Effects of Substance Use and Antisocial Personality on Neuroimaging-Based Machine Learning Prediction of Schizophrenia.

BACKGROUND AND HYPOTHESIS: Neuroimaging-based machine learning (ML) algorithms have the potential to aid the clinical diagnosis of schizophrenia. However, literature on the effect of prevalent comorbidities such as substance use disorder (SUD) and antisocial personality (ASPD) on these models' performance has remained unexplored. We investigated whether the presence of SUD or ASPD affects the performance of neuroimaging-based ML models trained to discern patients with schizophrenia (SCH) from controls. STUDY DESIGN: We trained an ML model on structural MRI data from public datasets to distinguish between SCH and controls (SCH = 347, controls = 341). We then investigated the model's performance in two independent samples of individuals undergoing forensic psychiatric examination: sample 1 was used for sensitivity analysis to discern ASPD (N = 52) from SCH (N = 66), and sample 2 was used for specificity analysis to discern ASPD (N = 26) from controls (N = 25). Both samples included individuals with SUD. STUDY RESULTS: In sample 1, 94.4% of SCH with comorbid ASPD and SUD were classified as SCH, followed by patients with SCH + SUD (78.8% classified as SCH) and patients with SCH (60.0% classified as SCH). The model failed to discern SCH without comorbidities from ASPD + SUD (AUC = 0.562, 95%CI = 0.400-0.723). In sample 2, the model's specificity to predict controls was 84.0%. In both samples, about half of the ASPD + SUD were misclassified as SCH. Data-driven functional characterization revealed associations between the classification as SCH and cognition-related brain regions. CONCLUSION: Altogether, ASPD and SUD appear to have effects on ML prediction performance, which potentially results from converging cognition-related brain abnormalities between SCH, ASPD, and SUD.

DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies.

The analysis of motor evoked potentials (MEPs) generated by transcranial magnetic stimulation (TMS) is crucial in research and clinical medical practice. MEPs are characterized by their latency and the treatment of a single patient may require the characterization of thousands of MEPs. Given the difficulty of developing reliable and accurate algorithms, currently the assessment of MEPs is performed with visual inspection and manual annotation by a medical expert; making it a time-consuming, inaccurate, and error-prone process. In this study, we developed DELMEP, a deep learning-based algorithm to automate the estimation of MEP latency. Our algorithm resulted in a mean absolute error of about 0.5 ms and an accuracy that was practically independent of the MEP amplitude. The low computational cost of the DELMEP algorithm allows employing it in on-the-fly characterization of MEPs for brain-state-dependent and closed-loop brain stimulation protocols. Moreover, its learning ability makes it a particularly promising option for artificial-intelligence-based personalized clinical applications.

Multi-locus transcranial magnetic stimulation system for electronically targeted brain stimulation.

BACKGROUND: Transcranial magnetic stimulation (TMS) allows non-invasive stimulation of the cortex. In multi-locus TMS (mTMS), the stimulating electric field (E-field) is controlled electronically without coil movement by adjusting currents in the coils of a transducer. OBJECTIVE: To develop an mTMS system that allows adjusting the location and orientation of the E-field maximum within a cortical region. METHODS: We designed and manufactured a planar 5-coil mTMS transducer to allow controlling the maximum of the induced E-field within a cortical region approximately 30 mm in diameter. We developed electronics with a design consisting of independently controlled H-bridge circuits to drive up to six TMS coils. To control the hardware, we programmed software that runs on a field-programmable gate array and a computer. To induce the desired E-field in the cortex, we developed an optimization method to calculate the currents needed in the coils. We characterized the mTMS system and conducted a proof-of-concept motor-mapping experiment on a healthy volunteer. In the motor mapping, we kept the transducer placement fixed while electronically shifting the E-field maximum on the precentral gyrus and measuring electromyography from the contralateral hand. RESULTS: The transducer consists of an oval coil, two figure-of-eight coils, and two four-leaf-clover coils stacked on top of each other. The technical characterization indicated that the mTMS system performs as designed. The measured motor evoked potential amplitudes varied consistently as a function of the location of the E-field maximum. CONCLUSION: The developed mTMS system enables electronically targeted brain stimulation within a cortical region.

Publisher Correction: Surface-wetting characterization using contact-angle measurements.

The version of this Protocol originally published contained typographical errors that affected the accuracy/readability of the text. In Fig. 4e, the line "Contact angle remainsstable" should have read "Contact angle remains stable." In Table 1, in the "Advantages" column, the second instance of "Simple" was incorrectly associated with the "Sessile-drop goniometry" method; it should have corresponded to the "Tilting plate" method. In Table 2, in the "Issues" column, the entry "Difficult to place baseline when the RCA is ~90°" was broken incorrectly in a way that might have suggested that "the RCA is ~90°" was a separate issue. These errors have been corrected in the HTML and PDF versions of the paper.

Surface-wetting characterization using contact-angle measurements.

Wetting, the process of water interacting with a surface, is critical in our everyday lives and in many biological and technological systems. The contact angle is the angle at the interface where water, air and solid meet, and its value is a measure of how likely the surface is to be wetted by the water. Low contact-angle values demonstrate a tendency of the water to spread and adhere to the surface, whereas high contact-angle values show the surface's tendency to repel water. The most common method for surface-wetting characterization is sessile-drop goniometry, due to its simplicity. The method determines the contact angle from the shape of the droplet and can be applied to a wide variety of materials, from biological surfaces to polymers, metals, ceramics, minerals and so on. The apparent simplicity of the method is misleading, however, and obtaining meaningful results requires minimization of random and systematic errors. This article provides a protocol for performing reliable and reproducible measurements of the advancing contact angle (ACA) and the receding contact angle (RCA) by slowly increasing and reducing the volume of a probe drop, respectively. One pair of  ACA and RCA measurements takes ~15-20 min to complete, whereas the whole protocol with repeat measurements may take ~1-2 h. This protocol focuses on using water as a probe liquid, and advice is given on how it can be modified for the use of other probe liquids.

Modifying native nanocellulose aerogels with carbon nanotubes for mechanoresponsive conductivity and pressure sensing.

Mechanically excellent native cellulose nanofibers that are cleaved from plant cell walls have been modified by functionalized few-walled carbon nanotubes for hybrid nanofiber/nanotube aerogels. They show elastic mechanical behavior in combination with reversible electrical response under compression allowing responsive conductivity and pressure sensing. The concept combines wide availability of nanocellulosics and electrical functionality of carbon nanotubes synergistically.

Reliable measurement of the receding contact angle.

Surface wettability is usually evaluated by the contact angle between the perimeter of a water drop and the surface. However, this single measurement is not enough for proper characterization, and the so-called advancing and receding contact angles also need to be measured. Measuring the receding contact angle can be challenging, especially for extremely hydrophobic surfaces. We demonstrate a reliable procedure by using the common needle-in-the-sessile-drop method. Generally, the contact line movement needs to be followed, and true receding movement has to be distinguished from "pseudo-movement" occurring before the receding angle is reached. Depending on the contact angle hysteresis, the initial size of the drop may need to be surprisingly large to achieve a reliable result. Although our motivation for this work was the characterization of superhydrophobic surfaces, we also show that this method works universally ranging from hydrophilic to superhydrophobic surfaces.

Reversible switching between superhydrophobic states on a hierarchically structured surface.

Nature offers exciting examples for functional wetting properties based on superhydrophobicity, such as the self-cleaning surfaces on plant leaves and trapped air on immersed insect surfaces allowing underwater breathing. They inspire biomimetic approaches in science and technology. Superhydrophobicity relies on the Cassie wetting state where air is trapped within the surface topography. Pressure can trigger an irreversible transition from the Cassie state to the Wenzel state with no trapped air--this transition is usually detrimental for nonwetting functionality and is to be avoided. Here we present a new type of reversible, localized and instantaneous transition between two Cassie wetting states, enabled by two-level (dual-scale) topography of a superhydrophobic surface, that allows writing, erasing, rewriting and storing of optically displayed information in plastrons related to different length scales.

Hydrophobic nanocellulose aerogels as floating, sustainable, reusable, and recyclable oil absorbents.

Highly porous nanocellulose aerogels can be prepared by vacuum freeze-drying from microfibrillated cellulose hydrogels. Here we show that by functionalizing the native cellulose nanofibrils of the aerogel with a hydrophobic but oleophilic coating, such as titanium dioxide, a selectively oil-absorbing material capable of floating on water is achieved. Because of the low density and the ability to absorb nonpolar liquids and oils up to nearly all of its initial volume, the surface modified aerogels allow to collect organic contaminants from the water surface. The materials can be reused after washing, recycled, or incinerated with the absorbed oil. The cellulose is renewable and titanium dioxide is not environmentally hazardous, thus promoting potential in environmental applications.

Inorganic hollow nanotube aerogels by atomic layer deposition onto native nanocellulose templates.

Hollow nano-objects have raised interest in applications such as sensing, encapsulation, and drug-release. Here we report on a new class of porous materials, namely inorganic nanotube aerogels that, unlike other aerogels, have a framework consisting of inorganic hollow nanotubes. First we show a preparation method for titanium dioxide, zinc oxide, and aluminum oxide nanotube aerogels based on atomic layer deposition (ALD) on biological nanofibrillar aerogel templates, that is, nanofibrillated cellulose (NFC), also called microfibrillated cellulose (MFC) or nanocellulose. The aerogel templates are prepared from nanocellulose hydrogels either by freeze-drying in liquid nitrogen or liquid propane or by supercritical drying, and they consist of a highly porous percolating network of cellulose nanofibrils. They can be prepared as films on substrates or as freestanding objects. We show that, in contrast to freeze-drying, supercritical drying produces nanocellulose aerogels without major interfibrillar aggregation even in thick films. Uniform oxide layers are readily deposited by ALD onto the fibrils leading to organic-inorganic core-shell nanofibers. We further demonstrate that calcination at 450 °C removes the organic core leading to purely inorganic self-supporting aerogels consisting of hollow nanotubular networks. They can also be dispersed by grinding, for example, in ethanol to create a slurry of inorganic hollow nanotubes, which in turn can be deposited to form a porous film. Finally we demonstrate the use of a titanium dioxide nanotube network as a resistive humidity sensor with a fast response.