Closed-Loop Brain Stimulation

In the same way that beauty lies in the eye of the beholder, what a stimulus does to the brain is determined not simply by the nature of the stimulus but by the nature of the brain that is receiving the stimulus at that instant in time. Over the past decades, therapeutic brain stimulation has typically applied open-loop fixed protocols and has largely ignored this principle. Only recent neurotechnological advancements have enabled us to predict the nature of the brain (i.e., the electrophysiological brain state in the next instance in time) with sufficient temporal precision in the range of milliseconds using feedforward algorithms applied to electroencephalography time-series data. This allows stimulation exclusively whenever the targeted brain area is in a prespecified excitability or connectivity state. Preclinical studies have shown that repetitive stimulation during a particular brain state (e.g., high-excitability state), but not during other states, results in lasting modification (e.g., long-term potentiation) of the stimulated circuits. Here, we survey the evidence that this is also possible at the systems level of the human cortex using electroencephalography-informed transcranial magnetic stimulation. We critically discuss opportunities and difficulties in developing brain state-dependent stimulation for more effective long-term modification of pathological brain networks (e.g., in major depressive disorder) than is achievable with conventional fixed protocols. The same real-time electroencephalography-informed transcranial magnetic stimulation technology will allow closing of the loop by recording the effects of stimulation. This information may enable stimulation protocol adaptation that maximizes treatment response. This way, brain states control brain stimulation, thereby introducing a paradigm shift from open-loop to closed-loop stimulation.

Improved dynamic functional connectivity estimation with an alternating hidden Markov model

Dynamic functional connectivity (DFC) analysis has been widely applied to functional magnetic resonance imaging (fMRI) data to reveal the time-varying functional interactions between brain regions. Although the sliding window (SW) method is popular for DFC analysis, the selection of window length is hard, and the temporal resolution is limited by the window length. The hidden Markov model (HMM) without the limitation of window length has been proven to be able to estimate time-varying brain states from fMRI data. However, HMM tends to be overfitted in DFC analysis of fMRI data because of the high spatial dimension and the limited sample size of fMRI data. In this study, we proposed an alternating HMM (aHMM) method that used the functional connectivity estimation of SW to initialize the covariance matrix of HMM and adopted an alternating HMM procedure to reduce the number of parameters during each optimization. The simulated and real fMRI resting data from the Human Connectome Projects showed that aHMM produced better robustness to noise, parameter number and sample size in DFC estimation than SW and HMM. For the real fMRI resting data of cerebral small vessel disease (CSVD), results of aHMM revealed that amnesia and mild cognitive impairment (aMCI) caused the CSVD with aMCI (CSVD-aMCI) group tended to spend more time on the brain state with overall weak connections and less time on the state with overall strong connections than the CSVD-controls. Moreover, CSVD-aMCI showed significantly lower connectivity amplitude and higher connectivity fluctuation than CSVD-control. In contrast, HMM did not detect intergroup differences of the connectivity amplitude and fluctuations and SW did not detect intergroup differences of connectivity fluctuations and fraction of time. The results further indicated that aHMM outperformed HMM and SW in detecting inter-group differences of temporal properties of DFC and connectivity fluctuations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09874-3.

Disentangling the impact of cerebrospinal fluid formation and neuronal activity on solute clearance from the brain

BACKGROUND

Despite recent attention, pathways and mechanisms of fluid transposition in the brain are still a matter of intense discussion and driving forces underlying waste clearance in the brain remain elusive. Consensus exists that net solute transport is a prerequisite for efficient clearance. The individual impact of neuronal activity and cerebrospinal fluid (CSF) formation, which both vary with brain state and anesthesia, remain unclear.

METHODS

To separate conditions with high and low neuronal activity and high and low CSF formation, different anesthetic regimens in naive rat were established, using Isoflurane (ISO), Medetomidine (MED), acetazolamide or combinations thereof. With dynamic contrast-enhanced MRI, after application of low molecular weight contrast agent (CA) Gadobutrol to cisterna magna, tracer distribution was monitored as surrogate for solute clearance. Simultaneous fiber-based Ca²⁺-recordings informed about the state of neuronal activity under different anesthetic regimen. T2-weighted MRI and diffusion-weighted MRI (DWI) provided size of subarachnoidal space and aqueductal flow as surrogates for CSF formation. Finally, a pathway and mechanism-independent two-compartment model was introduced to provide a measure of efficiency for solute clearance from the brain.

RESULTS

Anatomical imaging, DWI and Ca²⁺-recordings confirmed that conditions with distinct levels of neuronal activity and CSF formation were achieved. A sleep-resembling condition, with reduced neuronal activity and enhanced CSF formation was achieved using ISO+MED and an awake-like condition with high neuronal activity using MED alone. CA distribution in the brain correlated with the rate of CSF formation. The cortical brain state had major influence on tracer diffusion. Under conditions with low neuronal activity, higher diffusivity suggested enlargement of extracellular space, facilitating a deeper permeation of solutes into brain parenchyma. Under conditions with high neuronal activity, diffusion of solutes into parenchyma was hindered and clearance along paravascular pathways facilitated. Exclusively based on the measured time signal curves, the two-compartment model provided net exchange ratios, which were significantly larger for the sleep-resembling condition than for the awake-like condition.

CONCLUSIONS

Efficiency of solute clearance in brain changes with alterations in both state of neuronal activity and CSF formation. Our clearance pathway and mechanism agnostic kinetic model informs about net solute transport, solely based on the measured time signal curves. This rather simplifying approach largely accords with preclinical and clinical findings.

Amplitude of low-frequency fluctuation after taste exposure revealed by resting-state fMRI

Taste perception has been deeply explored from the behavioural level to delineating neural mechanisms. However, most previous studies about the neural underpinnings of taste perception have focused on task-related brain activation. Notably, evidence indicates that task-induced brain activation often involves interference from irrelevant task materials and only accounts for a small fraction of the brain's energy consumption. Investigation of the resting-state spontaneous brain activity would bring us a comprehensive understanding of the neural mechanism of taste perception. Here we acquired resting-state functional magnetic resonance imaging (rs-fMRI) data from twenty-two participants immediately after they received sweet, sour and tasteless gustatory stimulation. Our results showed that, in contrast to the tasteless condition, the sour exposure induced decreased amplitude of low-frequency fluctuation (ALFF) in the somatosensory cortex in the left post-central gyrus, and the sweet exposure led to increased ALFF in the bilateral putamen involved in reward processing. Moreover, in contrast to the sweet stimulation condition, the sour stimulation condition showed increased ALFF in the right superior frontal gyrus, which has been linked to functioning in high-order cognitive control. Altogether, our data indicate that taste exposure may affect the spontaneous functional activity in brain regions, including the somatosensory areas, reward processing areas and high-order cognitive functioning areas. Our findings may contribute to a further understanding the neural network and mechanisms after taste exposure.

Task matters: Individual MEG signatures from naturalistic and neurophysiological brain states

The discovery that human brain connectivity data can be used as a "fingerprint" to identify a given individual from a population, has become a burgeoning research area in the neuroscience field. Recent studies have identified the possibility to extract these brain signatures from the temporal rich dynamics of resting-state magneto encephalography (MEG) recordings. Nevertheless, it is still uncertain to what extent MEG signatures can serve as an indicator of human identifiability during task-related conduct. Here, using MEG data from naturalistic and neurophysiological tasks, we show that identification improves in tasks relative to resting-state, providing compelling evidence for a task dependent axis of MEG signatures. Notably, improvements in identifiability were more prominent in strictly controlled tasks. Lastly, the brain regions contributing most towards individual identification were also modified when engaged in task activities. We hope that this investigation advances our understanding of the driving factors behind brain identification from MEG signals.

Towards the understanding of state-independent neural traits underlying psychiatric disorders

Hampered by the symptom complexity and diversity, the understanding of fundamental mechanisms underlying psychiatric disorders remains elusive. Traditional neuroscience research focusing on each behavioral domain separately may lack an overarching view of the pathogenesis of an entire disorder, offering limited power to identify core neuropathology that could possibly account for the disorder's various symptoms. The search for neural traits that are robustly present across different brain functional states and disease stages may provide insights into the rudimentary changes beneath manifest clinical phenotypes and thus help penetrate the causal mechanisms underlying a complex disorder. In this review, I briefly summarize previous research on this topic, emphasize how neural traits may help boost the understanding of biological mechanisms underlying psychiatric disorders, and exemplify how the observed traits may aid individualized predictions for diagnosis and prognosis in precision psychiatry, in particular related to schizophrenia. I also discuss a proposed research framework that can be leveraged for future studies on neural traits, as well as considerations for future applications of this nascent research strategy.

Causal decoding of individual cortical excitability states

Brain responsiveness to stimulation fluctuates with rapidly shifting cortical excitability state, as reflected by oscillations in the electroencephalogram (EEG). For example, the amplitude of motor-evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS) of motor cortex changes from trial to trial. To date, individual estimation of the cortical processes leading to this excitability fluctuation has not been possible. Here, we propose a data-driven method to derive individually optimized EEG classifiers in healthy humans using a supervised learning approach that relates pre-TMS EEG activity dynamics to MEP amplitude. Our approach enables considering multiple brain regions and frequency bands, without defining them a priori, whose compound phase-pattern information determines the excitability. The individualized classifier leads to an increased classification accuracy of cortical excitability states from 57% to 67% when compared to μ-oscillation phase extracted by standard fixed spatial filters. Results show that, for the used TMS protocol, excitability fluctuates predominantly in the μ-oscillation range, and relevant cortical areas cluster around the stimulated motor cortex, but between subjects there is variability in relevant power spectra, phases, and cortical regions. This novel decoding method allows causal investigation of the cortical excitability state, which is critical also for individualizing therapeutic brain stimulation.

Combined resting state-fMRI and calcium recordings show stable brain states for task-induced fMRI in mice under combined ISO/MED anesthesia

For fMRI in animal models, the combination of low-dose anesthetic, isoflurane (ISO), and the sedative medetomidine (MED) has recently become an advocated regimen to achieve stable neuronal states and brain networks in rats that are required for reliable task-induced BOLD fMRI. However, in mice the temporal stability of neuronal states and networks in resting-state (rs)-fMRI experiments during the combined ISO/MED regimen has not been systematically investigated. Using a multimodal approach with optical calcium (Ca²⁺) recordings and rs-fMRI, we investigated cortical neuronal/astrocytic Ca²⁺activity states and brain networks at multiple time points while switching from anesthesia with 1% ISO to a combined ISO/MED regimen. We found that cortical activity states reached a steady-state 45 min following start of MED infusion as indicated by stable Ca²⁺ transients. Similarly, rs-networks were not statistically different between anesthesia with ISO and the combined ISO/MED regimen 45 and 100 min after start of MED. Importantly, during the transition time we identified changed rs-network signatures that likely reflect the different mode of action of the respective anesthetic; these included a dose-dependent increase in cortico-cortical functional connectivity (FC) presumably caused by reduction of ISO concentration and decreased FC in subcortical arousal nuclei due to MED infusion. Furthermore, we report detection of visual stimulation-induced BOLD fMRI during the stable ISO/MED neuronal state 45 min after induction. Based on our findings, we recommend a 45-minute waiting period after switching from ISO anesthesia to the combined ISO/MED regimen before performing rs- or task-induced fMRI experiments.

Dynamic inter-brain synchrony in real-life inter-personal cooperation: A functional near-infrared spectroscopy hyperscanning study

How two brains communicate with each other during social interaction is highly dynamic and complex. Multi-person (i.e., hyperscanning) studies to date have focused on analyzing the entire time series of brain signals to reveal an overall pattern of inter-brain synchrony (IBS). However, this approach does not account for the dynamic nature of social interaction. In the present study, we propose a data-driven approach based on sliding windows and k-mean clustering to capture the dynamic modulation of IBS patterns during interactive cooperation tasks. We used a portable functional near-infrared spectroscopy (fNIRS) system to measure brain hemodynamic response between interacting partners (20 dyads) engaged in a creative design task and a 3D model building task. Results indicated that inter-personal communication during naturalistic cooperation generally presented with a series of dynamic IBS states along the tasks. Compared to the model building task, the creative design task appeared to involve more complex and active IBS between multiple regions in specific dynamic IBS states. In summary, the proposed approach stands as a promising tool to distill complex inter-brain dynamics associated with social interaction into a set of representative brain states with more fine-grained temporal resolution. This approach holds promise for advancing our current understanding of the dynamic nature of neurocognitive processes underlying social interaction.

Increased sensitivity to strong perturbations in a whole-brain model of LSD

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Novel offline perturbational method applied on functional magnetic resonance imaging (fMRI) data under the effect of lysergic acid diethylamide (LSD).

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Shift of brain's global working point to more complex dynamics after LSD intake.

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Consistently longer recovery time after model perturbation under LSD influence.

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Strongest effects in resting state networks relevant for psychedelic experience.

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Higher response diversity across brain regions under LSD influence after an external in silico perturbation.

Lysergic acid diethylamide (LSD) is a potent psychedelic drug, which has seen a revival in clinical and pharmacological research within recent years. Human neuroimaging studies have shown fundamental changes in brain-wide functional connectivity and an expansion of dynamical brain states, thus raising the question about a mechanistic explanation of the dynamics underlying these alterations. Here, we applied a novel perturbational approach based on a whole-brain computational model, which opens up the possibility to externally perturb different brain regions in silico and investigate differences in dynamical stability of different brain states, i.e. the dynamical response of a certain brain region to an external perturbation. After adjusting the whole-brain model parameters to reflect the dynamics of functional magnetic resonance imaging (fMRI) BOLD signals recorded under the influence of LSD or placebo, perturbations of different brain areas were simulated by either promoting or disrupting synchronization in the regarding brain region. After perturbation offset, we quantified the recovery characteristics of the brain area to its basal dynamical state with the Perturbational Integration Latency Index (PILI) and used this measure to distinguish between the two brain states. We found significant changes in dynamical complexity with consistently higher PILI values after LSD intake on a global level, which indicates a shift of the brain's global working point further away from a stable equilibrium as compared to normal conditions. On a local level, we found that the largest differences were measured within the limbic network, the visual network and the default mode network. Additionally, we found a higher variability of PILI values across different brain regions after LSD intake, indicating higher response diversity under LSD after an external perturbation. Our results provide important new insights into the brain-wide dynamical changes underlying the psychedelic state - here provoked by LSD intake - and underline possible future clinical applications of psychedelic drugs in particular psychiatric disorders.

Functional connectivity patterns predict naturalistic viewing versus rest across development

Cognitive states, such as rest and task engagement, share an 'intrinsic' functional network organization that is subject to minimal variation over time and yields stable signatures within an individual. Importantly, there are also transient state-specific functional connectivity (FC) patterns that vary across neural states. Here, we examine functional brain organization differences that underlie distinct states in a cross-sectional developmental sample. We compare FC fMRI data acquired during naturalistic viewing (i.e., movie-watching) and resting-state paradigms in a large cohort of 157 children and young adults aged 6-20. Naturalistic paradigms are commonly implemented in pediatric research because they maintain the child's attention and contribute to reduced head motion. It remains unknown, however, to what extent the brain-wide functional network organization is comparable during movie-watching and rest across development. Here, we identify a widespread FC pattern that predicts whether individuals are watching a movie or resting. Specifically, we develop a model for prediction of multilevel neural effects (termed PrimeNet), which can with high reliability distinguish between movie-watching and rest irrespective of age and that generalizes across movies. In turn, we characterize FC patterns in the most predictive functional networks for movie-watching versus rest and show that these patterns can indeed vary as a function of development. Collectively, these effects highlight a 'core' FC pattern that is robustly associated with naturalistic viewing, which also exhibits change across age. These results, focused here on naturalistic viewing, provide a roadmap for quantifying state-specific functional neural organization across development, which may reveal key variation in neurodevelopmental trajectories associated with behavioral phenotypes.

The shaky ground truth of real-time phase estimation

Instantaneous phase of brain oscillations in electroencephalography (EEG) is a measure of brain state that is relevant to neuronal processing and modulates evoked responses. However, determining phase at the time of a stimulus with standard signal processing methods is not possible due to the stimulus artifact masking the future part of the signal. Here, we quantify the degree to which signal-to-noise ratio and instantaneous amplitude of the signal affect the variance of phase estimation error and the precision with which "ground truth" phase is even defined, using both the variance of equivalent estimators and realistic simulated EEG data with known synthetic phase. Necessary experimental conditions are specified in which pre-stimulus phase estimation is meaningfully possible based on instantaneous amplitude and signal-to-noise ratio of the oscillation of interest. An open source toolbox is made available for causal (using pre-stimulus signal only) phase estimation along with a EEG dataset consisting of recordings from 140 participants and a best practices workflow for algorithm optimization and benchmarking. As an illustration, post-hoc sorting of open-loop transcranial magnetic stimulation (TMS) trials according to pre-stimulus sensorimotor μ-rhythm phase is performed to demonstrate modulation of corticospinal excitability, as indexed by the amplitude of motor evoked potentials.

A computational paradigm for real-time MEG neurofeedback for dynamic allocation of spatial attention

Background

Neurofeedback aids volitional control of one’s own brain activity using non-invasive recordings of brain activity. The applications of neurofeedback include improvement of cognitive performance and treatment of various psychiatric and neurological disorders. During real-time magnetoencephalography (rt-MEG), sensor-level or source-localized brain activity is measured and transformed into a visual feedback cue to the subject. Recent real-time fMRI (rt-fMRI) neurofeedback studies have used pattern recognition techniques to decode and train a brain state to link brain activities and cognitive behaviors. Here, we utilize the real-time decoding technique similar to ones employed in rt-fMRI to analyze time-varying rt-MEG signals.

Results

We developed a novel rt-MEG method, state-based neurofeedback (sb-NFB), to decode a time-varying brain state, a state signal, from which timings are extracted for neurofeedback training. The approach is entirely data-driven: it uses sensor-level oscillatory activity to find relevant features that best separate the targeted brain states. In a psychophysical task of spatial attention switching, we trained five young, healthy subjects using the sb-NFB method to decrease the time necessary for switch spatial attention from one visual hemifield to the other (referred to as switch time). Training resulted in a decrease in switch time with training. We saw that the activity targeted by the training involved proportional changes in alpha and beta-band oscillations, in sensors at the occipital and parietal regions. We also found that the state signal that encodes whether subjects attend to the left or right visual field effectively switches consistently with the task.

Conclusion

We demonstrated the use of the sb-NFB method when the subject learns to increase the speed of shifting covert spatial attention from one visual field to the other. The sb-NFB method can target timing features that would otherwise also include extraneous features such as visual detection and motor response in a simple reaction time task.

State-Dependent Changes in Brain Glycogen Metabolism

A fundamental understanding of glycogen structure, concentration, polydispersity and turnover is critical to qualify the role of glycogen in the brain. These molecular and metabolic features are under the control of neuronal activity through the interdependent action of neuromodulatory tone, ionic homeostasis and availability of metabolic substrates, all variables that concur to define the state of the system. In this chapter, we briefly describe how glycogen responds to selected behavioral, nutritional, environmental, hormonal, developmental and pathological conditions. We argue that interpreting glycogen metabolism through the lens of brain state is an effective approach to establish the relevance of energetics in connecting molecular and cellular neurophysiology to behavior.

Circuit mechanisms and computational models of REM sleep

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REM sleep was discovered in the 1950s.

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Many hypothalamic and brainstem areas have been found to contribute to REM sleep.

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An up-to-date picture of REM-sleep-regulating circuits is reviewed.

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A brief overview of computational models for REM sleep regulation is provided.

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Outstanding issues for future studies are discussed.

Rapid eye movement (REM) sleep or paradoxical sleep is an elusive behavioral state. Since its discovery in the 1950s, our knowledge of the neuroanatomy, neurotransmitters and neuropeptides underlying REM sleep regulation has continually evolved in parallel with the development of novel technologies. Although the pons was initially discovered to be responsible for REM sleep, it has since been revealed that many components in the hypothalamus, midbrain, pons, and medulla also contribute to REM sleep. In this review, we first provide an up-to-date overview of REM sleep-regulating circuits in the brainstem and hypothalamus by summarizing experimental evidence from neuroanatomical, neurophysiological and gain- and loss-of-function studies. Second, because quantitative approaches are essential for understanding the complexity of REM sleep-regulating circuits and because mathematical models have provided valuable insights into the dynamics underlying REM sleep genesis and maintenance, we summarize computational studies of the sleep-wake cycle, with an emphasis on REM sleep regulation. Finally, we discuss outstanding issues for future studies.

Ancestral Circuits for the Coordinated Modulation of Brain State

Internal states of the brain profoundly influence behavior. Fluctuating states such as alertness can be governed by neuromodulation, but the underlying mechanisms and cell types involved are not fully understood. We developed a method to globally screen for cell types involved in behavior by integrating brain-wide activity imaging with high-content molecular phenotyping and volume registration at cellular resolution. We used this method (MultiMAP) to record from 22 neuromodulatory cell types in behaving zebrafish during a reaction-time task that reports alertness. We identified multiple monoaminergic, cholinergic, and peptidergic cell types linked to alertness and found that activity in these cell types was mutually correlated during heightened alertness. We next recorded from and controlled homologous neuromodulatory cells in mice; alertness-related cell-type dynamics exhibited striking evolutionary conservation and modulated behavior similarly. These experiments establish a method for unbiased discovery of cellular elements underlying behavior and reveal an evolutionarily conserved set of diverse neuromodulatory systems that collectively govern internal state.

Can brain state be manipulated to emphasize individual differences in functional connectivity?

While neuroimaging studies typically collapse data from many subjects, brain functional organization varies between individuals, and characterizing this variability is crucial for relating brain activity to behavioral phenotypes. Rest has become the default state for probing individual differences, chiefly because it is easy to acquire and a supposed neutral backdrop. However, the assumption that rest is the optimal condition for individual differences research is largely untested. In fact, other brain states may afford a better ratio of within- to between-subject variability, facilitating biomarker discovery. Depending on the trait or behavior under study, certain tasks may bring out meaningful idiosyncrasies across subjects, essentially enhancing the individual signal in networks of interest beyond what can be measured at rest. Here, we review theoretical considerations and existing work on how brain state influences individual differences in functional connectivity, present some preliminary analyses of within- and between-subject variability across conditions using data from the Human Connectome Project, and outline questions for future study.

Basal forebrain activation enhances between-trial reliability of low-frequency local field potentials (LFP) and spiking activity in tree shrew primary visual cortex (V1)

Brain state has profound effects on neural processing and stimulus encoding in sensory cortices. While the synchronized state is dominated by low-frequency local field potential (LFP) activity, low-frequency LFP power is suppressed in the desynchronized state, where a concurrent enhancement in gamma power is observed. Recently, it has been shown that cortical desynchronization co-occurs with enhanced between-trial reliability of spiking activity in sensory neurons, but it is currently unclear whether this effect is also evident in LFP signals. Here, we address this question by recording both spike trains and LFP in primary visual cortex during natural movie stimulation, and using isoflurane anesthesia and basal forebrain (BF) electrical activation as proxies for synchronized and desynchronized brain states. We show that indeed, low-frequency LFP modulations ("LFP events") also occur more reliably following BF activation. Interestingly, while being more reliable, these LFP events are smaller in amplitude compared to those generated in the synchronized brain state. We further demonstrate that differences in reliability of spiking activity between cortical states can be linked to amplitude and probability of LFP events. The correlated temporal dynamics between low-frequency LFP and spiking response reliability in visual cortex suggests that these effects may both be the result of the same neural circuit activation triggered by BF stimulation, which facilitates switching between processing of incoming sensory information in the desynchronized and reverberation of internal signals in the synchronized state.

Does Global Astrocytic Calcium Signaling Participate in Awake Brain State Transitions and Neuronal Circuit Function?

We continuously need to adapt to changing conditions within our surrounding environment, and our brain needs to quickly shift between resting and working activity states in order to allow appropriate behaviors. These global state shifts are intimately linked to the brain-wide release of the neuromodulators, noradrenaline and acetylcholine. Astrocytes have emerged as a new player participating in the regulation of brain activity, and have recently been implicated in brain state shifts. Astrocytes display global Ca²⁺ signaling in response to activation of the noradrenergic system, but whether astrocytic Ca²⁺ signaling is causative or correlative for shifts in brain state and neural activity patterns is not known. Here we review the current available literature on astrocytic Ca²⁺ signaling in awake animals in order to explore the role of astrocytic signaling in brain state shifts. Furthermore, we look at the development and availability of innovative new methodological tools that are opening up for new ways of visualizing and perturbing astrocyte activity in awake behaving animals. With these new tools at hand, the field of astrocyte research will likely be able to elucidate the causal and mechanistic roles of astrocytes in complex behaviors within a very near future.

Orexin/Hypocretin and Organizing Principles for a Diversity of Wake-Promoting Neurons in the Brain

An enigmatic feature of behavioural state control is the rich diversity of wake-promoting neural systems. This diversity has been rationalized as 'robustness via redundancy', wherein wakefulness control is not critically dependent on one type of neuron or molecule. Studies of the brain orexin/hypocretin system challenge this view by demonstrating that wakefulness control fails upon loss of this neurotransmitter system. Since orexin neurons signal arousal need, and excite other wake-promoting neurons, their actions illuminate nonredundant principles of arousal control. Here, we suggest such principles by reviewing the orexin system from a collective viewpoint of biology, physics and engineering. Orexin peptides excite other arousal-promoting neurons (noradrenaline, histamine, serotonin, acetylcholine neurons), either by activating mixed-cation conductances or by inhibiting potassium conductances. Ohm's law predicts that these opposite conductance changes will produce opposite effects on sensitivity of neuronal excitability to current inputs, thus enabling orexin to differentially control input-output gain of its target networks. Orexin neurons also produce other transmitters, including glutamate. When orexin cells fire, glutamate-mediated downstream excitation displays temporal decay, but orexin-mediated excitation escalates, as if orexin transmission enabled arousal controllers to compute a time integral of arousal need. Since the anatomical and functional architecture of the orexin system contains negative feedback loops (e.g. orexin ➔ histamine ➔ noradrenaline/serotonin-orexin), such computations may stabilize wakefulness via integral feedback, a basic engineering strategy for set point control in uncertain environments. Such dynamic behavioural control requires several distinct wake-promoting modules, which perform nonredundant transformations of arousal signals and are connected in feedback loops.

Evaluation of cortical local field potential diffusion in stereotactic electro-encephalography recordings: A glimpse on white matter signal

While there is a strong interest in meso-scale field potential recording using intracranial electroencephalography with penetrating depth electrodes (i.e. stereotactic EEG or S-EEG) in humans, the signal recorded in the white matter remains ignored. White matter is generally considered electrically neutral and often included in the reference montage. Moreover, re-referencing electrophysiological data is a critical preprocessing choice that could drastically impact signal content and consequently the results of any given analysis. In the present stereotactic electroencephalography study, we first illustrate empirically the consequences of commonly used references (subdermal, white matter, global average, local montage) on inter-electrode signal correlation. Since most of these reference montages incorporate white matter signal, we next consider the difference between signals recorded in cortical gray matter and white matter. Our results reveal that electrode contacts located in the white matter record a mixture of activity, with part arising from the volume conduction (zero time delay) of activity from nearby gray matter. Furthermore, our analysis shows that white matter signal may be correlated with distant gray matter signal. While residual passive electrical spread from nearby matter may account for this relationship, our results suggest the possibility that this long distance correlation arises from the white matter fiber tracts themselves (i.e. activity from distant gray matter traveling along axonal fibers with time lag larger than zero); yet definitive conclusions about the origin of the white matter signal would require further experimental substantiation. By characterizing the properties of signals recorded in white matter and in gray matter, this study illustrates the importance of including anatomical prior knowledge when analyzing S-EEG data.

Repetitive Transcorneal Alternating Current Stimulation Reduces Brain Idling State After Long-term Vision Loss

BACKGROUND

Deafferentation of visual system structures following brain or optic nerve injury leaves cortical areas deprived of visual input. Deprived cortical areas have a reduced sensory information processing and are characterized with localized enhanced or synchronized rhythms believed to represent an "idling state".

OBJECTIVE/HYPOTHESIS

We hypothesized that cortical idling can be modified with transcorneal alternating current stimulation (tACS) known to modulate cortical oscillations and thus change the functional state of the deafferented areas.

METHODS

tACS was applied in rat model of severe optic nerve crush using a protocol similar to our clinical studies (200 μA, 2-8 Hz) for 5 treatment days right after the lesion and at the chronic stage (3 months later). EEG and VEP were recorded over the visual cortices. In vivo confocal neuroimaging of the retina and histology of the optic nerves were performed.

RESULTS

Morphological investigations showed massive retinal ganglion cells death and degeneration of the optic nerves after crush. Visual loss was associated with increased EEG spectral power and lower coherence, indicating an "idling state". Stimulation induced a significant decrease of EEG power towards normal values. These effects were especially pronounced in the chronic stage.

CONCLUSION

Our results suggest that alternating current injected via the eye is able to modulate visually deprived brain areas and thus reduce cortical idling.

State-related functional integration and functional segregation brain networks in schizophrenia

Altered topological properties of brain connectivity networks have emerged as important features of schizophrenia. The aim of this study was to investigate how the state-related modulations to graph measures of functional integration and functional segregation brain networks are disrupted in schizophrenia. Firstly, resting state and auditory oddball discrimination (AOD) fMRI data of healthy controls (HCs) and schizophrenia patients (SZs) were decomposed into spatially independent components (ICs) by group independent component analysis (ICA). Then, weighted positive and negative functional integration (inter-component networks) and functional segregation (intra-component networks) brain networks were built in each subject. Subsequently, connectivity strength, clustering coefficient, and global efficiency of all brain networks were statistically compared between groups (HCs and SZs) in each state and between states (rest and AOD) within group. We found that graph measures of negative functional integration brain network and several positive functional segregation brain networks were altered in schizophrenia during AOD task. The metrics of positive functional integration brain network and one positive functional segregation brain network were higher during the resting state than during the AOD task only in HCs. These findings imply that state-related characteristics of both functional integration and functional segregation brain networks are impaired in schizophrenia which provides new insight into the altered brain performance in this brain disorder.