scholarly journals Bi-directional Modeling Between Cross-Scale Neural Activity

2020 ◽  
Author(s):  
Yin-Jui Chang ◽  
Yuan-I Chen ◽  
Hsin-Chih Yeh ◽  
Jose M. Carmena ◽  
Samantha R. Santacruz
Keyword(s):  
Multiple Scales ◽  
Brain Activity ◽  
Head Model ◽  
Brain Areas ◽  
Explanatory Theory ◽  
Neural Data ◽  
Multi Scale ◽  
Learning Techniques ◽  
Low Dimensional ◽  
The Brain

AbstractFundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas the population brain activity has been studied in the micro-to meso-scale in building the connections between the dynamical patterns and the behaviors, such studies were often done at a single length scale and lacked an explanatory theory that identifies the neuronal origin across multiple scales. Here we introduce the NeuroBondGraph Network, a dynamical system incorporating both biological-inspired components and deep learning techniques to capture cross-scale dynamics that can infer and map the neural data from multiple scales. We demonstrated our model is not only 3.5 times more accurate than the popular sphere head model but also extracts more synchronized phase and correlated low-dimensional latent dynamics. We also showed that we can extend our methods to robustly predict held-out data across 16 days. Accordingly, the NeuroBondGraph Network opens the door to revealing comprehensive understanding of the brain computation, where network mechanisms of multi-scale communications are critical.

Cannabidiol Has a Unique Effect on Global Brain Activity: A Pharmacological, Functional MRI Study in Awake Mice 

2021 ◽  
Author(s):  
Aymen Sadaka ◽  
Ana Ozuna ◽  
Richard Ortiz ◽  
Praveen Kulkarni ◽  
Clare Johnson ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Brain Activity ◽  
Stress Disorders ◽  
Functional Coupling ◽  
Specific Information ◽  
Imaging Data ◽  
Post Traumatic Stress ◽  
Brain Areas ◽  
Dose Dependent ◽  
The Brain

Abstract Background: The phytocannabinoid cannabidiol (CBD) is a potential treatment for post-traumatic stress disorders. How does CBD interact with the brain to alter behavior? We hypothesized that CBD would produce a dose-dependent reduction in brain activity and functional coupling in neural circuitry associated with fear and defense. Methods: During the scanning session awake mice were given vehicle or CBD (3, 10, or 30 mg/kg I.P.) and imaged for 10 min post treatment. Mice were also treated with the 10 mg/kg dose of CBD and imaged one hr later for resting state BOLD functional connectivity (rsFC). Imaging data were registered to a 3D MRI mouse atlas providing site-specific information on 138 different brain areas. Blood samples were collected for CBD measurements.Results: CBD produced a dose-dependent polarization of activation along the rostral-caudal axis of the brain. The olfactory bulb and prefrontal cortex showed an increase in positive BOLD whereas the brainstem and cerebellum showed a decrease in BOLD signal. This negative BOLD affected many areas connected to the ascending reticular activating system (ARAS). The ARAS was decoupled to much of the brain but was hyperconnected to the olfactory system and prefrontal cortex. The pattern of ARAS connectivity closely overlapped with brain areas showing high levels N-acyl-phosphatidylethanolamines-specific phospholipase D (NAPE-PLD) messenger RNA.Conclusion: The CBD-induced decrease in ARAS activity is consistent with an emerging literature suggesting that CBD reduces autonomic arousal under conditions of emotional and physical stress. The putative target and mechanism of action is NAPE-PLD the enzyme responsible for the biosynthesis of lipid signaling molecules like anandamide.

scholarly journals Distributed context-dependent choice information in mouse dorsal-parietal cortex

2021 ◽  
Author(s):  
Javier Orlandi ◽  
Mohammad Adbolrahmani ◽  
Ryo Aoki ◽  
Dmitry Lyamzin ◽  
Andrea Benucci
Keyword(s):  
Decision Making ◽  
Decision Variable ◽  
Visual Stream ◽  
Posterior Cortex ◽  
Neural Data ◽  
Context Dependent ◽  
Cortical Regions ◽  
Low Dimensional ◽  
Independent Decision ◽  
The Brain

Abstract Choice information appears in the brain as distributed signals with top-down and bottom-up components that together support decision-making computations. In sensory and associative cortical regions, the presence of choice signals, their strength, and area specificity are known to be elusive and changeable, limiting a cohesive understanding of their computational significance. In this study, examining the mesoscale activity in mouse posterior cortex during a complex visual discrimination task, we found that broadly distributed choice signals defined a decision variable in a low-dimensional embedding space of multi-area activations, particularly along the ventral visual stream. The subspace they defined was near-orthogonal to concurrently represented sensory and motor-related activations, and it was modulated by task difficulty and contextually by the animals’ attention state. To mechanistically relate choice representations to decision-making computations, we trained recurrent neural networks with the animals’ choices and found an equivalent decision variable whose context-dependent dynamics agreed with that of the neural data. In conclusion, our results demonstrated an independent decision variable broadly represented in the posterior cortex, controlled by task features and cognitive demands. Its dynamics reflected decision computations, possibly linked to context-dependent feedback signals used for probabilistic-inference computations in variable animal-environment interactions.

scholarly journals Resting-state connectivity studies as a marker of the acute and delayed effects of subanaesthetic ketamine administration in healthy and depressed individuals: A systematic review

2021 ◽  
Vol 5 ◽  
pp. 239821282110554
Author(s):  
Vasileia Kotoula ◽  
Toby Webster ◽  
James Stone ◽  
Mitul A Mehta
Keyword(s):  
Resting State ◽  
Emotional Regulation ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Reward Processing ◽  
Brain Areas ◽  
Delayed Effects ◽  
Treatment Naïve ◽  
Resting State Connectivity ◽  
The Brain

Acute ketamine administration has been widely used in neuroimaging research to mimic psychosis-like symptoms. Within the last two decades, ketamine has also emerged as a potent, fast-acting antidepressant. The delayed effects of the drug, observed 2–48 h after a single infusion, are associated with marked improvements in depressive symptoms. At the systems’ level, several studies have investigated the acute ketamine effects on brain activity and connectivity; however, several questions remain unanswered around the brain changes that accompany the drug’s antidepressant effects and how these changes relate to the brain areas that appear with altered function and connectivity in depression. This review aims to address some of these questions by focusing on resting-state brain connectivity. We summarise the studies that have examined connectivity changes in treatment-naïve, depressed individuals and those studies that have looked at the acute and delayed effects of ketamine in healthy and depressed volunteers. We conclude that brain areas that are important for emotional regulation and reward processing appear with altered connectivity in depression whereas the default mode network presents with increased connectivity in depressed individuals compared to healthy controls. This finding, however, is not as prominent as the literature often assumes. Acute ketamine administration causes an increase in brain connectivity in healthy volunteers. The delayed effects of ketamine on brain connectivity vary in direction and appear to be consistent with the drug normalising the changes observed in depression. The limited number of studies however, as well as the different approaches for resting-state connectivity analysis make it very difficult to draw firm conclusions and highlight the importance of data sharing and larger future studies.

scholarly journals Synchronization in the connectome: Metastable oscillatory modes emerge from interactions in the brain spacetime network

2022 ◽  
Author(s):  
Joana Cabral ◽  
Francesca Castaldo ◽  
Jakub Vohryzek ◽  
Vladimir Litvak ◽  
Christian Bick ◽  
...  
Keyword(s):  
Coupled Oscillators ◽  
Brain Activity ◽  
Power Spectra ◽  
Brain Areas ◽  
Neural Mass ◽  
Spatially Distributed ◽  
Weakly Stable ◽  
Human Brains ◽  
The Brain

A rich repertoire of oscillatory signals is detected from human brains with electro- and magnetoencephalography (EEG/MEG). However, the principles underwriting coherent oscillations and their link with neural activity remain unclear. Here, we hypothesise that the emergence of transient brain rhythms is a signature of weakly stable synchronization between spatially distributed brain areas, occurring at network-specific collective frequencies due to non-negligible conduction times. We test this hypothesis using a phenomenological network model to simulate interactions between neural mass potentials (resonating at 40Hz) in the structural connectome. Crucially, we identify a critical regime where metastable oscillatory modes emerge spontaneously in the delta (0.5-4Hz), theta (4-8Hz), alpha (8-13Hz) and beta (13-30Hz) frequency bands from weak synchronization of subsystems, closely approximating the MEG power spectra from 89 healthy individuals. Grounded in the physics of delay-coupled oscillators, these numerical analyses demonstrate the role of the spatiotemporal connectome in structuring brain activity in the frequency domain.

The Neurobiology of Sleep

2013 ◽  
pp. 1127-1143
Author(s):  
Giulio Tononi ◽  
Chiara Cirelli
Keyword(s):  
Sleep Deprivation ◽  
Brain Activity ◽  
Negative Consequences ◽  
Brain Areas ◽  
Environmental Stimuli ◽  
Essential Function ◽  
The Brain ◽  
Dreamless Sleep

Sleep is a state of reduced responsiveness to environmental stimuli, usually associated with immobility and stereotyped postures. It is universal and tightly regulated, suggesting that it is likely to serve some essential function. However, that function remains unclear. In this chapter we examine how sleep is traditionally subdivided into different stages that alternate in the course of the night, discuss the brain areas that determine whether we are asleep or awake, and summarize the negative consequences of sleep deprivation. We then discuss how brain activity changes between sleep and wakefulness and consider how this leads to the characteristic modifications of consciousness experienced during dreaming and dreamless sleep. Finally, we turn to the paramount but still mysterious question of sleep function.

scholarly journals Source Space Analysis of Event-Related Dynamic Reorganization of Brain Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Andreas A. Ioannides ◽  
Stavros I. Dimitriadis ◽  
George A. Saridis ◽  
Marotesa Voultsidou ◽  
Vahe Poghosyan ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Activity ◽  
Clinical Applications ◽  
Brain Areas ◽  
Connectivity Patterns ◽  
The Brain ◽  
Typical Subject ◽  
Different Parts ◽  
Neuroimaging Techniques

How the brain works is nowadays synonymous with how different parts of the brain work together and the derivation of mathematical descriptions for the functional connectivity patterns that can be objectively derived from data of different neuroimaging techniques. In most cases static networks are studied, often relying on resting state recordings. Here, we present a quantitative study of dynamic reconfiguration of connectivity for event-related experiments. Our motivation is the development of a methodology that can be used for personalized monitoring of brain activity. In line with this motivation, we use data with visual stimuli from a typical subject that participated in different experiments that were previously analyzed with traditional methods. The earlier studies identified well-defined changes in specific brain areas at specific latencies related to attention, properties of stimuli, and tasks demands. Using a recently introduced methodology, we track the event-related changes in network organization, at source space level, thus providing a more global and complete view of the stages of processing associated with the regional changes in activity. The results suggest the time evolving modularity as an additional brain code that is accessible with noninvasive means and hence available for personalized monitoring and clinical applications.

scholarly journals Beat Detection Recruits the Visual Cortex in Early Blind Subjects

Life ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 296
Author(s):  
Rodrigo Araneda ◽  
Sandra Silva Moura ◽  
Laurence Dricot ◽  
Anne G. De Volder
Keyword(s):  
Brain Plasticity ◽  
Brain Activity ◽  
Sensory Modality ◽  
Brain Regions ◽  
Rhythm Perception ◽  
Brain Areas ◽  
Sensory Modalities ◽  
Beat Detection ◽  
The Brain ◽  
Early Blindness

Using functional magnetic resonance imaging, here we monitored the brain activity in 12 early blind subjects and 12 blindfolded control subjects, matched for age, gender and musical experience, during a beat detection task. Subjects were required to discriminate regular (“beat”) from irregular (“no beat”) rhythmic sequences composed of sounds or vibrotactile stimulations. In both sensory modalities, the brain activity differences between the two groups involved heteromodal brain regions including parietal and frontal cortical areas and occipital brain areas, that were recruited in the early blind group only. Accordingly, early blindness induced brain plasticity changes in the cerebral pathways involved in rhythm perception, with a participation of the visually deprived occipital brain areas whatever the sensory modality for input. We conclude that the visually deprived cortex switches its input modality from vision to audition and vibrotactile sense to perform this temporal processing task, supporting the concept of a metamodal, multisensory organization of this cortex.

scholarly journals Temporo-parietal cortex involved in modeling one’s own and others’ attention

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Arvid Guterstam ◽  
Branden J Bio ◽  
Andrew I Wilterson ◽  
Michael Graziano
Keyword(s):  
Theory Of Mind ◽  
Pattern Analysis ◽  
Brain Activity ◽  
Activity Patterns ◽  
Exogenous Attention ◽  
Attentional State ◽  
Brain Areas ◽  
Multivoxel Pattern Analysis ◽  
Directing Attention ◽  
The Brain

In a traditional view, in social cognition, attention is equated with gaze and people track other people’s attention by tracking their gaze. Here, we used fMRI to test whether the brain represents attention in a richer manner. People read stories describing an agent (either oneself or someone else) directing attention to an object in one of two ways: either internally directed (endogenous) or externally induced (exogenous). We used multivoxel pattern analysis to examine how brain areas within the theory-of-mind network encoded attention type and agent type. Brain activity patterns in the left temporo-parietal junction (TPJ) showed significant decoding of information about endogenous versus exogenous attention. The left TPJ, left superior temporal sulcus (STS), precuneus, and medial prefrontal cortex (MPFC) significantly decoded agent type (self versus other). These findings show that the brain constructs a rich model of one’s own and others’ attentional state, possibly aiding theory of mind.

scholarly journals Distributed context-dependent choice information in mouse dorsal-parietal cortex

2021 ◽  
Author(s):  
Javier G. Orlandi ◽  
Mohammad Abdolrahmani ◽  
Ryo Aoki ◽  
Dmitry R. Lyamzin ◽  
Andrea Benucci
Keyword(s):  
Decision Making ◽  
Decision Variable ◽  
Visual Stream ◽  
Posterior Cortex ◽  
Neural Data ◽  
Context Dependent ◽  
Cortical Regions ◽  
Low Dimensional ◽  
Independent Decision ◽  
The Brain

Choice information appears in the brain as distributed signals with top-down and bottom-up components that together support decision-making computations. In sensory and associative cortical regions, the presence of choice signals, their strength, and area specificity are known to be elusive and changeable, limiting a cohesive understanding of their computational significance. In this study, examining the mesoscale activity in mouse posterior cortex during a complex visual discrimination task, we found that broadly distributed choice signals defined a decision variable in a low-dimensional embedding space of multi-area activations, particularly along the ventral visual stream. The subspace they defined was near-orthogonal to concurrently represented sensory and motor-related activations, and it was modulated by task difficulty and contextually by the animals’ attention state. To mechanistically relate choice representations to decision-making computations, we trained recurrent neural networks with the animals’ choices and found an equivalent decision variable whose context-dependent dynamics agreed with that of the neural data. In conclusion, our results demonstrated an independent decision variable broadly represented in the posterior cortex, controlled by task features and cognitive demands. Its dynamics reflected decision computations, possibly linked to context-dependent feedback signals used for probabilistic-inference computations in variable animal-environment interactions.

scholarly journals Representing Multiple Observed Actions in the Motor System

2018 ◽  
Vol 29 (8) ◽  
pp. 3631-3641 ◽  
Author(s):  
Emiel Cracco ◽  
Christian Keysers ◽  
Amanda Clauwaert ◽  
Marcel Brass
Keyword(s):  
Motor Cortex ◽  
Motor System ◽  
Brain Activity ◽  
Third Party ◽  
Potential Mechanism ◽  
Brain Areas ◽  
Fmri Study ◽  
Social Encounters ◽  
The Brain ◽  
Do So

Abstract There is now converging evidence that others’ actions are represented in the motor system. However, social cognition requires us to represent not only the actions but also the interactions of others. To do so, it is imperative that the motor system can represent multiple observed actions. The current fMRI study investigated whether this is possible by measuring brain activity from 29 participants while they observed 2 right hands performing sign language gestures. Three key results were obtained. First, brain activity in the premotor and parietal motor cortex was stronger when 2 hands performed 2 different gestures than when 1 hand performed a single gesture. Second, both individual observed gestures could be decoded from brain activity in the same 2 regions. Third, observing 2 different gestures compared with 2 identical gestures activated brain areas related to motor conflict, and this activity was correlated with parietal motor activity. Together, these results show that the motor system is able to represent multiple observed actions, and as such reveal a potential mechanism by which third-party social encounters could be processed in the brain.

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