Behind the Wheel: Unraveling the Impact of Experience Over Age Over the First 18 Months of Licensure.

OBJECTIVE: This study examined how driving attention develops with age and driving. METHODS: In this observational longitudinal study, 190 adolescents (53% female, 73% Black) were enrolled across four groups: 16- and 18-year olds with and without driving experience. They underwent driving simulation with eye-tracking technology seven times over 18 months. By using a combination of factorial and longitudinal designs, the study examined the individual and combined effects of age and driving experience on driving attention over time. RESULTS: Licensed participants had higher odds of glancing at safety-critical events initially (OR = 15.01, 95% CI: 1.36-165.53), but these odds decreased at higher driving speeds (b = -0.17, p<.01). Average glance length decreased over time (b = -0.26, p=.01), but less so in licensed participants (b=0.14, p=.01). Several visual behaviors were influenced by environmental and driving factors. CONCLUSIONS: Motor vehicle crashes (MVCs) are burdensome and costly to society. This study focused on the role of inattention in MVCs, particularly during the risky period of adolescence. Findings indicated that driving experience, as determined by licensure, had a considerable impact on visual behavior in both the short term (within two weeks of obtaining a license) and over the first 18 months of independent driving. Overall, these findings suggest that licensed adolescents are more likely to identify potential hazards on the road and navigate safely. To ensure effective guidance, pediatric psychologists and other professionals should consider the unique circumstances, needs, and concerns of individual patients.

Learning to play against any mixture of opponents.

Intuitively, experience playing against one mixture of opponents in a given domain should be relevant for a different mixture in the same domain. If the mixture changes, ideally we would not have to train from scratch, but rather could transfer what we have learned to construct a policy to play against the new mixture. We propose a transfer learning method, Q-Mixing, that starts by learning Q-values against each pure-strategy opponent. Then a Q-value for any distribution of opponent strategies is approximated by appropriately averaging the separately learned Q-values. From these components, we construct policies against all opponent mixtures without any further training. We empirically validate Q-Mixing in two environments: a simple grid-world soccer environment, and a social dilemma game. Our experiments find that Q-Mixing can successfully transfer knowledge across any mixture of opponents. Next, we consider the use of observations during play to update the believed distribution of opponents. We introduce an opponent policy classifier-trained reusing Q-learning data-and use the classifier results to refine the mixing of Q-values. Q-Mixing augmented with the opponent policy classifier performs better, with higher variance, than training directly against a mixed-strategy opponent.

Generative Adversarial Network (GAN) for Simulating Electroencephalography.

Electroencephalographs record the electrical activity of your brain through the scalp. Electroencephalography is difficult to obtain due to its sensitivity and variability. Applications of electroencephalography such as for diagnosis, education, brain-computer interfaces require large samples of electroencephalography recording, however, it is often difficult to obtain the required datasets. Generative adversarial networks are robust deep learning framework which have proven themselves to be capable of synthesizing data. The robust nature of a generative adversarial network was used to generate multi-channel electroencephalography data in order to see if generative adversarial networks could reconstruct the spatio-temporal aspects of multi-channel electroencephalography signals. We were able to find that the synthetic electroencephalography data was able to replicate fine details of electroencephalography data and could potentially help us to generate large sample synthetic resting-state electroencephalography data for use in simulation testing of neuroimaging analyses. Generative adversarial networks (GANs) are robust deep-learning frameworks that can be trained to be convincing replicants of real data GANs were capable of generating "fake" EEG data that replicated fine details and topographies of "real" resting-state EEG data.

Mastering the game of Stratego with model-free multiagent reinforcement learning.

We introduce DeepNash, an autonomous agent that plays the imperfect information game Stratego at a human expert level. Stratego is one of the few iconic board games that artificial intelligence (AI) has not yet mastered. It is a game characterized by a twin challenge: It requires long-term strategic thinking as in chess, but it also requires dealing with imperfect information as in poker. The technique underpinning DeepNash uses a game-theoretic, model-free deep reinforcement learning method, without search, that learns to master Stratego through self-play from scratch. DeepNash beat existing state-of-the-art AI methods in Stratego and achieved a year-to-date (2022) and all-time top-three ranking on the Gravon games platform, competing with human expert players.

Designing all-pay auctions using deep learning and multi-agent simulation.

We propose a multi-agent learning approach for designing crowdsourcing contests and All-Pay auctions. Prizes in contests incentivise contestants to expend effort on their entries, with different prize allocations resulting in different incentives and bidding behaviors. In contrast to auctions designed manually by economists, our method searches the possible design space using a simulation of the multi-agent learning process, and can thus handle settings where a game-theoretic equilibrium analysis is not tractable. Our method simulates agent learning in contests and evaluates the utility of the resulting outcome for the auctioneer. Given a large contest design space, we assess through simulation many possible contest designs within the space, and fit a neural network to predict outcomes for previously untested contest designs. Finally, we apply mirror ascent to optimize the design so as to achieve more desirable outcomes. Our empirical analysis shows our approach closely matches the optimal outcomes in settings where the equilibrium is known, and can produce high quality designs in settings where the equilibrium strategies are not solvable analytically.

The emerging postural instability phenotype in idiopathic Parkinson disease.

Identification of individuals at high risk for rapid progression of motor and cognitive signs in Parkinson disease (PD) is clinically significant. Postural instability and gait dysfunction (PIGD) are associated with greater motor and cognitive deterioration. We examined the relationship between baseline clinical factors and the development of postural instability using 5-year longitudinal de-novo idiopathic data (n = 301) from the Parkinson's Progressive Markers Initiative (PPMI). Logistic regression analysis revealed baseline features associated with future postural instability, and we designated this cohort the emerging postural instability (ePI) phenotype. We evaluated the resulting ePI phenotype rating scale validity in two held-out populations which showed a significantly higher risk of postural instability. Emerging PI phenotype was identified before onset of postural instability in 289 of 301 paired comparisons, with a median progression time of 972 days. Baseline cognitive performance was similar but declined more rapidly in ePI phenotype. We provide an ePI phenotype rating scale (ePIRS) for evaluation of individual risk at baseline for progression to postural instability.

The cap-proximal RNA secondary structure inhibits preinitiation complex formation on HAC1 mRNA.

Translation of HAC1 mRNA in the budding yeast Saccharomyces cerevisiae is derepressed when RNase Ire1 removes its intron via nonconventional cytosolic splicing in response to accumulation of unfolded proteins inside the endoplasmic reticulum. The spliced HAC1 mRNA is translated into a transcription factor that changes the cellular gene expression patterns to increase the protein folding capacity of cells. Previously, we showed that a segment of the intronic sequence interacts with the 5'-UTR of the unspliced mRNA, resulting in repression of HAC1 translation at the initiation stage. However, the exact mechanism of translational derepression is not clear. Here, we show that at least 11-base-pairing interactions between the 5'-UTR and intron (UI) are sufficient to repress HAC1 translation. We also show that overexpression of the helicase eukaryotic initiation factor 4A derepressed translation of an unspliced HAC1 mRNA containing only 11-bp interactions between the 5'-UTR and intronic sequences. In addition, our genetic screen identifies that single mutations in the UI interaction site could derepress translation of the unspliced HAC1 mRNA. Furthermore, we show that the addition of 24 RNA bases between the mRNA 5'-cap and the UI interaction site derepressed translation of the unspliced HAC1 mRNA. Together, our data provide a mechanistic explanation for why the cap-proximal UI-RNA duplex inhibits the recruitment of translating ribosomes to HAC1 mRNA, thus keeping mRNA translationally repressed.

Rapid genome surveillance of SARS-CoV-2 and study of risk factors using shipping container laboratories and portable DNA sequencing technology

In this paper we report on genome sequencing of 154 SARS-CoV-2 samples between June and July 2021 (Summer outbreak) in the Bailiwick of Jersey, a UK channel island. We have analysed extensive data collected on 598,155 RT-qPCR tests that identified 8,950 positive cases as part of public health surveillance from September 2020 to August 2021. Our study implemented an amplicon-based sequencing approach using the Oxford Nanopore Technology (ONT) portable device. This revealed the emergence of twelve AY sublineages and were clustered into the Delta sub-clades 21I and 21J. This was integrated alongside an existing RT-qPCR diagnostic laboratory to provide a sample-to-sequence turnaround time of approximately 30 hours with significant scope for optimisation. Owing to the geographic remoteness of the island from large scale sequencing infrastructure, this presents an opportunity to provide policy makers with near real-time sequencing findings. Our analysis suggests that age and sex remained a substantial risk factor for mortality. We observe viral loads are higher in advanced ages and unvaccinated individuals. The median age of SARS-CoV-2 positive individuals was higher during winter than the summer outbreak, and the contact tracing program showed that younger individuals stayed positive for longer.

Developmental trajectories of driving attention in adolescents: Preliminary findings from REACT.

OBJECTIVE: To characterize the trajectory of driving attention as a function of age and driving experience. Hypotheses. The rate of change in driving attention will be greater for 16- compared to 18-year-olds and those acquiring driving experience (vs. non-drivers). Age and driving experience will interact, with the effect of driving experience being stronger among 16- compared to 18-year-olds. METHODS: In this longitudinal study, 190 adolescents were enrolled into 4 groups: (1) 16-year-olds and (2) 18-year-olds recruited within 2 weeks of obtaining a full driver's license; (3) 16-year-olds and (4) 18-year-olds with no driving experience (no permit/license, no intention to obtain either over study period). At seven time points over 18 months, participants drove in a high-fidelity driving simulator integrated with eye tracking. Participants completed three experimental drives with three safety critical events and varying cognitive load conditions. Driving attention was measured by vertical and horizontal eye movements, number of glances, and glance length. A multilevel model using SAS PROC MIXED (SAS 9.4) will estimate the baseline intercept and slope of driving attention over time, with baseline age, driving experience, and their interaction serving as predictors of intercept and slope. RESULTS: Preliminary analyses suggest driving attention changes over time as a function of age, driving experience, and across cognitive load conditions. CONCLUSIONS: Inattention is the primary contributor to motor vehicle crashes. It is critical to gain a clear understanding of how driving attention changes during adolescence, the riskiest developmental period for drivers. Results will reveal how driving impacts attention development through practice, providing a target for intervention.

Validation of the attention-related driving errors scale in novice adolescent drivers.

INTRODUCTION: Motor vehicle collisions (MVCs) are a leading cause of death among adolescents. Identifying factors that contribute to adolescent MVCs is a pressing public health need. Exogenous (cell phones, passengers, music) and endogenous (stress, worry, mind-wandering) forms of driver inattention account for approximately 78% of all MVCs in the United States. Though both exogenous and endogenous distraction contribute to crash risk, prior work investigating adolescent crash risk has largely focused on exogenous distractors. The Attention-Related Driving Errors Scale (ARDES) is a promising measure assessing individual differences in endogenous driver inattention that has been validated in adult drivers. Its validation in an adolescent sample may prove useful in tailoring future interventions to decrease MVC risk in young drivers. METHODS: This study sought to validate the ARDES in novice adolescent drivers by investigating its underlying factor structure and its relations with self-reported measures of daily inattention, performance-based attention assessments, and a self-report measure of driving behavior. RESULTS: Replicating earlier work in adults, results suggested ARDES items can be classified according to their operational level of the driving. The ARDES had good internal reliability and construct validity, suggesting it is a valid self-report measure of the propensity for adolescents' attentional errors while driving. DISCUSSION: The ARDES provides a useful tool for researchers to identify adolescents at greater risk of attentional errors while driving. Future research should use the ARDES to better understand the role of driver inattention in adolescent crash risk.

White matter integrity, duration of untreated psychosis, and antipsychotic treatment response in medication-naïve first-episode psychosis patients.

It is becoming increasingly clear that longer duration of untreated psychosis (DUP) is associated with adverse clinical outcomes in patients with psychosis spectrum disorders. Because this association is often cited when justifying early intervention efforts, it is imperative to better understand underlying biological mechanisms. We enrolled 66 antipsychotic-naïve first-episode psychosis (FEP) patients and 45 matched healthy controls in this trial. At baseline, we used a human connectome style diffusion-weighted imaging (DWI) sequence to quantify white matter integrity in both groups. Patients then received 16 weeks of treatment with risperidone, 51 FEP completed the trial. We compared whole-brain fractional anisotropy (FA), mean diffusivity, axial diffusivity (AD), and radial diffusivity between groups. To test if structural white matter integrity mediates the relationship between longer DUP and poorer treatment response, we fit a mediator model and estimated indirect effects. We found decreased whole-brain FA and AD in medication-naive FEP compared with controls. In patients, lower FA was correlated with longer DUP (r = -0.32; p = 0.03) and poorer subsequent response to antipsychotic treatment (r = 0.40; p = 0.01). Importantly, we found a significant mediation effect for FA (indirect effect: -2.70; p = 0.03), indicating that DUP exerts its effects on treatment response through affecting white matter integrity. Our data provide empirical support to the idea the DUP may have fundamental pathogenic effects on the natural history of psychosis, suggest a biological mechanism underlying this phenomenon, and underscore the importance of early intervention efforts in this disabling neuropsychiatric syndrome.

Neurite Orientation Dispersion and Density Imaging (NODDI) and Duration of Untreated Psychosis in Antipsychotic Medication-Naïve First Episode Psychosis Patients.

Background: Diffusion tensor imaging suggests that white matter alterations are already evident in first episode psychosis patients (FEP) and may become more prominent as the duration of untreated psychosis (DUP) increases. But because the tensor model lacks specificity, it remains unclear how to interpret findings on a biological level. Here, we used a biophysical diffusion model, Neurite Orientation Dispersion and Density Imaging (NODDI), to map microarchitecture in FEP, and to investigate associations between DUP and microarchitectural integrity. Methods: We scanned 78 antipsychotic medication-naïve FEP and 64 healthy controls using a multi-shell diffusion weighted sequence and used the NODDI toolbox to compute neurite density (ND), orientation dispersion index (ODI) and extracellular free water (FW) maps. AFNI's 3dttest++ was used to compare diffusion maps between groups and to perform regression analyses with DUP. Results: We found that ND was decreased in commissural and association fibers but increased in projection fibers in FEP. ODI was largely increased regardless of fiber type, and FW showed a mix of increase in decrease across fiber tracts. We also demonstrated associations between DUP and microarchitecture for all NODDI indices. Conclusions: We demonstrated that complex microarchitecture abnormalities are already evident in antipsychotic-naïve FEP. ND alterations are differentially expressed depending on fiber type, while decreased fiber complexity appears to be a uniform marker of white matter deficit in the illness. Importantly, we identified an empirical link between longer DUP and greater white matter pathology across NODDI indices, underscoring the critical importance of early intervention in this devastating illness.

Gender moderates the relationship between media use and sleep quality.

With high screen time and poor sleep commonly reported in adolescents, it is important to more fully understand how screen time impacts sleep. Despite similar overall screen times, male and female media preferences and usages differ, making it critical to determine if different domains of screen time differentially affect sleep quality. The present study examined whether differing amounts and domains of screen-based media vary in impact on sleep quality of 16-year-old male and female adolescents over a 3-month period. A total of 98 adolescents (mean [SD] age 16.27 [0.29] years; 51% female) completed two online surveys spaced 3 months apart and comprised of well-validated self-reported measures of sleep quality, media usage, and depressive symptoms. The various domains of media were categorised into screen-based media with little-to-no peer-to-peer interaction involved (video-only) and screen-based media with interaction a predominant component to the usage (peer-to-peer interaction-involved). Self-reported sleep quality decreased across the 3-month study period. Gender moderated the effect of interactive screen time on sleep quality 3 months later, with interactive screen time associated with better sleep quality in males, but remaining poorer in females. Screen time competes with sleep time and may do so differentially depending on the media domain. Compared to females, interactive components of screen time may lessen worsening sleep quality over time in males. Understanding the relationships among screen time, its content, age, and gender may inform guidelines for educators, parents, and adolescents to help improve sleep quality of adolescents.

Effects of Noise Exposure on the Vestibular System: A Systematic Review.

Despite our understanding of the impact of noise-induced damage to the auditory system, much less is known about the impact of noise exposure on the vestibular system. In this article, we review the anatomical, physiological, and functional evidence for noise-induced damage to peripheral and central vestibular structures. Morphological studies in several animal models have demonstrated cellular damage throughout the peripheral vestibular system and particularly in the otolith organs; however, there is a paucity of data on the effect of noise exposure on human vestibular end organs. Physiological studies have corroborated morphological studies by demonstrating disruption across vestibular pathways with otolith-mediated pathways impacted more than semicircular canal-mediated pathways. Similar to the temporary threshold shifts observed in the auditory system, physiological studies in animals have suggested a capacity for recovery following noise-induced vestibular damage. Human studies have demonstrated that diminished sacculo-collic responses are related to the severity of noise-induced hearing loss, and dose-dependent vestibular deficits following noise exposure have been corroborated in animal models. Further work is needed to better understand the physiological and functional consequences of noise-induced vestibular impairment in animals and humans.

Psychosocial Health of School-aged Children during the Initial COVID-19 Safer-At-Home School Mandates in Florida: A cross-sectional study

BackgroundGiven the emerging literature regarding the impacts of lockdown measures on mental health, this study aims to identify risk factors in school-aged children for being at risk for psychosocial disorders during the COVID-19 Safer-at-Home School mandates in Florida MethodsA cross-sectional study was conducted in April 2020 (n=280). Bivariate analysis and logistic and multinomial logistic regression models are used to examine socio-demographic and knowledge, attitude, and practice (KAP) predictors of anxiety, depression, and obsessive- compulsive disorder (OCD). ResultsLoss of household income was associated with being at risk for depression [aOR=3.130, 95% CI= (1.41-6.97)], anxiety [aOR=2.531, 95%CI= (1.154-5.551)], and OCD [aOR=2.90, 95%CI= (1.32-6.36)]. Being female was associated with risk for depression [aOR=1.72, 95% CI=(1.02-2.93)], anxiety [aOR=1.75, 95% CI=(1.04-2.97)], and OCD[aOR=1.764, 95%CI= (1.027-3.028)]. Parental practices that are protective against COVID-19 were associated with children being at risk of depression [aOR=1.55, 95% CI= (1.04-2.31)]. Being at a lower school level was risk factor for anxiety and OCD. ConclusionsEfforts to address mental health risk in children, as a result schools should prioritize girls, younger children, and children of families who lose income. Limiting the spread of COVID-19 through school closure may exacerbate the risk of psychosocial disorders in children, thus school administrators should move quickly to target those at greatest risk.

CONTAIN: An open-source shipping container laboratory optimisedfor automated COVID-19 diagnostics

The COVID-19 pandemic has challenged diagnostic systems globally. Expanding testing capabilities to conduct population-wide screening for COVID-19 requires innovation in diagnostic services at both the molecular and industrial scale. No report to-date has considered the complexity of laboratory infrastructure in conjunction with the available molecular assays to offer a standardised solution to testing. Here we present CONTAIN. A modular biosafety level 2+ laboratory optimised for automated RT-qPCR COVID-19 testing based on a standard 40ft shipping container. Using open-source liquid-handling robots and RNA extraction reagents we demonstrate a reproducible workflow for RT-qPCR COVID-19 testing. With five OT2 liquid handlers, a single CONTAIN unit reaches a maximum daily testing capacity of 2400 tests/day. We validate this workflow for automated RT-qPCR testing, using both synthetic SARS-CoV-2 samples and patient samples from a local NHS hospital. Finally, we discuss the suitability of CONTAIN and its flexibility in a range of diagnostic testing scenarios including high-density urban environments and mobile response units. Visual abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/106625v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@18acad6org.highwire.dtl.DTLVardef@10ae5f1org.highwire.dtl.DTLVardef@7e34d3org.highwire.dtl.DTLVardef@1be3815_HPS_FORMAT_FIGEXP M_FIG C_FIG

Data Reduction Solution for Driving Simulator.

This paper describes the methods that have been developed and implemented to process research participant data generated by a high fidelity driving simulator that has been integrated with eye tracking equipment. The driving simulator is used for experimental studies to understand driving behavior. Solutions are implemented to programmatically process the output of the simulator and transform the raw data from these research experiments to an analysis ready format. The algorithm is tested across the data for numerous participants with varying scenarios within the experiments and is further curated to meet the requirements and standards of the research studies that require the use of driving simulator to generate data.

Fifty years of digestive endoscopy: Successes, setbacks, solutions and the future.

Flexible endoscopes became generally available 50 years ago and created a revolution in the practice of gastroenterology. They improved diagnosis enormously, enabled quicker, less invasive, and more cost-effective surgical treatment, while endoscopic screening has prevented many cancer deaths. The new technology stimulated research leading to a better understanding of gastrointestinal pathology, identifying new diseases and clarifying the etiology of others. Better-controlled clinical trials accelerated the use of newer and more effective drugs. National and international endoscopy societies supported nursing input, encouraged research, stimulated specialist journals, and devised guidelines that encouraged audit and quality assurance. Advances in instrument design and the manufacture of new accessories enhanced endoscopic technique, diagnostic ability, patient comfort, and safety. The risk of cross-infection inherent in the use of complex labile equipment that cannot be autoclaved remains a challenge. Endoscopy societies working closely with industry have established rigid protocols for high-level disinfection that minimize the risks, but strict adherence to guidelines and continued vigilance is essential, especially with the increasing prevalence of antibiotic-resistant commensals that can give rise to opportunistic infection. Government health departments have a responsibility to encourage and support research in this area by endoscopists, instrument manufacturers, and the pharmaceutical industry. Current trends suggest that in the future, artificial intelligence will greatly improve endoscopic diagnosis, and that therapeutic endoscopy will expand, encouraging endoscopists to subspecialize.

A longitudinal neurite and free water imaging study in patients with a schizophrenia spectrum disorder.

Diffusion tensor imaging (DTI) studies show widespread white matter abnormalities in schizophrenia, but it is difficult to directly relate these parameters to biological processes. Neurite orientation dispersion and density imaging (NODDI) is geared toward biophysical characterization of white matter microstructure, but only few studies have leveraged this technique to study white matter alterations. We recruited 42 schizophrenia patients (30 antipsychotic-naïve and 12 currently untreated) and 42 matched controls in this prospective study. We assessed the orientation dispersion index (ODI) and extracellular free water (FW) using single-shell DTI data before and after a 6-week trial of risperidone. Longitudinal data were available for 27 patients. Voxelwise analyses showed significantly increased ODI in the posterior limb of the internal capsule in unmedicated patients (242 voxels; x = -24; y = 6; z = 6; p < 0.01; α < 0.04), but no alterations in FW. Whole brain measures did not reveal alterations in ODI but a 6.3% trend-level increase in FW in unmedicated SZ (t = -1.873; p = 0.07). Baseline ODI was negatively correlated with subsequent response to antipsychotic treatment (r = -0.38; p = 0.049). Here, we demonstrated altered fiber complexity in medication-naïve and unmedicated patients with a schizophrenia spectrum illness. Lesser whole brain fiber uniformity was predictive of poor response to treatment, suggesting this measure may be a clinically relevant biomarker. Interestingly, we found no significant changes in NODDI indices after short-term treatment with risperidone. Our data show that biophysical diffusion models have promise for the in vivo evaluation of brain microstructure in this devastating neuropsychiatric syndrome.

Micro- and Macrostructural White Matter Integrity in Never-Treated and Currently Unmedicated Patients With Schizophrenia and Effects of Short-Term Antipsychotic Treatment.

BACKGROUND: Schizophrenia is associated with progressive white matter changes, but it is unclear whether antipsychotic medications contribute to these. Our objective was to characterize effects of short-term treatment with risperidone on white matter diffusion indices. METHODS: We recruited 42 patients with schizophrenia (30 never treated and 12 currently untreated) and 42 matched healthy control subjects in this prospective case-control neuroimaging study. Patients received a 6-week trial of risperidone. Using diffusion tensor imaging, we assessed microstructural (fractional anisotropy, mean diffusivity, and radial diffusivity) and macrostructural (radial fiber trophy) white matter integrity deficits in unmedicated patients compared with control subjects and change in white matter integrity in patients before and after antipsychotic treatment (mean risperidone dose at end point was 3.73 ± 1.72 mg). RESULTS: At baseline, fractional anisotropy was decreased in the left medial temporal white matter (cluster extent: 123 voxels; Montreal Neurological Institute peak coordinates: x = -51, y = -44, z = -7; α < .05), and mean diffusivity was increased in the fusiform/lingual gyrus white matter extending to the hippocampal part of the cingulum (cluster extent: 185 voxels; peak coordinates: x = -27, y = -49, z = 2; α < .04) in patients compared with control subjects. Radial diffusivity and macrostructure were not abnormal. None of the diffusion indices showed a significant change after 6 weeks of treatment with both voxelwise and whole-brain white matter analyses. CONCLUSIONS: We demonstrate microstructural white matter integrity abnormalities in the absence of macrostructural impairment in unmedicated patients with primarily early-stage schizophrenia. In our data, we found no significant white matter changes after short-term treatment with risperidone.