LIVIVO - Search results -

Search results

Result 1 - 10 of total 71

Search options

Book ; Online ; E-Book: Essentials of Autism Spectrum Disorders Evaluation and Assessment

Saulnier, Celine A. / Ventola, Pamela E. / Kaufman, Alan S. / Kaufman, Nadeen L.

(Essentials of Psychological Assessment Series)

2024

Series title	Essentials of Psychological Assessment Series
Subject code	616.85/882
Language	English
Size	1 online resource (242 pages)
Edition	2nd ed.
Publisher	John Wiley & Sons, Incorporated
Publishing place	Newark
Document type	Book ; Online ; E-Book
Remark	Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
ISBN	1-119-98252-9 ; 1-119-98251-0 ; 978-1-119-98252-4 ; 978-1-119-98251-7
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

Full text online

Accessible to users with ZB MED library card

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.

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Details ▾
- Full text online
- Order with fees

Article ; Online: Feasibility and Acceptability of Delivering Pivotal Response Treatment for Autism Spectrum Disorder via Telehealth: Pilot Pre-Post Study.

Drapalik, Krista N / Grodberg, David / Ventola, Pamela

JMIR pediatrics and parenting

2022 Volume 5, Issue 3, Page(s) e32520

Abstract: Background: Pivotal response treatment (PRT), an evidence-based and parent-delivered intervention, is designed to improve social communication in autistic individuals.: Objective: The aim of this study was to assess the feasibility, acceptability, ... ...

Abstract	Background: Pivotal response treatment (PRT), an evidence-based and parent-delivered intervention, is designed to improve social communication in autistic individuals. Objective: The aim of this study was to assess the feasibility, acceptability, and clinical effects of an online model of PRT delivered via MindNest Health, a telehealth platform that aims to provide self-directed and engaging online modules, real-time coaching and feedback, and accessible stepped-care to large populations of parents seeking resources for their autistic children. Methods: Male and female autistic children, aged 2-7 years with single-word to phrase-level speech, and their parents were eligible to participate in the study. Families were randomized to the online parent training condition or control condition. The online component of the intervention consisted of eight 20-minute online courses of content describing parent training principles in PRT. Four 1-hour videoconferences were held after course 1, course 3, course 5, and course 8. Parents were given 1-2 weeks to complete each course. Parents completed the Client Credibility Questionnaire (CCQ) at week 2 and at the study endpoint, as well as the Behavioral Intervention Rating Scale (BIRS) at the study endpoint to assess parental expectancies, and treatment acceptability and effectiveness. Results: Nine of 14 participants completed the study curriculum in the online parent training condition, and 6 of 12 participants completed the control condition. Thus, a total of 58% (15/26) participants across both groups completed the study curriculum by study closure. Within the online parent training condition, there was a significant increase in mean CCQ total scores, from 25.38 (SD 3.25) at baseline to 27.5 (SD 3.74) at study endpoint (P=.04); mean CCQ confidence scores, from 6.0 (SD 1.07) at baseline to 6.75 (SD 0.89) at study endpoint (P=.02); and mean CCQ other improvement scores, from 5.25 (SD 0.89) at baseline to 6.25 (SD 1.28) at study endpoint (P=.009). Within the control condition, a modest increase in mean CCQ scores was noted (Confidence, difference=+0.25; Recommend, difference=+0.25; Total Score, difference=+0.50), but the differences were not statistically significant (Confidence P=.38, Recommend P=.36, Total Score P=.43). Among the 11 parents who completed the BIRS at the study endpoint, 82% (n=9) endorsed that they slightly agree or agree with over 93% of the Acceptability factor items on the BIRS. Conclusions: The feasibility of this online treatment is endorsed by the high rate of online module completion and attendance to videoconferences within the online parent training group. Acceptability of treatment is supported by strong ratings on the CCQ and significant improvements in scores, as well as strong ratings on the BIRS. This study's small sample size limits the conclusions that can be drawn; however, the PRT MindNest Health platform holds promise to support parents of autistic children who are unable to access traditional, in-person parent-mediated interventions for their child.
Language	English
Publishing date	2022-09-06
Publishing country	Canada
Document type	Journal Article
ISSN	2561-6722
ISSN (online)	2561-6722
DOI	10.2196/32520
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article: ESTIMATING REPRODUCIBLE FUNCTIONAL NETWORKS ASSOCIATED WITH TASK DYNAMICS USING UNSUPERVISED LSTMS.

Dvornek, Nicha C / Ventola, Pamela / Duncan, James S

Proceedings. IEEE International Symposium on Biomedical Imaging

2020 Volume 2020

Abstract: We propose a method for estimating more reproducible functional networks that are more strongly associated with dynamic task activity by using recurrent neural networks with long short term memory (LSTMs). The LSTM model is trained in an unsupervised ... ...

Abstract	We propose a method for estimating more reproducible functional networks that are more strongly associated with dynamic task activity by using recurrent neural networks with long short term memory (LSTMs). The LSTM model is trained in an unsupervised manner to learn to generate the functional magnetic resonance imaging (fMRI) time-series data in regions of interest. The learned functional networks can then be used for further analysis, e.g., correlation analysis to determine functional networks that are strongly associated with an fMRI task paradigm. We test our approach and compare to other methods for decomposing functional networks from fMRI activity on 2 related but separate datasets that employ a biological motion perception task. We demonstrate that the functional networks learned by the LSTM model are more strongly associated with the task activity and dynamics compared to other approaches. Furthermore, the patterns of network association are more closely replicated across subjects within the same dataset as well as across datasets. More reproducible functional networks are essential for better characterizing the neural correlates of a target task.
Language	English
Publishing date	2020-05-22
Publishing country	United States
Document type	Journal Article
ISSN	1945-7928
ISSN	1945-7928
DOI	10.1109/isbi45749.2020.9098377
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: An adapted clinical global Impression of improvement for use in Angelman syndrome: Validation analyses utilizing data from the NEPTUNE study.

Ventola, Pamela / Jaeger, Judith / Keary, Christopher J / Kolevzon, Alexander / Adams, Maxwell / Keshavan, Bina / Zinger-Salmun, Celia / Ochoa-Lubinoff, Cesar

European journal of paediatric neurology : EJPN : official journal of the European Paediatric Neurology Society

2023 Volume 47, Page(s) 35–40

Abstract: Purpose: Angelman Syndrome (AS) is a rare, severe neurogenetic disorder that causes symptoms such as intellectual disability and motor impairments and is typically diagnosed in early childhood. The complexity and heterogeneity of AS confound ... ...

Abstract	Purpose: Angelman Syndrome (AS) is a rare, severe neurogenetic disorder that causes symptoms such as intellectual disability and motor impairments and is typically diagnosed in early childhood. The complexity and heterogeneity of AS confound characterization of disease severity and pose unique challenges when determining an individual's response to treatment. There is therefore a substantial unmet need for rating scales specifically designed for complex conditions such as AS. To address this, the Clinical Global Impressions (CGI) scale, which has components for both symptom severity (CGI-S) and improvement (CGI-I) was specifically adapted to measure severity (CGI-S-AS) and improvement (CGI-I-AS) in AS. Methods: The modified CGI-S/I-AS was used in the NEPTUNE trial of gaboxadol for the treatment of AS. Here we report on the validation of the CGI-I-AS using data from NEPTUNE and discuss insights for its potential use in future trials. Results: Improvements in the CGI-I-AS rating tended to be consistent with changes on other relevant rating scales. Sleep-related symptoms were particularly well represented, while communication-related symptoms were not. Conclusions: Our validation analysis of the CGI-I-AS demonstrates its usefulness along with possible areas of improvement. The CGI-I-AS is a potential tool for use in other trials of AS drug candidates, and the process for its development can serve as a road map for the development of assessment tools for other neuropsychiatric disorders with similar complexities and heterogeneity.
MeSH term(s)	Child, Preschool ; Humans ; Angelman Syndrome/diagnosis ; Psychiatric Status Rating Scales ; Severity of Illness Index ; Treatment Outcome ; Clinical Trials as Topic
Language	English
Publishing date	2023-09-04
Publishing country	England
Document type	Journal Article
ZDB-ID	1397146-3
ISSN	1532-2130 ; 1090-3798
ISSN (online)	1532-2130
ISSN	1090-3798
DOI	10.1016/j.ejpn.2023.08.003
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 4867: 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.MO 641: Show issues

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article: Pivotal response treatment for autism spectrum disorder: current perspectives.

Lei, Jiedi / Ventola, Pamela

Neuropsychiatric disease and treatment

2017 Volume 13, Page(s) 1613–1626

Abstract: Pivotal response treatment (PRT) is an evidence-based behavioral intervention based on applied behavior analysis principles aimed to improve social communication skills in individuals with autism spectrum disorder (ASD). PRT adopts a more naturalistic ... ...

Abstract	Pivotal response treatment (PRT) is an evidence-based behavioral intervention based on applied behavior analysis principles aimed to improve social communication skills in individuals with autism spectrum disorder (ASD). PRT adopts a more naturalistic approach and focuses on using a number of strategies to help increase children's motivation during intervention. Since its conceptualization, PRT has received much empirical support for eliciting therapeutic gains in greater use of functional social communication skills in individuals with ASD. Building upon the empirical evidence supporting PRT, recent advancements have increasingly turned to using interdisciplinary research integrating neuroimaging techniques and behavioral measures to help identify objective biomarkers of treatment, which have two primary purposes. First, neuroimaging results can help characterize how PRT may elicit change, and facilitate partitioning of the heterogeneous profiles of neural mechanisms underlying similar profile of behavioral changes observed over PRT. Second, neuroimaging provides an objective means to both map and track how biomarkers may serve as reliable and sensitive predictors of responder profiles to PRT, assisting clinicians to identify who will most likely benefit from PRT. Together, a better understanding of both mechanisms of change and predictors of responder profile will help PRT to serve as a more precise and targeted intervention for individuals with ASD, thus moving towards the goal of precision medicine and improving quality of care. This review focuses on the recent emerging neuroimaging evidences supporting PRT, offering current perspectives on the importance of interdisciplinary research to help clinicians better understand how PRT works and predict who will respond to PRT.
Language	English
Publishing date	2017-06-20
Publishing country	New Zealand
Document type	Journal Article ; Review
ZDB-ID	2186503-6
ISSN	1178-2021 ; 1176-6328
ISSN (online)	1178-2021
ISSN	1176-6328
DOI	10.2147/NDT.S120710
Database	MEDical Literature Analysis and Retrieval System OnLINE

Full text online

Accessible to users with ZB MED library card

In stock of ZB MED Cologne/Königswinter

Zs.A 6068: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾

Book ; Online: Estimating Reproducible Functional Networks Associated with Task Dynamics using Unsupervised LSTMs

Dvornek, Nicha C. / Ventola, Pamela / Duncan, James S.

2021

Abstract	We propose a method for estimating more reproducible functional networks that are more strongly associated with dynamic task activity by using recurrent neural networks with long short term memory (LSTMs). The LSTM model is trained in an unsupervised manner to learn to generate the functional magnetic resonance imaging (fMRI) time-series data in regions of interest. The learned functional networks can then be used for further analysis, e.g., correlation analysis to determine functional networks that are strongly associated with an fMRI task paradigm. We test our approach and compare to other methods for decomposing functional networks from fMRI activity on 2 related but separate datasets that employ a biological motion perception task. We demonstrate that the functional networks learned by the LSTM model are more strongly associated with the task activity and dynamics compared to other approaches. Furthermore, the patterns of network association are more closely replicated across subjects within the same dataset as well as across datasets. More reproducible functional networks are essential for better characterizing the neural correlates of a target task. Comment: IEEE International Symposium on Biomedical Imaging (ISBI) 2020
Keywords	Quantitative Biology - Quantitative Methods ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Image and Video Processing ; Statistics - Applications
Subject code	006
Publishing date	2021-05-06
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article: COMBINING PHENOTYPIC AND RESTING-STATE FMRI DATA FOR AUTISM CLASSIFICATION WITH RECURRENT NEURAL NETWORKS.

Dvornek, Nicha C / Ventola, Pamela / Duncan, James S

Proceedings. IEEE International Symposium on Biomedical Imaging

2018 Volume 2018, Page(s) 725–728

Abstract: Accurate identification of autism spectrum disorder (ASD) from resting-state functional magnetic resonance imaging (rsfMRI) is a challenging task due in large part to the heterogeneity of ASD. Recent work has shown better classification accuracy using a ... ...

Abstract	Accurate identification of autism spectrum disorder (ASD) from resting-state functional magnetic resonance imaging (rsfMRI) is a challenging task due in large part to the heterogeneity of ASD. Recent work has shown better classification accuracy using a recurrent neural network with rsfMRI time-series as inputs. However, phenotypic features, which are often available and likely carry predictive information, are excluded from the model, and combining such data with rsfMRI into the recurrent neural network is not a straightforward task. In this paper, we present several methodologies for incorporating phenotypic data with rsfMRI into a single deep learning framework for classifying ASD. We test the proposed architectures using a cross-validation framework on the large, heterogeneous first cohort from the Autism Brain Imaging Data Exchange. Our best model achieved an accuracy of 70.1%, outperforming prior work.
Language	English
Publishing date	2018-05-24
Publishing country	United States
Document type	Journal Article
ISSN	1945-7928
ISSN	1945-7928
DOI	10.1109/ISBI.2018.8363676
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Longitudinal Cognitive and Behavioral Presentation of Adult Female with Kabuki Syndrome.

Ventola, Pamela / Pomales-Ramos, Anamiguel / DeLucia, Elizabeth A

The American journal of case reports

2019 Volume 20, Page(s) 430–436

Abstract: BACKGROUND Kabuki syndrome (KS) is a rare disease with an estimated prevalence of approximately 1: 32 000. While the clinical presentation of KS is heterogeneous, manifestations may include: characteristic facial features, postnatal growth retardation, ... ...

Abstract	BACKGROUND Kabuki syndrome (KS) is a rare disease with an estimated prevalence of approximately 1: 32 000. While the clinical presentation of KS is heterogeneous, manifestations may include: characteristic facial features, postnatal growth retardation, and skeletal abnormalities. With regards to the cognitive profile, most individuals with KS have an Intellectual Disability, but the magnitude of the impairment ranges from mild to severe, and verbal abilities are generally stronger than nonverbal abilities (i.e., visual spatial and visual perception abilities). Given the low incidence of KS, there is limited literature illustrating the longitudinal development of individuals with the condition. This report presents the cognitive and behavioral trajectory of an individual with KS. CASE REPORT The patient in this case report was a 27-year-old female with KS. Her cognitive profile had remained in the average range over time, but consistent with the limited KS literature, her verbal abilities were significantly higher than her nonverbal abilities. Specifically, our patient demonstrated significant deficits in visual motor and visual perceptual skills. With regards to her core language skills, her expressive skills were average, yet her receptive skills were below average. Throughout the majority of her schooling, her academic achievement skills were mildly delayed. Notably, her performance on cognitive and academic assessments remained stable over time. During young adulthood, she developed significant internalizing symptoms, particularly depressive symptoms. CONCLUSIONS This is the first case report to illustrate the presentation of an individual with KS from toddlerhood through young adulthood. The patient's clinical presentation across time was relatively consistent with the KS literature to date; notable patterns of language, motor, cognitive and behavioral deficits illustrate the considerable heterogeneity that exists within the syndrome. This case report, particularly, illustrates the persistence of the cognitive profile over time and also the co-occurring psychiatric symptoms that might emerge.
MeSH term(s)	Abnormalities, Multiple ; Adult ; Anxiety/etiology ; Cognition Disorders/etiology ; Depression/etiology ; Face/abnormalities ; Female ; Hematologic Diseases/complications ; Humans ; Language Disorders/etiology ; Motor Skills Disorders/etiology ; Vestibular Diseases/complications
Language	English
Publishing date	2019-04-02
Publishing country	United States
Document type	Case Reports ; Journal Article
ZDB-ID	2517183-5
ISSN	1941-5923 ; 1941-5923
ISSN (online)	1941-5923
ISSN	1941-5923
DOI	10.12659/AJCR.913854
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article: Efficient Shapley Explanation For Features Importance Estimation Under Uncertainty.

Li, Xiaoxiao / Zhou, Yuan / Dvornek, Nicha C / Gu, Yufeng / Ventola, Pamela / Duncan, James S

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

2020 Volume 12261, Page(s) 792–801

Abstract: Complex deep learning models have shown their impressive power in analyzing high-dimensional medical image data. To increase the trust of applying deep learning models in medical field, it is essential to understand why a particular prediction was ... ...

Abstract	Complex deep learning models have shown their impressive power in analyzing high-dimensional medical image data. To increase the trust of applying deep learning models in medical field, it is essential to understand why a particular prediction was reached. Data feature importance estimation is an important approach to understand both the model and the underlying properties of data. Shapley value explanation (SHAP) is a technique to fairly evaluate input feature importance of a given model. However, the existing SHAP-based explanation works have limitations such as 1) computational complexity, which hinders their applications on high-dimensional medical image data; 2) being sensitive to noise, which can lead to serious errors. Therefore, we propose an uncertainty estimation method for the feature importance results calculated by SHAP. Then we theoretically justify the methods under a Shapley value framework. Finally we evaluate our methods on MNIST and a public neuroimaging dataset. We show the potential of our method to discover disease related biomarkers from neuroimaging data.
Language	English
Publishing date	2020-09-29
Publishing country	Germany
Document type	Journal Article
DOI	10.1007/978-3-030-59710-8_77
Database	MEDical Literature Analysis and Retrieval System OnLINE

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Details ▾

Article: Graph Embedding Using Infomax for ASD Classification and Brain Functional Difference Detection.

Li, Xiaoxiao / Dvornek, Nicha C / Zhuang, Juntang / Ventola, Pamela / Duncan, James

Proceedings of SPIE--the International Society for Optical Engineering

2020 Volume 11317

Abstract: Significant progress has been made using fMRI to characterize the brain changes that occur in ASD, a complex neuro-developmental disorder. However, due to the high dimensionality and low signal-to-noise ratio of fMRI, embedding informative and robust ... ...

Abstract	Significant progress has been made using fMRI to characterize the brain changes that occur in ASD, a complex neuro-developmental disorder. However, due to the high dimensionality and low signal-to-noise ratio of fMRI, embedding informative and robust brain regional fMRI representations for both graph-level classification and region-level functional difference detection tasks between ASD and healthy control (HC) groups is difficult. Here, we model the whole brain fMRI as a graph, which preserves geometrical and temporal information and use a Graph Neural Network (GNN) to learn from the graph-structured fMRI data. We investigate the potential of including mutual information (MI) loss (Infomax), which is an unsupervised term encouraging large MI of each nodal representation and its corresponding graph-level summarized representation to learn a better graph embedding. Specifically, this work developed a pipeline including a GNN encoder, a classifier and a discriminator, which forces the encoded nodal representations to both benefit classification and reveal the common nodal patterns in a graph. We simultaneously optimize graph-level classification loss and Infomax. We demonstrated that Infomax graph embedding improves classification performance as a regularization term. Furthermore, we found separable nodal representations of ASD and HC groups in prefrontal cortex, cingulate cortex, visual regions, and other social, emotional and execution related brain regions. In contrast with GNN with classification loss only, the proposed pipeline can facilitate training more robust ASD classification models. Moreover, the separable nodal representations can detect the functional differences between the two groups and contribute to revealing new ASD biomarkers.
Language	English
Publishing date	2020-02-28
Publishing country	United States
Document type	Journal Article
ISSN	0277-786X
ISSN	0277-786X
DOI	10.1117/12.2549451
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

To top

Full text online

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

Full text online

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

Inter-library loan at ZB MED