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Biomarkers in Psychiatry

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Cover of 'Biomarkers in Psychiatry'

Table of Contents

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    Book Overview
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    Chapter 41 Network Neuroscience: A Framework for Developing Biomarkers in Psychiatry
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    Chapter 42 Reappraising Preclinical Models of Separation Anxiety Disorder, Panic Disorder, and CO 2 Sensitivity: Implications for Methodology and Translation into New Treatments
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    Chapter 43 Immunological Processes in Schizophrenia Pathology: Potential Biomarkers?
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    Chapter 44 Translational Shifts in Preclinical Models of Depression: Implications for Biomarkers for Improved Treatments
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    Chapter 45 Neuroimmune Biomarkers in Mental Illness
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    Chapter 46 Imaging and Genetic Biomarkers Predicting Transition to Psychosis
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    Chapter 47 Using Pattern Classification to Identify Brain Imaging Markers in Autism Spectrum Disorder
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    Chapter 48 Deconstructing Schizophrenia: Advances in Preclinical Models for Biomarker Identification
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    Chapter 49 Imaging and Genetic Approaches to Inform Biomarkers for Anxiety Disorders, Obsessive–Compulsive Disorders, and PSTD
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    Chapter 50 Cognitive Phenotypes for Biomarker Identification in Mental Illness: Forward and Reverse Translation
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    Chapter 52 Genomic and Imaging Biomarkers in Schizophrenia
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    Chapter 57 Stem Cells to Inform the Neurobiology of Mental Illness
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    Chapter 58 Biomarkers in Neuropsychiatry: A Prospect for the Twenty-First Century?
  15. Altmetric Badge
    Chapter 64 Correction to: Imaging and Genetic Approaches to Inform Biomarkers for Anxiety Disorders, Obsessive–Compulsive Disorders, and PSTD
Attention for Chapter 57: Stem Cells to Inform the Neurobiology of Mental Illness
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Chapter title
Stem Cells to Inform the Neurobiology of Mental Illness
Chapter number 57
Book title
Biomarkers in Psychiatry
Published in
Current topics in behavioral neurosciences, January 2018
DOI 10.1007/7854_2018_57
Pubmed ID
Book ISBNs
978-3-31-999641-7, 978-3-31-999642-4
Authors

Mandy Johnstone, Robert F. Hillary, David St. Clair, Johnstone, Mandy, Hillary, Robert F., St. Clair, David

Abstract

The inception of human-induced pluripotent stem cell (hiPSCs) technology has provided an exciting platform upon which the modelling and treatment of human neurodevelopmental and neuropsychiatric disorders may be expedited. Although the genetic architecture of these disorders is far more complex than previously imagined, many key loci have at last been identified. This has allowed in vivo and in vitro technologies to be refined to model specific high-penetrant genetic loci involved in both disorders. Animal models of neurodevelopmental disorders, such as schizophrenia and autism spectrum disorders, show limitations in recapitulating the full complexity and heterogeneity of human neurodevelopmental disease states. Indeed, patient-derived hiPSCs offer distinct advantages over classical animal models in the study of human neuropathologies. Here we have discussed the current, relative translational merit of hiPSCs in investigating human neurodevelopmental and neuropsychiatric disorders with a specific emphasis on the utility of such systems to aid in the identification of biomarkers. We have highlighted the promises and pitfalls of reprogramming cell fate for the study of these disorders and provide recommendations for future directions in this field in order to overcome current limitations. Ultimately, this will aid in the development of effective clinical strategies for diverse patient populations affected by these disorders with the aim of also leading to biomarker identification.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 8 23%
Researcher 5 14%
Student > Master 3 9%
Student > Doctoral Student 2 6%
Other 1 3%
Other 0 0%
Unknown 16 46%
Readers by discipline Count As %
Psychology 4 11%
Neuroscience 4 11%
Agricultural and Biological Sciences 3 9%
Medicine and Dentistry 2 6%
Social Sciences 1 3%
Other 3 9%
Unknown 18 51%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 June 2019.
All research outputs
#14,421,028
of 23,096,849 outputs
Outputs from Current topics in behavioral neurosciences
#289
of 500 outputs
Outputs of similar age
#240,864
of 442,670 outputs
Outputs of similar age from Current topics in behavioral neurosciences
#6
of 10 outputs
Altmetric has tracked 23,096,849 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 500 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.0. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 442,670 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.