Detection of Children/Youth With Fetal Alcohol Spectrum Disorder Through Eye Movement, Psychometric, and Neuroimaging Data

Machine learning algorithms to the early diagnosis of fetal alcohol spectrum disorders

Introduction

Fetal alcohol spectrum disorders include a variety of physical and neurocognitive disorders caused by prenatal alcohol exposure. Although their overall prevalence is around 0.77%, FASD remains underdiagnosed and little known, partly due to the complexity of their diagnosis, which shares some symptoms with other pathologies such as autism spectrum, depression or hyperactivity disorders.

Methods

This study included 73 control and 158 patients diagnosed with FASD. Variables selected were based on IOM classification from 2016, including sociodemographic, clinical, and psychological characteristics. Statistical analysis included Kruskal-Wallis test for quantitative factors, Chi-square test for qualitative variables, and Machine Learning (ML) algorithms for predictions.

Results

This study explores the application ML in diagnosing FASD and its subtypes: Fetal Alcohol Syndrome (FAS), partial FAS (pFAS), and Alcohol-Related Neurodevelopmental Disorder (ARND). ML constructed a profile for FASD based on socio-demographic, clinical, and psychological data from children with FASD compared to a control group. Random Forest (RF) model was the most efficient for predicting FASD, achieving the highest metrics in accuracy (0.92), precision (0.96), sensitivity (0.92), F1 Score (0.94), specificity (0.92), and AUC (0.92). For FAS, XGBoost model obtained the highest accuracy (0.94), precision (0.91), sensitivity (0.91), F1 Score (0.91), specificity (0.96), and AUC (0.93). In the case of pFAS, RF model showed its effectiveness, with high levels of accuracy (0.90), precision (0.86), sensitivity (0.96), F1 Score (0.91), specificity (0.83), and AUC (0.90). For ARND, RF model obtained the best levels of accuracy (0.87), precision (0.76), sensitivity (0.93), F1 Score (0.84), specificity (0.83), and AUC (0.88). Our study identified key variables for efficient FASD screening, including traditional clinical characteristics like maternal alcohol consumption, lip-philtrum, microcephaly, height and weight impairment, as well as neuropsychological variables such as the Working Memory Index (WMI), aggressive behavior, IQ, somatic complaints, and depressive problems.

Discussion

Our findings emphasize the importance of ML analyses for early diagnoses of FASD, allowing a better understanding of FASD subtypes to potentially improve clinical practice and avoid misdiagnosis.

Machine learning approaches to the identification of children affected by prenatal alcohol exposure: A narrative review

Fetal alcohol spectrum disorders (FASDs) affect at least 0.8% of the population globally. The diagnosis of FASD is uniquely complex, with a heterogeneous physical and neurobehavioral presentation that requires multidisciplinary expertise for diagnosis. Many researchers have begun to incorporate machine learning approaches into FASD research to identify children who are affected by prenatal alcohol exposure, including those with FASD. This narrative review highlights these efforts. Following an introduction to machine learning, we summarize examples from the literature of neurobehavioral screening tools and physiologic markers of exposure. We discuss individual efforts, including models that classify FASD based on parent-reported neurocognitive or behavioral questionnaires, 3D facial imaging, brain imaging, DNA methylation patterns, microRNA profiles, cardiac orienting response, and dysmorphic facial features. We highlight model performance and discuss the limitations of these approaches. We conclude by considering the scalability of these approaches and how these machine learning models, largely developed from clinical samples or highly exposed birth cohorts, may perform in the general population.

Abnormal stability of spontaneous neuronal activity as a predictor of diagnosis conversion from major depressive disorder to bipolar disorder

OBJECTIVE

Bipolar disorder (BD) is often misdiagnosed as major depressive disorder (MDD) in the early stage, which may lead to inappropriate treatment. This study aimed to characterize the alterations of spontaneous neuronal activity in patients with depressive episodes whose diagnosis transferred from MDD to BD.

METHODS

532 patients with MDD and 132 healthy controls (HCs) were recruited over 10 years. During the follow-up period, 75 participants with MDD transferred to BD (tBD), and 157 participants remained with the diagnosis of unipolar depression (UD). After excluding participants with poor image quality and excessive head movement, 68 participants with the diagnosis of tBD, 150 participants with the diagnosis of UD, and 130 HCs were finally included in the analysis. The dynamic amplitude of low-frequency fluctuations (dALFF) of spontaneous neuronal activity was evaluated in tBD, UD and HC using functional magnetic resonance imaging at study inclusion. Receiver operating characteristic (ROC) analysis was performed to evaluate sensitivity and specificity of the conversion prediction from MDD to BD based on dALFF.

RESULTS

Compared to HC, tBD exhibited elevated dALFF at left premotor cortex (PMC_L), right lateral temporal cortex (LTC_R) and right early auditory cortex (EAC_R), and UD showed reduced dALFF at PMC_L, left paracentral lobule (PCL_L), bilateral medial prefrontal cortex (mPFC), right orbital frontal cortex (OFC_R), right dorsolateral prefrontal cortex (DLPFC_R), right posterior cingulate cortex (PCC_R) and elevated dALFF at LTC_R. Furthermore, tBD exhibited elevated dALFF at PMC_L, PCL_L, bilateral mPFC, bilateral OFC, DLPFC_R, PCC_R and LTC_R than UD. In addition, ROC analysis based on dALFF in differential areas obtained an area under the curve (AUC) of 72.7%.

CONCLUSIONS

The study demonstrated the temporal dynamic abnormalities of tBD and UD in the critical regions of the somatomotor network (SMN), default mode network (DMN), and central executive network (CEN). The differential abnormal patterns of temporal dynamics between the two diseases have the potential to predict the diagnosis transition from MDD to BD.

FASDetect as a machine learning-based screening app for FASD in youth with ADHD

Fetal alcohol-spectrum disorder (FASD) is underdiagnosed and often misdiagnosed as attention-deficit/hyperactivity disorder (ADHD). Here, we develop a screening tool for FASD in youth with ADHD symptoms. To develop the prediction model, medical record data from a German University outpatient unit are assessed including 275 patients aged 0-19 years old with FASD with or without ADHD and 170 patients with ADHD without FASD aged 0-19 years old. We train 6 machine learning models based on 13 selected variables and evaluate their performance. Random forest models yield the best prediction models with a cross-validated AUC of 0.92 (95% confidence interval [0.84, 0.99]). Follow-up analyses indicate that a random forest model with 6 variables - body length and head circumference at birth, IQ, socially intrusive behaviour, poor memory and sleep disturbance - yields equivalent predictive accuracy. We implement the prediction model in a web-based app called FASDetect - a user-friendly, clinically scalable FASD risk calculator that is freely available at https://fasdetect.dhc-lab.hpi.de .

Identifying Prenatal Alcohol Exposure and Children Affected by It: A Review of Biomarkers and Screening Tools

PURPOSE

Early identification of prenatal alcohol exposure (PAE) and of those in need of services resulting from this exposure is an important public health concern. This study reviewed the existing literature on potential biomarkers and screening tools of PAE and its impact.

SEARCH METHODS

Electronic databases were searched for articles published between January 1, 1996, and November 30, 2021, using the following search terms: ("fetal alcohol" or "prenatal alcohol" or "FASD" or "alcohol-related neurodevelopmental disorder" or "ARND" or "ND-PAE") and ("screening" or "identification" or "biomarker"). Duplicate articles were electronically eliminated. Titles and abstracts were reviewed for appropriateness, and selected articles were retrieved for further analysis. Additional articles were added that were referenced in the reviewed articles or identified from expert knowledge. Information about the characteristics of the sample, the biomarker or screening tool, and the predictive validity outcome data were abstracted. A narrative analysis of the studies was then performed on the data.

SEARCH RESULTS

A total of 3,813 articles were initially identified, and 1,215 were removed as duplicates. Of the remaining articles, 182 were identified as being within the scope of the review based on title and abstract inspection, and 181 articles were successfully retrieved. Of these, additional articles were removed because they were preclinical (3), were descriptive only (13), included only self-report of PAE (42), included only mean group comparison (17), were additional duplicates (2), focused on cost analysis (9), missed predictive validity data (24), or for other reasons (23). The remaining articles (n = 48) were abstracted. An additional 13 manuscripts were identified from these articles, and two more from expert knowledge. A total of 63 articles contributed to the review.

DISCUSSION AND CONCLUSIONS

Biomarkers and screening tools of PAE and its impact fall short of ideal predictive validity characteristics. Higher specificity than sensitivity was found for many of the biomarkers and screening tools used to identify PAE and its impact, suggesting that current methods continue to under-identify the full range of individuals impacted by PAE. Exceptions to this were found in recent investigations using microRNAs related to growth and vascular development, proteomic changes associated with PAE, and combinations of markers estimating levels of various cytokines. Replications of these findings are needed across other samples to confirm the limited data available. Future research on biomarkers and screening tools should attend to feasibility and scalability of implementation. This article also recommends a systematic process of evaluation to improve early identification of individuals impacted by PAE so that harm reduction and habilitative care efforts can be implemented.

Impact of prenatal alcohol exposure on neurodevelopmental outcomes: a systematic review

Background

Prenatal exposure to alcohol (PAE) represents a significant public health concern. Previous research linking PAE to neurodevelopmental outcomes has been mixed and often has limited focus on residual confounding or moderating factors.

Methods

A systematic review of prospective cohort studies (n = >1000) assessing the impact of PAE on neurodevelopmental outcomes was undertaken (neurophysiology, motor skills, cognition, language, academic achievement, memory, attention, executive function, affect regulation, and adaptive behaviour, social skills, or communication). Electronic searches of EMBASE, Medline, CINAHL, and Psychinfo were conducted in May 2021. A quality assessment was conducted using an adapted version of the Newcastle-Ottawa Scale (NOS).

Results

Thirty longitudinal cohort studies met the inclusion criteria. Evidence of the impact of PAE was mixed across domains. We found no evidence that PAE affects executive function, but there were impacts on motor skills, cognition, language, academic achievement, attention, affect regulation, and adaptive behaviour. The most consistent adverse effect was on affect regulation (nine out of thirteen studies, six of which found an association between heavy alcohol consumption or binge drinking during pregnancy). We found no protective factors. Few studies controlled for variables in the postnatal environment.

Discussion

This review was unable to conclude a safe level of alcohol consumption during pregnancy. Methodological improvements are needed to improve the quality and consistency in which PAE is studied. Further research into residual confounding variables is vital, including a greater focus on the postpartum environment.

Analysis and novel methods for capture of normative eye-tracking data in 2.5-month old infants

Development of attention systems is essential for both cognitive and social behavior maturation. Visual behavior has been used to assess development of these attention systems. Yet, given its importance, there is a notable lack of literature detailing successful methods and procedures for using eye-tracking in early infancy to assess oculomotor and attention dynamics. Here we show that eye-tracking technology can be used to automatically record and assess visual behavior in infants as young as 2.5 months, and present normative data describing fixation and saccade behavior at this age. Features of oculomotor dynamics were analyzed from 2.5-month old infants who viewed videos depicting live action, cartoons, geometric shapes, social and non-social scenes. Of the 54 infants enrolled, 50 infants successfully completed the eye-tracking task and high-quality data was collected for 32 of those infants. We demonstrate that modifications specifically tailored for the infant population allowed for consistent tracking of pupil and corneal reflection and minimal data loss. Additionally, we found consistent fixation and saccade behaviors across the entire six-minute duration of the videos, indicating that this is a feasible task for 2.5-month old infants. Moreover, normative oculomotor metrics for a free-viewing task in 2.5-month old infants are documented for the first time as a result of this high-quality data collection.

Distinguishing between depression in bipolar disorder and unipolar depression using magnetic resonance imaging: a systematic review

OBJECTIVES

Magnetic resonance imaging (MRI) studies comparing bipolar and unipolar depression characterize pathophysiological differences between these conditions. However, it is difficult to interpret the current literature due to differences in MRI modalities, analysis methods, and study designs.

METHODS

We conducted a systematic review of publications using MRI to compare individuals with bipolar and unipolar depression. We grouped studies according to MRI modality and task design. Within the discussion, we critically evaluated and summarized the functional MRI research and then further complemented these findings by reviewing the structural MRI literature.

RESULTS

We identified 88 MRI publications comparing participants with bipolar depression and unipolar depressive disorder. Compared to individuals with unipolar depression, participants with bipolar disorder exhibited heightened function, increased within network connectivity, and reduced grey matter volume in salience and central executive network brain regions. Group differences in default mode network function were less consistent but more closely associated with depressive symptoms in participants with unipolar depression but distractibility in bipolar depression.

CONCLUSIONS

When comparing mood disorder groups, the neuroimaging evidence suggests that individuals with bipolar disorder are more influenced by emotional and sensory processing when responding to their environment. In contrast, depressive symptoms and neurofunctional response to emotional stimuli were more closely associated with reduced central executive function and less adaptive cognitive control of emotionally oriented brain regions in unipolar depression. Researchers now need to replicate and refine network-level trends in these heterogeneous mood disorders and further characterize MRI markers associated with early disease onset, progression, and recovery.

Fetal alcohol spectrum disorders screening tools: A systematic review

BACKGROUND

Screening facilitates the early identification of fetal alcohol spectrum disorder (FASD) and prevalence estimation of FASD for timely prevention, diagnostic, and management planning. However, little is known about FASD screening tools.

AIMS

The aims of this systematic review are to identify FASD screening tools and examine their performance characteristics.

METHODS

Four electronic databases were searched for eligible studies that examined individuals with FASD or prenatal alcohol exposure and reported the sensitivity and specificity of FASD screening tools. The quality of the studies was assessed using the Quality Assessment of Diagnostic Studies-2 tool.

RESULTS

Sixteen studies were identified, comprising five fetal alcohol syndrome (FAS) and seven FASD screening tools. They varied in screening approach and performance characteristics and were linked to four different diagnostic criteria. FAS screening tools performed well in the identification of individuals at risk of FAS while the performance of FASD screening tools varied in the identification of individuals at risk of FASD.

CONCLUSION AND IMPLICATIONS

Results highlight the vast differences in the screening approaches performance characteristics, and diagnostic criteria linked to FASD screening tools. More research is needed to identify biomarkers unique to FASD to guide the development of accurate FASD screening tools.

School-aged children diagnosed with an FASD exhibit visuo-cortical network disturbance: A magnetoencephalography (MEG) study

Children with prenatal alcohol exposure (PAE) often suffer from cognitive and neurobehavioral dysfunction throughout their lives, which may rise to a level of concern such that children receive a diagnosis under the fetal alcohol spectrum disorders (FASD) umbrella. Magnetoencephalography (MEG) contributes direct insight into neural processing and functional connectivity measures with temporal precision to understand cortical processing disorders that manifest during development. The impairment of perception may become more consequential among school-aged children with an FASD in the process of intellectual functioning and behavioral maturation. Fifty participants with the age range of 8-13 years participated in our study following parental informed consent and child assent. For each participant, visual responses were recorded using magnetoencephalography (MEG) while performing a prosaccade task with central stimuli (fovea centralis) and peripheral stimuli (left and right of central) presented on a screen, requiring participants to shift their gaze to the stimuli. After source analysis using minimum norm estimation (MNE), we investigated visual responses from each participant by measuring the latency and amplitude of visual evoked fields. Delayed peak latency of the visual response was identified in the primary visual area (calcarine fissure) and visual association areas (v2, v3) in young children with an FASD for both stimulus types (central and peripheral). But the difference in visual response latency was only statistically significant (p ≤ 0.01) for the peripheral (right) stimulus. We also observed reduced amplitude (p ≤ 0.006) of visual evoked response in children with an FASD for the central stimulus type in both primary and visual association areas. Multiple visual areas show impairment in children with an FASD, with visual delay and conduction disturbance more prominent in response to peripheral stimuli. Children with an FASD also exhibit significantly reduced amplitude of neural activation to central stimuli. These sensory deficits may lead to slow cognitive processing speed through continued intra-cortical network disturbance in children with an FASD.

Pilot Study on Analysis of Electroencephalography Signals from Children with FASD with the Implementation of Naive Bayesian Classifiers

In this paper Naive Bayesian classifiers were applied for the purpose of differentiation between the EEG signals recorded from children with Fetal Alcohol Syndrome Disorders (FASD) and healthy ones. This work also provides a brief introduction to the FASD itself, explaining the social, economic and genetic reasons for the FASD occurrence. The obtained results were good and promising and indicate that EEG recordings can be a helpful tool for potential diagnostics of FASDs children affected with it, in particular those with invisible physical signs of these spectrum disorders.

Screening approaches for identifying fetal alcohol spectrum disorder in children, adolescents, and adults: A systematic review

BACKGROUND

Fetal alcohol spectrum disorder (FASD) is a prevalent neurodevelopmental disorder that is caused by prenatal alcohol exposure (PAE) and associated with a range of cognitive, affective, and health concerns. Although the identification of FASD can facilitate the provision of interventions and support, and plays a protective role against adverse outcomes, there are high rates of missed detection. The identification of FASD via screening may improve its recognition across settings. The current systematic review examined the available evidence on FASD screening tools and approaches across age groups and settings.

METHODS

A systematic search was carried out for both peer-reviewed studies and gray literature sources published between January 1990 and May 2020 and was preregistered with PROSPERO (#CRD42019122077). Studies included in the review focused on human applications of FASD screening in children, adolescents, and adults. The quality of the studies was assessed using the QUADAS-2 and GRADE frameworks.

RESULTS

The search yielded 20 screening tools and approaches across 45 studies, broadly characterized in 2 groups. The first group included approaches currently in use that aim to identify individuals at risk of FASD using a range of markers (n = 19) or associated sentinel dysmorphic facial features (n = 6). Another group of studies, characterized as emerging, focused on identifying promising biomarkers of PAE/FASD (n = 20). Overall, we identified limited research supporting the psychometric properties of most screening approaches. The quality review provided evidence of bias due to the common use of case-control designs and lack of adequate reference standards.

CONCLUSIONS

Although several FASD screening tools and approaches are available for use across a range of age groups and settings, the overall evidence base supporting their psychometric properties is weak, with most studies demonstrating significant risk of bias. Service providers should exercise caution in selecting and implementing FASD screening tools given these limitations. It is critically important to accurately identify individuals with FASD across ages and settings to support healthy outcomes. Thus, there is a pressing need for additional research in this area, particularly validation studies in large and representative samples using robust methodological approaches.

Detection of prenatal alcohol exposure using machine learning classification of resting-state functional network connectivity data

Fetal Alcohol Spectrum Disorder (FASD), a wide range of physical and neurobehavioral abnormalities associated with prenatal alcohol exposure (PAE), is recognized as a significant public health concern. Advancements in the diagnosis of FASD have been hindered by a lack of consensus in diagnostic criteria and limited use of objective biomarkers. Previous research from our group utilized resting-state functional magnetic resonance imaging (fMRI) to measure functional network connectivity (FNC), which revealed several sex- and region-dependent alterations in FNC as a result of moderate PAE relative to controls. Considering that FNC is sensitive to moderate PAE, this study explored the use of FNC data and machine learning methods to detect PAE among a sample of rodents exposed to alcohol prenatally and controls. We utilized previously acquired resting state fMRI data collected from adult rats exposed to moderate levels of prenatal alcohol (PAE) or a saccharin control solution (SAC) to assess FNC of resting state networks extracted by spatial group independent component analysis (GICA). FNC data were subjected to binary classification using support vector machine (SVM) -based algorithms and leave-one-out-cross validation (LOOCV) in an aggregated sample of males and females (n = 48; 12 male PAE, 12 female PAE, 12 male SAC, 12 female SAC), a males-only sample (n = 24; 12 PAE, 12 SAC), and a females-only sample (n = 24; 12 PAE, 12 SAC). Results revealed that a quadratic SVM (QSVM) kernel was significantly effective for PAE detection in females. QSVM kernel-based classification resulted in accuracy rates of 62.5% for all animals, 58.3% for males, and 79.2% for females. Additionally, qualitative evaluation of QSVM weights implicates an overarching theme of several hippocampal and cortical networks in contributing to the formation of correct classification decisions by QSVM. Our results suggest that binary classification using QSVM and adult female FNC data is a potential candidate for the translational development of novel and non-invasive techniques for the identification of FASD.

Imaging Time Series of Eye Tracking Data to Classify Attentional States

It has been shown that conclusions about the human mental state can be drawn from eye gaze behavior by several previous studies. For this reason, eye tracking recordings are suitable as input data for attentional state classifiers. In current state-of-the-art studies, the extracted eye tracking feature set usually consists of descriptive statistics about specific eye movement characteristics (i.e., fixations, saccades, blinks, vergence, and pupil dilation). We suggest an Imaging Time Series approach for eye tracking data followed by classification using a convolutional neural net to improve the classification accuracy. We compared multiple algorithms that used the one-dimensional statistical summary feature set as input with two different implementations of the newly suggested method for three different data sets that target different aspects of attention. The results show that our two-dimensional image features with the convolutional neural net outperform the classical classifiers for most analyses, especially regarding generalization over participants and tasks. We conclude that current attentional state classifiers that are based on eye tracking can be optimized by adjusting the feature set while requiring less feature engineering and our future work will focus on a more detailed and suited investigation of this approach for other scenarios and data sets.

Economic evaluations of big data analytics for clinical decision-making: a scoping review

OBJECTIVE

Much has been invested in big data analytics to improve health and reduce costs. However, it is unknown whether these investments have achieved the desired goals. We performed a scoping review to determine the health and economic impact of big data analytics for clinical decision-making.

MATERIALS AND METHODS

We searched Medline, Embase, Web of Science and the National Health Services Economic Evaluations Database for relevant articles. We included peer-reviewed papers that report the health economic impact of analytics that assist clinical decision-making. We extracted the economic methods and estimated impact and also assessed the quality of the methods used. In addition, we estimated how many studies assessed "big data analytics" based on a broad definition of this term.

RESULTS

The search yielded 12 133 papers but only 71 studies fulfilled all eligibility criteria. Only a few papers were full economic evaluations; many were performed during development. Papers frequently reported savings for healthcare payers but only 20% also included costs of analytics. Twenty studies examined "big data analytics" and only 7 reported both cost-savings and better outcomes.

DISCUSSION

The promised potential of big data is not yet reflected in the literature, partly since only a few full and properly performed economic evaluations have been published. This and the lack of a clear definition of "big data" limit policy makers and healthcare professionals from determining which big data initiatives are worth implementing.

A review of studies exploring fetal alcohol spectrum disorders through eye tracking measures

The widespread cognitive and cerebral consequences of prenatal alcohol exposure have been established during the last decades, through the exploration of fetal alcohol spectrum disorders (FASD) using neuropsychological and neuroscience tools. This research field has recently benefited from the emergence of innovative measures, among which eye tracking, allowing a precise measure of the eye movements indexing a large range of cognitive functions. We propose a comprehensive review, based on PRISMA guidelines, of the eye tracking studies performed in populations with FASD. Studies were selected from the PsycINFO, PubMed and Scopus databases, and were evaluated through a standardized methodological quality assessment. Studies were classified according to the eye tracking indexes recorded (saccade characteristics, initial fixation, number of fixations, dwell time, gaze pattern) and the process measured (perception, memory, executive functions). Eye tracking data showed that FASD are mostly associated with impaired ocular perceptive/motor abilities (i.e., altered eye movements, centrally for saccade initiation), lower accuracy as well as increased error rates in saccadic eye movements involving working memory abilities, and reduced inhibitory control on saccades. After identifying the main limitations presented by the reviewed studies, we propose guidelines for future research, underlining the need to increase the standardization of diagnosis and evaluation tools, and to improve the methodological quality of eye tracking measures.

Efficacy and safety of rituximab in autoimmune encephalitis: A meta-analysis

BACKGROUND

Autoimmune encephalitis (AE) is a rare but debilitating neurological disease where the body develops antibodies against neuronal cell surface/synaptic proteins. Rituximab is an anti-CD20 chimeric monoclonal antibody which shows promise in AE treatment observational studies. To our knowledge, there has been no previous meta-analysis providing robust evidence on the effectiveness and safety of rituximab as second-line therapy for the treatment for AE.

METHODS

This study was conducted according to the PRISMA (Preferred Reporting Items for Systematic review and Meta-Analysis) statement. Investigators independently searched PubMed, Web of Science, Google Scholar, WANFANG, CNKI, and J-STAGE for studies. Meta-analysis via representative forest plots was conducted for good functional outcome (mRS ≤ 2), proportion of relapse, and mRS score change pre- and post-treatment.

RESULTS

Good functional outcome at last follow-up following rituximab therapy occurred in 72.2% of patients (95% CI: 66.3%-77.4%). Mean mRS score decreased by 2.67 (95% CI: 2.04-3.3; P < .001). Relapses following the rituximab therapy occurred in only 14.2% of patients (95% CI: 9.5%-20.8%). Infusion related reactions, pneumonia, and severe sepsis were seen in 29 (15.7%), 11 (6.0%), and two patients (1.1%), respectively. The efficacy and side effect profile of rituximab are comparable to outcomes seen in rituximab use in other autoimmune and inflammatory CNS disease.

CONCLUSION

Our meta-analysis showed that rituximab is an effective second-line agent for AE with an acceptable toxicity profile.

Relationship Between Task-Based and Parent Report-Based Measures of Attention and Executive Function in Children with Fetal Alcohol Spectrum Disorders (FASD)

A majority of children with fetal alcohol spectrum disorders (FASD) have demonstrated attention and executive function deficits as measured by both parent report measures and performance on tasks requiring sustained levels of attention. However, prior studies have consistently reported a lack of association between parental report-based and task-based performance measures. The current study investigated whether changes in performance over time within-task (i.e., first-half versus second-half) better correspond to parental reports of executive function and temperament in children with FASD. Greater differences in split-half performance during a continuous performance task were found to be associated with higher parent-reported levels of behavioral regulation and inhibitory control. These findings suggest that within-task performance differences may more accurately reflect individual differences in executive function and temperament as measured by parental report and help to further inform the way in which cognitive processes are measured in children with FASD.

Amyotrophic lateral sclerosis (ALS), cancer, autoimmunity and metabolic disorders: An unsolved tantalizing challenge

Amyotrophic lateral sclerosis (ALS) commonly referred to as motor neurone disease, is a neurodegenerative disease of unknown pathogenesis that progresses rapidly and has attracted an increased amount of scholarly interest in recent years. The current conception of amyotrophic lateral sclerosis has transitioned into a more complex theory in which individual genetic risk, ageing and environmental factors interact, leading to disease onset in subjects in whom the sum of these factors reach a determined threshold. Based on this conceptualization, the environmental conditions, particularly those that are potentially modifiable, are becoming increasingly relevant. In this review, the current integrative model of the disease is discussed. In addition, we explore the role of cancer, autoimmunity and metabolic diseases as examples of novel, non-genetic and environmental factors. Together with the potential triggers or perpetuating pathogenic mechanisms along with new insights into potential lines of future research are provided. LINKED ARTICLES: This article is part of a themed issue on Neurochemistry in Japan. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v178.6/issuetoc.

Multivariate models of brain volume for identification of children and adolescents with fetal alcohol spectrum disorder

Magnetic resonance imaging (MRI) studies of fetal alcohol spectrum disorder (FASD) have shown reductions of brain volume associated with prenatal exposure to alcohol. Previous studies consider regional brain volumes independently but ignore potential relationships across numerous structures. This study aims to (a) identify a multivariate model based on regional brain volume that discriminates children/adolescents with FASD versus healthy controls, and (b) determine if FASD classification performance can be increased by building classification models separately for each sex. Three‐dimensional T1‐weighted MRI from two independent childhood/adolescent datasets were used for training (79 FASD, aged 5.7–18.9 years, 35 males; 81 controls, aged 5.8–18.5 years, 32 males) and testing (67 FASD, aged 6.0–19.6 years, 38 males; 74 controls, aged 5.2–19.5 years, 42 males) a classification model. Using FreeSurfer, 87 regional brain volumes were extracted for each subject and were used as input into a support vector machine generating a classification model from the training data. The model performed moderately well on the test data with accuracy 77%, sensitivity 64%, and specificity 88%. Regions that contributed heavily to prediction in this model included temporal lobe and subcortical gray matter. Further investigation of two separate models for males and females showed slightly decreased accuracy compared to the model including all subjects (male accuracy 70%; female accuracy 67%), but had different regional contributions suggesting sex differences. This work demonstrates the potential of multivariate analysis of brain volumes for discriminating children/adolescents with FASD and provides indication of the most affected regions.

Forty Years of Assessing Neurodevelopmental and Behavioral Effects of Prenatal Alcohol Exposure in Infants: What Have We Learned?

It has been known for over 4 decades that prenatal alcohol exposure (PAE) can adversely affect neurodevelopment and behavior (NDB). Yet, early detection of altered NDB due to PAE continues to present a major clinical challenge. Identification of altered NDB in the first 2 years of life, before higher-order cognitive processes develop, invites early interventions for affected children to improve long-term outcomes. Studies published in English from January of 1980 to July of 2018 were identified in PubMed/MEDLINE. The review focused on prospective birth cohort studies which used standardized NDB assessments in children up to 2 years of age, wherein PAE was the main exposure and NDB was the main outcome. NDB was categorized into the domains of neurocognitive, adaptive, and self-regulation based on the 2016 Updated Clinical Guidelines for Diagnosing fetal alcohol spectrum disorder. An initial search resulted in 1,867 articles for which we reviewed abstracts; 114 were selected for full-text review; and 3 additional abstracts were identified through review of references in eligible publications. Thirty-one publications met criteria and were included: of these, 24 reported neurocognitive outcomes, 24 reported adaptive behavior outcomes, and 12 reported outcomes in the domain of self-regulation. Although self-regulation was assessed in the fewest number of studies, 8/12 (75%) reported PAE-associated deficits. In contrast, results were mixed for the other 2 domains: 13/24 (54%) of the selected studies that included neurocognitive outcomes showed poorer performance following PAE, and 8/24 (33%) studies that assessed adaptive functioning found significant differences between PAE and comparison infants. There is considerable evidence to support the value of early-life assessments of infant NDB when PAE is known or suspected. More studies focusing on infant self-regulation, in particular, are needed to determine the utility of early evaluation of this critical developmental domain in infants with PAE.