Functional networks of the brain: from connectivity restoration to dynamic integration

Hramov, Aleksandr E.; Frolov, Nikita S.; Maksimenko, Vladimir A.; Kurkin, Semen A.; Kazantsev, Viktor; Pisarchik, Aleksandr N.

doi:10.3367/ufne.2020.06.038807

Cited by 59 publications

(14 citation statements)

References 304 publications

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“…In the context of diagnostics, particular interest is dedicated to large-scale brain networks [ 24 ]. The interactions between remote brain regions reflect the neural response to human cognitive and motor-related activity [ 25 ]. For instance, the fronto–parietal area processes working memory, attention, and cognitive control [ 26 , 27 , 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural Network

Pitsik

Frolov

Shusharina

et al. 2022

Sensors

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Large-scale functional connectivity is an important indicator of the brain’s normal functioning. The abnormalities in the connectivity pattern can be used as a diagnostic tool to detect various neurological disorders. The present paper describes the functional connectivity assessment based on artificial intelligence to reveal age-related changes in neural response in a simple motor execution task. Twenty subjects of two age groups performed repetitive motor tasks on command, while the whole-scalp EEG was recorded. We applied the model based on the feed-forward multilayer perceptron to detect functional relationships between five groups of sensors located over the frontal, parietal, left, right, and middle motor cortex. Functional dependence was evaluated with the predicted and original time series coefficient of determination. Then, we applied statistical analysis to highlight the significant features of the functional connectivity network assessed by our model. Our findings revealed the connectivity pattern is consistent with modern ideas of the healthy aging effect on neural activation. Elderly adults demonstrate a pronounced activation of the whole-brain theta-band network and decreased activation of frontal–parietal and motor areas of the mu-band. Between-subject analysis revealed a strengthening of inter-areal task-relevant links in elderly adults. These findings can be interpreted as an increased cognitive demand in elderly adults to perform simple motor tasks with the dominant hand, induced by age-related working memory decline.

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Section: Introductionmentioning

confidence: 99%

Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural Network

Pitsik

Frolov

Shusharina

et al. 2022

Sensors

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“…Brain connectivity is studied in three forms: functional, structural, and effective [2][3][4][5]. Structural connectivity identifies anatomical neural networks that show possible pathways for neural communication [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…Effective connectivity utilizes the functional connectivity information and additionally determines the direction of the dynamic information flow [9,10]. Effective and functional connectivity can be measured in the frequency domain (e.g., coherence [11]) and in the time domain (e.g., Granger causality [5] or artificial neuronal network-based functional connectivity [12]).…”

Section: Introductionmentioning

confidence: 99%

“…In the last two decades, significant progress has been achieved in the development of new computational algorithms that enable connectivity calculations directly between the different regions of the brain (source space) [5] instead of electrodes or coils (channel space). The source space analysis provides better anatomical localization [18] and enables inter-subject or group analysis as the brain activity now can be projected onto a more standardized space.…”

Section: Introductionmentioning

confidence: 99%

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Event-Related Coherence in Visual Cortex and Brain Noise: An MEG Study

et al. 2021

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The analysis of neurophysiological data using the two most widely used open-source MATLAB toolboxes, FieldTrip and Brainstorm, validates our hypothesis about the correlation between event-related coherence in the visual cortex and neuronal noise. The analyzed data were obtained from magnetoencephalography (MEG) experiments based on visual perception of flickering stimuli, in which fifteen subjects effectively participated. Before coherence and brain noise calculations, MEG data were first transformed from recorded channel data to brain source waveforms by solving the inverse problem. The inverse solution was obtained for a 2D cortical shape in Brainstorm and a 3D volume in FieldTrip. We found that stronger brain entrainment to the visual stimuli concurred with higher brain noise in both studies.

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“…Brain networks can be defined as a set of regions that exhibit correlated activity in resting-state condition or during task performance (6,7). In recent studies, researchers concentrated on building brain functional networks, as well as searching for abnormal communication between them in order to explain the pathophysiological mechanisms of MDD (8 Resting state networks are frequently used methodology for identifying different brain constructs based on their correlated activity over time, against a region of interest -ROI analysis, seed-based analysis etc.…”

Section: Introductionmentioning

confidence: 99%

Resting-State Functional Connectivity Impairment in Patients With the Major Depressive Episode

Stoyanov¹,

Khorev²,

Paunova³

et al. 2022

Preprint

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Aim. In the current study, we aimed at identifying resting-state brain networks, which are different in patients with depression compared to healthy individuals. Moreover, we analyzed the potential for clinical use of different network measures that could discriminate the two groups and thus help the diagnostic process. Method and subjects. We recruited 90 subjects: 49 healthy controls (HC) and 41 patients with a major depressive episode (MDE). All subjects underwent clinical evaluation and functional resting-state MRI. The data were processed to investigate functional connectivity network measures across the two groups using Brain Connectivity Toolbox. The statistical inferences were made at the functional networks level, using false discovery rate method. Independent samples t-test was performed on the network measures mean values to reveal differences between HC and MDE groups. Permutation-based statistical testing was used to test the significance of the difference between the distributions of the network measures by nodes for HC and MDE groups. Linear discriminant analysis was used to differentiate between the groups. Results and discussion. Significant differences in FC between depressed patients and healthy controls were found with the most prominent changes encompassing within-region as well as between-region connectivity of occipital lobe areas such as precuneus (PreCu), cuneus (Cu), superior occipital gyrus (SOG), lingual gyrus (LG), fusiform gyrus (FG), cerebellum, along with limbic structures including the hippocampus (Hipp) and cingulate gyrus. Linear discriminant analysis demonstrated that the full connectivity matrices, as well as those with only the significant connections identified in advance, were the most precise in differentiating between depression and health. These measures reached precision levels of 97% and 94%, respectively. Conclusion. Our study delivered further evidence about impairment of functional connectivity networks in MDE that may contribute to differentiate patients with depression from healthy subjects.

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Functional networks of the brain: from connectivity restoration to dynamic integration

Cited by 59 publications

References 304 publications

Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural Network

Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural Network

Event-Related Coherence in Visual Cortex and Brain Noise: An MEG Study

Resting-State Functional Connectivity Impairment in Patients With the Major Depressive Episode

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