Meta-analyses

Function/location meta-analysis

Table 17: Mathematical meta-analyses in functional neuroimaging.

Data source	Purpose	Method	Reference
BrainMapTM	Assessment of variation in activation focus	`Functional volumes modeling': Probability density modeling incorporating sample size	[Fox et al., 1997b,Fox et al., 1999,Fox et al., 2001,Fox et al., 1997a]
Lesion database (BRAID)	Determine association between 14 functional variables and 90 brain structural variables in elderly people	Manual delineation of infarct-like lesions. Chi-square contingency table test between pairs of functional and structural variables.	[Letovsky et al., 1998,Herskovits, 2000a]
Lesion database (BRAID)	Determine association between lesion sites and development of secondary attention-deficit hyperactivity disorder (S-ADHD) in children	Manual delineation of lesion from MRI, Mann-Whitney and Fisher exact test statistics in a brain image database (BRAID)	[Herskovits et al., 1999,Herskovits, 2000a]
Lesion database (BRAID)			[Herskovits, 2000b]
Literature	Determination of areas involved in language production		[Indefrey and Levelt, 2000,Indefrey and Levelt, 1999]
Literature (25 studies)	Distinction between dorsolateral and frontopolar cortex	Hotelling's $T^2$ statistics used to compare two groups of activations in Talairach space. $\chi^2$ used for region test.	[Christoff and Grabrieli, 2000, page 176]
Literature	Determining of different activation between 5 sets of activation foci each set involving different cognitive demand	Multidimensional Kolmogorov-Smirnov applied multiple times.	[Duncan and Owen, 2000]
Literature (28 studies)	Determination of areas involved in syntactic parsing	Region-based analysis (102 areas) with $P$ -value threshold determined from a binomial distribution.	[Indefrey, 2001]
BrainMapTM	Novelty detection in nomenclature	Probability density modeling through adaptive kernel density modeling conditioned on anatomical labels.	[Nielsen and Hansen, 2002c,Nielsen et al., 2001]
Literature (9 studies)	Prediction of areas involved in single word reading	Probability density modeling through kernel density modeling of 154 Talairach coordinates	[Turkeltaub et al., 2002,Turkeltaub et al., 2001]

Table 17 displays some of the meta-analyses that use mathematics/statistics. For an early review see [Fox et al., 1998].

Meta-analysis of Talairach coordinates was pioneered by Peter T. Fox et al. under the name ``functional volumes modeling'' (FVM) [Fox et al., 1997b,Fox et al., 1999,Fox et al., 1997a]. These original studies used parametric Gaussian models. Non-parametric modeling of the distribution of brain foci was first described in two unpublished studies with Gaussian mixture modeling [Nielsen and Hansen, 1999] and adaptive Gaussian kernel density modeling [Nielsen and Hansen, 2000]. Later studies include adaptive Gaussian kernel density modeling for database outlier detection [Nielsen and Hansen, 2002c,Nielsen et al., 2001,Nielsen et al., 2000], Gaussian kernel density modeling in connection with single word processing [Turkeltaub et al., 2002,Turkeltaub et al., 2001], kernel density estimation in connection with Broca's area and verbal working memory [Chein et al., 2002,Chein et al., 2001], and kernel density modeling with a spheric uniform kernel in emotion [Wager et al., 2003]. Another study is [Wager et al., 2004]. A large number of similar studies appears in a special issue of the Human Brain Mapping, volume 25, issue 1 [Fox et al., 2005b]. Functional volumes modeling is sometimes referred to as ``voxelization'' or ``activation likelihood estimation'' (ALE). Some form of meta-analysis with the use the BrainMap^TM database has been briefly described [Mahurin et al., 1995].

Determining statistical thresholds in one set of voxelized Talairach coordinates is described in [Turkeltaub et al., 2002,Turkeltaub et al., 2001] and [Nielsen, 2005]. Statistical methods for determining whether two sets are different are described in [Christoff and Grabrieli, 2000,Duncan and Owen, 2000] and [Nielsen and Hansen, 2004a,Nielsen et al., 2004b,Nielsen et al., 2005,Nielsen et al., 2004a] and [Laird et al., 2005a]. The multidimensional Kolmogorov-Smirnov used in [Duncan and Owen, 2000] is originally from [Peacock, 1983] and is also described and implemented in [Press et al., 1992, section 14.7]. Hotelling's $T^2$ test was also used in [Berman et al., 1999, page 212] but not in connection with a meta-analysis. Statistical tests on warped coordinates are described in [Steel and Lawrie, 2004].

The Brede Toolbox automatically performs multivariate analyses such as singular value decomposition (principal component analyses), independent component analyses, non-negative matrix factorization and K-means on voxelized Talairach coordinates on the entire Brede Database [Nielsen, 2003,Nielsen et al., 2004c]. Another multivariate analysis method, ``replicator dynamics'', is suggested in [Neumann et al., 2005].

A number of meta-analytic studies have grown out of the BRAID database: [Herskovits et al., 1999,Megalooikonomou et al., 1999,Megalooikonomou et al., 2000,Megalooikonomou and Herskovits, 2001,Lazarevic et al., 2001,Herskovits et al., 2002].

Descriptive statistics of activation foci appears in [Markowitsch and Tulving, 1994], where the fraction of fundus activations over 30 PET studies is found.

Table 18: Meta-analysis in Talairach space of brain function. KDE is kernel density estimation (in Talairach space).

.
Area	Function	Method	Description	Reference
Left inferior frontal cortex	Semantic and phonological processing	Tables, plots	Phonological processing dorsally while semantic ventrally	[Poldrack et al., 1999]
Anterior cingulate	Cognition, emotion			[Bush et al., 2000]
Many	Cognition	Tables, plots		[Cabeza and Nyberg, 2000]
Inferior frontal	Phonological processing	Plots		[Burton, 2001]
Prefrontal	Cognition, emotion	Plots, warp transformation, MANOVA, KDE	Resampling was used for significance test	[Steel and Lawrie, 2004]
Orbitofrontal		Plots		[Kringelbach and Rolls, 2004]
Medial frontal	Self/Other	Clustering(?)		[Seitz et al., 2005]
Posterior cingulate	As many as possible	Text clustering, Hotelling's test	Text mining on PubMed abstract for clustering articles. Thereafter determination of segregation between coordinates in clustered articles	[Nielsen et al., 2005]

Connectivity analyses

Table 19: Connectivity analyses

Species	System	Reports	Areas	Conn.	Levels	Method	References
Rat	Hippocampus	$>900$ (14000)	23		0-3, c, x	2D and 5D MDS (from SAS MDS), Cluster analysis (SAS 6.09 MODECLUS), Venn diagram	[Burns and Young, 2000]
Macaque	Cortical sensory	[Felleman and Van Essen, 1991]	30/14		$<, \leq, \emptyset, \geq, >$	Hierarchical analysis	[Hilgetag et al., 2000b]
Cat	Cortical sensory	[Scannell et al., 1995]	22		$<, \leq, \emptyset, \geq, >$	Hierarchical analysis	as above
Cat	Cortical	Part of [Scannell et al., 1999]	55	892	0-3	Optimal set analysis, MDS (SAS MDS), Cluster analysis (SAS MODECLUS), small-world coefficient	[Hilgetag et al., 2000a]
Macaque	Visual	[Felleman and Van Essen, 1991]	32	319		as above	as above
`Primate'		[Young, 1993]	73	834		as above	as above
Macaque	Somatosensory motor	[Felleman and Van Essen, 1991]	15	66		as above	as above
Primate	Cortical	19 (CoCoMac-Stry)	39	``3897 tests''	0-3	Optimal set analysis, MDS (SAS MDS), small-world coefficient	[Stephan et al., 2000a]

An early program for connectivity analysis is ``Connection'' [Nicolelis et al., 1990].

[Kaiser and Hilgetag, 2004] used data from CoCoMac together with spatial positions from Caret to get an approximation for the wiring length in cortex.

[Toro and Paus, 2006] described a co-activation analysis of functional activation recorded in the BrainMap^TM database and constructed a program for interactive visualization of the 6-D probabilistic map. A smaller co-activation study analysing data from 126 papers focused on connections from the basal ganglia [Postuma and Dagher, 2006].

A database with volumes for anatomo-functional connectivity might become available [Poupon et al., 2006].

Unclassified

Meta-analysis of ERP in schizophrenia [Bramon et al., 2001].

[Young and Scannell, 2000]