The escalating global burden of age-related neurodegenerative diseases and associated healthcare costs necessitates innovative interventions to stabilize or enhance cognitive functions. Deficits in working memory (WM) are linked to alterations in prefrontal theta-gamma cross-frequency coupling. Low-intensity transcranial alternating current stimulation (tACS) has emerged as a non-invasive, low-cost approach capable of modulating ongoing oscillations in targeted brain areas through entrainment. This study investigates the impact of multi-session peak-coupled theta-gamma cross-frequency tACS administered to the dorsolateral prefrontal cortex (DLPFC) on WM performance in older adults. In a randomized, sham-controlled, triple-blinded design, 77 participants underwent 16 stimulation sessions over six weeks while performing n-back tasks. Signal detection measures revealed increased 2-back sensitivity and robust modulations of response bias, indicating improved WM and decision-making adaptations, respectively. No effects were observed in the 1-back condition, emphasizing dependencies on cognitive load. Repeated tACS reinforces behavioral changes, indicated by increasing effect sizes. This study supports prior research correlating prefrontal theta-gamma coupling with WM processes and provides unique insights into the neurocognitive benefits of repeated tACS intervention. The well-tolerated and highly effective multi-session tACS intervention among the elderly underscores its therapeutic potential in vulnerable populations.

The electrical activity of the neural processes involved in cognitive functions is captured in EEG signals, allowing the exploration of the integration and coordination of neuronal oscillations across multiple spatiotemporal scales. We have proposed a novel approach that combines the transformation of EEG signal into image sequences, considering cross-frequency phase synchronisation (CFS) dynamics involved in low-level auditory processing, with the development of a two-stage deep learning model for the detection of developmental dyslexia (DD). This deep learning model exploits spatial and temporal information preserved in the image sequences to find discriminative patterns of phase synchronisation over time achieving a balanced accuracy of up to 83%. This result supports the existence of differential brain synchronisation dynamics between typical and dyslexic seven-year-old readers. Furthermore, we have obtained interpretable representations using a novel feature mask to link the most relevant regions during classification with the cognitive processes attributed to normal reading and those corresponding to compensatory mechanisms found in dyslexia.

UNASSIGNED: West syndrome (WS) is a devastating epileptic encephalopathy with onset in infancy and early childhood. It is characterized by clustered epileptic spasms, developmental arrest, and interictal hypsarrhythmia on electroencephalogram (EEG). Hypsarrhythmia is considered the hallmark of WS, but its visual assessment is challenging due to its wide variability and lack of a quantifiable definition. This study aims to analyze the EEG patterns in WS and identify computational diagnostic biomarkers of the disease.
UNASSIGNED: Linear and non-linear features derived from EEG recordings of 31 WS patients and 20 age-matched controls were compared. Subsequently, the correlation of the identified features with structural and genetic abnormalities was investigated.
UNASSIGNED: WS patients showed significantly elevated alpha-band activity (0.2516 vs. 0.1914, p < 0.001) and decreased delta-band activity (0.5117 vs. 0.5479, p < 0.001), particularly in the occipital region, as well as globally strengthened theta-band activity (0.2145 vs. 0.1655, p < 0.001) in power spectrum analysis. Moreover, wavelet-bicoherence analysis revealed significantly attenuated cross-frequency coupling in WS patients. Additionally, bi-channel coherence analysis indicated minor connectivity alterations in WS patients. Among the four non-linear characteristics of the EEG data (i.e., approximate entropy, sample entropy, permutation entropy, and wavelet entropy), permutation entropy showed the most prominent global reduction in the EEG of WS patients compared to controls (1.4411 vs. 1.5544, p < 0.001). Multivariate regression results suggested that genetic etiologies could influence the EEG profiles of WS, whereas structural factors could not.
UNASSIGNED: A combined global strengthening of theta activity and global reduction of permutation entropy can serve as computational EEG biomarkers for WS. Implementing these biomarkers in clinical practice may expedite diagnosis and treatment in WS, thereby improving long-term outcomes.

UNASSIGNED: Cross frequency coupling (CFC) between electrophysiological signals in the brain is a long-studied phenomenon and its abnormalities have been observed in conditions such as Parkinson\'s disease and epilepsy. More recently, CFC has been observed in stomach-brain electrophysiologic studies and thus becomes an enticing possible target for diseases involving aberrations of the gut-brain axis. However, current methods of detecting coupling, specifically phase-amplitude coupling (PAC), do not attempt to capture the phase and amplitude statistical relationships.
UNASSIGNED: In this paper, we first demonstrate a method of modeling these joint statistics with a flexible parametric approach, where we model the conditional distribution of amplitude given phase using a gamma distributed generalized linear model (GLM) with a Fourier basis of regressors. We perform model selection with minimum description length (MDL) principle, demonstrate a method for assessing goodness-of-fit (GOF), and showcase the efficacy of this approach in multiple electroencephalography (EEG) datasets. Secondly, we showcase how we can utilize the mutual information, which operates on the joint distribution, as a canonical measure of coupling, as it is non-zero and non-negative if and only if the phase and amplitude are not statistically independent. In addition, we build off of previous work by Martinez-Cancino et al., and Voytek et al., and show that the information density, evaluated using our method along the given sample path, is a promising measure of time-resolved PAC.
UNASSIGNED: Using synthetically generated gut-brain coupled signals, we demonstrate that our method outperforms the existing gold-standard methods for detectable low-levels of phase-amplitude coupling through receiver operating characteristic (ROC) curve analysis. To validate our method, we test on invasive EEG recordings by generating comodulograms, and compare our method to the gold standard PAC measure, Modulation Index, demonstrating comparable performance in exploratory analysis. Furthermore, to showcase its use in joint gut-brain electrophysiology data, we generate topoplots of simultaneous high-density EEG and electrgastrography recordings and reproduce seminal work by Richter et al. that demonstrated the existence of gut-brain PAC. Using simulated data, we validate our method for different types of time-varying coupling and then demonstrate its performance to track time-varying PAC in sleep spindle EEG and mismatch negativity (MMN) datasets.
UNASSIGNED: Our new measure of PAC using Gamma GLMs and mutual information demonstrates a promising new way to compute PAC values using the full joint distribution on amplitude and phase. Our measure outperforms the most common existing measures of PAC, and show promising results in identifying time varying PAC in electrophysiological datasets. In addition, we provide for using our method with multiple comparisons and show that our measure potentially has more statistical power in electrophysiologic recordings using simultaneous gut-brain datasets.

Recently, neuroscience has seen a shift from localist approaches to network-wide investigations of brain function. Neurophysiological signals across different spatial and temporal scales provide insight into neural communication. However, additional methodological considerations arise when investigating network-wide brain dynamics rather than local effects. Specifically, larger amounts of data, investigated across a higher dimensional space, are necessary. Here, we present FiNN (Find Neurophysiological Networks), a novel toolbox for the analysis of neurophysiological data with a focus on functional and effective connectivity. FiNN provides a wide range of data processing methods and statistical and visualization tools to facilitate inspection of connectivity estimates and the resulting metrics of brain dynamics. The Python toolbox and its documentation are freely available as Supporting Information. We evaluated FiNN against a number of established frameworks on both a conceptual and an implementation level. We found FiNN to require much less processing time and memory than other toolboxes. In addition, FiNN adheres to a design philosophy of easy access and modifiability, while providing efficient data processing implementations. Since the investigation of network-level neural dynamics is experiencing increasing interest, we place FiNN at the disposal of the neuroscientific community as open-source software.

Although one can recognize the environment by soundscape substituting vision to auditory signal, whether subjects could perceive the soundscape as visual or visual-like sensation has been questioned. In this study, we investigated hierarchical process to elucidate the recruitment mechanism of visual areas by soundscape stimuli in blindfolded subjects. Twenty-two healthy subjects were repeatedly trained to recognize soundscape stimuli converted by visual shape information of letters. An effective connectivity method called dynamic causal modeling (DCM) was employed to reveal how the brain was hierarchically organized to recognize soundscape stimuli. The visual mental imagery model generated cortical source signals of five regions of interest better than auditory bottom-up, cross-modal perception, and mixed models. Spectral couplings between brain areas in the visual mental imagery model were analyzed. While within-frequency coupling is apparent in bottom-up processing where sensory information is transmitted, cross-frequency coupling is prominent in top-down processing, corresponding to the expectation and interpretation of information. Sensory substitution in the brain of blindfolded subjects derived visual mental imagery by combining bottom-up and top-down processing.

UNASSIGNED: Cognitive deficits observed in Alzheimer\'s disease (AD) patients have been correlated with altered hippocampal activity. Although the mechanism remains under extensive study, neurofibrillary tangles and amyloid plaques have been proposed as responsible for brain activity alterations. Aiming to unveil the mechanism, researchers have developed several transgenic models of AD. Nevertheless, the variability in hippocampal oscillatory alterations found in different genetic backgrounds and ages remains unclear.
UNASSIGNED: To assess the oscillatory alterations in relation to animal developmental age and protein inclusion, amyloid-β (Aβ) load, and abnormally phosphorylated tau (pTau), we reviewed and analyzed the published data on peak power, frequency, and quantification of theta-gamma cross-frequency coupling (modulation index values).
UNASSIGNED: To ensure that the search was as current as possible, a systematic review was conducted to locate and abstract all studies published from January 2000 to February 2023 that involved in vivo hippocampal local field potential recording in transgenic mouse models of AD.
UNASSIGNED: The presence of Aβ was associated with electrophysiological alterations that are mainly reflected in power increases, frequency decreases, and lower modulation index values. Concomitantly, pTau accumulation was associated with electrophysiological alterations that are mainly reflected in power decreases, frequency decreases, and no significant alterations in modulation index values.
UNASSIGNED: In this study, we showed that electrophysiological parameters are altered from prodromal stages to the late stages of pathology. Thus, we found that Aβ deposition is associated with brain network hyperexcitability, whereas pTau deposition mainly leads to brain network hypoexcitability in transgenic models.

High frequency oscillations are important novel biomarkers of epileptogenic tissue. The interaction of oscillations across different time scales is revealed as cross-frequency coupling (CFC) representing a high-order structure in the functional organization of brain rhythms. New artificial intelligence methods such as deep learning neural networks can provide powerful tools for automated analysis of EEG. Here we present a Stacked Sparse Autoencoder (SSAE) trained to recognize absence seizure activity based on the cross-frequency patterns within scalp EEG. We used EEG records from the Temple University Hospital database. Absence seizures (n = 94) from 12 patients were taken into analysis along with segments of background activity. Half of the records were selected randomly for network training and the second half were used for testing. Power-to-power coupling was calculated between all frequencies 2-120 Hz pairwise using the EEGLAB toolbox. The resulting CFC matrices were used as training or testing inputs to the autoencoder. The trained network was able to recognize background and seizure segments (not used in training) with a sensitivity of 96.3%, specificity of 99.8% and overall accuracy of 98.5%. Our results provide evidence that the SSAE neural networks can be used for automated detection of absence seizures within scalp EEG.

As a crucial component of the cerebral cholinergic system and the Papez circuit in the basal forebrain, dysfunction of the nucleus basalis of Meynert (NBM) is associated with various neurodegenerative disorders. However, no drugs, including existing cholinesterase inhibitors, have been shown to reverse this dysfunction. Due to advancements in neuromodulation technology, researchers are exploring the use of deep brain stimulation (DBS) therapy targeting the NBM (NBM-DBS) to treat mental and neurological disorders as well as the related mechanisms. Herein, we provided an update on the research progress on cognition-related neural network oscillations and complex anatomical and projective relationships between the NBM and other cognitive structures and circuits. Furthermore, we reviewed previous animal studies of NBM lesions, NBM-DBS models, and clinical case studies to summarize the important functions of the NBM in neuromodulation. In addition to elucidating the mechanism of the NBM neural network, future research should focus on to other types of neurons in the NBM, despite the fact that cholinergic neurons are still the key target for cell type-specific activation by DBS.

Applications of causal techniques to neural time series have increased extensively over last decades, including a wide and diverse family of methods focusing on electroencephalogram (EEG) analysis. Besides connectivity inferred in defined frequency bands, there is a growing interest in the analysis of cross-frequency interactions, in particular phase and amplitude coupling and directionality. Some studies show contradicting results of coupling directionality from high frequency to low frequency signal components, in spite of generally considered modulation of a high-frequency amplitude by a low-frequency phase. We have compared two widely used methods to estimate the directionality in cross frequency coupling: conditional mutual information (CMI) and phase slope index (PSI). The latter, applied to infer cross-frequency phase-amplitude directionality from animal intracranial recordings, gives opposite results when comparing to CMI. Both metrics were tested in a numerically simulated example of unidirectionally coupled Rössler systems, which helped to find the explanation of the contradictory results: PSI correctly estimates the lead/lag relationship which, however, is not generally equivalent to causality in the sense of directionality of coupling in nonlinear systems, correctly inferred by using CMI with surrogate data testing.