Neuroimage. Reports最新文献

{"title":"Brain working memory network indices as landmarks of intelligence","authors":"Mohammadreza Khodaei ,&nbsp;Paul J. Laurienti ,&nbsp;Dale Dagenbach ,&nbsp;Sean L. Simpson","doi":"10.1016/j.ynirp.2023.100165","DOIUrl":"10.1016/j.ynirp.2023.100165","url":null,"abstract":"<div><p>Identifying the neural correlates of intelligence has long been a goal in neuroscience. Recently, the field of network neuroscience has attracted researchers' attention as a means for answering this question. In network neuroscience, the brain is considered as an integrated system whose systematic properties provide profound insights into health and behavioral outcomes. However, most network studies of intelligence have used univariate methods to investigate topological network measures, with their focus limited to a few measures. Furthermore, most studies have focused on resting state networks despite the fact that brain activation during working memory tasks has been linked to intelligence. Finally, the literature is still missing an investigation of the association between network assortativity and intelligence. To address these issues, here we employ a recently developed mixed-modeling framework for analyzing multi-task brain networks to elucidate the most critical working memory task network topological properties corresponding to individuals' intelligence differences. We used a data set of 379 subjects (22–35 y/o) from the Human Connectome Project (HCP). Each subject's data included composite intelligence scores, and fMRI during resting state and a 2-back working memory task. Following comprehensive quality control and preprocessing of the minimally preprocessed fMRI data, we extracted a set of the main topological network features, including global efficiency, degree, leverage centrality, modularity, and clustering coefficient. The estimated network features and subject's confounders were then incorporated into the multi-task mixed-modeling framework to investigate how brain network changes between working memory and resting state relate to intelligence score. Our results indicate that the general intelligence score (cognitive composite score) is associated with a change in the relationship between connection strength and multiple network topological properties, including global efficiency, leverage centrality, and degree difference during working memory as it is compared to resting state. More specifically, we observed a higher increase in the positive association between global efficiency and connection strength for the high intelligence group when they switch from resting state to working memory. The strong connections might form superhighways for a more efficient global flow of information through the brain network. Furthermore, we found an increase in the negative association between degree difference and leverage centrality with connection strength during working memory tasks for the high intelligence group. These indicate higher network resilience and assortativity along with higher circuit-specific information flow during working memory for those with a higher intelligence score. Although the exact neurobiological implications of our results are speculative at this point, our results provide evidence for the significant ass","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 2","pages":"Article 100165"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/53/01/nihms-1909521.PMC10327823.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10167693","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}

{"title":"Population modeling with machine learning can enhance measures of mental health - Open-data replication","authors":"Ty Easley ,&nbsp;Ruiqi Chen ,&nbsp;Kayla Hannon ,&nbsp;Rosie Dutt ,&nbsp;Janine Bijsterbosch","doi":"10.1016/j.ynirp.2023.100163","DOIUrl":"https://doi.org/10.1016/j.ynirp.2023.100163","url":null,"abstract":"<div><p>Efforts to predict trait phenotypes based on functional MRI data from large cohorts have been hampered by low prediction accuracy and/or small effect sizes. Although these findings are highly replicable, the small effect sizes are somewhat surprising given the presumed brain basis of phenotypic traits such as neuroticism and fluid intelligence. We aim to replicate previous work and additionally test multiple data manipulations that may improve prediction accuracy by addressing data pollution challenges. Specifically, we added additional fMRI features, averaged the target phenotype across multiple measurements to obtain more accurate estimates of the underlying trait, balanced the target phenotype's distribution through undersampling of majority scores, and identified data-driven subtypes to investigate the impact of between-participant heterogeneity. Our results replicated prior results from Dadi et al. (2021) in a larger sample. Each data manipulation further led to small but consistent improvements in prediction accuracy, which were largely additive when combining multiple data manipulations. Combining data manipulations (i.e., extended fMRI features, averaged target phenotype, balanced target phenotype distribution) led to a three-fold increase in prediction accuracy for fluid intelligence compared to prior work. These findings highlight the benefit of several relatively easy and low-cost data manipulations, which may positively impact future work.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 2","pages":"Article 100163"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50173413","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}

{"title":"Music interventions in 132 healthy older adults enhance cerebellar grey matter and auditory working memory, despite general brain atrophy","authors":"Damien Marie ,&nbsp;Cécile A.H. Müller ,&nbsp;Eckart Altenmüller ,&nbsp;Dimitri Van De Ville ,&nbsp;Kristin Jünemann ,&nbsp;Daniel S. Scholz ,&nbsp;Tillmann H.C. Krüger ,&nbsp;Florian Worschech ,&nbsp;Matthias Kliegel ,&nbsp;Christopher Sinke ,&nbsp;Clara E. James","doi":"10.1016/j.ynirp.2023.100166","DOIUrl":"10.1016/j.ynirp.2023.100166","url":null,"abstract":"<div><p>Normal aging is associated with brain atrophy and cognitive decline. Working memory, involved in cognitive functioning and daily living, is particularly affected. Music training gained momentum in research on brain plasticity and possible transfer effects of interventions on working memory, especially in the context of healthy aging. This longitudinal voxel-based morphometry study evaluated effects of 6-month music interventions on grey matter volume plasticity and auditory working memory performance in 132 healthy older adults. This study is part of a randomized controlled trial comparing two interventions: piano practice (experimental group) and musical culture (musical listening awareness, active control). We report significant grey matter volume increase at whole-brain level in the caudate nucleus, Rolandic operculum and inferior cerebellum when merging both groups, but no group differences. Cerebellar grey matter increase, training intensity metrics and sleep were positively associated with tonal working memory improvement. Digit Span Backward verbal working memory performance also increased. Using region of interest analyses, we showed a group difference in the right primary auditory cortex grey matter volume, decreasing in the musical group while staying stable in the piano group. In contrast, a significant 6-month whole-brain atrophy pattern consistent with longer-term investigations of the aging brain was revealed. We argue that education for seniors should become a major policy priority in the framework of healthy aging, to promote brain plasticity and cognitive reserve, through stimulating group interventions such as music-making and active listening.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 2","pages":"Article 100166"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43751261","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}

{"title":"Associative memory is more strongly predicted by age-related differences in the prefrontal cortex than medial temporal lobes","authors":"Tiago Guardia ,&nbsp;Negar Mazloum-Farzaghi ,&nbsp;Rosanna K. Olsen ,&nbsp;Kamen A. Tsvetanov ,&nbsp;Karen L. Campbell","doi":"10.1016/j.ynirp.2023.100168","DOIUrl":"10.1016/j.ynirp.2023.100168","url":null,"abstract":"<div><p>It is well established that episodic memory declines with age and one of the primary explanations for this decline is an age-related impairment in the ability to form new associations. At a neural level, both the medial temporal lobe (MTL) and lateral prefrontal cortex (PFC) are thought to be critical for associative memory, and grey matter volume loss in these regions has been associated with age-related declines in episodic memory. While some recent work has compared the relative contributions of grey matter volume in MTL and PFC regions to item and associative memory, studies investigating the unique and shared contributions of age-related differences in the MTL and PFC to memory differences are still rare. In this study, we use a lifespan approach to examine the relationship between grey matter volume within substructures of the MTL and PFC on the one hand and item and associative memory on the other. To this end, we used data from over 300 healthy individuals uniformly spread across the adult lifespan from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) and tested the multivariate relationship between grey matter volumes and item/associative memory scores using canonical correlation analysis. We show that structures of the PFC alone predict memory performance better than either structures of the MTL alone or PFC and MTL combined. Moreover, our results also indicate that grey matter volume in the inferior frontal gyrus - pars opercularis, superior frontal gyrus, and middle frontal gyrus relates most strongly to memory (particularly associative memory, which loaded higher than item memory) and this effect persists when controlling for age and education. Finally, we also show that the relationship between frontal grey matter volume and memory is not moderated by age or sex. Taken together, these findings emphasize the critical role of the frontal lobes, and the control processes they subserve, in determining the effects of age on associative memory.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 2","pages":"Article 100168"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45988155","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}

{"title":"Hand preference and the corpus callosum: Is there really no association?","authors":"Nora Raaf,&nbsp;René Westerhausen","doi":"10.1016/j.ynirp.2023.100160","DOIUrl":"https://doi.org/10.1016/j.ynirp.2023.100160","url":null,"abstract":"<div><p>Originating from a series of morphometric studies conducted in the 1980s, it appears a widely held belief in cognitive neuroscience that the corpus callosum is larger in left or mixed handers than in right handers (RH). However, a recent meta-analysis challenges this belief by not finding significant differences in corpus callosum size between handedness groups. Yet, relying on the available published data, the meta-analysis was not able to account for a series of factors potential influencing its outcome, such as confounding effects of brain size differences and a restricted spatial resolution of previous callosal segmentation strategies. To address these remaining questions, we here analysed N = 1057 participants' midsagittal corpus callosum of from the Human Connectome Project (HCP 1200 Young Adults) to compare handedness groups based on consistency (e.g., consistent RH vs. mixed handers, MH) and direction of hand preference (e.g., dominant RH vs. dominant left handers). A possible relevance of brain-size differences was addressed by analysing callosal variability by both using forebrain volume (FBV) as covariate and utilising relative area (callosal area/thickness divided by FBV) as a dependent variable. Callosal thickness was analysed at 100 measuring points along the structure to achieve high spatial resolution to detect subregional effects. However, neither of the conducted analyses was able to find significant handedness-related differences in the corpus callosum and the respective effect-sizes estimates were small. For example, comparing MH and consistent RH, the effect sizes for difference in callosal area were below a Cohen's <em>d</em> = 0.1 (irrespective of how FBV was included), and narrow confidence intervals allowed to exclude effects above |<em>d</em>| = 0.2. Analysing thickness, effect sizes were below <em>d</em> = 0.2 with confidence intervals not extending above |<em>d</em>| = 0.3. In this, the possible range of population effect sizes of hand preference on callosal morphology appears well below the effects commonly reported for factors like age, sex, or brain size. Effects on cognition or behaviour accordingly can be considered small, questioning the common practise to attribute performance differences between handedness groups to differences in callosal architecture.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 1","pages":"Article 100160"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50201112","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}

{"title":"“Computational analysis on verbal fluency reveals heterogeneity in subjective language interests and brain structure”","authors":"Francilia Zengaffinen ,&nbsp;Antje Stahnke ,&nbsp;Stephan Furger ,&nbsp;Roland Wiest ,&nbsp;Thomas Dierks ,&nbsp;Werner Strik ,&nbsp;Yosuke Morishima","doi":"10.1016/j.ynirp.2023.100159","DOIUrl":"10.1016/j.ynirp.2023.100159","url":null,"abstract":"<div><p>Language is an essential higher cognitive function in humans and is often affected by psychiatric and neurological disorders. Objective measures like the verbal fluency test are often used to determine language dysfunction. Recent applications of computational approaches broaden insights into language-related functions. In addition, individuals diagnosed with a psychiatric or neurological disorder also often report subjective difficulties in language-related functions. Therefore, we investigated the association between objective and subjective measures of language functioning, on the one hand, and inter-individual structural variations in language-related brain areas, on the other hand.</p><p>We performed a Latent Semantic analysis (LSA) on a semantic verbal fluency task in 101 healthy adult participants. To investigate if these objective measures are associated with a subjective one, we examined assessed subjective natural tendency of interest in language-related activity with a study-specific questionnaire. Lastly, a voxel-based brain morphometry (VBM) was conducted to reveal associations between objective (LSA) measures and structural changes in language-related brain areas.</p><p>We found a positive correlation between the LSA measure cosine similarity and the subjective interest in language. Furthermore, we found that higher cosine similarity corresponds to higher gray matter volume in the right cerebellum. The results suggest that people with higher interests in language access semantic knowledge in a more organized way exhibited by higher cosine similarity and have larger gray matter volume in the right cerebellum, when compared to people with lower interests.</p><p>In conclusion, we demonstrate that there is inter-individual diverseness of accessing the semantic knowledge space and that it is associated with subjective language interests as well as structural differences in the right cerebellum.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 1","pages":"Article 100159"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42851642","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}

{"title":"Machine learning classification of chronic traumatic brain injury using diffusion tensor imaging and NODDI: A replication and extension study","authors":"J. Michael Maurer ,&nbsp;Keith A. Harenski ,&nbsp;Subhadip Paul ,&nbsp;Victor M. Vergara ,&nbsp;David D. Stephenson ,&nbsp;Aparna R. Gullapalli ,&nbsp;Nathaniel E. Anderson ,&nbsp;Gerard J.B. Clarke ,&nbsp;Prashanth K. Nyalakanti ,&nbsp;Carla L. Harenski ,&nbsp;Jean Decety ,&nbsp;Andrew R. Mayer ,&nbsp;David B. Arciniegas ,&nbsp;Vince D. Calhoun ,&nbsp;Todd B. Parrish ,&nbsp;Kent A. Kiehl","doi":"10.1016/j.ynirp.2023.100157","DOIUrl":"10.1016/j.ynirp.2023.100157","url":null,"abstract":"<div><p>Individuals with acute and chronic traumatic brain injury (TBI) are associated with unique white matter (WM) structural abnormalities, including fractional anisotropy (FA) differences. Our research group previously used FA as a feature in a linear support vector machine (SVM) pattern classifier, observing high classification between individuals with and without acute TBI (i.e., an area under the curve [AUC] value of 75.50%). However, it is not known whether FA could similarly classify between individuals with and without history of chronic TBI. Here, we attempted to replicate our previous work with a new sample, investigating whether FA could similarly classify between incarcerated men with (<em>n</em> = 80) and without (<em>n</em> = 80) self-reported history of chronic TBI. Additionally, given limitations associated with FA, including underestimation of FA values in WM tracts containing crossing fibers, we extended upon our previous study by incorporating neurite orientation dispersion and density imaging (NODDI) metrics, including orientation dispersion (ODI) and isotropic volume (Viso). A linear SVM based classification approach, similar to our previous study, was incorporated here to classify between individuals with and without self-reported chronic TBI using FA and NODDI metrics as separate features. Overall classification rates were similar when incorporating FA and NODDI ODI metrics as features (AUC: 82.50%). Additionally, NODDI-based metrics provided the highest sensitivity (ODI: 85.00%) and specificity (Viso: 82.50%) rates. The current study serves as a replication and extension of our previous study, observing that multiple diffusion MRI metrics can reliably classify between individuals with and without self-reported history of chronic TBI.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 1","pages":"Article 100157"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10168530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9471923","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}

{"title":"Polygenic indices for cognition in healthy aging; the role of brain measures","authors":"A. Tsapanou ,&nbsp;N. Mourtzi ,&nbsp;Y. Gu ,&nbsp;C. Habeck ,&nbsp;D. Belsky ,&nbsp;Y. Stern","doi":"10.1016/j.ynirp.2022.100153","DOIUrl":"10.1016/j.ynirp.2022.100153","url":null,"abstract":"<div><h3>Background</h3><p>Genome-wide association studies (GWAS) have identified large numbers of genetic variants associated with cognition. However, little is known about how these genetic discoveries impact cognitive aging.</p></div><div><h3>Methods</h3><p>We conducted polygenic-index (PGI) analysis of cognitive performance in n = 168 European-ancestry adults aged 20–80. We computed PGIs based on GWAS of cognitive performance in young/middle-aged and older adults. We tested associations of the PGI with cognitive performance, as measured through neuropsychological evaluation. We explored whether these associations were accounted for by magnetic resonance imaging (MRI) measures of brain-aging phenotypes: total gray matter volume (GM), cortical thickness (CT), and white matter hyperintensities burden (WMH).</p></div><div><h3>Results</h3><p>Participants with higher PGI values performed better on cognitive tests (B = 0.627, SE = 0.196, <em>p</em> = 0.002) (age, sex, and principal components as covariates). Associations remained significant with inclusion of covariates for MRI measures of brain aging; B = 0.439, SE: 0.198, <em>p</em> = 0.028). PGI associations were stronger in young and middle-aged (age&lt;65) as compared to older adults. For further validation, linear regression for Cog PGI and cognition in the fully adjusted model and adding the interaction between age group and Cog PGI, showed significant results (B = 0.892, SE: 0.325, <em>p</em> = 0.007) driven by young and middle-aged adults (B = −0.403, SE: 0.193, <em>p</em> = 0.039). In ancillary analysis, the Cognitive PGI was not associated with any of the brain measures.</p></div><div><h3>Conclusions</h3><p>Genetics discovered in GWAS of cognition are associated with cognitive performance in healthy adults across age, but most strongly in young and middle-aged adults. Associations were not explained by brain-structural markers of brain aging. Genetics uncovered in GWAS of cognitive performance may contribute to individual differences established relatively early in life and may not reflect genetic mechanisms of cognitive aging.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 1","pages":"Article 100153"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ce/26/nihms-1883031.PMC10038095.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9199482","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}

{"title":"Deep learning based detection of enlarged perivascular spaces on brain MRI","authors":"Tanweer Rashid ,&nbsp;Hangfan Liu ,&nbsp;Jeffrey B. Ware ,&nbsp;Karl Li ,&nbsp;Jose Rafael Romero ,&nbsp;Elyas Fadaee ,&nbsp;Ilya M. Nasrallah ,&nbsp;Saima Hilal ,&nbsp;R. Nick Bryan ,&nbsp;Timothy M. Hughes ,&nbsp;Christos Davatzikos ,&nbsp;Lenore Launer ,&nbsp;Sudha Seshadri ,&nbsp;Susan R. Heckbert ,&nbsp;Mohamad Habes","doi":"10.1016/j.ynirp.2023.100162","DOIUrl":"10.1016/j.ynirp.2023.100162","url":null,"abstract":"<div><p>Deep learning has been demonstrated effective in many neuroimaging applications. However, in many scenarios, the number of imaging sequences capturing information related to small vessel disease lesions is insufficient to support data-driven techniques. Additionally, cohort-based studies may not always have the optimal or essential imaging sequences for accurate lesion detection. Therefore, it is necessary to determine which imaging sequences are crucial for precise detection. This study introduces a deep learning framework to detect enlarged perivascular spaces (ePVS) and aims to find the optimal combination of MRI sequences for deep learning-based quantification. We implemented an effective lightweight U-Net adapted for ePVS detection and comprehensively investigated different combinations of information from SWI, FLAIR, T1-weighted (T1w), and T2-weighted (T2w) MRI sequences. The experimental results showed that T2w MRI is the most important for accurate ePVS detection, and the incorporation of SWI, FLAIR and T1w MRI in the deep neural network had minor improvements in accuracy and resulted in the highest sensitivity and precision (sensitivity = 0.82, precision = 0.83). The proposed method achieved comparable accuracy at a minimal time cost compared to manual reading. The proposed automated pipeline enables robust and time-efficient readings of ePVS from MR scans and demonstrates the importance of T2w MRI for ePVS detection and the potential benefits of using multimodal images. Furthermore, the model provides whole-brain maps of ePVS, enabling a better understanding of their clinical correlates compared to the clinical rating methods within only a couple of brain regions.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 1","pages":"Article 100162"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10078801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9845865","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}

{"title":"An examination of the reliability of seed-to-seed resting state functional connectivity in tinnitus patients","authors":"Sara A. Schmidt ,&nbsp;Somayeh Shahsavarani ,&nbsp;Rafay A. Khan ,&nbsp;Yihsin Tai ,&nbsp;Elsa C. Granato ,&nbsp;Caterina M. Willson ,&nbsp;Pedro Ramos ,&nbsp;Paul Sherman ,&nbsp;Carlos Esquivel ,&nbsp;Bradley P. Sutton ,&nbsp;Fatima T. Husain","doi":"10.1016/j.ynirp.2023.100158","DOIUrl":"10.1016/j.ynirp.2023.100158","url":null,"abstract":"<div><p>Resting state functional connectivity (RS-FC) studies of tinnitus over the years have produced inconsistent results. While findings can be organized into broad categories, such as increased correlations between auditory and limbic areas in tinnitus patients and a disrupted default mode network, there has been little one-to-one correspondence of results across RS-FC studies of tinnitus. While some of this variation can be explained by the heterogeneity of the tinnitus population, including tinnitus severity, the sources of variability in RS-FC of tinnitus patients are unclear. To directly assess the reliability of RS-FC measures in tinnitus, both tinnitus and control participants from two different sites (University of Illinois at Urbana-Champaign, or UIUC, and the Wilford Hall Ambulatory Surgical Center, or WHASC, at the Lackland Airforce Base in San Antonio, Texas) participated in two resting state MRI scans separated by exactly one week. Seed-to-seed analysis assessing correlations between the fMRI activity of 27 regions in the default mode, dorsal attention, auditory, visual, salience, and emotional processing networks were examined in control and tinnitus participants separately for each site. Additionally, heart rate and respiration measures were collected at UIUC, and the effect of extra physiological corrections using these measures on reliability was examined within the UIUC participants. Intra-class correlation coefficients (ICCs) were used as the measure of reliability. Overall, RS-FC in a seed-to-seed analysis was as reliable in tinnitus participants as it was in control participants in the seed regions examined. As previously shown in studies of participants with normal hearing sensitivity, intra-network reliability was higher than inter-network reliability. Related to this, stronger correlations between two seed regions were predictive of stronger reliability of the connectivity between those regions. These effects were seen in both control and tinnitus populations. Additional physiological corrections did not have a significant impact on the ICC values. The current study demonstrates that, on a whole-brain level, RS-FC assessed via seed-to-seed analysis is reliable in tinnitus participants. We therefore must look to other sources as potential causes of discrepancies across studies, such as variability within analysis techniques or within the behavioral characteristics of tinnitus participants.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 1","pages":"Article 100158"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43086208","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}