scholarly journals Biophysical network models of phase-synchronization in MEG resting-state

2021 ◽  
Author(s):  
Nitin Williams ◽  
Benedetta Toselli ◽  
Felix Siebenhuhner ◽  
Satu Palva ◽  
Gabriele Arnulfo ◽  
...  
Keyword(s):  
Phase Synchronization ◽  
Network Models ◽  
Brain Regions ◽  
Null Models ◽  
Oscillatory Dynamics ◽  
Structural Connectome ◽  
Structural Connections ◽  
Magnetic Resonance Imaging Mri ◽  
Relationship Of ◽  
The Relationship

Magnetoencephalography (MEG) is used extensively to study functional connectivity (FC) networks of phase-synchronization, but the relationship of these networks to their biophysical substrates is poorly understood. Biophysical Network Models (BNMs) have been used to produce networks corresponding to MEG-derived networks of phase-synchronization, but the roles of inter-regional conduction delays, the structural connectome and dynamics of model of individual brain regions, in obtaining this correspondence remain unknown. In this study, we investigated the roles of conduction delays, the structural connectome, and dynamics of models of individual regions, in obtaining a correspondence between model-generated and MEG-derived networks between left-hemispheric regions. To do this, we compared three BNMs, respectively comprising Wilson-Cowan oscillators interacting with diffusion Magnetic Resonance Imaging (MRI)-based patterns of structural connections through zero delays, constant delays and distance-dependent delays respectively. For the BNM whose networks corresponded most closely to the MEG-derived network, we used comparisons against null models to identify specific features of each model component, e.g. the pattern of connections in the structure connectome, that contributed to the observed correspondence. The Wilson-Cowan zero delays model produced networks with a closer correspondence to the MEG-derived network than those produced by the constant delays model, and the same as those produced by the distance-dependent delays model. Hence, there is no evidence that including conduction delays improves the correspondence between model-generated and MEG-derived networks. Given this, we chose the Wilson-Cowan zero delays model for further investigation. Comparing the Wilson-Cowan zero delays model against null models revealed that both the pattern of structural connections and Wilson-Cowan oscillatory dynamics contribute to the correspondence between model-generated and MEG-derived networks. Our investigations yield insight into the roles of conduction delays, the structural connectome and dynamics of models of individual brain regions, in obtaining a correspondence between model-generated and MEG-derived networks. These findings result in a parsimonious BNM that produces networks corresponding closely to MEG-derived networks of phase-synchronization.

scholarly journals Damage to the structural connectome reflected in resting-state fMRI functional connectivity

2020 ◽  
Vol 4 (4) ◽  
pp. 1197-1218
Author(s):  
Anirudh Wodeyar ◽  
Jessica M. Cassidy ◽  
Steven C. Cramer ◽  
Ramesh Srinivasan
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Structural Damage ◽  
Partial Correlation ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Analysis Pipeline ◽  
Structural Connectome ◽  
Relationship Of ◽  
The Relationship

The relationship between structural and functional connectivity has been mostly examined in intact brains. Fewer studies have examined how differences in structure as a result of injury alters function. In this study we analyzed the relationship of structure to function across patients with stroke among whom infarcts caused heterogenous structural damage. We estimated relationships between distinct brain regions of interest (ROIs) from functional MRI in two pipelines. In one analysis pipeline, we measured functional connectivity by using correlation and partial correlation between 114 cortical ROIs. We found fMRI-BOLD partial correlation was altered at more edges as a function of the structural connectome (SC) damage, relative to the correlation. In a second analysis pipeline, we limited our analysis to fMRI correlations between pairs of voxels for which we possess SC information. We found that voxel-level functional connectivity showed the effect of structural damage that we could not see when examining correlations between ROIs. Further, the effects of structural damage on functional connectivity are consistent with a model of functional connectivity, diffusion, which expects functional connectivity to result from activity spreading over multiple edge anatomical paths.

scholarly journals Heritability and interindividual variability of regional structure-function coupling

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zijin Gu ◽  
Keith Wakefield Jamison ◽  
Mert Rory Sabuncu ◽  
Amy Kuceyeski
Keyword(s):  
Functional Connectivity ◽  
Structure Function ◽  
Regional Differences ◽  
Regional Scale ◽  
Brain Regions ◽  
Diffusion Weighted Mri ◽  
Regional Structure ◽  
Human Connectome Project ◽  
Structural Connections ◽  
The Relationship

AbstractWhite matter structural connections are likely to support flow of functional activation or functional connectivity. While the relationship between structural and functional connectivity profiles, here called SC-FC coupling, has been studied on a whole-brain, global level, few studies have investigated this relationship at a regional scale. Here we quantify regional SC-FC coupling in healthy young adults using diffusion-weighted MRI and resting-state functional MRI data from the Human Connectome Project and study how SC-FC coupling may be heritable and varies between individuals. We show that regional SC-FC coupling strength varies widely across brain regions, but was strongest in highly structurally connected visual and subcortical areas. We also show interindividual regional differences based on age, sex and composite cognitive scores, and that SC-FC coupling was highly heritable within certain networks. These results suggest regional structure-function coupling is an idiosyncratic feature of brain organisation that may be influenced by genetic factors.

Setting the Scene

2021 ◽  
pp. 1-8
Author(s):  
John Zerilli
Keyword(s):  
Neural Circuits ◽  
Brain Regions ◽  
Basic Assumptions ◽  
The Subject ◽  
Basic Argument ◽  
The Mind ◽  
Relationship Of ◽  
The Relationship

The modularity of mind has been understood in various ways, amended as evidence from neuroscience has forced the theory to shed various structural assumptions. Neuroplasticity has, for better or worse, challenged many of the orthodox conceptions of the mind that originally led cognitive scientists to postulate mental modules. Similarly, rapidly accumulating neuroscientific evidence of the reuse or redeployment of neural circuits, revealing the integrated and interactive structure of brain regions, has upset basic assumptions about the relationship of function to structure upon which modularity—not to say neuroscience itself—originally depended. These movements, developments, and cross-currents are the subject of this book. This chapter outlines the basic argument of the book and its motivation.

scholarly journals Tau seeding activity anticipates phospho-tau pathology in Alzheimer’s disease

2018 ◽  
Author(s):  
Sarah K. Kaufman ◽  
Kelly Del Tredici ◽  
Talitha L. Thomas ◽  
Heiko Braak ◽  
Marc I. Diamond
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Visual Cortex ◽  
Brain Regions ◽  
Tau Pathology ◽  
Age Related ◽  
Important Addition ◽  
Disease Stages ◽  
Relationship Of ◽  
The Relationship

AbstractAlzheimer’s disease (AD) is characterized by accumulation of tau neurofibrillary tangles (NFTs) and, according to the prion model, transcellular propagation of pathological “seeds” may underlie its progression. Staging of NFT pathology with phospho-tau antibody is useful to classify AD and primary age-related tauopathy (PART) cases. The locus coeruleus (LC) shows the earliest phospho-tau signal, whereas other studies suggest that pathology begins in the transentorhinal/entorhinal cortices (TRE/EC). The relationship of tau seeding activity, phospho-tau pathology, and progression of neurodegeneration remains obscure. Consequently, we employed an established cellular biosensor assay to quantify tau seeding activity in fixed human tissue, in parallel with AT8 phospho-tau staining of immediately adjacent sections. We studied four brain regions from each of n=247 individuals across a range of disease stages. We detected the earliest and most robust seeding activity in the TRE/EC. The LC did not uniformly exhibit seeding activity until later NFT stages. We also detected seeding activity in the first temporal gyrus and visual cortex at stages before NFTs and/or AT8-immunopositivity were detectable. AD and putative PART cases exhibited similar patterns of seeding activity that anticipated histopathology across all NFT stages. Our findings are consistent with the prion model and suggest that pathological seeding activity begins in the TRE/EC rather than in the LC, and may offer an important addition to classical histopathology.

scholarly journals Transmission time delays organize the brain network synchronization

2019 ◽  
Vol 377 (2153) ◽  
pp. 20180132 ◽  
Author(s):  
Spase Petkoski ◽  
Viktor K. Jirsa
Keyword(s):  
Time Delays ◽  
Phase Synchronization ◽  
Brain Regions ◽  
Brain Network ◽  
Temporal Organization ◽  
Delay Systems ◽  
Oscillatory Dynamics ◽  
Wide Range ◽  
Kuramoto Oscillators ◽  
The Brain

The timing of activity across brain regions can be described by its phases for oscillatory processes, and is of crucial importance for brain functioning. The structure of the brain constrains its dynamics through the delays due to propagation and the strengths of the white matter tracts. We use self-sustained delay-coupled, non-isochronous, nonlinearly damped and chaotic oscillators to study how spatio-temporal organization of the brain governs phase lags between the coherent activity of its regions. In silico results for the brain network model demonstrate a robust switching from in- to anti-phase synchronization by increasing the frequency, with a consistent lagging of the stronger connected regions. Relative phases are well predicted by an earlier analysis of Kuramoto oscillators, confirming the spatial heterogeneity of time delays as a crucial mechanism in shaping the functional brain architecture. Increased frequency and coupling are also shown to distort the oscillators by decreasing their amplitude, and stronger regions have lower, but more synchronized activity. These results indicate specific features in the phase relationships within the brain that need to hold for a wide range of local oscillatory dynamics, given that the time delays of the connectome are proportional to the lengths of the structural pathways. This article is part of the theme issue ‘Nonlinear dynamics of delay systems’.

Difference in Amyloid Load Between Single Memory Domain and Multidomain Subjective Cognitive Decline: A Study from the SILCODE

2021 ◽  
pp. 1-10
Author(s):  
Xiaoqi Wang ◽  
Qiuhui Bi ◽  
Jie Lu ◽  
Piu Chan ◽  
Xiaochen Hu ◽  
...  
Keyword(s):  
Cognitive Decline ◽  
Memory Function ◽  
Verbal Learning ◽  
Linear Regression Analysis ◽  
Brain Regions ◽  
Middle Temporal Gyrus ◽  
Subjective Cognitive Decline ◽  
Amyloid Load ◽  
Relationship Of ◽  
The Relationship

Background: Subjective cognitive decline (SCD), an at-risk condition of Alzheimer’s disease (AD), can involve various cognitive domains, such as memory, language, planning, and attention. Objective: We aims to explore the differences in amyloid load between the single memory domain SCD (sd-SCD) and the multidomain SCD (md-SCD) and assess the relationship of amyloid pathology with quantitative SCD scores and objective cognition. Methods: A total of 63 SCD participants from the SILCODE study underwent the clinical evaluation, neuropsychological assessment, and 18F-florbetapir PET scan. Global amyloid standard uptake value ratio (SUVr) was calculated. Additionally, regional amyloid SUVr was quantified in 12 brain regions of interests. A nonparametric rank ANCOVA was used to compare the global and regional amyloid SUVr between the md-SCD (n = 34) and sd-SCD (n = 29) groups. A multiple linear regression analysis was conducted to test the relationship of amyloid SUVr with quantitative SCD scores and objective cognition. Results: Compared with individuals with sd-SCD, individuals with md-SCD had increased global amyloid SUVr (F = 5.033, p = 0.029) and regional amyloid SUVr in the left middle temporal gyrus (F = 12.309, p = 0.001; Bonferroni corrected), after controlling for the effects of age, sex, and education. When pooling all SCD participants together, the increased global amyloid SUVr was related with higher SCD-plus sum scores and lower Auditory Verbal Learning Test-delayed recall scores. Conclusion: According to our findings, individuals with md-SCD showed higher amyloid accumulation than individuals with sd-SCD, suggesting that md-SCD may experience a more advanced stage of SCD. Additionally, increased global amyloid load was predictive of a poorer episodic memory function in SCD individuals.

The relationship between myocardial and hepatic T2 and T2* at 1.5T and 3T MRI in normal and iron-overloaded patients

2017 ◽  
Vol 59 (3) ◽  
pp. 355-362 ◽  
Author(s):  
Supika Kritsaneepaiboon ◽  
Natee Ina ◽  
Thirachit Chotsampancharoen ◽  
Supaporn Roymanee ◽  
Sirichai Cheewatanakornkul
Keyword(s):  
Iron Concentration ◽  
Liver Iron ◽  
3T Mri ◽  
Non Invasive ◽  
Asymptomatic Patients ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri ◽  
Relationship Of ◽  
The Relationship ◽  
Best Fit

Background Cardiac and liver iron assessment using magnetic resonance imaging (MRI) is non-invasive and used as a preclinical “endpoint” in asymptomatic patients and for serial iron measurements in iron-overloaded patients. Purpose To compare iron measurements between hepatic and myocardial T2* and T2 at 1.5T and 3T MRI in normal and iron-overloaded patients. Material and Methods The T2 and T2* values from the regions of interest (ROIs) at mid-left ventricle and mid-hepatic slices were evaluated by 1.5T and 3T MRI scans for healthy and iron-overloaded patients. Results For iron-overloaded patients, the myocardial T2 (1.5T) and myocardial T2 (3T) values were 60.3 ms (range = 56.2–64.8 ms) and 55 ms (range = 51.6–60.1 ms) (ρ = 0.3679) while the myocardial T2* (3T) 20.5 ms (range = 18.4–25.9 ms) was shorter than the myocardial T2* (1.5T) 35.9 ms (range = 31.4–39.5 ms) (ρ = 0.6454). The hepatic T2 at 1.5T and 3T were 19.1 ms (range = 14.8–27.9 ms) and 15.5 ms (14.6–20.4 ms) (ρ = 0.9444) and the hepatic T2* at 1.5T and 3T were 2.7 ms (range = 1.8–5.6 ms) and 1.8 ms (range = 1.1–2.9 ms) (ρ = 0.9826). The line of best fit exhibiting the linearity of the hepatic T2* (1.5T) and hepatic T2* (3T) had a slope of 2 and an intercept of –0.387 ms (R = 0.984). Conclusion Our study found myocardial T2 (1.5T) nearly equal to T2 (3T) with myocardial T2* (3T) 1.75 shorter than myocardial T2* (1.5T). The relationship of hepatic T2* (1.5T) and hepatic T2* (3T) was linear with T2* (1.5T) approximately double to T2* (3T) in iron-overloaded patients. This linear relationship between hepatic T2* (1.5T) and hepatic T2 (3T) could be an alternative method for estimating liver iron concentration (LIC) from 3T.

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