickness, surface area, and volume) across brain regions, and testing sites in the Adolescent Brain Cognitive Development (ABCD) study release v4.0. Longitudinal stability ICC estimates ranged from 0 to .98, depending on the measure, parcellation, and brain region. We used Intra-Class Effect Decomposition (ICED) to estimate between-subjects variance and error variance, and assess the relative contribution of each across brain regions and testing sites on longitudinal stability. In further exploratory analyses, we examined the influence of parcellation used (Desikan-Killiany-Tourville and Destrieux) on longitudinal stability. Our results highlight meaningful heterogeneity in longitudinal stability across brain regions, structural measures (cortical thickness in particular), parcellations, and ABCD testing sites. Differences in longitudinal stability across brain regions were largely driven by between-subjects variance, whereas differences in longitudinal stability across testing sites were largely driven by differences in error variance. We argue that investigations such as this are essential to capture patterns of longitudinal stability heterogeneity that would otherwise go undiagnosed. Such improved understanding allows the field to more accurately interpret results, compare effect sizes, and plan more powerful studies.
{"title":"Longitudinal stability of cortical grey matter measures varies across brain regions, imaging metrics, and testing sites in the ABCD study","authors":"Sam Parsons, A. Brandmaier, U. Lindenberger, R. Kievit","doi":"10.1162/imag_a_00086","DOIUrl":"https://doi.org/10.1162/imag_a_00086","url":null,"abstract":"Abstract Magnetic resonance imaging (MRI) is a vital tool for the study of brain structure and function. It is increasingly being used in individual differences research to examine brain-behaviour associations. Prior work has demonstrated low test-retest stability of functional MRI measures, highlighting the need to examine the longitudinal stability (test-retest reliability across long timespans) of MRI measures across brain regions and imaging metrics, particularly in adolescence. In this study, we examined the longitudinal stability of grey matter measures (cortical thickness, surface area, and volume) across brain regions, and testing sites in the Adolescent Brain Cognitive Development (ABCD) study release v4.0. Longitudinal stability ICC estimates ranged from 0 to .98, depending on the measure, parcellation, and brain region. We used Intra-Class Effect Decomposition (ICED) to estimate between-subjects variance and error variance, and assess the relative contribution of each across brain regions and testing sites on longitudinal stability. In further exploratory analyses, we examined the influence of parcellation used (Desikan-Killiany-Tourville and Destrieux) on longitudinal stability. Our results highlight meaningful heterogeneity in longitudinal stability across brain regions, structural measures (cortical thickness in particular), parcellations, and ABCD testing sites. Differences in longitudinal stability across brain regions were largely driven by between-subjects variance, whereas differences in longitudinal stability across testing sites were largely driven by differences in error variance. We argue that investigations such as this are essential to capture patterns of longitudinal stability heterogeneity that would otherwise go undiagnosed. Such improved understanding allows the field to more accurately interpret results, compare effect sizes, and plan more powerful studies.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"186 ","pages":"1-22"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140275510","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}
Jason S. Nomi, Danilo Bzdok, Jingwei Li, Taylor Bolt, Catie Chang, S. Kornfeld, Z. Goodman, B.T. Thomas Yeo, R. N. Spreng, L. Uddin
Abstract The global signal (GS) in resting-state functional MRI (fMRI), known to contain artifacts and non-neuronal physiological signals, also contains important neural information related to individual state and trait characteristics. Here, we show distinct linear and curvilinear relationships between GS topography and age in a cross-sectional sample of individuals (6-85 years old) representing a significant portion of the lifespan. Subcortical brain regions such as the thalamus and putamen show linear associations with the GS across age. The thalamus has stronger contributions to the GS in older-age individuals compared with younger-aged individuals, while the putamen has stronger contributions in younger individuals compared with older individuals. The subcortical nucleus basalis of Meynert shows a u-shaped pattern similar to cortical regions within the lateral frontoparietal network and dorsal attention network, where contributions of the GS are stronger at early and old age, and weaker in middle age. This differentiation between subcortical and cortical brain activity across age supports a dual-layer model of GS composition, where subcortical aspects of the GS are differentiated from cortical aspects of the GS. We find that these subcortical-cortical contributions to the GS depend strongly on age across the lifespan of human development. Our findings demonstrate how neurobiological information within the GS differs across development and highlight the need to carefully consider whether or not to remove this signal when investigating age-related functional differences in the brain.
{"title":"Systematic cross-sectional age-associations in global fMRI signal topography","authors":"Jason S. Nomi, Danilo Bzdok, Jingwei Li, Taylor Bolt, Catie Chang, S. Kornfeld, Z. Goodman, B.T. Thomas Yeo, R. N. Spreng, L. Uddin","doi":"10.1162/imag_a_00101","DOIUrl":"https://doi.org/10.1162/imag_a_00101","url":null,"abstract":"Abstract The global signal (GS) in resting-state functional MRI (fMRI), known to contain artifacts and non-neuronal physiological signals, also contains important neural information related to individual state and trait characteristics. Here, we show distinct linear and curvilinear relationships between GS topography and age in a cross-sectional sample of individuals (6-85 years old) representing a significant portion of the lifespan. Subcortical brain regions such as the thalamus and putamen show linear associations with the GS across age. The thalamus has stronger contributions to the GS in older-age individuals compared with younger-aged individuals, while the putamen has stronger contributions in younger individuals compared with older individuals. The subcortical nucleus basalis of Meynert shows a u-shaped pattern similar to cortical regions within the lateral frontoparietal network and dorsal attention network, where contributions of the GS are stronger at early and old age, and weaker in middle age. This differentiation between subcortical and cortical brain activity across age supports a dual-layer model of GS composition, where subcortical aspects of the GS are differentiated from cortical aspects of the GS. We find that these subcortical-cortical contributions to the GS depend strongly on age across the lifespan of human development. Our findings demonstrate how neurobiological information within the GS differs across development and highlight the need to carefully consider whether or not to remove this signal when investigating age-related functional differences in the brain.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"109 27","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140089939","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 Sharing research data has been widely promoted in the field of neuroimaging and has enhanced the rigor and reproducibility of neuroimaging studies. Yet the emergence of novel software tools and algorithms, such as face recognition, has raised concerns due to their potential to reidentify defaced neuroimaging data that are thought to have been deidentified. Despite the surge of privacy concerns, however, the risk of reidentification via these tools and algorithms has not yet been examined outside the limited settings for demonstration purposes. There is also a pressing need to carefully analyze regulatory implications of this new reidentification attack because concerns about the anonymity of data are the main reason that researchers think they are legally constrained from sharing their data. This study aims to tackle these gaps through rigorous technical and regulatory analyses. Using a simulation analysis, we first tested the generalizability of the matching accuracies in defaced neuroimaging data reported in a recent face recognition study (Schwarz et al., 2021). The results showed that the real-world likelihood of reidentification in defaced neuroimaging data via face recognition would be substantially lower than that reported in the previous studies. Next, by taking a US jurisdiction as a case study, we analyzed whether the novel reidentification threat posed by face recognition would place defaced neuroimaging data out of compliance under the current regulatory regime. Our analysis suggests that defaced neuroimaging data using existing tools would still meet the regulatory requirements for data deidentification. A brief comparison with the EU’s General Data Protection Regulation (GDPR) was also provided. Then, we examined the implication of NIH’s new Data Management and Sharing Policy on the current practice of neuroimaging data sharing based on the results of our simulation and regulatory analyses. Finally, we discussed future directions of open data sharing in neuroimaging.
摘要 共享研究数据在神经成像领域得到了广泛推广，并提高了神经成像研究的严谨性和可重复性。然而，新型软件工具和算法（如人脸识别）的出现引起了人们的担忧，因为它们有可能重新识别被认为已经去标识化的污损神经成像数据。然而，尽管隐私问题备受关注，但在有限的演示环境之外，人们尚未对通过这些工具和算法重新识别身份的风险进行研究。由于对数据匿名性的担忧是研究人员认为他们在共享数据方面受到法律限制的主要原因，因此还迫切需要仔细分析这种新的再识别攻击对监管的影响。本研究旨在通过严格的技术和监管分析来弥补这些不足。通过模拟分析，我们首先测试了最近一项人脸识别研究（Schwarz et al.）结果表明，现实世界中通过人脸识别对污损神经影像数据进行重新识别的可能性大大低于之前的研究报告。接下来，我们以美国司法管辖区为例，分析了人脸识别带来的新的再识别威胁是否会使污损的神经影像数据不符合现行的监管制度。我们的分析表明，使用现有工具对神经成像数据进行篡改仍然符合数据去标识化的监管要求。我们还提供了与《欧盟通用数据保护条例》（GDPR）的简要比较。然后，我们根据模拟和法规分析的结果，研究了美国国立卫生研究院（NIH）新的数据管理和共享政策对当前神经影像数据共享实践的影响。最后，我们讨论了神经影像学开放数据共享的未来方向。
{"title":"Demystifying the likelihood of reidentification in neuroimaging data: A technical and regulatory analysis","authors":"A. S. Jwa, Oluwasanmi Koyejo, Russell A. Poldrack","doi":"10.1162/imag_a_00111","DOIUrl":"https://doi.org/10.1162/imag_a_00111","url":null,"abstract":"Abstract Sharing research data has been widely promoted in the field of neuroimaging and has enhanced the rigor and reproducibility of neuroimaging studies. Yet the emergence of novel software tools and algorithms, such as face recognition, has raised concerns due to their potential to reidentify defaced neuroimaging data that are thought to have been deidentified. Despite the surge of privacy concerns, however, the risk of reidentification via these tools and algorithms has not yet been examined outside the limited settings for demonstration purposes. There is also a pressing need to carefully analyze regulatory implications of this new reidentification attack because concerns about the anonymity of data are the main reason that researchers think they are legally constrained from sharing their data. This study aims to tackle these gaps through rigorous technical and regulatory analyses. Using a simulation analysis, we first tested the generalizability of the matching accuracies in defaced neuroimaging data reported in a recent face recognition study (Schwarz et al., 2021). The results showed that the real-world likelihood of reidentification in defaced neuroimaging data via face recognition would be substantially lower than that reported in the previous studies. Next, by taking a US jurisdiction as a case study, we analyzed whether the novel reidentification threat posed by face recognition would place defaced neuroimaging data out of compliance under the current regulatory regime. Our analysis suggests that defaced neuroimaging data using existing tools would still meet the regulatory requirements for data deidentification. A brief comparison with the EU’s General Data Protection Regulation (GDPR) was also provided. Then, we examined the implication of NIH’s new Data Management and Sharing Policy on the current practice of neuroimaging data sharing based on the results of our simulation and regulatory analyses. Finally, we discussed future directions of open data sharing in neuroimaging.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"248 ","pages":"1-18"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140274272","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}
Quentin Uhl, Tommaso Pavan, Malwina Molendowska, Derek K. Jones, Marco Palombo, Ileana Jelescu
Abstract Biophysical models of diffusion tailored to quantify gray matter microstructure are gathering increasing interest. The two-compartment Neurite EXchange Imaging (NEXI) model has been proposed recently to account for neurites, extra-cellular space, and exchange across the cell membrane. NEXI parameter estimation requires multi-shell multi-diffusion time data and has so far only been implemented experimentally on animal data collected on a preclinical magnetic resonance imaging (MRI) set-up. In this work, the translation of NEXI to the human cortex in vivo was achieved using a 3 T Connectom MRI system with 300 mT/m gradients, that enables the acquisition of a broad range of b-values (0 – 7.5 ms/µm²) with a window covering short to intermediate diffusion times (20 – 49 ms) suitable for the characteristic exchange times (10 – 50 ms). Microstructure estimates of four model variants: NEXI, NEXIdot (its extension with the addition of a dot compartment), and their respective versions that correct for the Rician noise floor (NEXIRM and NEXIdot,RM) that particularly impacts high b-value signal, were compared. The reliability of estimates in each model variant was evaluated in synthetic and human in vivo data. In the latter, the intra-subject (scan-rescan) versus between-subjects variability of microstructure estimates was compared in the cortex. The better performance of NEXIRM highlights the importance of correcting for Rician bias in the NEXI model to obtain accurate estimates of microstructure parameters in the human cortex, and the sensitivity of the NEXI framework to individual differences in cortical microstructure. This application of NEXI in humans represents a significant step, unlocking new avenues for studying neurodevelopment, aging, and various neurodegenerative disorders.
{"title":"Quantifying human gray matter microstructure using neurite exchange imaging (NEXI) and 300 mT/m gradients","authors":"Quentin Uhl, Tommaso Pavan, Malwina Molendowska, Derek K. Jones, Marco Palombo, Ileana Jelescu","doi":"10.1162/imag_a_00104","DOIUrl":"https://doi.org/10.1162/imag_a_00104","url":null,"abstract":"Abstract Biophysical models of diffusion tailored to quantify gray matter microstructure are gathering increasing interest. The two-compartment Neurite EXchange Imaging (NEXI) model has been proposed recently to account for neurites, extra-cellular space, and exchange across the cell membrane. NEXI parameter estimation requires multi-shell multi-diffusion time data and has so far only been implemented experimentally on animal data collected on a preclinical magnetic resonance imaging (MRI) set-up. In this work, the translation of NEXI to the human cortex in vivo was achieved using a 3 T Connectom MRI system with 300 mT/m gradients, that enables the acquisition of a broad range of b-values (0 – 7.5 ms/µm²) with a window covering short to intermediate diffusion times (20 – 49 ms) suitable for the characteristic exchange times (10 – 50 ms). Microstructure estimates of four model variants: NEXI, NEXIdot (its extension with the addition of a dot compartment), and their respective versions that correct for the Rician noise floor (NEXIRM and NEXIdot,RM) that particularly impacts high b-value signal, were compared. The reliability of estimates in each model variant was evaluated in synthetic and human in vivo data. In the latter, the intra-subject (scan-rescan) versus between-subjects variability of microstructure estimates was compared in the cortex. The better performance of NEXIRM highlights the importance of correcting for Rician bias in the NEXI model to obtain accurate estimates of microstructure parameters in the human cortex, and the sensitivity of the NEXI framework to individual differences in cortical microstructure. This application of NEXI in humans represents a significant step, unlocking new avenues for studying neurodevelopment, aging, and various neurodegenerative disorders.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"10 6","pages":"1-19"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140086129","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}
Roshni Lulla, Leonardo Christov-Moore, A. Vaccaro, N. Reggente, Marco Iacoboni, Jonas T. Kaplan
Abstract Empathy seems to rely on our ability to faithfully simulate multiple aspects of others’ inferred experiences, often using brain structures we would use during a similar experience. Much neuroimaging work in this vein has related empathic tendencies to univariate correlates of simulation strength or salience. However, novel evidence suggests that empathy may rely on the multivariate distinctiveness of these simulations. Someone whose representations of painful and non-painful stimulation are more distinct from each other may more accurately simulate that experience upon seeing somebody else experience it. We sought to predict empathic tendencies from the dissimilarity between neural activity patterns evoked by observing other people experience pain and touch and compared those findings to traditional univariate analyses. In support of a simulationist perspective, diverse observed somatosensory experiences were best classified by activation patterns in contralateral somatosensory and insular cortices, the same areas that would be active were the subject experiencing the stimuli themselves. In support of our specific hypothesis, the degree of dissimilarity between patterns for pain and touch in distinct areas was each associated with different aspects of trait empathy. Furthermore, the pattern dissimilarity analysis proved more informative regarding individual differences than analogous univariate analyses. These results suggest that multiple facets of empathy are associated with an ability to robustly distinguish between the simulated states of others at corresponding levels of the processing hierarchy, observable via the distinguishability of neural patterns arising with those states. Activation pattern dissimilarity may be a useful tool for parsing the neuroimaging correlates of complex cognitive functions like empathy.
{"title":"Empathy from dissimilarity: Multivariate pattern analysis of neural activity during observation of somatosensory experience","authors":"Roshni Lulla, Leonardo Christov-Moore, A. Vaccaro, N. Reggente, Marco Iacoboni, Jonas T. Kaplan","doi":"10.1162/imag_a_00110","DOIUrl":"https://doi.org/10.1162/imag_a_00110","url":null,"abstract":"Abstract Empathy seems to rely on our ability to faithfully simulate multiple aspects of others’ inferred experiences, often using brain structures we would use during a similar experience. Much neuroimaging work in this vein has related empathic tendencies to univariate correlates of simulation strength or salience. However, novel evidence suggests that empathy may rely on the multivariate distinctiveness of these simulations. Someone whose representations of painful and non-painful stimulation are more distinct from each other may more accurately simulate that experience upon seeing somebody else experience it. We sought to predict empathic tendencies from the dissimilarity between neural activity patterns evoked by observing other people experience pain and touch and compared those findings to traditional univariate analyses. In support of a simulationist perspective, diverse observed somatosensory experiences were best classified by activation patterns in contralateral somatosensory and insular cortices, the same areas that would be active were the subject experiencing the stimuli themselves. In support of our specific hypothesis, the degree of dissimilarity between patterns for pain and touch in distinct areas was each associated with different aspects of trait empathy. Furthermore, the pattern dissimilarity analysis proved more informative regarding individual differences than analogous univariate analyses. These results suggest that multiple facets of empathy are associated with an ability to robustly distinguish between the simulated states of others at corresponding levels of the processing hierarchy, observable via the distinguishability of neural patterns arising with those states. Activation pattern dissimilarity may be a useful tool for parsing the neuroimaging correlates of complex cognitive functions like empathy.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"4 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140271918","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}
C. Olm, Claire S. Peterson, David J. Irwin, Eddie B. Lee, John Q. Trojanowski, Lauren Massimo, John A. Detre, C. Mcmillan, James C. Gee, M. Grossman
Abstract Regional cerebral blood flow (CBF) changes quantified using arterial spin labeling (ASL) are altered in neurodegenerative disorders such as frontotemporal lobar degeneration due to tau (FTLD-tau), but the relationship between ASL CBF and pathologic burden has not been assessed. Our objective was to determine whether regional ASL CBF acquired antemortem in patients with FTLD-tau is related to pathologic burden measured at autopsy in those same regions in the same patients to directly test the imaging-pathology relationship. In this case-control study, data were acquired between 3/4/2010 and 12/16/2018. Data processing and analysis were completed in 2023. Twenty-one participants with autopsy-confirmed FTLD-tau (N = 10 women, mean[SD] age 67.9[7.56] years) along with 25 control participants (N = 15 women, age 64.7[7.53]) were recruited through the cognitive neurology clinic at the University of Pennsylvania. All participants had ASL and T1-weighted images collected antemortem. ASL images were processed to estimate CBF and T1-weighted images were processed to estimate gray matter (GM) volumes in regions corresponding to regions sampled postmortem. Digital quantification of pathologic burden was performed to find the percent area occupied (%AO) of pathologic FTLD-tau at autopsy. Regional CBF and GM volumes were both related to pathologic burden in the same regions from the same participants. Strengths of model fits of imaging measures to pathologic burden were compared. CBF in FTLD-tau and controls were compared, with results considered significant at p < 0.05 after Bonferroni correction. We found that relative to controls, FTLD-tau displayed hypoperfusion in anterior cingulate, orbitofrontal, middle frontal, and superior temporal regions, as well as angular gyrus. For patients with FTLD-tau regional CBF was significantly associated with pathologic burden (beta = -1.07, t = -4.80, p < 0.005). Models including both GM volume and CBF provided significantly better fits to pathologic burden data than single modality models (p < 0.05, Bonferroni-corrected). Our results indicate that reduced CBF measured using ASL MRI is associated with increased pathologic burden in FTLD-tau and adds complementary predictive value of pathologic burden to structural MRI.
{"title":"Pathologic burden goes with the flow: MRI perfusion and pathologic burden in frontotemporal lobar degeneration due to tau","authors":"C. Olm, Claire S. Peterson, David J. Irwin, Eddie B. Lee, John Q. Trojanowski, Lauren Massimo, John A. Detre, C. Mcmillan, James C. Gee, M. Grossman","doi":"10.1162/imag_a_00118","DOIUrl":"https://doi.org/10.1162/imag_a_00118","url":null,"abstract":"Abstract Regional cerebral blood flow (CBF) changes quantified using arterial spin labeling (ASL) are altered in neurodegenerative disorders such as frontotemporal lobar degeneration due to tau (FTLD-tau), but the relationship between ASL CBF and pathologic burden has not been assessed. Our objective was to determine whether regional ASL CBF acquired antemortem in patients with FTLD-tau is related to pathologic burden measured at autopsy in those same regions in the same patients to directly test the imaging-pathology relationship. In this case-control study, data were acquired between 3/4/2010 and 12/16/2018. Data processing and analysis were completed in 2023. Twenty-one participants with autopsy-confirmed FTLD-tau (N = 10 women, mean[SD] age 67.9[7.56] years) along with 25 control participants (N = 15 women, age 64.7[7.53]) were recruited through the cognitive neurology clinic at the University of Pennsylvania. All participants had ASL and T1-weighted images collected antemortem. ASL images were processed to estimate CBF and T1-weighted images were processed to estimate gray matter (GM) volumes in regions corresponding to regions sampled postmortem. Digital quantification of pathologic burden was performed to find the percent area occupied (%AO) of pathologic FTLD-tau at autopsy. Regional CBF and GM volumes were both related to pathologic burden in the same regions from the same participants. Strengths of model fits of imaging measures to pathologic burden were compared. CBF in FTLD-tau and controls were compared, with results considered significant at p < 0.05 after Bonferroni correction. We found that relative to controls, FTLD-tau displayed hypoperfusion in anterior cingulate, orbitofrontal, middle frontal, and superior temporal regions, as well as angular gyrus. For patients with FTLD-tau regional CBF was significantly associated with pathologic burden (beta = -1.07, t = -4.80, p < 0.005). Models including both GM volume and CBF provided significantly better fits to pathologic burden data than single modality models (p < 0.05, Bonferroni-corrected). Our results indicate that reduced CBF measured using ASL MRI is associated with increased pathologic burden in FTLD-tau and adds complementary predictive value of pathologic burden to structural MRI.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"770 ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140281248","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}
Alexander Jaffray, C. Kames, Michelle Medina, Christina Graf, Adam Clansey, Alexander Rauscher
Abstract Functional Magnetic Resonance Imaging (fMRI) is typically acquired using gradient-echo sequences with a long echo time at high temporal resolution. Gradient-echo sequences inherently encode information about the magnetic field in the often discarded image phase. We demonstrate a method for processing the phase of reconstructed fMRI data to isolate temporal fluctuations in the harmonic fields associated with respiration by solving a blind source separation problem. The fMRI-derived field fluctuations are shown to be in strong agreement with breathing belt data acquired during the same scan. This work presents a concurrent, hardware-free measurement of respiration-induced field fluctuations, providing a respiratory regressor for fMRI analysis which is independent of local contrast changes, and with potential applications in image reconstruction and fMRI analysis.
{"title":"Detection of respiration-induced field modulations in fMRI: A concurrent and navigator-free approach","authors":"Alexander Jaffray, C. Kames, Michelle Medina, Christina Graf, Adam Clansey, Alexander Rauscher","doi":"10.1162/imag_a_00091","DOIUrl":"https://doi.org/10.1162/imag_a_00091","url":null,"abstract":"Abstract Functional Magnetic Resonance Imaging (fMRI) is typically acquired using gradient-echo sequences with a long echo time at high temporal resolution. Gradient-echo sequences inherently encode information about the magnetic field in the often discarded image phase. We demonstrate a method for processing the phase of reconstructed fMRI data to isolate temporal fluctuations in the harmonic fields associated with respiration by solving a blind source separation problem. The fMRI-derived field fluctuations are shown to be in strong agreement with breathing belt data acquired during the same scan. This work presents a concurrent, hardware-free measurement of respiration-induced field fluctuations, providing a respiratory regressor for fMRI analysis which is independent of local contrast changes, and with potential applications in image reconstruction and fMRI analysis.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"12 10-12","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":"139881939","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}
Hazhar Sufi Karimi, Arghya Pal, Lipeng Ning, Y. Rathi
Abstract Diffusion magnetic resonance imaging (dMRI) allows to estimate brain tissue microstructure as well as the connectivity of the white matter (known as tractography). Accurate estimation of the model parameters (by solving the inverse problem) is thus very important to infer the underlying biophysical tissue properties and fiber orientations. Although there has been extensive research on this topic with a myriad of dMRI models, most models use standard nonlinear optimization techniques and only provide an estimate of the model parameters without any information (quantification) about uncertainty in their estimation. Further, the effect of this uncertainty on the estimation of the derived dMRI microstructural measures downstream (e.g., fractional anisotropy) is often unknown and is rarely estimated. To address this issue, we first design a new deep-learning algorithm to identify the number of crossing fibers in each voxel. Then, at each voxel, we propose a robust likelihood-free deep learning method to estimate not only the mean estimate of the parameters of a multi-fiber dMRI model (e.g., the biexponential model), but also its full posterior distribution. The posterior distribution is then used to estimate the uncertainty in the model parameters as well as the derived measures. We perform several synthetic and in-vivo quantitative experiments to demonstrate the robustness of our approach for different noise levels and out-of-distribution test samples. Besides, our approach is computationally fast and requires an order of magnitude less time than standard nonlinear fitting techniques. The proposed method demonstrates much lower error (compared to existing methods) in estimating several metrics, including number of fibers in a voxel, fiber orientation, and tensor eigenvalues. The proposed methodology is quite general and can be used for the estimation of the parameters from any other dMRI model.
{"title":"Likelihood-free posterior estimation and uncertainty quantification for diffusion MRI models","authors":"Hazhar Sufi Karimi, Arghya Pal, Lipeng Ning, Y. Rathi","doi":"10.1162/imag_a_00088","DOIUrl":"https://doi.org/10.1162/imag_a_00088","url":null,"abstract":"Abstract Diffusion magnetic resonance imaging (dMRI) allows to estimate brain tissue microstructure as well as the connectivity of the white matter (known as tractography). Accurate estimation of the model parameters (by solving the inverse problem) is thus very important to infer the underlying biophysical tissue properties and fiber orientations. Although there has been extensive research on this topic with a myriad of dMRI models, most models use standard nonlinear optimization techniques and only provide an estimate of the model parameters without any information (quantification) about uncertainty in their estimation. Further, the effect of this uncertainty on the estimation of the derived dMRI microstructural measures downstream (e.g., fractional anisotropy) is often unknown and is rarely estimated. To address this issue, we first design a new deep-learning algorithm to identify the number of crossing fibers in each voxel. Then, at each voxel, we propose a robust likelihood-free deep learning method to estimate not only the mean estimate of the parameters of a multi-fiber dMRI model (e.g., the biexponential model), but also its full posterior distribution. The posterior distribution is then used to estimate the uncertainty in the model parameters as well as the derived measures. We perform several synthetic and in-vivo quantitative experiments to demonstrate the robustness of our approach for different noise levels and out-of-distribution test samples. Besides, our approach is computationally fast and requires an order of magnitude less time than standard nonlinear fitting techniques. The proposed method demonstrates much lower error (compared to existing methods) in estimating several metrics, including number of fibers in a voxel, fiber orientation, and tensor eigenvalues. The proposed methodology is quite general and can be used for the estimation of the parameters from any other dMRI model.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"23 2","pages":"1-22"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139815491","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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