scholarly journals Functional magnetic resonance imaging tomography in assessing the functional state of the brain in patients with opioid addiction

2018 ◽  
Vol 20 (3) ◽  
pp. 72-79
Author(s):  
D A Tarumov ◽  
Sh K Abdulaev ◽  
A G Trufanov ◽  
V L Ushakov ◽  
V K Shamrey ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Opioid Dependence ◽  
Control Group ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Passive Mode ◽  
Functional Connections ◽  
The Brain

The possibilities of functional magnetic resonance imaging in the diagnosis of opioid dependence syndrome are considered. It is known that opioid addiction is one of the leading problems of modern narcology. Despite the fact that the number of researches of the neurobiological effects of opioids is increasing every year, the pathogenetic effects of dependence on this narcotic substance are still not fully understood. Functional magnetic resonance imaging rest allows one to assess the functional connectivity of the remote from each other parts of the brain and makes a great contribution to understanding the mechanisms of development of addictive disorders in general. In patients with opioid dependence, an analysis was made of the neural network of the passive mode of the brain (default mode network). This resting network is associated with the processes of control and thinking, including emotional and cognitive components, and consists of medial frontal regions, posterior cingulate sections, precuneus, lower parietal and temporal divisions. It was found that, in comparison with the control group, in all patients suffering from opioid dependence, weakened functional connections of all structures of the cerebro-spinal cord system (p0,05). In this case, changes in the medial prefrontal cortex and precuneus are more pronounced in patients who are in the state of opioid intoxication, and in the parietal-temporal regions in patients who are in a state of remission up to 1 month. The correlation of cortical structures responsible for the «behavior control» system (orbitofrontal cortex, prefrontal cortex) with subcortical structures responsible for emotions in the limbic system was also evaluated. In comparison with the control group, in patients with early remission, weakened functional connections between cortical structures and left contiguous nucleus, almond-shaped body from two sides. In patients in a state of intoxication in addition to these changes, the functional relationship between the orbital frontal cortex and the shell on the left has been weakened. The weakening of functional links in the network of the passive mode of the brain in the groups of drug addicts suggests that they have violated the processes of control, thinking and making the right decision. The resulting functional changes can form the basis for creating biomarker maps for patients suffering from opioid dependence, which can be used to guide and evaluate the treatment of this pathology.

scholarly journals Modeling Brain Connectivity Dynamics in Functional Magnetic Resonance Imaging via Particle Filtering

2021 ◽  
Author(s):  
Pierfrancesco Ambrosi ◽  
Mauro Costagli ◽  
Ercan E Kuruoğlu ◽  
Laura Biagi ◽  
Guido Buonincontri ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Brain Connectivity ◽  
Time Varying ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Functional Connections ◽  
Hidden Parameters ◽  
The Brain

AbstractInterest in the studying of functional connections in the brain has grown considerably in the last decades, as many studies have pointed out that these interactions can play a role as markers of neurological diseases. Most studies in this field treat the brain network as a system of connections stationary in time, but dynamic features of brain connectivity can provide useful information, both on physiology and pathological conditions of the brain. In this paper, we propose the application of a computational methodology, named Particle Filter (PF), to study non-stationarities in brain connectivity in functional Magnetic Resonance Imaging (fMRI). The PF algorithm estimates time-varying hidden parameters of a first-order linear time-varying Vector Autoregressive model (VAR) through a Sequential Monte Carlo strategy. On simulated time series, the PF approach effectively detected and enabled to follow time-varying hidden parameters and it captured causal relationships among signals. The method was also applied to real fMRI data, acquired in presence of periodic tactile or visual stimulations, in different sessions. On these data, the PF estimates were consistent with current knowledge on brain functioning. Most importantly, the approach enabled to detect statistically significant modulations in the cause-effect relationship between brain areas, which correlated with the underlying visual stimulation pattern presented during the acquisition.

Resting state functional magnetic resonance imaging processing techniques in stroke studies

2016 ◽  
Vol 27 (8) ◽  
pp. 871-885 ◽  
Author(s):  
Golrokh Mirzaei ◽  
Hojjat Adeli
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Resting State ◽  
Brain Connectivity ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Stroke Patients ◽  
Processing Techniques ◽  
The Brain

AbstractIn recent years, there has been considerable research interest in the study of brain connectivity using the resting state functional magnetic resonance imaging (rsfMRI). Studies have explored the brain networks and connection between different brain regions. These studies have revealed interesting new findings about the brain mapping as well as important new insights in the overall organization of functional communication in the brain network. In this paper, after a general discussion of brain networks and connectivity imaging, the brain connectivity and resting state networks are described with a focus on rsfMRI imaging in stroke studies. Then, techniques for preprocessing of the rsfMRI for stroke patients are reviewed, followed by brain connectivity processing techniques. Recent research on brain connectivity using rsfMRI is reviewed with an emphasis on stroke studies. The authors hope this paper generates further interest in this emerging area of computational neuroscience with potential applications in rehabilitation of stroke patients.

scholarly journals Brain Functional Magnetic Resonance Imaging Highlights Altered Connections and Functional Networks in Patients With Hypertension

Hypertension ◽  
2020 ◽  
Vol 76 (5) ◽  
pp. 1480-1490 ◽  
Author(s):  
Lorenzo Carnevale ◽  
Angelo Maffei ◽  
Alessandro Landolfi ◽  
Giovanni Grillea ◽  
Daniela Carnevale ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Resting State ◽  
Diffusion Tensor ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Attention Network ◽  
Dorsal Attention Network ◽  
The Brain

Hypertension is one of the main risk factors for vascular dementia and Alzheimer disease. To predict the onset of these diseases, it is necessary to develop tools to detect the early effects of vascular risk factors on the brain. Resting-state functional magnetic resonance imaging can investigate how the brain modulates its resting activity and analyze how hypertension impacts cerebral function. Here, we used resting-state functional magnetic resonance imaging to explore brain functional-hemodynamic coupling across different regions and their connectivity in patients with hypertension, as compared to subjects with normotension. In addition, we leveraged multimodal imaging to identify the signature of hypertension injury on the brain. Our study included 37 subjects (18 normotensives and 19 hypertensives), characterized by microstructural integrity by diffusion tensor imaging and cognitive profile, who were subjected to resting-state functional magnetic resonance imaging analysis. We mapped brain functional connectivity networks and evaluated the connectivity differences among regions, identifying the altered connections in patients with hypertension compared with subjects with normotension in the (1) dorsal attention network and sensorimotor network; (2) dorsal attention network and visual network; (3) dorsal attention network and frontoparietal network. Then we tested how diffusion tensor imaging fractional anisotropy of superior longitudinal fasciculus correlates with the connections between dorsal attention network and default mode network and Montreal Cognitive Assessment scores with a widespread network of functional connections. Finally, based on our correlation analysis, we applied a feature selection to highlight those most relevant to describing brain injury in patients with hypertension. Our multimodal imaging data showed that hypertensive brains present a network of functional connectivity alterations that correlate with cognitive dysfunction and microstructural integrity. Registration— URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02310217.

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