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  • 1
    In: Clinical Child Psychology and Psychiatry, SAGE Publications, Vol. 28, No. 4 ( 2023-10), p. 1291-1304
    Abstract: This study investigates the self-reported impact of children’s psychiatric disorders on their siblings and assesses what forms of support such children most value. We used a qualitative research design with open interviews to stimulate children between 8 and 15 years old to talk about their experiences living with a brother or sister with a psychiatric disorder. Their stories were analysed within a hermeneutic phenomenological framework in order to identify narrative themes and interpret the meaning of shared experiences. From our analysis, nine shared narrative themes emerge. Overall, siblings report feeling conflicted about adapting their lives to their brother’s or sister’s disorder and signal a need for personalized attention from parents. They also indicate that being involved in the care for their brother or sister helps them to better understand their behaviour. Finally, siblings reveal that, in their experience, formal, protocolized forms of support foreground family problems and stress. Thus, we recommend to involve children in the care process; to acknowledge their personal needs and conflicts; and to be mindful of the style of support: help offered in an informal or playful way, instead of formal and protocolized, could be a more effective way of meeting siblings’ needs.
    Type of Medium: Online Resource
    ISSN: 1359-1045 , 1461-7021
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2023
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    SSG: 5,2
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  • 2
    In: Frontiers in Neuroscience, Frontiers Media SA, Vol. 16 ( 2022-5-12)
    Abstract: Neuronal excitation-inhibition (E/I) imbalances are considered an important pathophysiological mechanism in neurodevelopmental disorders. Preclinical studies on tuberous sclerosis complex (TSC), suggest that altered chloride homeostasis may impair GABAergic inhibition and thereby E/I-balance regulation. Correction of chloride homeostasis may thus constitute a treatment target to alleviate behavioral symptoms. Recently, we showed that bumetanide—a chloride-regulating agent—improved behavioral symptoms in the open-label study Bumetanide to Ameliorate Tuberous Sclerosis Complex Hyperexcitable Behaviors trial (BATSCH trial; Eudra-CT: 2016-002408-13). Here, we present resting-state EEG as secondary analysis of BATSCH to investigate associations between EEG measures sensitive to network-level changes in E/I balance and clinical response to bumetanide. EEGs of 10 participants with TSC (aged 8–21 years) were available. Spectral power, long-range temporal correlations (LRTC), and functional E/I ratio ( f E/I) in the alpha-frequency band were compared before and after 91 days of treatment. Pre-treatment measures were compared against 29 typically developing children (TDC). EEG measures were correlated with the Aberrant Behavioral Checklist-Irritability subscale (ABC-I), the Social Responsiveness Scale-2 (SRS-2), and the Repetitive Behavior Scale-Revised (RBS-R). At baseline, TSC showed lower alpha-band absolute power and f E/I than TDC. Absolute power increased through bumetanide treatment, which showed a moderate, albeit non-significant, correlation with improvement in RBS-R. Interestingly, correlations between baseline EEG measures and clinical outcomes suggest that most responsiveness might be expected in children with network characteristics around the E/I balance point. In sum, E/I imbalances pointing toward an inhibition-dominated network are present in TSC. We established neurophysiological effects of bumetanide although with an inconclusive relationship with clinical improvement. Nonetheless, our results further indicate that baseline network characteristics might influence treatment response. These findings highlight the possible utility of E/I-sensitive EEG measures to accompany new treatment interventions for TSC. Clinical Trial Registration EU Clinical Trial Register, EudraCT 2016-002408-13 ( www.clinicaltrialsregister.eu/ctr-search/trial/2016-002408-13/NL ). Registered 25 July 2016.
    Type of Medium: Online Resource
    ISSN: 1662-453X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2411902-7
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  • 3
    Online Resource
    Online Resource
    American Psychiatric Association Publishing ; 2001
    In:  American Journal of Psychiatry Vol. 158, No. 4 ( 2001-04-01), p. 644-646
    In: American Journal of Psychiatry, American Psychiatric Association Publishing, Vol. 158, No. 4 ( 2001-04-01), p. 644-646
    Type of Medium: Online Resource
    ISSN: 0002-953X , 1535-7228
    RVK:
    Language: English
    Publisher: American Psychiatric Association Publishing
    Publication Date: 2001
    detail.hit.zdb_id: 1500554-9
    detail.hit.zdb_id: 280045-7
    SSG: 5,2
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  • 4
    In: Schizophrenia Bulletin, Oxford University Press (OUP), Vol. 46, No. 1 ( 2020-01-04), p. 17-26
    Abstract: Despite the high level of interest in the use of machine learning (ML) and neuroimaging to detect psychosis at the individual level, the reliability of the findings is unclear due to potential methodological issues that may have inflated the existing literature. This study aimed to elucidate the extent to which the application of ML to neuroanatomical data allows detection of first episode psychosis (FEP), while putting in place methodological precautions to avoid overoptimistic results. We tested both traditional ML and an emerging approach known as deep learning (DL) using 3 feature sets of interest: (1) surface-based regional volumes and cortical thickness, (2) voxel-based gray matter volume (GMV) and (3) voxel-based cortical thickness (VBCT). To assess the reliability of the findings, we repeated all analyses in 5 independent datasets, totaling 956 participants (514 FEP and 444 within-site matched controls). The performance was assessed via nested cross-validation (CV) and cross-site CV. Accuracies ranged from 50% to 70% for surfaced-based features; from 50% to 63% for GMV; and from 51% to 68% for VBCT. The best accuracies (70%) were achieved when DL was applied to surface-based features; however, these models generalized poorly to other sites. Findings from this study suggest that, when methodological precautions are adopted to avoid overoptimistic results, detection of individuals in the early stages of psychosis is more challenging than originally thought. In light of this, we argue that the current evidence for the diagnostic value of ML and structural neuroimaging should be reconsidered toward a more cautious interpretation.
    Type of Medium: Online Resource
    ISSN: 0586-7614 , 1745-1701
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 2180196-4
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    SSG: 15,3
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  • 5
    Online Resource
    Online Resource
    Frontiers Media SA ; 2022
    In:  Frontiers in Psychiatry Vol. 13 ( 2022-4-28)
    In: Frontiers in Psychiatry, Frontiers Media SA, Vol. 13 ( 2022-4-28)
    Abstract: Relatively few studies have focused on the wellbeing, experiences and needs of the siblings of children with a psychiatric diagnosis. However, the studies that have been conducted suggest that the impact of such circumstances on these siblings is significant. Studying narratives of diagnosed children or relatives has proven to be a successful approach to gain insights that could help improve care. Only a few attempts have been made to study narratives in psychiatry utilizing a machine learning approach. Method In this current study, 13 narratives of the experiences of siblings of children with a neurodevelopmental disorders were collected through largely unstructured interviews. The interviews were analyzed using the traditional qualitative, hermeneutic phenomenology method as well as latent Dirichlet allocation (LDA), an unsupervised machine learning method clustering words from documents into topics. One aim of this study was to evaluate the experiences of the siblings in order to find leads to improve care and support for these siblings. Furthermore, the outcomes of both analyses were compared to evaluate the role of machine learning in analyzing narratives. Results Qualitative analysis of the interviews led to the formulation of nine main themes: confrontation with conflicts, coping strategies siblings, need for rest and time for myself, need for support and attention from personal circle, wish for normality, influence on personal choices and possibilities for development, doing things together, recommendations and advices, ambivalence and loyalty. Using unsupervised machine learning (LDA) 24 topics were formed that mostly overlapped with the qualitative themes found. Both the qualitative analysis and the LDA analysis detected themes that were unique to the respective analysis. Conclusion The present study found that studying narratives of siblings of children with a neurodevelopmental disorder contributes to a better understanding of the subjects' experiences. Siblings cope with ambivalent feelings toward their brother or sister and this emotional conflict often leads to adapted behavior. Several coping strategies are developed to deal with the behavior of their brother or sister like seeking support or ignoring. Devoted support, time and attention from close relatives, especially parents, is needed. The LDA analysis didn't appear useful to distract meaning and context from the narratives, but it was proposed that machine learning could be a valuable and quick addition to the traditional qualitative methods by finding overlooked topics and giving a rudimental overview of topics in narratives.
    Type of Medium: Online Resource
    ISSN: 1664-0640
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2564218-2
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  • 6
    In: Molecular Autism, Springer Science and Business Media LLC, Vol. 11, No. 1 ( 2020-12)
    Abstract: Tuberous sclerosis complex (TSC) is an autosomal dominant disease that affects multiple organs including the brain. TSC is strongly associated with broad neurodevelopmental disorders, including autism spectrum disorder symptomatology. Preclinical TSC studies have indicated altered neuronal chloride homeostasis affecting the polarity of γ-aminobutyric acid (GABA) ergic transmission as a potential treatment target. Bumetanide, a selective NKCC1 chloride importer antagonist, may attenuate depolarizing GABA action, and in that way reduce disease burden. In this open-label pilot study, we tested the effect of bumetanide on a variety of neurophysiological, cognitive, and behavioral measures in children with TSC. Methods Participants were treated with bumetanide (2dd 0.5–1.0 mg) for 13 weeks in an open-label trial. The Aberrant Behavior Checklist-Irritability (ABC-I) subscale was chosen as the primary endpoint. Secondary endpoints included other behavioral questionnaires in addition to event-related potentials (ERP) and neuropsychological tests if tolerated. Additionally, the treatment effect on seizure frequency and quality of life was assessed. Endpoint data were collected at baseline, after 91 days of treatment and after a 28-day wash-out period. Results Fifteen patients (8–21-years old) with TSC were included of which 13 patients completed the study. Treatment was well-tolerated with only expected adverse events due to the diuretic effects of bumetanide. Irritable behavior (ABC-I) showed significant improvement after treatment in 11 out of 13 patients ( t (12) = 4.41, p = .001, d = .773). A favorable effect was also found for social behavior (Social Responsiveness Scale) ( t (11) = 4.01, p = .002, d = .549) and hyperactive behavior (ABC-hyperactivity subscale) (t (12) = 3.65, p = .003, d = .686). Moreover, patients rated their own health-related quality of life higher after treatment. At baseline, TSC patients showed several atypical ERPs versus typically developing peers of which prepulse inhibition was significantly decreased in the TSC group. Neuropsychological measurements showed no change and bumetanide had no effect on seizure frequency. Limitations The sample size and open-label design of this pilot study warrant caution when interpreting outcome measures. Conclusions Bumetanide treatment is a potential treatment to alleviate the behavioral burden and quality of life associated with TSC. More elaborate trials are needed to determine the application and effect size of bumetanide for the TSC population. Trial registration EU Clinical Trial Register, EudraCT 2016-002408-13 ( www.clinicaltrialsregister.eu/ctr-search/trial/2016-002408-13/NL ). Registered 25 July 2016.
    Type of Medium: Online Resource
    ISSN: 2040-2392
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2540930-X
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  • 7
    Online Resource
    Online Resource
    Frontiers Media SA ; 2018
    In:  Frontiers in Psychiatry Vol. 9 ( 2018-6-5)
    In: Frontiers in Psychiatry, Frontiers Media SA, Vol. 9 ( 2018-6-5)
    Type of Medium: Online Resource
    ISSN: 1664-0640
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2018
    detail.hit.zdb_id: 2564218-2
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  • 8
    In: BMC Psychiatry, Springer Science and Business Media LLC, Vol. 22, No. 1 ( 2022-12)
    Abstract: Developing predictive models for precision psychiatry is challenging because of unavailability of the necessary data: extracting useful information from existing electronic health record (EHR) data is not straightforward, and available clinical trial datasets are often not representative for heterogeneous patient groups. The aim of this study was constructing a natural language processing (NLP) pipeline that extracts variables for building predictive models from EHRs. We specifically tailor the pipeline for extracting information on outcomes of psychiatry treatment trajectories, applicable throughout the entire spectrum of mental health disorders (“transdiagnostic”). Methods A qualitative study into beliefs of clinical staff on measuring treatment outcomes was conducted to construct a candidate list of variables to extract from the EHR. To investigate if the proposed variables are suitable for measuring treatment effects, resulting themes were compared to transdiagnostic outcome measures currently used in psychiatry research and compared to the HDRS (as a gold standard) through systematic review, resulting in an ideal set of variables. To extract these from EHR data, a semi-rule based NLP pipeline was constructed and tailored to the candidate variables using Prodigy. Classification accuracy and F1-scores were calculated and pipeline output was compared to HDRS scores using clinical notes from patients admitted in 2019 and 2020. Results Analysis of 34 questionnaires answered by clinical staff resulted in four themes defining treatment outcomes: symptom reduction, general well-being, social functioning and personalization. Systematic review revealed 242 different transdiagnostic outcome measures, with the 36-item Short-Form Survey for quality of life (SF36) being used most consistently, showing substantial overlap with the themes from the qualitative study. Comparing SF36 to HDRS scores in 26 studies revealed moderate to good correlations (0.62—0.79) and good positive predictive values (0.75—0.88). The NLP pipeline developed with notes from 22,170 patients reached an accuracy of 95 to 99 percent (F1 scores: 0.38 – 0.86) on detecting these themes, evaluated on data from 361 patients. Conclusions The NLP pipeline developed in this study extracts outcome measures from the EHR that cater specifically to the needs of clinical staff and align with outcome measures used to detect treatment effects in clinical trials.
    Type of Medium: Online Resource
    ISSN: 1471-244X
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2050438-X
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  • 9
    Online Resource
    Online Resource
    Elsevier BV ; 2018
    In:  Journal of the American Academy of Child & Adolescent Psychiatry Vol. 57, No. 10 ( 2018-10), p. S313-S314
    In: Journal of the American Academy of Child & Adolescent Psychiatry, Elsevier BV, Vol. 57, No. 10 ( 2018-10), p. S313-S314
    Type of Medium: Online Resource
    ISSN: 0890-8567
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2018
    detail.hit.zdb_id: 392535-3
    SSG: 5,2
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  • 10
    Online Resource
    Online Resource
    Elsevier BV ; 2018
    In:  Journal of the American Academy of Child & Adolescent Psychiatry Vol. 57, No. 10 ( 2018-10), p. S314-S315
    In: Journal of the American Academy of Child & Adolescent Psychiatry, Elsevier BV, Vol. 57, No. 10 ( 2018-10), p. S314-S315
    Type of Medium: Online Resource
    ISSN: 0890-8567
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2018
    detail.hit.zdb_id: 392535-3
    SSG: 5,2
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