Scientific Research by Jamie Feusner, M.D.: Multimodal Machine-Learning Classification of Body Dysmorphic Disorder, Anorexia Nervosa, and Non-Clinical Populations, and Predictions of Transdiagnostic Phenotypes
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A slide presentation created by Ky Trang Ho to present brain research conducted at UCLA. The take-home message: MRI, clinical data and demographic variables can be used to predict the severity of symptoms of anorexia nervosa, AN, and body dysmorphic disorder.
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NEW RESEARCHJOURNALVOLUMNeural Markers in Pediatric Bipo.docx
NEW RESEARCH
JOURNAL
VOLUM
Neural Markers in Pediatric Bipolar Disorder
and Familial Risk for Bipolar Disorder
Jillian Lee Wiggins, PhD, Melissa A. Brotman, PhD, Nancy E. Adleman, PhD, Pilyoung Kim, PhD,
Caroline G. Wambach, BS, Richard C. Reynolds, MS, Gang Chen, PhD, Kenneth Towbin, MD,
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Neuroimaging studies comparing unaffected youth at
high familial risk for BD (i.e., those with a first-degree
relative with the disorder; termed “high-risk” [HR]) to
“low-risk” (LR) youth (i.e., those without a first-degree
relative with BD) and to patients with BD may help
identify potential brain-based markers associated with
risk (i.e., regions where HRþBDsLR), resilience
(HRsBDþLR), or illness (BDsHRþLR).
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(fMRI), 99 youths (i.e., adolescents and young adults)
aged 9.8 to 24.8 years (36 BD, 22 HR, 41 LR) performed a
task probing face emotion labeling, previously shown to
be impaired behaviorally in youth with BD and HR youth.
Results: We found three patterns of results. Candidate
risk endophenotypes (i.e., where BD and HR shared def-
icits) included dysfunction in higher-order face processing
regions (e.g., middle temporal gyrus, dorsolateral pre-
frontal cortex). Candidate resilience markers and disorder
sequelae (where HR and BD, respectively, show unique
alterations relative to the other two groups) included
different patterns of neural responses across other regions
Supplemental material cited in this article is available online.
OF THE AMERICAN ACADEMY OF CHILD & ADOLESCENT PSYCHIATRY
E 56 NUMBER 1 JANUARY 2017
mediating face processing (e.g., fusiform), executive
function (e.g., inferior frontal gyrus), and social cognition
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parietal junction).
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additional populations, neural patterns suggesting risk
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Moreover, information about risk and resilience markers
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associated with risk.
Clinical trial registration information—Studies of Brain
Function and Course of Illness in Pediatric Bipolar Dis-
order and Child and Adolescent Bipolar Disorder Brain
Imaging and Treatment Study; http://clinicaltrials.gov/;
NCT00025935 and NCT00006177.
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Select two nursing theorists from the following list to compare and contrast:
· Jean Watson
· Patricia Benner
· Myra Estrin Levine
· Martha Rogers
·
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· Betty Neuman
· Imogene King
· Hildegard Peplau
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· Nola Pender
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image1.png
Frontiers in Psychiatry | www.frontiersin.or
Edited by:
Yanhui Liao,
Sir Run Run Shaw Hospital, China
Reviewed by:
Yu-Tao Xiang,
University of Macau, China
Xiao Jun Xiang,
Central South University, China
Yanbo Zhang,
University of Alberta, Canada
*Correspondence:
Yanqing Tang
[email protected]
Specialty section:
This article was submitted to
Addictive Disorders,
a section of the journal
Frontiers in Psychiatry
Received: 21 May 2020
Accepted: 16 July 2020
Published: 17 September 2020
Citation:
Liu L, Jia L, Jian P, Zhou Y, Zhou J,
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Cognition in the Elders: A Systematic
Review and Meta-Analysis
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Front. Psychiatry 11:00755.
doi: 10.3389/fpsyt.2020.00755
SYSTEMATIC REVIEW
published: 17 September 2020
doi: 10.3389/fpsyt.2020.00755
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This document discusses research analyzing the relationship between mobile phone location sensor data and measures of depressive symptom severity. The research replicated a previous study finding significant correlations between several GPS-derived features (location variance, entropy, circadian movement) and scores on the PHQ-9 depression scale. These relationships were stronger when analyzing weekend versus weekday GPS data. GPS features predicted PHQ-9 scores up to 10 weeks later, suggesting they may serve as early warning signals of depression. The findings provide further evidence that passively collected GPS data from smartphones can reliably predict depressive symptom severity.
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This document discusses the tendency of researchers to make spurious claims linking negative emotions to health outcomes. For over 50 years, researchers have claimed to find a modifiable connection between negative emotion and morbidity/mortality, but these claims often lead to embarrassment after being disconfirmed. The document argues that many studies actually find only uninformative risk markers rather than true modifiable risk factors. It warns against continuing to make premature or exaggerated claims without ruling out alternative explanations like residual confounding.
NEW RESEARCHJOURNALVOLUMNeural Markers in Pediatric Bipo.docx
NEW RESEARCH
JOURNAL
VOLUM
Neural Markers in Pediatric Bipolar Disorder
and Familial Risk for Bipolar Disorder
Jillian Lee Wiggins, PhD, Melissa A. Brotman, PhD, Nancy E. Adleman, PhD, Pilyoung Kim, PhD,
Caroline G. Wambach, BS, Richard C. Reynolds, MS, Gang Chen, PhD, Kenneth Towbin, MD,
Daniel S. Pine, MD, Ellen Leibenluft, MD
Objective: Bipolar disorder (BD) is highly heritable.
Neuroimaging studies comparing unaffected youth at
high familial risk for BD (i.e., those with a first-degree
relative with the disorder; termed “high-risk” [HR]) to
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relative with BD) and to patients with BD may help
identify potential brain-based markers associated with
risk (i.e., regions where HRþBDsLR), resilience
(HRsBDþLR), or illness (BDsHRþLR).
Method: During functional magnetic resonance imaging
(fMRI), 99 youths (i.e., adolescents and young adults)
aged 9.8 to 24.8 years (36 BD, 22 HR, 41 LR) performed a
task probing face emotion labeling, previously shown to
be impaired behaviorally in youth with BD and HR youth.
Results: We found three patterns of results. Candidate
risk endophenotypes (i.e., where BD and HR shared def-
icits) included dysfunction in higher-order face processing
regions (e.g., middle temporal gyrus, dorsolateral pre-
frontal cortex). Candidate resilience markers and disorder
sequelae (where HR and BD, respectively, show unique
alterations relative to the other two groups) included
different patterns of neural responses across other regions
Supplemental material cited in this article is available online.
OF THE AMERICAN ACADEMY OF CHILD & ADOLESCENT PSYCHIATRY
E 56 NUMBER 1 JANUARY 2017
mediating face processing (e.g., fusiform), executive
function (e.g., inferior frontal gyrus), and social cognition
(e.g., default network, superior temporal sulcus, temporo-
parietal junction).
Conclusion: If replicated in longitudinal studies and with
additional populations, neural patterns suggesting risk
endophenotypes could be used to identify individuals at
risk for BD who may benefit from prevention measures.
Moreover, information about risk and resilience markers
could be used to develop novel treatments that recruit
neural markers of resilience and attenuate neural patterns
associated with risk.
Clinical trial registration information—Studies of Brain
Function and Course of Illness in Pediatric Bipolar Dis-
order and Child and Adolescent Bipolar Disorder Brain
Imaging and Treatment Study; http://clinicaltrials.gov/;
NCT00025935 and NCT00006177.
Key words: bipolar,brain,adolescence,risk,endophenotype
J Am Acad Child Adolesc Psychiatry 2017;56(1):67–78.
ipolar disorder (BD), 1 of the 10 leading causes of
disability (per The Global Burden of Disease, 2004
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heritable, with estimates ranging from 59% to 85%.1,2 Neu-
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NUR3643 Research and TheoryModule 2 Competency Identi.docx
NUR3643 Research and Theory
Module 2
Competency: Identify the major concepts of selected nursing theories.
Select two nursing theorists from the following list to compare and contrast:
· Jean Watson
· Patricia Benner
· Myra Estrin Levine
· Martha Rogers
·
Dorothy Johnson
· Betty Neuman
· Imogene King
· Hildegard Peplau
· Margret Newman
· Nola Pender
· Rosemary Rizzo Parse
image1.png
Frontiers in Psychiatry | www.frontiersin.or
Edited by:
Yanhui Liao,
Sir Run Run Shaw Hospital, China
Reviewed by:
Yu-Tao Xiang,
University of Macau, China
Xiao Jun Xiang,
Central South University, China
Yanbo Zhang,
University of Alberta, Canada
*Correspondence:
Yanqing Tang
[email protected]
Specialty section:
This article was submitted to
Addictive Disorders,
a section of the journal
Frontiers in Psychiatry
Received: 21 May 2020
Accepted: 16 July 2020
Published: 17 September 2020
Citation:
Liu L, Jia L, Jian P, Zhou Y, Zhou J,
Wu F and Tang Y (2020) The Effects of
Benzodiazepine Use and Abuse on
Cognition in the Elders: A Systematic
Review and Meta-Analysis
of Comparative Studies.
Front. Psychiatry 11:00755.
doi: 10.3389/fpsyt.2020.00755
SYSTEMATIC REVIEW
published: 17 September 2020
doi: 10.3389/fpsyt.2020.00755
The Effects of Benzodiazepine Use
and Abuse on Cognition in the
Elders: A Systematic Review and
Meta-Analysis of Comparative
Studies
Linzi Liu1, Linna Jia1, Peiying Jian2, Yifang Zhou3, Jian Zhou1, Feng Wu1
and Yanqing Tang1,3*
1 Department of Psychiatry, The First Affiliated Hospital, China Medical University, Shenyang, China, 2 Department of
Psychology, Queen’s University, Kingston, ON, Canada, 3 Department of Geriatrics, The First Affiliated Hospital, China
Medical University, Shenyang, China
Objective: Benzodiazepines (BZD) are one of the most frequently prescribed drugs
worldwide. However, the cognitive effects of benzodiazepines in the elderly are highly
debated. This systematic review and meta-analysis aims to explore the following two
questions in the elderly population: (i) Do BZD lead to any impairments in cognitive
functions in elderly users? and (ii) Which specific cognitive domains are most affected by
BZD use and abuse?
Methods: First, we performed a literature search following the PRISMA guidelines.
Electronic databases, including PubMed, PsycINFO, EMBASE, Cochrane Library, and
Web of Science were searched until May 14th, 2020. After selecting the relevant articles,
we integrated the results of the selected studies with a standardized cognitive
classification method. Next, we performed meta-analyses with the random-effects
model on the cognitive results. Finally, we specifically examined the cognitive
impairments of BZD in the abuse subgroup.
Results: Of the included studies, eight of the thirteen had meta-analyzable data.
Compared to the controls, elderly BZD users had significantly lower digital symbol test
scores (n=253; SMD: -0.61, 95% CI: -0.91 to 0.31, I² = 0%, p < 0.0001). There .
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Depressive disorder is an illness that involves the body, mood and thoughts. It interferes with daily life, normal functioning and causes pain for both the person with the disorder and those who care about him/her. Severe depression may lead to serious illness or suicide. The most affected sector is the Adolescent Community. The biggest problem in diagnosing and treating depressive disorders is recognizing that someone is suffering from it. As various factors are involved, it is very difficult for the Psychologists to diagnose depressive disorders correctly at an early stage itself. Nowadays, computers are used in assisting Physicians to diagnose diseases and identify correct treatments according to the patient details. In the same way, computers can also be used in assisting psychologists to diagnose mental disorders and identify correct treatments according to the patient details. Various techniques are available to store the expert knowledge and computerize the diagnosis process. Bayesian Network is such a technique that combines statistics and expert knowledge to diagnose diseases effectively. This paper proposes a Framework for diagnosing depression level in adolescents using Bayesian Networks. Initially, Ontology should be constructed to provide a basis for Bayesian Networks. The ontology acts as the topology and shows the relationships between adolescent depression concepts. By applying probabilities to the relationships between concepts from the statistics, and by using Bayes Theorem, depression level of a patient can be diagnosed effectively. This framework may help novice psychologists to understand the domain concepts and also to diagnose the depression level and suggest correct treatments.
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Mb managing anxiety_in_practice_april_26
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Eko med 23-ispr.
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Hypothetical model of dynamic biomarkers of the alzheimer pathological cascade
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- 2. Don A. Vaughn, PhD Wesley T. Kerr, MD, PhD Teena D. Moody, PhD Aifeng F. Zhang, PhD Alex D. Leow, MD, PhD Michael A. Strober, PhD
- 5. GOALS Determine if body dysmorphic disorder, anorexia nervosa, and healthy controls can accurately be distinguished based on neurobiological, demographic and psychometric date 01 Determine the multivariate differences in neurobiology and psychometric scales that map to the diagnoses 02 Determine if neural activity and connectivity patterns in a multivariate context predict insight and obsessions/compulsions across disorders 03
- 6. METHODS
- 7. PARTICIPANT DEMOGRAPHICS Variable AN BDD CON p Number of Participants 24 29 31 N/A Age (years) 21 ± 5 23 ± 5 21 ± 5 0.12 Sex: female 23/24 (96%) 26/29 (90%) 25/31 (81%) 0.21 Illness Duration (log months) 3.7 ± 1.2 4.6 ± 0.7 N/A 0.01 BMI 20 ± 2 22 ± 3 22 ± 3 0.02 Lowest Lifetime BMI 16 ± 2 N/A N/A N/A
- 8. HAMA (anxiety) MADRS (depression) YBC/BDD-YBOCS (obsessions/compulsions)BABS (insight) PSYCHO- METRIC DATA
- 9. fMRI: visual matching task of bodies, faces, and houses fMRI DATA
- 10. Coherence values from ICA, selecting primary visual, secondary visual, and salience networks fMRI DATA
- 11. DWI DATA White matter connectivity: graph theory metric of normalized path length (NPL) from network derived from tractography and Freesurfer parcellation
- 13. MACHINE LEARNING • Multiple imputation • Logistic regression • Leave-one-out cross validation • Permutation testing for significance
- 14. RESULTS
- 15. Vaughn et al, PLOS ONE, 2019
- 17. CLASSIFICATIONSUMMARY Weight-Restored AN, BDD, and healthy individuals Can be accurately differentiated using multimodal neuroimaging and psychometric data White matter network connectivity: lower global efficiency →AN Insight: lower →BDD Informative features Replicate in independent and larger samples Test and validate in other populations: - early stages of illness - at risk of developing either disorder - border between AN and BDD FUTURE
- 18. PREDICTION OF INSIGHT AND OBSESSIONS/COMPULSIONS ACROSS BDD AND AN
- 20. Yᵢ = 𝛽ₒ + 𝛽₁Xᵢ₁ + 𝛽₂Xᵢ₂ + 𝛽₃Xᵢ₃ + 𝛽₄Xᵢ₄ + 𝛽₅Xᵢ₅ + 𝛽₆Xᵢ₆ + 𝛽₇Xᵢ₇ + 𝛽₈Xᵢ₈ + 𝛽₉Xᵢ₉ + 𝛽₁₀Xᵢ₁₀ + Ɛ₁ 2⁰ STATISTICAL MODELING: MULTIPLE LINEAR REGRESSION 1⁰ A B
- 21. RESULTS
- 22. p < 0.001 Poor insight across AN and BDD can be predicted by MRI, psychometric, clinical, and demographic variables Feusner et al. Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities, 2018
- 23. Group was the only factor that trended toward individually significant association; the BDD group having a 3.8 higher score than AN (SE 2.2, p = 0.08 )
- 24. p < 0.001 Severity of obsessions and compulsions across AN and BDD can be predicted by MRI, psychometric, clinical, and demographic variables
- 25. Significant individual associations: 1. Group; BDD unit effect of 8.2 (SE 2.7, p = 0.003) 2. Anxiety/depr ession (HAMADRS); unit effect of 6.4 (SE 2.5, p = 0.01)
- 26. Adding neuroimaging features improves the models compared to only including demographic, clinical variables and psychometric scores INSIGHT Deviance diff. 83.5, df = 4, p = 10−17 Deviance diff. 233.4, df = 4, p = 10−49 OBSESSIONS/ COMPULSIONS
- 27. CONCLUSIONS
- 28. ● Brain structure and function, symptoms, clinical variables, and demographics can be used to predict: ● BDD vs. AN vs. HC diagnosis ● Severity of symptoms: insight and obsessions/ compulsions ● Implications: ○ Shared and distinct pathophysiology ○ If validated in other samples, could have clinical utility