# Exploring the Anatomical Basis of Effective Connectivity Models with DTI-Based Fiber  Tractography

Authors: Hubert M. J. Fonteijn; David G. Norris; Frans A. J. Verstraten
Journal: International Journal of Biomedical Imaging
 (2008)

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## Abstract

Diffusion tensor imaging (DTI) is considered to be a promising tool for revealing the anatomical basis of functional networks. In this study, we investigate the potential of DTI to provide the anatomical basis of paths that are used in studies of effective connectivity, using structural equation modeling. We have taken regions of interest from eight previously published studies, and examined the connectivity as defined by DTI-based fiber tractography between these regions. The resulting fiber tracts were then compared with the paths proposed in the original studies. For a substantial number of connections, we found fiber tracts that corresponded to the proposed paths. More importantly, we have also identified a number of cases in which tractography suggested direct connections which were not included in the original analyses. We therefore conclude that DTI-based fiber tractography can be a valuable tool to study the anatomical basis of functional networks.


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## Body

## 1. Introduction

In functional neuroimaging,  and particularly in PET and fMRI,  study design and analysis have been dominated by the concept of  functionalsegregation, which emphasizes the specialization of a brain structure for a
specific part of a cognitive function. This has resulted in a large number of
studies in which differences in cognitive states are linked to the differential
activation of separate brain areas [1]. The concept of functional integration, on the other hand, has long been recognized as an equally important principle of brain organization. Functional integration
refers to the interaction between brain areas and has been studied with two
categories of analyses: functional connectivity analyses and effective
connectivity analyses. In functional connectivity analyses [2–4], the covariance structure of a
measure of brain activity is studied, from which differences in cognitive
states are linked to differences in correlations between regions. These
analysis methods are thus limited in their capacity to make inferences about
the directionality of these correlations, which makes it difficult to address, for
instance, the functional hierarchy of the brain structures under investigation.
In effective connectivity analyses, on the other hand, models are defined a priori, comprising the brain
structures of interest and assumptions about the afferent/efferent connections
between them [5, 6]. These models are then fitted to the
activity of these brain areas to obtain the strength of these connections,
which enables inferences on changes in connection strengths in relationship to
cognitive states.Effective
connectivity has been defined as the influence one neural system or region
exerts over another [7]. There are two ways by which this
influence can be mediated: via a direct path between two regions, or via an
indirect path in which a third region is involved. The analysis methods that
have been applied to functional neuroimaging data, structural equation modeling
(SEM) and dynamic causal modeling (DCM), differentiate between these two
possibilities, provided that the underlying model (regions and their paths) is
completely specified. If two regions are connected via a third region and
this third region has furthermore no other influence on the rest of the
network, one can choose to incorporate only an indirect path between the two
regions and to refrain from explicitly modeling the third region and its direct
paths. This is usually done to keep the model computationally tractable. We are
working here, however, on the premise that most of the specified paths should
reflect veridical direct paths, because we believe that too many “indirect”
paths, involving these “third party” regions, which are not explicitly taken
into account in the model, will seriously decrease the biological validity of
the model.SEM has
also extensively been applied in the social sciences, where these paths
represent abstract causal connections between variables. When this method is
applied to functional neuroimaging however, these paths should ultimately
correspond to the white matter connectivity of the brain regions under investigation.
This hence yields an extra source of model validation. Furthermore, McIntosh
and Gonzalez-Lima [8] have studied the effect of
erroneous model specification on the estimation of the path coefficients and
have found that it could seriously impinge on the estimation of these
coefficients. Therefore, it would be highly valuable to incorporate all
available knowledge regarding anatomical connections into effective
connectivity models. It should be noted that we do not imply that each existing
anatomical connection should result in an effective connection in each and every
cognitive task. However, it is the role of the model estimation to indicate
which anatomical connections have become effective for which task manipulation.
Therefore, leaving an anatomical connection, which is known to exist, out of a
model specification can only be justified when one has strong beliefs about the
functioning of this path in the present context, possibly combined with a
requirement for a decrease in model complexity.Unfortunately,
our knowledge about human anatomical connectivity is relatively sparse, because
a number of techniques that are used in other species (active tracers) are
highly invasive. Therefore, only two classical anatomical methods can be used:dissection
studies, which only provide information on a relatively coarse scale, and passive
tracer studies, which are not very well suited to investigate long-range
connectivity. Most effective connectivity studies thus can only validate their
connections with information from nonhuman primates, which in turn raises
problems with the homology of brain structures between different species.In the
last decade, a technique known as diffusion tensor imaging (DTI) [9, 10] has emerged as a good candidate to
resolve this situation. In DTI, the sensitivity of the (diffusion-weighted) MR signal
to the self-diffusion of water on a microscopic scale is employed to
characterize the anisotropic structure of white matter in vivo. It is assumed
that the direction in which diffusion is largest is collinear to the direction
of the axonal bundle in the voxel, because diffusion is assumed to be hindered
in directions perpendicular to this direction. With this information, fiber
tracking [11, 12] can be performed in which the main diffusion
directions of voxels are followed throughout white matter. This method already
has provided useful insights in, for instance, the anatomy of the thalamus and
the striatum [13, 14].So far,
researchers have acknowledged a number of limitations to this technique. First,
DTI provides no information about the afferent or efferent character of the axons,
because the diffusion of water does not differentiate between these two
situations. Second, the tensor model can only provide one main direction per
voxel, which considerably increases the likelihood of erroneous tracking
results through a region of crossing fibers. Finally, in cerebral gray matter
there is generally no dominant fiber direction, making it difficult to track
fibers to their cortical origin.The aim
of the current study is to investigate to what extent DTI-based tractography
can provide support for the anatomical basis of the networks, proposed in
effective connectivity studies. Furthermore, we have investigated whether
DTI-based tractography is able to reveal any connections that go beyond the
ones proposed in the original analysis. To address these questions, we have
chosen eight effective connectivity studies all using structural equation modeling.
SEM was introduced at an early stage of the development of PET and fMRI, with
the consequence that a body of SEM studies is to be found in the literature. DCM,
on the other hand, has only relatively recently been introduced, but is gaining
a rapid popularity.
It should be noted that any conclusions we are able to draw in this study
within the context of SEM models can be readily generalized to DCM studies as
there is no difference in the role of the underlying anatomical model in both
frameworks.We have
chosen networks spanning a number of different cognitive domains, including
learning [15, 16],
cognitive control [17, 18], working memory [19], visual and auditory perception [20], major depression [21], and the thalamocortical network
involved in general-anaesthetic-induced unconsciousness [22]. We performed a standard DTI
experiment on 6 subjects and used the coordinates of the network nodes as seed
regions for a fiber tracking analysis. In this analysis, we have established
whether DTI-based fiber tractography provides evidence about the direct nature
of every possible connection, whether or not it was proposed in the original
studies.


## 2. Materials and Methods

### 2.1. Subjects

We
studied 6 healthy subjects (2 females, age range 25–32 years) after
informed consent was given according to institutional guidelines of the local
ethics committee (CMO protocol region Arnhem-Nijmegen, The Netherlands).


### 2.2. Imaging

DTI was
performed using a twice refocused pulsed gradient spin echo EPI sequence [23] at 1.5 T(Sonata system, Siemens, Erlangen, Germany)with a standard head coil. Axial slices were
obtained using the following imaging parameters: repetition time = 9900 milliseconds,
echo time = 88 milliseconds, flip angle = 90°, 128 × 128 matrix, 320 mm × 320 mm field of view, and slice thickness = 2.5 mm with no gap (2.5 × 2.5 × 2.5 mm
isotropic voxels). Diffusion weighting was obtained along sixty noncollinear
directions [24] using a b-value of
700 s/mm2.
Five reference images with no diffusion weighting were also obtained. This
resulted in a scanning time of approximately 10 minutes per subject.


### 2.3. Analyses

For each
subject the five reference images with no diffusion weighting were averaged and
normalized to the MNI T2 template in SPM2 (Statistical Parametric Mapping,http://www.fil.ion.ucl.ac.uk/spm). The matrix of normalization parameters was
inverted to obtain the transformation matrix from standard space to world
space. We have done this to avoid the extensive resampling and reorientation of
the data that is involved in the normalization of DTI data [25, 26], because we hypothesize that this
would lead to a degradation of the finer details in the fiber tracts.Only linear terms were used in the
normalization to ensure that the transformation matrices could be inverted. No
motion correction was applied. Diffusion tensors and fractional anisotropy (FA)
maps were calculated using the diffusion toolbox [27] in SPM2. The FA maps were used for
displaying the anatomical location of the ROI coordinates. These anatomical
locations were then used as seed regions for fiber tracking. Fiber tracking was
performed in the DTI-Studio package [28] using the FACT algorithm [29]. Tracking was terminated when the
angle of two consecutive eigenvectors was larger than 85°, or when a voxel was
reached with an FA value smaller than 0.20.In most of the original studies [15–19, 22], the ROIs that were used for
effective connectivity analysis were all spheres of 8 mm radius. However, in
two studies [20, 21] only the peak voxels of a partial
least-squares analysis [30] were used. Because these voxels
represented larger clusters of voxels, we have used ROIs with roughly the same
size (spheres of 8 mm radius) as a starting point for all networks. We have
chosen not to transform the whole original ROI into subject space, as this
might lead to extensive seeding of the white matter adjacent to the seed
coordinate, and thus to many false positives. Instead, we have drawn ROIs on
the individual subject’s FA maps, taking care that the borders of the ROI were
at the border of gray and white matters (as visible on the FA map) and that the
ROI would approximately be of the same size as the original ROI. All
possible combinations of regions were tracked, including the connections that
were not proposed in the original effective connectivity studies.


## 2.1. Subjects

We
studied 6 healthy subjects (2 females, age range 25–32 years) after
informed consent was given according to institutional guidelines of the local
ethics committee (CMO protocol region Arnhem-Nijmegen, The Netherlands).


## 2.2. Imaging

DTI was
performed using a twice refocused pulsed gradient spin echo EPI sequence [23] at 1.5 T(Sonata system, Siemens, Erlangen, Germany)with a standard head coil. Axial slices were
obtained using the following imaging parameters: repetition time = 9900 milliseconds,
echo time = 88 milliseconds, flip angle = 90°, 128 × 128 matrix, 320 mm × 320 mm field of view, and slice thickness = 2.5 mm with no gap (2.5 × 2.5 × 2.5 mm
isotropic voxels). Diffusion weighting was obtained along sixty noncollinear
directions [24] using a b-value of
700 s/mm2.
Five reference images with no diffusion weighting were also obtained. This
resulted in a scanning time of approximately 10 minutes per subject.


## 2.3. Analyses

For each
subject the five reference images with no diffusion weighting were averaged and
normalized to the MNI T2 template in SPM2 (Statistical Parametric Mapping,http://www.fil.ion.ucl.ac.uk/spm). The matrix of normalization parameters was
inverted to obtain the transformation matrix from standard space to world
space. We have done this to avoid the extensive resampling and reorientation of
the data that is involved in the normalization of DTI data [25, 26], because we hypothesize that this
would lead to a degradation of the finer details in the fiber tracts.Only linear terms were used in the
normalization to ensure that the transformation matrices could be inverted. No
motion correction was applied. Diffusion tensors and fractional anisotropy (FA)
maps were calculated using the diffusion toolbox [27] in SPM2. The FA maps were used for
displaying the anatomical location of the ROI coordinates. These anatomical
locations were then used as seed regions for fiber tracking. Fiber tracking was
performed in the DTI-Studio package [28] using the FACT algorithm [29]. Tracking was terminated when the
angle of two consecutive eigenvectors was larger than 85°, or when a voxel was
reached with an FA value smaller than 0.20.In most of the original studies [15–19, 22], the ROIs that were used for
effective connectivity analysis were all spheres of 8 mm radius. However, in
two studies [20, 21] only the peak voxels of a partial
least-squares analysis [30] were used. Because these voxels
represented larger clusters of voxels, we have used ROIs with roughly the same
size (spheres of 8 mm radius) as a starting point for all networks. We have
chosen not to transform the whole original ROI into subject space, as this
might lead to extensive seeding of the white matter adjacent to the seed
coordinate, and thus to many false positives. Instead, we have drawn ROIs on
the individual subject’s FA maps, taking care that the borders of the ROI were
at the border of gray and white matters (as visible on the FA map) and that the
ROI would approximately be of the same size as the original ROI. All
possible combinations of regions were tracked, including the connections that
were not proposed in the original effective connectivity studies.


## 3. Results

The results for all networks under
investigation are visualized in Figures1–8, in which the
thickness of the connecting lines indicates the number of subjects in which a
particular connection was found. In the appendix, we have also listed these
results in Table 1. Our results support the proposed paths to a large extent.
The most striking class of paths which are not supported by our findings contains
frontal interhemispheric paths. This can however convincingly be explained by
methodological shortcomings in regions with crossing fibers in the frontal
parts of the brain. We will discuss these issues further in Section 4
. In half of the
studies, we have also found connections indicating paths that were not included
in the original studies. In the following, we will describe these findings for
each study separately.Table 1
Tractography results for all the proposed networks. The
number indicates the number of subjects in which this path was
found. Results in black indicate proposed paths, while results in
red indicate paths that were not proposed in the original
networks.Figure 1
The proposed network of Büchel et al. is shown on the left. The connections found in our analysis are shown on the right. The legend indicates the meaning of the color scale and the thickness of the lines.
This legend is valid for all the following figures.Figure 2
The network and results for Fletcher et
al.Figure 3
The network and results for Koechlin et
al.Figure 4
The network and results for Kondo et al.Figure 5
The network and results for McIntosh et al.Figure 6
The network and results for Rowe et al.Figure 7
The network and results for Seminowicz et al. BA = Brodmann area.Figure 8
The network and
results for White et al.The nomenclature of the original
studies is maintained throughout this whole article. This not only leads to the
situation that this nomenclature is inconsistent between studies, but may also
give the incorrect impression that similar connections are under investigation
in different networks. It is therefore important to note that the regions of
interest normally are spheres of approximately 8 mm in diameter,
and thus two regions with
the label “prefrontal cortex” can be quite widely separated.Büchel et al.
The network of this study can be separated
into a dorsal stream (V1, DE, PP, and LP) and a ventral stream (V1, ITp, and
ITa) of visual areas. The paths within these streams are supported in the
majority of subjects by our DTI results. However, the crucial path under
investigation is the path between the two streams (PP-ITp), which is
hypothesized to mediate the learning effect under investigation. Interestingly,
we have found no evidence for this path, but we have found support for a path (DE-ITa),
which was not included in the proposed network and which could be a potential
candidate to mediate this effect. Whereas we do not wish to suggest that the
original path (PP-ITp) should be dismissed, it would be interesting to
investigate whether a part of the learning related effects, reported in the
original study, is mediated by the new path we have reported.Fletcher et al.
In this network, a series of
regions (OCC, PAR, and PFC) is proposed in both hemispheres with symmetrical
paths within the hemispheres and extensive interhemispheric connections. The
paths between occipital and parietal cortices are supported by our findings as are the
interhemispheric connections between these regions. Furthermore, we have found
evidence for paths that were not included in the original model, namely,
interhemispheric connections between occipital and parietal cortices. This is quite
remarkable, because interhemispheric connections between nonhomologous regions
are rarely found in this kind of analyses.
Also of interest are the cases in
which the proposed paths are not supported by our results. The interhemispheric
connections we did not find (LPFC-RPFC, LPFC-RPAR, and RPFC-LPAR) fall into the
aforementioned problematic class of frontal interhemispheric connections. The
most interesting negative result is the lack of connections between right
parietal and right prefrontal cortices, in contrast to the presence of these connections in
the left hemisphere. In this case, methodological shortcomings are not likely
to affect the results, as this would indicate that these shortcomings would
exist for the right hemisphere but not for the left hemisphere. The same
argument makes the consideration of connections via an extra region, which has
not been included in the network, also unattractive. Therefore, we tentatively
interpret these findings as a support for an asymmetry in connectivity between
these areas in parietal and prefrontal cortices.Koechlin et al.
This network contains a set of
motor and prefrontal regions in both hemispheres which are symmetrically
connected within the hemispheres. Furthermore, homologous areas are connected
with each other.  This last set of paths falls again in the class of frontal
interhemispheric connections and thus it is not surprising that we have found
no support for these paths. We have found connections supporting all
intrahemispheric paths in most subjects. Interestingly, Koechlin et al. have
proposed an alternative network in their study, which contained also paths
directly from premotor cortex to rostral prefrontal cortex. This extra network
did not result in significant changes in the original paths, which is in
agreement with the lack of support for these paths in our results.Kondo et al.
Kondo et al. have proposed four
network nodes (PFC, ACC, SPL, and IFC). However, two of these nodes (SPL and
IFC) can be further split up in four anatomically distinct regions (SPL1, SPL2,
IFC1, and IFC2). We have thus decided to treat each of these regions as a
separate node and have studied the connections between these nodes. Our results
show that the regions which constitute each original node (e.g., IFC1 and IFC2)
are connected with each other, but they show differential connectivity patterns
with the rest of the network, which suggest that they also have a different
role within this network. The lack of support for paths from
and to the ACC was surprising, especially given the fact that the ACC is known to
connect extensively with the prefrontal cortex in the macaque [31].McIntosh et al.
In this network, a set of regions
is proposed ranging from occipital, temporal, premotor, and prefrontal regions.
Moreover, these regions were proposed to be highly interconnected. Given our
results in the other studies, it was not surprising that there was hardly any
support for the (nonhomologous) interhemispheric connections originating in
frontal and prefrontal cortices.
Support was found for paths between occipital cortex and frontal and superior
temporal cortices.
However, there are also two intrahemispheric paths for which no support was
given (A6-A18R, A10-A6). Furthermore, there was no evidence found for paths
which were not included in the original analysis. Therefore, within the context
of this network, DTI did not deliver any extra information.Rowe et al.
Rowe et al. have proposed a network
with bilateral parietal, prefrontal, and prestriate areas and a motor area in
the left hemisphere only. We have found only scarce support for the paths to
and from prestriate area, even after lowering the FA threshold. In the left
hemisphere, we have found consistent evidence for all other proposed paths.
However, as in the network of Fletcher et al., we have found support for
parietal-frontal connections in the left but not in the right hemisphere. Based
on the same arguments as in the network of Rowe et al., we again interpret this
as an evidence for an asymmetry in these connections. This is even more
remarkable, because the locations of parietal and especially prefrontal areas
in both studies are quite widely separated.Seminowicz et al.
The network of Seminowicz et al.
consists predominantly of prefrontal subcortical areas in the right hemisphere,
with the lateral prefrontal cortex as the only region in the left hemisphere.
This might directly explain why we have found only scarce evidence for paths
from this region to the rest of the network, because of the aforementioned
methodological problems with frontal interhemispheric connections.
In contrast to the study of Kondo
et al., we have found connections supporting the proposed paths from the anterior
cingulate cortex. Furthermore, we have found many connections from the
thalamus to the rest of the network which do not conform to proposed paths.
This is however not surprising, as it is well known that the different nuclei
in the thalamus extensively connect to different parts of the cortex [13]. The “extra” connections are
thus most probably the result of the fact that the seed region in this case was
too large to specifically select the nucleus of the thalamus involved in this
network. Further evidence for paths that were not proposed in the original
network was found for connections from medial prefrontal cortex to the
hippocampus and the subgenual cingulated cortex.
The hippocampus is a region of low
FA and thus in an initial analysis showed only very limited connectivity with
the rest of the network. We have therefore repeated the analysis with a lowered
FA threshold (0.15) which yielded support for the proposed paths from the
hippocampus.White et al.
White et al. have proposed a model
with the left motor cortex and supplementary motor area, the thalamus, and two
areas from the right cerebellum. We have found support for the intrahemispheric
paths but not for the interhemispheric ones. This last finding contradicts
findings from the macaque literature in which there are connections found
between the primary motor cortex and the contralateral cerebellum deep nucleus [32]. Moreover, there is evidence
that the pons, which is a region through which these fibers have to pass,
contains crossing fibers [33]. We, therefore, conclude that
it is valid to include these connections in an effective connectivity analysis.


## 4. Discussion

In this study, we have for the
first time used DTI-based fiber tractography to investigate the anatomical
basis of effective connectivity models.  The aim of this study was twofold.
First, we wanted to establish that DTI-based tractography is able to resolve the
connectivity between ROIs of the size typically used in effective connectivity
studies. We hypothesized that the majority of the proposed paths were indeed
valid, and compared the results of our DTI-based analysis with these paths. We
have found positive evidence for a substantial number of paths. The negative
findings will be discussed below. However, we believe that the greatest
potential advantage of using DTI-based tractography in the context of effective
connectivity models lies in establishing paths that are not suggested by the
available knowledge (e.g., macaque tracer literature). Our second aim was therefore
to investigate the evidence for connections which were not proposed in the
original studies. We have found evidence for such connections in half of the models.
In the following,
we will discuss these findings in the context of current methodological
limitations and we will evaluate their implications for the proposed models.When there was no evidence found
for a proposed path, there are two explanations possible:(i)
there is no
direct anatomical connection; in this case, the proposed path can only be
supported by an indirect connection with a third region;(ii)
there is a
direct connection, but it has not been found due to methodological limitations
(false negative).
The second explanation is
especially relevant to a class of frontal interhemispheric connections. As
mentioned in the introduction, DTI-based tractography has profound difficulties
when tracking through voxels with multiple fiber populations. The frontal
interhemispheric connections pass through the corona radiata, which is well
known to contain such voxels [34]. Therefore, any negative
findings about this class of connections have to be interpreted as being
inconclusive.There is another set of negative
findings which merits further discussion: in both networks of Fletcher et al.
and of Rowe et al., we have found evidence for a connection between parietal
and prefrontal cortices
in the left hemisphere but not in the right hemisphere Here, an explanation in terms of
methodological limitations seems unattractive, because this would suggest that
these limitations occur in the right hemisphere but not in the left hemisphere.
Therefore, we tentatively conclude that these connections are indeed absent in
the right hemisphere and that this is an evidence for an asymmetry in
parietal-frontal connectivity. We should point out, however, that this
asymmetry seems counterintuitive at first, since the right hemisphere is
hypothesized to be dominant for visuospatial processing and one would expect
that these connections would be especially strong for this hemisphere. However,
as we are dealing with relatively small ROIs within the frontal and parietal
lobes, this does not rule out the possibility of there being any connections
between these lobes in the right hemisphere.There are a number of paths for
which no evidence was found and which cannot be interpreted directly in terms
of methodological limitations. As in many neuroimaging studies, these negative
findings are difficult to classify and it would certainly be imprudent to
conclude that these paths do not exist. Moreover, it is also possible that a
path is mediated by connections from and to a third region, which was not
included in the network. This situation can be relatively harmless if the
function of the missing region is known to be restricted to “message passing”. However,
we hypothesize that, if there is a substantial number of such “indirect” connections
present in a network, the inclusion of these other regions, which mediate these
connections, becomes necessary to ensure the biological validity of the
network.Given the methodological issues
discussed above, it is clear that the potential contribution of DTI to
connectivity studies lies not in disproving the existence of postulated
connections, but in the unique potential for detecting hitherto unconsidered
direct anatomical connections. This is because DTI may be prone to type II
errors, but it is far less likely to consistently produce type I errors when
connections are averaged across subjects. It is hence highly significant that we
have also found evidence for a number of paths which have not been taken into
account in the original studies. Incorporating these paths in a new analysis of
these models can potentially have a significant impact on the interpretation of
these models, since they point to improvements in the anatomical validity of
the models, which in turn leads to more veridical path coefficients.At the current state of technology in effective connectivity, one has
considerable freedom to choose the connections in a model and to evaluate
different models with different connectivity profiles against each other [35]. This model selection procedure can
be augmented significantly with DTI-based tractography, because models in which
the connections to a large extent overlap with the connections found in a DTI analysis
should in turn be more likely. One could potentially formalize this in a
Bayesian framework by designing priors on the connections and by subsequently
making the priors on the “known” connections high and sharp and the priors on
the “unknown” connections relatively noninformative.We have observed a large intersubject
variability in our findings. If this would be a veridical variability, it would
be a surprising and new finding, since the intersubject variability of
anatomical connections is generally considered to be low and is furthermore difficult
to assess with either DTI or tracer methods. In DTI, the normalization of
findings still makes it difficult to compare findings across subjects, whereas
tracer studies are normally performed in very few animals because of ethical
considerations, which makes any discussion about differences between animals
extremely difficult. Although we have not normalized the fiber tracks to a
template, we do believe that normalization problems still play an important
role in our studies because they might cause the erroneous placement of seed
regions in some subjects, which in turn would lead to misleading tractography
results and the above-mentioned intersubject variability. The smoothing
strategy, employed in functional studies to reduce the effect of anatomical
differences between subjects, cannot be applied in our framework because the
directional information, used in the tracking procedure, would be smeared out
over other voxels and potentially other tracks, with unpredictable implications
for the veridicality of the tracks found. Currently, there is no convincing way
of solving this problem, as the basic anatomical landmarks vary substantially
over subjects.Recently, advances have been made
towards the estimation of multiple fiber directions within one voxel and also
in probabilistic tractography. We will now discuss each of these developments
and their potential use for our framework.The estimation of multiple fiber
compartments per voxel, in general, brings this technique closer to producing
veridical anatomical connections, and a number of techniques have been proposed
to achieve this [33, 36–39]. There is, however, one
problem which cannot be solved by this technique alone, and that is the kissing/crossing
fiber problem: when a fiber has to track through a voxel with multiple
compartments, it is uncertain which compartment has to be used to determine the
direction in which the track is to be continued. In a number of studies, the
direction that is most collinear with the incoming fiber was chosen, but this
does not necessarily have to be the true direction. Whereas the single tensor
model is probably too conservative in the connections it yields, multiple compartment
models might thus yield a number of false positives.In probabilistic fiber tracking, a
measure of uncertainty of the local fiber direction is estimated per voxel [40–44]. Fiber tracking is now done
in a Monte Carlo type experiment: the tracking
is performed multiple times, each time with a different orientation drawn from
the local fiber direction distributions. Subsequently, the number of times a
target voxel was hit by this procedure is calculated, which then is converted
to an informal measure of “probability of connection”. While this procedure by
itself seems valid, in practice this leads to widespread patterns of
connectivity and it is uncertain at which level of “probability” the map should
be thresholded. Moreover, the probability of connection tends to decrease with
increasing distance. In a SEM network, both relatively local and long
connections can be included, which makes the comparison of these connections
difficult. Thus while both techniques (multiple direction estimation and
probabilistic fiber tracking) can potentially alleviate some of the problems,
we have encountered (e.g., interhemispheric connections), unresolved issues
remain.In conclusion, we have shown that DTI-based tractography can be used to
explore the anatomical connections between regions, used in effective
connectivity studies, notwithstanding the current limitations of this method.
We have observed evidence for the proposed paths in a large number of cases and,
more importantly, we have shown in several cases direct connections that were
not included in the original models. We therefore conclude that DTI-based
tractography is a valuable tool for exploring the anatomical basis of
functional networks.



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*Source: 423192-2008-05-05.xml*