Frontal lobe hypoactivation in medication-free adults with bipolar II depression during response inhibition

In executive function, specifically in response inhibition, numerous studies support the essential role for the inferior frontal cortex (IFC). Hypoactivation of the IFC during response-inhibition tasks has been found consistently in subjects with bipolar disorder during manic and euthymic states. The aim of this study was to examine whether reduced IFC activation also exists in unmedicated subjects with bipolar disorder during the depressed phase of the disorder. Participants comprised 19 medication-free bipolar II (BP II) depressed patients and 20 healthy control subjects who underwent functional magnetic resonance imaging (fMRI) while performing a Go/NoGo response-inhibition task. Whole-brain analyses were conducted to assess activation differences within and between groups. The BP II depressed group, compared with the control group, showed significantly reduced activation in right frontal regions, including the IFC (Brodmann's area (BA) 47), middle frontal gyrus (BA 10), as well as other frontal and temporal regions. IFC hypoactivation may be a persistent deficit in subjects with bipolar disorder in both acute mood states as well as euthymia, thus representing a trait feature of bipolar disorder

Accurate, Affordable, and Generalisable Machine Learning Simulations of Transition Metal X-ray Absorption Spectra using the XANESNET Deep Neural Network

The affordable, accurate, and reliable prediction of spectroscopic observables plays a key role in the analysis of increasingly-complex experiments. In this Article, we develop and deploy a deep neural network (DNN) – XANESNET – for predicting the lineshape of first-row transition metal K-edge X-ray absorption near-edge structure (XANES) spectra. XANESNET predicts the spectral intensities using only information about the local coordination geometry ofthe transition metal complexes encoded in a feature vector of weighted atom-centred symmetry functions (wACSF). We address in detail the calibration of the feature vector for the particularities of the problem at hand, and we explore the individual feature importances to reveal the physical insight that XANESNET obtains at the Fe K-edge. XANESNET relies on only a few judiciously-selected features – radial information on the first and second coordination shells suffices, along with angular information sufficient to separate satisfactorily key coordination geometries. The feature importance is found to reflect the XANES spectral window under consideration and is consistent with the expected underlying physics. We subsequently apply XANESNET at nine first-row transition metal (Ti–Zn) K-edges. It can be optimised in as little as a minute, predicts instantaneously, and provides K-edge XANES spectra with an average accuracy of ca. ± 2–4% in which the positions of prominent peaks are matched with a > 90% hit rate to sub-eV (ca.0.8 eV) error

A Deep Neural Network for the Rapid Prediction of X-ray Absorption Spectra

X-ray spectroscopy delivers strong impact across the physical and biological sciences by providing end-users with highly-detailed information about the electronic and geometric structure of matter. To decode this information in challenging cases, e.g. in operando catalysts, batteries, and temporally-evolving systems, advanced theoretical calculations are necessary. The complexity and resource requirements often render these out of reach for end-users, and therefore data are often not interpreted exhaustively, leaving a wealth of valuable information unexploited. In this paper, we introduce supervised machine learning of X-ray absorption spectra, by developing a deep neural network (DNN) that is able to estimate Fe K-edge X-ray absorption near-edge structure spectra in less than a second with no input beyond geometric information about the local environment of the absorption site. We predict peak positions with sub-eV accuracy and peak intensities with errors over an order of magnitude smaller than the spectral variations that the model is engineered to capture. The performance of the DNN is promising, as illustrated by its application to the structural refinement of iron(II)tris(bipyridine) and nitrosylmyoglobin, but also highlights areas for which future developments should focus.</p

Beyond Structural Insight : A Deep Neural Network for the Prediction of Pt L2/3-edge X-ray Absorption Spectra

X-ray absorption spectroscopy at the L2/3 edge can be used to obtain detailed information about the local electronic and geometric structure of transition metal complexes. By virtue of the dipole selection rules, the transition metal L2/3 edge usually exhibits two distinct spectral regions: (i) the “white line”, which is dominated by bound electronic transitions from metal-centred 2p orbitals into unoccupied orbitals with d character; the intensity and shape of this band consequently reflects the d density of states (d-DOS), which is strongly modulated by mixing with ligand orbitals involved in chemical bonding, and (ii) the post-edge, where oscillations encode the local geometric structure around the X-ray absorption site. In this Article, we extend our recently-developed XANESNET deep neural network (DNN) beyond the K-edge to predict X-ray absorption spectra at the Pt L2/3 edge. We demonstrate that XANESNET is able to predict Pt L2/3 -edge X-ray absorption spectra, including both the parts containing electronic and geometric structural information. The performance of our DNN in practical situations is demonstrated by application to two Pt complexes, and by simulating the transient spectrum of a photoexcited dimeric Pt complex. Our discussion includes an analysis of the feature importance in our DNN which demonstrates the role of key features and assists with interpreting the performance of the network

An 'On-the-Fly' Deep Neural Network for Simulating Time-Resolved Spectroscopy : Predicting the Ultrafast Ring-Opening Dynamics of 1,2-Dithiane

Revolutionary developments in ultrafast light source technology are enabling experimental spectroscopists to probe the structural dynamics of molecules and materials on the femtosecond timescale. The capacity to investigate ultrafast processes afforded by these resources accordingly inspires theoreticians to carry out high-level simulations which facilitate the interpretation of the underlying dynamics probed during these ultrafast experiments. In this Article, we implement a deep neural network (DNN) to convert excited-state molecular dynamics simulations into time-resolved spectroscopic signals. Our DNN is trained on-the-fly from first-principles theoretical data obtained from a set of time-evolving molecular dynamics. The train-test process iterates for each time-step of the dynamics data until the network can predict spectra with sufficient accuracy to replace the computationally intensive quantum chemistry calculations required to produce them, at which point it simulates the time-resolved spectra for longer timescales. The potential of this approach is demonstrated by probing dynamics of the ring opening of 1,2-dithiane using sulphur K-edge X-ray absorption spectroscopy. The benefits of this strategy will be more markedly apparent for simulations of larger systems which will exhibit a more notable computational burden, making this approach applicable to the study of a diverse range of complex chemical dynamics

The Role of Structural Representation in the Performance of a Deep Neural Network for X-ray Spectroscopy

An important consideration when developing a deep neural network (DNN) for the prediction of molecular properties is the representation of the chemical space. Herein we explore the effect of the representation on the performance of our DNN engineered to predict Fe K-edge X-ray absorption near-edge structure (XANES) spectra, and address the question: How important is the choice of representation for the local environment around an arbitrary Fe absorption site? Using two popular representations of chemical space — the Coulomb matrix (CM) and pair-distribution/radial distribution curve (RDC) — we investigate the effect that the choice of representation has on the performance of our DNN. While CM and RDC featurisation are demonstrably robust descriptors, it is possible to obtain a smaller mean squared error (MSE) between the target and estimated XANES spectra when using RDC featurisation, and converge to this state a) faster and b) using fewer data samples. This is advantageous for future extension of our DNN to other X-ray absorption edges, and for reoptimisation of our DNN to reproduce results from higher levels of theory. In the latter case, dataset sizes will be limited more strongly by the resource-intensive nature of the underlying theoretical calculations

Enhancing the Analysis of Disorder in X-ray Absorption Spectra : Application of Deep Neural Networks to T-jump-X-ray Probe Experiments

Many chemical and biological reactions, including ligand exchange processes, require thermal energy for the reactants to overcome a transition barrier and reach the product state. Temperature-jump (T-jump) spectroscopy uses a near-infrared (NIR) pulse to rapidly heat a sample, offering an approach for triggering these processes and directly accessing thermally-activated pathways. However, thermal activation inherently increases the disorder of the system under study and, as a consequence, can make quantitative interpretations of structural changes challenging. In this Article, we optimise a deep neural network (DNN) for the instantaneous prediction of Co K-edge X-ray absorption near-edge structure (XANES) spectra. We apply our DNN to analyse T-jump pump/X-ray probe data pertaining to the ligand exchange processes and solvation dynamics of Co2+in chlorinated aqueous solution. Our analysis is greatly facilitated by machine learning, as our DNN is able to predict quickly and cost-effectively the XANES spectra of thousands of geometric configurations sampled fromab initiomolecular dynamics (MD) using nothing more than the local geometric environment around the X-ray absorption site. We identify directly the structural changes following the T-jump, which are dominated by sample heating and a commensurate increase in the Debye-Waller factor

Frontal lobe hypoactivation in medication-free adults with bipolar II depression during response inhibition

In executive function, specifically in response inhibition, numerous studies support the essential role for the inferior frontal cortex (IFC). Hypoactivation of the IFC during response-inhibition tasks has been found consistently in subjects with bipolar disorder during manic and euthymic states. The aim of this study was to examine whether reduced IFC activation also exists in unmedicated subjects with bipolar disorder during the depressed phase of the disorder. Participants comprised 19 medication-free bipolar II (BP II) depressed patients and 20 healthy control subjects who underwent functional magnetic resonance imaging (fMRI) while performing a Go/NoGo response-inhibition task. Whole-brain analyses were conducted to assess activation differences within and between groups. The BP II depressed group, compared with the control group, showed significantly reduced activation in right frontal regions, including the IFC (Brodmann's area (BA) 47), middle frontal gyrus (BA 10), as well as other frontal and temporal regions. IFC hypoactivation may be a persistent deficit in subjects with bipolar disorder in both acute mood states as well as euthymia, thus representing a trait feature of bipolar disorder