scholarly journals Amygdalar nuclei and hippocampal subfields on MRI: Test‐retest reliability of automated segmentation in old and young healthy volunteers

2020 ◽  
Vol 16 (S5) ◽  
Author(s):  
Giulia Quattrini ◽  
Michela Pievani ◽  
Jorge Jovicich ◽  
Marco Aiello ◽  
Nuria Bargalló ◽  
...  
Keyword(s):  
Healthy Volunteers ◽  
Automated Segmentation ◽  
Retest Reliability ◽  
Hippocampal Subfields ◽  
Test Retest Reliability

scholarly journals Amygdalar nuclei and hippocampal subfields on MRI: Test-retest reliability of automated volumetry across different MRI sites and vendors

NeuroImage ◽  
2020 ◽  
Vol 218 ◽  
pp. 116932 ◽  
Author(s):  
Giulia Quattrini ◽  
Michela Pievani ◽  
Jorge Jovicich ◽  
Marco Aiello ◽  
Núria Bargalló ◽  
...  
Keyword(s):  
Retest Reliability ◽  
Hippocampal Subfields ◽  
Test Retest Reliability

Test–retest reliability of pain location using three different body chart grids: a study on healthy volunteers

Physiotherapy ◽  
2015 ◽  
Vol 101 ◽  
pp. e860
Author(s):  
D. Leoni ◽  
C. Cescon ◽  
H. Carolin ◽  
G. Capra ◽  
R. Clijsen ◽  
...  
Keyword(s):  
Healthy Volunteers ◽  
Retest Reliability ◽  
Pain Location ◽  
Test Retest Reliability

Motor Unit Number Index (MUNIX): A novel neurophysiological marker for neuromuscular disorders; test–retest reliability in healthy volunteers

2011 ◽  
Vol 122 (9) ◽  
pp. 1867-1872 ◽  
Author(s):  
Christoph Neuwirth ◽  
Sanjeev Nandedkar ◽  
Erik Stålberg ◽  
Paul E. Barkhaus ◽  
Mamede de Carvalho ◽  
...  
Keyword(s):  
Healthy Volunteers ◽  
Motor Unit ◽  
Neuromuscular Disorders ◽  
Retest Reliability ◽  
Test Retest Reliability

scholarly journals Test–retest reliability of a functional MRI anticipatory anxiety paradigm in healthy volunteers

2008 ◽  
Vol 27 (3) ◽  
pp. 459-468 ◽  
Author(s):  
Thérèse Schunck ◽  
Gilles Erb ◽  
Alexandre Mathis ◽  
Nathalie Jacob ◽  
Christian Gilles ◽  
...  
Keyword(s):  
Healthy Volunteers ◽  
Functional Mri ◽  
Anticipatory Anxiety ◽  
Retest Reliability ◽  
Test Retest Reliability

Exploring test retest reliability and longitudinal stability of digital biomarkers for Parkinson’s disease in the m-Power dataset (Preprint)

2020 ◽  
Author(s):  
Mehran Sahandi Far ◽  
Simon B. Eickhoff ◽  
Maria Goni ◽  
Juergen Dukart
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Healthy Volunteers ◽  
Intraclass Correlation ◽  
Smart Devices ◽  
Longitudinal Stability ◽  
Retest Reliability ◽  
Medication Effects ◽  
Test Retest Reliability ◽  
Digital Biomarkers

BACKGROUND Digital biomarkers (DB) as captured using sensors embedded in modern smart devices are a promising technology for home-based symptom monitoring in Parkinson’s disease (PD). Despite extensive application in recent studies test-retest reliability and longitudinal stability of DB has not been well addressed in this context. OBJECTIVE We utilized the large-scale m-Power dataset to establish the test-retest reliability and longitudinal stability of gait, balance, voice and tapping tasks in an unsupervised and self-administered daily life setting in PD patients and healthy volunteers. METHODS Intraclass Correlation Coefficients (ICC) were computed to estimate the test-retest reliability of features that also differentiate between PD and healthy volunteers. In addition, we tested for longitudinal stability of DB measures in PD and HC as well as for their sensitivity to PD medication effects. RESULTS Among the features differing between PD and HC, only few tapping and voice features had good to excellent test-retest reliabilities and medium to large effect sizes. All other features performed poorly in this respect. Only few features were sensitive to medication effects. The longitudinal analyses revealed significant alterations over time across a variety of features and in particular for the tapping task. CONCLUSIONS These results indicate the need for further development of more standardized, sensitive and reliable DB for application in self-administered remote studies in PD patients. Motivational, learning and other confounds may cause a variation in performance that needs to be considered in DB longitudinal applications. CLINICALTRIAL Not applicable

Test–retest reliability of the BOLD pharmacological MRI response to ketamine in healthy volunteers

NeuroImage ◽  
2013 ◽  
Vol 64 ◽  
pp. 75-90 ◽  
Author(s):  
S. De Simoni ◽  
A.J. Schwarz ◽  
O.G. O'Daly ◽  
A.F. Marquand ◽  
C. Brittain ◽  
...  
Keyword(s):  
Healthy Volunteers ◽  
Retest Reliability ◽  
Pharmacological Mri ◽  
Test Retest Reliability

Test–retest reliability of DSC MRI CBV mapping in healthy volunteers

Neuroreport ◽  
2001 ◽  
Vol 12 (8) ◽  
pp. 1567-1569 ◽  
Author(s):  
Michael E. Henry ◽  
Marc J. Kaufman ◽  
Nicholas Lange ◽  
Mark E. Schmidt ◽  
Seth Purcell ◽  
...  
Keyword(s):  
Healthy Volunteers ◽  
Retest Reliability ◽  
Dsc Mri ◽  
Test Retest Reliability

scholarly journals Exploring test retest reliability and longitudinal stability of digital biomarkers for Parkinson’s disease in the m-Power dataset

2020 ◽  
Author(s):  
Mehran Sahandi Far ◽  
Simon B. Eickhoff ◽  
María Goñi ◽  
Juergen Dukart
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Healthy Volunteers ◽  
Intraclass Correlation ◽  
Smart Devices ◽  
Longitudinal Stability ◽  
Retest Reliability ◽  
Medication Effects ◽  
Test Retest Reliability ◽  
Digital Biomarkers

AbstractBackgroundDigital biomarkers (DB) as captured using sensors embedded in modern smart devices are a promising technology for home-based symptom monitoring in Parkinson’s disease (PD).ObjectiveDespite extensive application in recent studies test-retest reliability and longitudinal stability of DB has not been well addressed in this context. We utilized the large-scale m-Power dataset to establish the test-retest reliability and longitudinal stability of gait, balance, voice and tapping tasks in an unsupervised and self-administered daily life setting in PD patients and healthy volunteers.MethodsIntraclass Correlation Coefficients (ICC) were computed to estimate the test-retest reliability of features that also differentiate between PD and healthy volunteers. In addition, we tested for longitudinal stability of DB measures in PD and HC as well as for their sensitivity to PD medication effects.ResultsAmong the features differing between PD and HC, only few tapping and voice features had good to excellent test-retest reliabilities and medium to large effect sizes. All other features performed poorly in this respect. Only few features were sensitive to medication effects. The longitudinal analyses revealed significant alterations over time across a variety of features and in particular for the tapping task.ConclusionsThese results indicate the need for further development of more standardized, sensitive and reliable DB for application in self-administered remote studies in PD patients. Motivational, learning and other confounds may cause a variation in performance that needs to be considered in DB longitudinal applications.

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