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Abstract When people divide attention between two sensory modalities and respond rapidly to stimuli in either modality, the reaction times (RTs) are longer than when they selectively attend and respond to one sensory modality. There is a further increase in RT when subjects use different fingers to respond to stimuli from different modalities as compared to when they use the same finger for both modalities. Here we use functional magnetic resonance imaging to show that division of attention bilaterally activates the caudal prefrontal areas near the precentral sulcus and areas in the intraparietal sulcus. All RT tasks, whether different fingers for different modalities or one finger for both modalities was used, activated identical motor areas 4a, 4p, supplementary and cingulate, the basal ganglia and the ventral anterior thalami. The increased blood oxygen level-dependent signal from motor cortical areas was anatomically distinct from the prefrontal/parietal areas. These anatomically dissociable neural substrates of division of attention and motor control may be responsible for the different types of RT delays that we have found. In the brain, there were no differences in the BOLD signal irrespective of whether the effector was specified a priori or was specified only after the sensory signal was received. [ABSTRACT FROM AUTHOR]

Presents information on NEUROGENERATOR, a database generator for neuroimaging. Main objective of neuroimaging; Contents of the database; Benefits of the database.

A major goal of neuroscience is to reveal mechanisms supporting collaborative actions of neurons in local and larger-scale networks. However, no clear overall principle of operation has emerged despite decades-long experimental efforts. Here, we used an unbiased method to extract and identify the dynamics of local postsynaptic network states contained in the cortical field potential. Field potentials were recorded by depth electrodes targeting a wide selection of cortical regions during spontaneous activities, and sensory, motor, and cognitive experimental tasks. Despite different architectures and different activities, all local cortical networks generated the same type of dynamic confined to one region only of state space. Surprisingly, within this region, state trajectories expanded and contracted continuously during all brain activities and generated a single expansion followed by a contraction in a single trial. This behavior deviates from known attractors and attractor networks. The state-space contractions of particular subsets of brain regions cross-correlated during perceptive, motor, and cognitive tasks. Our results imply that the cortex does not need to change its dynamic to shift between different activities, making task-switching inherent in the dynamic of collective cortical operations. Our results provide a mathematically described general explanation of local and larger scale cortical dynamic. [ABSTRACT FROM AUTHOR]

Using 15O-butanol positron emission tomography (PET), we measured regional cerebral blood flow changes in five healthy young women during exposure to androstadienone, a putative human pheromone, as well as pleasant (γ-methyl-ionone), unpleasant (methyl-thio-butanoate), and neutral (dipropylene glycol; vehicle compound) odours. Compared with the odorous substances, androstadienone activated a widely distributed neuronal network. Two large cortical fields exhibited consistent activation in each contrast: the anterior part of the inferior lateral prefrontal cortex (PFC) and the posterior part of the superior temporal cortex (STP). Intriguingly, these areas were deactivated by γ-methyl-ionone and methyl-thio-butanoate. These brain regions can be identified as cortical fields underlying other than olfactory functions, including various aspects of social cognition and attention. [Copyright &y& Elsevier]

The cellular biology of brains is relatively well-understood, but neuroscientists have not yet generated a theory explaining how brains work. Explanations of how neurons collectively operate to produce what brains can do are tentative and incomplete. Without prior assumptions about the brain mechanisms, I attempt here to identify major obstacles to progress in neuroscientific understanding of brains and central nervous systems. Most of the obstacles to our understanding are conceptual. Neuroscience lacks concepts and models rooted in experimental results explaining how neurons interact at all scales. The cerebral cortex is thought to control awake activities, which contrasts with recent experimental results. There is ambiguity distinguishing task-related brain activities from spontaneous activities and organized intrinsic activities. Brains are regarded as driven by external and internal stimuli in contrast to their considerable autonomy. Experimental results are explained by sensory inputs, behavior, and psychological concepts. Time and space are regarded as mutually independent variables for spiking, post-synaptic events, and other measured variables, in contrast to experimental results. Dynamical systems theory and models describing evolution of variables with time as the independent variable are insufficient to account for central nervous system activities. Spatial dynamics may be a practical solution. The general hypothesis that measurements of changes in fundamental brain variables, action potentials, transmitter releases, post-synaptic transmembrane currents, etc., propagating in central nervous systems reveal how they work, carries no additional assumptions. Combinations of current techniques could reveal many aspects of spatial dynamics of spiking, post-synaptic processing, and plasticity in insects and rodents to start with. But problems defining baseline and reference conditions hinder interpretations of the results. Furthermore, the facts that pooling and averaging of data destroy their underlying dynamics imply that single-trial designs and statistics are necessary., Competing Interests: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Roland.)

In the cerebral cortex, membrane currents, i.e., action potentials and other membrane currents, express many forms of space-time dynamics. In the spontaneous asynchronous irregular state, their space-time dynamics are local non-propagating fluctuations and sparse spiking appearing at unpredictable positions. After transition to active spiking states, larger structured zones with active spiking neurons appear, propagating through the cortical network, driving it into various forms of widespread excitation, and engaging the network from microscopic scales to whole cortical areas. At each engaged cortical site, the amount of excitation in the network, after a delay, becomes matched by an equal amount of space-time fine-tuned inhibition that might be instrumental in driving the dynamics toward perception and action., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Brain dynamics are often taken to be temporal dynamics of spiking and membrane potentials in a balanced network. Almost all evidence for a balanced network comes from recordings of cell bodies of few single neurons, neglecting more than 99% of the cortical network. We examined the space-time dynamics of excitation and inhibition simultaneously in dendrites and axons over four visual areas of ferrets exposed to visual scenes with stationary and moving objects. The visual stimuli broke the tight balance between excitation and inhibition such that the network exhibited longer episodes of net excitation subsequently balanced by net inhibition, in contrast to a balanced network. Locally in all four areas the amount of net inhibition matched the amount of net excitation with a delay of 125 ms. The space-time dynamics of excitation-inhibition evolved to reduce the complexity of neuron interactions over the whole network to a flow on a low-(3)-dimensional manifold within 80 ms. In contrast to the pure temporal dynamics, the low dimensional flow evolved to distinguish the simple visual scenes.

Fluorescence molecular tomography (FMT) is a promising tool for real time in vivo quantification of neurotransmission (NT) as we pursue in our BRAIN initiative effort. However, the acquired image data are noisy and the reconstruction problem is ill-posed. Further, while spatial sparsity of the NT effects could be exploited, traditional compressive-sensing methods cannot be directly applied as the system matrix in FMT is highly coherent. To overcome these issues, we propose and assess a three-step reconstruction method. First, truncated singular value decomposition is applied on the data to reduce matrix coherence. The resultant image data are input to a homotopy-based reconstruction strategy that exploits sparsity via ℓ 1 regularization. The reconstructed image is then input to a maximum-likelihood expectation maximization (MLEM) algorithm that retains the sparseness of the input estimate and improves upon the quantitation by accurate Poisson noise modeling. The proposed reconstruction method was evaluated in a three-dimensional simulated setup with fluorescent sources in a cuboidal scattering medium with optical properties simulating human brain cortex (reduced scattering coefficient: 9.2 cm -1 , absorption coefficient: 0.1 cm -1 ) and tomographic measurements made using pixelated detectors. In different experiments, fluorescent sources of varying size and intensity were simulated. The proposed reconstruction method provided accurate estimates of the fluorescent source intensity, with a 20% lower root mean square error on average compared to the pure-homotopy method for all considered source intensities and sizes. Further, compared with conventional ℓ 2 regularized algorithm, overall, the proposed method reconstructed substantially more accurate fluorescence distribution. The proposed method shows considerable promise and will be tested using more realistic simulations and experimental setups.

Most neurons have a threshold separating the silent non-spiking state and the state of producing temporal sequences of spikes. But neurons in vivo also have a second threshold, found recently in granular layer neurons of the primary visual cortex, separating spontaneous ongoing spiking from visually evoked spiking driven by sharp transients. Here we examine whether this second threshold exists outside the granular layer and examine details of transitions between spiking states in ferrets exposed to moving objects. We found the second threshold, separating spiking states evoked by stationary and moving visual stimuli from the spontaneous ongoing spiking state, in all layers and zones of areas 17 and 18 indicating that the second threshold is a property of the network. Spontaneous and evoked spiking, thus can easily be distinguished. In addition, the trajectories of spontaneous ongoing states were slow, frequently changing direction. In single trials, sharp as well as smooth and slow transients transform the trajectories to be outward directed, fast and crossing the threshold to become evoked. Although the speeds of the evolution of the evoked states differ, the same domain of the state space is explored indicating uniformity of the evoked states. All evoked states return to the spontaneous evoked spiking state as in a typical mono-stable dynamical system. In single trials, neither the original spiking rates, nor the temporal evolution in state space could distinguish simple visual scenes.

Neurons in the primary visual cortex spontaneously spike even when there are no visual stimuli. It is unknown whether the spiking evoked by visual stimuli is just a modification of the spontaneous ongoing cortical spiking dynamics or whether the spontaneous spiking state disappears and is replaced by evoked spiking. This study of laminar recordings of spontaneous spiking and visually evoked spiking of neurons in the ferret primary visual cortex shows that the spiking dynamics does not change: the spontaneous spiking as well as evoked spiking is controlled by a stable and persisting fixed point attractor. Its existence guarantees that evoked spiking return to the spontaneous state. However, the spontaneous ongoing spiking state and the visual evoked spiking states are qualitatively different and are separated by a threshold (separatrix). The functional advantage of this organization is that it avoids the need for a system reorganization following visual stimulation, and impedes the transition of spontaneous spiking to evoked spiking and the propagation of spontaneous spiking from layer 4 to layers 2-3.

Apparent motion (AM) is a robust visual illusion, in which fast displays of static objects in successively different positions elicit the perception of object motion. Neurons in higher order areas 21 and 19 compute object motion under such conditions and send feedback to early visual areas 18 and 17, which is instrumental in eliciting computation of motion in those very areas. To explore the computational dynamics of AM, we made a neural field model consisting of two one-dimensional rings of simple neurons expressing firing rates, one for areas 17/18 and one for areas 19/21. The model neurons, without any orientation or direction selectivity, computed apparent motion for the range of space-timings of stimuli associated with short- and long-range AM in humans. The computation of long-range AM in 17/18 required two model areas and the presence of feedback and conduction/computation delays between those areas. As in the in vivo experiments of long-range AM, the stationary stimuli were initially mapped as stationary in model area 17/18, but after the feedback also these lower areas computed AM. The dynamics of the two-area network produces short-range and long-range apparent motion for a large range of feedback strengths and a small range of lateral excitation near the bifurcation to an amplitude instability. The computation of AM in higher order areas was due to the neurons in these areas having large receptive fields as a consequence of divergent feed-forward connectivity. This implies that these areas compute long-range AM when early areas 17 and 18 do not, and therefore higher order areas must enslave lower order areas to compute the same, if the whole network is to arrive at a coherent perceptual solution., (Copyright (c) 2010 Elsevier Inc. All rights reserved.)

When an object is introduced moving in the visual field of view, the object maps with different delays in each of the six cortical layers in many visual areas by mechanisms that are poorly understood. We combined voltage sensitive dye (VSD) recordings with laminar recordings of action potentials in visual areas 17, 18, 19 and 21 in ferrets exposed to stationary and moving bars. At the area 17/18 border a moving bar first elicited an ON response in layer 4 and then ON responses in supragranular and infragranular layers, identical to a stationary bar. Shortly after, the moving bar mapped as moving synchronous peak firing across layers. Complex dynamics evolved including feedback from areas 19/21, the computation of a spatially restricted pre-depolarization (SRP), and firing in the direction of cortical motion prior to the mapping of the bar. After 350 ms, the representations of the bar (peak firing and peak VSD signal) in areas 19/21 and 17/18 moved over the cortex in phase. The dynamics comprise putative mechanisms for automatic saliency of novel moving objects, coherent mapping of moving objects across layers and areas, and planning of catch-up saccades.

Motion can be perceived when static images are successively presented with a spatial shift. This type of motion is an illusion and is termed apparent motion (AM). Here we show, with a voltage sensitive dye applied to the visual cortex of the ferret, that presentation of a sequence of stationary, short duration, stimuli which are perceived to produce AM are, initially, mapped in areas 17 and 18 as separate stationary representations. But time locked to the offset of the 1st stimulus, a sequence of signals are elicited. First, an activation traverses cortical areas 19 and 21 in the direction of AM. Simultaneously, a motion dependent feedback signal from these areas activates neurons between areas 19/21 and areas 17/18. Finally, an activation is recorded, traveling always from the representation of the 1st to the representation of the next or succeeding stimuli. This activation elicits spikes from neurons situated between these stimulus representations in areas 17/18. This sequence forms a physiological mechanism of motion computation which could bind populations of neurons in the visual areas to interpret motion out of stationary stimuli.

Despite the lack of direct evidence, it is generally believed that top-down signals are mediated by the abundant feedback connections from higher- to lower-order sensory areas. Here we provide direct evidence for a top-down mechanism. We stained the visual cortex of the ferret with a voltage-sensitive dye and presented a short-duration contrast square. This elicited an initial feedforward and lateral spreading depolarization at the square representation in areas 17 and 18. After a delay, a broad feedback wave (FBW) of neuron peak depolarization traveled from areas 21 and 19 toward areas 18 and 17. In areas 18 and 17, the FBW contributed the peak depolarization of dendrites of the neurons representing the square, after which the neurons decreased their depolarization and firing. Thereafter, the peak depolarization surrounded the figure representation over most of areas 17 and 18 representing the background. Thus, the FBW is an example of a well behaved long-range communication from higher-order visual areas to areas 18 and 17, collectively addressing very large populations of neurons representing the visual scene. Through local interaction with feedforward and lateral spreading depolarization, the FBW differentially activates neurons representing the object and neurons representing the background.

In visual discrimination tasks, the relevant feature to discriminate is defined before stimulus presentation. In feature uncertainty tasks, a cue about the relevant feature is provided after stimulus offset. We used (15)O-butanol positron emission tomography (PET) in order to investigate brain activation during a feature uncertainty task. There was greater activity during the feature uncertainty task, compared with stimulus detection and discrimination of orientation and spatial frequency, in the lateral and medial prefrontal cortex, the cuneus, superior temporal and inferior parietal cortex, cortical motor areas, and the cerebellum. The most robust and consistent activation was observed in the dorsal anterior cingulate cortex (Brodmann area 32; x = 0 y = 16, z = 40). The insula, located near the claustrum (x = -38, y = 8, z = 4), was activated during the discrimination tasks compared with the feature uncertainty condition. These results suggest that the dorsal anterior cingulate cortex is important in feature uncertainty conditions, which include divided attention, expectancy under uncertainty, and cognitive monitoring., (Copyright 2003 Wiley-Liss, Inc.)

Recent physiological evidence shows that in response to stimuli and preceding motor activity, large fields of the upper layers of the cerebral cortex depolarize. It is argued that this finding is a general one and that these dynamic depolarization fields represent the computational elements of the cerebral cortex. Each depolarization field engages many more neurons than do columns and hyper-columns. These fields can be explained by cooperative neuronal computing in layers I-III of the cortex. In these layers, the computing modes might be general for all parts of the cerebral cortex and be sufficiently flexible to handle all sorts of cortical computations, including perception, memory storage, memory retrieval, thought and the production of behavior.

Objects in natural scenes are rarely seen in isolation, but are usually overlapping or partially occluding other objects. To recognize individual objects, the visual system must be able to segregate overlapping objects from one another. Evidence from lesions in humans and monkeys suggest that perceptual segregation of occluded or overlapping objects involves extrastriate visual cortex. In monkeys, area V4 has been shown to play an important role in recognizing occluded or poorly salient shapes. In humans, a retinotopic homologue of ventral V4 (V4v) has been described, but it is not known whether this area is also functionally homologous to area V4 in monkeys. In this study, we tried to localize the visual cortical regions involved in perceptual segregation of overlapping shapes using positron emission tomography (PET). Regional cerebral blood flow (rCBF) was measured in seven subjects while they discriminated the relative areas of simultaneously presented rectangular shapes. In the control condition, the shapes were displayed without overlaps; in a second condition, the shapes overlapped each other partially. In a third condition, the shapes did not overlap but had been reduced in salience by adding random noise to the stimuli. Contrasting the overlapping shape condition with the control condition identified a single region in the left posterior lateral occipital cortex. The rCBF in this region also increased, though more weakly, during discrimination of shapes embedded in noise, relative to the control condition. The region activated by segregation of overlapping shapes was located in the posterior occipital cortex close to the anterior border of area V2, near the average location of human V4v as determined by retinotopic mapping studies. The activation of this region of extrastriate visual cortex by a task that involved segregation of overlapping shapes is consistent with monkey V4 and human V4v being functionally homologous. We conclude that discrimination of overlapping shapes involves in particular a region of extrastriate visual cortex located in the left lateral occipital cortex and that this region may correspond to human V4v.