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  1. Article ; Online: Feedback Related Negativity Amplitude is Greatest Following Deceptive Feedback in Autistic Adolescents.

    Riek, Nathan T / Susam, Busra T / Hudac, Caitlin M / Conner, Caitlin M / Akcakaya, Murat / Yun, Jane / White, Susan W / Mazefsky, Carla A / Gable, Philip A

    Journal of autism and developmental disorders

    2023  

    Abstract: The purpose of this study is to investigate if feedback related negativity (FRN) can capture instantaneous elevated emotional reactivity in autistic adolescents. A measurement of elevated reactivity could allow clinicians to better support autistic ... ...

    Abstract The purpose of this study is to investigate if feedback related negativity (FRN) can capture instantaneous elevated emotional reactivity in autistic adolescents. A measurement of elevated reactivity could allow clinicians to better support autistic individuals without the need for self-reporting or verbal conveyance. The study investigated reactivity in 46 autistic adolescents (ages 12-21 years) completing the Affective Posner Task which utilizes deceptive feedback to elicit distress presented as frustration. The FRN event-related potential (ERP) served as an instantaneous quantitative neural measurement of emotional reactivity. We compared deceptive and distressing feedback to both truthful but distressing feedback and truthful and non-distressing feedback using the FRN, response times in the successive trial, and Emotion Dysregulation Inventory (EDI) reactivity scores. Results revealed that FRN values were most negative to deceptive feedback as compared to truthful non-distressing feedback. Furthermore, distressing feedback led to faster response times in the successive trial on average. Lastly, participants with higher EDI reactivity scores had more negative FRN values for non-distressing truthful feedback compared to participants with lower reactivity scores. The FRN amplitude showed changes based on both frustration and reactivity. The findings of this investigation support using the FRN to better understand emotion regulation processes for autistic adolescents in future work. Furthermore, the change in FRN based on reactivity suggests the possible need to subgroup autistic adolescents based on reactivity and adjust interventions accordingly.
    Language English
    Publishing date 2023-07-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 391999-7
    ISSN 1573-3432 ; 0162-3257
    ISSN (online) 1573-3432
    ISSN 0162-3257
    DOI 10.1007/s10803-023-06038-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 765: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  2. Article ; Online: Quantitative EEG Changes in Youth With ASD Following Brief Mindfulness Meditation Exercise.

    Susam, Busra T / Riek, Nathan T / Beck, Kelly / Eldeeb, Safaa / Hudac, Caitlin M / Gable, Philip A / Conner, Caitlin / Akcakaya, Murat / White, Susan / Mazefsky, Carla

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

    2022  Volume 30, Page(s) 2395–2405

    Abstract: Mindfulness has growing empirical support for improving emotion regulation in individuals with Autism Spectrum Disorder (ASD). Mindfulness is cultivated through meditation practices. Assessing the role of mindfulness in improving emotion regulation is ... ...

    Abstract Mindfulness has growing empirical support for improving emotion regulation in individuals with Autism Spectrum Disorder (ASD). Mindfulness is cultivated through meditation practices. Assessing the role of mindfulness in improving emotion regulation is challenging given the reliance on self-report tools. Electroencephalography (EEG) has successfully quantified neural responses to emotional arousal and meditation in other populations, making it ideal to objectively measure neural responses before and after mindfulness (MF) practice among individuals with ASD. We performed an EEG-based analysis during a resting state paradigm in 35 youth with ASD. Specifically, we developed a machine learning classifier and a feature and channel selection approach that separates resting states preceding (Pre-MF) and following (Post-MF) a mindfulness meditation exercise within participants. Across individuals, frontal and temporal channels were most informative. Total power in the beta band (16-30 Hz), Total power (4-30 Hz), relative power in alpha band (8-12 Hz) were the most informative EEG features. A classifier using a non-linear combination of selected EEG features from selected channel locations separated Pre-MF and Post-MF resting states with an average accuracy, sensitivity, and specificity of 80.76%, 78.24%, and 82.14% respectively. Finally, we validated that separation between Pre-MF and Post-MF is due to the MF prime rather than linear-temporal drift. This work underscores machine learning as a critical tool for separating distinct resting states within youth with ASD and will enable better classification of underlying neural responses following brief MF meditation.
    MeSH term(s) Adolescent ; Autism Spectrum Disorder ; Electroencephalography ; Emotions ; Humans ; Meditation ; Mindfulness
    Language English
    Publishing date 2022-09-02
    Publishing country United States
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
    ZDB-ID 1166307-8
    ISSN 1558-0210 ; 1063-6528 ; 1534-4320
    ISSN (online) 1558-0210
    ISSN 1063-6528 ; 1534-4320
    DOI 10.1109/TNSRE.2022.3199151
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4726: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  3. Article ; Online: Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction.

    Al-Zaiti, Salah S / Martin-Gill, Christian / Zègre-Hemsey, Jessica K / Bouzid, Zeineb / Faramand, Ziad / Alrawashdeh, Mohammad O / Gregg, Richard E / Helman, Stephanie / Riek, Nathan T / Kraevsky-Phillips, Karina / Clermont, Gilles / Akcakaya, Murat / Sereika, Susan M / Van Dam, Peter / Smith, Stephen W / Birnbaum, Yochai / Saba, Samir / Sejdic, Ervin / Callaway, Clifton W

    Nature medicine

    2023  Volume 29, Issue 7, Page(s) 1804–1813

    Abstract: Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are ... ...

    Abstract Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.
    MeSH term(s) Humans ; Emergency Service, Hospital ; Time Factors ; Myocardial Infarction/diagnosis ; Electrocardiography ; Risk Assessment
    Language English
    Publishing date 2023-06-29
    Publishing country United States
    Document type Observational Study ; Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 1220066-9
    ISSN 1546-170X ; 1078-8956
    ISSN (online) 1546-170X
    ISSN 1078-8956
    DOI 10.1038/s41591-023-02396-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4212: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MG 39: Show issues

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