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

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.

Download Full-text

Adolescent development of multiscale cortical wiring and functional connectivity in the human connectome

10.1101/2021.08.16.456455 ◽

2021 ◽

Author(s):

Bo-yong Park ◽

Casey Paquola ◽

Richard A.I. Bethlehem ◽

Oualid Benkarim ◽

Bratislav Misic ◽

...

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Brain Network ◽

Network Development ◽

Network Function ◽

Age Related ◽

Mri Features ◽

Functional Brain Network ◽

Structural Networks ◽

Advanced Model

Adolescence is a time of profound changes in the structural wiring of the brain and maturation of large-scale functional interactions. Here, we analyzed structural and functional brain network development in an accelerated longitudinal cohort spanning 14-25 years (n = 199). Core to our work was an advanced model of cortical wiring that incorporates multimodal MRI features of (i) cortico-cortical proximity, (ii) microstructural similarity, and (iii) diffusion tractography. Longitudinal analyses assessing age-related changes in cortical wiring during adolescence identified increases in cortical wiring within attention and default-mode networks, as well as between transmodal and attention, and sensory and limbic networks, indicative of a continued differentiation of cortico-cortical structural networks. Cortical wiring changes were statistically independent from age-related cortical thinning seen in the same subjects. Conversely, resting-state functional MRI analysis in the same subjects indicated an increasing segregation of sensory and transmodal systems during adolescence, with age-related reductions in their functional connectivity alongside with an increase in structural wiring distance. Our findings provide new insights into adolescent brain network development, illustrating how the maturation of structural wiring interacts with the development of macroscale network function.

Download Full-text

Age-related changes in spatial and temporal features of resting state fMRI

10.1101/109181 ◽

2017 ◽

Author(s):

Shruti G. Vij ◽

Jason S. Nomi ◽

Dina R. Dajani ◽

Lucina Q. Uddin

Keyword(s):

Resting State ◽

Large Scale ◽

Spectral Composition ◽

Brain Network ◽

Functional Networks ◽

Functional Changes ◽

Age Related ◽

Temporal Features ◽

Functional Brain Network ◽

Age Related Changes

AbstractDevelopment and aging are associated with functional changes in the brain across the lifespan. These changes can be detected in spatial and temporal features of resting state functional MRI (rs-fMRI) data. Independent vector analysis (IVA) is a whole-brain multivariate approach that can be used to comprehensively assess these changes in spatial and temporal features. We present a multi-dimensional approach to assessing age-related changes in spatial and temporal features of statistically independent components identified by IVA in a cross-sectional lifespan sample (ages 6-85 years). We show that while large-scale brain network configurations remain consistent throughout the lifespan, changes continue to occur in both local organization and in the spectral composition of these functional networks. We show that the spatial extent of functional networks decreases with age, but with no significant change in the peak functional loci of these networks. Additionally, we show differential age-related patterns across the frequency spectrum; lower frequency correlations decrease across the lifespan whereas higher-frequency correlations increase. These changes indicate an increasing stability of networks with age. In addition to replicating results from previous studies, the current results uncover new aspects of functional brain network changes across the lifespan that are frequency band-dependent.

Download Full-text

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.

Download Full-text

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

10.1101/2021.12.10.21267535 ◽

2021 ◽

Author(s):

Simone JT van Montfort ◽

Fienke L Ditzel ◽

Ilse MJ Kant ◽

Ellen Aarts ◽

Lisette M Vernooij ◽

...

Keyword(s):

Functional Connectivity ◽

Postoperative Delirium ◽

Elective Surgery ◽

Brain Network ◽

Major Surgery ◽

Functional Brain ◽

Functional Brain Network ◽

Trail Making ◽

Functional Connectivity Strength ◽

Connectivity Strength

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.

Download Full-text

Different Modes of Low-Frequency Focused Ultrasound-Mediated Attenuation of Epilepsy Based on the Topological Theory

Micromachines ◽

10.3390/mi12081001 ◽

2021 ◽

Vol 12 (8) ◽

pp. 1001

Author(s):

Minjian Zhang ◽

Bo Li ◽

Yafei Liu ◽

Rongyu Tang ◽

Yiran Lang ◽

...

Keyword(s):

Focused Ultrasound ◽

Continuous Wave ◽

Low Frequency ◽

Brain Regions ◽

Brain Network ◽

Functional Brain ◽

Functional Brain Network ◽

Significant Difference ◽

The Brain ◽

Brain Connections

Epilepsy is common brain dysfunction, where abnormal synchronized activities can be observed across multiple brain regions. Low-frequency focused pulsed ultrasound has been proven to modulate the epileptic brain network. In this study, we used two modes of low-intensity focused ultrasound (pulsed-wave and continuous-wave) to sonicate the brains of KA-induced epileptic rats, analyzed the EEG functional brain connections to explore their respective effect on the epileptic brain network, and discuss the mechanism of ultrasound neuromodulation. By comparing the brain network characteristics before and after sonication, we found that two modes of ultrasound both significantly affected the functional brain network, especially in the low-frequency band below 12 Hz. After two modes of sonication, the power spectral density of the EEG signals and the connection strength of the brain network were significantly reduced, but there was no significant difference between the two modes. Our results indicated that the ultrasound neuromodulation could effectively regulate the epileptic brain connections. The ultrasound-mediated attenuation of epilepsy was independent of modes of ultrasound.

Download Full-text

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.

Download Full-text

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.

Download Full-text

Age-related differences in functional brain network segregation are consistent with a cascade of cerebrovascular, structural, and cognitive effects

Network Neuroscience ◽

10.1162/netn_a_00110 ◽

2020 ◽

Vol 4 (1) ◽

pp. 89-114 ◽

Cited By ~ 2

Author(s):

Tania S. Kong ◽

Caterina Gratton ◽

Kathy A. Low ◽

Chin Hong Tan ◽

Antonio M. Chiarelli ◽

...

Keyword(s):

White Matter ◽

Cognitive Performance ◽

Cognitive Aging ◽

Optical Tomography ◽

Brain Network ◽

Cortical Atrophy ◽

Resting State Functional Connectivity ◽

Functional Brain ◽

Age Related ◽

Functional Brain Network

Age-related declines in cognition are associated with widespread structural and functional brain changes, including changes in resting-state functional connectivity and gray and white matter status. Recently we have shown that the elasticity of cerebral arteries also explains some of the variance in cognitive and brain health in aging. Here, we investigated how network segregation, cerebral arterial elasticity (measured with pulse-DOT—the arterial pulse based on diffuse optical tomography) and gray and white matter status jointly account for age-related differences in cognitive performance. We hypothesized that at least some of the variance in brain and cognitive aging is linked to reduced cerebrovascular elasticity, leading to increased cortical atrophy and white matter abnormalities, which, in turn, are linked to reduced network segregation and decreases in cognitive performance. Pairwise comparisons between these variables are consistent with an exploratory hierarchical model linking them, especially when focusing on association network segregation (compared with segregation in sensorimotor networks). These findings suggest that preventing or slowing age-related changes in one or more of these factors may induce a neurophysiological cascade beneficial for preserving cognition in aging.

Download Full-text

Research on Functional Brain Network Metrics for Depression Patients Automatic Identification

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.1440 ◽

2013 ◽

Vol 427-429 ◽

pp. 1440-1446

Author(s):

Chen Cheng ◽

Wen Zhao Liu ◽

Jun Jie Chen

Keyword(s):

Network Topology ◽

Neurological Diseases ◽

Brain Network ◽

Classification Model ◽

Support Vector ◽

Automatic Identification ◽

Functional Brain ◽

Functional Brain Network ◽

Network Metrics ◽

Abnormal Brain

Nowadays, Brain network as a means of emerging brain disease research has been fully recognized which is applied to the neurological diseases, such as major depressive disorder (MDD). It also can detect the exception of the whole brain network topological. But there is no evidence to prove that abnormal brain network topology metrics can be an effective feature in the classification model to distinguish the healthy control and MDD. So, we hypothesize the abnormal brain network topology metrics can be used as an valid classification feature. Resting state functional brain networks were constructed for 26 healthy controls and 34 MDD patients by thresholding partial correlation matrices of 90 regions. According to the theory-based approaches, the global and local metrics were calculated. Non-parametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in support vector machine algorithm. The current study demonstrate that MDD is associated with abnormal function brain network topological metrics and statistically significance network metrics can be successfully used for feature selection in classification algorithms.

Download Full-text