The ability to decode mood state over time from neural activity could enable closed-loop systems to treat neuropsychiatric disorders. However, this decoding has not been demonstrated, partly owing to the difficulty of modeling distributed mood-relevant neural dynamics while dealing with the sparsity of mood state measurements. Here we develop a modeling framework to decode mood state variations from multi-site intracranial recordings in seven human subjects with epilepsy who self-reported their mood state intermittently over multiple days. We built dynamic neural encoding models of mood state and corresponding decoders for each individual and demonstrated that mood state variations over time can be decoded from neural activity. Across subjects, the decoders largely recruited neural signals from limbic regions, whose spectro-spatial features were tuned to mood variations. The dynamic models also provided an analytical tool to compute the timescales of the decoded mood state. These results provide an initial line of evidence indicating the feasibility of mood state decoding.

Highlights d We investigated the effects of brain stimulation on mood state in epilepsy patients d Lateral OFC stimulation improved mood state in subjects with depression symptoms d This stimulation induced neural features associated with positive mood states d Lateral OFC is a promising new stimulation target for treatment of mood disorders

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The system identification framework with the new BN-modulated waveform and the clinical HIL simulation testbed can help develop future model-based closed-loop electrical brain stimulation systems for treatment of neurological and neuropsychiatric disorders.

Objective. Developing dynamic network models for multisite electrocorticogram (ECoG) activity can help study neural representations and design neurotechnologies in humans given the clinical promise of ECoG. However, dynamic network models have so far largely focused on spike recordings rather than ECoG. A dynamic network model for ECoG recordings, which constitute a network, should describe their temporal dynamics while also achieving dimensionality reduction given the inherent spatial and temporal correlations. Approach. We devise both linear and nonlinear dynamic models for ECoG power features and comprehensively evaluate their accuracy in predicting feature dynamics. Linear state-space models (LSSMs) provide a general linear dynamic network model and can simultaneously achieve dimensionality reduction by describing high-dimensional signals in terms of a lowdimensional latent state. We thus study whether and how well LSSMs can predict ECoG dynamics and achieve dimensionality reduction. Further, we fit a general family of nonlinear dynamic models termed radial basis function (RBF) auto-regressive (AR) models for ECoG to study how the linear form of LSSMs affects the prediction of ECoG dynamics. Finally, we study the differences in dynamics and predictability of ECoG power features across different frequency bands. We use both numerical simulations and large-scale ECoG activity recorded from 10 human epilepsy subjects to evaluate the models. Main results. First, we find that LSSMs can significantly predict the dynamics of ECoG power features using latent states with a much lower dimension compared to the number of features. Second, compared with LSSMs, nonlinear RBF-AR models do not improve the prediction of human ECoG power features, thus suggesting the usefulness of the linear assumption in describing ECoG dynamics. Finally, compared with other frequency bands, the dynamics of ECoG power features in 1-8 Hz (delta + theta) can be predicted significantly better and is more dominated by slow dynamics. Significance. Our results suggest that LSSMs with low-dimensional latent states can capture important dynamics in human large-scale ECoG power features, thus achieving

Through first-principles computations, we compared the photocatalytic properties of (102) and (001) facets within BiOBr. Due to the surface states, the (102) facets of BiOBr have lower conduction band minimum and higher valence band maximum, compared with the (001) facets. Therefore, the (102) facets have more efficient electron injection, higher redox potential of photoinduced hole, and smaller band gap, which may result in better photocatalytic performances. Also, we prepared BiOBr-102 and BiOBr-001 samples with dominantly exposed (102) and (001) facets, respectively, and found red-shift absorption, and enhanced photodegradation rate of Rhodamine B in BiOBr-102, which agree well with the computations. Therefore, BiOBr samples with dominantly exposed (102) facets are superior in photocatalysis, and the results demonstrate the critical role of facet orientation in photocatalyst design.

These results have significant implications for clinically viable CLAD design for a wide range of anesthetic states, with potential cost-saving and therapeutic benefits.

Mesoporous Ni0.85Se nanospheres grown on graphene were synthesized via the hydrothermal approach. Because of the exceptional electron-transfer pathway of graphene and the excellent catalytic ability of the mesoporous Ni0.85Se nanospheres, the nanocomposites exhibited excellent electrocatalytic property as the counter electrode (CE) of dye-sensitized solar cells. More catalytic active sites, better charge-transfer ability and faster reaction velocity of Ni0.85Se@RGO (RGO = reduced graphene oxide) CE led to faster and more complete I3(-) reduction than Pt, Ni0.85Se, and RGO CEs. Furthermore, the power conversion efficiency of Ni0.85Se@RGO CE reached 7.82%, which is higher than that of Pt CE (7.54%). Electrochemical impedance spectra, cyclic voltammetry, and Tafel polarization were obtained to demonstrate positive synergetic effect between Ni0.85Se and RGO, as well as the higher catalytic activity and the better charge-transfer ability of Ni0.85Se@RGO compared with Pt CE.