Christian Ewert, David Kügler, R. Stirnberg, A. Koch, A. Yendiki, Martin Reuter
Abstract Diffusion-weighted magnetic resonance imaging (dMRI) permits a detailed in-vivo analysis of neuroanatomical microstructure, invaluable for clinical and population studies. However, many measurements with different diffusion-encoding directions and possibly b-values are necessary to infer the underlying tissue microstructure within different imaging voxels accurately. Two challenges particularly limit the utility of dMRI: long acquisition times limit feasible scans to only a few directional measurements, and the heterogeneity of acquisition schemes across studies makes it difficult to combine datasets. Left unaddressed by previous learning-based methods that only accept dMRI data adhering to the specific acquisition scheme used for training, there is a need for methods that accept and predict signals for arbitrary diffusion encodings. Addressing these challenges, we describe the first geometric deep learning method for continuous dMRI signal reconstruction for arbitrary diffusion sampling schemes for both the input and output. Our method combines the reconstruction accuracy and robustness of previous learning-based methods with the flexibility of model-based methods, for example, spherical harmonics or SHORE. We demonstrate that our method outperforms model-based methods and performs on par with discrete learning-based methods on single-, multi-shell, and grid-based diffusion MRI datasets. Relevant for dMRI-derived analyses, we show that our reconstruction translates to higher-quality estimates of frequently used microstructure models compared to other reconstruction methods, enabling high-quality analyses even from very short dMRI acquisitions.
{"title":"Geometric deep learning for diffusion MRI signal reconstruction with continuous samplings (DISCUS)","authors":"Christian Ewert, David Kügler, R. Stirnberg, A. Koch, A. Yendiki, Martin Reuter","doi":"10.1162/imag_a_00121","DOIUrl":"https://doi.org/10.1162/imag_a_00121","url":null,"abstract":"Abstract Diffusion-weighted magnetic resonance imaging (dMRI) permits a detailed in-vivo analysis of neuroanatomical microstructure, invaluable for clinical and population studies. However, many measurements with different diffusion-encoding directions and possibly b-values are necessary to infer the underlying tissue microstructure within different imaging voxels accurately. Two challenges particularly limit the utility of dMRI: long acquisition times limit feasible scans to only a few directional measurements, and the heterogeneity of acquisition schemes across studies makes it difficult to combine datasets. Left unaddressed by previous learning-based methods that only accept dMRI data adhering to the specific acquisition scheme used for training, there is a need for methods that accept and predict signals for arbitrary diffusion encodings. Addressing these challenges, we describe the first geometric deep learning method for continuous dMRI signal reconstruction for arbitrary diffusion sampling schemes for both the input and output. Our method combines the reconstruction accuracy and robustness of previous learning-based methods with the flexibility of model-based methods, for example, spherical harmonics or SHORE. We demonstrate that our method outperforms model-based methods and performs on par with discrete learning-based methods on single-, multi-shell, and grid-based diffusion MRI datasets. Relevant for dMRI-derived analyses, we show that our reconstruction translates to higher-quality estimates of frequently used microstructure models compared to other reconstruction methods, enabling high-quality analyses even from very short dMRI acquisitions.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"55 2","pages":"1-18"},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140764635","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 In our everyday lives, we are often faced with situations in which we make choices that involve risky or delayed rewards. However, the extent to which we are willing to accept larger risky (over smaller certain) or larger delayed (over smaller immediate) rewards varies across individuals. Here, we investigated the relationship between cortical complexity in the medial prefrontal cortex and individual differences in risky and intertemporal preferences. We found that reduced cortical complexity in left ventromedial prefrontal cortex (vmPFC) was associated with a greater preference for risky and immediate rewards. In addition to these common structural associations in left vmPFC, we also found associations between lower cortical complexity and a greater preference for immediate rewards that extended into the left dorsomedial prefrontal cortex and right vmPFC. Furthermore, these structural associations occurred in a context where a preference for risky rewards was correlated with a preference for delayed rewards across individuals. These results suggest that risk and intertemporal preferences are distinct but related, and likely influenced by multiple neurocognitive processes, with cortical complexity in vmPFC reflecting one shared aspect possibly related to impulsiveness in terms of risky and impatient economic choice. Future work should elucidate the complex relationships between brain structure and behavioral preferences.
{"title":"Reduced cortical complexity in ventromedial prefrontal cortex is associated with a greater preference for risky and immediate rewards","authors":"F. Bergström, Caryn Lerman, J. Kable","doi":"10.1162/imag_a_00143","DOIUrl":"https://doi.org/10.1162/imag_a_00143","url":null,"abstract":"Abstract In our everyday lives, we are often faced with situations in which we make choices that involve risky or delayed rewards. However, the extent to which we are willing to accept larger risky (over smaller certain) or larger delayed (over smaller immediate) rewards varies across individuals. Here, we investigated the relationship between cortical complexity in the medial prefrontal cortex and individual differences in risky and intertemporal preferences. We found that reduced cortical complexity in left ventromedial prefrontal cortex (vmPFC) was associated with a greater preference for risky and immediate rewards. In addition to these common structural associations in left vmPFC, we also found associations between lower cortical complexity and a greater preference for immediate rewards that extended into the left dorsomedial prefrontal cortex and right vmPFC. Furthermore, these structural associations occurred in a context where a preference for risky rewards was correlated with a preference for delayed rewards across individuals. These results suggest that risk and intertemporal preferences are distinct but related, and likely influenced by multiple neurocognitive processes, with cortical complexity in vmPFC reflecting one shared aspect possibly related to impulsiveness in terms of risky and impatient economic choice. Future work should elucidate the complex relationships between brain structure and behavioral preferences.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"76 1","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140785374","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}
Chuan Huang, Thomas Hagan, Minos Kritikos, Daniel Suite, Tianyun Zhao, Melissa A. Carr, Stephanie Meija-Santiago, A. Invernizzi, Megan K. Horton, Roberto G. Lucchini, Evelyn J. Bromet, Roman Kotov, Sean A P Clouston, B. Luft
Abstract Multimodal imaging using network connectivity techniques shows promise for investigating neuropathology influencing Post-Traumatic Stress Disorder (PTSD) symptom maintenance and course. We recruited World Trade Center (WTC) responders who continued to suffer from chronic PTSD into a diffusion tensor neuroimaging protocol (n = 100), along with nine unexposed controls without PTSD from other sources. Using a graph theory approach to probe network alterations in brain diffusion images, we calculated weighted characteristics path length (wCPL) as a surrogate marker for the effective neuroanatomical distance between anatomical nodes. The sample (N = 109; 47 with chronic PTSD) was in their mid-fifties, and the majority were male. Responders were matched in terms of cognitive performance, occupation, and demographics. The anatomical connectivity graph was constructed for each participant using deterministic diffusion tractography. We identified a significant difference in wCPL between trauma-exposed WTC responders (Cohen’s d = 0.42, p < 0.001) that was highest in people with PTSD, and not explained by WTC exposure severity or duration. We also found that wCPL was associated with PTSD symptom severity in responders with PTSD. In the largest study to date to examine the relationship between chronic PTSD and anatomy, we examined the anatomical topography of neural connections and found that wCPL differed between the PTSD+ and PTSD- diagnostic categories.
{"title":"Graph theory-based analysis reveals neural anatomical network alterations in chronic post-traumatic stress disorder","authors":"Chuan Huang, Thomas Hagan, Minos Kritikos, Daniel Suite, Tianyun Zhao, Melissa A. Carr, Stephanie Meija-Santiago, A. Invernizzi, Megan K. Horton, Roberto G. Lucchini, Evelyn J. Bromet, Roman Kotov, Sean A P Clouston, B. Luft","doi":"10.1162/imag_a_00141","DOIUrl":"https://doi.org/10.1162/imag_a_00141","url":null,"abstract":"Abstract Multimodal imaging using network connectivity techniques shows promise for investigating neuropathology influencing Post-Traumatic Stress Disorder (PTSD) symptom maintenance and course. We recruited World Trade Center (WTC) responders who continued to suffer from chronic PTSD into a diffusion tensor neuroimaging protocol (n = 100), along with nine unexposed controls without PTSD from other sources. Using a graph theory approach to probe network alterations in brain diffusion images, we calculated weighted characteristics path length (wCPL) as a surrogate marker for the effective neuroanatomical distance between anatomical nodes. The sample (N = 109; 47 with chronic PTSD) was in their mid-fifties, and the majority were male. Responders were matched in terms of cognitive performance, occupation, and demographics. The anatomical connectivity graph was constructed for each participant using deterministic diffusion tractography. We identified a significant difference in wCPL between trauma-exposed WTC responders (Cohen’s d = 0.42, p < 0.001) that was highest in people with PTSD, and not explained by WTC exposure severity or duration. We also found that wCPL was associated with PTSD symptom severity in responders with PTSD. In the largest study to date to examine the relationship between chronic PTSD and anatomy, we examined the anatomical topography of neural connections and found that wCPL differed between the PTSD+ and PTSD- diagnostic categories.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"398 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140758035","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}
Haily Merritt, Joshua Faskowitz, Marlen Z. Gonzalez, Richard F. Betzel
Abstract The social environment has a critical influence on human development, cognition, and health. Research in health psychology and social neuroscience indicate an urgent need to understand how social relationships are associated with brain function and organization. To address this, we apply multilayer modeling and modularity maximization—both established tools in network neuroscience—to jointly cluster patterns of brain-behavior associations for seven social support measures. By using network approaches to map and analyze the connectivity between all pairs of brain regions simultaneously, we can clarify how relationships between brain regions (e.g. connectivity) change as a function of social relationships. This multilayer approach enables direct comparison of brain-behavior associations across social contexts for all brain regions and builds on both ecological and developmental neuroscientific findings and network neuroscientific approaches. In particular, we find that subcortical and control systems are especially sensitive to different constructs of perceived social support. Network nodes in these systems are highly flexible; their community affiliations, which reflect groups of nodes with similar patterns of brain-behavior associations, differ across social support measures. Additionally, our application of multilayer modeling to patterns of brain-behavior correlations, as opposed to just functional connectivity, represents an innovation in how multilayer models are used in human neuroscience. More than that, it offers a generalizable technique for studying the stability and variation of brain-behavior associations.
{"title":"Stability and variation of brain-behavior correlation patterns across measures of social support","authors":"Haily Merritt, Joshua Faskowitz, Marlen Z. Gonzalez, Richard F. Betzel","doi":"10.1162/imag_a_00133","DOIUrl":"https://doi.org/10.1162/imag_a_00133","url":null,"abstract":"Abstract The social environment has a critical influence on human development, cognition, and health. Research in health psychology and social neuroscience indicate an urgent need to understand how social relationships are associated with brain function and organization. To address this, we apply multilayer modeling and modularity maximization—both established tools in network neuroscience—to jointly cluster patterns of brain-behavior associations for seven social support measures. By using network approaches to map and analyze the connectivity between all pairs of brain regions simultaneously, we can clarify how relationships between brain regions (e.g. connectivity) change as a function of social relationships. This multilayer approach enables direct comparison of brain-behavior associations across social contexts for all brain regions and builds on both ecological and developmental neuroscientific findings and network neuroscientific approaches. In particular, we find that subcortical and control systems are especially sensitive to different constructs of perceived social support. Network nodes in these systems are highly flexible; their community affiliations, which reflect groups of nodes with similar patterns of brain-behavior associations, differ across social support measures. Additionally, our application of multilayer modeling to patterns of brain-behavior correlations, as opposed to just functional connectivity, represents an innovation in how multilayer models are used in human neuroscience. More than that, it offers a generalizable technique for studying the stability and variation of brain-behavior associations.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"16 3","pages":"1-18"},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140785128","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}
Hussein Bdair, Marie Sato-Fitoussi, Stéphane Planche, L. Moquin, M. Kang, A. Aliaga, A. Nagano-Saito, Kelly Smart, S. M. Cox, Jamie Near, A. Aguilar-Valles, G. Massarweh, Pedro Rosa-Neto, C. Benkelfat, j.-p. soucy, Alexey Kostikov, Alain Gratton, M. Leyton
Abstract The excitatory neurotransmitter glutamate plays a critical role in experience-dependent neuroplasticity, including addiction-related processes. To date, however, it is not possible to measure glutamate release in the living human brain. Positron emission tomography (PET) with [11C]ABP688, a selective allosteric antagonist of metabotropic type 5 glutamate (mGlu5) receptors, could offer an effective strategy. To test this proposition, we conducted a series of studies in rats using microdialysis and [11C]ABP688 microPET imaging, and in humans using PET and magnetic resonance spectroscopy (MRS). Significant calcium-dependent glutamate release was identified in the ventral striatum of awake rats (190.5 ± 34.7%, p < 0.05; n = 7) following administration of a low dose of ethanol (EtOH; 20%, 0.5 g/kg), a pharmacological challenge readily translatable to human research. Simultaneous microdialysis and microPET studies in anesthetized rats yielded concurrent increases in glutamate release (126.9 ± 5.3%, p < 0.001; n = 11) and decreases in striatal [11C]ABP688 binding (6.8 ± 9.6%, p < 0.05). These latter two effects, however, were not significantly correlated (r = 0.25, p = 0.46). In humans, a laboratory stressor yielded significant changes in self-reported mood (ps < 0.041), sympathetic system activations (ps < 0.042), and the MRS index of striatal glutamate reuptake following excitatory neurotransmission, Glx/Cr levels (p = 0.048). These effects, however, were not accompanied by significant changes in [11C]ABP688 BPND (ps > 0.21, n = 9) or correlated with each other (ps > 0.074). Together, these studies document EtOH-induced glutamate release from neurons, EtOH-induced decreases in [11C]ABP688 binding, and stress-induced changes in glutamate turnover, yet fail to provide evidence that the PET [11C]ABP688 method can be exploited to quantify moderate changes in glutamate release. The results underscore the need for highly controlled testing conditions during PET measures of mGlu5 receptors.
{"title":"Testing PET-[11C]ABP688 as a tool to quantify glutamate release in vivo","authors":"Hussein Bdair, Marie Sato-Fitoussi, Stéphane Planche, L. Moquin, M. Kang, A. Aliaga, A. Nagano-Saito, Kelly Smart, S. M. Cox, Jamie Near, A. Aguilar-Valles, G. Massarweh, Pedro Rosa-Neto, C. Benkelfat, j.-p. soucy, Alexey Kostikov, Alain Gratton, M. Leyton","doi":"10.1162/imag_a_00126","DOIUrl":"https://doi.org/10.1162/imag_a_00126","url":null,"abstract":"Abstract The excitatory neurotransmitter glutamate plays a critical role in experience-dependent neuroplasticity, including addiction-related processes. To date, however, it is not possible to measure glutamate release in the living human brain. Positron emission tomography (PET) with [11C]ABP688, a selective allosteric antagonist of metabotropic type 5 glutamate (mGlu5) receptors, could offer an effective strategy. To test this proposition, we conducted a series of studies in rats using microdialysis and [11C]ABP688 microPET imaging, and in humans using PET and magnetic resonance spectroscopy (MRS). Significant calcium-dependent glutamate release was identified in the ventral striatum of awake rats (190.5 ± 34.7%, p < 0.05; n = 7) following administration of a low dose of ethanol (EtOH; 20%, 0.5 g/kg), a pharmacological challenge readily translatable to human research. Simultaneous microdialysis and microPET studies in anesthetized rats yielded concurrent increases in glutamate release (126.9 ± 5.3%, p < 0.001; n = 11) and decreases in striatal [11C]ABP688 binding (6.8 ± 9.6%, p < 0.05). These latter two effects, however, were not significantly correlated (r = 0.25, p = 0.46). In humans, a laboratory stressor yielded significant changes in self-reported mood (ps < 0.041), sympathetic system activations (ps < 0.042), and the MRS index of striatal glutamate reuptake following excitatory neurotransmission, Glx/Cr levels (p = 0.048). These effects, however, were not accompanied by significant changes in [11C]ABP688 BPND (ps > 0.21, n = 9) or correlated with each other (ps > 0.074). Together, these studies document EtOH-induced glutamate release from neurons, EtOH-induced decreases in [11C]ABP688 binding, and stress-induced changes in glutamate turnover, yet fail to provide evidence that the PET [11C]ABP688 method can be exploited to quantify moderate changes in glutamate release. The results underscore the need for highly controlled testing conditions during PET measures of mGlu5 receptors.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"309 ","pages":"1-18"},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140755811","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}
Vinay S. Raghavan, James A. O'Sullivan, J. Herrero, Stephan Bickel, A. Mehta, N. Mesgarani
Abstract Listeners with hearing loss have trouble following a conversation in multitalker environments. While modern hearing aids can generally amplify speech, these devices are unable to tune into a target speaker without first knowing to which speaker a user aims to attend. Brain-controlled hearing aids have been proposed using auditory attention decoding (AAD) methods, but current methods use the same model to compare the speech stimulus and neural response, regardless of the dynamic overlap between talkers which is known to influence neural encoding. Here, we propose a novel framework that directly classifies event-related potentials (ERPs) evoked by glimpsed and masked acoustic events to determine whether the source of the event was attended. We present a system that identifies auditory events using the local maxima in the envelope rate of change, assesses the temporal masking of auditory events relative to competing speakers, and utilizes masking-specific ERP classifiers to determine if the source of the event was attended. Using intracranial electrophysiological recordings, we showed that high gamma ERPs from recording sites in auditory cortex can effectively decode the attention of subjects. This method of AAD provides higher accuracy, shorter switch times, and more stable decoding results compared with traditional correlational methods, permitting the quick and accurate detection of changes in a listener’s attentional focus. This framework also holds unique potential for detecting instances of divided attention and inattention. Overall, we extend the scope of AAD algorithms by introducing the first linear, direct-classification method for determining a listener’s attentional focus that leverages the latest research in multitalker speech perception. This work represents another step toward informing the development of effective and intuitive brain-controlled hearing assistive devices.
{"title":"Improving auditory attention decoding by classifying intracranial responses to glimpsed and masked acoustic events","authors":"Vinay S. Raghavan, James A. O'Sullivan, J. Herrero, Stephan Bickel, A. Mehta, N. Mesgarani","doi":"10.1162/imag_a_00148","DOIUrl":"https://doi.org/10.1162/imag_a_00148","url":null,"abstract":"Abstract Listeners with hearing loss have trouble following a conversation in multitalker environments. While modern hearing aids can generally amplify speech, these devices are unable to tune into a target speaker without first knowing to which speaker a user aims to attend. Brain-controlled hearing aids have been proposed using auditory attention decoding (AAD) methods, but current methods use the same model to compare the speech stimulus and neural response, regardless of the dynamic overlap between talkers which is known to influence neural encoding. Here, we propose a novel framework that directly classifies event-related potentials (ERPs) evoked by glimpsed and masked acoustic events to determine whether the source of the event was attended. We present a system that identifies auditory events using the local maxima in the envelope rate of change, assesses the temporal masking of auditory events relative to competing speakers, and utilizes masking-specific ERP classifiers to determine if the source of the event was attended. Using intracranial electrophysiological recordings, we showed that high gamma ERPs from recording sites in auditory cortex can effectively decode the attention of subjects. This method of AAD provides higher accuracy, shorter switch times, and more stable decoding results compared with traditional correlational methods, permitting the quick and accurate detection of changes in a listener’s attentional focus. This framework also holds unique potential for detecting instances of divided attention and inattention. Overall, we extend the scope of AAD algorithms by introducing the first linear, direct-classification method for determining a listener’s attentional focus that leverages the latest research in multitalker speech perception. This work represents another step toward informing the development of effective and intuitive brain-controlled hearing assistive devices.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"339 4","pages":"1-19"},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140781026","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}
Justin T. Fleming, J. M. Njoroge, Abigail L. Noyce, Tyler K. Perrachione, Barbara G. Shinn-Cunningham
Abstract Making sense of our environment requires us to extract temporal and spatial information from multiple sensory modalities, particularly audition and vision. Often, we must hold this sensory information in working memory (WM) to guide future actions, while simultaneously processing new sensory inputs as they arise. However, these processes of WM maintenance and perceptual processing can interfere with one another when the tasks rely on similar cognitive resources. fMRI studies have uncovered attention and WM networks that are specialized for either auditory-temporal or visual-spatial processing; the functional specialization of these networks makes specific predictions about patterns of interference between perceptual processing and WM. Specifically, we hypothesized that dual-task interference should increase when the tasks share a common sensory modality, a common information domain (temporal vs. spatial processing), or both. To test these predictions, we asked participants to store temporal or spatial information about auditory or visual stimuli in WM. On some trials, participants also performed an intervening auditory task, which was either temporal or spatial, during WM retention. Errors on WM recall and perceptual judgment tasks both generally increased when the tasks relied on shared modality- and domain-biased resources, with maximal interference when both tasks were auditory-temporal. Pupil dilations were also larger and started earlier when both tasks were auditory-temporal, indicating an increase in cognitive effort to overcome the interference. Event-related potentials (ERPs) and alpha-band oscillatory activity revealed neural signatures of domain-based interference even when the tasks were presented in different sensory modalities, when behavioral differences were masked by ceiling effects. These results demonstrate that sensory modality and information domain jointly affect how task information is represented in WM, consistent with past work demonstrating how tasks engage complementary auditory-temporal and visual-spatial cognitive control networks.
{"title":"Sensory modality and information domain contribute jointly to dual-task interference between working memory and perceptual processing","authors":"Justin T. Fleming, J. M. Njoroge, Abigail L. Noyce, Tyler K. Perrachione, Barbara G. Shinn-Cunningham","doi":"10.1162/imag_a_00130","DOIUrl":"https://doi.org/10.1162/imag_a_00130","url":null,"abstract":"Abstract Making sense of our environment requires us to extract temporal and spatial information from multiple sensory modalities, particularly audition and vision. Often, we must hold this sensory information in working memory (WM) to guide future actions, while simultaneously processing new sensory inputs as they arise. However, these processes of WM maintenance and perceptual processing can interfere with one another when the tasks rely on similar cognitive resources. fMRI studies have uncovered attention and WM networks that are specialized for either auditory-temporal or visual-spatial processing; the functional specialization of these networks makes specific predictions about patterns of interference between perceptual processing and WM. Specifically, we hypothesized that dual-task interference should increase when the tasks share a common sensory modality, a common information domain (temporal vs. spatial processing), or both. To test these predictions, we asked participants to store temporal or spatial information about auditory or visual stimuli in WM. On some trials, participants also performed an intervening auditory task, which was either temporal or spatial, during WM retention. Errors on WM recall and perceptual judgment tasks both generally increased when the tasks relied on shared modality- and domain-biased resources, with maximal interference when both tasks were auditory-temporal. Pupil dilations were also larger and started earlier when both tasks were auditory-temporal, indicating an increase in cognitive effort to overcome the interference. Event-related potentials (ERPs) and alpha-band oscillatory activity revealed neural signatures of domain-based interference even when the tasks were presented in different sensory modalities, when behavioral differences were masked by ceiling effects. These results demonstrate that sensory modality and information domain jointly affect how task information is represented in WM, consistent with past work demonstrating how tasks engage complementary auditory-temporal and visual-spatial cognitive control networks.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"103 2","pages":"1-22"},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140790347","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}
Huan Huang, Yuchao Jiang, Hechun Li, Hanxi Wu, Xiaorong Feng, Jinnan Gong, Sisi Jiang, Dezhong Yao, C. Luo
Abstract Gradients capture the underlying functional organization of the brain. Cortical gradients have been well characterized, however very little is known about the underlying gradient of the white matter. Here, we proposed a functionally gradient mapping of the corpus callosum by using blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD-fMRI), which for the first time uncovered three distinct but stable spatial axes: posterior-anterior, dorsal-ventral, and left-right. The three spatial patterns were replicated in another independent cohort and robust across scanning conditions. We further associated the three gradient maps with brain anatomy, connectome, and task-related brain functions, by using structural magnetic resonance imaging, both resting-state and task fMRI, and diffusion tensor imaging data. The posterior-anterior gradient distribution of the corpus callosum showed a similar pattern with the cerebral cortex, gradually extending from the primary cortex to the transmodal cortex. The dorsal-ventral gradient distribution revealed an N-shaped pattern from the primary cortex to the higher-order cognitive cortex. The posterior-anterior and dorsal-ventral gradient maps were also associated with white-matter microstructures, such as fractional anisotropy and myelin water fraction. The left-right gradient showed an inverted V-shaped pattern, which delineated the inter-hemisphere separation. These findings provide fundamental insight into the functional organization of the human corpus callosum, unveiling potential patterns of functional interaction with the cerebral cortex and their associations with cognitive behaviors.
摘要 梯度反映了大脑的基本功能组织。大脑皮层梯度已被很好地描述，但对白质的基本梯度却知之甚少。在此，我们利用血氧水平依赖性功能磁共振成像（BOLD-fMRI）提出了胼胝体的功能梯度图谱，首次发现了三个不同但稳定的空间轴：后-前、背-腹和左-右。这三种空间模式在另一个独立队列中得到了复制，并在不同扫描条件下保持稳定。通过使用结构磁共振成像、静息态和任务 fMRI 以及弥散张量成像数据，我们进一步将这三种梯度图与大脑解剖、连接组以及与任务相关的大脑功能联系起来。胼胝体的前后梯度分布显示出与大脑皮层相似的模式，从初级皮层逐渐延伸到跨模态皮层。背腹梯度分布显示了从初级皮层到高阶认知皮层的 N 型模式。后-前和背-腹梯度图还与白质微结构有关，如分数各向异性和髓鞘水分数。左右梯度图呈倒 V 形，划分了大脑半球之间的分隔。这些发现从根本上揭示了人类胼胝体的功能组织，揭示了其与大脑皮层功能相互作用的潜在模式及其与认知行为的关联。
{"title":"Functional organization of the human corpus callosum unveiled with BOLD-fMRI gradients","authors":"Huan Huang, Yuchao Jiang, Hechun Li, Hanxi Wu, Xiaorong Feng, Jinnan Gong, Sisi Jiang, Dezhong Yao, C. Luo","doi":"10.1162/imag_a_00115","DOIUrl":"https://doi.org/10.1162/imag_a_00115","url":null,"abstract":"Abstract Gradients capture the underlying functional organization of the brain. Cortical gradients have been well characterized, however very little is known about the underlying gradient of the white matter. Here, we proposed a functionally gradient mapping of the corpus callosum by using blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD-fMRI), which for the first time uncovered three distinct but stable spatial axes: posterior-anterior, dorsal-ventral, and left-right. The three spatial patterns were replicated in another independent cohort and robust across scanning conditions. We further associated the three gradient maps with brain anatomy, connectome, and task-related brain functions, by using structural magnetic resonance imaging, both resting-state and task fMRI, and diffusion tensor imaging data. The posterior-anterior gradient distribution of the corpus callosum showed a similar pattern with the cerebral cortex, gradually extending from the primary cortex to the transmodal cortex. The dorsal-ventral gradient distribution revealed an N-shaped pattern from the primary cortex to the higher-order cognitive cortex. The posterior-anterior and dorsal-ventral gradient maps were also associated with white-matter microstructures, such as fractional anisotropy and myelin water fraction. The left-right gradient showed an inverted V-shaped pattern, which delineated the inter-hemisphere separation. These findings provide fundamental insight into the functional organization of the human corpus callosum, unveiling potential patterns of functional interaction with the cerebral cortex and their associations with cognitive behaviors.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"153 2","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":"140403085","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}
Maëlan Q. Menétrey, M. Roinishvili, E. Chkonia, Michael H. Herzog, David Pascucci
Abstract Recent work suggests that the individual alpha peak frequency (IAPF) reflects the temporal resolution of visual processing: individuals with higher IAPF can segregate stimuli at shorter intervals compared to those with lower IAPF. However, this evidence mainly comes from studies focusing on short intervals, with stimulus onset asynchronies (SOA) rarely extending beyond a single alpha cycle (e.g., ~100 ms). Here, we investigated the relationship between IAPF and performance in visual backward masking (VBM), which allowed us to test the effects of IAPF for longer SOAs than an alpha cycle. A group of healthy controls (N = 79) and schizophrenia patients (N = 121), who generally exhibit lower IAPF, were tested in conditions with a Vernier shown alone, a Vernier followed by a mask at two SOAs (30 and 150 ms), or only a mask. Our results show that IAPF can predict VBM performance in all conditions with a Vernier. Furthermore, in both the control and schizophrenia groups, individuals with higher IAPF showed reduced masking effects, even when the SOA of 150 ms exceeded the alpha cycle. These findings challenge the notion that IAPF is exclusively related to temporal resolution and visual processing within a single alpha cycle. We discuss alternative mechanisms by which IAPF determines visual performance.
{"title":"Alpha peak frequency affects visual performance beyond temporal resolution","authors":"Maëlan Q. Menétrey, M. Roinishvili, E. Chkonia, Michael H. Herzog, David Pascucci","doi":"10.1162/imag_a_00107","DOIUrl":"https://doi.org/10.1162/imag_a_00107","url":null,"abstract":"Abstract Recent work suggests that the individual alpha peak frequency (IAPF) reflects the temporal resolution of visual processing: individuals with higher IAPF can segregate stimuli at shorter intervals compared to those with lower IAPF. However, this evidence mainly comes from studies focusing on short intervals, with stimulus onset asynchronies (SOA) rarely extending beyond a single alpha cycle (e.g., ~100 ms). Here, we investigated the relationship between IAPF and performance in visual backward masking (VBM), which allowed us to test the effects of IAPF for longer SOAs than an alpha cycle. A group of healthy controls (N = 79) and schizophrenia patients (N = 121), who generally exhibit lower IAPF, were tested in conditions with a Vernier shown alone, a Vernier followed by a mask at two SOAs (30 and 150 ms), or only a mask. Our results show that IAPF can predict VBM performance in all conditions with a Vernier. Furthermore, in both the control and schizophrenia groups, individuals with higher IAPF showed reduced masking effects, even when the SOA of 150 ms exceeded the alpha cycle. These findings challenge the notion that IAPF is exclusively related to temporal resolution and visual processing within a single alpha cycle. We discuss alternative mechanisms by which IAPF determines visual performance.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"16 22","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":"140277357","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}
Giuseppe de Alteriis, Eilidh MacNicol, Fran Hancock, Alessandro Ciaramella, Diana Cash, P. Expert, Federico E. Turkheimer
Abstract Dynamic Functional Connectivity (dFC) is the study of the dynamic patterns of interaction that characterise brain function. Numerous numerical methods are available to compute and analyse dFC from high-dimensional data. In fMRI, a number of them rely on the computation of the instantaneous Phase Alignment (iPA) matrix (also known as instantaneous Phase Locking). Their limitations are the high computational cost and the concomitant need to introduce approximations with ensuing information loss. Here, we introduce the analytical decomposition of the iPA. This has two advantages. Firstly, we achieve an up to 1000-fold reduction in computing time without information loss. Secondly, we can formally introduce two alternative approaches to the analysis of the resulting time-varying instantaneous connectivity patterns, Discrete and Continuous EiDA (Eigenvector Dynamic Analysis), and a related set of metrics to quantify the total amount of instantaneous connectivity, drawn from dynamical systems and information theory. We applied EiDA to a dataset from 48 rats that underwent functional magnetic resonance imaging (fMRI) at four stages during a longitudinal study of ageing. Using EiDA, we found that the metrics we introduce provided robust markers of ageing with decreases in total connectivity and metastability, and an increase in informational complexity over the life span. This suggests that ageing reduces the available functional repertoire that is postulated to support cognitive functions and overt behaviours, slows down the exploration of this reduced repertoire, and decreases the coherence of its structure. In summary, EiDA is a method to extract lossless connectivity information that requires significantly less computational time, and provides robust and analytically principled metrics for brain dynamics. These metrics are interpretable and promising for studies on neurodevelopmental and neurodegenerative disorders.
{"title":"EiDA: A lossless approach for dynamic functional connectivity; application to fMRI data of a model of ageing","authors":"Giuseppe de Alteriis, Eilidh MacNicol, Fran Hancock, Alessandro Ciaramella, Diana Cash, P. Expert, Federico E. Turkheimer","doi":"10.1162/imag_a_00113","DOIUrl":"https://doi.org/10.1162/imag_a_00113","url":null,"abstract":"Abstract Dynamic Functional Connectivity (dFC) is the study of the dynamic patterns of interaction that characterise brain function. Numerous numerical methods are available to compute and analyse dFC from high-dimensional data. In fMRI, a number of them rely on the computation of the instantaneous Phase Alignment (iPA) matrix (also known as instantaneous Phase Locking). Their limitations are the high computational cost and the concomitant need to introduce approximations with ensuing information loss. Here, we introduce the analytical decomposition of the iPA. This has two advantages. Firstly, we achieve an up to 1000-fold reduction in computing time without information loss. Secondly, we can formally introduce two alternative approaches to the analysis of the resulting time-varying instantaneous connectivity patterns, Discrete and Continuous EiDA (Eigenvector Dynamic Analysis), and a related set of metrics to quantify the total amount of instantaneous connectivity, drawn from dynamical systems and information theory. We applied EiDA to a dataset from 48 rats that underwent functional magnetic resonance imaging (fMRI) at four stages during a longitudinal study of ageing. Using EiDA, we found that the metrics we introduce provided robust markers of ageing with decreases in total connectivity and metastability, and an increase in informational complexity over the life span. This suggests that ageing reduces the available functional repertoire that is postulated to support cognitive functions and overt behaviours, slows down the exploration of this reduced repertoire, and decreases the coherence of its structure. In summary, EiDA is a method to extract lossless connectivity information that requires significantly less computational time, and provides robust and analytically principled metrics for brain dynamics. These metrics are interpretable and promising for studies on neurodevelopmental and neurodegenerative disorders.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"100 ","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":"140276191","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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