SwePub
Sök i SwePub databas

  Utökad sökning

Träfflista för sökning "WFRF:(Nichols TE) ;lar1:(ki)"

Sökning: WFRF:(Nichols TE) > Karolinska Institutet

  • Resultat 1-10 av 12
Sortera/gruppera träfflistan
   
NumreringReferensOmslagsbildHitta
1.
  •  
2.
  •  
3.
  •  
4.
  •  
5.
  •  
6.
  •  
7.
  • Petersson, KM, et al. (författare)
  • Statistical limitations in functional neuroimaging. I. Non-inferential methods and statistical models
  • 1999
  • Ingår i: Philosophical transactions of the Royal Society of London. Series B, Biological sciences. - : The Royal Society. - 0962-8436 .- 1471-2970. ; 354:1387, s. 1239-1260
  • Tidskriftsartikel (refereegranskat)abstract
    • Functional neuroimaging (FNI) provides experimental access to the intact living brain making it possible to study higher cognitive functions in humans. In this review and in a companion paper in this issue, we discuss some common methods used to analyse FNI data. The emphasis in both papers is on assumptions and limitations of the methods reviewed. There are several methods available to analyse FNI data indicating that none is optimal for all purposes. In order to make optimal use of the methods available it is important to know the limits of applicability. For the interpretation of FNI results it is also important to take into account the assumptions, approximations and inherent limitations of the methods used. This paper gives a brief overview over some non-inferential descriptive methods and common statistical models used in FNI. Issues relating to the complex problem of model selection are discussed. In general, proper model selection is a necessary prerequisite for the validity of the subsequent statistical inference. The non-inferential section describes methods that, combined with inspection of parameter estimates and other simple measures, can aid in the process of model selection and verification of assumptions. The section on statistical models covers approaches to global normalization and some aspects of univariate, multivariate, and Bayesian models. Finally, approaches to functional connectivity and effective connectivity are discussed. In the companion paper we review issues related to signal detection and statistical inference.
  •  
8.
  • Petersson, KM, et al. (författare)
  • Statistical limitations in functional neuroimaging. II. Signal detection and statistical inference
  • 1999
  • Ingår i: Philosophical transactions of the Royal Society of London. Series B, Biological sciences. - : The Royal Society. - 0962-8436 .- 1471-2970. ; 354:1387, s. 1261-1281
  • Tidskriftsartikel (refereegranskat)abstract
    • The field of functional neuroimaging (FNI) methodology has developed into a mature but evolving area of knowledge and its applications have been extensive. A general problem in the analysis of FNI data is finding a signal embedded in noise. This is sometimes called signal detection. Signal detection theory focuses in general on issues relating to the optimization of conditions for separating the signal from noise. When methods from probability theory and mathematical statistics are directly applied in this procedure it is also called statistical inference. In this paper we briefly discuss some aspects of signal detection theory relevant to FNI and, in addition, some common approaches to statistical inference used in FNI. Low–pass filtering in relation to functional–anatomical variability and some effects of filtering on signal detection of interest to FNI are discussed. Also, some general aspects of hypothesis testing and statistical inference are discussed. This includes the need for characterizing the signal in data when the null hypothesis is rejected, the problem of multiple comparisons that is central to FNI data analysis, omnibus tests and some issues related to statistical power in the context of FNI. In turn, random field, scale space, non–parametric and Monte Carlo approaches are reviewed, representing the most common approaches to statistical inference used in FNI. Complementary to these issues an overview and discussion of non–inferential descriptive methods, common statistical models and the problem of model selection is given in a companion paper. In general, model selection is an important prelude to subsequent statistical inference. The emphasis in both papers is on the assumptions and inherent limitations of the methods presented. Most of the methods described here generally serve their purposes well when the inherent assumptions and limitations are taken into account. Significant differences in results between different methods are most apparent in extreme parameter ranges, for example at low effective degrees of freedom or at small spatial autocorrelation. In such situations or in situations when assumptions and approximations are seriously violated it is of central importance to choose the most suitable method in order to obtain valid results.
  •  
9.
  •  
10.
  •  
Skapa referenser, mejla, bekava och länka
  • Resultat 1-10 av 12

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy