Complexity in neuronal noise depends on network interconnectivity

Increased excitability of dentate gyrus mossy cells occurs early in life in the Tg2576 model of Alzheimer's disease

BACKGROUND

Hyperexcitability in Alzheimer's disease (AD) is proposed to emerge early and contribute to disease progression. The dentate gyrus (DG) and its primary cell type, granule cells (GCs) are implicated in hyperexcitability in AD. Hence, we hypothesized that mossy cells (MCs), important regulators of GC excitability, contribute to early hyperexcitability in AD. Indeed, MCs and GCs are linked to hyperexcitability in epilepsy.

METHODS

Using the Tg2576 model of AD and WT mice (~ 1 month-old), we compared MCs and GCs electrophysiologically and morphologically, assessed the activity marker c-Fos, Aβ expression and a hippocampal- and MC-dependent memory task that is impaired at 3-4 months of age in Tg2576 mice.

RESULTS

Tg2576 MCs had increased spontaneous excitatory events (sEPSP/Cs) and decreased spontaneous inhibitory currents (sIPSCs), increasing the excitation/inhibition ratio. Additionally, Tg2576 MC intrinsic excitability was enhanced. Consistent with in vitro results, Tg2576 MCs showed enhanced c-Fos protein expression. Tg2576 MCs had increased intracellular Aβ expression, suggesting a reason for increased excitability. GCs showed increased excitatory and inhibitory input without changes in intrinsic properties, consistent with effects of increased MC activity. In support, increased GC activity was normalized by an antagonist of MC input to GCs. Also in support, Tg2576 MC axons showed sprouting to the area of GC dendrites. These effects occurred before an impairment in the memory task, suggesting they are extremely early alterations.

CONCLUSIONS

Alterations in Tg2576 MCs and GCs early in life suggest an early role for MCs in increased GC excitability. MCs may be a novel target to intervene in AD pathophysiology at early stages.

Epileptic network identification: insights from dynamic mode decomposition of sEEG data

Objective.For medically-refractory epilepsy patients, stereoelectroencephalography (sEEG) is a surgical method using intracranial electrode recordings to identify brain networks participating in early seizure organization and propagation (i.e. the epileptogenic zone, EZ). If identified, surgical EZ treatment via resection, ablation or neuromodulation can lead to seizure-freedom. To date, quantification of sEEG data, including its visualization and interpretation, remains a clinical and computational challenge. Given elusiveness of physical laws or governing equations modelling complex brain dynamics, data science offers unique insight into identifying unknown patterns within high-dimensional sEEG data. We apply here an unsupervised data-driven algorithm, dynamic mode decomposition (DMD), to sEEG recordings from five focal epilepsy patients (three with temporal lobe, and two with cingulate epilepsy), who underwent subsequent resective or ablative surgery and became seizure free.Approach.DMD obtains a linear approximation of nonlinear data dynamics, generating coherent structures ('modes') defining important signal features, used to extract frequencies, growth rates and spatial structures. DMD was adapted to produce dynamic modal maps (DMMs) across frequency sub-bands, capturing onset and evolution of epileptiform dynamics in sEEG data. Additionally, we developed a static estimate of EZ-localized electrode contacts, termed the higher-frequency mode-based norm index (MNI). DMM and MNI maps for representative patient seizures were validated against clinical sEEG results and seizure-free outcomes following surgery.Main results.DMD was most informative at higher frequencies, i.e. gamma (including high-gamma) and beta range, successfully identifying EZ contacts. Combined interpretation of DMM/MNI plots best identified spatiotemporal evolution of mode-specific network changes, with strong concordance to sEEG results and outcomes across all five patients. The method identified network attenuation in other contacts not implicated in the EZ.Significance.This is the first application of DMD to sEEG data analysis, supporting integration of neuroengineering, mathematical and machine learning methods into traditional workflows for sEEG review and epilepsy surgical decision-making.

Increased excitability of dentate gyrus mossy cells occurs early in life in the Tg2576 model of Alzheimer's disease

INTRODUCTION

Hyperexcitability in Alzheimer's disease (AD) emerge early and contribute to disease progression. The dentate gyrus (DG) is implicated in hyperexcitability in AD. We hypothesized that mossy cells (MCs), regulators of DG excitability, contribute to early hyperexcitability in AD. Indeed, MCs generate hyperexcitability in epilepsy.

METHODS

Using the Tg2576 model and WT mice (∼1month-old), we compared MCs electrophysiologically, assessed c-Fos activity marker, Aβ expression and mice performance in a hippocampal-dependent memory task.

RESULTS

Tg2576 MCs exhibit increased spontaneous excitatory events and decreased inhibitory currents, increasing the charge transfer excitation/inhibition ratio. Tg2576 MC intrinsic excitability was enhanced, and showed higher c-Fos, intracellular Aβ expression, and axon sprouting. Granule cells only showed changes in synaptic properties, without intrinsic changes. The effects occurred before a memory task is affected.

DISCUSSION

Early electrophysiological and morphological alterations in Tg2576 MCs are consistent with enhanced excitability, suggesting an early role in DG hyperexcitability and AD pathophysiology.

HIGHLIGHTS

∘ MCs from 1 month-old Tg2576 mice had increased spontaneous excitatory synaptic input. ∘ Tg2576 MCs had reduced spontaneous inhibitory synaptic input. ∘ Several intrinsic properties were abnormal in Tg2576 MCs. ∘ Tg2576 GCs had enhanced synaptic excitation but no changes in intrinsic properties. ∘ Tg2576 MCs exhibited high c-Fos expression, soluble Aβ and axonal sprouting.

Detection of Emotional Sensitivity Using fNIRS Based Dynamic Functional Connectivity

In this study, we proposed an analytical framework to identify dynamic task-based functional connectivity (FC) features as new biomarkers of emotional sensitivity in nursing students, by using a combination of unsupervised and supervised machine learning techniques. The dynamic FC was measured by functional Near-Infrared Spectroscopy (fNIRS), and computed using a sliding window correlation (SWC) analysis. A k -means clustering technique was applied to derive four recurring connectivity states. The states were characterized by both graph theory and semi-metric analysis. Occurrence probability and state transition were extracted as dynamic FC network features, and a Random Forest (RF) classifier was implemented to detect emotional sensitivity. The proposed method was trialled on 39 nursing students and 19 registered nurses during decision-making, where we assumed registered nurses have developed strategies to cope with emotional sensitivity. Emotional stimuli were selected from International Affective Digitized Sound System (IADS) database. Experiment results showed that registered nurses demonstrated single dominant connectivity state of task-relevance, while nursing students displayed in two states and had higher level of task-irrelevant state connectivity. The results also showed that students were more susceptive to emotional stimuli, and the derived dynamic FC features provided a stronger discriminating power than heart rate variability (accuracy of 81.65% vs 71.03%) as biomarkers of emotional sensitivity. This work forms the first study to demonstrate the stability of fNIRS based dynamic FC states as a biomarker. In conclusion, the results support that the state distribution of dynamic FC could help reveal the differentiating factors between the nursing students and registered nurses during decision making, and it is anticipated that the biomarkers might be used as indicators when developing professional training related to emotional sensitivity.

Early changes in synaptic and intrinsic properties of dentate gyrus granule cells in a mouse model of Alzheimer's disease neuropathology and atypical effects of the cholinergic antagonist atropine

It has been reported that hyperexcitability occurs in a subset of patients with Alzheimer's disease (AD) and hyperexcitability could contribute to the disease. Several studies have suggested that the hippocampal dentate gyrus (DG) may be an important area where hyperexcitability occurs. Therefore, we tested the hypothesis that the principal DG cell type, granule cells (GCs), would exhibit changes at the single-cell level which would be consistent with hyperexcitability and might help explain it. We used the Tg2576 mouse, where it has been shown that hyperexcitability is robust at 2-3 months of age. GCs from 2 to 3-month-old Tg2576 mice were compared to age-matched wild type (WT) mice. Effects of muscarinic cholinergic antagonism were tested because previously we found that Tg2576 mice exhibited hyperexcitability in vivo that was reduced by the muscarinic cholinergic antagonist atropine, counter to the dogma that in AD one needs to boost cholinergic function. The results showed that GCs from Tg2576 mice exhibited increased frequency of spontaneous excitatory postsynaptic potentials/currents (sEPSP/Cs) and reduced frequency of spontaneous inhibitory synaptic events (sIPSCs) relative to WT, increasing the excitation:inhibition (E:I) ratio. There was an inward NMDA receptor-dependent current that we defined here as a novel synaptic current (nsC) in Tg2576 mice because it was very weak in WT mice. Intrinsic properties were distinct in Tg2576 GCs relative to WT. In summary, GCs of the Tg2576 mouse exhibit early electrophysiological alterations that are consistent with increased synaptic excitation, reduced inhibition, and muscarinic cholinergic dysregulation. The data support previous suggestions that the DG contributes to hyperexcitability and there is cholinergic dysfunction early in life in AD mouse models.

1/f neural noise and electrophysiological indices of contextual prediction in aging

Prediction of upcoming words during reading has been suggested to enhance the efficiency of discourse processing. Emerging models have postulated that predictive mechanisms require synchronous firing of neural networks, but to date, this relationship has been investigated primarily through oscillatory activity in narrow frequency bands. A recently-developed measure proposed to reflect broadband neural activity - and thereby synchronous neuronal firing - is 1/f neural noise extracted from EEG spectral power. Previous research has indicated that this measure of 1/f neural noise changes across the lifespan, and these neural changes predict age-related behavioral impairments in visual working memory. Using a cross-sectional sample of young and older adults, we examined age-related changes in 1/f neural noise and whether this measure predicted ERP correlates of successful lexical prediction during discourse comprehension. 1/f neural noise across two different language tasks revealed high within-subject correlations, indicating that this measure can provide a reliable index of individualized patterns of neural activation. In addition to age, 1/f noise was a significant predictor of N400 effects of successful lexical prediction; however, noise did not mediate age-related declines in other ERP effects. We discuss broader implications of these findings for theories of predictive processing, as well as potential applications of 1/f noise across research populations.

Low frequency electric current noise in glioma cell populations

Measuring the electrical activity of large and defined populations of cells is currently a major technical challenge to electrophysiology, especially in the picoampere-range. For this purpose, we developed and applied a bidirectional transducer based on a chip with interdigitated gold electrodes to record the electrical response of cultured glioma cells. Recent research determined that also non-neural brain glia cells are electrically active and excitable. Their transformed counterparts, e.g. glioma cells, were suggested to partially retain these electric features. Such electrophysiological studies however are usually performed on individual cells and are limited in their predictive power for the overall electrical activity of the multicellular tumour bulk. Our extremely low-noise measuring system allowed us to detect not only prominent electrical bursts of neuronal cells but also minute, yet constantly occurring and functional, membrane capacitive current oscillations across large populations of C6 glioma cells, which we termed electric current noise. At the same time, tumour cells of non-brain origin (HeLa) proved to be electrically quiescent in comparison. Finally, we determined that the glioma cell activity is primarily caused by the opening of voltage-gated Na⁺ and K⁺ ion channels and can be efficiently abolished using specific pharmacological inhibitors. Thus, we offer here a unique approach for studying electrophysiological properties of large cancer cell populations as an in vitro reference for tumour bulks in vivo.

The stereotactic approach for mapping epileptic networks: a prospective study of 200 patients

Object

Stereoelectroencephalography (SEEG) is a methodology that permits accurate 3D in vivo electroclinical recordings of epileptiform activity. Among other general indications for invasive intracranial electroencephalography (EEG) monitoring, its advantages include access to deep cortical structures, its ability to localize the epileptogenic zone when subdural grids have failed to do so, and its utility in the context of possible multifocal seizure onsets with the need for bihemispheric explorations. In this context, the authors present a brief historical overview of the technique and report on their experience with 2 SEEG techniques (conventional Leksell frame-based stereotaxy and frameless stereotaxy under robotic guidance) for the purpose of invasively monitoring difficult-to-localize refractory focal epilepsy.

Methods

Over a period of 4 years, the authors prospectively identified 200 patients with refractory epilepsy who collectively underwent 2663 tailored SEEG electrode implantations for invasive intracranial EEG monitoring and extraoperative mapping. The first 122 patients underwent conventional Leksell frame-based SEEG electrode placement; the remaining 78 patients underwent frameless stereotaxy under robotic guidance, following acquisition of a stereotactic ROSA robotic device at the authors' institution. Electrodes were placed according to a preimplantation hypothesis of the presumed epileptogenic zone, based on a standardized preoperative workup including video-EEG monitoring, MRI, PET, ictal SPECT, and neuropsychological assessment. Demographic features, seizure semiology, number and location of implanted SEEG electrodes, and location of the epileptogenic zone were recorded and analyzed for all patients. For patients undergoing subsequent craniotomy for resection, the type of resection and procedure-related complications were prospectively recorded. These results were analyzed and correlated with pathological diagnosis and postoperative seizure outcomes.

Results

The epileptogenic zone was confirmed by SEEG in 154 patients (77%), of which 134 (87%) underwent subsequent craniotomy for epileptogenic zone resection. Within this cohort, 90 patients had a minimum follow-up of at least 12 months; therein, 61 patients (67.8%) remained seizure free, with an average follow-up period of 2.4 years. The most common pathological diagnosis was focal cortical dysplasia Type I (55 patients, 61.1%). Per electrode, the surgical complications included wound infection (0.08%), hemorrhagic complications (0.08%), and a transient neurological deficit (0.04%) in a total of 5 patients (2.5%). One patient (0.5%) ultimately died due to intracerebral hematoma directly ensuing from SEEG electrode placement.

Conclusions

Based on these results, SEEG methodology is safe, reliable, and effective. It is associated with minimal morbidity and mortality, and serves as a practical, minimally invasive approach to extraoperative localization of the epileptogenic zone in patients with refractory epilepsy.

Connectionist interpretation of the association between cognitive dissonance and attention switching

A novel connectionist model accounting for cognitive dissonance is described, in which the concepts of self and attention switching are considered. The model is composed of a unit corresponding to self, a bistable pair comprising two units relevant to two dissonant cognitions, and links whose weights correspond to cognitive evaluations. The model makes it possible to use mathematical formulas to represent the cognitive-dissonance process. Analysis reveals that the model fits experimental data of major paradigms in cognitive dissonance theory. The model shows that attention switching, which is produced by internal and external stimuli, causes building-up of cognitive dissonance and retardation of its reduction. The psychological phenomenon of selective exposure is interpreted on the basis of the operation of the model.

PHASE SYNCHRONIZATION OF NEURONAL NOISE IN MOUSE HIPPOCAMPAL EPILEPTIFORM DYNAMICS

Organized brain activity is the result of dynamical, segregated neuronal signals that may be used to investigate synchronization effects using sophisticated neuroengineering techniques. Phase synchrony analysis, in particular, has emerged as a promising methodology to study transient and frequency-specific coupling effects across multi-site signals. In this study, we investigated phase synchronization in intracellular recordings of interictal and ictal epileptiform events recorded from pairs of cells in the whole (intact) mouse hippocampus. In particular, we focused our analysis on the background noise-like activity (NLA), previously reported to exhibit complex neurodynamical properties. Our results show evidence for increased linear and nonlinear phase coupling in NLA across three frequency bands [theta (4–10 Hz), beta (12–30 Hz) and gamma (30–80 Hz)] in the ictal compared to interictal state dynamics. We also present qualitative and statistical evidence for increased phase synchronization in the theta, beta and gamma frequency bands from paired recordings of ictal NLA. Overall, our results validate the use of background NLA in the neurodynamical study of epileptiform transitions and suggest that what is considered "neuronal noise" is amenable to synchronization effects in the spatiotemporal domain.

Complexity and multifractality of neuronal noise in mouse and human hippocampal epileptiform dynamics

Fractal methods offer an invaluable means of investigating turbulent nonlinearity in non-stationary biomedical recordings from the brain. Here, we investigate properties of complexity (i.e. the correlation dimension, maximum Lyapunov exponent, 1/f(γ) noise and approximate entropy) and multifractality in background neuronal noise-like activity underlying epileptiform transitions recorded at the intracellular and local network scales from two in vitro models: the whole-intact mouse hippocampus and lesional human hippocampal slices. Our results show evidence for reduced dynamical complexity and multifractal signal features following transition to the ictal epileptiform state. These findings suggest that pathological breakdown in multifractal complexity coincides with loss of signal variability or heterogeneity, consistent with an unhealthy ictal state that is far from the equilibrium of turbulent yet healthy fractal dynamics in the brain. Thus, it appears that background noise-like activity successfully captures complex and multifractal signal features that may, at least in part, be used to classify and identify brain state transitions in the healthy and epileptic brain, offering potential promise for therapeutic neuromodulatory strategies for afflicted patients suffering from epilepsy and other related neurological disorders.