Pub Date : 2024-01-03DOI: 10.3389/fnimg.2023.1320989
Nicole Angenstein
Age-related changes of asymmetries in the auditory system and decreasing efficiency of hemispheric interaction have been discussed for some time. This mini-review discusses recent neuroimaging studies on alterations in lateralization of cortical processing and structural changes concerning the division of labor and interaction between hemispheres during auditory processing in elderly people with the focus on people without severe hearing loss. Several changes of asymmetries in anatomy, function and neurotransmitter concentration were observed in auditory cortical areas of older compared to younger adults. It was shown that connections between left and right auditory cortex are reduced during aging. Functionally, aging seems to lead to a reduction in asymmetry of auditory processing. However, the results do not always point into the same direction. Furthermore, correlations between function, anatomy and behavior in the left and right hemisphere appear to differ between younger and older adults. The changes in auditory cortex asymmetries with aging might be due to compensation of declining processing capacities, but at the same time these mechanisms could impair the balanced division of labor between the two hemispheres that is required for the processing of complex auditory stimuli such as speech. Neuroimaging studies are essential to follow the slow changes with aging as in the beginning no behavioral effects might be visible due to compensation. Future studies should control well for peripheral hearing loss and cognitive decline. Furthermore, for the interpretability of results it is necessary to use specific tasks with well-controlled task difficulty.
{"title":"Asymmetries and hemispheric interaction in the auditory system of elderly people","authors":"Nicole Angenstein","doi":"10.3389/fnimg.2023.1320989","DOIUrl":"https://doi.org/10.3389/fnimg.2023.1320989","url":null,"abstract":"Age-related changes of asymmetries in the auditory system and decreasing efficiency of hemispheric interaction have been discussed for some time. This mini-review discusses recent neuroimaging studies on alterations in lateralization of cortical processing and structural changes concerning the division of labor and interaction between hemispheres during auditory processing in elderly people with the focus on people without severe hearing loss. Several changes of asymmetries in anatomy, function and neurotransmitter concentration were observed in auditory cortical areas of older compared to younger adults. It was shown that connections between left and right auditory cortex are reduced during aging. Functionally, aging seems to lead to a reduction in asymmetry of auditory processing. However, the results do not always point into the same direction. Furthermore, correlations between function, anatomy and behavior in the left and right hemisphere appear to differ between younger and older adults. The changes in auditory cortex asymmetries with aging might be due to compensation of declining processing capacities, but at the same time these mechanisms could impair the balanced division of labor between the two hemispheres that is required for the processing of complex auditory stimuli such as speech. Neuroimaging studies are essential to follow the slow changes with aging as in the beginning no behavioral effects might be visible due to compensation. Future studies should control well for peripheral hearing loss and cognitive decline. Furthermore, for the interpretability of results it is necessary to use specific tasks with well-controlled task difficulty.","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"57 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139451560","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}
Pub Date : 2023-12-21DOI: 10.3389/fnimg.2023.1138193
Zhipeng Cao, Matthew McCabe, Peter Callas, R. Cupertino, J. Ottino-González, Alistair Murphy, Devarshi Pancholi, N. Schwab, Orr Catherine, Kent Hutchison, J. Cousijn, Alain Dagher, John J. Foxe, A. Goudriaan, Robert Hester, Chiang‐Shan R. Li, Wesley K. Thompson, Angelica M. Morales, Edythe D. London, V. Lorenzetti, M. Luijten, Rocio Martin-Santos, R. Momenan, Martin P. Paulus, L. Schmaal, Rajita Sinha, Nadia Solowij, D. Stein, Elliot A. Stein, A. Uhlmann, R. V. van Holst, D. Veltman, R. Wiers, Murat Yücel, Sheng Zhang, P. Conrod, S. Mackey, Hugh Garavan
There are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance.We estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method.The results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = −0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments.Our findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.
{"title":"Recalibrating single-study effect sizes using hierarchical Bayesian models","authors":"Zhipeng Cao, Matthew McCabe, Peter Callas, R. Cupertino, J. Ottino-González, Alistair Murphy, Devarshi Pancholi, N. Schwab, Orr Catherine, Kent Hutchison, J. Cousijn, Alain Dagher, John J. Foxe, A. Goudriaan, Robert Hester, Chiang‐Shan R. Li, Wesley K. Thompson, Angelica M. Morales, Edythe D. London, V. Lorenzetti, M. Luijten, Rocio Martin-Santos, R. Momenan, Martin P. Paulus, L. Schmaal, Rajita Sinha, Nadia Solowij, D. Stein, Elliot A. Stein, A. Uhlmann, R. V. van Holst, D. Veltman, R. Wiers, Murat Yücel, Sheng Zhang, P. Conrod, S. Mackey, Hugh Garavan","doi":"10.3389/fnimg.2023.1138193","DOIUrl":"https://doi.org/10.3389/fnimg.2023.1138193","url":null,"abstract":"There are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance.We estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method.The results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = −0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments.Our findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"40 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138950118","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}
Pub Date : 2023-12-21DOI: 10.3389/fnimg.2023.1331404
Arnaud Delorme, S. Makeig
{"title":"This is no “ICA bug”: response to the article, “ICA's bug: how ghost ICs emerge from effective rank deficiency caused by EEG electrode interpolation and incorrect re-referencing”","authors":"Arnaud Delorme, S. Makeig","doi":"10.3389/fnimg.2023.1331404","DOIUrl":"https://doi.org/10.3389/fnimg.2023.1331404","url":null,"abstract":"","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"52 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138951190","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}
Pub Date : 2023-12-15DOI: 10.3389/fnimg.2023.1324107
Susanne G. Mueller
Many neurodegenerative diseases affect the brainstem and often do so in an early stage. The overall goal of this project was (a) to develop a method to segment internal brainstem structures from T1 and T2 weighted sequences by taking advantage of the superior myelin contrast of the T1/T2 ratio image (RATIO) and (b) to test if this approach provides biological meaningful information by investigating the effects of aging on different brainstem gray matter structures.675 T1 and T2 weighted images were obtained from the Human Connectome Project Aging. The intensities of the T1 and T2 images were re-scaled and RATIO images calculated. The brainstem was isolated and k-means clustering used to identify five intensity clusters. Non-linear diffeomorphic mapping was used to warp the five intensity clusters in subject space into a common space to generate probabilistic group averages/priors that were used to inform the final probabilistic segmentations at the single subject level. The five clusters corresponded to five brainstem tissue types (two gray matters, two mixed gray/white, and 1 csf/tissue partial volume).These cluster maps were used to calculate Jacobian determinant maps and the mean Jacobians of 48 brainstem gray matter structures extracted. Significant linear or quadratic age effects were found for all but five structures.These findings suggest that it is possible to obtain a biologically meaningful segmentation of internal brainstem structures from T1 and T2 weighted sequences using a fully automated segmentation procedure.
{"title":"Mapping internal brainstem structures using T1 and T2 weighted 3T images","authors":"Susanne G. Mueller","doi":"10.3389/fnimg.2023.1324107","DOIUrl":"https://doi.org/10.3389/fnimg.2023.1324107","url":null,"abstract":"Many neurodegenerative diseases affect the brainstem and often do so in an early stage. The overall goal of this project was (a) to develop a method to segment internal brainstem structures from T1 and T2 weighted sequences by taking advantage of the superior myelin contrast of the T1/T2 ratio image (RATIO) and (b) to test if this approach provides biological meaningful information by investigating the effects of aging on different brainstem gray matter structures.675 T1 and T2 weighted images were obtained from the Human Connectome Project Aging. The intensities of the T1 and T2 images were re-scaled and RATIO images calculated. The brainstem was isolated and k-means clustering used to identify five intensity clusters. Non-linear diffeomorphic mapping was used to warp the five intensity clusters in subject space into a common space to generate probabilistic group averages/priors that were used to inform the final probabilistic segmentations at the single subject level. The five clusters corresponded to five brainstem tissue types (two gray matters, two mixed gray/white, and 1 csf/tissue partial volume).These cluster maps were used to calculate Jacobian determinant maps and the mean Jacobians of 48 brainstem gray matter structures extracted. Significant linear or quadratic age effects were found for all but five structures.These findings suggest that it is possible to obtain a biologically meaningful segmentation of internal brainstem structures from T1 and T2 weighted sequences using a fully automated segmentation procedure.","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"19 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138970565","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}
Pub Date : 2023-12-01DOI: 10.3389/fnimg.2023.1260893
Aino L. I. Alahäivälä, D. Thaploo, Simon Wein, Philipp Seidel, Marco Riebel, Thomas Hummel, J. Schwarzbach
In contrast to other sensory domains, detection of primary olfactory processes using functional magnetic resonance imaging has proven to be notably challenging with conventional block designs. This difficulty arises from significant habituation and hemodynamic responses in olfactory areas that do not appear to align with extended boxcar functions convolved with a generic hemodynamic response model. Consequently, some researchers have advocated for a transition to event-related designs, despite their known lower detection power compared to block designs.Here, we conducted a block design experiment with 16s of continuous odorant stimulation alternating with 16s of continuous odorless air stimulation in 33 healthy participants. We compared four statistical analyses that relied either on standard block designs (SBD1-2) or on block designs that were modulated by the participants' individual breathing patterns (MBD1-2).We found that such modulated block designs were comparatively more powerful than standard block designs, despite having a substantially lower design efficiency. Using whole-brain effect size maps, we observed that the right insular and medial aspects of the left piriform cortex exhibited a preference for a breathing-modulated analysis approach.Research in olfaction that necessitates designs with longer-lasting blocks, such as those employed in the investigation of state-dependent processing, will benefit from the breathing-modulated analyses outlined in this study.
{"title":"Inhalation-modulated detection of olfactory BOLD responses in the human brain","authors":"Aino L. I. Alahäivälä, D. Thaploo, Simon Wein, Philipp Seidel, Marco Riebel, Thomas Hummel, J. Schwarzbach","doi":"10.3389/fnimg.2023.1260893","DOIUrl":"https://doi.org/10.3389/fnimg.2023.1260893","url":null,"abstract":"In contrast to other sensory domains, detection of primary olfactory processes using functional magnetic resonance imaging has proven to be notably challenging with conventional block designs. This difficulty arises from significant habituation and hemodynamic responses in olfactory areas that do not appear to align with extended boxcar functions convolved with a generic hemodynamic response model. Consequently, some researchers have advocated for a transition to event-related designs, despite their known lower detection power compared to block designs.Here, we conducted a block design experiment with 16s of continuous odorant stimulation alternating with 16s of continuous odorless air stimulation in 33 healthy participants. We compared four statistical analyses that relied either on standard block designs (SBD1-2) or on block designs that were modulated by the participants' individual breathing patterns (MBD1-2).We found that such modulated block designs were comparatively more powerful than standard block designs, despite having a substantially lower design efficiency. Using whole-brain effect size maps, we observed that the right insular and medial aspects of the left piriform cortex exhibited a preference for a breathing-modulated analysis approach.Research in olfaction that necessitates designs with longer-lasting blocks, such as those employed in the investigation of state-dependent processing, will benefit from the breathing-modulated analyses outlined in this study.","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138613088","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}
Pub Date : 2023-12-01DOI: 10.3389/fnimg.2023.1252261
Corinne Donnay, H. Dieckhaus, C. Tsagkas, María Inés Gaitán, E. Beck, Andrew Mullins, Daniel S. Reich, G. Nair
Automatic whole brain and lesion segmentation at 7T presents challenges, primarily from bias fields, susceptibility artifacts including distortions, and registration errors. Here, we sought to use deep learning algorithms (D/L) to do both skull stripping and whole brain segmentation on multiple imaging contrasts generated in a single Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) acquisition on participants clinically diagnosed with multiple sclerosis (MS), bypassing registration errors.Brain scans Segmentation from 3T and 7T scanners were analyzed with software packages such as FreeSurfer, Classification using Derivative-based Features (C-DEF), nnU-net, and a novel 3T-to-7T transfer learning method, Pseudo-Label Assisted nnU-Net (PLAn). 3T and 7T MRIs acquired within 9 months from 25 study participants with MS (Cohort 1) were used for training and optimizing. Eight MS patients (Cohort 2) scanned only at 7T, but with expert annotated lesion segmentation, was used to further validate the algorithm on a completely unseen dataset. Segmentation results were rated visually by experts in a blinded fashion and quantitatively using Dice Similarity Coefficient (DSC).Of the methods explored here, nnU-Net and PLAn produced the best tissue segmentation at 7T for all tissue classes. In both quantitative and qualitative analysis, PLAn significantly outperformed nnU-Net (and other methods) in lesion detection in both cohorts. PLAn's lesion DSC improved by 16% compared to nnU-Net.Limited availability of labeled data makes transfer learning an attractive option, and pre-training a nnUNet model using readily obtained 3T pseudo-labels was shown to boost lesion detection capabilities at 7T.
{"title":"Pseudo-Label Assisted nnU-Net enables automatic segmentation of 7T MRI from a single acquisition","authors":"Corinne Donnay, H. Dieckhaus, C. Tsagkas, María Inés Gaitán, E. Beck, Andrew Mullins, Daniel S. Reich, G. Nair","doi":"10.3389/fnimg.2023.1252261","DOIUrl":"https://doi.org/10.3389/fnimg.2023.1252261","url":null,"abstract":"Automatic whole brain and lesion segmentation at 7T presents challenges, primarily from bias fields, susceptibility artifacts including distortions, and registration errors. Here, we sought to use deep learning algorithms (D/L) to do both skull stripping and whole brain segmentation on multiple imaging contrasts generated in a single Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) acquisition on participants clinically diagnosed with multiple sclerosis (MS), bypassing registration errors.Brain scans Segmentation from 3T and 7T scanners were analyzed with software packages such as FreeSurfer, Classification using Derivative-based Features (C-DEF), nnU-net, and a novel 3T-to-7T transfer learning method, Pseudo-Label Assisted nnU-Net (PLAn). 3T and 7T MRIs acquired within 9 months from 25 study participants with MS (Cohort 1) were used for training and optimizing. Eight MS patients (Cohort 2) scanned only at 7T, but with expert annotated lesion segmentation, was used to further validate the algorithm on a completely unseen dataset. Segmentation results were rated visually by experts in a blinded fashion and quantitatively using Dice Similarity Coefficient (DSC).Of the methods explored here, nnU-Net and PLAn produced the best tissue segmentation at 7T for all tissue classes. In both quantitative and qualitative analysis, PLAn significantly outperformed nnU-Net (and other methods) in lesion detection in both cohorts. PLAn's lesion DSC improved by 16% compared to nnU-Net.Limited availability of labeled data makes transfer learning an attractive option, and pre-training a nnUNet model using readily obtained 3T pseudo-labels was shown to boost lesion detection capabilities at 7T.","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":" 41","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138613506","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}
Pub Date : 2023-11-13DOI: 10.3389/fnimg.2023.1178359
Bonnie B. Smith, Yi Zhao, Martin A. Lindquist, Brian Caffo
Background Brain functional connectivity analysis of resting-state functional magnetic resonance imaging (fMRI) data is typically performed in a standardized template space assuming consistency of connections across subjects. Analysis methods can come in the form of one-edge-at-a-time analyses or dimension reduction/decomposition methods. Common to these approaches is an assumption that brain regions are functionally aligned across subjects; however, it is known that this functional alignment assumption is often violated. Methods In this paper, we use subject-level regression models to explain intra-subject variability in connectivity. Covariates can include factors such as geographic distance between two pairs of brain regions, whether the two regions are symmetrically opposite (homotopic), and whether the two regions are members of the same functional network. Additionally, a covariate for each brain region can be included, to account for the possibility that some regions have consistently higher or lower connectivity. This style of analysis allows us to characterize the fraction of variation explained by each type of covariate. Additionally, comparisons across subjects can then be made using the fitted connectivity regression models, offering a more parsimonious alternative to edge-at-a-time approaches. Results We apply our approach to Human Connectome Project data on 268 regions of interest (ROIs), grouped into eight functional networks. We find that a high proportion of variation is explained by region covariates and network membership covariates, while geographic distance and homotopy have high relative importance after adjusting for the number of predictors. We also find that the degree of data repeatability using our connectivity regression model—which uses only partial location information about pairs of ROI's—is comparably as high as the repeatability obtained using full location information. Discussion While our analysis uses data that have been transformed into a common template-space, we also envision the method being useful in multi-atlas registration settings, where subject data remains in its own geometry and templates are warped instead. These results suggest the tantalizing possibility that fMRI connectivity analysis can be performed in subject-space, using less aggressive registration, such as simple affine transformations, multi-atlas subject-space registration, or perhaps even no registration whatsoever.
{"title":"Regression models for partially localized fMRI connectivity analyses","authors":"Bonnie B. Smith, Yi Zhao, Martin A. Lindquist, Brian Caffo","doi":"10.3389/fnimg.2023.1178359","DOIUrl":"https://doi.org/10.3389/fnimg.2023.1178359","url":null,"abstract":"Background Brain functional connectivity analysis of resting-state functional magnetic resonance imaging (fMRI) data is typically performed in a standardized template space assuming consistency of connections across subjects. Analysis methods can come in the form of one-edge-at-a-time analyses or dimension reduction/decomposition methods. Common to these approaches is an assumption that brain regions are functionally aligned across subjects; however, it is known that this functional alignment assumption is often violated. Methods In this paper, we use subject-level regression models to explain intra-subject variability in connectivity. Covariates can include factors such as geographic distance between two pairs of brain regions, whether the two regions are symmetrically opposite (homotopic), and whether the two regions are members of the same functional network. Additionally, a covariate for each brain region can be included, to account for the possibility that some regions have consistently higher or lower connectivity. This style of analysis allows us to characterize the fraction of variation explained by each type of covariate. Additionally, comparisons across subjects can then be made using the fitted connectivity regression models, offering a more parsimonious alternative to edge-at-a-time approaches. Results We apply our approach to Human Connectome Project data on 268 regions of interest (ROIs), grouped into eight functional networks. We find that a high proportion of variation is explained by region covariates and network membership covariates, while geographic distance and homotopy have high relative importance after adjusting for the number of predictors. We also find that the degree of data repeatability using our connectivity regression model—which uses only partial location information about pairs of ROI's—is comparably as high as the repeatability obtained using full location information. Discussion While our analysis uses data that have been transformed into a common template-space, we also envision the method being useful in multi-atlas registration settings, where subject data remains in its own geometry and templates are warped instead. These results suggest the tantalizing possibility that fMRI connectivity analysis can be performed in subject-space, using less aggressive registration, such as simple affine transformations, multi-atlas subject-space registration, or perhaps even no registration whatsoever.","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"28 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136282590","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}
Pub Date : 2023-11-09DOI: 10.3389/fnimg.2023.1201682
Mashaal Syed, Jingya Miao, Anish Sathe, Kichang Kang, Arichena Manmatharayan, Michael Kogan, Caio M. Matias, Ashwini Sharan, Mahdi Alizadeh
Introduction It is now understood that in focal epilepsy, impacted neural regions are not limited to the epileptogenic zone. As such, further investigation into the underlying functional connectivity (FC) patterns in those enduring Temporal Lobe Epilepsy (TLE) with Mesial Temporal Sclerosis (MTS) is imperative to understanding the intricacies of the disease. Methods The rsfMRIs of 17 healthy participants, 10 left-sided TLE-MTS patients with a pre-operative history of focal impaired awareness seizures (FIA), and 13 left-sided TLE-MTS patients with a pre-operative history of focal aware seizures (FA) were compared to determine the existence of distinct FC patterns with respect to seizure types. Similarly, the rsfMRIs of the above-mentioned healthy participants, 16 left-sided TLE-MTS individuals who were seizure-free (SF) 12 months postoperatively, and 16 left-sided TLE-MTS persons without seizure freedom (nSF) were interrogated. The ROI-to-ROI connectivity analysis included a total of 175 regions of interest (ROIs) and accounted for both age and duration of epileptic activity. Significant correlations were determined via two-sample t- tests and Bonferroni correction (α = 0.05). Results Comparisons of FA and FIA groups depicted significant correlations between the contralateral anterior cingulate gyrus, subgenual region, and the contralateral cerebellum, lobule III ( p -value = 2.26e-4, mean z-score = −0.05 ± 0.28, T = −4.23). Comparisons of SF with nSF depicted two significantly paired-ROIs; the contralateral amygdala and the contralateral precuneus ( p- value = 2.9e-5, mean z-score = −0.12 ± 0.19, T = 4.98), as well as the contralateral locus coeruleus and the ipsilateral intralaminar nucleus ( p -value= 1.37e-4, mean z-score = 0.06 ± 0.17, T = −4.41). Significance FC analysis proves to be a lucrative modality for exploring unique signatures with respect to seizure types and postoperative outcomes. By furthering our understanding of the differences between epileptic phenotypes, we can achieve improvement in future treatment modalities not limited to targeting advancements.
{"title":"Profiles of resting state functional connectivity in temporal lobe epilepsy associated with post-laser interstitial thermal therapy seizure outcomes and semiologies","authors":"Mashaal Syed, Jingya Miao, Anish Sathe, Kichang Kang, Arichena Manmatharayan, Michael Kogan, Caio M. Matias, Ashwini Sharan, Mahdi Alizadeh","doi":"10.3389/fnimg.2023.1201682","DOIUrl":"https://doi.org/10.3389/fnimg.2023.1201682","url":null,"abstract":"Introduction It is now understood that in focal epilepsy, impacted neural regions are not limited to the epileptogenic zone. As such, further investigation into the underlying functional connectivity (FC) patterns in those enduring Temporal Lobe Epilepsy (TLE) with Mesial Temporal Sclerosis (MTS) is imperative to understanding the intricacies of the disease. Methods The rsfMRIs of 17 healthy participants, 10 left-sided TLE-MTS patients with a pre-operative history of focal impaired awareness seizures (FIA), and 13 left-sided TLE-MTS patients with a pre-operative history of focal aware seizures (FA) were compared to determine the existence of distinct FC patterns with respect to seizure types. Similarly, the rsfMRIs of the above-mentioned healthy participants, 16 left-sided TLE-MTS individuals who were seizure-free (SF) 12 months postoperatively, and 16 left-sided TLE-MTS persons without seizure freedom (nSF) were interrogated. The ROI-to-ROI connectivity analysis included a total of 175 regions of interest (ROIs) and accounted for both age and duration of epileptic activity. Significant correlations were determined via two-sample t- tests and Bonferroni correction (α = 0.05). Results Comparisons of FA and FIA groups depicted significant correlations between the contralateral anterior cingulate gyrus, subgenual region, and the contralateral cerebellum, lobule III ( p -value = 2.26e-4, mean z-score = −0.05 ± 0.28, T = −4.23). Comparisons of SF with nSF depicted two significantly paired-ROIs; the contralateral amygdala and the contralateral precuneus ( p- value = 2.9e-5, mean z-score = −0.12 ± 0.19, T = 4.98), as well as the contralateral locus coeruleus and the ipsilateral intralaminar nucleus ( p -value= 1.37e-4, mean z-score = 0.06 ± 0.17, T = −4.41). Significance FC analysis proves to be a lucrative modality for exploring unique signatures with respect to seizure types and postoperative outcomes. By furthering our understanding of the differences between epileptic phenotypes, we can achieve improvement in future treatment modalities not limited to targeting advancements.","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":" 43","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135292484","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}
Pub Date : 2023-10-31DOI: 10.3389/fnimg.2023.1153115
Alexander Asturias, Thomas Knoblauch, Alan Rodriguez, Cheryl Vanier, Caroline Le Tohic, Brandon Barrett, Michael Eisenberg, Rachelle Gibbert, Lennon Zimmerman, Shaunaq Parikh, Anh Nguyen, Sherwin Azad, Leo Germin, Enrico Fazzini, Travis Snyder
Background Mild traumatic brain injuries (mTBIs) comprise 80% of all TBI, but conventional MRI techniques are often insensitive to the subtle changes and injuries produced in a concussion. Diffusion tensor imaging (DTI) is one of the most sensitive MRI techniques for mTBI studies with outcome and symptom associations described. The corpus callosum (CC) is one of the most studied fiber tracts in TBI and mTBI, but the comprehensive post-mTBI symptom relationship has not fully been explored. Methods This is a retrospective observational study of how quantitative DTI data of the CC and its sub-regions may relate to clinical presentation of symptoms and timing of resolution of symptoms in patients diagnosed with uncomplicated mTBI. DTI and clinical data were obtained retrospectively from 446 (mean age 42 years, range 13–82) civilian patients. From patient medical charts, presentation of the following common post-concussive symptoms was noted: headache, balance issues, cognitive deficits, fatigue, anxiety, depression, and emotional lability. Also recorded was the time between injury and a visit to the physician when improvement or resolution of a particular symptom was reported. FA values from the total CC and 3 subregions of the CC (genu or anterior, mid body, and splenium or posterior) were obtained from hand tracing on the Olea Sphere v3.0 SP12 free-standing workstation. DTI data was obtained from 8 different 3T MRI scanners and harmonized via ComBat harmonization. The statistical models used to explore the association between regional Fractional Anisotropy (FA) values and symptom presentation and time to symptom resolution were logistic regression and interval-censored semi-parametric Cox proportional hazard models, respectively. Subgroups related to age and timing of first scan were also analyzed. Results Patients with the highest FA in the total CC ( p = 0.01), anterior CC ( p < 0.01), and mid-body CC ( p = 0.03), but not the posterior CC ( p = 0.91) recovered faster from post-concussive cognitive deficits. Patients with the highest FA in the posterior CC recovered faster from depression ( p = 0.04) and emotional lability ( p = 0.01). There was no evidence that FA in the CC or any of its sub-regions was associated with symptom presentation or with time to resolution of headache, balance issues, fatigue, or anxiety. Patients with mTBI under 40 had higher FA in the CC and the anterior and mid-body subregions (but not the posterior subregion: p = 1.00) compared to patients 40 or over ( p ≤ 0.01). There was no evidence for differences in symptom presentation based on loss of consciousness (LOC) or sex ( p ≥ 0.18). Conclusion This study suggests that FA of the CC has diagnostic and prognostic value for clinical assessment of mTBI in a large diverse civilian population, particularly in patients with cognitive symptoms.
{"title":"Diffusion in the corpus callosum predicts persistence of clinical symptoms after mild traumatic brain injury, a multi-scanner study","authors":"Alexander Asturias, Thomas Knoblauch, Alan Rodriguez, Cheryl Vanier, Caroline Le Tohic, Brandon Barrett, Michael Eisenberg, Rachelle Gibbert, Lennon Zimmerman, Shaunaq Parikh, Anh Nguyen, Sherwin Azad, Leo Germin, Enrico Fazzini, Travis Snyder","doi":"10.3389/fnimg.2023.1153115","DOIUrl":"https://doi.org/10.3389/fnimg.2023.1153115","url":null,"abstract":"Background Mild traumatic brain injuries (mTBIs) comprise 80% of all TBI, but conventional MRI techniques are often insensitive to the subtle changes and injuries produced in a concussion. Diffusion tensor imaging (DTI) is one of the most sensitive MRI techniques for mTBI studies with outcome and symptom associations described. The corpus callosum (CC) is one of the most studied fiber tracts in TBI and mTBI, but the comprehensive post-mTBI symptom relationship has not fully been explored. Methods This is a retrospective observational study of how quantitative DTI data of the CC and its sub-regions may relate to clinical presentation of symptoms and timing of resolution of symptoms in patients diagnosed with uncomplicated mTBI. DTI and clinical data were obtained retrospectively from 446 (mean age 42 years, range 13–82) civilian patients. From patient medical charts, presentation of the following common post-concussive symptoms was noted: headache, balance issues, cognitive deficits, fatigue, anxiety, depression, and emotional lability. Also recorded was the time between injury and a visit to the physician when improvement or resolution of a particular symptom was reported. FA values from the total CC and 3 subregions of the CC (genu or anterior, mid body, and splenium or posterior) were obtained from hand tracing on the Olea Sphere v3.0 SP12 free-standing workstation. DTI data was obtained from 8 different 3T MRI scanners and harmonized via ComBat harmonization. The statistical models used to explore the association between regional Fractional Anisotropy (FA) values and symptom presentation and time to symptom resolution were logistic regression and interval-censored semi-parametric Cox proportional hazard models, respectively. Subgroups related to age and timing of first scan were also analyzed. Results Patients with the highest FA in the total CC ( p = 0.01), anterior CC ( p &lt; 0.01), and mid-body CC ( p = 0.03), but not the posterior CC ( p = 0.91) recovered faster from post-concussive cognitive deficits. Patients with the highest FA in the posterior CC recovered faster from depression ( p = 0.04) and emotional lability ( p = 0.01). There was no evidence that FA in the CC or any of its sub-regions was associated with symptom presentation or with time to resolution of headache, balance issues, fatigue, or anxiety. Patients with mTBI under 40 had higher FA in the CC and the anterior and mid-body subregions (but not the posterior subregion: p = 1.00) compared to patients 40 or over ( p ≤ 0.01). There was no evidence for differences in symptom presentation based on loss of consciousness (LOC) or sex ( p ≥ 0.18). Conclusion This study suggests that FA of the CC has diagnostic and prognostic value for clinical assessment of mTBI in a large diverse civilian population, particularly in patients with cognitive symptoms.","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135872245","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}
Pub Date : 2023-10-26eCollection Date: 2023-01-01DOI: 10.3389/fnimg.2023.1272061
Jan Klein, Annika Gerken, Niklas Agethen, Sven Rothlübbers, Neeraj Upadhyay, Veronika Purrer, Carsten Schmeel, Valeri Borger, Maya Kovalevsky, Itay Rachmilevitch, Yeruham Shapira, Ullrich Wüllner, Jürgen Jenne
Introduction: Transcranial focused ultrasound therapy (tcFUS) offers precise thermal ablation for treating Parkinson's disease and essential tremor. However, the manual fine-tuning of fiber tracking and segmentation required for accurate treatment planning is time-consuming and demands expert knowledge of complex neuroimaging tools. This raises the question of whether a fully automated pipeline is feasible or if manual intervention remains necessary.
Methods: We investigate the dependence on fiber tractography algorithms, segmentation approaches, and degrees of automation, specifically for essential tremor therapy planning. For that purpose, we compare an automatic pipeline with a manual approach that requires the manual definition of the target point and is based on FMRIB software library (FSL) and other open-source tools.
Results: Our findings demonstrate the high feasibility of automatic fiber tracking and the automated determination of standard treatment coordinates. Employing an automatic fiber tracking approach and deep learning (DL)-supported standard coordinate calculation, we achieve anatomically meaningful results comparable to a manually performed FSL-based pipeline. Individual cases may still exhibit variations, often stemming from differences in region of interest (ROI) segmentation. Notably, the DL-based approach outperforms registration-based methods in producing accurate segmentations. Precise ROI segmentation proves crucial, surpassing the importance of fine-tuning parameters or selecting algorithms. Correct thalamus and red nucleus segmentation play vital roles in ensuring accurate pathway computation.
Conclusion: This study highlights the potential for automation in fiber tracking algorithms for tcFUS therapy, but acknowledges the ongoing need for expert verification and integration of anatomical expertise in treatment planning.
{"title":"Automatic planning of MR-guided transcranial focused ultrasound treatment for essential tremor.","authors":"Jan Klein, Annika Gerken, Niklas Agethen, Sven Rothlübbers, Neeraj Upadhyay, Veronika Purrer, Carsten Schmeel, Valeri Borger, Maya Kovalevsky, Itay Rachmilevitch, Yeruham Shapira, Ullrich Wüllner, Jürgen Jenne","doi":"10.3389/fnimg.2023.1272061","DOIUrl":"10.3389/fnimg.2023.1272061","url":null,"abstract":"<p><strong>Introduction: </strong>Transcranial focused ultrasound therapy (tcFUS) offers precise thermal ablation for treating Parkinson's disease and essential tremor. However, the manual fine-tuning of fiber tracking and segmentation required for accurate treatment planning is time-consuming and demands expert knowledge of complex neuroimaging tools. This raises the question of whether a fully automated pipeline is feasible or if manual intervention remains necessary.</p><p><strong>Methods: </strong>We investigate the dependence on fiber tractography algorithms, segmentation approaches, and degrees of automation, specifically for essential tremor therapy planning. For that purpose, we compare an automatic pipeline with a manual approach that requires the manual definition of the target point and is based on FMRIB software library (FSL) and other open-source tools.</p><p><strong>Results: </strong>Our findings demonstrate the high feasibility of automatic fiber tracking and the automated determination of standard treatment coordinates. Employing an automatic fiber tracking approach and deep learning (DL)-supported standard coordinate calculation, we achieve anatomically meaningful results comparable to a manually performed FSL-based pipeline. Individual cases may still exhibit variations, often stemming from differences in region of interest (ROI) segmentation. Notably, the DL-based approach outperforms registration-based methods in producing accurate segmentations. Precise ROI segmentation proves crucial, surpassing the importance of fine-tuning parameters or selecting algorithms. Correct thalamus and red nucleus segmentation play vital roles in ensuring accurate pathway computation.</p><p><strong>Conclusion: </strong>This study highlights the potential for automation in fiber tracking algorithms for tcFUS therapy, but acknowledges the ongoing need for expert verification and integration of anatomical expertise in treatment planning.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"2 ","pages":"1272061"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10637361/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89720937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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