Layer-specific dynamics of local field potentials in monkey V1 during electrical stimulation.

The mammalian neocortex, organized into six cellular layers or laminae, forms a cortical network within layers. Layer specific computations are crucial for sensory processing of visual stimuli within primary visual cortex. Laminar recordings of local field potentials (LFPs) are a powerful tool to study neural activity within cortical layers.

Intrinsic dynamic shapes responses to external stimulation in the human brain.

Sensory stimulation of the brain reverberates in its recurrent neuronal networks. However, current computational models of brain activity do not separate immediate sensory responses from intrinsic recurrent dynamics. We apply a vector-autoregressive model with external input (VARX), combining the concepts of "functional connectivity" and "encoding models", to intracranial recordings in humans.

Cross-species striatal hubs: Linking anatomy to resting-state connectivity.

Corticostriatal connections are essential for motivation, cognition, and behavioral flexibility. There is broad interest in using resting-state functional magnetic resonance imaging (rs-fMRI) to link circuit dysfunction in these connections with neuropsychiatric disorders. In this paper, we used tract-tracing data from non-human primates (NHPs) to assess the likelihood of monosynaptic connections being represented in rs-fMRI data of NHPs and humans.

Laminar organization of visual responses in core and parabelt auditory cortex.

Audiovisual (AV) interaction has been shown in many studies of auditory cortex. However, the underlying processes and circuits are unclear because few studies have used methods that delineate the timing and laminar distribution of net excitatory and inhibitory processes within areas, much less across cortical levels. This study examined laminar profiles of neuronal activity in auditory core (AC) and parabelt (PB) cortices recorded from macaques during active discrimination of conspecific faces and vocalizations.

Closed-loop transfer enables artificial intelligence to yield chemical knowledge.

Artificial intelligence-guided closed-loop experimentation has emerged as a promising method for optimization of objective functions, but the substantial potential of this traditionally black-box approach to uncovering new chemical knowledge has remained largely untapped. Here we report the integration of closed-loop experiments with physics-based feature selection and supervised learning, denoted as closed-loop transfer (CLT), to yield chemical insights in parallel with optimization of objective functions. CLT was used to examine the factors dictating the photostability in solution of light-harvesting donor-acceptor molecules used in a variety of organic electronics applications, and showed fundamental insights including the importance of high-energy regions of the triplet state manifold.

Shape-persistent ladder molecules exhibit nanogap-independent conductance in single-molecule junctions.

Molecular electronic devices require precise control over the flow of current in single molecules. However, the electron transport properties of single molecules critically depend on dynamic molecular conformations in nanoscale junctions. Here we report a unique strategy for controlling molecular conductance using shape-persistent molecules.

Secondary structure determines electron transport in peptides.

Proteins play a key role in biological electron transport, but the structure-function relationships governing the electronic properties of peptides are not fully understood. Despite recent progress, understanding the link between peptide conformational flexibility, hierarchical structures, and electron transport pathways has been challenging. Here, we use single-molecule experiments, molecular dynamics (MD) simulations, nonequilibrium Green's function-density functional theory (NEGF-DFT), and unsupervised machine learning to understand the role of secondary structure on electron transport in peptides.

Multiple energy dissipation modes in dynamic polymer networks with neutral and ionic junctions.

Polymer networks with controlled ratios of neutral and ionic dynamic crosslink points were prepared from ethylene glycol, boric acid, and lithium hydroxide. Both neutral and ionic sites led to the emergence of distinct damping modes separate from the glass transition. This work highlights the potential of polymer networks for multimodal damping spectra through dynamic bond selection.

Beyond nothingness in the formation and functional relevance of voids in polymer films.

Voids-the nothingness-broadly exist within nanomaterials and impact properties ranging from catalysis to mechanical response. However, understanding nanovoids is challenging due to lack of imaging methods with the needed penetration depth and spatial resolution. Here, we integrate electron tomography, morphometry, graph theory and coarse-grained molecular dynamics simulation to study the formation of interconnected nanovoids in polymer films and their impacts on permeance and nanomechanical behaviour.

Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics.

We developed a detailed model of macaque auditory thalamocortical circuits, including primary auditory cortex (A1), medial geniculate body (MGB), and thalamic reticular nucleus, utilizing the NEURON simulator and NetPyNE tool. The A1 model simulates a cortical column with over 12,000 neurons and 25 million synapses, incorporating data on cell-type-specific neuron densities, morphology, and connectivity across six cortical layers. It is reciprocally connected to the MGB thalamus, which includes interneurons and core and matrix-layer-specific projections to A1.

Dissociation of Centrally and Peripherally Induced Transcranial Magnetic Stimulation Effects in Nonhuman Primates.

Transcranial magnetic stimulation (TMS) is a noninvasive brain stimulation method that is rapidly growing in popularity for studying causal brain-behavior relationships. However, its dose-dependent centrally induced neural mechanisms and peripherally induced sensory costimulation effects remain debated. Understanding how TMS stimulation parameters affect brain responses is vital for the rational design of TMS protocols.

Predicting the phase distribution during multi-channel transcranial alternating current stimulation in silico and in vivo.

Background: Transcranial alternating current stimulation (tACS) is a widely used noninvasive brain stimulation (NIBS) technique to affect neural activity. TACS experiments have been coupled with computational simulations to predict the electromagnetic fields within the brain. However, existing simulations are focused on the magnitude of the field.

An open-access dataset of naturalistic viewing using simultaneous EEG-fMRI.

In this work, we present a dataset that combines functional magnetic imaging (fMRI) and electroencephalography (EEG) to use as a resource for understanding human brain function in these two imaging modalities. The dataset can also be used for optimizing preprocessing methods for simultaneously collected imaging data. The dataset includes simultaneously collected recordings from 22 individuals (ages: 23-51) across various visual and naturalistic stimuli.

Unexpected Slow Relaxation Dynamics in Pure Ring Polymers Arise from Intermolecular Interactions.

Ring polymers have fascinated scientists for decades, but experimental progress has been challenging due to the presence of linear chain contaminants that fundamentally alter dynamics. In this work, we report the unexpected slow stress relaxation behavior of concentrated ring polymers that arises due to ring-ring interactions and ring packing structure. Topologically pure, high molecular weight ring polymers are prepared without linear chain contaminants using cyclic poly(phthalaldehyde) (cPPA), a metastable polymer chemistry that rapidly depolymerizes from free ends at ambient temperatures.

Open Macromolecular Genome: Generative Design of Synthetically Accessible Polymers.

A grand challenge in polymer science lies in the predictive design of new polymeric materials with targeted functionality. However, design of functional polymers is challenging due to the vast chemical space and an incomplete understanding of structure-property relations. Recent advances in deep generative modeling have facilitated the efficient exploration of molecular design space, but data sparsity in polymer science is a major obstacle hindering progress.

Enhanced Electron Transport in Nonconjugated Radical Oligomers Occurs by Tunneling.

Incorporating temperature- and air-stable organic radical species into molecular designs is a potentially advantageous means of controlling the properties of electronic materials. However, we still lack a complete understanding of the structure-property relationships of organic radical species at the molecular level. In this work, the charge transport properties of (2,2,6,6-tetramethylpiperidin-1-yl)oxyl (TEMPO) radical-containing nonconjugated molecules are studied using single-molecule charge transport experiments and molecular modeling.

Semantic novelty modulates neural responses to visual change across the human brain.

Our continuous visual experience in daily life is dominated by change. Previous research has focused on visual change due to stimulus motion, eye movements or unfolding events, but not their combined impact across the brain, or their interactions with semantic novelty. We investigate the neural responses to these sources of novelty during film viewing.

Experimental validation of computational models for the prediction of phase distribution during multi-channel transcranial alternating current stimulation.

Transcranial alternating current stimulation (tACS) is a widely used noninvasive brain stimulation (NIBS) technique to affect neural activity. Neural oscillations exhibit phase-dependent associations with cognitive functions, and tools to manipulate local oscillatory phases can affect communication across remote brain regions. A recent study demonstrated that multi-channel tACS can generate electric fields with a phase gradient or traveling waves in the brain.

Saccadic modulation of neural excitability in auditory areas of the neocortex.

In natural "active" vision, humans and other primates use eye movements (saccades) to sample bits of information from visual scenes. In the visual cortex, non-retinal signals linked to saccades shift visual cortical neurons into a high excitability state as each saccade ends. The extent of this saccadic modulation outside of the visual system is unknown.

Automated Measurement of Electrogenerated Redox Species Degradation Using Multiplexed Interdigitated Electrode Arrays.

Characterizing the decomposition of electrogenerated species in solution is essential for applications involving electrosynthesis, homogeneous electrocatalysis, and energy storage with redox flow batteries. In this work, we present an automated, multiplexed, and highly robust platform for determining the rate constant of chemical reaction steps following electron transfer, known as the EC mechanism. We developed a generation-collection methodology based on microfabricated interdigitated electrode arrays (IDAs) with variable gap widths on a single device.

Electron Tomography and Machine Learning for Understanding the Highly Ordered Structure of Leafhopper Brochosomes.

Insects known as leafhoppers (Hemiptera: Cicadellidae) produce hierarchically structured nanoparticles known as brochosomes that are exuded and applied to the insect cuticle, thereby providing camouflage and anti-wetting properties to aid insect survival. Although the physical properties of brochosomes are thought to depend on the leafhopper species, the structure-function relationships governing brochosome behavior are not fully understood. Brochosomes have complex hierarchical structures and morphological heterogeneity across species, due to which a multimodal characterization approach is required to effectively elucidate their nanoscale structure and properties.

Self-Assembly of Repetitive Segment and Random Segment Polymer Architectures.

Recent advances in chemical synthesis have created new methodologies for synthesizing sequence-controlled synthetic polymers, but rational design of monomer sequence for desired properties remains challenging. In this work, we synthesize periodic polymers with repetitive segments using a sequence-controlled ring-opening metathesis polymerization (ROMP) method, which draws inspiration from proteins containing repetitive sequence motifs. The repetitive segment architecture is shown to dramatically affect the self-assembly behavior of these materials.

Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling.

General conditions for organic reactions are important but rare, and efforts to identify them usually consider only narrow regions of chemical space. Discovering more general reaction conditions requires considering vast regions of chemical space derived from a large matrix of substrates crossed with a high-dimensional matrix of reaction conditions, rendering exhaustive experimentation impractical. Here, we report a simple closed-loop workflow that leverages data-guided matrix down-selection, uncertainty-minimizing machine learning, and robotic experimentation to discover general reaction conditions.

Understanding Supramolecular Assembly of Supercharged Proteins.

Ordered supramolecular assemblies have recently been created using electrostatic interactions between oppositely charged proteins. Despite recent progress, the fundamental mechanisms governing the assembly of oppositely supercharged proteins are not fully understood. Here, we use a combination of experiments and computational modeling to systematically study the supramolecular assembly process for a series of oppositely supercharged green fluorescent protein variants.

Designing Multicomponent Polymer Colloids for Self-Stratifying Films.

Aqueous polymer colloids known as latexes are widely used in coating applications. Multicomponent latexes comprised of two incompatible polymeric species organized into a core-shell particle morphology are a promising system for self-stratifying coatings that spontaneously partition into multiple layers, thereby yielding complex structured coatings requiring only a single application step. Developing new materials for self-stratifying coatings requires a clear understanding of the thermodynamic and kinetic properties governing phase separation and polymeric species transport.