Ch11 - Statistical Analysis II: Advanced Approaches

Question 1

Hypothesis-driven analysis

Answer 1

Evaluation of data based on statistical tests of validity of a null hypothesis

Question 2

multiple regression

Answer 2

statistical approaches that evaluate the relative contributions of several independent variables to a dependent variable

Question 3

data-driven analysis

Answer 3

drawing inferences based on examination of intrinsic structure of data

Question 4

component (data-driven analysis)

Answer 4

feature of a data set that represents some aspect of its intrinsic structure

Question 5

data reduction

Answer 5

simplification of a data set by reducing its variability e.g, eliminating irrelevant, redundant, nonpredictive variables

Question 6

principle component analysis (PCA)

Answer 6

common technique for data reduction
- simplifies high-dimensional data into smaller set of components that retains most of the variability of the original data set

Question 7

orthogonal

Answer 7

property of 2 variables such that they are completely uncorrelated with each other

Question 8

eigenimage

Answer 8

spacial maps
- generated by PCA that reflect orthogonal components of a complex image (time series of images)

Question 9

eigenvector

Answer 9

set of values that describe component of the intrinsic variability in data
- following PCA

Question 10

eigenvalue

Answer 10

mathematical describtion of amount of variability in data set that is accounted for by a given component

Question 11

independent component analysis (ICA)

Answer 11

important class of data-driven analyses that identify stationary se of voxels whoses BOLD time course vary together over time and maximally distinguishable from other sets

Question 12

mixing matrix

Answer 12

statistical describtion of how set of hypothesized sources (e.g., components) combine to form the obsereved data

Question 13

spacial ICA

Answer 13

form of independent component analysis that generates components that have minimal spacial redundancy

Question 14

temporal ICA

Answer 14

form of independent component analysis that generates components that have minimal temporal redundancy

Question 15

resting-state connectivity

Answer 15

functional connectivity of a given brain region when measured while the participant is not performing any coordinated, purposeful task

Question 16

partial least squares (PLS)

Answer 16

approach to analyzing functional neuroimaging data that is used to identify components whose amplitude is influenced by an experimental manipulation

Question 17

latent variable

Answer 17

variable whose value is not directly measured but is inferred based on the values of other variables

Question 18

permutation (significance testing)

Answer 18

approach that involve resampling original data to determine the size of and effect that might be obsereved with a given alpha level

Question 19

hyperscanning

Answer 19

simultaneous collection of fMRI data from 2+ subjects who are interacting in an experimental paradigm

Question 20

intersubject correlation

Answer 20

functional MRI time course that are shared by different individuals while performing the same experimental task/stimuli

Question 21

reverse interference

Answer 21

reasoning from the outcome of a dependent variable to infer the state of a dependent variable

Question 22

fiber tracts

Answer 22

bundles of axons that convey signals, as action potentials, from one brain region to another

Question 23

structural connectivity

Answer 23

pattern of structural connections between regions based on known axonal projections

Question 24

coactivation

Answer 24

simultanous activation of 2+ brain regions within single experimental task
- doesn’t imply that regions are functionally connected!

Question 25

double dissociation

Answer 25

demonstration that 2 experimental manipulations have different effects on 2 dependent variables

Question 26

seed voxel

Answer 26

voxel chosen as a starting point for a connectivity analysis

Question 27

default network

Answer 27

set of brain regions whose activation tends to decrease during the performance of active, engaging tasks, but to increase during conditions of resting and reflection

Question 28

psychophysiological interaction (PPI)

Answer 28

statistical approach for identifying the effect of an experimental manipulation on the functional connectivity between 2 brain regions

Question 29

epiphenomenal

Answer 29

secondary consequence of a causal chain of processes, but playing no causal role in the process of interest

Question 30

structural equation modeling (SEM)

Answer 30

statistical approach for testing a model of the relationships among a set of independent and dependent variables

Question 31

dynamic causal modeling (DCM)

Answer 31

statistical approach for testing meodels of the connectivity between brain regions based on hypotheses about how experimental manipulations alter activation and connectivity between regions

Question 32

Granger causality

Answer 32

A form of time series analysis that quantifies the information gained by using the past history of one variable to improve predictions of future values of another variable

Question 33

Diffusion tensor imaging (DTI)

Answer 33

collection of images that provide information about the magnitude and direction of molecular diffusion
- for creation of fractional anisotropy

Question 34

fractional anisotropy (FA)

Answer 34

preference for molecules to diffuse in an anisotropic manner
- FA value of 1 = diffusion along prefered axis
- FA value of 0 = diffusion similar in all directions

Question 35

real-time analyses

Answer 35

set of computational steps designed for tge rapid analysis of fMRI data so that statistical tests can be conducted immediately following the acquisition of images

Question 36

presurgical planning

Answer 36

use of fMRI or another neuroscience technique to map particular functions in a single individual to guide clinical decision-making about the potential course and consequences of neurosurgery in that individual

Question 37

biofeedback

Answer 37

providing an explicit indicator of some physiological process (e.g. beating heart) so individual can attempt to regulate that activation/guide behavior

Question 38

biomarker

Answer 38

phenotypic feature (physical, pysiological, behavioral) that provides robust predictor of some experimentally or clinically important outcome

Question 39

subsequent memory

Answer 39

approach to fMRI analysis
- sorts experimental stimuli based on whether they were remembered or forgotten in later testing session
- allows identification of brain regions whose activation predicts successful encoding of stimulus

Question 40

logistical regression

Answer 40

subset of regression analysis that uses set of independent variables to predict binary outcome variable

Question 41

pattern classification

Answer 41

attemt to separate individual exemplars into different categories by constructing a set of decision rules based on some combination of their feature

E.g.:

Question 42

machine learning

Answer 42

subdicipline within computer science that develops algorithmic rules for relating input data to desirable outputs

Question 43

multi-voxel pattern analysis (MVPA)

Answer 43

approach for pattern classification in fMRI research that uses data of relative changes of activation across set of voxels as input

Question 44

feature selection

Answer 44

initial step in pattern classification that involves determination of which input variables should be included in the classification algorithm

Question 45

searchlight

Answer 45

approach to feature selection in pattern classification
- reflects geometrically defined ROI (e.g. sphere of 5 voxel radius) that can be moved through the brain

Question 46

training set

Answer 46

in pattern classification analysis
- part of data set that is used to develop the classification algorithm

Question 47

testing set

Answer 47

in pattern classification
- novel part of data set used to evaluate the robustness of the classification algorithm

Question 48

support vector machines (SVMs)

Answer 48

class of algorithm used in pattern classification that attempts to identify the combination of features in the original data set that can most effectively differentiate between 2 categories

Question 49

cross-validation

Answer 49

in pattern classification analysis
- approach to evaluate the effectiveness of classification using given feature set
- iterated generation and testing of classifiers based on different parts of the same training set