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  1. Article ; Online: Explainable AI-Based Identification of Contributing Factors to the Mood State Change in Children and Adolescents with Pre-Existing Psychiatric Disorders in the Context of COVID-19-Related Lockdowns in Greece

    Charis Ntakolia / Dimitrios Priftis / Konstantinos Kotsis / Konstantina Magklara / Mariana Charakopoulou-Travlou / Ioanna Rannou / Konstantina Ladopoulou / Iouliani Koullourou / Emmanouil Tsalamanios / Eleni Lazaratou / Aspasia Serdari / Aliki Grigoriadou / Neda Sadeghi / Kenny Chiu / Ioanna Giannopoulou

    BioMedInformatics, Vol 3, Iss 4, Pp 1040-

    2023  Volume 1059

    Abstract: The COVID-19 pandemic and its accompanying restrictions have significantly impacted people’s lives globally. There is an increasing interest in examining the influence of this unprecedented situation on our mental well-being, with less attention towards ... ...

    Abstract The COVID-19 pandemic and its accompanying restrictions have significantly impacted people’s lives globally. There is an increasing interest in examining the influence of this unprecedented situation on our mental well-being, with less attention towards the impact of the elongation of COVID-19-related measures on youth with a pre-existing psychiatric/developmental disorder. The majority of studies focus on individuals, such as students, adults, and youths, among others, with little attention being given to the elongation of COVID-19-related measures and their impact on a special group of individuals, such as children and adolescents with diagnosed developmental and psychiatric disorders. In addition, most of these studies adopt statistical methodologies to identify pair-wise relationships among factors, an approach that limits the ability to understand and interpret the impact of various factors. In response, this study aims to adopt an explainable machine learning approach to identify factors that explain the deterioration or amelioration of mood state in a youth clinical sample. The purpose of this study is to identify and interpret the impact of the greatest contributing features of mood state changes on the prediction output via an explainable machine learning pipeline. Among all the machine learning classifiers, the Random Forest model achieved the highest effectiveness, with 76% best AUC-ROC Score and 13 features. The explainability analysis showed that stress or positive changes derived from the imposing restrictions and COVID-19 pandemic are the top two factors that could affect mood state.
    Keywords COVID-19 pandemic ; mental health ; machine learning ; explainability ; children and adolescents ; Neurosciences. Biological psychiatry. Neuropsychiatry ; RC321-571 ; Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 360
    Language English
    Publishing date 2023-11-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Correction

    Charis Ntakolia / Dimitrios Priftis / Mariana Charakopoulou-Travlou / Ioanna Rannou / Konstantina Magklara / Ioanna Giannopoulou / Konstantinos Kotsis / Aspasia Serdari / Emmanouil Tsalamanios / Aliki Grigoriadou / Konstantina Ladopoulou / Iouliani Koullourou / Neda Sadeghi / Georgia O’Callaghan / Eleni Lazaratou

    Healthcare, Vol 10, Iss 657, p

    Ntakolia et al. An Explainable Machine Learning Approach for COVID-19’s Impact on Mood States of Children and Adolescents during the First Lockdown in Greece. Healthcare 2022, 10 , 149

    2022  Volume 657

    Abstract: Argyris Stringaris was initially included as an author in the original publication [.] ...

    Abstract Argyris Stringaris was initially included as an author in the original publication [.]
    Keywords n/a ; Medicine ; R
    Language English
    Publishing date 2022-03-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Hypoplasia of cerebellar afferent networks in Down syndrome revealed by DTI-driven tensor based morphometry

    Nancy Raitano Lee / Amritha Nayak / M. Okan Irfanoglu / Neda Sadeghi / Catherine J. Stoodley / Elizabeth Adeyemi / Liv S. Clasen / Carlo Pierpaoli

    Scientific Reports, Vol 10, Iss 1, Pp 1-

    2020  Volume 13

    Abstract: Abstract Quantitative magnetic resonance imaging (MRI) investigations of brain anatomy in children and young adults with Down syndrome (DS) are limited, with no diffusion tensor imaging (DTI) studies covering that age range. We used DTI-driven tensor ... ...

    Abstract Abstract Quantitative magnetic resonance imaging (MRI) investigations of brain anatomy in children and young adults with Down syndrome (DS) are limited, with no diffusion tensor imaging (DTI) studies covering that age range. We used DTI-driven tensor based morphometry (DTBM), a novel technique that extracts morphometric information from diffusion data, to investigate brain anatomy in 15 participants with DS and 15 age- and sex-matched typically developing (TD) controls, ages 6–24 years (mean age ~17 years). DTBM revealed marked hypoplasia of cerebellar afferent systems in DS, including fronto-pontine (middle cerebellar peduncle) and olivo-cerebellar (inferior cerebellar peduncle) connections. Prominent gray matter hypoplasia was observed in medial frontal regions, the inferior olives, and the cerebellum. Very few abnormalities were detected by classical diffusion MRI metrics, such as fractional anisotropy and mean diffusivity. Our results highlight the potential importance of cerebro-cerebellar networks in the clinical manifestations of DS and suggest a role for DTBM in the investigation of other brain disorders involving white matter hypoplasia or atrophy.
    Keywords Medicine ; R ; Science ; Q
    Subject code 616
    Language English
    Publishing date 2020-03-01T00:00:00Z
    Publisher Nature Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: An Explainable Machine Learning Approach for COVID-19’s Impact on Mood States of Children and Adolescents during the First Lockdown in Greece

    Charis Ntakolia / Dimitrios Priftis / Mariana Charakopoulou-Travlou / Ioanna Rannou / Konstantina Magklara / Ioanna Giannopoulou / Konstantinos Kotsis / Aspasia Serdari / Emmanouil Tsalamanios / Aliki Grigoriadou / Konstantina Ladopoulou / Iouliani Koullourou / Neda Sadeghi / Georgia O’Callaghan / Argyris Stringaris / Eleni Lazaratou

    Healthcare, Vol 10, Iss 149, p

    2022  Volume 149

    Abstract: The global spread of COVID-19 led the World Health Organization to declare a pandemic on 11 March 2020. To decelerate this spread, countries have taken strict measures that have affected the lifestyles and economies. Various studies have focused on the ... ...

    Abstract The global spread of COVID-19 led the World Health Organization to declare a pandemic on 11 March 2020. To decelerate this spread, countries have taken strict measures that have affected the lifestyles and economies. Various studies have focused on the identification of COVID-19’s impact on the mental health of children and adolescents via traditional statistical approaches. However, a machine learning methodology must be developed to explain the main factors that contribute to the changes in the mood state of children and adolescents during the first lockdown. Therefore, in this study an explainable machine learning pipeline is presented focusing on children and adolescents in Greece, where a strict lockdown was imposed. The target group consists of children and adolescents, recruited from children and adolescent mental health services, who present mental health problems diagnosed before the pandemic. The proposed methodology imposes: (i) data collection via questionnaires; (ii) a clustering process to identify the groups of subjects with amelioration, deterioration and stability to their mood state; (iii) a feature selection process to identify the most informative features that contribute to mood state prediction; (iv) a decision-making process based on an experimental evaluation among classifiers; (v) calibration of the best-performing model; and (vi) a post hoc interpretation of the features’ impact on the best-performing model. The results showed that a blend of heterogeneous features from almost all feature categories is necessary to increase our understanding regarding the effect of the COVID-19 pandemic on the mood state of children and adolescents.
    Keywords COVID-19 pandemic ; children and adolescents ; machine learning ; post hoc explainability ; model calibration ; Medicine ; R
    Subject code 360
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: The effect of neurofeedback training on EEG and balance performance in children with reading disorder

    Neda Sadeghi / Mohammad ALi Nazari / Mehdi Alizade / Mohammad Kamali

    Modern Rehabilitation, Vol 7, Iss 3, Pp 32-

    2013  Volume 39

    Abstract: Background and Aim: Reading disorder is a neurodevelopmental disorder with deficits in cognition and motor skills. According to available studies, the brain structure in thesechildren is intact, but the brain function is abnormal. So, neurofeedback as a ... ...

    Abstract Background and Aim: Reading disorder is a neurodevelopmental disorder with deficits in cognition and motor skills. According to available studies, the brain structure in thesechildren is intact, but the brain function is abnormal. So, neurofeedback as a new treatment, can improve brain function in this disorder through regulating abnormalities of electroencephalogram (EEG). The purpose of this study was to investigate the effectiveness of neurofeedback balance protocol (to inhibit 4-7 Hz while reinforcing 15-18 Hz at electrode sites O1 and O2) on EEG and balance performance in children with reading disorder. Materials and Methods: The study was conducted in a single subject design in 20 sessions. Participants were 4 children (1girl and 3 boys) aged between 8-12 years old who completed twelve 30-min neurofeedback sessions. Repeated measurements were performed during the baseline, treatment and follow-up by means of 2nd subtest of BOTMP for balance performance and EEG for brain waves changes. Results: The results showed that the effect of treatment on balance performance was high (Cohen’d > 0.8) in all subjects but there were no significant changes in absolute power of brain waves. Conclusion: The results of this study indicated that neurofeedback balance protocol can improve balance in children with reading disorder and may be more treatment sessions are needed for significant brain waves changes.
    Keywords Neurofeedback ; Reading Disorder ; Balance ; EEG ; Therapeutics. Psychotherapy ; RC475-489 ; Psychiatry ; RC435-571 ; Neurology. Diseases of the nervous system ; RC346-429 ; Neurosciences. Biological psychiatry. Neuropsychiatry ; RC321-571 ; Internal medicine ; RC31-1245 ; Medicine ; R
    Subject code 616
    Language Persian
    Publishing date 2013-09-01T00:00:00Z
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: The effect of neurofeedback training on balance performance and attention shifting in children with reading disorder

    Neda Sadeghi Naeinipour / Mohammad Ali Nazari / Mehdi Alizade Zarei / Mohammad Kamali

    مجله پژوهش در علوم توانبخشی, Vol 9, Iss 2, Pp 185-

    2013  Volume 196

    Abstract: Introduction: Reading impairment is a neurodevelopmental disorder with some deficits in cognition and motor skills. Neurofeedback, as a new treatment approach, can improve the functioning of individuals with the disorder through regulating ... ...

    Abstract Introduction: Reading impairment is a neurodevelopmental disorder with some deficits in cognition and motor skills. Neurofeedback, as a new treatment approach, can improve the functioning of individuals with the disorder through regulating electroencephalographic (EEG) abnormalities. The purpose of this study was to investigate the effectiveness of neurofeedback balance protocol on improving balance performance and attention shifting in children with reading disorders. Materials and Methods: In this 20-session single subject study, 4 children (1 female) aging 8 to12 years completed twelve 30-miniute neurofeedback sessions. Repeated were performed during the baseline, treatment and follow-up stages of the study by means of the 2nd subtest of BOTMP for balance performance and the Posner Paradigm for shifting attention. Results: The results showed that the effect of treatment on balance performance was high (Cohen’s d > 0.8) in all subjects but one and that attention shifting were improved only in some cases. Conclusion: Consequently, neurofeedback balance protocol can improve balance and, to some extents, attention shifting in children with reading disorders. Keywords: Neurofeedback, Reading disorder, Balance, Attention shifting
    Keywords Medicine ; R ; Therapeutics. Pharmacology ; RM1-950
    Subject code 796
    Language Persian
    Publishing date 2013-05-01T00:00:00Z
    Publisher Vesnu Publications
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Urine Exosomes for Non-Invasive Assessment of Gene Expression and Mutations of Prostate Cancer.

    Piruz Motamedinia / Anna N Scott / Kendall L Bate / Neda Sadeghi / Guillermo Salazar / Edan Shapiro / Jennifer Ahn / Michael Lipsky / James Lin / Greg W Hruby / Ketan K Badani / Daniel P Petrylak / Mitchell C Benson / Michael J Donovan / Wayne D Comper / James M McKiernan / Leileata M Russo

    PLoS ONE, Vol 11, Iss 5, p e

    2016  Volume 0154507

    Abstract: PURPOSE:The analysis of exosome/microvesicle (extracellular vesicles (EVs)) and the RNA packaged within them (exoRNA) has the potential to provide a non-invasive platform to detect and monitor disease related gene expression potentially in lieu of more ... ...

    Abstract PURPOSE:The analysis of exosome/microvesicle (extracellular vesicles (EVs)) and the RNA packaged within them (exoRNA) has the potential to provide a non-invasive platform to detect and monitor disease related gene expression potentially in lieu of more invasive procedures such as biopsy. However, few studies have tested the diagnostic potential of EV analysis in humans. EXPERIMENTAL DESIGN:The ability of EV analysis to accurately reflect prostate tissue mRNA expression was examined by comparing urinary EV TMPRSS2:ERG exoRNA from pre-radical prostatectomy (RP) patients versus corresponding RP tissue in 21 patients. To examine the differential expression of TMPRSS2:ERG across patient groups a random urine sample was taken without prostate massage from a cohort of 207 men including prostate biopsy negative (Bx Neg, n = 39), prostate biopsy positive (Bx Pos, n = 47), post-radical prostatectomy (post-RP, n = 37), un-biopsied healthy age-matched men (No Bx, n = 44), and young male controls (Cont, n = 40). The use of EVs was also examined as a potential platform to non-invasively differentiate Bx Pos versus Bx Neg patients via the detection of known prostate cancer genes TMPRSS2:ERG, BIRC5, ERG, PCA3 and TMPRSS2. RESULTS:In this technical pilot study urinary EVs had a sensitivity: 81% (13/16), specificity: 80% (4/5) and an overall accuracy: 81% (17/21) for non-invasive detection of TMPRSS2:ERG versus RP tissue. The rate of TMPRSS2:ERG exoRNA detection was found to increase with age and the expression level correlated with Bx Pos status. Receiver operator characteristic analyses demonstrated that various cancer-related genes could differentiate Bx Pos from Bx Neg patients using exoRNA isolated from urinary EVs: BIRC5 (AUC 0.674 (CI:0.560-0.788), ERG (AUC 0.785 (CI:0.680-0.890), PCA3 (AUC 0.681 (CI:0.567-0.795), TMPRSS2:ERG (AUC 0.744 (CI:0.600-0.888), and TMPRSS2 (AUC 0.637 (CI:0.519-0.754). CONCLUSION:This pilot study suggests that urinary EVs have the potential to be used as a platform to non-invasively differentiate ...
    Keywords Medicine ; R ; Science ; Q
    Subject code 610
    Language English
    Publishing date 2016-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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