Assessment of Directional Connectivity Between Neural Sources Using Effective Connectivity Measures and Particle Filters

2020 ◽  
pp. 2150149
Author(s):  
Santhosh Kumar Veeramalla ◽  
T. V. K. Hanumantha Rao
Keyword(s):  
Neural Activity ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Current Dipole ◽  
Suggested Approach ◽  
Brain Functions ◽  
Direct Exposure ◽  
Signal Processing Algorithms ◽  
Eeg Connectivity ◽  
Time Courses

Electrical neural activity monitoring and recording will increase our understanding of how the human brain works. Tracking mechanisms of neural activity led to better diagnosis and management of severe neurological conditions such as Parkinson’s disease and epilepsy. More importantly, these approaches were used to distinguish between various types of seizures based on the location and direction of the seizure foci, thereby increasing the outcomes of epilepsy surgery. A detailed study was carried out on the role of neural synchrony in brain functions with Electroencephalography (EEG). Most studies had been conducted on EEG connectivity analysis at sensor level. It is not easy to evaluate the connected networks because the volume conductive effect significantly distorts signals because of the electrical conductiveness of the head and often scalp electrodes derive input from the same sources in the brain. These factors help to estimate the real connectivity between brain regions inaccurately. The suggested approach is referred to as EEG source connectivity. The inverse problem is the estimation of the localized current dipole model from the EEG measurements. In order to solve the inverse EEG problem, advanced signal-processing algorithms such as the efficient implementation of PF have been built to facilitate direct exposure to neural dipole sources in real time and measure the connectivity of neural sources time courses using functional and effective connectivity measures.

Effective brain connectivity estimation between active brain regions in autism using the dual Kalman-based method

2020 ◽  
Vol 65 (1) ◽  
pp. 23-32
Author(s):  
Mehdi Rajabioun ◽  
Ali Motie Nasrabadi ◽  
Mohammad Bagher Shamsollahi ◽  
Robert Coben
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Estimation Error ◽  
Brain Regions ◽  
Estimation Methods ◽  
Estimation Errors ◽  
Normal Children ◽  
Brain Functions ◽  
The Brain

AbstractBrain connectivity estimation is a useful method to study brain functions and diagnose neuroscience disorders. Effective connectivity is a subdivision of brain connectivity which discusses the causal relationship between different parts of the brain. In this study, a dual Kalman-based method is used for effective connectivity estimation. Because of connectivity changes in autism, the method is applied to autistic signals for effective connectivity estimation. For method validation, the dual Kalman based method is compared with other connectivity estimation methods by estimation error and the dual Kalman-based method gives acceptable results with less estimation errors. Then, connectivities between active brain regions of autistic and normal children in the resting state are estimated and compared. In this simulation, the brain is divided into eight regions and the connectivity between regions and within them is calculated. It can be concluded from the results that in the resting state condition the effective connectivity of active regions is decreased between regions and is increased within each region in autistic children. In another result, by averaging the connectivity between the extracted active sources of each region, the connectivity between the left and right of the central part is more than that in other regions and the connectivity in the occipital part is less than that in others.

Connectivity-Related Roles of Contralesional Brain Regions for Motor Performance Early after Stroke

2020 ◽  
Author(s):  
Lukas Hensel ◽  
Caroline Tscherpel ◽  
Jana Freytag ◽  
Stella Ritter ◽  
Anne K Rehme ◽  
...  
Keyword(s):  
Neural Activity ◽  
Motor Performance ◽  
Primary Motor Cortex ◽  
Effective Connectivity ◽  
Premotor Cortex ◽  
Brain Regions ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Anterior Intraparietal Sulcus

Abstract Hemiparesis after stroke is associated with increased neural activity not only in the lesioned but also in the contralesional hemisphere. While most studies have focused on the role of contralesional primary motor cortex (M1) activity for motor performance, data on other areas within the unaffected hemisphere are scarce, especially early after stroke. We here combined functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) to elucidate the contribution of contralesional M1, dorsal premotor cortex (dPMC), and anterior intraparietal sulcus (aIPS) for the stroke-affected hand within the first 10 days after stroke. We used “online” TMS to interfere with neural activity at subject-specific fMRI coordinates while recording 3D movement kinematics. Interfering with aIPS activity improved tapping performance in patients, but not healthy controls, suggesting a maladaptive role of this region early poststroke. Analyzing effective connectivity parameters using a Lasso prediction model revealed that behavioral TMS effects were predicted by the coupling of the stimulated aIPS with dPMC and ipsilesional M1. In conclusion, we found a strong link between patterns of frontoparietal connectivity and TMS effects, indicating a detrimental influence of the contralesional aIPS on motor performance early after stroke.

scholarly journals Entropy, complexity, and maturity in children’s neural responses during naturalistic mathematics learning

2020 ◽  
Author(s):  
Marie Amalric ◽  
Jessica F. Cantlon
Keyword(s):  
Real World ◽  
Neural Activity ◽  
Mathematics Learning ◽  
Brain Regions ◽  
Multiscale Entropy ◽  
Neural Responses ◽  
Brain Functions ◽  
Temporal Properties ◽  
Neural Processes ◽  
New Evidence

AbstractA major goal of human neuroscience is to understand how the brain functions in the real world, and to measure neural processes under naturalistic conditions that are more ecologically valid than traditional laboratory tasks. A critical step toward this goal is understanding how neural activity during real world naturalistic tasks relates to neural activity in more traditional laboratory tasks. In the present study, we used intersubject correlations to locate reliable stimulus-driven neural processes among children and adults in naturalistic and laboratory versions of a mathematics task that shared the same content. We show that relative to a control condition with grammatical content, naturalistic and simplified mathematics tasks evoked overlapping activation within brain regions previously associated with math semantics. We further examined the temporal properties of children’s neural responses during the naturalistic and laboratory tasks to determine whether temporal patterns of neural activity change over development, or dissociate based on semantic or task content. We introduce a rather novel measure, not yet used in fMRI studies of child learning: neural multiscale entropy. In addition to showing new evidence of naturalistic mathematics processing in the developing brain, we show that neural maturity and neural entropy are two independent but complementary markers of functional brain development. We discuss the implications of these results for the development of neural complexity in children.

scholarly journals Spatial variability of low frequency brain signal differentiates brain states

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242330
Author(s):  
Yifeng Wang ◽  
Yujia Ao ◽  
Qi Yang ◽  
Yang Liu ◽  
Yujie Ouyang ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Neural Activity ◽  
Brain Function ◽  
Spatial Organization ◽  
Low Frequency ◽  
Brain Regions ◽  
Temporal Dimension ◽  
Previous Theory ◽  
Brain Functions ◽  
Signal Variability

Temporal variability of the neural signal has been demonstrated to be closely related to healthy brain function. Meanwhile, the evolving brain functions are supported by dynamic relationships among brain regions. We hypothesized that the spatial variability of brain signal might provide important information about brain function. Here we used the spatial sample entropy (SSE) to investigate the spatial variability of neuroimaging signal during a steady-state presented face detection task. Lower SSE was found during task state than during resting state, associating with more repetitive functional interactions between brain regions. The standard deviation (SD) of SSE during the task was negatively related to the SD of reaction time, suggesting that the spatial pattern of neural activity is reorganized according to particular cognitive function and supporting the previous theory that greater variability is associated with better task performance. These results were replicated with reordered data, implying the reliability of SSE in measuring the spatial organization of neural activity. Overall, the present study extends the research scope of brain signal variability from the temporal dimension to the spatial dimension, improving our understanding of the spatiotemporal characteristics of brain activities and the theory of brain signal variability.

scholarly journals On the Role of Bilateral Brain Hypofunction and Abnormal Lateralization of Cortical Information Flow as Neural Underpinnings of Conventional Metaphor Processing Impairment in Schizophrenia: An fMRI and EEG Study

2021 ◽  
Author(s):  
Przemysław Adamczyk ◽  
Martin Jáni ◽  
Tomasz S. Ligeza ◽  
Olga Płonka ◽  
Piotr Błądziński ◽  
...  
Keyword(s):  
Information Flow ◽  
Language Processing ◽  
Time Course ◽  
Figurative Language ◽  
Neural Circuits ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Blood Oxygenation ◽  
Control Group ◽  
Metaphor Processing

AbstractFigurative language processing (e.g. metaphors) is commonly impaired in schizophrenia. In the present study, we investigated the neural activity and propagation of information within neural circuits related to the figurative speech, as a neural substrate of impaired conventional metaphor processing in schizophrenia. The study included 30 schizophrenia outpatients and 30 healthy controls, all of whom were assessed with a functional Magnetic Resonance Imaging (fMRI) and electroencephalography (EEG) punchline-based metaphor comprehension task including literal (neutral), figurative (metaphorical) and nonsense (absurd) endings. The blood oxygenation level-dependent signal was recorded with 3T MRI scanner and direction and strength of cortical information flow in the time course of task processing was estimated with a 64-channel EEG input for directed transfer function. The presented results revealed that the behavioral manifestation of impaired figurative language in schizophrenia is related to the hypofunction in the bilateral fronto-temporo-parietal brain regions (fMRI) and various differences in effective connectivity in the fronto-temporo-parietal circuit (EEG). Schizophrenia outpatients showed an abnormal pattern of connectivity during metaphor processing which was related to bilateral (but more pronounced at the left hemisphere) hypoactivation of the brain. Moreover, we found reversed lateralization patterns, i.e. a rightward-shifted pattern during metaphor processing in schizophrenia compared to the control group. In conclusion, the presented findings revealed that the impairment of the conventional metaphor processing in schizophrenia is related to the bilateral brain hypofunction, which supports the evidence on reversed lateralization of the language neural network and the existence of compensatory recruitment of alternative neural circuits in schizophrenia.

scholarly journals Neural substrates of propranolol-induced impairments in the reconsolidation of nicotine-associated memories in smokers

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiao Lin ◽  
Jiahui Deng ◽  
Kai Yuan ◽  
Qiandong Wang ◽  
Lin Liu ◽  
...  
Keyword(s):  
Neural Activity ◽  
Neural Substrates ◽  
Brain Regions ◽  
Reward Processing ◽  
Memory Reconsolidation ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Postcentral Gyrus ◽  
Propranolol Group ◽  
Propranolol Administration

AbstractThe majority of smokers relapse even after successfully quitting because of the craving to smoking after unexpectedly re-exposed to smoking-related cues. This conditioned craving is mediated by reward memories that are frequently experienced and stubbornly resistant to treatment. Reconsolidation theory posits that well-consolidated memories are destabilized after retrieval, and this process renders memories labile and vulnerable to amnestic intervention. This study tests the retrieval reconsolidation procedure to decrease nicotine craving among people who smoke. In this study, 52 male smokers received a single dose of propranolol (n = 27) or placebo (n = 25) before the reactivation of nicotine-associated memories to impair the reconsolidation process. Craving for smoking and neural activity in response to smoking-related cues served as primary outcomes. Functional magnetic resonance imaging was performed during the memory reconsolidation process. The disruption of reconsolidation by propranolol decreased craving for smoking. Reactivity of the postcentral gyrus in response to smoking-related cues also decreased in the propranolol group after the reconsolidation manipulation. Functional connectivity between the hippocampus and striatum was higher during memory reconsolidation in the propranolol group. Furthermore, the increase in coupling between the hippocampus and striatum positively correlated with the decrease in craving after the reconsolidation manipulation in the propranolol group. Propranolol administration before memory reactivation disrupted the reconsolidation of smoking-related memories in smokers by mediating brain regions that are involved in memory and reward processing. These findings demonstrate the noradrenergic regulation of memory reconsolidation in humans and suggest that adjunct propranolol administration can facilitate the treatment of nicotine dependence. The present study was pre-registered at ClinicalTrials.gov (registration no. ChiCTR1900024412).

scholarly journals Dissociable oscillatory networks support gain and loss processing in human orbitofrontal cortex

2021 ◽  
Author(s):  
Ignacio Saez ◽  
Jack Lin ◽  
Edward Chang ◽  
Josef Parvizi ◽  
Robert T. Knight ◽  
...  
Keyword(s):  
Neural Activity ◽  
Orbitofrontal Cortex ◽  
Low Frequency ◽  
Brain Regions ◽  
Reward Processing ◽  
Neural Oscillations ◽  
Beta Band ◽  
Neuronal Ensembles ◽  
Gain And Loss

AbstractHuman neuroimaging and animal studies have linked neural activity in orbitofrontal cortex (OFC) to valuation of positive and negative outcomes. Additional evidence shows that neural oscillations, representing the coordinated activity of neuronal ensembles, support information processing in both animal and human prefrontal regions. However, the role of OFC neural oscillations in reward-processing in humans remains unknown, partly due to the difficulty of recording oscillatory neural activity from deep brain regions. Here, we examined the role of OFC neural oscillations (<30Hz) in reward processing by combining intracranial OFC recordings with a gambling task in which patients made economic decisions under uncertainty. Our results show that power in different oscillatory bands are associated with distinct components of reward evaluation. Specifically, we observed a double dissociation, with a selective theta band oscillation increase in response to monetary gains and a beta band increase in response to losses. These effects were interleaved across OFC in overlapping networks and were accompanied by increases in oscillatory coherence between OFC electrode sites in theta and beta band during gain and loss processing, respectively. These results provide evidence that gain and loss processing in human OFC are supported by distinct low-frequency oscillations in networks, and provide evidence that participating neuronal ensembles are organized functionally through oscillatory coherence, rather than local anatomical segregation.

scholarly journals Altered Prefrontal–Basal Ganglia Effective Connectivity in Patients With Poststroke Cognitive Impairment

2020 ◽  
Vol 11 ◽  
Author(s):  
Jing Zhang ◽  
Zixiao Li ◽  
Xingxing Cao ◽  
Lijun Zuo ◽  
Wei Wen ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Basal Ganglia ◽  
Cognitive Decline ◽  
Effective Connectivity ◽  
Network Connectivity ◽  
Brain Regions ◽  
Neuronal Model ◽  
Causal Modeling ◽  
Healthy Controls

We investigated the association between poststroke cognitive impairment and a specific effective network connectivity in the prefrontal–basal ganglia circuit. The resting-state effective connectivity of this circuit was modeled by employing spectral dynamic causal modeling in 11 poststroke patients with cognitive impairment (PSCI), 8 poststroke patients without cognitive impairment (non-PSCI) at baseline and 3-month follow-up, and 28 healthy controls. Our results showed that different neuronal models of effective connectivity in the prefrontal–basal ganglia circuit were observed among healthy controls, non-PSCI, and PSCI patients. Additional connected paths (extra paths) appeared in the neuronal models of stroke patients compared with healthy controls. Moreover, changes were detected in the extra paths of non-PSCI between baseline and 3-month follow-up poststroke, indicating reorganization in the ipsilesional hemisphere and suggesting potential compensatory changes in the contralesional hemisphere. Furthermore, the connectivity strengths of the extra paths from the contralesional ventral anterior nucleus of thalamus to caudate correlated significantly with cognitive scores in non-PSCI and PSCI patients. These suggest that the neuronal model of effective connectivity of the prefrontal–basal ganglia circuit may be sensitive to stroke-induced cognitive decline, and it could be a biomarker for poststroke cognitive impairment 3 months poststroke. Importantly, contralesional brain regions may play an important role in functional compensation of cognitive decline.

scholarly journals Locus Coeruleus to Paraventricular Thalamus Projections Facilitate Emergence From Isoflurane Anesthesia in Mice

2021 ◽  
Vol 12 ◽  
Author(s):  
Yawen Ao ◽  
Bo Yang ◽  
Caiju Zhang ◽  
Bo Wu ◽  
Xuefen Zhang ◽  
...  
Keyword(s):  
Locus Coeruleus ◽  
Neural Activity ◽  
Brain Regions ◽  
Cortical Arousal ◽  
Emergence Time ◽  
General Anesthetic ◽  
Common Pathway ◽  
Isoflurane Anesthesia ◽  
New Strategy ◽  
Paraventricular Thalamus

Locus coeruleus (LC) sends widespread outputs to many brain regions to modulate diverse functions, including sleep/wake states, attention, and the general anesthetic state. The paraventricular thalamus (PVT) is a critical thalamic area for arousal and receives dense tyrosine-hydroxylase (TH) inputs from the LC. Although anesthesia and sleep may share a common pathway, it is important to understand the processes underlying emergence from anesthesia. In this study, we hypothesize that LC TH neurons and the TH:LC-PVT circuit may be involved in regulating emergence from anesthesia. Only male mice are used in this study. Here, using c-Fos as a marker of neural activity, we identify LC TH expressing neurons are active during anesthesia emergence. Remarkably, chemogenetic activation of LC TH neurons shortens emergence time from anesthesia and promotes cortical arousal. Moreover, enhanced c-Fos expression is observed in the PVT after LC TH neurons activation. Optogenetic activation of the TH:LC-PVT projections accelerates emergence from anesthesia, whereas, chemogenetic inhibition of the TH:LC-PVT circuit prolongs time to wakefulness. Furthermore, optogenetic activation of the TH:LC-PVT projections produces electrophysiological evidence of arousal. Together, these results demonstrate that activation of the TH:LC-PVT projections is helpful in facilitating the transition from isoflurane anesthesia to an arousal state, which may provide a new strategy in shortening the emergence time after general anesthesia.

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