Processes and measurements: a framework for understanding neural oscillations in field potentials.

Various neuroscientific theories maintain that brain oscillations are important for neuronal computation, but opposing views claim that these macroscale dynamics are 'exhaust fumes' of more relevant processes. Here, we approach the question of whether oscillations are functional or epiphenomenal by distinguishing between measurements and processes, and by reviewing whether causal or inferentially useful links exist between field potentials, electric fields, and neurobiological events. We introduce a vocabulary for the role of brain signals and their underlying processes, demarcating oscillations as a distinct entity where both processes and measurements can exhibit periodicity.

Generating realistic neurophysiological time series with denoising diffusion probabilistic models.

Denoising diffusion probabilistic models (DDPMs) have recently been shown to accurately generate complicated data such as images, audio, or time series. Experimental and clinical neuroscience also stand to benefit from this progress, as the accurate generation of neurophysiological time series can enable or improve many neuroscientific applications. Here, we present a flexible DDPM-based method for modeling multichannel, densely sampled neurophysiological recordings.

PINK1-Mediated Mitochondrial Activity Confers Olaparib Resistance in Prostate Cancer Cells.

Olaparib, a PARP inhibitor, is effective against various cancers, including prostate cancer. However, resistance to olaparib poses a significant challenge. This study uncovers that mitochondrial alterations and PINK1 gene overexpression contribute to this resistance in prostate cancer cells.

Evaluating and Comparing Measures of Aperiodic Neural Activity.

Neuro-electrophysiological recordings contain prominent aperiodic activity - meaning irregular activity, with no characteristic frequency - which has variously been referred to as 1/f (or 1/f-like activity), fractal, or 'scale-free' activity. Previous work has established that aperiodic features of neural activity is dynamic and variable, relating (between subjects) to healthy aging and to clinical diagnoses, and also (within subjects) tracking conscious states and behavioral performance. There are, however, a wide variety of conceptual frameworks and associated methods for the analyses and interpretation of aperiodic activity - for example, time domain measures such as the autocorrelation, fractal measures, and/or various complexity and entropy measures, as well as measures of the aperiodic exponent in the frequency domain.

LX1 Dual Targets AR Variants and AKR1C3 in Advanced Prostate Cancer Therapy.

The development of resistance to current standard-of-care treatments, such as androgen receptor (AR) targeting therapies, remains a major challenge in the management of advanced prostate cancer. There is an urgent need for new therapeutic strategies targeting key resistant drivers, such as AR variants like AR-V7, and steroidogenic enzymes, such as aldo-keto reductase 1C3 (AKR1C3), to overcome drug resistance and improve outcomes for patients with advanced prostate cancer. Here, we have designed, synthesized, and characterized a novel class of LX compounds targeting both the AR/AR variants and AKR1C3 pathways.

Development of neuronal timescales in human cortical organoids and rat hippocampus dissociated cultures.

To support complex cognition, neuronal circuits must integrate information across multiple temporal scales, ranging from milliseconds to decades. Neuronal timescales describe the duration over which activity within a network persists, posing a putative explanatory mechanism for how information might be integrated over multiple temporal scales. Little is known about how timescales develop in human neural circuits or other model systems, limiting insight into how the functional dynamics necessary for cognition emerge.

Simulation-based inference for efficient identification of generative models in computational connectomics.

Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neuronal networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters, and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning.

Flexible and efficient simulation-based inference for models of decision-making.

Inferring parameters of computational models that capture experimental data are a central task in cognitive neuroscience. Bayesian statistical inference methods usually require the ability to evaluate the likelihood of the model-however, for many models of interest in cognitive neuroscience, the associated likelihoods cannot be computed efficiently. Simulation-based inference (SBI) offers a solution to this problem by only requiring access to simulations produced by the model.

Parameterizing neural power spectra into periodic and aperiodic components.

Electrophysiological signals exhibit both periodic and aperiodic properties. Periodic oscillations have been linked to numerous physiological, cognitive, behavioral and disease states. Emerging evidence demonstrates that the aperiodic component has putative physiological interpretations and that it dynamically changes with age, task demands and cognitive states.

Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture.

Complex cognitive functions such as working memory and decision-making require information maintenance over seconds to years, from transient sensory stimuli to long-term contextual cues. While theoretical accounts predict the emergence of a corresponding hierarchy of neuronal timescales, direct electrophysiological evidence across the human cortex is lacking. Here, we infer neuronal timescales from invasive intracranial recordings.

For the Sciences They Are A-Changin': A Response to Commentaries on Núñez et al.'s (2019) "What Happened to Cognitive Science?".

A recent issue of Topics in Cognitive Science featured 11 thoughtful commentaries responding to our article "What happened to cognitive science?" (Núñez et al., 2019). Here, we identify several themes that arose in those commentaries and respond to each.

NitroSynapsin ameliorates hypersynchronous neural network activity in Alzheimer hiPSC models.

Beginning at early stages, human Alzheimer's disease (AD) brains manifest hyperexcitability, contributing to subsequent extensive synapse loss, which has been linked to cognitive dysfunction. No current therapy for AD is disease-modifying. Part of the problem with AD drug discovery is that transgenic mouse models have been poor predictors of potential human treatment.

Complex Oscillatory Waves Emerging from Cortical Organoids Model Early Human Brain Network Development.

Structural and transcriptional changes during early brain maturation follow fixed developmental programs defined by genetics. However, whether this is true for functional network activity remains unknown, primarily due to experimental inaccessibility of the initial stages of the living human brain. Here, we developed human cortical organoids that dynamically change cellular populations during maturation and exhibited consistent increases in electrical activity over the span of several months.

What happened to cognitive science?

More than a half-century ago, the 'cognitive revolution', with the influential tenet 'cognition is computation', launched the investigation of the mind through a multidisciplinary endeavour called cognitive science. Despite significant diversity of views regarding its definition and intended scope, this new science, explicitly named in the singular, was meant to have a cohesive subject matter, complementary methods and integrated theories. Multiple signs, however, suggest that over time the prospect of an integrated cohesive science has not materialized.

Setd5 haploinsufficiency alters neuronal network connectivity and leads to autistic-like behaviors in mice.

SETD5, a gene linked to intellectual disability (ID) and autism spectrum disorder (ASD), is a member of the SET-domain family and encodes a putative histone methyltransferase (HMT). To date, the mechanism by which SETD5 haploinsufficiency causes ASD/ID remains an unanswered question. Setd5 is the highly conserved mouse homolog, and although the Setd5 null mouse is embryonic lethal, the heterozygote is viable.

Inferring synaptic excitation/inhibition balance from field potentials.

Neural circuits sit in a dynamic balance between excitation (E) and inhibition (I). Fluctuations in E:I balance have been shown to influence neural computation, working memory, and information flow, while more drastic shifts and aberrant E:I patterns are implicated in numerous neurological and psychiatric disorders. Current methods for measuring E:I dynamics require invasive procedures that are difficult to perform in behaving animals, and nearly impossible in humans.

Interpreting the electrophysiological power spectrum.

Recent experimental findings suggest that there may be rich physiological information embedded within the power spectrum of neurophysiological recordings, which, in addition to power in specific oscillatory frequencies, can be extracted with the appropriate model. This article reviews previous empirical and modeling results, as well as the canonical power law model that is often used to describe the power spectrum. In addition, a modified power law model with parameters estimating synaptic and spiking contributions is proposed.