Specific connectivity optimizes learning in thalamocortical loops

Thalamocortical loops have a central role in cognition and motor control, but precisely how they contribute to these processes is unclear. Recent studies showing evidence of plasticity in thalamocortical synapses indicate a role for the thalamus in shaping cortical dynamics through learning. Since signals undergo a compression from the cortex to the thalamus, we hypothesized that the computational role of the thalamus depends critically on the structure of corticothalamic connectivity. To test this, we identified the optimal corticothalamic structure that promotes biologically plausible learning in thalamocortical synapses. We found that corticothalamic projections specialized to communicate an efference copy of the cortical output benefit motor control, while communicating the modes of highest variance is optimal for working memory tasks. We analyzed neural recordings from mice performing grasping and delayed discrimination tasks and found corticothalamic communication consistent with these predictions. These results suggest that the thalamus orchestrates cortical dynamics in a functionally precise manner through structured connectivity.

The Network Systems Underlying Emotions: The Rational Foundation of Deep Brain Stimulation Psychosurgery

Science and philosophy have tried to understand the origin of emotions for centuries. However, only in the last 150 years have we started to try to understand them in a neuroscientific scope. Emotions include physiological changes involving different systems, such as the endocrine or the musculoskeletal, but they also cause a conscious experience of those changes that are embedded in memory. In addition to the cortico-striato-thalamo-cortical circuit, which is the most important of the basal ganglia, the limbic system and prefrontal circuit are primarily involved in the process of emotion perceptions, thoughts, and memories. The purpose of this review is to describe the anatomy and physiology of the different brain structures involved in circuits that underlie emotions and behaviour, underlying the symptoms of certain psychiatric pathologies. These circuits are targeted during deep brain stimulation (DBS) and knowledge of them is mandatory to understand the clinical-physiological implications for the treatment. We summarize the main outcomes of DBS treatment in several psychiatric illness such as obsessive compulsive disorder, refractory depression, erethism and other conditions, aiming to understand the rationale for selecting these neural systems as targets for DBS.

Methodological aspects of studying the mechanisms of consciousness

There are at least two approaches to the definition of consciousness. In the first case, certain aspects of consciousness, called qualia, are considered inaccessible for research from a third person and can only be described through subjective experience. This approach is inextricably linked with the so-called "hard problem of consciousness", that is, the question of why consciousness has qualia or how any physical changes in the environment can generate subjective experience. With this approach, some aspects of consciousness, by definition, cannot be explained on the basis of external observations and, therefore, are outside the scope of scientific research. In the second case, a priori constraints do not constrain the field of scientific investigation, and the best explanation of the experience in the first person is included as a possible subject of empirical research. Historically, in the study of cause-and-effect relationships in biology, it was customary to distinguish between proximate causation and ultimate causation existing in biological systems. Immediate causes are based on the immediate influencing factors [1]. Proximate causation has evolutionary explanations. When studying biological systems themselves, such an approach is undoubtedly justified, but it often seems insufficient when studying the interaction of consciousness and the brain [2,3]. Current scientific communities proceed from the assumption that the physical substrate for the generation of consciousness is a neural network that unites various types of neurons located in various brain structures. Many neuroscientists attach a key role in this process to the cortical and thalamocortical neural networks. This question is directly related to experimental and clinical research in the field of disorder of consciousness. Progress in this area of medicine depends on advances in neuroscience in this area and is also a powerful source of empirical information. In this area of consciousness research, a large amount of experimental data has been accumulated, and in this review an attempt was made to generalize and systematize.

A Framework for Collaborative Curation of Neuroscientific Literature

Large models of complex neuronal circuits require specifying numerous parameters, with values that often need to be extracted from the literature, a tedious and error-prone process. To help establishing shareable curated corpora of annotations, we have developed a literature curation framework comprising an annotation format, a Python API (NeuroAnnotation Toolbox; NAT), and a user-friendly graphical interface (NeuroCurator). This framework allows the systematic annotation of relevant statements and model parameters. The context of the annotated content is made explicit in a standard way by associating it with ontological terms (e.g., species, cell types, brain regions). The exact position of the annotated content within a document is specified by the starting character of the annotated text, or the number of the figure, the equation, or the table, depending on the context. Alternatively, the provenance of parameters can also be specified by bounding boxes. Parameter types are linked to curated experimental values so that they can be systematically integrated into models. We demonstrate the use of this approach by releasing a corpus describing different modeling parameters associated with thalamo-cortical circuitry. The proposed framework supports a rigorous management of large sets of parameters, solving common difficulties in their traceability. Further, it allows easier classification of literature information and more efficient and systematic integration of such information into models and analyses.

Correlation between volumetric oxygenation responses and electrophysiology identifies deep thalamocortical activity during epileptic seizures

Visualization of whole brain activity during epileptic seizures is essential for both fundamental research into the disease mechanisms and the development of efficient treatment strategies. It has been previously discussed that pathological synchronization originating from cortical areas may reinforce backpropagating signaling from the thalamic neurons, leading to massive seizures through enhancement of high frequency neural activity in the thalamocortical loop. However, the study of deep brain neural activity is challenging with the existing functional neuroimaging methods due to lack of adequate spatiotemporal resolution or otherwise insufficient penetration into subcortical areas. To investigate the role of thalamocortical activity during epileptic seizures, we developed a new functional neuroimaging framework based on spatiotemporal correlation of volumetric optoacoustic hemodynamic responses with the concurrent electroencephalogram recordings and anatomical brain landmarks. The method is shown to be capable of accurate three-dimensional mapping of the onset, spread, and termination of the epileptiform events in a 4-aminopyridine acute model of focal epilepsy. Our study is the first to demonstrate entirely noninvasive real-time visualization of synchronized epileptic foci in the whole mouse brain, including the neocortex and subcortical structures, thus opening new vistas in systematic studies toward the understanding of brain signaling and the origins of neurological disorders.

A Thalamic-Fronto-Parietal Structural Covariance Network Emerging in the Course of Recovery from Hand Paresis after Ischemic Stroke

AIM

To describe structural covariance networks of gray matter volume (GMV) change in 28 patients with first-ever stroke to the primary sensorimotor cortices, and to investigate their relationship to hand function recovery and local GMV change.

METHODS

Tensor-based morphometry maps derived from high-resolution structural images were subject to principal component analyses to identify the networks. We calculated correlations between network expression and local GMV change, sensorimotor hand function and lesion volume. To verify which of the structural covariance networks of GMV change have a significant relationship to hand function, we performed an additional multivariate regression approach.

RESULTS

Expression of the second network, explaining 9.1% of variance, correlated with GMV increase in the medio-dorsal (md) thalamus and hand motor skill. Patients with positive expression coefficients were distinguished by significantly higher GMV increase of this structure during stroke recovery. Significant nodes of this network were located in md thalamus, dorsolateral prefrontal cortex, and higher order sensorimotor cortices. Parameter of hand function had a unique relationship to the network and depended on an interaction between network expression and lesion volume. Inversely, network expression is limited in patients with large lesion volumes.

CONCLUSION

Chronic phase of sensorimotor cortical stroke has been characterized by a large scale co-varying structural network in the ipsilesional hemisphere associated specifically with sensorimotor hand skill. Its expression is related to GMV increase of md thalamus, one constituent of the network, and correlated with the cortico-striato-thalamic loop involved in control of motor execution and higher order sensorimotor cortices. A close relation between expression of this network with degree of recovery might indicate reduced compensatory resources in the impaired subgroup.

Cortical and thalamic contributions to response dynamics across layers of the primary somatosensory cortex during tactile discrimination

Tactile information processing in the rodent primary somatosensory cortex (S1) is layer specific and involves modulations from both thalamocortical and cortico-cortical loops. However, the extent to which these loops influence the dynamics of the primary somatosensory cortex while animals execute tactile discrimination remains largely unknown. Here, we describe neural dynamics of S1 layers across the multiple epochs defining a tactile discrimination task. We observed that neuronal ensembles within different layers of the S1 cortex exhibited significantly distinct neurophysiological properties, which constantly changed across the behavioral states that defined a tactile discrimination. Neural dynamics present in supragranular and granular layers generally matched the patterns observed in the ventral posterior medial nucleus of the thalamus (VPM), whereas the neural dynamics recorded from infragranular layers generally matched the patterns from the posterior nucleus of the thalamus (POM). Selective inactivation of contralateral S1 specifically switched infragranular neural dynamics from POM-like to those resembling VPM neurons. Meanwhile, ipsilateral M1 inactivation profoundly modulated the firing suppression observed in infragranular layers. This latter effect was counterbalanced by contralateral S1 block. Tactile stimulus encoding was layer specific and selectively affected by M1 or contralateral S1 inactivation. Lastly, causal information transfer occurred between all neurons in all S1 layers but was maximal from infragranular to the granular layer. These results suggest that tactile information processing in the S1 of awake behaving rodents is layer specific and state dependent and that its dynamics depend on the asynchronous convergence of modulations originating from ipsilateral M1 and contralateral S1.

Mindfulness starts with the body: somatosensory attention and top-down modulation of cortical alpha rhythms in mindfulness meditation

Using a common set of mindfulness exercises, mindfulness based stress reduction (MBSR) and mindfulness based cognitive therapy (MBCT) have been shown to reduce distress in chronic pain and decrease risk of depression relapse. These standardized mindfulness (ST-Mindfulness) practices predominantly require attending to breath and body sensations. Here, we offer a novel view of ST-Mindfulness's somatic focus as a form of training for optimizing attentional modulation of 7–14 Hz alpha rhythms that play a key role in filtering inputs to primary sensory neocortex and organizing the flow of sensory information in the brain. In support of the framework, we describe our previous finding that ST-Mindfulness enhanced attentional regulation of alpha in primary somatosensory cortex (SI). The framework allows us to make several predictions. In chronic pain, we predict somatic attention in ST-Mindfulness “de-biases” alpha in SI, freeing up pain-focused attentional resources. In depression relapse, we predict ST-Mindfulness's somatic attention competes with internally focused rumination, as internally focused cognitive processes (including working memory) rely on alpha filtering of sensory input. Our computational model predicts ST-Mindfulness enhances top-down modulation of alpha by facilitating precise alterations in timing and efficacy of SI thalamocortical inputs. We conclude by considering how the framework aligns with Buddhist teachings that mindfulness starts with “mindfulness of the body.” Translating this theory into neurophysiology, we hypothesize that with its somatic focus, mindfulness' top-down alpha rhythm modulation in SI enhances gain control which, in turn, sensitizes practitioners to better detect and regulate when the mind wanders from its somatic focus. This enhanced regulation of somatic mind-wandering may be an important early stage of mindfulness training that leads to enhanced cognitive regulation and metacognition.

Seeing via Miniature Eye Movements: A Dynamic Hypothesis for Vision

During natural viewing, the eyes are never still. Even during fixation, miniature movements of the eyes move the retinal image across tens of foveal photoreceptors. Most theories of vision implicitly assume that the visual system ignores these movements and somehow overcomes the resulting smearing. However, evidence has accumulated to indicate that fixational eye movements cannot be ignored by the visual system if fine spatial details are to be resolved. We argue that the only way the visual system can achieve its high resolution given its fixational movements is by seeing via these movements. Seeing via eye movements also eliminates the instability of the image, which would be induced by them otherwise. Here we present a hypothesis for vision, in which coarse details are spatially encoded in gaze-related coordinates, and fine spatial details are temporally encoded in relative retinal coordinates. The temporal encoding presented here achieves its highest resolution by encoding along the elongated axes of simple-cell receptive fields and not across these axes as suggested by spatial models of vision. According to our hypothesis, fine details of shape are encoded by inter-receptor temporal phases, texture by instantaneous intra-burst rates of individual receptors, and motion by inter-burst temporal frequencies. We further describe the ability of the visual system to readout the encoded information and recode it internally. We show how reading out of retinal signals can be facilitated by neuronal phase-locked loops (NPLLs), which lock to the retinal jitter; this locking enables recoding of motion information and temporal framing of shape and texture processing. A possible implementation of this locking-and-recoding process by specific thalamocortical loops is suggested. Overall it is suggested that high-acuity vision is based primarily on temporal mechanisms of the sort presented here and low-acuity vision is based primarily on spatial mechanisms.