scholarly journals Brain-wide electrical spatiotemporal dynamics encode reward anticipation

2019 ◽  
Author(s):  
Mai-Anh T. Vu ◽  
Lisa K. David ◽  
Gwenaëlle E. Thomas ◽  
Meghana Vagwala ◽  
Caley Burrus ◽  
...  
Keyword(s):  
Neural Activity ◽  
Brain Regions ◽  
Network Activity ◽  
Prelimbic Cortex ◽  
Reward Anticipation ◽  
Spatially Distributed ◽  
Subsequent Behavior ◽  
Subcortical Regions ◽  
Network Patterns ◽  
The Brain

AbstractAnticipation of an upcoming stimulus induces neural activity across cortical and subcortical regions and influences subsequent behavior. Nevertheless, the network mechanism whereby the brain integrates this information to signal the anticipation of rewards remains relatively unexplored. Here we employ multi-circuit electrical recordings from six brain regions as mice perform a sample-to-match task in which reward anticipation is operationalized as their progress towards obtaining a potential reward. We then use machine learning to discover the naturally occurring network patterns that integrate this neural activity across timescales. Only one of the networks that we uncovered signals responses linked to reward anticipation, specifically relative proximity and reward magnitude. Activity in this Electome (electrical functional connectivity) network is dominated by theta oscillations leading from prelimbic cortex and striatum that converge on ventral tegmental area, and by beta oscillations leading from striatum that converge on prelimbic cortex. Network activity is also synchronized with brain-wide cellular firing. Critically, this network generalizes to new groups of healthy mice, as well as a mouse line that models aberrant neural circuitry observed in brain disorders that show altered reward anticipation. Thus, our findings reveal the network-level architecture whereby the brain integrates spatially distributed activity across timescales to signal reward anticipation.

scholarly journals Brain-wide electrical dynamics encode an appetitive socioemotional state

2020 ◽  
Author(s):  
Stephen D. Mague ◽  
Austin Talbot ◽  
Cameron Blount ◽  
Lara J. Duffney ◽  
Kathryn K. Walder-Christensen ◽  
...  
Keyword(s):  
Social Preference ◽  
Brain Regions ◽  
Brain Network ◽  
Network Activity ◽  
Brain State ◽  
Genetic Mouse Model ◽  
Spatially Distributed ◽  
Subcortical Regions ◽  
Complex Social Behavior ◽  
The Brain

AbstractMany cortical and subcortical regions contribute to complex social behavior; nevertheless, the network level architecture whereby the brain integrates this information to encode appetitive socioemotional behavior remains unknown. Here we measure electrical activity from eight brain regions as mice engage in a social preference assay. We then use machine learning to discover an explainable brain network that encodes the extent to which mice chose to engage another mouse. This socioemotional network is organized by theta oscillations leading from prelimbic cortex and amygdala that converge on ventral tegmental area, and network activity is synchronized with brain-wide cellular firing. The network generalizes, on a mouse-by-mouse basis, to encode socioemotional behaviors in healthy animals, but fails to encode an appetitive socioemotional state in a ‘high confidence’ genetic mouse model of autism. Thus, our findings reveal the architecture whereby the brain integrates spatially distributed activity across timescales to encode an appetitive socioemotional brain state in health and disease.

scholarly journals HIV Infection Is Associated with Attenuated Frontostriatal Intrinsic Connectivity: A Preliminary Study

2015 ◽  
Vol 21 (3) ◽  
pp. 203-213 ◽  
Author(s):  
Jonathan C. Ipser ◽  
Gregory G. Brown ◽  
Amanda Bischoff-Grethe ◽  
Colm G. Connolly ◽  
Ronald J. Ellis ◽  
...  
Keyword(s):  
Hiv Infection ◽  
Brain Regions ◽  
Group Differences ◽  
Hiv Rna ◽  
Functional Connections ◽  
Intrinsic Connectivity ◽  
Dorsolateral Prefrontal ◽  
Independent Replication ◽  
Subcortical Regions ◽  
The Brain

AbstractHIV-associated cognitive impairments are prevalent, and are consistent with injury to both frontal cortical and subcortical regions of the brain. The current study aimed to assess the association of HIV infection with functional connections within the frontostriatal network, circuitry hypothesized to be highly vulnerable to HIV infection. Fifteen HIV-positive and 15 demographically matched control participants underwent 6 min of resting-state functional magnetic resonance imaging (RS-fMRI). Multivariate group comparisons of age-adjusted estimates of connectivity within the frontostriatal network were derived from BOLD data for dorsolateral prefrontal cortex (DLPFC), dorsal caudate and mediodorsal thalamic regions of interest. Whole-brain comparisons of group differences in frontostriatal connectivity were conducted, as were pairwise tests of connectivity associations with measures of global cognitive functioning and clinical and immunological characteristics (nadir and current CD4 count, duration of HIV infection, plasma HIV RNA). HIV – associated reductions in connectivity were observed between the DLPFC and the dorsal caudate, particularly in younger participants (<50 years, N=9). Seropositive participants also demonstrated reductions in dorsal caudate connectivity to frontal and parietal brain regions previously demonstrated to be functionally connected to the DLPFC. Cognitive impairment, but none of the assessed clinical/immunological variables, was also associated with reduced frontostriatal connectivity. In conclusion, our data indicate that HIV is associated with attenuated intrinsic frontostriatal connectivity. Intrinsic connectivity of this network may therefore serve as a marker of the deleterious effects of HIV infection on the brain, possibly via HIV-associated dopaminergic abnormalities. These findings warrant independent replication in larger studies. (JINS, 2015, 21, 1–11)

scholarly journals Regulation and Modulation of pH in the Brain

2003 ◽  
Vol 83 (4) ◽  
pp. 1183-1221 ◽  
Author(s):  
MITCHELL CHESLER
Keyword(s):  
Intracellular Ph ◽  
Neural Activity ◽  
Extracellular Fluid ◽  
Cell Types ◽  
Brain Regions ◽  
Metabolic Products ◽  
Homeostatic Function ◽  
Acid Extrusion ◽  
Activity Dependent ◽  
The Brain

Chesler, Mitchell. Regulation and Modulation of pH in the Brain. Physiol Rev 83: 1183-1221, 2003; 10.1152/physrev.00010.2003.—The regulation of pH is a vital homeostatic function shared by all tissues. Mechanisms that govern H+ in the intracellular and extracellular fluid are especially important in the brain, because electrical activity can elicit rapid pH changes in both compartments. These acid-base transients may in turn influence neural activity by affecting a variety of ion channels. The mechanisms responsible for the regulation of intracellular pH in brain are similar to those of other tissues and are comprised principally of forms of Na+/H+ exchange, Na+-driven Cl-/HCO3- exchange, Na+-HCO3- cotransport, and passive Cl-/HCO3- exchange. Differences in the expression or efficacy of these mechanisms have been noted among the functionally and morphologically diverse neurons and glial cells that have been studied. Molecular identification of transporter isoforms has revealed heterogeneity among brain regions and cell types. Neural activity gives rise to an assortment of extracellular and intracellular pH shifts that originate from a variety of mechanisms. Intracellular pH shifts in neurons and glia have been linked to Ca2+ transport, activation of acid extrusion systems, and the accumulation of metabolic products. Extracellular pH shifts can occur within milliseconds of neural activity, arise from an assortment of mechanisms, and are governed by the activity of extracellular carbonic anhydrase. The functional significance of these compartmental, activity-dependent pH shifts is discussed.

scholarly journals Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability

2018 ◽  
Author(s):  
Matthieu Gilson ◽  
Nikos E. Kouvaris ◽  
Gustavo Deco ◽  
Jean-François Mangin ◽  
Cyril Poupon ◽  
...  
Keyword(s):  
Network Analysis ◽  
Network Theory ◽  
Random Network ◽  
Effective Connectivity ◽  
Structural Connectivity ◽  
Real Data ◽  
Brain Regions ◽  
Network Activity ◽  
Whole Brain ◽  
The Brain

AbstractNeuroimaging techniques such as MRI have been widely used to explore the associations between brain areas. Structural connectivity (SC) captures the anatomical pathways across the brain and functional connectivity (FC) measures the correlation between the activity of brain regions. These connectivity measures have been much studied using network theory in order to uncover the distributed organization of brain structures, in particular FC for task-specific brain communication. However, the application of network theory to study FC matrices is often “static” despite the dynamic nature of time series obtained from fMRI. The present study aims to overcome this limitation by introducing a network-oriented analysis applied to whole-brain effective connectivity (EC) useful to interpret the brain dynamics. Technically, we tune a multivariate Ornstein-Uhlenbeck (MOU) process to reproduce the statistics of the whole-brain resting-state fMRI signals, which provides estimates for MOU-EC as well as input properties (similar to local excitabilities). The network analysis is then based on the Green function (or network impulse response) that describes the interactions between nodes across time for the estimated dynamics. This model-based approach provides time-dependent graph-like descriptor, named communicability, that characterize the roles that either nodes or connections play in the propagation of activity within the network. They can be used at both global and local levels, and also enables the comparison of estimates from real data with surrogates (e.g. random network or ring lattice). In contrast to classical graph approaches to study SC or FC, our framework stresses the importance of taking the temporal aspect of fMRI signals into account. Our results show a merging of functional communities over time (in which input properties play a role), moving from segregated to global integration of the network activity. Our formalism sets a solid ground for the analysis and interpretation of fMRI data, including task-evoked activity.

scholarly journals Age-related declines in neural distinctiveness correlate across brain areas and result from both decreased reliability and increased confusability

2021 ◽  
Author(s):  
Molly Simmonite ◽  
Thad A Polk
Keyword(s):  
Neural Activity ◽  
Healthy Aging ◽  
Brain Regions ◽  
Aging Brain ◽  
Activation Patterns ◽  
Neural Representations ◽  
Age Related ◽  
Behavioural Performance ◽  
The Difference ◽  
The Brain

According to the neural dedifferentiation hypothesis, age-related reductions in the distinctiveness of neural representations contribute to sensory, cognitive, and motor declines associated with aging: neural activity associated with different stimulus categories becomes more confusable with age and behavioural performance suffers as a result. Initial studies investigated age-related dedifferentiation in the visual cortex, but subsequent research has revealed declines in other brain regions, suggesting that dedifferentiation may be a general feature of the aging brain. In the present study, we used functional magnetic resonance imaging to investigate age-related dedifferentiation in the visual, auditory, and motor cortices. Participants were 58 young adults and 79 older adults. The similarity of activation patterns across different blocks of the same condition was calculated (within-condition correlation, a measure of reliability) as was the similarity of activation patterns elicited by different conditions (between-category correlations, a measure of confusability). Neural distinctiveness was defined as the difference between the mean within- and between-condition similarity. We found age-related reductions in neural distinctiveness in the visual, auditory, and motor cortices, which were driven by both decreases in within-category similarity and increases in between-category similarity. There were significant positive cross-region correlations between neural distinctiveness in different regions. These correlations were driven by within-category similarities, a finding that indicates that declines in the reliability of neural activity appear to occur in tandem across the brain. These findings suggest that the changes in neural distinctiveness that occur in healthy aging result from changes in both the reliability and confusability of patterns of neural activity.

scholarly journals Cerebellar neurodynamics during motor planning predict decision timing and outcome on single-trial level

2019 ◽  
Author(s):  
Qian Lin ◽  
Magdalena Helmreich ◽  
Friederike Schlumm ◽  
Jennifer M. Li ◽  
Drew N. Robson ◽  
...  
Keyword(s):  
Motor Planning ◽  
Brain Regions ◽  
Decision Time ◽  
Single Trial ◽  
Movement Initiation ◽  
Decision Outcomes ◽  
Spatially Distributed ◽  
Brain States ◽  
The Difference ◽  
Subcortical Regions

SUMMARYThe neuronal basis of goal-directed behavior requires interaction of multiple separated brain regions. How subcortical regions and their interactions with brain-wide activity are involved in action selection is less understood. We have investigated this question by developing an assay based on whole-brain volumetric calcium imaging using light-field microscopy combined with an operant-conditioning task in larval zebrafish. We find global and recurring dynamics of brain states to exhibit pre-motor bifurcations towards mutually exclusive decision outcomes which arises from a spatially distributed network. Within this network the cerebellum shows a particularly strong pre-motor activity, predictive of both the timing and outcome of behavior up to ∼10 seconds before movement initiation. Furthermore, on the single-trial level, decision directions can be inferred from the difference neuroactivity between the ipsilateral and contralateral hemispheres, while the decision time can be quantitatively predicted by the rate of bi-hemispheric population ramping activity. Our results point towards a cognitive role of the cerebellum and its importance in motor planning.

scholarly journals A spatiotemporal complexity architecture of human brain activity

2021 ◽  
Author(s):  
Stephan Krohn ◽  
Nina von Schwanenflug ◽  
Leonhard Waschke ◽  
Amy Romanello ◽  
Martin Gell ◽  
...  
Keyword(s):  
Human Brain ◽  
Neural Activity ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Organization ◽  
Functional Connections ◽  
Signal Complexity ◽  
The Brain

The human brain operates in large-scale functional networks, collectively subsumed as the functional connectome1-13. Recent work has begun to unravel the organization of the connectome, including the temporal dynamics of brain states14-20, the trade-off between segregation and integration9,15,21-23, and a functional hierarchy from lower-order unimodal to higher-order transmodal processing systems24-27. However, it remains unknown how these network properties are embedded in the brain and if they emerge from a common neural foundation. Here we apply time-resolved estimation of brain signal complexity to uncover a unifying principle of brain organization, linking the connectome to neural variability6,28-31. Using functional magnetic resonance imaging (fMRI), we show that neural activity is marked by spontaneous "complexity drops" that reflect episodes of increased pattern regularity in the brain, and that functional connections among brain regions are an expression of their simultaneous engagement in such episodes. Moreover, these complexity drops ubiquitously propagate along cortical hierarchies, suggesting that the brain intrinsically reiterates its own functional architecture. Globally, neural activity clusters into temporal complexity states that dynamically shape the coupling strength and configuration of the connectome, implementing a continuous re-negotiation between cost-efficient segregation and communication-enhancing integration9,15,21,23. Furthermore, complexity states resolve the recently discovered association between anatomical and functional network hierarchies comprehensively25-27,32. Finally, brain signal complexity is highly sensitive to age and reflects inter-individual differences in cognition and motor function. In sum, we identify a spatiotemporal complexity architecture of neural activity — a functional "complexome" that gives rise to the network organization of the human brain.

scholarly journals Network Dysfunction in Comorbid Psychiatric Illnesses and Epilepsy

2020 ◽  
Vol 20 (4) ◽  
pp. 205-210
Author(s):  
Phillip L. W. Colmers ◽  
Jamie Maguire
Keyword(s):  
Cognitive Impairments ◽  
Brain Regions ◽  
Network Activity ◽  
Hypothalamic Pituitary Adrenal ◽  
Psychiatric Illnesses ◽  
Ictal Activity ◽  
Episodic Nature ◽  
Network States ◽  
The Brain ◽  
Pituitary Adrenal Axis

The episodic nature of both epilepsy and psychiatric illnesses suggests that the brain switches between healthy and pathological states. The most obvious example of transitions between network states related to epilepsy is the manifestation of ictal events. In addition to seizures, there are more subtle changes in network communication within and between brain regions, which we propose may contribute to psychiatric illnesses associated with the epilepsies. This review will highlight evidence supporting aberrant network activity associated with epilepsy and the contribution to cognitive impairments and comorbid psychiatric illnesses. Further, we discuss potential mechanisms mediating the network dysfunction associated with comorbidities in epilepsy, including interneuron loss and hypothalamic–pituitary–adrenal axis dysfunction. Conceptually, it is necessary to think beyond ictal activity to appreciate the breadth of network dysfunction contributing to the spectrum of symptoms associated with epilepsy, including psychiatric comorbidities.

scholarly journals Neurosteroids increase tonic GABAergic inhibition in the lateral section of the central amygdala in mice

2015 ◽  
Vol 113 (9) ◽  
pp. 3421-3431 ◽  
Author(s):  
H. Romo-Parra ◽  
P. Blaesse ◽  
L. Sosulina ◽  
H.-C. Pape
Keyword(s):  
De Novo ◽  
Brain Regions ◽  
Network Activity ◽  
Aminobutyric Acid ◽  
Gabaergic Inhibition ◽  
Central Amygdala ◽  
Cognitive Behaviors ◽  
Excitatory Neurotransmission ◽  
Inhibitory Signaling ◽  
The Brain

Neurosteroids are formed de novo in the brain and can modulate both inhibitory and excitatory neurotransmission. Recent evidence suggests that the anxiolytic effects of neurosteroids are mediated by the amygdala, a key structure for emotional and cognitive behaviors. Tonic inhibitory signaling via extrasynaptic type A γ-aminobutyric acid receptors (GABAARs) is known to be crucially involved in regulating network activity in various brain regions including subdivisions of the amygdala. Here we provide evidence for the existence of tonic GABAergic inhibition generated by the activation of δ-subunit-containing GABAARs in neurons of the lateral section of the mouse central amygdala (CeAl). Furthermore, we show that neurosteroids play an important role in the modulation of tonic GABAergic inhibition in the CeAl. Taken together, these findings provide new mechanistic insights into the effects of pharmacologically relevant neurosteroids in the amygdala and might be extrapolated to the regulation of anxiety.

scholarly journals Topological conditions for propagation of spatially-distributed neural activity

2021 ◽  
Author(s):  
Àlex Tudoras ◽  
Alex D Reyes
Keyword(s):  
Nervous System ◽  
Spatial Patterns ◽  
Neural Activity ◽  
Large Population ◽  
Simplicial Complexes ◽  
Brain Regions ◽  
Important Task ◽  
Active Population ◽  
Spatially Distributed ◽  
Target Layer

An important task of the nervous system is to transmit information faithfully and reliably across brain regions, a process that involves the coordinated activity of a relatively large population of neurons. In topographically organized networks, where the entering and exiting axons of neurons terminate in confined areas, successful propagation depends on the spatial patterns of activity: the firing neurons in a presynaptic or source layer must be located sufficiently close to each other to ensure that cells in the postsynaptic or target layer receive the requisite number of convergent inputs to fire. Here, we use principles of topology to define the conditions for transmitting information across layers. We show that simplicial complexes formed by source neurons can be used to: 1) determine whether target neurons receive suprathreshold inputs; 2) identify neurons within the active population that contribute to firing; and 3) discriminate between single and multiple active clusters of neurons.

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