Timo Roine is a Finnish M.Sc. (Tech.). He graduated in 2009, and did research on computer vision applications in mineral technology for several years and worked as a consultant in healthcare for two years. In September 2012, he started his PhD research at the Vision Lab on high angular resolution diffusion MRI, constrained spherical deconvolution (CSD), and network analysis in collaboration with Alexander Leemans of the PROVIDI Lab.
See publications at: http://visielab.uantwerpen.be/people/timo-roine Supervisors: Prof. Dr. Jan Sijbers, Prof. Dr. Alexander Leemans, and Dr. Ben Jeurissen Address: Antwerp, Belgium and Helsinki, Finland
A grinding mill model based on discrete element method (DEM) simulations is being developed at Ou... more A grinding mill model based on discrete element method (DEM) simulations is being developed at Outotec Oyj (Finland) to be used in mill design optimization. The model can be used for many purposes; one example is the selection of the lining and the size of the mill to meet the requirements of the clients. To validate the accuracy of the DEM simulator, a laboratory-sized ball mill prototype was constructed and iron balls were used as the mill charge. The prototype mill has its front side made of transparent glass in order to be able to visually examine the behaviour of the ball batch while the mill is rotating. The idea behind this type of arrangement is to create an exact model of the prototype mill for the DEM simulator and then compare the results while running the simulator and the physical mill with identical filling and rotation speed parameters. Image analysis can be used to evaluate and compare the performance of the prototype mill and the DEM simulator. Because of the need t...
Asperger syndrome (AS) is a neurodevelopmental disorder, which belongs to autism spectrum disorde... more Asperger syndrome (AS) is a neurodevelopmental disorder, which belongs to autism spectrum disorders. Its main symptoms are deficits in social interactions, and restricted and stereotyped behavior. There is accumulating evidence that the white matter tracts connecting brain areas are atypical in AS (for a review, see Schipul et al. 2011). Here, we characterized white matter tracts with diffusion tensor imaging (DTI) in AS and neurotypical controls. In addition, we used classification methods to find out how well DTI data predict that a subject belongs to the AS or control group.
Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a noninvasive imaging method, which c... more Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a noninvasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. However, the voxel sizes used in DW-MRI are relatively large, making DW-MRI prone to significant partial volume effects (PVE). These PVEs can be caused both by complex (e.g. crossing) WM fiber configurations and non-WM tissue, such as gray matter (GM) and cerebrospinal fluid. High angular resolution diffusion imaging methods have been developed to correctly characterize complex WM fiber configurations, but significant non-WM PVEs are also present in a large proportion of WM voxels. In constrained spherical deconvolution (CSD), the full fiber orientation distribution function (fODF) is deconvolved from clinically feasible DW data using a response function (RF) representing the signal of a single coherently oriented population of fibers. Non-WM PVEs cause a loss of precision in the detected fiber orientations and an emergence of false peaks in CSD, more prominently in voxels with GM PVEs. We propose a method, informed CSD (iCSD), to improve the estimation of fODFs under non-WM PVEs by modifying the RF to account for non-WM PVEs locally. In practice the RF is modified based on tissue fractions estimated from high-resolution anatomical data. Results from simulation and in-vivo bootstrapping experiments demonstrate a significant improvement in the precision of the identified fiber orientations and in the number of false peaks detected under GM PVEs. Probabilistic whole brain tractography shows fiber density is increased in the major WM tracts and decreased in subcortical GM regions. The iCSD method significantly improves the fiber orientation estimation at the WM-GM interface, which is especially important in connectomics, where the connectivity between GM regions is analyzed.
Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a non-invasive imaging method, which ... more Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a non-invasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. Significant partial volume effects (PVEs) are present in the DW signal due to relatively large voxel sizes. These PVEs can be caused by both non-WM tissue, such as gray matter (GM) and cerebrospinal fluid (CSF), and by multiple non-parallel WM fiber populations. High angular resolution diffusion imaging (HARDI) methods have been developed to correctly characterize complex WM fiber configurations, but to date, many of the HARDI methods do not account for non-WM PVEs. In this work, we investigated the isotropic PVEs caused by non-WM tissue in WM voxels on fiber orientations extracted with constrained spherical deconvolution (CSD). Experiments were performed on simulated and real DW-MRI data. In particular, simulations were performed to demonstrate the effects of varying the diffusion weightings, signal-to-noise ratios (SNRs), fiber configurations, and tissue fractions. Our results show that the presence of non-WM tissue signal causes a decrease in the precision of the detected fiber orientations and an increase in the detection of false peaks in CSD. We estimated 35–50% of WM voxels to be affected by non-WM PVEs. For HARDI sequences, which typically have a relatively high degree of diffusion weighting, these adverse effects are most pronounced in voxels with GM PVEs. The non-WM PVEs become severe with 50% GM volume for maximum spherical harmonics orders of 8 and below, and already with 25% GM volume for higher orders. In addition, a low diffusion weighting or SNR increases the effects. The non-WM PVEs may cause problems in connectomics, where reliable fiber tracking at the WM–GM interface is especially important. We suggest acquiring data with high diffusion-weighting 2500–3000 s/mm2, reasonable SNR (~30) and using lower SH orders in GM contaminated regions to minimize the non-WM PVEs in CSD.
Background: Recent brain imaging findings suggest that there are widely distributed abnormalities... more Background: Recent brain imaging findings suggest that there are widely distributed abnormalities affecting the brain connectivity in individuals with autism spectrum disorder (ASD). Using graph theoretical analysis, it is possible to investigate both global and local properties of brain’s wiring diagram, i.e., the connectome. Methods: We acquired diffusion-weighted magnetic resonance imaging data from 14 adult males with high-functioning ASD and 19 age-, gender-, and IQ-matched controls. As with diffusion tensor imaging-based tractography, it is not possible to detect complex (e.g., crossing) fiber configurations, present in 60–90 % of white matter voxels; we performed constrained spherical deconvolution-based whole brain tractography. Unweighted and weighted structural brain networks were then reconstructed from these tractography data and analyzed with graph theoretical measures. Results: In subjects with ASD, global efficiency was significantly decreased both in the unweighted and the weighted networks, normalized characteristic path length was significantly increased in the unweighted networks, and strength was significantly decreased in the weighted networks. In the local analyses, betweenness centrality of the right caudate was significantly increased in the weighted networks, and the strength of the right superior temporal pole was significantly decreased in the unweighted networks in subjects with ASD. Conclusions: Our findings provide new insights into understanding ASD by showing that the integration of structural brain networks is decreased and that there are abnormalities in the connectivity of the right caudate and right superior temporal pole in subjects with ASD.
Background: The aim of this study was to investigate potential differences in neural structure in... more Background: The aim of this study was to investigate potential differences in neural structure in individuals with Asperger syndrome (AS), high-functioning individuals with autism spectrum disorder (ASD). The main symptoms of AS are severe impairments in social interactions and restricted or repetitive patterns of behaviors, interests or activities. Methods: Diffusion weighted magnetic resonance imaging data were acquired for 14 adult males with AS and 19 age, sex and IQ-matched controls. Voxelwise group differences in fractional anisotropy (FA) were studied with tract-based spatial statistics (TBSS). Based on the results of TBSS, a tract-level comparison was performed with constrained spherical deconvolution (CSD)-based tractography, which is able to detect complex (for example, crossing) fiber configurations. In addition, to investigate the relationship between the microstructural changes and the severity of symptoms, we looked for correlations between FA and the Autism Spectrum Quotient (AQ), Empathy Quotient and Systemizing Quotient. Results: TBSS revealed widely distributed local increases in FA bilaterally in individuals with AS, most prominent in the temporal part of the superior longitudinal fasciculus, corticospinal tract, splenium of corpus callosum, anterior thalamic radiation, inferior fronto-occipital fasciculus (IFO), posterior thalamic radiation, uncinate fasciculus and inferior longitudinal fasciculus (ILF). CSD-based tractography also showed increases in the FA in multiple tracts. However, only the difference in the left ILF was significant after a Bonferroni correction. These results were not explained by the complexity of microstructural organization, measured using the planar diffusion coefficient. In addition, we found a correlation between AQ and FA in the right IFO in the whole group. Conclusions: Our results suggest that there are local and tract-level abnormalities in white matter (WM) microstructure in our homogenous and carefully characterized group of adults with AS, most prominent in the left ILF.
A grinding mill model based on discrete element method (DEM) simulations is being developed at Ou... more A grinding mill model based on discrete element method (DEM) simulations is being developed at Outotec Oyj (Finland) to be used in mill design optimization. The model can be used for many purposes; one example is the selection of the lining and the size of the mill to meet the requirements of the clients. To validate the accuracy of the DEM simulator, a laboratory-sized ball mill prototype was constructed and iron balls were used as the mill charge. The prototype mill has its front side made of transparent glass in order to be able to visually examine the behaviour of the ball batch while the mill is rotating. The idea behind this type of arrangement is to create an exact model of the prototype mill for the DEM simulator and then compare the results while running the simulator and the physical mill with identical filling and rotation speed parameters. Image analysis can be used to evaluate and compare the performance of the prototype mill and the DEM simulator. Because of the need t...
Asperger syndrome (AS) is a neurodevelopmental disorder, which belongs to autism spectrum disorde... more Asperger syndrome (AS) is a neurodevelopmental disorder, which belongs to autism spectrum disorders. Its main symptoms are deficits in social interactions, and restricted and stereotyped behavior. There is accumulating evidence that the white matter tracts connecting brain areas are atypical in AS (for a review, see Schipul et al. 2011). Here, we characterized white matter tracts with diffusion tensor imaging (DTI) in AS and neurotypical controls. In addition, we used classification methods to find out how well DTI data predict that a subject belongs to the AS or control group.
Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a noninvasive imaging method, which c... more Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a noninvasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. However, the voxel sizes used in DW-MRI are relatively large, making DW-MRI prone to significant partial volume effects (PVE). These PVEs can be caused both by complex (e.g. crossing) WM fiber configurations and non-WM tissue, such as gray matter (GM) and cerebrospinal fluid. High angular resolution diffusion imaging methods have been developed to correctly characterize complex WM fiber configurations, but significant non-WM PVEs are also present in a large proportion of WM voxels. In constrained spherical deconvolution (CSD), the full fiber orientation distribution function (fODF) is deconvolved from clinically feasible DW data using a response function (RF) representing the signal of a single coherently oriented population of fibers. Non-WM PVEs cause a loss of precision in the detected fiber orientations and an emergence of false peaks in CSD, more prominently in voxels with GM PVEs. We propose a method, informed CSD (iCSD), to improve the estimation of fODFs under non-WM PVEs by modifying the RF to account for non-WM PVEs locally. In practice the RF is modified based on tissue fractions estimated from high-resolution anatomical data. Results from simulation and in-vivo bootstrapping experiments demonstrate a significant improvement in the precision of the identified fiber orientations and in the number of false peaks detected under GM PVEs. Probabilistic whole brain tractography shows fiber density is increased in the major WM tracts and decreased in subcortical GM regions. The iCSD method significantly improves the fiber orientation estimation at the WM-GM interface, which is especially important in connectomics, where the connectivity between GM regions is analyzed.
Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a non-invasive imaging method, which ... more Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a non-invasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. Significant partial volume effects (PVEs) are present in the DW signal due to relatively large voxel sizes. These PVEs can be caused by both non-WM tissue, such as gray matter (GM) and cerebrospinal fluid (CSF), and by multiple non-parallel WM fiber populations. High angular resolution diffusion imaging (HARDI) methods have been developed to correctly characterize complex WM fiber configurations, but to date, many of the HARDI methods do not account for non-WM PVEs. In this work, we investigated the isotropic PVEs caused by non-WM tissue in WM voxels on fiber orientations extracted with constrained spherical deconvolution (CSD). Experiments were performed on simulated and real DW-MRI data. In particular, simulations were performed to demonstrate the effects of varying the diffusion weightings, signal-to-noise ratios (SNRs), fiber configurations, and tissue fractions. Our results show that the presence of non-WM tissue signal causes a decrease in the precision of the detected fiber orientations and an increase in the detection of false peaks in CSD. We estimated 35–50% of WM voxels to be affected by non-WM PVEs. For HARDI sequences, which typically have a relatively high degree of diffusion weighting, these adverse effects are most pronounced in voxels with GM PVEs. The non-WM PVEs become severe with 50% GM volume for maximum spherical harmonics orders of 8 and below, and already with 25% GM volume for higher orders. In addition, a low diffusion weighting or SNR increases the effects. The non-WM PVEs may cause problems in connectomics, where reliable fiber tracking at the WM–GM interface is especially important. We suggest acquiring data with high diffusion-weighting 2500–3000 s/mm2, reasonable SNR (~30) and using lower SH orders in GM contaminated regions to minimize the non-WM PVEs in CSD.
Background: Recent brain imaging findings suggest that there are widely distributed abnormalities... more Background: Recent brain imaging findings suggest that there are widely distributed abnormalities affecting the brain connectivity in individuals with autism spectrum disorder (ASD). Using graph theoretical analysis, it is possible to investigate both global and local properties of brain’s wiring diagram, i.e., the connectome. Methods: We acquired diffusion-weighted magnetic resonance imaging data from 14 adult males with high-functioning ASD and 19 age-, gender-, and IQ-matched controls. As with diffusion tensor imaging-based tractography, it is not possible to detect complex (e.g., crossing) fiber configurations, present in 60–90 % of white matter voxels; we performed constrained spherical deconvolution-based whole brain tractography. Unweighted and weighted structural brain networks were then reconstructed from these tractography data and analyzed with graph theoretical measures. Results: In subjects with ASD, global efficiency was significantly decreased both in the unweighted and the weighted networks, normalized characteristic path length was significantly increased in the unweighted networks, and strength was significantly decreased in the weighted networks. In the local analyses, betweenness centrality of the right caudate was significantly increased in the weighted networks, and the strength of the right superior temporal pole was significantly decreased in the unweighted networks in subjects with ASD. Conclusions: Our findings provide new insights into understanding ASD by showing that the integration of structural brain networks is decreased and that there are abnormalities in the connectivity of the right caudate and right superior temporal pole in subjects with ASD.
Background: The aim of this study was to investigate potential differences in neural structure in... more Background: The aim of this study was to investigate potential differences in neural structure in individuals with Asperger syndrome (AS), high-functioning individuals with autism spectrum disorder (ASD). The main symptoms of AS are severe impairments in social interactions and restricted or repetitive patterns of behaviors, interests or activities. Methods: Diffusion weighted magnetic resonance imaging data were acquired for 14 adult males with AS and 19 age, sex and IQ-matched controls. Voxelwise group differences in fractional anisotropy (FA) were studied with tract-based spatial statistics (TBSS). Based on the results of TBSS, a tract-level comparison was performed with constrained spherical deconvolution (CSD)-based tractography, which is able to detect complex (for example, crossing) fiber configurations. In addition, to investigate the relationship between the microstructural changes and the severity of symptoms, we looked for correlations between FA and the Autism Spectrum Quotient (AQ), Empathy Quotient and Systemizing Quotient. Results: TBSS revealed widely distributed local increases in FA bilaterally in individuals with AS, most prominent in the temporal part of the superior longitudinal fasciculus, corticospinal tract, splenium of corpus callosum, anterior thalamic radiation, inferior fronto-occipital fasciculus (IFO), posterior thalamic radiation, uncinate fasciculus and inferior longitudinal fasciculus (ILF). CSD-based tractography also showed increases in the FA in multiple tracts. However, only the difference in the left ILF was significant after a Bonferroni correction. These results were not explained by the complexity of microstructural organization, measured using the planar diffusion coefficient. In addition, we found a correlation between AQ and FA in the right IFO in the whole group. Conclusions: Our results suggest that there are local and tract-level abnormalities in white matter (WM) microstructure in our homogenous and carefully characterized group of adults with AS, most prominent in the left ILF.
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voxels.
In constrained spherical deconvolution (CSD), the full fiber orientation distribution function (fODF) is deconvolved from clinically feasible DW data using a response function (RF) representing the signal of a single coherently oriented population of fibers. Non-WM PVEs cause a loss of precision in the detected fiber orientations and an emergence of false peaks in CSD, more prominently in voxels with GM PVEs. We propose a method, informed CSD (iCSD), to improve the estimation of fODFs under non-WM PVEs by modifying the RF to account for non-WM PVEs locally. In practice the RF is modified based on tissue fractions estimated from high-resolution
anatomical data.
Results from simulation and in-vivo bootstrapping experiments demonstrate a significant improvement in the precision of the identified fiber orientations and in the number of false peaks detected under GM PVEs. Probabilistic whole brain tractography shows fiber density is increased in the major
WM tracts and decreased in subcortical GM regions. The iCSD method significantly improves the fiber orientation estimation at the WM-GM interface, which is especially important in connectomics, where the connectivity between GM regions is analyzed.
brain connectivity in individuals with autism spectrum disorder (ASD). Using graph theoretical analysis, it is possible
to investigate both global and local properties of brain’s wiring diagram, i.e., the connectome.
Methods: We acquired diffusion-weighted magnetic resonance imaging data from 14 adult males with
high-functioning ASD and 19 age-, gender-, and IQ-matched controls. As with diffusion tensor imaging-based
tractography, it is not possible to detect complex (e.g., crossing) fiber configurations, present in 60–90 % of white
matter voxels; we performed constrained spherical deconvolution-based whole brain tractography. Unweighted
and weighted structural brain networks were then reconstructed from these tractography data and analyzed with
graph theoretical measures.
Results: In subjects with ASD, global efficiency was significantly decreased both in the unweighted and the
weighted networks, normalized characteristic path length was significantly increased in the unweighted networks,
and strength was significantly decreased in the weighted networks. In the local analyses, betweenness centrality
of the right caudate was significantly increased in the weighted networks, and the strength of the right superior
temporal pole was significantly decreased in the unweighted networks in subjects with ASD.
Conclusions: Our findings provide new insights into understanding ASD by showing that the integration of
structural brain networks is decreased and that there are abnormalities in the connectivity of the right caudate
and right superior temporal pole in subjects with ASD.
Asperger syndrome (AS), high-functioning individuals with autism spectrum disorder (ASD). The main symptoms
of AS are severe impairments in social interactions and restricted or repetitive patterns of behaviors, interests or
activities.
Methods: Diffusion weighted magnetic resonance imaging data were acquired for 14 adult males with AS and 19
age, sex and IQ-matched controls. Voxelwise group differences in fractional anisotropy (FA) were studied with
tract-based spatial statistics (TBSS). Based on the results of TBSS, a tract-level comparison was performed with
constrained spherical deconvolution (CSD)-based tractography, which is able to detect complex (for example,
crossing) fiber configurations. In addition, to investigate the relationship between the microstructural changes and
the severity of symptoms, we looked for correlations between FA and the Autism Spectrum Quotient (AQ), Empathy
Quotient and Systemizing Quotient.
Results: TBSS revealed widely distributed local increases in FA bilaterally in individuals with AS, most prominent in
the temporal part of the superior longitudinal fasciculus, corticospinal tract, splenium of corpus callosum, anterior
thalamic radiation, inferior fronto-occipital fasciculus (IFO), posterior thalamic radiation, uncinate fasciculus and
inferior longitudinal fasciculus (ILF). CSD-based tractography also showed increases in the FA in multiple tracts.
However, only the difference in the left ILF was significant after a Bonferroni correction. These results were not
explained by the complexity of microstructural organization, measured using the planar diffusion coefficient.
In addition, we found a correlation between AQ and FA in the right IFO in the whole group.
Conclusions: Our results suggest that there are local and tract-level abnormalities in white matter (WM) microstructure
in our homogenous and carefully characterized group of adults with AS, most prominent in the left ILF.
diffusion tensor imaging (DTI) in 14 male adults with Asperger syndrome (AS) and 19 gender-, age-, and intelligence
quotient-matched controls. We focused on individuals with high-functioning autism spectrum disorder (ASD), AS, to
decrease heterogeneity caused by large variation in the cognitive profile. Previous DTI studies of ASD have mainly
focused on finding local changes in fractional anisotropy (FA) and mean diffusivity (MD), two indexes used to
characterize microstructural properties of WM. Although the local or voxel-based approaches may be able to provide
detailed information in terms of location of the observed differences, such results are known to be highly sensitive to
partial volume effects, registration errors, or placement of the regions of interest. Therefore, we performed global
histogram analyses of (a) whole-brain tractography results and (b) skeletonized WM masks. In addition to the FA and
MD, the planar diffusion coefficient (CP) was computed as it can provide more specific information of the complexity
of the neural structure. Our main finding indicated that adults with AS had higher mean FA values than controls. A less
complex neural structure in adults with AS could have explained the results, but no significant difference in CP was
found. Our results suggest that there are global abnormalities in the WM tissue of adults with AS. Autism Res 2013, 6:
642–650. © 2013 International Society for Autism Research, Wiley Periodicals, Inc.
voxels.
In constrained spherical deconvolution (CSD), the full fiber orientation distribution function (fODF) is deconvolved from clinically feasible DW data using a response function (RF) representing the signal of a single coherently oriented population of fibers. Non-WM PVEs cause a loss of precision in the detected fiber orientations and an emergence of false peaks in CSD, more prominently in voxels with GM PVEs. We propose a method, informed CSD (iCSD), to improve the estimation of fODFs under non-WM PVEs by modifying the RF to account for non-WM PVEs locally. In practice the RF is modified based on tissue fractions estimated from high-resolution
anatomical data.
Results from simulation and in-vivo bootstrapping experiments demonstrate a significant improvement in the precision of the identified fiber orientations and in the number of false peaks detected under GM PVEs. Probabilistic whole brain tractography shows fiber density is increased in the major
WM tracts and decreased in subcortical GM regions. The iCSD method significantly improves the fiber orientation estimation at the WM-GM interface, which is especially important in connectomics, where the connectivity between GM regions is analyzed.
brain connectivity in individuals with autism spectrum disorder (ASD). Using graph theoretical analysis, it is possible
to investigate both global and local properties of brain’s wiring diagram, i.e., the connectome.
Methods: We acquired diffusion-weighted magnetic resonance imaging data from 14 adult males with
high-functioning ASD and 19 age-, gender-, and IQ-matched controls. As with diffusion tensor imaging-based
tractography, it is not possible to detect complex (e.g., crossing) fiber configurations, present in 60–90 % of white
matter voxels; we performed constrained spherical deconvolution-based whole brain tractography. Unweighted
and weighted structural brain networks were then reconstructed from these tractography data and analyzed with
graph theoretical measures.
Results: In subjects with ASD, global efficiency was significantly decreased both in the unweighted and the
weighted networks, normalized characteristic path length was significantly increased in the unweighted networks,
and strength was significantly decreased in the weighted networks. In the local analyses, betweenness centrality
of the right caudate was significantly increased in the weighted networks, and the strength of the right superior
temporal pole was significantly decreased in the unweighted networks in subjects with ASD.
Conclusions: Our findings provide new insights into understanding ASD by showing that the integration of
structural brain networks is decreased and that there are abnormalities in the connectivity of the right caudate
and right superior temporal pole in subjects with ASD.
Asperger syndrome (AS), high-functioning individuals with autism spectrum disorder (ASD). The main symptoms
of AS are severe impairments in social interactions and restricted or repetitive patterns of behaviors, interests or
activities.
Methods: Diffusion weighted magnetic resonance imaging data were acquired for 14 adult males with AS and 19
age, sex and IQ-matched controls. Voxelwise group differences in fractional anisotropy (FA) were studied with
tract-based spatial statistics (TBSS). Based on the results of TBSS, a tract-level comparison was performed with
constrained spherical deconvolution (CSD)-based tractography, which is able to detect complex (for example,
crossing) fiber configurations. In addition, to investigate the relationship between the microstructural changes and
the severity of symptoms, we looked for correlations between FA and the Autism Spectrum Quotient (AQ), Empathy
Quotient and Systemizing Quotient.
Results: TBSS revealed widely distributed local increases in FA bilaterally in individuals with AS, most prominent in
the temporal part of the superior longitudinal fasciculus, corticospinal tract, splenium of corpus callosum, anterior
thalamic radiation, inferior fronto-occipital fasciculus (IFO), posterior thalamic radiation, uncinate fasciculus and
inferior longitudinal fasciculus (ILF). CSD-based tractography also showed increases in the FA in multiple tracts.
However, only the difference in the left ILF was significant after a Bonferroni correction. These results were not
explained by the complexity of microstructural organization, measured using the planar diffusion coefficient.
In addition, we found a correlation between AQ and FA in the right IFO in the whole group.
Conclusions: Our results suggest that there are local and tract-level abnormalities in white matter (WM) microstructure
in our homogenous and carefully characterized group of adults with AS, most prominent in the left ILF.
diffusion tensor imaging (DTI) in 14 male adults with Asperger syndrome (AS) and 19 gender-, age-, and intelligence
quotient-matched controls. We focused on individuals with high-functioning autism spectrum disorder (ASD), AS, to
decrease heterogeneity caused by large variation in the cognitive profile. Previous DTI studies of ASD have mainly
focused on finding local changes in fractional anisotropy (FA) and mean diffusivity (MD), two indexes used to
characterize microstructural properties of WM. Although the local or voxel-based approaches may be able to provide
detailed information in terms of location of the observed differences, such results are known to be highly sensitive to
partial volume effects, registration errors, or placement of the regions of interest. Therefore, we performed global
histogram analyses of (a) whole-brain tractography results and (b) skeletonized WM masks. In addition to the FA and
MD, the planar diffusion coefficient (CP) was computed as it can provide more specific information of the complexity
of the neural structure. Our main finding indicated that adults with AS had higher mean FA values than controls. A less
complex neural structure in adults with AS could have explained the results, but no significant difference in CP was
found. Our results suggest that there are global abnormalities in the WM tissue of adults with AS. Autism Res 2013, 6:
642–650. © 2013 International Society for Autism Research, Wiley Periodicals, Inc.