Functional brain network and trail making test changes after major surgery and delirium

AbstractBackgroundDelirium is a frequent complication of elective surgery in elderly patients, associated with an increased risk of long-term cognitive impairment and dementia. Disturbances in the functional brain network were previously reported during delirium. We hypothesized persisting alterations in functional brain networks three months after elective surgery in patients with postoperative delirium, and hypothesized that postoperative brain connectivity changes (irrespective of delirium) are related to cognitive decline.MethodsElderly patients (N=554) undergoing elective surgery underwent clinical assessments (including Trail Making Test B (TMT-B) and resting-state functional magnetic resonance imaging (rs-fMRI) before and three months after surgery. Delirium was assessed on the first seven postoperative days. After strict motion correction, rs-fMRI connectivity strength and network characteristics were calculated in 246 patients (130 patients underwent scans at both timepoints), of whom 38 (16%) developed postoperative delirium.ResultsRs-fMRI functional connectivity strength increased after surgery in the total study population (β=0.006, 95%CI=0.000–0.012, p=0.021), but decreased after postoperative delirium (β=-0.014, 95%CI=0.000–0.012, p=0.026). No difference in TMT-B scores was found at follow-up between patients with and without postoperative delirium. Patients who decreased in functional connectivity strength declined in TMT-B scores compared to the group that did not (β=11.04, 95%CI=0.85-21.2, p=0.034).ConclusionsDelirium was associated with decreased functional connectivity strength three months after the syndrome was clinically resolved, which implies that delirium has lasting impact on brain networks. Decreased connectivity strength was associated with statistically significant (but not necessarily clinically relevant) cognitive deterioration after major surgery, which was not specifically related to delirium.Summary statementDelirium was associated with decreased resting-state fMRI functional connectivity strength three months after the syndrome was clinically resolved. Irrespective of delirium, decreased connectivity strength after major surgery was associated with a statistically significant cognitive deterioration.

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The trend of disruption in the functional brain network topology of Alzheimer’s disease

10.21203/rs.3.rs-440491/v1 ◽

2021 ◽

Author(s):

Alireza Fathian ◽

Yousef Jamali ◽

Mohammad Reza Raoufy

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Functional Connectivity ◽

Small World ◽

Brain Network ◽

Cognitively Normal ◽

Functional Brain ◽

Rich Club ◽

Functional Brain Network ◽

Progressive Disorder

Abstract Alzheimer’s disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain’s functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study analyzed the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer’s disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression, and these trends behaved differently at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, The methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process.

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Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach

Brain Sciences ◽

10.3390/brainsci10110777 ◽

2020 ◽

Vol 10 (11) ◽

pp. 777

Author(s):

Nicholas John Simos ◽

Stavros I. Dimitriadis ◽

Eleftherios Kavroulakis ◽

Georgios C. Manikis ◽

George Bertsias ◽

...

Keyword(s):

Machine Learning ◽

Functional Connectivity ◽

Lupus Erythematosus ◽

Statistical Power ◽

Functional Neuroimaging ◽

Brain Regions ◽

Brain Network ◽

Functional Brain ◽

Graph Metrics ◽

Functional Brain Network

Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients.

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Age-related changes in functional connectivity between young adulthood and late adulthood

Analytical Methods ◽

10.1039/c5ay00699f ◽

2015 ◽

Vol 7 (10) ◽

pp. 4111-4122 ◽

Cited By ~ 4

Author(s):

Xin Xu ◽

Qifan Kuang ◽

Yongqing Zhang ◽

Huijun Wang ◽

Zhining Wen ◽

...

Keyword(s):

Functional Connectivity ◽

Young Adulthood ◽

Brain Network ◽

Late Adulthood ◽

Functional Brain ◽

Age Related ◽

Functional Brain Network ◽

Significant Difference ◽

Network Metrics ◽

Age Related Changes

The functional brain network in late adulthood has been found to show a significant difference from that in young adulthood using a variety of network metrics.

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Automated Extraction of Human Functional Brain Network Properties Associated with Working Memory Load through a Machine Learning-Based Feature Selection Algorithm

Computational Intelligence and Neuroscience ◽

10.1155/2018/4835676 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Satoru Hiwa ◽

Shogo Obuchi ◽

Tomoyuki Hiroyasu

Keyword(s):

Working Memory ◽

Functional Connectivity ◽

Brain Regions ◽

Brain Network ◽

Clustering Coefficient ◽

Support Vector ◽

Neural Basis ◽

Functional Brain ◽

Functional Brain Network ◽

Network Properties

Working memory (WM) load-dependent changes of functional connectivity networks have previously been investigated by graph theoretical analysis. However, the extraordinary number of nodes represented within the complex network of the human brain has hindered the identification of functional regions and their network properties. In this paper, we propose a novel method for automatically extracting characteristic brain regions and their graph theoretical properties that reflect load-dependent changes in functional connectivity using a support vector machine classification and genetic algorithm optimization. The proposed method classified brain states during 2- and 3-back test conditions based upon each of the three regional graph theoretical metrics (degree, clustering coefficient, and betweenness centrality) and automatically identified those brain regions that were used for classification. The experimental results demonstrated that our method achieved a >90% of classification accuracy using each of the three graph metrics, whereas the accuracy of the conventional manual approach of assigning brain regions was only 80.4%. It has been revealed that the proposed framework can extract meaningful features of a functional brain network that is associated with WM load from a large number of nodal graph theoretical metrics without prior knowledge of the neural basis of WM.

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Brain network connectivity differs in early-onset neurodegenerative dementia

Neurology ◽

10.1212/wnl.0000000000004577 ◽

2017 ◽

Vol 89 (17) ◽

pp. 1764-1772 ◽

Cited By ~ 32

Author(s):

Massimo Filippi ◽

Silvia Basaia ◽

Elisa Canu ◽

Francesca Imperiale ◽

Alessandro Meani ◽

...

Keyword(s):

Functional Connectivity ◽

Early Onset ◽

Network Architecture ◽

Parietal Lobe ◽

Brain Network ◽

Clustering Coefficient ◽

Functional Network ◽

Graph Analysis ◽

Functional Brain ◽

Functional Brain Network

Objective:To investigate functional brain network architecture in early-onset Alzheimer disease (EOAD) and behavioral variant frontotemporal dementia (bvFTD).Methods:Thirty-eight patients with bvFTD, 37 patients with EOAD, and 32 age-matched healthy controls underwent 3D T1-weighted and resting-state fMRI. Graph analysis and connectomics assessed global and local functional topologic network properties, regional functional connectivity, and intrahemispheric and interhemispheric between-lobe connectivity.Results:Despite similarly extensive cognitive impairment relative to controls, patients with EOAD showed severe global functional network alterations (lower mean nodal strength, local efficiency, clustering coefficient, and longer path length), while patients with bvFTD showed relatively preserved global functional brain architecture. Patients with bvFTD demonstrated reduced nodal strength in the frontoinsular lobe and a relatively focal altered functional connectivity of frontoinsular and temporal regions. Functional connectivity breakdown in the posterior brain nodes, particularly in the parietal lobe, differentiated patients with EOAD from those with bvFTD. While EOAD was associated with widespread loss of both intrahemispheric and interhemispheric functional correlations, bvFTD showed a preferential disruption of the intrahemispheric connectivity.Conclusions:Disease-specific patterns of functional network topology and connectivity alterations were observed in patients with EOAD and bvFTD. Graph analysis and connectomics may aid clinical diagnosis and help elucidate pathophysiologic differences between neurodegenerative dementias.

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Investigating functional brain network integrity using a traditional and novel diagnostic system for neurodevelopmental disorders

10.1101/396317 ◽

2018 ◽

Author(s):

Dina R. Dajani ◽

Catherine A. Burrows ◽

Paola Odriozola ◽

Adriana Baez ◽

Mary Beth Nebel ◽

...

Keyword(s):

Executive Function ◽

Functional Connectivity ◽

Neurodevelopmental Disorders ◽

Autism Spectrum ◽

Brain Network ◽

Group Differences ◽

Behavioral Measures ◽

Functional Brain ◽

Diagnostic Categories ◽

Functional Brain Network

AbstractBackgroundCurrent diagnostic systems for neurodevelopmental disorders do not have clear links to underlying neurobiology, limiting their utility in identifying targeted treatments for individuals. Several factors contribute to this issue, including the use of small samples in neuroimaging research and heterogeneity within diagnostic categories. Here, we aimed to investigate differences in functional brain network integrity between traditional diagnostic categories (autism spectrum disorder [ASD], attention-deficit/hyperactivity disorder [ADHD], typically developing [TD]) and carefully consider the impact of comorbid ASD and ADHD on functional brain network integrity in a large sample. We also assess the neurobiological validity of a novel, potential alternative nosology based on behavioral measures of executive function.MethodFive-minute resting-state fMRI data were obtained from 168 children (128 boys, 40 girls) with ASD, ADHD, comorbid ASD and ADHD, and TD children. Independent component analysis and dual regression were used to compute within- and between-network functional connectivity metrics at the individual level.ResultsNo significant group differences in within- nor between-network functional connectivity were observed between traditional diagnostic categories (ASD, ADHD, TD) even when stratified by comorbidity (ASD+ADHD, ASD, ADHD, TD). Similarly, subgroups classified by executive functioning levels showed no group differences.ConclusionsUsing clinical diagnosis and behavioral measures of executive function, no group differences were observed among the categories examined. Therefore, we suggest that brain imaging metrics may more effectively define clinical subgroups than behavioral metrics, and may contribute to the establishment of a neurobiologically valid nosology for neurodevelopmental disorders.

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The Effects of Intermittent Theta Burst Stimulation on Functional Brain Network Following Stroke: An Electroencephalography Study

Frontiers in Neuroscience ◽

10.3389/fnins.2021.755709 ◽

2021 ◽

Vol 15 ◽

Author(s):

Qian Ding ◽

Shunxi Zhang ◽

Songbin Chen ◽

Jixiang Chen ◽

Xiaotong Li ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Stroke Rehabilitation ◽

Brain Network ◽

Stroke Survivors ◽

Theta Burst Stimulation ◽

Burst Stimulation ◽

Functional Brain ◽

Functional Brain Network ◽

Theta Burst

Objective: Intermittent theta burst stimulation (iTBS) is a special form of repetitive transcranial magnetic stimulation (rTMS), which effectively increases cortical excitability and has been widely used as a neural modulation approach in stroke rehabilitation. As effects of iTBS are typically investigated by motor evoked potentials, how iTBS influences functional brain network following stroke remains unclear. Resting-state electroencephalography (EEG) has been suggested to be a sensitive measure for evaluating effects of rTMS on brain functional activity and network. Here, we used resting-state EEG to investigate the effects of iTBS on functional brain network in stroke survivors.Methods: We studied thirty stroke survivors (age: 63.1 ± 12.1 years; chronicity: 4.0 ± 3.8 months; UE FMA: 26.6 ± 19.4/66) with upper limb motor dysfunction. Stroke survivors were randomly divided into two groups receiving either Active or Sham iTBS over the ipsilesional primary motor cortex. Resting-state EEG was recorded at baseline and immediately after iTBS to assess the effects of iTBS on functional brain network.Results: Delta and theta bands interhemispheric functional connectivity were significantly increased after Active iTBS (P = 0.038 and 0.011, respectively), but were not significantly changed after Sham iTBS (P = 0.327 and 0.342, respectively). Delta and beta bands global efficiency were also significantly increased after Active iTBS (P = 0.013 and 0.0003, respectively), but not after Sham iTBS (P = 0.586 and 0.954, respectively).Conclusion: This is the first study that used EEG to investigate the acute neuroplastic changes after iTBS following stroke. Our findings for the first time provide evidence that iTBS modulates brain network functioning in stroke survivors. Acute increase in interhemispheric functional connectivity and global efficiency after iTBS suggest that iTBS has the potential to normalize brain network functioning following stroke, which can be utilized in stroke rehabilitation.

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