A multimodal neuroimaging classifier for alcohol dependence

With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence

A multimodal neuroimaging classifier for alcohol dependence

With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence

Attosecond Time-Domain Measurement of Core-Level-Exciton Decay in Magnesium Oxide.

Excitation of ionic solids with extreme ultraviolet pulses creates localized core-level excitons, which in some cases couple strongly to the lattice. Here, core-level-exciton states of magnesium oxide are studied in the time domain at the Mg L_{2,3} edge with attosecond transient reflectivity spectroscopy. Attosecond pulses trigger the excitation of these short-lived quasiparticles, whose decay is perturbed by time-delayed near-infrared pulses. Combined with a few-state theoretical model, this reveals that the infrared pulse shifts the energy of bright (dipole-allowed) core-level-exciton states as well as induces features arising from dark core-level excitons. We report coherence lifetimes for the two lowest core-level excitons of 2.3±0.2 and 1.6±0.5  fs and show that these are primarily a consequence of strong exciton-phonon coupling, disclosing the drastic influence of structural effects in this ultrafast relaxation process

Real-time and Sub-wavelength Ultrafast Coherent Diffraction Imaging in the Extreme Ultraviolet

Coherent Diffraction Imaging is a technique to study matter with nanometer-scale spatial resolution based on coherent illumination of the sample with hard X-ray, soft X-ray or extreme ultraviolet light delivered from synchrotrons or more recently X-ray Free-Electron Lasers. This robust technique simultaneously allows quantitative amplitude and phase contrast imaging. Laser-driven high harmonic generation XUV-sources allow table-top realizations. However, the low conversion efficiency of lab-based sources imposes either a large scale laser system or long exposure times, preventing many applications. Here we present a lensless imaging experiment combining a high numerical aperture (NA=0.8) setup with a high average power fibre laser driven high harmonic source. The high flux and narrow-band harmonic line at 33.2 nm enables either sub-wavelength spatial resolution close to the Abbe limit (Delta r=0.8 lambda) for long exposure time, or sub-70 nm imaging in less than one second. The unprecedented high spatial resolution, compactness of the setup together with the real-time capability paves the way for a plethora of applications in fundamental and life sciences

Photoacoustic imaging of squirrel monkey cortical and subcortical brain regions during peripheral electrical stimulation

The investigation of neuronal activity in non-human primate models is of critical importance due to their genetic similarity to human brains. In this study, we tested the feasibility of using photoacoustic imaging for the detection of cortical and subcortical responses due to peripheral electrical stimulation in a squirrel monkey model. Photoacoustic computed tomography and photoacoustic microscopy were applied on squirrel monkeys for real-time deep subcortical imaging and optical-resolution cortical imaging, respectively. The electrically evoked hemodynamic changes in primary somatosensory cortex, premotor cortices, primary motor cortex, and underlying subcortical areas were measured. Hemodynamic responses were observed in both cortical and subcortical brain areas at the cortices during external stimulation, demonstrating the feasibility of photoacoustic technique for functional imaging of non-human primate brain

SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals

Neuronal signals generally represent activation of the neuronal networks and give insights into brain functionalities. They are considered as fingerprints of actions and their processing across different structures of the brain. These recordings generate a large volume of data that are susceptible to noise and artifacts. Therefore, the review of these data to ensure high quality by automatically detecting and removing the artifacts is imperative. Toward this aim, this work proposes a custom-developed automatic artifact removal toolbox named, SANTIA (SigMate Advanced: a Novel Tool for Identification of Artifacts in Neuronal Signals). Developed in Matlab, SANTIA is an open-source toolbox that applies neural network-based machine learning techniques to label and train models to detect artifacts from the invasive neuronal signals known as local field potentials