scholarly journals Resistive index of internal carotid artery and brain networks in patients with chronic cerebral ischemia

2021 ◽  
Author(s):  
VF Fokin ◽  
NV Ponomareva ◽  
RB Medvedev ◽  
RN Konovalov ◽  
MV Krotenkova ◽  
...  
Keyword(s):  
Cerebral Ischemia ◽  
Carotid Artery ◽  
Cognitive Functions ◽  
Brain Networks ◽  
Resistive Index ◽  
Brain Regions ◽  
Brain Network ◽  
Fmri Data ◽  
Chronic Cerebral Ischemia ◽  
Effective Prevention

Quantitative assessment of cerebral hemodynamics is important for patients with chronic cerebral ischemia (CCI), as it helps to reveal the pathogenesis of the disease and set the course for effective prevention and treatment. The study was aimed to assess the correlation of the left carotid artery (ICA) resistive index (RI) with cognitive functions and brain network organization based on fMRI data in patients with CCI (51 males and 105 females). The listed above indicators were studied in patients with the left ICA RI values below and above the average (0.54 ± 0.013). The lower, normal physiological ICA resistance levels corresponded to the more successful realization of verbal cognitive functions. In the first group, RI was within normal range (RI = 0.42 ± 0.007), and in the second group RI exceeded normal levels (RI = 0.61 ± 0.01). Variation of the right ICA RI did not correlate with the characteristics of verbal cognitive functions. FMRI data analysis was used to assess the differences in connectivity between various brain regions in the groups with low and high RI. The normal physiological and elevated RI values of the left ICA correlated with differences in the organization of brain networks: normal physiological RI values corresponded to a better organization of hemispheric connections in the basal ganglia and brainstem, and high RI values corresponded to a better organization of connections between the frontal regions and the cerebellum as well as occipital areas of the cerebral cortex. The left ICA RI can be considered as a biomarker of cognitive decline and brain networks reorganization in patients with CCI.

scholarly journals Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data

2021 ◽  
Author(s):  
Guoqiang Hu ◽  
Huanjie Li ◽  
Wei Zhao ◽  
Yuxing Hao ◽  
Zonglei Bai ◽  
...  
Keyword(s):  
Spectral Clustering ◽  
Large Scale ◽  
Brain Networks ◽  
Motor Task ◽  
Component Analysis ◽  
Brain Regions ◽  
Brain Network ◽  
Tensor Component ◽  
Fmri Data ◽  
Neural Responses

AbstractThe study of brain network interactions during naturalistic stimuli facilitates a deeper understanding of human brain function. Intersubject correlation (ISC) analysis of functional magnetic resonance imaging (fMRI) data is a widely used method that can measure neural responses to naturalistic stimuli that are consistent across subjects. However, interdependent correlation values in ISC artificially inflated the degrees of freedom, which hinders the investigation of individual differences. Besides, the existing ISC model mainly focus on similarities between subjects but fails to distinguish neural responses to different stimuli features. To estimate large-scale brain networks evoked with naturalistic stimuli, we propose a novel analytic framework to characterize shared spatio-temporal patterns across subjects in a purely data-driven manner. In the framework, a third-order tensor is constructed from the timeseries extracted from all brain regions from a given parcellation, for all participants, with modes of the tensor corresponding to spatial distribution, time series and participants. Tensor component analysis (TCA) will then reveal spatially and temporally shared components, i.e., naturalistic stimuli evoked networks, their temporal courses of activity and subject loadings of each component. To enhance the reproducibility of the estimation with TCA, a novel spectral clustering method, tensor spectral clustering, was proposed and applied to evaluate the stability of TCA algorithm. We demonstrate the effectiveness of the proposed framework via simulations and real fMRI data collected during a motor task with a traditional fMRI study design. We also apply the proposed framework to fMRI data collected during passive movie watching to illustrate how reproducible brain networks are identified evoked by naturalistic movie viewing.

scholarly journals Reconfiguration of human evolving large-scale epileptic brain networks prior to seizures: an evaluation with node centralities

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Rieke Fruengel ◽  
Timo Bröhl ◽  
Thorsten Rings ◽  
Klaus Lehnertz
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Theoretical Concept ◽  
Global Network ◽  
Time Resolved ◽  
Functional Connections ◽  
Node Centrality ◽  
Network Mechanism

AbstractPrevious research has indicated that temporal changes of centrality of specific nodes in human evolving large-scale epileptic brain networks carry information predictive of impending seizures. Centrality is a fundamental network-theoretical concept that allows one to assess the role a node plays in a network. This concept allows for various interpretations, which is reflected in a number of centrality indices. Here we aim to achieve a more general understanding of local and global network reconfigurations during the pre-seizure period as indicated by changes of different node centrality indices. To this end, we investigate—in a time-resolved manner—evolving large-scale epileptic brain networks that we derived from multi-day, multi-electrode intracranial electroencephalograpic recordings from a large but inhomogeneous group of subjects with pharmacoresistant epilepsies with different anatomical origins. We estimate multiple centrality indices to assess the various roles the nodes play while the networks transit from the seizure-free to the pre-seizure period. Our findings allow us to formulate several major scenarios for the reconfiguration of an evolving epileptic brain network prior to seizures, which indicate that there is likely not a single network mechanism underlying seizure generation. Rather, local and global aspects of the pre-seizure network reconfiguration affect virtually all network constituents, from the various brain regions to the functional connections between them.

scholarly journals Psychological resilience correlates with EEG source-space brain network flexibility

2019 ◽  
Vol 3 (2) ◽  
pp. 539-550 ◽  
Author(s):  
Véronique Paban ◽  
Julien Modolo ◽  
Ahmad Mheich ◽  
Mahmoud Hassan
Keyword(s):  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Personality Testing ◽  
Functional Networks ◽  
Psychological Resilience ◽  
Full Spectrum ◽  
Sliding Windows ◽  
Resilience Scale ◽  
Network Flexibility

We aimed at identifying the potential relationship between the dynamical properties of the human functional network at rest and one of the most prominent traits of personality, namely resilience. To tackle this issue, we used resting-state EEG data recorded from 45 healthy subjects. Resilience was quantified using the 10-item Connor-Davidson Resilience Scale (CD-RISC). By using a sliding windows approach, brain networks in each EEG frequency band (delta, theta, alpha, and beta) were constructed using the EEG source-space connectivity method. Brain networks dynamics were evaluated using the network flexibility, linked with the tendency of a given node to change its modular affiliation over time. The results revealed a negative correlation between the psychological resilience and the brain network flexibility for a limited number of brain regions within the delta, alpha, and beta bands. This study provides evidence that network flexibility, a metric of dynamic functional networks, is strongly correlated with psychological resilience as assessed from personality testing. Beyond this proof-of-principle that reliable EEG-based quantities representative of personality traits can be identified, this motivates further investigation regarding the full spectrum of personality aspects and their relationship with functional networks.

scholarly journals Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xin Wang ◽  
Yanshuang Ren ◽  
Wensheng Zhang
Keyword(s):  
Pearson Correlation ◽  
Brain Regions ◽  
Brain Network ◽  
Classification Performance ◽  
Low Rank ◽  
Fmri Data ◽  
Healthy Controls ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Depression Disorder

Study of functional brain network (FBN) based on functional magnetic resonance imaging (fMRI) has proved successful in depression disorder classification. One popular approach to construct FBN is Pearson correlation. However, it only captures pairwise relationship between brain regions, while it ignores the influence of other brain regions. Another common issue existing in many depression disorder classification methods is applying only single local feature extracted from constructed FBN. To address these issues, we develop a new method to classify fMRI data of patients with depression and healthy controls. First, we construct the FBN using a sparse low-rank model, which considers the relationship between two brain regions given all the other brain regions. Moreover, it can automatically remove weak relationship and retain the modular structure of FBN. Secondly, FBN are effectively measured by eight graph-based features from different aspects. Tested on fMRI data of 31 patients with depression and 29 healthy controls, our method achieves 95% accuracy, 96.77% sensitivity, and 93.10% specificity, which outperforms the Pearson correlation FBN and sparse FBN. In addition, the combination of graph-based features in our method further improves classification performance. Moreover, we explore the discriminative brain regions that contribute to depression disorder classification, which can help understand the pathogenesis of depression disorder.

Graph Theory-Based Brain Network Connectivity Analysis and Classification of Alzheimer’s Disease

2021 ◽  
Author(s):  
A. Thushara ◽  
C. Ushadevi Amma ◽  
Ansamma John
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Neurodegenerative Disorder ◽  
Brain Networks ◽  
Network Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Fiber Tracts ◽  
The Brain

Alzheimer’s Disease (AD) is basically a progressive neurodegenerative disorder associated with abnormal brain networks that affect millions of elderly people and degrades their quality of life. The abnormalities in brain networks are due to the disruption of White Matter (WM) fiber tracts that connect the brain regions. Diffusion-Weighted Imaging (DWI) captures the brain’s WM integrity. Here, the correlation betwixt the WM degeneration and also AD is investigated by utilizing graph theory as well as Machine Learning (ML) algorithms. By using the DW image obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, the brain graph of each subject is constructed. The features extracted from the brain graph form the basis to differentiate between Mild Cognitive Impairment (MCI), Control Normal (CN) and AD subjects. Performance evaluation is done using binary and multiclass classification algorithms and obtained an accuracy that outperforms the current top-notch DWI-based studies.

scholarly journals Cognitive functions, quality of life in patients with dyscirculatory (vascular) encephalopathy II stage in northerners from the position of ICF

2021 ◽  
Vol 27 (3) ◽  
pp. 351-364
Author(s):  
E. D. Yakovchuk ◽  
G. O. Penina
Keyword(s):  
Quality Of Life ◽  
Cerebral Ischemia ◽  
Emotional Disorders ◽  
Cognitive Functions ◽  
Komi Republic ◽  
International Classification Of Functioning ◽  
Chronic Cerebral Ischemia ◽  
Sf 36

Objective. The aim of the study was to analyze cognitive functions, emotional disorders, the quality of life with the application of the International Classification of Functioning (ICF) in patients with chronic cerebrovascular diseases, dyscirculatory (vascular) encephalopathy II stage in the Komi Republic. Design and methods. In the clinics of the Komi Republic, we examined 126 people (mean age — 65,8 ± 10,1 years; women comprised 76 participants, 60,3 %, p ≤ 0,05) with dyscirculatory (vascular) encephalopathy. Complaints and anamnesis were analyzed, somatic and neurological status, cognitive functions, emotional status, and sleep were assessed. Results. Out of 126 patients, 44,5 % patients are employed (56 people). Among non-working patients with an established group of disability, 35,7 % (in 14,3 % disability 2nd degree (10 people), in 21,4 % — disability 3rd degree (15 people), p ≤ 0,05). Among the workers, 3 people (5,3%) had disability 3rd degree. Hypertension (HTN) I stage was detected in 10,6 %, HTN II stage — in 44,4 %, HTN III stage — in 45 % (p ≤ 0,05); 27,8 % had history of ischemic heart disease, 7,2 % — myocardial infarction, 19,1% — cerebrovascular accident, 8,6% — heart rhythm disturbances, 15,8 % — significant stenosis of the brachiocephalic arteries. Based on memory function assessment by ICF, none demonstrated normal results. Mild cognitive impairment was usually found, and there was no case of dementia. Dysfunction in the domain of blood pressure function was found in all patients. The dysfunction of blood vessels was found in all patients with chronic cerebral ischemia II stage. Daily activities were reduced due to the physical dysfunction. According to the total assessment of health status by the SF-36 scale, the physical and mental components of health were similarly decreased. Conclusions. The SF-36 (questionnaire for assessing the quality of life) and ICF (International Classification of Functioning, Disabilities and Health) complement each other. Cognitive scales are the basic tools for examination of patients with chronic cerebral ischemia, making diagnosis according to ICF, and treating cognitive, physical and emotional disorders in dyscirculatory (vascular) encephalopathy.

scholarly journals Analysis of fMRI data using noise-diffusion network models: a new covariance-coding perspective

2017 ◽  
Author(s):  
Matthieu Gilson
Keyword(s):  
Resting State ◽  
Effective Connectivity ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Brain Network ◽  
Fmri Data ◽  
Second Order Statistics ◽  
Proposed Model ◽  
The Many

AbstractSince the middle of the 1990s, studies of resting-state fMRI/BOLD data have explored the correlation patterns of activity across the whole brain, which is referred to as functional connectivity (FC). Among the many methods that have been developed to interpret FC, a recently proposed model-based approach describes the propagation of fluctuating BOLD activity within the recurrently connected brain network by inferring the effective connectivity (EC). In this model, EC quantifies the strengths of directional interactions between brain regions, viewed from the proxy of BOLD activity. In addition, the tuning procedure for the model provides estimates for the local variability (input variances) to explain how the observed FC is generated. Generalizing, the network dynamics can be studied in the context of an input-output mapping - determined by EC - for the second-order statistics of fluctuating nodal activities. The present paper focuses on the following detection paradigm: observing output covariances, how discriminative is the (estimated) network model with respect to various input covariance patterns? An application with the model fitted to experimental fMRI data - movie viewing versus resting state - illustrates that changes in excitability and changes in brain coordination go hand in hand.

scholarly journals Complex Brain Network Analysis and Its Applications to Brain Disorders: A Survey

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-27 ◽  
Author(s):  
Jin Liu ◽  
Min Li ◽  
Yi Pan ◽  
Wei Lan ◽  
Ruiqing Zheng ◽  
...  
Keyword(s):  
Network Analysis ◽  
Brain Networks ◽  
Small World ◽  
Brain Regions ◽  
Brain Network ◽  
Brain Diseases ◽  
Brain Disorders ◽  
Comprehensive Overview ◽  
Rich Club ◽  
New Perspective

It is well known that most brain disorders are complex diseases, such as Alzheimer’s disease (AD) and schizophrenia (SCZ). In general, brain regions and their interactions can be modeled as complex brain network, which describe highly efficient information transmission in a brain. Therefore, complex brain network analysis plays an important role in the study of complex brain diseases. With the development of noninvasive neuroimaging and electrophysiological techniques, experimental data can be produced for constructing complex brain networks. In recent years, researchers have found that brain networks constructed by using neuroimaging data and electrophysiological data have many important topological properties, such as small-world property, modularity, and rich club. More importantly, many brain disorders have been found to be associated with the abnormal topological structures of brain networks. These findings provide not only a new perspective to explore the pathological mechanisms of brain disorders, but also guidance for early diagnosis and treatment of brain disorders. The purpose of this survey is to provide a comprehensive overview for complex brain network analysis and its applications to brain disorders.

scholarly journals Evaluation of the efficacy and tolerability of naftidrofuryl in the therapy of chronic cerebral ischemia

2021 ◽  
Vol 13 (1) ◽  
pp. 38-43
Author(s):  
M. N. Dadasheva ◽  
I. A. Zolotovskaya ◽  
R. V. Gorenkov ◽  
K. N. Dadasheva ◽  
D. I. Lebedeva
Keyword(s):  
Cerebral Ischemia ◽  
Anxiety Disorders ◽  
Cognitive Functions ◽  
High Efficiency ◽  
Rating Scale ◽  
Chronic Cerebral Ischemia ◽  
Open Label ◽  
Multicenter Observational Study ◽  
Self Rating ◽  
Number Of Patients

Objective: to study the clinical efficacy and tolerability of naftidrofuryl (Duzofarm®) in patients with chronic cerebral ischemia (CCI).Patients and methods. A prospective open-label multicenter observational study evaluated the clinical efficiency and tolerability of naftidrofuryl treatment in patients with CCI. To statistically evaluate the efficacy and tolerability of naftidrofuryl, the investigators used data from 200 outpatients with Stages I–II CCI who were included in the program of treatment and received its full cycle. The patients were prescribed naftidrofuryl at a dose of 100 mg (2 tablets) thrice daily. Basic therapy that the patients had received before their inclusion in the observation program was not discontinued. The patients' status was evaluated using a specially designed questionnaire. Four visits were scheduled. All the patients underwent neurological examination. To study the patients' cognitive functions and emotional state, the investigators used the following tests and scales: the Mini-Cog test, the Mini-Mental State Examination (MMSE), the Zung Self-Rating Anxiety Scale, the Asthenia Subjective Assessment Scale, and the Modified Scoring Scale for Subjective Sleep Characteristics. Adverse reactions were recorded to evaluate the tolerability of therapy.Results and discussion. Naftidrofuryl therapy significantly improved health and reversed complaints in the patients. By the end of the treatment cycle, there was a significant improvement on all scales, which suggested a decrease in the severity of cognitive impairment and asthenic and mild anxiety disorders. When performing the Mini-Cog test, the proportion of patients who were able to remember and recall three words without errors increased from 43 to 86%, and when doing the clock drawing test, the proportion increased by 65%. Cognitive functions on the MMSE were observed to statistically significantly improve by 1.2 scores. According to the Zung Anxiety Self-Rating Scale, the number of patients with anxiety disorders decreased by 24%, those with insomnia significantly declined by 31% compared with the baseline level.Conclusion. The findings showed the high efficiency and appropriateness of naftidrofuryl administration to patients with Stages I–II CCI and hypertension.

Sign in / Sign up

Export Citation Format

Share Document