Brain state dependent activity in the cortex and thalamus

McCormick, David A.; McGinley, Matthew J.; Salkoff, David B.

doi:10.1016/j.conb.2014.10.003

Cited by 186 publications

(210 citation statements)

References 74 publications

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“…Classically, three cortical states are differentiated, including wake, SWS and REM sleep (for ref see Brown et al, 2012; McCormick et al, 2015). However, recent studies suggest a more complex scenario that is characterized by a continuum of states rather than sharp transitions and, both in SWS and waking, finer distinctions can be made in terms of internal cortical dynamics and responsiveness to external stimuli (Harris and Thiele, 2011; McGinley et al, 2015; Vyazovskiy et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

The Input-Output Relationship of the Cholinergic Basal Forebrain

2017

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Summary Basal forebrain cholinergic neurons influence cortical state, plasticity, learning and attention. They collectively innervate the entire cerebral cortex, differentially controlling acetylcholine efflux across different cortical areas and timescales. Such control might be achieved by differential inputs driving separable cholinergic outputs, although no input-output relationship on a brain-wide level has ever been demonstrated. Here we identify input neurons to cholinergic cells projecting to specific cortical regions by infecting cholinergic axon terminals with a monosynaptically-restricted viral tracer. This approach revealed several circuit motifs, such as central amygdala neurons synapsing onto basolateral amygdala-projecting cholinergic neurons or strong somatosensory cortical input to motor cortex-projecting cholinergic neurons. The presence of input cells in the parasympathetic midbrain nuclei contacting frontally projecting cholinergic neurons suggest that the network regulating the inner eye muscles are additionally regulating cortical state via acetylcholine efflux. This dataset enables future circuit-level experiments to identify drivers of known cholinergic cortical functions.

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Section: Discussionmentioning

confidence: 99%

The Input-Output Relationship of the Cholinergic Basal Forebrain

2017

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“…Thalamo-cortical circuits, particularly the somatosensory thalamo-cortical circuits in rodents, are excellent models of longrange projections often studied for their straightforward circuit architecture with monosynaptic excitatory cortical input (25,26). In addition, the thalamus can control cortical states (27-29) and modulate spontaneous cortical activity based on different cortical states (30,31). These studies suggest that the thalamo-corticothalamic networks are integral to facilitating diverse functional neural integration across the entire brain.…”

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confidence: 99%

Long-range projections coordinate distributed brain-wide neural activity with a specific spatiotemporal profile

Leong

Chan

Gao

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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One challenge in contemporary neuroscience is to achieve an integrated understanding of the large-scale brain-wide interactions, particularly the spatiotemporal patterns of neural activity that give rise to functions and behavior. At present, little is known about the spatiotemporal properties of long-range neuronal networks. We examined brain-wide neural activity patterns elicited by stimulating ventral posteromedial (VPM) thalamo-cortical excitatory neurons through combined optogenetic stimulation and functional MRI (fMRI). We detected robust optogenetically evoked fMRI activation bilaterally in primary visual, somatosensory, and auditory cortices at low (1 Hz) but not high frequencies (5-40 Hz). Subsequent electrophysiological recordings indicated interactions over long temporal windows across thalamo-cortical, cortico-cortical, and interhemispheric callosal projections at low frequencies. We further observed enhanced visually evoked fMRI activation during and after VPM stimulation in the superior colliculus, indicating that visual processing was subcortically modulated by low-frequency activity originating from VPM. Stimulating posteromedial complex thalamo-cortical excitatory neurons also evoked brain-wide bloodoxygenation-level-dependent activation, although with a distinct spatiotemporal profile. Our results directly demonstrate that lowfrequency activity governs large-scale, brain-wide connectivity and interactions through long-range excitatory projections to coordinate the functional integration of remote brain regions. This low-frequency phenomenon contributes to the neural basis of long-range functional connectivity as measured by resting-state fMRI.fMRI | optogenetic | brain connectivity | low frequency | thalamus T he brain is a highly complex, interconnected structure with parallel and hierarchical networks distributed within and between neural systems (1, 2). This integrative architecture dictates the underlying principles of how brain-wide neural connectivity supports and organizes sensory, behavioral, and cognitive processes (3). Recent advances in structural (2, 4) and functional connectivity (5-12) mapping, as well as neural circuit modulatory tools, such as optogenetics (13, 14), permit detailed, high-resolution neural examinations at unprecedented scales. In particular, functional connectivity mapping using resting-state functional MRI (rsfMRI) produces noninvasive visualization of slow and spontaneous hemodynamic fluctuations in humans (5-9) and animals (10-12). Coherent low-frequency (<1 Hz) fluctuations across functionally coupled, large-scale networks is an intriguing property, although the exact underlying neural bases and functional significance remain unclear. Previous studies integrating large-scale electrical recordings, voltage-sensitive dye (VSD), and Ca 2+ -imaging techniques (15-18) support the hypothesis that slow, oscillating neural activity constrains and elicits these hemodynamic fluctuations. Furthermore, low-frequency activity can temporally synchronize remote brain region...

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“…Further, the ability to predict spikes from stimuli will likely play a critical role in neuroprosthetic devices to restore sensation, such as artificial cochleas (Brown and Balkany, 2007), retinas (Trenholm and Roska, 2014; Nirenberg and Pandarinath, 2012), semi-circular canals (Merfeld and Lewis, 2012), and even artificial proprioception (Tabot et al, 2013). Third, the feature vectors and the nonlinearity serve as a basis to quantify changes in computation with context (Fairhall et al, 2001; Fairhall, 2013; Geffen et al, 2007), attention (Rabinowitz et al, 2015), learning (Shulz et al, 2000), and through neuromodulation (McCormick et al, 2015). …”

Section: Discussionmentioning

confidence: 99%

Analysis of Neuronal Spike Trains, Deconstructed

Aljadeff

Lansdell

Fairhall

et al. 2016

Neuron

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As information flows through the brain, neuronal firing progresses from encoding the world as sensed by the animal to driving the motor output of subsequent behavior. One of the more tractable goals of quantitative neuroscience is to develop predictive models that relate the sensory or motor streams with neuronal firing. Here we review and contrast analytical tools used to accomplish this task. We focus on classes of models in which the external variable is compared with one or more feature vectors to extract a low-dimensional representation, the history of spiking and other variables are potentially incorporated, and these factors are nonlinearly transformed to predict the occurrences of spikes. We illustrate these techniques in application to datasets of different degrees of complexity. In particular, we address the fitting of models in the presence of strong correlations in the external variable, as occurs in natural sensory stimuli and in movement. Spectral correlation between predicted and measured spike trains is introduced to contrast the relative success of different methods.

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Brain state dependent activity in the cortex and thalamus

Cited by 186 publications

References 74 publications

The Input-Output Relationship of the Cholinergic Basal Forebrain

The Input-Output Relationship of the Cholinergic Basal Forebrain

Long-range projections coordinate distributed brain-wide neural activity with a specific spatiotemporal profile

Analysis of Neuronal Spike Trains, Deconstructed

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