Constrained spherical deconvolution-based tractography and tract-based spatial statistics show abnormal microstructural organization in Asperger syndrome
© Roine et al.; licensee BioMed Central. 2015
Received: 20 May 2014
Accepted: 11 December 2014
Published: 16 January 2015
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.
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.
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.
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.
Keywordsautism spectrum disorder diffusion magnetic resonance imaging fractional anisotropy white matter tract inferior longitudinal fasciculus
Asperger syndrome (AS) is a neurodevelopmental autism spectrum disorder (ASD), which affects 0.6 to 0.7% of the population . The main symptoms of AS are severe impairments in social interactions and restricted, repetitive patterns of behaviors, interests, and activities. Although the heritability of ASD has been estimated to be as high as 90% , no single autism locus has been implicated. Rather, autism seems to be a behavioral manifestation of multiple underlying genetic disorders . This heterogeneity, the diversity of symptoms, and the variation in the degree of their severity among individuals, also suggest that the neurobiological background is very complex with multiple brain areas being involved. It has been suggested that there are widely distributed abnormalities in brain connectivity in individuals with ASD [4–6].
Functional imaging studies have shown that there is reduced long-distance and increased short-distance connectivity in ASD [4–6]. Recently, diffusion-weighted (DW) magnetic resonance imaging (MRI) has enabled also the in vivo investigation of anatomical brain connectivity [7–10]. In white matter (WM) tracts, the diffusion of water molecules is hindered more by the cell walls of the axons than along the main orientation of the axons, and thus, diffusion is likely to be anisotropic in WM. Fractional anisotropy (FA) is the most commonly used index to describe the degree of anisotropy and can be used to quantify the microstructural coherence or organization of WM tracts . Mean diffusivity (MD) represents the average diffusion rate in all directions and with the planar diffusion coefficient (CP), the degree of fiber complexity can be quantified . In particular, a higher CP describes a more disc-shaped diffusion, typically caused by ‘crossing fibers’ [13–15].
The findings of diffusion MRI studies in ASD are not consistent. Both decreased and increased FA values have been reported in many WM tracts in ASD, compared to typically developing control subjects [16, 17]. Most of the studies have been performed in either children or adolescents with ASD . In adults, mainly decreased FA values in ASD subjects have been reported [18–23]. Bloemen and coworkers  used a voxelwise method to analyze the FA values of the WM in 13 adult males with AS and 13 age and IQ-matched male controls and mainly found regions with decreased FA in adults with AS. The discrepancy across studies could be partly due to the use of different image acquisition parameters or analysis approaches [17, 24–26], but also the variation in age and cognitive profile of the subjects across the different studies may play a role in this context.
The aim of our study was to investigate potential differences in FA and MD in adults with ASD. To narrow down the variation in the cognitive profile of the subjects, we only investigated high-functioning individuals with AS. Individuals with AS and autism share the same core symptoms, but according to DSM-IV, subjects with AS do not have a clinically significant delay in speech and cognitive development, although in DSM-5, autism and AS (among others) were placed on the same spectrum of autistic disorders.
As different analytic approaches may also contribute to the varying results in DWI studies in ASD, we have chosen to use multiple approaches to confirm that the results support each other. In our previous histogram-based study, we used a dual approach, a skeletonized white matter histogram and a whole-brain tractography histogram, and found a global increase of FA values in adult males with AS compared to age-, sex- and IQ-matched controls, suggesting a more coherent neural tract organization in the individuals with AS . This raised a question about the location of the differences. Thus, in the current study, the aim was to find out if the differences would be widely distributed or if they could be pointed more specifically to certain locations or white matter tracts. We chose again a dual approach and used both tract-based spatial statistics (TBSS)  and tractography to investigate both voxelwise differences and tract-level differences. Furthermore, as diffusion tensor imaging (DTI) is unable to correctly characterize crossing fiber configurations, which are present in up to 90% of the WM tissue , we used constrained spherical deconvolution (CSD)-based tractography [30, 31]. In CSD, multiple fibers passing through a voxel with distinct orientations can be reliably estimated. So far, CSD-based tractography has only been used in two studies in ASD [32, 33], and only a few tracts have been investigated.
Fourteen individuals with AS and 19 control subjects without any neuropsychiatric disorders were included in this study. All subjects were male, and the individuals with AS were age- and IQ-matched with the controls. To minimize the effect of age-related changes on the neural structure, only individuals aged 40 years or less were eligible for the study. The mean age of individuals with AS was 28.6 ± 5.7 years and that of controls 26.4 ± 4.7 years. The mean IQs for the AS and control groups were 125.1 ± 14.5 and 127.9 ± 10.0, respectively (Wechsler’s Adult Intelligence Scale-Third Edition; The Psychological Corporation; 2005). The patients were recruited from a private neuropsychiatric clinic (NeuroMental) in Helsinki and from the neuropsychiatric clinic in Helsinki University Central Hospital. Only individuals fulfilling ICD-10 (International Classification of Disease; World Health Organization; 1993) criteria, diagnosed by experienced clinicians specialized in developmental neuropsychiatry, were included in the study. Both individuals with AS and controls had a full psychiatric evaluation before inclusion in the study. Diagnostic process for the AS group included full developmental history, acquired using multiple sources of information (for example, all previous medical records, parental interviews when possible). Benton Facial Recognition Test (FRT)  and Reading the Mind in the Eyes Test (Eyes Test)  were carried out for all subjects. In addition, all subjects completed Autism Spectrum Quotient (AQ) , Empathy Quotient (EQ)  and Systemizing Quotient (SQ)  questionnaires, which had been translated into Finnish, and the translation was confirmed by a back-translation. A two-tailed t-test was used to test for group differences. There were no significant differences in total IQ, verbal IQ, performance IQ, FRT, Eyes test or SQ, whereas individuals with AS had significantly higher AQ scores (P = 0.0000001) and significantly lower EQ scores than controls (P = 0.0017) (see Table one in Roine et al. ). Control subjects were paid for their attendance in the study and for individuals with AS the expenses and the loss of income were compensated. The Ethics Committee of the Hospital District of Helsinki and Uusimaa approved the research protocol, and all participants signed a written informed consent form before participating in the study.
The MR images were acquired with a Signa VH/i 3.0 T scanner with HDxt upgrade (General Electric, Milwaukee, WI, USA). A quadrature receiving eight-channel high-resolution brain array coil was used (MRI Devices Corporation, FL). The maximum field gradient amplitude of the MRI system was 40 mT/m with a slew rate of 150 T/m/s. A high-order shimming with 24-cm field of view (FOV) was applied prior to DW imaging. A spin echo pulsed sequence of 60 unique gradient orientations arranged on the unit sphere was used. Eight nondiffusion weighted B0-images were acquired and all of the 60 orientations were imaged twice, resulting in 120 diffusion-weighted images in total. The b-value, which controls the diffusion weighting, was 1000 s/mm2. Echo time (TE) was set to the minimum (approximately 98 ms). Repetition time (TR) was 10 s, and the number of excitations (NEX) was one. The imaging area covered the whole brain with 53 contiguous axial slices. The acquired in-plane resolution of the slices was 1.875 mm × 1.875 mm, and the thickness of the slices was 3.0 mm. The matrix size was 128 × 128.
The DW acquisitions of the two groups were tested for possible differences in subject motion using a two-tailed t-test. All of the parameters of the affine transform (12 degrees of freedom) were tested both for absolute and relative differences . No group differences were found (P < 0.05) in any of the parameters.
Voxelwise analysis with tract-based spatial statistics
The voxelwise statistical analysis of the diffusion data was carried out using TBSS , which belongs to the Functional MRI of the Brain (FMRIB) Software Library (FSL) tools . FMRIB’s Diffusion Toolbox (FDT) was used for the preprocessing of the data. DW images were corrected for subject motion and eddy-current-induced distortions, and the nonbrain tissue was removed. Diffusion tensors were fitted resulting in FA and MD images in addition to the images containing eigenvalues of the diffusion tensor. As differences in FA could result from changes in the fiber complexity [29, 41], CP images were calculated from the eigenvalues . FA images were transformed into standard space by nonlinear registration based on free-form deformations and B-splines , after which a mean FA image of all subjects was calculated and thinned, resulting in a mean FA skeleton image. Then FA data of all subjects were projected onto the mean FA skeleton by selecting the highest FA values perpendicular to the FA skeleton. For MD and CP, the same nonlinear warps and projection vectors as used for FA images were used. ‘Randomise’, a permutation program that enables modeling and inference using standard general linear model design setup, was used for statistical testing of the voxelwise differences between the two groups. Threshold-free cluster enhancement (TFCE) was used to enhance cluster-like structures in the data . Permutation-based non-parametric testing was used to correct for multiple comparisons across space. The TBSS results were also transformed back to native space of each subject to confirm that a given point in the skeleton was derived from the correct anatomically corresponding region. We tested for differences in FA, MD and CP values between subjects with AS and controls. In addition, we correlated AQ, SQ and EQ with FA for the whole group including individuals with AS and controls. Analyses were controlled for age and IQ.
Constrained spherical deconvolution-based tractography
Results and discussion
A voxelwise comparison of FA, MD and CP values between individuals with AS and age, sex and IQ-matched controls was performed with TBSS, and the findings were confirmed by CSD-based tractography.
Voxelwise analysis with tract-based spatial statistics
As TBSS is designed for investigation of voxelwise differences in WM tracts, and only the voxels with the highest FA value in the cross-section of the WM tracts are analyzed, the analyses are restricted to WM. This way, the partial volume effect (PVE) is minimized in terms of adverse contributions of GM or cerebrospinal fluid to WM voxels. Furthermore, no smoothing is needed. However, abnormalities in the WM might reduce the FA value of a tract locally, and it is possible that the voxel chosen to the skeleton is not actually in the center of the tract. Therefore, we transformed the TBSS results back to native space of each subject to confirm that a given point in the skeleton was derived from the correct anatomically corresponding region. A limitation of TBSS is that by studying only the voxels on the skeleton with the highest FA, a large part of WM remains uninvestigated. The tract-level analysis with the CSD-based tractography method complements the more local TBSS approach, as it is not limited to the voxels with the highest FA in the cross-section of the tract.
Tract-level analysis with constrained spherical deconvolution tractography
Fractional anisotropy (FA) values in individuals with Asperger syndrome (AS) and controls in the white matter (WM) tracts reconstructed with tractography
WM a tract
FA a (AS a subjects) b
FA a (controls) b
Corrected Pvalue c
ATR a L
0.3416 ± 0.0381
0.3335 ± 0.0330
ATR a R
0.3511 ± 0.0399
0.3453 ± 0.0332
0.4521 ± 0.0225
0.4401 ± 0.0553
CST a L
0.4957 ± 0.0254
0.4848 ± 0.0405
CST a R
0.4915 ± 0.0278
0.4713 ± 0.0420
IFO a L
0.4381 ± 0.0336
0.4129 ± 0.0523
IFO a R
0.4365 ± 0.0261
0.4112 ± 0.0342
ILF a L
0.3864 ± 0.0277
0.3521 ± 0.0358
ILF a R
0.3697 ± 0.0258
0.3511 ± 0.0380
SLF a L
0.4266 ± 0.0154
0.3978 ± 0.0433
SLF a R
0.3899 ± 0.0255
0.3620 ± 0.0434
UNC a L
0.3426 ± 0.0368
0.3109 ± 0.0424
UNC a R
0.3353 ± 0.0373
0.3143 ± 0.0408
Correlation of autism spectrum quotient (AQ), empathy quotient (EQ) and systemizing quotient (SQ) with fractional anisotropy of the white matter tracts
WM a tracts
AQ a and FA a
EQ a and FA a
SQ a and FA a
ATR a L
ATR a R
CST a L
CST a R
IFO a L
IFO a R
ILF a L
ILF a R
SLF a L
SLF a R
UNC a L
UNC a R
Possible causes for increased fractional anisotropy
Anisotropic diffusion is primarily caused by dense packing of axons and their cell membranes . Other tissue properties such as myelination can also affect the degree of anisotropy [15, 51]. However, one of the most important factors affecting the FA is the complexity of the underlying WM fiber structure [29, 41]: less crossing fiber configurations in individuals with AS could be a cause for the increased FA. However, no between-group differences were observed in CP with either TBSS or with CSD-based tractography. Thus, the higher FA in subjects with AS was not explained by a lower degree of crossing fibers . In addition, it has been recently reported that differences in subject motion may affect DW-MRI metrics in ASD [52, 53]. However, in our sample, there were no differences in subject motion between the groups. Thus, subject motion does not confound our results. FA can increase or decrease without changes in MD. For instance, if axial diffusivity increases and radial diffusivity decreases, FA can increase without changes in MD.
Increased FA indicates that the coherence of the WM tracts in subjects with AS may be higher than in controls . However, it has been suggested that individuals with ASD have difficulties in differentiating signal from noise, and thus, strong physical connectivity does not necessarily equal high computational connectivity . Interestingly, asynchronous brain activity during film watching in individuals with AS was reported in a partly overlapping sample . It is also possible that more intensive training of the social and communication (or other lacking) skills may lead to increased FA values in adults with AS, as in two studies with healthy subjects it has been shown that training can induce an increase in FA [56, 57]. In addition, Pardini and coworkers found an increase in FA in individuals with ASD who highly adhered to therapy, compared to those who adhered to it only moderately .
Comparison to the previous findings
Although most of the previous studies in adults with ASD have reported decreased FA values [18–23], increased FA values have also been found [19, 20]. In addition, a recent study reported a positive correlation between FA and autistic traits in subjects with ADHD most prominent in socio-communicative skills, which are impaired in AS . In some studies in adults with ASD, no group differences in FA related measures were reported [60, 61]. A voxel-based meta-analysis showed increased WM volume in the right arcuate, left inferior fronto-occipital and uncinate fasciculi in individuals with ASD . In studies performed in children and adolescents, more increases in FA have been found than in adults [63–68]. However, the methods and also regions of interest vary a lot between different studies.
CSD-based tractography has been previously used in only two studies in ASD [32, 33]. McGrath and coworkers investigated the WM integrity of the arcuate fasciculus (AF) and IFO, and found a decrease in FA in the right IFO in individuals with ASD . We did not perform tractography of the AF, as it did not appear in the TBSS results, and the FA of the right IFO was increased in our subjects with AS compared to controls in TBSS. At the tract-level, the difference in the right IFO did not endure the correction for multiple comparisons. However, the difference of the absolute values is bigger in our study than in McGrath and coworkers’ study. The samples also differ, as the patients in our study are older (28.6 ± 5.7 versus 17.28 ± 2.87), and have a higher IQ (125.1 ± 14.5 versus 106.84 ± 14.54). McGrath and coworkers also investigated WM microstructure based on the results of functional connectivity and found reduced FA in the WM connecting the left Brodmann area 19 to the left caudate head and to the left thalamus in individuals with ASD .
The inconsistent findings in the previous studies may be partly due to the use of different methods, but also to the heterogeneity of the samples, as in ASD, the diversity of symptoms is large and variation in the degree of their severity among individuals varies a lot. Differences in age and IQ between the different samples may also affect the results, as it has been shown that FA values correlate with IQ  and that FA changes with age [53, 70]. Langen and coworkers have hypothesized that there might be an earlier peak in the WM maturation and an earlier onset of an age-related decrease in FA in autism . In our study, we have chosen a homogeneous sample of subjects with ASD, as subjects with AS do not have a clinically significant delay in speech and cognitive development (International Classification of Disease; World Health Organization; 1993). In addition, our subjects with AS are high-functioning, with a mean IQ of 125.1 ± 14.5.
Advantages of the chosen methodology
The large amount of different analysis methods makes it more difficult to compare different studies with each other, as each of the methods has its limitations [24, 25, 71, 72]. Because of the inconsistency in the results of the previous studies, we have used two different techniques to reliably detect the possible WM changes in male adults with AS. In TBSS, the FA of the WM is investigated at a more local level than in the tractography-based analysis, where the mean FA is calculated for the whole tract. Therefore, possible differences in some part of the WM tract seen in TBSS may not be seen in the mean FA of the whole tract. In that sense, the TBSS analysis might be more sensitive, but the tractography-based analysis might be more robust. Furthermore, in the tractography approach we have used CSD-based tractography instead of DTI-based tractography. Unlike in DTI, with CSD it is possible to reliably investigate crossing fibers, which are present in up to 90% of the WM tissue .
Thus, TBSS and CSD-based tractography complement each other, and both indicate increased FA in individuals with AS. Furthermore, these results are supported by our earlier findings of globally increased FA in subjects with AS , but provide more information concerning the location of the differences.
Limitations of our study include a relatively small sample size. However, our results survived corrections for multiple comparisons and thus, can be considered robust. Finland is an isolated and genetically homogeneous country , which can be beneficial as it has been suggested that heritable factors play a strong role in WM organization . Another limitation concerns the Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule (ADOS). They are standard instruments in the diagnostics of ASD in many countries, but were not in use in Finland at the time of the study. Therefore, we do not have this information for our subjects. However, we used AQ, EQ and SQ questionnaires specifically designed for high-functioning individuals with ASD, and in addition, all subjects were thoroughly screened to exclude other psychiatric disorders. In addition, the DW-MRI acquisition was suboptimal for CSD-based tractography , as the diffusion-weighting was relatively low and the voxel size was anisotropic. Nevertheless, fiber crossings, present in 60 to 90% of the WM voxels , can reliably be identified with CSD [48, 74, 75], and thus, using CSD is highly beneficial in comparison to DTI, where fiber crossings cannot be identified. Finally, we used a semi-automated tract segmentation method to extract the 13 fiber tracts in 33 subjects . While this approach has been successfully applied for DTI-based tractography using a single representative subject [50, 76], performance characteristics for CSD-based tractography, combined with the use of more advanced population-specific atlas templates [77, 78], remain to be determined.
We investigated local microstructural differences in the WM of individuals with AS by comparing their FA, MD and CP values to those of age-, sex- and IQ-matched controls. We used a dual approach and performed first a more local voxelwise analysis, TBSS, and then confirmed the findings with a tract-level analysis approach, CSD-based tractography. Our results suggest that there are widely distributed abnormalities in the WM tracts of adults with AS, and that these are most pronounced in the left ILF. The changes were not explained by differences in the complexity of the microstructural organization of the WM. Furthermore, these results are in line with our earlier findings of globally increased FA in subjects with AS .
Our study was restricted to adult males with AS. In the future, longitudinal studies beginning in the childhood and following the diagnosis and possible rehabilitation should be performed to more thoroughly understand the neural deficits and the effects of age and IQ in ASD. Furthermore, as ASD is a very broad and heterogeneous disorder, the study samples should be as homogeneous as possible, and different symptom domains should be investigated separately. Finally, the WM tracts could be segmented and thus, short parts of the tracts could be investigated separately in addition to looking at the mean FA of the whole tract.
UR has two academic degrees, a Master of Science (M.Sc.) in Technology from Aalto University (2010) and medical degree from the University of Helsinki (2011), acquired simultaneously. In the beginning of her medical studies she was chosen to a graduate program for medical doctors, and she will soon finish her doctoral studies, during which she has attended multiple scientific conferences and courses and visited research groups abroad. She’s especially interested in neuropsychiatry, diffusion magnetic resonance imaging and applying the newest technology to clinical studies. JS is a researcher and a psychologist. He has a PhD in psychology. He is currently working as a clinical psychologist helping children and adults with neurodevelopmental disabilities and as a post-doctoral researcher in the field of cognitive neuroscience. TR received his M.Sc. (Tech.) in 2009 from Helsinki University of Technology (currently Aalto University), Finland. He did research on mineral processing technology in the Control Engineering group of Aalto University from 2005 to 2010. From 2010 to 2012 he worked as a consultant for healthcare management in Nordic Healthcare Group, Finland. In 2012 he started his PhD about diffusion MRI and constrained spherical deconvolution at the iMinds-Vision Lab, Department of Physics, University of Antwerp, Belgium under the supervision of Ben Jeurissen, Alexander Leemans and Jan Sijbers. TNvW is a pediatrician and a child neurologist. She did her PhD on Asperger syndrome, and she is a specialist in diagnostics, neuroimaging and molecular genetics of AS. TNvW has a wide experience in neuropsychiatric diagnostics, and she treats both children and adults with AS. She is the managing director of the Neuropsychiatric Rehabilitation and Medical Centre Neuromental in Helsinki, Finland. SL is an adjunct professor of psychiatry at the University of Helsinki. He has ten years of experience with developmental neuropsychiatric disorders and is especially interested in clinical manifestations of ADHD and ASD in adulthood. In addition, he has done research on mood disorders and chronobiology. PR is an MD and a PhD. He has two specialties regarding neuropsychiatric disorders: he is a pediatric neurologist and an adolescent psychiatrist. In addition, he has five years of brain imaging experience with neuropsychiatric disorders at UCLA PET-Center and Neuropsychiatric Center. In Finland, he has clinical work experience with neuropsychiatric disorders including Asperger syndrome and other ASD, and he works at the Helsinki University Central Hospital Adolescent Psychiatry Clinic. His main field is evaluating neuropsychiatric patients and their comorbid conditions in addition to medical treatment and rehabilitation. PT is an adjunct professor of psychiatry at the University of Helsinki. He has more than ten years of experience on developmental neuropsychiatric disorders and is especially interested in clinical manifestation of ASD in adulthood. In addition, he has done research in forensic psychiatry. AL is a physicist who received his PhD in 2006 at the University of Antwerp, Belgium. From 2007 to 2009, he worked as a postdoctoral researcher at the Cardiff University Brain Research Imaging Center (CUBRIC), Cardiff University, Wales, United Kingdom. In 2009, he joined the Image Sciences Institute (ISI), University Medical Center Utrecht, the Netherlands, where he currently holds a tenured faculty position as Associate Professor. His current research interests include modeling, processing, visualizing and analyzing diffusion MRI data for investigating microstructural and architectural tissue organization. He heads the PROVIDI Lab and is the developer of the software ExploreDTI. MS is a professor of cognitive neuroscience in Aalto University. He is very experienced in noninvasive study of neural basis of cognitive functions such as attention, speech perception, auditory perception and multisensory integration. Now his research is focused on studying brain activity in naturalistic conditions.
Autism Diagnostic Interview–revised
Autism Diagnostic Observation Schedule
autism spectrum quotient
autism spectrum disorder
anterior thalamic radiation
planar diffusion coefficient
constrained spherical deconvolution
diffusion tensor imaging
- Eyes Test:
Reading the Mind in the Eyes Test
FMRIB’s diffusion toolbox
Functional MRI of the Brain
fiber orientation distribution
field of view
Benton Facial Recognition Test
FMRIB software library
inferior fronto-occipital fasciculus
inferior longitudinal fasciculus
magnetic resonance imaging
number of excitations
posterior thalamic radiation
partial volume effect
splenium of corpus callosum
superior longitudinal fasciculus
tract-based spatial statistics
threshold-free cluster enhancement
The authors would like to thank the radiographer Marita Kattelus from the Advanced Magnetic Imaging Centre of the Aalto University. The study was supported by the Academy of Finland (National Centres of Excellence program 2006-2011, grants #129670, #130412, #138145, #259752, #259952), and the aivoAALTO project grant from the Aalto University. The work of TR was supported by the Fund for Scientific Research-Flanders (FWO), and by the Interuniversity Attraction Poles Program (P7/11) initiated by the Belgian Science Policy Office. The research of AL is supported by Vidi Grant 639.072.411 from the Netherlands Organisation for Scientific Research (NWO). We gratefully acknowledge the DTI sequence and recon code from Drs Roland Bammer, Michael Moseley and Gary Glover, supported by the NIH NCRR grant ‘Stanford Center for Advanced Magnetic Resonance Technology’, P41 RR09784 (PI: G. Glover).
- Fombonne E: Epidemiology of pervasive developmental disorders. Pediatr Res 2009, 65:591–8. 10.1203/PDR.0b013e31819e7203View ArticlePubMedGoogle Scholar
- Folstein SE, Rosen-Sheidley B: Genetics of autism: complex aetiology for a heterogeneous disorder. Nat Rev Genet 2001, 2:943–55. 10.1038/35103559View ArticlePubMedGoogle Scholar
- Betancur C: Etiological heterogeneity in autism spectrum disorders: more than 100 genetic and genomic disorders and still counting. Brain Res 2011, 1380:42–77.View ArticlePubMedGoogle Scholar
- Belmonte MK, Allen G, Beckel-Mitchener A, Boulanger LM, Carper RA, Webb SJ: Autism and abnormal development of brain connectivity. J Neurosci 2004, 24:9228–31. 10.1523/JNEUROSCI.3340-04.2004View ArticlePubMedGoogle Scholar
- Wass S: Distortions and disconnections: disrupted brain connectivity in autism. Brain Cogn 2011, 75:18–28. 10.1016/j.bandc.2010.10.005View ArticlePubMedGoogle Scholar
- Schipul SE, Keller TA, Just MA: Inter-regional brain communication and its disturbance in autism. Front Syst Neurosci 2011, 22:5–10.Google Scholar
- Basser PJ, Mattiello J, LeBihan D: MR diffusion tensor spectroscopy and imaging. Biophys J 1994, 66:259–67. 10.1016/S0006-3495(94)80775-1View ArticlePubMed CentralPubMedGoogle Scholar
- Jones DK, Knösche TR, Turner R: White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. NeuroImage 2013, 73:239–54.View ArticlePubMedGoogle Scholar
- Jones DK, Leemans A: Diffusion tensor imaging. Methods Mol Biol 2011, 711:127–44. 10.1007/978-1-61737-992-5_6View ArticlePubMedGoogle Scholar
- Tournier JD, Mori S, Leemans A: Diffusion tensor imaging and beyond. Magnet Reson Med 2011, 65:1532–56. 10.1002/mrm.22924View ArticleGoogle Scholar
- Beaulieu C: The basis of anisotropic water diffusion in the nervous system - a technical review. NMR Biomed 2002, 15:435–55. 10.1002/nbm.782View ArticlePubMedGoogle Scholar
- Westin CF, Maier SE, Mamata H, Nabavi A, Jolesz FA, Kikinis R: Processing and visualization for diffusion tensor MRI. Med Image Anal 2002, 6:93–108. 10.1016/S1361-8415(02)00053-1View ArticlePubMedGoogle Scholar
- Wiegell MR, Larsson HB, Wedeen VJ: Fiber crossing in human brain depicted with diffusion tensor MR imaging. Radiology 2000, 217:897–903. 10.1148/radiology.217.3.r00nv43897View ArticlePubMedGoogle Scholar
- Ennis DB, Kindlmann G: Orthogonal tensor invariants and the analysis of diffusion tensor magnetic resonance images. Magn Reson Med 2006, 55:136–46. 10.1002/mrm.20741View ArticlePubMedGoogle Scholar
- Vos SB, Jones DK, Jeurissen B, Viergever MA, Leemans A: The influence of complex white matter architecture on the mean diffusivity in diffusion tensor MRI of the human brain. NeuroImage 2012, 59:2208–16. 10.1016/j.neuroimage.2011.09.086View ArticlePubMedGoogle Scholar
- Pina-Camacho L, Villero S, Fraguas D, Boada L, Janssen J, Navas-Sánchez FJ, Mayoral M, Llorente C, Arango C, Parellada M: Autism spectrum disorder: does neuroimaging support the DSM-5 proposal for a symptom dyad? A systematic review of functional magnetic resonance imaging and diffusion tensor imaging studies. J Autism Dev Disord 2012, 42:1326–41. 10.1007/s10803-011-1360-4View ArticlePubMedGoogle Scholar
- Travers BG, Adluru N, Ennis C, Tromp DPM, Destiche D, Doran S, Bigler ED, Lange N, Lainhart JE, Alexander AL: Diffusion tensor imaging in autism spectrum disorder: a review. Autism Res 2012, 5:289–313. 10.1002/aur.1243View ArticlePubMed CentralPubMedGoogle Scholar
- Catani M, Jones DK, Daly E, Embiricos N, Deeley Q, Pugliese L, Curran S, Robertson D, Murphy DGM: Altered cerebellar feedback projections in Asperger syndrome. NeuroImage 2008, 41:1184–91. 10.1016/j.neuroimage.2008.03.041View ArticlePubMedGoogle Scholar
- Thakkar KN, Polli FE, Joseph RM, Tuch DS, Hadjikhani N, Barton JJ, Manoach D: Response monitoring, repetitive behaviour and anterior cingulate abnormalities in autism spectrum disorders (ASD). Brain 2008, 131:2464–78. 10.1093/brain/awn099View ArticlePubMed CentralPubMedGoogle Scholar
- Bloemen OJN, Deeley Q, Sundram F, Daly EM, Barker GJ, Jones DK, van Amelsvoort TA, Schmitz N, Robertson D, Murphy KC, Murphy DG: White matter integrity in asperger syndrome: a preliminary diffusion tensor magnetic resonance imaging study in adults. Autism Res 2010, 3:203–13. 10.1002/aur.146View ArticlePubMedGoogle Scholar
- Kleinhans NM, Pauley G, Richards T, Neuhaus E, Martin N, Corrigan NM, Shaw DW, Estes A, Dager SR: Age-related abnormalities in white matter microstructure in autism spectrum disorders. Brain Res 2012, 1479:1–16.View ArticlePubMed CentralPubMedGoogle Scholar
- Langen M, Leemans A, Johnston P, Ecker C, Daly E, Murphy CM, Dell’Acqua F, Durston S: Fronto-striatal circuitry and inhibitory control in autism: findings from diffusion tensor imaging tractography. Cortex 2012, 48:183–93. 10.1016/j.cortex.2011.05.018View ArticlePubMedGoogle Scholar
- Gibbard CR, Ren J, Seunarine KK, Clayden JD, Skuse DH, Clark CA: White matter microstructure correlates with autism trait severity in a combined clinical–control sample of high-functioning adults. NeuroImage 2013, 3:106–14.View ArticlePubMed CentralPubMedGoogle Scholar
- Cercignani M: Strategies for patient-control comparison of diffusion MRI data. In Diffusion MRI: theory, methods, and applications. Edited by: Jones DK. Oxford: Oxford University Press; 2010:485–99.View ArticleGoogle Scholar
- Jones DK, Cercignani M: Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed 2010, 23:803–20. 10.1002/nbm.1543View ArticlePubMedGoogle Scholar
- Deprez S, Billiet T, Sunaert S, Leemans A: Diffusion tensor MRI of chemotherapy-induced cognitive impairment in non-CNS cancer patients: a review. Brain Imaging Behav 2013, 7:409–35. 10.1007/s11682-012-9220-1View ArticlePubMedGoogle Scholar
- Roine U, Roine T, Salmi J, Nieminen-Von Wendt T, Leppämäki S, Rintahaka P, Tani P, Leemans A, Sams M: Increased coherence of white matter fiber tract organization in adults with Asperger syndrome: a diffusion tensor imaging study. Autism Res 2013, 6:642–50. 10.1002/aur.1332View ArticlePubMedGoogle Scholar
- Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, Watkins KE, Ciccarelli O, Cader MZ, Matthews PM, Behrens TE: Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage 2006, 31:1487–505. 10.1016/j.neuroimage.2006.02.024View ArticlePubMedGoogle Scholar
- Jeurissen B, Leemans A, Tournier JD, Jones DK, Sijbers J: Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Hum Brain Mapp 2013, 34:2747–66. 10.1002/hbm.22099View ArticlePubMedGoogle Scholar
- Tournier JD, Calamante F, Gadian DG, Connelly A: Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage 2004, 23:1176–85. 10.1016/j.neuroimage.2004.07.037View ArticlePubMedGoogle Scholar
- Tournier JD, Calamante F, Connelly A: Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. NeuroImage 2007, 35:1459–72. 10.1016/j.neuroimage.2007.02.016View ArticlePubMedGoogle Scholar
- McGrath J, Johnson K, O'Hanlon E, Garavan H, Gallagher L, Leemans A: White matter and visuospatial processing in autism: a constrained spherical deconvolution tractography study. Autism Res 2013, 6:307–19. 10.1002/aur.1290View ArticlePubMedGoogle Scholar
- McGrath J, Johnson K, O'Hanlon E, Garavan H, Leemans A, Gallagher L: Abnormal functional connectivity during visuospatial processing is associated with disrupted organisation of white matter in autism. Front Hum Neurosci 2013, 7:434.View ArticlePubMed CentralPubMedGoogle Scholar
- Benton AL, Sivan AB, Hamsher K, Vareny NR, Spreen O: Facial recognition: stimulus and multiple choice pictures. In Contributions to neuropsychological assessment. Edited by: Benton AL, Sivan AB, Hamsher KDS, Varney NR, Speen O. New York: Oxford University Press; 1983:30–40.Google Scholar
- Baron-Cohen S, Wheelwright S, Hill J, Raste Y, Plumb I: The "reading the mind in the eyes" test revised version: a study with normal adults, and adults with Asperger syndrome or high-functioning autism. J Child Psychol Psychiatry 2001, 42:241–51. 10.1111/1469-7610.00715View ArticlePubMedGoogle Scholar
- Baron-Cohen S, Wheelwright S, Skinner R, Martin J, Clubley E: The autism-spectrum quotient (AQ): evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. J Autism Dev Disord 2001, 31:5–17. 10.1023/A:1005653411471View ArticlePubMedGoogle Scholar
- Baron-Cohen S, Wheelwright S: The empathy quotient: an investigation of adults with Asperger syndrome or high functioning autism, and normal sex differences. J Autism Dev Disord 2004, 34:163–75.View ArticlePubMedGoogle Scholar
- Baron-Cohen S, Richler J, Bisarya D, Gurunathan N, Wheelwright S: The systemizing quotient: an investigation of adults with Asperger syndrome or high-functioning autism, and normal sex differences. Philos Trans R Soc Lond B Biol Sci 2003, 358:361–74. 10.1098/rstb.2002.1206View ArticlePubMed CentralPubMedGoogle Scholar
- Franco AR, Ling J, Cañive JM: Assessment and quantification of head motion in neuropsychiatric functional imaging research as applied to schizophrenia. J Int Neuropsychol Soc 2007, 13:839–45.PubMedGoogle Scholar
- Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers J, Zhang Y, De Stefano N, Brady JM, Matthews PM: Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 2004, 23:208-S219.View ArticleGoogle Scholar
- Wedeen VJ, Rosene DL, Wang R, Dai G, Mortazavi F, Hagmann P, Kaas JH, Tseng W-YI: The geometric structure of the brain fiber pathways. Science 2012, 335:1628–34. 10.1126/science.1215280View ArticlePubMed CentralPubMedGoogle Scholar
- Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ: Non-rigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging 1999, 18:712–21. 10.1109/42.796284View ArticlePubMedGoogle Scholar
- Smith SM, Nichols TE: Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage 2009, 44:83–98. 10.1016/j.neuroimage.2008.03.061View ArticlePubMedGoogle Scholar
- Leemans A, Jeurissen B, Sijbers J, Jones DK: ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. In Proceedings of the 17th Annual Meeting of International Society for Magnetic Resonance in Medicine. Hawaii: Curran Associates Inc; 2009:3537.Google Scholar
- Jeurissen B, Leemans A, Jones DK, Tournier JD, Sijbers J: Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution. Hum Brain Mapp 2011, 32:461–79. 10.1002/hbm.21032View ArticlePubMedGoogle Scholar
- Leemans A, Jones DK: The B‒matrix must be rotated when correcting for subject motion in DTI data. Magn Reson Med 2009, 61:1336–49. 10.1002/mrm.21890View ArticlePubMedGoogle Scholar
- Veraart J, Rajan J, Peeters RR, Leemans A, Sunaert S, Sijbers J: Comprehensive framework for accurate diffusion MRI parameter estimation. Magn Reson Med 2013, 70:972–84. 10.1002/mrm.24529View ArticlePubMedGoogle Scholar
- Tournier JD, Yeh CH, Calamante F, Cho KH, Connelly A, Lin CP: Resolving crossing fibres using constrained spherical deconvolution: validation using diffusion-weighted imaging phantom data. NeuroImage 2008, 42:617–25. 10.1016/j.neuroimage.2008.05.002View ArticlePubMedGoogle Scholar
- Wakana S, Caprihan A, Panzenboeck MM, Fallon JH, Perry M, Gollub RL, Hua K, Zhang J, Jiang H, Dubey P, Blitz A, van Zijl P, Mori S: Reproducibility of quantitative tractography methods applied to cerebral white matter. NeuroImage 2007, 36:630–44. 10.1016/j.neuroimage.2007.02.049View ArticlePubMed CentralPubMedGoogle Scholar
- Lebel C, Walker L, Leemans A, Phillips L, Beaulieu C: Microstructural maturation of the human brain from childhood to adulthood. NeuroImage 2008, 40:1044–55. 10.1016/j.neuroimage.2007.12.053View ArticlePubMedGoogle Scholar
- Vos SB, Jones DK, Viergever MA, Leemans A: Partial volume effect as a hidden covariate in DTI analyses. NeuroImage 2011, 55:1566–76. 10.1016/j.neuroimage.2011.01.048View ArticlePubMedGoogle Scholar
- Yendiki A, Koldewyn K, Kakunoori S, Kanwisher N, Fischl B: Spurious group differences due to head motion in a diffusion MRI study. NeuroImage 2014, 88:79–90.View ArticleGoogle Scholar
- Koldewyn K, Yendiki A, Weigelt S, Gweon H, Julian J, Richardson H, Malloy C, Saxe R, Fischl B, Kanwisher N: Differences in the right inferior longitudinal fasciculus but no general disruption of white matter tracts in children with autism spectrum disorder. Proc Natl Acad Sci U S A 2014, 111:1981–6. 10.1073/pnas.1324037111View ArticlePubMed CentralPubMedGoogle Scholar
- Choe AS, Stepniewska I, Colvin D, Ding Z, Anderson AW: Validation of diffusion tensor MRI in the central nervous system using light microscopy: quantitative comparison of fiber properties. NMR Biomed 2012, 25:900–8. 10.1002/nbm.1810View ArticlePubMedGoogle Scholar
- Salmi J, Roine U, Glerean E, Lahnakoski J, Nieminen-von Wendt T, Tani P, Leppämäki S, Nummenmaaa L, Jääskeläinen IP, Carlson S, Rintahaka P, Sams M: The brains of high functioning autistic individuals do not synchronize with those of others. NeuroImage 2013, 3:489–97.View ArticlePubMed CentralPubMedGoogle Scholar
- Scholz J, Klein MC, Behrens TE, Johansen-Berg H: Training induces changes in white-matter architecture. Nat Neurosci 2009, 12:1370–1. 10.1038/nn.2412View ArticlePubMed CentralPubMedGoogle Scholar
- Schlegel AA, Rudelson JJ, Tse PU: White matter structure changes as adults learn a second language. J Cogn Neurosci 2012, 24:1664–70. 10.1162/jocn_a_00240View ArticlePubMedGoogle Scholar
- Pardini M, Elia M, Garaci FG, Guida S, Coniglione F, Krueger F, Benassi F, Emberti Gialloreti L: Long-term cognitive and behavioral therapies, combined with augmentative communication, are related to uncinate fasciculus integrity in autism. J Autism Dev Disord 2012, 42:585–92. 10.1007/s10803-011-1281-2View ArticlePubMedGoogle Scholar
- Cooper M, Thapar A, Jones DK: White matter microstructure predicts autistic traits in attention-deficit/hyperactivity disorder. J Autism Dev Disord 2014. Epub Ahead of Print, doi: 10.1007/s10803–014–2131–9Google Scholar
- Conturo TE, Williams DL, Smith CD, Gultepe E, Akbudak E, Minshew NJ: Neuronal fiber pathway abnormalities in autism: an initial MRI diffusion tensor tracking study of hippocampo-fusiform and amygdalo-fusiform pathways. J Int Neuropsychol Soc 2008, 14:933–46. 10.1017/S1355617708081381View ArticlePubMed CentralPubMedGoogle Scholar
- Thomas C, Humphreys K, Jung KJ, Minshew N, Behrmann M: The anatomy of the callosal and visual-association pathways in high-functioning autism: a DTI tractography study. Cortex 2011, 47:863–73. 10.1016/j.cortex.2010.07.006View ArticlePubMed CentralPubMedGoogle Scholar
- Radua J, Via E, Catani M, Mataix-Cols D: Voxel-based meta-analysis of regional white-matter volume differences in autism spectrum disorder versus healthy controls. Psychol Med 2011, 41:1539. 10.1017/S0033291710002187View ArticlePubMedGoogle Scholar
- Ben Bashat D, Kronfeld-Duenias V, Zachor DA, Ekstein PM, Hendler T, Tarrasch R, Even A, Levy Y, Ben Sira L: Accelerated maturation of white matter in young children with autism: a high b value DWI study. NeuroImage 2007, 37:40–7. 10.1016/j.neuroimage.2007.04.060View ArticlePubMedGoogle Scholar
- Cheung C, Chua SE, Cheung V, Khong PL, Tai KS, Wong TK, Ho TP, McAlonan GM: White matter fractional anisotrophy differences and correlates of diagnostic symptoms in autism. J Child Psychol Psychiatry 2009, 50:1102–12. 10.1111/j.1469-7610.2009.02086.xView ArticlePubMedGoogle Scholar
- Cheng Y, Chou KH, Chen IY, Fan YT, Decety J, Lin CP: Atypical development of white matter microstructure in adolescents with autism spectrum disorders. NeuroImage 2010, 50:873–82. 10.1016/j.neuroimage.2010.01.011View ArticlePubMedGoogle Scholar
- Sahyoun CP, Belliveau JW, Mody M: White matter integrity and pictorial reasoning in high-functioning children with autism. Brain Cogn 2010, 73:180. 10.1016/j.bandc.2010.05.002View ArticlePubMed CentralPubMedGoogle Scholar
- Bode MK, Mattila ML, Kiviniemi V, Rahko J, Moilanen I, Ebeling H, Tervonen O, Nikkinen J: White matter in autism spectrum disorders - evidence of impaired fiber formation. Acta Radiol 2011, 52:1169–74. 10.1258/ar.2011.110197View ArticlePubMedGoogle Scholar
- Weinstein M, Ben-Sira L, Levy Y, Zachor DA, Itzhak EB, Artzi M, Tarrasch R, Eksteine PM, Hendler T, Ben Bashat D: Abnormal white matter integrity in young children with autism. Hum Brain Mapp 2011, 32:534–43. 10.1002/hbm.21042View ArticlePubMedGoogle Scholar
- Chiang MC, Barysheva M, Shattuck DW, Lee AD, Madsen SK, Avedissian C, Klunder AD, Toga AW, McMahon KL, de Zubicaray GI, Wright MJ, Srivastava A, Balov N, Thompson PM: Genetics of brain fiber architecture and intellectual performance. J Neurosci 2009, 29:2212–24. 10.1523/JNEUROSCI.4184-08.2009View ArticlePubMed CentralPubMedGoogle Scholar
- Salat DH, Tuch DS, Greve DN, van der Kouwe AJ, Hevelone ND, Zaleta AK, Rosen BR, Fischl B, Corkin S, Rosas HD, Dale AM: Age-related alterations in white matter microstructure measured by diffusion tensor imaging. Neurobiol Aging 2005, 26:1215–27. 10.1016/j.neurobiolaging.2004.09.017View ArticlePubMedGoogle Scholar
- Bach M, Laun FB, Leemans A, Tax CM, Biessels GJ, Stieltjes B, Maier-Hein KH: Methodological considerations on tract-based spatial statistics (TBSS). NeuroImage 2014, 100:358–69.View ArticlePubMedGoogle Scholar
- Roine T, Jeurissen B, Perrone D, Aelterman J, Leemans A, Philips W, Sijbers J: Isotropic non-white matter partial volume effects in constrained spherical deconvolution. Front Neuroinform 2014, 8:28.View ArticlePubMed CentralPubMedGoogle Scholar
- Peltonen L, Jalanko A, Varilo T: Molecular genetics of the Finnish disease heritage. Hum Mol Genet 1999, 8:1913–23. 10.1093/hmg/8.10.1913View ArticlePubMedGoogle Scholar
- Farquharson S, Tournier JD, Calamante F, Fabinyi G, Schneider-Kolsky M, Jackson GD, Connelly A: White matter fiber tractography: why we need to move beyond DTI. J Neurosurg 2013, 118:1367–77. 10.3171/2013.2.JNS121294View ArticlePubMedGoogle Scholar
- Kristo G, Leemans A, Raemaekers M, Rutten GJ, de Gelder B, Ramsey NF: Reliability of two clinically relevant fiber pathways reconstructed with constrained spherical deconvolution. Magn Reson Med 2013, 70:1544–56. 10.1002/mrm.24602View ArticlePubMedGoogle Scholar
- Aarnink SH, Vos SB, Leemans A, Jernigan TL, Madsen KS, Baaré WF: Automated longitudinal intra-subject analysis (ALISA) for diffusion MRI tractography. NeuroImage 2014, 86:404–16.View ArticlePubMedGoogle Scholar
- Van Hecke W, Sijbers J, D'Agostino E, Maes F, De Backer S, Vandervliet E, Parizel PM, Leemans A: On the construction of an inter-subject diffusion tensor magnetic resonance atlas of the healthy human brain. NeuroImage 2008, 43:69–80. 10.1016/j.neuroimage.2008.07.006View ArticlePubMedGoogle Scholar
- Van Hecke W, Leemans A, Sage CA, Emsell L, Veraart J, Sijbers J, Sunaert S, Parizel PM: The effect of template selection on diffusion tensor voxel-based analysis results. NeuroImage 2011, 55:566–73. 10.1016/j.neuroimage.2010.12.005View ArticlePubMedGoogle Scholar
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