• MeSH
• Brain physiology   MeSH
• Synaptic Transmission MeSH
• Neurons physiology   MeSH
• Models, Theoretical MeSH

• Conspectus
• Patologie. Klinická medicína
• NML Fields
• neurovědy

Objective.Functional specialization is fundamental to neural information processing. Here, we study whether and how functional specialization emerges in artificial deep convolutional neural networks (CNNs) during a brain-computer interfacing (BCI) task.Approach.We trained CNNs to predict hand movement speed from intracranial electroencephalography (iEEG) and delineated how units across the different CNN hidden layers learned to represent the iEEG signal.Main results.We show that distinct, functionally interpretable neural populations emerged as a result of the training process. While some units became sensitive to either iEEG amplitude or phase, others showed bimodal behavior with significant sensitivity to both features. Pruning of highly sensitive units resulted in a steep drop of decoding accuracy not observed for pruning of less sensitive units, highlighting the functional relevance of the amplitude- and phase-specialized populations.Significance.We anticipate that emergent functional specialization as uncovered here will become a key concept in research towards interpretable deep learning for neuroscience and BCI applications.

• MeSH
• Algorithms MeSH
• Electroencephalography methods   MeSH
• Brain MeSH
• Neural Networks, Computer MeSH
• Brain-Computer Interfaces * MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

• MeSH
• Cognition MeSH
• Humans MeSH
• Neural Networks, Computer MeSH
• Neuronal Plasticity MeSH
• Perception MeSH
• Models, Theoretical MeSH
• Check Tag
• Humans MeSH

Epilepsy may affect connectivity between the putamen and cortex even during the resting state. Putamen is part of the basal ganglia resting state network (BG-RSN) which is anti-correlated with the default mode network (DMN) in healthy subjects. Therefore, we aimed at studying the functional brain connectivity (FC) of the putamen with the cortical areas engaged in the DMN as well as with the primary somatomotor cortex which is a cortical region engaged in the BG-RSN. We compared the data obtained in patients with epilepsy with that in healthy controls (HC). Functional magnetic resonance imaging (fMRI) was performed in 10 HC and 24 patients with epilepsy: 14 patients with extratemporal epilepsy (PE) and 10 patients with temporal epilepsy (PT). Resting state fMRI data was obtained using the 1.5 T Siemens Symphony scanner. The Group ICA of fMRI Toolbox (GIFT) program was used for independent component analysis. The component representing the DMN was chosen according to a spatial correlation with a mask typical for DMN. The FC between the putamen and the primary somatomotor cortex was studied to assess the connectivity of the putamen within the BG-RSN. A second-level analysis was calculated to evaluate differences among the groups using SPM software. In patients with epilepsy as compared to HC, the magnitude of anti-correlation between the putamen and brain regions engaged in the DMN was significantly lower. In fact, the correlation changed the connectivity direction from negative in HC to positive in PE and PT. The disturbed FC of the BG in patients with epilepsy as compared with HC was further illustrated by a significant decrease in connectivity between the left/right putamen and the left/right somatomotor cortex, i.e. between regions that are engaged in the BG-RSN. The FC between the putamen and the cortex is disturbed in patients with epilepsy. This may reflect an altered function of the BG in epilepsy.

• MeSH
• Basal Ganglia anatomy & histology   physiology   MeSH
• Adult MeSH
• Epilepsy, Temporal Lobe pathology   physiopathology   MeSH
• Epilepsy pathology   physiopathology   MeSH
• Middle Aged MeSH
• Humans MeSH
• Magnetic Resonance Imaging MeSH
• Brain Mapping MeSH
• Motor Cortex pathology   physiopathology   MeSH
• Brain anatomy & histology   pathology   physiology   MeSH
• Cerebral Cortex pathology   physiopathology   MeSH
• Nerve Net anatomy & histology   physiology   MeSH
• Putamen pathology   physiopathology   MeSH
• Case-Control Studies MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Regular drug use can lead to addiction, but not everyone who takes drugs makes this transition. How exactly drugs of abuse interact with individual vulnerability is not fully understood, nor is it clear how individuals defy the risks associated with drugs or addiction vulnerability. We used resting-state functional MRI (fMRI) in 162 participants to characterize risk- and resilience-related changes in corticostriatal functional circuits in individuals exposed to stimulant drugs both with and without clinically diagnosed drug addiction, siblings of addicted individuals, and control volunteers. The likelihood of developing addiction, whether due to familial vulnerability or drug use, was associated with significant hypoconnectivity in orbitofrontal and ventromedial prefrontal cortical-striatal circuits-pathways critically implicated in goal-directed decision-making. By contrast, resilience against a diagnosis of substance use disorder was associated with hyperconnectivity in two networks involving 1) the lateral prefrontal cortex and medial caudate nucleus and 2) the supplementary motor area, superior medial frontal cortex, and putamen-brain circuits respectively implicated in top-down inhibitory control and the regulation of habits. These findings point toward a predisposing vulnerability in the causation of addiction, related to impaired goal-directed actions, as well as countervailing resilience systems implicated in behavioral regulation, and may inform novel strategies for therapeutic and preventative interventions.

• MeSH
• Adult MeSH
• Genetic Predisposition to Disease MeSH
• Humans MeSH
• Brain physiopathology   MeSH
• Nerve Net physiology   MeSH
• Substance-Related Disorders * MeSH
• Psychology MeSH
• Central Nervous System Stimulants * MeSH
• Case-Control Studies MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

• MeSH
• Neuropsychology MeSH
• Social Values MeSH
• Publication type
• Popular Work MeSH

• Conspectus
• Fyziologie člověka a srovnávací fyziologie
• NML Fields
• neurologie
• psychologie, klinická psychologie
• sociologie

Nervové systémy mozku vykazují charakteristické vzorce časové korelace, jež jsou výsledkem funkční interakce složité strukturální sítě. Ve světle nových studií přibývají důkazy o tom, že tyto specifické vzorce nervové aktivace a funkční konektivity jsou neuronálním korelátem percepČních a kognitivních procesů. Článek přináší pohled na dynamické systémy mozku a jejich funkční a efektivní konektivitu a na metody, jež umožňují jejich deskripci.

Neural systems exhibit characteristic patterns of temporal correlations that emerge as the result of functional interactions within a structural network. There is mounting evidence that specific patterns of neuronal activation as well as patterns of functional connectivity are possible neural correlates of perceptual and cognitive processes. The article reviews the dynamic brain systems and their functional and effective connectivity as well as methods, which allow the description of these processes.

• MeSH
• Central Nervous System physiology   MeSH
• Electroencephalography methods   utilization   MeSH
• Electromagnetic Fields MeSH
• Research Support as Topic MeSH
• Humans MeSH
• Magnetic Resonance Imaging methods   MeSH
• Magnetoencephalography methods   utilization   MeSH
• Brain anatomy & histology   physiopathology   pathology   MeSH
• Systems Theory MeSH
• Tomography methods   MeSH
• Check Tag
• Humans MeSH

Complex spatiotemporal patterns, called chimera states, consist of coexisting coherent and incoherent domains and can be observed in networks of coupled oscillators. The interplay of synchrony and asynchrony in complex brain networks is an important aspect in studies of both the brain function and disease. We analyse the collective dynamics of FitzHugh-Nagumo neurons in complex networks motivated by its potential application to epileptology and epilepsy surgery. We compare two topologies: an empirical structural neural connectivity derived from diffusion-weighted magnetic resonance imaging and a mathematically constructed network with modular fractal connectivity. We analyse the properties of chimeras and partially synchronized states and obtain regions of their stability in the parameter planes. Furthermore, we qualitatively simulate the dynamics of epileptic seizures and study the influence of the removal of nodes on the network synchronizability, which can be useful for applications to epileptic surgery.

• MeSH
• Diffusion Magnetic Resonance Imaging MeSH
• Epilepsy diagnostic imaging   MeSH
• Humans MeSH
• Brain * physiology   MeSH
• Nerve Net physiology   MeSH
• Neural Conduction physiology   MeSH
• Models, Theoretical MeSH
• Seizures diagnostic imaging   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH
• Comparative Study MeSH

Recently a new so-called energy complexity measure has been introduced and studied for feedforward perceptron networks. This measure is inspired by the fact that biological neurons require more energy to transmit a spike than not to fire, and the activity of neurons in the brain is quite sparse, with only about 1% of neurons firing. In this letter, we investigate the energy complexity of recurrent networks, which counts the number of active neurons at any time instant of a computation. We prove that any deterministic finite automaton with m states can be simulated by a neural network of optimal size [Formula: see text] with the time overhead of [Formula: see text] per one input bit, using the energy O(e), for any e such that [Formula: see text] and e=O(s), which shows the time-energy trade-off in recurrent networks. In addition, for the time overhead [Formula: see text] satisfying [Formula: see text], we obtain the lower bound of [Formula: see text] on the energy of such a simulation for some constant c>0 and for infinitely many s.

• MeSH
• Action Potentials physiology   MeSH
• Algorithms MeSH
• Brain physiology   MeSH
• Neural Networks, Computer * MeSH
• Neurons physiology   MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

We spend much our lives interacting with others in various social contexts. Although we deal with this myriad of interpersonal exchanges with apparent ease, each one relies upon a broad array of sophisticated cognitive processes. Recent research suggests that the cognitive operations supporting interactive behaviour are themselves underpinned by several canonical functional brain networks (CFNs) that integrate dynamically with one another in response to changing situational demands. Dynamic integrations among these CFNs should therefore play a pivotal role in coordinating interpersonal behaviour. Further, different types of interaction should present different demands on cognitive systems, thereby eliciting distinct patterns of dynamism among these CFNs. To investigate this, the present study performed functional magnetic resonance imaging (fMRI) on 30 individuals while they interacted with one another cooperatively or competitively. By applying a novel combination of analytical techniques to these brain imaging data, we identify six states of dynamic functional connectivity characterised by distinct patterns of integration and segregation among specific CFNs that differ systematically between these opposing types of interaction. Moreover, applying these same states to fMRI data acquired from an independent sample engaged in the same kinds of interaction, we were able to classify interpersonal exchanges as cooperative or competitive. These results provide the first direct evidence for the systematic involvement of CFNs during social interactions, which should guide neurocognitive models of interactive behaviour and investigations into biomarkers for the interpersonal dysfunction characterizing many neurological and psychiatric disorders.

• MeSH
• Mental Disorders * MeSH
• Humans MeSH
• Magnetic Resonance Imaging methods   MeSH
• Brain Mapping MeSH
• Brain physiology   MeSH
• Social Interaction * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH