Scaling brain size, keeping timing: evolutionary preservation of brain rhythms

Breathing coordinates cortico-hippocampal dynamics in mice during offline states

Network dynamics have been proposed as a mechanistic substrate for the information transfer across cortical and hippocampal circuits. However, little is known about the mechanisms that synchronize and coordinate these processes across widespread brain regions during offline states. Here we address the hypothesis that breathing acts as an oscillatory pacemaker, persistently coupling distributed brain circuit dynamics. Using large-scale recordings from a number of cortical and subcortical brain regions in behaving mice, we uncover the presence of an intracerebral respiratory corollary discharge, that modulates neural activity across these circuits. During offline states, the respiratory modulation underlies the coupling of hippocampal sharp-wave ripples and cortical DOWN/UP state transitions, which mediates systems memory consolidation. These results highlight breathing, a perennial brain rhythm, as an oscillatory scaffold for the functional coordination of the limbic circuit that supports the segregation and integration of information flow across neuronal networks during offline states.

EEG-Driven Prediction Model of Oxcarbazepine Treatment Outcomes in Patients With Newly-Diagnosed Focal Epilepsy

Objective: Antiseizure medicine (ASM) is the first choice for patients with epilepsy. The choice of ASM is determined by the type of epilepsy or epileptic syndrome, which may not be suitable for certain patients. This initial choice of a particular drug affects the long-term prognosis of patients, so it is critical to select the appropriate ASMs based on the individual characteristics of a patient at the early stage of the disease. The purpose of this study is to develop a personalized prediction model to predict the probability of achieving seizure control in patients with focal epilepsy, which will help in providing a more precise initial medication to patients.

Methods: Based on response to oxcarbazepine (OXC), enrolled patients were divided into two groups: seizure-free (52 patients), not seizure-free (NSF) (22 patients). We created models to predict patients' response to OXC monotherapy by combining Electroencephalogram (EEG) complexities and 15 clinical features. The prediction models were gradient boosting decision tree-Kolmogorov complexity (GBDT-KC) and gradient boosting decision tree-Lempel-Ziv complexity (GBDT-LZC). We also constructed two additional prediction models, support vector machine-Kolmogorov complexity (SVM-KC) and SVM-LZC, and these two models were compared with the GBDT models. The performance of the models was evaluated by calculating the accuracy, precision, recall, F1-score, sensitivity, specificity, and area under the curve (AUC) of these models.

Results: The mean accuracy, precision, recall, F1-score, sensitivity, specificity, AUC of GBDT-LZC model after five-fold cross-validation were 81%, 84%, 91%, 87%, 91%, 64%, 81%, respectively. The average accuracy, precision, recall, F1-score, sensitivity, specificity, AUC of GBDT-KC model with five-fold cross-validation were 82%, 84%, 92%, 88%, 83%, 92%, 83%, respectively. We used the rank of absolute weights to separately calculate the features that have the most significant impact on the classification of the two models.

Conclusion: (1) The GBDT-KC model has the potential to be used in the clinic to predict seizure-free with OXC monotherapy. (2). Electroencephalogram complexity, especially Kolmogorov complexity (KC) may be a potential biomarker in predicting the treatment efficacy of OXC in newly diagnosed patients with focal epilepsy.

Reduced Theta Sampling in Infants at Risk for Dyslexia across the Sensitive Period of Native Phoneme Learning

Research on children and adults with developmental dyslexia-a specific difficulty in learning to read and spell-suggests that phonological deficits in dyslexia are linked to basic auditory deficits in temporal sampling. However, it remains undetermined whether such deficits are already present in infancy, especially during the sensitive period when the auditory system specializes in native phoneme perception. Because dyslexia is strongly hereditary, it is possible to examine infants for early predictors of the condition before detectable symptoms emerge. This study examines low-level auditory temporal sampling in infants at risk for dyslexia across the sensitive period of native phoneme learning. Using magnetoencephalography (MEG), we found deficient auditory sampling at theta in at-risk infants at both 6 and 12 months, indicating atypical auditory sampling at the syllabic rate in those infants across the sensitive period for native-language phoneme learning. This interpretation is supported by our additional finding that auditory sampling at theta predicted later vocabulary comprehension, nonlinguistic communication and the ability to combine words. Our results indicate a possible early marker of risk for dyslexia.

Deep brain electrophysiology in freely moving sheep

Although rodents are arguably the easiest animals to use for studying brain function, relying on them as model species for translational research comes with its own set of limitations. Here, we propose sheep as a practical large animal species to use for in vivo brain function studies performed in naturalistic settings. We conducted proof-of-principle deep brain electrophysiological recording experiments using unrestrained sheep during behavioral testing. Recordings were made from cortex and hippocampus, both while sheep performed goal-directed behaviors (two-choice discrimination tasks) and across states of vigilance, including sleep. Hippocampal and cortical oscillatory rhythms were consistent with those seen in rodents and non-human primates, and included cortical alpha oscillations and hippocampal sharp wave ripple oscillations (∼150 Hz) during immobility and hippocampal theta oscillations (5-6 Hz) during locomotion. Recordings were conducted over a period of many months during which time the animals participated willingly in the experiments. Over 3,000 putative neurons were identified, including examples whose activity was modulated by task, speed of locomotion, spatial position, reward and vigilance states, and one whose firing rate was potentially modulated by the sight of the investigator. Together, these experiments demonstrate that sheep are excellent experimental animals to use for longitudinal studies requiring a large-brained mammal and/or large-scale recordings across distributed neuronal networks. Sheep could be used safely for studying not only neural encoding of decision-making and spatial-mapping in naturalistic environments outside the confines of the traditional laboratory but also the neural basis of both intra- and inter-species social interactions.

Transcranial stimulation of alpha oscillations up-regulates the default mode network

The default mode network (DMN) is the most-prominent intrinsic connectivity network, serving as a key architecture of the brain's functional organization. Conversely, dysregulated DMN is characteristic of major neuropsychiatric disorders. However, the field still lacks mechanistic insights into the regulation of the DMN and effective interventions for DMN dysregulation. The current study approached this problem by manipulating neural synchrony, particularly alpha (8 to 12 Hz) oscillations, a dominant intrinsic oscillatory activity that has been increasingly associated with the DMN in both function and physiology. Using high-definition alpha-frequency transcranial alternating current stimulation (α-tACS) to stimulate the cortical source of alpha oscillations, in combination with simultaneous electroencephalography and functional MRI (EEG-fMRI), we demonstrated that α-tACS (versus Sham control) not only augmented EEG alpha oscillations but also strengthened fMRI and (source-level) alpha connectivity within the core of the DMN. Importantly, increase in alpha oscillations mediated the DMN connectivity enhancement. These findings thus identify a mechanistic link between alpha oscillations and DMN functioning. That transcranial alpha modulation can up-regulate the DMN further highlights an effective noninvasive intervention to normalize DMN functioning in various disorders.

An Integrated Neuroimaging Approach to Inform Transcranial Electrical Stimulation Targeting in Visual Hallucinations

For decades, noninvasive brain stimulation (NIBS), such as transcranial electrical stimulation (tES), has been used to directly modulate human brain mechanisms of visual perception, setting the groundwork for the development of novel circuit-based therapies. While the field of NIBS has grown considerably over recent years, few studies have used these technologies to treat visual hallucinations (VH). Here, we review the NIBS-VH literature and find mixed results due to shortcomings that may potentially be addressed with a unique multimodal neuroimaging-NIBS approach. We highlight methodological advances in NIBS research that have provided researchers with more precise anatomical measurements that may improve our ability to influence brain activity. Specifically, we propose a methodology that combines neuroimaging advances, clinical neuroscience developments such as the identification of brain regions causally involved in VH, and personalized NIBS approaches that improve anatomical targeting. This methodology may enable us to reconcile existing discrepancies in tES-VH research and pave the way for more effective, VH-specific protocols for treating a number of neuropsychiatric disorders with VH as a core symptom.

Greater male than female variability in regional brain structure across the lifespan

For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.

Magnifying Traveling Waves on the Scalp

Traveling waves appear in various signals that measure neuronal activity. Some signals measured in animals have singles-cell resolution and directly point to neuronal activity. In those cases, activation of distributed neurons forms a wave front, and the front propagates across the cortical surface. Other signals are variants of neuroelectric potentials, i.e. electroencephalography, electrocorticography and field potentials. Instead of having fine spatial resolution, these signals reflect the activity of neuronal populations via volume conduction (VC). Sources of traveling waves in neuroelectric potentials have not been well addressed so far. As animal studies show propagating activation of neurons that spread in measured areas, it is often considered that neuronal activations during scalp waves have similar trajectories of activation, spreading like scalp waves. However, traveling waves on the scalp differ from those found directly on the cortical surface in several dimensions: traveling velocity, traveling distance and areal size occupied by single polarity. We describe that the simplest sources can produce scalp waves with perceived spatial dimensions which are actually a magnification of neuronal activity emanating from local sources due to VC. This viewpoint is not a rigorous proof of our magnification concept. However, we suggest the possibility that the actual dimensions of neuronal activity producing traveling waves is not as large as the dimension of the traveling waves.

NAS-optimized topology-preserving transfer learning for differentiating cortical folding patterns

Increasing evidence suggests that cortical folding patterns of human cerebral cortex manifest overt structural and functional differences. However, for interpretability, few studies leverage advanced techniques (e.g., deep learning) to investigate the difference among cortical folds, resulting in more differences yet to be extensively explored. To this end, we proposed an effective topology-preserving transfer learning framework to differentiate cortical fMRI time series extracted from cortical folds. Our framework consists of three main parts: (1) Neural architecture search (NAS), which is used to devise a well-performing network structure based on an initialized hand-designed super-graph in an image dataset; (2) Topology-preserving transfer, which takes the model searched by NAS as the source network, keeping the topological connectivity in the network unchanged, while transforming all 2D operations including convolution and pooling into 1D, therefore resulting in a topology-preserving network, named TPNAS-Net; (3) Classification and correlation analysis, which involves using the TPNAS-Net to classify 1D cortical fMRI time series for each individual brain, and performing a group difference analysis between autism spectrum disorder (ASD) and healthy control (HC) and correlation analysis with clinical information (i.e., age). Extensive experiments on two ASD datasets obtain consistent results, demonstrating that the TPNAS-Net not only discriminates cortical folding patterns at high classification accuracy, but also captures subtle differences between ASD and HC (p-value = 0.042). In addition, we discover that there is a positive correlation between the classification accuracy and age in ASD (r = 0.39, p-value = 0.04). These findings together suggest that structural and functional differences in cortical folding patterns between ASD and HC may provide a potentially useful biomarker for the diagnosis of ASD.

Sex differences in neuronal systems function and behaviour: beyond a single diagnosis in autism spectrum disorders

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is associated with functional brain alterations that underlie the expression of behaviour. Males are diagnosed up to four times more than females, and sex differences have been identified in memory, cognitive flexibility, verbal fluency, and social communication. Unfortunately, there exists a lack of information on the sex-dependent mechanisms of ASD, as well as biological markers to distinguish sex-specific symptoms in ASD. This can often result in a standardized diagnosis for individuals across the spectrum, despite significant differences in the various ASD subtypes. Alterations in neuronal connectivity and oscillatory activity, such as is observed in ASD, are highly coupled to behavioural states. Yet, despite the well-identified sexual dimorphisms that exist in ASD, these functional patterns have rarely been analyzed in the context of sex differences or symptomology. This review summarizes alterations in neuronal oscillatory function in ASD, discusses the age, region, symptom and sex-specific differences that are currently observed across the spectrum, and potential targets for regulating neuronal oscillatory activity in ASD. The need to identify sex-specific biomarkers, in order to facilitate specific diagnostic criteria and allow for more targeted therapeutic approaches for ASD will also be discussed.

Current findings and perspectives on aberrant neural oscillations in schizophrenia

There is now consistent evidence that neural oscillation at low- and high-frequencies constitute an important aspect of the pathophysiology of schizophrenia. Specifically, impaired rhythmic activity may underlie the deficit to generate coherent cognition and behavior, leading to the characteristic symptoms of psychosis and cognitive deficits. Importantly, the generating mechanisms of neural oscillations are relatively well-understood and thus enable the targeted search for the underlying circuit impairments and novel treatment targets. In the following review, we will summarize and assess the evidence for aberrant rhythmic activity in schizophrenia through evaluating studies that have utilized Electro/Magnetoencephalography to examine neural oscillations during sensory and cognitive tasks as well as during resting-state measurements. These data will be linked to current evidence from post-mortem, neuroimaging, genetics, and animal models that have implicated deficits in GABAergic interneurons and glutamatergic neurotransmission in oscillatory deficits in schizophrenia. Finally, we will highlight methodological and analytical challenges as well as provide recommendations for future research.

A biomarker-authenticated model of schizophrenia implicating NPTX2 loss of function

Schizophrenia is a polygenetic disorder whose clinical onset is often associated with behavioral stress. Here, we present a model of disease pathogenesis that builds on our observation that the synaptic immediate early gene NPTX2 is reduced in cerebrospinal fluid of individuals with recent onset schizophrenia. NPTX2 plays an essential role in maintaining excitatory homeostasis by adaptively enhancing circuit inhibition. NPTX2 function requires activity-dependent exocytosis and dynamic shedding at synapses and is coupled to circadian behavior. Behavior-linked NPTX2 trafficking is abolished by mutations that disrupt select activity-dependent plasticity mechanisms of excitatory neurons. Modeling NPTX2 loss of function results in failure of parvalbumin interneurons in their adaptive contribution to behavioral stress, and animals exhibit multiple neuropsychiatric domains. Because the genetics of schizophrenia encompasses diverse proteins that contribute to excitatory synapse plasticity, the identified vulnerability of NPTX2 function can provide a framework for assessing the impact of genetics and the intersection with stress.

A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials

Epilepsy presurgical investigation may include focal intracortical single-pulse electrical stimulations with depth electrodes, which induce cortico-cortical evoked potentials at distant sites because of white matter connectivity. Cortico-cortical evoked potentials provide a unique window on functional brain networks because they contain sufficient information to infer dynamical properties of large-scale brain connectivity, such as preferred directionality and propagation latencies.

 Here, we developed a biologically informed modelling approach to estimate the neural physiological parameters of brain functional networks from the cortico-cortical evoked potentials recorded in a large multicentric database. Specifically, we considered each cortico-cortical evoked potential as the output of a transient stimulus entering the stimulated region, which directly propagated to the recording region. Both regions were modelled as coupled neural mass models, the parameters of which were estimated from the first cortico-cortical evoked potential component, occurring before 80 ms, using dynamic causal modelling and Bayesian model inversion. This methodology was applied to the data of 780 patients with epilepsy from the F-TRACT database, providing a total of 34 354 bipolar stimulations and 774 445 cortico-cortical evoked potentials. The cortical mapping of the local excitatory and inhibitory synaptic time constants and of the axonal conduction delays between cortical regions was obtained at the population level using anatomy-based averaging procedures, based on the Lausanne2008 and the HCP-MMP1 parcellation schemes, containing 130 and 360 parcels, respectively. To rule out brain maturation effects, a separate analysis was performed for older (>15 years) and younger patients (<15 years). In the group of older subjects, we found that the cortico-cortical axonal conduction delays between parcels were globally short (median = 10.2 ms) and only 16% were larger than 20 ms. This was associated to a median velocity of 3.9 m/s. Although a general lengthening of these delays with the distance between the stimulating and recording contacts was observed across the cortex, some regions were less affected by this rule, such as the insula for which almost all efferent and afferent connections were faster than 10 ms. Synaptic time constants were found to be shorter in the sensorimotor, medial occipital and latero-temporal regions, than in other cortical areas. Finally, we found that axonal conduction delays were significantly larger in the group of subjects younger than 15 years, which corroborates that brain maturation increases the speed of brain dynamics.

To our knowledge, this study is the first to provide a local estimation of axonal conduction delays and synaptic time constants across the whole human cortex in vivo, based on intracerebral electrophysiological recordings.

Low-beta repetitive transcranial magnetic stimulation to human dorsolateral prefrontal cortex during object recognition memory sample presentation, at a task-related frequency observed in local field potentials in homologous macaque cortex, impairs subsequent recollection but not familiarity

According to dual‐process signal‐detection (DPSD) theories, short‐ and long‐term recognition memory draws upon both familiarity and recollection. It remains unclear how primate prefrontal cortex (PFC) contributes to these processes, but frequency‐specific neuronal activities are considered to play a key role. In Experiment 1, nonhuman primate (NHP) local field potential (LFP) electrophysiological recordings in macaque left dorsolateral PFC (dlPFC) revealed performance‐related differences in a low‐beta frequency range during the sample presentation phase of a visual object recognition memory task. Experiment 2 employed a similar task in humans and targeted left dlPFC (and vertex as a control) with repetitive transcranial magnetic stimulation (rTMS) at 12.5 Hz during occasional sample presentations. This low‐beta frequency rTMS to dlPFC decreased DPSD derived indices of recollection, but not familiarity, in subsequent memory tests of the targeted samples after short delays. The same number of rTMS pulses over the same total duration albeit at a random frequency had no effect on either recollection or familiarity. Neither stimulation protocols had any causal effect upon behaviour when targeted to the control site (vertex). In this study, our hypotheses for our human TMS study were derived from our observations in NHPs; this approach might inspire further translational research through investigation of homologous brain regions and tasks across species using similar neuroscientific methodologies to advance the neural mechanism of recognition memory in primates.

Aberrant Patterns of Sensory-Evoked Activity in the Olfactory Bulb of LRRK2 Knockout Mice

The LRRK2 gene is the major genetic determinant of familiar Parkinson's disease (PD). Leucine-rich repeat kinase 2 (LRRK2) is a multidomain protein involved in several intracellular signaling pathways. A wealth of evidence indicates that LRRK2 is enriched at the presynaptic compartment where it regulates vesicle trafficking and neurotransmitter release. However, whether the role of LRRK2 affects neuronal networks dynamic at systems level remains unknown. Addressing this question is critical to unravel the impact of LRRK2 on brain function. Here, combining behavioral tests, electrophysiological recordings, and functional imaging, we investigated neuronal network dynamics, in vivo, in the olfactory bulb of mice carrying a null mutation in LRRK2 gene (LRRK2 knockout, LRRK2 KO, mice). We found that LRRK2 KO mice exhibit olfactory behavioral deficits. At the circuit level, the lack of LRRK2 expression results in altered gamma rhythms and odorant-evoked activity with significant impairments, while the spontaneous activity exhibited limited alterations. Overall, our data in the olfactory bulb suggest that the multifaced role of LRRK2 has a strong impact at system level when the network is engaged in active sensory processing.

Neuromodulatory effects of HD-tACS/tDCS on the prefrontal cortex: A resting-state fNIRS-EEG study

Transcranial direct and alternating current stimulation (tDCS and tACS, respectively) can modulate human brain dynamics and cognition. However, these modalities have not been compared using multiple imaging techniques concurrently. In this study, 15 participants participated in an experiment involving two sessions with a gap of 10 d. In the first and second sessions, tACS and tDCS were administered to the participants. The anode for tDCS was positioned at point FpZ, and four cathodes were positioned over the left and right prefrontal cortices (PFCs) to target the frontal regions simultaneously. tDCS was administered with 1 mA current. tACS was supplied with a current of 1 mA (zero-to-peak value) at 10 Hz frequency. Stimulation was applied concomitantly with functional near-infrared spectroscopy and electroencephalography acquisitions in the resting-state. The statistical test showed significant alteration (p < 0.001) in the mean hemodynamic responses during and after tDCS and tACS periods. Between-group comparison revealed a significantly less (p < 0.001) change in the mean hemodynamic response caused by tACS compared with tDCS. As hypothesized, we successfully increased the hemodynamics in both left and right PFCs using tDCS and tACS. Moreover, a significant increase in alpha-band power (p < 0.01) and low beta band power (p < 0.05) due to tACS was observed after the stimulation period. Although tDCS is not frequency-specific, it increased but not significantly (p > 0.05) the powers of most bands including delta, theta, alpha, low beta, high beta, and gamma. These findings suggest that both hemispheres can be targeted and that both tACS and tDCS are equally effective in high-definition configurations, which may be of clinical relevance.

Neuronal Electrical Ongoing Activity as Cortical Areas Signature: An Insight from MNI Intracerebral Recording Atlas

The time course of the neuronal activity in the brain network, the neurodynamics, reflects the structure and functionality of the generating neuronal pools. Here, using the intracranial stereo-electroencephalographic (sEEG) recordings of the public Montreal Neurological Institute (MNI) atlas, we investigated the neurodynamics of primary motor (M1), somatosensory (S1) and auditory (A1) cortices measuring power spectral densities (PSD) and Higuchi fractal dimension (HFD) in the same subject (M1 vs. S1 in 16 subjects, M1 vs. A1 in 9, S1 vs. A1 in 6). We observed specific spectral features in M1, which prevailed above beta band, S1 in the alpha band, and A1 in the delta band. M1 HFD was higher than S1, both higher than A1. A clear distinction of neurodynamics properties of specific primary cortices supports the efforts in cortical parceling based on this expression of the local cytoarchitecture and connectivity. In this perspective, we selected within the MNI intracortical database a first set of primary motor, somatosensory and auditory cortices' representatives to query in recognizing ongoing patterns of neuronal communication. Potential clinical impact stands primarily in exploiting such exchange patterns to enhance the efficacy of neuromodulation intervention to cure symptoms secondary to neuronal activity unbalances.

Respiration-Driven Brain Oscillations in Emotional Cognition

Respiration paces brain oscillations and the firing of individual neurons, revealing a profound impact of rhythmic breathing on brain activity. Intriguingly, respiration-driven entrainment of neural activity occurs in a variety of cortical areas, including those involved in higher cognitive functions such as associative neocortical regions and the hippocampus. Here we review recent findings of respiration-entrained brain activity with a particular focus on emotional cognition. We summarize studies from different brain areas involved in emotional behavior such as fear, despair, and motivation, and compile findings of respiration-driven activities across species. Furthermore, we discuss the proposed cellular and network mechanisms by which cortical circuits are entrained by respiration. The emerging synthesis from a large body of literature suggests that the impact of respiration on brain function is widespread across the brain and highly relevant for distinct cognitive functions. These intricate links between respiration and cognitive processes call for mechanistic studies of the role of rhythmic breathing as a timing signal for brain activity.

Dissociable Components of Information Encoding in Human Perception

Brain decoding can predict visual perception from non-invasive electrophysiological data by combining information across multiple channels. However, decoding methods typically conflate the composite and distributed neural processes underlying perception that are together present in the signal, making it unclear what specific aspects of the neural computations involved in perception are reflected in this type of macroscale data. Using MEG data recorded while participants viewed a large number of naturalistic images, we analytically decomposed the brain signal into its oscillatory and non-oscillatory components, and used this decomposition to show that there are at least three dissociable stimulus-specific aspects to the brain data: a slow, non-oscillatory component, reflecting the temporally stable aspect of the stimulus representation; a global phase shift of the oscillation, reflecting the overall speed of processing of specific stimuli; and differential patterns of phase across channels, likely reflecting stimulus-specific computations. Further, we show that common cognitive interpretations of decoding analysis, in particular about how representations generalize across time, can benefit from acknowledging the multicomponent nature of the signal in the study of perception.

Modality-specific tracking of attention and sensory statistics in the human electrophysiological spectral exponent

A hallmark of electrophysiological brain activity is its 1/f-like spectrum - power decreases with increasing frequency. The steepness of this 'roll-off' is approximated by the spectral exponent, which in invasively recorded neural populations reflects the balance of excitatory to inhibitory neural activity (E:I balance). Here, we first establish that the spectral exponent of non-invasive electroencephalography (EEG) recordings is highly sensitive to general (i.e., anaesthesia-driven) changes in E:I balance. Building on the EEG spectral exponent as a viable marker of E:I, we then demonstrate its sensitivity to the focus of selective attention in an EEG experiment during which participants detected targets in simultaneous audio-visual noise. In addition to these endogenous changes in E:I balance, EEG spectral exponents over auditory and visual sensory cortices also tracked auditory and visual stimulus spectral exponents, respectively. Individuals' degree of this selective stimulus-brain coupling in spectral exponents predicted behavioural performance. Our results highlight the rich information contained in 1/f-like neural activity, providing a window into diverse neural processes previously thought to be inaccessible in non-invasive human recordings.

Modality-specific tracking of attention and sensory statistics in the human electrophysiological spectral exponent

A hallmark of electrophysiological brain activity is its 1/f-like spectrum - power decreases with increasing frequency. The steepness of this 'roll-off' is approximated by the spectral exponent, which in invasively recorded neural populations reflects the balance of excitatory to inhibitory neural activity (E:I balance). Here, we first establish that the spectral exponent of non-invasive electroencephalography (EEG) recordings is highly sensitive to general (i.e., anaesthesia-driven) changes in E:I balance. Building on the EEG spectral exponent as a viable marker of E:I, we then demonstrate its sensitivity to the focus of selective attention in an EEG experiment during which participants detected targets in simultaneous audio-visual noise. In addition to these endogenous changes in E:I balance, EEG spectral exponents over auditory and visual sensory cortices also tracked auditory and visual stimulus spectral exponents, respectively. Individuals' degree of this selective stimulus-brain coupling in spectral exponents predicted behavioural performance. Our results highlight the rich information contained in 1/f-like neural activity, providing a window into diverse neural processes previously thought to be inaccessible in non-invasive human recordings.

Investigating the spectral features of the brain meso-scale structure at rest

Recent studies provide novel insights into the meso-scale organization of the brain, highlighting the co-occurrence of different structures: classic assortative (modular), disassortative, and core-periphery. However, the spectral properties of the brain meso-scale remain mostly unexplored. To fill this knowledge gap, we investigated how the meso-scale structure is organized across the frequency domain. We analyzed the resting state activity of healthy participants with source-localized high-density electroencephalography signals. Then, we inferred the community structure using weighted stochastic block-model (WSBM) to capture the landscape of meso-scale structures across the frequency domain. We found that different meso-scale modalities co-exist and are diversely organized over the frequency spectrum. Specifically, we found a core-periphery structure dominance, but we also highlighted a selective increase of disassortativity in the low frequency bands (<8 Hz), and of assortativity in the high frequency band (30-50 Hz). We further described other features of the meso-scale organization by identifying those brain regions which, at the same time, (a) exhibited the highest degree of assortativity, disassortativity, and core-peripheriness (i.e., participation) and (b) were consistently assigned to the same community, irrespective from the granularity imposed by WSBM (i.e., granularity-invariance). In conclusion, we observed that the brain spontaneous activity shows frequency-specific meso-scale organization, which may support spatially distributed and local information processing.

Comparison of resting-state EEG between adults with Down syndrome and typically developing controls

Background

Down syndrome (DS) is the most common genetic cause of intellectual disability (ID) worldwide. Understanding electrophysiological characteristics associated with DS provides potential mechanistic insights into ID, helping inform biomarkers and targets for intervention. Currently, electrophysiological characteristics associated with DS remain unclear due to methodological differences between studies and inadequate controls for cognitive decline as a potential cofounder.

Methods

Eyes-closed resting-state EEG measures (specifically delta, theta, alpha, and beta absolute and relative powers, and alpha peak amplitude, frequency and frequency variance) in occipital and frontal regions were compared between adults with DS (with no diagnosis of dementia or evidence of cognitive decline) and typically developing (TD) matched controls (n = 25 per group).

Results

We report an overall ‘slower’ EEG spectrum, characterised by higher delta and theta power, and lower alpha and beta power, for both regions in people with DS. Alpha activity in particular showed strong group differences, including lower power, lower peak amplitude and greater peak frequency variance in people with DS.

Conclusions

Such EEG ‘slowing’ has previously been associated with cognitive decline in both DS and TD populations. These findings indicate the potential existence of a universal EEG signature of cognitive impairment, regardless of origin (neurodevelopmental or neurodegenerative), warranting further exploration.

Supplementary Information

The online version contains supplementary material available at 10.1186/s11689-021-09392-z.

Disorganization of Oscillatory Activity in Animal Models of Schizophrenia

Schizophrenia is a chronic, debilitating disorder with diverse symptomatology, including disorganized cognition and behavior. Despite considerable research effort, we have only a limited understanding of the underlying brain dysfunction. In this article, we review the potential role of oscillatory circuits in the disorder with a particular focus on the hippocampus, a region that encodes sequential information across time and space, as well as the frontal cortex. Several mechanistic explanations of schizophrenia propose that a loss of oscillatory synchrony between and within these brain regions may underlie some of the symptoms of the disorder. We describe how these oscillations are affected in several animal models of schizophrenia, including models of genetic risk, maternal immune activation (MIA) models, and models of NMDA receptor hypofunction. We then critically discuss the evidence for disorganized oscillatory activity in these models, with a focus on gamma, sharp wave ripple, and theta activity, including the role of cross-frequency coupling as a synchronizing mechanism. Finally, we focus on phase precession, which is an oscillatory phenomenon whereby individual hippocampal place cells systematically advance their firing phase against the background theta oscillation. Phase precession is important because it allows sequential experience to be compressed into a single 120 ms theta cycle (known as a 'theta sequence'). This time window is appropriate for the induction of synaptic plasticity. We describe how disruption of phase precession could disorganize sequential processing, and thereby disrupt the ordered storage of information. A similar dysfunction in schizophrenia may contribute to cognitive symptoms, including deficits in episodic memory, working memory, and future planning.

Psilocin, LSD, mescaline, and DOB all induce broadband desynchronization of EEG and disconnection in rats with robust translational validity

Serotonergic psychedelics are recently gaining a lot of attention as a potential treatment of several neuropsychiatric disorders. Broadband desynchronization of EEG activity and disconnection in humans have been repeatedly shown; however, translational data from animals are completely lacking. Therefore, the main aim of our study was to assess the effects of tryptamine and phenethylamine psychedelics (psilocin 4 mg/kg, LSD 0.2 mg/kg, mescaline 100 mg/kg, and DOB 5 mg/kg) on EEG in freely moving rats. A system consisting of 14 cortical EEG electrodes, co-registration of behavioral activity of animals with subsequent analysis only in segments corresponding to behavioral inactivity (resting-state-like EEG) was used in order to reach a high level of translational validity. Analyses of the mean power, topographic brain-mapping, and functional connectivity revealed that all of the psychedelics irrespective of the structural family induced overall and time-dependent global decrease/desynchronization of EEG activity and disconnection within 1-40 Hz. Major changes in activity were localized on the large areas of the frontal and sensorimotor cortex showing some subtle spatial patterns characterizing each substance. A rebound of occipital theta (4-8 Hz) activity was detected at later stages after treatment with mescaline and LSD. Connectivity analyses showed an overall decrease in global connectivity for both the components of cross-spectral and phase-lagged coherence. Since our results show almost identical effects to those known from human EEG/MEG studies, we conclude that our method has robust translational validity.

Electrophysiological biomarkers of behavioral dimensions from cross-species paradigms

There has been a fundamental failure to translate preclinically supported research into clinically efficacious treatments for psychiatric disorders. One of the greatest impediments toward improving this species gap has been the difficulty of identifying translatable neurophysiological signals that are related to specific behavioral constructs. Here, we present evidence from three paradigms that were completed by humans and mice using analogous procedures, with each task eliciting candidate a priori defined electrophysiological signals underlying effortful motivation, reinforcement learning, and cognitive control. The effortful motivation was assessed using a progressive ratio breakpoint task, yielding a similar decrease in alpha-band activity over time in both species. Reinforcement learning was assessed via feedback in a probabilistic learning task with delta power significantly modulated by reward surprise in both species. Additionally, cognitive control was assessed in the five-choice continuous performance task, yielding response-locked theta power seen across species, and modulated by difficulty in humans. Together, these successes, and also the teachings from these failures, provide a roadmap towards the use of electrophysiology as a method for translating findings from the preclinical assays to the clinical settings.

Suggested visual blockade during hypnosis: Top-down modulation of stimulus processing in a visual oddball task

Several theories of hypnosis assume that responses to hypnotic suggestions are implemented through top-down modulations via a frontoparietal network that is involved in monitoring and cognitive control. The current study addressed this issue re-analyzing previously published event-related-potentials (ERP) (N1, P2, and P3b amplitudes) and combined it with source reconstruction and connectivity analysis methods. ERP data were obtained from participants engaged in a visual oddball paradigm composed of target, standard, and distractor stimuli during a hypnosis (HYP) and a control (CON) condition. In both conditions, participants were asked to count the rare targets presented on a video screen. During HYP participants received suggestions that a wooden board in front of their eyes would obstruct their view of the screen. The results showed that participants’ counting accuracy was significantly impaired during HYP compared to CON. ERP components in the N1 and P2 window revealed no amplitude differences between CON and HYP at sensor-level. In contrast, P3b amplitudes in response to target stimuli were significantly reduced during HYP compared to CON. Source analysis of the P3b amplitudes in response to targets indicated that HYP was associated with reduced source activities in occipital and parietal brain areas related to stimulus categorization and attention. We further explored how these brain sources interacted by computing time-frequency effective connectivity between electrodes that best represented frontal, parietal, and occipital sources. This analysis revealed reduced directed information flow from parietal attentional to frontal executive sources during processing of target stimuli. These results provide preliminary evidence that hypnotic suggestions of a visual blockade are associated with a disruption of the coupling within the frontoparietal network implicated in top-down control.

Supervision of a self-driving vehicle unmasks latent sleepiness relative to manually controlled driving

Human error has been implicated as a causal factor in a large proportion of road accidents. Automated driving systems purport to mitigate this risk, but self-driving systems that allow a driver to entirely disengage from the driving task also require the driver to monitor the environment and take control when necessary. Given that sleep loss impairs monitoring performance and there is a high prevalence of sleep deficiency in modern society, we hypothesized that supervising a self-driving vehicle would unmask latent sleepiness compared to manually controlled driving among individuals following their typical sleep schedules. We found that participants felt sleepier, had more involuntary transitions to sleep, had slower reaction times and more attentional failures, and showed substantial modifications in brain synchronization during and following an autonomous drive compared to a manually controlled drive. Our findings suggest that the introduction of partial self-driving capabilities in vehicles has the potential to paradoxically increase accident risk.

Spacetime in the brain: rapid brain network reorganization in visual processing and recovery

Functional connectivity networks (FCN) are the physiological basis of brain synchronization to integrating neural activity. They are not rigid but can reorganize under pathological conditions or during mental or behavioral states. However, because mental acts can be very fast, like the blink of an eye, we now used the visual system as a model to explore rapid FCN reorganization and its functional impact in normal, abnormal and post treatment vision. EEG-recordings were time-locked to visual stimulus presentation; graph analysis of neurophysiological oscillations were used to characterize millisecond FCN dynamics in healthy subjects and in patients with optic nerve damage before and after neuromodulation with alternating currents stimulation and were correlated with visual performance. We showed that rapid and transient FCN synchronization patterns in humans can evolve and dissolve in millisecond speed during visual processing. This rapid FCN reorganization is functionally relevant because disruption and recovery after treatment in optic nerve patients correlated with impaired and recovered visual performance, respectively. Because FCN hub and node interactions can evolve and dissolve in millisecond speed to manage spatial and temporal neural synchronization during visual processing and recovery, we propose “Brain Spacetime” as a fundamental principle of the human mind not only in visual cognition but also in vision restoration.

The influence of auditory rhythms on the speed of inferred motion

The present research explored the influence of isochronous auditory rhythms on the timing of movement-related prediction in two experiments. In both experiments, participants observed a moving disc that was visible for a predetermined period before disappearing behind a small, medium, or large occluded area for the remainder of its movement. In Experiment 1, the disc was visible for 1 s. During this period, participants were exposed to either a fast or slow auditory rhythm, or they heard nothing. They were instructed to press a key to indicate when they believed the moving disc had reached a specified location on the other side of the occluded area. The procedure measured the (signed) error in participants' estimate of the time it would take for a moving object to contact a stationary one. The principal results of Experiment 1 were main effects of the rate of the auditory rhythm and of the size of the occlusion on participants' judgments. In Experiment 2, the period of visibility was varied with size of the occlusion area to keep the total movement time constant for all three levels of occlusion. The results replicated the main effect of rhythm found in Experiment 1 and showed a small, significant interaction, but indicated no main effect of occlusion size. Overall, the results indicate that exposure to fast isochronous auditory rhythms during an interval of inferred motion can influence the imagined rate of such motion and suggest a possible role of an internal rhythmicity in the maintenance of temporally accurate dynamic mental representations.

The Antidepressant-Like and Analgesic Effects of Kratom Alkaloids are accompanied by Changes in Low Frequency Oscillations but not ΔFosB Accumulation

Mitragyna speciosa (“kratom”), employed as a traditional medicine to improve mood and relieve pain, has shown increased use in Europe and North America. Here, the dose-dependent effects of a purified alkaloid kratom extract on neuronal oscillatory systems function, analgesia, and antidepressant-like behaviour were evaluated and kratom-induced changes in ΔFosB expression determined. Male rats were administered a low or high dose of kratom (containing 0.5 or 1 mg/kg of mitragynine, respectively) for seven days. Acute or repeated low dose kratom suppressed ventral tegmental area (VTA) theta oscillatory power whereas acute or repeated high dose kratom increased delta power, and reduced theta power, in the nucleus accumbens (NAc), prefrontal cortex (PFC), cingulate cortex (Cg) and VTA. The repeated administration of low dose kratom additionally elevated delta power in PFC, decreased theta power in NAc and PFC, and suppressed beta and low gamma power in Cg. Suppressed high gamma power in NAc and PFC was seen selectively following repeated high dose kratom. Both doses of kratom elevated NAc-PFC, VTA-NAc, and VTA-Cg coherence. Low dose kratom had antidepressant-like properties whereas both doses produced analgesia. No kratom-induced changes in ΔFosB expression were evident. These results support a role for kratom as having both antidepressant and analgesic properties that are accompanied by specific changes in neuronal circuit function. However, the absence of drug-induced changes in ΔFosB expression suggest that the drug may circumvent this cellular signaling pathway, a pathway known for its significant role in addiction.

Alpha-Band Phase Modulates Bottom-up Feature Processing

Recent studies reveal that attention operates in a rhythmic manner, that is, sampling each location or feature alternatively over time. However, most evidence derives from top-down tasks, and it remains elusive whether bottom-up processing also entails dynamic coordination. Here, we developed a novel feature processing paradigm and combined time-resolved behavioral measurements and electroencephalogram (EEG) recordings to address the question. Specifically, a salient color in a multicolor display serves as a noninformative cue to capture attention and presumably reset the oscillations of feature processing. We then measured the behavioral performance of a probe stimulus associated with either high- or low-salient color at varied temporal lags after the cue. First, the behavioral results (i.e., reaction time) display an alpha-band (~8 Hz) profile with a consistent phase lag between high- and low-salient conditions. Second, simultaneous EEG recordings show that behavioral performance is modulated by the phase of alpha-band neural oscillation at the onset of the probes. Finally, high- and low-salient probes are associated with distinct preferred phases of alpha-band neural oscillations. Taken together, our behavioral and neural results convergingly support a central function of alpha-band rhythms in feature processing, that is, features with varied saliency levels are processed at different phases of alpha neural oscillations.

Recurrent dynamics in the cerebral cortex: Integration of sensory evidence with stored knowledge

Current concepts of sensory processing in the cerebral cortex emphasize serial extraction and recombination of features in hierarchically structured feed-forward networks in order to capture the relations among the components of perceptual objects. These concepts are implemented in convolutional deep learning networks and have been validated by the astounding similarities between the functional properties of artificial systems and their natural counterparts. However, cortical architectures also display an abundance of recurrent coupling within and between the layers of the processing hierarchy. This massive recurrence gives rise to highly complex dynamics whose putative function is poorly understood. Here a concept is proposed that assigns specific functions to the dynamics of cortical networks and combines, in a unifying approach, the respective advantages of recurrent and feed-forward processing. It is proposed that the priors about regularities of the world are stored in the weight distributions of feed-forward and recurrent connections and that the high-dimensional, dynamic space provided by recurrent interactions is exploited for computations. These comprise the ultrafast matching of sensory evidence with the priors covertly represented in the correlation structure of spontaneous activity and the context-dependent grouping of feature constellations characterizing natural objects. The concept posits that information is encoded not only in the discharge frequency of neurons but also in the precise timing relations among the discharges. Results of experiments designed to test the predictions derived from this concept support the hypothesis that cerebral cortex exploits the high-dimensional recurrent dynamics for computations serving predictive coding.

The brain and its time: intrinsic neural timescales are key for input processing

We process and integrate multiple timescales into one meaningful whole. Recent evidence suggests that the brain displays a complex multiscale temporal organization. Different regions exhibit different timescales as described by the concept of intrinsic neural timescales (INT); however, their function and neural mechanisms remains unclear. We review recent literature on INT and propose that they are key for input processing. Specifically, they are shared across different species, i.e., input sharing. This suggests a role of INT in encoding inputs through matching the inputs' stochastics with the ongoing temporal statistics of the brain's neural activity, i.e., input encoding. Following simulation and empirical data, we point out input integration versus segregation and input sampling as key temporal mechanisms of input processing. This deeply grounds the brain within its environmental and evolutionary context. It carries major implications in understanding mental features and psychiatric disorders, as well as going beyond the brain in integrating timescales into artificial intelligence.

Rendering neuronal state equations compatible with the principle of stationary action

The principle of stationary action is a cornerstone of modern physics, providing a powerful framework for investigating dynamical systems found in classical mechanics through to quantum field theory. However, computational neuroscience, despite its heavy reliance on concepts in physics, is anomalous in this regard as its main equations of motion are not compatible with a Lagrangian formulation and hence with the principle of stationary action. Taking the Dynamic Causal Modelling (DCM) neuronal state equation as an instructive archetype of the first-order linear differential equations commonly found in computational neuroscience, we show that it is possible to make certain modifications to this equation to render it compatible with the principle of stationary action. Specifically, we show that a Lagrangian formulation of the DCM neuronal state equation is facilitated using a complex dependent variable, an oscillatory solution, and a Hermitian intrinsic connectivity matrix. We first demonstrate proof of principle by using Bayesian model inversion to show that both the original and modified models can be correctly identified via in silico data generated directly from their respective equations of motion. We then provide motivation for adopting the modified models in neuroscience by using three different types of publicly available in vivo neuroimaging datasets, together with open source MATLAB code, to show that the modified (oscillatory) model provides a more parsimonious explanation for some of these empirical timeseries. It is our hope that this work will, in combination with existing techniques, allow people to explore the symmetries and associated conservation laws within neural systems - and to exploit the computational expediency facilitated by direct variational techniques.

Epilepsy as a dynamical system, a most needed paradigm shift in epileptology

The idea of the epileptic brain being highly excitable and facilitated to synchronic activity has guided pharmacological treatment since the early twentieth century. Although tackling epilepsy's seizure-prone feature, by tonically modifying overall circuit excitability and/or connectivity, the last 50 years of drug development has not seen a substantial improvement in seizure suppression of refractory epilepsies. This review presents a new conceptual framework for epilepsy in which the temporal dynamics of the disease plays a more critical role in both its understanding and therapeutic strategies. The repetitive epileptiform pattern (characteristic during ictal activity) and other well-defined electrographic signatures (i.e., present during the interictal period) are discussed in terms of the sequential activation of the circuit motifs. Lessons learned from the physiological activation of neural circuitry are used to further corroborate the argument and explore the transition from proper function to a state of instability. Furthermore, the review explores how interfering in the temporally dependent abnormal connectivity between circuits may work as a therapeutic approach. We also review the use of probing stimulation to access network connectivity and evaluate its power to determine transitional states of the dynamical system as it moves towards regions of instability, especially when conventional electrographic monitoring is proven inefficient. Unorthodox cases, with little or no scalp electrographic correlate, in which ictogenic circuitry and/or seizure spread is temporally restricted to neurovegetative, cognitive, and motivational areas are shown as possible explanations for sudden death in epilepsy (SUDEP) and other psychiatric comorbidities. In short, this review presents a paradigm shift in the way that we address the disease and is aimed to encourage debate rather than narrow the rationale epilepsy is currently engaged in. This article is part of the Special Issue "NEWroscience 2018".

Measuring Phase-Amplitude Coupling Based on the Jensen-Shannon Divergence and Correlation Matrix

Phase-amplitude coupling (PAC) measures the relationship between the phase of low-frequency oscillations (LFO) and the amplitude of high-frequency oscillations (HFO). It plays an important functional role in neural information processing and cognition. Thus, we propose a novel method based on the Jensen-Shannon (JS) divergence and correlation matrix. The method takes the amplitude distributions of the HFO located in the corresponding phase bins of the LFO as multichannel inputs to construct a correlation matrix, where the elements are calculated based on the JS divergence between pairs of amplitude distributions. Then, the omega complexity extracted from the correlation matrix is used to estimate the PAC strength. The simulation results demonstrate that the proposed method can effectively reflect the PAC strength and slightly vary with the data length. Moreover, it outperforms five frequently used algorithms in the performance against additive white Gaussian noise and spike noise and the ability of detecting PAC in wide frequency ranges. To validate our proposed method with real data, it was applied to analyze the local field potential recorded from the dorsomedial striatum in a male Sprague-Dawley rat. It can replicate previous results obtained with other PAC metrics. In conclusion, these results suggest that our proposed method is a powerful tool for measuring the PAC between neural oscillations.

Mutual Interactions between Brain States and Alzheimer's Disease Pathology: A Focus on Gamma and Slow Oscillations

Brain state varies from moment to moment. While brain state can be defined by ongoing neuronal population activity, such as neuronal oscillations, this is tightly coupled with certain behavioural or vigilant states. In recent decades, abnormalities in brain state have been recognised as biomarkers of various brain diseases and disorders. Intriguingly, accumulating evidence also demonstrates mutual interactions between brain states and disease pathologies: while abnormalities in brain state arise during disease progression, manipulations of brain state can modify disease pathology, suggesting a therapeutic potential. In this review, by focusing on Alzheimer's disease (AD), the most common form of dementia, we provide an overview of how brain states change in AD patients and mouse models, and how controlling brain states can modify AD pathology. Specifically, we summarise the relationship between AD and changes in gamma and slow oscillations. As pathological changes in these oscillations correlate with AD pathology, manipulations of either gamma or slow oscillations can modify AD pathology in mouse models. We argue that neuromodulation approaches to target brain states are a promising non-pharmacological intervention for neurodegenerative diseases.

Riding the slow wave: Exploring the role of entrained low-frequency oscillations in memory formation

Neural oscillations are proposed to support a variety of behaviors, including long-term memory, yet their functional significance remains an active area of research. Here, we explore a potential functional role of low-frequency cortical oscillations in episodic memory formation. Recent theories suggest that low-frequency oscillations orchestrate rhythmic attentional sampling of the environment by dynamically modulating neural excitability across time. When these oscillations entrain to low-frequency rhythms present in the environment, such as speech or music, the brain can build temporal predictions about the onset of relevant events so that these events can be more efficiently processed. Building upon this literature, we propose that entrained low-frequency oscillations may similarly influence the temporal dynamics of episodic memory by rhythmically modulating encoding across time (mnemonic sampling). Central to this proposal is the phenomenon of cross-frequency phase-amplitude coupling, whereby the amplitudes of faster (higher frequency) rhythms, such as gamma oscillations, couple to the phase of slower (lower-frequency) rhythms entrained to environmental stimuli. By imposing temporal structure on higher-frequency oscillatory activity previously linked to memory formation, entrained low-frequency oscillations could dynamically orchestrate memory formation and optimize encoding at specific moments in time. We discuss prior experimental and theoretical work relevant to this proposal.

Dynamics of coherent activity between cortical areas defines a two-stage process of top-down attention

Analysing a visual scene requires the brain to briefly keep in memory potentially relevant items of that scene and then direct attention to their locations for detailed processing. To reveal the neuronal basis of the underlying working memory and top-down attention processes, we trained macaques to match two patterns presented with a delay between them. As the above processes are likely to require communication between brain regions, and the parietal cortex is known to be involved in spatial attention, we simultaneously recorded neuronal activities from the interconnected parietal and middle temporal areas. We found that mnemonic information about features of the first pattern was retained in coherent oscillating activity between the two areas in high-frequency bands, followed by coherent activity in lower frequency bands mediating top-down attention on the relevant spatial location. Oscillations maintaining featural information also modulated activity of the cells of the parietal cortex that mediate attention. This could potentially enable transfer of information to organize top-down signals necessary for selective attention. Our results provide evidence in support of a two-stage model of visual attention where the first stage involves creating a saliency map representing a visual scene and at the second stage attentional feedback is provided to cortical areas involved in detailed analysis of the attended parts of a scene.

The link between gliomas infiltration and white matter architecture investigated with electron microscopy and diffusion tensor imaging

Diffuse low-grade gliomas (DLGG) display different preferential locations in eloquent and secondary associative brain areas. The reason for this tendency is still unknown. We hypothesized that the intrinsic architecture and water diffusion properties of the white matter bundles in these regions may facilitate gliomas infiltration. Magnetic resonance imaging of sixty-seven diffuse low-grade gliomas patients were normalized to/and segmented in MNI space to create three probabilistic infiltration weighted gradient maps according to the molecular status of each tumor group (IDH mutated, IDH wild-type and IDH mutated/1p19q co-deleted). Diffusion tensor imaging (DTI)- based parameters were derived for five major white matter bundles, displaying regional differences in the grade of infiltration, averaged over 20 healthy individuals acquired from the Human connectome project (HCP) database. Transmission electron microscopy (TEM) was used to analyze fiber density, fiber diameter and g-ratio in 100 human white matter regions, sampled from cadaver specimens, reflecting areas with different gliomas infiltration in each white matter bundle. Histological results and DTI-based parameters were compared in anatomical regions of high- and low grade of infiltration (HIF and LIF) respectively. We detected differences in the white matter infiltration of five major white matter bundles in three groups. Astrocytomas IDHm infiltrated left fronto-temporal subcortical areas. Astrocytomas IDHwt were detected in the posterior-temporal and temporo-parietal regions bilaterally. Oligodendrogliomas IDHm/1p19q infiltrated anterior subcortical regions of the frontal lobes bilaterally. Regional differences within the same white matter bundles were detected by both TEM- and DTI analysis linked to different topographical variables. Our multimodal analysis showed that HIF regions, common to all the groups, displayed a smaller fiber diameter, lower FA and higher RD compared with LIF regions. Our results suggest that the both morphological features and diffusion parameters of the white matter may be different in regions linked to the preferential location of DLGG.

Neurobiology of brain oscillations in acute and chronic pain

Pain is a complex perceptual phenomenon. Coordinated activity among local and distant brain networks is a central element of the neural underpinnings of pain. Brain oscillatory rhythms across diverse frequency ranges provide a functional substrate for coordinating activity across local neuronal ensembles and anatomically distant brain areas in pain networks. This review addresses parallels between insights from human and rodent analyses of oscillatory rhythms in acute and chronic pain and discusses recent rodent-based studies that have shed light on mechanistic underpinnings of brain oscillatory dynamics in pain-related behaviors. We highlight the potential for therapeutic modulation of oscillatory rhythms, and identify outstanding questions and challenges to be addressed in future research.

Optical Stimulation of Thalamic Spindle Circuitry Sustains Electroencephalogram Patterns of General Anesthesia but not Duration of Loss of Consciousness

Alterations in thalamic GABAergic signaling are implicated in mediating the rise in 12-30 Hz electroencephalogram (EEG) activity that signals anesthetic-induced loss-of-consciousness with GABA_A receptor-targeting general anesthetics. A number of modeling studies have identified that anesthetic-induced alterations in thalamocortico-corticothalamic signaling in the same network that generates sleep spindles would be sufficient to elicit this key EEG signature of anesthetic hypnosis with general anesthetic agents. Accordingly, we hypothesize that targeted stimulation of this thalamic GABAergic circuitry into a sleep-spindle mode of activity would promote the general anesthetic effects of etomidate. We recorded EEG activity and loss-of-righting reflex in transgenic mice expressing channel rhodopsin-2 on GABAergic neurons (ChR2-VGAT, n = 8) and control, wild-type mice (C57BL/6J, n = 8). On two consecutive days mice were randomly assigned to receive spindle-rhythm stimulation via an optical probe targeting the left reticular thalamic nucleus or no stimulation. After an initial 30-minute recording, mice were administered etomidate (12 mg/kg, intraperitoneal) and recorded for 90 min with or without optical stimulation. Etomidate elicited an increase in 12-30 Hz EEG power in wild-type and ChR2-VGAT mice for 20 min following administration (p < 0.001). Optical spindle-rhythm stimulation prolonged the increase in 12-30 Hz activity in ChR2-VGAT mice only (p = 0.023). Spindle-rhythm stimulation also increased the incidence and duration of sleep spindle-like oscillations in ChR2-VGAT mice only (all p ≤ 0.001). Despite the maintained anesthetic-like changes in EEG activity, optical spindle-rhythm stimulation was not associated with changes in the time to and duration of the loss-of-righting reflex, a behavioral endpoint of etomidate-induced general anesthesia in rodents.

From Univariate to Multivariate Coupling Between Continuous Signals and Point Processes: A Mathematical Framework

Time series data sets often contain heterogeneous signals, composed of both continuously changing quantities and discretely occurring events. The coupling between these measurements may provide insights into key underlying mechanisms of the systems under study. To better extract this information, we investigate the asymptotic statistical properties of coupling measures between continuous signals and point processes. We first introduce martingale stochastic integration theory as a mathematical model for a family of statistical quantities that include the phase locking value, a classical coupling measure to characterize complex dynamics. Based on the martingale central limit theorem, we can then derive the asymptotic gaussian distribution of estimates of such coupling measure that can be exploited for statistical testing. Second, based on multivariate extensions of this result and random matrix theory, we establish a principled way to analyze the low-rank coupling between a large number of point processes and continuous signals. For a null hypothesis of no coupling, we establish sufficient conditions for the empirical distribution of squared singular values of the matrix to converge, as the number of measured signals increases, to the well-known Marchenko-Pastur (MP) law, and the largest squared singular value converges to the upper end of the MP support. This justifies a simple thresholding approach to assess the significance of multivariate coupling. Finally, we illustrate with simulations the relevance of our univariate and multivariate results in the context of neural time series, addressing how to reliably quantify the interplay between multichannel local field potential signals and the spiking activity of a large population of neurons.

Functional brain network topology across the menstrual cycle is estradiol dependent and correlates with individual well-being

The menstrual cycle (MC) is a sex hormone‐related phenomenon that repeats itself cyclically during the woman's reproductive life. In this explorative study, we hypothesized that coordinated variations of multiple sex hormones may affect the large‐scale organization of the brain functional network and that, in turn, such changes might have psychological correlates, even in the absence of overt clinical signs of anxiety and/or depression. To test our hypothesis, we investigated longitudinally, across the MC, the relationship between the sex hormones and both brain network and psychological changes. We enrolled 24 naturally cycling women and, at the early‐follicular, peri‐ovulatory, and mid‐luteal phases of the MC, we performed: (a) sex hormone dosage, (b) magnetoencephalography recording to study the brain network topology, and (c) psychological questionnaires to quantify anxiety, depression, self‐esteem, and well‐being. We showed that during the peri‐ovulatory phase, in the alpha band, the leaf fraction and the tree hierarchy of the brain network were reduced, while the betweenness centrality (BC) of the right posterior cingulate gyrus (rPCG) was increased. Furthermore, the increase in BC was predicted by estradiol levels. Moreover, during the luteal phase, the variation of estradiol correlated positively with the variations of both the topological change and environmental mastery dimension of the well‐being test, which, in turn, was related to the increase in the BC of rPCG. Our results highlight the effects of sex hormones on the large‐scale brain network organization as well as on their possible relationship with the psychological state across the MC. Moreover, the fact that physiological changes in the brain topology occur throughout the MC has widespread implications for neuroimaging studies.

Stimulus-induced gamma rhythms are weaker in human elderly with mild cognitive impairment and Alzheimer's disease

Alzheimer's disease (AD) in elderly adds substantially to socioeconomic burden necessitating early diagnosis. While recent studies in rodent models of AD have suggested diagnostic and therapeutic value for gamma rhythms in brain, the same has not been rigorously tested in humans. In this case-control study, we recruited a large population (N = 244; 106 females) of elderly (>49 years) subjects from the community, who viewed large gratings that induced strong gamma oscillations in their electroencephalogram (EEG). These subjects were classified as healthy (N = 227), mild cognitively impaired (MCI; N = 12), or AD (N = 5) based on clinical history and Clinical Dementia Rating scores. Surprisingly, stimulus-induced gamma rhythms, but not alpha or steady-state visually evoked responses, were significantly lower in MCI/AD subjects compared to their age- and gender-matched controls. This reduction was not due to differences in eye movements or baseline power. Our results suggest that gamma could be used as a potential screening tool for MCI/AD in humans.

Dichotic listening deficits in amblyaudia are characterized by aberrant neural oscillations in auditory cortex

OBJECTIVE

Children diagnosed with auditory processing disorder (APD) show deficits in processing complex sounds that are associated with difficulties in higher-order language, learning, cognitive, and communicative functions. Amblyaudia (AMB) is a subcategory of APD characterized by abnormally large ear asymmetries in dichotic listening tasks.

METHODS

Here, we examined frequency-specific neural oscillations and functional connectivity via high-density electroencephalography (EEG) in children with and without AMB during passive listening of nonspeech stimuli.

RESULTS

Time-frequency maps of these "brain rhythms" revealed stronger phase-locked beta-gamma (~35 Hz) oscillations in AMB participants within bilateral auditory cortex for sounds presented to the right ear, suggesting a hypersynchronization and imbalance of auditory neural activity. Brain-behavior correlations revealed neural asymmetries in cortical responses predicted the larger than normal right-ear advantage seen in participants with AMB. Additionally, we found weaker functional connectivity in the AMB group from right to left auditory cortex, despite their stronger neural responses overall.

CONCLUSION

Our results reveal abnormally large auditory sensory encoding and an imbalance in communication between cerebral hemispheres (ipsi- to -contralateral signaling) in AMB.

SIGNIFICANCE

These neurophysiological changes might lead to the functionally poorer behavioral capacity to integrate information between the two ears in children with AMB.

Stochastic resonance and 'gamma band' synchronization in the human visual system

Cortical synchronization in the gamma-frequency range (above ~30.0 Hz) and the signal/noise interplay described by stochastic resonance models have been proposed as basic mechanisms in neuronal synchronization and sensory information processing, particularly in vision. Here we report an observation in humans of linear and inverted-U distributions of the electrophysiological (EEG) responses to visual contrast stimulation in the gamma band and in the low frequency components of the visual evoked responses (VER), respectively. The combination of linear and inverted-U distributions is described by a stochastic resonance model (SR). The observation needs replication in larger subjects' samples. It nevertheless adds to the available evidence of a role of gamma oscillatory signals and SR mechanisms in neuronal synchronization and visual processing. Some functional adaptation in human vision appears conceivable and further investigation is warranted.

High-Frequency Visual Stimulation Primes Gamma Oscillations for Visually Evoked Phase Reset and Enhances Spatial Acuity

The temporal frequency of sensory stimulation is a decisive factor in the plasticity of perceptual detection thresholds. However, surprisingly little is known about how distinct temporal parameters of sensory input differentially recruit activity of neuronal circuits in sensory cortices. Here we demonstrate that brief repetitive visual stimulation induces long-term plasticity of visual responses revealed 24 h after stimulation and that the location and generalization of visual response plasticity is determined by the temporal frequency of the visual stimulation. Brief repetitive low-frequency stimulation (2 Hz) is sufficient to induce a visual response potentiation that is expressed exclusively in visual cortex layer 4 and in response to a familiar stimulus. In contrast, brief, repetitive high-frequency stimulation (HFS, 20 Hz) is sufficient to induce a visual response potentiation that is expressed in all cortical layers and transfers to novel stimuli. HFS induces a long-term suppression of the activity of fast-spiking interneurons and primes ongoing gamma oscillatory rhythms for phase reset by subsequent visual stimulation. This novel form of generalized visual response enhancement induced by HFS is paralleled by an increase in visual acuity, measured as improved performance in a visual detection task.

m.3243A > G-Induced Mitochondrial Dysfunction Impairs Human Neuronal Development and Reduces Neuronal Network Activity and Synchronicity

Epilepsy, intellectual and cortical sensory deficits, and psychiatric manifestations are the most frequent manifestations of mitochondrial diseases. How mitochondrial dysfunction affects neural structure and function remains elusive, mostly because of a lack of proper in vitro neuronal model systems with mitochondrial dysfunction. Leveraging induced pluripotent stem cell technology, we differentiated excitatory cortical neurons (iNeurons) with normal (low heteroplasmy) and impaired (high heteroplasmy) mitochondrial function on an isogenic nuclear DNA background from patients with the common pathogenic m.3243A > G variant of mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS). iNeurons with high heteroplasmy exhibited mitochondrial dysfunction, delayed neural maturation, reduced dendritic complexity, and fewer excitatory synapses. Micro-electrode array recordings of neuronal networks displayed reduced network activity and decreased synchronous network bursting. Impaired neuronal energy metabolism and compromised structural and functional integrity of neurons and neural networks could be the primary drivers of increased susceptibility to neuropsychiatric manifestations of mitochondrial disease.