scholarly journals Enhanced Hippocampus-Nidopallium Caudolaterale Connectivity during Route Formation in Goal-Directed Spatial Learning of Pigeons

Animals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 2003
Author(s):  
Meng-Meng Li ◽  
Jian-Tao Fan ◽  
Shu-Guan Cheng ◽  
Li-Fang Yang ◽  
Long Yang ◽  
...  
Keyword(s):  
Spatial Learning ◽  
Spectral Power ◽  
Brain Regions ◽  
Brain Network ◽  
Spatial Task ◽  
Behavioral Changes ◽  
Current Location ◽  
Theta Frequency ◽  
Hippocampal Theta ◽  
Insight Into

Goal-directed spatial learning is crucial for the survival of animals, in which the formation of the route from the current location to the goal is one of the central problems. A distributed brain network comprising the hippocampus and prefrontal cortex has been shown to support such capacity, yet it is not fully understood how the most similar brain regions in birds, the hippocampus (Hp) and nidopallium caudolaterale (NCL), cooperate during route formation in goal-directed spatial learning. Hence, we examined neural activity in the Hp-NCL network of pigeons and explored the connectivity dynamics during route formation in a goal-directed spatial task. We found that behavioral changes in spatial learning during route formation are accompanied by modifications in neural patterns in the Hp-NCL network. Specifically, as pigeons learned to solve the task, the spectral power in both regions gradually decreased. Meanwhile, elevated hippocampal theta (5 to 12 Hz) connectivity and depressed connectivity in NCL were also observed. Lastly, the interregional functional connectivity was found to increase with learning, specifically in the theta frequency band during route formation. These results provide insight into the dynamics of the Hp-NCL network during spatial learning, serving to reveal the potential mechanism of avian spatial navigation.

scholarly journals Causally informed activity flow models provide mechanistic insight into the emergence of cognitive processes from brain network interactions

2021 ◽  
Author(s):  
Ruben Sanchez-Romero ◽  
Takuya Ito ◽  
Ravi D. Mill ◽  
Stephen José Hanson ◽  
Michael W. Cole
Keyword(s):  
Functional Connectivity ◽  
Effective Connectivity ◽  
Pearson Correlation ◽  
Explanatory Power ◽  
Memory Task ◽  
Brain Regions ◽  
Brain Network ◽  
List Type ◽  
Flow Models ◽  
Insight Into

AbstractBrain activity flow models estimate the movement of task-evoked activity over brain connections to help explain the emergence of task-related functionality. Activity flow estimates have been shown to accurately predict task-evoked brain activations across a wide variety of brain regions and task conditions. However, these predictions have had limited explanatory power, given known issues with causal interpretations of the standard functional connectivity measures used to parameterize activity flow models. We show here that functional/effective connectivity (FC) measures grounded in causal principles facilitate mechanistic interpretation of activity flow models. Starting from Pearson correlation (the current field standard), we progress from FC measures with poor to excellent causal grounding, demonstrating a continuum of causal validity using simulations and empirical fMRI data. Finally, we apply a causal FC method to a dorsolateral prefrontal cortex region, demonstrating causal network mechanisms contributing to its strong activation during a 2-back (relative to a 0-back) working memory task. Together, these results reveal the promise of parameterizing activity flow models using causal FC methods to identify network mechanisms underlying cognitive computations in the human brain.Highlights-Activity flow models provide insight into how cognitive neural effects emerge from brain network interactions.-Functional connectivity methods grounded in causal principles facilitate mechanistic interpretations of task activity flow models.-Mechanistic activity flow models accurately predict task-evoked neural effects across a wide variety of brain regions and cognitive tasks.

scholarly journals Speed modulation of hippocampal theta frequency and power predicts water maze learning

2020 ◽  
Author(s):  
Calvin K. Young ◽  
Ming Ruan ◽  
Neil McNaughton
Keyword(s):  
Spatial Learning ◽  
Cognitive Processes ◽  
Water Maze ◽  
Maze Learning ◽  
Theta Oscillations ◽  
Movement Speed ◽  
Learning Rates ◽  
Speed Modulation ◽  
Theta Frequency ◽  
Hippocampal Theta

AbstractTheta oscillations in the hippocampus have many behavioural correlates, with the magnitude and vigour of ongoing movement being the most salient. Many consider correlates of locomotion with hippocampal theta to be a confound in delineating theta contributions to cognitive processes. But, theory and empirical experiments suggest theta-movement relationships are important if spatial navigation is to support higher cognitive processes. In the current study, we tested if variations in speed modulation of hippocampal theta can predict spatial learning rates in the water maze. Using multi-step regression, we find the magnitude and robustness of hippocampal theta frequency versus speed scaling can predict water maze learning rates. Using generalised linear models, we also demonstrate that speed and water maze learning are the best predictors of hippocampal theta frequency and power. Theta oscillations recorded from the supramammillary area showed much weaker, or non-existent, relationships, which supports the idea that hippocampal theta has specific roles in speed representation and spatial learning. Our findings suggest movement-speed correlations with hippocampal theta frequency may be actively used in spatial learning.

scholarly journals Fear-induced brain activations distinguish anxious and trauma-exposed brains

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhenfu Wen ◽  
Marie-France Marin ◽  
Jennifer Urbano Blackford ◽  
Zhe Sage Chen ◽  
Mohammed R. Milad
Keyword(s):  
Neural Network ◽  
Fear Conditioning ◽  
Psychiatric Diagnosis ◽  
Data Analytics ◽  
Conditioned Fear ◽  
Brain Regions ◽  
Brain Network ◽  
Neuroimaging Data ◽  
Task Irrelevant ◽  
Translational Models

AbstractTranslational models of fear conditioning and extinction have elucidated a core neural network involved in the learning, consolidation, and expression of conditioned fear and its extinction. Anxious or trauma-exposed brains are characterized by dysregulated neural activations within regions of this fear network. In this study, we examined how the functional MRI activations of 10 brain regions commonly activated during fear conditioning and extinction might distinguish anxious or trauma-exposed brains from controls. To achieve this, activations during four phases of a fear conditioning and extinction paradigm in 304 participants with or without a psychiatric diagnosis were studied. By training convolutional neural networks (CNNs) using task-specific brain activations, we reliably distinguished the anxious and trauma-exposed brains from controls. The performance of models decreased significantly when we trained our CNN using activations from task-irrelevant brain regions or from a brain network that is irrelevant to fear. Our results suggest that neuroimaging data analytics of task-induced brain activations within the fear network might provide novel prospects for development of brain-based psychiatric diagnosis.

scholarly journals Mechanisms Underlying the Cognitive and Behavioural Effects of Maternal Obesity

Nutrients ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 240
Author(s):  
Kyoko Hasebe ◽  
Michael D. Kendig ◽  
Margaret J. Morris
Keyword(s):  
Animal Models ◽  
Adverse Effects ◽  
Gut Microbiome ◽  
Maternal Obesity ◽  
Maternal Diet ◽  
Affective State ◽  
Brain Regions ◽  
Brain Network ◽  
Physical And Mental Health ◽  
Obesogenic Diet

The widespread consumption of ‘western’-style diets along with sedentary lifestyles has led to a global epidemic of obesity. Epidemiological, clinical and preclinical evidence suggests that maternal obesity, overnutrition and unhealthy dietary patterns programs have lasting adverse effects on the physical and mental health of offspring. We review currently available preclinical and clinical evidence and summarise possible underlying neurobiological mechanisms by which maternal overnutrition may perturb offspring cognitive function, affective state and psychosocial behaviour, with a focus on (1) neuroinflammation; (2) disrupted neuronal circuities and connectivity; and (3) dysregulated brain hormones. We briefly summarise research implicating the gut microbiota in maternal obesity-induced changes to offspring behaviour. In animal models, maternal obesogenic diet consumption disrupts CNS homeostasis in offspring, which is critical for healthy neurodevelopment, by altering hypothalamic and hippocampal development and recruitment of glial cells, which subsequently dysregulates dopaminergic and serotonergic systems. The adverse effects of maternal obesogenic diets are also conferred through changes to hormones including leptin, insulin and oxytocin which interact with these brain regions and neuronal circuits. Furthermore, accumulating evidence suggests that the gut microbiome may directly and indirectly contribute to these maternal diet effects in both human and animal studies. As the specific pathways shaping abnormal behaviour in offspring in the context of maternal obesogenic diet exposure remain unknown, further investigations are needed to address this knowledge gap. Use of animal models permits investigation of changes in neuroinflammation, neurotransmitter activity and hormones across global brain network and sex differences, which could be directly and indirectly modulated by the gut microbiome.

scholarly journals Reconfiguration of human evolving large-scale epileptic brain networks prior to seizures: an evaluation with node centralities

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Rieke Fruengel ◽  
Timo Bröhl ◽  
Thorsten Rings ◽  
Klaus Lehnertz
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Theoretical Concept ◽  
Global Network ◽  
Time Resolved ◽  
Functional Connections ◽  
Node Centrality ◽  
Network Mechanism

AbstractPrevious research has indicated that temporal changes of centrality of specific nodes in human evolving large-scale epileptic brain networks carry information predictive of impending seizures. Centrality is a fundamental network-theoretical concept that allows one to assess the role a node plays in a network. This concept allows for various interpretations, which is reflected in a number of centrality indices. Here we aim to achieve a more general understanding of local and global network reconfigurations during the pre-seizure period as indicated by changes of different node centrality indices. To this end, we investigate—in a time-resolved manner—evolving large-scale epileptic brain networks that we derived from multi-day, multi-electrode intracranial electroencephalograpic recordings from a large but inhomogeneous group of subjects with pharmacoresistant epilepsies with different anatomical origins. We estimate multiple centrality indices to assess the various roles the nodes play while the networks transit from the seizure-free to the pre-seizure period. Our findings allow us to formulate several major scenarios for the reconfiguration of an evolving epileptic brain network prior to seizures, which indicate that there is likely not a single network mechanism underlying seizure generation. Rather, local and global aspects of the pre-seizure network reconfiguration affect virtually all network constituents, from the various brain regions to the functional connections between them.

scholarly journals Pain Detection and the Privacy of Subjective Experience

2007 ◽  
Vol 33 (2-3) ◽  
pp. 433-456 ◽  
Author(s):  
Adam J. Kolber
Keyword(s):  
Subjective Experience ◽  
Brain Regions ◽  
Physical Pain ◽  
Medical Evidence ◽  
Functional Magnetic Resonance ◽  
Positron Emission ◽  
Pain Symptoms ◽  
The Brain ◽  
Insight Into ◽  
Neuroimaging Techniques

A neurologist with abdominal pain goes to see a gastroenterologist for treatment. The gastroenterologist asks the neurologist where it hurts. The neurologist replies, “In my head, of course.” Indeed, while we can feel pain throughout much of our bodies, pain signals undergo most of their processing in the brain. Using neuroimaging techniques like functional magnetic resonance imaging (“fMRI”) and positron emission tomography (“PET”), researchers have more precisely identified brain regions that enable us to experience physical pain. Certain regions of the brain's cortex, for example, increase in activation when subjects are exposed to painful stimuli. Furthermore, the amount of activation increases with the intensity of the painful stimulus. These findings suggest that we may be able to gain insight into the amount of pain a particular person is experiencing by non-invasively imaging his brain.Such insight could be particularly valuable in the courtroom where we often have no definitive medical evidence to prove or disprove claims about the existence and extent of pain symptoms.

The Cortical Organization of Writing Sequence: Evidence From Observing Chinese Characters in Motion

2021 ◽  
Author(s):  
Zhaoqi Zhang ◽  
Qiming Yuan ◽  
Zeping Liu ◽  
Man Zhang ◽  
Junjie Wu ◽  
...  
Keyword(s):  
Structural Equation ◽  
Chinese Character ◽  
Inferior Frontal Gyrus ◽  
Hierarchical Control ◽  
Brain Regions ◽  
Brain Network ◽  
Equation Model ◽  
Chinese Characters ◽  
General Motor ◽  
The Right

Abstract Writing sequences play an important role in handwriting of Chinese characters. However, little is known regarding the integral brain patterns and network mechanisms of processing Chinese character writing sequences. The present study decoded brain patterns during observing Chinese characters in motion by using multi-voxel pattern analysis (MVPA), meta-analytic decoding analysis, and extended unified structural equation model (euSEM). We found that perception of Chinese character writing sequence recruited brain regions not only for general motor schema processing, i.e., the right inferior frontal gyrus, shifting and inhibition functions, i.e., the right postcentral gyrus and bilateral pre-SMA/dACC, but also for sensorimotor functions specific for writing sequences. More importantly, these brain regions formed a cooperatively top-down brain network where information was transmitted from brain regions for general motor schema processing to those specific for writing sequences. These findings not only shed light on the neural mechanisms of Chinese character writing sequences, but also extend the hierarchical control model on motor schema processing.

Building the Arrow of Time… Over Time: A Sequence of Brain Activity Mapping Imagined Events in Time and Space

2018 ◽  
Vol 29 (10) ◽  
pp. 4398-4414 ◽  
Author(s):  
Baptiste Gauthier ◽  
Karin Pestke ◽  
Virginie van Wassenhove
Keyword(s):  
Brain Activity ◽  
Brain Regions ◽  
Spatial Task ◽  
Brain Response ◽  
Time And Space ◽  
Time Resolved ◽  
The Future ◽  
Spatial Dimensions ◽  
Future Events ◽  
Neural Signatures

Abstract When moving, the spatiotemporal unfolding of events is bound to our physical trajectory, and time and space become entangled in episodic memory. When imagining past or future events, or being in different geographical locations, the temporal and spatial dimensions of mental events can be independently accessed and manipulated. Using time-resolved neuroimaging, we characterized brain activity while participants ordered historical events from different mental perspectives in time (e.g., when imagining being 9 years in the future) or in space (e.g., when imagining being in Cayenne). We describe 2 neural signatures of temporal ordinality: an early brain response distinguishing whether participants were mentally in the past, the present or the future (self-projection in time), and a graded activity at event retrieval, indexing the mental distance between the representation of the self in time and the event. Neural signatures of ordinality and symbolic distances in time were distinct from those observed in the homologous spatial task: activity indicating spatial order and distances overlapped in latency in distinct brain regions. We interpret our findings as evidence that the conscious representation of time and space share algorithms (egocentric mapping, distance, and ordinality computations) but different implementations with a distinctive status for the psychological “time arrow.”

scholarly journals A Computational Network Control Theory Analysis of Depression Symptoms

2018 ◽  
Vol 1 ◽  
Author(s):  
Yoed N. Kenett ◽  
Roger E. Beaty ◽  
John D. Medaglia
Keyword(s):  
Control Theory ◽  
Diffusion Tensor ◽  
Brain Regions ◽  
Brain Network ◽  
Network Control ◽  
Depression Symptoms ◽  
Theory Approach ◽  
Imaging Data ◽  
The Brain ◽  
Subclinical Depression

AbstractRumination and impaired inhibition are considered core characteristics of depression. However, the neurocognitive mechanisms that contribute to these atypical cognitive processes remain unclear. To address this question, we apply a computational network control theory approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how network control theory relates to individual differences in subclinical depression. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that subclinical depression is negatively related to higher integration abilities in the right anterior insula, replicating and extending previous studies implicating atypical switching between the default mode and Executive Control Networks in depression. We also find that subclinical depression is related to the ability to “drive” the brain system into easy to reach neural states in several brain regions, including the bilateral lingual gyrus and lateral occipital gyrus. These findings highlight brain regions less known in their role in depression, and clarify their roles in driving the brain into different neural states related to depression symptoms.

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