Bibliography on Path Analysis in Functional Neuroimaging

Finn Årup Nielsen
CIMBI at DTU Informatics and NRU Rigshospitalet
Lyngby and Copenhagen, Denmark

  $Revision: 1.58 $
  $Date: 2008/10/23 11:28:32 $


References for path analysis (structural equation modeling) and related connectivity analyses (``functional integration'') for functional neuroimaging are collected.

This bibliography is part of a larger collection of bibliographies that was begun in 2001 see The bibliography is written in LATEX and BIBTeX and should be available both as HTML, PDF and PostScript:

The bibliography is probably far from complete, but new references are added whenever the author finds new material and has the time to add them. You can email the author if corrections are required or you have found some reference that you fell ought to be included:

Funding is from Lundbeck Foundation, the European Union project MAPAWAMO, International Neuroimaging Consortium (INC) HBM project, THOR Center for Neuroinformatics, the Villum Kann Rasmussen Foundation and the Lundbeck Foundation.


Path analysis in functional neuroimaging is usually used to described the network between brain regions. In functional neuroimaging the term structural equation modeling (SEM) is more common. It has also been called covariance structural equation modelling (CSEM) (Taylor et al., 2000; McIntosh and Gonzalez-Lima, 1994b). The aspect revealed by path analysis in functional neuroimaging has been termed effective connectivity by Karl J. Friston, -- in contrast to functional connectivity which describes the correlation among brain regions (Friston, 2004,1994), cf. the related concept in spike train analysis, e.g., (Espinosa and Gerstein, 1988). Systems-level neural modeling has also been used to denote path analysis on the large scale brain regions (Horwitz et al., 1999, box on page 92). Analysis of the network dynamics might reveal the transient response plasticity (McIntosh, 2000).

General references

Bollen (1998a,1989,1998b) gives general introductions to structural equation modeling and path analysis, and Ferron and Hess (2007) make a concrete example with maximum likelihood estimation for the structural equation model. Sánchez et al. (2005) review structural equation modeling and give an example application in environment epidemiology.

The ``Computational approaches to network analysis in functional imaging'' special issue of the journal Human Brain Mapping, volume 2, numbers 1 and 2, 1994, contains 8 contributions, e.g., (Grafton et al., 1994; Horwitz, 1994; Alexander and Moeller, 1994; Gonzalez-Lima and McIntosh, 1994; Friston, 1994; McIntosh and Gonzalez-Lima, 1994b).

Introductions to path and related analyses in functional neuroimaging are given by Büchel and Friston (1997b,a).

Analysis types for brain networks

If a broad angle is taken to path analysis a number of different analysis types can be regarded as ``path analysis'', e.g., principal component analysis (PCA), ordinary structural equation modeling, see table 1. For independent component analysis (ICA) see the Bibliography on Independent Component Analysis in Functional Neuroimaging,

The functional connectivity can be assessed by cross-correlation analysis between voxels, also called ``seed voxel correlation analysis''. This can be done by selecting a few important voxels and examining their correlation with the rest of the brain. Worsley et al. (1998b,a,2005a) compute the correlation from all voxels to all voxel and a threshold for on the 6D correlation random field is applied (Cao and Worsley, 1998). Worsley et al. (2005b) compare cross-correlation and SVD.

Table 1: Analysis types for brain networks
Abbrv. Name Comments References
CC Cross-correlation analysis Cross-correlation between one region/voxel to another, also: ``correlational analysis'' or ``seed voxel correlation analysis'' (SVCA) Horwitz et al. (1984); Cao and Worsley (1998); Metter et al. (1984b)
  Regression with PCA Voxel-Voxel regression with principal component analysis Friston et al. (1993)
CCA Canonical correlation analysis   Bullmore et al. (1996); Friston et al. (1995a)
CRA Canonical ridge analysis Regularization of the canonical correlation analysis model so it can be perform on neuroimaging data Nielsen et al. (1998)
PLS Partial least squares   McIntosh and Lobaugh (2004)
PPI Psychophysiological interaction   Friston et al. (1997)
RAM Reticular action model   McArdle and McDonald (1984); Steele et al. (2004)
SEM Structural equation modeling Also called path analysis Penny et al. (2004)
RD Replicator dynamics   Lohmann and Bohn (2002)
SVD Singular value decomposition Similar to principal component analysis Sychra et al. (1994): On fMRI
SSM Scaled subprofile model   Moeller et al. (1987)
DCM Dynamic Causal Modeling   Friston et al. (2003); Friston (2003); Penny et al. (2004)

The validity of the inference made by dynamic causal modeling is explored by Lee et al. (2006).

Mathematical description of structural equations model

A general form of structural equations is (Bollen, 1989, eqs. 2.4, 2.8 and 2.9):

$\displaystyle {\bf N}$ $\displaystyle = {\bf N}{\bf B} + {\boldsymbol \Xi}{\boldsymbol \Gamma} + {\bf Z}$ (1)
$\displaystyle {\bf X}$ $\displaystyle = {\boldsymbol \Xi}{\boldsymbol \Lambda}_x + {\boldsymbol \Delta}$ (2)
$\displaystyle {\bf Y}$ $\displaystyle = {\bf N} {\boldsymbol \Lambda}_y + {\bf E}$ (3)

The first equation is for the latent variables, while the second and third equations (``the measurement model'') relate the latent variables to the observed variables, $ {\bf X}$ and $ {\bf Y}$. The diagonal of $ \bf B$ should be zero.

If there is no measurement noise, $ {\boldsymbol \Delta} = {\bf0}$ and $ \bf E = {\bf0}$, and there is a one-to-one relationship between the latent and observed variables, $ {\boldsymbol \Lambda}_x = {\bf I}$ and $ {\boldsymbol
\Lambda}_y = {\bf I}$, then the structural equations can be written as

$\displaystyle {\bf Y}$ $\displaystyle = {\bf Y}{\bf B} + {\bf X}{\boldsymbol \Gamma} + {\bf Z}$ (4)

In econometrics one finds the so-called ``structural form'' in ``simultaneous equation systems'' (Mardia et al., 1979, section 7.3, equation 7.3.1)

$\displaystyle {\bf Y}{\bf B} + {\bf X}{\boldsymbol \Gamma}$ $\displaystyle = {\bf U}$ (5)

This is equivalent to equation 4 with suitable redefinitions, e.g., $ {\bf B} \rightarrow {\bf I - B}$, $ {\boldsymbol \Gamma} \rightarrow -{\boldsymbol \Gamma}$ and $ {\bf Z}
\rightarrow {\bf U}$.

$ {\boldsymbol \Xi}$ and $ {\bf X}$ are called the exogenous or independent variables while $ {\bf N}$ and $ {\bf Y}$ are called the endogenous variables. If there are no exogenous variables, $ {\bf X} = {\bf0}$, then equation 4 simplifies to

$\displaystyle {\bf Y}$ $\displaystyle = {\bf Y}{\bf B} + {\bf Z}$ (6)

When regarding this equation as a network the columns of the $ {\bf Y}$ are the nodes of the network, while the $ {\bf B}$ matrix describes the links between the nodes.

Functional neuroimaging

Functional neuroimaging tends to use the relatively simple equation 6, though, e.g., with this renaming

$\displaystyle {\bf X}$ $\displaystyle = {\bf X}{\bf K} + {\bf U}$ (7)

Most often $ {\bf X}(N \times P)$ will contain data from brain scannings, e.g., as a $ {\bf X}($scans$ \times$   brain regions$ )$ matrix, while $ {\bf
K}(\text{brain regions} \times \text{brain regions})$ is the ``network'' one wants to estimate and this is typically regarded as sparse, i.e., many elements are zero.

An example taken from (Bullmore et al., 2000, page 295) with a transposed notation for a single scan

$\displaystyle {\bf x}_{(n)}^{\sf T}$ $\displaystyle = {\bf K}^{\sf T} {\bf x}_{(n)}^{\sf T} + {\bf u}_{(n)}^{\sf T}$ (8)
$\displaystyle \left[ \begin{array}{c} \text{VEC} \\ \text{PFC} \\ \text{SMA} \\ \text{IFG} \\ \text{IPL} \end{array} \right]$ $\displaystyle = \left[ \begin{array}{ccccc} 0 & 0 & 0 & 0 & \theta_1 \\ \theta_...
...array}{c} \psi_1 \\ \psi_2 \\ \psi_3 \\ \psi_4 \\ \psi_5 \\ \end{array} \right]$ (9)

In this kind of application of structural equation modeling the brain regions are the nodes of the network. Multisubject extension to this scheme make nodes also over subject so the matrix $ {\bf X}$ gets the size $ ($scans$ \times ($brain regions$ \times$   subjects$ )\,)$ (Mechelli et al., 2002).

Number of networks

The number of different networks (in terms of zero structure) for even small sized structure matrices is very large. For a two-by-two structure matrix, $ {\bf K}(2 \times 2)$, there are 3 non-zero networks and 4 if we allow for the zero network: There are two elements of the structure matrix that can either by zero or non-zero independent of each other. This gives all compinations: $ 2^2 = 4$. Generally, for a $ N$-by-$ N$ structure matrix, $ {\bf K}(N \times N)$, the form for the number of networks $ L(N)$ is:

$\displaystyle L(N) = 2^{N\times N -N} = 2 ^{N(N-1)}$ (10)

Some examples: $ L(3)=64$, $ L(4)=4\,096$, $ L(5)=1\,048\,576$, $ L(6)=1\,073\,741\,824$.

If one considers a growing network where one non-zero element in the structure matrix is added at a time, and when a non-zero element is added it is maintained in the network, then the number of possible networks shrinks dramatically. The number of possible non-zero elements to start with is $ M = N(N-1)$. When the first element is added and the network is incremented with a new non-zero element then there are $ M-1$ elements left to choose from. In the next step only $ M-2$ and so on until the all off-diagonal element of the structure matrix is non-zero. The form for the total number of networks that is traversed is

$\displaystyle L_{\text{grow}}(N)$ $\displaystyle = \sum_{m=0}^M (M-m) = M(M+1)- \frac{M(M+1)}{2} = \frac{M(M+1)}{2}$ (11)
  $\displaystyle = \frac{N(N-1)\left(N(N-1)+1\right)}{2} = \frac{N^4 - 2N^3 + 2N^2 - N}{2}$ (12)

Some examples: $ L_{\text{grow}}(2)=3$, $ L_{\text{grow}}(3)=21$, $ L_{\text{grow}}(4)=78$, $ L_{\text{grow}}(5)=210$, $ L_{\text{grow}}(6)=465$ and $ L_{\text{grow}}(7)=903$.

Other remarks

Panel analysis is a dynamic (longitudinal) form of path analysis, see, e.g., Easdon and McIntosh (2000) for an application in functional neuroimaging. Büchel et al. (1999) investigated the change in path coefficients over time in associative learning.

Structural equation modeling on BOLD fMRI may be confounded by 1/f-noise and/or cardiac and respiratory noise that can cause nuisance connectivity, see, e.g., a comment by Lund (2001).


Some of the few tools that enable path and related analyses are listed in in table 2. Haughton et al. (2006) describes three software packages for directed acyclic graphs: MIM, Tetrad and WinMine.

Table: Analysis types for brain networks.
Name Description References
gR Graphical modeling in R  
LiNGAM ``Discovery of non-gaussian linear causal models'' matlab programs Shimizu et al. (2006),
MIM   Edwards (1995,2000),,
Mplus Commercial windows program for structural equations modeling Muthén and Muthén (2006),
Mx Binary programs for Linux, Mac, Unix and Windows Neale et al. (2003),
SEM (Steele) Structural equation modeling implemented in Matlab by J. Douglas Steele
SPM2 DCM Dynamic Causal Modeling as implemented in SPM2 Friston et al. (2003); Friston (2003)
LISREL Commercial general structural equation modeling program


Some early examples: Høedt-Rasmussen and Skinhøj (1964) compared hemispheric cerebral blood flow based on measurements with Krypton-85. Paulson (1970); Paulson et al. (1970) compute the ``interchannel coefficient of variation'' (or ``interregional coefficient of variation'') between 16 channels measuring cerebral blood flow with Xenon-133.

Most studies analyze functional brain scans. However, there has also been a study that considered the covariance between gray matter density in different brain regions via voxel-based morphometry (Mechelli et al., 2005). .

Table 3: Path analyses in functional neuroimaging. VEC: ventral extrastriate cortex, PFC: prefrontal cortex, SMA: supplementary motor area, IFG: Interior frontal gyrus, IPL: Inferior parietal lobule.
Type Scan Variables/Regions Behavioral domain Remarks Reference
    -- -- Review McIntosh (1999)
          Horwitz et al. (1999)
    -- -- Review Taylor et al. (2000)
-- -- -- Overview Brief describtion in section 3.1 Horwitz et al. (2000a)
CC PET $ 2\times 23+ 3$ Resting Partial correlation, kappa statistics Horwitz et al. (1984)
CC PET $ 2\times ?$ Normals   Metter et al. (1984b)
CC PET $ 2\times ?$ Resting Normals, Alzheimer, Huntington, Parkinson Metter et al. (1984a)
SEM PET $ 2\times 7$: BA 17/18, 19d, 19v, 7, 37, 21, 46 Object and spatial vision   McIntosh et al. (1994); McIntosh and Gonzalez-Lima (1994b)
? ? ? ? ? Horwitz (1994)
SEM PET 5: SMA/cing, motor, putamen, GP, thalamus Movement Controls and Parkinson's disease patients Grafton et al. (1994)
CC fMRI   Vision   Kleinschmidt et al. (1994)
CC fMRI ? Resting state   Biswal et al. (1995)
SEM PET ? Face matching with Alzheimer patients   Horwitz et al. (1995)
CC/SEM PET 11 regions reading, visual word recognition   Nyberg et al. (1996)
SEM fMRI ? Visual motion Modulation modeled with interaction term Büchel and Friston (1997c)
? PET ? Semantic processing in schizophrenia   Jennings et al. (1998)
CC PET   Reading and dyslexia   Horwitz et al. (1998)
CC PET All voxels Vigilance task Correlation field threshold via random field theory Worsley et al. (1998b,a)
SEM fMRI 2 $ \times$ 7 Motor task Low-frequency BOLD fMRI Lowe (1999)
? PET ? Face encoding and recognition   Rajah et al. (1999)
? PET ? Episodic encoding and retrieval of words   Krause et al. (1999)
? fMRI ? Associative learning   Büchel et al. (1999)
CC fMRI A few voxels in hippocambus and thalamus Resting state   Stein et al. (2000)
? PET   Language processing   Petersson et al. (2000)
CC PET Wernicke, Broca, others Language production   Horwitz et al. (2000b)
CC fMRI Voxels in Rolandic cortex, ventrolateral thalamus, anterior putamen correlated with the rest of the brain Motor   Mopritz et al. (2000)
Panel/PLS PET Left cerebellum, left superior fronal cortex Eyeblink conditioning   Easdon and McIntosh (2000)
PLS PET   Short-term memory wrt. age   Della-Maggiore et al. (2000)
SEM fMRI 5: VEC, PFC, SMA, IFG, IPL Semantic decision, subvocal rehearsal Model order determination by P-values, AIC and Bollen's ``parsimonious fit index'' Bullmore et al. (2000)
SEM fMRI 7 Memory retrieval   Maguire et al. (2000)
SEM PET ? ?   Nezafat et al. (2001)
CC fMRI From/to anterior cerebellum Simple motor task Schizophrenia and control subjects Stephan et al. (2001)
? fMRI 9: EC, BA37, IPS, SMA, FEF, VPC, IFG, PSTS, AG Implicit language processing   ?
SEM PET 12 Working memory Split-half validation, AIC and RMSEA Glabus et al. (2003)
PLS ? 39   FDG, rats, Nair and Gon-za-lez-Lima (2003)
SEM fMRI 10 Visual attention In normals and Williams syndrome Meyer-Lindenberg et al. (2004)
RAM fMRI 5 Predictive error signal Depressive illness Steele et al. (2004)
CC fMRI Voxels Finger opposition Power law, clustering coefficient and path length computed (small world variables) Eguíluz et al. (2005)
SEM fMRI 10 Flanker task (attentional control)   Erickson et al. (2005)
SEM fMRI 4 Emotional face processing In normals and Williams syndrome Meyer-Lindenberg et al. (2005)
SEM fMRI 8 regions around amygdala Negative emotional faces Minimization with adaptive simulated annealing and with split half verification Stein et al. (2006)
CC fMRI 90 regions Resting state with pharmacological stimulation Wavelet correlation analysis in the frequency interval 0.06-0.11 and with metrics of network efficiency (small world Achard and Bullmore (2007)
SEM MRI   None Size of brain regions Colibazzi et al. (2008)

Resting state path analysis

Functional connectivity has been assessed with resting state BOLD fMRI (Biswal et al., 1995), e.g., Stein et al. (2000) pick a few seed voxels in the thalamus and the hippocambus and compute the correlation coefficient between these (each at a time) and the rest of the brain, thresholding at 0.5. The correlation is high for low frequencies ($ < 0.1$ Hz), and hypercapnia results in a substantial decrease in the correlation (Biswal et al., 1997). Lowe et al. (1998) report low-frequency resting state fluctuation with low sampling rate multislice. Xiong et al. (1999) pick the seed in the primary motor cortex.


Kim et al. (2007): MAR, SEM, GLM

Clark et al. (1984)

Koch et al. (2002): Comparison of functional and anatomical connectivity.

McIntosh and Gonzalez-Lima (1991): SEM on auditory system.

McIntosh and Gonzalez-Lima (1992): SEM on the visual system of the rat.

McIntosh and Gonzalez-Lima (1994a)

Anatomically based structural equation modeling (SEM) Rajah et al. (1999)

Friston et al. (1995b) ``regression''.

Cordes et al. (2001)

(Büchel and Friston (1998): ``variable parameter regression'' and Kalman filtering)

Cordes 2000, AJNR, 21:1636

"Structural equation" and PET

From PubMed: Nezafat et al. (2001), Della-Maggiore et al. (2000), Petersson et al. (2000), Taylor et al. (2000), McIntosh (1999), Rajah et al. (1999), Horwitz et al. (1999), McIntosh (1998), Jennings et al. (1998), Cabeza et al. (1997), McIntosh et al. (1994).

Other connectivity analyses

Brain connectivity may also be obtained from tractography of diffusion spectrum imaging (Hagmann et al., 2008).

Examples of networks

Object and spatial vision

The following functional networks are originally from McIntosh et al. (1994). The network descriptions are in the dot file format Koutsofios and North (1996) and figures 1 and 2 display the output from the program. Negative path coefficients are indicated by dotted lines.

digraph ObjectVision {
    "17/18" -> "19v" 
    "17/18" -> "19d" [style=dotted]
    "19v" -> "37" 
    "19v" -> "19d"
    "19d" -> "7" [style=dotted]
    "19d" -> "46" [style=dotted]
    "37" -> "21"
    "37" -> "7" [style=dotted]
    "7" -> "21"
    "7" -> "46" [style=dotted]
    "21" -> "46"
    "46" -> "19v" [style=dotted]

digraph SpatialVision {
    "17/18" -> "19v" 
    "17/18" -> "19d" 
    "19v" -> "37" 
    "19v" -> "19d"
    "19d" -> "7" 
    "19d" -> "46"
    "37" -> "21"
    "37" -> "7" [style=dotted]
    "7" -> "21" [style=dotted]
    "7" -> "46" 
    "21" -> "46" [style=dotted]
    "46" -> "19v"

Figure 1: Object vision functional network for the right hemisphere. Adapted from (Horwitz, 1994, figure 3) which is adapted from McIntosh et al. (1994).

Figure 2: Spatial vision functional network for the right hemisphere. Adapted from (Horwitz, 1994, figure 3) which is adapted from McIntosh et al. (1994).

McIntosh and Gonzalez-Lima (1994b) consider interhemispheric functional models for the same task.

Motor system

Motor system connectivity is examined by Grafton et al. (1994) who used a cortical-subcortical network proposed by Alexander et al. (1990); DeLong (1990). Some of the results from a LISREL estimation are displayed in figure 3 and the corresponding dot file is shown below.

digraph GraftonS1994Network {
        subgraph clusterNormalMovement {
                label="Normal subjects, Movement task";
                { rank = same;  
                        NM1 [label="SMA & Cingulate\n Motor Areas" ];
                        NM2 [label="Motor cortex"];
                NM3 [label="Putamen"]
                { rank = same;
                        NM4 [label="Globus pallidus"];
                        NM5 [label="Ventrolateral\n Thalamus"];
                NM1 -> NM2
                NM1 -> NM3 [style=dotted]
                NM1 -> NM5 
                NM2 -> NM3 [style=dotted]
                NM2 -> NM5 [style=dotted]
                NM3 -> NM4
                NM4 -> NM5
                NM5 -> NM1 
                NM5 -> NM2 [style=dotted]
        subgraph clusterParkinsonBefore {
                label="Parkinson patients, before pallidotomy";
                { rank = same;  
                        PB1 [label="SMA & Cingulate\n Motor Areas" ];
                        PB2 [label="Motor cortex"];
                PB3 [label="Putamen"]
                { rank = same;
                        PB4 [label="Globus pallidus"];
                        PB5 [label="Ventrolateral\n Thalamus"];
                PB1 -> PB2
                PB1 -> PB3 [style=dotted]
                PB1 -> PB5 
                PB2 -> PB3 
                PB2 -> PB5 
                PB3 -> PB4
                PB4 -> PB5
                PB5 -> PB1 
                PB5 -> PB2 

Figure 3: Movement network from Grafton et al. (1994).


A visual implicit language processing network:

digraph McKiernanK2001Development {
    IFG -> VPC
    SMA -> VPC
    FEF -> SMA 
    FEF -> VPC
    FEF -> IPC
    PSTS -> IFG
    PSTS -> FEF
    PSTS -> AG
    AG -> IFG [style=dotted]
    IPS -> IFG
    IPS -> VPC 
    IPS -> AG
    BA37 -> PSTS
    BA37 -> VPC [style=dotted]
    BA37 -> IPS
    EC -> BA37

Figure 4: Visual implicit language processing McKiernan et al. (2001).


Achard, S. and Bullmore, E. (2007).
Efficiency and cost of economical brain functional networks.
PLoS Computational Biology, 3(2):e17. DOI: 10.1371/journal.pcbi.0030017.
cited: Applications

Alexander, G. E., Crutcher, M. D., and DeLong, M. R. (1990).
Basal ganglia thalamo-cortical circuits: Parallel substrates for motor, oculomotor, ``prefrontal'' and ``limbic'' functions.
Prog. Brain Res., 85:119-146.
cited: Motor system

Alexander, G. E. and Moeller, J. R. (1994).
Application of the scaled subprofile model to functional imaging in neuropsychiatric disorders: A principal component approach to modeling brain function in disease.
Human Brain Mapping, 2(1 and 2):79-94. ISSN 1065-9471 [ ] .
cited: General references

Biswal, B., Hudetz, A. G., Yetkin, F. Z., Haughton, V. M., and Hyde, J. S. (1997).
Hypercapnia reversibly suppresses low-frequency fluctuations in the human motor cortex during rest using echo-planar MRI.
Journal of Cerebral Blood Flow and Metabolism, 17(3):301-308. PMID: 9119903.
cited: Resting state path analysis

Biswal, B., Yetkin, F. Z., Haughton, V. M., and Hyde, J. S. (1995).
Functional connectivity in the motor cortex of resting human brain using echo-planar MRI.
Magnetic Resonance in Medicine, 34(4):537-541. PMID: 8524021. ISSN 0740-3194 [ ] .
cited: Applications | Resting state path analysis

Bollen, K. A. (1989).
Structural Equations with Latent Variables.
Wiley Series in Probability and Mathematical Statistics. John Wiley & Sons, New York.
cited: General references | Mathematical description of structural

Bollen, K. A. (1998a).
Path analysis.
In Armitage, P. and Colton, T., editors, Encyclopedia of biostatistics, pages 3280-3284. John Wiley & Sons, West Sussex, England. ISBN 0471975761 [ | ] .
cited: General references

Bollen, K. A. (1998b).
Structural equation models.
In Armitage, P. and Colton, T., editors, Encyclopedia of biostatistics, pages 4363-4372. John Wiley & Sons, West Sussex, England. ISBN 0471975761 [ | ] .
cited: General references

Büchel, C., Coull, J. T., and Friston, K. J. (1999).
The predictive value of changes in effective connectivity for human learning.
Science, 283(5407):1538-1541. PMID: 10066177. ISSN 0036-8075 [ ] .
cited: Other remarks | Applications

Büchel, C. and Friston, K. J. (1997a).
Characterising functional integration.
In Frackowiak, R. S. J., Friston, K. J., Frith, C. D., Dolan, R. J., and Mazziotta, J. C., editors, Human Brain Function, chapter 7, pages 127-140. Academic Press, San Diego, California.
cited: General references

Büchel, C. and Friston, K. J. (1997b).
Effective connectivity and neuroimaging.
In SPMcourse, short course notes, chapter 6. Institute of Neurology, Wellcome Department of Cognitive Neurology, London, UK.
cited: General references

Büchel, C. and Friston, K. J. (1997c).
Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI.
Cerebral Cortex, 7(8):768-778. PMID: 9408041.
cited: Applications

Büchel, C. and Friston, K. J. (1998).
Dynamic changes in effective connectivity characterized by variable parameter regression and Kalman filtering.
Human Brain Mapping, 6(5-6):403-408. ISSN 1065-9471 [ ] .
cited: Unclassified

Bullmore, E., Horwitz, B., Honey, G., Brammer, M., Williams, S., and Sharma, T. (2000).
How good is good enough in path analysis of fMRI data?
NeuroImage, 11(4):289-301. PMID: 10725185. 2000.pdf. Path analysis with fMRI data on five brain regions with model order determination through P-values, Akaike's AIC and Bollen's parsimonous fit index.
cited: Functional neuroimaging | Applications

Bullmore, E. T., Rabe-Hesketh, S., Morris, R. G., Williams, S. C. R., and Gregory, L. (1996).
Functional magnetic resonance image analysis of a large-scale neurocognitive network.
NeuroImage, 4(1):16-33. PMID: 9345494. DOI: 10.1006/nimg.1996.0026. WOBIB: 113. ISSN 1053-8119 [ ] .
cited: Analysis types for brain

Cabeza, R., McIntosh, A. R., Tulving, E., Nyberg, L., and Grady, C. L. (1997).
Age-related differences in effective neural connectivity during encoding and recall.
NeuroReport, 8(16):3479-3483. PMID: 9427311.
cited: "Structural equation" and PET

Cao, J. and Worsley, K. J. (1998).
The geometry of correlation fields with an application to functional connectivity of the brain.
Annals of Applied Probability, 9:1021-1057.
cited: Analysis types for brain | Analysis types for brain

Clark, C. M., Kessler, R., Buchsbaum, M. S., Margolin, R. A., and Holcomb, H. H. (1984).
Correlational methods for determining regional coupling of cerebral glucose metabolism: a pilot study.
Biological Psychiatry, 19(5):663-678.
cited: Unclassified

Colibazzi, T., Zhu, H., Bansal, R., Schultz, R. T., Wang, Z., and Peterson, B. S. (2008).
Latent volumetric structure of the human brain: Exploratory factor analysis and structural equation modeling of gray matter volumes in healthy children and adults.
Human Brain Mapping, 29(11):1302-1312. DOI: 10.1002/hbm.20466.
cited: Applications

Cordes, D., Haughton, V., Arfanakis, K., Carew, J., and Turski, P. (2001).
Decomposition of cross correlation maps into frequency components to measure functional connectivity in resting state MRI data.
NeuroImage, 13(6):S99.
cited: Unclassified

Della-Maggiore, V., Sekuler, A. B., Grady, C. L., Bennett, P. J., Sekuler, R., and McIntosh, A. R. (2000).
Corticolimbic interactions associated with performance on a short-term memory task are modified by age.
Journal of Neuroscience, 20(22):8410-8416. PMID: 11069948.
cited: Applications | "Structural equation" and PET

DeLong, M. R. (1990).
Primate models of movement disorders of basal ganglia origin.
Trends in Neurosciences, 13(281-285).
cited: Motor system

Easdon, C. and McIntosh, A. R. (2000).
Measuring dynamic connectivity in eyeblink conditioning: A panel analysis.
NeuroImage, 11(5, part 2):S572.
Sixth Annual Meeting of the Organization For Human Brain Mapping.
cited: Other remarks | Applications

Edwards, D. (1995).
Introduction to Graphical Modelling.
Springer texts in statistics. Springer Verlag, Heidelberg. ISBN 0387944834 [ | ] .
cited: Tools

Edwards, D. (2000).
Introduction to graphical modelling.
Springer texts in statistics. Springer. ISBN 0387950540 [ | ] .
cited: Tools

Eguíluz, V. M., Chialvo, D. R., Cecchi, G. A., Baliki, M., and Apkarian, A. V. (2005).
Scale-free brain functional networks.
Physical Review Letters, 94(018102):1-4.
cited: Applications

Erickson, K. I., Ho, M.-H. R., Colcomb, S. J., and Kramer, A. F. (2005).
A structural equation modeling analysis of attentional control: an event-related fMRI study.
Cognitive Brain Research, 22:349-357. DOI: 10.1016/j.cogbrainres.2004.09.004.
cited: Applications

Espinosa, I. E. and Gerstein, G. L. (1988).
Cortical auditory neuron interactions during presentation of 3-tone sequences: effective connectivity.
Brain Research, 450(1-2):39-50. PMID: 3401720.
cited: Terminology

Ferron, J. M. and Hess, M. R. (2007).
Estimation in SEM: A concrete example.
Journal of Educational and Behavioral Statistics, 32(1):110-120. DOI: 10.3102/1076998606298025.
cited: General references

Friston, K. J. (1994).
Functional and effective connectivity in neuroimaging: A synthesis.
Human Brain Mapping, 2(1 and 2):56-78. DOI: 10.1002/hbm.460020107. ISSN 1065-9471 [ ] .
cited: Terminology | General references

Friston, K. J. (2003).
Dynamical causal models.
In Frackowiak, R. S. J., Friston, K. J., Dolan, R., and Price, C., editors, Human Brain Function, chapter 22. Academic Press, second edition. ISBN 0122648412 [ | ] .
cited: Analysis types for brain | Tools

Friston, K. J. (2004).
Functional integration in the brain.
In Frackowiak, R. S. J., Friston, K. J., Frith, C. D., Dolan, R. J., Price, C. J., Zeki, S., Ashburner, J., and Penny, W., editors, Human Brain Function, chapter 48, pages 971-997. Elsevier, Amsterdam, Holland, second edition.
cited: Terminology

Friston, K. J., Büchel, C., Fink, G. R., Morris, J., Rolls, E., and Dolan, R. J. (1997).
Psychophysiological and modulatory interactions in neuroimaging.
NeuroImage, 6(3):218-229. PMID: 9344826.
cited: Analysis types for brain

Friston, K. J., Frith, C. D., and Frackowiak, R. S. J. (1993).
Time-dependent changes in effective connectivity measured with PET.
Human Brain Mapping, 1(1):69-80. DOI: 10.1002/hbm.460010108. Presents a model with regression from voxels to voxels by first performing a principal component analysis on the neuroimaging data and then estimate the regression parameters from the principal components to the neuroimaging data.
cited: Analysis types for brain

Friston, K. J., Frith, C. D., Frackowiak, R. S. J., and Turner, R. (1995a).
Characterizing dynamic brain responses with fMRI: A multivariate approach.
NeuroImage, 2(2):166-172. DOI: 10.1006/nimg.1995.1019.
cited: Analysis types for brain

Friston, K. J., Harrison, L., and Penny, W. (2003).
Dynamic causal modelling.
NeuroImage, 19(4):1273-1302. PMID: 12948688.
cited: Analysis types for brain | Tools

Friston, K. J., Ungeleider, L. G., Jezzard, P., and Turner, R. (1995b).
Characterizing modulatory interactions between V1 and V2 in human cortex with fMRI.
Human Brain Mapping, 2:211-224.
cited: Unclassified

Glabus, M. F., Horwitz, B., Holt, J. L., Kohn, P. D., Gerton, B. K., Callicott, J. H., Meyer-Lindenberg, A., and Berman, K. F. (2003).
Interindividual differences in functional interactions among prefrontal, parietal and parahippocampal regions during working memory.
Cerebral Cortex, 13(12):1352-1361. PMID: 14615300. DOI: 10.1093/cercor/bhg082. Structural equations modeling of PET images with normal subjects performing a working memory task. The MX software was used with AIC and root-means quare error of approximation and a split-half validation. Talairach coordinates and path coefficients are reported.
cited: Applications

Gonzalez-Lima, F. and McIntosh, A. R. (1994).
Neural network interactions related to auditory learning analyzed with structural equation modeling.
Human Brain Mapping, 2(1 and 2):23-44. ISSN 1065-9471 [ ] .
cited: General references

Grafton, S. T., Sutton, J., Couldwell, W., Lew, M., and Waters, C. (1994).
Network analysis of motor system connectivity in parkinson's disease: Modulation of thalamocortical interactions after pallidotomy.
Human Brain Mapping, 2(1 and 2):45-55. ISSN 1065-9471 [ ] .
cited: General references | Applications | Motor system | Motor system

Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., and Wedeen, V. J. (2008).
Mapping the structural core of human cerebral cortex.
PLoS Biology, 6(7):e157. DOI: 10.1371/journal.pbio.0060159.
cited: Other connectivity analyses

Haughton, D., Kamis, A., and Scholten, P. (2006).
A review of three directed acyclic graphs software packages: MIM, Tetrad, and WinMine.
The American Statistician, 60(3):272-286. DOI: 10.1198/000313006X117972.
cited: Tools

Høedt-Rasmussen, K. and Skinhøj, E. (1964).
Transneural depression of the cerebral hemispheric metabolism in man.
Acta Neurologica Scandinavica, 40:41-46. 2-channel measurements of regional cerebral blood flow with Krypton-85. The measurements are compared between the hemispheres.
cited: Applications

Horwitz, B. (1994).
Data analysis paradigms for metabolic-flow data: through the skull: Advanced EEGs use MRIs to accurately measure combining neural modeling and functional neuroimaging.
Human Brain Mapping, 2(1 and 2):112-122. ISSN 1065-9471 [ ] .
cited: General references | Applications | Object and spatial vision | Object and spatial vision

Horwitz, B., Duara, R., and Rapoport, S. I. (1984).
Intercorrelations of glucose metabolic rates between brain regions: Application to healthy males in a state of reduced sensory input.
Journal of Cerebral Blood Flow and Metabolism, 4(4):484-499. ISSN 0271-678X [ ] .
cited: Analysis types for brain | Applications

Horwitz, B., Friston, K. J., and Taylor, J. G. (2000a).
Neural modeling and functional brain imaging: an overview.
Neural Networks, 13(8-9):829-846. PMID: 11156195.
cited: Applications

Horwitz, B., Jeffries, K. J., and Braun, A. R. (2000b).
Functional connectivity among language areas during speech production.
In Fox, P. T. and Lancaster, J. L., editors, Sixth International Conference on Functional Mapping of the Human Brain, NeuroImage, volume 11, page S284. Academic Press. ISSN 1053-8119 [ ] . Demonstrates varying cross-correlation among Wenicke's and Broca's during different language production tasks.
cited: Applications

Horwitz, B., McIntosh, A. R., Haxby, J. V., Furey, M., Salerno, J. A., Schapiro, M. B., Rapoport, S. I., and Grady, C. L. (1995).
Network analysis of PET-mapped visual pathways in Alzheimer type dementia.
NeuroReport, 6(17):2287-2292. PMID: 8747138.
cited: Applications

Horwitz, B., Rumsey, J. M., and Donohue, B. C. (1998).
Functional connectivity of the angular gyrus in normal reading and dyslexia.
Proceedings of the National Academy of Sciences, 95(15):8939-8944.
cited: Applications

Horwitz, B., Tagamets, M. A., and McIntosh, A. R. (1999).
Neural modeling, functional brain imaging, and cognition.
Trends in Cognitive Science, 3(3):91-98. PMID: 10322460.
cited: Terminology | Applications | "Structural equation" and PET

Jennings, J. M., McIntosh, A. R., Kapur, S., Zipursky, R. B., and Houle, S. (1998).
Functional network differences in schizophrenia: a rCBF study of semantic processing.
NeuroReport, 9(8):1697-1700. PMID: 9665585.
cited: Applications | "Structural equation" and PET

Kim, J., Zhu, W., Chang, L., Bentler, P. M., and Ernst, T. (2007).
Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data.
Human Brain Mapping, 28(2):85-93. DOI: 10.1002/hbm.20259.
cited: Unclassified

Kleinschmidt, A., Merboldt, K. D., Hanicke, W., Steinmetz, H., and Frahm, J. (1994).
Correlational imaging of thalamocortical coupling in the primary visual pathway of the human brain.
Journal of Cerebral Blood Flow and Metabolism, 14(6):954-957. PMID: 7929658.
cited: Applications

Koch, M. A., Norris, D. G., and Hund-Georgiadis, M. (2002).
An investigation of functional and anatomical connectivity using magnetic resonance imaging.
NeuroImage, 16(1):241-250. Assess anatomical connectivity by diffusion-weighted magnetic resonance imaging and functional connectivity by resting state BOLD fMRI (a la Biswal). The functional connectivity showed little correlation for white matter and high correlation among grey matter areas particularly corresponding areas collateral.
cited: Unclassified

Koutsofios, E. and North, S. C. (1996).
Drawing graphs with dot.
AT&T Bell Laboratories, Murray Hill, New Jersey.
cited: Object and spatial vision

Krause, B. J., Horwitz, B., Taylor, J. G., Schmidt, D., Mottaghy, F. M., Herzog, H., Halsband, U., and Muller-Gartner, H. (1999).
Network analysis in episodic encoding and retrieval of word-pair associates: a PET study.
European Journal of Neuroscience, 11(9):3293-3301. PMID: 10510193. This is a network analysis with no Talairach coordinates.
cited: Applications

Lee, L., Friston, K. J., and Horwitz, B. (2006).
Large-scale neural models and dynamic causal modelling.
NeuroImage, 30(4):1243-1254. PMID: 16387513.
cited: Analysis types for brain

Lohmann, G. and Bohn, S. (2002).
Using replicator dynamics for analyzing fMRI data of the human brain.
IEEE Transactions on Medical Imaging, 21(5):485-492.
cited: Analysis types for brain

Lowe, M. J. (1999).
Functional connectivity with continuous state fMRI assessed with structural equations.
NeuroImage, 9(6):S197.
cited: Applications

Lowe, M. J., Mock, B. J., and Sorenson, J. A. (1998).
Functional connectivity in single and multislice echoplanar imaging using resting-state fluctualations.
NeuroImage, 7(2):119-132. PMID: 9558644.
cited: Resting state path analysis

Lund, T. E. (2001).
fcMRI - mapping functional connectivity or correlating cardiac-induced noise?
Magnetic Resonance in Medicine, 46(3):628. DOI: 10.1002/mrm.1238.
cited: Other remarks

Maguire, E. A., Mummery, C. J., and Büchel, C. (2000).
Patterns of hippocampal-cortical interaction dissociate temporal lobe memory subsystems.
Hippocampus, 10:475-482. fMRI study.
cited: Applications

Mardia, K. V., Kent, J. T., and Bibby, J. M. (1979).
Multivariate Analysis.
Probability and Mathematical Statistics. Academic Press, London. ISBN 0124712525 [ | ] .
cited: Mathematical description of structural

McArdle, J. J. and McDonald, R. P. (1984).
Some algebraic properties of the reticular action model for moment structures.
British Journal of Mathematical and Statistical Psychology, 37:234-251. PMID: 6509005.
cited: Analysis types for brain

McIntosh, A. R. (1998).
Understanding neural interactions in learning and memory using functional neuroimaging.
Ann. New York Academy of Sci., 855:556-571. PMID: 9929651.
cited: "Structural equation" and PET

McIntosh, A. R. (1999).
Mapping cognition to the brain through neural interactions.
Memory, 7(5-6):523-548. PMID: 10659085.
cited: Applications | "Structural equation" and PET

McIntosh, A. R. (2000).
Towards a network theory of cognition.
Neural Network, 13:861-870.
cited: Terminology

McIntosh, A. R. and Gonzalez-Lima, F. (1991).
Structural modeling of functional neural pathways mapped with 2-deoxyglucose: Effects of acoustic startle habituation on the auditory system.
Brain Research, 547(2):295-302. PMID: 1884204.
cited: Unclassified

McIntosh, A. R. and Gonzalez-Lima, F. (1992).
Structural modeling of functional visual pathways mapped with 2-deoxyglucose: Effects of patterned light and footshock.
Brain Research, 578(1-2):75-86. PMID: 1511292. ISSN 0006-8993 [ ] .
cited: Unclassified

McIntosh, A. R. and Gonzalez-Lima, F. (1994a).
Network interactions among limbic cortices, basal forebrain, and cerebellum differentiate a tone conditioned as a Pavlovian excitor or inhibitor: fluorodeoxyglucose mapping and covariance structural modeling.
Journal of Neurophysiology, 72(4):1717-1733. PMID: 7823097.
cited: Unclassified

McIntosh, A. R. and Gonzalez-Lima, F. (1994b).
Structural equation modeling and its application to network analysis in functional brain imaging.
Human Brain Mapping, 2(1 and 2):2-22. ISSN 1065-9471 [ ] . General introduction to structural equation modeling in functional brain imaging. Examples are given with object and spatial vision in a human PET study and a rat brain study for the geniculocortical circuit.
cited: Terminology | General references | Applications | Object and spatial vision

McIntosh, A. R., Grady, C. L., Ungerleider, L. G., Haxby, J. V., Rapoport, S. I., and Horwitz, B. (1994).
Network analysis of cortical visual pathways mapped with PET.
Jornal of Neuroscience, 14(2):655-666. PMID: 8301356.
cited: Applications | "Structural equation" and PET | Object and spatial vision | Object and spatial vision | Object and spatial vision

McIntosh, A. R. and Lobaugh, N. L. (2004).
Partial least squares analysis of neuroimaging data: applications and advances.
NeuroImage, 23(1):S250-S263. DOI: 10.1016/j.neuroimage.2004.07.020.
cited: Analysis types for brain

McKiernan, K. A., Conant, L. L., Chen, A., and Binder, J. R. (2001).
Development and cross-validation of a model of linguistic processing using neural network and path analyses with FMRI data.
NeuroImage, 13(6):S200. Shortly describes a connection analysis on fMRI data with ``path analysis'' and an associative neural network in connection with visual implicit language processing.
cited: Cognition

Mechelli, A., Friston, K. J., Frackowiak, R. S. J., and Price, C. J. (2005).
Structural covariance in the human cortex.
The Journal of Neuroscience, 25(36):8303-8310. DOI: 10.1523/JNEUROSCI.0357-05.2005.
cited: Applications

Mechelli, A., Penny, W. D., Price, C. J., Gitelman, D. R., and Friston, K. J. (2002).
Effective connectivity and intersubject variability: using a multisubject network to test differences and commonalities.
NeuroImage, 17(3):1459-1469. PMID: 12414285.
cited: Functional neuroimaging

Metter, E. J., Riege, W. H., Kameyama, M., Kuhl, D. E., and Phelps, M. E. (1984a).
Cerebral metabolic relationships for selected brain regions in Alzheimer's, Huntington's, and Parkinson's diseases.
Journal of Cerebral Blood Flow and Metabolism, 4(4):500-506.
cited: Applications

Metter, E. J., Riege, W. H., Kuhl, D. E., and Phelps, M. E. (1984b).
Cerebral metabolic relationships for selected brain regions in healthy adults.
Journal of Cerebral Blood Flow and Metabolism, 4(1):1-7. ISSN 0271-678X [ ] .
cited: Analysis types for brain | Applications

Meyer-Lindenberg, A., Hariri, A. R., Munoz, K. E., Mervis, C. B., Mattay, V. S., colleen A. Morris, and Berman, K. F. (2005).
Neural correlates of genetically abnormal social cognition in Williams syndrome.
Nature Neuroscience, 8(8):991-993. DOI: 10.1038/nn1494.
cited: Applications

Meyer-Lindenberg, A., Kohn, P., Mervis, C. B., Kippenhan, J. S., Olsen, R. K., Morris, C. A., and Berman, K. F. (2004).
Nerual basis of genetically determined visuospatial construction deficit in Williams syndrome.
Neuron, 43:623-631. fMRI, voxel-based morphometry and structural equation modeling neuroimaging study.
cited: Applications

Moeller, J. R., Strother, S. C., Sidtis, J. J., and Rottenberg, D. A. (1987).
Scaled subprofile model: A statistical approach to the analysis of functional patterns in positron emission tomographic data.
Journal of Cerebral Blood Flow and Metabolism, 7:649-658.
cited: Analysis types for brain

Mopritz, C., Arfanakis, K., Cordes, D., Haughton, V., and Meyerand, M. E. (2000).
Connectivity analysis of fMRI activation datasets.
NeuroImage, 11(5):S575. Cross-correlation with time-series from seed voxels located in rolandic cortex, ventrolateral thalamus and anterior putamen.
cited: Applications

Muthén, L. K. and Muthén, B. O. (2006).
Mplus. Statistical Analysis With Latent Variables. User's Guide.
Muthén and Muthén, Los Angeles, California.
Version 4.1.
cited: Tools

Nair, H. P. and Gonzalez-Lima, F. (2003).
Large-scale networks in learning analyzed with partial least squares.
In Sommer, F. T. and Wichert, A., editors, Exploratory analysis and data modeling in functional neuroimaging, pages 273-294. MIT Press, Cambridge, MA, USA. ISBN 0262194813 [ | ] .
cited: Applications

Neale, M. C., Boker, S. M., Xie, G., and Maes, H. H. (2003).
Mx: Statistical Modeling.
Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, sixth edition.
cited: Tools

Nezafat, R., Shadmehr, R., and Holcomb, H. H. (2001).
Long-term adaptation to dynamics of reaching movements: a PET study.
Experimental Brain Research, 140(1):66-76. PMID: 11500799.
cited: Applications | "Structural equation" and PET

Nielsen, F. Å., Hansen, L. K., and Strother, S. C. (1998).
Canonical ridge analysis with ridge parameter optimization.
NeuroImage, 7(4, part 2):S758.
4th International Conference on Functional Mapping of the Human Brain. June 7-12, 1998, Montreal, Quebec, Canada.
cited: Analysis types for brain

Nyberg, L., McIntosh, A. R., Cabeza, R., Nilsson, L.-G., Houle, S., Habib, R., and Tulving, E. (1996).
Network analysis of positron emission tomography regional cerebral blood flow data: ensemble inhibition during episodic memory retrieval.
Journal of Neuroscience, 16(11):3753-3759. PMID: 8642418. ISSN 0270-6474 [ ] .
cited: Applications

Paulson, O. B. (1970).
Regional cerebral blood flow in apoplexy due to occlusion of the middle cerebral artery.
Neurology, 20(1):63-77. PMID: 5460771.
cited: Applications

Paulson, O. B., Lassen, N. A., and Skinhøj, E. (1970).
Regional cerebral blood flow in apoplexy without arterial occlusion.
Neurology, 20(1):125-138. PMID: 5460771.
cited: Applications

Penny, W. D., Stephan, K. E., Mechelli, A., and Friston, K. J. (2004).
Modelling functional integration: a comparison of structural equation and dynamic causal models.
NeuroImage, 23(Supplement 1):S264-S274. PMID: 15501096.
cited: Analysis types for brain | Analysis types for brain

Petersson, K. M., Reis, A., Askelof, S., Castro-Caldas, A., and Ingvar, M. (2000).
Language processing modulated by literacy: a network analysis of verbal repetition in literate and illiterate subjects.
Journal of Cognitive Neuroscience, 12(3):364-382. PMID: 10931764.
cited: Applications | "Structural equation" and PET

Rajah, M. N., McIntosh, A. R., and Grady, C. L. (1999).
Frontotemporal interactions in face encoding and recognition.
Brain Research. Cognitive Brain Research, 8(3):259-269. PMID: 10556604.
cited: Applications | Unclassified | "Structural equation" and PET

Sánchez, B. N., Budtz-Jørgensen, E., Ryan, L. M., and Hu, H. (2005).
Structural equation models: A review with applications to environmental epidemiology.
Journal of the American Statistical Association, 100(472):1443-1455. DOI: 10.1198/016214505000001005.
cited: General references

Shimizu, S., Hoyer, P. O., Hyvärinen, A., and Kerminen, A. (2006).
A linear nongaussian acyclic model for causal discovery.
Journal of Machine learning Research, 7:2003-2030.
cited: Tools

Steele, J. D., Meyer, M., and Ebmeier, K. P. (2004).
Neural predictive error signal correlates with depressive illness severity in a game paradigm.
NeuroImage, 23(1):269-280. PMID: 15325374. DOI: 10.1016/j.neuroimage.2004.04.023.
cited: Analysis types for brain | Applications

Stein, J. L., Wiedholz, L. M., Weinberg, D., Mattay, V. S., and Meyer-Lindenberg, A. (2006).
Automatic construction and stringent validation of path models from human fMRI data.
In Neuroscience. Society for Neuroscience.
#492.10/PP85. A Bullmore-like construction of network models (structural equation models) with minimization by adaptive simulated annealing and split half validation. The title on the poster was ``A Validated Network of Effect Amygdala Connectivity During Perceptual Processing of Negative Emotional Stimuli''.
cited: Applications

Stein, T., Moritz, C., Quigley, M., Cordes, D., Haughton, V., and Meyerand, E. (2000).
Functional connectivity in the thalamus and hippocambus studied with functional MR imaging.
AJNR American Journal of Neuroradiology, 21:1397-1401.
cited: Applications | Resting state path analysis

Stephan, K. E., Magnotta, V. A., White, T., Arndt, S., Flaum, M., O'Leary, D. S., and Andreasen, N. C. (2001).
Effects of olanzapine on cerebellar functional connectivity in schizophrenia measured by fMRI during a simple motor task.
Psychological Medicine, 31(6):1065-1078. PMID: 11513374.
cited: Applications

Sychra, J. J., Bandettini, P. A., Bhattacharya, N., and Lin, Q. (1994).
Synthetic images by subspace transform. I. principal components images and related filters.
Medical Physics, 21(2):193-201. PMID: 8177152.
cited: Analysis types for brain

Taylor, J. G., Krause, B., Shah, N. J., Horwitz, B., and Mueller-Gaertner, H. W. (2000).
On the relation between brain imagings and brain neural networks.
Human Brain Mapping, 9(3):165-182. PMID: 10739367.
cited: Terminology | Applications | "Structural equation" and PET

Worsley, K. J., Cao, J., Paus, T., Petrides, M., and Evans, A. C. (1998a).
Applications of random field theory to functional connectivity.
Human Brain Mapping, 6:364-367.
cited: Analysis types for brain | Applications

Worsley, K. J., Cao, J., Paus, T., Petrides, M., and Evans, A. C. (1998b).
Detecting functional connectivity by threshlding correlation random fields.
NeuroImage, 7:S36. Short description of determination of functional connectivity by examining the cross-correlation between voxels and determing a threshold setting from random field theory. The method is exemplified on a positron emission tomography data set.
cited: Analysis types for brain | Applications

Worsley, K. J., Charil, A., Lerch, J., and Evans, A. C. (2005a).
Connectivity of anatomical and functional MRI data.
In International Joint Conference on Neural Networks, July 31-August 4, 2005, Montreal, Quebec, Canada.
cited: Analysis types for brain

Worsley, K. J., Chen, J.-I., Lerch, J., and Evans, A. C. (2005b).
Comparing connectivity via thresholding correlations and SVD.
Philosophical Transactions of the Royal Society, 360:913-920.
cited: Analysis types for brain

Xiong, J., Parsons, L. M., Gao, J.-H., and Fox, P. T. (1999).
Interregional connectivity to primary motor cortex revealed using MRI resting state images.
Human Brain Mapping, 8(2-3):151-156.
cited: Resting state path analysis


Achard and Bullmore (2007)
Alexander and Moeller (1994)
Alexander et al. (1990)
Büchel and Friston (1997a)
Büchel and Friston (1997b)
Büchel and Friston (1997c)
Büchel and Friston (1998)
Büchel et al. (1999)
Biswal et al. (1995)
Biswal et al. (1997)
Bollen (1989)
Bollen (1998a)
Bollen (1998b)
Bullmore et al. (1996)
Bullmore et al. (2000)
Cabeza et al. (1997)
Cao and Worsley (1998)
Analysis types for brain
Clark et al. (1984)
Colibazzi et al. (2008)
Cordes et al. (2001)
Analysis types for brain
Analysis types for brain | Tools
Della-Maggiore et al. (2000)
DeLong (1990)
Easdon and McIntosh (2000)
Edwards (1995)
Edwards (2000)
effective connectivity
Eguíluz et al. (2005)
emotional faces
Erickson et al. (2005)
Espinosa and Gerstein (1988)
Ferron and Hess (2007)
finger opposition
Friston et al. (1993)
Friston et al. (1995a)
Friston et al. (1995b)
Friston et al. (1997)
Friston et al. (2003)
Friston (1994)
Friston (2003)
Friston (2004)
functional connectivity
Glabus et al. (2003)
Gonzalez-Lima and McIntosh (1994)
Grafton et al. (1994)
Høedt-Rasmussen and Skinhøj (1964)
Hagmann et al. (2008)
Haughton et al. (2006)
Horwitz et al. (1984)
Horwitz et al. (1995)
Horwitz et al. (1998)
Horwitz et al. (1999)
Horwitz et al. (2000a)
Horwitz et al. (2000b)
Horwitz (1994)
Analysis types for brain
Jennings et al. (1998)
Kim et al. (2007)
Kleinschmidt et al. (1994)
Koch et al. (2002)
Koutsofios and North (1996)
Krause et al. (1999)
latent variable
Mathematical description of structural
Lee et al. (2006)
Lohmann and Bohn (2002)
Lowe et al. (1998)
Lowe (1999)
Lund (2001)
Maguire et al. (2000)
Mardia et al. (1979)
McArdle and McDonald (1984)
McIntosh and Gonzalez-Lima (1991)
McIntosh and Gonzalez-Lima (1992)
McIntosh and Gonzalez-Lima (1994a)
McIntosh and Gonzalez-Lima (1994b)
McIntosh and Lobaugh (2004)
McIntosh et al. (1994)
McIntosh (1998)
McIntosh (1999)
McIntosh (2000)
McKiernan et al. (2001)
measurement model
Mathematical description of structural
Mechelli et al. (2002)
Mechelli et al. (2005)
Metter et al. (1984a)
Metter et al. (1984b)
Meyer-Lindenberg et al. (2004)
Meyer-Lindenberg et al. (2005)
Moeller et al. (1987)
Mopritz et al. (2000)
Muthén and Muthén (2006)
Nair and Gon-za-lez-Lima (2003)
Neale et al. (2003)
Mathematical description of structural
Nezafat et al. (2001)
Nielsen et al. (1998)
Mathematical description of structural
Nyberg et al. (1996)
Paulson et al. (1970)
Paulson (1970)
Analysis types for brain
Penny et al. (2004)
no title
Petersson et al. (2000)
Analysis types for brain
Analysis types for brain
principal component analysis
Analysis types for brain
Rajah et al. (1999)
Analysis types for brain | Applications
Analysis types for brain
replicator dynamics
Analysis types for brain
Sánchez et al. (2005)
seed voxel correlation analysis
Analysis types for brain
Analysis types for brain | Tools
Shimizu et al. (2006)
simulated annealing
simultaneous equation systems
Mathematical description of structural
small world
Applications | Applications
Functional neuroimaging
Analysis types for brain
Steele et al. (2004)
Stein et al. (2000)
Stein et al. (2006)
Stephan et al. (2001)
structural form
Mathematical description of structural
Analysis types for brain
Sychra et al. (1994)
Taylor et al. (2000)
no title | Tools | Tools
transient response plasticity
voxel-based morphometry
Worsley et al. (1998a)
Worsley et al. (1998b)
Worsley et al. (2005a)
Worsley et al. (2005b)
Xiong et al. (1999)

Finn Årup Nielsen 2010-04-23