How far neuroscience is from understanding brains.

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.

Local networks from different parts of the human cerebral cortex generate and share the same population dynamic.

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.

Emerging principles of spacetime in brains: Meeting report on spatial neurodynamics.

How do neurons and networks of neurons interact spatially? Here, we overview recent discoveries revealing how spatial dynamics of spiking and postsynaptic activity efficiently expose and explain fundamental brain and brainstem mechanisms behind detection, perception, learning, and behavior.

Cortical dynamics subserving visual apparent motion.

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.

I feel my hand moving: a new role of the primary motor cortex in somatic perception of limb movement.

The primary motor cortex (MI) is regarded as the site for motor control. Occasional reports that MI neurons react to sensory stimuli have either been ignored or attributed to guidance of voluntary movements. Here, we show that MI activation is necessary for the somatic perception of movement of our limbs. We made use of an illusion: when the wrist tendon of one hand is vibrated, it is perceived as the hand moving. If the vibrated hand has skin contact with the other hand, it is perceived as both hands bending. Using fMRI and TMS, we show that the activation in MI controlling the nonvibrated hand is compulsory for the somatic perception of the hand movement. This novel function of MI contrasts with its traditional role as the executive locus of voluntary limb movement.

Space-Time Dynamics of Membrane Currents Evolve to Shape Excitation, Spiking, and Inhibition in the Cortex at Small and Large Scales.

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.

Breaking the Excitation-Inhibition Balance Makes the Cortical Network's Space-Time Dynamics Distinguish Simple Visual Scenes.

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.

A three-step reconstruction method for fluorescence molecular tomography based on compressive sensing.

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 ℓ1 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 ℓ2 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.

Dynamic depolarization fields in the cerebral cortex.

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.

The Second Spiking Threshold: Dynamics of Laminar Network Spiking in the Visual Cortex.

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.

Somatotopy and attentional modulation of the human parietal and opercular regions.

The somatotopical organization of the postcentral gyrus is well known, but less is known about the somatotopical organization of area 2, the somatosensory association areas in the postparietal cortex, and the parietal operculum. The extent to which these areas are modulated by attention is also poorly understood. For these reasons, we measured the BOLD signal when rectangular parallelepipeds of varying shape were presented to the immobile right hand or right foot of 10 subjects either discriminating these or just being stimulated. Activation areas in each subject were mapped against cytoarchitectural probability maps of area 2, IP1, and IP2 along the intraparietal sulcus and the parietal opercular areas OP1-OP4. In area 2, the somatotopical representation of the hand and foot were distinctly separate, whereas there was considerable overlap in IP1 and no clear evidence of separate representations in OP1, OP4, and IP2. The overlap of hand and foot representations increased in the following order: area 3a, 3b, 1, 2, IP1, OP4, IP2, and OP1. There were significant foot representations but no hand representations in right (ipsilateral) areas 3a, 3b, and 1. Shape discrimination using the foot as opposed to stimulation enhanced the signal in OP4 bilaterally, whereas discrimination with the hand enhanced the signal bilaterally in area 2, IP1, and IP2. These results indicate that somatosensory areas in humans are arranged from strong somatotopy into no somatotopy in the following order: 3a, 3b, 1, 2, IP1, OP4, IP2, and OP1. Higher order areas such as IP1, IP2, and OP4 showed task-related attentional enhancement.

Visually Evoked Spiking Evolves While Spontaneous Ongoing Dynamics Persist.

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.

Learning of Sequential Finger Movements in Man: A Combined Kinematic and Positron Emission Tomography (PET) Study.

The cerebral structures participating in learning of a manual skill were mapped with regional cerebral blood flow (rCBF) measurements and positron emission tomography in nine healthy volunteers. The task was a complicated right-hand finger movement sequence. The subjects were examined at three stages: during initial practice of the finger movement sequence, in an advanced stage of learning, and after they had learnt the finger movement sequence. Quantitative evaluation of video tapes and electromyographic records of the right forearm and hand muscles demonstrated that the finger movements significantly accelerated and became more regular. Significant mean rCBF increases were induced in the left motor hand area, the left premotor cortex, the left supplementary motor area, the left sensory hand area, the left supplementary sensory area and the right anterior lobe of the cerebellum. During the learning process significant depressions of the mean rCBF occurred bilaterally in the superior parietal lobule, the anterior parietal cortex and the pars triangularis of the right inferior frontal cortex. The mean rCBF increases in these structures during the initial stage of learning were related to somatosensory feedback processing and internal language for the guidance of the finger movements. These activations disappeared when the subjects had learnt the finger movement sequence. Conversely, the mean rCBF significantly rose during the course of learning in the midsector of the putamen and globus pallidus on the left side. It is suggested that during the learning phase of this movement sequence, the basal ganglia were critically involved in the establishment of the final motor programme.

Changes in Regional Cerebral Oxidative Metabolism Induced by Tactile Learning and Recognition in Man.

We measured the regional cerebral oxidative metabolism (rCMRO2) with positron emission tomography in normal healthy volunteers in three different stages: rest, tactile learning, and tactile recognition of complicated geometrical objects. The frequency of manipulatory movements during tactile recognition was twice that of tactile learning. Tactile recognition with the right hand increased rCMRO2 in six prefrontal cortical areas, bilaterally in the supplementary motor areas, the premotor areas and supplementary sensory areas, in the left primary motor and primary sensory area, in the left anterior superior parietal lobule, bilaterally in the secondary somatosensory area, the anterior insula, lingual gyri, hippocampus, basal ganglia, anterior parasagittal cerebellum, and lobus posterior cerebelli. These structures have in other studies been found to participate in manipulatory movements and analysis of somatosensory information. Tactile learning increased rCMRO2 in the same structures as did tactile recognition. Thus we found no differences in the anatomical structures participating in storage and retrieval. However the rCMRO2 increases in the left premotor cortex, supplementary motor area, and left somatosensory hand area were larger during tactile recognition in accordance with the higher frequency of manipulatory movements and higher flux of somatosensory information from the periphery during recognition. Despite this the rCMRO2 was significantly higher in the neocerebellar cortex during tactile learning. Since there were no learning effects on the manipulatory movements, this extra metabolic activity in the lateral cerebellum was attributed to energy demanding processes associated with climbing fibre activity during storage of somatosensory information.

Somatosensory Discrimination of Shape: Tactile Exploration and Cerebral Activation.

This study of somatosensory discrimination of rectangular parallelepipeda with the right hand had three purposes: (i) to describe the exploratory finger movements; (ii) to reveal the anatomical brain structures specifically engaged in the production of exploratory finger movements; and (iii) to reveal the anatomical structures specifically engaged in the discrimination of tactually sensed shape. The thumb was the most active finger, moving with a mean exploration frequency of 2.4 Hz, as evident from videotape records of the exploratory finger movements. The cerebral structures activated during somatosensory discrimination were mapped by measurements of regional cerebral blood flow (rCBF) in six healthy male volunteers with positron emission tomography (PET) and the use of the computerized brain atlas of Greitz et al. (1991, J. Comp. Ass. Tomogr., 15, 26 - 38). The rCBF changes caused by somatosensory discrimination were compared point-to-point to a PET-study on right-hand finger movements and a PET-study on vibration stimulation of the right hand. From these results the following conclusions were drawn. The rCBF increase in the left superior parietal lobule indicated the site engaged in the analysis of shape. The rCBF increases in the left supplementary sensory area, bilaterally in premotor areas, in the left putamen, the right dentate nucleus and bilaterally in the posterior cerebellum were related to the control of the tactile exploratory finger movements. The rCBF increases in the right homologue of Broca's area, bilaterally in the superior prefrontal cortex and in the right midfrontal cortex probably resulted from working memory, the direction of attention, and the discrimination process.

The putative pheromone androstadienone activates cortical fields in the human brain related to social cognition.

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 (gamma-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 gamma-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.

Cortico-cortical communication dynamics.

In principle, cortico-cortical communication dynamics is simple: neurons in one cortical area communicate by sending action potentials that release glutamate and excite their target neurons in other cortical areas. In practice, knowledge about cortico-cortical communication dynamics is minute. One reason is that no current technique can capture the fast spatio-temporal cortico-cortical evolution of action potential transmission and membrane conductances with sufficient spatial resolution. A combination of optogenetics and monosynaptic tracing with virus can reveal the spatio-temporal cortico-cortical dynamics of specific neurons and their targets, but does not reveal how the dynamics evolves under natural conditions. Spontaneous ongoing action potentials also spread across cortical areas and are difficult to separate from structured evoked and intrinsic brain activity such as thinking. At a certain state of evolution, the dynamics may engage larger populations of neurons to drive the brain to decisions, percepts and behaviors. For example, successfully evolving dynamics to sensory transients can appear at the mesoscopic scale revealing how the transient is perceived. As a consequence of these methodological and conceptual difficulties, studies in this field comprise a wide range of computational models, large-scale measurements (e.g., by MEG, EEG), and a combination of invasive measurements in animal experiments. Further obstacles and challenges of studying cortico-cortical communication dynamics are outlined in this critical review.

Six principles of visual cortical dynamics.

A fundamental goal in vision science is to determine how many neurons in how many areas are required to compute a coherent interpretation of the visual scene. Here I propose six principles of cortical dynamics of visual processing in the first 150 ms following the appearance of a visual stimulus. Fast synaptic communication between neurons depends on the driving neurons and the biophysical history and driving forces of the target neurons. Under these constraints, the retina communicates changes in the field of view driving large populations of neurons in visual areas into a dynamic sequence of feed-forward communication and integration of the inward current of the change signal into the dendrites of higher order area neurons (30-70 ms). Simultaneously an even larger number of neurons within each area receiving feed-forward input are pre-excited to sub-threshold levels. The higher order area neurons communicate the results of their computations as feedback adding inward current to the excited and pre-excited neurons in lower areas. This feedback reconciles computational differences between higher and lower areas (75-120 ms). This brings the lower area neurons into a new dynamic regime characterized by reduced driving forces and sparse firing reflecting the visual areas interpretation of the current scene (140 ms). The population membrane potentials and net-inward/outward currents and firing are well behaved at the mesoscopic scale, such that the decoding in retinotopic cortical space shows the visual areas' interpretation of the current scene. These dynamics have plausible biophysical explanations. The principles are theoretical, predictive, supported by recent experiments and easily lend themselves to experimental tests or computational modeling.

Cortical Membrane Potential Dynamics and Laminar Firing during Object Motion.

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.