Brain connectivity最新文献

{"title":"Attention Please! Unravelling the Link Between Brain Network Connectivity and Cognitive Attention Following Acquired Brain Injury: A Systematic Review of Structural and Functional Measures.","authors":"Athena Stein, Jacob R Thorstensen, Jonathan M Ho, Daniel P Ashley, Kartik K Iyer, Karen M Barlow","doi":"10.1089/brain.2023.0067","DOIUrl":"10.1089/brain.2023.0067","url":null,"abstract":"<p><p>Traumatic brain injury (TBI) and stroke are the most common causes of acquired brain injury (ABI), annually affecting 69 million and 15 million people, respectively. Following ABI, the relationship between brain network disruption and common cognitive issues including attention dysfunction is heterogenous. Using PRISMA guidelines, we systematically reviewed 43 studies published by February 2023 that reported correlations between attention and connectivity. Across all ages and stages of recovery, following TBI, greater attention was associated with greater structural efficiency within/between executive control network (ECN), salience network (SN), and default mode network (DMN) and greater functional connectivity (fc) within/between ECN and DMN, indicating DMN interference. Following stroke, greater attention was associated with greater structural connectivity (sc) within ECN; or greater fc within the dorsal attention network (DAN). In childhood ABI populations, decreases in structural network segregation were associated with greater attention. Longitudinal recovery from TBI was associated with normalization of DMN activity, and in stroke, normalization of DMN and DAN activity. Results improve clinical understanding of attention-related connectivity changes after ABI. Recommendations for future research include increased use of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to measure connectivity at the point of care, standardized attention and connectivity outcome measures and analysis pipelines, detailed reporting of patient symptomatology, and casual analysis of attention-related connectivity using brain stimulation.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":" ","pages":"4-38"},"PeriodicalIF":2.4,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138450817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Feeling of Time Passing Is Associated with Recurrent Sustained Activity and Theta Rhythms Across the Cortex.","authors":"Emma M Millon, Ali E Haddad, Han Yan M Chang, Laleh Najafizadeh, Tracey J Shors","doi":"10.1089/brain.2023.0010","DOIUrl":"10.1089/brain.2023.0010","url":null,"abstract":"<p><p><b><i>Introduction:</i></b> We are constantly estimating how much time has passed, and yet know little about the brain mechanisms through which this process occurs. In this pilot study, we evaluated so-called subjective time estimation with the temporal bisection task, while recording brain activity from electroencephalography (EEG). <b><i>Methods:</i></b> Nine adult participants were trained to distinguish between two durations of visual stimuli as either \"short\" (400 msec) or \"long\" (1600 msec). They were then presented with stimulus durations in between the long and short stimuli. EEG data from 128 electrodes were examined with a novel analytical method that identifies segments of sustained cortical activity during the task. <b><i>Results:</i></b> Participants tended to categorize intermediate durations as \"long\" more frequently than \"short\" and were thus experiencing time as moving faster while overestimating the amount of time passing. Their mean bisection point (during which frequency of selecting short vs. long is equal) was closer to the geometric mean of task stimuli (800 msec) rather than the arithmetic mean (1000 msec). In contrast, sustained brain activity occurred closer to the arithmetic mean. The recurrence rate of this activity was highly related to the bisection point, especially when analyzed within naturally occurring theta oscillations (4-8 Hz) (<i>r</i> = -0.90). <b><i>Discussion:</i></b> Sustained activity across the cortex within the theta range may reflect temporal durations, whereas its repeated appearance relates to the subjective feeling of time passing.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":" ","pages":"39-47"},"PeriodicalIF":3.4,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138450818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dysregulated Salience Network Control over Default-Mode and Central-Executive Networks in Schizophrenia Revealed Using Stochastic Dynamical Causal Modeling.","authors":"Deepa S Thakuri, Puskar Bhattarai, Dean F Wong, Ganesh B Chand","doi":"10.1089/brain.2023.0054","DOIUrl":"10.1089/brain.2023.0054","url":null,"abstract":"<p><p><b><i>Introduction:</i></b> Neuroimaging studies suggest that the human brain consists of intrinsically organized, large-scale neural networks. Among these networks, the interplay among the default-mode network (DMN), salience network (SN), and central-executive network (CEN) has been widely used to understand the functional interaction patterns in health and disease. This triple network model suggests that the SN causally controls over the DMN and CEN in healthy individuals. This interaction is often referred to as SN's dynamic regulating mechanism. However, such interactions are not well understood in individuals with schizophrenia. <b><i>Methods:</i></b> In this study, we leveraged resting-state functional magnetic resonance imaging data from schizophrenia (<i>n</i> = 67) and healthy controls (<i>n</i> = 81) and evaluated the directional functional interactions among DMN, SN, and CEN using stochastic dynamical causal modeling methodology. <b><i>Results:</i></b> In healthy controls, our analyses replicated previous findings that SN regulates DMN and CEN activities (Mann-Whitney <i>U</i> test; <i>p</i> < 10^-8). In schizophrenia, however, our analyses revealed a disrupted SN-based controlling mechanism over the DMN and CEN (Mann-Whitney <i>U</i> test; <i>p</i> < 10^-16). <b><i>Conclusions:</i></b> These results indicate that the disrupted controlling mechanism of SN over the other two neural networks may be a candidate neuroimaging phenotype in schizophrenia.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":" ","pages":"70-79"},"PeriodicalIF":2.4,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10890948/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139073347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>Brain Connectivity:</i> <i>A Journal of Clinical Neurology, Neuroscience, & Neuroimaging</i>.","authors":"Paul Edison","doi":"10.1089/brain.2024.29059.editorial","DOIUrl":"10.1089/brain.2024.29059.editorial","url":null,"abstract":"","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":"14 1","pages":"1-3"},"PeriodicalIF":3.4,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139929975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Language First, Cognition Later: Different Trajectories of Subcomponents of the Future-Reading Network in Processing Narratives from Kindergarten to Adolescence.","authors":"Tzipi Horowitz-Kraus, Raya Meri, Scott K Holland, Rola Farah, Tamara Rohana, Narmeen Haj","doi":"10.1089/brain.2023.0012","DOIUrl":"10.1089/brain.2023.0012","url":null,"abstract":"<p><p>Narrative comprehension is a linguistic ability that emerges early in life and has a critical role in language development, reading acquisition, and comprehension. According to the Simple View of Reading model, reading is acquired through word decoding and linguistic comprehension. Here, within and between networks, functional connectivity in several brain networks supporting both language and reading abilities was examined from prereading to proficient reading age in 32 healthy children, ages 5-18 years, scanned annually while listening to stories over 12 years. Functional connectivity changes within and between the networks were assessed and compared between the years using hierarchical linear regression and were related to reading abilities. At prereading age, the networks related to basic language processing accounted for 32.5% of the variation of reading ability at reading age (at 12-14 years) (<i>R</i>² = 0.325, <i>p</i> = 0.05). At age 17, more complex cognitive networks were involved and accounted for 97.4% of the variation in reading ability (<i>R</i>² = 0.974, <i>p</i> = 0.022). Overall, networks composing the future-reading network are highly involved in processing narratives along development; however, networks related to semantic, phonological, and syntactic processing predict reading ability earlier in life, and more complex networks predict reading proficiency later in life.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":" ","pages":"60-69"},"PeriodicalIF":2.4,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10890959/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139541385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acknowledgment of Reviewers 2023.","authors":"","doi":"10.1089/brain.2023.29058.ack","DOIUrl":"https://doi.org/10.1089/brain.2023.29058.ack","url":null,"abstract":"","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138799075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acknowledgment of Reviewers 2023.","authors":"","doi":"10.1089/jicm.2023.29127.ack","DOIUrl":"https://doi.org/10.1089/jicm.2023.29127.ack","url":null,"abstract":"","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":"18 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139003671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differential Effect of Global Signal Regression Between Awake and Anesthetized Conditions in Mice.","authors":"Da Wang, Hui Li, Mengyang Xu, Binshi Bo, Mengchao Pei, Zhifeng Liang, Garth J. Thompson","doi":"10.1089/brain.2023.0032","DOIUrl":"https://doi.org/10.1089/brain.2023.0032","url":null,"abstract":"","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":"74 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138586759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Blood-Based Lipid Profile Associated With Hippocampal Volume and Brain Resting-State Activation Within Obese Adults from the UK Biobank.","authors":"Guocheng Jiang, Jennifer S Rabin, Sandra E Black, Walter Swardfager, Bradley J MacIntosh","doi":"10.1089/brain.2023.0018","DOIUrl":"10.1089/brain.2023.0018","url":null,"abstract":"<p><p><b><i>Objectives:</i></b> Obesity and dyslipidemia may be associated with hippocampal alterations and may increase the risk of neurodegeneration. This study studied hippocampal anatomical and functional association with a lipid profile based on high-density lipoprotein, low-density lipoprotein, and triglyceride related to dyslipidemia in obese and nonobese adults. A whole-brain analysis was also conducted to examine the effect of dyslipidemia on resting-state function across the brain. <b><i>Participants and Methods:</i></b> In total, 553 UK Biobank participants comprised three groups based on body mass index (BMI) rankings: obese adults with high BMI (O_High, <i>n</i> = 184, 32.7 kg/m² ≤ BMI ≤53.4 kg/m²), obese adults with a lower BMI (O_Low, <i>n</i> = 182, 30.3 kg/m² ≤ BMI ≤32.6 kg/m²), and nonobese controls (<i>n</i> = 187). Structural MRI and functional MRI data were accessed. The fractional amplitude of low-frequency fluctuations (fALFFs) maps was calculated to reflect resting-state brain activity. A lipid health factor was created using principal component analysis. Linear models tested for associations between the lipid health score and hippocampal MRI readouts. <b><i>Results:</i></b> With a higher lipid health factor corresponding to a lower dyslipidemia risk, we found a positive correlation between hippocampal volume with the lipid health factor exclusively in group O_Low (<i>p</i> = 0.01). We also found a positive association between the lipid health factor and hippocampal fALFF in group O_High (<i>p</i> = 0.02). Additional fALFF voxel-wise analysis to group O_High also implicated that the premotor cortex, amygdala, thalamus, subcallosal cortex, temporal fusiform cortex, and middle temporal gyrus brain regions are related with lipid. <b><i>Conclusion:</i></b> The study finds novel associations among circulating lipid, hippocampal structure, and hippocampal function exclusively in the obese adults.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":" ","pages":"578-588"},"PeriodicalIF":3.4,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71477976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>Brain Connectivity:</i> <i>A Journal of Clinical Neurology, Neuroscience, & Neuroimaging</i> Advancing the Field of Neurology.","authors":"Paul Edison","doi":"10.1089/brain.2023.29057.editorial","DOIUrl":"10.1089/brain.2023.29057.editorial","url":null,"abstract":"","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":"13 10","pages":"575-577"},"PeriodicalIF":3.4,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139039505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}