Interlimb neural interactions in the corticospinal pathways

Depletion of estrogens and progesterone at menopause has been linked to an increased risk for the development of Alzheimer’s disease (AD) in women. A currently controversial literature indicates that although treatment of postmenopausal women with hormone therapy (HT) may reduce the risk of AD, several parameters of HT may limit its potential efficacy and perhaps, even exacerbate AD risk. One such parameter is continuous vs. cyclic delivery of the progestogen component of HT. Recent experimental evidence suggests that continuous progesterone can attenuate neural actions of estradiol (E2). In the present study, we compared the effects of continuous and cyclic progesterone treatment in the presence and absence of E2 in ovariectomized 3×Tg-AD mice, a transgenic mouse model of AD. We found that ovariectomy-induced hormone depletion increases AD-like pathology in female 3×Tg-AD mice, including accumulation of β-amyloid, tau hyperphosphorylation, and impaired hippocampal-dependent behavior. E2 treatment alone prevents the increases in pathology. Continuous progesterone did not affect β-amyloid levels when delivered alone but blocked the Aβ-lowering action of E2. In contrast, cyclic progesterone significantly reduced β-amyloid levels by itself and enhanced rather than inhibited the E2 effects. These results provide new insight into the neural interactions between E2 and progesterone that may prove valuable in optimizing HT regimens in postmenopausal women.

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Effects of Local Nicotinic Activation of the Superior Colliculus on Saccades in Monkeys

Journal of Neurophysiology ◽

10.1152/jn.00558.2004 ◽

2005 ◽

Vol 93 (1) ◽

pp. 519-534 ◽

Cited By ~ 21

Author(s):

Masayuki Watanabe ◽

Yasushi Kobayashi ◽

Yuka Inoue ◽

Tadashi Isa

Keyword(s):

Superior Colliculus ◽

Reaction Times ◽

Low Frequency ◽

Visually Guided ◽

Population Activity ◽

Affected Area ◽

Movement Field ◽

Neural Interactions ◽

Restricted Region

To examine the role of competitive and cooperative neural interactions within the intermediate layer of superior colliculus (SC), we elevated the basal SC neuronal activity by locally injecting a cholinergic agonist nicotine and analyzed its effects on saccade performance. After microinjection, spontaneous saccades were directed toward the movement field of neurons at the injection site (affected area). For visually guided saccades, reaction times were decreased when targets were presented close to the affected area. However, when visual targets were presented remote from the affected area, reaction times were not increased regardless of the rostrocaudal level of the injection sites. The endpoints of visually guided saccades were biased toward the affected area when targets were presented close to the affected area. After this endpoint effect diminished, the trajectories of visually guided saccades remained modestly curved toward the affected area. Compared with the effects on endpoints, the effects on reaction times were more localized to the targets close to the affected area. These results are consistent with a model that saccades are triggered by the activities of neurons within a restricted region, and the endpoints and trajectories of the saccades are determined by the widespread population activity in the SC. However, because increased reaction times were not observed for saccades toward targets remote from the affected area, inhibitory interactions in the SC may not be strong enough to shape the spatial distribution of the low-frequency preparatory activities in the SC.

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BOLD Coherence Reveals Segregated Functional Neural Interactions When Adapting to Distinct Torque Perturbations

Journal of Neurophysiology ◽

10.1152/jn.00405.2006 ◽

2007 ◽

Vol 97 (3) ◽

pp. 2107-2120 ◽

Cited By ~ 16

Author(s):

Eugene Tunik ◽

Paul J. Schmitt ◽

Scott T. Grafton

Keyword(s):

Basal Ganglia ◽

Contextual Information ◽

Natural World ◽

Adaptive Responses ◽

Response Preparation ◽

Target Capture ◽

Neural Processes ◽

Neural Interactions ◽

The Right ◽

Level Analysis

In the natural world, we experience and adapt to multiple extrinsic perturbations. This poses a challenge to neural circuits in discriminating between different context-appropriate responses. Using event-related fMRI, we characterized the neural dynamics involved in this process by randomly delivering a position- or velocity-dependent torque perturbation to subjects’ arms during a target-capture task. Each perturbation was color-cued during movement preparation to provide contextual information. Although trajectories differed between perturbations, subjects significantly reduced error under both conditions. This was paralleled by reduced BOLD signal in the right dentate nucleus, the left sensorimotor cortex, and the left intraparietal sulcus. Trials included “NoGo” conditions to dissociate activity related to preparation from execution and adaptation. Subsequent analysis identified perturbation-specific neural processes underlying preparation (“NoGo”) and adaptation (“Go”) early and late into learning. Between-perturbation comparisons of BOLD magnitude revealed negligible differences for both preparation and adaptation trials. However, a network-level analysis of BOLD coherence revealed that by late learning, response preparation (“NoGo”) was attributed to a relative focusing of coherence within cortical and basal ganglia networks in both perturbation conditions, demonstrating a common network interaction for establishing arbitrary visuomotor associations. Conversely, late-learning adaptation (“Go”) was attributed to a focusing of BOLD coherence between a cortical–basal ganglia network in the viscous condition and between a cortical–cerebellar network in the positional condition. Our findings demonstrate that trial-to-trial acquisition of two distinct adaptive responses is attributed not to anatomically segregated regions, but to differential functional interactions within common sensorimotor circuits.

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Dynamical Latent State Computation in the Posterior Parietal Cortex

10.1101/2022.01.12.476065 ◽

2022 ◽

Author(s):

Kaushik J Lakshminarasimhan ◽

Eric Avila ◽

Xaq Pitkow ◽

Dora E Angelaki

Keyword(s):

Parietal Cortex ◽

Posterior Parietal Cortex ◽

Target Location ◽

Neural Representation ◽

World Model ◽

Population Activity ◽

The World ◽

Neural Interactions ◽

Posterior Parietal ◽

Hidden States

Success in many real-world tasks depends on our ability to dynamically track hidden states of the world. To understand the underlying neural computations, we recorded brain activity in posterior parietal cortex (PPC) of monkeys navigating by optic flow to a hidden target location within a virtual environment, without explicit position cues. In addition to sequential neural dynamics and strong interneuronal interactions, we found that the hidden state -- monkey's displacement from the goal -- was encoded in single neurons, and could be dynamically decoded from population activity. The decoded estimates predicted navigation performance on individual trials. Task manipulations that perturbed the world model induced substantial changes in neural interactions, and modified the neural representation of the hidden state, while representations of sensory and motor variables remained stable. The findings were recapitulated by a task-optimized recurrent neural network model, suggesting that neural interactions in PPC embody the world model to consolidate information and track task-relevant hidden states.

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Dynamical differential covariance recovers directional network structure in multiscale neural systems

10.1101/2021.06.18.448901 ◽

2021 ◽

Author(s):

Yusi Chen ◽

Burke Q Rosen ◽

Terrence J Sejnowski

Keyword(s):

Structural Connectivity ◽

Network Models ◽

Ground Truth ◽

Neural Systems ◽

Noise Tolerance ◽

Functional Magnetic Resonance ◽

Resonance Imaging ◽

Dynamical Models ◽

Wide Range ◽

Neural Interactions

Investigating causal neural interactions are essential to understanding sub- sequent behaviors. Many statistical methods have been used for analyzing neural activity, but efficiently and correctly estimating the direction of net- work interactions remains difficult. Here, we derive dynamical differential covariance (DDC), a new method based on dynamical network models that detects directional interactions with low bias and high noise tolerance with- out the stationary assumption. The method is first validated on networks with false positive motifs and multiscale neural simulations where the ground truth connectivity is known. Then, applying DDC to recordings of resting-state functional magnetic resonance imaging (rs-fMRI) from over 1,000 individual subjects, DDC consistently detected regional interactions with strong structural connectivity. DDC can be generalized to a wide range of dynamical models and recording techniques.

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Immune stimulation reduces sleep and memory ability in Drosophila melanogaster

10.7287/peerj.preprints.285v1 ◽

2014 ◽

Author(s):

Eamonn B Mallon ◽

Akram Alghamdi ◽

Robert T.K. Holdbrook ◽

Ezio Rosato

Keyword(s):

Immune Response ◽

Drosophila Melanogaster ◽

Immune System ◽

Receptor Protein ◽

Immune Stimulation ◽

Night Sleep ◽

Conditioning Paradigm ◽

Memory Ability ◽

Neural Interactions ◽

Aversive Classical Conditioning

Psychoneuroimmunology studies the increasing number of connections between neurobiology, immunology and behaviour. We establish Drosophila melanogaster as a tractable model in this field by demonstrating the effects of the immune response on two fundamental behaviours: sleep and memory ability. We used the Geneswitch system to upregulate peptidoglycan receptor protein (PGRP) expression, thereby stimulating the immune system in the absence of infection. Geneswitch was activated by feeding the steroid RU486, to the flies. We used an aversive classical conditioning paradigm to quantify memory and measures of activity to infer sleep. Immune stimulated flies exhibited reduced levels of sleep, which could not be explained by a generalised increase in waking activity. The effects on sleep were more pronounced for day compared to night sleep. Immune stimulated flies also showed a reduction in memory abilities. These results establish Drosophila as a model for immune-neural interactions and suggest a possible role for sleep in the interplay between the immune response and memory.

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Fuzzy and Neural: Interactions and Applications

10.1007/978-3-7908-1881-9 ◽

1998 ◽

Cited By ~ 3

Author(s):

James J. Buckley ◽

Thomas Feuring

Keyword(s):

Neural Interactions

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Integrating TMS, EEG, and MRI as an Approach for Studying Brain Connectivity

The Neuroscientist ◽

10.1177/1073858420916452 ◽

2020 ◽

Vol 26 (5-6) ◽

pp. 471-486

Author(s):

Romina Esposito ◽

Marta Bortoletto ◽

Carlo Miniussi

Keyword(s):

Brain Function ◽

Closed Loop ◽

Current Knowledge ◽

Brain Connectivity ◽

Brain Activity ◽

Brain Regions ◽

Integrative Approach ◽

Resonance Imaging ◽

Neural Communication ◽

Neural Interactions

The human brain is a complex network in which hundreds of brain regions are interconnected via thousands of axonal pathways. The capability of such a complex system emerges from specific interactions among smaller entities, a set of events that can be described by the activation of interconnections between brain areas. Studies that focus on brain connectivity have the aim of understanding and modeling brain function, taking into account the spatiotemporal dynamics of neural communication between brain regions. Much of the current knowledge regarding brain connectivity has been obtained from stand-alone neuroimaging methods. Nevertheless, the use of a multimodal approach seems to be a powerful way to investigate effective brain connectivity, overcoming the limitations of unimodal approaches. In this review, we will present the advantages of an integrative approach in which transcranial magnetic stimulation–electroencephalography coregistration is combined with magnetic resonance imaging methods to explore effective neural interactions. Moreover, we will describe possible implementations of the integrative approach in open- and closed-loop frameworks where real-time brain activity becomes a contributor to the study of cognitive brain networks.

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