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  1. Article ; Online: Understanding Environmental Exposures and ADHD: a Pathway Forward.

    Faraone, Stephen V

    Prevention science : the official journal of the Society for Prevention Research

    2024  

    Abstract: This commentary addresses a series of articles in Prevention Science about environmental causes of attention deficit hyperactivity disorder (ADHD). It provides an overview of their key findings and places them in a broader context to facilitate their ... ...

    Abstract This commentary addresses a series of articles in Prevention Science about environmental causes of attention deficit hyperactivity disorder (ADHD). It provides an overview of their key findings and places them in a broader context to facilitate their interpretation. Each of the articles included in the special issue is a meta-analysis assessing the association of ADHD with several environmental exposures. Each of the author teams systematically searched for articles and defined eligibility criteria. They assured that the measurement of risk factors preceded the measurement of ADHD. Most of the analyses are based on many studies with many participants in the constituent studies. As is typical of any observational epidemiologic study, the constituent studies could not correct for all possible confounds because some were not measured, and some are unknown. For this reason, these meta-analyses may have documented confounded associations, which calls for cautious interpretations. None of the constituent studies assessed what might be the most important type of confounding, familial, and genetic confounding, which occurs when the environmental exposure being studied is correlated with the genetic risk of the disorder being studied or other familial risk factors. Addressing familial/genetic confounding requires a genetically informed study. Because of these issues, the results presented here are intriguing but require further examination before one can conclude that the reported associations correspond to causal events for ADHD. A pathway forward is suggested by drawing parallels between genomic and exposure research.
    Language English
    Publishing date 2024-03-21
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2251270-6
    ISSN 1573-6695 ; 1389-4986
    ISSN (online) 1573-6695
    ISSN 1389-4986
    DOI 10.1007/s11121-024-01672-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: In Memoriam: Joseph Biederman.

    Faraone, Stephen V

    Biological psychiatry

    2023  Volume 93, Issue 11, Page(s) 956–958

    Language English
    Publishing date 2023-03-17
    Publishing country United States
    Document type Journal Article
    ZDB-ID 209434-4
    ISSN 1873-2402 ; 0006-3223
    ISSN (online) 1873-2402
    ISSN 0006-3223
    DOI 10.1016/j.biopsych.2023.03.023
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book: ADHD: Non-pharmacologic interventions

    Faraone, Stephen V. / Antshel, Kevin M.

    (Child and adolescent psychiatric clinics of North America ; 23,4)

    2014  

    Author's details ed. Stephen V. Faraone ; Kevin M. Antshel
    Series title Child and adolescent psychiatric clinics of North America ; 23,4
    Collection
    Language English
    Size XIV S., S. 687 - 981 : graph. Darst.
    Publisher Elsevier
    Publishing place Philadelphia, Pa
    Publishing country United States
    Document type Book
    HBZ-ID HT018534060
    ISBN 978-0-323-32601-8 ; 0-323-32601-3
    Database Catalogue ZB MED Medicine, Health

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    Shelf markLoan statusLocation
    Se 1345 -23,4- verfügbar in Königswinter Königswinter

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  4. Article ; Online: Primary prevention of prescription stimulant misuse in first-year college students.

    Antshel, Kevin M / Park, Aesoon / Maisto, Stephen / Faraone, Stephen V

    Journal of American college health : J of ACH

    2024  , Page(s) 1–9

    Abstract: Objective: ...

    Abstract Objective:
    Language English
    Publishing date 2024-01-16
    Publishing country United States
    Document type Journal Article
    ZDB-ID 604907-2
    ISSN 1940-3208 ; 0744-8481
    ISSN (online) 1940-3208
    ISSN 0744-8481
    DOI 10.1080/07448481.2023.2299409
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  5. Article ; Online: The Impact of Attention-Deficit/Hyperactivity Disorder Medications on Suicidality: Implications and Mechanisms.

    Faraone, Stephen V

    Biological psychiatry

    2020  Volume 88, Issue 6, Page(s) 436–437

    MeSH term(s) Attention Deficit Disorder with Hyperactivity/drug therapy ; Humans ; Suicide ; Suicide, Attempted
    Language English
    Publishing date 2020-08-27
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Comment
    ZDB-ID 209434-4
    ISSN 1873-2402 ; 0006-3223
    ISSN (online) 1873-2402
    ISSN 0006-3223
    DOI 10.1016/j.biopsych.2020.06.024
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  6. Article ; Online: Genomic Machine Learning Meta-regression: Insights on Associations of Study Features With Reported Model Performance.

    Barnett, Eric J / Onete, Daniel G / Salekin, Asif / Faraone, Stephen V

    IEEE/ACM transactions on computational biology and bioinformatics

    2024  Volume 21, Issue 1, Page(s) 169–177

    Abstract: Many studies have been conducted with the goal of correctly predicting diagnostic status of a disorder using the combination of genomic data and machine learning. It is often hard to judge which components of a study led to better results and whether ... ...

    Abstract Many studies have been conducted with the goal of correctly predicting diagnostic status of a disorder using the combination of genomic data and machine learning. It is often hard to judge which components of a study led to better results and whether better reported results represent a true improvement or an uncorrected bias inflating performance. We extracted information about the methods used and other differentiating features in genomic machine learning models. We used these features in linear regressions predicting model performance. We tested for univariate and multivariate associations as well as interactions between features. Of the models reviewed, 46% used feature selection methods that can lead to data leakage. Across our models, the number of hyperparameter optimizations reported, data leakage due to feature selection, model type, and modeling an autoimmune disorder were significantly associated with an increase in reported model performance. We found a significant, negative interaction between data leakage and training size. Our results suggest that methods susceptible to data leakage are prevalent among genomic machine learning research, resulting in inflated reported performance. Best practice guidelines that promote the avoidance and recognition of data leakage may help the field avoid biased results.
    MeSH term(s) Machine Learning ; Genomics
    Language English
    Publishing date 2024-02-05
    Publishing country United States
    Document type Journal Article
    ISSN 1557-9964
    ISSN (online) 1557-9964
    DOI 10.1109/TCBB.2023.3343808
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: The pharmacology of amphetamine and methylphenidate: Relevance to the neurobiology of attention-deficit/hyperactivity disorder and other psychiatric comorbidities.

    Faraone, Stephen V

    Neuroscience and biobehavioral reviews

    2018  Volume 87, Page(s) 255–270

    Abstract: Psychostimulants, including amphetamines and methylphenidate, are first-line pharmacotherapies for individuals with attention-deficit/hyperactivity disorder (ADHD). This review aims to educate physicians regarding differences in pharmacology and ... ...

    Abstract Psychostimulants, including amphetamines and methylphenidate, are first-line pharmacotherapies for individuals with attention-deficit/hyperactivity disorder (ADHD). This review aims to educate physicians regarding differences in pharmacology and mechanisms of action between amphetamine and methylphenidate, thus enhancing physician understanding of psychostimulants and their use in managing individuals with ADHD who may have comorbid psychiatric conditions. A systematic literature review of PubMed was conducted in April 2017, focusing on cellular- and brain system-level effects of amphetamine and methylphenidate. The primary pharmacologic effect of both amphetamine and methylphenidate is to increase central dopamine and norepinephrine activity, which impacts executive and attentional function. Amphetamine actions include dopamine and norepinephrine transporter inhibition, vesicular monoamine transporter 2 (VMAT-2) inhibition, and monoamine oxidase activity inhibition. Methylphenidate actions include dopamine and norepinephrine transporter inhibition, agonist activity at the serotonin type 1A receptor, and redistribution of the VMAT-2. There is also evidence for interactions with glutamate and opioid systems. Clinical implications of these actions in individuals with ADHD with comorbid depression, anxiety, substance use disorder, and sleep disturbances are discussed.
    MeSH term(s) Adrenergic Uptake Inhibitors/pharmacology ; Adrenergic Uptake Inhibitors/therapeutic use ; Amphetamine/pharmacology ; Amphetamine/therapeutic use ; Animals ; Attention Deficit Disorder with Hyperactivity/complications ; Attention Deficit Disorder with Hyperactivity/drug therapy ; Attention Deficit Disorder with Hyperactivity/physiopathology ; Brain/drug effects ; Central Nervous System Stimulants/pharmacology ; Central Nervous System Stimulants/therapeutic use ; Comorbidity ; Dopamine Uptake Inhibitors/pharmacology ; Dopamine Uptake Inhibitors/therapeutic use ; Humans ; Mental Disorders/complications ; Mental Disorders/drug therapy ; Mental Disorders/physiopathology ; Methylphenidate/pharmacology ; Methylphenidate/therapeutic use
    Chemical Substances Adrenergic Uptake Inhibitors ; Central Nervous System Stimulants ; Dopamine Uptake Inhibitors ; Methylphenidate (207ZZ9QZ49) ; Amphetamine (CK833KGX7E)
    Language English
    Publishing date 2018-02-08
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 282464-4
    ISSN 1873-7528 ; 0149-7634
    ISSN (online) 1873-7528
    ISSN 0149-7634
    DOI 10.1016/j.neubiorev.2018.02.001
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    Zs.A 1371: Show issues Location:
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  8. Article ; Online: Differences in Primary Care Management of Patients With Adult Attention Deficit Hyperactivity Disorder (ADHD) Based on Race and Ethnicity.

    Alai, Jillian / Callen, Elisabeth F / Clay, Tarin / Goodman, David W / Adler, Lenard A / Faraone, Stephen V

    Journal of attention disorders

    2024  Volume 28, Issue 5, Page(s) 923–935

    Abstract: Objective: Examine differences in care patterns around adult ADHD between race (White/Non-White) and ethnic (Hispanic/Non-Hispanic) groups utilizing existing quality measures (QMs), concerning diagnosis, treatment, and medication prescribing.: Methods! ...

    Abstract Objective: Examine differences in care patterns around adult ADHD between race (White/Non-White) and ethnic (Hispanic/Non-Hispanic) groups utilizing existing quality measures (QMs), concerning diagnosis, treatment, and medication prescribing.
    Methods: The AAFP National Research Network in partnership with SUNY Upstate Medical used an EHR dataset to evaluate achievement of 10 ADHD QMs. The dataset was obtained from DARTNet Institute and includes 4 million patients of 873 behavioral and primary care practices with at least 100 patients from 2010 to 2020. Patients 18-years or older with adult ADHD were included in this analysis.
    Results: White patients and Non-Hispanic/Latinx patients were more likely to achieve these QMs than Non-White patients and Hispanic/Latinx patients, respectively. Differences between groups concerning medication and monitoring demonstrate a disparity for Non-White and Hispanic/Latinx populations.
    Conclusions: Using QMs in EHR data can help identify gaps in ADHD research. There is a need to continue investigating disparities of quality adult ADHD care.
    MeSH term(s) Adult ; Humans ; Ethnicity ; Attention Deficit Disorder with Hyperactivity/diagnosis ; Attention Deficit Disorder with Hyperactivity/drug therapy ; Hispanic or Latino ; Drug Prescriptions ; Primary Health Care
    Language English
    Publishing date 2024-01-12
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2004350-8
    ISSN 1557-1246 ; 1087-0547
    ISSN (online) 1557-1246
    ISSN 1087-0547
    DOI 10.1177/10870547231218038
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Use of big data and machine learning algorithms to extract possible treatment targets in neurodevelopmental disorders.

    Malik, Muhammad Ammar / Faraone, Stephen V / Michoel, Tom / Haavik, Jan

    Pharmacology & therapeutics

    2023  Volume 250, Page(s) 108530

    Abstract: Neurodevelopmental disorders (NDDs) impact multiple aspects of an individual's functioning, including social interactions, communication, and behaviors. The underlying biological mechanisms of NDDs are not yet fully understood, and pharmacological ... ...

    Abstract Neurodevelopmental disorders (NDDs) impact multiple aspects of an individual's functioning, including social interactions, communication, and behaviors. The underlying biological mechanisms of NDDs are not yet fully understood, and pharmacological treatments have been limited in their effectiveness, in part due to the complex nature of these disorders and the heterogeneity of symptoms across individuals. Identifying genetic loci associated with NDDs can help in understanding biological mechanisms and potentially lead to the development of new treatments. However, the polygenic nature of these complex disorders has made identifying new treatment targets from genome-wide association studies (GWAS) challenging. Recent advances in the fields of big data and high-throughput tools have provided radically new insights into the underlying biological mechanism of NDDs. This paper reviews various big data approaches, including classical and more recent techniques like deep learning, which can identify potential treatment targets from GWAS and other omics data, with a particular emphasis on NDDs. We also emphasize the increasing importance of explainable and causal machine learning (ML) methods that can aid in identifying genes, molecular pathways, and more complex biological processes that may be future targets of intervention in these disorders. We conclude that these new developments in genetics and ML hold promise for advancing our understanding of NDDs and identifying novel treatment targets.
    MeSH term(s) Humans ; Genome-Wide Association Study ; Big Data ; Neurodevelopmental Disorders/drug therapy ; Neurodevelopmental Disorders/genetics ; Algorithms ; Machine Learning
    Language English
    Publishing date 2023-09-12
    Publishing country England
    Document type Journal Article ; Review ; Research Support, Non-U.S. Gov't
    ZDB-ID 194735-7
    ISSN 1879-016X ; 0163-7258
    ISSN (online) 1879-016X
    ISSN 0163-7258
    DOI 10.1016/j.pharmthera.2023.108530
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  10. Article ; Online: WHO Essential Medicines List and methylphenidate for ADHD in children and adolescents - Authors' reply.

    Cortese, Samuele / Coghill, David / Mattingly, Gregory W / Rohde, Luis A / Wong, Ian C K / Faraone, Stephen V

    The lancet. Psychiatry

    2024  Volume 11, Issue 2, Page(s) 93–95

    MeSH term(s) Child ; Adolescent ; Humans ; Methylphenidate/therapeutic use ; Attention Deficit Disorder with Hyperactivity/drug therapy ; Central Nervous System Stimulants/therapeutic use ; World Health Organization
    Chemical Substances Methylphenidate (207ZZ9QZ49) ; Central Nervous System Stimulants
    Language English
    Publishing date 2024-01-19
    Publishing country England
    Document type Letter
    ISSN 2215-0374
    ISSN (online) 2215-0374
    DOI 10.1016/S2215-0366(23)00437-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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