Insight and inference for DVARS

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Insight and inference for DVARS

Estimates of functional connectivity using resting state functional Magnetic Resonance Imaging (rs-fMRI) are acutely sensitive to artifacts and large scale nuisance variation. As a result much effort is dedicated to preprocessing rs-fMRI data and using diagnostic measures to identify bad scans. One such diagnostic measure is DVARS, the spatial root mean square of the data after temporal differe...

متن کامل

p-Values: The Insight to Modern Statistical Inference

I introduce a p-value function that derives from the continuity inherent in a wide range of regular statistical models. This provides confidence bounds and confidence sets, tests, and estimates that all reflect model continuity. The development starts with the scalar-variable scalar-parameter exponential model and extends to the vector-parameter model with scalar interest parameter, then to gen...

متن کامل

Notes on Creating a Standardized Version of DVARS

By constructing a sampling distribution for DVARS we can create a standardized version of DVARS that should be more similar across scanners and datasets.

متن کامل

Bayesian inference for the stochastic identification of elastoplastic material parameters: Introduction, misconceptions and additional insight

We discuss Bayesian inference (BI) for the probabilistic identification of material parameters. This contribution aims to shed light on the use of BI for the identification of elastoplastic material parameters. For this purpose a single spring is considered, for which the stress-strain curves are artificially created. Besides offering a didactic introduction to BI, this paper proposes an approa...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: NeuroImage

سال: 2018

ISSN: 1053-8119

DOI: 10.1016/j.neuroimage.2017.12.098