Elizabeth Keeling, Maurizio Bergamino, Sudarshan Ragunathan, C. Quarles, Allen T. Newton, Ashley M. Stokes
Abstract The purpose of this study was to optimize and validate a multi-contrast, multi-echo fMRI method using a combined spin- and gradient-echo (SAGE) acquisition. It was hypothesized that SAGE-based blood oxygen level-dependent (BOLD) functional MRI (fMRI) will improve sensitivity and spatial specificity while reducing signal dropout. SAGE-fMRI data were acquired with five echoes (2 gradient-echoes, 2 asymmetric spin-echoes, and 1 spin-echo) across 12 protocols with varying acceleration factors, and temporal SNR (tSNR) was assessed. The optimized protocol was then implemented in working memory and vision tasks in 15 healthy subjects. Task-based analysis was performed using individual echoes, quantitative dynamic relaxation times T2* and T2, and echo time-dependent weighted combinations of dynamic signals. These methods were compared to determine the optimal analysis method for SAGE-fMRI. Implementation of a multiband factor of 2 and sensitivity encoding (SENSE) factor of 2.5 yielded adequate spatiotemporal resolution while minimizing artifacts and loss in tSNR. Higher BOLD contrast-to-noise ratio (CNR) and tSNR were observed for SAGE-fMRI relative to single-echo fMRI, especially in regions with large susceptibility effects and for T2-dominant analyses. Using a working memory task, the extent of activation was highest with T2*-weighting, while smaller clusters were observed with quantitative T2* and T2. SAGE-fMRI couples the high BOLD sensitivity from multi-gradient-echo acquisitions with improved spatial localization from spin-echo acquisitions, providing two contrasts for analysis. SAGE-fMRI provides substantial advantages, including improving CNR and tSNR for more accurate analysis.
{"title":"Optimization and validation of multi-echo, multi-contrast SAGE acquisition in fMRI","authors":"Elizabeth Keeling, Maurizio Bergamino, Sudarshan Ragunathan, C. Quarles, Allen T. Newton, Ashley M. Stokes","doi":"10.1162/imag_a_00217","DOIUrl":"https://doi.org/10.1162/imag_a_00217","url":null,"abstract":"Abstract The purpose of this study was to optimize and validate a multi-contrast, multi-echo fMRI method using a combined spin- and gradient-echo (SAGE) acquisition. It was hypothesized that SAGE-based blood oxygen level-dependent (BOLD) functional MRI (fMRI) will improve sensitivity and spatial specificity while reducing signal dropout. SAGE-fMRI data were acquired with five echoes (2 gradient-echoes, 2 asymmetric spin-echoes, and 1 spin-echo) across 12 protocols with varying acceleration factors, and temporal SNR (tSNR) was assessed. The optimized protocol was then implemented in working memory and vision tasks in 15 healthy subjects. Task-based analysis was performed using individual echoes, quantitative dynamic relaxation times T2* and T2, and echo time-dependent weighted combinations of dynamic signals. These methods were compared to determine the optimal analysis method for SAGE-fMRI. Implementation of a multiband factor of 2 and sensitivity encoding (SENSE) factor of 2.5 yielded adequate spatiotemporal resolution while minimizing artifacts and loss in tSNR. Higher BOLD contrast-to-noise ratio (CNR) and tSNR were observed for SAGE-fMRI relative to single-echo fMRI, especially in regions with large susceptibility effects and for T2-dominant analyses. Using a working memory task, the extent of activation was highest with T2*-weighting, while smaller clusters were observed with quantitative T2* and T2. SAGE-fMRI couples the high BOLD sensitivity from multi-gradient-echo acquisitions with improved spatial localization from spin-echo acquisitions, providing two contrasts for analysis. SAGE-fMRI provides substantial advantages, including improving CNR and tSNR for more accurate analysis.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"14 9","pages":"1-20"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141690690","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 General intelligence, referred to as g, is hypothesized to emerge from the capacity to dynamically and adaptively reorganize macroscale brain connectivity. Temporal reconfiguration can be assessed using dynamic functional connectivity (dFC), which captures the propensity of brain connectivity to transition between a recurring repertoire of distinct states. Conventional dFC metrics commonly focus on categorical state switching frequencies which do not fully assess individual variation in continuous connectivity reconfiguration. Here, we supplement frequency measures by quantifying within-state connectivity consistency, dissimilarity between connectivity across states, and conformity of individual connectivity to group-average state connectivity. We utilized resting-state functional magnetic resonance imaging (fMRI) data from the large-scale Human Connectome Project and applied data-driven multivariate Partial Least Squares Correlation to explore emergent associations between dynamic network properties and cognitive ability. Our findings reveal a positive association between g and the stable maintenance of states characterized by distinct connectivity between higher-order networks, efficient reconfiguration (i.e., minimal connectivity changes during transitions between similar states, large connectivity changes between dissimilar states), and ability to sustain connectivity close to group-average state connectivity. This hints at fundamental properties of brain–behavior organization, suggesting that general cognitive processing capacity may be supported by the ability to efficiently reconfigure between stable and population-typical connectivity patterns.
{"title":"Higher general intelligence is associated with stable, efficient, and typical dynamic functional brain connectivity patterns","authors":"Justin Ng, Ju-Chi Yu, J. D. Feusner, Colin Hawco","doi":"10.1162/imag_a_00234","DOIUrl":"https://doi.org/10.1162/imag_a_00234","url":null,"abstract":"Abstract General intelligence, referred to as g, is hypothesized to emerge from the capacity to dynamically and adaptively reorganize macroscale brain connectivity. Temporal reconfiguration can be assessed using dynamic functional connectivity (dFC), which captures the propensity of brain connectivity to transition between a recurring repertoire of distinct states. Conventional dFC metrics commonly focus on categorical state switching frequencies which do not fully assess individual variation in continuous connectivity reconfiguration. Here, we supplement frequency measures by quantifying within-state connectivity consistency, dissimilarity between connectivity across states, and conformity of individual connectivity to group-average state connectivity. We utilized resting-state functional magnetic resonance imaging (fMRI) data from the large-scale Human Connectome Project and applied data-driven multivariate Partial Least Squares Correlation to explore emergent associations between dynamic network properties and cognitive ability. Our findings reveal a positive association between g and the stable maintenance of states characterized by distinct connectivity between higher-order networks, efficient reconfiguration (i.e., minimal connectivity changes during transitions between similar states, large connectivity changes between dissimilar states), and ability to sustain connectivity close to group-average state connectivity. This hints at fundamental properties of brain–behavior organization, suggesting that general cognitive processing capacity may be supported by the ability to efficiently reconfigure between stable and population-typical connectivity patterns.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"19 S1","pages":"1-34"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141702960","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 investigation of the brain’s functional connectome and its dynamic changes can provide valuable insights into brain organization and its reconfiguration. However, the analysis of dynamic functional connectivity (dFC) using functional magnetic resonance imaging (fMRI) faces major challenges, including the high dimensionality of brain networks, unknown latent sources underlying observed dFC, and the large number of brain connections that increase the risk of spurious findings. In this paper, we propose a new regularized blind source separation (BSS) method called dyna-LOCUS to address these challenges. dyna-LOCUS decomposes observed dFC measures to reveal latent source connectivity traits and their dynamic temporal expression profiles. By utilizing low-rank factorization and novel regularizations, dyna-LOCUS achieves efficient and reliable mapping of connectivity traits underlying the dynamic brain functional connectome, characterizes temporal changes of the connectivity traits that contribute to the reconfiguration in the observed dFC, and generates parsimonious and interpretable results in identifying whole-brain dFC states. We introduce a highly efficient iterative Node-Rotation algorithm that solves the nonconvex optimization problem for learning dyna-LOCUS. Simulation studies demonstrate the advantages of our proposed method. Application of dyna-LOCUS to the Philadelphia Neurodevelopmental Cohort (PNC) study unveils latent connectivity traits and key brain connections and regions driving each of these neural circuits, reveals temporal expression levels and interactions of these connectivity traits, and generates new findings regarding gender differences in the neurodevelopment of an executive function-related connectivity trait.
{"title":"Unveiling hidden sources of dynamic functional connectome through a novel regularized blind source separation approach","authors":"Jialu Ran, Yikai Wang, Ying Guo","doi":"10.1162/imag_a_00220","DOIUrl":"https://doi.org/10.1162/imag_a_00220","url":null,"abstract":"Abstract The investigation of the brain’s functional connectome and its dynamic changes can provide valuable insights into brain organization and its reconfiguration. However, the analysis of dynamic functional connectivity (dFC) using functional magnetic resonance imaging (fMRI) faces major challenges, including the high dimensionality of brain networks, unknown latent sources underlying observed dFC, and the large number of brain connections that increase the risk of spurious findings. In this paper, we propose a new regularized blind source separation (BSS) method called dyna-LOCUS to address these challenges. dyna-LOCUS decomposes observed dFC measures to reveal latent source connectivity traits and their dynamic temporal expression profiles. By utilizing low-rank factorization and novel regularizations, dyna-LOCUS achieves efficient and reliable mapping of connectivity traits underlying the dynamic brain functional connectome, characterizes temporal changes of the connectivity traits that contribute to the reconfiguration in the observed dFC, and generates parsimonious and interpretable results in identifying whole-brain dFC states. We introduce a highly efficient iterative Node-Rotation algorithm that solves the nonconvex optimization problem for learning dyna-LOCUS. Simulation studies demonstrate the advantages of our proposed method. Application of dyna-LOCUS to the Philadelphia Neurodevelopmental Cohort (PNC) study unveils latent connectivity traits and key brain connections and regions driving each of these neural circuits, reveals temporal expression levels and interactions of these connectivity traits, and generates new findings regarding gender differences in the neurodevelopment of an executive function-related connectivity trait.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"11 19","pages":"1-30"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141700950","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}
D. Scheinost, Joseph Chang, Emma Brennan‐Wydra, C. Lacadie, R. Constable, K. Chawarska, Laura R. Ment
Abstract The default mode (DMN), frontoparietal (FPN), and salience (SN) networks interact to support a range of behaviors, are vulnerable to environmental insults, and are disrupted in neurodevelopmental disorders. However, their development across the third trimester and perinatal transition remains unknown. Employing resting-state functional MRI at 30 to 32, 34 to 36, and 40 to 44 weeks postmenstrual age (PMA), we examined developmental trajectories of the intra- and internetwork connectivity of the 3 networks measured in 84 fetuses and neonates. A secondary analysis addressed the impact of maternal mental health on these networks. The DMN, FPN, and SN intranetwork connectivity evidenced significant increases between 36 and 44 weeks PMA, with connectivity measures reaching values significantly greater than 0 at 40 weeks PMA for all 3 networks. Connectivity between SN and DMN and between SN and FPN decreased significantly with the connectivity values significantly below 0 at 36–44 weeks. However, DMN-FPN connectivity increased between 30 and 44 weeks with the connectivity greater than 0 already at 36 months. Finally, higher maternal stress levels negatively affected the SN across 30-44 weeks PMA. These data provide a normative framework to compare fetuses and neonates at risk for neurobehavioral disorders and assess the impact of the environment on the developing brain.
{"title":"Developmental trajectories of the default mode, frontoparietal, and salience networks from the third trimester through the newborn period","authors":"D. Scheinost, Joseph Chang, Emma Brennan‐Wydra, C. Lacadie, R. Constable, K. Chawarska, Laura R. Ment","doi":"10.1162/imag_a_00201","DOIUrl":"https://doi.org/10.1162/imag_a_00201","url":null,"abstract":"Abstract The default mode (DMN), frontoparietal (FPN), and salience (SN) networks interact to support a range of behaviors, are vulnerable to environmental insults, and are disrupted in neurodevelopmental disorders. However, their development across the third trimester and perinatal transition remains unknown. Employing resting-state functional MRI at 30 to 32, 34 to 36, and 40 to 44 weeks postmenstrual age (PMA), we examined developmental trajectories of the intra- and internetwork connectivity of the 3 networks measured in 84 fetuses and neonates. A secondary analysis addressed the impact of maternal mental health on these networks. The DMN, FPN, and SN intranetwork connectivity evidenced significant increases between 36 and 44 weeks PMA, with connectivity measures reaching values significantly greater than 0 at 40 weeks PMA for all 3 networks. Connectivity between SN and DMN and between SN and FPN decreased significantly with the connectivity values significantly below 0 at 36–44 weeks. However, DMN-FPN connectivity increased between 30 and 44 weeks with the connectivity greater than 0 already at 36 months. Finally, higher maternal stress levels negatively affected the SN across 30-44 weeks PMA. These data provide a normative framework to compare fetuses and neonates at risk for neurobehavioral disorders and assess the impact of the environment on the developing brain.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"10 16","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141698552","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}
M. Ortiz-Rios, Nikoloz Sirmpilatze, Jessica König, Susann Boreitus
Abstract Initiatives towards acquiring large-scale neuroimaging data in non-human primates promise improving translational neuroscience and cross-species comparisons. Crucial among these efforts is the need to expand sample sizes while reducing the impact of anesthesia on the functional properties of brain networks. Yet, the effects of anesthesia on non-human primate brain networks remain unclear. Here, we demonstrate using functional magnetic resonance imaging (fMRI) at 9.4 tesla that isoflurane anesthesia induces a variety of brain states in the marmoset brain with dramatically altered functional connectivity profiles. As an alternative, we recommend using a continuous infusion of the sedative medetomidine, supplemented with a low concentration of isoflurane. Using this protocol in eight marmosets, we observed robust visual activation during flickering light stimulation and identified resting-state networks similar to the awake state. In contrast, isoflurane alone led to a suppressed visual activation and the absence of awake-like network patterns. Comparing states using a graph-theoretical approach, we confirmed that the structure of functional networks is preserved under our proposed anesthesia protocol but is lost using isoflurane alone at concentration levels greater than 1%. We believe that the widespread adoption of this protocol will be a step towards advancing translational neuroscience initiatives in non-human primate neuroimaging. To promote the collaborative use of neuroimaging resources, we openly share our datasets (https://zenodo.org/records/11118775).
{"title":"An anesthetic protocol for preserving functional network structure in the marmoset monkey brain","authors":"M. Ortiz-Rios, Nikoloz Sirmpilatze, Jessica König, Susann Boreitus","doi":"10.1162/imag_a_00230","DOIUrl":"https://doi.org/10.1162/imag_a_00230","url":null,"abstract":"Abstract Initiatives towards acquiring large-scale neuroimaging data in non-human primates promise improving translational neuroscience and cross-species comparisons. Crucial among these efforts is the need to expand sample sizes while reducing the impact of anesthesia on the functional properties of brain networks. Yet, the effects of anesthesia on non-human primate brain networks remain unclear. Here, we demonstrate using functional magnetic resonance imaging (fMRI) at 9.4 tesla that isoflurane anesthesia induces a variety of brain states in the marmoset brain with dramatically altered functional connectivity profiles. As an alternative, we recommend using a continuous infusion of the sedative medetomidine, supplemented with a low concentration of isoflurane. Using this protocol in eight marmosets, we observed robust visual activation during flickering light stimulation and identified resting-state networks similar to the awake state. In contrast, isoflurane alone led to a suppressed visual activation and the absence of awake-like network patterns. Comparing states using a graph-theoretical approach, we confirmed that the structure of functional networks is preserved under our proposed anesthesia protocol but is lost using isoflurane alone at concentration levels greater than 1%. We believe that the widespread adoption of this protocol will be a step towards advancing translational neuroscience initiatives in non-human primate neuroimaging. To promote the collaborative use of neuroimaging resources, we openly share our datasets (https://zenodo.org/records/11118775).","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"15 3","pages":"1-23"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141704297","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 potential of using neural data to predict academic outcomes has always been at the heart of educational neuroscience, an emerging field at the crossroad of psychology, neuroscience, and education sciences. Although this prospect has long been elusive, the exponential use of advanced techniques in machine learning in neuroimaging may change this state of affairs. Here we provide a review of neuroimaging studies that have used machine learning to predict literacy and numeracy outcomes in adults and children, in both the context of learning disability and typical performance. We notably review the cross-sectional and longitudinal designs used in such studies, and describe how they can be coupled with regression and classification approaches. Our review highlights the promise of these methods for predicting literacy and numeracy outcomes, as well as their difficulties. However, we also found a large variability in terms of algorithms and underlying brain circuits across studies, and a relative lack of studies investigating longitudinal prediction of outcomes in young children before the onset of formal education. We argue that the field needs a standardization of methods, as well as a greater use of accessible and portable neuroimaging methods that have more applicability potential than lab-based neuroimaging techniques.
{"title":"From brain to education through machine learning: Predicting literacy and numeracy skills from neuroimaging data","authors":"Tomoya Nakai, Coumarane Tirou, Jérôme Prado","doi":"10.1162/imag_a_00219","DOIUrl":"https://doi.org/10.1162/imag_a_00219","url":null,"abstract":"Abstract The potential of using neural data to predict academic outcomes has always been at the heart of educational neuroscience, an emerging field at the crossroad of psychology, neuroscience, and education sciences. Although this prospect has long been elusive, the exponential use of advanced techniques in machine learning in neuroimaging may change this state of affairs. Here we provide a review of neuroimaging studies that have used machine learning to predict literacy and numeracy outcomes in adults and children, in both the context of learning disability and typical performance. We notably review the cross-sectional and longitudinal designs used in such studies, and describe how they can be coupled with regression and classification approaches. Our review highlights the promise of these methods for predicting literacy and numeracy outcomes, as well as their difficulties. However, we also found a large variability in terms of algorithms and underlying brain circuits across studies, and a relative lack of studies investigating longitudinal prediction of outcomes in young children before the onset of formal education. We argue that the field needs a standardization of methods, as well as a greater use of accessible and portable neuroimaging methods that have more applicability potential than lab-based neuroimaging techniques.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"20 1","pages":"1-24"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141704984","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}
S. Fuglsang, Jonatan Märcher-Rørsted, Kristoffer H. Madsen, Ditte H. Frantzen, Gerard Encina-Llamas, Charlotte Sørensen, T. Dyrby, Torsten Dau, Jens Hjortkjær, H. Siebner
Abstract Age-related alterations in the auditory system have been suggested to affect the processing of temporal envelope amplitude modulations (AM) at different levels of the auditory hierarchy, yet few studies have used functional magnetic resonance imaging (fMRI) to study this noninvasively in humans with high spatial resolution. In this study, we utilized sparse-sampling fMRI at 3 Tesla (3T) to investigate regional blood oxygenation level-dependent (BOLD) responses to AM noise stimuli in 65 individuals ranging in age from 19 to 77 years. We contrasted BOLD responses to AM noise stimuli modulated at 4 Hz or 80 Hz with responses to unmodulated stimuli. This allowed us to derive functional measures of regional neural sensitivity to the imposed AM. Compared with unmodulated noise, slowly varying 4 Hz AM noise stimuli elicited significantly greater BOLD responses in the left and right auditory cortex along the Heschl’s gyrus (HG). BOLD responses to the 80 Hz AM stimuli were significantly greater than responses to unmodulated stimuli in putatively primary auditory cortical regions in the lateral HG. BOLD responses to 4 Hz AM stimuli were significantly greater in magnitude than responses to 80 Hz AM stimuli in auditory cortical regions. We find no discernible effects of age on the functional recruitment of the auditory cortex by AM stimuli. While the results affirm the involvement of the auditory cortex in processing temporal envelope rate information, they provide no support for age-related effects on these measures. We discuss potential caveats in assessing age-related changes in responses to AM stimuli in the auditory pathway.
{"title":"BOLD fMRI responses to amplitude-modulated sounds across age in adult listeners","authors":"S. Fuglsang, Jonatan Märcher-Rørsted, Kristoffer H. Madsen, Ditte H. Frantzen, Gerard Encina-Llamas, Charlotte Sørensen, T. Dyrby, Torsten Dau, Jens Hjortkjær, H. Siebner","doi":"10.1162/imag_a_00238","DOIUrl":"https://doi.org/10.1162/imag_a_00238","url":null,"abstract":"Abstract Age-related alterations in the auditory system have been suggested to affect the processing of temporal envelope amplitude modulations (AM) at different levels of the auditory hierarchy, yet few studies have used functional magnetic resonance imaging (fMRI) to study this noninvasively in humans with high spatial resolution. In this study, we utilized sparse-sampling fMRI at 3 Tesla (3T) to investigate regional blood oxygenation level-dependent (BOLD) responses to AM noise stimuli in 65 individuals ranging in age from 19 to 77 years. We contrasted BOLD responses to AM noise stimuli modulated at 4 Hz or 80 Hz with responses to unmodulated stimuli. This allowed us to derive functional measures of regional neural sensitivity to the imposed AM. Compared with unmodulated noise, slowly varying 4 Hz AM noise stimuli elicited significantly greater BOLD responses in the left and right auditory cortex along the Heschl’s gyrus (HG). BOLD responses to the 80 Hz AM stimuli were significantly greater than responses to unmodulated stimuli in putatively primary auditory cortical regions in the lateral HG. BOLD responses to 4 Hz AM stimuli were significantly greater in magnitude than responses to 80 Hz AM stimuli in auditory cortical regions. We find no discernible effects of age on the functional recruitment of the auditory cortex by AM stimuli. While the results affirm the involvement of the auditory cortex in processing temporal envelope rate information, they provide no support for age-related effects on these measures. We discuss potential caveats in assessing age-related changes in responses to AM stimuli in the auditory pathway.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"39 5","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141693720","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}
Mark D. Zuppichini, Abbey M. Hamlin, Quan Zhou, Esther Kim, Shreya K. Rajagopal, A. Beltz, T. Polk
Abstract One factor that might contribute to functional deterioration in healthy older adults is a decline in the brain’s major inhibitory neurotransmitter, gamma-aminobutyric acid (GABA). Previous studies have reported mixed results regarding whether GABA declines in healthy aging. These previous studies were cross-sectional and therefore cannot provide insight into GABA changes over time within aging individuals. Furthermore, aging is associated with gray and white matter atrophy that may confound age-related differences in GABA. In the present study, we utilized a repeated-measures, longitudinal design and MR spectroscopy to measure GABA levels in bilateral auditory, sensorimotor, and ventrovisual voxels of interest (VOI) in 30 healthy older adults at two time points a few years apart. Furthermore, we applied two of the most common tissue correction strategies to control for the effects of tissue composition on GABA estimates. Results from mixed-effects models showed that longitudinal change in age is a significant predictor of tissue-corrected longitudinal change in GABA levels: as age increases, GABA declines. In contrast, there was no cross-sectional effect of age on GABA in our sample (e.g., the oldest old did not have lower GABA levels than the youngest old). In conclusion, results from this study provide support for within-person, age-related declines in GABA over time, even after controlling for tissue composition.
{"title":"GABA levels decline with age: A longitudinal study","authors":"Mark D. Zuppichini, Abbey M. Hamlin, Quan Zhou, Esther Kim, Shreya K. Rajagopal, A. Beltz, T. Polk","doi":"10.1162/imag_a_00224","DOIUrl":"https://doi.org/10.1162/imag_a_00224","url":null,"abstract":"Abstract One factor that might contribute to functional deterioration in healthy older adults is a decline in the brain’s major inhibitory neurotransmitter, gamma-aminobutyric acid (GABA). Previous studies have reported mixed results regarding whether GABA declines in healthy aging. These previous studies were cross-sectional and therefore cannot provide insight into GABA changes over time within aging individuals. Furthermore, aging is associated with gray and white matter atrophy that may confound age-related differences in GABA. In the present study, we utilized a repeated-measures, longitudinal design and MR spectroscopy to measure GABA levels in bilateral auditory, sensorimotor, and ventrovisual voxels of interest (VOI) in 30 healthy older adults at two time points a few years apart. Furthermore, we applied two of the most common tissue correction strategies to control for the effects of tissue composition on GABA estimates. Results from mixed-effects models showed that longitudinal change in age is a significant predictor of tissue-corrected longitudinal change in GABA levels: as age increases, GABA declines. In contrast, there was no cross-sectional effect of age on GABA in our sample (e.g., the oldest old did not have lower GABA levels than the youngest old). In conclusion, results from this study provide support for within-person, age-related declines in GABA over time, even after controlling for tissue composition.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"5 11","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141700374","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}
Weikang Gong, Christian F. Beckmann, Stephen M. Smith
Abstract It remains largely unknown whether individualised longitudinal changes of brain imaging features can be predicted based only on the baseline brain images. This would be of great value, for example, for longitudinal data imputation, longitudinal brain-behaviour associations, and early prediction of brain-related diseases. We explore this possibility using longitudinal data of multiple modalities from UK Biobank brain imaging, with around 3,500 subjects. As baseline and follow-up images are generally similar in the case of short follow-up time intervals (e.g., 2 years), a simple copy of the baseline image may have a very good prediction performance. Therefore, for the first time, we propose a new mathematical framework for guiding the longitudinal prediction of brain images, providing answers to fundamental questions: (1) what is a suitable definition of longitudinal change; (2) how to detect the existence of changes; (3) what is the “null” prediction performance; and (4) can we distinguish longitudinal change prediction from simple data denoising. Building on these, we designed a deep U-Net based model for predicting longitudinal changes in multimodal brain images. Our results show that the proposed model can predict to a modest degree individualised longitudinal changes in almost all modalities, and outperforms other potential models. Furthermore, compared with the true longitudinal changes computed from real data, the predicted longitudinal changes have a similar or even improved accuracy in predicting subjects’ non-imaging phenotypes, and have a high between-subject discriminability. Our study contributes a new theoretical framework for longitudinal brain imaging studies, and our results show the potential for longitudinal data imputation, along with highlighting several caveats when performing longitudinal data analysis.
{"title":"Individualised prediction of longitudinal change in multimodal brain imaging","authors":"Weikang Gong, Christian F. Beckmann, Stephen M. Smith","doi":"10.1162/imag_a_00215","DOIUrl":"https://doi.org/10.1162/imag_a_00215","url":null,"abstract":"Abstract It remains largely unknown whether individualised longitudinal changes of brain imaging features can be predicted based only on the baseline brain images. This would be of great value, for example, for longitudinal data imputation, longitudinal brain-behaviour associations, and early prediction of brain-related diseases. We explore this possibility using longitudinal data of multiple modalities from UK Biobank brain imaging, with around 3,500 subjects. As baseline and follow-up images are generally similar in the case of short follow-up time intervals (e.g., 2 years), a simple copy of the baseline image may have a very good prediction performance. Therefore, for the first time, we propose a new mathematical framework for guiding the longitudinal prediction of brain images, providing answers to fundamental questions: (1) what is a suitable definition of longitudinal change; (2) how to detect the existence of changes; (3) what is the “null” prediction performance; and (4) can we distinguish longitudinal change prediction from simple data denoising. Building on these, we designed a deep U-Net based model for predicting longitudinal changes in multimodal brain images. Our results show that the proposed model can predict to a modest degree individualised longitudinal changes in almost all modalities, and outperforms other potential models. Furthermore, compared with the true longitudinal changes computed from real data, the predicted longitudinal changes have a similar or even improved accuracy in predicting subjects’ non-imaging phenotypes, and have a high between-subject discriminability. Our study contributes a new theoretical framework for longitudinal brain imaging studies, and our results show the potential for longitudinal data imputation, along with highlighting several caveats when performing longitudinal data analysis.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"19 2","pages":"1-19"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141709540","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}
A. Vigotsky, R. Jabakhanji, Paulo Branco, Gian Domenico Iannetti, Marwan N. Baliki, A. V. Apkarian
Abstract How does the human brain generate coherent, subjective perceptions—transforming yellow and oblong visual sensory information into the perception of an edible banana? This is a hard problem. According to the standard viewpoint, processing in groups of dedicated regions—identified as active “blobs” when using functional magnetic resonance imaging (fMRI)—gives rise to perception. Here, we reveal a new organizational concept by discovering that stimulus-specific information distributed throughout the whole brain. Using fMRI, we found stimulus-specific information across the neocortex, even in voxels previously considered “noise,” challenging traditional analytical approaches. Surprisingly, these stimulus-specific signals were also present in the subcortex and cerebellum and could be detected from across-subject variances. Finally, we observed that stimulus-specific signal in brain regions beyond the primary and secondary sensory cortices is influenced by sedation levels, suggesting a connection to perception rather than sensory encoding. We hypothesize that these widespread, stimulus-specific, and consciousness level-dependent signals may underlie coherent and subjective perceptions.
{"title":"Widespread, perception-related information in the human brain scales with levels of consciousness","authors":"A. Vigotsky, R. Jabakhanji, Paulo Branco, Gian Domenico Iannetti, Marwan N. Baliki, A. V. Apkarian","doi":"10.1162/imag_a_00240","DOIUrl":"https://doi.org/10.1162/imag_a_00240","url":null,"abstract":"Abstract How does the human brain generate coherent, subjective perceptions—transforming yellow and oblong visual sensory information into the perception of an edible banana? This is a hard problem. According to the standard viewpoint, processing in groups of dedicated regions—identified as active “blobs” when using functional magnetic resonance imaging (fMRI)—gives rise to perception. Here, we reveal a new organizational concept by discovering that stimulus-specific information distributed throughout the whole brain. Using fMRI, we found stimulus-specific information across the neocortex, even in voxels previously considered “noise,” challenging traditional analytical approaches. Surprisingly, these stimulus-specific signals were also present in the subcortex and cerebellum and could be detected from across-subject variances. Finally, we observed that stimulus-specific signal in brain regions beyond the primary and secondary sensory cortices is influenced by sedation levels, suggesting a connection to perception rather than sensory encoding. We hypothesize that these widespread, stimulus-specific, and consciousness level-dependent signals may underlie coherent and subjective perceptions.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"21 5","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141849556","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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