scholarly journals Is there any incremental benefit to conducting neuroimaging and neurocognitive assessments in the diagnosis of ADHD in young children? A machine learning investigation

2020 ◽  
Author(s):  
Ilke Öztekin ◽  
Mark A. Finlayson ◽  
Paulo A. Graziano ◽  
Anthony S. Dick
Keyword(s):  
Machine Learning ◽  
Executive Function ◽  
Young Children ◽  
Diagnostic Category ◽  
Teacher Ratings ◽  
Future Research ◽  
Cognitive Measures ◽  
Hyperactivity Disorder ◽  
Incremental Value ◽  
Extensive Evaluation

ABSTRACTGiven the negative trajectories of early behavior problems associated with Attention-Deficit/Hyperactivity Disorder (ADHD), early diagnosis of ADHD is considered critical to enable early intervention and treatment. To this end, the current investigation employed machine learning to evaluate the relative predictive value of parent/teacher ratings, as well as behavioral and neural measures of executive function in predicting ADHD diagnostic category in a sample consisting of 162 young children (53.7% ADHD, ages 4 to 7, mean age 5.55, 67.9% male, 82.6% Hispanic/Latino). Among all the target measures assessed in the study, teacher ratings of executive function were identified as by far the most important measure in predicting ADHD diagnostic category. While a more extensive evaluation of neural measures, such as diffusion-weighted imaging, may provide more information as they relate to the underlying cognitive deficits associated with ADHD, the current study indicates that commonly used structural imaging measures of cortical thickness, as well as widely used cognitive measures of executive function, have little incremental value in differentiating typically developing children from those diagnosed with ADHD. Future research evaluating the importance of such measures in predicting children’s functional impairment in academic and social areas would provide additional insight into their contributing role in ADHD.

scholarly journals Detection of Leek Rust Disease under Field Conditions Using Hyperspectral Proximal Sensing and Machine Learning

2021 ◽  
Vol 13 (7) ◽  
pp. 1341
Author(s):  
Simon Appeltans ◽  
Jan G. Pieters ◽  
Abdul M. Mouazen
Keyword(s):  
Machine Learning ◽  
Field Conditions ◽  
Environmental Cost ◽  
Supervised Machine Learning ◽  
Future Research ◽  
Complex Data ◽  
Rust Disease ◽  
Proximal Sensing ◽  
Full Field ◽  
Extensive Evaluation

Rust disease is an important problem for leek cultivation worldwide. It reduces market value and in extreme cases destroys the entire harvest. Farmers have to resort to periodical full-field fungicide applications to prevent the spread of disease, once every 1 to 5 weeks, depending on the cultivar and weather conditions. This implies an economic cost for the farmer and an environmental cost for society. Hyperspectral sensors have been extensively used to address this issue in research, but their application in the field has been limited to a relatively low number of crops, excluding leek, due to the high investment costs and complex data gathering and analysis associated with these sensors. To fill this gap, a methodology was developed for detecting leek rust disease using hyperspectral proximal sensing data combined with supervised machine learning. First, a hyperspectral library was constructed containing 43,416 spectra with a waveband range of 400–1000 nm, measured under field conditions. Then, an extensive evaluation of 11 common classifiers was performed using the scikit-learn machine learning library in Python, combined with a variety of wavelength selection techniques and preprocessing strategies. The best performing model was a (linear) logistic regression model that was able to correctly classify rust disease with an accuracy of 98.14 %, using reflectance values at 556 and 661 nm, combined with the value of the first derivative at 511 nm. This model was used to classify unlabelled hyperspectral images, confirming that the model was able to accurately classify leek rust disease symptoms. It can be concluded that the results in this work are an important step towards the mapping of leek rust disease, and that future research is needed to overcome certain challenges before variable rate fungicide applications can be adopted against leek rust disease.

scholarly journals Structural and diffusion weighted brain imaging biomarkers for ADHD and its symptomology in very young (4–7-year-old) children

2021 ◽  
Author(s):  
Ilke Oztekin ◽  
Dea Garic ◽  
Mark Finlayson ◽  
Paulo Graziano ◽  
Anthony Steven Dick
Keyword(s):  
Emotion Regulation ◽  
Executive Function ◽  
Brain Imaging ◽  
Diagnostic Category ◽  
Teacher Ratings ◽  
Developmental Trajectory ◽  
Imaging Biomarkers ◽  
Impulsive Behavior ◽  
Diffusion Weighted ◽  
And Diffusion

The current study aimed to identify the key neurobiology of Attention-Deficit/Hyperactivity Disorder (ADHD), as it relates to ADHD diagnostic category and symptoms of hyperactive/impulsive behavior and inattention. To do so, we adapted a predictive modeling approach to identify the key structural and diffusion weighted brain imaging measures, and their relative standing with respect to teacher ratings of executive function – EF (measured by the Metacognition Index of the Behavior Rating Inventory of Executive Function– BRIEF), negativity and emotion regulation – ER, (measured by the Emotion Regulation Checklist, ERC), in a critical young age range (ages 4 to 7, mean age 5.52 years, 82.2% Hispanic/Latino), where initial contact with educators and clinicians typically take place. Teacher ratings of EF and ER were predictive of both ADHD diagnostic category and symptoms of hyperactive/impulsive behavior and inattention. Among the neural measures evaluated, the current study identified the critical importance of the largely understudied diffusion weighted imaging measures for the underlying neurobiology of ADHD and its associated symptomology. Specifically, our analyses implicated the inferior frontal gyrus, the pericallosal sulcus, and the caudate as critical predictors of ADHD diagnostic category and its associated symptomology, above and beyond teacher ratings of EF and ER. Collectively, the current set of findings have implications for theories of ADHD, the relative utility of neurobiological measures with respect to teacher ratings of EF and ER, and the developmental trajectory of its underlying neurobiology.

scholarly journals Executive functions in French-Greek early bilinguals: In search of the suggested bilingual advantage

2020 ◽  
Vol 25 (2) ◽  
pp. 76
Author(s):  
Elisavet Chrysochoou ◽  
Styliani Kanaki ◽  
Ana B. Vivas
Keyword(s):  
Executive Function ◽  
Daily Basis ◽  
Task Demands ◽  
Cognitive Tasks ◽  
Future Research ◽  
Verbal Intelligence ◽  
Cognitive Measures ◽  
Top Down ◽  
Bilingual Advantage ◽  
Linguistic Interference

Bilinguals must manage two languages on a daily basis, which requires, among other things, dealing with cross-linguistic interference. Such cognitive training is assumed to underlie better performance of bilinguals, relative to monolinguals, in non-verbal cognitive tasks. Ηowever, the suggested advantage has recently been questioned. The present study aimed at shedding light into this debate, focusing on French-Greek early bilingual adults. Exposure to two languages from the first few years of life has been suggested to favour the demonstration of an advantage. Bilinguals were compared to Greek-speaking monolingual adults (matched for age, gender, non-verbal intelligence, and SES) on executive function tasks, tapping switching, inhibition, and updating processes. Task demands were also manipulated. In line with the suggested advantage and as expected, in the switching paradigm, bilinguals performed faster overall and demonstrated a smaller mixing cost; this can be assumed to reflect better general monitoring and top-down processing for bilingual participants. In contrast, the groups did not differ on switching cost, neither on the inhibition and updating measures. Moreover, contrary to what was expected, the cognitive measures did not correlate with an index of how balanced bilingualism was. Findings do not support a general and robust cognitive advantage in a sample of early bilinguals. Factors that might influence its observation are discussed, along with lines of future research.

Attention-Deficit/Hyperactivity Disorder

2013 ◽  
Author(s):  
Edmund J. S. Sonuga-Barke
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Causal Structure ◽  
Diagnostic Category ◽  
Future Research ◽  
Hyperactivity Disorder ◽  
Preschool Period ◽  
Underlying Pathophysiology ◽  
Continuity And Discontinuity

In this chapter I review the literature on attention-deficit/hyperactivity disorder (ADHD) with the aim of providing a developmental synthesis. In the first section I ask: What is ADHD? I conclude that it is a relatively broad construct that, although having validity as a mental disorder dimension and utility as diagnostic category, is frequently comorbid with, but can be distinguished from, other disorders, and is highly heterogeneous. In the second section I ask: What causes ADHD? I conclude that ADHD has a complex set of causes implicating multiple genetic and environmental risks (and their interaction) reflected in alterations in diverse brain systems. The causal structure of ADHD is heterogeneous, with different children displaying different etiological and pathophysiological profiles. In the third section I reflect on developmental considerations. I conclude that ADHD-type problems present in different forms throughout the lifespan from the preschool period to adulthood and that existing data suggest patterns of continuity and discontinuity that support a lifespan perspective both at the level of clinical phenotype and underlying pathophysiology. In the light of this I argue for a developmental reconceptualization of the disorder, grounded in a biopsychosocial framework that would allow the complexity and heterogeneity of the condition to be understood in terms of risk, resilience, and protective factors, as well as mediating and moderating processes. I review the implications of the developmental perspective for nosological and diagnostic formulations of the condition. In the last section I set out priorities for future research in the genetics, imaging, neuropsychology, and treatment of the condition.

Executive Functions and Clinical and Academic Outcomes in Speech-Language Pathology Graduate Students

2020 ◽  
Vol 5 (5) ◽  
pp. 1221-1230
Author(s):  
Jane Roitsch ◽  
Kimberly A. Murphy ◽  
Anastasia M. Raymer
Keyword(s):  
Executive Function ◽  
Academic Outcomes ◽  
Graduate Program ◽  
Performance Outcomes ◽  
Future Research ◽  
Speech Language Pathology ◽  
Grade Point ◽  
Praxis Ii ◽  
Language Pathology ◽  
Preliminary Study

Purpose The purpose of this study was to investigate executive function measures as they relate to clinical and academic performance outcomes of graduate speech-language pathology students. Method An observational design incorporating correlations and stepwise multiple regressions was used to determine the strength of the relationships between clinical outcomes that occurred at various time points throughout the graduate program (clinical coursework grades throughout the program and case study paper scores at the end of the program), academic outcomes (graduate grade point average and Praxis II exam in speech-language pathology scores), and executive function (EF) scores (EF assessment scores, self-reported EF scores). Participants were 37 students (36 women, M age = 24.1) in a master's degree program in speech-language pathology at a southeastern U.S. university during the 2017–2018 academic year. Results Findings of this preliminary study indicated that a limited number of objective EF scores and self-reported EF scores were related to clinical and academic outcomes of graduate speech-language pathology students. Conclusion As results of this preliminary study suggest that EF tests may be related to clinical and academic outcomes, future research can move to study the potential role of EF measures in the graduate admissions process in clinical graduate programs such as speech-language pathology.

The Influence of Background Auditory Noise on P50 and N100 Suppression Elicited by the Paired-Click Paradigm

2020 ◽  
Vol 34 (3) ◽  
pp. 171-178
Author(s):  
Samantha Major ◽  
Kimberly Carpenter ◽  
Logan Beyer ◽  
Hannah Kwak ◽  
Geraldine Dawson ◽  
...  
Keyword(s):  
Sensory Gating ◽  
Autism Spectrum ◽  
Inhibitory Response ◽  
Future Research ◽  
Typically Developing ◽  
Optimal Outcomes ◽  
Hyperactivity Disorder ◽  
Auditory Sensory Gating ◽  
Auditory Noise ◽  
P50 Suppression

Abstract. Auditory sensory gating is commonly assessed using the Paired-Click Paradigm (PCP), an electroencephalography (EEG) task in which two identical sounds are presented sequentially and the brain’s inhibitory response to the second sound is measured. Many clinical populations demonstrate reduced P50 and/or N100 suppression. Testing sensory gating in children may help to identify individuals at risk for neurodevelopmental disorders earlier, including autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), which could lead to more optimal outcomes. Minimal research has been done with children because of the difficulty of performing lengthy EEG experiments with young children, requiring them to sit still for long periods of time. We designed a modified, potentially child-friendly version of the PCP and evaluated it in typically developing adults. The PCP was administered twice, once in a traditional silent room (silent movie condition) and once with an audible movie playing (audible movie condition) to minimize boredom and enhance behavioral compliance. We tested whether P50 and N100 suppression were influenced by the presence of the auditory background noise from the movie. N100 suppression was observed in both hemispheres in the silent movie condition and in the left hemisphere only during the audible movie condition, though suppression was attenuated in the audible movie condition. P50 suppression was not observed in either condition. N100 sensory gating was successfully elicited with an audible movie playing during the PCP, supporting the use of the modified task for future research in both children and adults.

Relations between Executive Function and Social Understanding in Young Children

2007 ◽  
Author(s):  
Stuart I. Hammond ◽  
Max B. Bibok ◽  
Dana P. Liebermann ◽  
Ulrich Mueller ◽  
Jeremy Carpendale
Keyword(s):  
Executive Function ◽  
Young Children ◽  
Social Understanding

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

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