Pub Date : 2024-02-02DOI: 10.1016/j.ynirp.2024.100195
Aaron Carass , Danielle Greenman , Blake E. Dewey , Peter A. Calabresi , Jerry L. Prince , Dzung L. Pham
Clinical magnetic resonance images (MRIs) lack a standard intensity scale due to differences in scanner hardware and the pulse sequences used to acquire the images. When MRIs are used for quantification, as in the evaluation of white matter lesions (WMLs) in multiple sclerosis, this lack of intensity standardization becomes a critical problem affecting both the staging and tracking of the disease and its treatment. This paper presents a study of harmonization on WML segmentation consistency, which is evaluated using an object detection classification scheme that incorporates manual delineations from both the original and harmonized MRIs. A cohort of ten people scanned on two different imaging platforms was studied. An expert rater, blinded to the image source, manually delineated WMLs on images from both scanners before and after harmonization. It was found that there is closer agreement in both global and per-lesion WML volume and spatial distribution after harmonization, demonstrating the importance of image harmonization prior to the creation of manual delineations. These results could lead to better truth models in both the development and evaluation of automated lesion segmentation algorithms.
{"title":"Image harmonization improves consistency of intra-rater delineations of MS lesions in heterogeneous MRI","authors":"Aaron Carass , Danielle Greenman , Blake E. Dewey , Peter A. Calabresi , Jerry L. Prince , Dzung L. Pham","doi":"10.1016/j.ynirp.2024.100195","DOIUrl":"https://doi.org/10.1016/j.ynirp.2024.100195","url":null,"abstract":"<div><p>Clinical magnetic resonance images (MRIs) lack a standard intensity scale due to differences in scanner hardware and the pulse sequences used to acquire the images. When MRIs are used for quantification, as in the evaluation of white matter lesions (WMLs) in multiple sclerosis, this lack of intensity standardization becomes a critical problem affecting both the staging and tracking of the disease and its treatment. This paper presents a study of harmonization on WML segmentation consistency, which is evaluated using an object detection classification scheme that incorporates manual delineations from both the original and harmonized MRIs. A cohort of ten people scanned on two different imaging platforms was studied. An expert rater, blinded to the image source, manually delineated WMLs on images from both scanners before and after harmonization. It was found that there is closer agreement in both global and per-lesion WML volume and spatial distribution after harmonization, demonstrating the importance of image harmonization prior to the creation of manual delineations. These results could lead to better truth models in both the development and evaluation of automated lesion segmentation algorithms.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"4 1","pages":"Article 100195"},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666956024000011/pdfft?md5=b2927579d2bc3da98e6ecc6c594b70c0&pid=1-s2.0-S2666956024000011-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139674793","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}
Pub Date : 2024-02-02DOI: 10.1016/j.ynirp.2024.100197
Karel Joineau , Mathilde Boussac , Patrice Peran , David Devos , Jean Luc Houeto , Sophie Drapier , David Maltete , Jesus Aguilar , Estelle Harroch , Margherita Fabbri , Clémence Leung , Fabienne Ory-Magne , Melissa Tir , Christine Tranchant , Hayet Salhi , Solène Frismand , Frederique Fluchere , Ana Marques , Olivier Rascol , Emeline Descamps , Christine Brefel-Courbon
Pain is a frequent and disabling non-motor symptom of Parkinson’s Disease (PD). Yet, no treatment to date can efficiently reduce this pain. This article investigates the brain functional connectivity of PD patients with central pain and the effects of levodopa and oxycodone on this connectivity.
Thirty-eight PD patients received either levodopa, oxycodone, or a placebo during an eight-week period. Pain intensity was evaluated using the Visual Analogue Scale and resting-state functional connectivity was measured before and after treatments. PD patients were also separated into two groups: responders and non-responders.
At baseline, the intensity of pain was correlated with the connectivity between the anterior insula and the posterior cingulate cortex and between the nucleus accumbens, the brainstem, and the hippocampus. Levodopa and oxycodone had no specific effects on functional connectivity. Responders had a decrease in connectivity between the anterior insula and the posterior cingulate cortex, while non-responders showed an increase in connectivity.
The correlation between pain intensity and specific brain connectivity may represent a “hyper-awareness” of pain and a distortion of learning and memory systems in PD patients with central pain, leading to a state of chronic pain. The placebo effect could explain the changes in connectivity that are associated with a potential reduction in pain awareness.
{"title":"Parkinsonian central pain is linked to the connectivity of the nucleus accumbens and the anterior insula","authors":"Karel Joineau , Mathilde Boussac , Patrice Peran , David Devos , Jean Luc Houeto , Sophie Drapier , David Maltete , Jesus Aguilar , Estelle Harroch , Margherita Fabbri , Clémence Leung , Fabienne Ory-Magne , Melissa Tir , Christine Tranchant , Hayet Salhi , Solène Frismand , Frederique Fluchere , Ana Marques , Olivier Rascol , Emeline Descamps , Christine Brefel-Courbon","doi":"10.1016/j.ynirp.2024.100197","DOIUrl":"https://doi.org/10.1016/j.ynirp.2024.100197","url":null,"abstract":"<div><p>Pain is a frequent and disabling non-motor symptom of Parkinson’s Disease (PD). Yet, no treatment to date can efficiently reduce this pain. This article investigates the brain functional connectivity of PD patients with central pain and the effects of levodopa and oxycodone on this connectivity.</p><p>Thirty-eight PD patients received either levodopa, oxycodone, or a placebo during an eight-week period. Pain intensity was evaluated using the Visual Analogue Scale and resting-state functional connectivity was measured before and after treatments. PD patients were also separated into two groups: responders and non-responders.</p><p>At baseline, the intensity of pain was correlated with the connectivity between the anterior insula and the posterior cingulate cortex and between the nucleus accumbens, the brainstem, and the hippocampus. Levodopa and oxycodone had no specific effects on functional connectivity. Responders had a decrease in connectivity between the anterior insula and the posterior cingulate cortex, while non-responders showed an increase in connectivity.</p><p>The correlation between pain intensity and specific brain connectivity may represent a “hyper-awareness” of pain and a distortion of learning and memory systems in PD patients with central pain, leading to a state of chronic pain. The placebo effect could explain the changes in connectivity that are associated with a potential reduction in pain awareness.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"4 1","pages":"Article 100197"},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666956024000035/pdfft?md5=1bb58d787b77d2dcb6ec6db83b26ec91&pid=1-s2.0-S2666956024000035-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139674363","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}
Pub Date : 2024-01-29DOI: 10.1016/j.ynirp.2024.100196
Freda Werdiger , Vignan Yogendrakumar , Milanka Visser , James Kolacz , Christina Lam , Mitchell Hill , Chushuang Chen , Mark W. Parsons , Andrew Bivard
Introduction
During the subacute phase of ischemic stroke, MR diffusion-weighted imaging (DWI) is used to assess the extent of tissue injury. Segmentation of DWI infarct is challenging due to disease variability, but Deep Learning (DL) provides a solution, outperforming existing methods on small datasets. However, a lack of clinically meaningful performance evaluation hinders clinical translation. Here we develop a DL DWI segmentation tool and provide clinical performance review.
Methods
Subjects in this retrospective study presented with stroke symptoms and later underwent DWI imaging. DL architectures U-Net and DenseNet were used to develop a DWI segmentation tool. The Dice Similarly Coefficient (DSC) was used to select the best- and worst-performing model. Clinical experts reviewed these models on the clinical test set, agreeing with the model if no 'significant’ error was present. The average agreement with the model and interrater agreement was also derived.
Results
In total, 573 participants with an ischemic stroke were included. The DenseNet delivered the best model (DSC = 0.831 ± 0.064) with a mean inference time of 0.07 s. Clinicians compared this with the worst model (U-Net, DSC = 0.759 ± 0.122), agreeing with the DenseNet predictions more than the U-Net (83.8 % vs. 79.3 %). Clinicians also agreed with each other more over performance interpretation when evaluating the DenseNet over the U-Net (87.9 % vs. 72.7 %).
Conclusion
Our DWI segmentation tool achieved high performance with clinical review providing meaningful performance evaluation. Model development will continue towards prospective deployment before which clinical review will be repeated. This work will benefit physicians in assessing patient prognosis.
{"title":"Clinical performance review for 3-D Deep Learning segmentation of stroke infarct from diffusion-weighted images","authors":"Freda Werdiger , Vignan Yogendrakumar , Milanka Visser , James Kolacz , Christina Lam , Mitchell Hill , Chushuang Chen , Mark W. Parsons , Andrew Bivard","doi":"10.1016/j.ynirp.2024.100196","DOIUrl":"https://doi.org/10.1016/j.ynirp.2024.100196","url":null,"abstract":"<div><h3>Introduction</h3><p>During the subacute phase of ischemic stroke, MR diffusion-weighted imaging (DWI) is used to assess the extent of tissue injury. Segmentation of DWI infarct is challenging due to disease variability, but Deep Learning (DL) provides a solution, outperforming existing methods on small datasets. However, a lack of clinically meaningful performance evaluation hinders clinical translation. Here we develop a DL DWI segmentation tool and provide clinical performance review.</p></div><div><h3>Methods</h3><p>Subjects in this retrospective study presented with stroke symptoms and later underwent DWI imaging. DL architectures U-Net and DenseNet were used to develop a DWI segmentation tool. The Dice Similarly Coefficient (DSC) was used to select the best- and worst-performing model. Clinical experts reviewed these models on the clinical test set, agreeing with the model if no 'significant’ error was present. The average agreement with the model and interrater agreement was also derived.</p></div><div><h3>Results</h3><p>In total, 573 participants with an ischemic stroke were included. The DenseNet delivered the best model (DSC = 0.831 ± 0.064) with a mean inference time of 0.07 s. Clinicians compared this with the worst model (U-Net, DSC = 0.759 ± 0.122), agreeing with the DenseNet predictions more than the U-Net (83.8 % vs. 79.3 %). Clinicians also agreed with each other more over performance interpretation when evaluating the DenseNet over the U-Net (87.9 % vs. 72.7 %).</p></div><div><h3>Conclusion</h3><p>Our DWI segmentation tool achieved high performance with clinical review providing meaningful performance evaluation. Model development will continue towards prospective deployment before which clinical review will be repeated. This work will benefit physicians in assessing patient prognosis.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"4 1","pages":"Article 100196"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666956024000023/pdfft?md5=f324aa4c5c3ee9cb6266753d69b4de8d&pid=1-s2.0-S2666956024000023-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139653519","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}
Pub Date : 2023-12-21DOI: 10.1016/j.ynirp.2023.100194
Rozemarijn M. Mattiesing , Serena Stel , Alysha S. Mangroe , Iman Brouwer , Adriaan Versteeg , Ronald A. van Schijndel , Bernard M.J. Uitdehaag , Frederik Barkhof , Hugo Vrenken , Joost P.A. Kuijer
Background
The detection and quantification of changes in white matter lesions in the brain is important to monitor treatment effects in patients with multiple sclerosis (MS). Existing automatic tools predominantly require FLAIR images as input which are not always available, or only focus on new/enlarging activity. Therefore, we developed and validated a semi-automated method to quantify lesion volume changes based on 2D proton-density (PD)-weighted images and image subtraction. This semi-automated method provides insight in both “positive” activity (defined as new and enlarging lesions) and “negative” activity (disappearing and shrinking lesions).
Methods
Yearly MRI scans of patients with early MS from the REFLEX/REFLEXION studies were used. The maximum follow-up period was 5 years. Two PD-weighted images were normalized, registered to a common halfway-space, intensity-matched, and subsequently subtracted. Within manual lesion masks, lesion changes were quantified using a subtraction intensity threshold and total lesion volume change (TLVC) was calculated. Reproducibility was measured by assessing transitivity, specifically, we calculated the intraclass correlation coefficient for the absolute agreement (ICCtrans) and the difference (Δtrans) between the direct one-step and indirect multi-step measurements of TLVC between two visits. Accuracy was assessed by calculating both the intraclass correlation coefficient for absolute agreement (ICCacc) and the difference (Δacc) between the one-step semi-automated TLVC and manually measured lesion volume change (numerical difference) between two visits. Spearman's correlations (rs) were used to assess the relation of global and central atrophy, manually measured T2 lesion volume, and lesion volume change with the method's performance as reflected by the difference measures |Δtrans| and Δacc. An alpha of 0.05 was used as the cut-off for significance.
Results
Reproducibility was excellent, with ICCtrans values ranging from 0.90 to 0.96. Accuracy was good overall, with ICCacc values ranging from 0.67 to 0.86. The standard deviation of Δtrans ranged from 0.25 to 0.86 mL. The mean of Δacc ranged from 0.11 to 0.37 mL and was significantly different from zero. Both global and central atrophy significantly correlated with lower reproducibility (correlation of |Δtrans| with global atrophy, rs = −0.19 to −0.28, and correlation of |Δtrans| with central atrophy, rs = 0.22 to 0.34). There was generally no significant correlation between global/central atrophy and accuracy. Higher lesion volume was significantly correlated with lower reproducibility (rs = 0.62). Higher lesion volume change was significantly correlated with lower reproducibility (rs = 0.22) and lower acc
{"title":"Validation of a semi-automated method to quantify lesion volume changes in multiple sclerosis on 2D proton-density-weighted scans based on image subtraction","authors":"Rozemarijn M. Mattiesing , Serena Stel , Alysha S. Mangroe , Iman Brouwer , Adriaan Versteeg , Ronald A. van Schijndel , Bernard M.J. Uitdehaag , Frederik Barkhof , Hugo Vrenken , Joost P.A. Kuijer","doi":"10.1016/j.ynirp.2023.100194","DOIUrl":"https://doi.org/10.1016/j.ynirp.2023.100194","url":null,"abstract":"<div><h3>Background</h3><p>The detection and quantification of changes in white matter lesions in the brain is important to monitor treatment effects in patients with multiple sclerosis (MS). Existing automatic tools predominantly require FLAIR images as input which are not always available, or only focus on new/enlarging activity. Therefore, we developed and validated a semi-automated method to quantify lesion volume changes based on 2D proton-density (PD)-weighted images and image subtraction. This semi-automated method provides insight in both “positive” activity (defined as new and enlarging lesions) and “negative” activity (disappearing and shrinking lesions).</p></div><div><h3>Methods</h3><p>Yearly MRI scans of patients with early MS from the REFLEX/REFLEXION studies were used. The maximum follow-up period was 5 years. Two PD-weighted images were normalized, registered to a common halfway-space, intensity-matched, and subsequently subtracted. Within manual lesion masks, lesion changes were quantified using a subtraction intensity threshold and total lesion volume change (TLVC) was calculated. Reproducibility was measured by assessing transitivity, specifically, we calculated the intraclass correlation coefficient for the absolute agreement (ICC<sub>trans</sub>) and the difference (Δ<sub>trans</sub>) between the direct one-step and indirect multi-step measurements of TLVC between two visits. Accuracy was assessed by calculating both the intraclass correlation coefficient for absolute agreement (ICC<sub>acc</sub>) and the difference (Δ<sub>acc</sub>) between the one-step semi-automated TLVC and manually measured lesion volume change (numerical difference) between two visits. Spearman's correlations (r<sub>s</sub>) were used to assess the relation of global and central atrophy, manually measured T2 lesion volume, and lesion volume change with the method's performance as reflected by the difference measures |Δ<sub>trans</sub>| and Δ<sub>acc</sub>. An alpha of 0.05 was used as the cut-off for significance.</p></div><div><h3>Results</h3><p>Reproducibility was excellent, with ICC<sub>trans</sub> values ranging from 0.90 to 0.96. Accuracy was good overall, with ICC<sub>acc</sub> values ranging from 0.67 to 0.86. The standard deviation of Δ<sub>trans</sub> ranged from 0.25 to 0.86 mL. The mean of Δ<sub>acc</sub> ranged from 0.11 to 0.37 mL and was significantly different from zero. Both global and central atrophy significantly correlated with lower reproducibility (correlation of |Δ<sub>trans</sub>| with global atrophy, r<sub>s</sub> = −0.19 to −0.28, and correlation of |Δ<sub>trans</sub>| with central atrophy, r<sub>s</sub> = 0.22 to 0.34). There was generally no significant correlation between global/central atrophy and accuracy. Higher lesion volume was significantly correlated with lower reproducibility (r<sub>s</sub> = 0.62). Higher lesion volume change was significantly correlated with lower reproducibility (r<sub>s</sub> = 0.22) and lower acc","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"4 1","pages":"Article 100194"},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666956023000399/pdfft?md5=938a53ab9f8e3d9c298e4de27ddb9da2&pid=1-s2.0-S2666956023000399-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139033848","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}
Pub Date : 2023-11-07DOI: 10.1016/j.ynirp.2023.100191
Anton Orlichenko , Grant Daly , Ziyu Zhou , Anqi Liu , Hui Shen , Hong-Wen Deng , Yu-Ping Wang
Most packages for the analysis of fMRI-based functional connectivity (FC) and genomic data are used with a programming language interface, lacking an easy-to-navigate GUI frontend. This exacerbates two problems found in these types of data: demographic confounds and quality control in the face of high dimensionality of features. The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset. FC in particular usually contains tens of thousands of features per subject, and can only be summarized and efficiently explored using visualizations. To remedy this situation, we have developed ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level demographic, genomic, and imaging features. The software is Python-based, runs in a self-contained Docker image, and contains a browser-based GUI frontend. We demonstrate the usefulness of ImageNomer by identifying an unexpected race confound when predicting achievement scores in the Philadelphia Neurodevelopmental Cohort (PNC) dataset, which contains multitask fMRI and single nucleotide polymorphism (SNP) data of healthy adolescents. In the past, many studies have attempted to use FC to identify achievement-related features in fMRI. Using ImageNomer to visualize trends in achievement scores between races, we find a clear potential for confounding effects if race can be predicted using FC. Using correlation analysis in the ImageNomer software, we show that FCs correlated with Wide Range Achievement Test (WRAT) score are in fact more highly correlated with race. Investigating further, we find that whereas both FC and SNP (genomic) features can account for 10–15% of WRAT score variation, this predictive ability disappears when controlling for race. We also use ImageNomer to investigate race-FC correlation in the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP) dataset. In this work, we demonstrate the advantage of our ImageNomer GUI tool in data exploration and confound detection. Additionally, this work identifies race as a strong confound in FC data and casts doubt on the possibility of finding unbiased achievement-related features in fMRI and SNP data of healthy adolescents.
{"title":"ImageNomer: Description of a functional connectivity and omics analysis tool and case study identifying a race confound","authors":"Anton Orlichenko , Grant Daly , Ziyu Zhou , Anqi Liu , Hui Shen , Hong-Wen Deng , Yu-Ping Wang","doi":"10.1016/j.ynirp.2023.100191","DOIUrl":"https://doi.org/10.1016/j.ynirp.2023.100191","url":null,"abstract":"<div><p>Most packages for the analysis of fMRI-based functional connectivity (FC) and genomic data are used with a programming language interface, lacking an easy-to-navigate GUI frontend. This exacerbates two problems found in these types of data: demographic confounds and quality control in the face of high dimensionality of features. The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset. FC in particular usually contains tens of thousands of features per subject, and can only be summarized and efficiently explored using visualizations. To remedy this situation, we have developed ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level demographic, genomic, and imaging features. The software is Python-based, runs in a self-contained Docker image, and contains a browser-based GUI frontend. We demonstrate the usefulness of ImageNomer by identifying an unexpected race confound when predicting achievement scores in the Philadelphia Neurodevelopmental Cohort (PNC) dataset, which contains multitask fMRI and single nucleotide polymorphism (SNP) data of healthy adolescents. In the past, many studies have attempted to use FC to identify achievement-related features in fMRI. Using ImageNomer to visualize trends in achievement scores between races, we find a clear potential for confounding effects if race can be predicted using FC. Using correlation analysis in the ImageNomer software, we show that FCs correlated with Wide Range Achievement Test (WRAT) score are in fact more highly correlated with race. Investigating further, we find that whereas both FC and SNP (genomic) features can account for 10–15% of WRAT score variation, this predictive ability disappears when controlling for race. We also use ImageNomer to investigate race-FC correlation in the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP) dataset. In this work, we demonstrate the advantage of our ImageNomer GUI tool in data exploration and confound detection. Additionally, this work identifies race as a strong confound in FC data and casts doubt on the possibility of finding unbiased achievement-related features in fMRI and SNP data of healthy adolescents.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 4","pages":"Article 100191"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666956023000363/pdfft?md5=63d2f8e7b388542a1c1f1741653bcaa3&pid=1-s2.0-S2666956023000363-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92061984","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}
Pub Date : 2023-11-02DOI: 10.1016/j.ynirp.2023.100192
Peter A. Hall , Mohammad Nazmus Sakib , Anna Hudson , Alkarim Billawala , Geoffrey T. Fong , Hasan Ayaz
Objective
Introduction of brain hypoxia by frequent mask-wearing is a concern voiced by some who resist masking mandates. Studies have examined acute effects of one-shot mask-wearing on peripheral and cerebral oxygenation in the laboratory, but not effects of everyday mask-wearing frequencies on task-related functional activation. The objective of the current study was to examine whether frequency of mask-wearing in daily life is associated with lower task-related brain oxygenation levels, and whether the magnitude of any such effects vary by age and sex.
Methods
Participants were 78 community-dwelling adults between the ages of 18 and 84 years, all of whom were vaccinated at the time of participation; 65.4% (n = 51) were female. Frequency of mask-wearing was assessed using survey questions on mask-wearing practice during an active COVID-19 mask mandate. Recordings of task-related cerebral oxygenation were taken during the completion of a simple reaction time task using 16-channel functional near-infrared spectroscopy (fNIRS).
Results
The psychomotor vigilance task elicited reliable increases in cerebral oxygenation within the right mid-frontal gyrus (F(1,61.345) = 15.975, p < .001). However, there was no significant association between everyday masking frequency and performance on the psychomotor vigilance task (b = 0.059, SE = 0.092 (95% CI [-0.122, 0.241]), t = .646, p = .520), nor any association between everyday masking frequency and task-related brain oxygenation on any measurement channel (all ps < .05).
Conclusions
Higher mask-wearing frequency in daily life is not associated with significantly lower levels of task-related brain oxygenation, or worse performance on a sustained attention task.
目的:经常戴口罩引起脑缺氧是一些反对戴口罩的人所关注的问题。在实验室中，研究已经检查了一次性戴口罩对外周和大脑氧合的急性影响，但没有检查日常戴口罩频率对任务相关功能激活的影响。目前这项研究的目的是研究日常生活中戴口罩的频率是否与较低的脑氧合水平有关，以及这种影响的程度是否因年龄和性别而异。研究对象为78名年龄在18岁至84岁之间的社区居民，他们在参与研究时均接种了疫苗;65.4% (n = 51)为女性。在COVID-19口罩强制执行期间，使用关于佩戴口罩实践的调查问题评估佩戴口罩的频率。在完成简单的反应时间任务时，使用16通道功能近红外光谱(fNIRS)记录任务相关的脑氧合。结果精神运动警觉性任务可引起右侧额叶中回脑氧合增加(F(1,61.345) = 15.975, p <措施)。然而，日常掩蔽频率与精神运动警觉性任务的表现之间没有显著关联(b = 0.059, SE = 0.092 (95% CI [-0.122, 0.241])， t = 0.646, p = 0.520)，日常掩蔽频率与任何测量通道上的任务相关脑氧合之间也没有任何关联(所有ps <. 05)。结论日常生活中戴口罩频率高与任务相关脑氧合水平显著降低或持续注意力任务的表现不相关。
{"title":"Task-related oxygenation in the prefrontal cortex as a function of mask-wearing frequency: An empirical test using functional near-infrared spectroscopy","authors":"Peter A. Hall , Mohammad Nazmus Sakib , Anna Hudson , Alkarim Billawala , Geoffrey T. Fong , Hasan Ayaz","doi":"10.1016/j.ynirp.2023.100192","DOIUrl":"https://doi.org/10.1016/j.ynirp.2023.100192","url":null,"abstract":"<div><h3>Objective</h3><p>Introduction of brain hypoxia by frequent mask-wearing is a concern voiced by some who resist masking mandates. Studies have examined acute effects of one-shot mask-wearing on peripheral and cerebral oxygenation in the laboratory, but not effects of everyday mask-wearing frequencies on task-related functional activation. The objective of the current study was to examine whether frequency of mask-wearing in daily life is associated with lower task-related brain oxygenation levels, and whether the magnitude of any such effects vary by age and sex.</p></div><div><h3>Methods</h3><p>Participants were 78 community-dwelling adults between the ages of 18 and 84 years, all of whom were vaccinated at the time of participation; 65.4% (<em>n</em> = 51) were female. Frequency of mask-wearing was assessed using survey questions on mask-wearing practice during an active COVID-19 mask mandate. Recordings of task-related cerebral oxygenation were taken during the completion of a simple reaction time task using 16-channel functional near-infrared spectroscopy (fNIRS).</p></div><div><h3>Results</h3><p>The psychomotor vigilance task elicited reliable increases in cerebral oxygenation within the right mid-frontal gyrus (<em>F</em>(1,61.345) = 15.975, <em>p</em> < .001). However, there was no significant association between everyday masking frequency and performance on the psychomotor vigilance task (<em>b</em> = 0.059, SE = 0.092 (95% CI [-0.122, 0.241]), <em>t</em> = .646, <em>p</em> = .520), nor any association between everyday masking frequency and task-related brain oxygenation on any measurement channel (all <em>p</em>s < .05).</p></div><div><h3>Conclusions</h3><p>Higher mask-wearing frequency in daily life is not associated with significantly lower levels of task-related brain oxygenation, or worse performance on a sustained attention task.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 4","pages":"Article 100192"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666956023000375/pdfft?md5=8749ad281d06b9ff7bc27cc28b20d09b&pid=1-s2.0-S2666956023000375-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92061980","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}
Pub Date : 2023-11-01DOI: 10.1016/j.ynirp.2023.100193
Johanna Philipson , Amar Awad , Lena Lindström , Patric Blomstedt , Marjan Jahanshahi , Johan Eriksson
Essential tremor (ET) is characterized by bilateral upper limb postural and/or kinetic tremor, but also cognitive deficits. Tremor in ET, as well as aspects of cognitive deficits associated with ET, have been suggested to be linked to dysfunction in the cerebello-thalamo-cerebral circuit. In ET patients with disabling and medically intractable motor symptoms, Deep Brain Stimulation (DBS) is effective in reducing tremor. DBS in the caudal Zona incerta (cZi) has been shown to modulate the activity of the sensorimotor cerebello-cerebral circuit during motor tasks. Whether the activity in the cerebello-cerebral circuit is modulated by DBS during tasks involving working memory is unknown. The present study therefore aimed to investigate the possible effects of cZi DBS on working-memory processing in ET patients by means of task-based blood oxygen level-dependent (BOLD) fMRI.
Thirteen ET patients completed a working-memory task during DBS OFF and ON conditions. The task involved three conditions: maintenance, manipulation, and control. Behaviorally, there was no significant effect from DBS on accuracy, but a marginally significant Task x DBS interaction was detected for response times (RTs). However, post hoc comparisons for each condition failed to reach statistical significance. FMRI analyses revealed that DBS did not alter BOLD signal in regions of interest (lateral prefrontal cortex, parietal cortex, and the cerebellum), or in a complementary whole-brain analysis.
The present study indicates that DBS in the cZi in patients with ET has at most marginal effects on working memory, which is consistent with the results of pre- and post-DBS neuropsychological assessment showing minimal cognitive effects of surgery.
{"title":"Evaluation of the effects of DBS in the caudal Zona incerta on brain activity during a working memory task in patients with essential tremor","authors":"Johanna Philipson , Amar Awad , Lena Lindström , Patric Blomstedt , Marjan Jahanshahi , Johan Eriksson","doi":"10.1016/j.ynirp.2023.100193","DOIUrl":"https://doi.org/10.1016/j.ynirp.2023.100193","url":null,"abstract":"<div><p>Essential tremor (ET) is characterized by bilateral upper limb postural and/or kinetic tremor, but also cognitive deficits. Tremor in ET, as well as aspects of cognitive deficits associated with ET, have been suggested to be linked to dysfunction in the cerebello-thalamo-cerebral circuit. In ET patients with disabling and medically intractable motor symptoms, Deep Brain Stimulation (DBS) is effective in reducing tremor. DBS in the caudal Zona incerta (cZi) has been shown to modulate the activity of the sensorimotor cerebello-cerebral circuit during motor tasks. Whether the activity in the cerebello-cerebral circuit is modulated by DBS during tasks involving working memory is unknown. The present study therefore aimed to investigate the possible effects of cZi DBS on working-memory processing in ET patients by means of task-based blood oxygen level-dependent (BOLD) fMRI.</p><p>Thirteen ET patients completed a working-memory task during DBS OFF and ON conditions. The task involved three conditions: maintenance, manipulation, and control. Behaviorally, there was no significant effect from DBS on accuracy, but a marginally significant Task x DBS interaction was detected for response times (RTs). However, post hoc comparisons for each condition failed to reach statistical significance. FMRI analyses revealed that DBS did not alter BOLD signal in regions of interest (lateral prefrontal cortex, parietal cortex, and the cerebellum), or in a complementary whole-brain analysis.</p><p>The present study indicates that DBS in the cZi in patients with ET has at most marginal effects on working memory, which is consistent with the results of pre- and post-DBS neuropsychological assessment showing minimal cognitive effects of surgery.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 4","pages":"Article 100193"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666956023000387/pdfft?md5=1e223601e57c5277baa024b597c482be&pid=1-s2.0-S2666956023000387-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92061983","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}
Pub Date : 2023-10-30DOI: 10.1016/j.ynirp.2023.100189
P. Andersson , G. Samrani , M. Andersson , J. Persson
In memory, familiar but no longer relevant information may disrupt encoding and retrieval of to-be-learned information. While it has been demonstrated that the ability to resolve proactive interference (PI) in working memory (WM) is reduced in aging, the neuroanatomical components of this decline have yet to be determined. Hippocampal (HC) involvement in age-related decline in control of PI is currently not known. In particular, the association between HC subfield volumes and control of PI in WM has not been examined previously. Here we investigate the associations between mean level and 5-year trajectories of gray matter subfield volumes and PI in WM across the adult life span (N = 157). Longitudinal analyses over 5-years across all participants revealed that reduced volume in the subiculum was related to impaired control of PI. Age-stratified analyses showed that this association was most pronounced in older adults. Furthermore, we found that in older adults the effect of age on PI was mediated by GM volume in the HC. The current results show that HC volume is associated with the ability to control PI in WM, and that these associations are modulated by age.
{"title":"Hippocampal subfield volumes contribute to working memory interference control in aging: Evidence from longitudinal associations over 5 years","authors":"P. Andersson , G. Samrani , M. Andersson , J. Persson","doi":"10.1016/j.ynirp.2023.100189","DOIUrl":"https://doi.org/10.1016/j.ynirp.2023.100189","url":null,"abstract":"<div><p>In memory, familiar but no longer relevant information may disrupt encoding and retrieval of to-be-learned information. While it has been demonstrated that the ability to resolve proactive interference (PI) in working memory (WM) is reduced in aging, the neuroanatomical components of this decline have yet to be determined. Hippocampal (HC) involvement in age-related decline in control of PI is currently not known. In particular, the association between HC subfield volumes and control of PI in WM has not been examined previously. Here we investigate the associations between mean level and 5-year trajectories of gray matter subfield volumes and PI in WM across the adult life span (N = 157). Longitudinal analyses over 5-years across all participants revealed that reduced volume in the subiculum was related to impaired control of PI. Age-stratified analyses showed that this association was most pronounced in older adults. Furthermore, we found that in older adults the effect of age on PI was mediated by GM volume in the HC. The current results show that HC volume is associated with the ability to control PI in WM, and that these associations are modulated by age.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 4","pages":"Article 100189"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266695602300034X/pdfft?md5=1c3a203dc606062fa73537dbd89a5644&pid=1-s2.0-S266695602300034X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92061981","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}
Pub Date : 2023-10-19DOI: 10.1016/j.ynirp.2023.100190
Stijn Michielse , Jindra Bakker , Iris Lange , Tim Batink , Liesbet Goossens , Marieke Wichers , Ritsaert Lieverse , Inez Myin-Germeys , Koen Schruers , Therese van Amelsvoort , Wolfgang Viechtbauer , Jim van Os , Machteld Marcelis
Background
Research indicates that Acceptance and Commitment Therapy in Daily Life (ACT-DL) is effective in reducing symptoms of depression, anxiety and psychosis. During adolescence, vulnerability to psychopathology peaks, creating a window for early interventions, while white matter development is ongoing. This study aims to examine microstructural white matter after ACT-DL intervention in youngsters with mild psychopathology.
Methods
Forty-five individuals with mild psychopathology were randomly allocated to ACT-DL (n=20) or topic discussion control (TD, n=25). Symptomatology was assessed with the Community Assessment of Psychic Experiences (CAPE), Montgomery–Åsberg Depression Rating Scale (MADRS) and the Experience Sampling Method (ESM). Diffusion Weighted Imaging (DWI) and network-connectivity parameters were obtained and compared before and after the intervention/control condition. Interactions between microstructural white matter change and condition were examined in models of CAPE positive symptoms and ESM subclinical psychotic experiences (PE) and negative affect (NA) levels.
Results
ACT-DL, compared to TD, was associated with changes on subclinical depressive and psychotic symptom levels. There was no significant change in DWI or network connectivity in either condition and no significant difference between both conditions. In the model of NA, several regional interactions between condition and network measures were significant, but stratification per condition provided no significant associations. There were no significant interactions between DWI or network connectivity parameters and condition in the models of the CAPE positive symptoms, MADRS and PE.
Conclusions
The findings suggest that behavioral (symptom) changes are more sensitive to a five-week psychological training than microstructural white matter changes which did not show significant changes over time.
{"title":"Acceptance and Commitment Therapy and white matter plasticity in individuals with subclinical depression and psychotic experiences: A Randomised Controlled Trial","authors":"Stijn Michielse , Jindra Bakker , Iris Lange , Tim Batink , Liesbet Goossens , Marieke Wichers , Ritsaert Lieverse , Inez Myin-Germeys , Koen Schruers , Therese van Amelsvoort , Wolfgang Viechtbauer , Jim van Os , Machteld Marcelis","doi":"10.1016/j.ynirp.2023.100190","DOIUrl":"https://doi.org/10.1016/j.ynirp.2023.100190","url":null,"abstract":"<div><h3>Background</h3><p>Research indicates that Acceptance and Commitment Therapy in Daily Life (ACT-DL) is effective in reducing symptoms of depression, anxiety and psychosis. During adolescence, vulnerability to psychopathology peaks, creating a window for early interventions, while white matter development is ongoing. This study aims to examine microstructural white matter after ACT-DL intervention in youngsters with mild psychopathology.</p></div><div><h3>Methods</h3><p>Forty-five individuals with mild psychopathology were randomly allocated to ACT-DL (n=20) or topic discussion control (TD, n=25). Symptomatology was assessed with the Community Assessment of Psychic Experiences (CAPE), Montgomery–Åsberg Depression Rating Scale (MADRS) and the Experience Sampling Method (ESM). Diffusion Weighted Imaging (DWI) and network-connectivity parameters were obtained and compared before and after the intervention/control condition. Interactions between microstructural white matter change and condition were examined in models of CAPE positive symptoms and ESM subclinical psychotic experiences (PE) and negative affect (NA) levels.</p></div><div><h3>Results</h3><p>ACT-DL, compared to TD, was associated with changes on subclinical depressive and psychotic symptom levels. There was no significant change in DWI or network connectivity in either condition and no significant difference between both conditions. In the model of NA, several regional interactions between condition and network measures were significant, but stratification per condition provided no significant associations. There were no significant interactions between DWI or network connectivity parameters and condition in the models of the CAPE positive symptoms, MADRS and PE.</p></div><div><h3>Conclusions</h3><p>The findings suggest that behavioral (symptom) changes are more sensitive to a five-week psychological training than microstructural white matter changes which did not show significant changes over time.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 4","pages":"Article 100190"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49881738","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 : 2023-09-29DOI: 10.1016/j.ynirp.2023.100186
Charles A. Ellis , Robyn L. Miller , Vince D. Calhoun
Many studies have analyzed resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data to elucidate the effects of neurological and neuropsychiatric disorders upon the interactions of brain regions over time. Existing studies often use either machine learning classification or clustering algorithms. Additionally, several studies have used clustering algorithms to extract features related to brain states trajectories that can be used to train interpretable classifiers. However, the combination of explainable dFNC classifiers followed by clustering algorithms is highly underutilized. In this study, we show how such an approach can be used to study the effects of schizophrenia (SZ) upon brain activity. Specifically, we train an explainable deep learning model to classify between individuals with SZ and healthy controls. We then cluster the resulting explanations, identifying discriminatory states of dFNC. We lastly apply several novel measures to quantify aspects of the classifier explanations and obtain additional insights into the effects of SZ upon brain network dynamics. Specifically, we uncover effects of schizophrenia upon subcortical, sensory, and cerebellar network interactions. We also find that individuals with SZ likely have reduced variability in overall brain activity and that the effects of SZ may be temporally localized. In addition to uncovering effects of SZ upon brain network dynamics, our approach could provide novel insights into a variety of neurological and neuropsychiatric disorders in future dFNC studies.
{"title":"Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics","authors":"Charles A. Ellis , Robyn L. Miller , Vince D. Calhoun","doi":"10.1016/j.ynirp.2023.100186","DOIUrl":"https://doi.org/10.1016/j.ynirp.2023.100186","url":null,"abstract":"<div><p>Many studies have analyzed resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data to elucidate the effects of neurological and neuropsychiatric disorders upon the interactions of brain regions over time. Existing studies often use either machine learning classification or clustering algorithms. Additionally, several studies have used clustering algorithms to extract features related to brain states trajectories that can be used to train interpretable classifiers. However, the combination of explainable dFNC classifiers followed by clustering algorithms is highly underutilized. In this study, we show how such an approach can be used to study the effects of schizophrenia (SZ) upon brain activity. Specifically, we train an explainable deep learning model to classify between individuals with SZ and healthy controls. We then cluster the resulting explanations, identifying discriminatory states of dFNC. We lastly apply several novel measures to quantify aspects of the classifier explanations and obtain additional insights into the effects of SZ upon brain network dynamics. Specifically, we uncover effects of schizophrenia upon subcortical, sensory, and cerebellar network interactions. We also find that individuals with SZ likely have reduced variability in overall brain activity and that the effects of SZ may be temporally localized. In addition to uncovering effects of SZ upon brain network dynamics, our approach could provide novel insights into a variety of neurological and neuropsychiatric disorders in future dFNC studies.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 4","pages":"Article 100186"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49881739","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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