Faculty Bibliography

Background: The burden of mental, neurological, and substance (MNS) disorders is greater in low- and middle-income countries (LMICs). The rapid growth of digital health (i.e., eHealth) approaches offer new solutions for transforming pediatric mental health services and have the potential to address multiple resource and system barriers. However, little work has been done in applying eHealth to promote young children's mental health in LMICs. It is also not clear how eHealth has been and might be applied to translating existing evidence-based practices/strategies (EBPs) to enable broader access to child mental health interventions and services. Methods: A scoping review was conducted to summarize current eHealth applications and evidence in child mental health. The review focuses on 1) providing an overview of existing eHealth applications, research methods, and effectiveness evidence in child mental health promotion (focused on children of 0-12 years of age) across diverse service contexts; and 2) drawing lessons learned from the existing research about eHealth design strategies and usability data in order to inform future eHealth design in LMICs. Results: Thirty-two (32) articles fitting our inclusion criteria were reviewed. The child mental health eHealth studies were grouped into three areas: i) eHealth interventions targeting families that promote child and family wellbeing; ii) eHealth for improving school mental health services (e.g., promote school staff's knowledge and management skills); and iii) eHealth for improving behavioral health care in the pediatric care system (e.g., promote use of integrated patient-portal and electronic decision support systems). Most eHealth studies have reported positive impacts. Although most pediatric eHealth studies were conducted in high-income countries, many eHealth design strategies can be adapted and modified to fit LMIC contexts. Most user-engagement strategies identified from high-income countries are also relevant for populations in LMICs. Conclusions: This review synthesizes patterns of eHealth use across a spectrum of individual/family and system level of eHealth interventions that can be applied to promote child mental health and strengthen mental health service systems. This review also summarizes critical lessons to guide future eHealth design and delivery models in LMICs. However, more research in testing combinations of eHealth strategies in LMICs is needed.

Altered structural brain asymmetry in autism spectrum disorder (ASD) has been reported. However, findings have been inconsistent, likely due to limited sample sizes. Here we investigated 1,774 individuals with ASD and 1,809 controls, from 54 independent data sets of the ENIGMA consortium. ASD was significantly associated with alterations of cortical thickness asymmetry in mostly medial frontal, orbitofrontal, cingulate and inferior temporal areas, and also with asymmetry of orbitofrontal surface area. These differences generally involved reduced asymmetry in individuals with ASD compared to controls. Furthermore, putamen volume asymmetry was significantly increased in ASD. The largest case-control effect size was Cohen's d = -0.13, for asymmetry of superior frontal cortical thickness. Most effects did not depend on age, sex, IQ, severity or medication use. Altered lateralized neurodevelopment may therefore be a feature of ASD, affecting widespread brain regions with diverse functions. Large-scale analysis was necessary to quantify subtle alterations of brain structural asymmetry in ASD.

This tutorial introduces recent developments in precision medicine for estimating treatment decision rules. The objective of these developments is to advance personalised healthcare by identifying an optimal treatment option for each individual patient based on each patient's characteristics. The methods detailed in this tutorial define composite variables from the patient measures that can be viewed as 'biosignatures' for differential treatment response, which we have termed 'generated effect modifiers'. In contrast to most machine learning approaches to precision medicine, these biosignatures are derived from linear and non-linear regression models and thus have the advantage of easy visualisation and ready interpretation. The methods are illustrated using examples from randomised clinical trials.

Spectral imaging is a ubiquitous tool in modern biochemistry. Despite acquiring dozens to thousands of spectral channels, existing technology cannot capture spectral images at the same spatial resolution as structural microscopy. Due to partial voluming and low light exposure, spectral images are often difficult to interpret and analyze. This highlights a need to upsample the low-resolution spectral image by using spatial information contained in the high-resolution image, thereby creating a fused representation with high specificity both spatially and spectrally. In this paper, we propose a framework for the fusion of co-registered structural and spectral microscopy images to create super-resolved representations of spectral images. As a first application, we super-resolve spectral images of retinal tissue imaged with confocal laser scanning microscopy, by using spatial information from structured illumination microscopy. Second, we super-resolve mass spectroscopic images of mouse brain tissue, by using spatial information from high-resolution histology images. We present a systematic validation of model assumptions crucial towards maintaining the original nature of spectra and the applicability of super-resolution. Goodness-of-fit for spectral predictions are evaluated through functional R² values, and the spatial quality of the super-resolved images are evaluated using normalized mutual information.

The sparse activity of hippocampal dentate gyrus (DG) granule cells (GCs) is thought to be critical for cognition and behavior, whereas excessive DG activity may contribute to disorders such as temporal lobe epilepsy (TLE). Glutamatergic mossy cells (MCs) of the DG are potentially critical to normal and pathological functions of the DG because they can regulate GC activity through innervation of GCs or indirectly through GABAergic neurons. Here, we test the hypothesis that MC excitation of GCs is normally weak, but under pathological conditions, MC excitation of GCs is dramatically strengthened. We show that selectively inhibiting MCs during severe seizures reduced manifestations of those seizures, hippocampal injury, and chronic epilepsy. In contrast, selectively activating MCs was pro-convulsant. Mechanistic in vitro studies using optogenetics further demonstrated the unanticipated ability of MC axons to excite GCs under pathological conditions. These results demonstrate an excitatory and epileptogenic effect of MCs in the DG.

BACKGROUND/OBJECTIVE/OBJECTIVE:Sleep problems are commonly reported by individuals with Autism Spectrum Disorder (ASD). However, to date, no quantitative evidence synthesis of available studies has been performed to quantify sleep alterations in adults with ASD. We performed a systematic review and meta-analysis of objective (ie, based on actigraphy or polysomnography [PSG]) and subjective (ie, based on sleep diaries/questionnaires) studies comparing sleep parameters in adults with ASD and in a typically developing (TD) control group. METHODS:PubMed, OVID databases and Web of Knowledge were systematically searched up to February 2019 with no language restrictions. Original studies including adults with a diagnosis of ASD according to DSM, ICD, or based on standard diagnostic tools (eg, ADOS), and a TD control group were included. Random-effects models were used. Study quality was evaluated with the Newcastle Ottawa Scale (NOS). Analyses were conducted using Comprehensive Meta-Analysis. RESULTS:From initial pool of 1948 references, 14 publications including 8 datasets, (194 ASD and 277 controls) met the inclusion criteria. Compared to controls, individuals with ASD were significantly more impaired in six out of 11 subjective parameters, including lower sleep efficiency (SE, SMD = -0.87, CI = -1.14 - 0.60) and in 10 out of 17 objective outcomes, including longer sleep onset latency (PSG) (SMD = 0.86, CI = 0.29-1.07) and wake after sleep onset (WASO, actigraphy) (SMD = 0.57, CI = 0.28-0.87). The mean NOS score was 4.88/6. CONCLUSIONS:Individuals with ASD demonstrated impaired sleep compared to controls in most subjective and objective measures.

BACKGROUND:Nonhuman primate (NHP) models are commonly used to advance our understanding of brain function and organization. However, to date, they have offered few insights into individual differences among NHPs. In large part, this is due to the logistical challenges of NHP research, which limit most studies to 5 subjects or fewer. METHODS:We leveraged the availability of a large-scale open NHP imaging resource to provide an initial examination of individual differences in the functional organization of the NHP brain. Specifically, we selected one awake functional magnetic resonance imaging dataset (Newcastle University: n = 10) and two anesthetized functional magnetic resonance imaging datasets (Oxford University: n = 19; University of California, Davis: n = 19) to examine individual differences in functional connectivity characteristics across the cortex as well as potential state dependencies. RESULTS:We noted significant individual variations of functional connectivity across the macaque cortex. Similar to the findings in humans, during the awake state, the primary sensory and motor cortices showed lower variability than the high-order association regions. This variability pattern was significantly correlated with T1-weighted and T2-weighted mapping and degree of long-distance connectivity, but not short-distance connectivity. The interindividual variability under anesthesia exhibited a very distinct pattern, with lower variability in medial frontal cortex, precuneus, and somatomotor regions and higher variability in the lateral ventral frontal and insular cortices. CONCLUSIONS:This work has implications for our understanding of the evolutionary origins of individual variation in the human brain and methodological implications that must be considered in any pursuit to study individual variation in NHP models.